commit d3ddab7c5d5e3643b7e89d45846d3f94b60be4c9 Author: 张菲 Date: Tue Oct 7 22:42:55 2025 +0800 Upload to Main diff --git a/.idea/.gitignore b/.idea/.gitignore new file mode 100644 index 0000000..13566b8 --- /dev/null +++ b/.idea/.gitignore @@ -0,0 +1,8 @@ +# Default ignored files +/shelf/ +/workspace.xml +# Editor-based HTTP Client requests +/httpRequests/ +# Datasource local storage ignored files +/dataSources/ +/dataSources.local.xml diff --git a/.idea/inspectionProfiles/Project_Default.xml b/.idea/inspectionProfiles/Project_Default.xml new file mode 100644 index 0000000..c07d4a9 --- /dev/null +++ b/.idea/inspectionProfiles/Project_Default.xml @@ -0,0 +1,58 @@ + + + + \ No newline at end of file diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml new file mode 100644 index 0000000..105ce2d --- /dev/null +++ b/.idea/inspectionProfiles/profiles_settings.xml @@ -0,0 +1,6 @@ + + + + \ No newline at end of file diff --git a/.idea/misc.xml b/.idea/misc.xml new file mode 100644 index 0000000..84fe515 --- /dev/null +++ b/.idea/misc.xml @@ -0,0 +1,4 @@ + + + + \ No newline at end of file diff --git a/.idea/modules.xml b/.idea/modules.xml new file mode 100644 index 0000000..4d400be --- /dev/null +++ b/.idea/modules.xml @@ -0,0 +1,8 @@ + + + + + + + + \ No newline at end of file diff --git a/.idea/multi-research.iml b/.idea/multi-research.iml new file mode 100644 index 0000000..2176380 --- /dev/null +++ b/.idea/multi-research.iml @@ -0,0 +1,14 @@ + + + + + + + + + + + + \ No newline at end of file diff --git a/.idea/runConfigurations/traial.xml b/.idea/runConfigurations/traial.xml new file mode 100644 index 0000000..54c9670 --- /dev/null +++ b/.idea/runConfigurations/traial.xml @@ -0,0 +1,25 @@ + + + + + \ No newline at end of file diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..4656a3b --- /dev/null +++ b/LICENSE @@ -0,0 +1,22 @@ +MIT License + +Copyright (c) 2022 Muhammad Uzair Khattak +Copyright (c) 2021 Kaiyang Zhou + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/README.md b/README.md new file mode 100644 index 0000000..4fa70fe --- /dev/null +++ b/README.md @@ -0,0 +1,89 @@ +# DAPT [T-PAMI 2025] + + + +> [**Decouple before Align: Visual Disentanglement Enhances Prompt Tuning**](https://arxiv.org/abs/2210.03117)
+> [Fei Zhang](https://scholar.google.com/citations?hl=zh-CN&user=Omrg6UkAAAAJ), [Tianfei Zhou](https://www.tfzhou.com/), [Jiangchao Yao](https://sunarker.github.io/), [Ya Zhang](http://scholar.google.com/citations?user=pbjw9sMAAAAJ&hl=zh-CN), [Ivor W. Tsang](https://scholar.google.com.sg/citations?user=rJMOlVsAAAAJ&hl=en), [Yanfeng Wang](https://ieeexplore.ieee.org/author/37085615187) + + + +Official implementation of the paper "[Decouple before Align: Visual Disentanglement +Enhances Prompt Tuning](https://arxiv.org/pdf/2508.00395)". + + + + + +## Highlights + +![main figure](docs/main_fig.png) +> **

Abstract:** *Prompt tuning (PT), as an emerging resource-efficient fine-tuning paradigm, has showcased remarkable effectiveness in +improving the task-specific transferability of vision-language models. This paper delves into a previously overlooked information +asymmetry issue in PT, where the visual modality mostly conveys more context than the object-oriented textual modality. Correspondingly, +coarsely aligning these two modalities could result in the biased attention, driving the model to merely focus on the context area. To +address this, we propose DAPT, an effective PT framework based on an intuitive decouple-before-align concept. First, we propose to +explicitly decouple the visual modality into the foreground and background representation via exploiting coarse-and-fine visual +segmenting cues, and then both of these decoupled patterns are aligned with the original foreground texts and the hand-crafted +background classes, thereby symmetrically strengthening the modal alignment. To further enhance the visual concentration, we propose +a visual pull-push regularization tailored for the foreground-background patterns, directing the original visual representation towards +unbiased attention on the region-of-interest object. We demonstrate the power of architecture-free DAPT through few-shot learning, +base-to-novel generalization, and data-efficient learning, all of which yield superior performance across prevailing benchmarks.*

+ +## Main Contributions + +1) **Multi-modal prompt learning:** Adapt CLIP using a novel prompting technique which prompts both the vision and language branch of CLIP. +2) **Vision Decoupling:** We propose the visual disentanglement that exploits the +visual cues of different levels to highlight the text-oriented +object in the visual modality. +3) **Fine-grained v.s. Coarse Visual Decoupling:** Different Masks are explored to serve effective decoupling visual signal. + +## Results +>SOTA performance is made, and such a method could be seamlessly integrated on other methods. + +![main figure](docs/fig.png) + + +## Installation +For installation and other package requirements, please follow the instructions detailed in [INSTALL.md](docs/INSTALL.md). + +## Data preparation +Please follow the instructions at [DATASETS.md](docs/DATASETS.md) to prepare all datasets. + + +**DAPT-S**: Then, you should download the **Segementation MASK** from [Here](https://drive.google.com/file/d/12BDM8X3ZynLVNqmkAMEvxVMk7vU9ILzv/view?usp=sharing), and put them correspondingly to each root data directory. + +These masks are generated with [SEEM](https://github.com/UX-Decoder/Segment-Everything-Everywhere-All-At-Once), and you can also generate masks by other tools, Just suit yourself. + +(PS: It contains all segmentation masks for ImageNet, so it is convenient to use them for other studies) + + + +## Training and Evaluation +Please refer to the [RUN.md](docs/RUN.md) for detailed instructions on training, evaluating and reproducing the results using our pre-trained models. (All implementations could also refer to [MaPLe](https://github.com/muzairkhattak/multimodal-prompt-learning/tree/main)) + + +
+ +## Citation +If you use our work, please consider citing: +```bibtex +@ARTICLE{11106768, + author={Zhang, Fei and Zhou, Tianfei and Yao, Jiangchao and Zhang, Ya and Tsang, Ivor W. and Wang, Yanfeng}, + journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, + title={Decouple Before Align: Visual Disentanglement Enhances Prompt Tuning}, + year={2025}, + volume={47}, + number={11}, + pages={10619-10632}, + keywords={Visualization;Tuning;Semantics;Artificial intelligence;Object oriented modeling;Accuracy;Image recognition;Context modeling;Training;Technological innovation;Prompt tuning;visual disentanglement;multi-modal learning}, + doi={10.1109/TPAMI.2025.3594894}} +``` + +## Contact +If you have any questions, please create an issue on this repository or contact at ferenas@sjtu.edu.cn. + + +## Acknowledgements + +Our code is based on [MaPLe](https://github.com/muzairkhattak/multimodal-prompt-learning/tree/main) repository. We thank the authors for releasing their code. If you use our model and code, please consider citing these works as well. + diff --git a/clip/__init__.py b/clip/__init__.py new file mode 100644 index 0000000..dcc5619 --- /dev/null +++ b/clip/__init__.py @@ -0,0 +1 @@ +from .clip import * diff --git a/clip/__pycache__/__init__.cpython-39.pyc b/clip/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 0000000..376955e Binary files /dev/null and b/clip/__pycache__/__init__.cpython-39.pyc differ diff --git a/clip/__pycache__/clip.cpython-39.pyc b/clip/__pycache__/clip.cpython-39.pyc new file mode 100644 index 0000000..508c9ee Binary files /dev/null and b/clip/__pycache__/clip.cpython-39.pyc differ diff --git a/clip/__pycache__/model.cpython-39.pyc b/clip/__pycache__/model.cpython-39.pyc new file mode 100644 index 0000000..e24179e Binary files /dev/null and b/clip/__pycache__/model.cpython-39.pyc differ diff --git a/clip/__pycache__/simple_tokenizer.cpython-39.pyc b/clip/__pycache__/simple_tokenizer.cpython-39.pyc new file mode 100644 index 0000000..2e675d8 Binary files /dev/null and b/clip/__pycache__/simple_tokenizer.cpython-39.pyc differ diff --git a/clip/bpe_simple_vocab_16e6.txt.gz b/clip/bpe_simple_vocab_16e6.txt.gz new file mode 100644 index 0000000..7b5088a Binary files /dev/null and b/clip/bpe_simple_vocab_16e6.txt.gz differ diff --git a/clip/clip.py b/clip/clip.py new file mode 100644 index 0000000..b1e4140 --- /dev/null +++ b/clip/clip.py @@ -0,0 +1,221 @@ +import hashlib +import os +import urllib +import warnings +from typing import Union, List + +import torch +from PIL import Image +from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize +from tqdm import tqdm + +from .model import build_model +from .simple_tokenizer import SimpleTokenizer as _Tokenizer + +try: + from torchvision.transforms import InterpolationMode + BICUBIC = InterpolationMode.BICUBIC +except ImportError: + BICUBIC = Image.BICUBIC + + +if torch.__version__.split(".") < ["1", "7", "1"]: + warnings.warn("PyTorch version 1.7.1 or higher is recommended") + + +__all__ = ["available_models", "load", "tokenize"] +_tokenizer = _Tokenizer() + +_MODELS = { + "RN50": "https://openaipublic.azureedge.net/clip/models/afeb0e10f9e5a86da6080e35cf09123aca3b358a0c3e3b6c78a7b63bc04b6762/RN50.pt", + "RN101": "https://openaipublic.azureedge.net/clip/models/8fa8567bab74a42d41c5915025a8e4538c3bdbe8804a470a72f30b0d94fab599/RN101.pt", + "RN50x4": "https://openaipublic.azureedge.net/clip/models/7e526bd135e493cef0776de27d5f42653e6b4c8bf9e0f653bb11773263205fdd/RN50x4.pt", + "RN50x16": "https://openaipublic.azureedge.net/clip/models/52378b407f34354e150460fe41077663dd5b39c54cd0bfd2b27167a4a06ec9aa/RN50x16.pt", + "ViT-B/32": "https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt", + "ViT-B/16": "https://openaipublic.azureedge.net/clip/models/5806e77cd80f8b59890b7e101eabd078d9fb84e6937f9e85e4ecb61988df416f/ViT-B-16.pt", +} + + +def _download(url: str, root: str = os.path.expanduser("~/.cache/clip")): + os.makedirs(root, exist_ok=True) + filename = os.path.basename(url) + + expected_sha256 = url.split("/")[-2] + download_target = os.path.join(root, filename) + + if os.path.exists(download_target) and not os.path.isfile(download_target): + raise RuntimeError(f"{download_target} exists and is not a regular file") + + if os.path.isfile(download_target): + if hashlib.sha256(open(download_target, "rb").read()).hexdigest() == expected_sha256: + return download_target + else: + warnings.warn(f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file") + + with urllib.request.urlopen(url) as source, open(download_target, "wb") as output: + with tqdm(total=int(source.info().get("Content-Length")), ncols=80, unit='iB', unit_scale=True) as loop: + while True: + buffer = source.read(8192) + if not buffer: + break + + output.write(buffer) + loop.update(len(buffer)) + + if hashlib.sha256(open(download_target, "rb").read()).hexdigest() != expected_sha256: + raise RuntimeError(f"Model has been downloaded but the SHA256 checksum does not not match") + + return download_target + + +def _transform(n_px): + return Compose([ + Resize(n_px, interpolation=BICUBIC), + CenterCrop(n_px), + lambda image: image.convert("RGB"), + ToTensor(), + Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)), + ]) + + +def available_models() -> List[str]: + """Returns the names of available CLIP models""" + return list(_MODELS.keys()) + + +def load(name: str, device: Union[str, torch.device] = "cuda" if torch.cuda.is_available() else "cpu", jit=False): + """Load a CLIP model + + Parameters + ---------- + name : str + A model name listed by `clip.available_models()`, or the path to a model checkpoint containing the state_dict + + device : Union[str, torch.device] + The device to put the loaded model + + jit : bool + Whether to load the optimized JIT model or more hackable non-JIT model (default). + + Returns + ------- + model : torch.nn.Module + The CLIP model + + preprocess : Callable[[PIL.Image], torch.Tensor] + A torchvision transform that converts a PIL image into a tensor that the returned model can take as its input + """ + if name in _MODELS: + model_path = _download(_MODELS[name]) + elif os.path.isfile(name): + model_path = name + else: + raise RuntimeError(f"Model {name} not found; available models = {available_models()}") + + try: + # loading JIT archive + model = torch.jit.load(model_path, map_location=device if jit else "cpu").eval() + state_dict = None + except RuntimeError: + # loading saved state dict + if jit: + warnings.warn(f"File {model_path} is not a JIT archive. Loading as a state dict instead") + jit = False + state_dict = torch.load(model_path, map_location="cpu") + + if not jit: + model = build_model(state_dict or model.state_dict()).to(device) + if str(device) == "cpu": + model.float() + return model, _transform(model.visual.input_resolution) + + # patch the device names + device_holder = torch.jit.trace(lambda: torch.ones([]).to(torch.device(device)), example_inputs=[]) + device_node = [n for n in device_holder.graph.findAllNodes("prim::Constant") if "Device" in repr(n)][-1] + + def patch_device(module): + try: + graphs = [module.graph] if hasattr(module, "graph") else [] + except RuntimeError: + graphs = [] + + if hasattr(module, "forward1"): + graphs.append(module.forward1.graph) + + for graph in graphs: + for node in graph.findAllNodes("prim::Constant"): + if "value" in node.attributeNames() and str(node["value"]).startswith("cuda"): + node.copyAttributes(device_node) + + model.apply(patch_device) + patch_device(model.encode_image) + patch_device(model.encode_text) + + # patch dtype to float32 on CPU + if str(device) == "cpu": + float_holder = torch.jit.trace(lambda: torch.ones([]).float(), example_inputs=[]) + float_input = list(float_holder.graph.findNode("aten::to").inputs())[1] + float_node = float_input.node() + + def patch_float(module): + try: + graphs = [module.graph] if hasattr(module, "graph") else [] + except RuntimeError: + graphs = [] + + if hasattr(module, "forward1"): + graphs.append(module.forward1.graph) + + for graph in graphs: + for node in graph.findAllNodes("aten::to"): + inputs = list(node.inputs()) + for i in [1, 2]: # dtype can be the second or third argument to aten::to() + if inputs[i].node()["value"] == 5: + inputs[i].node().copyAttributes(float_node) + + model.apply(patch_float) + patch_float(model.encode_image) + patch_float(model.encode_text) + + model.float() + + return model, _transform(model.input_resolution.item()) + + +def tokenize(texts: Union[str, List[str]], context_length: int = 77, truncate: bool = False) -> torch.LongTensor: + """ + Returns the tokenized representation of given input string(s) + + Parameters + ---------- + texts : Union[str, List[str]] + An input string or a list of input strings to tokenize + + context_length : int + The context length to use; all CLIP models use 77 as the context length + + truncate: bool + Whether to truncate the text in case its encoding is longer than the context length + + Returns + ------- + A two-dimensional tensor containing the resulting tokens, shape = [number of input strings, context_length] + """ + if isinstance(texts, str): + texts = [texts] + + sot_token = _tokenizer.encoder["<|startoftext|>"] + eot_token = _tokenizer.encoder["<|endoftext|>"] + all_tokens = [[sot_token] + _tokenizer.encode(text) + [eot_token] for text in texts] + result = torch.zeros(len(all_tokens), context_length, dtype=torch.long) + + for i, tokens in enumerate(all_tokens): + if len(tokens) > context_length: + if truncate: + tokens = tokens[:context_length] + tokens[-1] = eot_token + else: + raise RuntimeError(f"Input {texts[i]} is too long for context length {context_length}") + result[i, :len(tokens)] = torch.tensor(tokens) + + return result diff --git a/clip/model.py b/clip/model.py new file mode 100644 index 0000000..545f0da --- /dev/null +++ b/clip/model.py @@ -0,0 +1,741 @@ +from collections import OrderedDict +from typing import Tuple, Union + +import numpy as np +import torch +import torch.nn.functional as F +from torch import nn + + +class Bottleneck(nn.Module): + expansion = 4 + + def __init__(self, inplanes, planes, stride=1): + super().__init__() + + # all conv layers have stride 1. an avgpool is performed after the second convolution when stride > 1 + self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False) + self.bn1 = nn.BatchNorm2d(planes) + + self.conv2 = nn.Conv2d(planes, planes, 3, padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) + + self.avgpool = nn.AvgPool2d(stride) if stride > 1 else nn.Identity() + + self.conv3 = nn.Conv2d(planes, planes * self.expansion, 1, bias=False) + self.bn3 = nn.BatchNorm2d(planes * self.expansion) + + self.relu = nn.ReLU(inplace=True) + self.downsample = None + self.stride = stride + + if stride > 1 or inplanes != planes * Bottleneck.expansion: + # downsampling layer is prepended with an avgpool, and the subsequent convolution has stride 1 + self.downsample = nn.Sequential(OrderedDict([ + ("-1", nn.AvgPool2d(stride)), + ("0", nn.Conv2d(inplanes, planes * self.expansion, 1, stride=1, bias=False)), + ("1", nn.BatchNorm2d(planes * self.expansion)) + ])) + + def forward(self, x: torch.Tensor): + identity = x + + out = self.relu(self.bn1(self.conv1(x))) + out = self.relu(self.bn2(self.conv2(out))) + out = self.avgpool(out) + out = self.bn3(self.conv3(out)) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + out = self.relu(out) + return out + + +class AttentionPool2d(nn.Module): + def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None): + super().__init__() + self.positional_embedding = nn.Parameter(torch.randn(spacial_dim ** 2 + 1, embed_dim) / embed_dim ** 0.5) + self.k_proj = nn.Linear(embed_dim, embed_dim) + self.q_proj = nn.Linear(embed_dim, embed_dim) + self.v_proj = nn.Linear(embed_dim, embed_dim) + self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim) + self.num_heads = num_heads + + def forward(self, x): + x = x.reshape(x.shape[0], x.shape[1], x.shape[2] * x.shape[3]).permute(2, 0, 1) # NCHW -> (HW)NC + x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0) # (HW+1)NC + x = x + self.positional_embedding[:, None, :].to(x.dtype) # (HW+1)NC + x, _ = F.multi_head_attention_forward( + query=x, key=x, value=x, + embed_dim_to_check=x.shape[-1], + num_heads=self.num_heads, + q_proj_weight=self.q_proj.weight, + k_proj_weight=self.k_proj.weight, + v_proj_weight=self.v_proj.weight, + in_proj_weight=None, + in_proj_bias=torch.cat([self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]), + bias_k=None, + bias_v=None, + add_zero_attn=False, + dropout_p=0, + out_proj_weight=self.c_proj.weight, + out_proj_bias=self.c_proj.bias, + use_separate_proj_weight=True, + training=self.training, + need_weights=False + ) + + return x[0] + + +class ModifiedResNet(nn.Module): + """ + A ResNet class that is similar to torchvision's but contains the following changes: + - There are now 3 "stem" convolutions as opposed to 1, with an average pool instead of a max pool. + - Performs anti-aliasing strided convolutions, where an avgpool is prepended to convolutions with stride > 1 + - The final pooling layer is a QKV attention instead of an average pool + """ + + def __init__(self, layers, output_dim, heads, input_resolution=224, width=64): + super().__init__() + self.output_dim = output_dim + self.input_resolution = input_resolution + + # the 3-layer stem + self.conv1 = nn.Conv2d(3, width // 2, kernel_size=3, stride=2, padding=1, bias=False) + self.bn1 = nn.BatchNorm2d(width // 2) + self.conv2 = nn.Conv2d(width // 2, width // 2, kernel_size=3, padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(width // 2) + self.conv3 = nn.Conv2d(width // 2, width, kernel_size=3, padding=1, bias=False) + self.bn3 = nn.BatchNorm2d(width) + self.avgpool = nn.AvgPool2d(2) + self.relu = nn.ReLU(inplace=True) + + # residual layers + self._inplanes = width # this is a *mutable* variable used during construction + self.layer1 = self._make_layer(width, layers[0]) + self.layer2 = self._make_layer(width * 2, layers[1], stride=2) + self.layer3 = self._make_layer(width * 4, layers[2], stride=2) + self.layer4 = self._make_layer(width * 8, layers[3], stride=2) + + embed_dim = width * 32 # the ResNet feature dimension + self.attnpool = AttentionPool2d(input_resolution // 32, embed_dim, heads, output_dim) + + def _make_layer(self, planes, blocks, stride=1): + layers = [Bottleneck(self._inplanes, planes, stride)] + + self._inplanes = planes * Bottleneck.expansion + for _ in range(1, blocks): + layers.append(Bottleneck(self._inplanes, planes)) + + return nn.Sequential(*layers) + + def forward(self, x): + def stem(x): + for conv, bn in [(self.conv1, self.bn1), (self.conv2, self.bn2), (self.conv3, self.bn3)]: + x = self.relu(bn(conv(x))) + x = self.avgpool(x) + return x + + x = x.type(self.conv1.weight.dtype) + x = stem(x) + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + x = self.attnpool(x) + + return x + + +class LayerNorm(nn.LayerNorm): + """Subclass torch's LayerNorm to handle fp16.""" + + def forward(self, x: torch.Tensor): + orig_type = x.dtype + ret = super().forward(x.type(torch.float32)) + return ret.type(orig_type) + + +class QuickGELU(nn.Module): + def forward(self, x: torch.Tensor): + return x * torch.sigmoid(1.702 * x) + + +class ResidualAttentionBlock(nn.Module): + def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None): + super().__init__() + + self.attn = nn.MultiheadAttention(d_model, n_head) + self.ln_1 = LayerNorm(d_model) + self.mlp = nn.Sequential(OrderedDict([ + ("c_fc", nn.Linear(d_model, d_model * 4)), + ("gelu", QuickGELU()), + ("c_proj", nn.Linear(d_model * 4, d_model)) + ])) + self.ln_2 = LayerNorm(d_model) + self.attn_mask = attn_mask + + def attention(self, x: torch.Tensor): + self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None + return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0] + + def forward(self, x: torch.Tensor): + x = x + self.attention(self.ln_1(x)) + x = x + self.mlp(self.ln_2(x)) + return x + + +class ResidualAttentionBlock_IVLP(nn.Module): + def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None, add_prompt=False, + text_layer=False, i=0, design_details=None): + super().__init__() + + self.attn = nn.MultiheadAttention(d_model, n_head) + self.ln_1 = LayerNorm(d_model) + self.mlp = nn.Sequential(OrderedDict([ + ("c_fc", nn.Linear(d_model, d_model * 4)), + ("gelu", QuickGELU()), + ("c_proj", nn.Linear(d_model * 4, d_model)) + ])) + self.ln_2 = LayerNorm(d_model) + # Only add learnable tokens if flag is set True + # For the first iteration i, we should not add the learnable parameters + # as it is already been taken care of in the very start, for both text + # and the visual branch + self.text_layer = text_layer + self.attn_mask = attn_mask + if i != 0: + self.add_prompt = add_prompt + if self.add_prompt: + if self.text_layer: + self.n_ctx_text = design_details["language_ctx"] # hyperparameter + ctx_vectors = torch.empty(self.n_ctx_text, d_model) + else: + self.n_ctx_visual = design_details["vision_ctx"] # hyperparameter + ctx_vectors = torch.empty(self.n_ctx_visual, d_model) + # Code snippet for per layer visual prompts + nn.init.normal_(ctx_vectors, std=0.02) + self.VPT_shallow = nn.Parameter(ctx_vectors) + else: + self.add_prompt = False + + def attention(self, x: torch.Tensor): + self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None + return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0] + + def forward(self, x: torch.Tensor): + # Will need to append the learnable tokens for this layer here + # Check if flag was set for this layer or not + if self.add_prompt: + # Also see if this is textual transformer layer or not + if not self.text_layer: + # Remove the outputs produced by learnable tokens of previous layer + prefix = x[0:x.shape[0] - self.n_ctx_visual, :, :] + # Create/configure learnable tokens of this layer + visual_context = self.VPT_shallow.expand(x.shape[1], -1, -1).permute(1, 0, 2).half() + # Add the learnable tokens of this layer with the input, by replacing the previous + # layer learnable tokens + x = torch.cat([prefix, visual_context], dim=0) + else: + # Appending the learnable tokens in different way + # x -> [77, NCLS, DIM] + # First remove the learnable tokens from previous layer + prefix = x[:1, :, :] + suffix = x[1 + self.n_ctx_text:, :, :] + # Create/configure learnable tokens of this layer + textual_context = self.VPT_shallow.expand(x.shape[1], -1, -1).permute(1, 0, 2).half() + # Add the learnable tokens of this layer with the input, replaced by previous + # layer learnable tokens + x = torch.cat([prefix, textual_context, suffix], dim=0) + + x = x + self.attention(self.ln_1(x)) + x = x + self.mlp(self.ln_2(x)) + return x + + +class ResidualAttentionBlock_MaPLe(nn.Module): + def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None, design_details=None, + text_layer=False, i=0): + super().__init__() + + self.attn = nn.MultiheadAttention(d_model, n_head) + self.ln_1 = LayerNorm(d_model) + self.mlp = nn.Sequential(OrderedDict([ + ("c_fc", nn.Linear(d_model, d_model * 4)), + ("gelu", QuickGELU()), + ("c_proj", nn.Linear(d_model * 4, d_model)) + ])) + self.ln_2 = LayerNorm(d_model) + # For the first iteration i, we do not need to add the learnable parameters here + # as it will be added in the beginning, for both text and the vision branch + self.text_layer = text_layer + self.attn_mask = attn_mask + # This must be consistent with the config file prompt + self.compound_prompt_nctx = design_details['maple_length'] + if i == 0: + self.first_layer = True + else: + self.first_layer = False + + def attention(self, x: torch.Tensor): + self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None + return self.attn(x, x, x, need_weights=True, attn_mask=self.attn_mask) + + def forward(self, inputs): + # For the first layer, we do not need to add any duplicate, as it is already added + # as the shallow version + x = inputs[0] + compound_prompts_deeper = inputs[1] + counter = inputs[2] + if not self.first_layer: + if len(compound_prompts_deeper) > 0: + # This means that deeper compound prompts are turned on + # Here it behaves differently for text and visual side + # Forward function is same for both + + if not self.text_layer: + # First check if the ith layer needs compound prompts or not + if not (counter > len(compound_prompts_deeper) - 1): + # Remove the outputs produced by learnable tokens of previous layer + prefix = x[0:x.shape[0] - self.compound_prompt_nctx, :, :] + # Create/configure learnable tokens of this layer + visual_context = compound_prompts_deeper[counter] # extract the correct index + visual_context = visual_context.expand(x.shape[1], -1, -1).permute(1, 0, 2).half() + # Add the learnable tokens of this layer with the input, by replacing previous + # layer learnable tokens + x = torch.cat([prefix, visual_context], dim=0) + + # Once done, update the counter, so that the next time, it does not use same learnable tokens + counter += 1 + else: + # First check if the ith layer needs compound prompts or not + if not (counter > len(compound_prompts_deeper) - 1): + # Appending the learnable tokens in different way + # x -> [77, NCLS, DIM] + # First remove the learnable tokens from previous layer + prefix = x[:1, :, :] + suffix = x[1 + self.compound_prompt_nctx:, :, :] + # Create/configure learnable tokens of this layer + textual_context = compound_prompts_deeper[counter] + textual_context = textual_context.expand(x.shape[1], -1, -1).permute(1, 0, 2).half() + # Add the learnable tokens of this layer with the input, replaced by previous + # layer learnable tokens + x = torch.cat([prefix, textual_context, suffix], dim=0) + # Once done, update the counter, so that the next time, it does not use same learnable tokens + counter += 1 + inp,attn_mask = self.attention(self.ln_1(x)) + x = x + inp + x = x + self.mlp(self.ln_2(x)) + if self.text_layer: + return [x, compound_prompts_deeper, counter] + else: + return [x, compound_prompts_deeper, counter, attn_mask] # return again as a list, so that nn.seq can work + + +class Transformer(nn.Module): + def __init__(self, width: int, layers: int, heads: int, attn_mask: torch.Tensor = None, prompts_needed=0, + text_layer=False, design_details=None): + super().__init__() + self.width = width + self.layers = layers + # Implements respective encoder blocks for a given design choice + current_trainer = design_details['trainer'] + if current_trainer == 'IVLP' or current_trainer == 'VPT': + self.resblocks = nn.Sequential(*[ResidualAttentionBlock_IVLP(width, heads, attn_mask, True, + text_layer, i, + design_details) if prompts_needed > i + else ResidualAttentionBlock_IVLP(width, heads, attn_mask, False, + text_layer, i, design_details) + for i in range(layers)]) + elif current_trainer == 'MaPLe': + self.resblocks = nn.Sequential( + *[ResidualAttentionBlock_MaPLe(width, heads, attn_mask, design_details, text_layer, i) + for i in range(layers)]) + else: + # Corresponds to default CoOp or CoCoOp + assert current_trainer == 'CoOp' or current_trainer == 'CoCoOp' + self.resblocks = nn.Sequential(*[ResidualAttentionBlock(width, heads, attn_mask) for _ in range(layers)]) + + def forward(self, x: torch.Tensor): + return self.resblocks(x) + + +class Transformer_normal(nn.Module): + def __init__(self, width: int, layers: int, heads: int, attn_mask: torch.Tensor = None, prompts_needed=0, + text_layer=False, design_details=None): + super().__init__() + self.width = width + self.layers = layers + # Implements respective encoder blocks for a given design choice + # current_trainer = design_details['trainer'] + # if current_trainer == 'IVLP' or current_trainer == 'VPT': + # self.resblocks = nn.Sequential(*[ResidualAttentionBlock_IVLP(width, heads, attn_mask, True, + # text_layer, i, + # design_details) if prompts_needed > i + # else ResidualAttentionBlock_IVLP(width, heads, attn_mask, False, + # text_layer, i, design_details) + # for i in range(layers)]) + # elif current_trainer == 'MaPLe': + # self.resblocks = nn.Sequential( + # *[ResidualAttentionBlock_MaPLe(width, heads, attn_mask, design_details, text_layer, i) + # for i in range(layers)]) + # else: + # # Corresponds to default CoOp or CoCoOp + # assert current_trainer == 'CoOp' or current_trainer == 'CoCoOp' + self.resblocks = nn.Sequential(*[ResidualAttentionBlock(width, heads, attn_mask) for _ in range(layers)]) + + def forward(self, x: torch.Tensor): + return self.resblocks(x) + + +class VisionTransformer(nn.Module): + def __init__(self, input_resolution: int, patch_size: int, width: int, layers: int, heads: int, + output_dim: int, design_details): + super().__init__() + self.input_resolution = input_resolution + self.output_dim = output_dim + self.conv1 = nn.Conv2d(in_channels=3, out_channels=width, kernel_size=patch_size, stride=patch_size, bias=False) + if design_details["vision_depth"] == 0: + self.VPT_shallow = False + else: + self.VPT_shallow = True + if self.VPT_shallow: + # Add visual prompt tokens here + n_ctx = design_details["vision_ctx"] # hyperparameter + ctx_vectors = torch.empty(n_ctx, width) + nn.init.normal_(ctx_vectors, std=0.02) + self.VPT = nn.Parameter(ctx_vectors) + # self.VPT.half() + scale = width ** -0.5 + self.class_embedding = nn.Parameter(scale * torch.randn(width)) + self.positional_embedding = nn.Parameter(scale * torch.randn((input_resolution // patch_size) ** 2 + 1, width)) + self.ln_pre = LayerNorm(width) + # hyper-parameter if need to add prompt embeddings inside to the input + # of transformer block or not: + self.prompt_till_layer_visual = design_details["vision_depth"] + self.transformer = Transformer_normal(width, layers, heads, prompts_needed=self.prompt_till_layer_visual, + design_details=design_details) + + self.ln_post = LayerNorm(width) + self.proj = nn.Parameter(scale * torch.randn(width, output_dim)) + + def forward(self, x: torch.Tensor): + x = self.conv1(x) # shape = [*, width, grid, grid] + x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2] + x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width] + x = torch.cat( + [self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), + x], dim=1) # shape = [*, grid ** 2 + 1, width] + x = x + self.positional_embedding.to(x.dtype) + + # After positional embeddings, we will attach prompts with the model, remember only those + # are trainable parameters here in whole image encoder. + if self.VPT_shallow: + visual_ctx = self.VPT.expand(x.shape[0], -1, -1).half() + x = torch.cat([x, visual_ctx], dim=1) + else: + assert self.prompt_till_layer_visual == 0 + + # Normal code as before + x = self.ln_pre(x) + + x = x.permute(1, 0, 2) # NLD -> LND + x = self.transformer(x) + x = x.permute(1, 0, 2) # LND -> NLD + + x = self.ln_post(x[:, 0, :]) + + if self.proj is not None: + x = x @ self.proj + + return x + + +class VisionTransformer_MaPLe(nn.Module): + def __init__(self, input_resolution: int, patch_size: int, width: int, layers: int, heads: int, output_dim: int, + design_details): + super().__init__() + self.input_resolution = input_resolution + self.output_dim = output_dim + self.conv1 = nn.Conv2d(in_channels=3, out_channels=width, kernel_size=patch_size, stride=patch_size, bias=False) + self.VPT_shallow = True + scale = width ** -0.5 + self.patch_num = self.input_resolution // patch_size + self.class_embedding = nn.Parameter(scale * torch.randn(width)) + self.positional_embedding = nn.Parameter(scale * torch.randn((input_resolution // patch_size) ** 2 + 1, width)) + self.ln_pre = LayerNorm(width) + # hyper-parameter if need to add prompt embeddings inside to the input + # of transformer block or not: + self.prompt_till_layer_visual = 0 + self.transformer = Transformer(width, layers, heads, design_details=design_details) + + self.ln_post = LayerNorm(width) + self.proj = nn.Parameter(scale * torch.randn(width, output_dim)) + + def forward(self, x: torch.Tensor, shared_ctx, compound_deeper_prompts): + x = self.conv1(x) # shape = [*, width, grid, grid] + x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2] + x = x.permute(0, 2, 1) # shape = [*, grid ** 2, output_size] + x = torch.cat( + [self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), + x], dim=1) # shape = [*, grid ** 2 + 1, width] + x = x + self.positional_embedding.to(x.dtype) + + # After positional embeddings, we will attach prompts with the model, remember only those + # are trainable parameters here in whole image encoder. + if self.VPT_shallow: + visual_ctx = shared_ctx.expand(x.shape[0], -1, -1).half() + x = torch.cat([x, visual_ctx], dim=1) + else: + assert self.prompt_till_layer_visual == 0 + + # Normal code as before + x = self.ln_pre(x) + + x = x.permute(1, 0, 2) # NLD -> LND + # Again combine the inputs, so nn.sequential can work + outputs = self.transformer([x, compound_deeper_prompts, 0]) # third argument is counter + x = outputs[0] + mask = outputs[3] + x = x.permute(1, 0, 2) # LND -> NLD + visual_ctx = x[:,-shared_ctx.shape[0]:,:] + x = self.ln_post(x[:, 0, :]) #only cls embedding is selected + visual_ctx = self.ln_post(visual_ctx) + if self.proj is not None: + x = x @ self.proj + visual_ctx = visual_ctx @ self.proj + return x,visual_ctx,mask + + +class CLIP(nn.Module): + def __init__(self, + embed_dim: int, + # vision + image_resolution: int, + vision_layers: Union[Tuple[int, int, int, int], int], + vision_width: int, + vision_patch_size: int, + # text + context_length: int, + vocab_size: int, + transformer_width: int, + transformer_heads: int, + transformer_layers: int, + design_details + ): + super().__init__() + + self.context_length = context_length + trainer = design_details['trainer'] + + if isinstance(vision_layers, (tuple, list)): + vision_heads = vision_width * 32 // 64 + self.visual = ModifiedResNet( + layers=vision_layers, + output_dim=embed_dim, + heads=vision_heads, + input_resolution=image_resolution, + width=vision_width + ) + + else: + vision_heads = vision_width // 64 + if trainer == "MaPLe": + self.visual = VisionTransformer_MaPLe( + input_resolution=image_resolution, + patch_size=vision_patch_size, + width=vision_width, + layers=vision_layers, + heads=vision_heads, + output_dim=embed_dim, + design_details=design_details + ) + self.visual_ori = VisionTransformer( + input_resolution=image_resolution, + patch_size=vision_patch_size, + width=vision_width, + layers=vision_layers, + heads=vision_heads, + output_dim=embed_dim, + design_details=design_details + ) + else: + self.visual = VisionTransformer( + input_resolution=image_resolution, + patch_size=vision_patch_size, + width=vision_width, + layers=vision_layers, + heads=vision_heads, + output_dim=embed_dim, + design_details=design_details + ) + # hyper-parameter if need to add prompt embeddings inside to the input + # of transformer block or not: + prompt_till_layer_text = design_details['language_depth'] + self.transformer = Transformer( + width=transformer_width, + layers=transformer_layers, + heads=transformer_heads, + attn_mask=self.build_attention_mask(), + prompts_needed=prompt_till_layer_text, + text_layer=True, + design_details=design_details + ) + + self.vocab_size = vocab_size + self.token_embedding = nn.Embedding(vocab_size, transformer_width) + self.positional_embedding = nn.Parameter(torch.empty(self.context_length, transformer_width)) + self.ln_final = LayerNorm(transformer_width) + + self.text_projection = nn.Parameter(torch.empty(transformer_width, embed_dim)) + self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07)) + + self.initialize_parameters() + + def initialize_parameters(self): + nn.init.normal_(self.token_embedding.weight, std=0.02) + nn.init.normal_(self.positional_embedding, std=0.01) + + if isinstance(self.visual, ModifiedResNet): + if self.visual.attnpool is not None: + std = self.visual.attnpool.c_proj.in_features ** -0.5 + nn.init.normal_(self.visual.attnpool.q_proj.weight, std=std) + nn.init.normal_(self.visual.attnpool.k_proj.weight, std=std) + nn.init.normal_(self.visual.attnpool.v_proj.weight, std=std) + nn.init.normal_(self.visual.attnpool.c_proj.weight, std=std) + + for resnet_block in [self.visual.layer1, self.visual.layer2, self.visual.layer3, self.visual.layer4]: + for name, param in resnet_block.named_parameters(): + if name.endswith("bn3.weight"): + nn.init.zeros_(param) + + proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5) + attn_std = self.transformer.width ** -0.5 + fc_std = (2 * self.transformer.width) ** -0.5 + for block in self.transformer.resblocks: + nn.init.normal_(block.attn.in_proj_weight, std=attn_std) + nn.init.normal_(block.attn.out_proj.weight, std=proj_std) + nn.init.normal_(block.mlp.c_fc.weight, std=fc_std) + nn.init.normal_(block.mlp.c_proj.weight, std=proj_std) + + if self.text_projection is not None: + nn.init.normal_(self.text_projection, std=self.transformer.width ** -0.5) + + def build_attention_mask(self): + # lazily create causal attention mask, with full attention between the vision tokens + # pytorch uses additive attention mask; fill with -inf + mask = torch.empty(self.context_length, self.context_length) + mask.fill_(float("-inf")) + mask.triu_(1) # zero out the lower diagonal + return mask + + @property + def dtype(self): + return self.visual.conv1.weight.dtype + + def encode_image(self, image): + return self.visual(image.type(self.dtype)) + + def encode_text(self, text): + x = self.token_embedding(text).type(self.dtype) # [batch_size, n_ctx, d_model] + + x = x + self.positional_embedding.type(self.dtype) + x = x.permute(1, 0, 2) # NLD -> LND + x = self.transformer(x) + x = x.permute(1, 0, 2) # LND -> NLD + x = self.ln_final(x).type(self.dtype) + + # x.shape = [batch_size, n_ctx, transformer.width] + # take features from the eot embedding (eot_token is the highest number in each sequence) + x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection + + return x + + def forward(self, image, text): + image_features = self.encode_image(image) + text_features = self.encode_text(text) + + # normalized features + image_features = image_features / image_features.norm(dim=-1, keepdim=True) + text_features = text_features / text_features.norm(dim=-1, keepdim=True) + + # cosine similarity as logits + logit_scale = self.logit_scale.exp() + logits_per_image = logit_scale * image_features @ text_features.t() + logits_per_text = logit_scale * text_features @ image_features.t() + + # shape = [global_batch_size, global_batch_size] + return logits_per_image, logits_per_text + + +def convert_weights(model: nn.Module): + """Convert applicable model parameters to fp16""" + + def _convert_weights_to_fp16(l): + if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)): + l.weight.data = l.weight.data.half() + if l.bias is not None: + l.bias.data = l.bias.data.half() + + if isinstance(l, nn.MultiheadAttention): + for attr in [*[f"{s}_proj_weight" for s in ["in", "q", "k", "v"]], "in_proj_bias", "bias_k", "bias_v"]: + tensor = getattr(l, attr) + if tensor is not None: + tensor.data = tensor.data.half() + + for name in ["text_projection", "proj"]: + if hasattr(l, name): + attr = getattr(l, name) + if attr is not None: + attr.data = attr.data.half() + + model.apply(_convert_weights_to_fp16) + + +def build_model(state_dict: dict, design_details): + vit = "visual.proj" in state_dict + + if vit: + vision_width = state_dict["visual.conv1.weight"].shape[0] + vision_layers = len( + [k for k in state_dict.keys() if k.startswith("visual.") and k.endswith(".attn.in_proj_weight")]) + vision_patch_size = state_dict["visual.conv1.weight"].shape[-1] + grid_size = round((state_dict["visual.positional_embedding"].shape[0] - 1) ** 0.5) + image_resolution = vision_patch_size * grid_size + else: + counts: list = [len(set(k.split(".")[2] for k in state_dict if k.startswith(f"visual.layer{b}"))) for b in + [1, 2, 3, 4]] + vision_layers = tuple(counts) + vision_width = state_dict["visual.layer1.0.conv1.weight"].shape[0] + output_width = round((state_dict["visual.attnpool.positional_embedding"].shape[0] - 1) ** 0.5) + vision_patch_size = None + assert output_width ** 2 + 1 == state_dict["visual.attnpool.positional_embedding"].shape[0] + image_resolution = output_width * 32 + + embed_dim = state_dict["text_projection"].shape[1] + context_length = state_dict["positional_embedding"].shape[0] + vocab_size = state_dict["token_embedding.weight"].shape[0] + transformer_width = state_dict["ln_final.weight"].shape[0] + transformer_heads = transformer_width // 64 + transformer_layers = len(set(k.split(".")[2] for k in state_dict if k.startswith(f"transformer.resblocks"))) + + model = CLIP( + embed_dim, + image_resolution, vision_layers, vision_width, vision_patch_size, + context_length, vocab_size, transformer_width, transformer_heads, transformer_layers, design_details + ) + + for key in ["input_resolution", "context_length", "vocab_size"]: + if key in state_dict: + del state_dict[key] + + convert_weights(model) + try: + model.load_state_dict(state_dict) + except: + missing_keys, _ = model.load_state_dict(state_dict, strict=False) + print('Weights not found for some missing keys: ', missing_keys) + return model.eval() diff --git a/clip/simple_tokenizer.py b/clip/simple_tokenizer.py new file mode 100644 index 0000000..0a66286 --- /dev/null +++ b/clip/simple_tokenizer.py @@ -0,0 +1,132 @@ +import gzip +import html +import os +from functools import lru_cache + +import ftfy +import regex as re + + +@lru_cache() +def default_bpe(): + return os.path.join(os.path.dirname(os.path.abspath(__file__)), "bpe_simple_vocab_16e6.txt.gz") + + +@lru_cache() +def bytes_to_unicode(): + """ + Returns list of utf-8 byte and a corresponding list of unicode strings. + The reversible bpe codes work on unicode strings. + This means you need a large # of unicode characters in your vocab if you want to avoid UNKs. + When you're at something like a 10B token dataset you end up needing around 5K for decent coverage. + This is a signficant percentage of your normal, say, 32K bpe vocab. + To avoid that, we want lookup tables between utf-8 bytes and unicode strings. + And avoids mapping to whitespace/control characters the bpe code barfs on. + """ + bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1)) + cs = bs[:] + n = 0 + for b in range(2**8): + if b not in bs: + bs.append(b) + cs.append(2**8+n) + n += 1 + cs = [chr(n) for n in cs] + return dict(zip(bs, cs)) + + +def get_pairs(word): + """Return set of symbol pairs in a word. + Word is represented as tuple of symbols (symbols being variable-length strings). + """ + pairs = set() + prev_char = word[0] + for char in word[1:]: + pairs.add((prev_char, char)) + prev_char = char + return pairs + + +def basic_clean(text): + text = ftfy.fix_text(text) + text = html.unescape(html.unescape(text)) + return text.strip() + + +def whitespace_clean(text): + text = re.sub(r'\s+', ' ', text) + text = text.strip() + return text + + +class SimpleTokenizer(object): + def __init__(self, bpe_path: str = default_bpe()): + self.byte_encoder = bytes_to_unicode() + self.byte_decoder = {v: k for k, v in self.byte_encoder.items()} + merges = gzip.open(bpe_path).read().decode("utf-8").split('\n') + merges = merges[1:49152-256-2+1] + merges = [tuple(merge.split()) for merge in merges] + vocab = list(bytes_to_unicode().values()) + vocab = vocab + [v+'' for v in vocab] + for merge in merges: + vocab.append(''.join(merge)) + vocab.extend(['<|startoftext|>', '<|endoftext|>']) + self.encoder = dict(zip(vocab, range(len(vocab)))) + self.decoder = {v: k for k, v in self.encoder.items()} + self.bpe_ranks = dict(zip(merges, range(len(merges)))) + self.cache = {'<|startoftext|>': '<|startoftext|>', '<|endoftext|>': '<|endoftext|>'} + self.pat = re.compile(r"""<\|startoftext\|>|<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""", re.IGNORECASE) + + def bpe(self, token): + if token in self.cache: + return self.cache[token] + word = tuple(token[:-1]) + ( token[-1] + '',) + pairs = get_pairs(word) + + if not pairs: + return token+'' + + while True: + bigram = min(pairs, key = lambda pair: self.bpe_ranks.get(pair, float('inf'))) + if bigram not in self.bpe_ranks: + break + first, second = bigram + new_word = [] + i = 0 + while i < len(word): + try: + j = word.index(first, i) + new_word.extend(word[i:j]) + i = j + except: + new_word.extend(word[i:]) + break + + if word[i] == first and i < len(word)-1 and word[i+1] == second: + new_word.append(first+second) + i += 2 + else: + new_word.append(word[i]) + i += 1 + new_word = tuple(new_word) + word = new_word + if len(word) == 1: + break + else: + pairs = get_pairs(word) + word = ' '.join(word) + self.cache[token] = word + return word + + def encode(self, text): + bpe_tokens = [] + text = whitespace_clean(basic_clean(text)).lower() + for token in re.findall(self.pat, text): + token = ''.join(self.byte_encoder[b] for b in 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a/configs/datasets/eurosat.yaml b/configs/datasets/eurosat.yaml new file mode 100644 index 0000000..88e9b83 --- /dev/null +++ b/configs/datasets/eurosat.yaml @@ -0,0 +1,2 @@ +DATASET: + NAME: "EuroSAT" diff --git a/configs/datasets/fgvc_aircraft.yaml b/configs/datasets/fgvc_aircraft.yaml new file mode 100644 index 0000000..fa72f46 --- /dev/null +++ b/configs/datasets/fgvc_aircraft.yaml @@ -0,0 +1,2 @@ +DATASET: + NAME: "FGVCAircraft" diff --git a/configs/datasets/food101.yaml b/configs/datasets/food101.yaml new file mode 100644 index 0000000..c9f8bfe --- /dev/null +++ b/configs/datasets/food101.yaml @@ -0,0 +1,2 @@ +DATASET: + NAME: "Food101" diff --git a/configs/datasets/imagenet.yaml b/configs/datasets/imagenet.yaml new file mode 100644 index 0000000..d1170ff --- /dev/null +++ b/configs/datasets/imagenet.yaml @@ -0,0 +1,2 @@ +DATASET: + NAME: "ImageNet" diff --git a/configs/datasets/imagenet_a.yaml b/configs/datasets/imagenet_a.yaml new file mode 100644 index 0000000..ccd3b30 --- /dev/null +++ b/configs/datasets/imagenet_a.yaml @@ -0,0 +1,2 @@ +DATASET: + NAME: "ImageNetA" diff --git a/configs/datasets/imagenet_r.yaml b/configs/datasets/imagenet_r.yaml new file mode 100644 index 0000000..f34269f --- /dev/null +++ b/configs/datasets/imagenet_r.yaml @@ -0,0 +1,2 @@ +DATASET: + NAME: "ImageNetR" diff --git a/configs/datasets/imagenet_sketch.yaml b/configs/datasets/imagenet_sketch.yaml new file mode 100644 index 0000000..feff22b --- /dev/null +++ b/configs/datasets/imagenet_sketch.yaml @@ -0,0 +1,2 @@ +DATASET: + NAME: "ImageNetSketch" diff --git a/configs/datasets/imagenetv2.yaml b/configs/datasets/imagenetv2.yaml new file mode 100644 index 0000000..9fcdc0f --- /dev/null +++ b/configs/datasets/imagenetv2.yaml @@ -0,0 +1,2 @@ +DATASET: + NAME: "ImageNetV2" diff --git a/configs/datasets/oxford_flowers.yaml b/configs/datasets/oxford_flowers.yaml new file mode 100644 index 0000000..769ecfa --- /dev/null +++ b/configs/datasets/oxford_flowers.yaml @@ -0,0 +1,2 @@ +DATASET: + NAME: "OxfordFlowers" \ No newline at end of file diff --git a/configs/datasets/oxford_pets.yaml b/configs/datasets/oxford_pets.yaml new file mode 100644 index 0000000..14ea509 --- /dev/null +++ b/configs/datasets/oxford_pets.yaml @@ -0,0 +1,2 @@ +DATASET: + NAME: "OxfordPets" \ No newline at end of file diff --git a/configs/datasets/pascal_voc.yaml b/configs/datasets/pascal_voc.yaml new file mode 100644 index 0000000..6e94373 --- /dev/null +++ b/configs/datasets/pascal_voc.yaml @@ -0,0 +1,2 @@ +DATASET: + NAME: "VOC12" \ No newline at end of file diff --git a/configs/datasets/stanford_cars.yaml b/configs/datasets/stanford_cars.yaml new file mode 100644 index 0000000..0362aff --- /dev/null +++ b/configs/datasets/stanford_cars.yaml @@ -0,0 +1,2 @@ +DATASET: + NAME: "StanfordCars" diff --git a/configs/datasets/sun397.yaml b/configs/datasets/sun397.yaml new file mode 100644 index 0000000..461bfb6 --- /dev/null +++ b/configs/datasets/sun397.yaml @@ -0,0 +1,2 @@ +DATASET: + NAME: "SUN397" diff --git a/configs/datasets/ucf101.yaml b/configs/datasets/ucf101.yaml new file mode 100644 index 0000000..5467a6d --- /dev/null +++ b/configs/datasets/ucf101.yaml @@ -0,0 +1,2 @@ +DATASET: + NAME: "UCF101" diff --git a/configs/glip_Swin_T_O365_GoldG.yaml b/configs/glip_Swin_T_O365_GoldG.yaml new file mode 100644 index 0000000..1cf3528 --- /dev/null +++ b/configs/glip_Swin_T_O365_GoldG.yaml @@ -0,0 +1,100 @@ +MODEL: + META_ARCHITECTURE: "GeneralizedVLRCNN" + WEIGHT: "swin_tiny_patch4_window7_224.pth" + RPN_ONLY: True + RPN_ARCHITECTURE: "VLDYHEAD" + + BACKBONE: + CONV_BODY: "SWINT-FPN-RETINANET" + OUT_CHANNELS: 256 + FREEZE_CONV_BODY_AT: -1 + + LANGUAGE_BACKBONE: + FREEZE: False + MODEL_TYPE: "bert-base-uncased" # "roberta-base", "clip" + MASK_SPECIAL: False + + RPN: + USE_FPN: True + ANCHOR_SIZES: (64, 128, 256, 512, 1024) + ANCHOR_STRIDE: (8, 16, 32, 64, 128) + ASPECT_RATIOS: (1.0,) + SCALES_PER_OCTAVE: 1 + + DYHEAD: + CHANNELS: 256 + NUM_CONVS: 6 + USE_GN: True + USE_DYRELU: True + USE_DFCONV: True + USE_DYFUSE: True + TOPK: 9 # topk for selecting candidate positive samples from each level + SCORE_AGG: "MEAN" + LOG_SCALE: 0.0 + + FUSE_CONFIG: + EARLY_FUSE_ON: True + TYPE: "MHA-B" + USE_CLASSIFICATION_LOSS: False + USE_TOKEN_LOSS: False + USE_CONTRASTIVE_ALIGN_LOSS: False + CONTRASTIVE_HIDDEN_DIM: 64 + USE_DOT_PRODUCT_TOKEN_LOSS: True + USE_FUSED_FEATURES_DOT_PRODUCT: True + USE_LAYER_SCALE: True + CLAMP_MIN_FOR_UNDERFLOW: True + CLAMP_MAX_FOR_OVERFLOW: True + CLAMP_BERTATTN_MIN_FOR_UNDERFLOW: True + CLAMP_BERTATTN_MAX_FOR_OVERFLOW: True + CLAMP_DOT_PRODUCT: True + + USE_CHECKPOINT: True + +TEST: + DURING_TRAINING: False + IMS_PER_BATCH: 64 + +# use for grounding model +DATASETS: + TRAIN: ("object365_dt_train", "mixed_train_no_coco", "flickr30k_train", ) + TEST: ("coco_2014_val", ) + DISABLE_SHUFFLE: False + ADD_DET_PROMPT: False + RANDOM_SAMPLE_NEG: 85 + CONTROL_PROB: (0.0, 0.0, 0.5, 0.0) + + SEPARATION_TOKENS: ". " + +INPUT: + PIXEL_MEAN: [ 103.530, 116.280, 123.675 ] + PIXEL_STD: [ 57.375, 57.120, 58.395 ] + MIN_SIZE_TRAIN: 800 + MAX_SIZE_TRAIN: 1333 + MIN_SIZE_TEST: 800 + MAX_SIZE_TEST: 1333 + +AUGMENT: + MULT_MIN_SIZE_TRAIN: (480,560,640,720,800) + +DATALOADER: + SIZE_DIVISIBILITY: 32 + +SOLVER: + OPTIMIZER: ADAMW + BASE_LR: 0.0001 + LANG_LR: 0.00001 + WEIGHT_DECAY: 0.0001 + STEPS: (0.67, 0.89) + MAX_EPOCH: 30 + IMS_PER_BATCH: 64 + WARMUP_ITERS: 2000 + WARMUP_FACTOR: 0.001 + USE_AMP: True + MODEL_EMA: 0.999 + FIND_UNUSED_PARAMETERS: False + + CLIP_GRADIENTS: + ENABLED: True + CLIP_TYPE: "full_model" + CLIP_VALUE: 1.0 + NORM_TYPE: 2.0 \ No newline at end of file diff --git a/configs/trainers/CoCoOp/vit_b16_c16_ep10_batch1.yaml b/configs/trainers/CoCoOp/vit_b16_c16_ep10_batch1.yaml new file mode 100644 index 0000000..b403143 --- /dev/null +++ b/configs/trainers/CoCoOp/vit_b16_c16_ep10_batch1.yaml @@ -0,0 +1,35 @@ +DATALOADER: + TRAIN_X: + BATCH_SIZE: 1 + TEST: + BATCH_SIZE: 100 + NUM_WORKERS: 8 + +INPUT: + SIZE: (224, 224) + INTERPOLATION: "bicubic" + PIXEL_MEAN: [0.48145466, 0.4578275, 0.40821073] + PIXEL_STD: [0.26862954, 0.26130258, 0.27577711] + TRANSFORMS: ["random_resized_crop", "random_flip", "normalize"] + +OPTIM: + NAME: "sgd" + LR: 0.002 + MAX_EPOCH: 10 + LR_SCHEDULER: "cosine" + WARMUP_EPOCH: 1 + WARMUP_TYPE: "constant" + WARMUP_CONS_LR: 1e-5 + +TRAIN: + PRINT_FREQ: 20 + +MODEL: + BACKBONE: + NAME: "ViT-B/16" + +TRAINER: + COCOOP: + N_CTX: 16 + CTX_INIT: "" + PREC: "fp16" \ No newline at end of file diff --git a/configs/trainers/CoCoOp/vit_b16_c4_ep10_batch1.yaml b/configs/trainers/CoCoOp/vit_b16_c4_ep10_batch1.yaml new file mode 100644 index 0000000..048d9a2 --- /dev/null +++ b/configs/trainers/CoCoOp/vit_b16_c4_ep10_batch1.yaml @@ -0,0 +1,35 @@ +DATALOADER: + TRAIN_X: + BATCH_SIZE: 1 + TEST: + BATCH_SIZE: 100 + NUM_WORKERS: 8 + +INPUT: + SIZE: (224, 224) + INTERPOLATION: "bicubic" + PIXEL_MEAN: [0.48145466, 0.4578275, 0.40821073] + PIXEL_STD: [0.26862954, 0.26130258, 0.27577711] + TRANSFORMS: ["random_resized_crop", "random_flip", "normalize"] + +OPTIM: + NAME: "sgd" + LR: 0.002 + MAX_EPOCH: 10 + LR_SCHEDULER: "cosine" + WARMUP_EPOCH: 1 + WARMUP_TYPE: "constant" + WARMUP_CONS_LR: 1e-5 + +TRAIN: + PRINT_FREQ: 20 + +MODEL: + BACKBONE: + NAME: "ViT-B/16" + +TRAINER: + COCOOP: + N_CTX: 4 + CTX_INIT: "" + PREC: "fp16" \ No newline at end of file diff --git a/configs/trainers/CoCoOp/vit_b16_c4_ep10_batch1_ctxv1.yaml b/configs/trainers/CoCoOp/vit_b16_c4_ep10_batch1_ctxv1.yaml new file mode 100644 index 0000000..4611d87 --- /dev/null +++ b/configs/trainers/CoCoOp/vit_b16_c4_ep10_batch1_ctxv1.yaml @@ -0,0 +1,35 @@ +DATALOADER: + TRAIN_X: + BATCH_SIZE: 1 + TEST: + BATCH_SIZE: 100 + NUM_WORKERS: 8 + +INPUT: + SIZE: (224, 224) + INTERPOLATION: "bicubic" + PIXEL_MEAN: [0.48145466, 0.4578275, 0.40821073] + PIXEL_STD: [0.26862954, 0.26130258, 0.27577711] + TRANSFORMS: ["random_resized_crop", "random_flip", "normalize"] + +OPTIM: + NAME: "sgd" + LR: 0.002 + MAX_EPOCH: 10 + LR_SCHEDULER: "cosine" + WARMUP_EPOCH: 1 + WARMUP_TYPE: "constant" + WARMUP_CONS_LR: 1e-5 + +TRAIN: + PRINT_FREQ: 20 + +MODEL: + BACKBONE: + NAME: "ViT-B/16" + +TRAINER: + COCOOP: + N_CTX: 4 + CTX_INIT: "a photo of a" + PREC: "fp16" \ No newline at end of file diff --git a/configs/trainers/CoCoOp/vit_b16_c8_ep10_batch1.yaml b/configs/trainers/CoCoOp/vit_b16_c8_ep10_batch1.yaml new file mode 100644 index 0000000..3349c14 --- /dev/null +++ b/configs/trainers/CoCoOp/vit_b16_c8_ep10_batch1.yaml @@ -0,0 +1,35 @@ +DATALOADER: + TRAIN_X: + BATCH_SIZE: 1 + TEST: + BATCH_SIZE: 100 + NUM_WORKERS: 8 + +INPUT: + SIZE: (224, 224) + INTERPOLATION: "bicubic" + PIXEL_MEAN: [0.48145466, 0.4578275, 0.40821073] + PIXEL_STD: [0.26862954, 0.26130258, 0.27577711] + TRANSFORMS: ["random_resized_crop", "random_flip", "normalize"] + +OPTIM: + NAME: "sgd" + LR: 0.002 + MAX_EPOCH: 10 + LR_SCHEDULER: "cosine" + WARMUP_EPOCH: 1 + WARMUP_TYPE: "constant" + WARMUP_CONS_LR: 1e-5 + +TRAIN: + PRINT_FREQ: 20 + +MODEL: + BACKBONE: + NAME: "ViT-B/16" + +TRAINER: + COCOOP: + N_CTX: 8 + CTX_INIT: "" + PREC: "fp16" \ No newline at end of file diff --git a/configs/trainers/CoOp/rn101.yaml b/configs/trainers/CoOp/rn101.yaml new file mode 100644 index 0000000..c67838f --- /dev/null +++ b/configs/trainers/CoOp/rn101.yaml @@ -0,0 +1,29 @@ +DATALOADER: + TRAIN_X: + BATCH_SIZE: 32 + TEST: + BATCH_SIZE: 100 + NUM_WORKERS: 8 + +INPUT: + SIZE: (224, 224) + INTERPOLATION: "bicubic" + PIXEL_MEAN: [0.48145466, 0.4578275, 0.40821073] + PIXEL_STD: [0.26862954, 0.26130258, 0.27577711] + TRANSFORMS: ["random_resized_crop", "random_flip", "normalize"] + +OPTIM: + NAME: "sgd" + LR: 0.002 + MAX_EPOCH: 200 + LR_SCHEDULER: "cosine" + WARMUP_EPOCH: 1 + WARMUP_TYPE: "constant" + WARMUP_CONS_LR: 1e-5 + +TRAIN: + PRINT_FREQ: 5 + +MODEL: + BACKBONE: + NAME: "RN101" \ No newline at end of file diff --git a/configs/trainers/CoOp/rn101_ep50.yaml b/configs/trainers/CoOp/rn101_ep50.yaml new file mode 100644 index 0000000..a291895 --- /dev/null +++ b/configs/trainers/CoOp/rn101_ep50.yaml @@ -0,0 +1,29 @@ +DATALOADER: + TRAIN_X: + BATCH_SIZE: 32 + TEST: + BATCH_SIZE: 100 + NUM_WORKERS: 8 + +INPUT: + SIZE: (224, 224) + INTERPOLATION: "bicubic" + PIXEL_MEAN: [0.48145466, 0.4578275, 0.40821073] + PIXEL_STD: [0.26862954, 0.26130258, 0.27577711] + TRANSFORMS: ["random_resized_crop", "random_flip", "normalize"] + +OPTIM: + NAME: "sgd" + LR: 0.002 + MAX_EPOCH: 50 + LR_SCHEDULER: "cosine" + WARMUP_EPOCH: 1 + WARMUP_TYPE: "constant" + WARMUP_CONS_LR: 1e-5 + +TRAIN: + PRINT_FREQ: 5 + +MODEL: + BACKBONE: + NAME: "RN101" \ No newline at end of file diff --git a/configs/trainers/CoOp/rn50.yaml b/configs/trainers/CoOp/rn50.yaml new file mode 100644 index 0000000..7638b1b --- /dev/null +++ b/configs/trainers/CoOp/rn50.yaml @@ -0,0 +1,29 @@ +DATALOADER: + TRAIN_X: + BATCH_SIZE: 32 + TEST: + BATCH_SIZE: 100 + NUM_WORKERS: 8 + +INPUT: + SIZE: (224, 224) + INTERPOLATION: "bicubic" + PIXEL_MEAN: [0.48145466, 0.4578275, 0.40821073] + PIXEL_STD: [0.26862954, 0.26130258, 0.27577711] + TRANSFORMS: ["random_resized_crop", "random_flip", "normalize"] + +OPTIM: + NAME: "sgd" + LR: 0.002 + MAX_EPOCH: 200 + LR_SCHEDULER: "cosine" + WARMUP_EPOCH: 1 + WARMUP_TYPE: "constant" + WARMUP_CONS_LR: 1e-5 + +TRAIN: + PRINT_FREQ: 5 + +MODEL: + BACKBONE: + NAME: "RN50" \ No newline at end of file diff --git a/configs/trainers/CoOp/rn50_ctxv1.yaml b/configs/trainers/CoOp/rn50_ctxv1.yaml new file mode 100644 index 0000000..e902b56 --- /dev/null +++ b/configs/trainers/CoOp/rn50_ctxv1.yaml @@ -0,0 +1,33 @@ +DATALOADER: + TRAIN_X: + BATCH_SIZE: 32 + TEST: + BATCH_SIZE: 100 + NUM_WORKERS: 8 + +INPUT: + SIZE: (224, 224) + INTERPOLATION: "bicubic" + PIXEL_MEAN: [0.48145466, 0.4578275, 0.40821073] + PIXEL_STD: [0.26862954, 0.26130258, 0.27577711] + TRANSFORMS: ["random_resized_crop", "random_flip", "normalize"] + +OPTIM: + NAME: "sgd" + LR: 0.002 + MAX_EPOCH: 200 + LR_SCHEDULER: "cosine" + WARMUP_EPOCH: 1 + WARMUP_TYPE: "constant" + WARMUP_CONS_LR: 1e-5 + +TRAIN: + PRINT_FREQ: 5 + +MODEL: + BACKBONE: + NAME: "RN50" + +TRAINER: + COOP: + CTX_INIT: "a photo of a" diff --git a/configs/trainers/CoOp/rn50_ep100.yaml b/configs/trainers/CoOp/rn50_ep100.yaml new file mode 100644 index 0000000..fd0968f --- /dev/null +++ b/configs/trainers/CoOp/rn50_ep100.yaml @@ -0,0 +1,29 @@ +DATALOADER: + TRAIN_X: + BATCH_SIZE: 32 + TEST: + BATCH_SIZE: 100 + NUM_WORKERS: 8 + +INPUT: + SIZE: (224, 224) + INTERPOLATION: "bicubic" + PIXEL_MEAN: [0.48145466, 0.4578275, 0.40821073] + PIXEL_STD: [0.26862954, 0.26130258, 0.27577711] + TRANSFORMS: ["random_resized_crop", "random_flip", "normalize"] + +OPTIM: + NAME: "sgd" + LR: 0.002 + MAX_EPOCH: 100 + LR_SCHEDULER: "cosine" + WARMUP_EPOCH: 1 + WARMUP_TYPE: "constant" + WARMUP_CONS_LR: 1e-5 + +TRAIN: + PRINT_FREQ: 5 + +MODEL: + BACKBONE: + NAME: "RN50" diff --git a/configs/trainers/CoOp/rn50_ep50.yaml b/configs/trainers/CoOp/rn50_ep50.yaml new file mode 100644 index 0000000..8d7ef34 --- /dev/null +++ b/configs/trainers/CoOp/rn50_ep50.yaml @@ -0,0 +1,29 @@ +DATALOADER: + TRAIN_X: + BATCH_SIZE: 32 + TEST: + BATCH_SIZE: 100 + NUM_WORKERS: 8 + +INPUT: + SIZE: (224, 224) + INTERPOLATION: "bicubic" + PIXEL_MEAN: [0.48145466, 0.4578275, 0.40821073] + PIXEL_STD: [0.26862954, 0.26130258, 0.27577711] + TRANSFORMS: ["random_resized_crop", "random_flip", "normalize"] + +OPTIM: + NAME: "sgd" + LR: 0.002 + MAX_EPOCH: 50 + LR_SCHEDULER: "cosine" + WARMUP_EPOCH: 1 + WARMUP_TYPE: "constant" + WARMUP_CONS_LR: 1e-5 + +TRAIN: + PRINT_FREQ: 5 + +MODEL: + BACKBONE: + NAME: "RN50" \ No newline at end of file diff --git a/configs/trainers/CoOp/rn50_ep50_ctxv1.yaml b/configs/trainers/CoOp/rn50_ep50_ctxv1.yaml new file mode 100644 index 0000000..5bf06f9 --- /dev/null +++ b/configs/trainers/CoOp/rn50_ep50_ctxv1.yaml @@ -0,0 +1,33 @@ +DATALOADER: + TRAIN_X: + BATCH_SIZE: 32 + TEST: + BATCH_SIZE: 100 + NUM_WORKERS: 8 + +INPUT: + SIZE: (224, 224) + INTERPOLATION: "bicubic" + PIXEL_MEAN: [0.48145466, 0.4578275, 0.40821073] + PIXEL_STD: [0.26862954, 0.26130258, 0.27577711] + TRANSFORMS: ["random_resized_crop", "random_flip", "normalize"] + +OPTIM: + NAME: "sgd" + LR: 0.002 + MAX_EPOCH: 50 + LR_SCHEDULER: "cosine" + WARMUP_EPOCH: 1 + WARMUP_TYPE: "constant" + WARMUP_CONS_LR: 1e-5 + +TRAIN: + PRINT_FREQ: 5 + +MODEL: + BACKBONE: + NAME: "RN50" + +TRAINER: + COOP: + CTX_INIT: "a photo of a" \ No newline at end of file diff --git a/configs/trainers/CoOp/rn50_val.yaml b/configs/trainers/CoOp/rn50_val.yaml new file mode 100644 index 0000000..6b0e97e --- /dev/null +++ b/configs/trainers/CoOp/rn50_val.yaml @@ -0,0 +1,17 @@ +DATALOADER: + TRAIN_X: + BATCH_SIZE: 200 + TEST: + BATCH_SIZE: 200 + NUM_WORKERS: 8 + +INPUT: + SIZE: (224, 224) + INTERPOLATION: "bicubic" + PIXEL_MEAN: [0.48145466, 0.4578275, 0.40821073] + PIXEL_STD: [0.26862954, 0.26130258, 0.27577711] + TRANSFORMS: ["random_resized_crop", "random_flip", "normalize"] + +MODEL: + BACKBONE: + NAME: "RN50" \ No newline at end of file diff --git a/configs/trainers/CoOp/vit_b16.yaml b/configs/trainers/CoOp/vit_b16.yaml new file mode 100644 index 0000000..00e1df5 --- /dev/null +++ b/configs/trainers/CoOp/vit_b16.yaml @@ -0,0 +1,29 @@ +DATALOADER: + TRAIN_X: + BATCH_SIZE: 32 + TEST: + BATCH_SIZE: 100 + NUM_WORKERS: 8 + +INPUT: + SIZE: (224, 224) + INTERPOLATION: "bicubic" + PIXEL_MEAN: [0.48145466, 0.4578275, 0.40821073] + PIXEL_STD: [0.26862954, 0.26130258, 0.27577711] + TRANSFORMS: ["random_resized_crop", "random_flip", "normalize"] + +OPTIM: + NAME: "sgd" + LR: 0.002 + MAX_EPOCH: 200 + LR_SCHEDULER: "cosine" + WARMUP_EPOCH: 1 + WARMUP_TYPE: "constant" + WARMUP_CONS_LR: 1e-5 + +TRAIN: + PRINT_FREQ: 5 + +MODEL: + BACKBONE: + NAME: "ViT-B/16" \ No newline at end of file diff --git a/configs/trainers/CoOp/vit_b16_ep100.yaml b/configs/trainers/CoOp/vit_b16_ep100.yaml new file mode 100644 index 0000000..31bd7bc --- /dev/null +++ b/configs/trainers/CoOp/vit_b16_ep100.yaml @@ -0,0 +1,29 @@ +DATALOADER: + TRAIN_X: + BATCH_SIZE: 32 + TEST: + BATCH_SIZE: 100 + NUM_WORKERS: 8 + +INPUT: + SIZE: (224, 224) + INTERPOLATION: "bicubic" + PIXEL_MEAN: [0.48145466, 0.4578275, 0.40821073] + PIXEL_STD: [0.26862954, 0.26130258, 0.27577711] + TRANSFORMS: ["random_resized_crop", "random_flip", "normalize"] + +OPTIM: + NAME: "sgd" + LR: 0.002 + MAX_EPOCH: 100 + LR_SCHEDULER: "cosine" + WARMUP_EPOCH: 1 + WARMUP_TYPE: "constant" + WARMUP_CONS_LR: 1e-5 + +TRAIN: + PRINT_FREQ: 5 + +MODEL: + BACKBONE: + NAME: "ViT-B/16" \ No newline at end of file diff --git a/configs/trainers/CoOp/vit_b16_ep50.yaml b/configs/trainers/CoOp/vit_b16_ep50.yaml new file mode 100644 index 0000000..786b980 --- /dev/null +++ b/configs/trainers/CoOp/vit_b16_ep50.yaml @@ -0,0 +1,29 @@ +DATALOADER: + TRAIN_X: + BATCH_SIZE: 32 + TEST: + BATCH_SIZE: 100 + NUM_WORKERS: 8 + +INPUT: + SIZE: (224, 224) + INTERPOLATION: "bicubic" + PIXEL_MEAN: [0.48145466, 0.4578275, 0.40821073] + PIXEL_STD: [0.26862954, 0.26130258, 0.27577711] + TRANSFORMS: ["random_resized_crop", "random_flip", "normalize"] + +OPTIM: + NAME: "sgd" + LR: 0.002 + MAX_EPOCH: 50 + LR_SCHEDULER: "cosine" + WARMUP_EPOCH: 1 + WARMUP_TYPE: "constant" + WARMUP_CONS_LR: 1e-5 + +TRAIN: + PRINT_FREQ: 5 + +MODEL: + BACKBONE: + NAME: "ViT-B/16" \ No newline at end of file diff --git a/configs/trainers/CoOp/vit_b32.yaml b/configs/trainers/CoOp/vit_b32.yaml new file mode 100644 index 0000000..9919840 --- /dev/null +++ b/configs/trainers/CoOp/vit_b32.yaml @@ -0,0 +1,29 @@ +DATALOADER: + TRAIN_X: + BATCH_SIZE: 32 + TEST: + BATCH_SIZE: 100 + NUM_WORKERS: 8 + +INPUT: + SIZE: (224, 224) + INTERPOLATION: "bicubic" + PIXEL_MEAN: [0.48145466, 0.4578275, 0.40821073] + PIXEL_STD: [0.26862954, 0.26130258, 0.27577711] + TRANSFORMS: ["random_resized_crop", "random_flip", "normalize"] + +OPTIM: + NAME: "sgd" + LR: 0.002 + MAX_EPOCH: 200 + LR_SCHEDULER: "cosine" + WARMUP_EPOCH: 1 + WARMUP_TYPE: "constant" + WARMUP_CONS_LR: 1e-5 + +TRAIN: + PRINT_FREQ: 5 + +MODEL: + BACKBONE: + NAME: "ViT-B/32" \ No newline at end of file diff --git a/configs/trainers/CoOp/vit_b32_ep50.yaml b/configs/trainers/CoOp/vit_b32_ep50.yaml new file mode 100644 index 0000000..cf9f8e1 --- /dev/null +++ b/configs/trainers/CoOp/vit_b32_ep50.yaml @@ -0,0 +1,29 @@ +DATALOADER: + TRAIN_X: + BATCH_SIZE: 32 + TEST: + BATCH_SIZE: 100 + NUM_WORKERS: 8 + +INPUT: + SIZE: (224, 224) + INTERPOLATION: "bicubic" + PIXEL_MEAN: [0.48145466, 0.4578275, 0.40821073] + PIXEL_STD: [0.26862954, 0.26130258, 0.27577711] + TRANSFORMS: ["random_resized_crop", "random_flip", "normalize"] + +OPTIM: + NAME: "sgd" + LR: 0.002 + MAX_EPOCH: 50 + LR_SCHEDULER: "cosine" + WARMUP_EPOCH: 1 + WARMUP_TYPE: "constant" + WARMUP_CONS_LR: 1e-5 + +TRAIN: + PRINT_FREQ: 5 + +MODEL: + BACKBONE: + NAME: "ViT-B/32" \ No newline at end of file diff --git a/configs/trainers/IVLP/vit_b16_c2_ep5_batch4_2+2ctx.yaml b/configs/trainers/IVLP/vit_b16_c2_ep5_batch4_2+2ctx.yaml new file mode 100644 index 0000000..70ecad2 --- /dev/null +++ b/configs/trainers/IVLP/vit_b16_c2_ep5_batch4_2+2ctx.yaml @@ -0,0 +1,39 @@ +# Deep independent V-L Prompting +DATALOADER: + TRAIN_X: + BATCH_SIZE: 4 + TEST: + BATCH_SIZE: 100 + NUM_WORKERS: 8 + +INPUT: + SIZE: (224, 224) + INTERPOLATION: "bicubic" + PIXEL_MEAN: [0.48145466, 0.4578275, 0.40821073] + PIXEL_STD: [0.26862954, 0.26130258, 0.27577711] + TRANSFORMS: ["random_resized_crop", "random_flip", "normalize"] + +OPTIM: + NAME: "sgd" + LR: 0.0035 + MAX_EPOCH: 5 + LR_SCHEDULER: "cosine" + WARMUP_EPOCH: 1 + WARMUP_TYPE: "constant" + WARMUP_CONS_LR: 1e-5 + +TRAIN: + PRINT_FREQ: 20 + +MODEL: + BACKBONE: + NAME: "ViT-B/16" + +TRAINER: + IVLP: + N_CTX_VISION: 2 + N_CTX_TEXT: 2 + CTX_INIT: "a photo of a" + PREC: "fp16" + PROMPT_DEPTH_VISION: 12 + PROMPT_DEPTH_TEXT: 12 \ No newline at end of file diff --git a/configs/trainers/IVLP/vit_b16_c2_ep5_batch4_4ctx_language_only.yaml b/configs/trainers/IVLP/vit_b16_c2_ep5_batch4_4ctx_language_only.yaml new file mode 100644 index 0000000..94a962e --- /dev/null +++ b/configs/trainers/IVLP/vit_b16_c2_ep5_batch4_4ctx_language_only.yaml @@ -0,0 +1,39 @@ +# Deep language prompting +DATALOADER: + TRAIN_X: + BATCH_SIZE: 4 + TEST: + BATCH_SIZE: 100 + NUM_WORKERS: 8 + +INPUT: + SIZE: (224, 224) + INTERPOLATION: "bicubic" + PIXEL_MEAN: [0.48145466, 0.4578275, 0.40821073] + PIXEL_STD: [0.26862954, 0.26130258, 0.27577711] + TRANSFORMS: ["random_resized_crop", "random_flip", "normalize"] + +OPTIM: + NAME: "sgd" + LR: 0.0025 + MAX_EPOCH: 5 + LR_SCHEDULER: "cosine" + WARMUP_EPOCH: 1 + WARMUP_TYPE: "constant" + WARMUP_CONS_LR: 1e-5 + +TRAIN: + PRINT_FREQ: 20 + +MODEL: + BACKBONE: + NAME: "ViT-B/16" + +TRAINER: + IVLP: + N_CTX_VISION: 0 + N_CTX_TEXT: 4 + CTX_INIT: "a photo of a" + PREC: "fp16" + PROMPT_DEPTH_VISION: 0 + PROMPT_DEPTH_TEXT: 12 diff --git a/configs/trainers/MaPLe/glib_t.yaml b/configs/trainers/MaPLe/glib_t.yaml new file mode 100644 index 0000000..cef8421 --- /dev/null +++ b/configs/trainers/MaPLe/glib_t.yaml @@ -0,0 +1,53 @@ +DATASET: + SELECTION_BATCH_SIZE: 50 + SUBSAMPLE_CLASSES: all + + +DATALOADER: + RETURN_IMG0: true + TRAIN_X: + BATCH_SIZE: 16 + TEST: + BATCH_SIZE: 64 + NUM_WORKERS: 2 + +INPUT: + SIZE: (224, 224) + INTERPOLATION: "bicubic" + PIXEL_MEAN: [0.48145466, 0.4578275, 0.40821073] + PIXEL_STD: [0.26862954, 0.26130258, 0.27577711] + TRANSFORMS: ["random_resized_crop", "random_flip", "normalize"] +# CUTOUT_N: 1 +# CUTOUT_LEN: 128 + +OPTIM: + NAME: "sgd" + LR: 0.0026 #0.0035 0.0026 for crossdata + MAX_EPOCH: 5 + LR_SCHEDULER: "cosine" + WARMUP_EPOCH: 1 + WARMUP_TYPE: "constant" + WARMUP_CONS_LR: 1e-5 + +TRAIN: + PRINT_FREQ: 20 + CHECKPOINT_FREQ: 1 + +MODEL: + BACKBONE: + NAME: "ViT-B/16" + + + +TEST: + PER_CLASS_RESULT: false + FINAL_MODEL: "best_val" + + + +TRAINER: + MAPLEG: + N_CTX: 4 + CTX_INIT: "a photo of a" + PREC: "fp16" + PROMPT_DEPTH: 9 \ No newline at end of file diff --git a/configs/trainers/MaPLe/vit_b16_base.yaml b/configs/trainers/MaPLe/vit_b16_base.yaml new file mode 100644 index 0000000..bd1f666 --- /dev/null +++ b/configs/trainers/MaPLe/vit_b16_base.yaml @@ -0,0 +1,52 @@ +DATASET: + SELECTION_BATCH_SIZE: 50 + SUBSAMPLE_CLASSES: base + + +DATALOADER: + RETURN_IMG0: true + TRAIN_X: + BATCH_SIZE: 1 + TEST: + BATCH_SIZE: 256 + NUM_WORKERS: 4 + +INPUT: + SIZE: (224, 224) + INTERPOLATION: "bicubic" + PIXEL_MEAN: [0.48145466, 0.4578275, 0.40821073] + PIXEL_STD: [0.26862954, 0.26130258, 0.27577711] + TRANSFORMS: ["random_resized_crop", "random_flip", "normalize"] +# CUTOUT_N: 1 +# CUTOUT_LEN: 128 + +OPTIM: + NAME: "sgd" + LR: 0.0035 + MAX_EPOCH: 5 + LR_SCHEDULER: "cosine" + WARMUP_EPOCH: 1 + WARMUP_TYPE: "constant" + WARMUP_CONS_LR: 1e-5 + +TRAIN: + PRINT_FREQ: 20 + +MODEL: + BACKBONE: + NAME: "ViT-B/16" + + + +TEST: + PER_CLASS_RESULT: false + FINAL_MODEL: "best_val" + + + +TRAINER: + MAPLE: + N_CTX: 2 + CTX_INIT: "a photo of a" + PREC: "fp16" + PROMPT_DEPTH: 9 \ No newline at end of file diff --git a/configs/trainers/MaPLe/vit_b16_c2_ep5_batch4_2ctx.yaml b/configs/trainers/MaPLe/vit_b16_c2_ep5_batch4_2ctx.yaml new file mode 100644 index 0000000..23cfd47 --- /dev/null +++ b/configs/trainers/MaPLe/vit_b16_c2_ep5_batch4_2ctx.yaml @@ -0,0 +1,41 @@ +DATALOADER: + TRAIN_X: + BATCH_SIZE: 4 + TEST: + BATCH_SIZE: 100 + NUM_WORKERS: 4 + +INPUT: + SIZE: (224, 224) + INTERPOLATION: "bicubic" + PIXEL_MEAN: [0.48145466, 0.4578275, 0.40821073] + PIXEL_STD: [0.26862954, 0.26130258, 0.27577711] + TRANSFORMS: ["random_resized_crop", "random_flip", "normalize"] + +OPTIM: + NAME: "sgd" + LR: 0.0035 + MAX_EPOCH: 5 + LR_SCHEDULER: "cosine" + WARMUP_EPOCH: 1 + WARMUP_TYPE: "constant" + WARMUP_CONS_LR: 1e-5 + + + +TRAIN: + PRINT_FREQ: 20 + +TEST: + FINAL_MODEL: "best_val" + +MODEL: + BACKBONE: + NAME: "ViT-B/16" + +TRAINER: + MAPLE: + N_CTX: 2 + CTX_INIT: "a photo of a" + PREC: "fp16" + PROMPT_DEPTH: 9 \ No newline at end of file diff --git a/configs/trainers/MaPLe/vit_b16_c2_ep5_batch4_2ctx_cross_datasets.yaml b/configs/trainers/MaPLe/vit_b16_c2_ep5_batch4_2ctx_cross_datasets.yaml new file mode 100644 index 0000000..bf62084 --- /dev/null +++ b/configs/trainers/MaPLe/vit_b16_c2_ep5_batch4_2ctx_cross_datasets.yaml @@ -0,0 +1,36 @@ +DATALOADER: + TRAIN_X: + BATCH_SIZE: 4 + TEST: + BATCH_SIZE: 100 + NUM_WORKERS: 8 + +INPUT: + SIZE: (224, 224) + INTERPOLATION: "bicubic" + PIXEL_MEAN: [0.48145466, 0.4578275, 0.40821073] + PIXEL_STD: [0.26862954, 0.26130258, 0.27577711] + TRANSFORMS: ["random_resized_crop", "random_flip", "normalize"] + +OPTIM: + NAME: "sgd" + LR: 0.0026 + MAX_EPOCH: 2 + LR_SCHEDULER: "cosine" + WARMUP_EPOCH: 1 + WARMUP_TYPE: "constant" + WARMUP_CONS_LR: 1e-5 + +TRAIN: + PRINT_FREQ: 20 + +MODEL: + BACKBONE: + NAME: "ViT-B/16" + +TRAINER: + MAPLE: + N_CTX: 2 + CTX_INIT: "a photo of a" + PREC: "fp16" + PROMPT_DEPTH: 9 \ No newline at end of file diff --git a/configs/trainers/MaPLe/vit_b16_t.yaml b/configs/trainers/MaPLe/vit_b16_t.yaml new file mode 100644 index 0000000..effa91b --- /dev/null +++ b/configs/trainers/MaPLe/vit_b16_t.yaml @@ -0,0 +1,53 @@ +DATASET: + SELECTION_BATCH_SIZE: 50 + SUBSAMPLE_CLASSES: all + + +DATALOADER: + RETURN_IMG0: true + TRAIN_X: + BATCH_SIZE: 4 + TEST: + BATCH_SIZE: 128 + NUM_WORKERS: 4 + +INPUT: + SIZE: (224, 224) + INTERPOLATION: "bicubic" + PIXEL_MEAN: [0.48145466, 0.4578275, 0.40821073] + PIXEL_STD: [0.26862954, 0.26130258, 0.27577711] + TRANSFORMS: ["random_resized_crop", "random_flip", "normalize"] +# CUTOUT_N: 1 +# CUTOUT_LEN: 128 + +OPTIM: + NAME: "sgd" + LR: 0.0035 #0.0035 0.0026 for crossdata + MAX_EPOCH: 10 + LR_SCHEDULER: "cosine" + WARMUP_EPOCH: 1 + WARMUP_TYPE: "constant" + WARMUP_CONS_LR: 1e-5 + +TRAIN: + PRINT_FREQ: 20 + CHECKPOINT_FREQ: 1 + +MODEL: + BACKBONE: + NAME: "ViT-B/16" + + + +TEST: + PER_CLASS_RESULT: false + FINAL_MODEL: "best_val" + + + +TRAINER: + MAPLE: + N_CTX: 2 + CTX_INIT: "A photo of a" + PREC: "fp16" + PROMPT_DEPTH: 9 \ No newline at end of file diff --git a/configs/trainers/VPT/vit_b16_c2_ep5_batch4_4.yaml b/configs/trainers/VPT/vit_b16_c2_ep5_batch4_4.yaml new file mode 100644 index 0000000..73e32b5 --- /dev/null +++ b/configs/trainers/VPT/vit_b16_c2_ep5_batch4_4.yaml @@ -0,0 +1,37 @@ +# Deep vision prompting +DATALOADER: + TRAIN_X: + BATCH_SIZE: 4 + TEST: + BATCH_SIZE: 100 + NUM_WORKERS: 8 + +INPUT: + SIZE: (224, 224) + INTERPOLATION: "bicubic" + PIXEL_MEAN: [0.48145466, 0.4578275, 0.40821073] + PIXEL_STD: [0.26862954, 0.26130258, 0.27577711] + TRANSFORMS: ["random_resized_crop", "random_flip", "normalize"] + +OPTIM: + NAME: "sgd" + LR: 0.0025 + MAX_EPOCH: 5 + LR_SCHEDULER: "cosine" + WARMUP_EPOCH: 1 + WARMUP_TYPE: "constant" + WARMUP_CONS_LR: 1e-5 + +TRAIN: + PRINT_FREQ: 20 + +MODEL: + BACKBONE: + NAME: "ViT-B/16" + +TRAINER: + VPT: + N_CTX_VISION: 8 + CTX_INIT: "a photo of a" + PREC: "fp16" + PROMPT_DEPTH_VISION: 12 \ No newline at end of file diff --git a/datasets/__init__.py 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+IGNORED = ["BACKGROUND_Google", "Faces_easy"] +NEW_CNAMES = { + "airplanes": "airplane", + "Faces": "face", + "Leopards": "leopard", + "Motorbikes": "motorbike", +} + + +@DATASET_REGISTRY.register() +class Caltech101(DatasetBase): + + dataset_dir = "caltech-101" + + def __init__(self, cfg): + root = os.path.abspath(os.path.expanduser(cfg.DATASET.ROOT)) + self.dataset_dir = os.path.join(root, self.dataset_dir) + self.image_dir = os.path.join(self.dataset_dir, "101_ObjectCategories") + self.split_path = os.path.join(self.dataset_dir, "split_zhou_Caltech101.json") + self.split_fewshot_dir = os.path.join(self.dataset_dir, "split_fewshot") + mkdir_if_missing(self.split_fewshot_dir) + + if os.path.exists(self.split_path): + train, val, test = OxfordPets.read_split(self.split_path, self.image_dir) + else: + train, val, test = DTD.read_and_split_data(self.image_dir, ignored=IGNORED, new_cnames=NEW_CNAMES) + OxfordPets.save_split(train, val, test, self.split_path, self.image_dir) + + num_shots = cfg.DATASET.NUM_SHOTS + + if num_shots >= 1: + seed = cfg.SEED + preprocessed = os.path.join(self.split_fewshot_dir, f"shot_{num_shots}-seed_{seed}.pkl") + + if os.path.exists(preprocessed): + print(f"Loading preprocessed few-shot data from {preprocessed}") + with open(preprocessed, "rb") as file: + data = pickle.load(file) + train, val = data["train"], data["val"] + else: + train = self.generate_fewshot_dataset(train, num_shots=num_shots) + val = self.generate_fewshot_dataset(val, num_shots=min(num_shots, 4)) + data = {"train": train, "val": val} + print(f"Saving preprocessed few-shot data to {preprocessed}") + with open(preprocessed, "wb") as file: + pickle.dump(data, file, protocol=pickle.HIGHEST_PROTOCOL) + + subsample = cfg.DATASET.SUBSAMPLE_CLASSES + train, val, test = OxfordPets.subsample_classes(train, val, test, subsample=subsample) + + super().__init__(train_x=train, val=val, test=test) + diff --git a/datasets/data_manager.py b/datasets/data_manager.py new file mode 100644 index 0000000..baaa628 --- /dev/null +++ b/datasets/data_manager.py @@ -0,0 +1,481 @@ +import torch +import torchvision.transforms as T +import numpy as np +from tabulate import tabulate +from torch.utils.data import Dataset as TorchDataset +import os +from dassl.utils import read_image + +from dassl.data.datasets import build_dataset +from dassl.data.samplers import build_sampler +from dassl.data.transforms import INTERPOLATION_MODES, build_transform +from .new_da import RandomResizedCropPair, build_transform_pair +from PIL import Image + +def build_data_loader( + cfg, + sampler_type="SequentialSampler", + data_source=None, + batch_size=64, + n_domain=0, + n_ins=2, + tfm=None, + is_train=True, + dataset_wrapper=None, + weight=None, +): + # Build sampler + sampler = build_sampler( + sampler_type, + cfg=cfg, + data_source=data_source, + batch_size=batch_size, + n_domain=n_domain, + n_ins=n_ins + ) + + if dataset_wrapper is None: + dataset_wrapper = DatasetWrapper + + # Build data loader + data_loader = torch.utils.data.DataLoader( + dataset_wrapper(cfg, data_source,transform=tfm, is_train=is_train,weight=weight), + batch_size=batch_size, + sampler=sampler, + num_workers=cfg.DATALOADER.NUM_WORKERS, + drop_last=is_train and len(data_source) >= batch_size, + pin_memory=(torch.cuda.is_available() and cfg.USE_CUDA) + ) + assert len(data_loader) > 0 + + return data_loader + + + +def build_data_loader_mask( + cfg, + dataset, + sampler_type="SequentialSampler", + data_source=None, + batch_size=64, + n_domain=0, + n_ins=2, + tfm=None, + is_train=True, + dataset_wrapper=None, + weight=None, +): + # Build sampler + sampler = build_sampler( + sampler_type, + cfg=cfg, + data_source=data_source, + batch_size=batch_size, + n_domain=n_domain, + n_ins=n_ins + ) + + if dataset_wrapper is None: + dataset_wrapper = DatasetWrapperMask + + # Build data loader + data_loader = torch.utils.data.DataLoader( + dataset_wrapper(cfg, dataset,data_source,transform=tfm, is_train=is_train,weight=weight), + batch_size=batch_size, + sampler=sampler, + num_workers=cfg.DATALOADER.NUM_WORKERS, + drop_last=is_train and len(data_source) >= batch_size, + pin_memory=(torch.cuda.is_available() and cfg.USE_CUDA) + ) + assert len(data_loader) > 0 + + return data_loader + +def select_dm_loader(cfg,dataset,s_ind=None,is_train=False): + + tfm = build_transform(cfg, is_train=is_train) + if is_train: + dataloader = build_data_loader( + cfg, + sampler_type=cfg.DATALOADER.TRAIN_X.SAMPLER, + data_source=list(np.asarray(dataset)[s_ind]) if s_ind is not None else dataset, + batch_size=cfg.DATALOADER.TRAIN_X.BATCH_SIZE, #cfg.DATALOADER.TRAIN_X.BATCH_SIZE* + n_domain=cfg.DATALOADER.TRAIN_X.N_DOMAIN, + n_ins=cfg.DATALOADER.TRAIN_X.N_INS, + tfm=tfm, + is_train=is_train, + dataset_wrapper=None, + ) + else: + dataloader = build_data_loader( + cfg, + sampler_type=cfg.DATALOADER.TEST.SAMPLER, + data_source=list(np.asarray(dataset)[s_ind]) if s_ind is not None else dataset, + batch_size=cfg.DATASET.SELECTION_BATCH_SIZE, + n_domain=cfg.DATALOADER.TRAIN_X.N_DOMAIN, + n_ins=cfg.DATALOADER.TRAIN_X.N_INS, + tfm=tfm, + is_train=is_train, + dataset_wrapper=None, + ) + + return dataloader + +class DataManager: + + def __init__( + self, + cfg, + dataset, + s_ind=None, + custom_tfm_train=None, + custom_tfm_test=None, + dataset_wrapper=None, + weight=None, + ): + # # Load dataset + # dataset = build_dataset(cfg) + + # Build transform + if custom_tfm_train is None: + ###pair is for + tfm_train_pair = build_transform_pair(cfg, is_train=True) + tfm_train = build_transform(cfg,is_train=True) + else: + print("* Using custom transform for training") + tfm_train = custom_tfm_train + + if custom_tfm_test is None: + tfm_test = build_transform(cfg, is_train=False) + else: + print("* Using custom transform for testing") + tfm_test = custom_tfm_test + + + # Build train_loader_x + + train_loader_x = build_data_loader_mask( + cfg, + dataset, + sampler_type=cfg.DATALOADER.TRAIN_X.SAMPLER, + data_source=list(np.asarray(dataset.train_x)[s_ind]) if s_ind is not None else dataset.train_x, + batch_size=cfg.DATALOADER.TRAIN_X.BATCH_SIZE, + n_domain=cfg.DATALOADER.TRAIN_X.N_DOMAIN, + n_ins=cfg.DATALOADER.TRAIN_X.N_INS, + tfm=tfm_train_pair, + is_train=True, + dataset_wrapper=dataset_wrapper, + weight=weight + ) + + + train_loader_xmore = build_data_loader( + cfg, + sampler_type=cfg.DATALOADER.TRAIN_X.SAMPLER, + data_source=list(np.asarray(dataset.train_x)[s_ind]) if s_ind is not None else dataset.train_x, + batch_size=cfg.DATASET.SELECTION_BATCH_SIZE, + n_domain=cfg.DATALOADER.TRAIN_X.N_DOMAIN, + n_ins=cfg.DATALOADER.TRAIN_X.N_INS, + tfm=tfm_train, + is_train=True, + dataset_wrapper=dataset_wrapper, + weight=weight + ) + + # Build train_loader_u + train_loader_u = None + if dataset.train_u: + sampler_type_ = cfg.DATALOADER.TRAIN_U.SAMPLER + batch_size_ = cfg.DATALOADER.TRAIN_U.BATCH_SIZE + n_domain_ = cfg.DATALOADER.TRAIN_U.N_DOMAIN + n_ins_ = cfg.DATALOADER.TRAIN_U.N_INS + + if cfg.DATALOADER.TRAIN_U.SAME_AS_X: + sampler_type_ = cfg.DATALOADER.TRAIN_X.SAMPLER + batch_size_ = cfg.DATALOADER.TRAIN_X.BATCH_SIZE + n_domain_ = cfg.DATALOADER.TRAIN_X.N_DOMAIN + n_ins_ = cfg.DATALOADER.TRAIN_X.N_INS + + train_loader_u = build_data_loader( + cfg, + sampler_type=sampler_type_, + data_source=dataset.train_u, + batch_size=batch_size_, + n_domain=n_domain_, + n_ins=n_ins_, + tfm=tfm_train, + is_train=True, + dataset_wrapper=dataset_wrapper + ) + + # Build val_loader + val_loader = None + if dataset.val: + val_loader = build_data_loader( + cfg, + sampler_type=cfg.DATALOADER.TEST.SAMPLER, + data_source=dataset.val, + batch_size=cfg.DATALOADER.TEST.BATCH_SIZE, + tfm=tfm_test, + is_train=False, + dataset_wrapper=dataset_wrapper + ) + + # Build test_loader + test_loader = build_data_loader( + cfg, + sampler_type=cfg.DATALOADER.TEST.SAMPLER, + data_source=dataset.test, + batch_size=cfg.DATALOADER.TEST.BATCH_SIZE, + tfm=tfm_test, + is_train=False, + dataset_wrapper=dataset_wrapper + ) + + # Attributes + self._num_classes = dataset.num_classes + self._num_source_domains = len(cfg.DATASET.SOURCE_DOMAINS) + self._lab2cname = dataset.lab2cname + + # Dataset and data-loaders + self.dataset = dataset + self.train_loader_x = train_loader_x + self.train_loader_u = train_loader_u + self.train_loader_xmore = train_loader_xmore + self.val_loader = val_loader + self.test_loader = test_loader + + if cfg.VERBOSE: + self.show_dataset_summary(cfg) + + @property + def num_classes(self): + return self._num_classes + + @property + def num_source_domains(self): + return self._num_source_domains + + @property + def lab2cname(self): + return self._lab2cname + + def show_dataset_summary(self, cfg): + dataset_name = cfg.DATASET.NAME + source_domains = cfg.DATASET.SOURCE_DOMAINS + target_domains = cfg.DATASET.TARGET_DOMAINS + + table = [] + table.append(["Dataset", dataset_name]) + if source_domains: + table.append(["Source", source_domains]) + if target_domains: + table.append(["Target", target_domains]) + table.append(["# classes", f"{self.num_classes:,}"]) + table.append(["# train_x", f"{len(self.dataset.train_x):,}"]) + if self.dataset.train_u: + table.append(["# train_u", f"{len(self.dataset.train_u):,}"]) + if self.dataset.val: + table.append(["# val", f"{len(self.dataset.val):,}"]) + table.append(["# test", f"{len(self.dataset.test):,}"]) + + print(tabulate(table)) + + +class DatasetWrapperMask(TorchDataset): + + def __init__(self, cfg, dataset,data_source,transform=None, is_train=False,weight=None): + self.cfg = cfg + self.data_source = data_source + self.transform = transform # accept list (tuple) as input + self.is_train = is_train + self.data_path = dataset.dataset_dir + self.mask_path = os.path.join(dataset.dataset_dir,'mask') + # Augmenting an image K>1 times is only allowed during training + self.k_tfm = cfg.DATALOADER.K_TRANSFORMS if is_train else 1 + self.return_img0 = cfg.DATALOADER.RETURN_IMG0 + + if weight is not None: + self.weight = weight + else: + self.weight = None + + if self.k_tfm > 1 and transform is None: + raise ValueError( + "Cannot augment the image {} times " + "because transform is None".format(self.k_tfm) + ) + + # Build transform that doesn't apply any data augmentation + interp_mode = INTERPOLATION_MODES[cfg.INPUT.INTERPOLATION] + to_tensor = [] + to_tensor += [T.Resize(cfg.INPUT.SIZE, interpolation=interp_mode)] + to_tensor += [T.ToTensor()] + if "normalize" in cfg.INPUT.TRANSFORMS: + normalize = T.Normalize( + mean=cfg.INPUT.PIXEL_MEAN, std=cfg.INPUT.PIXEL_STD + ) + to_tensor += [normalize] + self.to_tensor = T.Compose(to_tensor) + + def __len__(self): + return len(self.data_source) + + def __getitem__(self, idx): + item = self.data_source[idx] + + if self.weight is None: + output = { + "label": item.label, + "domain": item.domain, + "impath": item.impath, + "index": idx + } + else: + output = { + "label": item.label, + "domain": item.domain, + "impath": item.impath, + "index": idx, + "weight": self.weight[idx] + } + + # img_path = os.path.join('/'.join(item.impath.split('/')[:-1]),'mask',item.impath.split('/')[-1]) ('/').join(item.impath.split('/')[-2:]) + if self.cfg.DATASET.NAME in ['Food101','Caltech101','DescribableTextures','EuroSAT','UCF101']: + mask = read_image(os.path.join(self.mask_path,('/').join(item.impath.split('/')[-2:]))) + elif self.cfg.DATASET.NAME in ['SUN397']: + mask = read_image(os.path.join(self.mask_path,('/').join(item.impath.split('/')[7:]))) + elif self.cfg.DATASET.NAME in ['ImageNet']: + mask = read_image(os.path.join(self.mask_path,('/').join(item.impath.split('/')[7:]))) + elif self.cfg.DATASET.NAME in ['VOC12']: + mask_path = os.path.join(self.data_path,'VOCdevkit/VOC2012/SegmentationClass_All',item.impath.split('/')[-1][:-3]+'png') + mask = read_image(mask_path) + else: + mask = read_image(os.path.join(self.mask_path, item.impath.split('/')[-1])) + img0 = read_image(item.impath) + mask = mask.resize(img0.size) + if self.transform is not None: + if isinstance(self.transform, (list, tuple)): + for i, tfm in enumerate(self.transform): + img = self._transform_image(tfm, img0,img0) + keyname = "img" + if (i + 1) > 1: + keyname += str(i + 1) + output[keyname] = img + else: + img,mask = self._transform_image(self.transform, img0,mask) + output["img"] = img + output["mask"] = mask + else: + output["img"] = img0 + + if self.return_img0: + output["img0"] = self.to_tensor(img0) # without any augmentation + + return output + + def _transform_image(self, tfm, img0,mask): + img_list = [] + for k in range(self.k_tfm): + img_list.append(tfm(img0,mask)) + + img = img_list + if len(img_list) == 1: + img = img_list[0][0] + mask = img_list[0][1] + + return img,mask + + +class DatasetWrapper(TorchDataset): + + def __init__(self, cfg, data_source,transform=None, is_train=False,weight=None): + self.cfg = cfg + self.data_source = data_source + self.transform = transform # accept list (tuple) as input + self.is_train = is_train + self.mask_path = ('/').join(data_source[0].impath.split('/')[:-2])+'/mask' + # Augmenting an image K>1 times is only allowed during training + self.k_tfm = cfg.DATALOADER.K_TRANSFORMS if is_train else 1 + self.return_img0 = cfg.DATALOADER.RETURN_IMG0 + + if weight is not None: + self.weight = weight + else: + self.weight = None + + if self.k_tfm > 1 and transform is None: + raise ValueError( + "Cannot augment the image {} times " + "because transform is None".format(self.k_tfm) + ) + + # Build transform that doesn't apply any data augmentation + interp_mode = INTERPOLATION_MODES[cfg.INPUT.INTERPOLATION] + to_tensor = [] + to_tensor += [T.Resize(cfg.INPUT.SIZE, interpolation=interp_mode)] + to_tensor += [T.ToTensor()] + if "normalize" in cfg.INPUT.TRANSFORMS: + normalize = T.Normalize( + mean=cfg.INPUT.PIXEL_MEAN, std=cfg.INPUT.PIXEL_STD + ) + to_tensor += [normalize] + self.to_tensor = T.Compose(to_tensor) + + def __len__(self): + return len(self.data_source) + + def __getitem__(self, idx): + item = self.data_source[idx] + + if self.weight is None: + output = { + "label": item.label, + "domain": item.domain, + "impath": item.impath, + "index": idx + } + else: + output = { + "label": item.label, + "domain": item.domain, + "impath": item.impath, + "index": idx, + "weight": self.weight[idx] + } + + # img0 = read_image(item.impath) + img0 = read_image(item.impath) + # img0 = img0.resize(mask.size) + # mask = read_image(item.impath.split('/')[:-1].join('/')) + if self.transform is not None: + if isinstance(self.transform, (list, tuple)): + for i, tfm in enumerate(self.transform): + img = self._transform_image(tfm, img0) + keyname = "img" + if (i + 1) > 1: + keyname += str(i + 1) + output[keyname] = img + else: + img = self._transform_image(self.transform, img0) + output["img"] = img + output['mask'] = 1 + else: + output["img"] = img0 + + if self.return_img0: + output["img0"] = self.to_tensor(img0) # without any augmentation + + return output + + def _transform_image(self, tfm, img0): + img_list = [] + + for k in range(self.k_tfm): + img_list.append(tfm(img0)) + + img = img_list + if len(img) == 1: + img = img[0] + + return img diff --git a/datasets/dtd.py b/datasets/dtd.py new file mode 100644 index 0000000..4005f1d --- /dev/null +++ b/datasets/dtd.py @@ -0,0 +1,95 @@ +import os +import pickle +import random + +from dassl.data.datasets import DATASET_REGISTRY, Datum, DatasetBase +from dassl.utils import listdir_nohidden, mkdir_if_missing + +from .oxford_pets import OxfordPets + + +@DATASET_REGISTRY.register() +class DescribableTextures(DatasetBase): + + dataset_dir = "dtd" + + def __init__(self, cfg): + root = os.path.abspath(os.path.expanduser(cfg.DATASET.ROOT)) + self.dataset_dir = os.path.join(root, self.dataset_dir) + self.image_dir = os.path.join(self.dataset_dir, "images") + self.split_path = os.path.join(self.dataset_dir, "split_zhou_DescribableTextures.json") + self.split_fewshot_dir = os.path.join(self.dataset_dir, "split_fewshot") + mkdir_if_missing(self.split_fewshot_dir) + + if os.path.exists(self.split_path): + train, val, test = OxfordPets.read_split(self.split_path, self.image_dir) + else: + train, val, test = self.read_and_split_data(self.image_dir) + OxfordPets.save_split(train, val, test, self.split_path, self.image_dir) + + num_shots = cfg.DATASET.NUM_SHOTS + if num_shots >= 1: + seed = cfg.SEED + preprocessed = os.path.join(self.split_fewshot_dir, f"shot_{num_shots}-seed_{seed}.pkl") + + if os.path.exists(preprocessed): + print(f"Loading preprocessed few-shot data from {preprocessed}") + with open(preprocessed, "rb") as file: + data = pickle.load(file) + train, val = data["train"], data["val"] + else: + train = self.generate_fewshot_dataset(train, num_shots=num_shots) + val = self.generate_fewshot_dataset(val, num_shots=min(num_shots, 4)) + data = {"train": train, "val": val} + print(f"Saving preprocessed few-shot data to {preprocessed}") + with open(preprocessed, "wb") as file: + pickle.dump(data, file, protocol=pickle.HIGHEST_PROTOCOL) + + subsample = cfg.DATASET.SUBSAMPLE_CLASSES + train, val, test = OxfordPets.subsample_classes(train, val, test, subsample=subsample) + + super().__init__(train_x=train, val=val, test=test) + + @staticmethod + def read_and_split_data(image_dir, p_trn=0.5, p_val=0.2, ignored=[], new_cnames=None): + # The data are supposed to be organized into the following structure + # ============= + # images/ + # dog/ + # cat/ + # horse/ + # ============= + categories = listdir_nohidden(image_dir) + categories = [c for c in categories if c not in ignored] + categories.sort() + + p_tst = 1 - p_trn - p_val + print(f"Splitting into {p_trn:.0%} train, {p_val:.0%} val, and {p_tst:.0%} test") + + def _collate(ims, y, c): + items = [] + for im in ims: + item = Datum(impath=im, label=y, classname=c) # is already 0-based + items.append(item) + return items + + train, val, test = [], [], [] + for label, category in enumerate(categories): + category_dir = os.path.join(image_dir, category) + images = listdir_nohidden(category_dir) + images = [os.path.join(category_dir, im) for im in images] + random.shuffle(images) + n_total = len(images) + n_train = round(n_total * p_trn) + n_val = round(n_total * p_val) + n_test = n_total - n_train - n_val + assert n_train > 0 and n_val > 0 and n_test > 0 + + if new_cnames is not None and category in new_cnames: + category = new_cnames[category] + + train.extend(_collate(images[:n_train], label, category)) + val.extend(_collate(images[n_train : n_train + n_val], label, category)) + test.extend(_collate(images[n_train + n_val :], label, category)) + + return train, val, test diff --git a/datasets/eurosat.py b/datasets/eurosat.py new file mode 100644 index 0000000..297ede2 --- /dev/null +++ b/datasets/eurosat.py @@ -0,0 +1,73 @@ +import os +import pickle + +from dassl.data.datasets import DATASET_REGISTRY, Datum, DatasetBase +from dassl.utils import mkdir_if_missing + +from .oxford_pets import OxfordPets +from .dtd import DescribableTextures as DTD + +NEW_CNAMES = { + "AnnualCrop": "Annual Crop Land", + "Forest": "Forest", + "HerbaceousVegetation": "Herbaceous Vegetation Land", + "Highway": "Highway or Road", + "Industrial": "Industrial Buildings", + "Pasture": "Pasture Land", + "PermanentCrop": "Permanent Crop Land", + "Residential": "Residential Buildings", + "River": "River", + "SeaLake": "Sea or Lake", +} + + +@DATASET_REGISTRY.register() +class EuroSAT(DatasetBase): + + dataset_dir = "eurosat" + + def __init__(self, cfg): + root = os.path.abspath(os.path.expanduser(cfg.DATASET.ROOT)) + self.dataset_dir = os.path.join(root, self.dataset_dir) + self.image_dir = os.path.join(self.dataset_dir, "2750") + self.split_path = os.path.join(self.dataset_dir, "split_zhou_EuroSAT.json") + self.split_fewshot_dir = os.path.join(self.dataset_dir, "split_fewshot") + mkdir_if_missing(self.split_fewshot_dir) + + if os.path.exists(self.split_path): + train, val, test = OxfordPets.read_split(self.split_path, self.image_dir) + else: + train, val, test = DTD.read_and_split_data(self.image_dir, new_cnames=NEW_CNAMES) + OxfordPets.save_split(train, val, test, self.split_path, self.image_dir) + + num_shots = cfg.DATASET.NUM_SHOTS + if num_shots >= 1: + seed = cfg.SEED + preprocessed = os.path.join(self.split_fewshot_dir, f"shot_{num_shots}-seed_{seed}.pkl") + + if os.path.exists(preprocessed): + print(f"Loading preprocessed few-shot data from {preprocessed}") + with open(preprocessed, "rb") as file: + data = pickle.load(file) + train, val = data["train"], data["val"] + else: + train = self.generate_fewshot_dataset(train, num_shots=num_shots) + val = self.generate_fewshot_dataset(val, num_shots=min(num_shots, 4)) + data = {"train": train, "val": val} + print(f"Saving preprocessed few-shot data to {preprocessed}") + with open(preprocessed, "wb") as file: + pickle.dump(data, file, protocol=pickle.HIGHEST_PROTOCOL) + + subsample = cfg.DATASET.SUBSAMPLE_CLASSES + train, val, test = OxfordPets.subsample_classes(train, val, test, subsample=subsample) + + super().__init__(train_x=train, val=val, test=test) + + def update_classname(self, dataset_old): + dataset_new = [] + for item_old in dataset_old: + cname_old = item_old.classname + cname_new = NEW_CNAMES[cname_old] + item_new = Datum(impath=item_old.impath, label=item_old.label, classname=cname_new) + dataset_new.append(item_new) + return dataset_new diff --git a/datasets/fgvc_aircraft.py b/datasets/fgvc_aircraft.py new file mode 100644 index 0000000..d04182a --- /dev/null +++ b/datasets/fgvc_aircraft.py @@ -0,0 +1,71 @@ +import os +import pickle + +from dassl.data.datasets import DATASET_REGISTRY, Datum, DatasetBase +from dassl.utils import mkdir_if_missing + +from .oxford_pets import OxfordPets + + +@DATASET_REGISTRY.register() +class FGVCAircraft(DatasetBase): + + dataset_dir = "fgvc_aircraft" + + def __init__(self, cfg): + root = os.path.abspath(os.path.expanduser(cfg.DATASET.ROOT)) + self.dataset_dir = os.path.join(root, self.dataset_dir) + self.image_dir = os.path.join(self.dataset_dir, "images") + self.split_fewshot_dir = os.path.join(self.dataset_dir, "split_fewshot") + mkdir_if_missing(self.split_fewshot_dir) + + classnames = [] + with open(os.path.join(self.dataset_dir, "variants.txt"), "r") as f: + lines = f.readlines() + for line in lines: + classnames.append(line.strip()) + cname2lab = {c: i for i, c in enumerate(classnames)} + + train = self.read_data(cname2lab, "images_variant_train.txt") + val = self.read_data(cname2lab, "images_variant_val.txt") + test = self.read_data(cname2lab, "images_variant_test.txt") + + num_shots = cfg.DATASET.NUM_SHOTS + if num_shots >= 1: + seed = cfg.SEED + preprocessed = os.path.join(self.split_fewshot_dir, f"shot_{num_shots}-seed_{seed}.pkl") + + if os.path.exists(preprocessed): + print(f"Loading preprocessed few-shot data from {preprocessed}") + with open(preprocessed, "rb") as file: + data = pickle.load(file) + train, val = data["train"], data["val"] + else: + train = self.generate_fewshot_dataset(train, num_shots=num_shots) + val = self.generate_fewshot_dataset(val, num_shots=min(num_shots, 4)) + data = {"train": train, "val": val} + print(f"Saving preprocessed few-shot data to {preprocessed}") + with open(preprocessed, "wb") as file: + pickle.dump(data, file, protocol=pickle.HIGHEST_PROTOCOL) + + subsample = cfg.DATASET.SUBSAMPLE_CLASSES + train, val, test = OxfordPets.subsample_classes(train, val, test, subsample=subsample) + + super().__init__(train_x=train, val=val, test=test) + + def read_data(self, cname2lab, split_file): + filepath = os.path.join(self.dataset_dir, split_file) + items = [] + + with open(filepath, "r") as f: + lines = f.readlines() + for line in lines: + line = line.strip().split(" ") + imname = line[0] + ".jpg" + classname = " ".join(line[1:]) + impath = os.path.join(self.image_dir, imname) + label = cname2lab[classname] + item = Datum(impath=impath, label=label, classname=classname) + items.append(item) + + return items diff --git a/datasets/food101.py b/datasets/food101.py new file mode 100644 index 0000000..01725ba --- /dev/null +++ b/datasets/food101.py @@ -0,0 +1,51 @@ +import os +import pickle + +from dassl.data.datasets import DATASET_REGISTRY, Datum, DatasetBase +from dassl.utils import mkdir_if_missing + +from .oxford_pets import OxfordPets +from .dtd import DescribableTextures as DTD + + +@DATASET_REGISTRY.register() +class Food101(DatasetBase): + + dataset_dir = "food-101" + + def __init__(self, cfg): + root = os.path.abspath(os.path.expanduser(cfg.DATASET.ROOT)) + self.dataset_dir = os.path.join(root, self.dataset_dir) + self.image_dir = os.path.join(self.dataset_dir, "images") + self.split_path = os.path.join(self.dataset_dir, "split_zhou_Food101.json") + self.split_fewshot_dir = os.path.join(self.dataset_dir, "split_fewshot") + mkdir_if_missing(self.split_fewshot_dir) + + if os.path.exists(self.split_path): + train, val, test = OxfordPets.read_split(self.split_path, self.image_dir) + else: + train, val, test = DTD.read_and_split_data(self.image_dir) + OxfordPets.save_split(train, val, test, self.split_path, self.image_dir) + + num_shots = cfg.DATASET.NUM_SHOTS + if num_shots >= 1: + seed = cfg.SEED + preprocessed = os.path.join(self.split_fewshot_dir, f"shot_{num_shots}-seed_{seed}.pkl") + + if os.path.exists(preprocessed): + print(f"Loading preprocessed few-shot data from {preprocessed}") + with open(preprocessed, "rb") as file: + data = pickle.load(file) + train, val = data["train"], data["val"] + else: + train = self.generate_fewshot_dataset(train, num_shots=num_shots) + val = self.generate_fewshot_dataset(val, num_shots=min(num_shots, 4)) + data = {"train": train, "val": val} + print(f"Saving preprocessed few-shot data to {preprocessed}") + with open(preprocessed, "wb") as file: + pickle.dump(data, file, protocol=pickle.HIGHEST_PROTOCOL) + + subsample = cfg.DATASET.SUBSAMPLE_CLASSES + train, val, test = OxfordPets.subsample_classes(train, val, test, subsample=subsample) + + super().__init__(train_x=train, val=val, test=test) diff --git a/datasets/imagenet.py b/datasets/imagenet.py new file mode 100644 index 0000000..a2b0b87 --- /dev/null +++ b/datasets/imagenet.py @@ -0,0 +1,92 @@ +import os +import pickle +from collections import OrderedDict + +from dassl.data.datasets import DATASET_REGISTRY, Datum, DatasetBase +from dassl.utils import listdir_nohidden, mkdir_if_missing + +from .oxford_pets import OxfordPets +from random import sample + +@DATASET_REGISTRY.register() +class ImageNet(DatasetBase): + + dataset_dir = "imagenet" + + def __init__(self, cfg): + root = os.path.abspath(os.path.expanduser(cfg.DATASET.ROOT)) + self.dataset_dir = os.path.join(root, self.dataset_dir) + self.image_dir = os.path.join(self.dataset_dir, "images") + self.preprocessed = os.path.join(self.dataset_dir, "preprocessed.pkl") + self.split_fewshot_dir = os.path.join(self.dataset_dir, "split_fewshot") + mkdir_if_missing(self.split_fewshot_dir) + + if os.path.exists(self.preprocessed): + with open(self.preprocessed, "rb") as f: + preprocessed = pickle.load(f) + train = preprocessed["train"] + test = preprocessed["test"] + else: + text_file = os.path.join(self.dataset_dir, "classnames.txt") + classnames = self.read_classnames(text_file) + train = self.read_data(classnames, "train") + # Follow standard practice to perform evaluation on the val set + # Also used as the val set (so evaluate the last-step model) + test = self.read_data(classnames, "val") + + preprocessed = {"train": train, "test": test} + with open(self.preprocessed, "wb") as f: + pickle.dump(preprocessed, f, protocol=pickle.HIGHEST_PROTOCOL) + + num_shots = cfg.DATASET.NUM_SHOTS + if num_shots >= 1000: + seed = cfg.SEED + preprocessed = os.path.join(self.split_fewshot_dir, f"shot_{num_shots}-seed_{seed}.pkl") + + if os.path.exists(preprocessed): + print(f"Loading preprocessed few-shot data from {preprocessed}") + with open(preprocessed, "rb") as file: + data = pickle.load(file) + train = data["train"] + else: + train = self.generate_fewshot_dataset(train, num_shots=num_shots) + data = {"train": train} + print(f"Saving preprocessed few-shot data to {preprocessed}") + with open(preprocessed, "wb") as file: + pickle.dump(data, file, protocol=pickle.HIGHEST_PROTOCOL) + + subsample = cfg.DATASET.SUBSAMPLE_CLASSES + train, test = OxfordPets.subsample_classes(train, test, subsample=subsample) + + + super().__init__(train_x=sample(train,int(len(train)*0.8)), val=sample(test,5000), test=test) + + @staticmethod + def read_classnames(text_file): + """Return a dictionary containing + key-value pairs of : . + """ + classnames = OrderedDict() + with open(text_file, "r") as f: + lines = f.readlines() + for line in lines: + line = line.strip().split(" ") + folder = line[0] + classname = " ".join(line[1:]) + classnames[folder] = classname + return classnames + + def read_data(self, classnames, split_dir): + split_dir = os.path.join(self.image_dir, split_dir) + folders = sorted(f.name for f in os.scandir(split_dir) if f.is_dir()) + items = [] + + for label, folder in enumerate(folders): ##sub evaluation + imnames = listdir_nohidden(os.path.join(split_dir, folder)) + classname = classnames[folder] + for imname in imnames: + impath = os.path.join(split_dir, folder, imname) + item = Datum(impath=impath, label=label, classname=classname) + items.append(item) + + return items diff --git a/datasets/imagenet_a.py b/datasets/imagenet_a.py new file mode 100644 index 0000000..6079850 --- /dev/null +++ b/datasets/imagenet_a.py @@ -0,0 +1,46 @@ +import os + +from dassl.data.datasets import DATASET_REGISTRY, Datum, DatasetBase +from dassl.utils import listdir_nohidden + +from .imagenet import ImageNet + +TO_BE_IGNORED = ["README.txt"] + + +@DATASET_REGISTRY.register() +class ImageNetA(DatasetBase): + """ImageNet-A(dversarial). + + This dataset is used for testing only. + """ + + dataset_dir = "imagenet-adversarial" + + def __init__(self, cfg): + root = os.path.abspath(os.path.expanduser(cfg.DATASET.ROOT)) + self.dataset_dir = os.path.join(root, self.dataset_dir) + self.image_dir = os.path.join(self.dataset_dir, "imagenet-a") + + text_file = os.path.join(self.dataset_dir, "classnames.txt") + classnames = ImageNet.read_classnames(text_file) + + data = self.read_data(classnames) + + super().__init__(train_x=data, test=data) + + def read_data(self, classnames): + image_dir = self.image_dir + folders = listdir_nohidden(image_dir, sort=True) + folders = [f for f in folders if f not in TO_BE_IGNORED] + items = [] + + for label, folder in enumerate(folders): + imnames = listdir_nohidden(os.path.join(image_dir, folder)) + classname = classnames[folder] + for imname in imnames: + impath = os.path.join(image_dir, folder, imname) + item = Datum(impath=impath, label=label, classname=classname) + items.append(item) + + return items diff --git a/datasets/imagenet_r.py b/datasets/imagenet_r.py new file mode 100644 index 0000000..396902c --- /dev/null +++ b/datasets/imagenet_r.py @@ -0,0 +1,46 @@ +import os + +from dassl.data.datasets import DATASET_REGISTRY, Datum, DatasetBase +from dassl.utils import listdir_nohidden + +from .imagenet import ImageNet + +TO_BE_IGNORED = ["README.txt"] + + +@DATASET_REGISTRY.register() +class ImageNetR(DatasetBase): + """ImageNet-R(endition). + + This dataset is used for testing only. + """ + + dataset_dir = "imagenet-rendition" + + def __init__(self, cfg): + root = os.path.abspath(os.path.expanduser(cfg.DATASET.ROOT)) + self.dataset_dir = os.path.join(root, self.dataset_dir) + self.image_dir = os.path.join(self.dataset_dir, "imagenet-r") + + text_file = os.path.join(self.dataset_dir, "classnames.txt") + classnames = ImageNet.read_classnames(text_file) + + data = self.read_data(classnames) + + super().__init__(train_x=data, test=data) + + def read_data(self, classnames): + image_dir = self.image_dir + folders = listdir_nohidden(image_dir, sort=True) + folders = [f for f in folders if f not in TO_BE_IGNORED] + items = [] + + for label, folder in enumerate(folders): + imnames = listdir_nohidden(os.path.join(image_dir, folder)) + classname = classnames[folder] + for imname in imnames: + impath = os.path.join(image_dir, folder, imname) + item = Datum(impath=impath, label=label, classname=classname) + items.append(item) + + return items diff --git a/datasets/imagenet_sketch.py b/datasets/imagenet_sketch.py new file mode 100644 index 0000000..b0069eb --- /dev/null +++ b/datasets/imagenet_sketch.py @@ -0,0 +1,43 @@ +import os + +from dassl.data.datasets import DATASET_REGISTRY, Datum, DatasetBase +from dassl.utils import listdir_nohidden + +from .imagenet import ImageNet + + +@DATASET_REGISTRY.register() +class ImageNetSketch(DatasetBase): + """ImageNet-Sketch. + + This dataset is used for testing only. + """ + + dataset_dir = "imagenet-sketch" + + def __init__(self, cfg): + root = os.path.abspath(os.path.expanduser(cfg.DATASET.ROOT)) + self.dataset_dir = os.path.join(root, self.dataset_dir) + self.image_dir = os.path.join(self.dataset_dir, "images") + + text_file = os.path.join(self.dataset_dir, "classnames.txt") + classnames = ImageNet.read_classnames(text_file) + + data = self.read_data(classnames) + + super().__init__(train_x=data, test=data) + + def read_data(self, classnames): + image_dir = self.image_dir + folders = listdir_nohidden(image_dir, sort=True) + items = [] + + for label, folder in enumerate(folders): + imnames = listdir_nohidden(os.path.join(image_dir, folder)) + classname = classnames[folder] + for imname in imnames: + impath = os.path.join(image_dir, folder, imname) + item = Datum(impath=impath, label=label, classname=classname) + items.append(item) + + return items diff --git a/datasets/imagenetv2.py b/datasets/imagenetv2.py new file mode 100644 index 0000000..69f9a8a --- /dev/null +++ b/datasets/imagenetv2.py @@ -0,0 +1,46 @@ +import os + +from dassl.data.datasets import DATASET_REGISTRY, Datum, DatasetBase +from dassl.utils import listdir_nohidden + +from .imagenet import ImageNet + + +@DATASET_REGISTRY.register() +class ImageNetV2(DatasetBase): + """ImageNetV2. + + This dataset is used for testing only. + """ + + dataset_dir = "imagenetv2" + + def __init__(self, cfg): + root = os.path.abspath(os.path.expanduser(cfg.DATASET.ROOT)) + self.dataset_dir = os.path.join(root, self.dataset_dir) + image_dir = "imagenetv2-matched-frequency-format-val" + self.image_dir = os.path.join(self.dataset_dir, image_dir) + + text_file = os.path.join(self.dataset_dir, "classnames.txt") + classnames = ImageNet.read_classnames(text_file) + + data = self.read_data(classnames) + + super().__init__(train_x=data, test=data) + + def read_data(self, classnames): + image_dir = self.image_dir + folders = list(classnames.keys()) + items = [] + + for label in range(1000): + class_dir = os.path.join(image_dir, str(label)) + imnames = listdir_nohidden(class_dir) + folder = folders[label] + classname = classnames[folder] + for imname in imnames: + impath = os.path.join(class_dir, imname) + item = Datum(impath=impath, label=label, classname=classname) + items.append(item) + + return items diff --git a/datasets/new_da.py b/datasets/new_da.py new file mode 100644 index 0000000..33dfc43 --- /dev/null +++ b/datasets/new_da.py @@ -0,0 +1,567 @@ +import torch +from torchvision.transforms import RandomResizedCrop,InterpolationMode +from torchvision.transforms import functional as F +import numpy as np +import random +import torch +import torchvision.transforms.functional as F +from torchvision.transforms import ( + Resize, Compose, ToTensor, Normalize, CenterCrop, RandomCrop, ColorJitter, + RandomApply, GaussianBlur, RandomGrayscale, RandomResizedCrop, + RandomHorizontalFlip +) +from torchvision.transforms.functional import InterpolationMode + +from dassl.data.transforms.transforms import SVHNPolicy, CIFAR10Policy, ImageNetPolicy +from dassl.data.transforms.transforms import RandAugment, RandAugment2, RandAugmentFixMatch +from PIL import Image, ImageFilter + +class RandomResizedCropPair(RandomResizedCrop): + def __init__(self, size, scale=(0.08, 1.0), ratio=(3. / 4., 4. / 3.), interpolation=InterpolationMode.BILINEAR): + super(RandomResizedCropPair, self).__init__(size, scale, ratio, interpolation) + + def __call__(self, img,mask): + i,j,h,w = self.get_params(img,self.scale,self.ratio) + return F.resized_crop(img,i,j,h,w,self.size,self.interpolation),F.resized_crop(mask,i,j,h,w,self.size,self.interpolation) + + +class ComposePair: + def __init__(self, transforms): + self.transforms = transforms + + def __call__(self, img,mask): + + for t in self.transforms: + if isinstance(t,Normalize): + img = t(img) + elif isinstance(t,ToTensor): + img = t(img) + mask = torch.from_numpy(np.array(mask,dtype=np.float16)).permute(2,0,1)[:1] + + + ###design the mask split + mask[mask==255] = 0 + mask[mask > 1] = 1 + else: + img,mask = t(img,mask) + + return img,mask + + def __repr__(self): + format_string = self.__class__.__name__ + '(' + for t in self.transforms: + format_string += '\n' + format_string += ' {0}'.format(t) + format_string += '\n)' + return format_string + +class RandomHorizontalFlipPair(RandomHorizontalFlip): + def __init__(self, p=0.5): + super().__init__(p) + + def __call__(self, img, mask): + if torch.rand(1) < self.p: + return F.hflip(img),F.hflip(mask) + return img,mask + + + + + + + + +AVAI_CHOICES = [ + "random_flip", + "random_resized_crop", + "normalize", + "instance_norm", + "random_crop", + "random_translation", + "center_crop", # This has become a default operation during testing + "cutout", + "imagenet_policy", + "cifar10_policy", + "svhn_policy", + "randaugment", + "randaugment_fixmatch", + "randaugment2", + "gaussian_noise", + "colorjitter", + "randomgrayscale", + "gaussian_blur", + + "random_flip_pair", + "random_resized_crop_pair", +] + +INTERPOLATION_MODES = { + "bilinear": InterpolationMode.BILINEAR, + "bicubic": InterpolationMode.BICUBIC, + "nearest": InterpolationMode.NEAREST, +} + + +class Random2DTranslation: + """Given an image of (height, width), we resize it to + (height*1.125, width*1.125), and then perform random cropping. + + Args: + height (int): target image height. + width (int): target image width. + p (float, optional): probability that this operation takes place. + Default is 0.5. + interpolation (int, optional): desired interpolation. Default is + ``torchvision.transforms.functional.InterpolationMode.BILINEAR`` + """ + + def __init__( + self, height, width, p=0.5, interpolation=InterpolationMode.BILINEAR + ): + self.height = height + self.width = width + self.p = p + self.interpolation = interpolation + + def __call__(self, img): + if random.uniform(0, 1) > self.p: + return F.resize( + img=img, + size=[self.height, self.width], + interpolation=self.interpolation + ) + + new_width = int(round(self.width * 1.125)) + new_height = int(round(self.height * 1.125)) + resized_img = F.resize( + img=img, + size=[new_height, new_width], + interpolation=self.interpolation + ) + x_maxrange = new_width - self.width + y_maxrange = new_height - self.height + x1 = int(round(random.uniform(0, x_maxrange))) + y1 = int(round(random.uniform(0, y_maxrange))) + croped_img = F.crop( + img=resized_img, + top=y1, + left=x1, + height=self.height, + width=self.width + ) + + return croped_img + + +class InstanceNormalization: + """Normalize data using per-channel mean and standard deviation. + + Reference: + - Ulyanov et al. Instance normalization: The missing in- gredient + for fast stylization. ArXiv 2016. + - Shu et al. A DIRT-T Approach to Unsupervised Domain Adaptation. + ICLR 2018. + """ + + def __init__(self, eps=1e-8): + self.eps = eps + + def __call__(self, img): + C, H, W = img.shape + img_re = img.reshape(C, H * W) + mean = img_re.mean(1).view(C, 1, 1) + std = img_re.std(1).view(C, 1, 1) + return (img-mean) / (std + self.eps) + + +class Cutout: + """Randomly mask out one or more patches from an image. + + https://github.com/uoguelph-mlrg/Cutout + + Args: + n_holes (int, optional): number of patches to cut out + of each image. Default is 1. + length (int, optinal): length (in pixels) of each square + patch. Default is 16. + """ + + def __init__(self, n_holes=1, length=16): + self.n_holes = n_holes + self.length = length + + def __call__(self, img): + """ + Args: + img (Tensor): tensor image of size (C, H, W). + + Returns: + Tensor: image with n_holes of dimension + length x length cut out of it. + """ + h = img.size(1) + w = img.size(2) + + mask = np.ones((h, w), np.float32) + + for n in range(self.n_holes): + y = np.random.randint(h) + x = np.random.randint(w) + + y1 = np.clip(y - self.length // 2, 0, h) + y2 = np.clip(y + self.length // 2, 0, h) + x1 = np.clip(x - self.length // 2, 0, w) + x2 = np.clip(x + self.length // 2, 0, w) + + mask[y1:y2, x1:x2] = 0.0 + + mask = torch.from_numpy(mask) + mask = mask.expand_as(img) + return img * mask + + +class GaussianNoise: + """Add gaussian noise.""" + + def __init__(self, mean=0, std=0.15, p=0.5): + self.mean = mean + self.std = std + self.p = p + + def __call__(self, img): + if random.uniform(0, 1) > self.p: + return img + noise = torch.randn(img.size()) * self.std + self.mean + return img + noise + + +def build_transform(cfg, is_train=True, choices=None): + """Build transformation function. + + Args: + cfg (CfgNode): config. + is_train (bool, optional): for training (True) or test (False). + Default is True. + choices (list, optional): list of strings which will overwrite + cfg.INPUT.TRANSFORMS if given. Default is None. + """ + if cfg.INPUT.NO_TRANSFORM: + print("Note: no transform is applied!") + return None + + if choices is None: + choices = cfg.INPUT.TRANSFORMS + + for choice in choices: + assert choice in AVAI_CHOICES + + target_size = f"{cfg.INPUT.SIZE[0]}x{cfg.INPUT.SIZE[1]}" + + normalize = Normalize(mean=cfg.INPUT.PIXEL_MEAN, std=cfg.INPUT.PIXEL_STD) + + if is_train: + return _build_transform_train(cfg, choices, target_size, normalize) + else: + return _build_transform_test(cfg, choices, target_size, normalize) + + +def build_transform_pair(cfg, is_train=True, choices=None): + """Build transformation function. + + Args: + cfg (CfgNode): config. + is_train (bool, optional): for training (True) or test (False). + Default is True. + choices (list, optional): list of strings which will overwrite + cfg.INPUT.TRANSFORMS if given. Default is None. + """ + if cfg.INPUT.NO_TRANSFORM: + print("Note: no transform is applied!") + return None + + if choices is None: + choices = cfg.INPUT.TRANSFORMS + + for choice in choices: + assert choice in AVAI_CHOICES + + target_size = f"{cfg.INPUT.SIZE[0]}x{cfg.INPUT.SIZE[1]}" + + normalize = Normalize(mean=cfg.INPUT.PIXEL_MEAN, std=cfg.INPUT.PIXEL_STD) + + if is_train: + return _build_transform_train_pair(cfg, choices, target_size, normalize) + else: + return _build_transform_test(cfg, choices, target_size, normalize) + +def _build_transform_train_pair(cfg, choices, target_size, normalize): + print("Building transform_train_pair") + tfm_train = [] + + interp_mode = INTERPOLATION_MODES[cfg.INPUT.INTERPOLATION] + input_size = cfg.INPUT.SIZE + + # Make sure the image size matches the target size + conditions = [] + conditions += ["random_crop" not in choices] + conditions += ["random_resized_crop" not in choices] + if all(conditions): + print(f"+ resize to {target_size}") + tfm_train += [Resize(input_size, interpolation=interp_mode)] + + # if "random_translation" in choices: + # print("+ random translation") + # tfm_train += [Random2DTranslation(input_size[0], input_size[1])] + # + # if "random_crop" in choices: + # crop_padding = cfg.INPUT.CROP_PADDING + # print(f"+ random crop (padding = {crop_padding})") + # tfm_train += [RandomCrop(input_size, padding=crop_padding)] + + if "random_resized_crop" in choices: + s_ = cfg.INPUT.RRCROP_SCALE + print(f"+ random resized crop pair (size={input_size}, scale={s_})") + tfm_train += [ + RandomResizedCropPair(input_size, scale=s_, interpolation=interp_mode) + ] + + if "random_flip" in choices: + print("+ random flip pair") + tfm_train += [RandomHorizontalFlipPair()] + + if "imagenet_policy" in choices: + print("+ imagenet policy") + tfm_train += [ImageNetPolicy()] + + if "cifar10_policy" in choices: + print("+ cifar10 policy") + tfm_train += [CIFAR10Policy()] + + if "svhn_policy" in choices: + print("+ svhn policy") + tfm_train += [SVHNPolicy()] + + if "randaugment" in choices: + n_ = cfg.INPUT.RANDAUGMENT_N + m_ = cfg.INPUT.RANDAUGMENT_M + print(f"+ randaugment (n={n_}, m={m_})") + tfm_train += [RandAugment(n_, m_)] + + if "randaugment_fixmatch" in choices: + n_ = cfg.INPUT.RANDAUGMENT_N + print(f"+ randaugment_fixmatch (n={n_})") + tfm_train += [RandAugmentFixMatch(n_)] + + if "randaugment2" in choices: + n_ = cfg.INPUT.RANDAUGMENT_N + print(f"+ randaugment2 (n={n_})") + tfm_train += [RandAugment2(n_)] + + if "colorjitter" in choices: + b_ = cfg.INPUT.COLORJITTER_B + c_ = cfg.INPUT.COLORJITTER_C + s_ = cfg.INPUT.COLORJITTER_S + h_ = cfg.INPUT.COLORJITTER_H + print( + f"+ color jitter (brightness={b_}, " + f"contrast={c_}, saturation={s_}, hue={h_})" + ) + tfm_train += [ + ColorJitter( + brightness=b_, + contrast=c_, + saturation=s_, + hue=h_, + ) + ] + + if "randomgrayscale" in choices: + print("+ random gray scale") + tfm_train += [RandomGrayscale(p=cfg.INPUT.RGS_P)] + + if "gaussian_blur" in choices: + print(f"+ gaussian blur (kernel={cfg.INPUT.GB_K})") + gb_k, gb_p = cfg.INPUT.GB_K, cfg.INPUT.GB_P + tfm_train += [RandomApply([GaussianBlur(gb_k)], p=gb_p)] + + print("+ to torch tensor of range [0, 1]") + tfm_train += [ToTensor()] + + if "cutout" in choices: + cutout_n = cfg.INPUT.CUTOUT_N + cutout_len = cfg.INPUT.CUTOUT_LEN + print(f"+ cutout (n_holes={cutout_n}, length={cutout_len})") + tfm_train += [Cutout(cutout_n, cutout_len)] + + if "normalize" in choices: + print( + f"+ normalization (mean={cfg.INPUT.PIXEL_MEAN}, std={cfg.INPUT.PIXEL_STD})" + ) + tfm_train += [normalize] + + if "gaussian_noise" in choices: + print( + f"+ gaussian noise (mean={cfg.INPUT.GN_MEAN}, std={cfg.INPUT.GN_STD})" + ) + tfm_train += [GaussianNoise(cfg.INPUT.GN_MEAN, cfg.INPUT.GN_STD)] + + if "instance_norm" in choices: + print("+ instance normalization") + tfm_train += [InstanceNormalization()] + + tfm_train = ComposePair(tfm_train) + + + return tfm_train + + +def _build_transform_train(cfg, choices, target_size, normalize): + print("Building transform_train") + tfm_train = [] + + interp_mode = INTERPOLATION_MODES[cfg.INPUT.INTERPOLATION] + input_size = cfg.INPUT.SIZE + + # Make sure the image size matches the target size + conditions = [] + conditions += ["random_crop" not in choices] + conditions += ["random_resized_crop" not in choices] + if all(conditions): + print(f"+ resize to {target_size}") + tfm_train += [Resize(input_size, interpolation=interp_mode)] + + if "random_translation" in choices: + print("+ random translation") + tfm_train += [Random2DTranslation(input_size[0], input_size[1])] + + if "random_crop" in choices: + crop_padding = cfg.INPUT.CROP_PADDING + print(f"+ random crop (padding = {crop_padding})") + tfm_train += [RandomCrop(input_size, padding=crop_padding)] + + if "random_resized_crop" in choices: + s_ = cfg.INPUT.RRCROP_SCALE + print(f"+ random resized crop (size={input_size}, scale={s_})") + tfm_train += [ + RandomResizedCrop(input_size, scale=s_, interpolation=interp_mode) + ] + + if "random_flip" in choices: + print("+ random flip") + tfm_train += [RandomHorizontalFlip()] + + if "imagenet_policy" in choices: + print("+ imagenet policy") + tfm_train += [ImageNetPolicy()] + + if "cifar10_policy" in choices: + print("+ cifar10 policy") + tfm_train += [CIFAR10Policy()] + + if "svhn_policy" in choices: + print("+ svhn policy") + tfm_train += [SVHNPolicy()] + + if "randaugment" in choices: + n_ = cfg.INPUT.RANDAUGMENT_N + m_ = cfg.INPUT.RANDAUGMENT_M + print(f"+ randaugment (n={n_}, m={m_})") + tfm_train += [RandAugment(n_, m_)] + + if "randaugment_fixmatch" in choices: + n_ = cfg.INPUT.RANDAUGMENT_N + print(f"+ randaugment_fixmatch (n={n_})") + tfm_train += [RandAugmentFixMatch(n_)] + + if "randaugment2" in choices: + n_ = cfg.INPUT.RANDAUGMENT_N + print(f"+ randaugment2 (n={n_})") + tfm_train += [RandAugment2(n_)] + + if "colorjitter" in choices: + b_ = cfg.INPUT.COLORJITTER_B + c_ = cfg.INPUT.COLORJITTER_C + s_ = cfg.INPUT.COLORJITTER_S + h_ = cfg.INPUT.COLORJITTER_H + print( + f"+ color jitter (brightness={b_}, " + f"contrast={c_}, saturation={s_}, hue={h_})" + ) + tfm_train += [ + ColorJitter( + brightness=b_, + contrast=c_, + saturation=s_, + hue=h_, + ) + ] + + if "randomgrayscale" in choices: + print("+ random gray scale") + tfm_train += [RandomGrayscale(p=cfg.INPUT.RGS_P)] + + if "gaussian_blur" in choices: + print(f"+ gaussian blur (kernel={cfg.INPUT.GB_K})") + gb_k, gb_p = cfg.INPUT.GB_K, cfg.INPUT.GB_P + tfm_train += [RandomApply([GaussianBlur(gb_k)], p=gb_p)] + + print("+ to torch tensor of range [0, 1]") + tfm_train += [ToTensor()] + + if "cutout" in choices: + cutout_n = cfg.INPUT.CUTOUT_N + cutout_len = cfg.INPUT.CUTOUT_LEN + print(f"+ cutout (n_holes={cutout_n}, length={cutout_len})") + tfm_train += [Cutout(cutout_n, cutout_len)] + + if "normalize" in choices: + print( + f"+ normalization (mean={cfg.INPUT.PIXEL_MEAN}, std={cfg.INPUT.PIXEL_STD})" + ) + tfm_train += [normalize] + + if "gaussian_noise" in choices: + print( + f"+ gaussian noise (mean={cfg.INPUT.GN_MEAN}, std={cfg.INPUT.GN_STD})" + ) + tfm_train += [GaussianNoise(cfg.INPUT.GN_MEAN, cfg.INPUT.GN_STD)] + + if "instance_norm" in choices: + print("+ instance normalization") + tfm_train += [InstanceNormalization()] + + tfm_train = Compose(tfm_train) + + return tfm_train + + +def _build_transform_test(cfg, choices, target_size, normalize): + print("Building transform_test") + tfm_test = [] + + interp_mode = INTERPOLATION_MODES[cfg.INPUT.INTERPOLATION] + input_size = cfg.INPUT.SIZE + + print(f"+ resize the smaller edge to {max(input_size)}") + tfm_test += [Resize(max(input_size), interpolation=interp_mode)] + + print(f"+ {target_size} center crop") + tfm_test += [CenterCrop(input_size)] + + print("+ to torch tensor of range [0, 1]") + tfm_test += [ToTensor()] + + if "normalize" in choices: + print( + f"+ normalization (mean={cfg.INPUT.PIXEL_MEAN}, std={cfg.INPUT.PIXEL_STD})" + ) + tfm_test += [normalize] + + if "instance_norm" in choices: + print("+ instance normalization") + tfm_test += [InstanceNormalization()] + + tfm_test = Compose(tfm_test) + + return tfm_test + diff --git a/datasets/oxford_flowers.py b/datasets/oxford_flowers.py new file mode 100644 index 0000000..514dc3e --- /dev/null +++ b/datasets/oxford_flowers.py @@ -0,0 +1,89 @@ +import os +import pickle +import random +from scipy.io import loadmat +from collections import defaultdict + +from dassl.data.datasets import DATASET_REGISTRY, Datum, DatasetBase +from dassl.utils import read_json, mkdir_if_missing + +from .oxford_pets import OxfordPets + + +@DATASET_REGISTRY.register() +class OxfordFlowers(DatasetBase): + + dataset_dir = "oxford_flowers" + + def __init__(self, cfg): + root = os.path.abspath(os.path.expanduser(cfg.DATASET.ROOT)) + self.dataset_dir = os.path.join(root, self.dataset_dir) + self.image_dir = os.path.join(self.dataset_dir, "jpg") + self.label_file = os.path.join(self.dataset_dir, "imagelabels.mat") + self.lab2cname_file = os.path.join(self.dataset_dir, "cat_to_name.json") + self.split_path = os.path.join(self.dataset_dir, "split_zhou_OxfordFlowers.json") + self.split_fewshot_dir = os.path.join(self.dataset_dir, "split_fewshot") + mkdir_if_missing(self.split_fewshot_dir) + + if os.path.exists(self.split_path): + train, val, test = OxfordPets.read_split(self.split_path, self.image_dir) + else: + train, val, test = self.read_data() + OxfordPets.save_split(train, val, test, self.split_path, self.image_dir) + + num_shots = cfg.DATASET.NUM_SHOTS + if num_shots >= 1: + seed = cfg.SEED + preprocessed = os.path.join(self.split_fewshot_dir, f"shot_{num_shots}-seed_{seed}.pkl") + + if os.path.exists(preprocessed): + print(f"Loading preprocessed few-shot data from {preprocessed}") + with open(preprocessed, "rb") as file: + data = pickle.load(file) + train, val = data["train"], data["val"] + else: + train = self.generate_fewshot_dataset(train, num_shots=num_shots) + val = self.generate_fewshot_dataset(val, num_shots=min(num_shots, 4)) + data = {"train": train, "val": val} + print(f"Saving preprocessed few-shot data to {preprocessed}") + with open(preprocessed, "wb") as file: + pickle.dump(data, file, protocol=pickle.HIGHEST_PROTOCOL) + + subsample = cfg.DATASET.SUBSAMPLE_CLASSES + train, val, test = OxfordPets.subsample_classes(train, val, test, subsample=subsample) + + super().__init__(train_x=train, val=val, test=test) + + def read_data(self): + tracker = defaultdict(list) + label_file = loadmat(self.label_file)["labels"][0] + for i, label in enumerate(label_file): + imname = f"image_{str(i + 1).zfill(5)}.jpg" + impath = os.path.join(self.image_dir, imname) + label = int(label) + tracker[label].append(impath) + + print("Splitting data into 50% train, 20% val, and 30% test") + + def _collate(ims, y, c): + items = [] + for im in ims: + item = Datum(impath=im, label=y - 1, classname=c) # convert to 0-based label + items.append(item) + return items + + lab2cname = read_json(self.lab2cname_file) + train, val, test = [], [], [] + for label, impaths in tracker.items(): + random.shuffle(impaths) + n_total = len(impaths) + n_train = round(n_total * 0.5) + n_val = round(n_total * 0.2) + n_test = n_total - n_train - n_val + assert n_train > 0 and n_val > 0 and n_test > 0 + cname = lab2cname[str(label)] + train.extend(_collate(impaths[:n_train], label, cname)) + val.extend(_collate(impaths[n_train : n_train + n_val], label, cname)) + test.extend(_collate(impaths[n_train + n_val :], label, cname)) + + return train, val, test diff --git a/datasets/oxford_pets.py b/datasets/oxford_pets.py new file mode 100644 index 0000000..9d47352 --- /dev/null +++ b/datasets/oxford_pets.py @@ -0,0 +1,186 @@ +import os +import pickle +import math +import random +from collections import defaultdict + +from dassl.data.datasets import DATASET_REGISTRY, Datum, DatasetBase +from dassl.utils import read_json, write_json, mkdir_if_missing + + +@DATASET_REGISTRY.register() +class OxfordPets(DatasetBase): + + dataset_dir = "oxford_pets" + + def __init__(self, cfg): + root = os.path.abspath(os.path.expanduser(cfg.DATASET.ROOT)) + self.dataset_dir = os.path.join(root, self.dataset_dir) + self.image_dir = os.path.join(self.dataset_dir, "images") + self.anno_dir = os.path.join(self.dataset_dir, "annotations") + self.split_path = os.path.join(self.dataset_dir, "split_zhou_OxfordPets.json") + self.split_fewshot_dir = os.path.join(self.dataset_dir, "split_fewshot") + mkdir_if_missing(self.split_fewshot_dir) + + if os.path.exists(self.split_path): + train, val, test = self.read_split(self.split_path, self.image_dir) + else: + trainval = self.read_data(split_file="trainval.txt") + test = self.read_data(split_file="test.txt") + train, val = self.split_trainval(trainval) + self.save_split(train, val, test, self.split_path, self.image_dir) + + num_shots = cfg.DATASET.NUM_SHOTS + if num_shots >= 1: + seed = cfg.SEED + preprocessed = os.path.join(self.split_fewshot_dir, f"shot_{num_shots}-seed_{seed}.pkl") + + if os.path.exists(preprocessed): + print(f"Loading preprocessed few-shot data from {preprocessed}") + with open(preprocessed, "rb") as file: + data = pickle.load(file) + train, val = data["train"], data["val"] + else: + train = self.generate_fewshot_dataset(train, num_shots=num_shots) + val = self.generate_fewshot_dataset(val, num_shots=min(num_shots, 4)) + data = {"train": train, "val": val} + print(f"Saving preprocessed few-shot data to {preprocessed}") + with open(preprocessed, "wb") as file: + pickle.dump(data, file, protocol=pickle.HIGHEST_PROTOCOL) + + subsample = cfg.DATASET.SUBSAMPLE_CLASSES + train, val, test = self.subsample_classes(train, val, test, subsample=subsample) + + super().__init__(train_x=train, val=val, test=test) + + def read_data(self, split_file): + filepath = os.path.join(self.anno_dir, split_file) + items = [] + + with open(filepath, "r") as f: + lines = f.readlines() + for line in lines: + line = line.strip() + imname, label, species, _ = line.split(" ") + breed = imname.split("_")[:-1] + breed = "_".join(breed) + breed = breed.lower() + imname += ".jpg" + impath = os.path.join(self.image_dir, imname) + label = int(label) - 1 # convert to 0-based index + item = Datum(impath=impath, label=label, classname=breed) + items.append(item) + + return items + + @staticmethod + def split_trainval(trainval, p_val=0.2): + p_trn = 1 - p_val + print(f"Splitting trainval into {p_trn:.0%} train and {p_val:.0%} val") + tracker = defaultdict(list) + for idx, item in enumerate(trainval): + label = item.label + tracker[label].append(idx) + + train, val = [], [] + for label, idxs in tracker.items(): + n_val = round(len(idxs) * p_val) + assert n_val > 0 + random.shuffle(idxs) + for n, idx in enumerate(idxs): + item = trainval[idx] + if n < n_val: + val.append(item) + else: + train.append(item) + + return train, val + + @staticmethod + def save_split(train, val, test, filepath, path_prefix): + def _extract(items): + out = [] + for item in items: + impath = item.impath + label = item.label + classname = item.classname + impath = impath.replace(path_prefix, "") + if impath.startswith("/"): + impath = impath[1:] + out.append((impath, label, classname)) + return out + + train = _extract(train) + val = _extract(val) + test = _extract(test) + + split = {"train": train, "val": val, "test": test} + + write_json(split, filepath) + print(f"Saved split to {filepath}") + + @staticmethod + def read_split(filepath, path_prefix): + def _convert(items): + out = [] + for impath, label, classname in items: + impath = os.path.join(path_prefix, impath) + item = Datum(impath=impath, label=int(label), classname=classname) + out.append(item) + return out + + print(f"Reading split from {filepath}") + split = read_json(filepath) + train = _convert(split["train"]) + val = _convert(split["val"]) + test = _convert(split["test"]) + + return train, val, test + + @staticmethod + def subsample_classes(*args, subsample="all"): + """Divide classes into two groups. The first group + represents base classes while the second group represents + new classes. + + Args: + args: a list of datasets, e.g. train, val and test. + subsample (str): what classes to subsample. + """ + assert subsample in ["all", "base", "new"] + + if subsample == "all": + return args + + dataset = args[0] + labels = set() + for item in dataset: + labels.add(item.label) + labels = list(labels) + labels.sort() + n = len(labels) + # Divide classes into two halves + m = math.ceil(n / 2) + + print(f"SUBSAMPLE {subsample.upper()} CLASSES!") + if subsample == "base": + selected = labels[:m] # take the first half + else: + selected = labels[m:] # take the second half + relabeler = {y: y_new for y_new, y in enumerate(selected)} + + output = [] + for dataset in args: + dataset_new = [] + for item in dataset: + if item.label not in selected: + continue + item_new = Datum( + impath=item.impath, + label=relabeler[item.label], + classname=item.classname + ) + dataset_new.append(item_new) + output.append(dataset_new) + + return output diff --git a/datasets/pascal_voc.py b/datasets/pascal_voc.py new file mode 100644 index 0000000..0f27074 --- /dev/null +++ b/datasets/pascal_voc.py @@ -0,0 +1,229 @@ +import os +import pickle +from collections import OrderedDict + +from dassl.data.datasets import DATASET_REGISTRY, Datum, DatasetBase +from dassl.utils import listdir_nohidden, mkdir_if_missing + +from .oxford_pets import OxfordPets +import numpy as np +from pathlib import Path +from collections import defaultdict +import random +import math +CAT_LIST = ['aeroplane', + 'bicycle', + 'bird', + 'boat', + 'bottle', + 'bus', + 'car', + 'cat', + 'chair', + 'cow', + 'table', + 'dog', + 'horse', + 'motorbike', + 'person', + 'plant', + 'sheep', + 'sofa', + 'train', + 'tvmonitor'] + +CAT_LIST_TO_NAME = dict(zip(range(len(CAT_LIST)) ,CAT_LIST)) + + +def _collate(ims, y, c): + return Datum(impath=ims, label=y, classname=c) + +def load_img_name_list(dataset_path): + + img_gt_name_list = open(dataset_path).readlines() + img_name_list = [img_gt_name.strip() for img_gt_name in img_gt_name_list] + + return img_name_list + +def load_image_label_list_from_npy(data_root,img_name_list, label_file_path=None): + if label_file_path is None: + label_file_path = 'voc12/cls_labels.npy' + cls_labels_dict = np.load(label_file_path, allow_pickle=True).item() + label_list = [] + data_dtm = [] + + for id in img_name_list: + if id not in cls_labels_dict.keys(): + img_name = id + '.jpg' + else: + img_name = id + label = cls_labels_dict[img_name] + label_idx = np.where(label==1)[0] + class_name = [CAT_LIST[idx] for idx in range(len(label_idx))] + data_dtm.append(_collate(os.path.join(data_root,img_name+'.jpg'),label,class_name)) + + return data_dtm + + + + + + + + + +@DATASET_REGISTRY.register() +class VOC12(DatasetBase): + dataset_dir = "voc12data" + + def __init__(self, cfg): + root = os.path.abspath(os.path.expanduser(cfg.DATASET.ROOT)) + self.dataset_dir = os.path.join(root, self.dataset_dir) + self.image_dir = os.path.join(self.dataset_dir,'VOCdevkit/VOC2012/JPEGImages') + train_img_name_list_path = os.path.join('voc12/train_aug_id.txt') + val_img_name_list_path = os.path.join('voc12/val_id.txt') + + train = load_image_label_list_from_npy(self.image_dir,load_img_name_list(train_img_name_list_path)) + val = load_image_label_list_from_npy(self.image_dir,load_img_name_list(val_img_name_list_path)) + self.split_fewshot_dir = os.path.join(self.dataset_dir, "split_fewshot") + mkdir_if_missing(self.split_fewshot_dir) + + num_shots = cfg.DATASET.NUM_SHOTS + if num_shots >= 1: + seed = cfg.SEED + preprocessed = os.path.join(self.split_fewshot_dir, f"shot_{num_shots}-seed_{seed}.pkl") + + if os.path.exists(preprocessed): + print(f"Loading preprocessed few-shot data from {preprocessed}") + with open(preprocessed, "rb") as file: + data = pickle.load(file) + train = data["train"] + else: + train = self.generate_fewshot_dataset(train, num_shots=num_shots) + data = {"train": train} + print(f"Saving preprocessed few-shot data to {preprocessed}") + with open(preprocessed, "wb") as file: + pickle.dump(data, file, protocol=pickle.HIGHEST_PROTOCOL) + + subsample = cfg.DATASET.SUBSAMPLE_CLASSES + train, val = self.subsample_classes(train, val, subsample=subsample) + + super().__init__(train_x=train, val=val, test=val) + + @staticmethod + def subsample_classes(*args, subsample="all"): + """Divide classes into two groups. The first group + represents base classes while the second group represents + new classes. + + Args: + args: a list of datasets, e.g. train, val and test. + subsample (str): what classes to subsample. + """ + assert subsample in ["all", "base", "new"] + + if subsample == "all": + return args + + dataset = args[0] + labels = set() + for item in dataset: + label_idx = random.choices(np.where(item.label == 1)[0])[0] + labels.add(label_idx) + labels = list(labels) + labels.sort() + n = len(labels) + # Divide classes into two halves + m = math.ceil(n / 2) + + print(f"SUBSAMPLE {subsample.upper()} CLASSES!") + if subsample == "base": + selected = labels[:m] # take the first half + else: + selected = labels[m:] # take the second half + relabeler = {y: y_new for y_new, y in enumerate(selected)} + + output = [] + for dataset in args: + dataset_new = [] + for item in dataset: + label_idx = random.choices(np.where(item.label == 1)[0])[0] + if label_idx not in selected: + continue + + item_new = Datum( + impath=item.impath, + label=item.label, + classname=item.classname + ) + dataset_new.append(item_new) + output.append(dataset_new) + + return output + + + @staticmethod + def get_num_classes(data_source): + """Count number of classes. + + Args: + data_source (list): a list of Datum objects. + """ + return len(CAT_LIST) + + @staticmethod + def get_lab2cname(data_source): + """Get a label-to-classname mapping (dict). + + Args: + data_source (list): a list of Datum objects. + """ + return CAT_LIST_TO_NAME, CAT_LIST + + def split_dataset_by_label(self, data_source): + """Split a dataset, i.e. a list of Datum objects, + into class-specific groups stored in a dictionary. + + Args: + data_source (list): a list of Datum objects. + """ + output = defaultdict(list) + + for item in data_source: + one_hot_label = item.label + label_idx = random.choices(np.where(one_hot_label==1)[0])[0] + output[label_idx].append(item) + + return output + + @staticmethod + def read_classnames(text_file): + """Return a dictionary containing + key-value pairs of : . + """ + classnames = OrderedDict() + with open(text_file, "r") as f: + lines = f.readlines() + for line in lines: + line = line.strip().split(" ") + folder = line[0] + classname = " ".join(line[1:]) + classnames[folder] = classname + return classnames + + def read_data(self, classnames, split_dir): + split_dir = os.path.join(self.image_dir, split_dir) + folders = sorted(f.name for f in os.scandir(split_dir) if f.is_dir()) + items = [] + + for label, folder in enumerate(folders): + imnames = listdir_nohidden(os.path.join(split_dir, folder)) + classname = classnames[folder] + for imname in imnames: + impath = os.path.join(split_dir, folder, imname) + item = Datum(impath=impath, label=label, classname=classname) + items.append(item) + + return items + + diff --git a/datasets/stanford_cars.py b/datasets/stanford_cars.py new file mode 100644 index 0000000..ddbb072 --- /dev/null +++ b/datasets/stanford_cars.py @@ -0,0 +1,75 @@ +import os +import pickle +from scipy.io import loadmat + +from dassl.data.datasets import DATASET_REGISTRY, Datum, DatasetBase +from dassl.utils import mkdir_if_missing + +from .oxford_pets import OxfordPets +import numpy as np + +@DATASET_REGISTRY.register() +class StanfordCars(DatasetBase): + + dataset_dir = "stanford_cars" + + def __init__(self, cfg): + root = os.path.abspath(os.path.expanduser(cfg.DATASET.ROOT)) + self.dataset_dir = os.path.join(root, self.dataset_dir) + self.split_path = os.path.join(self.dataset_dir, "split_zhou_StanfordCars.json") + self.split_fewshot_dir = os.path.join(self.dataset_dir, "split_fewshot") + mkdir_if_missing(self.split_fewshot_dir) + + if os.path.exists(self.split_path): + train, val, test = OxfordPets.read_split(self.split_path, self.dataset_dir) + else: + trainval_file = os.path.join(self.dataset_dir, "devkit", "cars_train_annos.mat") + test_file = os.path.join(self.dataset_dir, "cars_test_annos_withlabels.mat") + meta_file = os.path.join(self.dataset_dir, "devkit", "cars_meta.mat") + trainval = self.read_data("cars_train", trainval_file, meta_file) + test = self.read_data("cars_test", test_file, meta_file) + train, val = OxfordPets.split_trainval(trainval) + OxfordPets.save_split(train, val, test, self.split_path, self.dataset_dir) + + num_shots = cfg.DATASET.NUM_SHOTS + if num_shots >= 1: + seed = cfg.SEED + preprocessed = os.path.join(self.split_fewshot_dir, f"shot_{num_shots}-seed_{seed}.pkl") + + if os.path.exists(preprocessed): + print(f"Loading preprocessed few-shot data from {preprocessed}") + with open(preprocessed, "rb") as file: + data = pickle.load(file) + train, val = data["train"], data["val"] + else: + train = self.generate_fewshot_dataset(train, num_shots=num_shots) + val = self.generate_fewshot_dataset(val, num_shots=min(num_shots, 4)) + data = {"train": train, "val": val} + print(f"Saving preprocessed few-shot data to {preprocessed}") + with open(preprocessed, "wb") as file: + pickle.dump(data, file, protocol=pickle.HIGHEST_PROTOCOL) + + subsample = cfg.DATASET.SUBSAMPLE_CLASSES + train, val, test = OxfordPets.subsample_classes(train, val, test, subsample=subsample) + + super().__init__(train_x=train, val=val, test=test) + + def read_data(self, image_dir, anno_file, meta_file): + anno_file = loadmat(anno_file)["annotations"][0] + meta_file = loadmat(meta_file)["class_names"][0] + items = [] + + for i in range(len(anno_file)): + imname = anno_file[i]["fname"][0] + impath = os.path.join(self.dataset_dir, image_dir, imname) + label = anno_file[i]["class"][0, 0] + label = int(label) - 1 # convert to 0-based index + classname = meta_file[label][0] + names = classname.split(" ") + year = names.pop(-1) + names.insert(0, year) + classname = " ".join(names) + item = Datum(impath=impath, label=label, classname=classname) + items.append(item) + + return items diff --git a/datasets/sun397.py b/datasets/sun397.py new file mode 100644 index 0000000..19df8b6 --- /dev/null +++ b/datasets/sun397.py @@ -0,0 +1,81 @@ +import os +import pickle + +from dassl.data.datasets import DATASET_REGISTRY, Datum, DatasetBase +from dassl.utils import mkdir_if_missing + +from .oxford_pets import OxfordPets + + +@DATASET_REGISTRY.register() +class SUN397(DatasetBase): + + dataset_dir = "sun397" + + def __init__(self, cfg): + root = os.path.abspath(os.path.expanduser(cfg.DATASET.ROOT)) + self.dataset_dir = os.path.join(root, self.dataset_dir) + self.image_dir = os.path.join(self.dataset_dir, "SUN397") + self.split_path = os.path.join(self.dataset_dir, "split_zhou_SUN397.json") + self.split_fewshot_dir = os.path.join(self.dataset_dir, "split_fewshot") + mkdir_if_missing(self.split_fewshot_dir) + + if os.path.exists(self.split_path): + train, val, test = OxfordPets.read_split(self.split_path, self.image_dir) + else: + classnames = [] + with open(os.path.join(self.dataset_dir, "ClassName.txt"), "r") as f: + lines = f.readlines() + + for line in lines: + line = line.strip()[1:] # remove / + classnames.append(line) + cname2lab = {c: i for i, c in enumerate(classnames)} + trainval = self.read_data(cname2lab, "Training_01.txt") + test = self.read_data(cname2lab, "Testing_01.txt") + train, val = OxfordPets.split_trainval(trainval) + OxfordPets.save_split(train, val, test, self.split_path, self.image_dir) + + num_shots = cfg.DATASET.NUM_SHOTS + if num_shots >= 1: + seed = cfg.SEED + preprocessed = os.path.join(self.split_fewshot_dir, f"shot_{num_shots}-seed_{seed}.pkl") + + if os.path.exists(preprocessed): + print(f"Loading preprocessed few-shot data from {preprocessed}") + with open(preprocessed, "rb") as file: + data = pickle.load(file) + train, val = data["train"], data["val"] + else: + train = self.generate_fewshot_dataset(train, num_shots=num_shots) + val = self.generate_fewshot_dataset(val, num_shots=min(num_shots, 4)) + data = {"train": train, "val": val} + print(f"Saving preprocessed few-shot data to {preprocessed}") + with open(preprocessed, "wb") as file: + pickle.dump(data, file, protocol=pickle.HIGHEST_PROTOCOL) + + subsample = cfg.DATASET.SUBSAMPLE_CLASSES + train, val, test = OxfordPets.subsample_classes(train, val, test, subsample=subsample) + + super().__init__(train_x=train, val=val, test=test) + + def read_data(self, cname2lab, text_file): + text_file = os.path.join(self.dataset_dir, text_file) + items = [] + + with open(text_file, "r") as f: + lines = f.readlines() + for line in lines: + imname = line.strip()[1:] # remove / + classname = os.path.dirname(imname) + label = cname2lab[classname] + impath = os.path.join(self.image_dir, imname) + + names = classname.split("/")[1:] # remove 1st letter + names = names[::-1] # put words like indoor/outdoor at first + classname = " ".join(names) + + item = Datum(impath=impath, label=label, classname=classname) + items.append(item) + + return items diff --git a/datasets/ucf101.py b/datasets/ucf101.py new file mode 100644 index 0000000..94439e4 --- /dev/null +++ b/datasets/ucf101.py @@ -0,0 +1,84 @@ +import os +import pickle +import re + +from dassl.data.datasets import DATASET_REGISTRY, Datum, DatasetBase +from dassl.utils import mkdir_if_missing + +from .oxford_pets import OxfordPets + + +@DATASET_REGISTRY.register() +class UCF101(DatasetBase): + + dataset_dir = "ucf101" + + def __init__(self, cfg): + root = os.path.abspath(os.path.expanduser(cfg.DATASET.ROOT)) + self.dataset_dir = os.path.join(root, self.dataset_dir) + self.image_dir = os.path.join(self.dataset_dir, "UCF-101-midframes") + self.split_path = os.path.join(self.dataset_dir, "split_zhou_UCF101.json") + self.split_fewshot_dir = os.path.join(self.dataset_dir, "split_fewshot") + mkdir_if_missing(self.split_fewshot_dir) + + if os.path.exists(self.split_path): + train, val, test = OxfordPets.read_split(self.split_path, self.image_dir) + else: + cname2lab = {} + filepath = os.path.join(self.dataset_dir, "ucfTrainTestlist/classInd.txt") + with open(filepath, "r") as f: + lines = f.readlines() + for line in lines: + label, classname = line.strip().split(" ") + label = int(label) - 1 # conver to 0-based index + cname2lab[classname] = label + + trainval = self.read_data(cname2lab, "ucfTrainTestlist/trainlist01.txt") + test = self.read_data(cname2lab, "ucfTrainTestlist/testlist01.txt") + train, val = OxfordPets.split_trainval(trainval) + OxfordPets.save_split(train, val, test, self.split_path, self.image_dir) + + num_shots = cfg.DATASET.NUM_SHOTS + if num_shots >= 1: + seed = cfg.SEED + preprocessed = os.path.join(self.split_fewshot_dir, f"shot_{num_shots}-seed_{seed}.pkl") + + if os.path.exists(preprocessed): + print(f"Loading preprocessed few-shot data from {preprocessed}") + with open(preprocessed, "rb") as file: + data = pickle.load(file) + train, val = data["train"], data["val"] + else: + train = self.generate_fewshot_dataset(train, num_shots=num_shots) + val = self.generate_fewshot_dataset(val, num_shots=min(num_shots, 4)) + data = {"train": train, "val": val} + print(f"Saving preprocessed few-shot data to {preprocessed}") + with open(preprocessed, "wb") as file: + pickle.dump(data, file, protocol=pickle.HIGHEST_PROTOCOL) + + subsample = cfg.DATASET.SUBSAMPLE_CLASSES + train, val, test = OxfordPets.subsample_classes(train, val, test, subsample=subsample) + + super().__init__(train_x=train, val=val, test=test) + + def read_data(self, cname2lab, text_file): + text_file = os.path.join(self.dataset_dir, text_file) + items = [] + + with open(text_file, "r") as f: + lines = f.readlines() + for line in lines: + line = line.strip().split(" ")[0] # trainlist: filename, label + action, filename = line.split("/") + label = cname2lab[action] + + elements = re.findall("[A-Z][^A-Z]*", action) + renamed_action = "_".join(elements) + + filename = filename.replace(".avi", ".jpg") + impath = os.path.join(self.image_dir, renamed_action, filename) + + item = Datum(impath=impath, label=label, classname=renamed_action) + items.append(item) + + return items diff --git a/deepcore/__init__.py b/deepcore/__init__.py new file mode 100644 index 0000000..203562b --- /dev/null +++ b/deepcore/__init__.py @@ -0,0 +1 @@ +# __init__.py \ No newline at end of file diff --git a/deepcore/__pycache__/__init__.cpython-39.pyc b/deepcore/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 0000000..d71f79a Binary files /dev/null and b/deepcore/__pycache__/__init__.cpython-39.pyc differ diff --git a/deepcore/datasets/__init__.py b/deepcore/datasets/__init__.py new file mode 100644 index 0000000..11b8ff6 --- /dev/null +++ b/deepcore/datasets/__init__.py @@ -0,0 +1,8 @@ +from .cifar10 import * +from .cifar100 import * +from .fashionmnist import * +from .imagenet import * +from .mnist import * +from .qmnist import * +from .svhn import * +from .tinyimagenet import * diff --git a/deepcore/datasets/cifar10.py b/deepcore/datasets/cifar10.py new file mode 100644 index 0000000..e3cc2c1 --- /dev/null +++ b/deepcore/datasets/cifar10.py @@ -0,0 +1,19 @@ +from torchvision import datasets, transforms +from torch import tensor, long + + +def CIFAR10(data_path): + channel = 3 + im_size = (32, 32) + num_classes = 10 + mean = [0.4914, 0.4822, 0.4465] + std = [0.2470, 0.2435, 0.2616] + + transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean=mean, std=std)]) + dst_train = datasets.CIFAR10(data_path, train=True, download=False, transform=transform) + dst_test = datasets.CIFAR10(data_path, train=False, download=False, transform=transform) + class_names = dst_train.classes + dst_train.targets = tensor(dst_train.targets, dtype=long) + dst_test.targets = tensor(dst_test.targets, dtype=long) + + return channel, im_size, num_classes, class_names, mean, std, dst_train, dst_test diff --git a/deepcore/datasets/cifar100.py b/deepcore/datasets/cifar100.py new file mode 100644 index 0000000..401e0dc --- /dev/null +++ b/deepcore/datasets/cifar100.py @@ -0,0 +1,17 @@ +from torchvision import datasets, transforms +from torch import tensor, long + + +def CIFAR100(data_path): + channel = 3 + im_size = (32, 32) + num_classes = 100 + mean = [0.5071, 0.4865, 0.4409] + std = [0.2673, 0.2564, 0.2762] + transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean=mean, std=std)]) + dst_train = datasets.CIFAR100(data_path, train=True, download=True, transform=transform) + dst_test = datasets.CIFAR100(data_path, train=False, download=True, transform=transform) + class_names = dst_train.classes + dst_train.targets = tensor(dst_train.targets, dtype=long) + dst_test.targets = tensor(dst_test.targets, dtype=long) + return channel, im_size, num_classes, class_names, mean, std, dst_train, dst_test diff --git a/deepcore/datasets/fashionmnist.py b/deepcore/datasets/fashionmnist.py new file mode 100644 index 0000000..8346bf4 --- /dev/null +++ b/deepcore/datasets/fashionmnist.py @@ -0,0 +1,14 @@ +from torchvision import datasets, transforms + + +def FashionMNIST(data_path): + channel = 1 + im_size = (28, 28) + num_classes = 10 + mean = [0.2861] + std = [0.3530] + transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean=mean, std=std)]) + dst_train = datasets.FashionMNIST(data_path, train=True, download=True, transform=transform) + dst_test = datasets.FashionMNIST(data_path, train=False, download=True, transform=transform) + class_names = dst_train.classes + return channel, im_size, num_classes, class_names, mean, std, dst_train, dst_test diff --git a/deepcore/datasets/imagenet.py b/deepcore/datasets/imagenet.py new file mode 100644 index 0000000..f4ec2be --- /dev/null +++ b/deepcore/datasets/imagenet.py @@ -0,0 +1,27 @@ +from torchvision import datasets, transforms +from torch import tensor, long + + +def ImageNet(data_path): + channel = 3 + im_size = (224, 224) + num_classes = 1000 + mean = [0.485, 0.456, 0.406] + std = [0.229, 0.224, 0.225] + normalize = transforms.Normalize(mean, std) + dst_train = datasets.ImageNet(data_path, split="train", transform=transforms.Compose([ + transforms.Resize(256), + transforms.CenterCrop(224), + transforms.ToTensor(), + normalize, + ])) + dst_test = datasets.ImageNet(data_path, split="val", transform=transforms.Compose([ + transforms.Resize(256), + transforms.CenterCrop(224), + transforms.ToTensor(), + normalize, + ])) + class_names = dst_train.classes + dst_train.targets = tensor(dst_train.targets, dtype=long) + dst_test.targets = tensor(dst_test.targets, dtype=long) + return channel, im_size, num_classes, class_names, mean, std, dst_train, dst_test diff --git a/deepcore/datasets/mnist.py b/deepcore/datasets/mnist.py new file mode 100644 index 0000000..fa2f8d2 --- /dev/null +++ b/deepcore/datasets/mnist.py @@ -0,0 +1,25 @@ +from torchvision import datasets, transforms +import numpy as np + + +def MNIST(data_path, permuted=False, permutation_seed=None): + channel = 1 + im_size = (28, 28) + num_classes = 10 + mean = [0.1307] + std = [0.3081] + transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean=mean, std=std)]) + if permuted: + np.random.seed(permutation_seed) + pixel_permutation = np.random.permutation(28 * 28) + transform = transforms.Compose( + [transform, transforms.Lambda(lambda x: x.view(-1, 1)[pixel_permutation].view(1, 28, 28))]) + + dst_train = datasets.MNIST(data_path, train=True, download=True, transform=transform) + dst_test = datasets.MNIST(data_path, train=False, download=True, transform=transform) + class_names = [str(c) for c in range(num_classes)] + return channel, im_size, num_classes, class_names, mean, std, dst_train, dst_test + + +def permutedMNIST(data_path, permutation_seed=None): + return MNIST(data_path, True, permutation_seed) diff --git a/deepcore/datasets/qmnist.py b/deepcore/datasets/qmnist.py new file mode 100644 index 0000000..c951b08 --- /dev/null +++ b/deepcore/datasets/qmnist.py @@ -0,0 +1,18 @@ +from torchvision import datasets, transforms + + +def QMNIST(data_path): + channel = 1 + im_size = (28, 28) + num_classes = 10 + mean = [0.1308] + std = [0.3088] + transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean=mean, std=std)]) + dst_train = datasets.QMNIST(data_path, train=True, download=True, transform=transform) + dst_test = datasets.QMNIST(data_path, train=False, download=True, transform=transform) + class_names = [str(c) for c in range(num_classes)] + dst_train.targets = dst_train.targets[:, 0] + dst_test.targets = dst_test.targets[:, 0] + dst_train.compat = False + dst_test.compat = False + return channel, im_size, num_classes, class_names, mean, std, dst_train, dst_test diff --git a/deepcore/datasets/svhn.py b/deepcore/datasets/svhn.py new file mode 100644 index 0000000..abf9ea1 --- /dev/null +++ b/deepcore/datasets/svhn.py @@ -0,0 +1,19 @@ +from torchvision import datasets, transforms +from torch import tensor, long + + +def SVHN(data_path): + channel = 3 + im_size = (32, 32) + num_classes = 10 + mean = [0.4377, 0.4438, 0.4728] + std = [0.1980, 0.2010, 0.1970] + transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean=mean, std=std)]) + dst_train = datasets.SVHN(data_path, split='train', download=True, transform=transform) + dst_test = datasets.SVHN(data_path, split='test', download=True, transform=transform) + class_names = [str(c) for c in range(num_classes)] + dst_train.classes = list(class_names) + dst_test.classes = list(class_names) + dst_train.targets = tensor(dst_train.labels, dtype=long) + dst_test.targets = tensor(dst_test.labels, dtype=long) + return channel, im_size, num_classes, class_names, mean, std, dst_train, dst_test diff --git a/deepcore/datasets/tinyimagenet.py b/deepcore/datasets/tinyimagenet.py new file mode 100644 index 0000000..4ab66f2 --- /dev/null +++ b/deepcore/datasets/tinyimagenet.py @@ -0,0 +1,35 @@ +from torchvision import datasets, transforms +import os +import requests +import zipfile + + +def TinyImageNet(data_path, downsize=True): + if not os.path.exists(os.path.join(data_path, "tiny-imagenet-200")): + url = "http://cs231n.stanford.edu/tiny-imagenet-200.zip" # 248MB + print("Downloading Tiny-ImageNet") + r = requests.get(url, stream=True) + with open(os.path.join(data_path, "tiny-imagenet-200.zip"), "wb") as f: + for chunk in r.iter_content(chunk_size=1024): + if chunk: + f.write(chunk) + + print("Unziping Tiny-ImageNet") + with zipfile.ZipFile(os.path.join(data_path, "tiny-imagenet-200.zip")) as zf: + zf.extractall(path=data_path) + + channel = 3 + im_size = (32, 32) if downsize else (64, 64) + num_classes = 200 + mean = (0.4802, 0.4481, 0.3975) + std = (0.2770, 0.2691, 0.2821) + + transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean=mean, std=std)]) + if downsize: + transform = transforms.Compose([transforms.Resize(32), transform]) + + dst_train = datasets.ImageFolder(root=os.path.join(data_path, 'tiny-imagenet-200/train'), transform=transform) + dst_test = datasets.ImageFolder(root=os.path.join(data_path, 'tiny-imagenet-200/test'), transform=transform) + + class_names = dst_train.classes + return channel, im_size, num_classes, class_names, mean, std, dst_train, dst_test diff --git a/deepcore/methods/__init__.py b/deepcore/methods/__init__.py new file mode 100644 index 0000000..a23cc10 --- /dev/null +++ b/deepcore/methods/__init__.py @@ -0,0 +1,17 @@ +from .cal import * +from .contextualdiversity import * +from .coresetmethod import * +from .craig import * +from .deepfool import * +from .earlytrain import * +from .forgetting import * +from .full import * +from .glister import * +from .grand import * +from .gradmatch import * +from .herding import * +from .kcentergreedy import * +from .submodular import * +from .uncertainty import * +from .uniform import * + diff --git a/deepcore/methods/__pycache__/__init__.cpython-39.pyc b/deepcore/methods/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 0000000..cdd248b Binary files 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a/deepcore/methods/__pycache__/uniform.cpython-39.pyc b/deepcore/methods/__pycache__/uniform.cpython-39.pyc new file mode 100644 index 0000000..8f6fc3b Binary files /dev/null and b/deepcore/methods/__pycache__/uniform.cpython-39.pyc differ diff --git a/deepcore/methods/cal.py b/deepcore/methods/cal.py new file mode 100644 index 0000000..8658574 --- /dev/null +++ b/deepcore/methods/cal.py @@ -0,0 +1,146 @@ +from .earlytrain import EarlyTrain +from .methods_utils.euclidean import euclidean_dist_pair_np +from .methods_utils.cossim import cossim_pair_np +import numpy as np +import torch +from tqdm import tqdm +from .. import nets +from copy import deepcopy +from torchvision import transforms + + +class Cal(EarlyTrain): + def __init__(self, dst_train, args, fraction=0.5, random_seed=None, epochs=200, specific_model=None, + balance=False, metric="euclidean", neighbors: int = 10, pretrain_model: str = "ResNet18", **kwargs): + super().__init__(dst_train, args, fraction, random_seed, epochs, specific_model, **kwargs) + + self.balance = balance + + assert neighbors > 0 and neighbors < 100 + self.neighbors = neighbors + + if metric == "euclidean": + self.metric = euclidean_dist_pair_np + elif metric == "cossim": + self.metric = lambda a, b: -1. * cossim_pair_np(a, b) + elif callable(metric): + self.metric = metric + else: + self.metric = euclidean_dist_pair_np + + self.pretrain_model = pretrain_model + + def num_classes_mismatch(self): + raise ValueError("num_classes of pretrain dataset does not match that of the training dataset.") + + #Initial achievement, may not optimal + def mixing_feature(self,img_fea,text_fea,lam=0.5): + # return img_fea + return lam*img_fea + (1-lam)*text_fea + + def find_knn(self): + """ + Find k-nearest-neighbor data points with the pretrained embedding model + :return: knn matrix + """ + + # Initialize pretrained model + # model = nets.__dict__[self.pretrain_model](channel=self.args.channel, num_classes=self.args.num_classes, + # im_size=(224, 224), record_embedding=True, no_grad=True, + # pretrained=True).to(self.args.device) + self.model.eval() + probs = [] + # # Resize dst_train to 224*224 + # if self.args.im_size[0] != 224 or self.args.im_size[1] != 224: + # dst_train = deepcopy(self.dst_train) + # dst_train.transform = transforms.Compose([dst_train.transform, transforms.Resize(224)]) + # else: + # dst_train = self.dst_train + + # Calculate the distance matrix and return knn results + if self.balance: + knn = [] + for c in tqdm(range(self.num_classes)): + print(f'Start processing class {c}/{self.num_classes}') + class_index = np.arange(self.n_train)[self.dst_train_label == c] + + # Start recording embedding vectors + # batch_loader = torch.utils.data.DataLoader(torch.utils.data.Subset(dst_train, class_index), + # batch_size=self.args.selection_batch, + # num_workers=self.args.workers) + embdeddings = [] + c_probs = np.zeros([len(class_index), self.num_classes]) + data_loader = self.select_dm(self.dst_train, class_index, is_train=False) + for i, batch in enumerate(data_loader): + image, label = batch['img'].cuda(), batch['label'].cuda() + img_f, text_f,logit = self.model(image, label, record=True) + final_feature = self.mixing_feature(img_f,text_f) + embdeddings.append(final_feature.cpu().numpy()) + c_probs[i * self.args.DATASET.SELECTION_BATCH_SIZE:(i + 1) * self.args.DATASET.SELECTION_BATCH_SIZE] = \ + torch.softmax(logit, dim=1).detach().cpu() + + embdeddings = np.concatenate(embdeddings, axis=0) + probs.append(c_probs) + knn.append(np.argsort(self.metric(embdeddings), axis=1)[:, 1:(self.neighbors + 1)]) + self.probs = np.concatenate(probs,axis=0) + return knn + else: + # Start recording embedding vectors + embdeddings = [] + batch_loader = self.select_dm(self.dst_train, None, is_train=False) + print(f'Start processing all class') + for i, batch in enumerate(tqdm(batch_loader)): + image, label = batch['img'].cuda(), batch['label'].cuda() + img_f, text_f,logit = self.model(image, label, record=True) + final_feature = self.mixing_feature(img_f, text_f) + embdeddings.append(final_feature.cpu().numpy()) + probs[i * self.args.DATASET.SELECTION_BATCH_SIZE:(i + 1) * self.args.DATASET.SELECTION_BATCH_SIZE] = \ + torch.softmax(logit, dim=1).detach().cpu() + embdeddings = np.concatenate(embdeddings, axis=0) + self.probs = np.concatenate(probs, axis=0) + return np.argsort(self.metric(embdeddings), axis=1)[:, 1:(self.neighbors + 1)] + + def calc_kl(self, knn, index=None): + self.model.eval() + self.model.no_grad = True + sample_num = self.n_train if index is None else len(index) + # probs = np.zeros([sample_num, self.num_classes]) + # + # batch_loader = torch.utils.data.DataLoader( + # self.dst_train if index is None else torch.utils.data.Subset(self.dst_train, index), + # batch_size=self.args.selection_batch, num_workers=self.args.workers) + # batch_num = len(batch_loader) + # + # for i, (inputs, _) in enumerate(batch_loader): + # probs[i * self.args.selection_batch:(i + 1) * self.args.selection_batch] = torch.nn.functional.softmax( + # self.model(inputs.to(self.args.device)), dim=1).detach().cpu() + probs = self.probs[index] + s = np.zeros(sample_num) + for i in range(0, sample_num, self.args.DATASET.SELECTION_BATCH_SIZE): + + print("| Caculating KL-divergence for batch [%3d/%3d] with batchsize [%3d]" % (i, sample_num, self.args.DATASET.SELECTION_BATCH_SIZE)) + aa = np.expand_dims(probs[i:(i + self.args.DATASET.SELECTION_BATCH_SIZE)], 1).repeat(self.neighbors, 1) + bb = probs[knn[i:(i + self.args.DATASET.SELECTION_BATCH_SIZE)], :] + s[i:(i + self.args.DATASET.SELECTION_BATCH_SIZE)] = np.mean( + np.sum(0.5 * aa * np.log(aa / bb) + 0.5 * bb * np.log(bb / aa), axis=2), axis=1) + self.model.no_grad = False + return s + + def finish_run(self): + scores=[] + if self.balance: + selection_result = np.array([], dtype=np.int32) + for c, knn in zip(range(self.num_classes), self.knn): + class_index = np.arange(self.n_train)[self.dst_train_label == c] + scores.append(self.calc_kl(knn, class_index)) + selection_result = np.append(selection_result, class_index[np.argsort( + #self.calc_kl(knn, class_index))[::1][:round(self.fraction * len(class_index))]]) + scores[-1])[::1][:round(self.fraction * len(class_index))]]) + else: + selection_result = np.argsort(self.calc_kl(self.knn))[::1][:self.coreset_size] + return {"indices": selection_result, "scores":scores} + + def select(self, **kwargs): + self.knn = self.find_knn() + selection_result = self.run() + return selection_result \ No newline at end of file diff --git a/deepcore/methods/contextualdiversity.py b/deepcore/methods/contextualdiversity.py new file mode 100644 index 0000000..6a025d7 --- /dev/null +++ b/deepcore/methods/contextualdiversity.py @@ -0,0 +1,33 @@ +from .kcentergreedy import kCenterGreedy +import torch + + +# Acknowlegement to: +# https://github.com/sharat29ag/CDAL + + +class ContextualDiversity(kCenterGreedy): + def __init__(self, dst_train, args, fraction=0.5, random_seed=None, epochs=200, + specific_model=None, balance=True, already_selected=[], torchvision_pretrain: bool = False, **kwargs): + super(ContextualDiversity, self).__init__(dst_train, args, fraction, random_seed, epochs=epochs, specific_model=specific_model, balance=balance, already_selected=already_selected, torchvision_pretrain=torchvision_pretrain, **kwargs) + self.metric = self._metric + + def _metric(self, a_output, b_output): + with torch.no_grad(): + # Overload self.metric function for kCenterGreedy Algorithm + aa = a_output.view(a_output.shape[0], 1, a_output.shape[1]).repeat(1, b_output.shape[0], 1) + bb = b_output.view(1, b_output.shape[0], b_output.shape[1]).repeat(a_output.shape[0], 1, 1) + return torch.sum(0.5 * aa * torch.log(aa / bb) + 0.5 * bb * torch.log(bb / aa), dim=2) + + def construct_matrix(self, index=None): + self.model.eval() + self.model.no_grad = True + sample_num = self.n_train if index is None else len(index) + matrix = torch.zeros([sample_num, self.args.num_classes], requires_grad=False).to(self.args.device) + batch_loader = torch.utils.data.DataLoader(self.dst_train if index is None else + torch.utils.data.Subset(self.dst_train, index), batch_size=self.args.selection_batch + ,num_workers=self.args.workers) + for i, (inputs, _) in enumerate(batch_loader): + matrix[i * self.args.selection_batch:min((i + 1) * self.args.selection_batch, sample_num)] = torch.nn.functional.softmax(self.model(inputs.to(self.args.device)), dim=1) + self.model.no_grad = False + return matrix diff --git a/deepcore/methods/coresetmethod.py b/deepcore/methods/coresetmethod.py new file mode 100644 index 0000000..8fad776 --- /dev/null +++ b/deepcore/methods/coresetmethod.py @@ -0,0 +1,49 @@ +import numpy as np +import os + +class CoresetMethod(object): + def __init__(self, dst_train, args, fraction=0.5, random_seed=None,**kwargs): + if fraction <= 0.0 or fraction > 1.0: + raise ValueError("Illegal Coreset Size.") + + self.dm = dst_train + self.dst_train = dst_train.dataset.train_x + self.num_classes = dst_train.dataset.num_classes + self.fraction = fraction + self.random_seed = random_seed + self.index = [] + self.args = args + self.dst_train_label = self.get_train_label(self.dst_train) + self.n_train = len(self.dst_train) + self.coreset_size = round(self.n_train * fraction) + self.max_epoch = self.args.OPTIM_SELECTION.MAX_EPOCH + + def select(self, **kwargs): + return + + def get_train_label(self,dst_train): + ####Readable + ind = [] + for i,item in enumerate(dst_train): + ind.append(item.label) + return np.asarray(ind) + def pre_run(self): + self.train_indx = np.arange(self.n_train) + print(f'Start pre-funing CLIP with all datasets by {self.max_epoch} epoch') + file_save_name = self.args.DATASET.NAME + '_' + str(self.args.SEED) + '.pth' + output_checkpoint_dir = os.path.join('checkpoints', file_save_name) + if self.max_epoch > 0: + + if os.path.exists(output_checkpoint_dir): + print(f'The checkpiont exists! Load that shit') + ckpt = torch.load(output_checkpoint_dir) + self.model.load_state_dict(ckpt) + else: + for epoch in range(self.epoch, self.max_epoch): + # list_of_train_idx = np.random.choice(np.arange(self.n_pretrain if self.if_dst_pretrain else self.n_train), + # self.n_pretrain_size, replace=False) + self.before_epoch() # PASS + self.train(epoch) + self.test(epoch) + self.after_epoch() + torch.save(self.model.state_dict(), output_checkpoint_dir) diff --git a/deepcore/methods/craig.py b/deepcore/methods/craig.py new file mode 100644 index 0000000..ea5f1c1 --- /dev/null +++ b/deepcore/methods/craig.py @@ -0,0 +1,126 @@ +from .earlytrain import EarlyTrain +import torch +from .methods_utils import FacilityLocation, submodular_optimizer +import numpy as np +from .methods_utils.euclidean import euclidean_dist_pair_np +from ..nets.nets_utils import MyDataParallel +from tqdm import tqdm + +class Craig(EarlyTrain): + def __init__(self, dst_train, args, fraction=0.5, random_seed=None, epochs=200, specific_model=None, + balance=True, greedy="LazyGreedy", **kwargs): + super().__init__(dst_train, args, fraction, random_seed, epochs, specific_model, **kwargs) + + if greedy not in submodular_optimizer.optimizer_choices: + raise ModuleNotFoundError("Greedy optimizer not found.") + self._greedy = greedy + self.balance = balance + + def before_train(self): + pass + + def after_loss(self, outputs, loss, targets, batch_inds, epoch): + pass + + def before_epoch(self): + pass + + def after_epoch(self): + pass + + def before_run(self): + pass + + def num_classes_mismatch(self): + raise ValueError("num_classes of pretrain dataset does not match that of the training dataset.") + + # def while_update(self, outputs, loss, targets, epoch, batch_idx, batch_size): + # if batch_idx % self.args.print_freq == 0: + # print('| Epoch [%3d/%3d] Iter[%3d/%3d]\t\tLoss: %.4f' % ( + # epoch, self.epochs, batch_idx + 1, (self.n_pretrain_size // batch_size) + 1, loss.item())) + + # def calc_gradient(self, index=None): + # self.model.eval() + # + # batch_loader = torch.utils.data.DataLoader( + # self.dst_train if index is None else torch.utils.data.Subset(self.dst_train, index), + # batch_size=self.args.selection_batch, num_workers=self.args.workers) + # sample_num = len(self.dst_val.targets) if index is None else len(index) + # self.embedding_dim = self.model.get_last_layer().in_features + # + # gradients = [] + # + # for i, (input, targets) in enumerate(batch_loader): + # self.model_optimizer.zero_grad() + # outputs = self.model(input.to(self.args.device)) + # loss = self.criterion(outputs.requires_grad_(True), + # targets.to(self.args.device)).sum() + # batch_num = targets.shape[0] + # with torch.no_grad(): + # bias_parameters_grads = torch.autograd.grad(loss, outputs)[0] + # weight_parameters_grads = self.model.embedding_recorder.embedding.view(batch_num, 1, + # self.embedding_dim).repeat(1, + # self.args.num_classes, + # 1) * bias_parameters_grads.view( + # batch_num, self.args.num_classes, 1).repeat(1, 1, self.embedding_dim) + # gradients.append( + # torch.cat([bias_parameters_grads, weight_parameters_grads.flatten(1)], dim=1).cpu().numpy()) + # + # gradients = np.concatenate(gradients, axis=0) + # + # self.model.train() + # return euclidean_dist_pair_np(gradients) + + def calc_weights(self, matrix, result): + min_sample = np.argmax(matrix[result], axis=0) + weights = np.ones(np.sum(result) if result.dtype == bool else len(result)) + for i in min_sample: + weights[i] = weights[i] + 1 + return weights + + def finish_run(self): + if isinstance(self.model, MyDataParallel): + self.model = self.model.module + + self.model.no_grad = True + grad = self.calc_gradient() + grad_matrix = euclidean_dist_pair_np(grad) + # with self.model.embedding_recorder: + if self.balance: + + # Do selection by class + selection_result = np.array([], dtype=np.int32) + weights = np.array([]) + for c in tqdm(range(self.num_classes)): + class_index = np.arange(self.n_train)[self.dst_train_label == c] + matrix = -1. * grad_matrix[class_index[:,None],class_index] # Change to column index + # matrix = -1. * self.calc_gradient(class_index) + matrix -= np.min(matrix) - 1e-3 #The least is zero + submod_function = FacilityLocation(index=class_index, similarity_matrix=matrix) + submod_optimizer = submodular_optimizer.__dict__[self._greedy](args=self.args, index=class_index, + budget=round(self.fraction * len( + class_index))) + class_result = submod_optimizer.select(gain_function=submod_function.calc_gain, + update_state=submod_function.update_state) + selection_result = np.append(selection_result, class_result) + weights = np.append(weights, self.calc_weights(matrix, np.isin(class_index, class_result))) + else: + matrix = np.zeros([self.n_train, self.n_train]) + all_index = np.arange(self.n_train) + for c in range(self.num_classes): # Sparse Matrix + class_index = np.arange(self.n_train)[self.dst_train_label== c] + matrix[np.ix_(class_index, class_index)] = -1. * self.calc_gradient(class_index) + matrix[np.ix_(class_index, class_index)] -= np.min(matrix[np.ix_(class_index, class_index)]) - 1e-3 + submod_function = FacilityLocation(index=all_index, similarity_matrix=matrix) + submod_optimizer = submodular_optimizer.__dict__[self._greedy](args=self.args, index=all_index, + budget=self.coreset_size) + selection_result = submod_optimizer.select(gain_function=submod_function.calc_gain_batch, + update_state=submod_function.update_state, + batch=self.args.selection_batch) + weights = self.calc_weights(matrix, selection_result) + self.model.no_grad = False + return {"indices": selection_result, "weights": weights} + + def select(self, **kwargs): + selection_result = self.run() + return selection_result diff --git a/deepcore/methods/deepfool.py b/deepcore/methods/deepfool.py new file mode 100644 index 0000000..63d386c --- /dev/null +++ b/deepcore/methods/deepfool.py @@ -0,0 +1,120 @@ +from .earlytrain import EarlyTrain +import torch +import numpy as np + + +class DeepFool(EarlyTrain): + def __init__(self, dst_train, args, fraction=0.5, random_seed=None, epochs=200, + specific_model=None, balance: bool = False, max_iter: int = 50, **kwargs): + super().__init__(dst_train, args, fraction, random_seed, epochs, specific_model, **kwargs) + + self.balance = balance + self.max_iter = max_iter + + def num_classes_mismatch(self): + raise ValueError("num_classes of pretrain dataset does not match that of the training dataset.") + + def while_update(self, outputs, loss, targets, epoch, batch_idx, batch_size): + if batch_idx % self.args.print_freq == 0: + print('| Epoch [%3d/%3d] Iter[%3d/%3d]\t\tLoss: %.4f' % ( + epoch, self.epochs, batch_idx + 1, (self.n_pretrain_size // batch_size) + 1, loss.item())) + + def finish_run(self): + self.model.no_grad = False + + # Create a data loader for self.dst_train with batch size self.args.selection_batch + batch_loader = torch.utils.data.DataLoader(self.dst_train, batch_size=self.args.selection_batch + , num_workers=self.args.workers) + + r = np.zeros(self.n_train, dtype=np.float32) + batch_num = len(batch_loader) + for i, (inputs, targets) in enumerate(batch_loader): + if i % self.args.print_freq == 0: + print('| Selecting Batch [%3d/%3d]' % (i + 1, batch_num)) + r[(i * self.args.selection_batch):(i * self.args.selection_batch + targets.shape[0])] = self.deep_fool( + inputs) + + if self.balance: + selection_result = np.array([], dtype=np.int64) + for c in range(self.args.num_classes): + class_index = np.arange(self.n_train)[self.dst_train.targets == c] + selection_result = np.append(selection_result, class_index[ + r[class_index].argsort()[:round(len(class_index) * self.fraction)]]) + else: + selection_result = r.argsort()[:self.coreset_size] + return {"indices": selection_result, "scores": r} + + def deep_fool(self, inputs): + # Here, start running DeepFool algorithm. + self.model.eval() + + # Initialize a boolean mask indicating if selection has been stopped at corresponding positions. + sample_size = inputs.shape[0] + boolean_mask = np.ones(sample_size, dtype=bool) + all_idx = np.arange(sample_size) + + # A matrix to store total pertubations. + r_tot = np.zeros([sample_size, inputs.shape[1] * inputs.shape[2] * inputs.shape[3]]) + + # Set requires_grad for inputs. + cur_inputs = inputs.requires_grad_(True).to(self.args.device) + + original_shape = inputs.shape[1:] + + # set requires_grad for all parametres in network as False to accelerate autograd + for p in self.model.parameters(): + p.requires_grad_(False) + + self.model.no_grad = True + first_preds = self.model(cur_inputs).argmax(dim=1) + self.model.no_grad = False + + for i in range(self.max_iter): + f_all = self.model(cur_inputs) + + w_k = [] + for c in range(self.args.num_classes): + w_k.append(torch.autograd.grad(f_all[:, c].sum(), cur_inputs, + retain_graph=False if c + 1 == self.args.num_classes else True)[ + 0].flatten(1)) + w_k = torch.stack(w_k, dim=0) + w_k = w_k - w_k[first_preds, boolean_mask[boolean_mask]].unsqueeze(0) + w_k_norm = w_k.norm(dim=2) + + w_k_norm[first_preds, boolean_mask[ + boolean_mask]] = 1. # Set w_k_norm for preds positions to 1. to avoid division by zero. + + l_all = (f_all - f_all[boolean_mask[boolean_mask], first_preds].unsqueeze(1)).detach().abs() / w_k_norm.T + l_all[boolean_mask[ + boolean_mask], first_preds] = np.inf # Set l_k for preds positions to inf, as the argmin for each + # row will be calculated soon. + + l_hat = l_all.argmin(dim=1) + r_i = l_all[boolean_mask[boolean_mask], l_hat].unsqueeze(1) / w_k_norm[ + l_hat, boolean_mask[boolean_mask]].T.unsqueeze(1) * w_k[l_hat, boolean_mask[boolean_mask]] + + # Update r_tot values. + r_tot[boolean_mask] += r_i.cpu().numpy() + + cur_inputs += r_i.reshape([r_i.shape[0]] + list(original_shape)) + + # Re-input the updated sample into the network and get new predictions. + self.model.no_grad = True + preds = self.model(cur_inputs).argmax(dim=1) + self.model.no_grad = False + + # In DeepFool algorithm, the iteration stops when the updated sample produces a different prediction + # in the model. + index_unfinished = (preds == first_preds) + if torch.all(~index_unfinished): + break + + cur_inputs = cur_inputs[index_unfinished] + first_preds = first_preds[index_unfinished] + boolean_mask[all_idx[boolean_mask][~index_unfinished.cpu().numpy()]] = False + + return (r_tot * r_tot).sum(axis=1) + + def select(self, **kwargs): + selection_result = self.run() + return selection_result diff --git a/deepcore/methods/earlytrain.py b/deepcore/methods/earlytrain.py new file mode 100644 index 0000000..22a0958 --- /dev/null +++ b/deepcore/methods/earlytrain.py @@ -0,0 +1,322 @@ +from .coresetmethod import CoresetMethod +import torch, time +from torch import nn +import numpy as np +from copy import deepcopy +from .. import nets +from torchvision import transforms +from datasets.data_manager import select_dm_loader +from dassl.utils import MetricMeter, AverageMeter +from torch.cuda.amp import GradScaler, autocast +import datetime +from tqdm import tqdm +import os + +class EarlyTrain(CoresetMethod): + ''' + Core code for training related to coreset selection methods when pre-training is required. + ''' + + def __init__(self, dst_train, args,fraction=0.5, random_seed=None, epochs=200, specific_model=None, + torchvision_pretrain: bool = False, dst_pretrain_dict: dict = {}, fraction_pretrain=1., dst_test=None, + **kwargs): + super().__init__(dst_train, args, fraction, random_seed) + self.epochs = epochs + self.n_train = len(self.dst_train) + self.coreset_size = round(self.n_train * fraction) + self.model = specific_model + self.train_loader = self.dm.train_loader_x + self.test_loader = self.dm.test_loader + + + if kwargs: + # self.text_feature = kwargs['text_feature'] + self.optim = kwargs['optim'] + self.sche = kwargs['schedule'] + self.scar = kwargs['scar'] + + + + self.start_epoch = self.epoch = 0 + self.max_epoch = self.args.OPTIM_SELECTION.MAX_EPOCH + + if fraction_pretrain <= 0. or fraction_pretrain > 1.: + raise ValueError("Illegal pretrain fraction value.") + self.fraction_pretrain = fraction_pretrain + + if dst_pretrain_dict.__len__() != 0: + dict_keys = dst_pretrain_dict.keys() + if 'im_size' not in dict_keys or 'channel' not in dict_keys or 'dst_train' not in dict_keys or \ + 'num_classes' not in dict_keys: + raise AttributeError( + 'Argument dst_pretrain_dict must contain imszie, channel, dst_train and num_classes.') + if dst_pretrain_dict['im_size'][0] != args.im_size[0] or dst_pretrain_dict['im_size'][0] != args.im_size[0]: + raise ValueError("im_size of pretrain dataset does not match that of the training dataset.") + if dst_pretrain_dict['channel'] != args.channel: + raise ValueError("channel of pretrain dataset does not match that of the training dataset.") + if dst_pretrain_dict['num_classes'] != args.num_classes: + self.num_classes_mismatch() + + self.dst_pretrain_dict = dst_pretrain_dict + self.torchvision_pretrain = torchvision_pretrain + self.if_dst_pretrain = (len(self.dst_pretrain_dict) != 0) + + if torchvision_pretrain: + # Pretrained models in torchvision only accept 224*224 inputs, therefore we resize current + # datasets to 224*224. + if args.im_size[0] != 224 or args.im_size[1] != 224: + self.dst_train = deepcopy(dst_train) + self.dst_train.transform = transforms.Compose([self.dst_train.transform, transforms.Resize(224)]) + if self.if_dst_pretrain: + self.dst_pretrain_dict['dst_train'] = deepcopy(dst_pretrain_dict['dst_train']) + self.dst_pretrain_dict['dst_train'].transform = transforms.Compose( + [self.dst_pretrain_dict['dst_train'].transform, transforms.Resize(224)]) + if self.if_dst_pretrain: + self.n_pretrain = len(self.dst_pretrain_dict['dst_train']) + self.n_pretrain_size = round( + self.fraction_pretrain * (self.n_pretrain if self.if_dst_pretrain else self.n_train)) + self.dst_test = dst_test + + + def train(self, epoch, list_of_train_idx=None, **kwargs): + """ Train model for one epoch """ + + self.before_train() + self.model.train() + + losses = MetricMeter() + batch_time = AverageMeter() + data_time = AverageMeter() + + + end = time.time() + + print('\n=> Training Pre-tuning Epoch #%d' % epoch) + train_loader = select_dm_loader(self.args,self.dst_train,is_train=True) + self.num_batches = len(train_loader) + + # trainset_permutation_inds = np.random.permutation(list_of_train_idx) + # batch_sampler = torch.utils.data.BatchSampler(trainset_permutation_inds, batch_size=self.args.selection_batch, + # drop_last=False) + # trainset_permutation_inds = list(batch_sampler) + # + # train_loader = torch.utils.data.DataLoader(self.dst_pretrain_dict['dst_train'] if self.if_dst_pretrain + # else self.dst_train, shuffle=False, batch_sampler=batch_sampler, + # + # + # num_workers=self.args.workers, pin_memory=True) + + for i, batch in enumerate(train_loader): + data_time.update(time.time() - end) + image, label,real_ind = batch['img'].cuda(),batch['label'].cuda(),batch['index'].cuda() + + model = self.model + optim = self.optim + scaler = self.scar + + prec = self.args.TRAINER.MAPLE.PREC + if prec == "amp": + with autocast(): + loss,outputs = model(image, label) + optim.zero_grad() + scaler.scale(loss).backward() + scaler.step(optim) + scaler.update() + else: + loss,outputs = model(image, label) + optim.zero_grad() + loss.backward() + optim.step() + + self.after_loss(outputs, loss, label, real_ind, epoch) + self.while_update(outputs, loss, label, epoch, i, self.args.DATALOADER.TRAIN_X.BATCH_SIZE) + + loss_summary = {"loss": loss.item()} + + if (i + 1) == self.num_batches: + self.sche.step() + batch_time.update(time.time() - end) + losses.update(loss_summary) + + meet_freq = (i + 1) % self.args.TRAIN.PRINT_FREQ == 0 + only_few_batches = self.num_batches < self.args.TRAIN.PRINT_FREQ + + if meet_freq or only_few_batches: + nb_remain = 0 + nb_remain += self.num_batches - i - 1 + nb_remain += (self.max_epoch - self.epoch - 1) * self.num_batches + eta_seconds = batch_time.avg * nb_remain + eta = str(datetime.timedelta(seconds=int(eta_seconds))) + + info = [] + info += [f"epoch [{self.epoch + 1}/{self.max_epoch}]"] + info += [f"batch [{i + 1}/{self.num_batches}]"] + info += [f"time {batch_time.val:.3f} ({batch_time.avg:.3f})"] + info += [f"data {data_time.val:.3f} ({data_time.avg:.3f})"] + info += [f"{losses}"] + info += [f"lr {optim.param_groups[0]['lr']:.4e}"] + info += [f"eta {eta}"] + print(" ".join(info)) + + # n_iter = self.epoch * self.num_batches + i + # for name, meter in losses.meters.items(): + # self.write_scalar("train/" + name, meter.avg, n_iter) + # self.write_scalar("train/lr", self.get_current_lr(), n_iter) + + end = time.time() + + return self.finish_train() + + def run(self): + self.train_indx = np.arange(self.n_train) + self.before_run() + print(f'Start pre-funing CLIP with all datasets by {self.max_epoch} epoch') + file_save_name = self.args.DATASET.NAME + '_' + str(self.args.SEED) + '.pth' + output_checkpoint_dir = os.path.join('checkpoints', file_save_name) + if self.max_epoch > 0: + + if os.path.exists(output_checkpoint_dir): + print(f'The checkpiont exists! Load that shit') + ckpt = torch.load(output_checkpoint_dir) + self.model.load_state_dict(ckpt) + else: + for epoch in range(self.epoch,self.max_epoch): + # list_of_train_idx = np.random.choice(np.arange(self.n_pretrain if self.if_dst_pretrain else self.n_train), + # self.n_pretrain_size, replace=False) + self.before_epoch() #PASS + self.train(epoch) + self.test(epoch) + self.after_epoch() + torch.save(self.model.state_dict(),output_checkpoint_dir) + + return self.finish_run() + + def test(self, epoch): + self.model.no_grad = True + self.model.eval() + + + correct = 0. + total = 0. + + print('\n=> Testing Tuning Epoch #%d' % epoch) + + for batch_idx, batch in enumerate(self.test_loader): + image, target = batch['img'].cuda(), batch['label'] + output = self.model(image, target.cuda()) + + + predicted = torch.max(output.data, 1).indices.cpu() + correct += predicted.eq(target).sum().item() + total += target.size(0) + + # if batch_idx % self.args.print_freq == 0: + # print('| Test Epoch [%3d/%3d] Iter[%3d/%3d]\t\t Test Acc: %.3f%%' % ( + # epoch, self.epochs, batch_idx + 1, (round(len(self.dst_test) * self.args.selection_test_fraction) // + # self.args.selection_batch) + 1, loss.item(), + # 100. * correct / total)) + print(f'| Test Epoch {epoch} Test Acc: {100. * correct / total:.3f}%') + self.model.no_grad = False + + def num_classes_mismatch(self): + pass + + def before_train(self): + pass + + def after_loss(self, outputs, loss, targets, batch_inds, epoch): + pass + + def while_update(self, outputs, loss, targets, epoch, batch_idx, batch_size): + pass + + def finish_train(self): + pass + + def before_epoch(self): + pass + + def after_epoch(self): + pass + + def before_run(self): + pass + + def finish_run(self): + pass + + def select(self, **kwargs): + selection_result = self.run() + return selection_result + + def select_without_train(self, **kwargs): + return self.finish_run() + + @torch.no_grad() + def calcluate_clip_probability(self,batch): + input = batch["img"].cuda() + + self.specific_model = self.specific_model.cuda() + image_features = self.specific_model.encode_image(input) + image_features = image_features / image_features.norm(dim=-1, keepdim=True) + logit_scale = self.specific_model.logit_scale.exp() + return logit_scale * image_features @ self.text_feature.t() + + # using the defined select_dm + def select_dm(self,data,ind=None,is_train=None): + return select_dm_loader(self.args,data,ind,is_train) + + + def parse_batch_test(self, batch): + input = batch["img"] + label = batch["label"] + + input = input.cuda() + label = label.cuda() + + return input, label + + def parse_batch_train(self, batch): + input = batch["img"].cuda() + label = batch["label"].cuda() + domain = batch["index"].cuda() + + return input, label, domain + + + + def calc_gradient(self, index=None): + ''' + Calculate gradients matrix on current network for specified training dataset. + ''' + self.model.eval() + data_loader = self.select_dm(self.dst_train, index, is_train=False) + # Initialize a matrix to save gradients. + # (on cpu) + gradients = [] + lam = 0.5 + for i, batch in enumerate(tqdm(data_loader)): + self.optim.zero_grad() + image, label = batch['img'].cuda(), batch['label'].cuda() + bs_size = image.shape[0] + loss, visual_embedding, logit= self.model(image, label, cal_gradient=True) + embed_dim = visual_embedding.shape[-1] + with torch.no_grad(): + bias_parameters_grads = torch.autograd.grad(loss, logit)[0] + weight_parameters_grads = visual_embedding.view(bs_size, 1, + -1).repeat(1, self.num_classes, 1) * \ + bias_parameters_grads.view(bs_size, self.num_classes, + 1).repeat(1, 1, embed_dim) + # weight_parameters_grads_t = text_embedding.view(bs_size, 1, + # -1).repeat(1, self.num_classes, 1) * \ + # bias_parameters_grads.view(bs_size, self.num_classes, + # 1).repeat(1, 1, embed_dim) + # final_weight = torch.abs(weight_parameters_grads-weight_parameters_grads_t) + gradients.append(torch.cat([bias_parameters_grads, weight_parameters_grads.flatten(1)], + dim=1).cpu().numpy()) + + gradients = np.concatenate(gradients, axis=0, dtype=np.float32) + print('Finish Gradient Calculation') + self.model.train() + return gradients + diff --git a/deepcore/methods/forgetting.py b/deepcore/methods/forgetting.py new file mode 100644 index 0000000..a122e2e --- /dev/null +++ b/deepcore/methods/forgetting.py @@ -0,0 +1,99 @@ +from .earlytrain import EarlyTrain +import torch, time +from torch import nn +import numpy as np +from datasets.data_manager import select_dm_loader + +# Acknowledgement to +# https://github.com/mtoneva/example_forgetting + +class Forgetting(EarlyTrain): + def __init__(self, dst_train, args, fraction=0.5, random_seed=None, epochs=200, specific_model=None, balance=True, #default True + dst_test=None, **kwargs): + super().__init__(dst_train, args, fraction, random_seed, epochs, specific_model=specific_model, + dst_test=dst_test,**kwargs) + + self.balance = balance + + def get_hms(self, seconds): + # Format time for printing purposes + + m, s = divmod(seconds, 60) + h, m = divmod(m, 60) + + return h, m, s + + def before_train(self): + self.train_loss = 0. + self.correct = 0. + self.total = 0. + + def after_loss(self, outputs, loss, targets, batch_inds, epoch): + with torch.no_grad(): + _, predicted = torch.max(outputs.data, 1) + + cur_acc = (predicted == targets).clone().detach().requires_grad_(False).type(torch.float32) + self.forgetting_events[batch_inds.clone().detach()[(self.last_acc[batch_inds]-cur_acc)>0.01]]+=1. + self.last_acc[batch_inds] = cur_acc + + def while_update(self, outputs, loss, targets, epoch, batch_idx, batch_size): + pass + # self.train_loss += loss.item() + # self.total += targets.size(0) + # _, predicted = torch.max(outputs.data, 1) + # self.correct += predicted.eq(targets.data).cpu().sum() + # + # if batch_idx % self.args.print_freq == 0: + # print('| Epoch [%3d/%3d] Iter[%3d/%3d]\t\tLoss: %.4f Acc@1: %.3f%%' % ( + # epoch, self.epochs, batch_idx + 1, (self.n_train // batch_size) + 1, loss.item(), + # 100. * self.correct.item() / self.total)) + + + + def after_epoch(self): + pass + # epoch_time = time.time() - self.start_time + # self.elapsed_time += epoch_time + # print('| Elapsed time : %d:%02d:%02d' % (self.get_hms(self.elapsed_time))) + + def before_run(self): + self.elapsed_time = 0 + self.forgetting_events = torch.zeros(self.n_train, requires_grad=False).cuda() + self.test_initial_acc() + # self.last_acc = torch.zeros(self.n_train, requires_grad=False).cuda() + + def test_initial_acc(self): + self.model.no_grad = True + self.model.eval() + self.last_acc = torch.zeros(self.n_train, requires_grad=False).cuda() + + print('\n=> Testing Initial acc for Forgetting') + train_loader = select_dm_loader(self.args, self.dst_train) + for batch_idx, batch in enumerate(train_loader): + image, target,batch_inds = batch['img'].cuda(), batch['label'].cuda(), batch['index'].cuda() + output = self.model(image, target) + predicted = torch.max(output.data, 1).indices + + cur_acc = (predicted == target).clone().detach().requires_grad_(False).type(torch.float32) + self.last_acc[batch_inds] = cur_acc + + + self.model.no_grad = False + + def finish_run(self): + pass + + def select(self, **kwargs): + self.run() + + if not self.balance: + top_examples = self.train_indx[np.argsort(self.forgetting_events.cpu().numpy())][::-1][:self.coreset_size] + else: + top_examples = np.array([], dtype=np.int64) + for c in range(self.num_classes): + c_indx = self.train_indx[self.dst_train_label == c] + budget = round(self.fraction * len(c_indx)) + top_examples = np.append(top_examples, + c_indx[np.argsort(self.forgetting_events[c_indx].cpu().numpy())[::-1][:budget]]) + + return {"indices": top_examples, "scores": self.forgetting_events} diff --git a/deepcore/methods/full.py b/deepcore/methods/full.py new file mode 100644 index 0000000..3960cf1 --- /dev/null +++ b/deepcore/methods/full.py @@ -0,0 +1,10 @@ +import numpy as np +from .coresetmethod import CoresetMethod + + +class Full(CoresetMethod): + def __init__(self, dst_train, args, fraction, random_seed, **kwargs): + self.n_train = len(dst_train) + + def select(self, **kwargs): + return {"indices": np.arange(self.n_train)} diff --git a/deepcore/methods/glister.py b/deepcore/methods/glister.py new file mode 100644 index 0000000..1117654 --- /dev/null +++ b/deepcore/methods/glister.py @@ -0,0 +1,210 @@ +from .earlytrain import EarlyTrain +from .methods_utils import submodular_optimizer +import torch +import numpy as np +from ..nets.nets_utils import MyDataParallel +from tqdm import tqdm + +class Glister(EarlyTrain): + def __init__(self, dst_train, args, fraction=0.5, random_seed=None, epochs=200, specific_model=None, + balance: bool = True, greedy="StochasticGreedy", eta=None, dst_val=None, **kwargs): + super().__init__(dst_train, args, fraction, random_seed, epochs, specific_model, **kwargs) + + self.balance = balance + self.eta = args.OPTIM_SELECTION.LR if eta is None else eta + self.dst_val = dst_train.dataset.val + self.dst_val_label = self.get_train_label(self.dst_val) + self.n_val = len(self.dst_val) + + if greedy not in submodular_optimizer.optimizer_choices: + raise ModuleNotFoundError("Greedy optimizer not found.") + self._greedy = greedy + + def calc_gradient(self, index=None,val=False): + ''' + Calculate gradients matrix on current network for specified training dataset. + ''' + self.model.eval() + if val: + val_str = 'Val' + data_loader = self.select_dm(self.dst_val, index, is_train=False) + # self.init_out = [] + # self.init_emb = [] + # self.init_y = [] + else: + val_str = 'Train' + data_loader = self.select_dm(self.dst_train, index, is_train=False) + # Initialize a matrix to save gradients. + # (on cpu) + gradients = [] + + for i, batch in enumerate(tqdm(data_loader)): + + self.optim.zero_grad() + image, label = batch['img'].cuda(), batch['label'].cuda() + bs_size = image.shape[0] + loss,visual_embedding,logit = self.model(image,label,cal_gradient=True) + embed_dim = visual_embedding.shape[-1] + with torch.no_grad(): + bias_parameters_grads = torch.autograd.grad(loss, logit)[0] + weight_parameters_grads = visual_embedding.view(bs_size, 1, + -1).repeat(1, self.num_classes, 1) *\ + bias_parameters_grads.view(bs_size, self.num_classes, + 1).repeat(1, 1, embed_dim) + gradients.append(torch.cat([bias_parameters_grads, weight_parameters_grads.flatten(1)], + dim=1).cpu().numpy()) + + # if val: + # self.init_out.append(logit.cpu()) + # self.init_emb.append(visual_embedding.cpu()) + # self.init_y.append(label.cpu()) + + + # if val: + # with torch.no_grad(): + # self.init_out = torch.cat(self.init_out,dim=0).numpy().astype(dtype=np.float32) + # self.init_emb = torch.cat(self.init_emb,dim=0).numpy().astype(dtype=np.float32) + # self.init_y = torch.cat(self.init_y,dim=0).numpy().astype(dtype=np.float32) + + gradients = np.concatenate(gradients, axis=0,dtype=np.float32) + print(f'Finish Gradient Calculation on {val_str} dataset') + return gradients + + # def calc_gradient(self, index=None, val=False, record_val_detail=False): + # ''' + # Calculate gradients matrix on current network for training or validation dataset. + # ''' + # + # self.model.eval() + # + # if val: + # batch_loader = torch.utils.data.DataLoader( + # self.dst_val if index is None else torch.utils.data.Subset(self.dst_val, index), + # batch_size=self.args.selection_batch, num_workers=self.args.workers) + # else: + # batch_loader = torch.utils.data.DataLoader( + # self.dst_train if index is None else torch.utils.data.Subset(self.dst_train, index), + # batch_size=self.args.selection_batch, num_workers=self.args.workers) + # + # self.embedding_dim = self.model.get_last_layer().in_features + # gradients = [] + # if val and record_val_detail: + # self.init_out = [] + # self.init_emb = [] + # self.init_y = [] + # + # for i, (input, targets) in enumerate(batch_loader): + # self.model_optimizer.zero_grad() + # outputs = self.model(input.to(self.args.device)) + # loss = self.criterion(outputs.requires_grad_(True), targets.to(self.args.device)).sum() + # batch_num = targets.shape[0] + # with torch.no_grad(): + # bias_parameters_grads = torch.autograd.grad(loss, outputs)[0] + # weight_parameters_grads = self.model.embedding_recorder.embedding.view(batch_num, 1, + # self.embedding_dim).repeat(1, self.args.num_classes, 1) *\ + # bias_parameters_grads.view( + # batch_num, self.args.num_classes, 1).repeat(1, 1, self.embedding_dim) + # gradients.append(torch.cat( + # [bias_parameters_grads, weight_parameters_grads.flatten(1)], dim=1).cpu()) + # + # if val and record_val_detail: + # self.init_out.append(outputs.cpu()) + # self.init_emb.append(self.model.embedding_recorder.embedding.cpu()) + # self.init_y.append(targets) + # + # gradients = torch.cat(gradients, dim=0) + # if val: + # self.val_grads = torch.mean(gradients, dim=0) + # if self.dst_val == self.dst_train: + # # No validation set was provided while instantiating Glister, so self.dst_val == self.dst_train + # self.train_grads = gradients + # else: + # self.train_grads = gradients + # if val and record_val_detail: + # with torch.no_grad(): + # self.init_out = torch.cat(self.init_out, dim=0) + # self.init_emb = torch.cat(self.init_emb, dim=0) + # self.init_y = torch.cat(self.init_y) + # + # self.model.train() + + #PASS, worth disussion + def update_val_gradients(self, new_selection, selected_for_train): + + sum_selected_train_gradients = np.mean(self.train_gradients[selected_for_train], axis=0) + + new_outputs = self.init_out - self.eta * sum_selected_train_gradients[:self.num_classes].reshape(1, + -1).repeat(self.init_out.shape[0], 1) - self.eta * torch.matmul(self.init_emb, + sum_selected_train_gradients[self.num_classes:].view(self.num_classes, -1).T) + + sample_num = new_outputs.shape[0] + gradients = torch.zeros([sample_num, self.args.num_classes * (self.embedding_dim + 1)], requires_grad=False) + i = 0 + while i * self.args.selection_batch < sample_num: + batch_indx = np.arange(sample_num)[i * self.args.selection_batch:min((i + 1) * self.args.selection_batch, + sample_num)] + new_out_puts_batch = new_outputs[batch_indx].clone().detach().requires_grad_(True) + loss = self.criterion(new_out_puts_batch, self.init_y[batch_indx]) + batch_num = len(batch_indx) + bias_parameters_grads = torch.autograd.grad(loss.sum(), new_out_puts_batch, retain_graph=True)[0] + + weight_parameters_grads = self.init_emb[batch_indx].view(batch_num, 1, self.embedding_dim).repeat(1, + self.args.num_classes, 1) * bias_parameters_grads.view(batch_num, + self.args.num_classes, 1).repeat(1, 1, self.embedding_dim) + gradients[batch_indx] = torch.cat([bias_parameters_grads, weight_parameters_grads.flatten(1)], dim=1).cpu() + i += 1 + + self.val_grads = torch.mean(gradients, dim=0) + + def finish_run(self): + if isinstance(self.model, MyDataParallel): + self.model = self.model.module + + self.model.no_grad = True + + self.train_indx = np.arange(self.n_train) + self.val_indx = np.arange(self.n_val) + + train_gradients = self.calc_gradient(index=None) + val_gradients = self.calc_gradient(index=None,val=True) + if self.balance: + selection_result = np.array([], dtype=np.int64) + #weights = np.array([], dtype=np.float32) + for c in range(self.num_classes): + c_indx = self.train_indx[self.dst_train_label == c] + c_val_inx = self.val_indx[self.dst_val_label == c] + self.train_gradients = train_gradients[c_indx] + self.val_gradients = val_gradients[c_val_inx].mean(axis=0) + + # self.init_out = self.init_out[c_val_inx] + # self.init_emb = self.init_emb[c_val_inx] + # self.init_y = self.init_y[c_val_inx] + + submod_optimizer = submodular_optimizer.__dict__[self._greedy](args=self.args, index=c_indx, + budget=round(self.fraction * len(c_indx))) + #conditioal gain uses taylor series approximation + c_selection_result = submod_optimizer.select(gain_function=lambda idx_gain, selected, + **kwargs: np.dot(self.train_gradients[idx_gain], + self.val_gradients.reshape(-1, 1)). + flatten(), update_state=None) #self.update val + selection_result = np.append(selection_result, c_selection_result) + + else: + self.train_gradients = train_gradients + self.val_gradients = val_gradients.mean(axis=0) + submod_optimizer = submodular_optimizer.__dict__[self._greedy](args=self.args, + index=np.arange(self.n_train), budget=self.coreset_size) + selection_result = submod_optimizer.select(gain_function=lambda idx_gain, selected, + **kwargs: torch.matmul(self.train_gradients[idx_gain], + self.val_gradients.view(-1, 1)).detach().cpu().numpy().flatten(), + upadate_state=self.update_val_gradients) + + + self.model.no_grad = False + return {"indices": selection_result} + + def num_classes_mismatch(self): + raise ValueError("num_classes of pretrain dataset does not match that of the training dataset.") + + + diff --git a/deepcore/methods/gradmatch.py b/deepcore/methods/gradmatch.py new file mode 100644 index 0000000..4bf2e25 --- /dev/null +++ b/deepcore/methods/gradmatch.py @@ -0,0 +1,213 @@ +import torch +import numpy as np +from scipy.linalg import lstsq +from scipy.optimize import nnls +from .earlytrain import EarlyTrain +from ..nets.nets_utils import MyDataParallel + + +# https://github.com/krishnatejakk/GradMatch + +class GradMatch(EarlyTrain): + def __init__(self, dst_train, args, fraction=0.5, random_seed=None, epochs=200, specific_model=None, + balance=True, dst_val=None, lam: float = 1., **kwargs): + super().__init__(dst_train, args, fraction, random_seed, epochs, specific_model, **kwargs) + self.balance = balance + self.dst_val = dst_val + + def num_classes_mismatch(self): + raise ValueError("num_classes of pretrain dataset does not match that of the training dataset.") + + def while_update(self, outputs, loss, targets, epoch, batch_idx, batch_size): + if batch_idx % self.args.print_freq == 0: + print('| Epoch [%3d/%3d] Iter[%3d/%3d]\t\tLoss: %.4f' % ( + epoch, self.epochs, batch_idx + 1, (self.n_pretrain_size // batch_size) + 1, loss.item())) + + def orthogonal_matching_pursuit(self, A, b, budget: int, lam: float = 1.): + '''approximately solves min_x |x|_0 s.t. Ax=b using Orthogonal Matching Pursuit + Acknowlegement to: + https://github.com/krishnatejakk/GradMatch/blob/main/GradMatch/selectionstrategies/helpers/omp_solvers.py + Args: + A: design matrix of size (d, n) + b: measurement vector of length d + budget: selection budget + lam: regularization coef. for the final output vector + Returns: + vector of length n + ''' + with torch.no_grad(): + d, n = A.shape + if budget <= 0: + budget = 0 + elif budget > n: + budget = n + + x = np.zeros(n, dtype=np.float32) + resid = b.clone() + indices = [] + boolean_mask = torch.ones(n, dtype=bool, device="cuda") + all_idx = torch.arange(n, device='cuda') + + for i in range(budget): + if i % self.args.print_freq == 0: + print("| Selecting [%3d/%3d]" % (i + 1, budget)) + projections = torch.matmul(A.T, resid) + index = torch.argmax(projections[boolean_mask]) + index = all_idx[boolean_mask][index] + + indices.append(index.item()) + boolean_mask[index] = False + + if indices.__len__() == 1: + A_i = A[:, index] + x_i = projections[index] / torch.dot(A_i, A_i).view(-1) + A_i = A[:, index].view(1, -1) + else: + A_i = torch.cat((A_i, A[:, index].view(1, -1)), dim=0) + temp = torch.matmul(A_i, torch.transpose(A_i, 0, 1)) + lam * torch.eye(A_i.shape[0], device="cuda") + x_i, _ = torch.lstsq(torch.matmul(A_i, b).view(-1, 1), temp) + resid = b - torch.matmul(torch.transpose(A_i, 0, 1), x_i).view(-1) + if budget > 1: + x_i = nnls(temp.cpu().numpy(), torch.matmul(A_i, b).view(-1).cpu().numpy())[0] + x[indices] = x_i + elif budget == 1: + x[indices[0]] = 1. + return x + + def orthogonal_matching_pursuit_np(self, A, b, budget: int, lam: float = 1.): + '''approximately solves min_x |x|_0 s.t. Ax=b using Orthogonal Matching Pursuit + Acknowlegement to: + https://github.com/krishnatejakk/GradMatch/blob/main/GradMatch/selectionstrategies/helpers/omp_solvers.py + Args: + A: design matrix of size (d, n) + b: measurement vector of length d + budget: selection budget + lam: regularization coef. for the final output vector + Returns: + vector of length n + ''' + d, n = A.shape + if budget <= 0: + budget = 0 + elif budget > n: + budget = n + + x = np.zeros(n, dtype=np.float32) + resid = np.copy(b) + indices = [] + boolean_mask = np.ones(n, dtype=bool) + all_idx = np.arange(n) + + for i in range(budget): + if i % self.args.print_freq == 0: + print("| Selecting [%3d/%3d]" % (i + 1, budget)) + projections = A.T.dot(resid) + index = np.argmax(projections[boolean_mask]) + index = all_idx[boolean_mask][index] + + indices.append(index.item()) + boolean_mask[index] = False + + if indices.__len__() == 1: + A_i = A[:, index] + x_i = projections[index] / A_i.T.dot(A_i) + else: + A_i = np.vstack([A_i, A[:, index]]) + x_i = lstsq(A_i.dot(A_i.T) + lam * np.identity(A_i.shape[0]), A_i.dot(b))[0] + resid = b - A_i.T.dot(x_i) + if budget > 1: + x_i = nnls(A_i.dot(A_i.T) + lam * np.identity(A_i.shape[0]), A_i.dot(b))[0] + x[indices] = x_i + elif budget == 1: + x[indices[0]] = 1. + return x + + def calc_gradient(self, index=None, val=False): + self.model.eval() + if val: + batch_loader = torch.utils.data.DataLoader( + self.dst_val if index is None else torch.utils.data.Subset(self.dst_val, index), + batch_size=self.args.selection_batch, num_workers=self.args.workers) + sample_num = len(self.dst_val.targets) if index is None else len(index) + else: + batch_loader = torch.utils.data.DataLoader( + self.dst_train if index is None else torch.utils.data.Subset(self.dst_train, index), + batch_size=self.args.selection_batch, num_workers=self.args.workers) + sample_num = self.n_train if index is None else len(index) + + self.embedding_dim = self.model.get_last_layer().in_features + gradients = torch.zeros([sample_num, self.args.num_classes * (self.embedding_dim + 1)], + requires_grad=False, device=self.args.device) + + for i, (input, targets) in enumerate(batch_loader): + self.model_optimizer.zero_grad() + outputs = self.model(input.to(self.args.device)).requires_grad_(True) + loss = self.criterion(outputs, targets.to(self.args.device)).sum() + batch_num = targets.shape[0] + with torch.no_grad(): + bias_parameters_grads = torch.autograd.grad(loss, outputs, retain_graph=True)[0].cpu() + weight_parameters_grads = self.model.embedding_recorder.embedding.cpu().view(batch_num, 1, + self.embedding_dim).repeat(1,self.args.num_classes,1) *\ + bias_parameters_grads.view(batch_num, self.args.num_classes, + 1).repeat(1, 1, self.embedding_dim) + gradients[i * self.args.selection_batch:min((i + 1) * self.args.selection_batch, sample_num)] =\ + torch.cat([bias_parameters_grads, weight_parameters_grads.flatten(1)], dim=1) + + return gradients + + def finish_run(self): + if isinstance(self.model, MyDataParallel): + self.model = self.model.module + + self.model.no_grad = True + with self.model.embedding_recorder: + if self.dst_val is not None: + val_num = len(self.dst_val.targets) + + if self.balance: + selection_result = np.array([], dtype=np.int64) + weights = np.array([], dtype=np.float32) + for c in range(self.args.num_classes): + class_index = np.arange(self.n_train)[self.dst_train.targets == c] + cur_gradients = self.calc_gradient(class_index) + if self.dst_val is not None: + # Also calculate gradients of the validation set. + val_class_index = np.arange(val_num)[self.dst_val.targets == c] + cur_val_gradients = torch.mean(self.calc_gradient(val_class_index, val=True), dim=0) + else: + cur_val_gradients = torch.mean(cur_gradients, dim=0) + if self.args.device == "cpu": + # Compute OMP on numpy + cur_weights = self.orthogonal_matching_pursuit_np(cur_gradients.numpy().T, + cur_val_gradients.numpy(), + budget=round(len(class_index) * self.fraction)) + else: + cur_weights = self.orthogonal_matching_pursuit(cur_gradients.to(self.args.device).T, + cur_val_gradients.to(self.args.device), + budget=round(len(class_index) * self.fraction)) + selection_result = np.append(selection_result, class_index[np.nonzero(cur_weights)[0]]) + weights = np.append(weights, cur_weights[np.nonzero(cur_weights)[0]]) + else: + cur_gradients = self.calc_gradient() + if self.dst_val is not None: + # Also calculate gradients of the validation set. + cur_val_gradients = torch.mean(self.calc_gradient(val=True), dim=0) + else: + cur_val_gradients = torch.mean(cur_gradients, dim=0) + if self.args.device == "cpu": + # Compute OMP on numpy + cur_weights = self.orthogonal_matching_pursuit_np(cur_gradients.numpy().T, + cur_val_gradients.numpy(), + budget=self.coreset_size) + else: + cur_weights = self.orthogonal_matching_pursuit(cur_gradients.T, cur_val_gradients, + budget=self.coreset_size) + selection_result = np.nonzero(cur_weights)[0] + weights = cur_weights[selection_result] + self.model.no_grad = False + return {"indices": selection_result, "weights": weights} + + def select(self, **kwargs): + selection_result = self.run() + return selection_result + diff --git a/deepcore/methods/grand.py b/deepcore/methods/grand.py new file mode 100644 index 0000000..2d1b133 --- /dev/null +++ b/deepcore/methods/grand.py @@ -0,0 +1,108 @@ +from .earlytrain import EarlyTrain +import torch, time +import numpy as np +from ..nets.nets_utils import MyDataParallel +from tqdm import tqdm + +class GraNd(EarlyTrain): + def __init__(self, dst_train, args, fraction=0.5, random_seed=None, epochs=200, repeat=1, + specific_model=None, balance=False, **kwargs): + super().__init__(dst_train, args, fraction, random_seed, epochs, specific_model,**kwargs) + self.epochs = epochs + self.n_train = len(self.dst_train) + self.coreset_size = round(self.n_train * fraction) + self.specific_model = specific_model + self.repeat = repeat + + self.balance = balance + + # def while_update(self, outputs, loss, targets, epoch, batch_idx, batch_size): + # if batch_idx % self.args.print_freq == 0: + # print('| Epoch [%3d/%3d] Iter[%3d/%3d]\t\tLoss: %.4f' % ( + # epoch, self.epochs, batch_idx + 1, (self.n_train // batch_size) + 1, loss.item())) + + def before_run(self): + if isinstance(self.model, MyDataParallel): + self.model = self.model.module + + def calc_gradient(self, index=None): + ''' + Calculate gradients matrix on current network for specified training dataset. + ''' + self.model.eval() + data_loader = self.select_dm(self.dst_train, index, is_train=False) + # Initialize a matrix to save gradients. + # (on cpu) + gradients = [] + + for i, batch in enumerate(tqdm(data_loader)): + self.optim.zero_grad() + image, label = batch['img'].cuda(), batch['label'].cuda() + bs_size = image.shape[0] + loss, visual_embedding, logit = self.model(image, label, cal_gradient=True) + embed_dim = visual_embedding.shape[-1] + with torch.no_grad(): + bias_parameters_grads = torch.autograd.grad(loss, logit)[0] + weight_parameters_grads = visual_embedding.view(bs_size, 1, + -1).repeat(1, self.num_classes, 1) * \ + bias_parameters_grads.view(bs_size, self.num_classes, + 1).repeat(1, 1, embed_dim) + gradients.append(torch.cat([bias_parameters_grads, weight_parameters_grads.flatten(1)], + dim=1).cpu().numpy()) + + gradients = np.concatenate(gradients, axis=0, dtype=np.float32) + print('Finish Gradient Calculation') + self.model.train() + return gradients + + def finish_run(self): + # self.model.embedding_recorder.record_embedding = True # recording embedding vector + + gradients = self.calc_gradient() + self.norm_matrix[:,0] = np.linalg.norm(gradients,axis=1) + + + + # embedding_dim = self.model.get_last_layer().in_features + # data_loader = self.select_dm(self.dst_train, None, is_train=False) + # sample_num = self.n_train + # + # for i, batch in enumerate(data_loader): + # self.optim.zero_grad() + # image, target,batch_inds = batch['img'].cuda(), batch['label'].cuda(), batch['index'].cuda() + # + # outputs = self.model(image) + # loss = self.criterion(outputs.requires_grad_(True), + # targets.to(self.args.device)).sum() + # batch_num = targets.shape[0] + # with torch.no_grad(): + # bias_parameters_grads = torch.autograd.grad(loss, outputs)[0] + # self.norm_matrix[i * self.args.selection_batch:min((i + 1) * self.args.selection_batch, sample_num), + # self.cur_repeat] = torch.norm(torch.cat([bias_parameters_grads, ( + # self.model.embedding_recorder.embedding.view(batch_num, 1, embedding_dim).repeat(1, + # self.args.num_classes, 1) * bias_parameters_grads.view( + # batch_num, self.args.num_classes, 1).repeat(1, 1, embedding_dim)). + # view(batch_num, -1)], dim=1), dim=1, p=2) + # + # self.model.train() + + + def select(self, **kwargs): + # Initialize a matrix to save norms of each sample on idependent runs + self.norm_matrix = np.zeros([self.n_train, self.repeat]) + + # for self.cur_repeat in range(self.repeat): + self.run() + # self.random_seed = self.random_seed + 5 + + self.norm_mean = np.mean(self.norm_matrix, axis=1) + if not self.balance: + top_examples = self.train_indx[np.argsort(self.norm_mean)][::-1][:self.coreset_size] + else: + top_examples = np.array([], dtype=np.int64) + for c in tqdm(range(self.num_classes)): + c_indx = self.train_indx[self.dst_train_label == c] + budget = round(self.fraction * len(c_indx)) + top_examples = np.append(top_examples, c_indx[np.argsort(self.norm_mean[c_indx])[::-1][:budget]]) + + return {"indices": top_examples, "scores": self.norm_mean} diff --git a/deepcore/methods/herding.py b/deepcore/methods/herding.py new file mode 100644 index 0000000..25aa830 --- /dev/null +++ b/deepcore/methods/herding.py @@ -0,0 +1,109 @@ +from .earlytrain import EarlyTrain +import torch +import numpy as np +from .methods_utils import euclidean_dist +from ..nets.nets_utils import MyDataParallel + + +class Herding(EarlyTrain): + def __init__(self, dst_train, args, fraction=0.5, random_seed=None, epochs=200, + specific_model="ResNet18", balance: bool = False, metric="euclidean", **kwargs): + super().__init__(dst_train, args, fraction, random_seed, epochs=epochs, specific_model=specific_model, **kwargs) + + if metric == "euclidean": + self.metric = euclidean_dist + elif callable(metric): + self.metric = metric + else: + self.metric = euclidean_dist + self.run = lambda: self.finish_run() + + def _construct_matrix(index=None): + data_loader = torch.utils.data.DataLoader( + self.dst_train if index is None else torch.utils.data.Subset(self.dst_train, index), + batch_size=self.n_train if index is None else len(index), num_workers=self.args.workers) + inputs, _ = next(iter(data_loader)) + return inputs.flatten(1).requires_grad_(False).to(self.args.device) + + self.construct_matrix = _construct_matrix + + self.balance = balance + self.select_bs = self.args.DATASET.SELECTION_BATCH_SIZE + + def num_classes_mismatch(self): + raise ValueError("num_classes of pretrain dataset does not match that of the training dataset.") + + def while_update(self, outputs, loss, targets, epoch, batch_idx, batch_size): + pass + + #Initial achievement, may not optimal + def mixing_feature(self,img_fea,text_fea,lam=0.5): + # return img_fea + return lam*img_fea + (1-lam)*text_fea + + def construct_matrix(self, index=None): + self.model.eval() + self.model.no_grad = True + with torch.no_grad(): + # with self.model.embedding_recorder: + sample_num = self.n_train if index is None else len(index) + matrix = torch.zeros([sample_num, self.emb_dim], requires_grad=False).cuda() + data_loader = self.select_dm(self.dst_train,index,is_train=False) + for i, batch in enumerate(data_loader): + image,label = batch['img'].cuda(),batch['label'].cuda() + img_f,text_f,_ = self.model(image, label, record=True) + final_embed = self.mixing_feature(img_f,text_f) #Using the mixed image_feature and text_feature + matrix[i * self.select_bs:min((i + 1) * self.select_bs, sample_num)] = final_embed + + self.model.no_grad = False + self.model.train() + return matrix + + def before_run(self): + self.emb_dim = self.model.image_encoder.output_dim + + def herding(self, matrix, budget: int, index=None): + + sample_num = matrix.shape[0] + + if budget < 0: + raise ValueError("Illegal budget size.") + elif budget > sample_num: + budget = sample_num + + indices = np.arange(sample_num) + with torch.no_grad(): + mu = torch.mean(matrix, dim=0) + select_result = np.zeros(sample_num, dtype=bool) + + for i in range(budget): + if i % self.args.TRAIN.PRINT_FREQ == 0: + print("| Selecting [%3d/%3d]" % (i + 1, budget)) + dist = self.metric(((i + 1) * mu - torch.sum(matrix[select_result], dim=0)).view(1, -1), + matrix[~select_result]) + p = torch.argmax(dist).item() + p = indices[~select_result][p] + select_result[p] = True + if index is None: + index = indices + return index[select_result] + + def finish_run(self): + if isinstance(self.model, MyDataParallel): + self.model = self.model.module + + if self.balance: + selection_result = np.array([], dtype=np.int32) + for c in range(self.num_classes): + class_index = np.arange(self.n_train)[self.dst_train_label == c] + selection_result = np.append(selection_result, self.herding(self.construct_matrix(class_index), + budget=round(self.fraction * len(class_index)), index=class_index)) + else: + selection_result = self.herding(self.construct_matrix(), budget=self.coreset_size) + return {"indices": selection_result} + + def select(self, **kwargs): + selection_result = self.run() + return selection_result + + diff --git a/deepcore/methods/kcentergreedy.py b/deepcore/methods/kcentergreedy.py new file mode 100644 index 0000000..482c549 --- /dev/null +++ b/deepcore/methods/kcentergreedy.py @@ -0,0 +1,182 @@ +from .earlytrain import EarlyTrain +import torch +import numpy as np +from .methods_utils import euclidean_dist +from ..nets.nets_utils import MyDataParallel + + +def k_center_greedy(matrix, budget: int, metric, device, random_seed=None, index=None, already_selected=None, + print_freq: int = 20): + if type(matrix) == torch.Tensor: + assert matrix.dim() == 2 + elif type(matrix) == np.ndarray: + assert matrix.ndim == 2 + matrix = torch.from_numpy(matrix).requires_grad_(False).to(device) + + sample_num = matrix.shape[0] + assert sample_num >= 1 + + if budget < 0: + raise ValueError("Illegal budget size.") + elif budget > sample_num: + budget = sample_num + + if index is not None: + assert matrix.shape[0] == len(index) + else: + index = np.arange(sample_num) + + assert callable(metric) + + already_selected = np.array(already_selected) + + with torch.no_grad(): + np.random.seed(random_seed) + if already_selected.__len__() == 0: + select_result = np.zeros(sample_num, dtype=bool) + # Randomly select one initial point. + already_selected = [np.random.randint(0, sample_num)] + budget -= 1 + select_result[already_selected] = True + else: + select_result = np.in1d(index, already_selected) + + num_of_already_selected = np.sum(select_result) + + # Initialize a (num_of_already_selected+budget-1)*sample_num matrix storing distances of pool points from + # each clustering center. + dis_matrix = -1 * torch.ones([num_of_already_selected + budget - 1, sample_num], requires_grad=False).to(device) + + dis_matrix[:num_of_already_selected, ~select_result] = metric(matrix[select_result], matrix[~select_result]) + + mins = torch.min(dis_matrix[:num_of_already_selected, :], dim=0).values + + for i in range(budget): + if i % print_freq == 0: + print("| Selecting [%3d/%3d]" % (i + 1, budget)) + p = torch.argmax(mins).item() + select_result[p] = True + + if i == budget - 1: + break + mins[p] = -1 + dis_matrix[num_of_already_selected + i, ~select_result] = metric(matrix[[p]], matrix[~select_result]) + mins = torch.min(mins, dis_matrix[num_of_already_selected + i]) + return index[select_result] + + +class kCenterGreedy(EarlyTrain): + def __init__(self, dst_train, args, fraction=0.5, random_seed=None, epochs=0, + specific_model="ResNet18", balance: bool = False, already_selected=[], metric="euclidean", + torchvision_pretrain: bool = True, **kwargs): + super().__init__(dst_train, args, fraction, random_seed, epochs=epochs, specific_model=specific_model, + torchvision_pretrain=torchvision_pretrain, **kwargs) + + if already_selected.__len__() != 0: + if min(already_selected) < 0 or max(already_selected) >= self.n_train: + raise ValueError("List of already selected points out of the boundary.") + self.already_selected = np.array(already_selected) + + self.min_distances = None + + if metric == "euclidean": + self.metric = euclidean_dist + elif callable(metric): + self.metric = metric + else: + self.metric = euclidean_dist + self.run = lambda : self.finish_run() + def _construct_matrix(index=None): + data_loader = torch.utils.data.DataLoader( + self.dst_train if index is None else torch.utils.data.Subset(self.dst_train, index), + batch_size=self.n_train if index is None else len(index), + num_workers=self.args.workers) + inputs, _ = next(iter(data_loader)) + return inputs.flatten(1).requires_grad_(False).to(self.args.device) + self.construct_matrix = _construct_matrix + + self.balance = balance + + def num_classes_mismatch(self): + raise ValueError("num_classes of pretrain dataset does not match that of the training dataset.") + + def while_update(self, outputs, loss, targets, epoch, batch_idx, batch_size): + if batch_idx % self.args.print_freq == 0: + print('| Epoch [%3d/%3d] Iter[%3d/%3d]\t\tLoss: %.4f' % ( + epoch, self.epochs, batch_idx + 1, (self.n_pretrain_size // batch_size) + 1, loss.item())) + + def old_construct_matrix(self, index=None): + self.model.eval() + self.model.no_grad = True + with torch.no_grad(): + with self.model.embedding_recorder: + sample_num = self.n_train if index is None else len(index) + matrix = torch.zeros([sample_num, self.emb_dim], requires_grad=False).to(self.args.device) + + data_loader = torch.utils.data.DataLoader(self.dst_train if index is None else + torch.utils.data.Subset(self.dst_train, index), + batch_size=self.args.selection_batch, + num_workers=self.args.workers) + + for i, (inputs, _) in enumerate(data_loader): + self.model(inputs.to(self.args.device)) + matrix[i * self.args.selection_batch:min((i + 1) * self.args.selection_batch, + sample_num)] = self.model.embedding_recorder.embedding + + self.model.no_grad = False + return matrix + + def construct_matrix(self, index=None): + self.model.eval() + self.model.no_grad = True + with torch.no_grad(): + with self.model.embedding_recorder: + sample_num = self.n_train if index is None else len(index) + matrix = [] + + data_loader = torch.utils.data.DataLoader(self.dst_train if index is None else + torch.utils.data.Subset(self.dst_train, index), + batch_size=self.args.selection_batch, + num_workers=self.args.workers) + + for i, (inputs, _) in enumerate(data_loader): + self.model(inputs.to(self.args.device)) + matrix.append(self.model.embedding_recorder.embedding) + + self.model.no_grad = False + return torch.cat(matrix, dim=0) + + def before_run(self): + self.emb_dim = self.model.get_last_layer().in_features + + def finish_run(self): + if isinstance(self.model, MyDataParallel): + self.model = self.model.module + + def select(self, **kwargs): + self.run() + if self.balance: + selection_result = np.array([], dtype=np.int32) + for c in range(self.args.num_classes): + class_index = np.arange(self.n_train)[self.dst_train.targets == c] + + selection_result = np.append(selection_result, k_center_greedy(self.construct_matrix(class_index), + budget=round( + self.fraction * len(class_index)), + metric=self.metric, + device=self.args.device, + random_seed=self.random_seed, + index=class_index, + already_selected=self.already_selected[ + np.in1d(self.already_selected, + class_index)], + print_freq=self.args.print_freq)) + else: + matrix = self.construct_matrix() + del self.model_optimizer + del self.model + selection_result = k_center_greedy(matrix, budget=self.coreset_size, + metric=self.metric, device=self.args.device, + random_seed=self.random_seed, + already_selected=self.already_selected, print_freq=self.args.print_freq) + return {"indices": selection_result} diff --git a/deepcore/methods/methods_utils/__init__.py b/deepcore/methods/methods_utils/__init__.py new file mode 100644 index 0000000..d8bc26a --- /dev/null +++ b/deepcore/methods/methods_utils/__init__.py @@ -0,0 +1,4 @@ +from .euclidean import * +from .cossim import * +from .submodular_function import * +from .submodular_optimizer import * diff --git a/deepcore/methods/methods_utils/__pycache__/__init__.cpython-39.pyc b/deepcore/methods/methods_utils/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 0000000..be4d93d Binary files /dev/null and b/deepcore/methods/methods_utils/__pycache__/__init__.cpython-39.pyc differ diff --git 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b/deepcore/methods/methods_utils/__pycache__/submodular_optimizer.cpython-39.pyc new file mode 100644 index 0000000..eb190a3 Binary files /dev/null and b/deepcore/methods/methods_utils/__pycache__/submodular_optimizer.cpython-39.pyc differ diff --git a/deepcore/methods/methods_utils/cossim.py b/deepcore/methods/methods_utils/cossim.py new file mode 100644 index 0000000..782bbfb --- /dev/null +++ b/deepcore/methods/methods_utils/cossim.py @@ -0,0 +1,35 @@ +import numpy as np +import torch + + +def cossim_np(v1, v2): + # return cossim(torch.tensor(v1),torch.tensor(v2)).cpu().numpy() + num = np.dot(v1, v2.T) + denom = np.linalg.norm(v1, axis=1).reshape(-1, 1) * np.linalg.norm(v2, axis=1) + res = num / (denom + 1e-6) + res[np.isneginf(res)] = 0. + return 0.5 + 0.5 * res + +def cossim_pair_np(v1): + num = np.dot(v1, v1.T) + norm = np.linalg.norm(v1, axis=1) + denom = norm.reshape(-1, 1) * norm + res = num / (denom + 1e-6) + res[np.isneginf(res)] = 0. + return 0.5 + 0.5 * res + +def cossim(v1, v2): + num = torch.matmul(v1, v2.T) + denom = torch.norm(v1, dim=1).view(-1, 1) * torch.norm(v2, dim=1) + res = num / (denom + 1e-6) + res[torch.isneginf(res)] = 0. + return 0.5 + 0.5 * res + +def cossim_pair(v1): + num = torch.matmul(v1, v1.T) + norm = torch.norm(v1, dim=1) + denom = norm.view(-1, 1) * norm + res = num / (denom + 1e-6) + res[torch.isneginf(res)] = 0. + return 0.5 + 0.5 * res + diff --git a/deepcore/methods/methods_utils/euclidean.py b/deepcore/methods/methods_utils/euclidean.py new file mode 100644 index 0000000..e5f5483 --- /dev/null +++ b/deepcore/methods/methods_utils/euclidean.py @@ -0,0 +1,36 @@ +import torch +import numpy as np + + +def euclidean_dist(x, y): + m, n = x.size(0), y.size(0) + xx = torch.pow(x, 2).sum(1, keepdim=True).expand(m, n) + yy = torch.pow(y, 2).sum(1, keepdim=True).expand(n, m).t() + dist = xx + yy + dist.addmm_(1, -2, x, y.t()) + dist = dist.clamp(min=1e-12).sqrt() + return dist + + +def euclidean_dist_pair(x): + m = x.size(0) + xx = torch.pow(x, 2).sum(1, keepdim=True).expand(m, m) + dist = xx + xx.t() + dist.addmm_(1, -2, x, x.t()) + dist = dist.clamp(min=1e-12).sqrt() + return dist + +def euclidean_dist_np(x, y): + (rowx, colx) = x.shape + (rowy, coly) = y.shape + xy = np.dot(x, y.T) + x2 = np.repeat(np.reshape(np.sum(np.multiply(x, x), axis=1), (rowx, 1)), repeats=rowy, axis=1) + y2 = np.repeat(np.reshape(np.sum(np.multiply(y, y), axis=1), (rowy, 1)), repeats=rowx, axis=1).T + return np.sqrt(np.clip(x2 + y2 - 2. * xy, 1e-12, None)) + +#calculate the euclidean distance of each sample in x, return a N*N matrix, whose diag is zero +def euclidean_dist_pair_np(x): + (rowx, colx) = x.shape + xy = np.dot(x, x.T) + x2 = np.repeat(np.reshape(np.sum(np.multiply(x, x), axis=1), (rowx, 1)), repeats=rowx, axis=1) + return np.sqrt(np.clip(x2 + x2.T - 2. * xy, 1e-12, None)) diff --git a/deepcore/methods/methods_utils/submodular_function.py b/deepcore/methods/methods_utils/submodular_function.py new file mode 100644 index 0000000..e2e593e --- /dev/null +++ b/deepcore/methods/methods_utils/submodular_function.py @@ -0,0 +1,144 @@ +import numpy as np + + +class SubmodularFunction(object): + def __init__(self, index, similarity_kernel=None, similarity_matrix=None, already_selected=[]): + self.index = index + self.n = len(index) + + self.already_selected = already_selected + + assert similarity_kernel is not None or similarity_matrix is not None + + # For the sample similarity matrix, the method supports two input modes, one is to input a pairwise similarity + # matrix for the whole sample, and the other case allows the input of a similarity kernel to be used to + # calculate similarities incrementally at a later time if required. + if similarity_kernel is not None: + assert callable(similarity_kernel) + self.similarity_kernel = self._similarity_kernel(similarity_kernel) + else: + assert similarity_matrix.shape[0] == self.n and similarity_matrix.shape[1] == self.n + self.similarity_matrix = similarity_matrix + self.similarity_kernel = lambda a, b: self.similarity_matrix[np.ix_(a, b)] + + def _similarity_kernel(self, similarity_kernel): + return similarity_kernel + + +class FacilityLocation(SubmodularFunction): + def __init__(self, **kwargs): + super().__init__(**kwargs) + + if self.already_selected.__len__()==0: + self.cur_max = np.zeros(self.n, dtype=np.float32) + else: + self.cur_max = np.max(self.similarity_kernel(np.arange(self.n), self.already_selected), axis=1) + + self.all_idx = np.ones(self.n, dtype=bool) + + def _similarity_kernel(self, similarity_kernel): + # Initialize a matrix to store similarity values of sample points. + self.sim_matrix = np.zeros([self.n, self.n], dtype=np.float32) + self.if_columns_calculated = np.zeros(self.n, dtype=bool) + + def _func(a, b): + if not np.all(self.if_columns_calculated[b]): + if b.dtype != bool: + temp = ~self.all_idx + temp[b] = True + b = temp + not_calculated = b & ~self.if_columns_calculated + self.sim_matrix[:, not_calculated] = similarity_kernel(self.all_idx, not_calculated) + self.if_columns_calculated[not_calculated] = True + return self.sim_matrix[np.ix_(a, b)] + return _func + + def calc_gain(self, idx_gain, selected, **kwargs): + gains = np.maximum(0., self.similarity_kernel(self.all_idx, idx_gain) - self.cur_max.reshape(-1, 1)).sum(axis=0) + return gains + + def calc_gain_batch(self, idx_gain, selected, **kwargs): + batch_idx = ~self.all_idx + batch_idx[0:kwargs["batch"]] = True + gains = np.maximum(0., self.similarity_kernel(batch_idx, idx_gain) - self.cur_max[batch_idx].reshape(-1, 1)).sum(axis=0) + for i in range(kwargs["batch"], self.n, kwargs["batch"]): + batch_idx = ~self.all_idx + batch_idx[i * kwargs["batch"]:(i + 1) * kwargs["batch"]] = True + gains += np.maximum(0., self.similarity_kernel(batch_idx, idx_gain) - self.cur_max[batch_idx].reshape(-1,1)).sum(axis=0) + return gains + + def update_state(self, new_selection, total_selected, **kwargs): + self.cur_max = np.maximum(self.cur_max, np.max(self.similarity_kernel(self.all_idx, new_selection), axis=1)) + #self.cur_max = np.max(np.append(self.cur_max.reshape(-1, 1), self.similarity_kernel(self.all_idx, new_selection), axis=1), axis=1) + + +class GraphCut(SubmodularFunction): + def __init__(self, lam: float = 1., **kwargs): + super().__init__(**kwargs) + self.lam = lam + + if 'similarity_matrix' in kwargs: + self.sim_matrix_cols_sum = np.sum(self.similarity_matrix, axis=0) + self.all_idx = np.ones(self.n, dtype=bool) + + def _similarity_kernel(self, similarity_kernel): + # Initialize a matrix to store similarity values of sample points. + self.sim_matrix = np.zeros([self.n, self.n], dtype=np.float32) + self.sim_matrix_cols_sum = np.zeros(self.n, dtype=np.float32) + self.if_columns_calculated = np.zeros(self.n, dtype=bool) + + def _func(a, b): + if not np.all(self.if_columns_calculated[b]): + if b.dtype != bool: + temp = ~self.all_idx + temp[b] = True + b = temp + not_calculated = b & ~self.if_columns_calculated + self.sim_matrix[:, not_calculated] = similarity_kernel(self.all_idx, not_calculated) + self.sim_matrix_cols_sum[not_calculated] = np.sum(self.sim_matrix[:, not_calculated], axis=0) + self.if_columns_calculated[not_calculated] = True + return self.sim_matrix[np.ix_(a, b)] + return _func + + def calc_gain(self, idx_gain, selected, **kwargs): + # Conditional gain + # return the sum distance of each unselected sample to the any other one (selected, idx_gain) is for fun. _func() + gain = -2. * np.sum(self.similarity_kernel(selected, idx_gain), axis=0) + self.lam * self.sim_matrix_cols_sum[idx_gain] + + return gain + + def update_state(self, new_selection, total_selected, **kwargs): + pass + + +class LogDeterminant(SubmodularFunction): + def __init__(self, **kwargs): + super().__init__(**kwargs) + + self.all_idx = np.ones(self.n, dtype=bool) + + def _similarity_kernel(self, similarity_kernel): + # Initialize a matrix to store similarity values of sample points. + self.sim_matrix = np.zeros([self.n, self.n], dtype=np.float32) + self.if_columns_calculated = np.zeros(self.n, dtype=bool) + + def _func(a, b): + if not np.all(self.if_columns_calculated[b]): + if b.dtype != bool: + temp = ~self.all_idx + temp[b] = True + b = temp + not_calculated = b & ~self.if_columns_calculated + self.sim_matrix[:, not_calculated] = similarity_kernel(self.all_idx, not_calculated) + self.if_columns_calculated[not_calculated] = True + return self.sim_matrix[np.ix_(a, b)] + return _func + + def calc_gain(self, idx_gain, selected, **kwargs): + # Gain for LogDeterminant can be written as $f(x | A ) = \log\det(S_{a} - S_{a,A}S_{A}^{-1}S_{x,A}^T)$. + sim_idx_gain = self.similarity_kernel(selected, idx_gain).T + sim_selected = self.similarity_kernel(selected, selected) + return (np.dot(sim_idx_gain, np.linalg.pinv(sim_selected)) * sim_idx_gain).sum(-1) + + def update_state(self, new_selection, total_selected, **kwargs): + pass diff --git a/deepcore/methods/methods_utils/submodular_optimizer.py b/deepcore/methods/methods_utils/submodular_optimizer.py new file mode 100644 index 0000000..38068ac --- /dev/null +++ b/deepcore/methods/methods_utils/submodular_optimizer.py @@ -0,0 +1,155 @@ +import numpy as np +from tqdm import tqdm + +optimizer_choices = ["NaiveGreedy", "LazyGreedy", "StochasticGreedy", "ApproximateLazyGreedy"] + +class optimizer(object): + def __init__(self, args, index, budget:int, already_selected=[]): + self.args = args + self.index = index + + if budget <= 0 or budget > index.__len__(): + raise ValueError("Illegal budget for optimizer.") + + self.n = len(index) + self.budget = budget + self.already_selected = already_selected + + +class NaiveGreedy(optimizer): + def __init__(self, args, index, budget:int, already_selected=[]): + super(NaiveGreedy, self).__init__(args, index, budget, already_selected) + + def select(self, gain_function, update_state=None, **kwargs): + assert callable(gain_function) + if update_state is not None: + assert callable(update_state) + selected = np.zeros(self.n, dtype=bool) + selected[self.already_selected] = True + + greedy_gain = np.zeros(len(self.index)) + for i in range(sum(selected), self.budget): + if i % self.args.TRAIN.PRINT_FREQ == 0: + print("| Selecting [%3d/%3d]" % (i + 1, self.budget)) + greedy_gain[~selected] = gain_function(~selected, selected, **kwargs) + current_selection = greedy_gain.argmax() + selected[current_selection] = True + greedy_gain[current_selection] = -np.inf + if update_state is not None: + update_state(np.array([current_selection]), selected, **kwargs) + return self.index[selected] + + +class LazyGreedy(optimizer): + def __init__(self, args, index, budget:int, already_selected=[]): + super(LazyGreedy, self).__init__(args, index, budget, already_selected) + + def select(self, gain_function, update_state=None, **kwargs): + assert callable(gain_function) + if update_state is not None: + assert callable(update_state) + selected = np.zeros(self.n, dtype=bool) + selected[self.already_selected] = True + + greedy_gain = np.zeros(len(self.index)) + greedy_gain[~selected] = gain_function(~selected, selected, **kwargs) + greedy_gain[selected] = -np.inf + + for i in tqdm(range(sum(selected), self.budget)): + if i % self.args.TRAIN.PRINT_FREQ == 0: + print("| Selecting [%3d/%3d]" % (i + 1, self.budget)) + best_gain = -np.inf + last_max_element = -1 + while True: + cur_max_element = greedy_gain.argmax() + if last_max_element == cur_max_element: + # Select cur_max_element into the current subset + selected[cur_max_element] = True + greedy_gain[cur_max_element] = -np.inf + + if update_state is not None: + update_state(np.array([cur_max_element]), selected, **kwargs) + break + new_gain = gain_function(np.array([cur_max_element]), selected, **kwargs)[0] + greedy_gain[cur_max_element] = new_gain + if new_gain >= best_gain: + best_gain = new_gain + last_max_element = cur_max_element + return self.index[selected] + + +class StochasticGreedy(optimizer): + def __init__(self, args, index, budget:int, already_selected=[], epsilon: float=0.9): + super(StochasticGreedy, self).__init__(args, index, budget, already_selected) + self.epsilon = epsilon + + def select(self, gain_function, update_state=None, **kwargs): + assert callable(gain_function) + if update_state is not None: + assert callable(update_state) + selected = np.zeros(self.n, dtype=bool) + selected[self.already_selected] = True + + sample_size = max(round(-np.log(self.epsilon) * self.n / self.budget), 1) + + greedy_gain = np.zeros(len(self.index)) + all_idx = np.arange(self.n) + for i in range(sum(selected), self.budget): + if i % self.args.TRAIN.PRINT_FREQ == 0: + print("| Selecting [%3d/%3d]" % (i + 1, self.budget)) + + # Uniformly select a subset from unselected samples with size sample_size + subset = np.random.choice(all_idx[~selected], replace=False, size=min(sample_size, self.n - i)) + + if subset.__len__() == 0: + break + + greedy_gain[subset] = gain_function(subset, selected, **kwargs) + current_selection = greedy_gain[subset].argmax() + selected[subset[current_selection]] = True + greedy_gain[subset[current_selection]] = -np.inf + if update_state is not None: + update_state(np.array([subset[current_selection]]), selected, **kwargs) + return self.index[selected] + + +class ApproximateLazyGreedy(optimizer): + def __init__(self, args, index, budget:int, already_selected=[], beta: float=0.9): + super(ApproximateLazyGreedy, self).__init__(args, index, budget, already_selected) + self.beta = beta + + def select(self, gain_function, update_state=None, **kwargs): + assert callable(gain_function) + if update_state is not None: + assert callable(update_state) + selected = np.zeros(self.n, dtype=bool) + selected[self.already_selected] = True + + greedy_gain = np.zeros(len(self.index)) + greedy_gain[~selected] = gain_function(~selected, selected, **kwargs) + greedy_gain[selected] = -np.inf + + for i in range(sum(selected), self.budget): + if i % self.args.TRAIN.PRINT_FREQ == 0: + print("| Selecting [%3d/%3d]" % (i + 1, self.budget)) + while True: + cur_max_element = greedy_gain.argmax() + max_gain = greedy_gain[cur_max_element] + + new_gain = gain_function(np.array([cur_max_element]), selected, **kwargs)[0] + + if new_gain >= self.beta * max_gain: + # Select cur_max_element into the current subset + selected[cur_max_element] = True + greedy_gain[cur_max_element] = -np.inf + + if update_state is not None: + update_state(np.array([cur_max_element]), selected, **kwargs) + break + else: + greedy_gain[cur_max_element] = new_gain + return self.index[selected] + + + + diff --git a/deepcore/methods/submodular.py b/deepcore/methods/submodular.py new file mode 100644 index 0000000..3c578db --- /dev/null +++ b/deepcore/methods/submodular.py @@ -0,0 +1,116 @@ +from .earlytrain import EarlyTrain +import numpy as np +import torch +from .methods_utils import cossim_np, submodular_function, submodular_optimizer +from ..nets.nets_utils import MyDataParallel + + +class Submodular(EarlyTrain): + def __init__(self, dst_train, args, fraction=0.5, random_seed=None, epochs=200, specific_model=None, balance=True, + function="GraphCut", greedy="LazyGreedy", metric="cossim", **kwargs): + super(Submodular, self).__init__(dst_train, args, fraction, random_seed, epochs, specific_model, **kwargs) + + if greedy not in submodular_optimizer.optimizer_choices: + raise ModuleNotFoundError("Greedy optimizer not found.") + print(f"The Submodular Method is {function}") + self._greedy = greedy + self._metric = metric + self._function = function + + self.balance = balance + + def before_train(self): + pass + + def after_loss(self, outputs, loss, targets, batch_inds, epoch): + pass + + def before_epoch(self): + pass + + def after_epoch(self): + pass + + def before_run(self): + pass + + def num_classes_mismatch(self): + raise ValueError("num_classes of pretrain dataset does not match that of the training dataset.") + + + def calc_gradient(self, index=None): + ''' + Calculate gradients matrix on current network for specified training dataset. + ''' + self.model.eval() + data_loader = self.select_dm(self.dst_train, index, is_train=False) + # Initialize a matrix to save gradients. + # (on cpu) + gradients = [] + + for i, batch in enumerate(data_loader): + + self.optim.zero_grad() + image, label = batch['img'].cuda(), batch['label'].cuda() + bs_size = image.shape[0] + loss,visual_embedding,logit = self.model(image,label,cal_gradient=True) + embed_dim = visual_embedding.shape[-1] + with torch.no_grad(): + bias_parameters_grads = torch.autograd.grad(loss, logit)[0] + weight_parameters_grads = visual_embedding.view(bs_size, 1, + -1).repeat(1, self.num_classes, 1) *\ + bias_parameters_grads.view(bs_size, self.num_classes, + 1).repeat(1, 1, embed_dim) + gradients.append(torch.cat([bias_parameters_grads, weight_parameters_grads.flatten(1)], + dim=1).cpu().numpy()) + + gradients = np.concatenate(gradients, axis=0,dtype=np.float32) + print('Finish Gradient Calculation') + return gradients + + def finish_run(self): + if isinstance(self.model, MyDataParallel): + self.model = self.model.module + + # Turn on the embedding recorder and the no_grad flag + + self.model.no_grad = True + self.train_indx = np.arange(self.n_train) + + gradients = self.calc_gradient(index=None) + + if self.balance: + selection_result = np.array([], dtype=np.int64) + for c in range(self.num_classes): + print(f'class {c}') + c_indx = self.train_indx[self.dst_train_label == c] + # Calculate gradients into a matrix + c_gradients = gradients[c_indx] + # Instantiate a submodular function + submod_function = submodular_function.__dict__[self._function](index=c_indx, + similarity_kernel=lambda a, b:cossim_np(c_gradients[a], c_gradients[b])) + submod_optimizer = submodular_optimizer.__dict__[self._greedy](args=self.args, + index=c_indx, budget=round(self.fraction * len(c_indx)), already_selected=[]) + + c_selection_result = submod_optimizer.select(gain_function=submod_function.calc_gain, + update_state=submod_function.update_state) + selection_result = np.append(selection_result, c_selection_result) + else: + # Calculate gradients into a matrix + gradients = self.calc_gradient() + # Instantiate a submodular function + submod_function = submodular_function.__dict__[self._function](index=self.train_indx, + similarity_kernel=lambda a, b: cossim_np(gradients[a], gradients[b])) + submod_optimizer = submodular_optimizer.__dict__[self._greedy](args=self.args, index=self.train_indx, + budget=self.coreset_size) + selection_result = submod_optimizer.select(gain_function=submod_function.calc_gain, + update_state=submod_function.update_state) + + self.model.no_grad = False + return {"indices": selection_result} + + def select(self, **kwargs): + selection_result = self.run() + return selection_result + + diff --git a/deepcore/methods/uncertainty.py b/deepcore/methods/uncertainty.py new file mode 100644 index 0000000..e59ced2 --- /dev/null +++ b/deepcore/methods/uncertainty.py @@ -0,0 +1,120 @@ +from .earlytrain import EarlyTrain +import torch +import numpy as np +from datasets.data_manager import select_dm_loader +import time + +class Uncertainty(EarlyTrain): + def __init__(self, dst_train, args,fraction=0.5, random_seed=None, epochs=200, selection_method="Margin", + specific_model=None, balance=False, **kwargs): + super().__init__(dst_train, args, fraction, random_seed, epochs, specific_model, **kwargs) + + selection_choices = ["LeastConfidence", + "Entropy", + "Margin"] + if selection_method not in selection_choices: + raise NotImplementedError("Selection algorithm unavailable.") + self.selection_method = selection_method + + self.epochs = epochs + self.balance = balance + + def before_train(self): + pass + + def after_loss(self, outputs, loss, targets, batch_inds, epoch): + pass + + + def after_epoch(self): + pass + + def before_run(self): + pass + + def num_classes_mismatch(self): + raise ValueError("num_classes of pretrain dataset does not match that of the training dataset.") + + def while_update(self, outputs, loss, targets, epoch, batch_idx, batch_size): + pass + + def finish_run(self): + if self.balance: + selection_result = np.array([], dtype=np.int64) + scores = [] + for c in range(self.num_classes): + print(f"Balance Processing on the train set class {c}") + class_index = np.arange(self.n_train)[self.dst_train_label == c] + scores.append(self.rank_uncertainty_clip(class_index)) + selection_result = np.append(selection_result, class_index[np.argsort(scores[-1])[ + :round(len(class_index) * self.fraction)]]) + else: + print(f"Imbalance Processing on the train set class") + scores = self.rank_uncertainty_clip() + selection_result = np.argsort(scores)[::-1][:self.coreset_size] + return {"indices": selection_result, "scores": scores} + + def rank_uncertainty(self,index=None): + self.specific_model.eval() + with torch.no_grad(): + train_loader = torch.utils.data.DataLoader( + self.dst_train if index is None else torch.utils.data.Subset(self.dst_train, index), + batch_size=self.args.selection_batch, + num_workers=self.args.workers) + + scores = np.array([]) + batch_num = len(train_loader) + + for i, (input, _) in enumerate(train_loader): + if i % self.args.print_freq == 0: + print("| Selecting for batch [%3d/%3d]" % (i + 1, batch_num)) + if self.selection_method == "LeastConfidence": + scores = np.append(scores, self.model(input.to(self.args.device)).max(axis=1).values.cpu().numpy()) + elif self.selection_method == "Entropy": + preds = torch.nn.functional.softmax(self.model(input.to(self.args.device)), dim=1).cpu().numpy() + scores = np.append(scores, (np.log(preds + 1e-6) * preds).sum(axis=1)) + elif self.selection_method == 'Margin': + preds = torch.nn.functional.softmax(self.model(input.to(self.args.device)), dim=1) + preds_argmax = torch.argmax(preds, dim=1) + max_preds = preds[torch.ones(preds.shape[0], dtype=bool), preds_argmax].clone() + preds[torch.ones(preds.shape[0], dtype=bool), preds_argmax] = -1.0 + preds_sub_argmax = torch.argmax(preds, dim=1) + scores = np.append(scores, (max_preds - preds[ + torch.ones(preds.shape[0], dtype=bool), preds_sub_argmax]).cpu().numpy()) + return scores + + + def rank_uncertainty_clip(self,index=None): + self.model.eval() + with torch.no_grad(): + train_loader = select_dm_loader(self.args,self.dst_train,index) + scores = np.array([]) + + for i, batch in enumerate(train_loader): + # if i % self.args.print_freq == 0: + # print("| Selecting for batch [%3d/%3d]" % (i + 1, batch_num)) + image, label = batch['img'].cuda(), batch['label'].cuda() + logits = self.model(image,label) ##Eval mode + if self.selection_method == "LeastConfidence": + scores = np.append(scores, logits.max(axis=1).values.cpu().numpy()) + elif self.selection_method == "Entropy": + preds = torch.softmax(logits, dim=1).cpu().numpy() + scores = np.append(scores, (np.log(preds + 1e-6) * preds).sum(axis=1)) + elif self.selection_method == 'Margin': + preds = torch.softmax(logits, dim=1) + preds_argmax = torch.argmax(preds, dim=1) + max_preds = preds[torch.ones(preds.shape[0], dtype=bool), preds_argmax].clone() + preds[torch.ones(preds.shape[0], dtype=bool), preds_argmax] = -1.0 + preds_sub_argmax = torch.argmax(preds, dim=1) + scores = np.append(scores, (max_preds - preds[torch.ones(preds.shape[0], dtype=bool), preds_sub_argmax]).cpu().numpy()) + self.model.train() + return scores + + + def select(self, **kwargs): + selection_result = self.run() + return selection_result + + def select_without_train(self): + selection_result = self.finish_run() + return selection_result diff --git a/deepcore/methods/uniform.py b/deepcore/methods/uniform.py new file mode 100644 index 0000000..3ee1a98 --- /dev/null +++ b/deepcore/methods/uniform.py @@ -0,0 +1,34 @@ +import numpy as np +from .coresetmethod import CoresetMethod + + +class Uniform(CoresetMethod): + def __init__(self, dst_train, args, fraction=0.5, random_seed=None, balance=True, replace=False, **kwargs): + super().__init__(dst_train, args, fraction, random_seed) + self.balance = balance + self.replace = replace + self.n_train = len(self.dst_train) + + def select_balance(self): + """The same sampling proportions were used in each class separately.""" + np.random.seed(self.random_seed) + self.index = np.array([], dtype=np.int64) + all_index = np.arange(self.n_train) + for c in range(self.num_classes): + + c_index = (self.dst_train_label == c) + self.index = np.append(self.index, + np.random.choice(all_index[c_index], round(self.fraction * c_index.sum().item()), + replace=self.replace)) + return self.index + + def select_no_balance(self): + np.random.seed(self.random_seed) + self.index = np.random.choice(np.arange(self.n_train), round(self.n_train * self.fraction), + replace=self.replace) + + return self.index + + def select(self, **kwargs): + + return {"indices": self.select_balance() if self.balance else self.select_no_balance()} diff --git a/deepcore/nets/__init__.py b/deepcore/nets/__init__.py new file mode 100644 index 0000000..1ee844b --- /dev/null +++ b/deepcore/nets/__init__.py @@ -0,0 +1,8 @@ +from .alexnet import * +from .inceptionv3 import * +from .lenet import * +from .mlp import * +from .mobilenetv3 import * +from .resnet import * +from .vgg import * +from .wideresnet import * diff --git a/deepcore/nets/__pycache__/__init__.cpython-39.pyc b/deepcore/nets/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 0000000..12c3e38 Binary files /dev/null and b/deepcore/nets/__pycache__/__init__.cpython-39.pyc differ diff --git a/deepcore/nets/__pycache__/alexnet.cpython-39.pyc b/deepcore/nets/__pycache__/alexnet.cpython-39.pyc new file mode 100644 index 0000000..52401d9 Binary files /dev/null and b/deepcore/nets/__pycache__/alexnet.cpython-39.pyc differ diff --git a/deepcore/nets/__pycache__/inceptionv3.cpython-39.pyc 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Acknowledgement to +# https://github.com/kuangliu/pytorch-cifar, +# https://github.com/BIGBALLON/CIFAR-ZOO, + +class AlexNet_32x32(nn.Module): + def __init__(self, channel, num_classes, record_embedding=False, no_grad=False): + super().__init__() + self.features = nn.Sequential( + nn.Conv2d(channel, 128, kernel_size=5, stride=1, padding=4 if channel == 1 else 2), + nn.ReLU(inplace=True), + nn.MaxPool2d(kernel_size=2, stride=2), + nn.Conv2d(128, 192, kernel_size=5, padding=2), + nn.ReLU(inplace=True), + nn.MaxPool2d(kernel_size=2, stride=2), + nn.Conv2d(192, 256, kernel_size=3, padding=1), + nn.ReLU(inplace=True), + nn.Conv2d(256, 192, kernel_size=3, padding=1), + nn.ReLU(inplace=True), + nn.Conv2d(192, 192, kernel_size=3, padding=1), + nn.ReLU(inplace=True), + nn.MaxPool2d(kernel_size=2, stride=2), + ) + self.fc = nn.Linear(192 * 4 * 4, num_classes) + + self.embedding_recorder = EmbeddingRecorder(record_embedding) + self.no_grad = no_grad + + def get_last_layer(self): + return self.fc + + def forward(self, x): + with set_grad_enabled(not self.no_grad): + x = self.features(x) + x = x.view(x.size(0), -1) + x = self.embedding_recorder(x) + x = self.fc(x) + return x + + +class AlexNet_224x224(models.AlexNet): + def __init__(self, channel: int, num_classes: int, record_embedding: bool = False, + no_grad: bool = False, **kwargs): + super().__init__(num_classes, **kwargs) + self.embedding_recorder = EmbeddingRecorder(record_embedding) + if channel != 3: + self.features[0] = nn.Conv2d(channel, 64, kernel_size=11, stride=4, padding=2) + self.fc = self.classifier[-1] + self.classifier[-1] = self.embedding_recorder + self.classifier.add_module("fc", self.fc) + + self.no_grad = no_grad + + def get_last_layer(self): + return self.fc + + def forward(self, x: torch.Tensor) -> torch.Tensor: + with set_grad_enabled(not self.no_grad): + x = self.features(x) + x = self.avgpool(x) + x = torch.flatten(x, 1) + x = self.classifier(x) + return x + + +def AlexNet(channel: int, num_classes: int, im_size, record_embedding: bool = False, no_grad: bool = False, + pretrained: bool = False): + if pretrained: + if im_size[0] != 224 or im_size[1] != 224: + raise NotImplementedError("torchvison pretrained models only accept inputs with size of 224*224") + net = AlexNet_224x224(channel=3, num_classes=1000, record_embedding=record_embedding, no_grad=no_grad) + + from torch.hub import load_state_dict_from_url + state_dict = load_state_dict_from_url('https://download.pytorch.org/models/alexnet-owt-7be5be79.pth' + , progress=True) + net.load_state_dict(state_dict) + + if channel != 3: + net.features[0] = nn.Conv2d(channel, 64, kernel_size=11, stride=4, padding=2) + if num_classes != 1000: + net.fc = nn.Linear(4096, num_classes) + net.classifier[-1] = net.fc + + elif im_size[0] == 224 and im_size[1] == 224: + net = AlexNet_224x224(channel=channel, num_classes=num_classes, record_embedding=record_embedding, + no_grad=no_grad) + + elif (channel == 1 and im_size[0] == 28 and im_size[1] == 28) or ( + channel == 3 and im_size[0] == 32 and im_size[1] == 32): + net = AlexNet_32x32(channel=channel, num_classes=num_classes, record_embedding=record_embedding, + no_grad=no_grad) + else: + raise NotImplementedError("Network Architecture for current dataset has not been implemented.") + return net diff --git a/deepcore/nets/inceptionv3.py b/deepcore/nets/inceptionv3.py new file mode 100644 index 0000000..4d7bd94 --- /dev/null +++ b/deepcore/nets/inceptionv3.py @@ -0,0 +1,426 @@ +import torch +import torch.nn as nn +from torchvision.models import inception +from .nets_utils import EmbeddingRecorder + + +class BasicConv2d(nn.Module): + + def __init__(self, input_channels, output_channels, **kwargs): + super().__init__() + self.conv = nn.Conv2d(input_channels, output_channels, bias=False, **kwargs) + self.bn = nn.BatchNorm2d(output_channels) + self.relu = nn.ReLU(inplace=True) + + def forward(self, x): + x = self.conv(x) + x = self.bn(x) + x = self.relu(x) + + return x + + +# same naive inception module +class InceptionA(nn.Module): + + def __init__(self, input_channels, pool_features): + super().__init__() + self.branch1x1 = BasicConv2d(input_channels, 64, kernel_size=1) + + self.branch5x5 = nn.Sequential( + BasicConv2d(input_channels, 48, kernel_size=1), + BasicConv2d(48, 64, kernel_size=5, padding=2) + ) + + self.branch3x3 = nn.Sequential( + BasicConv2d(input_channels, 64, kernel_size=1), + BasicConv2d(64, 96, kernel_size=3, padding=1), + BasicConv2d(96, 96, kernel_size=3, padding=1) + ) + + self.branchpool = nn.Sequential( + nn.AvgPool2d(kernel_size=3, stride=1, padding=1), + BasicConv2d(input_channels, pool_features, kernel_size=3, padding=1) + ) + + def forward(self, x): + # x -> 1x1(same) + branch1x1 = self.branch1x1(x) + + # x -> 1x1 -> 5x5(same) + branch5x5 = self.branch5x5(x) + # branch5x5 = self.branch5x5_2(branch5x5) + + # x -> 1x1 -> 3x3 -> 3x3(same) + branch3x3 = self.branch3x3(x) + + # x -> pool -> 1x1(same) + branchpool = self.branchpool(x) + + outputs = [branch1x1, branch5x5, branch3x3, branchpool] + + return torch.cat(outputs, 1) + + +# downsample +# Factorization into smaller convolutions +class InceptionB(nn.Module): + + def __init__(self, input_channels): + super().__init__() + + self.branch3x3 = BasicConv2d(input_channels, 384, kernel_size=3, stride=2) + + self.branch3x3stack = nn.Sequential( + BasicConv2d(input_channels, 64, kernel_size=1), + BasicConv2d(64, 96, kernel_size=3, padding=1), + BasicConv2d(96, 96, kernel_size=3, stride=2) + ) + + self.branchpool = nn.MaxPool2d(kernel_size=3, stride=2) + + def forward(self, x): + # x - > 3x3(downsample) + branch3x3 = self.branch3x3(x) + + # x -> 3x3 -> 3x3(downsample) + branch3x3stack = self.branch3x3stack(x) + + # x -> avgpool(downsample) + branchpool = self.branchpool(x) + + # """We can use two parallel stride 2 blocks: P and C. P is a pooling + # layer (either average or maximum pooling) the activation, both of + # them are stride 2 the filter banks of which are concatenated as in + # figure 10.""" + outputs = [branch3x3, branch3x3stack, branchpool] + + return torch.cat(outputs, 1) + + +# Factorizing Convolutions with Large Filter Size +class InceptionC(nn.Module): + def __init__(self, input_channels, channels_7x7): + super().__init__() + self.branch1x1 = BasicConv2d(input_channels, 192, kernel_size=1) + + c7 = channels_7x7 + + # In theory, we could go even further and argue that one can replace any n × n + # convolution by a 1 × n convolution followed by a n × 1 convolution and the + # computational cost saving increases dramatically as n grows (see figure 6). + self.branch7x7 = nn.Sequential( + BasicConv2d(input_channels, c7, kernel_size=1), + BasicConv2d(c7, c7, kernel_size=(7, 1), padding=(3, 0)), + BasicConv2d(c7, 192, kernel_size=(1, 7), padding=(0, 3)) + ) + + self.branch7x7stack = nn.Sequential( + BasicConv2d(input_channels, c7, kernel_size=1), + BasicConv2d(c7, c7, kernel_size=(7, 1), padding=(3, 0)), + BasicConv2d(c7, c7, kernel_size=(1, 7), padding=(0, 3)), + BasicConv2d(c7, c7, kernel_size=(7, 1), padding=(3, 0)), + BasicConv2d(c7, 192, kernel_size=(1, 7), padding=(0, 3)) + ) + + self.branch_pool = nn.Sequential( + nn.AvgPool2d(kernel_size=3, stride=1, padding=1), + BasicConv2d(input_channels, 192, kernel_size=1), + ) + + def forward(self, x): + # x -> 1x1(same) + branch1x1 = self.branch1x1(x) + + # x -> 1layer 1*7 and 7*1 (same) + branch7x7 = self.branch7x7(x) + + # x-> 2layer 1*7 and 7*1(same) + branch7x7stack = self.branch7x7stack(x) + + # x-> avgpool (same) + branchpool = self.branch_pool(x) + + outputs = [branch1x1, branch7x7, branch7x7stack, branchpool] + + return torch.cat(outputs, 1) + + +class InceptionD(nn.Module): + + def __init__(self, input_channels): + super().__init__() + + self.branch3x3 = nn.Sequential( + BasicConv2d(input_channels, 192, kernel_size=1), + BasicConv2d(192, 320, kernel_size=3, stride=2) + ) + + self.branch7x7 = nn.Sequential( + BasicConv2d(input_channels, 192, kernel_size=1), + BasicConv2d(192, 192, kernel_size=(1, 7), padding=(0, 3)), + BasicConv2d(192, 192, kernel_size=(7, 1), padding=(3, 0)), + BasicConv2d(192, 192, kernel_size=3, stride=2) + ) + + self.branchpool = nn.AvgPool2d(kernel_size=3, stride=2) + + def forward(self, x): + # x -> 1x1 -> 3x3(downsample) + branch3x3 = self.branch3x3(x) + + # x -> 1x1 -> 1x7 -> 7x1 -> 3x3 (downsample) + branch7x7 = self.branch7x7(x) + + # x -> avgpool (downsample) + branchpool = self.branchpool(x) + + outputs = [branch3x3, branch7x7, branchpool] + + return torch.cat(outputs, 1) + + +# same +class InceptionE(nn.Module): + def __init__(self, input_channels): + super().__init__() + self.branch1x1 = BasicConv2d(input_channels, 320, kernel_size=1) + + self.branch3x3_1 = BasicConv2d(input_channels, 384, kernel_size=1) + self.branch3x3_2a = BasicConv2d(384, 384, kernel_size=(1, 3), padding=(0, 1)) + self.branch3x3_2b = BasicConv2d(384, 384, kernel_size=(3, 1), padding=(1, 0)) + + self.branch3x3stack_1 = BasicConv2d(input_channels, 448, kernel_size=1) + self.branch3x3stack_2 = BasicConv2d(448, 384, kernel_size=3, padding=1) + self.branch3x3stack_3a = BasicConv2d(384, 384, kernel_size=(1, 3), padding=(0, 1)) + self.branch3x3stack_3b = BasicConv2d(384, 384, kernel_size=(3, 1), padding=(1, 0)) + + self.branch_pool = nn.Sequential( + nn.AvgPool2d(kernel_size=3, stride=1, padding=1), + BasicConv2d(input_channels, 192, kernel_size=1) + ) + + def forward(self, x): + # x -> 1x1 (same) + branch1x1 = self.branch1x1(x) + + # x -> 1x1 -> 3x1 + # x -> 1x1 -> 1x3 + # concatenate(3x1, 1x3) + # """7. Inception modules with expanded the filter bank outputs. + # This architecture is used on the coarsest (8 × 8) grids to promote + # high dimensional representations, as suggested by principle + # 2 of Section 2.""" + branch3x3 = self.branch3x3_1(x) + branch3x3 = [ + self.branch3x3_2a(branch3x3), + self.branch3x3_2b(branch3x3) + ] + branch3x3 = torch.cat(branch3x3, 1) + + # x -> 1x1 -> 3x3 -> 1x3 + # x -> 1x1 -> 3x3 -> 3x1 + # concatenate(1x3, 3x1) + branch3x3stack = self.branch3x3stack_1(x) + branch3x3stack = self.branch3x3stack_2(branch3x3stack) + branch3x3stack = [ + self.branch3x3stack_3a(branch3x3stack), + self.branch3x3stack_3b(branch3x3stack) + ] + branch3x3stack = torch.cat(branch3x3stack, 1) + + branchpool = self.branch_pool(x) + + outputs = [branch1x1, branch3x3, branch3x3stack, branchpool] + + return torch.cat(outputs, 1) + + +class InceptionV3_32x32(nn.Module): + + def __init__(self, channel, num_classes, record_embedding=False, no_grad=False): + super().__init__() + self.Conv2d_1a_3x3 = BasicConv2d(channel, 32, kernel_size=3, padding=3 if channel == 1 else 1) + self.Conv2d_2a_3x3 = BasicConv2d(32, 32, kernel_size=3, padding=1) + self.Conv2d_2b_3x3 = BasicConv2d(32, 64, kernel_size=3, padding=1) + self.Conv2d_3b_1x1 = BasicConv2d(64, 80, kernel_size=1) + self.Conv2d_4a_3x3 = BasicConv2d(80, 192, kernel_size=3) + + # naive inception module + self.Mixed_5b = InceptionA(192, pool_features=32) + self.Mixed_5c = InceptionA(256, pool_features=64) + self.Mixed_5d = InceptionA(288, pool_features=64) + + # downsample + self.Mixed_6a = InceptionB(288) + + self.Mixed_6b = InceptionC(768, channels_7x7=128) + self.Mixed_6c = InceptionC(768, channels_7x7=160) + self.Mixed_6d = InceptionC(768, channels_7x7=160) + self.Mixed_6e = InceptionC(768, channels_7x7=192) + + # downsample + self.Mixed_7a = InceptionD(768) + + self.Mixed_7b = InceptionE(1280) + self.Mixed_7c = InceptionE(2048) + + # 6*6 feature size + self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) + self.dropout = nn.Dropout2d() + self.linear = nn.Linear(2048, num_classes) + + self.embedding_recorder = EmbeddingRecorder(record_embedding) + self.no_grad = no_grad + + def get_last_layer(self): + return self.linear + + def forward(self, x): + with torch.set_grad_enabled(not self.no_grad): + # 32 -> 30 + x = self.Conv2d_1a_3x3(x) + x = self.Conv2d_2a_3x3(x) + x = self.Conv2d_2b_3x3(x) + x = self.Conv2d_3b_1x1(x) + x = self.Conv2d_4a_3x3(x) + + # 30 -> 30 + x = self.Mixed_5b(x) + x = self.Mixed_5c(x) + x = self.Mixed_5d(x) + + # 30 -> 14 + # Efficient Grid Size Reduction to avoid representation + # bottleneck + x = self.Mixed_6a(x) + + # 14 -> 14 + # """In practice, we have found that employing this factorization does not + # work well on early layers, but it gives very good results on medium + # grid-sizes (On m × m feature maps, where m ranges between 12 and 20). + # On that level, very good results can be achieved by using 1 × 7 convolutions + # followed by 7 × 1 convolutions.""" + x = self.Mixed_6b(x) + x = self.Mixed_6c(x) + x = self.Mixed_6d(x) + x = self.Mixed_6e(x) + + # 14 -> 6 + # Efficient Grid Size Reduction + x = self.Mixed_7a(x) + + # 6 -> 6 + # We are using this solution only on the coarsest grid, + # since that is the place where producing high dimensional + # sparse representation is the most critical as the ratio of + # local processing (by 1 × 1 convolutions) is increased compared + # to the spatial aggregation.""" + x = self.Mixed_7b(x) + x = self.Mixed_7c(x) + + # 6 -> 1 + x = self.avgpool(x) + x = self.dropout(x) + x = x.view(x.size(0), -1) + x = self.embedding_recorder(x) + x = self.linear(x) + return x + + +class InceptionV3_224x224(inception.Inception3): + def __init__(self, channel: int, num_classes: int, record_embedding: bool = False, + no_grad: bool = False, **kwargs): + super().__init__(num_classes=num_classes, **kwargs) + self.embedding_recorder = EmbeddingRecorder(record_embedding) + if channel != 3: + self.Conv2d_1a_3x3 = inception.conv_block(channel, 32, kernel_size=3, stride=2) + self.no_grad = no_grad + + def get_last_layer(self): + return self.fc + + def _forward(self, x): + with torch.set_grad_enabled(not self.no_grad): + # N x 3 x 299 x 299 + x = self.Conv2d_1a_3x3(x) + # N x 32 x 149 x 149 + x = self.Conv2d_2a_3x3(x) + # N x 32 x 147 x 147 + x = self.Conv2d_2b_3x3(x) + # N x 64 x 147 x 147 + x = self.maxpool1(x) + # N x 64 x 73 x 73 + x = self.Conv2d_3b_1x1(x) + # N x 80 x 73 x 73 + x = self.Conv2d_4a_3x3(x) + # N x 192 x 71 x 71 + x = self.maxpool2(x) + # N x 192 x 35 x 35 + x = self.Mixed_5b(x) + # N x 256 x 35 x 35 + x = self.Mixed_5c(x) + # N x 288 x 35 x 35 + x = self.Mixed_5d(x) + # N x 288 x 35 x 35 + x = self.Mixed_6a(x) + # N x 768 x 17 x 17 + x = self.Mixed_6b(x) + # N x 768 x 17 x 17 + x = self.Mixed_6c(x) + # N x 768 x 17 x 17 + x = self.Mixed_6d(x) + # N x 768 x 17 x 17 + x = self.Mixed_6e(x) + # N x 768 x 17 x 17 + aux = None + if self.AuxLogits is not None: + if self.training: + aux = self.AuxLogits(x) + # N x 768 x 17 x 17 + x = self.Mixed_7a(x) + # N x 1280 x 8 x 8 + x = self.Mixed_7b(x) + # N x 2048 x 8 x 8 + x = self.Mixed_7c(x) + # N x 2048 x 8 x 8 + # Adaptive average pooling + x = self.avgpool(x) + # N x 2048 x 1 x 1 + x = self.dropout(x) + # N x 2048 x 1 x 1 + x = torch.flatten(x, 1) + # N x 2048 + x = self.embedding_recorder(x) + x = self.fc(x) + # N x 1000 (num_classes) + return x, aux + + +def InceptionV3(channel: int, num_classes: int, im_size, record_embedding: bool = False, no_grad: bool = False, + pretrained: bool = False): + if pretrained: + if im_size[0] != 224 or im_size[1] != 224: + raise NotImplementedError("torchvison pretrained models only accept inputs with size of 224*224") + net = InceptionV3_224x224(channel=3, num_classes=1000, record_embedding=record_embedding, no_grad=no_grad) + + from torch.hub import load_state_dict_from_url + state_dict = load_state_dict_from_url(inception.model_urls["inception_v3_google"], progress=True) + net.load_state_dict(state_dict) + + if channel != 3: + net.Conv2d_1a_3x3 = inception.conv_block(channel, 32, kernel_size=3, stride=2) + if num_classes != 1000: + net.fc = nn.Linear(net.fc.in_features, num_classes) + + elif im_size[0] == 224 and im_size[1] == 224: + net = InceptionV3_224x224(channel=channel, num_classes=num_classes, record_embedding=record_embedding, + no_grad=no_grad) + elif (channel == 1 and im_size[0] == 28 and im_size[1] == 28) or ( + channel == 3 and im_size[0] == 32 and im_size[1] == 32): + net = InceptionV3_32x32(channel=channel, num_classes=num_classes, record_embedding=record_embedding, + no_grad=no_grad) + else: + raise NotImplementedError("Network Architecture for current dataset has not been implemented.") + + return net diff --git a/deepcore/nets/lenet.py b/deepcore/nets/lenet.py new file mode 100644 index 0000000..682df5a --- /dev/null +++ b/deepcore/nets/lenet.py @@ -0,0 +1,43 @@ +import torch.nn as nn +import torch.nn.functional as F +from torch import set_grad_enabled +from .nets_utils import EmbeddingRecorder + + +# Acknowledgement to +# https://github.com/kuangliu/pytorch-cifar, +# https://github.com/BIGBALLON/CIFAR-ZOO, + +class LeNet(nn.Module): + def __init__(self, channel, num_classes, im_size, record_embedding: bool = False, no_grad: bool = False, + pretrained: bool = False): + if pretrained: + raise NotImplementedError("torchvison pretrained models not available.") + super(LeNet, self).__init__() + self.features = nn.Sequential( + nn.Conv2d(channel, 6, kernel_size=5, padding=2 if channel == 1 else 0), + nn.ReLU(inplace=True), + nn.MaxPool2d(kernel_size=2, stride=2), + nn.Conv2d(6, 16, kernel_size=5), + nn.ReLU(inplace=True), + nn.MaxPool2d(kernel_size=2, stride=2), + ) + self.fc_1 = nn.Linear(16 * 53 * 53 if im_size[0] == im_size[1] == 224 else 16 * 5 * 5, 120) + self.fc_2 = nn.Linear(120, 84) + self.fc_3 = nn.Linear(84, num_classes) + + self.embedding_recorder = EmbeddingRecorder(record_embedding) + self.no_grad = no_grad + + def get_last_layer(self): + return self.fc_3 + + def forward(self, x): + with set_grad_enabled(not self.no_grad): + x = self.features(x) + x = x.view(x.size(0), -1) + x = F.relu(self.fc_1(x)) + x = F.relu(self.fc_2(x)) + x = self.embedding_recorder(x) + x = self.fc_3(x) + return x diff --git a/deepcore/nets/mlp.py b/deepcore/nets/mlp.py new file mode 100644 index 0000000..ac2f19b --- /dev/null +++ b/deepcore/nets/mlp.py @@ -0,0 +1,37 @@ +import torch.nn as nn +import torch.nn.functional as F +from torch import set_grad_enabled +from .nets_utils import EmbeddingRecorder + +# Acknowledgement to +# https://github.com/kuangliu/pytorch-cifar, +# https://github.com/BIGBALLON/CIFAR-ZOO, + + +''' MLP ''' + + +class MLP(nn.Module): + def __init__(self, channel, num_classes, im_size, record_embedding: bool = False, no_grad: bool = False, + pretrained: bool = False): + if pretrained: + raise NotImplementedError("torchvison pretrained models not available.") + super(MLP, self).__init__() + self.fc_1 = nn.Linear(im_size[0] * im_size[1] * channel, 128) + self.fc_2 = nn.Linear(128, 128) + self.fc_3 = nn.Linear(128, num_classes) + + self.embedding_recorder = EmbeddingRecorder(record_embedding) + self.no_grad = no_grad + + def get_last_layer(self): + return self.fc_3 + + def forward(self, x): + with set_grad_enabled(not self.no_grad): + out = x.view(x.size(0), -1) + out = F.relu(self.fc_1(out)) + out = F.relu(self.fc_2(out)) + out = self.embedding_recorder(out) + out = self.fc_3(out) + return out diff --git a/deepcore/nets/mobilenetv3.py b/deepcore/nets/mobilenetv3.py new file mode 100644 index 0000000..9a2452b --- /dev/null +++ b/deepcore/nets/mobilenetv3.py @@ -0,0 +1,304 @@ +import torch.nn as nn +from torch import set_grad_enabled, flatten, Tensor +from torchvision.models import mobilenetv3 +from .nets_utils import EmbeddingRecorder +import math + +'''MobileNetV3 in PyTorch. +Paper： "Inverted Residuals and Linear Bottlenecks:Mobile Networks for Classification, Detection and Segmentation" + +Acknowlegement to: +https://github.com/d-li14/mobilenetv3.pytorch/blob/master/mobilenetv3.py +''' + + +def _make_divisible(v, divisor, min_value=None): + """ + This function is taken from the original tf repo. + It ensures that all layers have a channel number that is divisible by 8 + It can be seen here: + https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py + """ + if min_value is None: + min_value = divisor + new_v = max(min_value, int(v + divisor / 2) // divisor * divisor) + # Make sure that round down does not go down by more than 10%. + if new_v < 0.9 * v: + new_v += divisor + return new_v + + +class h_sigmoid(nn.Module): + def __init__(self, inplace=True): + super(h_sigmoid, self).__init__() + self.relu = nn.ReLU6(inplace=inplace) + + def forward(self, x): + return self.relu(x + 3) / 6 + + +class h_swish(nn.Module): + def __init__(self, inplace=True): + super(h_swish, self).__init__() + self.sigmoid = h_sigmoid(inplace=inplace) + + def forward(self, x): + return x * self.sigmoid(x) + + +class SELayer(nn.Module): + def __init__(self, channel, reduction=4): + super(SELayer, self).__init__() + self.avg_pool = nn.AdaptiveAvgPool2d(1) + self.fc = nn.Sequential( + nn.Linear(channel, _make_divisible(channel // reduction, 8)), + nn.ReLU(inplace=True), + nn.Linear(_make_divisible(channel // reduction, 8), channel), + h_sigmoid() + ) + + def forward(self, x): + b, c, _, _ = x.size() + y = self.avg_pool(x).view(b, c) + y = self.fc(y).view(b, c, 1, 1) + return x * y + + +def conv_3x3_bn(inp, oup, stride, padding=1): + return nn.Sequential( + nn.Conv2d(inp, oup, 3, stride, padding, bias=False), + nn.BatchNorm2d(oup), + h_swish() + ) + + +def conv_1x1_bn(inp, oup): + return nn.Sequential( + nn.Conv2d(inp, oup, 1, 1, 0, bias=False), + nn.BatchNorm2d(oup), + h_swish() + ) + + +class InvertedResidual(nn.Module): + def __init__(self, inp, hidden_dim, oup, kernel_size, stride, use_se, use_hs): + super(InvertedResidual, self).__init__() + assert stride in [1, 2] + + self.identity = stride == 1 and inp == oup + + if inp == hidden_dim: + self.conv = nn.Sequential( + # dw + nn.Conv2d(hidden_dim, hidden_dim, kernel_size, stride, (kernel_size - 1) // 2, groups=hidden_dim, + bias=False), + nn.BatchNorm2d(hidden_dim), + h_swish() if use_hs else nn.ReLU(inplace=True), + # Squeeze-and-Excite + SELayer(hidden_dim) if use_se else nn.Identity(), + # pw-linear + nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False), + nn.BatchNorm2d(oup), + ) + else: + self.conv = nn.Sequential( + # pw + nn.Conv2d(inp, hidden_dim, 1, 1, 0, bias=False), + nn.BatchNorm2d(hidden_dim), + h_swish() if use_hs else nn.ReLU(inplace=True), + # dw + nn.Conv2d(hidden_dim, hidden_dim, kernel_size, stride, (kernel_size - 1) // 2, groups=hidden_dim, + bias=False), + nn.BatchNorm2d(hidden_dim), + # Squeeze-and-Excite + SELayer(hidden_dim) if use_se else nn.Identity(), + h_swish() if use_hs else nn.ReLU(inplace=True), + # pw-linear + nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False), + nn.BatchNorm2d(oup), + ) + + def forward(self, x): + if self.identity: + return x + self.conv(x) + else: + return self.conv(x) + + +class MobileNetV3_32x32(nn.Module): + def __init__(self, cfgs, mode, channel=3, num_classes=1000, record_embedding=False, + no_grad=False, width_mult=1.): + super(MobileNetV3_32x32, self).__init__() + # setting of inverted residual blocks + self.cfgs = cfgs + assert mode in ['mobilenet_v3_large', 'mobilenet_v3_small'] + + self.embedding_recorder = EmbeddingRecorder(record_embedding) + self.no_grad = no_grad + + # building first layer + input_channel = _make_divisible(16 * width_mult, 8) + layers = [conv_3x3_bn(channel, input_channel, 2, padding=3 if channel == 1 else 1)] + # building inverted residual blocks + block = InvertedResidual + for k, t, c, use_se, use_hs, s in self.cfgs: + output_channel = _make_divisible(c * width_mult, 8) + exp_size = _make_divisible(input_channel * t, 8) + layers.append(block(input_channel, exp_size, output_channel, k, s, use_se, use_hs)) + input_channel = output_channel + self.features = nn.Sequential(*layers) + # building last several layers + self.conv = conv_1x1_bn(input_channel, exp_size) + self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) + output_channel = {'mobilenet_v3_large': 1280, 'mobilenet_v3_small': 1024} + output_channel = _make_divisible(output_channel[mode] * width_mult, 8) if width_mult > 1.0 else output_channel[ + mode] + self.classifier = nn.Sequential( + nn.Linear(exp_size, output_channel), + h_swish(), + nn.Dropout(0.2), + self.embedding_recorder, + nn.Linear(output_channel, num_classes), + ) + + self._initialize_weights() + + def forward(self, x): + with set_grad_enabled(not self.no_grad): + x = self.features(x) + x = self.conv(x) + x = self.avgpool(x) + x = x.view(x.size(0), -1) + x = self.classifier(x) + return x + + def _initialize_weights(self): + for m in self.modules(): + if isinstance(m, nn.Conv2d): + n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels + m.weight.data.normal_(0, math.sqrt(2. / n)) + if m.bias is not None: + m.bias.data.zero_() + elif isinstance(m, nn.BatchNorm2d): + m.weight.data.fill_(1) + m.bias.data.zero_() + elif isinstance(m, nn.Linear): + m.weight.data.normal_(0, 0.01) + m.bias.data.zero_() + + def get_last_layer(self): + return self.classifier[-1] + + +class MobileNetV3_224x224(mobilenetv3.MobileNetV3): + def __init__(self, inverted_residual_setting, last_channel, + channel=3, num_classes=1000, record_embedding=False, no_grad=False, **kwargs): + super(MobileNetV3_224x224, self).__init__(inverted_residual_setting, last_channel, + num_classes=num_classes, **kwargs) + + self.embedding_recorder = EmbeddingRecorder(record_embedding) + + self.fc = self.classifier[-1] + self.classifier[-1] = self.embedding_recorder + self.classifier.add_module("fc", self.fc) + + self.no_grad = no_grad + + def get_last_layer(self): + return self.fc + + def _forward_impl(self, x: Tensor) -> Tensor: + with set_grad_enabled(not self.no_grad): + x = self.features(x) + x = self.avgpool(x) + x = flatten(x, 1) + x = self.classifier(x) + return x + + +def MobileNetV3(arch: str, channel: int, num_classes: int, im_size, record_embedding: bool = False, + no_grad: bool = False, + pretrained: bool = False, **kwargs): + arch = arch.lower() + if pretrained: + if channel != 3: + raise NotImplementedError("Network Architecture for current dataset has not been implemented.") + + inverted_residual_setting, last_channel = mobilenetv3._mobilenet_v3_conf(arch) + net = MobileNetV3_224x224(inverted_residual_setting=inverted_residual_setting, last_channel=last_channel, + channel=3, num_classes=1000, record_embedding=record_embedding, no_grad=no_grad, + **kwargs) + + from torch.hub import load_state_dict_from_url + state_dict = load_state_dict_from_url(mobilenetv3.model_urls[arch], progress=True) + net.load_state_dict(state_dict) + + if num_classes != 1000: + net.fc = nn.Linear(last_channel, num_classes) + net.classifier[-1] = net.fc + + elif im_size[0] == 224 and im_size[1] == 224: + if channel != 3: + raise NotImplementedError("Network Architecture for current dataset has not been implemented.") + inverted_residual_setting, last_channel = mobilenetv3._mobilenet_v3_conf(arch) + net = MobileNetV3_224x224(inverted_residual_setting=inverted_residual_setting, last_channel=last_channel, + channel=channel, num_classes=num_classes, record_embedding=record_embedding, + no_grad=no_grad, **kwargs) + + elif (channel == 1 and im_size[0] == 28 and im_size[1] == 28) or ( + channel == 3 and im_size[0] == 32 and im_size[1] == 32): + if arch == "mobilenet_v3_large": + cfgs = [ + # k, t, c, SE, HS, s + [3, 1, 16, 0, 0, 1], + [3, 4, 24, 0, 0, 2], + [3, 3, 24, 0, 0, 1], + [5, 3, 40, 1, 0, 2], + [5, 3, 40, 1, 0, 1], + [5, 3, 40, 1, 0, 1], + [3, 6, 80, 0, 1, 2], + [3, 2.5, 80, 0, 1, 1], + [3, 2.3, 80, 0, 1, 1], + [3, 2.3, 80, 0, 1, 1], + [3, 6, 112, 1, 1, 1], + [3, 6, 112, 1, 1, 1], + [5, 6, 160, 1, 1, 2], + [5, 6, 160, 1, 1, 1], + [5, 6, 160, 1, 1, 1] + ] + net = MobileNetV3_32x32(cfgs, arch, channel=channel, num_classes=num_classes, + record_embedding=record_embedding, no_grad=no_grad) + elif arch == "mobilenet_v3_small": + cfgs = [ + # k, t, c, SE, HS, s + [3, 1, 16, 1, 0, 2], + [3, 4.5, 24, 0, 0, 2], + [3, 3.67, 24, 0, 0, 1], + [5, 4, 40, 1, 1, 2], + [5, 6, 40, 1, 1, 1], + [5, 6, 40, 1, 1, 1], + [5, 3, 48, 1, 1, 1], + [5, 3, 48, 1, 1, 1], + [5, 6, 96, 1, 1, 2], + [5, 6, 96, 1, 1, 1], + [5, 6, 96, 1, 1, 1], + ] + net = MobileNetV3_32x32(cfgs, arch, channel=channel, num_classes=num_classes, + record_embedding=record_embedding, no_grad=no_grad) + else: + raise ValueError("Model architecture not found.") + else: + raise NotImplementedError("Network Architecture for current dataset has not been implemented.") + return net + + +def MobileNetV3Large(channel: int, num_classes: int, im_size, record_embedding: bool = False, no_grad: bool = False, + pretrained: bool = False, **kwargs): + return MobileNetV3("mobilenet_v3_large", channel, num_classes, im_size, record_embedding, no_grad, + pretrained, **kwargs) + + +def MobileNetV3Small(channel: int, num_classes: int, im_size, record_embedding: bool = False, no_grad: bool = False, + pretrained: bool = False, **kwargs): + return MobileNetV3("mobilenet_v3_small", channel, num_classes, im_size, record_embedding, no_grad, + pretrained, **kwargs) diff --git a/deepcore/nets/nets_utils/__init__.py b/deepcore/nets/nets_utils/__init__.py new file mode 100644 index 0000000..100eb9d --- /dev/null +++ b/deepcore/nets/nets_utils/__init__.py @@ -0,0 +1,2 @@ +from .parallel import * +from .recorder import * \ No newline at end of file diff --git a/deepcore/nets/nets_utils/__pycache__/__init__.cpython-39.pyc b/deepcore/nets/nets_utils/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 0000000..4226948 Binary files /dev/null and b/deepcore/nets/nets_utils/__pycache__/__init__.cpython-39.pyc differ diff --git a/deepcore/nets/nets_utils/__pycache__/parallel.cpython-39.pyc b/deepcore/nets/nets_utils/__pycache__/parallel.cpython-39.pyc new file mode 100644 index 0000000..ff37f2b Binary files /dev/null and b/deepcore/nets/nets_utils/__pycache__/parallel.cpython-39.pyc differ diff --git a/deepcore/nets/nets_utils/__pycache__/recorder.cpython-39.pyc b/deepcore/nets/nets_utils/__pycache__/recorder.cpython-39.pyc new file mode 100644 index 0000000..fb8babb Binary files /dev/null and b/deepcore/nets/nets_utils/__pycache__/recorder.cpython-39.pyc differ diff --git a/deepcore/nets/nets_utils/parallel.py b/deepcore/nets/nets_utils/parallel.py new file mode 100644 index 0000000..88e0e37 --- /dev/null +++ b/deepcore/nets/nets_utils/parallel.py @@ -0,0 +1,16 @@ +from torch.nn import DataParallel + + +class MyDataParallel(DataParallel): + def __getattr__(self, name): + try: + return super().__getattr__(name) + except AttributeError: + return getattr(self.module, name) + def __setattr__(self, name, value): + try: + if name == "no_grad": + return setattr(self.module, name, value) + return super().__setattr__(name, value) + except AttributeError: + return setattr(self.module, name, value) diff --git a/deepcore/nets/nets_utils/recorder.py b/deepcore/nets/nets_utils/recorder.py new file mode 100644 index 0000000..988b8ac --- /dev/null +++ b/deepcore/nets/nets_utils/recorder.py @@ -0,0 +1,18 @@ +from torch import nn + + +class EmbeddingRecorder(nn.Module): + def __init__(self, record_embedding: bool = False): + super().__init__() + self.record_embedding = record_embedding + + def forward(self, x): + if self.record_embedding: + self.embedding = x + return x + + def __enter__(self): + self.record_embedding = True + + def __exit__(self, exc_type, exc_val, exc_tb): + self.record_embedding = False \ No newline at end of file diff --git a/deepcore/nets/resnet.py b/deepcore/nets/resnet.py new file mode 100644 index 0000000..bcf762c --- /dev/null +++ b/deepcore/nets/resnet.py @@ -0,0 +1,241 @@ +import torch.nn as nn +import torch.nn.functional as F +from torch import set_grad_enabled, flatten, Tensor +from .nets_utils import EmbeddingRecorder +from torchvision.models import resnet + + +# Acknowledgement to +# https://github.com/kuangliu/pytorch-cifar, +# https://github.com/BIGBALLON/CIFAR-ZOO, + + +def conv3x3(in_planes, out_planes, stride=1): + return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False) + + +class BasicBlock(nn.Module): + expansion = 1 + + def __init__(self, in_planes, planes, stride=1): + super(BasicBlock, self).__init__() + self.conv1 = conv3x3(in_planes, planes, stride) + self.bn1 = nn.BatchNorm2d(planes) + self.conv2 = conv3x3(planes, planes) + self.bn2 = nn.BatchNorm2d(planes) + + self.shortcut = nn.Sequential() + if stride != 1 or in_planes != self.expansion * planes: + self.shortcut = nn.Sequential( + nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(self.expansion * planes) + ) + + def forward(self, x): + out = F.relu(self.bn1(self.conv1(x))) + out = self.bn2(self.conv2(out)) + out += self.shortcut(x) + out = F.relu(out) + return out + + +class Bottleneck(nn.Module): + expansion = 4 + + def __init__(self, in_planes, planes, stride=1): + super(Bottleneck, self).__init__() + self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False) + self.bn1 = nn.BatchNorm2d(planes) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) + self.conv3 = nn.Conv2d(planes, self.expansion * planes, kernel_size=1, bias=False) + self.bn3 = nn.BatchNorm2d(self.expansion * planes) + + self.shortcut = nn.Sequential() + if stride != 1 or in_planes != self.expansion * planes: + self.shortcut = nn.Sequential( + nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(self.expansion * planes) + ) + + def forward(self, x): + out = F.relu(self.bn1(self.conv1(x))) + out = F.relu(self.bn2(self.conv2(out))) + out = self.bn3(self.conv3(out)) + out += self.shortcut(x) + out = F.relu(out) + return out + + +class ResNet_32x32(nn.Module): + def __init__(self, block, num_blocks, channel=3, num_classes=10, record_embedding: bool = False, + no_grad: bool = False): + super().__init__() + self.in_planes = 64 + + self.conv1 = conv3x3(channel, 64) + self.bn1 = nn.BatchNorm2d(64) + self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1) + self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2) + self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2) + self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2) + self.linear = nn.Linear(512 * block.expansion, num_classes) + + self.embedding_recorder = EmbeddingRecorder(record_embedding) + self.no_grad = no_grad + + def get_last_layer(self): + return self.linear + + def _make_layer(self, block, planes, num_blocks, stride): + strides = [stride] + [1] * (num_blocks - 1) + layers = [] + for stride in strides: + layers.append(block(self.in_planes, planes, stride)) + self.in_planes = planes * block.expansion + return nn.Sequential(*layers) + + def forward(self, x): + with set_grad_enabled(not self.no_grad): + out = F.relu(self.bn1(self.conv1(x))) + out = self.layer1(out) + out = self.layer2(out) + out = self.layer3(out) + out = self.layer4(out) + out = F.avg_pool2d(out, 4) + out = out.view(out.size(0), -1) + out = self.embedding_recorder(out) + out = self.linear(out) + return out + + +class ResNet_224x224(resnet.ResNet): + def __init__(self, block, layers, channel: int, num_classes: int, record_embedding: bool = False, + no_grad: bool = False, **kwargs): + super().__init__(block, layers, **kwargs) + self.embedding_recorder = EmbeddingRecorder(record_embedding) + if channel != 3: + self.conv1 = nn.Conv2d(channel, 64, kernel_size=7, stride=2, padding=3, bias=False) + if num_classes != 1000: + self.fc = nn.Linear(self.fc.in_features, num_classes) + self.no_grad = no_grad + + def get_last_layer(self): + return self.fc + + def _forward_impl(self, x: Tensor) -> Tensor: + # See note [TorchScript super()] + with set_grad_enabled(not self.no_grad): + x = self.conv1(x) + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) + + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + + x = self.avgpool(x) + x = flatten(x, 1) + x = self.embedding_recorder(x) + x = self.fc(x) + + return x + + +def ResNet(arch: str, channel: int, num_classes: int, im_size, record_embedding: bool = False, no_grad: bool = False, + pretrained: bool = False): + arch = arch.lower() + if pretrained: + if arch == "resnet18": + net = ResNet_224x224(resnet.BasicBlock, [2, 2, 2, 2], channel=3, num_classes=1000, + record_embedding=record_embedding, no_grad=no_grad) + elif arch == "resnet34": + net = ResNet_224x224(resnet.BasicBlock, [3, 4, 6, 3], channel=3, num_classes=1000, + record_embedding=record_embedding, no_grad=no_grad) + elif arch == "resnet50": + net = ResNet_224x224(resnet.Bottleneck, [3, 4, 6, 3], channel=3, num_classes=1000, + record_embedding=record_embedding, no_grad=no_grad) + elif arch == "resnet101": + net = ResNet_224x224(resnet.Bottleneck, [3, 4, 23, 3], channel=3, num_classes=1000, + record_embedding=record_embedding, no_grad=no_grad) + elif arch == "resnet152": + net = ResNet_224x224(resnet.Bottleneck, [3, 8, 36, 3], channel=3, num_classes=1000, + record_embedding=record_embedding, no_grad=no_grad) + else: + raise ValueError("Model architecture not found.") + from torch.hub import load_state_dict_from_url + state_dict = load_state_dict_from_url(resnet.model_urls[arch], progress=True) + net.load_state_dict(state_dict) + + if channel != 3: + net.conv1 = nn.Conv2d(channel, 64, kernel_size=7, stride=2, padding=3, bias=False) + if num_classes != 1000: + net.fc = nn.Linear(net.fc.in_features, num_classes) + + elif im_size[0] == 224 and im_size[1] == 224: + if arch == "resnet18": + net = ResNet_224x224(resnet.BasicBlock, [2, 2, 2, 2], channel=channel, num_classes=num_classes, + record_embedding=record_embedding, no_grad=no_grad) + elif arch == "resnet34": + net = ResNet_224x224(resnet.BasicBlock, [3, 4, 6, 3], channel=channel, num_classes=num_classes, + record_embedding=record_embedding, no_grad=no_grad) + elif arch == "resnet50": + net = ResNet_224x224(resnet.Bottleneck, [3, 4, 6, 3], channel=channel, num_classes=num_classes, + record_embedding=record_embedding, no_grad=no_grad) + elif arch == "resnet101": + net = ResNet_224x224(resnet.Bottleneck, [3, 4, 23, 3], channel=channel, num_classes=num_classes, + record_embedding=record_embedding, no_grad=no_grad) + elif arch == "resnet152": + net = ResNet_224x224(resnet.Bottleneck, [3, 8, 36, 3], channel=channel, num_classes=num_classes, + record_embedding=record_embedding, no_grad=no_grad) + else: + raise ValueError("Model architecture not found.") + elif (channel == 1 and im_size[0] == 28 and im_size[1] == 28) or ( + channel == 3 and im_size[0] == 32 and im_size[1] == 32): + if arch == "resnet18": + net = ResNet_32x32(BasicBlock, [2, 2, 2, 2], channel=channel, num_classes=num_classes, + record_embedding=record_embedding, no_grad=no_grad) + elif arch == "resnet34": + net = ResNet_32x32(BasicBlock, [3, 4, 6, 3], channel=channel, num_classes=num_classes, + record_embedding=record_embedding, no_grad=no_grad) + elif arch == "resnet50": + net = ResNet_32x32(Bottleneck, [3, 4, 6, 3], channel=channel, num_classes=num_classes, + record_embedding=record_embedding, no_grad=no_grad) + elif arch == "resnet101": + net = ResNet_32x32(Bottleneck, [3, 4, 23, 3], channel=channel, num_classes=num_classes, + record_embedding=record_embedding, no_grad=no_grad) + elif arch == "resnet152": + net = ResNet_32x32(Bottleneck, [3, 8, 36, 3], channel=channel, num_classes=num_classes, + record_embedding=record_embedding, no_grad=no_grad) + else: + raise ValueError("Model architecture not found.") + else: + raise NotImplementedError("Network Architecture for current dataset has not been implemented.") + return net + + +def ResNet18(channel: int, num_classes: int, im_size, record_embedding: bool = False, no_grad: bool = False, + pretrained: bool = False): + return ResNet("resnet18", channel, num_classes, im_size, record_embedding, no_grad, pretrained) + + +def ResNet34(channel: int, num_classes: int, im_size, record_embedding: bool = False, no_grad: bool = False, + pretrained: bool = False): + return ResNet("resnet34", channel, num_classes, im_size, record_embedding, no_grad, pretrained) + + +def ResNet50(channel: int, num_classes: int, im_size, record_embedding: bool = False, no_grad: bool = False, + pretrained: bool = False): + return ResNet("resnet50", channel, num_classes, im_size, record_embedding, no_grad, pretrained) + + +def ResNet101(channel: int, num_classes: int, im_size, record_embedding: bool = False, no_grad: bool = False, + pretrained: bool = False): + return ResNet("resnet101", channel, num_classes, im_size, record_embedding, no_grad, pretrained) + + +def ResNet152(channel: int, num_classes: int, im_size, record_embedding: bool = False, no_grad: bool = False, + pretrained: bool = False): + return ResNet("resnet152", channel, num_classes, im_size, record_embedding, no_grad, pretrained) diff --git a/deepcore/nets/vgg.py b/deepcore/nets/vgg.py new file mode 100644 index 0000000..b6ec0e1 --- /dev/null +++ b/deepcore/nets/vgg.py @@ -0,0 +1,128 @@ +import torch.nn as nn +from torch import set_grad_enabled, flatten, Tensor +from .nets_utils import EmbeddingRecorder +from torchvision.models import vgg + +# Acknowledgement to +# https://github.com/kuangliu/pytorch-cifar, +# https://github.com/BIGBALLON/CIFAR-ZOO, + +cfg_vgg = { + 'vgg11': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'], + 'vgg13': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'], + 'vgg16': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'], + 'vgg19': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'], +} + + +class VGG_32x32(nn.Module): + def __init__(self, vgg_name, channel, num_classes, record_embedding=False, no_grad=False): + super(VGG_32x32, self).__init__() + self.channel = channel + self.features = self._make_layers(cfg_vgg[vgg_name]) + self.classifier = nn.Linear(512 if vgg_name != 'VGGS' else 128, num_classes) + + self.embedding_recorder = EmbeddingRecorder(record_embedding) + self.no_grad = no_grad + + def forward(self, x): + with set_grad_enabled(not self.no_grad): + x = self.features(x) + x = x.view(x.size(0), -1) + x = self.embedding_recorder(x) + x = self.classifier(x) + return x + + def get_last_layer(self): + return self.classifier + + def _make_layers(self, cfg): + layers = [] + in_channels = self.channel + for ic, x in enumerate(cfg): + if x == 'M': + layers += [nn.MaxPool2d(kernel_size=2, stride=2)] + else: + layers += [nn.Conv2d(in_channels, x, kernel_size=3, padding=3 if self.channel == 1 and ic == 0 else 1), + nn.BatchNorm2d(x), + nn.ReLU(inplace=True)] + in_channels = x + layers += [nn.AvgPool2d(kernel_size=1, stride=1)] + return nn.Sequential(*layers) + + +class VGG_224x224(vgg.VGG): + def __init__(self, features: nn.Module, channel: int, num_classes: int, record_embedding: bool = False, + no_grad: bool = False, **kwargs): + super(VGG_224x224, self).__init__(features, num_classes, **kwargs) + self.embedding_recorder = EmbeddingRecorder(record_embedding) + if channel != 3: + self.features[0] = nn.Conv2d(channel, 64, kernel_size=3, padding=1) + self.fc = self.classifier[-1] + self.classifier[-1] = self.embedding_recorder + self.classifier.add_module("fc", self.fc) + + self.no_grad = no_grad + + def get_last_layer(self): + return self.fc + + def forward(self, x: Tensor) -> Tensor: + with set_grad_enabled(not self.no_grad): + x = self.features(x) + x = self.avgpool(x) + x = flatten(x, 1) + x = self.classifier(x) + return x + + +def VGG(arch: str, channel: int, num_classes: int, im_size, record_embedding: bool = False, no_grad: bool = False, + pretrained: bool = False): + arch = arch.lower() + if pretrained: + if im_size[0] != 224 or im_size[1] != 224: + raise NotImplementedError("torchvison pretrained models only accept inputs with size of 224*224") + net = VGG_224x224(features=vgg.make_layers(cfg_vgg[arch], True), channel=3, num_classes=1000, + record_embedding=record_embedding, no_grad=no_grad) + + from torch.hub import load_state_dict_from_url + state_dict = load_state_dict_from_url(vgg.model_urls[arch], progress=True) + net.load_state_dict(state_dict) + + if channel != 3: + net.features[0] = nn.Conv2d(channel, 64, kernel_size=3, padding=1) + + if num_classes != 1000: + net.fc = nn.Linear(4096, num_classes) + net.classifier[-1] = net.fc + + elif im_size[0] == 224 and im_size[1] == 224: + net = VGG_224x224(features=vgg.make_layers(cfg_vgg[arch], True), channel=channel, num_classes=num_classes, + record_embedding=record_embedding, no_grad=no_grad) + + elif (channel == 1 and im_size[0] == 28 and im_size[1] == 28) or ( + channel == 3 and im_size[0] == 32 and im_size[1] == 32): + net = VGG_32x32(arch, channel, num_classes=num_classes, record_embedding=record_embedding, no_grad=no_grad) + else: + raise NotImplementedError("Network Architecture for current dataset has not been implemented.") + return net + + +def VGG11(channel: int, num_classes: int, im_size, record_embedding: bool = False, no_grad: bool = False, + pretrained: bool = False): + return VGG("vgg11", channel, num_classes, im_size, record_embedding, no_grad, pretrained) + + +def VGG13(channel: int, num_classes: int, im_size, record_embedding: bool = False, no_grad: bool = False, + pretrained: bool = False): + return VGG('vgg13', channel, num_classes, im_size, record_embedding, no_grad, pretrained) + + +def VGG16(channel: int, num_classes: int, im_size, record_embedding: bool = False, no_grad: bool = False, + pretrained: bool = False): + return VGG('vgg16', channel, num_classes, im_size, record_embedding, no_grad, pretrained) + + +def VGG19(channel: int, num_classes: int, im_size, record_embedding: bool = False, no_grad: bool = False, + pretrained: bool = False): + return VGG('vgg19', channel, num_classes, im_size, record_embedding, no_grad, pretrained) diff --git a/deepcore/nets/wideresnet.py b/deepcore/nets/wideresnet.py new file mode 100644 index 0000000..4d50e37 --- /dev/null +++ b/deepcore/nets/wideresnet.py @@ -0,0 +1,181 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from .nets_utils import EmbeddingRecorder +from torchvision.models import resnet +from .resnet import ResNet_224x224 + + +# Acknowledgement to +# https://github.com/xternalz/WideResNet-pytorch + +class BasicBlock(nn.Module): + def __init__(self, in_planes, out_planes, stride, dropRate=0.0): + super(BasicBlock, self).__init__() + self.bn1 = nn.BatchNorm2d(in_planes) + self.relu1 = nn.ReLU(inplace=True) + self.conv1 = nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, + padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(out_planes) + self.relu2 = nn.ReLU(inplace=True) + self.conv2 = nn.Conv2d(out_planes, out_planes, kernel_size=3, stride=1, + padding=1, bias=False) + self.droprate = dropRate + self.equalInOut = (in_planes == out_planes) + self.convShortcut = (not self.equalInOut) and nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, + padding=0, bias=False) or None + + def forward(self, x): + if not self.equalInOut: + x = self.relu1(self.bn1(x)) + else: + out = self.relu1(self.bn1(x)) + out = self.relu2(self.bn2(self.conv1(out if self.equalInOut else x))) + if self.droprate > 0: + out = F.dropout(out, p=self.droprate, training=self.training) + out = self.conv2(out) + return torch.add(x if self.equalInOut else self.convShortcut(x), out) + + +class NetworkBlock(nn.Module): + def __init__(self, nb_layers, in_planes, out_planes, block, stride, dropRate=0.0): + super(NetworkBlock, self).__init__() + self.layer = self._make_layer(block, in_planes, out_planes, nb_layers, stride, dropRate) + + def _make_layer(self, block, in_planes, out_planes, nb_layers, stride, dropRate): + layers = [] + for i in range(int(nb_layers)): + layers.append(block(i == 0 and in_planes or out_planes, out_planes, i == 0 and stride or 1, dropRate)) + return nn.Sequential(*layers) + + def forward(self, x): + return self.layer(x) + + +class WideResNet_32x32(nn.Module): + def __init__(self, depth, num_classes, channel=3, widen_factor=1, drop_rate=0.0, record_embedding=False, + no_grad=False): + super(WideResNet_32x32, self).__init__() + nChannels = [16, 16 * widen_factor, 32 * widen_factor, 64 * widen_factor] + assert ((depth - 4) % 6 == 0) + n = (depth - 4) / 6 + block = BasicBlock + # 1st conv before any network block + self.conv1 = nn.Conv2d(channel, nChannels[0], kernel_size=3, stride=1, + padding=3 if channel == 1 else 1, bias=False) + # 1st block + self.block1 = NetworkBlock(n, nChannels[0], nChannels[1], block, 1, drop_rate) + # 2nd block + self.block2 = NetworkBlock(n, nChannels[1], nChannels[2], block, 2, drop_rate) + # 3rd block + self.block3 = NetworkBlock(n, nChannels[2], nChannels[3], block, 2, drop_rate) + # global average pooling and classifier + self.bn1 = nn.BatchNorm2d(nChannels[3]) + self.relu = nn.ReLU(inplace=True) + self.fc = nn.Linear(nChannels[3], num_classes) + self.nChannels = nChannels[3] + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + elif isinstance(m, nn.BatchNorm2d): + m.weight.data.fill_(1) + m.bias.data.zero_() + elif isinstance(m, nn.Linear): + m.bias.data.zero_() + + self.embedding_recorder = EmbeddingRecorder(record_embedding) + self.no_grad = no_grad + + def get_last_layer(self): + return self.fc + + def forward(self, x): + with torch.set_grad_enabled(not self.no_grad): + out = self.conv1(x) + out = self.block1(out) + out = self.block2(out) + out = self.block3(out) + out = self.relu(self.bn1(out)) + out = F.avg_pool2d(out, 8) + out = out.view(-1, self.nChannels) + out = self.embedding_recorder(out) + return self.fc(out) + + +def WideResNet(arch: str, channel: int, num_classes: int, im_size, record_embedding: bool = False, + no_grad: bool = False, pretrained: bool = False): + arch = arch.lower() + if pretrained: + if im_size[0] != 224 or im_size[1] != 224: + raise NotImplementedError("torchvison pretrained models only accept inputs with size of 224*224") + if arch == "wrn502": + arch = "wide_resnet50_2" + net = ResNet_224x224(resnet.Bottleneck, [3, 4, 6, 3], channel=3, num_classes=1000, + record_embedding=record_embedding, no_grad=no_grad, width_per_group=64 * 2) + elif arch == "wrn1012": + arch = "wide_resnet101_2" + net = ResNet_224x224(resnet.Bottleneck, [3, 4, 23, 3], channel=3, num_classes=1000, + record_embedding=record_embedding, no_grad=no_grad, width_per_group=64 * 2) + else: + raise ValueError("Model architecture not found.") + from torch.hub import load_state_dict_from_url + state_dict = load_state_dict_from_url(resnet.model_urls[arch], progress=True) + net.load_state_dict(state_dict) + + if channel != 3: + net.conv1 = nn.Conv2d(channel, 64, kernel_size=7, stride=2, padding=3, bias=False) + if num_classes != 1000: + net.fc = nn.Linear(net.fc.in_features, num_classes) + + elif im_size[0] == 224 and im_size[1] == 224: + # Use torchvision models without pretrained parameters + if arch == "wrn502": + arch = "wide_resnet50_2" + net = ResNet_224x224(resnet.Bottleneck, [3, 4, 6, 3], channel=channel, num_classes=num_classes, + record_embedding=record_embedding, no_grad=no_grad, width_per_group=64 * 2) + elif arch == "wrn1012": + arch = "wide_resnet101_2" + net = ResNet_224x224(resnet.Bottleneck, [3, 4, 23, 3], channel=channel, num_classes=num_classes, + record_embedding=record_embedding, no_grad=no_grad, width_per_group=64 * 2) + else: + raise ValueError("Model architecture not found.") + + elif (channel == 1 and im_size[0] == 28 and im_size[1] == 28) or ( + channel == 3 and im_size[0] == 32 and im_size[1] == 32): + if arch == "wrn168": + net = WideResNet_32x32(16, num_classes, channel, 8) + elif arch == "wrn2810": + net = WideResNet_32x32(28, num_classes, channel, 10) + elif arch == "wrn282": + net = WideResNet_32x32(28, num_classes, channel, 2) + else: + raise ValueError("Model architecture not found.") + else: + raise NotImplementedError("Network Architecture for current dataset has not been implemented.") + return net + + +def WRN168(channel: int, num_classes: int, im_size, record_embedding: bool = False, no_grad: bool = False, + pretrained: bool = False): + return WideResNet("wrn168", channel, num_classes, im_size, record_embedding, no_grad, pretrained) + + +def WRN2810(channel: int, num_classes: int, im_size, record_embedding: bool = False, no_grad: bool = False, + pretrained: bool = False): + return WideResNet("wrn2810", channel, num_classes, im_size, record_embedding, no_grad, pretrained) + + +def WRN282(channel: int, num_classes: int, im_size, record_embedding: bool = False, no_grad: bool = False, + pretrained: bool = False): + return WideResNet('wrn282', channel, num_classes, im_size, record_embedding, no_grad, pretrained) + + +def WRN502(channel: int, num_classes: int, im_size, record_embedding: bool = False, no_grad: bool = False, + pretrained: bool = False): + return WideResNet("wrn502", channel, num_classes, im_size, record_embedding, no_grad, pretrained) + + +def WRN1012(channel: int, num_classes: int, im_size, record_embedding: bool = False, no_grad: bool = False, + pretrained: bool = False): + return WideResNet("wrn1012", channel, num_classes, im_size, record_embedding, no_grad, pretrained) diff --git a/docs/Co-CoOp.md b/docs/Co-CoOp.md new file mode 100644 index 0000000..afa642d --- /dev/null +++ b/docs/Co-CoOp.md @@ -0,0 +1,99 @@ +# Conditional Prompt Learning for Vision-Language Models (Co-CoOp, CVPR'22) +[![paper](https://img.shields.io/badge/arXiv-Paper-.svg)](https://arxiv.org/abs/2203.05557) + +We provide the scripts in [scripts/cocoop](../scripts/cocoop) to reproduce Co-CoOp results (CVPR'22). + +Make sure to configure the dataset paths in environment variable `DATA` and run the commands from the main directory `MaPLe/`. + +## Generalization From Base to New Classes + +This corresponds to the experiments in Section 4.1, i.e., Table 1. + +You will need both `scripts/cocoop/base2new_train.sh` and `scripts/cocoop/base2new_test.sh`. The former trains a model on bash classes while the latter evaluates the trained model on new classes. Both scripts have two input arguments, i.e., `DATASET` and `SEED`. + +`DATASET` takes as input a dataset name, like `imagenet` or `caltech101`. The valid names are the files' names in `CoOp/configs/datasets/`. + +Below we provide an example on how to evaluate the model on ImageNet. + +```bash +# seed=1 +bash scripts/cocoop/base2new_train.sh imagenet 1 +bash scripts/cocoop/base2new_test.sh imagenet 1 + +# seed=2 +bash scripts/cocoop/base2new_train.sh imagenet 2 +bash scripts/cocoop/base2new_test.sh imagenet 2 + +# seed=3 +bash scripts/cocoop/base2new_train.sh imagenet 3 +bash scripts/cocoop/base2new_test.sh imagenet 3 +``` + +When the evaluation is done, you can use `parse_test_res.py` to automatically calculate the average results. For instance, after you finish the evaluation (including `base2new_train.sh` and `base2new_test.sh`) on ImageNet using the aforementioned commands, you would get + +``` +output +|–– base2new/ +| |–– test_new/ +| | |–– imagenet/ +| | | |–– shots_16/ +| | | | |–– CoCoOp/ +| | | | | |–– vit_b16_c4_ep10_batch1_ctxv1/ +| | | | | | |–– seed1/ +| | | | | | |–– seed2/ +| | | | | | |–– seed3/ +| |–– train_base/ +| | |–– imagenet/ +| | | |–– shots_16/ +| | | | |–– CoCoOp/ +| | | | | |–– vit_b16_c4_ep10_batch1_ctxv1/ +| | | | | | |–– seed1/ +| | | | | | |–– seed2/ +| | | | | | |–– seed3/ +``` + +Then, to get the average performance on the base classes, run + +```bash +python parse_test_res.py output/base2new/train_base/imagenet/shots_16/CoCoOp/vit_b16_c4_ep10_batch1_ctxv1 +``` + +To get the average performance on the new classes, run + +```bash +python parse_test_res.py output/base2new/test_new/imagenet/shots_16/CoCoOp/vit_b16_c4_ep10_batch1_ctxv1 --test-log +``` + +## Cross-Dataset Transfer + +This corresponds to the experiments in Section 4.2, i.e., Table 2. + +The relevant scripts are `scripts/cocoop/xd_train.sh` and `scripts/cocoop/xd_test.sh` where the `DATASET` variable is set to the default, namely `imagenet`. To train the model, run + +```bash +# seed=1 +bash scripts/cocoop/xd_train.sh 1 + +# seed=2 +bash scripts/cocoop/xd_train.sh 2 + +# seed=3 +bash scripts/cocoop/xd_train.sh 3 +``` + +Then, you evaluate the model on other datasets, e.g., + +```bash +for SEED in 1 2 3 +do + bash scripts/cocoop/xd_test.sh caltech101 ${SEED} + bash scripts/cocoop/xd_test.sh oxford_pets ${SEED} + bash scripts/cocoop/xd_test.sh stanford_cars ${SEED} +done +``` + +## Domain Generalization + +This corresponds to the experiments in Section 4.3, i.e., Table 3. + +The steps are similar to those discussed in "Cross-Dataset Transfer" except you evaluate the model on the variants of ImageNet, i.e., `imagenetv2`, `imagenet_sketch`, `imagenet_a` and `imagenet_r`. \ No newline at end of file diff --git a/docs/CoOp.md b/docs/CoOp.md new file mode 100644 index 0000000..c7a4e7b --- /dev/null +++ b/docs/CoOp.md @@ -0,0 +1,110 @@ +# Learning to Prompt for Vision-Language Models (CoOp, IJCV'22) +[![paper](https://img.shields.io/badge/arXiv-Paper-.svg)](https://arxiv.org/abs/2109.01134) + +We provide the scripts in [scripts/coop](../scripts/coop) to reproduce CoOp results (IJCV'22). + +Make sure to configure the dataset paths in environment variable `DATA` and run the commands from the main directory `MaPLe/`. + +## Few-Shot Learning + +All you need is `scripts/coop/main.sh`, which contains six input arguments. + +`DATASET` takes as input a dataset name, like `imagenet` or `caltech101`. The valid names are the files' names in `configs/datasets/`. + +`CFG` means which config file to use, such as `rn50`, `rn101` or `vit_b32` (see `configs/trainers/coop/`). Note that for ImageNet, we use `configs/trainers/coop/*_ep50.yaml` for all settings (please follow the implementation details shown in the paper). + +Below we provide examples on how to run CoOp on Caltech101. + +**CLIP + CoOp (M=16, end)**: +- 1 shot: `bash scripts/coop/main.sh caltech101 rn50_ep50 end 16 1 False` +- 2 shots: `bash scripts/coop/main.sh caltech101 rn50_ep100 end 16 2 False` +- 4 shots: `bash scripts/coop/main.sh caltech101 rn50_ep100 end 16 4 False` +- 8 shots: `bash scripts/coop/main.sh caltech101 rn50 end 16 8 False` +- 16 shots: `bash scripts/coop/main.sh caltech101 rn50 end 16 16 False` + +**CLIP + CoOp (M=16, mid)**: +- 1 shot: `bash scripts/coop/main.sh caltech101 rn50_ep50 middle 16 1 False` +- 2 shots: `bash scripts/coop/main.sh caltech101 rn50_ep100 middle 16 2 False` +- 4 shots: `bash scripts/coop/main.sh caltech101 rn50_ep100 middle 16 4 False` +- 8 shots: `bash scripts/coop/main.sh caltech101 rn50 middle 16 8 False` +- 16 shots: `bash scripts/coop/main.sh caltech101 rn50 middle 16 16 False` + +**CLIP + CoOp (M=16, end, CSC)**: +- 1 shot: `bash scripts/coop/main.sh caltech101 rn50_ep50 end 16 1 True` +- 2 shots: `bash scripts/coop/main.sh caltech101 rn50_ep100 end 16 2 True` +- 4 shots: `bash scripts/coop/main.sh caltech101 rn50_ep100 end 16 4 True` +- 8 shots: `bash scripts/coop/main.sh caltech101 rn50 end 16 8 True` +- 16 shots: `bash scripts/coop/main.sh caltech101 rn50 end 16 16 True` + +**CLIP + CoOp (M=16, mid, CSC)**: +- 1 shot: `bash scripts/coop/main.sh caltech101 rn50_ep50 middle 16 1 True` +- 2 shots: `bash scripts/coop/main.sh caltech101 rn50_ep100 middle 16 2 True` +- 4 shots: `bash scripts/coop/main.sh caltech101 rn50_ep100 middle 16 4 True` +- 8 shots: `bash scripts/coop/main.sh caltech101 rn50 middle 16 8 True` +- 16 shots: `bash scripts/coop/main.sh caltech101 rn50 middle 16 16 True` + +After the experiments are finished, you can use `parse_test_res.py` to calculate the average results instead of manually looking into the log files. Say the structure of `output/` is + +``` +output +|–– caltech101/ +| |–– CoOp/ +| | |–– rn50_16shots/ +| | | |–– nctx16_cscFalse_ctpend/ +| | | | |–– seed1/ +| | | | |–– seed2/ +| | | | |–– seed3/ +| | |–– rn50_8shots/ +| | | |–– nctx16_cscFalse_ctpend/ +| | | | |–– seed1/ +| | | | |–– seed2/ +| | | | |–– seed3/ +``` + +To calculate the average results for the folder `rn50_16shots/nctx16_cscFalse_ctpend/`, you can run + +```bash +python parse_test_res.py output/caltech101/CoOp/rn50_16shots/nctx16_cscFalse_ctpend +``` + +Then, you will see something like this in your terminal + +```bash +Parsing files in output/caltech101/CoOp/rn50_16shots/nctx16_cscFalse_ctpend +file: output/caltech101/CoOp/rn50_16shots/nctx16_cscFalse_ctpend/seed1/log.txt. accuracy: 91.81%. error: 8.19%. +file: output/caltech101/CoOp/rn50_16shots/nctx16_cscFalse_ctpend/seed2/log.txt. accuracy: 92.01%. error: 7.99%. +file: output/caltech101/CoOp/rn50_16shots/nctx16_cscFalse_ctpend/seed3/log.txt. accuracy: 92.17%. error: 7.83%. +=== +Summary of directory: output/caltech101/CoOp/rn50_16shots/nctx16_cscFalse_ctpend +* accuracy: 92.00% +- 0.15% +* error: 8.00% +- 0.15% +=== +``` + +**How to initialize the context tokens with pre-trained word vectors?** Specify the words for the parameter `TRAINER.COOP.CTX_INIT` in your config file. In our paper, we use `configs/trainers/rn50_ctxv1.yaml` (give this file to `--config-file`, see `scripts/coop/main.sh`), which uses "a photo of a" as the initialization words. + +**How to visualize nearest words for the learned context tokens?** All you need is `interpret_prompt.py`. Say the learned tokens are saved in `a/b/c/prompt_learner/model.pth.tar` and you would like to see the top-3 nearest words for each token. In this case, run `python interpret_prompt.py a/b/c/prompt_learner/model.pth.tar 3` + +## Robustness to Distribution Shift +To reproduce the robustness experiments, you can simply load the models learned on ImageNet and evaluate them on the following datasets: `imagenetv2`, `imagenet-sketch`, `imagenet-a` and `imagenet-r`. + +The command is provided in `scripts/coop/eval.sh`. The key arguments are `--model-dir`, `--load-epoch` and `--eval-only`. `--model-dir` indicates the directory where the models are saved (i.e. the entire folder containing `log.txt`, the tensorboard file and `prompt_learner/`). `--load-epoch` tells the code to load the model saved at a specific epoch, like `--load-epoch 50` for ImageNet (see the [source code](https://github.com/KaiyangZhou/Dassl.pytorch/blob/master/dassl/engine/trainer.py#L169) for more details). + +For example, to evaluate `CLIP + CoOp (M=16, end)` on ImageNetV2, you can do + +```bash +# Don't need to use rn5_ep50 here as no training is performed +bash scripts/coop/eval.sh imagenetv2 rn50 +``` + +The default setting is `SHOTS=16`. Feel free to modify the script. + +Again, you can use `parse_test_res.py` to automate the calculation of average performance. This time you should append `--test-log`, e.g., `python parse_test_res.py directory --test-log`. + +## Zero-Shot CLIP + +See `**scripts/zsclip/zeroshot.sh**`. + +## Linear Probe CLIP + +Please move to [lpclip/](lpclip/). \ No newline at end of file diff --git a/docs/DATASETS.md b/docs/DATASETS.md new file mode 100644 index 0000000..d07c672 --- /dev/null +++ b/docs/DATASETS.md @@ -0,0 +1,233 @@ +# How to install datasets + +### Acknowledgement: This readme file for installing datasets has been borrowed directly from [CoOp's](https://github.com/KaiyangZhou/CoOp/blob/main/DATASETS.md) official repository. + +We suggest putting all datasets under the same folder (say `$DATA`) to ease management and following the instructions below to organize datasets to avoid modifying the source code. The file structure looks like + +``` +$DATA/ +|–– imagenet/ +|–– caltech-101/ +|–– oxford_pets/ +|–– stanford_cars/ +``` + +If you have some datasets already installed somewhere else, you can create symbolic links in `$DATA/dataset_name` that point to the original data to avoid duplicate download. + +Datasets list: +- [ImageNet](#imagenet) +- [Caltech101](#caltech101) +- [OxfordPets](#oxfordpets) +- [StanfordCars](#stanfordcars) +- [Flowers102](#flowers102) +- [Food101](#food101) +- [FGVCAircraft](#fgvcaircraft) +- [SUN397](#sun397) +- [DTD](#dtd) +- [EuroSAT](#eurosat) +- [UCF101](#ucf101) +- [ImageNetV2](#imagenetv2) +- [ImageNet-Sketch](#imagenet-sketch) +- [ImageNet-A](#imagenet-a) +- [ImageNet-R](#imagenet-r) + +The instructions to prepare each dataset are detailed below. To ensure reproducibility and fair comparison for future work, we provide fixed train/val/test splits for all datasets except ImageNet where the validation set is used as test set. The fixed splits are either from the original datasets (if available) or created by us. + +### ImageNet +- Create a folder named `imagenet/` under `$DATA`. +- Create `images/` under `imagenet/`. +- Download the dataset from the [official website](https://image-net.org/index.php) and extract the training and validation sets to `$DATA/imagenet/images`. The directory structure should look like +``` +imagenet/ +|–– images/ +| |–– train/ # contains 1,000 folders like n01440764, n01443537, etc. +| |–– val/ +``` +- If you had downloaded the ImageNet dataset before, you can create symbolic links to map the training and validation sets to `$DATA/imagenet/images`. +- Download the `classnames.txt` to `$DATA/imagenet/` from this [link](https://drive.google.com/file/d/1-61f_ol79pViBFDG_IDlUQSwoLcn2XXF/view?usp=sharing). The class names are copied from [CLIP](https://github.com/openai/CLIP/blob/main/notebooks/Prompt_Engineering_for_ImageNet.ipynb). + +### Caltech101 +- Create a folder named `caltech-101/` under `$DATA`. +- Download `101_ObjectCategories.tar.gz` from http://www.vision.caltech.edu/Image_Datasets/Caltech101/101_ObjectCategories.tar.gz and extract the file under `$DATA/caltech-101`. +- Download `split_zhou_Caltech101.json` from this [link](https://drive.google.com/file/d/1hyarUivQE36mY6jSomru6Fjd-JzwcCzN/view?usp=sharing) and put it under `$DATA/caltech-101`. + +The directory structure should look like +``` +caltech-101/ +|–– 101_ObjectCategories/ +|–– split_zhou_Caltech101.json +``` + +### OxfordPets +- Create a folder named `oxford_pets/` under `$DATA`. +- Download the images from https://www.robots.ox.ac.uk/~vgg/data/pets/data/images.tar.gz. +- Download the annotations from https://www.robots.ox.ac.uk/~vgg/data/pets/data/annotations.tar.gz. +- Download `split_zhou_OxfordPets.json` from this [link](https://drive.google.com/file/d/1501r8Ber4nNKvmlFVQZ8SeUHTcdTTEqs/view?usp=sharing). + +The directory structure should look like +``` +oxford_pets/ +|–– images/ +|–– annotations/ +|–– split_zhou_OxfordPets.json +``` + +### StanfordCars +- Create a folder named `stanford_cars/` under `$DATA`. +- Download the train images http://ai.stanford.edu/~jkrause/car196/cars_train.tgz. +- Download the test images http://ai.stanford.edu/~jkrause/car196/cars_test.tgz. +- Download the train labels https://ai.stanford.edu/~jkrause/cars/car_devkit.tgz. +- Download the test labels http://ai.stanford.edu/~jkrause/car196/cars_test_annos_withlabels.mat. +- Download `split_zhou_StanfordCars.json` from this [link](https://drive.google.com/file/d/1ObCFbaAgVu0I-k_Au-gIUcefirdAuizT/view?usp=sharing). + +The directory structure should look like +``` +stanford_cars/ +|–– cars_test\ +|–– cars_test_annos_withlabels.mat +|–– cars_train\ +|–– devkit\ +|–– split_zhou_StanfordCars.json +``` + +### Flowers102 +- Create a folder named `oxford_flowers/` under `$DATA`. +- Download the images and labels from https://www.robots.ox.ac.uk/~vgg/data/flowers/102/102flowers.tgz and https://www.robots.ox.ac.uk/~vgg/data/flowers/102/imagelabels.mat respectively. +- Download `cat_to_name.json` from [here](https://drive.google.com/file/d/1AkcxCXeK_RCGCEC_GvmWxjcjaNhu-at0/view?usp=sharing). +- Download `split_zhou_OxfordFlowers.json` from [here](https://drive.google.com/file/d/1Pp0sRXzZFZq15zVOzKjKBu4A9i01nozT/view?usp=sharing). + +The directory structure should look like +``` +oxford_flowers/ +|–– cat_to_name.json +|–– imagelabels.mat +|–– jpg/ +|–– split_zhou_OxfordFlowers.json +``` + +### Food101 +- Download the dataset from https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/ and extract the file `food-101.tar.gz` under `$DATA`, resulting in a folder named `$DATA/food-101/`. +- Download `split_zhou_Food101.json` from [here](https://drive.google.com/file/d/1QK0tGi096I0Ba6kggatX1ee6dJFIcEJl/view?usp=sharing). + +The directory structure should look like +``` +food-101/ +|–– images/ +|–– license_agreement.txt +|–– meta/ +|–– README.txt +|–– split_zhou_Food101.json +``` + +### FGVCAircraft +- Download the data from https://www.robots.ox.ac.uk/~vgg/data/fgvc-aircraft/archives/fgvc-aircraft-2013b.tar.gz. +- Extract `fgvc-aircraft-2013b.tar.gz` and keep only `data/`. +- Move `data/` to `$DATA` and rename the folder to `fgvc_aircraft/`. + +The directory structure should look like +``` +fgvc_aircraft/ +|–– images/ +|–– ... # a bunch of .txt files +``` + +### SUN397 +- Create a folder named `sun397/` under `$DATA`. +- Download the images http://vision.princeton.edu/projects/2010/SUN/SUN397.tar.gz. +- Download the partitions https://vision.princeton.edu/projects/2010/SUN/download/Partitions.zip. +- Extract these files under `$DATA/sun397/`. +- Download `split_zhou_SUN397.json` from this [link](https://drive.google.com/file/d/1y2RD81BYuiyvebdN-JymPfyWYcd8_MUq/view?usp=sharing). + +The directory structure should look like +``` +sun397/ +|–– SUN397/ +|–– split_zhou_SUN397.json +|–– ... # a bunch of .txt files +``` + +### DTD +- Download the dataset from https://www.robots.ox.ac.uk/~vgg/data/dtd/download/dtd-r1.0.1.tar.gz and extract it to `$DATA`. This should lead to `$DATA/dtd/`. +- Download `split_zhou_DescribableTextures.json` from this [link](https://drive.google.com/file/d/1u3_QfB467jqHgNXC00UIzbLZRQCg2S7x/view?usp=sharing). + +The directory structure should look like +``` +dtd/ +|–– images/ +|–– imdb/ +|–– labels/ +|–– split_zhou_DescribableTextures.json +``` + +### EuroSAT +- Create a folder named `eurosat/` under `$DATA`. +- Download the dataset from http://madm.dfki.de/files/sentinel/EuroSAT.zip and extract it to `$DATA/eurosat/`. +- Download `split_zhou_EuroSAT.json` from [here](https://drive.google.com/file/d/1Ip7yaCWFi0eaOFUGga0lUdVi_DDQth1o/view?usp=sharing). + +The directory structure should look like +``` +eurosat/ +|–– 2750/ +|–– split_zhou_EuroSAT.json +``` + +### UCF101 +- Create a folder named `ucf101/` under `$DATA`. +- Download the zip file `UCF-101-midframes.zip` from [here](https://drive.google.com/file/d/10Jqome3vtUA2keJkNanAiFpgbyC9Hc2O/view?usp=sharing) and extract it to `$DATA/ucf101/`. This zip file contains the extracted middle video frames. +- Download `split_zhou_UCF101.json` from this [link](https://drive.google.com/file/d/1I0S0q91hJfsV9Gf4xDIjgDq4AqBNJb1y/view?usp=sharing). + +The directory structure should look like +``` +ucf101/ +|–– UCF-101-midframes/ +|–– split_zhou_UCF101.json +``` + +### ImageNetV2 +- Create a folder named `imagenetv2/` under `$DATA`. +- Go to this github repo https://github.com/modestyachts/ImageNetV2. +- Download the matched-frequency dataset from https://s3-us-west-2.amazonaws.com/imagenetv2public/imagenetv2-matched-frequency.tar.gz and extract it to `$DATA/imagenetv2/`. +- Copy `$DATA/imagenet/classnames.txt` to `$DATA/imagenetv2/`. + +The directory structure should look like +``` +imagenetv2/ +|–– imagenetv2-matched-frequency-format-val/ +|–– classnames.txt +``` + +### ImageNet-Sketch +- Download the dataset from https://github.com/HaohanWang/ImageNet-Sketch. +- Extract the dataset to `$DATA/imagenet-sketch`. +- Copy `$DATA/imagenet/classnames.txt` to `$DATA/imagenet-sketch/`. + +The directory structure should look like +``` +imagenet-sketch/ +|–– images/ # contains 1,000 folders whose names have the format of n* +|–– classnames.txt +``` + +### ImageNet-A +- Create a folder named `imagenet-adversarial/` under `$DATA`. +- Download the dataset from https://github.com/hendrycks/natural-adv-examples and extract it to `$DATA/imagenet-adversarial/`. +- Copy `$DATA/imagenet/classnames.txt` to `$DATA/imagenet-adversarial/`. + +The directory structure should look like +``` +imagenet-adversarial/ +|–– imagenet-a/ # contains 200 folders whose names have the format of n* +|–– classnames.txt +``` + +### ImageNet-R +- Create a folder named `imagenet-rendition/` under `$DATA`. +- Download the dataset from https://github.com/hendrycks/imagenet-r and extract it to `$DATA/imagenet-rendition/`. +- Copy `$DATA/imagenet/classnames.txt` to `$DATA/imagenet-rendition/`. + +The directory structure should look like +``` +imagenet-rendition/ +|–– imagenet-r/ # contains 200 folders whose names have the format of n* +|–– classnames.txt +``` \ No newline at end of file diff --git a/docs/INSTALL.md b/docs/INSTALL.md new file mode 100644 index 0000000..40c66b8 --- /dev/null +++ b/docs/INSTALL.md @@ -0,0 +1,46 @@ +# Installation + +This codebase is tested on Ubuntu 20.04.2 LTS with python 3.8. Follow the below steps to create environment and install dependencies. + +* Setup conda environment (recommended). +```bash +# Create a conda environment +conda create -y -n dapt python=3.8 + +# Activate the environment +conda activate dapt + +# Install torch (requires version >= 1.8.1) and torchvision +# Please refer to https://pytorch.org/ if you need a different cuda version +pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 torchaudio==0.9.0 -f https://download.pytorch.org/whl/torch_stable.html +``` + +* Install dassl library. +```bash +# Instructions borrowed from https://github.com/KaiyangZhou/Dassl.pytorch#installation + +# Clone this repo +git clone https://github.com/KaiyangZhou/Dassl.pytorch.git +cd Dassl.pytorch/ + +# Install dependencies +pip install -r requirements.txt + +# Install this library (no need to re-build if the source code is modified) +python setup.py develop +cd .. +``` + +* Clone DAPT code repository and install requirements +```bash +# Clone MaPLe code base +git clone https://github.com/muzairkhattak/multimodal-prompt-learning.git + +cd DAPT/ +# Install requirements + +pip install -r requirements.txt + +# Update setuptools package +pip install setuptools==59.5.0 +``` diff --git a/docs/RUN.md b/docs/RUN.md new file mode 100644 index 0000000..d3eed88 --- /dev/null +++ b/docs/RUN.md @@ -0,0 +1,226 @@ +# Training and Evaluation + +We provide bash scripts in [scripts/](../scripts) for each prompting variant including MaPLe, vision, language and independent V-L prompting. +Make sure to configure the dataset paths in environment variable `DATA` and run the commands from the main directory `DAPT/`. +Below we provide training and evaluation instructions for MaPLe. The same instructions applies for all other variants including *Vision (VPT), Language and independent V-L prompting* . + + +### Training time and compute +We train MaPLe+DAPT on each dataset with a batch size of 4 using a **single** NVIDIA 3090 GPU. + +## Quick Start +Here we provide an illustrative script in `scripts/dapt/dp_few_shot.sh`, where you could directly bash it to run for any dataset you like, and the core command is like: +```bash +python train.py --root /home/ubuntu/Data_file/few_shot_data --seed 1 --trainer MaPLe --dataset-config-file configs/datasets/oxford_pets.yaml --config-file configs/trainers/MaPLe/vit_b16_t.yaml --output-dir output/DAPT --mode dapt-g +DATASET.NUM_SHOTS +1 +DATASET.SELECTION_RATIO +1.0 +``` +Here `--mode` refers to whether GradCAM or SEEM-generated masks (`dapt-s`) are used for visual decoupling, and `DATASET.SELECTION_RATIO` refers to the scale of data selection, where you could set it to `[0,1]` + + +## Normal Pipieline of DAPT + +> Below are the original MaPLe scripts, you may refer to the follwing settings to perform other experiments: + + +#### (1) Base-to-Novel class generalization setting +The default training settings are provided in config file at `configs/trainers/MaPLe/vit_b16_c2_ep5_batch4_2ctx.yaml`. All hyper-parameters such as prompt length, prompt depth, etc., can be modified using this config file. + +Below, we provide instructions to train MaPLe on imagenet. + + +```bash +# Other possible dataset values includes [caltech101, food101, dtd, ucf101, oxford_flowers, oxford_pets, fgvc_aircraft, stanford_cars, sun397, eurosat] + +# seed=1 +# trains and evaluates on base classes +bash scripts/dapt/base2new_train_maple.sh imagenet 1 +# evaluates on novel classes +bash scripts/dapt/base2new_test_maple.sh imagenet 1 + +# seed=2 +# trains and evaluates on base classes +bash scripts/dapt/base2new_train_maple.sh imagenet 2 +# evaluates on novel classes +bash scripts/dapt/base2new_test_maple.sh imagenet 2 + +# seed=3 +# trains and evaluates on base classes +bash scripts/dapt/base2new_train_maple.sh imagenet 3 +# evaluates on novel classes +bash scripts/dapt/base2new_test_maple.sh imagenet 3 +``` + +#### Averaging results over 3 seeds: +Once the above trainings and evaluations are completed, the `output/` directory should have the following structure: + +``` +output +|–– base2new/ +| |–– test_new/ +| | |–– imagenet/ +| | | |–– shots_16/ +| | | | |–– MaPLe/ +| | | | | |–– vit_b16_c2_ep5_batch4_2ctx/ +| | | | | | |–– seed1/ +| | | | | | |–– seed2/ +| | | | | | |–– seed3/ +| |–– train_base/ +| | |–– imagenet/ +| | | |–– shots_16/ +| | | | |–– MaPLe/ +| | | | | |–– vit_b16_c2_ep5_batch4_2ctx/ +| | | | | | |–– seed1/ +| | | | | | |–– seed2/ +| | | | | | |–– seed3/ +``` + +Now use the script `parse_test_res.py` and run the commands below to calculate the averaged results: +```bash +# prints averaged results for base classes +python parse_test_res.py output/base2new/train_base/imagenet/shots_16/MaPLe/vit_b16_c2_ep5_batch4_2ctx +# averaged results for novel classes +python parse_test_res.py output/base2new/test_new/imagenet/shots_16/MaPLe/vit_b16_c2_ep5_batch4_2ctx --test-log +``` + +The above steps can be repeated for other individual datasets. + +#### Reproducing results using pre-trained weights for base-to-novel generalization setting + +We show an example to reproduce results for imagenet. Follow the instructions below to reproduce results using our pre-trained model weights: +* Download the zipped folder containing pre-trained weights for a single dataset from this [link](https://drive.google.com/drive/folders/1-tB6BUDBzs9CXTOJ7p5hM4Svq1tL_mGz?usp=sharing). Additionally we also provide the log files for both training and evaluation. After unzipping, the directory should look like this: + +``` +imagenet +|–– base/ +| |–– seed1/ +| |–– seed2/ +| |–– seed3/ +|–– novel/ +| |–– seed1/ +| |–– seed2/ +| |–– seed3/ +``` + +Now use the evaluation script `scripts/maple/reproduce_maple.sh` and run the commands below to calculate the averaged results: +```bash +# evaluate on base and novel classes for SEED1 +bash scripts/dapt/reproduce_maple.sh imagenet 1 /path/to/imagenet/weights/folder +# evaluate on base and novel classes for SEED2 +bash scripts/dapt/reproduce_maple.sh imagenet 2 /path/to/imagenet/weights/folder +# evaluate on base and novel classes for SEED3 +bash scripts/dapt/reproduce_maple.sh imagenet 3 /path/to/imagenet/weights/folder +``` + +This should evaluate and save the log files in `output/` directory. To obtain the averaged results, run: + +```bash +# prints averaged results for base classes +python parse_test_res.py output/base2new/train_base/imagenet/shots_16/MaPLe/vit_b16_c2_ep5_batch4_2ctx +# averaged results for novel classes +python parse_test_res.py output/base2new/test_new/imagenet/shots_16/MaPLe/vit_b16_c2_ep5_batch4_2ctx --test-log +``` + + +#### (2) Cross-Dataset Transfer +We provide instructions to train MaPLe on imageNet using all 1000 classes and then evaluating it directory on new downstream datasets. +We provide cross-dataset config for MaPLe: `configs/MaPLe/vit_b16_c2_ep5_batch4_2ctx_cross_datasets.yaml`. +* Firstly, train MaPLe on imagenet in few-shot manner (for all 3 seeds). + +```bash +# seed=1 +bash scripts/dapt/xd_train_maple.sh imagenet 1 +# seed=2 +bash scripts/dapt/xd_train_maple.sh imagenet 2 +# seed=3 +bash scripts/dapt/xd_train_maple.sh imagenet 3 +``` + +* Now evaluate imageNet model on downstream datasets. + +```bash +for SEED in 1 2 3 +do + bash scripts/dapt/xd_test_maple.sh caltech101 ${SEED} + bash scripts/dapt/xd_test_maple.sh oxford_pets ${SEED} + bash scripts/dapt/xd_test_maple.sh stanford_cars ${SEED} +done +``` + +#### (3) Domain Generalization +We use imagenet trained MaPLe model for domain generalization experiments. The steps are similar to above cross-dataset experiments, however, model is evaluated on imagenet variants. +* Evaluate imageNet model on variants of imagenet (domain shift datasets). + +```bash +for SEED in 1 2 3 +do + bash scripts/dapt/xd_test_maple.sh imagenetv2 ${SEED} + bash scripts/dapt/xd_test_maple.sh imagenet_sketch ${SEED} + bash scripts/dapt/xd_test_maple.sh imagenet_a ${SEED} + bash scripts/dapt/xd_test_maple.sh imagenet_r ${SEED} +done +``` + + +You can obtain averaged results by using the script `parse_test_res.py` and following the similar steps as provided in base-to-novel generalization experiments. +
+ + +#### Reproducing official results for cross-dataset and domain generalization setting + +We provide the instructions below to reproduce domain-generalization and cross-datasets results using our pre-trained imagenet model weights for MaPLe: +* Download the zipped folder containing pre-trained weights for imagenet from this [link](https://drive.google.com/drive/folders/1bmhvmNZc13WJ5U71qt0t8k91wyuoemVF?usp=sharing). Additionally, we also provide the log files for both training and evaluation. After unzipping, the directory should look like this: + +``` +imagenet +|–– seed1/ +|–– seed2/ +|–– seed3/ +``` + +Now use the evaluation script `scripts/maple/reproduce_maple_xd.sh` and run the commands below to calculate the averaged results: +```bash +# evaluate on given dataset for SEED1 +bash scripts/dapt/reproduce_maple_xd.sh food101 1 /path/to/imagenet/weights/folder +# evaluate on given dataset for SEED2 +bash scripts/dapt/reproduce_maple_xd.sh food101 2 /path/to/imagenet/weights/folder +# evaluate on given dataset for SEED3 +bash scripts/dapt/reproduce_maple_xd.sh food101 3 /path/to/imagenet/weights/folder +``` + +This should evaluate and save the log files in `output/` directory. To obtain the averaged results, run: + +```bash +# prints averaged results for food101 dataset +python parse_test_res.py output/evaluation/MaPLe/vit_b16_c2_ep5_batch4_2ctx_cross_datasets_16shots/food101 --test-log +``` + + +#### Training and Evaluating other variants + +For other variants including vision, language and independent V-L prompting techniques, we provide their corresponding configs and scripts as follows. + +``` +configs +|–– datasets/ +|–– trainers/ +| |–– CoCoOp/ +| |–– CoOp/ +| |–– MaPLe/ +| |–– IVLP/ +| |–– VPT/ +``` + +``` +scripts +|–– cocoop/ +|–– coop/ +|–– language-prompting/ +|–– maple/ +|–– independent-vlp/ +``` + +Please use the corresponding config and script files and follow the same instructions as provided for MaPLe in order to train and evaluate the other variants. Same instructions can be followed to reproduce results of other variants using provided pretrained weights. +This repository also supports using official [CoOp](CoOp.md) and [Co-CoOp](Co-CoOp.md) configs and models. diff --git a/docs/fig.png b/docs/fig.png new file mode 100644 index 0000000..7df22ee Binary files /dev/null and b/docs/fig.png differ diff --git a/docs/main_fig.png b/docs/main_fig.png new file mode 100644 index 0000000..bdf0150 Binary files /dev/null and b/docs/main_fig.png differ diff --git a/interpret_prompts/clip_words.csv b/interpret_prompts/clip_words.csv new file mode 100644 index 0000000..29009c3 --- /dev/null +++ b/interpret_prompts/clip_words.csv @@ -0,0 +1,49409 @@ +,0 +0,! +1,"""" +2,# +3,$ +4,% +5,& +6,' +7,( +8,) +9,* +10,+ +11,"," +12,- +13,. +14,/ +15,0 +16,1 +17,2 +18,3 +19,4 +20,5 +21,6 +22,7 +23,8 +24,9 +25,: +26,; +27,< +28,= +29,> +30,? +31,@ +32,A +33,B +34,C +35,D +36,E +37,F +38,G +39,H +40,I +41,J +42,K +43,L +44,M +45,N +46,O +47,P +48,Q +49,R 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+1,84 @@ +import os +import sys +import argparse +import torch + +from clip.simple_tokenizer import SimpleTokenizer +from clip import clip + +# "ViT-B/16" +# "RN50" +def load_clip_to_cpu(backbone_name="ViT-B/16"): + url = clip._MODELS[backbone_name] + model_path = clip._download(url) + + try: + # loading JIT archive + model = torch.jit.load(model_path, map_location="cpu").eval() + state_dict = None + + except RuntimeError: + state_dict = torch.load(model_path, map_location="cpu") + + model = clip.build_model(state_dict or model.state_dict()) + + return model + + +# parser = argparse.ArgumentParser() +# parser.add_argument("fpath", type=str, help="Path to the learned prompt") +# parser.add_argument("topk", type=int, help="Select top-k similar words") +# args = parser.parse_args() + +fpath = "./compound_prompt_weights/train_base/food101/shots_16/cocoop/vit_b16_c4_ep10_batch1_ctxv1/seed1/prompt_learner/model.pth.tar-5" +topk = 10 + +assert os.path.exists(fpath) + +print(f"Return the top-{topk} matched words") + +tokenizer = SimpleTokenizer() +clip_model = load_clip_to_cpu() +token_embedding = clip_model.token_embedding.weight +print(f"Size of token embedding: {token_embedding.shape}") + +prompt_learner = torch.load(fpath, map_location="cpu")["state_dict"] +# Extract the input tokens +ctx = prompt_learner["prompt_learner.ctx"] +ctx = ctx.float() +# Now extract the intermediate tokens +intermediate_embeddings = [] +depth = 9 - 1 +for i in range(depth): + # Now extract the prompt embeddings and store it + query = 'prompt_learner.compound_prompts_text.' + str(i) + temp = prompt_learner[query].float() + intermediate_embeddings.append(temp) + +print(f"Size of context: {ctx.shape}") + +# Now repeat this for all layer context embeddings + +all_layer_ctx = [ctx] + intermediate_embeddings + +for idx, single_ctx in enumerate(all_layer_ctx): + print("SHOWING RESULTS FOR CTX Vectors of Layer: ", idx + 1) + ctx = single_ctx + if ctx.dim() == 2: + # Generic context + distance = torch.cdist(ctx, token_embedding) + print(f"Size of distance matrix: {distance.shape}") + sorted_idxs = torch.argsort(distance, dim=1) + sorted_idxs = sorted_idxs[:, :topk] + + for m, idxs in enumerate(sorted_idxs): + words = [tokenizer.decoder[idx.item()] for idx in idxs] + dist = [f"{distance[m, idx].item():.4f}" for idx in idxs] + print(f"{m+1}: {words} {dist}") + + elif ctx.dim() == 3: + # Class-specific context + raise NotImplementedError + + print("##############################") + print("##############################") \ No newline at end of file diff --git a/lpclip/README.md b/lpclip/README.md new file mode 100644 index 0000000..390d0bb --- /dev/null +++ b/lpclip/README.md @@ -0,0 +1,17 @@ +# Linear Probe CLIP + +To run linear probe baselines, make sure that your current working directory is `lpclip/`. + +Step 1: Extract Features using the CLIP Image Encoder +```bash +sh feat_extractor.sh +``` + +Step 2: Train few-shot linear probe +```bash +sh linear_probe.sh +``` + +We follow the instructions stated in the Appendix A3 (pp.38) of [the original CLIP paper](https://arxiv.org/pdf/2103.00020.pdf), with a careful hyperparameter sweep. + +Note: please pull the latest Dassl (version >= `606a2c6`). diff --git a/lpclip/feat_extractor.py b/lpclip/feat_extractor.py new file mode 100644 index 0000000..440192a --- /dev/null +++ b/lpclip/feat_extractor.py @@ -0,0 +1,189 @@ +import os, argparse +import numpy as np +import torch +import sys + +sys.path.append(os.path.abspath("..")) + +from datasets.oxford_pets import OxfordPets +from datasets.oxford_flowers import OxfordFlowers +from datasets.fgvc_aircraft import FGVCAircraft +from datasets.dtd import DescribableTextures +from datasets.eurosat import EuroSAT +from datasets.stanford_cars import StanfordCars +from datasets.food101 import Food101 +from datasets.sun397 import SUN397 +from datasets.caltech101 import Caltech101 +from datasets.ucf101 import UCF101 +from datasets.imagenet import ImageNet +from datasets.imagenetv2 import ImageNetV2 +from datasets.imagenet_sketch import ImageNetSketch +from datasets.imagenet_a import ImageNetA +from datasets.imagenet_r import ImageNetR + +from dassl.utils import setup_logger, set_random_seed, collect_env_info +from dassl.config import get_cfg_default +from dassl.data.transforms import build_transform +from dassl.data import DatasetWrapper + +import clip + +# import pdb; pdb.set_trace() + + +def print_args(args, cfg): + print("***************") + print("** Arguments **") + print("***************") + optkeys = list(args.__dict__.keys()) + optkeys.sort() + for key in optkeys: + print("{}: {}".format(key, args.__dict__[key])) + print("************") + print("** Config **") + print("************") + print(cfg) + + +def reset_cfg(cfg, args): + if args.root: + cfg.DATASET.ROOT = args.root + + if args.output_dir: + cfg.OUTPUT_DIR = args.output_dir + + if args.trainer: + cfg.TRAINER.NAME = args.trainer + + if args.backbone: + cfg.MODEL.BACKBONE.NAME = args.backbone + + if args.head: + cfg.MODEL.HEAD.NAME = args.head + + +def extend_cfg(cfg): + """ + Add new config variables. + + E.g. + from yacs.config import CfgNode as CN + cfg.TRAINER.MY_MODEL = CN() + cfg.TRAINER.MY_MODEL.PARAM_A = 1. + cfg.TRAINER.MY_MODEL.PARAM_B = 0.5 + cfg.TRAINER.MY_MODEL.PARAM_C = False + """ + from yacs.config import CfgNode as CN + + cfg.TRAINER.OURS = CN() + cfg.TRAINER.OURS.N_CTX = 10 # number of context vectors + cfg.TRAINER.OURS.CSC = False # class-specific context + cfg.TRAINER.OURS.CTX_INIT = "" # initialize context vectors with given words + cfg.TRAINER.OURS.WEIGHT_U = 0.1 # weight for the unsupervised loss + + +def setup_cfg(args): + cfg = get_cfg_default() + extend_cfg(cfg) + + # 1. From the dataset config file + if args.dataset_config_file: + cfg.merge_from_file(args.dataset_config_file) + + # 2. From the method config file + if args.config_file: + cfg.merge_from_file(args.config_file) + + # 3. From input arguments + reset_cfg(cfg, args) + + cfg.freeze() + + return cfg + + +def main(args): + cfg = setup_cfg(args) + if cfg.SEED >= 0: + print("Setting fixed seed: {}".format(cfg.SEED)) + set_random_seed(cfg.SEED) + setup_logger(cfg.OUTPUT_DIR) + + if torch.cuda.is_available() and cfg.USE_CUDA: + torch.backends.cudnn.benchmark = True + + print_args(args, cfg) + print("Collecting env info ...") + print("** System info **\n{}\n".format(collect_env_info())) + + ###################################### + # Setup DataLoader + ###################################### + dataset = eval(cfg.DATASET.NAME)(cfg) + + if args.split == "train": + dataset_input = dataset.train_x + elif args.split == "val": + dataset_input = dataset.val + else: + dataset_input = dataset.test + + tfm_train = build_transform(cfg, is_train=False) + data_loader = torch.utils.data.DataLoader( + DatasetWrapper(cfg, dataset_input, transform=tfm_train, is_train=False), + batch_size=cfg.DATALOADER.TRAIN_X.BATCH_SIZE, + sampler=None, + shuffle=False, + num_workers=cfg.DATALOADER.NUM_WORKERS, + drop_last=False, + pin_memory=(torch.cuda.is_available() and cfg.USE_CUDA), + ) + + ######################################## + # Setup Network + ######################################## + clip_model, _ = clip.load("RN50", "cuda", jit=False) + clip_model.eval() + ################################################################################################################### + # Start Feature Extractor + feature_list = [] + label_list = [] + train_dataiter = iter(data_loader) + for train_step in range(1, len(train_dataiter) + 1): + batch = next(train_dataiter) + data = batch["img"].cuda() + feature = clip_model.visual(data) + feature = feature.cpu() + for idx in range(len(data)): + feature_list.append(feature[idx].tolist()) + label_list.extend(batch["label"].tolist()) + save_dir = os.path.join(cfg.OUTPUT_DIR, cfg.DATASET.NAME) + os.makedirs(save_dir, exist_ok=True) + save_filename = f"{args.split}" + np.savez( + os.path.join(save_dir, save_filename), + feature_list=feature_list, + label_list=label_list, + ) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--root", type=str, default="", help="path to dataset") + parser.add_argument("--output-dir", type=str, default="", help="output directory") + parser.add_argument("--config-file", type=str, default="", help="path to config file") + parser.add_argument( + "--dataset-config-file", + type=str, + default="", + help="path to config file for dataset setup", + ) + parser.add_argument("--num-shot", type=int, default=1, help="number of shots") + parser.add_argument("--split", type=str, choices=["train", "val", "test"], help="which split") + parser.add_argument("--trainer", type=str, default="", help="name of trainer") + parser.add_argument("--backbone", type=str, default="", help="name of CNN backbone") + parser.add_argument("--head", type=str, default="", help="name of head") + parser.add_argument("--seed", type=int, default=-1, help="only positive value enables a fixed seed") + parser.add_argument("--eval-only", action="store_true", help="evaluation only") + args = parser.parse_args() + main(args) diff --git a/lpclip/feat_extractor.sh b/lpclip/feat_extractor.sh new file mode 100644 index 0000000..c1b5a1b --- /dev/null +++ b/lpclip/feat_extractor.sh @@ -0,0 +1,20 @@ +# sh feat_extractor.sh +DATA=/path/to/datasets +OUTPUT='./clip_feat/' +SEED=1 + +# oxford_pets oxford_flowers fgvc_aircraft dtd eurosat stanford_cars food101 sun397 caltech101 ucf101 imagenet +for DATASET in oxford_pets +do + for SPLIT in train val test + do + python feat_extractor.py \ + --split ${SPLIT} \ + --root ${DATA} \ + --seed ${SEED} \ + --dataset-config-file ../configs/datasets/${DATASET}.yaml \ + --config-file ../configs/trainers/CoOp/rn50_val.yaml \ + --output-dir ${OUTPUT} \ + --eval-only + done +done diff --git a/lpclip/linear_probe.py b/lpclip/linear_probe.py new file mode 100644 index 0000000..455453d --- /dev/null +++ b/lpclip/linear_probe.py @@ -0,0 +1,129 @@ +import numpy as np +import os +from sklearn.linear_model import LogisticRegression +import argparse + +parser = argparse.ArgumentParser() +parser.add_argument("--dataset", type=str, default="", help="path to dataset") +parser.add_argument("--num_step", type=int, default=8, help="number of steps") +parser.add_argument("--num_run", type=int, default=10, help="number of runs") +parser.add_argument("--feature_dir", type=str, default="clip_feat", help="feature dir path") +args = parser.parse_args() + +dataset = args.dataset +dataset_path = os.path.join(f"{args.feature_dir}", dataset) + +train_file = np.load(os.path.join(dataset_path, "train.npz")) +train_feature, train_label = train_file["feature_list"], train_file["label_list"] +val_file = np.load(os.path.join(dataset_path, "val.npz")) +val_feature, val_label = val_file["feature_list"], val_file["label_list"] +test_file = np.load(os.path.join(dataset_path, "test.npz")) +test_feature, test_label = test_file["feature_list"], test_file["label_list"] + +os.makedirs("report", exist_ok=True) + +val_shot_list = {1: 1, 2: 2, 4: 4, 8: 4, 16: 4} + +for num_shot in [1, 2, 4, 8, 16]: + test_acc_step_list = np.zeros([args.num_run, args.num_step]) + for seed in range(1, args.num_run + 1): + np.random.seed(seed) + print(f"-- Seed: {seed} --------------------------------------------------------------") + # Sampling + all_label_list = np.unique(train_label) + selected_idx_list = [] + for label in all_label_list: + label_collection = np.where(train_label == label)[0] + selected_idx = np.random.choice(label_collection, size=num_shot, replace=False) + selected_idx_list.extend(selected_idx) + + fewshot_train_feature = train_feature[selected_idx_list] + fewshot_train_label = train_label[selected_idx_list] + + val_num_shot = val_shot_list[num_shot] + val_selected_idx_list = [] + for label in all_label_list: + label_collection = np.where(val_label == label)[0] + selected_idx = np.random.choice(label_collection, size=val_num_shot, replace=False) + val_selected_idx_list.extend(selected_idx) + + fewshot_val_feature = val_feature[val_selected_idx_list] + fewshot_val_label = val_label[val_selected_idx_list] + + # search initialization + search_list = [1e6, 1e4, 1e2, 1, 1e-2, 1e-4, 1e-6] + acc_list = [] + for c_weight in search_list: + clf = LogisticRegression(solver="lbfgs", max_iter=1000, penalty="l2", C=c_weight).fit(fewshot_train_feature, fewshot_train_label) + pred = clf.predict(fewshot_val_feature) + acc_val = sum(pred == fewshot_val_label) / len(fewshot_val_label) + acc_list.append(acc_val) + + print(acc_list, flush=True) + + # binary search + peak_idx = np.argmax(acc_list) + c_peak = search_list[peak_idx] + c_left, c_right = 1e-1 * c_peak, 1e1 * c_peak + + def binary_search(c_left, c_right, seed, step, test_acc_step_list): + clf_left = LogisticRegression(solver="lbfgs", max_iter=1000, penalty="l2", C=c_left).fit(fewshot_train_feature, fewshot_train_label) + pred_left = clf_left.predict(fewshot_val_feature) + acc_left = sum(pred_left == fewshot_val_label) / len(fewshot_val_label) + print("Val accuracy (Left): {:.2f}".format(100 * acc_left), flush=True) + + clf_right = LogisticRegression(solver="lbfgs", max_iter=1000, penalty="l2", C=c_right).fit(fewshot_train_feature, fewshot_train_label) + pred_right = clf_right.predict(fewshot_val_feature) + acc_right = sum(pred_right == fewshot_val_label) / len(fewshot_val_label) + print("Val accuracy (Right): {:.2f}".format(100 * acc_right), flush=True) + + # find maximum and update ranges + if acc_left < acc_right: + c_final = c_right + clf_final = clf_right + # range for the next step + c_left = 0.5 * (np.log10(c_right) + np.log10(c_left)) + c_right = np.log10(c_right) + else: + c_final = c_left + clf_final = clf_left + # range for the next step + c_right = 0.5 * (np.log10(c_right) + np.log10(c_left)) + c_left = np.log10(c_left) + + pred = clf_final.predict(test_feature) + test_acc = 100 * sum(pred == test_label) / len(pred) + print("Test Accuracy: {:.2f}".format(test_acc), flush=True) + test_acc_step_list[seed - 1, step] = test_acc + + saveline = "{}, seed {}, {} shot, weight {}, test_acc {:.2f}\n".format(dataset, seed, num_shot, c_final, test_acc) + with open( + "./report/{}_s{}r{}_details.txt".format(args.feature_dir, args.num_step, args.num_run), + "a+", + ) as writer: + writer.write(saveline) + return ( + np.power(10, c_left), + np.power(10, c_right), + seed, + step, + test_acc_step_list, + ) + + for step in range(args.num_step): + print( + f"{dataset}, {num_shot} Shot, Round {step}: {c_left}/{c_right}", + flush=True, + ) + c_left, c_right, seed, step, test_acc_step_list = binary_search(c_left, c_right, seed, step, test_acc_step_list) + # save results of last step + test_acc_list = test_acc_step_list[:, -1] + acc_mean = np.mean(test_acc_list) + acc_std = np.std(test_acc_list) + save_line = "{}, {} Shot, Test acc stat: {:.2f} ({:.2f})\n".format(dataset, num_shot, acc_mean, acc_std) + print(save_line, flush=True) + with open( + "./report/{}_s{}r{}.txt".format(args.feature_dir, args.num_step, args.num_run), + "a+", + ) as writer: + writer.write(save_line) diff --git a/lpclip/linear_probe.sh b/lpclip/linear_probe.sh new file mode 100644 index 0000000..2c654c3 --- /dev/null +++ b/lpclip/linear_probe.sh @@ -0,0 +1,10 @@ +feature_dir=clip_feat + +for DATASET in OxfordPets +do + python linear_probe.py \ + --dataset ${DATASET} \ + --feature_dir ${feature_dir} \ + --num_step 8 \ + --num_run 3 +done diff --git a/parse_test_res.py b/parse_test_res.py new file mode 100644 index 0000000..7046cc5 --- /dev/null +++ b/parse_test_res.py @@ -0,0 +1,267 @@ +""" +Goal +--- +1. Read test results from log.txt.txt files +2. Compute mean and std across different folders (seeds) + +Usage +--- +Assume the output files are saved under output/my_experiment, +which contains results of different seeds, e.g., + +my_experiment/ + seed1/ + log.txt.txt + seed2/ + log.txt.txt + seed3/ + log.txt.txt + +Run the following command from the root directory: + +$ python tools/parse_test_res.py output/my_experiment + +Add --ci95 to the argument if you wanna get 95% confidence +interval instead of standard deviation: + +$ python tools/parse_test_res.py output/my_experiment --ci95 + +If my_experiment/ has the following structure, + +my_experiment/ + exp-1/ + seed1/ + log.txt.txt + ... + seed2/ + log.txt.txt + ... + seed3/ + log.txt.txt + ... + exp-2/ + ... + exp-3/ + ... + +Run + +$ python tools/parse_test_res.py output/my_experiment --multi-exp +""" +import re +import numpy as np +import os.path as osp +import argparse +from collections import OrderedDict, defaultdict +import os + +from dassl.utils import check_isfile, listdir_nohidden +import matplotlib.pyplot as plt +import matplotlib +from matplotlib.pyplot import MultipleLocator +import pickle + + +def compute_ci95(res): + return 1.96 * np.std(res) / np.sqrt(len(res)) + + +def parse_function(*metrics, directory="", args=None, end_signal=None): + # print(f"Parsing files in {directory}") + subdirs = listdir_nohidden(directory, sort=True) + + outputs = [] + + for subdir in subdirs: + fpath = osp.join(directory, subdir, "log.txt") + assert check_isfile(fpath) + good_to_go = False + output = OrderedDict() + + with open(fpath, "r") as f: + lines = f.readlines() + + for line in lines: + line = line.strip() + + if line == end_signal: + good_to_go = True + + for metric in metrics: + match = metric["regex"].search(line) + if match and good_to_go: + if "file" not in output: + output["file"] = fpath + num = float(match.group(1)) + name = metric["name"] + output[name] = num + + if output: + outputs.append(output) + + assert len(outputs) > 0, f"Nothing found in {directory}" + + metrics_results = defaultdict(list) + + for output in outputs: + msg = "" + for key, value in output.items(): + if isinstance(value, float): + msg += f"{key}: {value:.2f}%. " + else: + msg += f"{key}: {value}. " + if key != "file": + metrics_results[key].append(value) + # print(msg) + + output_results = OrderedDict() + + # print("===") + # print(f"Summary of directory: {directory}") + for key, values in metrics_results.items(): + avg = np.mean(values) + std = compute_ci95(values) if args.ci95 else np.std(values) + # print(f"* {key}: {avg:.2f}% +- {std:.2f}%") + output_results[key] = avg + output_results['std'] = std + # print("===") + + return output_results + + +def main(args, end_signal): + metric = { + "name": args.keyword, + "regex": re.compile(fr"\* {args.keyword}: ([\.\deE+-]+)%"), + } + + if args.multi_exp: + final_results = defaultdict(list) + + for directory in listdir_nohidden(args.directory, sort=True): + directory = osp.join(args.directory, directory) + results = parse_function( + metric, directory=directory, args=args, end_signal=end_signal + ) + + for key, value in results.items(): + final_results[key].append(value) + + print("Average performance") + for key, values in final_results.items(): + avg = np.mean(values) + print(f"* {key}: {avg:.2f}%") + + + else: + z = parse_function( + metric, directory=args.directory, args=args, end_signal=end_signal + ) + + return z + +if __name__ == "__main__": + case = 'btn' #final, few, btn + data = 'ImageNet' #[ OxfordPets Caltech101 FGVCAircraft StanfordCars DescribableTextures Food101 + # SUN397 UCF101 OxfordFlowers EuroSAT] + + Method = ["Uniform"] #Forgetting ,"Uncertainty", "Herding", "Submodular", "Glister", "GraNd", "Craig", "Cal" + if case == 'final': + scope = [0.05,0.1,0.2,0.3,0.5,1.0] #0.05, 0.1, 0.2, 0.3, 0.5, 1.0 + few_scope = [16] #[1, 2, 4, 8, 16] + elif case == 'few': + scope = [1.0] #0.05, 0.1, 0.2, 0.3, 0.5, 1.0 + few_scope = [1, 2, 4, 8, 16] #[1, 2, 4, 8, 16] + elif case == 'btn': + scope = [1.0] #0.05, 0.1, 0.2, 0.3, 0.5, 1.0 + few_scope = [16] #[1, 2, 4, 8, 16] + # 'Forgetting', 'Herding', 'Submodular' (GraphCut/Facility Location), 'Glister', + # 'GraNd', 'Craig', 'Cal'] + parser = argparse.ArgumentParser() + parser.add_argument("--directory", type=str,default='output', help="path to directory") + parser.add_argument( + "--ci95", action="store_true", help=r"compute 95\% confidence interval" + ) + parser.add_argument("--test-log", action="store_true", help="parse test-only logs") + parser.add_argument( + "--multi-exp", action="store_true", help="parse multiple experiments" + ) + parser.add_argument( + "--keyword", default="accuracy", type=str, help="which keyword to extract" + ) + args = parser.parse_args() + + end_signal = "Finish training" + if args.test_log: + end_signal = "=> result" + + pallete = [ + '95a2ff', + 'fa8080', + 'ffc076', + 'bf19ff', + '87e885', + 'f9e264', + 'bdb76b', + '5f45ff', + 'cb9bff', + '009db2', + '0090ff', + '314976', + '765005', + ] + res_l = [] + plt.figure(figsize=(8,8)) #30,15 + plt.style.use('seaborn-darkgrid') + ax = plt.subplot() + for num_m,m in enumerate(Method): + if m != 'Uniform': + s = scope[:-1] + else: + s = scope + for i in range(len(s)): + for few in range(len(few_scope)): + args.directory = os.path.join('output_'+case,data,m + '_'+ str(s[i]) + '_'+str(few_scope[few])) + res = main(args, end_signal) + if case == 'final': + args.directory = os.path.join('/home/ubuntu/VLTuning/multimodal-prompt-learning-main/output',data,m + '_'+ str(s[i])) + res_base = main(args, end_signal) + print(f"{data}_Baseline_{s[i]}/{data}_Updated_{s[i]}: {res_base['accuracy']:.2f}+{res_base['std']:.2f} / {res['accuracy']:.2f}+{res['std']:.2f}") + elif case == 'btn': + args.directory = os.path.join('output_'+case + '_test',data,m + '_'+ str(s[i]) + '_'+str(few_scope[few])) + res_base = main(args, 'Evaluate on the *test* set') + harmony = 2 / (1/res_base['accuracy'] + 1/res['accuracy']) + print(f"{data}_Base_{s[i]}/{data}_Novel_{s[i]}/HM: {res['accuracy']:.2f} / {res_base['accuracy']:.2f} / {harmony:.2f}") + else: + print(f"{data}_Baseline_{s[i]}_shot_{few_scope[few]}: {res['accuracy']:.2f}+{res['std']:.2f}") + + + if m == 'Uniform' and i == (len(s)-1): + final = res['accuracy'] + res_l.append(res['accuracy']) + if m != 'Uniform': + res_l.append(final) + + x = range(0, len(scope)) + # if m != 'Uniform': + # ax.plot(x, res_l, label=Method[num_m], linewidth=2, linestyle='-',color='#'+pallete[num_m], marker='o',markersize=6) + # else: + # ax.plot(x, res_l, label=Method[num_m], linewidth=5, linestyle='--',color='#'+pallete[num_m], marker='o',markersize=10) + + res_l = [] + + ax.grid(True) + ax.set_xlabel('Data Volume', fontsize=20, fontdict={'family': 'Times New Roman', 'weight': "medium"}) + ax.set_ylabel('Accuracy (%)', fontsize=20, fontdict={'family': 'Times New Roman', 'weight': "medium"}) + ax.legend(prop={'size': 18, 'family': 'Times New Roman', 'weight': "bold"}, frameon=True) + + plt.title(data, fontdict={'family': 'Times New Roman', 'weight': "semibold"}, fontsize=20) + plt.xticks(range(len(scope)),scope,fontsize=15) + plt.yticks(fontsize=15) + # plt.ylim((65,79)) + + # save_name = 'Basline_fig2' + # if not os.path.exists(save_name): + # os.mkdir(save_name) + # plt.savefig(os.path.join(save_name,data+'.pdf'),bbox_inches='tight') + # plt.show() diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..49d8dce --- /dev/null +++ b/requirements.txt @@ -0,0 +1,275 @@ +absl-py @ file:///croot/absl-py_1666362940888/work +accelerate==0.21.0 +addict==2.4.0 +aiofiles==23.2.1 +aiohttp @ file:///tmp/build/80754af9/aiohttp_1646806365504/work +aiosignal @ file:///tmp/build/80754af9/aiosignal_1637843061372/work +altair==5.1.2 +annotated-types==0.6.0 +antlr4-python3-runtime==4.9.3 +anyio==3.7.1 +apex @ file:///home/ubuntu/apex +appdirs==1.4.4 +asttokens==2.2.1 +async-timeout @ file:///opt/conda/conda-bld/async-timeout_1664876359750/work +attrs==23.1.0 +backcall==0.2.0 +basicsr==1.4.2 +beautifulsoup4==4.12.2 +black==23.10.0 +blinker==1.4 +boto3==1.28.17 +botocore==1.31.17 +Bottleneck @ file:///tmp/build/80754af9/bottleneck_1648028898966/work +braceexpand==0.1.7 +brotlipy==0.7.0 +bytecode==0.15.0 +cachetools @ file:///tmp/build/80754af9/cachetools_1619597386817/work +certifi @ file:///croot/certifi_1690232220950/work/certifi +cffi @ file:///opt/conda/conda-bld/cffi_1642701102775/work +charset-normalizer @ file:///tmp/build/80754af9/charset-normalizer_1630003229654/work +click @ file:///tmp/build/80754af9/click_1646056590078/work +clip==0.2.0 +cloudpickle==3.0.0 +cmake==3.26.3 +colorama==0.4.6 +contourpy==1.0.7 +cryptography @ file:///croot/cryptography_1677533068310/work +cycler==0.11.0 +# Editable install with no version control (dassl==20.3.4) +-e /home/ubuntu/anaconda3/envs/py37/lib/python3.9/site-packages +datadings==3.4.6 +decorator==5.1.1 +detectron2==0.6 +diffdist==0.1 +diffusers==0.27.2 +dill @ file:///croot/dill_1667919592856/work +docker-pycreds==0.4.0 +einops==0.6.1 +entrypoints==0.3 +exceptiongroup==1.1.3 +executing==1.2.0 +faiss-gpu==1.7.2 +fastapi==0.104.0 +ffmpeg==1.4 +ffmpy==0.3.1 +filelock==3.12.0 +fire==0.5.0 +flake8==3.7.9 +flit_core @ file:///opt/conda/conda-bld/flit-core_1644941570762/work/source/flit_core +fonttools==4.39.4 +frozenlist @ file:///tmp/build/80754af9/frozenlist_1637767113340/work +fsspec==2023.5.0 +ftfy==6.1.1 +future==0.18.3 +fvcore==0.1.5.post20221221 +gdown==4.7.1 +gitdb==4.0.10 +GitPython==3.1.40 +google-auth==2.21.0 +google-auth-oauthlib==0.5.0 +gradio==3.50.2 +gradio_client==0.6.1 +grpcio==1.56.0 +h11==0.14.0 +httpcore==0.18.0 +httpx==0.25.0 +huggingface-hub==0.22.2 +hydra-core==1.3.2 +idna @ file:///croot/idna_1666125576474/work +imageio==2.28.1 +importlib-metadata @ file:///croot/importlib-metadata_1678997070253/work +importlib-resources==5.12.0 +infinibatch==0.1.1 +iopath==0.1.9 +ipdb==0.13.9 +ipython==8.13.2 +isort==4.3.21 +jedi==0.18.2 +Jinja2==3.1.2 +jmespath==1.0.1 +joblib @ file:///croot/joblib_1666298844297/work +jsonschema==4.19.1 +jsonschema-specifications==2023.7.1 +kestrel==0.0.1 +kiwisolver==1.4.4 +kornia==0.7.0 +lazy_loader==0.2 +liac-arff==2.5.0 +lightning-utilities==0.11.2 +lit==16.0.3 +littleutils==0.2.2 +llvmlite==0.41.1 +lmdb==1.4.1 +lxml==4.9.3 +Markdown @ file:///croot/markdown_1671541909495/work +markdown-it-py==2.2.0 +MarkupSafe @ file:///opt/conda/conda-bld/markupsafe_1654597864307/work +matplotlib==3.7.1 +matplotlib-inline==0.1.6 +mccabe==0.6.1 +mdurl==0.1.2 +minio==7.1.15 +mkl-fft==1.3.0 +mkl-random==1.0.2 +mkl-service==2.3.0 +mmcv-full==1.3.16 +mmengine==0.7.3 +mmsegmentation==0.18.0 +model-index==0.1.11 +more-itertools==10.1.0 +mpi4py @ file:///croot/mpi4py_1671223370575/work +mpmath==1.3.0 +msgpack==1.0.5 +msgpack-numpy==0.4.8 +multidict @ file:///opt/conda/conda-bld/multidict_1640703752579/work +MultiScaleDeformableAttention==1.0 +mypy-extensions==1.0.0 +natsort==8.4.0 +networkx==3.1 +ninja==1.11.1 +nltk @ file:///opt/conda/conda-bld/nltk_1645628263994/work +numba==0.58.1 +numexpr @ file:///tmp/build/80754af9/numexpr_1614798502122/work +numpy==1.23.5 +nvidia-cublas-cu11==11.10.3.66 +nvidia-cublas-cu12==12.1.3.1 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file:///croot/pandarallel_1674548472471/work +pandas==1.4.2 +parso==0.8.3 +pathspec==0.11.2 +pathtools==0.1.2 +pexpect==4.8.0 +pickleshare==0.7.5 +Pillow==9.5.0 +platformdirs==3.11.0 +portalocker==2.8.2 +prefetch-generator==1.0.3 +prettytable==3.7.0 +prompt-toolkit==3.0.38 +protobuf==3.20.3 +psutil @ file:///tmp/build/80754af9/psutil_1612297992929/work +ptyprocess==0.7.0 +pure-eval==0.2.2 +pyarrow==12.0.1 +pyasn1 @ file:///Users/ktietz/demo/mc3/conda-bld/pyasn1_1629708007385/work +pyasn1-modules==0.2.1 +pycocotools==2.0.7 +pycodestyle==2.5.0 +pycparser @ file:///tmp/build/80754af9/pycparser_1636541352034/work +pydantic==2.4.2 +pydantic_core==2.10.1 +pydub==0.25.1 +pyflakes==2.1.1 +Pygments==2.15.1 +PyJWT @ file:///opt/conda/conda-bld/pyjwt_1657544592787/work +pyOpenSSL @ file:///croot/pyopenssl_1677607685877/work +pyparsing==3.0.9 +PySocks @ file:///tmp/build/80754af9/pysocks_1605305812635/work +python-dateutil @ file:///tmp/build/80754af9/python-dateutil_1626374649649/work 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+thop==0.1.1.post2209072238 +threadpoolctl==3.1.0 +tifffile==2023.4.12 +tiktoken==0.3.3 +timm==0.4.12 +tokenizers==0.13.3 +toml==0.10.2 +tomli==2.0.1 +toolz==0.12.0 +torch==1.10.1+cu111 +torchaudio==0.10.1+cu111 +torchmetrics==1.3.2 +torchvision==0.11.2+cu111 +tqdm @ file:///croot/tqdm_1679561862951/work +traitlets==5.9.0 +transformers==4.29.1 +triton==2.2.0 +ttach==0.0.3 +typing_extensions==4.8.0 +urllib3 @ file:///croot/urllib3_1680254681959/work +uvicorn==0.23.2 +visualizer==0.0.1 +wandb==0.15.12 +wcwidth==0.2.6 +webdataset==0.2.48 +websockets==11.0.3 +Werkzeug @ file:///croot/werkzeug_1679489717957/work +wilds @ file:///home/ubuntu/wilds-1.2.2-py3-none-any.whl +xmltodict==0.13.0 +yacs==0.1.8 +yapf==0.29.0 +yarl @ file:///tmp/build/80754af9/yarl_1606939947528/work +yfcc100m==1.0.1 +zipp @ file:///croot/zipp_1672387121353/work \ No newline at end of file diff --git a/scripts/dapt/dp_few_shot.sh b/scripts/dapt/dp_few_shot.sh new file mode 100644 index 0000000..cb1c08b --- /dev/null +++ b/scripts/dapt/dp_few_shot.sh @@ -0,0 +1,38 @@ +#!/bin/bash + +#cd ../.. + +# custom config +DATA="/home/ubuntu/Data_file/few_shot_data" #Your data path +TRAINER=MaPLe + +DATASET=$1 #10 dataset from, like "stanford_cars" +CFG=vit_b16_t +MODE=dapt-g + +#seed = ("0" "1" "2") +#method "Uniform" "Uncertainty" "Herding" "Submodular" "Glister" "GraNd" "Craig" "Cal" "Forgetting" +#sample_rate = 0.05 0.1 0.2 0.3 0.5 1.0 #"Uncertainty" "Herding" +# Normally method == "Uniform" and sample_rate = "1.0", which means no data selection process + +for seed in 1 2 3; do + for rate in 1.0; do # + for shot in 1 2 4 8 16; do + for method in "Uniform"; do + echo "Running with seed =${seed} and sample rate=${rate} and Method=${method}" + CUDA_VISIBLE_DEVICES=1 python train.py \ + --root ${DATA} \ + --seed ${seed} \ + --trainer ${TRAINER} \ + --dataset-config-file configs/datasets/${DATASET}.yaml \ + --config-file configs/trainers/${TRAINER}/${CFG}.yaml \ + --output-dir output_few \ + --mode ${MODE} \ + DATASET.NUM_SHOTS ${shot} \ + DATASET.SUBSAMPLE_CLASSES all \ + DATASET.SELECTION_RATIO ${rate} \ + DATASET.SELECTION_METHOD ${method} + done + done + done +done diff --git a/train.py b/train.py new file mode 100644 index 0000000..d3a879b --- /dev/null +++ b/train.py @@ -0,0 +1,367 @@ +import argparse +import torch + +from dassl.utils import setup_logger, set_random_seed, collect_env_info +from dassl.config import get_cfg_default +from dassl.engine import build_trainer +# os['CUDA_LAUNCH_BLOCKING'] = 1 +# custom +import datasets.oxford_pets +import datasets.oxford_flowers +import datasets.fgvc_aircraft +import datasets.dtd +import datasets.eurosat +import datasets.stanford_cars +import datasets.food101 +import datasets.sun397 +import datasets.caltech101 +import datasets.ucf101 +import datasets.imagenet +import datasets.pascal_voc +import datasets.imagenet_sketch +import datasets.imagenetv2 +import datasets.imagenet_a +import datasets.imagenet_r + +import trainers.coop +import trainers.cocoop +import trainers.zsclip +import trainers.maple +import trainers.independentVL +import trainers.vpt +import os +os.environ['CUDA_VISIBLE_DEVICES'] = '1' +import copy + +def print_args(args, cfg): + print("***************") + print("** Arguments **") + print("***************") + optkeys = list(args.__dict__.keys()) + optkeys.sort() + for key in optkeys: + print("{}: {}".format(key, args.__dict__[key])) + print("************") + print("** Config **") + print("************") + print(cfg) + + +def reset_cfg(cfg, args): + if args.root: + cfg.DATASET.ROOT = args.root + + if args.output_dir: + cfg.OUTPUT_DIR = args.output_dir + + if args.resume: + cfg.RESUME = args.resume + + if args.seed: + cfg.SEED = args.seed + + if args.source_domains: + cfg.DATASET.SOURCE_DOMAINS = args.source_domains + + if args.target_domains: + cfg.DATASET.TARGET_DOMAINS = args.target_domains + + if args.transforms: + cfg.INPUT.TRANSFORMS = args.transforms + + if args.trainer: + cfg.TRAINER.NAME = args.trainer + + if args.backbone: + cfg.MODEL.BACKBONE.NAME = args.backbone + + if args.head: + cfg.MODEL.HEAD.NAME = args.head + + if args.dapt_mode: + cfg.TRAINER.DAPT_MODE = args.dapt_mode + +def extend_cfg(cfg): + """ + Add new config variables. + + E.g. + from yacs.config import CfgNode as CN + cfg.TRAINER.MY_MODEL = CN() + cfg.TRAINER.MY_MODEL.PARAM_A = 1. + cfg.TRAINER.MY_MODEL.PARAM_B = 0.5 + cfg.TRAINER.MY_MODEL.PARAM_C = False + """ + from yacs.config import CfgNode as CN + + cfg.TRAINER.COOP = CN() + cfg.TRAINER.COOP.N_CTX = 16 # number of context vectors + cfg.TRAINER.COOP.CSC = False # class-specific context + cfg.TRAINER.COOP.CTX_INIT = "" # initialization words + cfg.TRAINER.COOP.PREC = "fp16" # fp16, fp32, amp + cfg.TRAINER.COOP.CLASS_TOKEN_POSITION = "end" # 'middle' or 'end' or 'front' + + cfg.TRAINER.COCOOP = CN() + cfg.TRAINER.COCOOP.N_CTX = 16 # number of context vectors + cfg.TRAINER.COCOOP.CTX_INIT = "" # initialization words + cfg.TRAINER.COCOOP.PREC = "fp16" # fp16, fp32, amp + + # Config for MaPLe + cfg.TRAINER.MAPLE = CN() + cfg.TRAINER.MAPLE.N_CTX = 2 # number of context vectors + cfg.TRAINER.MAPLE.CTX_INIT = "a photo of a" # initialization words + cfg.TRAINER.MAPLE.PREC = "fp16" # fp16, fp32, amp + cfg.TRAINER.MAPLE.PROMPT_DEPTH = 9 # Max 12, minimum 0, for 1 it will act as shallow MaPLe (J=1) + cfg.DATASET.SUBSAMPLE_CLASSES = "all" # all, base or new + + # Config for independent Vision Language prompting (independent-vlp) + cfg.TRAINER.IVLP = CN() + cfg.TRAINER.IVLP.N_CTX_VISION = 2 # number of context vectors at the vision branch + cfg.TRAINER.IVLP.N_CTX_TEXT = 2 # number of context vectors at the language branch + cfg.TRAINER.IVLP.CTX_INIT = "a photo of a" # initialization words (only for language prompts) + cfg.TRAINER.IVLP.PREC = "fp16" # fp16, fp32, amp + # If both variables below are set to 0, 0, will the config will degenerate to COOP model + cfg.TRAINER.IVLP.PROMPT_DEPTH_VISION = 9 # Max 12, minimum 0, for 0 it will act as shallow MaPLe (J=1) + cfg.TRAINER.IVLP.PROMPT_DEPTH_TEXT = 9 # Max 12, minimum 0, for 0 it will act as shallow MaPLe (J=1) + cfg.DATASET.SUBSAMPLE_CLASSES = "all" # all, base or new + + # Config for only vision side prompting + cfg.TRAINER.VPT = CN() + cfg.TRAINER.VPT.N_CTX_VISION = 2 # number of context vectors at the vision branch + cfg.TRAINER.VPT.CTX_INIT = "a photo of a" # initialization words + cfg.TRAINER.VPT.PREC = "fp16" # fp16, fp32, amp + cfg.TRAINER.VPT.PROMPT_DEPTH_VISION = 1 # if set to 1, will represent shallow vision prompting only + cfg.DATASET.SUBSAMPLE_CLASSES = "all" # all, base or new + + #Configs for Data Selection + #Method ['Uniform', 'Uncertainty' (Entropy, least confidence, margin), + # 'Forgetting', 'Herding', 'Submodular' (GraphCut/Facility Location), 'Glister', + # 'GraNd', 'Craig', 'Cal'] + cfg.DATASET.SELECTION_METHOD = 'Uniform' + cfg.DATASET.SELECTION_RATIO = 1.0 + cfg.DATASET.SELECTION_BATCH_SIZE = 50 + + cfg.OPTIM_SELECTION = copy.deepcopy(cfg.OPTIM) + cfg.OPTIM_SELECTION.NAME = 'sgd' + cfg.OPTIM_SELECTION.LR = 0.0035 + cfg.OPTIM_SELECTION.MAX_EPOCH = 0 #Forgetting is needed + cfg.OPTIM_SELECTION.LR_SCHEDULER = 'cosine' + cfg.OPTIM_SELECTION.WARMUP_EPOCH = 1 + cfg.OPTIM_SELECTION.WARMUP_TYPE = 'constant' + cfg.OPTIM_SELECTION.WARMUP_CONS_LR = 1e-5 + + +def extend_cfg2(cfg): + """ + Add new config variables. + + E.g. + from yacs.config import CfgNode as CN + cfg.TRAINER.MY_MODEL = CN() + cfg.TRAINER.MY_MODEL.PARAM_A = 1. + cfg.TRAINER.MY_MODEL.PARAM_B = 0.5 + cfg.TRAINER.MY_MODEL.PARAM_C = False + """ + from yacs.config import CfgNode as CN + + cfg.TRAINER.COOP = CN() + cfg.TRAINER.COOP.N_CTX = 16 # number of context vectors + cfg.TRAINER.COOP.CSC = False # class-specific context + cfg.TRAINER.COOP.CTX_INIT = "" # initialization words + cfg.TRAINER.COOP.PREC = "fp16" # fp16, fp32, amp + cfg.TRAINER.COOP.CLASS_TOKEN_POSITION = "end" # 'middle' or 'end' or 'front' + + cfg.TRAINER.COCOOP = CN() + cfg.TRAINER.COCOOP.N_CTX = 16 # number of context vectors + cfg.TRAINER.COCOOP.CTX_INIT = "" # initialization words + cfg.TRAINER.COCOOP.PREC = "fp16" # fp16, fp32, amp + + # Config for MaPLe + cfg.TRAINER.MAPLE = CN() + cfg.TRAINER.MAPLE.N_CTX = 2 # number of context vectors + cfg.TRAINER.MAPLE.CTX_INIT = "a photo of a" # initialization words + cfg.TRAINER.MAPLE.PREC = "fp16" # fp16, fp32, amp + cfg.TRAINER.MAPLE.PROMPT_DEPTH = 9 # Max 12, minimum 0, for 1 it will act as shallow MaPLe (J=1) + cfg.DATASET.SUBSAMPLE_CLASSES = "all" # all, base or new + + # Config for independent Vision Language prompting (independent-vlp) + cfg.TRAINER.IVLP = CN() + cfg.TRAINER.IVLP.N_CTX_VISION = 2 # number of context vectors at the vision branch + cfg.TRAINER.IVLP.N_CTX_TEXT = 2 # number of context vectors at the language branch + cfg.TRAINER.IVLP.CTX_INIT = "a photo of a" # initialization words (only for language prompts) + cfg.TRAINER.IVLP.PREC = "fp16" # fp16, fp32, amp + # If both variables below are set to 0, 0, will the config will degenerate to COOP model + cfg.TRAINER.IVLP.PROMPT_DEPTH_VISION = 9 # Max 12, minimum 0, for 0 it will act as shallow MaPLe (J=1) + cfg.TRAINER.IVLP.PROMPT_DEPTH_TEXT = 9 # Max 12, minimum 0, for 0 it will act as shallow MaPLe (J=1) + cfg.DATASET.SUBSAMPLE_CLASSES = "all" # all, base or new + + # Config for only vision side prompting + cfg.TRAINER.VPT = CN() + cfg.TRAINER.VPT.N_CTX_VISION = 2 # number of context vectors at the vision branch + cfg.TRAINER.VPT.CTX_INIT = "a photo of a" # initialization words + cfg.TRAINER.VPT.PREC = "fp16" # fp16, fp32, amp + cfg.TRAINER.VPT.PROMPT_DEPTH_VISION = 1 # if set to 1, will represent shallow vision prompting only + cfg.DATASET.SUBSAMPLE_CLASSES = "all" # all, base or new + + #selection + #Method ['Uniform', 'Uncertainty' (Entropy, least confidence, margin), + # 'Forgetting', 'Herding', 'Submodular' (GraphCut/Facility Location), 'Glister', + # 'GraNd', 'Craig', 'Cal'] + cfg.DATASET.SELECTION_METHOD = 'GraNd' + cfg.DATASET.SELECTION_RATIO = 0.5 + cfg.DATASET.SELECTION_BATCH_SIZE = 50 + + cfg.OPTIM_SELECTION = copy.deepcopy(cfg.OPTIM) + cfg.OPTIM_SELECTION.NAME = 'sgd' + cfg.OPTIM_SELECTION.LR = 0.0035 + cfg.OPTIM_SELECTION.MAX_EPOCH = 0 #Forgetting is needed + cfg.OPTIM_SELECTION.LR_SCHEDULER = 'cosine' + cfg.OPTIM_SELECTION.WARMUP_EPOCH = 1 + cfg.OPTIM_SELECTION.WARMUP_TYPE = 'constant' + cfg.OPTIM_SELECTION.WARMUP_CONS_LR = 1e-5 + +def setup_cfg(args): + cfg = get_cfg_default() + extend_cfg(cfg) + + # 1. From the dataset config file + if args.dataset_config_file: + cfg.merge_from_file(args.dataset_config_file) + + # 2. From the method config file + if args.config_file: + cfg.merge_from_file(args.config_file) + + # 3. From input arguments + reset_cfg(cfg, args) + + # 4. From optional input arguments + cfg.merge_from_list(args.opts) + cfg.OUTPUT_DIR = os.path.join(cfg.OUTPUT_DIR,cfg.DATASET.NAME,cfg.DATASET.SELECTION_METHOD+'_'+str(cfg.DATASET.SELECTION_RATIO),'seed'+str(cfg.SEED)) + cfg.freeze() + + return cfg + + +def setup_cfg2(args): + cfg = get_cfg_default() + extend_cfg2(cfg) + + # 1. From the dataset config file + if args.dataset_config_file: + cfg.merge_from_file(args.dataset_config_file) + + # 2. From the method config file + if args.config_file: + cfg.merge_from_file(args.config_file) + + # 3. From input arguments + reset_cfg(cfg, args) + + # 4. From optional input arguments + cfg.merge_from_list(args.opts) + cfg.OUTPUT_DIR = os.path.join(cfg.OUTPUT_DIR,cfg.DATASET.NAME,cfg.DATASET.SELECTION_METHOD+'_'+str(cfg.DATASET.SELECTION_RATIO),'seed'+str(cfg.SEED)) + cfg.freeze() + + return cfg + +def main(args): + cfg = setup_cfg(args) + cfg2 = setup_cfg2(args) + if cfg.SEED >= 0: + print("Setting fixed seed: {}".format(cfg.SEED)) + set_random_seed(cfg.SEED) + setup_logger(cfg.OUTPUT_DIR) + + if torch.cuda.is_available() and cfg.USE_CUDA: + torch.backends.cudnn.benchmark = True + + print_args(args, cfg) + print("Collecting env info ...") + print("** System info **\n{}\n".format(collect_env_info())) + + trainer = build_trainer(cfg) + # trainer2 = build_trainer(cfg2) + + + if args.eval_only: + if cfg.DATASET.SUBSAMPLE_CLASSES == 'new': + original_weight_output = 'output/'+'/'.join(trainer.output_dir.split('/')[1:]) + trainer.load_model(original_weight_output) + trainer.test_withlabel() + # trainer.test_multi_label() Evaluation for VOC12 + import shutil + shutil.rmtree(original_weight_output+'/MultiModalPromptLearner') + else: + original_weight_output = 'output/' + '/'.join(trainer.output_dir.split('/')[1:]) + trainer.load_model(original_weight_output) + # trainer.load_model(args.model_dir,epoch=5) + trainer.test() + return + + + if not args.no_train: + trainer.train() + if cfg.DATASET.SUBSAMPLE_CLASSES != 'base': + import shutil + shutil.rmtree(trainer.output_dir+'/MultiModalPromptLearner') + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--root", type=str, default="", help="path to dataset") + parser.add_argument("--output-dir", type=str, default="", help="output directory") + parser.add_argument( + "--resume", + type=str, + default="", + help="checkpoint directory (from which the training resumes)", + ) + parser.add_argument( + "--seed", type=int, default=-1, help="only positive value enables a fixed seed" + ) + parser.add_argument( + "--source-domains", type=str, nargs="+", help="source domains for DA/DG" + ) + parser.add_argument( + "--target-domains", type=str, nargs="+", help="target domains for DA/DG" + ) + parser.add_argument( + "--transforms", type=str, nargs="+", help="data augmentation methods" + ) + parser.add_argument( + "--config-file", type=str, default="", help="path to config file" + ) + parser.add_argument( + "--dataset-config-file", + type=str, + default="", + help="path to config file for dataset setup", + ) + parser.add_argument("--trainer", type=str, default="", help="name of trainer") + parser.add_argument("--backbone", type=str, default="", help="name of CNN backbone") + parser.add_argument("--head", type=str, default="", help="name of head") + parser.add_argument("--eval-only", action="store_true", help="evaluation only") + parser.add_argument( + "--model-dir", + type=str, + default="", + help="load model from this directory for eval-only mode", + ) + parser.add_argument( + "--load-epoch", type=int, help="load model weights at this epoch for evaluation" + ) + parser.add_argument( + "--no-train", action="store_true", help="do not call trainer.train()" + ) + parser.add_argument( + "--dapt-mode", default="dapt-s", help="whether using dapt-s or dapt-g" + ) + parser.add_argument( + "opts", + default=None, + nargs=argparse.REMAINDER, + help="modify config options using the command-line", + ) + args = parser.parse_args() + main(args) diff --git a/trainers/__init__.py b/trainers/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/trainers/__pycache__/__init__.cpython-39.pyc b/trainers/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 0000000..ed654d6 Binary files /dev/null and b/trainers/__pycache__/__init__.cpython-39.pyc differ diff --git a/trainers/__pycache__/cocoop.cpython-39.pyc b/trainers/__pycache__/cocoop.cpython-39.pyc new file mode 100644 index 0000000..61bb840 Binary files /dev/null and b/trainers/__pycache__/cocoop.cpython-39.pyc differ diff --git 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TRAINER_REGISTRY, TrainerX +from dassl.metrics import compute_accuracy +from dassl.utils import load_pretrained_weights, load_checkpoint +from dassl.optim import build_optimizer, build_lr_scheduler + +from clip import clip +from clip.simple_tokenizer import SimpleTokenizer as _Tokenizer + +_tokenizer = _Tokenizer() + + +def load_clip_to_cpu(cfg): + backbone_name = cfg.MODEL.BACKBONE.NAME + url = clip._MODELS[backbone_name] + model_path = clip._download(url) + + try: + # loading JIT archive + model = torch.jit.load(model_path, map_location="cpu").eval() + state_dict = None + + except RuntimeError: + state_dict = torch.load(model_path, map_location="cpu") + design_details = {"trainer": 'CoCoOp', + "vision_depth": 0, + "language_depth": 0, "vision_ctx": 0, + "language_ctx": 0} + model = clip.build_model(state_dict or model.state_dict(), design_details) + + return model + + +class TextEncoder(nn.Module): + def __init__(self, clip_model): + super().__init__() + self.transformer = clip_model.transformer + self.positional_embedding = clip_model.positional_embedding + self.ln_final = clip_model.ln_final + self.text_projection = clip_model.text_projection + self.dtype = clip_model.dtype + + def forward(self, prompts, tokenized_prompts): + x = prompts + self.positional_embedding.type(self.dtype) + x = x.permute(1, 0, 2) # NLD -> LND + x = self.transformer(x) + x = x.permute(1, 0, 2) # LND -> NLD + x = self.ln_final(x).type(self.dtype) + + # x.shape = [batch_size, n_ctx, transformer.width] + # take features from the eot embedding (eot_token is the highest number in each sequence) + x = x[torch.arange(x.shape[0]), tokenized_prompts.argmax(dim=-1)] @ self.text_projection + + return x + + +class PromptLearner(nn.Module): + def __init__(self, cfg, classnames, clip_model): + super().__init__() + n_cls = len(classnames) + n_ctx = cfg.TRAINER.COCOOP.N_CTX + ctx_init = cfg.TRAINER.COCOOP.CTX_INIT + dtype = clip_model.dtype + ctx_dim = clip_model.ln_final.weight.shape[0] + vis_dim = clip_model.visual.output_dim + clip_imsize = clip_model.visual.input_resolution + cfg_imsize = cfg.INPUT.SIZE[0] + assert cfg_imsize == clip_imsize, f"cfg_imsize ({cfg_imsize}) must equal to clip_imsize ({clip_imsize})" + + if ctx_init: + # use given words to initialize context vectors + ctx_init = ctx_init.replace("_", " ") + n_ctx = len(ctx_init.split(" ")) + prompt = clip.tokenize(ctx_init) + with torch.no_grad(): + embedding = clip_model.token_embedding(prompt).type(dtype) + ctx_vectors = embedding[0, 1: 1 + n_ctx, :] + prompt_prefix = ctx_init + else: + # random initialization + ctx_vectors = torch.empty(n_ctx, ctx_dim, dtype=dtype) + nn.init.normal_(ctx_vectors, std=0.02) + prompt_prefix = " ".join(["X"] * n_ctx) + + print(f'Initial context: "{prompt_prefix}"') + print(f"Number of context words (tokens): {n_ctx}") + + self.ctx = nn.Parameter(ctx_vectors) + + self.meta_net = nn.Sequential(OrderedDict([ + ("linear1", nn.Linear(vis_dim, vis_dim // 16)), + ("relu", nn.ReLU(inplace=True)), + ("linear2", nn.Linear(vis_dim // 16, ctx_dim)) + ])) + + if cfg.TRAINER.COCOOP.PREC == "fp16": + self.meta_net.half() + + classnames = [name.replace("_", " ") for name in classnames] + name_lens = [len(_tokenizer.encode(name)) for name in classnames] + prompts = [prompt_prefix + " " + name + "." for name in classnames] + + tokenized_prompts = torch.cat([clip.tokenize(p) for p in prompts]) # (n_cls, n_tkn) + with torch.no_grad(): + embedding = clip_model.token_embedding(tokenized_prompts).type(dtype) + + # These token vectors will be saved when in save_model(), + # but they should be ignored in load_model() as we want to use + # those computed using the current class names + self.register_buffer("token_prefix", embedding[:, :1, :]) # SOS + self.register_buffer("token_suffix", embedding[:, 1 + n_ctx:, :]) # CLS, EOS + + self.n_cls = n_cls + self.n_ctx = n_ctx + self.tokenized_prompts = tokenized_prompts # torch.Tensor + self.name_lens = name_lens + + def construct_prompts(self, ctx, prefix, suffix, label=None): + # dim0 is either batch_size (during training) or n_cls (during testing) + # ctx: context tokens, with shape of (dim0, n_ctx, ctx_dim) + # prefix: the sos token, with shape of (n_cls, 1, ctx_dim) + # suffix: remaining tokens, with shape of (n_cls, *, ctx_dim) + + if label is not None: + prefix = prefix[label] + suffix = suffix[label] + + prompts = torch.cat( + [ + prefix, # (dim0, 1, dim) + ctx, # (dim0, n_ctx, dim) + suffix, # (dim0, *, dim) + ], + dim=1, + ) + + return prompts + + def forward(self, im_features): + prefix = self.token_prefix + suffix = self.token_suffix + ctx = self.ctx # (n_ctx, ctx_dim) + bias = self.meta_net(im_features) # (batch, ctx_dim) + bias = bias.unsqueeze(1) # (batch, 1, ctx_dim) + ctx = ctx.unsqueeze(0) # (1, n_ctx, ctx_dim) + ctx_shifted = ctx + bias # (batch, n_ctx, ctx_dim) + + # Use instance-conditioned context tokens for all classes + prompts = [] + for ctx_shifted_i in ctx_shifted: + ctx_i = ctx_shifted_i.unsqueeze(0).expand(self.n_cls, -1, -1) + pts_i = self.construct_prompts(ctx_i, prefix, suffix) # (n_cls, n_tkn, ctx_dim) + prompts.append(pts_i) + prompts = torch.stack(prompts) + + return prompts + + +class CustomCLIP(nn.Module): + def __init__(self, cfg, classnames, clip_model): + super().__init__() + self.prompt_learner = PromptLearner(cfg, classnames, clip_model) + self.tokenized_prompts = self.prompt_learner.tokenized_prompts + self.image_encoder = clip_model.visual + self.text_encoder = TextEncoder(clip_model) + self.logit_scale = clip_model.logit_scale + self.dtype = clip_model.dtype + + def forward(self, image, label=None): + tokenized_prompts = self.tokenized_prompts + logit_scale = self.logit_scale.exp() + + image_features = self.image_encoder(image.type(self.dtype)) + image_features = image_features / image_features.norm(dim=-1, keepdim=True) + + prompts = self.prompt_learner(image_features) + + logits = [] + for pts_i, imf_i in zip(prompts, image_features): + text_features = self.text_encoder(pts_i, tokenized_prompts) + text_features = text_features / text_features.norm(dim=-1, keepdim=True) + l_i = logit_scale * imf_i @ text_features.t() + logits.append(l_i) + logits = torch.stack(logits) + + if self.prompt_learner.training: + return F.cross_entropy(logits, label) + + return logits + + +@TRAINER_REGISTRY.register() +class CoCoOp(TrainerX): + def check_cfg(self, cfg): + assert cfg.TRAINER.COCOOP.PREC in ["fp16", "fp32", "amp"] + + def build_model(self): + cfg = self.cfg + classnames = self.dm.dataset.classnames + + print(f"Loading CLIP (backbone: {cfg.MODEL.BACKBONE.NAME})") + clip_model = load_clip_to_cpu(cfg) + + if cfg.TRAINER.COCOOP.PREC == "fp32" or cfg.TRAINER.COCOOP.PREC == "amp": + # CLIP's default precision is fp16 + clip_model.float() + + print("Building custom CLIP") + self.model = CustomCLIP(cfg, classnames, clip_model) + + print("Turning off gradients in both the image and the text encoder") + name_to_update = "prompt_learner" + + for name, param in self.model.named_parameters(): + if name_to_update not in name: + param.requires_grad_(False) + + # Double check + enabled = set() + for name, param in self.model.named_parameters(): + if param.requires_grad: + enabled.add(name) + print(f"Parameters to be updated: {enabled}") + + if cfg.MODEL.INIT_WEIGHTS: + load_pretrained_weights(self.model.prompt_learner, cfg.MODEL.INIT_WEIGHTS) + + self.model.to(self.device) + # NOTE: only give prompt_learner to the optimizer + self.optim = build_optimizer(self.model.prompt_learner, cfg.OPTIM) + self.sched = build_lr_scheduler(self.optim, cfg.OPTIM) + self.register_model("prompt_learner", self.model.prompt_learner, self.optim, self.sched) + + self.scaler = GradScaler() if cfg.TRAINER.COCOOP.PREC == "amp" else None + + # Note that multi-gpu training could be slow because CLIP's size is + # big, which slows down the copy operation in DataParallel + device_count = torch.cuda.device_count() + if device_count > 1: + print(f"Multiple GPUs detected (n_gpus={device_count}), use all of them!") + self.model = nn.DataParallel(self.model) + + def forward_backward(self, batch): + image, label = self.parse_batch_train(batch) + + model = self.model + optim = self.optim + scaler = self.scaler + + prec = self.cfg.TRAINER.COCOOP.PREC + if prec == "amp": + with autocast(): + loss = model(image, label) + optim.zero_grad() + scaler.scale(loss).backward() + scaler.step(optim) + scaler.update() + else: + loss = model(image, label) + optim.zero_grad() + loss.backward() + optim.step() + + loss_summary = {"loss": loss.item()} + + if (self.batch_idx + 1) == self.num_batches: + self.update_lr() + + return loss_summary + + def parse_batch_train(self, batch): + input = batch["img"] + label = batch["label"] + input = input.to(self.device) + label = label.to(self.device) + return input, label + + def load_model(self, directory, epoch=None): + if not directory: + print("Note that load_model() is skipped as no pretrained model is given") + return + + names = self.get_model_names() + + # By default, the best model is loaded + model_file = "model-best.pth.tar" + + if epoch is not None: + model_file = "model.pth.tar-" + str(epoch) + + for name in names: + model_path = osp.join(directory, name, model_file) + + if not osp.exists(model_path): + raise FileNotFoundError('Model not found at "{}"'.format(model_path)) + + checkpoint = load_checkpoint(model_path) + state_dict = checkpoint["state_dict"] + epoch = checkpoint["epoch"] + + # Ignore fixed token vectors + if "token_prefix" in state_dict: + del state_dict["token_prefix"] + + if "token_suffix" in state_dict: + del state_dict["token_suffix"] + + print("Loading weights to {} " 'from "{}" (epoch = {})'.format(name, model_path, epoch)) + # set strict=False + self._models[name].load_state_dict(state_dict, strict=False) diff --git a/trainers/coop.py b/trainers/coop.py new file mode 100644 index 0000000..9ada6ec --- /dev/null +++ b/trainers/coop.py @@ -0,0 +1,328 @@ +import os.path as osp + +import torch +import torch.nn as nn +from torch.nn import functional as F +from torch.cuda.amp import GradScaler, autocast + +from dassl.engine import TRAINER_REGISTRY, TrainerX +from dassl.metrics import compute_accuracy +from dassl.utils import load_pretrained_weights, load_checkpoint +from dassl.optim import build_optimizer, build_lr_scheduler + +from clip import clip +from clip.simple_tokenizer import SimpleTokenizer as _Tokenizer + +_tokenizer = _Tokenizer() + + +def load_clip_to_cpu(cfg): + backbone_name = cfg.MODEL.BACKBONE.NAME + url = clip._MODELS[backbone_name] + model_path = clip._download(url) + + try: + # loading JIT archive + model = torch.jit.load(model_path, map_location="cpu").eval() + state_dict = None + + except RuntimeError: + state_dict = torch.load(model_path, map_location="cpu") + design_details = {"trainer": 'CoOp', + "vision_depth": 0, + "language_depth": 0, "vision_ctx": 0, + "language_ctx": 0} + model = clip.build_model(state_dict or model.state_dict(), design_details) + + return model + + +class TextEncoder(nn.Module): + def __init__(self, clip_model): + super().__init__() + self.transformer = clip_model.transformer + self.positional_embedding = clip_model.positional_embedding + self.ln_final = clip_model.ln_final + self.text_projection = clip_model.text_projection + self.dtype = clip_model.dtype + + def forward(self, prompts, tokenized_prompts): + x = prompts + self.positional_embedding.type(self.dtype) + x = x.permute(1, 0, 2) # NLD -> LND + x = self.transformer(x) + x = x.permute(1, 0, 2) # LND -> NLD + x = self.ln_final(x).type(self.dtype) + + # x.shape = [batch_size, n_ctx, transformer.width] + # take features from the eot embedding (eot_token is the highest number in each sequence) + x = x[torch.arange(x.shape[0]), tokenized_prompts.argmax(dim=-1)] @ self.text_projection + + return x + + +class PromptLearner(nn.Module): + def __init__(self, cfg, classnames, clip_model): + super().__init__() + n_cls = len(classnames) + n_ctx = cfg.TRAINER.COOP.N_CTX + ctx_init = cfg.TRAINER.COOP.CTX_INIT + dtype = clip_model.dtype + ctx_dim = clip_model.ln_final.weight.shape[0] + clip_imsize = clip_model.visual.input_resolution + cfg_imsize = cfg.INPUT.SIZE[0] + assert cfg_imsize == clip_imsize, f"cfg_imsize ({cfg_imsize}) must equal to clip_imsize ({clip_imsize})" + + if ctx_init: + # use given words to initialize context vectors + ctx_init = ctx_init.replace("_", " ") + n_ctx = len(ctx_init.split(" ")) + prompt = clip.tokenize(ctx_init) + with torch.no_grad(): + embedding = clip_model.token_embedding(prompt).type(dtype) + ctx_vectors = embedding[0, 1 : 1 + n_ctx, :] + prompt_prefix = ctx_init + + else: + # random initialization + if cfg.TRAINER.COOP.CSC: + print("Initializing class-specific contexts") + ctx_vectors = torch.empty(n_cls, n_ctx, ctx_dim, dtype=dtype) + else: + print("Initializing a generic context") + ctx_vectors = torch.empty(n_ctx, ctx_dim, dtype=dtype) + nn.init.normal_(ctx_vectors, std=0.02) + prompt_prefix = " ".join(["X"] * n_ctx) + + print(f'Initial context: "{prompt_prefix}"') + print(f"Number of context words (tokens): {n_ctx}") + + self.ctx = nn.Parameter(ctx_vectors) # to be optimized + + classnames = [name.replace("_", " ") for name in classnames] + name_lens = [len(_tokenizer.encode(name)) for name in classnames] + prompts = [prompt_prefix + " " + name + "." for name in classnames] + + tokenized_prompts = torch.cat([clip.tokenize(p) for p in prompts]) + with torch.no_grad(): + embedding = clip_model.token_embedding(tokenized_prompts).type(dtype) + + # These token vectors will be saved when in save_model(), + # but they should be ignored in load_model() as we want to use + # those computed using the current class names + self.register_buffer("token_prefix", embedding[:, :1, :]) # SOS + self.register_buffer("token_suffix", embedding[:, 1 + n_ctx :, :]) # CLS, EOS + + self.n_cls = n_cls + self.n_ctx = n_ctx + self.tokenized_prompts = tokenized_prompts # torch.Tensor + self.name_lens = name_lens + self.class_token_position = cfg.TRAINER.COOP.CLASS_TOKEN_POSITION + + def forward(self): + ctx = self.ctx + if ctx.dim() == 2: + ctx = ctx.unsqueeze(0).expand(self.n_cls, -1, -1) + + prefix = self.token_prefix + suffix = self.token_suffix + + if self.class_token_position == "end": + prompts = torch.cat( + [ + prefix, # (n_cls, 1, dim) + ctx, # (n_cls, n_ctx, dim) + suffix, # (n_cls, *, dim) + ], + dim=1, + ) + + elif self.class_token_position == "middle": + half_n_ctx = self.n_ctx // 2 + prompts = [] + for i in range(self.n_cls): + name_len = self.name_lens[i] + prefix_i = prefix[i : i + 1, :, :] + class_i = suffix[i : i + 1, :name_len, :] + suffix_i = suffix[i : i + 1, name_len:, :] + ctx_i_half1 = ctx[i : i + 1, :half_n_ctx, :] + ctx_i_half2 = ctx[i : i + 1, half_n_ctx:, :] + prompt = torch.cat( + [ + prefix_i, # (1, 1, dim) + ctx_i_half1, # (1, n_ctx//2, dim) + class_i, # (1, name_len, dim) + ctx_i_half2, # (1, n_ctx//2, dim) + suffix_i, # (1, *, dim) + ], + dim=1, + ) + prompts.append(prompt) + prompts = torch.cat(prompts, dim=0) + + elif self.class_token_position == "front": + prompts = [] + for i in range(self.n_cls): + name_len = self.name_lens[i] + prefix_i = prefix[i : i + 1, :, :] + class_i = suffix[i : i + 1, :name_len, :] + suffix_i = suffix[i : i + 1, name_len:, :] + ctx_i = ctx[i : i + 1, :, :] + prompt = torch.cat( + [ + prefix_i, # (1, 1, dim) + class_i, # (1, name_len, dim) + ctx_i, # (1, n_ctx, dim) + suffix_i, # (1, *, dim) + ], + dim=1, + ) + prompts.append(prompt) + prompts = torch.cat(prompts, dim=0) + + else: + raise ValueError + + return prompts + + +class CustomCLIP(nn.Module): + def __init__(self, cfg, classnames, clip_model): + super().__init__() + self.prompt_learner = PromptLearner(cfg, classnames, clip_model) + self.tokenized_prompts = self.prompt_learner.tokenized_prompts + self.image_encoder = clip_model.visual + self.text_encoder = TextEncoder(clip_model) + self.logit_scale = clip_model.logit_scale + self.dtype = clip_model.dtype + + def forward(self, image): + image_features = self.image_encoder(image.type(self.dtype)) + + prompts = self.prompt_learner() + tokenized_prompts = self.tokenized_prompts + text_features = self.text_encoder(prompts, tokenized_prompts) + + image_features = image_features / image_features.norm(dim=-1, keepdim=True) + text_features = text_features / text_features.norm(dim=-1, keepdim=True) + + logit_scale = self.logit_scale.exp() + logits = logit_scale * image_features @ text_features.t() + + return logits + + +@TRAINER_REGISTRY.register() +class CoOp(TrainerX): + """Context Optimization (CoOp). + + Learning to Prompt for Vision-Language Models + https://arxiv.org/abs/2109.01134 + """ + + def check_cfg(self, cfg): + assert cfg.TRAINER.COOP.PREC in ["fp16", "fp32", "amp"] + + def build_model(self): + cfg = self.cfg + classnames = self.dm.dataset.classnames + + print(f"Loading CLIP (backbone: {cfg.MODEL.BACKBONE.NAME})") + clip_model = load_clip_to_cpu(cfg) + + if cfg.TRAINER.COOP.PREC == "fp32" or cfg.TRAINER.COOP.PREC == "amp": + # CLIP's default precision is fp16 + clip_model.float() + + print("Building custom CLIP") + self.model = CustomCLIP(cfg, classnames, clip_model) + + print("Turning off gradients in both the image and the text encoder") + for name, param in self.model.named_parameters(): + if "prompt_learner" not in name: + param.requires_grad_(False) + + if cfg.MODEL.INIT_WEIGHTS: + load_pretrained_weights(self.model.prompt_learner, cfg.MODEL.INIT_WEIGHTS) + + self.model.to(self.device) + # NOTE: only give prompt_learner to the optimizer + self.optim = build_optimizer(self.model.prompt_learner, cfg.OPTIM) + self.sched = build_lr_scheduler(self.optim, cfg.OPTIM) + self.register_model("prompt_learner", self.model.prompt_learner, self.optim, self.sched) + + self.scaler = GradScaler() if cfg.TRAINER.COOP.PREC == "amp" else None + + # Note that multi-gpu training could be slow because CLIP's size is + # big, which slows down the copy operation in DataParallel + device_count = torch.cuda.device_count() + if device_count > 1: + print(f"Multiple GPUs detected (n_gpus={device_count}), use all of them!") + self.model = nn.DataParallel(self.model) + + def forward_backward(self, batch): + image, label = self.parse_batch_train(batch) + + prec = self.cfg.TRAINER.COOP.PREC + if prec == "amp": + with autocast(): + output = self.model(image) + loss = F.cross_entropy(output, label) + self.optim.zero_grad() + self.scaler.scale(loss).backward() + self.scaler.step(self.optim) + self.scaler.update() + else: + output = self.model(image) + loss = F.cross_entropy(output, label) + self.model_backward_and_update(loss) + + loss_summary = { + "loss": loss.item(), + "acc": compute_accuracy(output, label)[0].item(), + } + + if (self.batch_idx + 1) == self.num_batches: + self.update_lr() + + return loss_summary + + def parse_batch_train(self, batch): + input = batch["img"] + label = batch["label"] + input = input.to(self.device) + label = label.to(self.device) + return input, label + + def load_model(self, directory, epoch=None): + if not directory: + print("Note that load_model() is skipped as no pretrained model is given") + return + + names = self.get_model_names() + + # By default, the best model is loaded + model_file = "model-best.pth.tar" + + if epoch is not None: + model_file = "model.pth.tar-" + str(epoch) + + for name in names: + model_path = osp.join(directory, name, model_file) + + if not osp.exists(model_path): + raise FileNotFoundError('Model not found at "{}"'.format(model_path)) + + checkpoint = load_checkpoint(model_path) + state_dict = checkpoint["state_dict"] + epoch = checkpoint["epoch"] + + # Ignore fixed token vectors + if "token_prefix" in state_dict: + del state_dict["token_prefix"] + + if "token_suffix" in state_dict: + del state_dict["token_suffix"] + + print("Loading weights to {} " 'from "{}" (epoch = {})'.format(name, model_path, epoch)) + # set strict=False + self._models[name].load_state_dict(state_dict, strict=False) diff --git a/trainers/imagenet_templates.py b/trainers/imagenet_templates.py new file mode 100644 index 0000000..560c5a5 --- /dev/null +++ b/trainers/imagenet_templates.py @@ -0,0 +1,94 @@ +# source: https://github.com/openai/CLIP/blob/main/notebooks/Prompt_Engineering_for_ImageNet.ipynb + +IMAGENET_TEMPLATES = [ + "a bad photo of a {}.", + "a photo of many {}.", + "a sculpture of a {}.", + "a photo of the hard to see {}.", + "a low resolution photo of the {}.", + "a rendering of a {}.", + "graffiti of a {}.", + "a bad photo of the {}.", + "a cropped photo of the {}.", + "a tattoo of a {}.", + "the embroidered {}.", + "a photo of a hard to see {}.", + "a bright photo of a {}.", + "a photo of a clean {}.", + "a photo of a dirty {}.", + "a dark photo of the {}.", + "a drawing of a {}.", + "a photo of my {}.", + "the plastic {}.", + "a photo of the cool {}.", + "a close-up photo of a {}.", + "a black and white photo of the {}.", + "a painting of the {}.", + "a painting of a {}.", + "a pixelated photo of the {}.", + "a sculpture of the {}.", + "a bright photo of the {}.", + "a cropped photo of a {}.", + "a plastic {}.", + "a photo of the dirty {}.", + "a jpeg corrupted photo of a {}.", + "a blurry photo of the {}.", + "a photo of the {}.", + "a good photo of the {}.", + "a rendering of the {}.", + "a {} in a video game.", + "a photo of one {}.", + "a doodle of a {}.", + "a close-up photo of the {}.", + "a photo of a {}.", + "the origami {}.", + "the {} in a video game.", + "a sketch of a {}.", + "a doodle of the {}.", + "a origami {}.", + "a low resolution photo of a {}.", + "the toy {}.", + "a rendition of the {}.", + "a photo of the clean {}.", + "a photo of a large {}.", + "a rendition of a {}.", + "a photo of a nice {}.", + "a photo of a weird {}.", + "a blurry photo of a {}.", + "a cartoon {}.", + "art of a {}.", + "a sketch of the {}.", + "a embroidered {}.", + "a pixelated photo of a {}.", + "itap of the {}.", + "a jpeg corrupted photo of the {}.", + "a good photo of a {}.", + "a plushie {}.", + "a photo of the nice {}.", + "a photo of the small {}.", + "a photo of the weird {}.", + "the cartoon {}.", + "art of the {}.", + "a drawing of the {}.", + "a photo of the large {}.", + "a black and white photo of a {}.", + "the plushie {}.", + "a dark photo of a {}.", + "itap of a {}.", + "graffiti of the {}.", + "a toy {}.", + "itap of my {}.", + "a photo of a cool {}.", + "a photo of a small {}.", + "a tattoo of the {}.", +] + +IMAGENET_TEMPLATES_SELECT = [ + "itap of a {}.", + "a bad photo of the {}.", + "a origami {}.", + "a photo of the large {}.", + "a {} in a video game.", + "art of the {}.", + "a photo of the small {}.", +] diff --git a/trainers/independentVL.py b/trainers/independentVL.py new file mode 100644 index 0000000..7e51b04 --- /dev/null +++ b/trainers/independentVL.py @@ -0,0 +1,304 @@ +import os.path as osp +from collections import OrderedDict +import math + +import torch +import torch.nn as nn +from torch.nn import functional as F +from torch.cuda.amp import GradScaler, autocast + +from dassl.engine import TRAINER_REGISTRY, TrainerX +from dassl.metrics import compute_accuracy +from dassl.utils import load_pretrained_weights, load_checkpoint +from dassl.optim import build_optimizer, build_lr_scheduler + +from clip import clip +from clip.simple_tokenizer import SimpleTokenizer as _Tokenizer + +_tokenizer = _Tokenizer() + + +def load_clip_to_cpu(cfg): + backbone_name = cfg.MODEL.BACKBONE.NAME + url = clip._MODELS[backbone_name] + model_path = clip._download(url) + + try: + # loading JIT archive + model = torch.jit.load(model_path, map_location="cpu").eval() + state_dict = None + + except RuntimeError: + state_dict = torch.load(model_path, map_location="cpu") + design_details = {"trainer": 'IVLP', + "vision_depth": cfg.TRAINER.IVLP.PROMPT_DEPTH_VISION, + "language_depth": cfg.TRAINER.IVLP.PROMPT_DEPTH_TEXT, "vision_ctx": cfg.TRAINER.IVLP.N_CTX_VISION, + "language_ctx": cfg.TRAINER.IVLP.N_CTX_TEXT} + model = clip.build_model(state_dict or model.state_dict(), design_details) + + return model + + +class TextEncoder(nn.Module): + def __init__(self, clip_model): + super().__init__() + self.transformer = clip_model.transformer + self.positional_embedding = clip_model.positional_embedding + self.ln_final = clip_model.ln_final + self.text_projection = clip_model.text_projection + self.dtype = clip_model.dtype + + def forward(self, prompts, tokenized_prompts): + x = prompts + self.positional_embedding.type(self.dtype) + x = x.permute(1, 0, 2) # NLD -> LND + x = self.transformer(x) + x = x.permute(1, 0, 2) # LND -> NLD + x = self.ln_final(x).type(self.dtype) + + # x.shape = [batch_size, n_ctx, transformer.width] + # take features from the eot embedding (eot_token is the highest number in each sequence) + x = x[torch.arange(x.shape[0]), tokenized_prompts.argmax(dim=-1)] @ self.text_projection + + return x + + +class VLPromptLearner(nn.Module): + def __init__(self, cfg, classnames, clip_model): + super().__init__() + n_cls = len(classnames) + # Make sure Language depth >= 1 + assert cfg.TRAINER.IVLP.PROMPT_DEPTH_TEXT >= 1, "In Independent VL prompting, Language prompt depth should be >=1" \ + "\nPlease use VPT trainer if you want to learn only vision " \ + "branch " + n_ctx = cfg.TRAINER.IVLP.N_CTX_TEXT + ctx_init = cfg.TRAINER.IVLP.CTX_INIT + dtype = clip_model.dtype + ctx_dim = clip_model.ln_final.weight.shape[0] + vis_dim = clip_model.visual.output_dim + clip_imsize = clip_model.visual.input_resolution + cfg_imsize = cfg.INPUT.SIZE[0] + assert cfg_imsize == clip_imsize, f"cfg_imsize ({cfg_imsize}) must equal to clip_imsize ({clip_imsize})" + + if ctx_init and (n_ctx) <= 4: + # use given words to initialize context vectors + ctx_init = ctx_init.replace("_", " ") + n_ctx = n_ctx + prompt = clip.tokenize(ctx_init) + with torch.no_grad(): + embedding = clip_model.token_embedding(prompt).type(dtype) + ctx_vectors = embedding[0, 1: 1 + n_ctx, :] + prompt_prefix = ctx_init + else: + # random initialization + ctx_vectors = torch.empty(n_ctx, ctx_dim, dtype=dtype) + nn.init.normal_(ctx_vectors, std=0.02) + prompt_prefix = " ".join(["X"] * n_ctx) + print(f"Independent V-L design") + print(f'Initial text context: "{prompt_prefix}"') + print(f"Number of context words (tokens) for Language prompting: {n_ctx}") + print(f"Number of context words (tokens) for Vision prompting: {cfg.TRAINER.IVLP.N_CTX_VISION}") + self.ctx = nn.Parameter(ctx_vectors) + + classnames = [name.replace("_", " ") for name in classnames] + name_lens = [len(_tokenizer.encode(name)) for name in classnames] + prompts = [prompt_prefix + " " + name + "." for name in classnames] + + tokenized_prompts = torch.cat([clip.tokenize(p) for p in prompts]) # (n_cls, n_tkn) + with torch.no_grad(): + embedding = clip_model.token_embedding(tokenized_prompts).type(dtype) + + # These token vectors will be saved when in save_model(), + # but they should be ignored in load_model() as we want to use + # those computed using the current class names + self.register_buffer("token_prefix", embedding[:, :1, :]) # SOS + self.register_buffer("token_suffix", embedding[:, 1 + n_ctx:, :]) # CLS, EOS + + self.n_cls = n_cls + self.n_ctx = n_ctx + self.tokenized_prompts = tokenized_prompts # torch.Tensor + self.name_lens = name_lens + + def construct_prompts(self, ctx, prefix, suffix, label=None): + # dim0 is either batch_size (during training) or n_cls (during testing) + # ctx: context tokens, with shape of (dim0, n_ctx, ctx_dim) + # prefix: the sos token, with shape of (n_cls, 1, ctx_dim) + # suffix: remaining tokens, with shape of (n_cls, *, ctx_dim) + + if label is not None: + prefix = prefix[label] + suffix = suffix[label] + + prompts = torch.cat( + [ + prefix, # (dim0, 1, dim) + ctx, # (dim0, n_ctx, dim) + suffix, # (dim0, *, dim) + ], + dim=1, + ) + + return prompts + + def forward(self): + ctx = self.ctx + if ctx.dim() == 2: + ctx = ctx.unsqueeze(0).expand(self.n_cls, -1, -1) + + prefix = self.token_prefix + suffix = self.token_suffix + prompts = self.construct_prompts(ctx, prefix, suffix) + + return prompts + + +class CustomCLIP(nn.Module): + def __init__(self, cfg, classnames, clip_model): + super().__init__() + self.prompt_learner = VLPromptLearner(cfg, classnames, clip_model) + self.tokenized_prompts = self.prompt_learner.tokenized_prompts + self.image_encoder = clip_model.visual + self.text_encoder = TextEncoder(clip_model) + self.logit_scale = clip_model.logit_scale + self.dtype = clip_model.dtype + + def forward(self, image, label=None): + tokenized_prompts = self.tokenized_prompts + logit_scale = self.logit_scale.exp() + + prompts = self.prompt_learner() + text_features = self.text_encoder(prompts, tokenized_prompts) + image_features = self.image_encoder(image.type(self.dtype)) + + image_features = image_features / image_features.norm(dim=-1, keepdim=True) + text_features = text_features / text_features.norm(dim=-1, keepdim=True) + logits = logit_scale * image_features @ text_features.t() + + if self.prompt_learner.training: + return F.cross_entropy(logits, label) + + return logits + + +@TRAINER_REGISTRY.register() +class IVLP(TrainerX): + def check_cfg(self, cfg): + assert cfg.TRAINER.IVLP.PREC in ["fp16", "fp32", "amp"] + + def build_model(self): + cfg = self.cfg + classnames = self.dm.dataset.classnames + + print(f"Loading CLIP (backbone: {cfg.MODEL.BACKBONE.NAME})") + clip_model = load_clip_to_cpu(cfg) + + if cfg.TRAINER.IVLP.PREC == "fp32" or cfg.TRAINER.IVLP.PREC == "amp": + # CLIP's default precision is fp16 + clip_model.float() + + print("Building custom CLIP") + self.model = CustomCLIP(cfg, classnames, clip_model) + + print("Turning off gradients in both the image and the text encoder") + name_to_update = "prompt_learner" + + for name, param in self.model.named_parameters(): + if name_to_update not in name: + # Make sure that VPT prompts are updated + if "VPT" in name: + param.requires_grad_(True) + else: + param.requires_grad_(False) + + # Double check + enabled = set() + for name, param in self.model.named_parameters(): + if param.requires_grad: + enabled.add(name) + print(f"Parameters to be updated: {enabled}") + + if cfg.MODEL.INIT_WEIGHTS: + load_pretrained_weights(self.model, cfg.MODEL.INIT_WEIGHTS) + + self.model.to(self.device) + # NOTE: only give prompt_learner to the optimizer + self.optim = build_optimizer(self.model, cfg.OPTIM) + self.sched = build_lr_scheduler(self.optim, cfg.OPTIM) + self.register_model("VLPromptLearner", self.model, self.optim, self.sched) + + self.scaler = GradScaler() if cfg.TRAINER.IVLP.PREC == "amp" else None + + # Note that multi-gpu training could be slow because CLIP's size is + # big, which slows down the copy operation in DataParallel + device_count = torch.cuda.device_count() + if device_count > 1: + print(f"Multiple GPUs detected (n_gpus={device_count}), use all of them!") + self.model = nn.DataParallel(self.model) + + def forward_backward(self, batch): + image, label = self.parse_batch_train(batch) + + model = self.model + optim = self.optim + scaler = self.scaler + + prec = self.cfg.TRAINER.IVLP.PREC + if prec == "amp": + with autocast(): + loss = model(image, label) + optim.zero_grad() + scaler.scale(loss).backward() + scaler.step(optim) + scaler.update() + else: + loss = model(image, label) + optim.zero_grad() + loss.backward() + optim.step() + + loss_summary = {"loss": loss.item()} + + if (self.batch_idx + 1) == self.num_batches: + self.update_lr() + + return loss_summary + + def parse_batch_train(self, batch): + input = batch["img"] + label = batch["label"] + input = input.to(self.device) + label = label.to(self.device) + return input, label + + def load_model(self, directory, epoch=None): + if not directory: + print("Note that load_model() is skipped as no pretrained model is given") + return + + names = self.get_model_names() + + # By default, the best model is loaded + model_file = "model-best.pth.tar" + + if epoch is not None: + model_file = "model.pth.tar-" + str(epoch) + + for name in names: + model_path = osp.join(directory, name, model_file) + + if not osp.exists(model_path): + raise FileNotFoundError('Model not found at "{}"'.format(model_path)) + + checkpoint = load_checkpoint(model_path) + state_dict = checkpoint["state_dict"] + epoch = checkpoint["epoch"] + + # Ignore fixed token vectors + if "prompt_learner.token_prefix" in state_dict: + del state_dict["prompt_learner.token_prefix"] + + if "prompt_learner.token_suffix" in state_dict: + del state_dict["prompt_learner.token_suffix"] + + print("Loading weights to {} " 'from "{}" (epoch = {})'.format(name, model_path, epoch)) + # set strict=False + self._models[name].load_state_dict(state_dict, strict=False) diff --git a/trainers/maple.py b/trainers/maple.py new file mode 100644 index 0000000..dcf5f07 --- /dev/null +++ b/trainers/maple.py @@ -0,0 +1,928 @@ +import os.path as osp +import random +from collections import OrderedDict +import math +import copy +import torch +import torch.nn as nn +import time +import os +import pickle +import deepcore.methods as s_method +import numpy as np + +from torch.nn import functional as F +from torch.cuda.amp import GradScaler, autocast + +from dassl.engine import TRAINER_REGISTRY, TrainerX +from dassl.metrics import compute_accuracy +from dassl.utils import load_pretrained_weights, load_checkpoint, mkdir_if_missing +from dassl.optim import build_optimizer, build_lr_scheduler +from dassl.evaluation import Classification,EvaluatorBase +from pygrad.pcgrad import PCGrad +from datasets.data_manager import DataManager +from dassl.data.datasets import build_dataset + + +from clip import clip +from clip.simple_tokenizer import SimpleTokenizer as _Tokenizer +from trainers.zsclip import CUSTOM_TEMPLATES +from .coop import load_clip_to_cpu as lcp +from tqdm import tqdm +from sklearn.metrics import f1_score, confusion_matrix +from collections import OrderedDict, defaultdict +from .util import GradCAM,denorm +import cv2 +_tokenizer = _Tokenizer() + +BACKGROUND_CATEGORY = ['ground','land','grass','tree','building','wall','sky','lake','water','river','sea','railway','railroad','keyboard','helmet', + 'cloud','house','mountain','ocean','road','rock','street','valley','bridge','sign',] + + +#['ground','land','grass','tree','building','wall','sky','lake','water','river','sea','railway','railroad','keyboard','helmet', + #'cloud','house','mountain','ocean','road','rock','street','valley','bridge','sign', + #] + +BACKGROUND_CATEGORY_FOOD = ['table','forks','tablecloth','hands','spoon','glasses','dishes'] + +def load_clip_to_cpu(cfg): + backbone_name = cfg.MODEL.BACKBONE.NAME + url = clip._MODELS[backbone_name] + model_path = clip._download(url) + + try: + # loading JIT archive + model = torch.jit.load(model_path, map_location="cpu").eval() + state_dict = None + + except RuntimeError: + state_dict = torch.load(model_path, map_location="cpu") + design_details = {"trainer": 'MaPLe', + "vision_depth": 0, + "language_depth": 0, "vision_ctx": 0, + "language_ctx": 0, + "maple_length": cfg.TRAINER.MAPLE.N_CTX} + + model = clip.build_model(state_dict or model.state_dict(), design_details) + + return model + + + + + +class TextEncoder(nn.Module): + def __init__(self, clip_model): + super().__init__() + self.transformer = clip_model.transformer + self.positional_embedding = clip_model.positional_embedding + self.ln_final = clip_model.ln_final + self.text_projection = clip_model.text_projection + self.dtype = clip_model.dtype + + def forward(self, prompts, tokenized_prompts, compound_prompts_deeper_text): + x = prompts + self.positional_embedding.type(self.dtype) + x = x.permute(1, 0, 2) # NLD -> LND + # Pass as the list, as nn.sequential cannot process multiple arguments in the forward pass + combined = [x, compound_prompts_deeper_text, 0] # third argument is the counter which denotes depth of prompt + outputs = self.transformer(combined) + x = outputs[0] # extract the x back from here + x = x.permute(1, 0, 2) # LND -> NLD + x = self.ln_final(x).type(self.dtype) + + # x.shape = [batch_size, n_ctx, transformer.width] + # take features from the eot embedding (eot_token is the highest number in each sequence) + x = x[torch.arange(x.shape[0]), tokenized_prompts.argmax(dim=-1)] @ self.text_projection + + return x + + +class MultiModalPromptLearner(nn.Module): + def __init__(self, cfg, classnames, clip_model): + super().__init__() + n_cls = len(classnames) + n_ctx = cfg.TRAINER.MAPLE.N_CTX # n_ctx + ctx_init = cfg.TRAINER.MAPLE.CTX_INIT # a photo of + dtype = clip_model.dtype + ctx_dim = clip_model.ln_final.weight.shape[0] #512 + clip_imsize = clip_model.visual.input_resolution #224 + cfg_imsize = cfg.INPUT.SIZE[0] #224 + # Default is 1, which is compound shallow prompting + assert cfg.TRAINER.MAPLE.PROMPT_DEPTH >= 1, "For MaPLe, PROMPT_DEPTH should be >= 1" + self.compound_prompts_depth = cfg.TRAINER.MAPLE.PROMPT_DEPTH #9 # max=12, but will create 11 such shared prompts + assert cfg_imsize == clip_imsize, f"cfg_imsize ({cfg_imsize}) must equal to clip_imsize ({clip_imsize})" + + if ctx_init and (n_ctx) <= 4: + # use given words to initialize context vectors + ctx_init = ctx_init.replace("_", " ") + n_ctx = n_ctx + prompt = clip.tokenize(ctx_init) + with torch.no_grad(): + embedding = clip_model.token_embedding(prompt).type(dtype) + ctx_vectors = embedding[0, 1: 1 + n_ctx, :] + prompt_prefix = ctx_init + else: + # random initialization + ctx_vectors = torch.empty(n_ctx, ctx_dim, dtype=dtype) + nn.init.normal_(ctx_vectors, std=0.02) + prompt_prefix = " ".join(["X"] * n_ctx) + print('MaPLe design: Multi-modal Prompt Learning') + print(f'Initial context: "{prompt_prefix}"') + print(f"Number of MaPLe context words (tokens): {n_ctx}") + # These below, related to the shallow prompts + # Linear layer so that the tokens will project to 512 and will be initialized from 768 + self.proj = nn.Linear(ctx_dim, 768) + self.proj.half() + self.ctx = nn.Parameter(ctx_vectors) #[2 512] + # These below parameters related to the shared prompts + # Define the compound prompts for the deeper layers + + # Minimum can be 1, which defaults to shallow MaPLe + # compound prompts + self.compound_prompts_text = nn.ParameterList([nn.Parameter(torch.empty(n_ctx, 512)) + for _ in range(self.compound_prompts_depth - 1)]) + for single_para in self.compound_prompts_text: + nn.init.normal_(single_para, std=0.02) + # Also make corresponding projection layers, for each prompt + single_layer = nn.Linear(ctx_dim, 768) + self.compound_prompt_projections = _get_clones(single_layer, self.compound_prompts_depth - 1) + + + + + + + + classnames = [name.replace("_", " ") for name in classnames] + + name_lens = [len(_tokenizer.encode(name)) for name in classnames] + prompts = [prompt_prefix + " " + name + "." for name in classnames] + tokenized_prompts = torch.cat([clip.tokenize(p) for p in prompts]) # (n_cls, n_tkn) + + + ###Introduce Background + bg_template = 'a clean origami {}.' + + bg_classesnames = [bg_template.format(name) for name in BACKGROUND_CATEGORY +BACKGROUND_CATEGORY_FOOD ] + tokenized_bg_prompts = torch.cat([clip.tokenize(bg) for bg in bg_classesnames]) + bg_num = len(BACKGROUND_CATEGORY) + len(BACKGROUND_CATEGORY_FOOD) + tokenized_prompts = torch.cat((tokenized_prompts,tokenized_bg_prompts),dim=0) + + with torch.no_grad(): + embedding = clip_model.token_embedding(tokenized_prompts).type(dtype) + self.bg_embeding = embedding[-bg_num:] + + # These token vectors will be saved when in save_model(), + # but they should be ignored in load_model() as we want to use + # those computed using the current class names + self.register_buffer("token_prefix", embedding[:-bg_num, :1, :]) # SOS + self.register_buffer("token_suffix", embedding[:-bg_num, 1 + n_ctx:, :]) # CLS, EOS + + self.n_cls = n_cls + self.n_ctx = n_ctx + self.tokenized_prompts = tokenized_prompts # torch.Tensor [class_num 77] [:-bg_num] + self.name_lens = name_lens + + def construct_prompts(self, ctx, prefix, suffix, label=None): + # dim0 is either batch_size (during training) or n_cls (during testing) + # ctx: context tokens, with shape of (dim0, n_ctx, ctx_dim) + # prefix: the sos token, with shape of (n_cls, 1, ctx_dim) + # suffix: remaining tokens, with shape of (n_cls, *, ctx_dim) + + + + if label is not None: + prefix = prefix[label] + suffix = suffix[label] + + prompts = torch.cat( + [ + prefix, # (dim0, 1, dim) + ctx, # (dim0, n_ctx, dim) + suffix, # (dim0, *, dim) + ], + dim=1, + ) + + final_prompts = torch.cat((prompts,self.bg_embeding.cuda()),dim=0) + return final_prompts + + def forward(self): + ctx = self.ctx + + if ctx.dim() == 2: + ctx = ctx.unsqueeze(0).expand(self.n_cls, -1, -1) + + prefix = self.token_prefix + suffix = self.token_suffix + prompts = self.construct_prompts(ctx, prefix, suffix) + + # Before returning, need to transform + # prompts to 768 for the visual side + visual_deep_prompts = [] + for index, layer in enumerate(self.compound_prompt_projections): + visual_deep_prompts.append(layer(self.compound_prompts_text[index])) + # Now the other way around + # We will project the textual prompts from 512 to 768 + return prompts, self.proj(self.ctx), self.compound_prompts_text, visual_deep_prompts # pass here original, as for visual 768 is required + + +class CustomCLIP(nn.Module): + def __init__(self, cfg, classnames, clip_model): + super().__init__() + self.prompt_learner = MultiModalPromptLearner(cfg, classnames, clip_model) + self.tokenized_prompts = self.prompt_learner.tokenized_prompts + self.image_encoder = clip_model.visual + self.image_encoder_ori = clip_model.visual_ori + self.text_encoder = TextEncoder(clip_model) + self.logit_scale = clip_model.logit_scale + self.dtype = clip_model.dtype + self.txt_f = [] + self.img_f = [] + self.one_hot_label = [] + self.vtx = [] + self.loaded_mask = None + # self.loss_weights = torch.nn.Parameter(torch.tensor([0.8,0.03],dtype=self.dtype)) + + + + def get_uniform_ball_noise(self,input_shape,radius=1.0): + uniform_noise_ball = torch.randn(input_shape).cuda() + uniform_noise_sphere = F.normalize(uniform_noise_ball,dim=1) + u = torch.rand(input_shape[0]).cuda() + u = u **(1. / input_shape[1]) + uniform_noise_ball = (uniform_noise_sphere.T *u *radius).T + return uniform_noise_ball.type(self.dtype) + + + def get_learnable_noise(self,input_shape): + para = 0.05 + noise = torch.nn.Parameter(torch.randn(input_shape)*para).cuda() + + return noise.type(self.dtype) + + def cos_sim(self,a,b): + return F.cosine_similarity(a,b) + + def forward(self, image, label=None,record=False,cal_gradient=False,weight=None,epoch=None,index=None,cfg=None,mask=None): + tokenized_prompts = self.tokenized_prompts + logit_scale = self.logit_scale.exp() + + prompts, shared_ctx, deep_compound_prompts_text, deep_compound_prompts_vision = self.prompt_learner() + text_features = self.text_encoder(prompts, tokenized_prompts, deep_compound_prompts_text) + text_features_fg = text_features[:-len(BACKGROUND_CATEGORY)] + ori_image_input = image.type(self.dtype) + # text_features = text_features + self.get_learnable_noise(text_features.shape) + + text_features_fg = text_features_fg / text_features_fg.norm(dim=-1, keepdim=True) + + image_features, visual_ctx, mask_similarity = self.image_encoder(ori_image_input, shared_ctx, + deep_compound_prompts_vision) + + + + image_features = image_features / image_features.norm(dim=-1, keepdim=True) + # if label is not None: + # image_features = image_features + self.get_uniform_ball_noise(image_features.shape) + + logits = logit_scale * image_features @ text_features_fg.t() + + + + + + if mask != None: + + + + text_features_bg = text_features[-len(BACKGROUND_CATEGORY):] + text_features_bg = text_features_bg / text_features_bg.norm(dim=-1, keepdim=True) + image_features_fg,_,_ = self.image_encoder(ori_image_input*mask, shared_ctx, deep_compound_prompts_vision) #, shared_ctx, deep_compound_prompts_vision + + + image_features_fg = image_features_fg / image_features_fg.norm(dim=-1, keepdim=True) + image_features_bg,_,_ = self.image_encoder(ori_image_input*(1-mask), shared_ctx, deep_compound_prompts_vision) + image_features_bg = image_features_bg / image_features_bg.norm(dim=-1, keepdim=True) + + + loss_re1 = F.triplet_margin_loss(image_features,image_features_fg.detach(),image_features_bg.detach(),margin=1.5) + + # image_features_fg_ori = self.image_encoder_ori(ori_image_input*mask_random) + # image_features_bg_ori = self.image_encoder_ori(ori_image_input*(1-mask_random)) + # image_features_fg_ori = image_features_fg_ori / image_features_fg_ori.norm(dim=-1, keepdim=True) + # image_features_bg_ori = image_features_bg_ori / image_features_bg_ori.norm(dim=-1,keepdim=True) + # image_features_all_ori = image_features_fg_ori + image_features_bg_ori + # image_features_all_ori = image_features_all_ori / image_features_all_ori.norm(dim=-1,keepdim=True) + # loss_reo = torch.abs(image_features_all_ori.detach() - image_features).mean() + + foreground_score = logit_scale*image_features_fg.detach()@text_features_fg.t() + pseudo_label = torch.argmax(image_features_bg @ text_features_bg.t(), dim=-1) + logits_bg = logit_scale*(image_features_bg) @ text_features_bg.t() + + para_bg = 0.5 + para_fg = 0.1 + para_vd = 0.8 + + + loss_bg = F.cross_entropy(logits_bg,pseudo_label) + loss_fg = F.cross_entropy(foreground_score,label) + + if epoch > 6: #Tunable parameters + loss_re = para_fg*loss_fg + para_bg*loss_bg + else: + loss_re = para_vd*loss_re1 #loss_reo would be effective in base2novel setting + + + if self.prompt_learner.training: + if weight is None: + return F.cross_entropy(logits,label)+loss_re,logits,{'loss_vd':loss_re1.item(),'loss_bg':loss_bg.item(),'loss_fg':loss_fg.item()} + else: + return F.cross_entropy(weight.unsqueeze(-1)*logits,label), logits + + if record: #store the embeeding + one_hot_label = F.one_hot(label,num_classes=text_features.shape[0]).to(torch.float16) + return image_features.detach(),(one_hot_label @ text_features).detach(), logits + + if cal_gradient: + #Treating this as initial gradient + # one_hot_label = F.one_hot(label,num_classes=text_features.shape[0]).to(torch.float16) + return F.cross_entropy(logits.requires_grad_(True), label), image_features.detach(), logits #,(one_hot_label @ text_features).detach() + return logits + + def grad_norm(self,loss_group,original_loss_group): + alpha = 0.10 + self.loss_weights.grad.data = self.loss_weights.grad.data * 0.0 + W = self.prompt_learner.compound_prompt_projections[0] + norms = [] + for i in range(len(loss_group)): + gygw = torch.autograd.grad(loss_group[i],W.parameters(),retain_graph=True) + norms.append(torch.norm(torch.mul(self.loss_weights[i],gygw[0]))) + norms = torch.stack(norms) + loss_ratio = loss_group.data.cpu().numpy() / original_loss_group + inverse_train_rate = loss_ratio / np.mean(loss_ratio) + mean_norm = np.mean(norms.data.cpu().numpy()) + constant_norm = torch.tensor(mean_norm*(inverse_train_rate**alpha),requires_grad=False).cuda() + grad_norm_loss = torch.sum(torch.abs(norms - constant_norm)) + + + + self.loss_weights.grad = torch.autograd.grad(grad_norm_loss,self.loss_weights)[0] + + + + + def forward_test(self, image, label=None,record=False,cal_gradient=False,weight=None,cfg=None,attn_mask=False): + tokenized_prompts = self.tokenized_prompts + logit_scale = self.logit_scale.exp() + + prompts, shared_ctx, deep_compound_prompts_text, deep_compound_prompts_vision = self.prompt_learner() + text_features = self.text_encoder(prompts, tokenized_prompts, deep_compound_prompts_text) + image_features,visual_ctx,mask = self.image_encoder(image.type(self.dtype), shared_ctx, deep_compound_prompts_vision) + + + image_features = image_features / image_features.norm(dim=-1, keepdim=True) + text_features = text_features / text_features.norm(dim=-1, keepdim=True) + logits = logit_scale * image_features @ text_features.t() + + + if self.prompt_learner.training: + if weight is None: + return F.cross_entropy(logits, label),logits + else: + return F.cross_entropy(weight.unsqueeze(-1)*logits,label), logits + + if record: #store the embeeding + one_hot_label = F.one_hot(label,num_classes=text_features.shape[0]).to(torch.float16) + return image_features.detach(),(one_hot_label @ text_features).detach(), logits + if attn_mask: + return logits,mask + if cal_gradient: + #Treating this as initial gradient + # one_hot_label = F.one_hot(label,num_classes=text_features.shape[0]).to(torch.float16) + return F.cross_entropy(logits.requires_grad_(True), label), image_features.detach(), logits #,(one_hot_label @ text_features).detach() + return logits + +def _get_clones(module, N): + return nn.ModuleList([copy.deepcopy(module) for i in range(N)]) + + +@TRAINER_REGISTRY.register() +class MaPLe(TrainerX): + + + + def check_cfg(self, cfg): + assert cfg.TRAINER.MAPLE.PREC in ["fp16", "fp32", "amp"] + + + + def build_model(self): + cfg = self.cfg + classnames = self.dm.dataset.classnames + + print(f"Loading CLIP (backbone: {cfg.MODEL.BACKBONE.NAME})") + clip_model = load_clip_to_cpu(cfg) + + if cfg.TRAINER.MAPLE.PREC == "fp32" or cfg.TRAINER.MAPLE.PREC == "amp": + # CLIP's default precision is fp16 + clip_model.float() + + print("Building custom CLIP") + self.model = CustomCLIP(cfg, classnames, clip_model) + + print("Turning off gradients in both the image and the text encoder") + name_to_update = "prompt_learner" + + for name, param in self.model.named_parameters(): + + + if name_to_update not in name: + # Make sure that VPT prompts are updated + if "VPT" in name: + param.requires_grad_(True) + else: + param.requires_grad_(False) + + + # Double check + enabled = set() + for name, param in self.model.named_parameters(): + if param.requires_grad: + enabled.add(name) + print(f"Parameters to be updated: {enabled}") + + if cfg.MODEL.INIT_WEIGHTS: + load_pretrained_weights(self.model, cfg.MODEL.INIT_WEIGHTS) + + self.model.to(self.device) + # self.model.loss_weights.requires_grad_(True) #open gradient for loss_weights + # NOTE: only give prompt_learner to the optimizer + + + self.optim = build_optimizer(self.model, cfg.OPTIM) + self.sched = build_lr_scheduler(self.optim, cfg.OPTIM) + + + + self.selected_optim = build_optimizer(self.model, cfg.OPTIM_SELECTION) + self.selected_sched = build_lr_scheduler(self.optim, cfg.OPTIM_SELECTION) + + self.register_model("MultiModalPromptLearner", self.model, self.optim, self.sched) + + self.scaler = GradScaler() if cfg.TRAINER.MAPLE.PREC == "amp" else None + + # Note that multi-gpu training could be slow because CLIP's size is + # big, which slows down the copy operation in DataParallel + # device_count = torch.cuda.device_count() + # if device_count > 1: + # print(f"Multiple GPUs detected (n_gpus={device_count}), use all of them!") + # self.model = nn.DataParallel(self.model) + + + # def generate_text_feature(self): + # cfg = self.cfg + # classnames = self.dm.dataset.classnames + # # + # # print(f"Loading Custom CLIP (backbone: {cfg.MODEL.BACKBONE.NAME}) for selection") + # # clip_model = lcp(cfg) + # # clip_model.to(self.device) + # + # temp = CUSTOM_TEMPLATES[cfg.DATASET.NAME] + # prompts = [temp.format(c.replace("_", " ")) for c in classnames] + # print(f"Prompts: {prompts}") + # prompts = torch.cat([clip.tokenize(p) for p in prompts]) + # prompts = prompts.to(self.device) + # + # p, _, deep_compound_prompts_text, _ = self.model.prompt_learner() + # with torch.no_grad(): + # text = self.model.text_encoder(prompts) + # text_features = self.model.encode_text(prompts, tokenized_prompts, deep_compound_prompts_text) + # text_features = text_features / text_features.norm(dim=-1, keepdim=True) + # + # self.ori_text_features = text_features + + + + + + + def forward_backward(self, batch): + if self.sample_weights is not None: + image, label,index,mask = self.parse_batch_train_pair(batch) + else: + image, label,index,mask = self.parse_batch_train_pair(batch) + weight = None + + model = self.model + optim = self.optim + scaler = self.scaler + + + prec = self.cfg.TRAINER.MAPLE.PREC + if prec == "amp": + with autocast(): + loss,_ = model(image, label, weight=weight,mask=mask) + optim.zero_grad() + scaler.scale(loss).backward() + scaler.step(optim) + scaler.update() + else: + loss,_,loss_dict = model(image, label, weight=weight,epoch=self.epoch,index=index,cfg=self.cfg,mask=mask) + optim.zero_grad() + + # optim.pc_backward(loss_task) + loss.backward() + # if self.epoch == 0: + # self.loss_o1 = loss_task.data.cpu().numpy() + # model.grad_norm(loss_task,self.loss_o1) + + optim.step() + + # normalized_coeff = 2 / torch.sum(model.loss_weights.data,dim=0) + # model.loss_weights.data *= normalized_coeff + + + + + loss_summary = loss_dict + + if (self.batch_idx + 1) == self.num_batches: + self.update_lr() + + + return loss_summary + + def parse_batch_train_pair(self, batch): + input = batch["img"] + label = batch["label"] + index = batch["index"] + mask = batch['mask'] + input = input.to(self.device) + label = label.to(self.device) + mask = mask.to(self.device) + + if self.sample_weights is not None: + # weight = batch['weight'].cuda() + return input, label,index,mask + else: + return input, label,index,mask + + + + def parse_batch_train(self, batch): + input = batch["img"] + label = batch["label"] + index = batch["index"] + input = input.to(self.device) + label = label.to(self.device) + + + if self.sample_weights is not None: + weight = batch['weight'].cuda() + return input, label,weight,index + else: + return input, label,index + + def load_model(self, directory, epoch=None): + if not directory: + print("Note that load_model() is skipped as no pretrained model is given") + return + + names = self.get_model_names() + + # By default, the best model is loaded + model_file = "model-best.pth.tar" + + if epoch is not None: + model_file = "model.pth.tar-" + str(epoch) + + for name in names: + model_path = osp.join(directory, name, model_file) + + if not osp.exists(model_path): + raise FileNotFoundError('Model not found at "{}"'.format(model_path)) + + checkpoint = load_checkpoint(model_path) + state_dict = checkpoint["state_dict"] + epoch = checkpoint["epoch"] + + # Ignore fixed token vectors + if "prompt_learner.token_prefix" in state_dict: + del state_dict["prompt_learner.token_prefix"] + + if "prompt_learner.token_suffix" in state_dict: + del state_dict["prompt_learner.token_suffix"] + + print("Loading weights to {} " 'from "{}" (epoch = {})'.format(name, model_path, epoch)) + # set strict=False + self._models[name].load_state_dict(state_dict, strict=False) + + def before_train(self): + directory = self.cfg.OUTPUT_DIR + if self.cfg.RESUME: + directory = self.cfg.RESUME + # self.start_epoch = self.resume_model_if_exist(directory) #in case of loading pre-trained weight + + + # Redefine the dataloader + selected_res = self.selector() + if 'weights' in selected_res: + c_weight = np.zeros(len(self.dm.dataset.train_x)) + c_weight[selected_res['indices']] = selected_res['weights'] + self.sample_weights = c_weight[selected_res['indices']] + else: + self.sample_weights = None + + + + self.build_final_data_loader(selected_res['indices'],self.sample_weights) + print(f'Finish the selecting process, now continue tune CLIP') + # Initialize summary writer + writer_dir = osp.join(self.output_dir, "tensorboard") + mkdir_if_missing(writer_dir) + self.init_writer(writer_dir) + + # Remember the starting time (for computing the elapsed time) + self.time_start = time.time() + + print(f"Now generate the attentive masking in {self.cfg.TRAINER.DAPT_MODE} \n") + + + if self.cfg.TRAINER.DAPT_MODE == 'dapt-s': + self.generate_mask_train() + else: + self.generate_gradcam_train(split='train') + + + + def after_epoch(self): + last_epoch = (self.epoch + 1) == self.max_epoch + do_test = not self.cfg.TEST.NO_TEST + meet_checkpoint_freq = ( + (self.epoch + 1) % self.cfg.TRAIN.CHECKPOINT_FREQ == 0 + if self.cfg.TRAIN.CHECKPOINT_FREQ > 0 else False) + + if do_test and self.cfg.TEST.FINAL_MODEL == "best_val": + curr_result = self.test(split="val") + is_best = curr_result > self.best_result + if is_best: + self.best_result = curr_result + self.save_model( + self.epoch, + self.output_dir, + val_result=curr_result, + model_name="model-best.pth.tar" + ) + + # if meet_checkpoint_freq or last_epoch: + # self.save_model(self.epoch, self.output_dir) + + print(f"Now generate the attentive masking in {self.cfg.TRAINER.DAPT_MODE} \n") + + + if self.cfg.TRAINER.DAPT_MODE == 'dapt-s': + self.generate_mask_train() + else: + self.generate_gradcam_train(split='train') + + + + + def build_final_data_loader(self,selected_ind=None,weight=None): + new_dm = DataManager(self.cfg,self.dm.dataset,selected_ind,weight=weight) + self.train_loader_x = new_dm.train_loader_x + self.train_loader_xmore = new_dm.train_loader_xmore #for generate the attentive masking + self.mask_list = torch.zeros((selected_ind.shape[0], 1, *self.cfg.INPUT.SIZE),dtype=torch.float16) + + def selector(self): + selection_ratio = self.cfg.DATASET.SELECTION_RATIO + seed = self.cfg.SEED + method = self.cfg.DATASET.SELECTION_METHOD + print(f"Selecting {selection_ratio*100}% data by {method}") + + if self.cfg.DATASET.SELECTION_METHOD == 'Uniform': + + + selector = s_method.Uniform(self.dm, self.cfg,selection_ratio, seed) + else: + + selector = s_method.__dict__[method](dst_train=self.dm, + args=self.cfg, + fraction=selection_ratio, + random_seed=seed, + specific_model=self.model, + optim = self.selected_optim, + schedule = self.selected_sched, + scar = self.scaler, + balance = True + ) + + + return selector.select() + + @torch.no_grad() + def test_withlabel(self, split=None): + """A generic testing pipeline.""" + self.set_model_mode("eval") + new_estimate = NewClassification(self.cfg,self.evaluator._lab2cname) + new_estimate.reset() + + if split is None: + split = self.cfg.TEST.SPLIT + + if split == "val" and self.val_loader is not None: + data_loader = self.val_loader + else: + split = "test" # in case val_loader is None + data_loader = self.test_loader + + print(f"Evaluate on the *{split}* set") + + for batch_idx, batch in enumerate(tqdm(data_loader)): + input, label = self.parse_batch_test(batch) + + output = self.model.forward_test(input,label,cfg = self.cfg) + new_estimate.process(output, label) + + results = new_estimate.evaluate() + + for k, v in results.items(): + tag = f"{split}/{k}" + self.write_scalar(tag, v, self.epoch) + + return list(results.values())[0] + + + def generate_gradcam(self, split=None,attn_mask=False): + """A generic pipeline for generating GradCAM""" + self.set_model_mode("eval") + model_dict = {'arch':self.model,'layer_name':'target.layer'} + cam = GradCAM(model_dict) + # new_estimate = NewClassification(self.cfg,self.evaluator._lab2cname) + # new_estimate.reset() + + img_split = 'wrong' #true/wrong + if split is None: + split = self.cfg.TEST.SPLIT + + if split == "val" and self.val_loader is not None: + data_loader = self.val_loader + else: + split = "test" # in case val_loader is None + data_loader = self.test_loader + + print(f"Generate GradCAM on the *{split}* set") + + save_path = self.cfg.OUTPUT_DIR + '/'+f'{split}_{img_split}_promptcamother' + if not os.path.exists(save_path): + os.mkdir(save_path) + for batch_idx, batch in enumerate(tqdm(data_loader)): + input, label = self.parse_batch_test(batch) + img_name = batch['impath'][0].split('/')[-1] + img_save_path = os.path.join(save_path, img_name) + img0 = denorm(batch['img0'].numpy(),self.cfg.INPUT.PIXEL_MEAN,self.cfg.INPUT.PIXEL_STD) + saliency_map = cam.forward(input,label,cfg = self.cfg,split=img_split,attn_mask=attn_mask) + if saliency_map != None: + final_map = cam.show_cam(img0,saliency_map.detach().cpu(),img_save_path) + + + + + def generate_mask_train(self): + for batch_idx, batch in enumerate(tqdm(self.train_loader_xmore)): + input, _, index = self.parse_batch_train(batch) + b,c,h,w = input.shape + mask = torch.ones((1,h,w),dtype=torch.float16) + grid_sizes = [32,16] + hide_prob = 0.5 + grid_size = grid_sizes[torch.randint(0,len(grid_sizes),size=(1,))] + + if (grid_size != 0): + for x in range(0,h,grid_size): + for y in range(0,w,grid_size): + x_end,y_end = min(h, x+grid_size),min(w,y+grid_size) + if (random.random() <= hide_prob): + mask[:,x:x_end,y:y_end] = 0 + self.mask_list[index, :] = mask + self.model.loaded_mask = self.mask_list + + + def generate_mask_bg(self): + for batch_idx, batch in enumerate(tqdm(self.train_loader_xmore)): + input, _, index = self.parse_batch_train(batch) + b,c,h,w = input.shape + mask = torch.ones((1,h,w),dtype=torch.float16) + grid_sizes = [64,128] + hide_prob = 0.5 + grid_size = grid_sizes[torch.randint(0,len(grid_sizes),size=(1,))] + + if (grid_size != 0): + for x in range(0,h,grid_size): + for y in range(0,w,grid_size): + x_end,y_end = min(h, x+grid_size),min(w,y+grid_size) + if (random.random() <= hide_prob): + mask[:,x:x_end,y:y_end] = 0 + self.mask_list[index, :] = mask + self.model.loaded_mask = self.mask_list + + + def generate_gradcam_train(self, split=None,attn_mask=False): + """A generic pipeline for generating GradCAM""" + self.set_model_mode("eval") + model_dict = {'arch':self.model,'layer_name':'target.layer'} + cam = GradCAM(model_dict) + # new_estimate = NewClassification(self.cfg,self.evaluator._lab2cname) + # new_estimate.reset() + + print(f"Generate GradCAM on the *{split}* set") + + # save_path = self.cfg.OUTPUT_DIR + '/'+f'{split}_{img_split}_promptcamother' + # if not os.path.exists(save_path): + # os.mkdir(save_path) + for batch_idx, batch in enumerate(tqdm(self.train_loader_xmore)): + input, label, index = self.parse_batch_train(batch) + # img0 = denorm(batch['img0'].numpy(),self.cfg.INPUT.PIXEL_MEAN,self.cfg.INPUT.PIXEL_STD) + saliency_map = cam.forward_train(input,label,cfg = self.cfg,attn_mask=attn_mask) + self.mask_list[index,:] = saliency_map.detach().cpu() + # if saliency_map != None: + # final_map = cam.show_cam(img0,saliency_map.detach().cpu(),img_save_path) + self.model.loaded_mask = self.mask_list + + +class NewClassification(Classification): + def __init__(self, cfg, lab2cname=None, **kwargs): + super(NewClassification, self).__init__(cfg,lab2cname) + self._lab2cname = lab2cname + self._correct = 0 + self._total = 0 + self._per_class_res = None + self._y_true = [] + self._y_pred = [] + if cfg.TEST.PER_CLASS_RESULT: + assert lab2cname is not None + self._per_class_res = defaultdict(list) + + def evaluate(self): + results = OrderedDict() + acc = 100.0 * self._correct / self._total + err = 100.0 - acc + macro_f1 = 100.0 * f1_score( + self._y_true, + self._y_pred, + average="macro", + labels=np.unique(self._y_true) + ) + + # The first value will be returned by trainer.test() + results["accuracy"] = acc + results["error_rate"] = err + results["macro_f1"] = macro_f1 + + wrong_ind = np.array(self._y_true) != np.array(self._y_pred) + np.save(self.cfg.OUTPUT_DIR + '/'+'wrongind.npy',wrong_ind) + print( + "=> result\n" + f"* total: {self._total:,}\n" + f"* correct: {self._correct:,}\n" + f"* accuracy: {acc:.1f}%\n" + f"* error: {err:.1f}%\n" + f"* macro_f1: {macro_f1:.1f}%" + ) + + if self._per_class_res is not None: + labels = list(self._per_class_res.keys()) + labels.sort() + + print("=> per-class result") + accs = [] + + for label in labels: + classname = self._lab2cname[label] + res = self._per_class_res[label] + correct = sum(res) + total = len(res) + acc = 100.0 * correct / total + accs.append(acc) + + print( + f"* class: {label} ({classname})\t" + f"total: {total:,}\t" + f"correct: {correct:,}\t" + f"acc: {acc:.1f}%" + ) + + mean_acc = np.mean(accs) + np.save(self.cfg.OUTPUT_DIR + '/'+'per-class.npy',{'per_cls':accs, 'mean_acc':mean_acc}) + print(f"* average: {mean_acc:.1f}%") + + results["perclass_accuracy"] = mean_acc + + if self.cfg.TEST.COMPUTE_CMAT: + cmat = confusion_matrix( + self._y_true, self._y_pred, normalize="true" + ) + save_path = osp.join(self.cfg.OUTPUT_DIR, "cmat.pt") + torch.save(cmat, save_path) + print(f"Confusion matrix is saved to {save_path}") + + return results diff --git a/trainers/util.py b/trainers/util.py new file mode 100644 index 0000000..add45d3 --- /dev/null +++ b/trainers/util.py @@ -0,0 +1,265 @@ +import torch +import torch.nn.functional as F +import numpy as np +import cv2 +from PIL import Image +import os + +BACKGROUND_CATEGORY = ['ground','land','grass','tree','building','wall','sky','lake','water','river','sea','railway','railroad','keyboard','helmet', + 'cloud','house','mountain','ocean','road','rock','street','valley','bridge','sign', + ] + +class GradCAM(object): + def __init__(self,model_dict): + layer_name = model_dict['layer_name'] + self.model_arch = model_dict['arch'] + + self.gradient = dict() + self.activation = dict() + + self.gradient_t = dict() + self.activation_t = dict() + + def backward_hook(module,grad_input,grad_output): + self.gradient['value'] = grad_output[0] + return None + + def forward_hook(module,input,output): + self.activation['value'] = output + return None + + def backward_hook_t(module,grad_input,grad_output): + self.gradient_t['value'] = grad_output[0] + return None + + def forward_hook_t(module,input,output): + self.activation_t['value'] = output + return None + + target_layer = self.model_arch.image_encoder.transformer.resblocks[-1].ln_1 + # target_layer_t = self.model_arch.image_encoder.transformer.resblocks[-2].mlp.c_proj + target_layer.register_forward_hook(forward_hook) + target_layer.register_backward_hook(backward_hook) + + # target_layer_t.register_forward_hook(forward_hook_t) + # target_layer_t.register_backward_hook(backward_hook_t) + + def forward(self,input,labels,cfg=None,retain_graph=False,split=None,attn_mask=False): + + + + + b,c,h,w = input.shape + patch_num,ori_size = self.model_arch.image_encoder.patch_num, self.model_arch.image_encoder.input_resolution + + if attn_mask: + logit,mask = self.model_arch.forward_test(input,labels,cfg=cfg,attn_mask=attn_mask) + cls_mask = mask[:,1:-self.model_arch.prompt_learner.n_ctx,:1].reshape(b,-1,patch_num,patch_num) #+ mask[:,1:-self.model_arch.prompt_learner.n_ctx,:1].permute(0,2,1) + aff = mask[:,1:-self.model_arch.prompt_learner.n_ctx, 1:-self.model_arch.prompt_learner.n_ctx] + # aff = (aff + aff.permute(0,2,1)) / 2 + aff = aff / (aff.sum(dim=1,keepdim=True) + 1e-6) + # aff = aff / (aff.sum(dim=1,keepdim=True) + 1e-6) + + + # aff = (aff + aff.permute(0,2,1)) / 2 + + # aff = torch.bmm(aff,aff) + + # aff = F.softmax(aff,dim=1) + # cls_mask = torch.bmm(cls_mask, aff).reshape(b,-1,patch_num,patch_num) + + + # cls_mask = mask[:,1:-self.model_arch.prompt_learner.n_ctx,:1].permute(0,2,1).reshape(b,-1,patch_num,patch_num) + # # cls_mask = mask[:,-self.model_arch.prompt_learner.n_ctx:,1:-self.model_arch.prompt_learner.n_ctx].reshape(b,-1,patch_num,patch_num).mean(dim=1,keepdim=True) + # final_cls_mask = F.upsample(cls_mask, size=(ori_size, ori_size), mode='bilinear', + # align_corners=True) + # final_cls_feature_min, final_cls_feature_max = final_cls_mask.min(), final_cls_mask.max() + # final_cls_mask = (final_cls_mask - final_cls_feature_min) / ( + # final_cls_feature_max - final_cls_feature_min + 1e-6) + # final_cls_mask = final_cls_mask / (final_cls_mask.max() + 1e-6) + + else: + logit = self.model_arch.forward_test(input,labels,cfg=cfg) + pred_label = torch.argmax(logit[:,:-len(BACKGROUND_CATEGORY)]) + sign = pred_label == labels + # if (split == 'true' and sign == False) or (split == 'wrong' and sign == True): + # print(f'Ignore the not {split} sample') + # return None + + # if attn_mask: + # return final_cls_mask + pred = logit[:,:-len(BACKGROUND_CATEGORY)].argmax(dim=-1) + background_logit = logit[:,-len(BACKGROUND_CATEGORY):] + one_hot_labels = F.one_hot(labels, num_classes=logit.shape[1]-len(BACKGROUND_CATEGORY)).to(torch.float16) + + loss = (F.softmax(logit[:,:-len(BACKGROUND_CATEGORY)])*one_hot_labels).mean() #+ background_logit.mean() #(logit[:,:-len(BACKGROUND_CATEGORY)]*one_hot_labels).mean() #F.cross_entropy(logit.requires_grad_(True), labels) + + # score = logit[:,labels] + self.model_arch.zero_grad() + loss.backward(retain_graph=retain_graph) + gradients = self.gradient['value'] + activations = self.activation['value'] + + # gradients_t = self.gradient_t['value'] + # activations_t = self.activation_t['value'] + + visual_feature = activations[1:-self.model_arch.prompt_learner.n_ctx] + # visual_feature = activations[1:-self.model_arch.prompt_learner.n_ctx] + # cls = gradients[1:-self.model_arch.prompt_learner.n_ctx,:,:] + # cls_token_gradient = gradients[-self.model_arch.prompt_learner.n_ctx:,:,:].mean(dim=0,keepdim=True)#gradients[:1,:,:] + cls_token_gradient,prompt_gradient = gradients[:1,:,:], gradients[-self.model_arch.prompt_learner.n_ctx:,:,:].mean(keepdim=True,dim=0) + visual_gradient = torch.mean(gradients[1:-self.model_arch.prompt_learner.n_ctx],keepdim=True,dim=0) + + lam = 0.5 + # cls_token_gradient = cls_token_gradient / (cls_token_gradient.max(dim=-1,keepdim=True)[0] + 1e-6) + # prompt_gradient = prompt_gradient / (prompt_gradient.max(dim=-1,keepdim=True)[0] + 1e-6) + + # sim = F.cosine_similarity(prompt_gradient.mean(dim=0,keepdim=True),cls_token_gradient,dim=-1) + # print(sim) + # cls_token_gradient = gradients[-self.model_arch.prompt_learner.n_ctx:,:,:].max(dim=0,keepdim=True)[0]#gradients[:1,:,:] + + # token_gradient = cls_token_gradient + # token_gradient = cls_token_gradient#*(prompt_gradient.mean(dim=0,keepdim=True)) + # propmt_mean = prompt_gradient.mean(dim=0,keepdim=True) + token_gradient = visual_gradient + + final_visual_feature = torch.bmm(visual_feature.permute(1,0,2),token_gradient.permute(1,2,0)) + final_visual_feature = F.relu(final_visual_feature).permute(0,2,1) + # if attn_mask: + # final_visual_feature = torch.bmm(final_visual_feature, aff) + + final_visual_feature = final_visual_feature.reshape(final_visual_feature.shape[0],1, patch_num, patch_num) + final_visual_feature = F.upsample(final_visual_feature,size=(ori_size,ori_size),mode='bilinear',align_corners=True) + + # saliency_map = final_visual_feature / final_visual_feature.max() + final_visual_feature_min, final_visual_feature_max = final_visual_feature.min(), final_visual_feature.max() + saliency_map = final_visual_feature / (final_visual_feature_max + 1e-6)#(final_visual_feature-final_visual_feature_min) / (final_visual_feature_max - final_visual_feature_min + 1e-6) + + threshold = 0.5 + # saliency_map[saliency_map >= threshold] = 1 + saliency_map[saliency_map < threshold] = 0 + + return saliency_map + + + def forward_train(self,input,labels,cfg=None,retain_graph=False,split=None,attn_mask=False): + + + + + b,c,h,w = input.shape + patch_num,ori_size = self.model_arch.image_encoder.patch_num, self.model_arch.image_encoder.input_resolution + + if attn_mask: + logit,mask = self.model_arch.forward_test(input,labels,cfg=cfg,attn_mask=attn_mask) + cls_mask = mask[:,1:-self.model_arch.prompt_learner.n_ctx,:1].reshape(b,-1,patch_num,patch_num) #+ mask[:,1:-self.model_arch.prompt_learner.n_ctx,:1].permute(0,2,1) + aff = mask[:,1:-self.model_arch.prompt_learner.n_ctx, 1:-self.model_arch.prompt_learner.n_ctx] + # aff = (aff + aff.permute(0,2,1)) / 2 + aff = aff / (aff.sum(dim=1,keepdim=True) + 1e-6) + # aff = aff / (aff.sum(dim=1,keepdim=True) + 1e-6) + + + # aff = (aff + aff.permute(0,2,1)) / 2 + + # aff = torch.bmm(aff,aff) + + # aff = F.softmax(aff,dim=1) + # cls_mask = torch.bmm(cls_mask, aff).reshape(b,-1,patch_num,patch_num) + + + # cls_mask = mask[:,1:-self.model_arch.prompt_learner.n_ctx,:1].permute(0,2,1).reshape(b,-1,patch_num,patch_num) + # # cls_mask = mask[:,-self.model_arch.prompt_learner.n_ctx:,1:-self.model_arch.prompt_learner.n_ctx].reshape(b,-1,patch_num,patch_num).mean(dim=1,keepdim=True) + # final_cls_mask = F.upsample(cls_mask, size=(ori_size, ori_size), mode='bilinear', + # align_corners=True) + # final_cls_feature_min, final_cls_feature_max = final_cls_mask.min(), final_cls_mask.max() + # final_cls_mask = (final_cls_mask - final_cls_feature_min) / ( + # final_cls_feature_max - final_cls_feature_min + 1e-6) + # final_cls_mask = final_cls_mask / (final_cls_mask.max() + 1e-6) + + else: + logit = self.model_arch.forward_test(input,labels,cfg=cfg) + pred_label = torch.argmax(logit) + sign = pred_label == labels + # if (split == 'true' and sign == False) or (split == 'wrong' and sign == True): + # print(f'Ignore the not {split} sample') + # return None + + # if attn_mask: + # return final_cls_mask + # pred = logit[:,-len(BACKGROUND_CATEGORY):].argmax(dim=-1) + # background_logit = logit[:,-len(BACKGROUND_CATEGORY):] + one_hot_labels = F.one_hot(labels, num_classes=logit.shape[1]).to(torch.float16) + loss = (logit*one_hot_labels).mean() #+ background_logit.mean() #(logit[:,:-len(BACKGROUND_CATEGORY)]*one_hot_labels).mean() #F.cross_entropy(logit.requires_grad_(True), labels) + # score = logit[:,labels] + self.model_arch.zero_grad() + loss.backward(retain_graph=retain_graph) + gradients = self.gradient['value'] + activations = self.activation['value'] + + # gradients_t = self.gradient_t['value'] + # activations_t = self.activation_t['value'] + + visual_feature = activations[1:-self.model_arch.prompt_learner.n_ctx] + # visual_feature = activations[1:-self.model_arch.prompt_learner.n_ctx] + # cls = gradients[1:-self.model_arch.prompt_learner.n_ctx,:,:] + # cls_token_gradient = gradients[-self.model_arch.prompt_learner.n_ctx:,:,:].mean(dim=0,keepdim=True)#gradients[:1,:,:] + cls_token_gradient,prompt_gradient = gradients[:1,:,:], gradients[-self.model_arch.prompt_learner.n_ctx:,:,:].mean(keepdim=True,dim=0) + visual_gradient = torch.mean(gradients[1:-self.model_arch.prompt_learner.n_ctx],keepdim=True,dim=0) + + lam = 0.5 + # cls_token_gradient = cls_token_gradient / (cls_token_gradient.max(dim=-1,keepdim=True)[0] + 1e-6) + # prompt_gradient = prompt_gradient / (prompt_gradient.max(dim=-1,keepdim=True)[0] + 1e-6) + + # sim = F.cosine_similarity(prompt_gradient.mean(dim=0,keepdim=True),cls_token_gradient,dim=-1) + # print(sim) + # cls_token_gradient = gradients[-self.model_arch.prompt_learner.n_ctx:,:,:].max(dim=0,keepdim=True)[0]#gradients[:1,:,:] + + # token_gradient = cls_token_gradient + # token_gradient = cls_token_gradient#*(prompt_gradient.mean(dim=0,keepdim=True)) + # propmt_mean = prompt_gradient.mean(dim=0,keepdim=True) + token_gradient = visual_gradient + + final_visual_feature = torch.bmm(visual_feature.permute(1,0,2),token_gradient.permute(1,2,0)) + final_visual_feature = F.relu(final_visual_feature).permute(0,2,1) + # if attn_mask: + # final_visual_feature = torch.bmm(final_visual_feature, aff) + + final_visual_feature = final_visual_feature.reshape(final_visual_feature.shape[0],1, patch_num, patch_num) + final_visual_feature = F.upsample(final_visual_feature,size=(ori_size,ori_size),mode='bilinear',align_corners=True) + + # saliency_map = final_visual_feature / final_visual_feature.max() + final_visual_feature_min, final_visual_feature_max = final_visual_feature.min(), final_visual_feature.max() + saliency_map = final_visual_feature / (final_visual_feature_max + 1e-6)#(final_visual_feature-final_visual_feature_min) / (final_visual_feature_max - final_visual_feature_min + 1e-6) + + threshold = 0.5 + saliency_map[saliency_map >= threshold] = 1 + saliency_map[saliency_map < threshold] = 0 + + return saliency_map + + + def show_cam(self,img,mask,save_path=None): + + heat_map = cv2.applyColorMap(np.uint8(255*mask.squeeze()), cv2.COLORMAP_JET) + heatmap = torch.from_numpy(heat_map).permute(2,0,1).float().div(255) + b,g,r = heatmap.split(1) + heatmap = torch.cat([r,g,b]) + rate = 0.5 + res = rate*heatmap + (1-rate)*img + res = res.div(res.max()).squeeze() + res = np.transpose(np.uint8(255*res),(1,2,0)) + + pil_image = Image.fromarray(res) + # pil_image.save('test1.jpg') + pil_image.save(save_path) + return pil_image + + + +def denorm(img,mean,std): + mean,std = np.array(mean),np.array(std) + img = img*std[:, None, None] + mean[:, None, None] + # img = np.clip(img*255, 0, 255) #.clamp(0,255) + # img = img / 255 + return img + diff --git a/trainers/vpt.py b/trainers/vpt.py new file mode 100644 index 0000000..4a45769 --- /dev/null +++ b/trainers/vpt.py @@ -0,0 +1,239 @@ +import os.path as osp +from collections import OrderedDict +import math + +import torch +import torch.nn as nn +from torch.nn import functional as F +from torch.cuda.amp import GradScaler, autocast + +from dassl.engine import TRAINER_REGISTRY, TrainerX +from dassl.metrics import compute_accuracy +from dassl.utils import load_pretrained_weights, load_checkpoint +from dassl.optim import build_optimizer, build_lr_scheduler + +from clip import clip +from clip.simple_tokenizer import SimpleTokenizer as _Tokenizer + +_tokenizer = _Tokenizer() + + +def load_clip_to_cpu(cfg): + backbone_name = cfg.MODEL.BACKBONE.NAME + url = clip._MODELS[backbone_name] + model_path = clip._download(url) + + try: + # loading JIT archive + model = torch.jit.load(model_path, map_location="cpu").eval() + state_dict = None + + except RuntimeError: + state_dict = torch.load(model_path, map_location="cpu") + design_details = { "trainer": "VPT", + "vision_depth": cfg.TRAINER.VPT.PROMPT_DEPTH_VISION, + "vision_ctx": cfg.TRAINER.VPT.N_CTX_VISION, + "language_depth": 0, + "language_ctx": 0} + assert cfg.TRAINER.VPT.PROMPT_DEPTH_VISION >= 1, "For Vision Prompting, PROMPT_DEPTH_VISION should be >= 1" + model = clip.build_model(state_dict or model.state_dict(), design_details) + + return model.float() + + +class TextEncoder(nn.Module): + def __init__(self, clip_model): + super().__init__() + self.transformer = clip_model.transformer + self.positional_embedding = clip_model.positional_embedding + self.ln_final = clip_model.ln_final + self.text_projection = clip_model.text_projection + self.dtype = clip_model.dtype + + def forward(self, prompts, tokenized_prompts): + x = prompts + self.positional_embedding.type(self.dtype) + x = x.permute(1, 0, 2) # NLD -> LND + x = self.transformer(x) + x = x.permute(1, 0, 2) # LND -> NLD + x = self.ln_final(x).type(self.dtype) + + # x.shape = [batch_size, n_ctx, transformer.width] + # take features from the eot embedding (eot_token is the highest number in each sequence) + x = x[torch.arange(x.shape[0]), tokenized_prompts.argmax(dim=-1)] @ self.text_projection + + return x + + +class FixedEmbeddings(): + def __init__(self, cfg, classnames, clip_model): + clip_imsize = clip_model.visual.input_resolution + cfg_imsize = cfg.INPUT.SIZE[0] + assert cfg_imsize == clip_imsize, f"cfg_imsize ({cfg_imsize}) must equal to clip_imsize ({clip_imsize})" + + prompt_prefix = "a photo of a" + print('Vision Prompting Design') + print(f'Initial context: "{prompt_prefix}"') + print(f"Number of context words (tokens) for Vision prompting: {cfg.TRAINER.VPT.N_CTX_VISION}") + print(f"Using fixed hand crated prompts") + + classnames = [name.replace("_", " ") for name in classnames] + prompts = [prompt_prefix + " " + name + "." for name in classnames] + + tokenized_prompts = torch.cat([clip.tokenize(p) for p in prompts]) + with torch.no_grad(): + text_features = clip_model.encode_text(tokenized_prompts) + + self.fixed_embeddings = text_features + + def return_fixed_embeddings(self): + return self.fixed_embeddings + + +class CustomCLIP(nn.Module): + def __init__(self, cfg, classnames, clip_model): + super().__init__() + self.embeddings = FixedEmbeddings(cfg, classnames, clip_model) + self.image_encoder = clip_model.visual + self.text_encoder = TextEncoder(clip_model) + self.logit_scale = clip_model.logit_scale + self.dtype = clip_model.dtype + + def forward(self, image, label=None, training=False): + logit_scale = self.logit_scale.exp() + + text_features = self.embeddings.return_fixed_embeddings().cuda() + image_features = self.image_encoder(image.type(self.dtype)) + + image_features = image_features / image_features.norm(dim=-1, keepdim=True) + text_features = text_features / text_features.norm(dim=-1, keepdim=True) + logits = logit_scale * image_features @ text_features.t() + + if training: + return F.cross_entropy(logits, label) + + return logits + + +@TRAINER_REGISTRY.register() +class VPT(TrainerX): + def check_cfg(self, cfg): + assert cfg.TRAINER.VPT.PREC in ["fp16", "fp32", "amp"] + + def build_model(self): + cfg = self.cfg + classnames = self.dm.dataset.classnames + + print(f"Loading CLIP (backbone: {cfg.MODEL.BACKBONE.NAME})") + clip_model = load_clip_to_cpu(cfg) + + if cfg.TRAINER.VPT.PREC == "fp32" or cfg.TRAINER.VPT.PREC == "amp": + # CLIP's default precision is fp16 + clip_model.float() + + print("Building custom CLIP") + self.model = CustomCLIP(cfg, classnames, clip_model) + + print("Turning off gradients in both the image and the text encoder") + name_to_update = "prompt_learner" + + for name, param in self.model.named_parameters(): + if name_to_update not in name: + # Make sure that VPT prompts are updated + if "VPT" in name: + param.requires_grad_(True) + else: + param.requires_grad_(False) + + # Double check + enabled = set() + for name, param in self.model.named_parameters(): + if param.requires_grad: + enabled.add(name) + print(f"Parameters to be updated: {enabled}") + + if cfg.MODEL.INIT_WEIGHTS: + load_pretrained_weights(self.model, cfg.MODEL.INIT_WEIGHTS) + + self.model.to(self.device) + # NOTE: only give prompt_learner to the optimizer + self.optim = build_optimizer(self.model, cfg.OPTIM) + self.sched = build_lr_scheduler(self.optim, cfg.OPTIM) + self.register_model("prompt_learner", self.model, self.optim, self.sched) + + self.scaler = GradScaler() if cfg.TRAINER.VPT.PREC == "amp" else None + + # Note that multi-gpu training could be slow because CLIP's size is + # big, which slows down the copy operation in DataParallel + device_count = torch.cuda.device_count() + if device_count > 1: + print(f"Multiple GPUs detected (n_gpus={device_count}), use all of them!") + self.model = nn.DataParallel(self.model) + + def forward_backward(self, batch): + image, label = self.parse_batch_train(batch) + + model = self.model + optim = self.optim + scaler = self.scaler + + prec = self.cfg.TRAINER.VPT.PREC + if prec == "amp": + with autocast(): + loss = model(image, label) + optim.zero_grad() + scaler.scale(loss).backward() + scaler.step(optim) + scaler.update() + else: + loss = model(image, label, training=True) + optim.zero_grad() + loss.backward() + optim.step() + + loss_summary = {"loss": loss.item()} + + if (self.batch_idx + 1) == self.num_batches: + self.update_lr() + + return loss_summary + + def parse_batch_train(self, batch): + input = batch["img"] + label = batch["label"] + input = input.to(self.device) + label = label.to(self.device) + return input, label + + def load_model(self, directory, epoch=None): + if not directory: + print("Note that load_model() is skipped as no pretrained model is given") + return + + names = self.get_model_names() + + # By default, the best model is loaded + model_file = "model-best.pth.tar" + + if epoch is not None: + model_file = "model.pth.tar-" + str(epoch) + + for name in names: + model_path = osp.join(directory, name, model_file) + + if not osp.exists(model_path): + raise FileNotFoundError('Model not found at "{}"'.format(model_path)) + + checkpoint = load_checkpoint(model_path) + state_dict = checkpoint["state_dict"] + epoch = checkpoint["epoch"] + + # Ignore fixed token vectors + if "prompt_learner.token_prefix" in state_dict: + del state_dict["prompt_learner.token_prefix"] + + if "prompt_learner.token_suffix" in state_dict: + del state_dict["prompt_learner.token_suffix"] + + print("Loading weights to {} " 'from "{}" (epoch = {})'.format(name, model_path, epoch)) + # set strict=False + self._models[name].load_state_dict(state_dict, strict=False) diff --git a/trainers/zsclip.py b/trainers/zsclip.py new file mode 100644 index 0000000..f939118 --- /dev/null +++ b/trainers/zsclip.py @@ -0,0 +1,99 @@ +import torch +import torch.nn as nn + +from dassl.engine import TRAINER_REGISTRY, TrainerX +from dassl.optim import build_optimizer, build_lr_scheduler + +from clip import clip +from clip.model import convert_weights + +from .coop import load_clip_to_cpu +from .imagenet_templates import IMAGENET_TEMPLATES, IMAGENET_TEMPLATES_SELECT + +CUSTOM_TEMPLATES = { + "OxfordPets": "a photo of a {}, a type of pet.", + "OxfordFlowers": "a photo of a {}, a type of flower.", + "FGVCAircraft": "a photo of a {}, a type of aircraft.", + "DescribableTextures": "{} texture.", + "EuroSAT": "a centered satellite photo of {}.", + "StanfordCars": "a photo of a {}.", + "Food101": "a photo of {}, a type of food.", + "SUN397": "a photo of a {}.", + "Caltech101": "a photo of a {}.", + "UCF101": "a photo of a person doing {}.", + "ImageNet": "a photo of a {}.", + "ImageNetSketch": "a photo of a {}.", + "ImageNetV2": "a photo of a {}.", + "ImageNetA": "a photo of a {}.", + "ImageNetR": "a photo of a {}.", +} + + +@TRAINER_REGISTRY.register() +class ZeroshotCLIP(TrainerX): + def build_model(self): + cfg = self.cfg + classnames = self.dm.dataset.classnames + + print(f"Loading CLIP (backbone: {cfg.MODEL.BACKBONE.NAME})") + clip_model = load_clip_to_cpu(cfg) + clip_model.to(self.device) + + temp = CUSTOM_TEMPLATES[cfg.DATASET.NAME] + prompts = [temp.format(c.replace("_", " ")) for c in classnames] + print(f"Prompts: {prompts}") + prompts = torch.cat([clip.tokenize(p) for p in prompts]) + prompts = prompts.to(self.device) + + with torch.no_grad(): + text_features = clip_model.encode_text(prompts) + text_features = text_features / text_features.norm(dim=-1, keepdim=True) + + self.text_features = text_features + self.clip_model = clip_model + + def model_inference(self, image): + image_features = self.clip_model.encode_image(image) + image_features = image_features / image_features.norm(dim=-1, keepdim=True) + logit_scale = self.clip_model.logit_scale.exp() + logits = logit_scale * image_features @ self.text_features.t() + return logits + + +@TRAINER_REGISTRY.register() +class ZeroshotCLIP2(ZeroshotCLIP): + """Prompt ensembling.""" + + # templates = IMAGENET_TEMPLATES + templates = IMAGENET_TEMPLATES_SELECT + + def build_model(self): + cfg = self.cfg + classnames = self.dm.dataset.classnames + + print(f"Loading CLIP (backbone: {cfg.MODEL.BACKBONE.NAME})") + clip_model = load_clip_to_cpu(cfg) + clip_model.to(self.device) + + for params in clip_model.parameters(): + params.requires_grad_(False) + + # add custom-made prompt + if cfg.DATASET.NAME != "ImageNet": + self.templates += [CUSTOM_TEMPLATES[cfg.DATASET.NAME]] + + num_temp = len(self.templates) + print(f"Prompt ensembling (n={num_temp})") + + mean_text_features = 0 + for i, temp in enumerate(self.templates): + prompts = [temp.format(c.replace("_", " ")) for c in classnames] + prompts = torch.cat([clip.tokenize(p) for p in prompts]).to(self.device) + text_features = clip_model.encode_text(prompts) + text_features = text_features / text_features.norm(dim=-1, keepdim=True) + mean_text_features = mean_text_features + text_features + mean_text_features = mean_text_features / num_temp + mean_text_features = mean_text_features / mean_text_features.norm(dim=-1, keepdim=True) + + self.text_features = 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