411 lines
17 KiB
Python
411 lines
17 KiB
Python
import copy
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import os.path as osp
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import numpy as np
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import torch
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import torch.nn as nn
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from torch.nn import functional as F
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from torch.cuda.amp import GradScaler, autocast
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import json
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from dassl.engine import TRAINER_REGISTRY, TrainerX
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from dassl.utils import load_pretrained_weights, load_checkpoint
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from dassl.optim import build_optimizer, build_lr_scheduler
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from clip import clip
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from clip.simple_tokenizer import SimpleTokenizer as _Tokenizer
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_tokenizer = _Tokenizer()
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DESC_LLM = "gpt-4.1"
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DESC_TOPK = 4
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def load_clip_to_cpu(cfg, zero_shot_model=False):
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backbone_name = cfg.MODEL.BACKBONE.NAME
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url = clip._MODELS[backbone_name]
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model_path = clip._download(url)
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try:
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# loading JIT archive
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model = torch.jit.load(model_path, map_location="cpu").eval()
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state_dict = None
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except RuntimeError:
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state_dict = torch.load(model_path, map_location="cpu")
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if not zero_shot_model:
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design_details = {"trainer": 'IVLP',
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"vision_depth": cfg.TRAINER.PROMPTSRC.PROMPT_DEPTH_VISION,
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"language_depth": cfg.TRAINER.PROMPTSRC.PROMPT_DEPTH_TEXT,
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"vision_ctx": cfg.TRAINER.PROMPTSRC.N_CTX_VISION,
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"language_ctx": cfg.TRAINER.PROMPTSRC.N_CTX_TEXT}
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model = clip.build_model(state_dict or model.state_dict(), design_details)
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else:
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# Return original CLIP model for generating frozen VL features
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design_details = {"trainer": 'IVLP',
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"vision_depth": 0,
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"language_depth": 0, "vision_ctx": 0,
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"language_ctx": 0}
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model = clip.build_model(state_dict or model.state_dict(), design_details)
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return model
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return model
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class TextEncoder(nn.Module):
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def __init__(self, clip_model):
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super().__init__()
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self.transformer = clip_model.transformer
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self.positional_embedding = clip_model.positional_embedding
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self.ln_final = clip_model.ln_final
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self.text_projection = clip_model.text_projection
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self.dtype = clip_model.dtype
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def forward(self, prompts, tokenized_prompts):
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x = prompts + self.positional_embedding.type(self.dtype)
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x = x.permute(1, 0, 2) # NLD -> LND
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x = self.transformer(x)
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x = x.permute(1, 0, 2) # LND -> NLD
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x = self.ln_final(x).type(self.dtype)
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# x.shape = [batch_size, n_ctx, transformer.width]
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# take features from the eot embedding (eot_token is the highest number in each sequence)
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x = x[torch.arange(x.shape[0]), tokenized_prompts.argmax(dim=-1)] @ self.text_projection
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return x
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class VLPromptLearner(nn.Module):
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def __init__(self, cfg, classnames, clip_model):
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super().__init__()
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n_cls = len(classnames)
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# Make sure Language depth >= 1
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assert cfg.TRAINER.PROMPTSRC.PROMPT_DEPTH_TEXT >= 1, "In Independent VL prompting, Language prompt depth should be >=1" \
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"\nPlease use VPT trainer if you want to learn only vision " \
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"branch"
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n_ctx = cfg.TRAINER.PROMPTSRC.N_CTX_TEXT
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ctx_init = cfg.TRAINER.PROMPTSRC.CTX_INIT
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dtype = clip_model.dtype
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ctx_dim = clip_model.ln_final.weight.shape[0]
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clip_imsize = clip_model.visual.input_resolution
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cfg_imsize = cfg.INPUT.SIZE[0]
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assert cfg_imsize == clip_imsize, f"cfg_imsize ({cfg_imsize}) must equal to clip_imsize ({clip_imsize})"
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if ctx_init and n_ctx <= 4:
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# use given words to initialize context vectors
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ctx_init = ctx_init.replace("_", " ")
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n_ctx = n_ctx
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prompt = clip.tokenize(ctx_init)
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with torch.no_grad():
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embedding = clip_model.token_embedding(prompt).type(dtype)
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ctx_vectors = embedding[0, 1: 1 + n_ctx, :]
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prompt_prefix = ctx_init
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else:
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# random initialization
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ctx_vectors = torch.empty(n_ctx, ctx_dim, dtype=dtype)
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nn.init.normal_(ctx_vectors, std=0.02)
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prompt_prefix = " ".join(["X"] * n_ctx)
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print(f"Independent V-L design")
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print(f'Initial text context: "{prompt_prefix}"')
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print(f"Number of context words (tokens) for Language prompting: {n_ctx}")
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print(f"Number of context words (tokens) for Vision prompting: {cfg.TRAINER.PROMPTSRC.N_CTX_VISION}")
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self.ctx = nn.Parameter(ctx_vectors)
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classnames = [name.replace("_", " ") for name in classnames]
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name_lens = [len(_tokenizer.encode(name)) for name in classnames]
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prompts = [prompt_prefix + " " + name + "." for name in classnames]
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tokenized_prompts = torch.cat([clip.tokenize(p) for p in prompts]) # (n_cls, n_tkn)
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# Also create frozen CLIP
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clip_model_temp = load_clip_to_cpu(cfg, True).float().cuda()
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clip_model_temp_image = load_clip_to_cpu(cfg, True)
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with torch.no_grad():
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embedding = clip_model.token_embedding(tokenized_prompts).type(dtype)
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self.ZS_image_encoder = clip_model_temp_image.visual
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# Now pre-compute the frozen VL embeddings from LLM descriptions
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all_teacher_features = []
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desc_file = f"./desc/{DESC_LLM}/descriptions_top{DESC_TOPK}/{cfg.DATASET.NAME}.json"
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with open(desc_file, "r") as f:
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all_desc = json.load(f)
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for cls in classnames:
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cls_descs = [f"a photo of {cls}, {desc}" for desc in all_desc[cls]]
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cls_token = torch.cat([clip.tokenize(cls_desc) for cls_desc in cls_descs]).cuda()
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with torch.no_grad():
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cls_feature = clip_model_temp.encode_text(cls_token)
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cls_feature = cls_feature / cls_feature.norm(dim=-1, keepdim=True)
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cls_feature = torch.mean(cls_feature, dim=0)
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all_teacher_features.append(cls_feature)
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self.fixed_embeddings = torch.stack(all_teacher_features)
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print(f"Using LLM descriptions from: {desc_file}")
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# These token vectors will be saved when in save_model(),
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# but they should be ignored in load_model() as we want to use
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# those computed using the current class names
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self.register_buffer("token_prefix", embedding[:, :1, :]) # SOS
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self.register_buffer("token_suffix", embedding[:, 1 + n_ctx:, :]) # CLS, EOS
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self.n_cls = n_cls
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self.n_ctx = n_ctx
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self.tokenized_prompts = tokenized_prompts # torch.Tensor
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self.name_lens = name_lens
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def construct_prompts(self, ctx, prefix, suffix, label=None):
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# dim0 is either batch_size (during training) or n_cls (during testing)
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# ctx: context tokens, with shape of (dim0, n_ctx, ctx_dim)
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# prefix: the sos token, with shape of (n_cls, 1, ctx_dim)
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# suffix: remaining tokens, with shape of (n_cls, *, ctx_dim)
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if label is not None:
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prefix = prefix[label]
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suffix = suffix[label]
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prompts = torch.cat(
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[
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prefix, # (dim0, 1, dim)
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ctx, # (dim0, n_ctx, dim)
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suffix, # (dim0, *, dim)
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],
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dim=1,
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)
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return prompts
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def forward(self):
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ctx = self.ctx
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if ctx.dim() == 2:
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ctx = ctx.unsqueeze(0).expand(self.n_cls, -1, -1)
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prefix = self.token_prefix
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suffix = self.token_suffix
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prompts = self.construct_prompts(ctx, prefix, suffix)
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return prompts
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class CustomCLIP(nn.Module):
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def __init__(self, cfg, classnames, clip_model):
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super().__init__()
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self.prompt_learner = VLPromptLearner(cfg, classnames, clip_model)
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self.tokenized_prompts = self.prompt_learner.tokenized_prompts
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self.image_encoder = clip_model.visual
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self.text_encoder = TextEncoder(clip_model)
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self.logit_scale = clip_model.logit_scale
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self.dtype = clip_model.dtype
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self.total_epochs = cfg.OPTIM.MAX_EPOCH
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self.n_cls = len(classnames)
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def forward(self, image, label=None):
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tokenized_prompts = self.tokenized_prompts
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logit_scale = self.logit_scale.exp()
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prompts = self.prompt_learner()
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# Compute the prompted image and text features
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text_features = self.text_encoder(prompts, tokenized_prompts)
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image_features = self.image_encoder(image.type(self.dtype))
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image_features = image_features / image_features.norm(dim=-1, keepdim=True)
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text_features = text_features / text_features.norm(dim=-1, keepdim=True)
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# Compute the prompted logits
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logits = logit_scale * image_features @ text_features.t()
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if self.prompt_learner.training:
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# Now calculate the frozen pre-trained features
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fixed_embeddings = self.prompt_learner.fixed_embeddings # precomputed pre-trained frozen textual features
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fixed_embeddings = fixed_embeddings / fixed_embeddings.norm(dim=-1, keepdim=True)
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with torch.no_grad():
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zero_shot_features = self.prompt_learner.ZS_image_encoder(image.type(self.dtype))
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zero_shot_features = zero_shot_features / zero_shot_features.norm(dim=-1, keepdim=True)
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# Compute pre-trained frozen visual features
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zero_shot_logits = logit_scale * zero_shot_features.cuda() @ fixed_embeddings.half().cuda().t()
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return F.cross_entropy(logits,
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label), text_features, fixed_embeddings, zero_shot_features, \
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image_features, zero_shot_logits, logits
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else:
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return logits
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@TRAINER_REGISTRY.register()
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class PromptSRC(TrainerX):
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def check_cfg(self, cfg):
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assert cfg.TRAINER.PROMPTSRC.PREC in ["fp16", "fp32", "amp"]
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def build_model(self):
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cfg = self.cfg
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classnames = self.dm.dataset.classnames
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print(f"Loading CLIP (backbone: {cfg.MODEL.BACKBONE.NAME})")
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clip_model = load_clip_to_cpu(cfg)
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if cfg.TRAINER.PROMPTSRC.PREC == "fp32" or cfg.TRAINER.PROMPTSRC.PREC == "amp":
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# CLIP's default precision is fp16
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clip_model.float()
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print("Building custom CLIP")
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self.model = CustomCLIP(cfg, classnames, clip_model)
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print("Turning off gradients in both the image and the text encoder")
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name_to_update = "prompt_learner"
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for name, param in self.model.named_parameters():
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if name_to_update not in name:
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# Make sure that VPT prompts are updated
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if "VPT" in name:
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param.requires_grad_(True)
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else:
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param.requires_grad_(False)
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else:
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if "ZS_image_encoder" in name:
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param.requires_grad_(False)
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# Double check
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enabled = set()
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for name, param in self.model.named_parameters():
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if param.requires_grad:
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enabled.add(name)
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print(f"Parameters to be updated: {enabled}")
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print(f"Parameters count: {len(enabled)}")
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if cfg.MODEL.INIT_WEIGHTS:
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load_pretrained_weights(self.model, cfg.MODEL.INIT_WEIGHTS)
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self.model.to(self.device)
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# NOTE: only give prompt_learner to the optimizer
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self.optim = build_optimizer(self.model, cfg.OPTIM)
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self.sched = build_lr_scheduler(self.optim, cfg.OPTIM)
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self.register_model("VLPromptLearner", self.model, self.optim, self.sched)
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# Cosine scheduler
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self.total_epochs = cfg.OPTIM.MAX_EPOCH
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self.step_counter = 1
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N = cfg.OPTIM.MAX_EPOCH
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mean = cfg.TRAINER.PROMPTSRC.GPA_MEAN
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stdev = cfg.TRAINER.PROMPTSRC.GPA_STD
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gauss = self.get_gauss(mean, stdev)
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self.gauss = np.array([gauss(a) for a in range(1, N + 1)])
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self.gauss = self.gauss / sum(self.gauss)
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self.scaler = GradScaler() if cfg.TRAINER.PROMPTSRC.PREC == "amp" else None
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# Note that multi-gpu training could be slow because CLIP's size is
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# big, which slows down the copy operation in DataParallel
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device_count = torch.cuda.device_count()
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if device_count > 1:
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print(f"Multiple GPUs detected (n_gpus={device_count}), use all of them!")
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self.model = nn.DataParallel(self.model)
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# Keep model with GPA
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self.previous_model_gpa = None
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def forward_backward(self, batch):
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image, label = self.parse_batch_train(batch)
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model = self.model
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optim = self.optim
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scaler = self.scaler
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prec = self.cfg.TRAINER.PROMPTSRC.PREC
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if prec == "amp":
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with autocast():
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loss = model(image, label)
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optim.zero_grad()
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scaler.scale(loss).backward()
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scaler.step(optim)
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scaler.update()
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else:
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loss_ce, normalized_text_features, zs_clip_text_embeddings, zs_image_embedd, image_ft, \
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zero_shot_logits, logits = model(image, label)
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# Calculate the L_SCL_text loss
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loss_scl_text = F.l1_loss(normalized_text_features, zs_clip_text_embeddings.cuda(),
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reduction='mean') * self.cfg.TRAINER.PROMPTSRC.TEXT_LOSS_WEIGHT
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# Calculate the L_SCL_image loss
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loss_scl_image = F.l1_loss(image_ft, zs_image_embedd.cuda(),
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reduction='mean') * self.cfg.TRAINER.PROMPTSRC.IMAGE_LOSS_WEIGHT
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# Now calculate L_SCL_logits
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L_SCL_logits = F.kl_div(
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F.log_softmax(logits / 1, dim=1),
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F.log_softmax(zero_shot_logits / 1, dim=1),
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reduction='sum',
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log_target=True
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) * (1 * 1) / logits.numel()
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L_SCL = (L_SCL_logits + loss_scl_text + loss_scl_image)
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loss = (loss_ce + L_SCL)
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optim.zero_grad()
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loss.backward()
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optim.step()
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loss_summary = {"loss": loss.item()}
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if (self.batch_idx + 1) == self.num_batches:
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self.update_lr()
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# Means one epoch is completed, perform GPA
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self.step_counter = self.step_counter + 1
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current_epoch_weight = self.gauss[self.step_counter - 2]
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current_model_weights = copy.deepcopy(model.state_dict())
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weighted_state_dict = self.state_dict_weighting(current_model_weights, current_epoch_weight)
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if self.previous_model_gpa is None:
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self.previous_model_gpa = weighted_state_dict
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else:
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self.previous_model_gpa = self.state_dict_add(weighted_state_dict, self.previous_model_gpa)
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if self.step_counter == self.model.total_epochs + 1:
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print("Using GPA model for final inference...")
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model.load_state_dict(self.previous_model_gpa)
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self.model.load_state_dict(self.previous_model_gpa)
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return loss_summary
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def state_dict_weighting(self, main_dict, weightage, prompt_only=False):
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# Average all parameters
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updated_dict = copy.deepcopy(main_dict)
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if not prompt_only:
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for key in main_dict:
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updated_dict[key] = main_dict[key] * weightage
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return updated_dict
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else:
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return main_dict * weightage
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def state_dict_add(self, dict1, dict2, prompt_only=False):
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# Average all parameters
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if not prompt_only:
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modified_dict = dict2
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for key in dict1:
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modified_dict[key] = (modified_dict[key] + dict1[key])
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return modified_dict
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else:
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return dict1 + dict2
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def get_gauss(self, mu, sigma):
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gauss = lambda x: (1 / (sigma * np.sqrt(2 * np.pi))) * np.exp(-0.5 * ((x - mu) / sigma) ** 2)
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return gauss
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def parse_batch_train(self, batch):
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input = batch["img"]
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label = batch["label"]
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input = input.to(self.device)
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label = label.to(self.device)
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return input, label
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def load_model(self, directory, epoch=None):
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if not directory:
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print("Note that load_model() is skipped as no pretrained model is given")
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return
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names = self.get_model_names()
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# By default, the best model is loaded
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model_file = "model-best.pth.tar"
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if epoch is not None:
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model_file = "model.pth.tar-" + str(epoch)
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for name in names:
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model_path = osp.join(directory, name, model_file)
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if not osp.exists(model_path):
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raise FileNotFoundError('Model not found at "{}"'.format(model_path))
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checkpoint = load_checkpoint(model_path)
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state_dict = checkpoint["state_dict"]
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epoch = checkpoint["epoch"]
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# Ignore fixed token vectors
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if "prompt_learner.token_prefix" in state_dict:
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del state_dict["prompt_learner.token_prefix"]
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if "prompt_learner.token_suffix" in state_dict:
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del state_dict["prompt_learner.token_suffix"]
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print("Loading weights to {} " 'from "{}" (epoch = {})'.format(name, model_path, epoch))
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# set strict=False
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self._models[name].load_state_dict(state_dict, strict=False) |