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张菲
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trainers/cocoop.py
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318
trainers/cocoop.py
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import os.path as osp
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from collections import OrderedDict
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import math
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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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from dassl.engine import TRAINER_REGISTRY, TrainerX
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from dassl.metrics import compute_accuracy
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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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def load_clip_to_cpu(cfg):
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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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design_details = {"trainer": 'CoCoOp',
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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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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 PromptLearner(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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n_ctx = cfg.TRAINER.COCOOP.N_CTX
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ctx_init = cfg.TRAINER.COCOOP.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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vis_dim = clip_model.visual.output_dim
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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:
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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 = len(ctx_init.split(" "))
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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'Initial context: "{prompt_prefix}"')
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print(f"Number of context words (tokens): {n_ctx}")
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self.ctx = nn.Parameter(ctx_vectors)
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self.meta_net = nn.Sequential(OrderedDict([
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("linear1", nn.Linear(vis_dim, vis_dim // 16)),
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("relu", nn.ReLU(inplace=True)),
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("linear2", nn.Linear(vis_dim // 16, ctx_dim))
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]))
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if cfg.TRAINER.COCOOP.PREC == "fp16":
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self.meta_net.half()
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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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with torch.no_grad():
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embedding = clip_model.token_embedding(tokenized_prompts).type(dtype)
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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, im_features):
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prefix = self.token_prefix
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suffix = self.token_suffix
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ctx = self.ctx # (n_ctx, ctx_dim)
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bias = self.meta_net(im_features) # (batch, ctx_dim)
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bias = bias.unsqueeze(1) # (batch, 1, ctx_dim)
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ctx = ctx.unsqueeze(0) # (1, n_ctx, ctx_dim)
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ctx_shifted = ctx + bias # (batch, n_ctx, ctx_dim)
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# Use instance-conditioned context tokens for all classes
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prompts = []
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for ctx_shifted_i in ctx_shifted:
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ctx_i = ctx_shifted_i.unsqueeze(0).expand(self.n_cls, -1, -1)
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pts_i = self.construct_prompts(ctx_i, prefix, suffix) # (n_cls, n_tkn, ctx_dim)
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prompts.append(pts_i)
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prompts = torch.stack(prompts)
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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 = PromptLearner(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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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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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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prompts = self.prompt_learner(image_features)
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logits = []
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for pts_i, imf_i in zip(prompts, image_features):
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text_features = self.text_encoder(pts_i, tokenized_prompts)
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text_features = text_features / text_features.norm(dim=-1, keepdim=True)
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l_i = logit_scale * imf_i @ text_features.t()
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logits.append(l_i)
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logits = torch.stack(logits)
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if self.prompt_learner.training:
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return F.cross_entropy(logits, label)
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return logits
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@TRAINER_REGISTRY.register()
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class CoCoOp(TrainerX):
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def check_cfg(self, cfg):
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assert cfg.TRAINER.COCOOP.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.COCOOP.PREC == "fp32" or cfg.TRAINER.COCOOP.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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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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if cfg.MODEL.INIT_WEIGHTS:
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load_pretrained_weights(self.model.prompt_learner, 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.prompt_learner, cfg.OPTIM)
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self.sched = build_lr_scheduler(self.optim, cfg.OPTIM)
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self.register_model("prompt_learner", self.model.prompt_learner, self.optim, self.sched)
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self.scaler = GradScaler() if cfg.TRAINER.COCOOP.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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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.COCOOP.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 = model(image, label)
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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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return loss_summary
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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 "token_prefix" in state_dict:
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del state_dict["token_prefix"]
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if "token_suffix" in state_dict:
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del state_dict["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)
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