319 lines
11 KiB
Python
319 lines
11 KiB
Python
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": '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)
|