diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..d045fe0 --- /dev/null +++ b/.gitignore @@ -0,0 +1,212 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[codz] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py.cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# UV +# Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +#uv.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock +#poetry.toml + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +# pdm recommends including project-wide configuration in pdm.toml, but excluding .pdm-python. +# https://pdm-project.org/en/latest/usage/project/#working-with-version-control +#pdm.lock +#pdm.toml +.pdm-python +.pdm-build/ + +# pixi +# Similar to Pipfile.lock, it is generally recommended to include pixi.lock in version control. +#pixi.lock +# Pixi creates a virtual environment in the .pixi directory, just like venv module creates one +# in the .venv directory. It is recommended not to include this directory in version control. +.pixi + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.envrc +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +#.idea/ + +# Abstra +# Abstra is an AI-powered process automation framework. +# Ignore directories containing user credentials, local state, and settings. +# Learn more at https://abstra.io/docs +.abstra/ + +# Visual Studio Code +# Visual Studio Code specific template is maintained in a separate VisualStudioCode.gitignore +# that can be found at https://github.com/github/gitignore/blob/main/Global/VisualStudioCode.gitignore +# and can be added to the global gitignore or merged into this file. However, if you prefer, +# you could uncomment the following to ignore the entire vscode folder +# .vscode/ + +# Ruff stuff: +.ruff_cache/ + +# PyPI configuration file +.pypirc + +# Cursor +# Cursor is an AI-powered code editor. `.cursorignore` specifies files/directories to +# exclude from AI features like autocomplete and code analysis. Recommended for sensitive data +# refer to https://docs.cursor.com/context/ignore-files +.cursorignore +.cursorindexingignore + +# Marimo +marimo/_static/ +marimo/_lsp/ +__marimo__/ + + +# Custom +**/output* +**/__pycache__ diff --git a/.gitmodules b/.gitmodules new file mode 100644 index 0000000..1f946d5 --- /dev/null +++ b/.gitmodules @@ -0,0 +1,3 @@ +[submodule "Dassl.pytorch"] + path = Dassl.pytorch + url = https://github.com/KaiyangZhou/Dassl.pytorch.git diff --git a/Dassl.pytorch b/Dassl.pytorch new file mode 160000 index 0000000..c61a1b5 --- /dev/null +++ b/Dassl.pytorch @@ -0,0 +1 @@ +Subproject commit c61a1b570ac6333bd50fb5ae06aea59002fb20bb diff --git a/clip/__pycache__/__init__.cpython-39.pyc b/clip/__pycache__/__init__.cpython-39.pyc deleted file mode 100644 index 376955e..0000000 Binary files a/clip/__pycache__/__init__.cpython-39.pyc and /dev/null differ diff --git a/clip/__pycache__/clip.cpython-39.pyc b/clip/__pycache__/clip.cpython-39.pyc deleted file mode 100644 index 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b/deepcore/nets/nets_utils/__pycache__/recorder.cpython-39.pyc deleted file mode 100644 index fb8babb..0000000 Binary files a/deepcore/nets/nets_utils/__pycache__/recorder.cpython-39.pyc and /dev/null differ diff --git a/run.sh b/run.sh new file mode 100644 index 0000000..dcd95e7 --- /dev/null +++ b/run.sh @@ -0,0 +1,43 @@ +#!/bin/bash + +# 定义种子列表 +seeds=(1 2 3) + +# 定义数据集列表 +datasets=( + "ucf101" + "eurosat" + "oxford_pets" + "food101" + "oxford_flowers" + "dtd" + "caltech101" + "fgvc_aircraft" + "stanford_cars" + # "sun397" + # "imagenet" +) + +# 对于每个种子，遍历所有数据集 +for seed in "${seeds[@]}"; do + for dataset in "${datasets[@]}"; do + echo "正在运行训练: 数据集=${dataset}, 种子=${seed}" + + # 运行训练命令 + CUDA_VISIBLE_DEVICES=0 python train.py \ + --root ~/Datasets/CoOp \ + --seed "$seed" \ + --trainer MaPLe \ + --dataset-config-file "configs/datasets/${dataset}.yaml" \ + --config-file configs/trainers/MaPLe/vit_b16_t.yaml \ + --output-dir "output/DAPT_${dataset}_seed${seed}" \ + --mode dapt-g \ + DATASET.NUM_SHOTS ${SHOTS} + + echo "完成: 数据集=${dataset}, 种子=${seed}" + echo "----------------------------------------" + done +done + +echo "所有训练任务完成!" + diff --git a/scripts/maple/base2new_test_maple.sh b/scripts/maple/base2new_test_maple.sh new file mode 100644 index 0000000..661dc33 --- /dev/null +++ b/scripts/maple/base2new_test_maple.sh @@ -0,0 +1,54 @@ +#!/bin/bash + +#cd ../.. + +# custom config +DATA="~/Datasets/CoOp" +TRAINER=MaPLe + +DATASET=$1 +SEED=$2 + +CFG=vit_b16_c2_ep5_batch4_2ctx +SHOTS=16 +# LOADEP=10 +SUB=new + + +COMMON_DIR=${DATASET}/shots_${SHOTS}/${TRAINER}/${CFG}/seed${SEED} +MODEL_DIR=output/base2new/train_base/${COMMON_DIR} +DIR=output/base2new/test_${SUB}/${COMMON_DIR} +if [ -d "$DIR" ]; then + echo "Evaluating model" + echo "Results are available in ${DIR}. Resuming..." + + 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 ${DIR} \ + --model-dir ${MODEL_DIR} \ + # --load-epoch ${LOADEP} \ + --eval-only \ + DATASET.NUM_SHOTS ${SHOTS} \ + DATASET.SUBSAMPLE_CLASSES ${SUB} + +else + echo "Evaluating model" + echo "Runing the first phase job and save the output to ${DIR}" + + 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 ${DIR} \ + --model-dir ${MODEL_DIR} \ + # --load-epoch ${LOADEP} \ + --eval-only \ + DATASET.NUM_SHOTS ${SHOTS} \ + DATASET.SUBSAMPLE_CLASSES ${SUB} +fi diff --git a/scripts/maple/base2new_train_maple.sh b/scripts/maple/base2new_train_maple.sh new file mode 100644 index 0000000..a8769ef --- /dev/null +++ b/scripts/maple/base2new_train_maple.sh @@ -0,0 +1,40 @@ +#!/bin/bash + +#cd ../.. + +# custom config +DATA="~/Datasets/CoOp" +TRAINER=MaPLe + +DATASET=$1 +SEED=$2 + +CFG=vit_b16_c2_ep5_batch4_2ctx +# CFG=vit_b16_base +SHOTS=16 + + +DIR=output/base2new/train_base/${DATASET}/shots_${SHOTS}/${TRAINER}/${CFG}/seed${SEED} +if [ -d "$DIR" ]; then + echo "Results are available in ${DIR}. Resuming..." + 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 ${DIR} \ + DATASET.NUM_SHOTS ${SHOTS} \ + DATASET.SUBSAMPLE_CLASSES base +else + echo "Run this job and save the output to ${DIR}" + 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 ${DIR} \ + DATASET.NUM_SHOTS ${SHOTS} \ + DATASET.SUBSAMPLE_CLASSES base +fi diff --git a/scripts/maple/reproduce_maple.sh b/scripts/maple/reproduce_maple.sh new file mode 100644 index 0000000..3a173a4 --- /dev/null +++ b/scripts/maple/reproduce_maple.sh @@ -0,0 +1,58 @@ +#!/bin/bash + +#cd ../.. + +# custom config +DATA="/path/to/dataset/folder" +TRAINER=MaPLe + +DATASET=$1 +SEED=$2 +WEIGHTSPATH=$3 + +CFG=vit_b16_c2_ep5_batch4_2ctx +SHOTS=16 +LOADEP=5 +SUB_base=base +SUB_novel=new + +COMMON_DIR=${DATASET}/shots_${SHOTS}/${TRAINER}/${CFG}/seed${SEED} +MODEL_DIR=${WEIGHTSPATH}/base/seed${SEED} +DIR_base=output/base2new/test_${SUB_base}/${COMMON_DIR} +DIR_novel=output/base2new/test_${SUB_novel}/${COMMON_DIR} +if [ -d "$DIR" ]; then + echo "Results are already available in ${DIR}. Skipping..." +else + echo "Evaluating model" + echo "Runing the first phase job and save the output to ${DIR}" + # Evaluate on base classes + 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 ${DIR_base} \ + --model-dir ${MODEL_DIR} \ + --load-epoch ${LOADEP} \ + --eval-only \ + DATASET.NUM_SHOTS ${SHOTS} \ + DATASET.SUBSAMPLE_CLASSES ${SUB_base} + + # Evaluate on novel classes + 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 ${DIR_novel} \ + --model-dir ${MODEL_DIR} \ + --load-epoch ${LOADEP} \ + --eval-only \ + DATASET.NUM_SHOTS ${SHOTS} \ + DATASET.SUBSAMPLE_CLASSES ${SUB_novel} + + + +fi \ No newline at end of file diff --git a/scripts/maple/reproduce_maple_xd.sh b/scripts/maple/reproduce_maple_xd.sh new file mode 100644 index 0000000..7f8548f --- /dev/null +++ b/scripts/maple/reproduce_maple_xd.sh @@ -0,0 +1,38 @@ +#!/bin/bash + +#cd ../.. + +# custom config +DATA="/path/to/dataset/folder" +TRAINER=MaPLe + +DATASET=$1 +SEED=$2 +WEIGHTSPATH=$3 + +CFG=vit_b16_c2_ep5_batch4_2ctx_cross_datasets +SHOTS=16 +LOADEP=2 + +MODEL_DIR=${WEIGHTSPATH}/seed${SEED} + +DIR=output/evaluation/${TRAINER}/${CFG}_${SHOTS}shots/${DATASET}/seed${SEED} +if [ -d "$DIR" ]; then + echo "Results are already available in ${DIR}. Skipping..." +else + echo "Evaluating model" + echo "Runing the first phase job and save the output to ${DIR}" + # Evaluate on evaluation datasets + 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 ${DIR} \ + --model-dir ${MODEL_DIR} \ + --load-epoch ${LOADEP} \ + --eval-only \ + DATASET.NUM_SHOTS ${SHOTS} \ + +fi \ No newline at end of file diff --git a/scripts/maple/xd_test_maple.sh b/scripts/maple/xd_test_maple.sh new file mode 100644 index 0000000..11828ec --- /dev/null +++ b/scripts/maple/xd_test_maple.sh @@ -0,0 +1,32 @@ +#!/bin/bash + +#cd ../.. + +# custom config +DATA="/path/to/dataset/folder" +TRAINER=MaPLe + +DATASET=$1 +SEED=$2 + +CFG=vit_b16_c2_ep5_batch4_2ctx_cross_datasets +SHOTS=16 + + +DIR=output/evaluation/${TRAINER}/${CFG}_${SHOTS}shots/${DATASET}/seed${SEED} +if [ -d "$DIR" ]; then + echo "Results are available in ${DIR}. Skip this job" +else + echo "Run this job and save the output to ${DIR}" + + 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 ${DIR} \ + --model-dir output/imagenet/${TRAINER}/${CFG}_${SHOTS}shots/seed${SEED} \ + --load-epoch 2 \ + --eval-only +fi \ No newline at end of file diff --git a/scripts/maple/xd_train_maple.sh b/scripts/maple/xd_train_maple.sh new file mode 100644 index 0000000..2829c51 --- /dev/null +++ b/scripts/maple/xd_train_maple.sh @@ -0,0 +1,30 @@ +#!/bin/bash + +#cd ../.. + +# custom config +DATA="/path/to/dataset/folder" +TRAINER=MaPLe + +DATASET=$1 +SEED=$2 + +CFG=vit_b16_c2_ep5_batch4_2ctx_cross_datasets +SHOTS=16 + + +DIR=output/${DATASET}/${TRAINER}/${CFG}_${SHOTS}shots/seed${SEED} +if [ -d "$DIR" ]; then + echo "Results are available in ${DIR}." +else + echo "Run this job and save the output to ${DIR}" + + 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 ${DIR} \ + DATASET.NUM_SHOTS ${SHOTS} +fi \ No newline at end of file diff --git a/train.py b/train.py index d3a879b..65ae51a 100644 --- a/train.py +++ b/train.py @@ -291,7 +291,7 @@ def main(args): trainer.test_withlabel() # trainer.test_multi_label() Evaluation for VOC12 import shutil - shutil.rmtree(original_weight_output+'/MultiModalPromptLearner') + # shutil.rmtree(original_weight_output+'/MultiModalPromptLearner') else: original_weight_output = 'output/' + '/'.join(trainer.output_dir.split('/')[1:]) trainer.load_model(original_weight_output) @@ -304,7 +304,7 @@ def main(args): trainer.train() if cfg.DATASET.SUBSAMPLE_CLASSES != 'base': import shutil - shutil.rmtree(trainer.output_dir+'/MultiModalPromptLearner') + # shutil.rmtree(trainer.output_dir+'/MultiModalPromptLearner') if __name__ == "__main__": diff --git a/trainers/__pycache__/__init__.cpython-39.pyc b/trainers/__pycache__/__init__.cpython-39.pyc deleted file mode 100644 index ed654d6..0000000 Binary files a/trainers/__pycache__/__init__.cpython-39.pyc and /dev/null differ diff --git a/trainers/__pycache__/cocoop.cpython-39.pyc b/trainers/__pycache__/cocoop.cpython-39.pyc deleted file mode 100644 index 61bb840..0000000 Binary files a/trainers/__pycache__/cocoop.cpython-39.pyc and /dev/null differ diff --git a/trainers/__pycache__/coop.cpython-39.pyc b/trainers/__pycache__/coop.cpython-39.pyc deleted file mode 100644 index 83902c4..0000000 Binary files a/trainers/__pycache__/coop.cpython-39.pyc and /dev/null differ diff --git a/trainers/__pycache__/imagenet_templates.cpython-39.pyc b/trainers/__pycache__/imagenet_templates.cpython-39.pyc deleted file mode 100644 index a3cb01f..0000000 Binary files a/trainers/__pycache__/imagenet_templates.cpython-39.pyc and /dev/null differ diff --git a/trainers/__pycache__/independentVL.cpython-39.pyc b/trainers/__pycache__/independentVL.cpython-39.pyc deleted file mode 100644 index d153b46..0000000 Binary files a/trainers/__pycache__/independentVL.cpython-39.pyc and /dev/null differ diff --git a/trainers/__pycache__/maple.cpython-39.pyc b/trainers/__pycache__/maple.cpython-39.pyc deleted file mode 100644 index 87bfb62..0000000 Binary files a/trainers/__pycache__/maple.cpython-39.pyc and /dev/null differ diff --git a/trainers/__pycache__/util.cpython-39.pyc b/trainers/__pycache__/util.cpython-39.pyc deleted file mode 100644 index 69d5a04..0000000 Binary files a/trainers/__pycache__/util.cpython-39.pyc and /dev/null differ diff --git a/trainers/__pycache__/vpt.cpython-39.pyc b/trainers/__pycache__/vpt.cpython-39.pyc deleted file mode 100644 index 776852c..0000000 Binary files a/trainers/__pycache__/vpt.cpython-39.pyc and /dev/null differ diff --git a/trainers/__pycache__/zsclip.cpython-39.pyc b/trainers/__pycache__/zsclip.cpython-39.pyc deleted file mode 100644 index aee16eb..0000000 Binary files a/trainers/__pycache__/zsclip.cpython-39.pyc and /dev/null differ diff --git a/trainers/maple.py b/trainers/maple.py index dcf5f07..23146bf 100644 --- a/trainers/maple.py +++ b/trainers/maple.py @@ -19,7 +19,6 @@ 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 @@ -35,16 +34,6 @@ 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] @@ -159,28 +148,18 @@ class MultiModalPromptLearner(nn.Module): 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.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 [class_num 77] [:-bg_num] + self.tokenized_prompts = tokenized_prompts # torch.Tensor [class_num, 77] self.name_lens = name_lens def construct_prompts(self, ctx, prefix, suffix, label=None): @@ -204,8 +183,7 @@ class MultiModalPromptLearner(nn.Module): dim=1, ) - final_prompts = torch.cat((prompts,self.bg_embeding.cuda()),dim=0) - return final_prompts + return prompts def forward(self): ctx = self.ctx @@ -264,17 +242,44 @@ class CustomCLIP(nn.Module): def cos_sim(self,a,b): return F.cosine_similarity(a,b) + def contrastive_loss(self, anchor, positive, negative, temperature=0.07): + """ + InfoNCE contrastive loss for foreground-background discrimination + + Args: + anchor: Complete image features [B, D] + positive: Foreground features [B, D] + negative: Background features [B, D] + temperature: Temperature parameter for softmax + + Returns: + loss: Contrastive learning loss value + """ + # Calculate similarity + sim_pos = F.cosine_similarity(anchor, positive, dim=-1) # [B] + sim_neg = F.cosine_similarity(anchor, negative, dim=-1) # [B] + + # Apply temperature scaling + sim_pos = sim_pos / temperature + sim_neg = sim_neg / temperature + + # InfoNCE loss: -log(exp(sim_pos) / (exp(sim_pos) + exp(sim_neg))) + logits = torch.stack([sim_pos, sim_neg], dim=1) # [B, 2] + labels = torch.zeros(logits.shape[0], dtype=torch.long, device=logits.device) + + loss = F.cross_entropy(logits, labels) + return loss + 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) + text_features = text_features / text_features.norm(dim=-1, keepdim=True) image_features, visual_ctx, mask_similarity = self.image_encoder(ori_image_input, shared_ctx, deep_compound_prompts_vision) @@ -285,18 +290,15 @@ class CustomCLIP(nn.Module): # 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() - - - + logits = logit_scale * image_features @ text_features.t() + loss_re = torch.tensor(0.0, dtype=self.dtype, device=image.device) + loss_fg = torch.tensor(0.0, dtype=self.dtype, device=image.device) + loss_contrastive = torch.tensor(0.0, dtype=self.dtype, device=image.device) 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 @@ -305,37 +307,27 @@ class CustomCLIP(nn.Module): 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_contrastive = self.contrastive_loss(image_features, image_features_fg.detach(), image_features_bg.detach(), temperature=0.07) - loss_bg = F.cross_entropy(logits_bg,pseudo_label) - loss_fg = F.cross_entropy(foreground_score,label) + para_fg = 0.2 + para_vd = 0.6 - if epoch > 6: #Tunable parameters - loss_re = para_fg*loss_fg + para_bg*loss_bg + + if label is not None: + loss_fg = F.cross_entropy(logit_scale*image_features_fg.detach()@text_features.t(), label) else: - loss_re = para_vd*loss_re1 #loss_reo would be effective in base2novel setting + loss_fg = torch.tensor(0.0, dtype=self.dtype, device=image.device) + + if epoch is not None and epoch > 6: #Tunable parameters + loss_re = para_fg*loss_fg + else: + loss_re = para_vd*loss_contrastive 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()} + return F.cross_entropy(logits,label)+loss_re,logits,{'loss_contrastive':loss_contrastive.item(),'loss_fg':loss_fg.item()} else: return F.cross_entropy(weight.unsqueeze(-1)*logits,label), logits @@ -674,8 +666,8 @@ class MaPLe(TrainerX): model_name="model-best.pth.tar" ) - # if meet_checkpoint_freq or last_epoch: - # self.save_model(self.epoch, self.output_dir) + 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") diff --git a/trainers/util.py b/trainers/util.py index add45d3..8cc60c5 100644 --- a/trainers/util.py +++ b/trainers/util.py @@ -5,10 +5,6 @@ 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'] @@ -80,7 +76,7 @@ class GradCAM(object): else: logit = self.model_arch.forward_test(input,labels,cfg=cfg) - pred_label = torch.argmax(logit[:,:-len(BACKGROUND_CATEGORY)]) + 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') @@ -88,11 +84,10 @@ class GradCAM(object): # 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) + pred = logit.argmax(dim=-1) + one_hot_labels = F.one_hot(labels, num_classes=logit.shape[1]).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) + loss = (F.softmax(logit)*one_hot_labels).mean() # score = logit[:,labels] self.model_arch.zero_grad() @@ -186,10 +181,8 @@ class GradCAM(object): # 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) + loss = (logit*one_hot_labels).mean() # score = logit[:,labels] self.model_arch.zero_grad() loss.backward(retain_graph=retain_graph)