temp

.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__

.gitmodules

@@ -0,0 +1,3 @@
+[submodule "Dassl.pytorch"]
+	path = Dassl.pytorch
+	url = https://github.com/KaiyangZhou/Dassl.pytorch.git

configs/trainers/MaPLe/vit_b16_c2_ep5_batch4_2ctx.yaml

@@ -15,7 +15,7 @@ INPUT:
 OPTIM:
   NAME: "sgd"
   LR: 0.0035
-  MAX_EPOCH: 5
+  MAX_EPOCH: 10
   LR_SCHEDULER: "cosine"
   WARMUP_EPOCH: 1
   WARMUP_TYPE: "constant"

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!"
+

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

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

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

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

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

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

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__":

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")
 

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)