styleEDL

.gitignore

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+*.pyc
+runs/
+logs/
+.vscode/

README.md

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+
+## 运行
+```bash
+python main.py --tag 
+```
++ `--tag` 指定保存目录的前缀，默认为cache
+
+
+
+## 添加新的模型
+
+1. 在`network`文件夹下新建新的文件
+1. 在`network`下`__init__`中的`models`加入新的网络模型
+1. 在`configs`文件夹中新建对应的配置文件
+
+
+## 添加新的trainer
+
+1. 在`trainer`文件夹中新建文件，继承`basetrainer`
+1. 在`trainer`下`__init__`中的`trainers`加入新的trainer
+1. 运行时，由配置文件中的`trainer`指定

configs/base.yaml

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+model: backbone
+trainer: backbone
+
+# ===========================
+# train
+seed: 330
+gpu_id: 0
+epochs: 90
+batch_size: 8
+
+
+image_size: 448
+isNormalize: True
+num_workers: 12
+
+dataset: Twitter_LDL
+num_classes: 8
+data_path: /media/Harddisk_A/emotion_dataset/
+
+
+resume_path:
+save_interval: 2000
+save_mark: 0.4
+display_interval: 20
+
+# ===========================
+# optimizer
+momentum: 0.1
+
+# ===========================
+# learning rate
+lr: 0.01
+
+# ===========================
+# scheduler
+scheduler: scheduler_cos
+
+# stepLR
+scheduler_stepLR:
+  step_size: 15
+  gamma: 0.5
+
+# MultiStepLR
+scheduler_multi:
+  milestones: [10, 20, 50]
+  gamma: 0.5
+
+# ExponentialLR
+scheduler_exp:
+  gamma: 0.5
+
+# CosineAnnealingLR
+scheduler_cos:
+  T_max: 40
+  eta_min: 0
+
+# CyclicLR
+scheduler_cyclic:
+  max_lr: 0.05
+  up: 10
+  down: 10
+
+# lambda
+scheduler_lambda:
+  lr_lambda: None

configs/train.yaml

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+model: gmg
+trainer: gmg
+seed: 330
+
+
+mu: 0.6
+lambda: 0.6
+parts: 2
+
+
+gpu_id: 1
+batch_size: 16
+
+epochs: 90
+
+lr: 0.005
+scheduler: scheduler_multi
+scheduler_multi:
+  milestones: [10, 30, 50, 70]
+  gamma: 0.1
+
+scheduler_cos:
+  T_max: 40
+  eta_min: 0
+
+momentum: 0.1
+weight_decay: 1e-7
+# resume_path: ./logs/Mar-01_14:45:22_final/epoch_55.pth

dataset/__init__.py

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+from .dataset import Dataset_LDL

dataset/dataset.py

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+# -*- encoding: utf-8 -*-
+
+import os
+import torch
+from PIL import Image, ImageFile
+from torch.utils.data import Dataset
+
+ImageFile.LOAD_TRUNCATED_IMAGES = True
+
+
+class Dataset_LDL(Dataset):
+    def __init__(
+        self,
+        root_path,
+        mode,
+        transforms,
+    ):
+        super().__init__()
+        self.transforms = transforms
+        self.images_path = []
+        self.labels = []
+        self.cls = []
+
+        if mode == "train":
+            file_name = 'ground_truth_train.txt'
+        elif mode == "test":
+            file_name = 'ground_truth_test.txt'
+
+        # 读入文件
+        with open(os.path.join(root_path, file_name), 'r') as f:
+            file = f.readlines()
+
+        for line in file:
+            temp = line.rstrip('\n').rstrip(' ').split(' ')
+            self.images_path.append(os.path.join(root_path, "images", temp[0]))
+            label = [eval(i) for i in temp[1:-1]]
+            self.cls.append(eval(temp[-1]))
+            self.labels.append(label)
+
+    def __getitem__(self, index):
+        original_img = Image.open(self.images_path[index]).convert('RGB')
+        label = torch.FloatTensor(self.labels[index])
+        cls = self.cls[index]
+        original_img = self.transforms(original_img)
+        return original_img, label, cls
+
+    def __len__(self) -> int:
+        return len(self.images_path)

envs.yaml

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+name: torch
+channels:
+  - pytorch
+  - defaults
+dependencies:
+  - _libgcc_mutex=0.1=main
+  - _openmp_mutex=5.1=1_gnu
+  - asttokens=2.0.5=pyhd3eb1b0_0
+  - backcall=0.2.0=pyhd3eb1b0_0
+  - blas=1.0=mkl
+  - bzip2=1.0.8=h7b6447c_0
+  - ca-certificates=2022.10.11=h06a4308_0
+  - charset-normalizer=2.0.4=pyhd3eb1b0_0
+  - cudatoolkit=11.3.1=h2bc3f7f_2
+  - decorator=5.1.1=pyhd3eb1b0_0
+  - executing=0.8.3=pyhd3eb1b0_0
+  - ffmpeg=4.2.2=h20bf706_0
+  - fftw=3.3.9=h27cfd23_1
+  - filelock=3.6.0=pyhd3eb1b0_0
+  - freetype=2.12.1=h4a9f257_0
+  - ftfy=5.8=py_0
+  - giflib=5.2.1=h7b6447c_0
+  - gmp=6.2.1=h295c915_3
+  - gnutls=3.6.15=he1e5248_0
+  - huggingface_hub=0.2.1=pyhd3eb1b0_0
+  - importlib_metadata=4.11.3=hd3eb1b0_0
+  - intel-openmp=2021.4.0=h06a4308_3561
+  - jpeg=9e=h7f8727e_0
+  - jupyter_client=7.3.5=py310h06a4308_0
+  - jupyter_core=4.11.1=py310h06a4308_0
+  - lame=3.100=h7b6447c_0
+  - lcms2=2.12=h3be6417_0
+  - ld_impl_linux-64=2.38=h1181459_1
+  - lerc=3.0=h295c915_0
+  - libdeflate=1.8=h7f8727e_5
+  - libffi=3.3=he6710b0_2
+  - libgcc-ng=11.2.0=h1234567_1
+  - libgfortran-ng=11.2.0=h00389a5_1
+  - libgfortran5=11.2.0=h1234567_1
+  - libgomp=11.2.0=h1234567_1
+  - libidn2=2.3.2=h7f8727e_0
+  - libopus=1.3.1=h7b6447c_0
+  - libpng=1.6.37=hbc83047_0
+  - libsodium=1.0.18=h7b6447c_0
+  - libstdcxx-ng=11.2.0=h1234567_1
+  - libtasn1=4.16.0=h27cfd23_0
+  - libtiff=4.4.0=hecacb30_0
+  - libunistring=0.9.10=h27cfd23_0
+  - libuuid=1.0.3=h7f8727e_2
+  - libuv=1.40.0=h7b6447c_0
+  - libvpx=1.7.0=h439df22_0
+  - libwebp=1.2.4=h11a3e52_0
+  - libwebp-base=1.2.4=h5eee18b_0
+  - lz4-c=1.9.3=h295c915_1
+  - mkl=2021.4.0=h06a4308_640
+  - mkl_fft=1.3.1=py310hd6ae3a3_0
+  - mkl_random=1.2.2=py310h00e6091_0
+  - ncurses=6.3=h5eee18b_3
+  - nettle=3.7.3=hbbd107a_1
+  - numpy-base=1.23.3=py310h8e6c178_0
+  - openh264=2.1.1=h4ff587b_0
+  - openssl=1.1.1s=h7f8727e_0
+  - packaging=21.3=pyhd3eb1b0_0
+  - parso=0.8.3=pyhd3eb1b0_0
+  - pexpect=4.8.0=pyhd3eb1b0_3
+  - pickleshare=0.7.5=pyhd3eb1b0_1003
+  - prompt-toolkit=3.0.20=pyhd3eb1b0_0
+  - ptyprocess=0.7.0=pyhd3eb1b0_2
+  - pure_eval=0.2.2=pyhd3eb1b0_0
+  - pycparser=2.21=pyhd3eb1b0_0
+  - pygments=2.11.2=pyhd3eb1b0_0
+  - pyopenssl=22.0.0=pyhd3eb1b0_0
+  - python=3.10.6=haa1d7c7_1
+  - python-dateutil=2.8.2=pyhd3eb1b0_0
+  - pytorch=1.11.0=py3.10_cuda11.3_cudnn8.2.0_0
+  - pytorch-mutex=1.0=cuda
+  - readline=8.2=h5eee18b_0
+  - sacremoses=0.0.43=pyhd3eb1b0_0
+  - six=1.16.0=pyhd3eb1b0_1
+  - sqlite=3.39.3=h5082296_0
+  - stack_data=0.2.0=pyhd3eb1b0_0
+  - threadpoolctl=2.2.0=pyh0d69192_0
+  - tk=8.6.12=h1ccaba5_0
+  - traitlets=5.1.1=pyhd3eb1b0_0
+  - typing_extensions=4.3.0=py310h06a4308_0
+  - tzdata=2022f=h04d1e81_0
+  - wcwidth=0.2.5=pyhd3eb1b0_0
+  - wheel=0.37.1=pyhd3eb1b0_0
+  - x264=1!157.20191217=h7b6447c_0
+  - xz=5.2.6=h5eee18b_0
+  - yaml=0.2.5=h7b6447c_0
+  - zeromq=4.3.4=h2531618_0
+  - zlib=1.2.13=h5eee18b_0
+  - zstd=1.5.2=ha4553b6_0
+  - pip:
+    - absl-py==1.3.0
+    - bottleneck==1.3.5
+    - brotlipy==0.7.0
+    - cachetools==5.2.0
+    - certifi==2022.12.7
+    - cffi==1.15.1
+    - click==8.0.4
+    - contourpy==1.0.7
+    - cryptography==38.0.1
+    - cycler==0.11.0
+    - debugpy==1.5.1
+    - easydict==1.10
+    - entrypoints==0.4
+    - fonttools==4.39.2
+    - fvcore==0.1.5.post20221221
+    - google-auth==2.15.0
+    - google-auth-oauthlib==0.4.6
+    - grpcio==1.51.1
+    - idna==3.4
+    - importlib-metadata==4.11.3
+    - iopath==0.1.10
+    - ipykernel==6.15.2
+    - ipython==8.4.0
+    - jedi==0.18.1
+    - jieba==0.42.1
+    - joblib==1.1.1
+    - jupyter-client==7.3.5
+    - jupyter-core==4.11.1
+    - kiwisolver==1.4.4
+    - markdown==3.4.1
+    - markupsafe==2.1.1
+    - matplotlib==3.7.1
+    - matplotlib-inline==0.1.6
+    - mkl-fft==1.3.1
+    - mkl-random==1.2.2
+    - mkl-service==2.4.0
+    - nest-asyncio==1.5.5
+    - numexpr==2.8.3
+    - numpy==1.23.3
+    - nvidia-ml-py3==7.352.0
+    - oauthlib==3.2.2
+    - pandas==1.4.4
+    - pillow==9.2.0
+    - pip==22.2.2
+    - portalocker==2.7.0
+    - protobuf==3.20.3
+    - psutil==5.9.0
+    - pyasn1==0.4.8
+    - pyasn1-modules==0.2.8
+    - pyparsing==3.0.9
+    - pysocks==1.7.1
+    - pytz==2022.1
+    - pyyaml==6.0
+    - pyzmq==23.2.0
+    - regex==2022.7.9
+    - requests==2.28.1
+    - requests-oauthlib==1.3.1
+    - rsa==4.9
+    - scikit-learn==1.1.1
+    - scipy==1.9.1
+    - setuptools==65.5.0
+    - tabulate==0.9.0
+    - tensorboard==2.11.0
+    - tensorboard-data-server==0.6.1
+    - tensorboard-plugin-wit==1.8.1
+    - termcolor==2.2.0
+    - tokenizers==0.11.4
+    - torch==1.11.0
+    - torchaudio==0.11.0
+    - torchsummary==1.5.1
+    - torchvision==0.12.0
+    - tornado==6.2
+    - tqdm==4.64.1
+    - transformers==4.18.0
+    - typing-extensions==4.3.0
+    - urllib3==1.26.12
+    - werkzeug==2.2.2
+    - yacs==0.1.8
+    - yapf==0.32.0
+    - zipp==3.8.0
+prefix: /home/wj/.conda/envs/mmsa

loss/EMD.py

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+import torch
+import torch.nn as nn
+
+
+def single_emd_loss(p, q, r=2):
+    """
+    Earth Mover's Distance of one sample
+
+    Args:
+        p: true distribution of shape num_classes × 1
+        q: estimated distribution of shape num_classes × 1
+        r: norm parameter
+    """
+    assert p.shape == q.shape, "Length of the two distribution must be the same"
+    length = p.shape[0]
+    emd_loss = 0.0
+    for i in range(1, length + 1):
+        emd_loss += torch.abs(sum(p[:i] - q[:i]))**r
+    return (emd_loss / length)**(1. / r)
+
+
+def emd_loss(p, q, r=2):
+    """
+    Earth Mover's Distance on a batch
+
+    Args:
+        p: true distribution of shape mini_batch_size × num_classes × 1
+        q: estimated distribution of shape mini_batch_size × num_classes × 1
+        r: norm parameters
+    """
+    assert p.shape == q.shape, "Shape of the two distribution batches must be the same."
+    mini_batch_size = p.shape[0]
+    loss_vector = []
+    for i in range(mini_batch_size):
+        loss_vector.append(single_emd_loss(p[i], q[i], r=r))
+    return sum(loss_vector) / mini_batch_size
+
+
+class EMDLoss(nn.Module):
+    def __init__(self) -> None:
+        super(EMDLoss, self).__init__()
+
+    def forward(self, x, y):
+        return emd_loss(x, y)

loss/__init__.py

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+from .adv_div import AdvDivLoss, ProgressiveCircularLoss
+from .EMD import EMDLoss
+from .share_specific import SharedAndSpecificLoss