增加代码规范，删除一些无用的function

docs/zh_CN/training/semi_supervised_learning/FixMatchCCSSL.md

@@ -79,14 +79,14 @@ python tools/train.py -c ppcls/configs/ssl/FixMatchCCSSL/FixMatchCCSSL_cifar10_4
 python -m paddle.distributed.launch --gpus='0,1,2,3' tools/train.py -c ppcls/configs/ssl/FixMatchCCSSL/FixMatchCCSSL_cifar10_4000_4gpu.yaml
 ```
 
-2. **查看训练日志和保存的模型参数文件** 训练过程中屏幕会实时打印loss等指标信息，同时会保存日志文件 `train.log` ，模型参数文件 `*.pdparams`，优化器参数文件 `*.pdopt` 等内容到`Global.output_dir`指定的文件夹下，默认在 `PaddleClas/output/WideResNet/`文件夹下。
+2. **查看训练日志和保存的模型参数文件** 训练过程中屏幕会实时打印loss等指标信息，同时会保存日志文件 `train.log` ，模型参数文件 `*.pdparams`，优化器参数文件 `*.pdopt` 等内容到`Global.output_dir`指定的文件夹下，默认在 `PaddleClas/output/RecModel/`文件夹下。
 
 ## 5. 模型评估与推理部署
 ### 5.1 模型评估
 准备用于评估的 `*.pdparams` 模型参数文件，可以使用训练好的模型，也可以使用 *4. 模型训练* 中保存的模型。
 * 以训练过程中保存的 `best_model_ema.ema.pdparams`为例，执行如下命令即可进行评估。
 ```
-python3.7 tools/eval.py -c ppcls/configs/ssl/FixMatchCCSSL/FixMatchCCSSL_cifar10_4000_4gpu.yaml -o Global.pretrained_model="./output/WideResNet/best_model_ema.ema"
+python3.7 tools/eval.py -c ppcls/configs/ssl/FixMatchCCSSL/FixMatchCCSSL_cifar10_4000_4gpu.yaml -o Global.pretrained_model="./output/RecModel/best_model_ema.ema"
 ```
 
 * 以训练好的模型为例，下载提供的已经训练好的模型，到 `PaddleClas/pretrained_models` 文件夹中，执行如下命令即可进行评估。
@@ -98,7 +98,7 @@ cd pretrained_models
 wget 
 cd ..
 # 评估
-python3.7 tools/eval.py -c ppcls/configs/ssl/FixMatch_CCSSL_cifar10_4000.yaml -o Global.pretrained_model=""
+python3.7 tools/eval.py -c ppcls/configs/ssl/FixMatchCCSSL_cifar10_4000.yaml -o Global.pretrained_model="./output/RecModel/best_model_ema.ema"
 ```
 **注：** `pretrained_model` 后填入的地址不需要加 `.pdparams`后缀，在程序运行时会自动补上。
 
@@ -114,15 +114,15 @@ python3.7 tools/eval.py -c ppcls/configs/ssl/FixMatch_CCSSL_cifar10_4000.yaml -o
 [2022/12/08 09:36:16] ppcls INFO: [Eval][Epoch 0][Iter: 140/157]CELoss: 0.03242, loss: 0.03242, top1: 0.95601, top5: 0.99945, batch_cost: 0.02084s, reader_cost: 0.00075, ips: 3071.00311 images/sec
 [2022/12/08 09:36:16] ppcls INFO: [Eval][Epoch 0][Avg]CELoss: 0.16041, loss: 0.16041, top1: 0.95610, top5: 0.99950
 ```
-默认评估日志保存在 `PaddleClas/output/WideResNetCCSSL/eval.log`中，可以看到我们提供的模型在cifar10数据集上的评估指标为top1: 95.61, top5: 99.95
+默认评估日志保存在 `PaddleClas/output/RecModel/eval.log`中，可以看到我们提供的模型在cifar10数据集上的评估指标为top1: 95.57, top5: 99.95
 
 ### 5.2 模型推理
 #### 5.2.1 推理模型准备
 将训练过程中保存的模型文件转成inference模型，同样以 `best_model_ema.ema_pdparams`为例，执行以下命令进行转换
 ```
 python3.7 tools/export_model.py \
--c ppcls/configs/ssl/FixMatch_CCSSL/FixMatch_CCSSL_cifar10_4000.yaml \
+-c ppcls/configs/ssl/FixMatchCCSSL/FixMatchCCSSL_cifar10_4000.yaml \
--o Global.pretrained_model="output/WideResNetCCSSL/best_model_ema.ema" \
+-o Global.pretrained_model="output/RecModel/best_model_ema.ema" \
 -o Global.save_inference_fir="./deploy/inference"
 ```
 

ppcls/arch/gears/decoup.py

@@ -1,16 +0,0 @@
-import paddle
-import paddle.nn as nn
-
-
-class Decoup(nn.Layer):
-    def __init__(self, logits_index, features_index, **kwargs):
-        super(Decoup, self).__init__()
-        self.logits_index = logits_index
-        self.features_index = features_index
-
-
-    def forward(self, out, **kwargs):
-        assert isinstance(out, (list, tuple)), 'out must  be list or tuple'
-        out = {'logits': out[self.logits_index], 'features':out[self.features_index]}
-        return out
-

ppcls/configs/ssl/FixMatchCCSSL/FixMatchCCSSL_cifar10_4000_4gpu.yaml

@@ -64,10 +64,9 @@ Optimizer:
   use_nesterov: true
   weight_decay: 0.001
   lr:
-    name: 'cosine_schedule_with_warmup'
+    name: 'CosineFixmatch'
     learning_rate: 0.03
     num_warmup_steps: 0
-    num_training_steps: 524800
 
 DataLoader:
   mean: [0.4914, 0.4822, 0.4465]

ppcls/data/dataloader/__init__.py

@@ -13,4 +13,3 @@ from ppcls.data.dataloader.person_dataset import Market1501, MSMT17
 from ppcls.data.dataloader.face_dataset import AdaFaceDataset, FiveValidationDataset
 from ppcls.data.dataloader.custom_label_dataset import CustomLabelDataset
 from ppcls.data.dataloader.cifar import Cifar10, Cifar100
-# from ppcls.data.dataloader.cifar import CIFAR10SSL, CIFAR100SSL

ppcls/data/dataloader/cifar.py

@@ -15,16 +15,11 @@
 from __future__ import print_function
 import numpy as np
 import cv2
-import shutil
 from ppcls.data import preprocess
 from ppcls.data.preprocess import transform
-# from ppcls.data.preprocess import BaseTransform, ListTransform
 from ppcls.data.dataloader.common_dataset import create_operators
 from paddle.vision.datasets import Cifar10 as Cifar10_paddle
 from paddle.vision.datasets import Cifar100 as Cifar100_paddle
-# from paddle.vision.datasets import cifar
-import os
-# from PIL import Image
 
 
 class Cifar10(Cifar10_paddle):
@@ -128,176 +123,4 @@ class Cifar100(Cifar100_paddle):
             image3 = transform(image, self._transform_ops_strong)
             image3 = image3.transpose((2, 0, 1))
 
-            return (image2, image3, np.int64(label))
+            return (image2, image3, np.int64(label))
-
-
-# def np_convert_pil(array):
-#     """
-#     array conver image
-#     Args:
-#         array: array and dim is 1
-#     """
-#     assert len(array.shape), "dim of array should 1"
-#     img = Image.fromarray(array.reshape(3, 32, 32).transpose(1, 2, 0))
-#     return img
-
-
-# class CIFAR10(cifar.Cifar10):
-#     """
-#     cifar10 dataset
-#     """
-#     def __init__(self, data_file, download=True, mode='train'):
-#         super().__init__(download=download, mode=mode)
-#         if data_file is not None:
-#             os.makedirs(data_file, exist_ok=True)
-#             if not os.path.exists(os.path.join(data_file, 'cifar-10-python.tar.gz')):
-#                 shutil.move('~/.cache/paddle/dataset/cifar/cifar-10-python.tar.gz', data_file)
-#         self.num_classes = 10
-#         self.x = []
-#         self.y = []
-#         for d in self.data:
-#             self.x.append(d[0])
-#             self.y.append(d[1])
-
-#         self.x = np.array(self.x)
-#         self.y = np.array(self.y)
-
-#     def __getitem__(self, idx):
-#         return self.x[idx], self.y[idx]
-
-#     def __len__(self):
-#         return self.x.shape[0]
-
-
-# class CIFAR100(cifar.Cifar100):
-#     """
-#     cifar10 dataset
-#     """
-#     def __init__(self, data_file, download=True, mode='train'):
-#         super().__init__(download=download, mode=mode)
-#         if data_file is not None:
-#             os.makedirs(data_file, exist_ok=True)
-#             if not os.path.exists(os.path.join(data_file, 'cifar-100-python.tar.gz')):
-#                 shutil.move('~/.cache/paddle/dataset/cifar/cifar-100-python.tar.gz', data_file)
-#         self.num_classes = 100
-#         self.x = []
-#         self.y = []
-#         for d in self.data:
-#             self.x.append(d[0])
-#             self.y.append(d[1])
-
-#         self.x = np.array(self.x)
-#         self.y = np.array(self.y)
-
-#     def __getitem__(self, idx):
-#         return self.x[idx], self.y[idx]
-
-#     def __len__(self):
-#         return self.x.shape[0]
-
-
-# class CIFAR10SSL(CIFAR10):
-#     """
-#     from Cifar10
-#     """
-
-#     def __init__(self, 
-#                  data_file=None, 
-#                  sample_per_label=None, 
-#                  download=True,
-#                  expand_labels=1,
-#                  mode='train',
-#                  transform_ops=None,
-#                  transform_w=None,
-#                  transform_s1=None,
-#                  transform_s2=None):
-#         super().__init__(data_file, download=download, mode=mode)
-#         self.data_type = 'unlabeled_train' if mode == 'train' else 'val'
-#         if transform_ops is not None and sample_per_label is not None:
-#             index = []
-#             self.data_type = 'labeled_train'
-#             for c in range(self.num_classes):
-#                 idx = np.where(self.y == c)[0]
-#                 idx = np.random.choice(idx, sample_per_label, False)
-#                 index.extend(idx)
-#             index = index * expand_labels
-#             # print(index)
-#             self.x = self.x[index]
-#             self.y = self.y[index]
-#         self.transforms = [transform_ops] if transform_ops is not None else [transform_w, transform_s1, transform_s2]
-#         self.mode = mode
-
-#     def __getitem__(self, idx):
-#         img, label = np_convert_pil(self.x[idx]), self.y[idx]
-#         results = ListTransform(self.transforms)(img)
-#         if self.data_type == 'unlabeled_train':
-#             return results
-#         return results[0], label
-        
-#     def __len__(self):
-#         return self.x.shape[0]
-        
-
-
-
-# class CIFAR100SSL(CIFAR100):
-#     """
-#     from Cifar100
-#     """
-
-#     def __init__(self, 
-#                  data_file=None, 
-#                  sample_per_label=None, 
-#                  download=True,
-#                  expand_labels=1,
-#                  mode='train',
-#                  transform_ops=None,
-#                  transform_w=None,
-#                  transform_s1=None,
-#                  transform_s2=None):
-#         super().__init__(data_file, download=download, mode=mode)
-#         self.data_type = 'unlabeled_train' if mode == 'train' else 'val'
-#         if transform_ops is not None and sample_per_label is not None:
-#             index = []
-#             self.data_type = 'labeled_train'
-#             for c in range(self.num_classes):
-#                 idx = np.where(self.y == c)[0]
-#                 idx = np.random.choice(idx, sample_per_label, False)
-#                 index.extend(idx)
-#             index = index * expand_labels
-#             # print(index)
-#             self.x = self.x[index]
-#             self.y = self.y[index]
-#         self.transforms = [transform_ops] if transform_ops is not None else [transform_w, transform_s1, transform_s2]
-#         self.mode = mode
-
-#     def __getitem__(self, idx):
-#         img, label = np_convert_pil(self.x[idx]), self.y[idx]
-#         results = ListTransform(self.transforms)(img)
-#         if self.data_type == 'unlabeled_train':
-#             return results
-#         return results[0], label
-        
-#     def __len__(self):
-#         return self.x.shape[0]
-
-
-# def x_u_split(num_labeled, num_classes, label):
-#     """
-#     split index of dataset to labeled x and unlabeled u
-#     Args:
-#         num_labeled: num of labeled dataset
-#         label: list or array, label
-#     """
-#     assert num_labeled <= len(label), "arg num_labeled should <= num of label"
-#     label = np.array(label) if isinstance(label, list) else label
-#     label_per_class = num_labeled // num_classes
-#     labeled_idx = []
-#     unlabeled_idx = np.array(list(range(label.shape[0])))
-#     for c in range(num_classes):
-#         idx = np.where(label == c)[0]
-#         idx = np.random.choice(idx, label_per_class, False)
-#         labeled_idx.extend(idx)
-    
-#     np.random.shuffle(labeled_idx)
-#     return labeled_idx, unlabeled_idx

ppcls/data/preprocess/__init__.py

@@ -56,8 +56,6 @@ from ppcls.data.preprocess.batch_ops.batch_operators import MixupCutmixHybrid
 import numpy as np
 from PIL import Image
 import random
-from paddle.vision.transforms import transforms as T
-from paddle.vision.transforms.transforms import RandomCrop, ToTensor, Normalize
 
 
 def transform(data, ops=[]):
@@ -123,41 +121,4 @@ class TimmAutoAugment(RawTimmAutoAugment):
             img = np.asarray(img)
 
         return img
-
+        
-
-# class BaseTransform:
-#     def __init__(self, cfg) -> None:
-#         """
-#         Args:
-#             cfg: list [dict, dict, dict]
-#         """
-#         ts = []
-#         for op in cfg:
-#             name = list(op.keys())[0]
-#             if op[name] is None:
-#                 ts.append(eval(name)())
-#             else:
-#                 ts.append(eval(name)(**(op[name])))
-
-#         self.t = T.Compose(ts)
-
-#     def __call__(self, img):
-        
-#         return self.t(img)
-
-
-# class ListTransform:
-#     def __init__(self, ops) -> None:
-#         """
-#         Args:
-#             ops: list[list[dict, dict], ...]
-#         """
-#         self.ts = []
-#         for op in ops:
-#             self.ts.append(BaseTransform(op))
-
-#     def __call__(self, img):
-#         results = []
-#         for op in self.ts:
-#             results.append(op(img))
-#         return results

ppcls/loss/__init__.py

@@ -22,7 +22,6 @@ from .pairwisecosface import PairwiseCosface
 from .dmlloss import DMLLoss
 from .distanceloss import DistanceLoss
 from .softtargetceloss import SoftTargetCrossEntropy
-from .ccssl_loss import CCSSLLoss
 from .distillationloss import DistillationCELoss
 from .distillationloss import DistillationGTCELoss
 from .distillationloss import DistillationDMLLoss

ppcls/loss/ccssl_loss.py

@@ -1,53 +1,14 @@
-
 from ppcls.engine.train.train import forward
 from .softsuploss import SoftSupConLoss
 import copy
 import paddle.nn as nn
 
 
-class CCSSLLoss(nn.Layer):
-    def __init__(self, **kwargs):
-        super(CCSSLLoss, self).__init__()
-        ce_cfg = copy.deepcopy(kwargs['CELoss'])
-        self.ce_weight = ce_cfg.pop('weight')
-        softsupconloss_cfg = copy.deepcopy(kwargs['SoftSupConLoss'])
-        self.softsupconloss_weight = softsupconloss_cfg.pop('weight')
-
-        self.softsuploss = SoftSupConLoss(**softsupconloss_cfg)
-        self.celoss = nn.CrossEntropyLoss(reduction='none')
-
-    def forward(self, feats, batch, **kwargs):
-        """
-        Args:
-            feats: feature of s1 and s2, (n, 2, d)
-            batch: dict 
-        """
-        logits_w = batch['logits_w']
-        logits_s1 = batch['logits_s1']
-        p_targets_u_w = batch['p_targets_u_w']
-        mask = batch['mask']
-
-        max_probs = batch['max_probs']
-        # reduction = batch['reduction']
-       
-
-        loss_u = self.celoss(logits_s1, p_targets_u_w) * mask
-        loss_u = loss_u.mean()
-
-        loss_c = self.softsuploss(feats, max_probs, p_targets_u_w)
-
-        return {'CCSSLLoss': self.ce_weight*loss_u + self.softsupconloss_weight * loss_c}
-
-
-
-
-
 class CCSSLCeLoss(nn.Layer):
     def __init__(self, **kwargs):
         super(CCSSLCeLoss, self).__init__()
         self.celoss = nn.CrossEntropyLoss(reduction='none')
 
-
     def forward(self, inputs, batch, **kwargs):
         p_targets_u_w = batch['p_targets_u_w']
         logits_s1 = batch['logits_s1']
@@ -56,6 +17,3 @@ class CCSSLCeLoss(nn.Layer):
         loss_u = loss_u.mean()
 
         return {'CCSSLCeLoss': loss_u}
-
-
-

ppcls/loss/softsuploss.py

@@ -41,7 +41,6 @@ class SoftSupConLoss(nn.Layer):
             
             score_mask = paddle.matmul(max_probs, max_probs.T)
             
-            
             mask = paddle.multiply(mask, score_mask)
             
         contrast_count = feat.shape[1]
@@ -55,7 +54,6 @@ class SoftSupConLoss(nn.Layer):
         mask = paddle.concat([mask, mask], axis=0)
         mask = paddle.concat([mask, mask], axis=1)
         
-        # mask = paddle.repeat_interleave(paddle.repeat_interleave(mask, 2, 0), 2, 1)
         logits_mask = 1 - paddle.eye(batch_size * contrast_count, dtype=paddle.float64)
         mask = mask * logits_mask
         exp_logits = paddle.exp(logits) * logits_mask
@@ -68,6 +66,4 @@ class SoftSupConLoss(nn.Layer):
             loss = loss.mean()
 
         return {"SoftSupConLoss": loss}
-    
+    
-
-

ppcls/optimizer/learning_rate.py

@@ -519,21 +519,3 @@ class CosineFixmatch(LRBase):
             last_epoch=self.last_epoch)
         setattr(learning_rate, "by_epoch", self.by_epoch)
         return learning_rate
-
-
-
-def cosine_schedule_with_warmup(learning_rate,
-                                num_warmup_steps,
-                                num_training_steps,
-                                num_cycles=7./16,
-                                last_epoch=-1,
-                                **kwargs):
-    
-    def _lr_lambda(current_step):
-        if current_step < num_warmup_steps:
-            return float(current_step) /  float(max(1, num_warmup_steps))
-        no_progress = float(current_step - num_warmup_steps) / float(max(1, num_training_steps - num_warmup_steps))
-        return max(0., math.cos(math.pi * num_cycles * no_progress))
-
-    return lr.LambdaDecay(learning_rate=learning_rate, lr_lambda=_lr_lambda)
-