增加代码规范，删除无用空格

ppcls/arch/__init__.py

@@ -91,9 +91,7 @@ class RecModel(TheseusLayer):
         
         out = dict()
         x = self.backbone(x)
-        
         out["backbone"] = x
-
         if self.neck is not None:
             feat = self.neck(x)
             out["neck"] = feat

ppcls/arch/backbone/model_zoo/wideresnet.py

@@ -143,7 +143,6 @@ class Wide_ResNet(TheseusLayer):
         # if use the output of projection head for classification
         self.proj_after = proj_after
         self.low_dim = low_dim
-
         channels = [
             16, 16 * widen_factor, 32 * widen_factor, 64 * widen_factor
         ]
@@ -184,7 +183,6 @@ class Wide_ResNet(TheseusLayer):
         else:
             self.fc = nn.Linear(channels[3], num_classes)
         self.channels = channels[3]
-
         # projection head
         if self.proj:
             self.l2norm = Normalize(2)
@@ -202,7 +200,6 @@ class Wide_ResNet(TheseusLayer):
         feat = self.relu(self.bn1(feat))
         feat = F.adaptive_avg_pool2d(feat, 1)
         feat = paddle.reshape(feat, [-1, self.channels])
-        
         if self.proj:
             pfeat = self.fc1(feat)
             pfeat = self.relu_mlp(pfeat)

ppcls/configs/ssl/FixMatchCCSSL/FixMatchCCSSL_cifar100_10000_4gpu.yaml

@@ -60,7 +60,7 @@ Loss:
   
 UnLabelLoss:
   Train:
-    - CCSSLCeLoss:
+    - CCSSLCELoss:
         weight: 1.
     - SoftSupConLoss:
         weight: 1.0

ppcls/engine/train/train_fixmatch_ccssl.py

@@ -53,14 +53,12 @@ def train_epoch_fixmatch_ccssl(engine, epoch_id, print_batch_step):
 
         inputs_x, targets_x = label_data_batch
         inputs_w, inputs_s1, inputs_s2 = unlabel_data_batch[:3]
-
         batch_size_label = inputs_x.shape[0]
         inputs = paddle.concat([inputs_x, inputs_w, inputs_s1, inputs_s2], axis=0)
 
         loss_dict, logits_label = get_loss(engine, inputs, batch_size_label, 
                                            temperture, threshold, targets_x,
                                            )
-
         loss = loss_dict['loss']
         loss.backward()
         
@@ -76,13 +74,9 @@ def train_epoch_fixmatch_ccssl(engine, epoch_id, print_batch_step):
 
         if engine.ema:
             engine.model_ema.update(engine.model)
-        
         update_metric(engine, logits_label, label_data_batch, batch_size)
-
         update_loss(engine, loss_dict, batch_size)
-
         engine.time_info['batch_cost'].update(time.time() - tic)
-
         if iter_id % print_batch_step == 0:
             log_info(engine, batch_size, epoch_id, iter_id)
 
@@ -101,6 +95,7 @@ def get_loss(engine,
              **kwargs
              ):
     out = engine.model(inputs)
+
     logits, feats = out['logits'], out['features']
     feat_w, feat_s1, feat_s2 = feats[batch_size_label:].chunk(3)
     feat_x = feats[:batch_size_label]
@@ -118,9 +113,7 @@ def get_loss(engine,
              'mask': mask,
              'max_probs': max_probs,
              }
-
     unlabel_loss = engine.unlabel_train_loss_func(feats, batch)
-
     loss_dict = {}
     for k, v in loss_dict_label.items():
         if k != 'loss':

ppcls/loss/__init__.py

@@ -17,7 +17,7 @@ from .triplet import TripletLoss, TripletLossV2
 from .tripletangularmarginloss import TripletAngularMarginLoss, TripletAngularMarginLoss_XBM
 from .supconloss import SupConLoss
 from .softsuploss import SoftSupConLoss
-from .ccssl_loss import CCSSLCeLoss
+from .ccssl_loss import CCSSLCELoss
 from .pairwisecosface import PairwiseCosface
 from .dmlloss import DMLLoss
 from .distanceloss import DistanceLoss

ppcls/loss/ccssl_loss.py

@@ -4,9 +4,9 @@ import copy
 import paddle.nn as nn
 
 
-class CCSSLCeLoss(nn.Layer):
+class CCSSLCELoss(nn.Layer):
     def __init__(self, **kwargs):
-        super(CCSSLCeLoss, self).__init__()
+        super(CCSSLCELoss, self).__init__()
         self.celoss = nn.CrossEntropyLoss(reduction='none')
 
     def forward(self, inputs, batch, **kwargs):
@@ -16,4 +16,4 @@ class CCSSLCeLoss(nn.Layer):
         loss_u = self.celoss(logits_s1, p_targets_u_w) * mask
         loss_u = loss_u.mean()
 
-        return {'CCSSLCeLoss': loss_u}
+        return {'CCSSLCELoss': loss_u}

ppcls/loss/softsuploss.py

-#   Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
 #
-#     http://www.apache.org/licenses/LICENSE-2.0
+#    http://www.apache.org/licenses/LICENSE-2.0
 #
 # Unless required by applicable law or agreed to in writing, software
 # distributed under the License is distributed on an "AS IS" BASIS,
@@ -47,9 +47,7 @@ class SoftSupConLoss(nn.Layer):
             labels = labels.reshape((-1, 1))
             mask = paddle.equal(labels, labels.T).astype('float32')
             max_probs = max_probs.reshape((-1, 1))
-            
             score_mask = paddle.matmul(max_probs, max_probs.T)
-            
             mask = paddle.multiply(mask, score_mask)
             
         contrast_count = feat.shape[1]