Merge remote-tracking branch 'upstream/dygraph' into dygraph

Update repo

README.md

@@ -6,15 +6,13 @@
 
 PaddleClas is a toolset for image classification tasks prepared for the industry and academia. It helps users train better computer vision models and apply them in real scenarios.
 
-
 **Recent update**
+- 2020.09.17 Add `Res2Net50_vd_26w_4s_ssld` pretrained model, whose Top-1 Acc on ImageNet-1k dataset reaches 83.1%. Add `Res2Net101_vd_26w_4s_ssld` pretrained model, whose Top-1 Acc on ImageNet-1k dataset reaches 83.9%.
 - 2020.10.12 Add Paddle-Lite demo。
 - 2020.10.10 Add cpp inference demo and improve FAQ tutorial.
 - 2020.09.17 Add `HRNet_W48_C_ssld` pretrained model, whose Top-1 Acc on ImageNet-1k dataset reaches 83.62%. Add `ResNet34_vd_ssld` pretrained model, whose Top-1 Acc on ImageNet-1k dataset reaches 79.72%.
 - 2020.09.07 Add `HRNet_W18_C_ssld` pretrained model, whose Top-1 Acc on ImageNet-1k dataset reaches 81.16%.
 - 2020.07.14 Add `Res2Net200_vd_26w_4s_ssld` pretrained model, whose Top-1 Acc on ImageNet-1k dataset reaches 85.13%. Add `Fix_ResNet50_vd_ssld_v2` pretrained model, whose Top-1 Acc on ImageNet-1k dataset reaches 84.00%.
-- 2020.06.17 Add English documents.
-- 2020.06.12 Add support for training and evaluation on Windows or CPU.
 - [more](./docs/en/update_history_en.md)
 
 

README_cn.md

@@ -7,13 +7,12 @@
 飞桨图像分类套件PaddleClas是飞桨为工业界和学术界所准备的一个图像分类任务的工具集，助力使用者训练出更好的视觉模型和应用落地。
 
 **近期更新**
+- 2020.10.20 添加 `Res2Net50_vd_26w_4s_ssld `模型，在ImageNet-1k上Top-1 Acc可达83.1%；添加 `Res2Net101_vd_26w_4s_ssld `模型，在ImageNet-1k上Top-1 Acc可达83.9%。
 - 2020.10.12 添加Paddle-Lite demo。
 - 2020.10.10 添加cpp inference demo，完善`FAQ 30问`教程。
 - 2020.09.17 添加 `HRNet_W48_C_ssld `模型，在ImageNet-1k上Top-1 Acc可达83.62%；添加 `ResNet34_vd_ssld `模型，在ImageNet-1k上Top-1 Acc可达79.72%。
 - 2020.09.07 添加 `HRNet_W18_C_ssld `模型，在ImageNet-1k上Top-1 Acc可达81.16%；添加 `MobileNetV3_small_x0_35_ssld `模型，在ImageNet-1k上Top-1 Acc可达55.55%。
 - 2020.07.14 添加 `Res2Net200_vd_26w_4s_ssld `模型，在ImageNet-1k上Top-1 Acc可达85.13%；添加 `Fix_ResNet50_vd_ssld_v2 `模型，在ImageNet-1k上Top-1 Acc可达84.0%。
-- 2020.06.17 添加英文文档。
-- 2020.06.12 添加对windows和CPU环境的训练与评估支持。
 - [more](./docs/zh_CN/update_history.md)
 
 

configs/EfficientNet/EfficientNetB0.yaml

@@ -2,7 +2,6 @@ mode: 'train'
 ARCHITECTURE:
     name: "EfficientNetB0"
     params:
-        is_test: False
         padding_type : "SAME"
         override_params:
             drop_connect_rate: 0.1

docs/en/models/SEResNext_and_Res2Net_en.md

@@ -30,8 +30,10 @@ At present, there are a total of 24 pretrained models of the three categories op
 |:--:|:--:|:--:|:--:|:--:|:--:|:--:|
 | Res2Net50_26w_4s      | 0.793  | 0.946  | 0.780             | 0.936             | 8.520        | 25.700            |
 | Res2Net50_vd_26w_4s   | 0.798  | 0.949  |                   |                   | 8.370        | 25.060            |
+| Res2Net50_vd_26w_4s_ssld   | 0.831  | 0.966  |                   |                   | 8.370        | 25.060            |
 | Res2Net50_14w_8s      | 0.795  | 0.947  | 0.781             | 0.939             | 9.010        | 25.720            |
 | Res2Net101_vd_26w_4s  | 0.806  | 0.952  |                   |                   | 16.670       | 45.220            |
+| Res2Net101_vd_26w_4s_ssld  | 0.839  | 0.971  |                   |                   | 16.670       | 45.220            |
 | Res2Net200_vd_26w_4s  | 0.812  | 0.957  |                   |                   | 31.490       | 76.210            |
 | Res2Net200_vd_26w_4s_ssld  | **0.851**  | 0.974  |                   |                   | 31.490       | 76.210            |
 | ResNeXt50_32x4d       | 0.778  | 0.938  | 0.778             |                   | 8.020        | 23.640            |

docs/en/update_history_en.md

 # Release Notes
 
+* 2020.10.20
+    * Add `Res2Net50_vd_26w_4s_ssld` pretrained model, whose Top-1 Acc on ImageNet1k dataset reaches 83.1%.
+    * Add `Res2Net101_vd_26w_4s_ssld` pretrained model, whose Top-1 Acc on ImageNet1k dataset reaches 83.9%.
+
 - 2020.10.12
     * Add Paddle-Lite demo.
 

docs/zh_CN/models/SEResNext_and_Res2Net.md

@@ -29,8 +29,10 @@ Res2Net是2019年提出的一种全新的对ResNet的改进方案，该方案可
 |:--:|:--:|:--:|:--:|:--:|:--:|:--:|
 | Res2Net50_26w_4s      | 0.793  | 0.946  | 0.780             | 0.936             | 8.520        | 25.700            |
 | Res2Net50_vd_26w_4s   | 0.798  | 0.949  |                   |                   | 8.370        | 25.060            |
+| Res2Net50_vd_26w_4s_ssld   | 0.831  | 0.966  |                   |                   | 8.370        | 25.060            |
 | Res2Net50_14w_8s      | 0.795  | 0.947  | 0.781             | 0.939             | 9.010        | 25.720            |
 | Res2Net101_vd_26w_4s  | 0.806  | 0.952  |                   |                   | 16.670       | 45.220            |
+| Res2Net101_vd_26w_4s_ssld  | 0.839  | 0.971  |                   |                   | 16.670       | 45.220            |
 | Res2Net200_vd_26w_4s  | 0.812  | 0.957  |                   |                   | 31.490       | 76.210            |
 | Res2Net200_vd_26w_4s_ssld  | **0.851**  | 0.974  |                   |                   | 31.490       | 76.210            |
 | ResNeXt50_32x4d       | 0.778  | 0.938  | 0.778             |                   | 8.020        | 23.640            |

docs/zh_CN/update_history.md

 # 更新日志
 
+- 2020.10.20
+    * 添加Res2Net50_vd_26w_4s_ssld模型，在ImageNet上Top-1 Acc可达0.831；添加Res2Net101_vd_26w_4s_ssld模型，在ImageNet上Top-1 Acc可达0.839。
+
 - 2020.10.12
     * 添加Paddle-Lite demo。
 

ppcls/modeling/architectures/efficientnet.py

@@ -518,7 +518,6 @@ class MbConvBlock(nn.Layer):
                  use_se,
                  name=None,
                  drop_connect_rate=None,
-                 is_test=False,
                  model_name=None,
                  cur_stage=None):
         super(MbConvBlock, self).__init__()
@@ -530,7 +529,6 @@ class MbConvBlock(nn.Layer):
         self.id_skip = block_args.id_skip
         self.expand_ratio = block_args.expand_ratio
         self.drop_connect_rate = drop_connect_rate
-        self.is_test = is_test
 
         if self.expand_ratio != 1:
             self._ecn = ExpandConvNorm(
@@ -583,7 +581,7 @@ class MbConvBlock(nn.Layer):
                 self.block_args.stride == 1 and \
                 self.block_args.input_filters == self.block_args.output_filters:
             if self.drop_connect_rate:
-                x = _drop_connect(x, self.drop_connect_rate, self.is_test)
+                x = _drop_connect(x, self.drop_connect_rate, not self.training)
             x = paddle.elementwise_add(x, inputs)
         return x
 
@@ -623,7 +621,6 @@ class ExtractFeatures(nn.Layer):
                  _global_params,
                  padding_type,
                  use_se,
-                 is_test,
                  model_name=None):
         super(ExtractFeatures, self).__init__()
 
@@ -661,7 +658,7 @@ class ExtractFeatures(nn.Layer):
                 num_repeat=round_repeats(block_args.num_repeat,
                                          _global_params))
 
-            drop_connect_rate = self._global_params.drop_connect_rate if not is_test else 0
+            drop_connect_rate = self._global_params.drop_connect_rate
             if drop_connect_rate:
                 drop_connect_rate *= float(idx) / block_size
 
@@ -682,7 +679,7 @@ class ExtractFeatures(nn.Layer):
                 block_args = block_args._replace(
                     input_filters=block_args.output_filters, stride=1)
             for _ in range(block_args.num_repeat - 1):
-                drop_connect_rate = self._global_params.drop_connect_rate if not is_test else 0
+                drop_connect_rate = self._global_params.drop_connect_rate
                 if drop_connect_rate:
                     drop_connect_rate *= float(idx) / block_size
                 _mc_block = self.add_sublayer(
@@ -711,7 +708,6 @@ class ExtractFeatures(nn.Layer):
 class EfficientNet(nn.Layer):
     def __init__(self,
                  name="b0",
-                 is_test=True,
                  padding_type="SAME",
                  override_params=None,
                  use_se=True,
@@ -724,7 +720,6 @@ class EfficientNet(nn.Layer):
             model_name, override_params)
         self.padding_type = padding_type
         self.use_se = use_se
-        self.is_test = is_test
 
         self._ef = ExtractFeatures(
             3,
@@ -732,7 +727,6 @@ class EfficientNet(nn.Layer):
             self._global_params,
             self.padding_type,
             self.use_se,
-            self.is_test,
             model_name=self.name)
 
         output_channels = round_filters(1280, self._global_params)
@@ -785,14 +779,12 @@ class EfficientNet(nn.Layer):
         return x
 
 
-def EfficientNetB0_small(is_test=True,
-                         padding_type='DYNAMIC',
+def EfficientNetB0_small(padding_type='DYNAMIC',
                          override_params=None,
                          use_se=False,
                          **args):
     model = EfficientNet(
         name='b0',
-        is_test=is_test,
         padding_type=padding_type,
         override_params=override_params,
         use_se=use_se,
@@ -800,14 +792,12 @@ def EfficientNetB0_small(is_test=True,
     return model
 
 
-def EfficientNetB0(is_test=False,
-                   padding_type='SAME',
+def EfficientNetB0(padding_type='SAME',
                    override_params=None,
                    use_se=True,
                    **args):
     model = EfficientNet(
         name='b0',
-        is_test=is_test,
         padding_type=padding_type,
         override_params=override_params,
         use_se=use_se,
@@ -815,14 +805,12 @@ def EfficientNetB0(is_test=False,
     return model
 
 
-def EfficientNetB1(is_test=False,
-                   padding_type='SAME',
+def EfficientNetB1(padding_type='SAME',
                    override_params=None,
                    use_se=True,
                    **args):
     model = EfficientNet(
         name='b1',
-        is_test=is_test,
         padding_type=padding_type,
         override_params=override_params,
         use_se=use_se,
@@ -830,14 +818,12 @@ def EfficientNetB1(is_test=False,
     return model
 
 
-def EfficientNetB2(is_test=False,
-                   padding_type='SAME',
+def EfficientNetB2(padding_type='SAME',
                    override_params=None,
                    use_se=True,
                    **args):
     model = EfficientNet(
         name='b2',
-        is_test=is_test,
         padding_type=padding_type,
         override_params=override_params,
         use_se=use_se,
@@ -845,14 +831,12 @@ def EfficientNetB2(is_test=False,
     return model
 
 
-def EfficientNetB3(is_test=False,
-                   padding_type='SAME',
+def EfficientNetB3(padding_type='SAME',
                    override_params=None,
                    use_se=True,
                    **args):
     model = EfficientNet(
         name='b3',
-        is_test=is_test,
         padding_type=padding_type,
         override_params=override_params,
         use_se=use_se,
@@ -860,14 +844,12 @@ def EfficientNetB3(is_test=False,
     return model
 
 
-def EfficientNetB4(is_test=False,
-                   padding_type='SAME',
+def EfficientNetB4(padding_type='SAME',
                    override_params=None,
                    use_se=True,
                    **args):
     model = EfficientNet(
         name='b4',
-        is_test=is_test,
         padding_type=padding_type,
         override_params=override_params,
         use_se=use_se,
@@ -875,14 +857,12 @@ def EfficientNetB4(is_test=False,
     return model
 
 
-def EfficientNetB5(is_test=False,
-                   padding_type='SAME',
+def EfficientNetB5(padding_type='SAME',
                    override_params=None,
                    use_se=True,
                    **args):
     model = EfficientNet(
         name='b5',
-        is_test=is_test,
         padding_type=padding_type,
         override_params=override_params,
         use_se=use_se,
@@ -890,14 +870,12 @@ def EfficientNetB5(is_test=False,
     return model
 
 
-def EfficientNetB6(is_test=False,
-                   padding_type='SAME',
+def EfficientNetB6(padding_type='SAME',
                    override_params=None,
                    use_se=True,
                    **args):
     model = EfficientNet(
         name='b6',
-        is_test=is_test,
         padding_type=padding_type,
         override_params=override_params,
         use_se=use_se,
@@ -905,14 +883,12 @@ def EfficientNetB6(is_test=False,
     return model
 
 
-def EfficientNetB7(is_test=False,
-                   padding_type='SAME',
+def EfficientNetB7(padding_type='SAME',
                    override_params=None,
                    use_se=True,
                    **args):
     model = EfficientNet(
         name='b7',
-        is_test=is_test,
         padding_type=padding_type,
         override_params=override_params,
         use_se=use_se,

ppcls/modeling/architectures/res2net_vd.py

@@ -57,7 +57,6 @@ class ConvBNLayer(nn.Layer):
             stride=stride,
             padding=(filter_size - 1) // 2,
             groups=groups,
-            act=None,
             weight_attr=ParamAttr(name=name + "_weights"),
             bias_attr=False)
         if name == "conv1":
@@ -111,8 +110,7 @@ class BottleneckBlock(nn.Layer):
                     act='relu',
                     name=name + '_branch2b_' + str(s + 1)))
             self.conv1_list.append(conv1)
-        self.pool2d_avg = AvgPool2d(
-            kernel_size=3, stride=stride, padding=1, ceil_mode=True)
+        self.pool2d_avg = AvgPool2d(kernel_size=3, stride=stride, padding=1)
 
         self.conv2 = ConvBNLayer(
             num_channels=num_filters,