add resnest metrics

README.md

@@ -286,8 +286,8 @@ ResNeSt与RegNet系列模型的精度、速度指标如下表所示，更多关
 
 | 模型                     | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | Flops(G) | Params(M) | 下载地址                                                                                                 |
 |------------------------|-----------|-----------|------------------|------------------|----------|-----------|------------------------------------------------------------------------------------------------------|
-| ResNeSt50_<br>fast_1s1x64d | 0.8035    | 0.9528    | -                | -                | 8.68     | 26.3      | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeSt50_fast_1s1x64d_pretrained.pdparams) |
-| ResNeSt50              | 0.8102    | 0.9542    | -                | -                | 10.78    | 27.5      | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeSt50_pretrained.pdparams)              |
+| ResNeSt50_<br>fast_1s1x64d | 0.8035    | 0.9528    | 3.45405                | 8.72680                | 8.68     | 26.3      | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeSt50_fast_1s1x64d_pretrained.pdparams) |
+| ResNeSt50              | 0.8102    | 0.9542    | 6.69042    | 8.01664                | 10.78    | 27.5      | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeSt50_pretrained.pdparams)              |
 | RegNetX_4GF            | 0.785     | 0.9416    |    6.46478              |      11.19862           | 8        | 22.1      | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/RegNetX_4GF_pretrained.pdparams)            |
 
 

docs/en/models/ResNeSt_RegNet_en.md

@@ -17,6 +17,6 @@ RegNet was proposed in 2020 by Facebook to deepen the concept of design space. B
 
 | Models             | Crop Size | Resize Short Size | FP16<br>Batch Size=1<br>(ms) | FP16<br>Batch Size=4<br>(ms) | FP16<br>Batch Size=8<br>(ms) | FP32<br>Batch Size=1<br>(ms) | FP32<br>Batch Size=4<br>(ms) | FP32<br>Batch Size=8<br>(ms) |
 |--------------------|-----------|-------------------|------------------------------|------------------------------|------------------------------|------------------------------|------------------------------|------------------------------|
-| ResNeSt50_fast_1s1x64d          | 224       | 256   | -           | -            | -          | -      |   -    |    -     |
-| ResNeSt50         | 224       | 256               | -           | -            | -          | -      |   -    |    -     |
+| ResNeSt50_fast_1s1x64d          | 224       | 256   | 3.46466           | 5.56647           | 9.11848          | 3.45405      |   8.72680    |    15.48710     |
+| ResNeSt50         | 224       | 256               | 7.05851           | 8.97676            | 13.34704          | 6.16248      |   12.0633    |    21.49936     |
 | RegNetX_4GF | 224       | 256       | 6.69042    | 8.01664            | 11.60608       | 6.46478     |   11.19862    |    16.89089    |

docs/zh_CN/models/ResNeSt_RegNet.md

@@ -20,6 +20,6 @@ RegNet是由facebook于2020年提出，旨在深化设计空间理念的概念
 
 | Models             | Crop Size | Resize Short Size | FP16<br>Batch Size=1<br>(ms) | FP16<br>Batch Size=4<br>(ms) | FP16<br>Batch Size=8<br>(ms) | FP32<br>Batch Size=1<br>(ms) | FP32<br>Batch Size=4<br>(ms) | FP32<br>Batch Size=8<br>(ms) |
 |--------------------|-----------|-------------------|------------------------------|------------------------------|------------------------------|------------------------------|------------------------------|------------------------------|
-| ResNeSt50_fast_1s1x64d          | 224       | 256   | -           | -            | -          | -      |   -    |    -     |
-| ResNeSt50         | 224       | 256               | -           | -            | -          | -      |   -    |    -     |
+| ResNeSt50_fast_1s1x64d          | 224       | 256   | 3.46466           | 5.56647           | 9.11848          | 3.45405      |   8.72680    |    15.48710     |
+| ResNeSt50         | 224       | 256               | 7.05851           | 8.97676            | 13.34704          | 6.16248      |   12.0633    |    21.49936     |
 | RegNetX_4GF | 224       | 256       | 6.69042    | 8.01664            | 11.60608       | 6.46478     |   11.19862    |    16.89089    |

ppcls/modeling/architectures/resnest.py

@@ -104,11 +104,7 @@ class ResNeSt():
             is_first=False,
             name="layer1")
         x = self.resnest_layer(
-            x=x, 
-            planes=128, 
-            blocks=self.layers[1], 
-            stride=2, 
-            name="layer2")
+            x=x, planes=128, blocks=self.layers[1], stride=2, name="layer2")
         if self.dilated or self.dilation == 4:
             x = self.resnest_layer(
                 x=x,
@@ -152,10 +148,8 @@ class ResNeSt():
                 blocks=self.layers[3],
                 stride=2,
                 name="layer4")
-        x = fluid.layers.pool2d(
-            input=x, pool_type="avg", global_pooling=True)
-        x = fluid.layers.dropout(
-            x=x, dropout_prob=self.final_drop)
+        x = fluid.layers.pool2d(input=x, pool_type="avg", global_pooling=True)
+        x = fluid.layers.dropout(x=x, dropout_prob=self.final_drop)
         stdv = 1.0 / math.sqrt(x.shape[1] * 1.0)
         x = fluid.layers.fc(
             input=x,
@@ -266,8 +260,7 @@ class ResNeSt():
             param_attr=ParamAttr(
                 name=name + "_splat_weights", initializer=MSRA()),
             bias_attr=False)
-        atten = self.rsoftmax(
-            x=atten, radix=radix, cardinality=groups)
+        atten = self.rsoftmax(x=atten, radix=radix, cardinality=groups)
         atten = fluid.layers.reshape(x=atten, shape=[-1, atten.shape[1], 1, 1])
 
         if radix > 1:
@@ -275,10 +268,10 @@ class ResNeSt():
                 input=atten, num_or_sections=radix, dim=1)
             out = fluid.layers.sum([
                 fluid.layers.elementwise_mul(
-                    x=att, y=split) for (att, split) in zip(attens, splited)
+                    x=split, y=att) for (att, split) in zip(attens, splited)
             ])
         else:
-            out = fluid.layers.elementwise_mul(atten, x)
+            out = fluid.layers.elementwise_mul(x, atten)
         return out
 
     def bottleneck(self,