f

configs/MobileNetV3/MobileNetV3_large_x0_35.yaml

@@ -15,7 +15,7 @@ topk: 5
 image_shape: [3, 224, 224]
 
 LEARNING_RATE:
-    function: 'CosineWarmup'
+    function: 'Cosine'
     params:
         lr: 2.6
         warmup_epoch: 5

configs/MobileNetV3/MobileNetV3_large_x0_5.yaml

@@ -15,7 +15,7 @@ topk: 5
 image_shape: [3, 224, 224]
 
 LEARNING_RATE:
-    function: 'CosineWarmup'
+    function: 'Cosine'
     params:
         lr: 1.3
         warmup_epoch: 5

configs/MobileNetV3/MobileNetV3_large_x0_75.yaml

@@ -15,7 +15,7 @@ topk: 5
 image_shape: [3, 224, 224]
 
 LEARNING_RATE:
-    function: 'CosineWarmup'
+    function: 'Cosine'
     params:
         lr: 1.3
         warmup_epoch: 5

configs/MobileNetV3/MobileNetV3_large_x1_0.yaml

@@ -15,7 +15,7 @@ topk: 5
 image_shape: [3, 224, 224]
 
 LEARNING_RATE:
-    function: 'CosineWarmup'
+    function: 'Cosine'
     params:
         lr: 2.6
         warmup_epoch: 5

configs/MobileNetV3/MobileNetV3_large_x1_25.yaml

@@ -15,7 +15,7 @@ topk: 5
 image_shape: [3, 224, 224]
 
 LEARNING_RATE:
-    function: 'CosineWarmup'
+    function: 'Cosine'
     params:
         lr: 0.65
         warmup_epoch: 5

configs/MobileNetV3/MobileNetV3_small_x0_35.yaml

@@ -14,7 +14,7 @@ topk: 5
 image_shape: [3, 224, 224]
 
 LEARNING_RATE:
-    function: 'CosineWarmup'
+    function: 'Cosine'
     params:
         lr: 2.6
         warmup_epoch: 5

configs/MobileNetV3/MobileNetV3_small_x0_5.yaml

@@ -15,7 +15,7 @@ topk: 5
 image_shape: [3, 224, 224]
 
 LEARNING_RATE:
-    function: 'CosineWarmup'
+    function: 'Cosine'
     params:
         lr: 2.6
         warmup_epoch: 5

configs/MobileNetV3/MobileNetV3_small_x0_75.yaml

@@ -15,7 +15,7 @@ topk: 5
 image_shape: [3, 224, 224]
 
 LEARNING_RATE:
-    function: 'CosineWarmup'
+    function: 'Cosine'
     params:
         lr: 2.6
         warmup_epoch: 5

configs/MobileNetV3/MobileNetV3_small_x1_0.yaml

@@ -15,7 +15,7 @@ topk: 5
 image_shape: [3, 224, 224]
 
 LEARNING_RATE:
-    function: 'CosineWarmup'
+    function: 'Cosine'
     params:
         lr: 2.6
         warmup_epoch: 5

configs/MobileNetV3/MobileNetV3_small_x1_25.yaml

@@ -15,7 +15,7 @@ topk: 5
 image_shape: [3, 224, 224]
 
 LEARNING_RATE:
-    function: 'CosineWarmup'
+    function: 'Cosine'
     params:
         lr: 1.3
         warmup_epoch: 5

configs/ShuffleNet/ShuffleNetV2.yaml

@@ -14,7 +14,7 @@ topk: 5
 image_shape: [3, 224, 224]
 
 LEARNING_RATE:
-    function: 'CosineWarmup'
+    function: 'Cosine'
     params:
         lr: 0.5
         warmup_epoch: 5

configs/ShuffleNet/ShuffleNetV2_swish.yaml

@@ -14,7 +14,7 @@ topk: 5
 image_shape: [3, 224, 224]
 
 LEARNING_RATE:
-    function: 'CosineWarmup'
+    function: 'Cosine'
     params:
         lr: 0.5
         warmup_epoch: 5

configs/ShuffleNet/ShuffleNetV2_x0_25.yaml

@@ -14,7 +14,7 @@ topk: 5
 image_shape: [3, 224, 224]
 
 LEARNING_RATE:
-    function: 'CosineWarmup'
+    function: 'Cosine'
     params:
         lr: 0.5
         warmup_epoch: 5

configs/ShuffleNet/ShuffleNetV2_x0_33.yaml

@@ -14,7 +14,7 @@ topk: 5
 image_shape: [3, 224, 224]
 
 LEARNING_RATE:
-    function: 'CosineWarmup'
+    function: 'Cosine'
     params:
         lr: 0.5
         warmup_epoch: 5

configs/ShuffleNet/ShuffleNetV2_x0_5.yaml

@@ -14,7 +14,7 @@ topk: 5
 image_shape: [3, 224, 224]
 
 LEARNING_RATE:
-    function: 'CosineWarmup'
+    function: 'Cosine'
     params:
         lr: 0.5
         warmup_epoch: 5

configs/ShuffleNet/ShuffleNetV2_x1_5.yaml

@@ -14,7 +14,7 @@ topk: 5
 image_shape: [3, 224, 224]
 
 LEARNING_RATE:
-    function: 'CosineWarmup'
+    function: 'Cosine'
     params:
         lr: 0.25
         warmup_epoch: 5

configs/ShuffleNet/ShuffleNetV2_x2_0.yaml

@@ -14,7 +14,7 @@ topk: 5
 image_shape: [3, 224, 224]
 
 LEARNING_RATE:
-    function: 'CosineWarmup'
+    function: 'Cosine'
     params:
         lr: 0.25
         warmup_epoch: 5

docs/zh_CN/feature_visiualization/get_started.md

+# 特征图可视化指南
+
+## 一、概述
+
+特征图是输入图片在卷积网络中的特征表达，对特征图的研究可以有利于我们对于模型的理解与设计，所以基于动态图我们使用本工具来可视化特征图。
+
+## 二、准备工作
+
+首先需要选定研究的模型，本文设定ResNet50作为研究模型，将resnet.py从[模型库](../../../ppcls/modeling/architecture/)拷贝到当前目录下，并下载预训练模型[预训练模型](../../zh_CN/models/models_intro), 复制resnet50的模型链接，使用下列命令下载并解压预训练模型。
+
+```bash
+wget The Link for Pretrained Model
+tar -xf Downloaded Pretrained Model
+```
+
+以resnet50为例：
+```bash
+wget https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_pretrained.tar
+tar -xf ResNet50_pretrained.tar
+```
+
+## 三、修改模型
+
+找到我们所需要的特征图位置，设置self.fm将其fetch出来，本文以resnet50中的stem层之后的特征图为例。
+
+在fm_vis.py中修改模型的名字。
+
+在ResNet50的__init__函数中定义self.fm
+```python
+self.fm = None
+```
+在ResNet50的forward函数中指定特征图
+```python
+def forward(self, inputs):
+    y = self.conv(inputs)
+    self.fm = y
+    y = self.pool2d_max(y)
+    for bottleneck_block in self.bottleneck_block_list:
+        y = bottleneck_block(y)
+    y = self.pool2d_avg(y)
+    y = fluid.layers.reshape(y, shape=[-1, self.pool2d_avg_output])
+    y = self.out(y)
+    return y, self.fm
+```
+执行函数
+```bash
+python tools/feature_maps_visualization/fm_vis.py -i the image you want to test \
+                                                -c channel_num -p pretrained model \
+                                                --show whether to show \
+                                                --interpolation interpolation method\
+                                                --save_path where to save \
+                                                --use_gpu whether to use gpu
+```
+参数说明：
++ `-i`：待预测的图片文件路径，如 `./test.jpeg`
++ `-c`：特征图维度，如 `./resnet50_vd/model`
++ `-p`：权重文件路径，如 `./ResNet50_pretrained/`
++ `--show`：是否展示图片，默认值 False
++ `--interpolation`: 图像插值方式， 默认值 1
++ `--save_path`：保存路径，如：`./tools/`
++ `--use_gpu`：是否使用 GPU 预测，默认值：True
+
+## 四、结果
+输入图片：  
+
+![](../../../tools/feature_maps_visualization/test.jpg)  
+
+输出特征图：  
+
+![](../../../tools/feature_maps_visualization/fm.jpg)

ppcls/modeling/architectures/__init__.py

@@ -12,7 +12,19 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from .resnet_name import *
+from .resnet import ResNet18, ResNet34, ResNet50, ResNet101, ResNet152
+from .resnet_vc import ResNet18_vc, ResNet34_vc, ResNet50_vc, ResNet101_vc, ResNet152_vc
+from .resnet_vd import ResNet18_vd, ResNet34_vd, ResNet50_vd, ResNet101_vd, ResNet152_vd, ResNet200_vd
+from .resnext import ResNeXt50_32x4d, ResNeXt50_64x4d, ResNeXt101_32x4d, ResNeXt101_64x4d, ResNeXt152_32x4d, ResNeXt152_64x4d
+from .resnext_vd import ResNeXt50_vd_32x4d, ResNeXt50_vd_64x4d, ResNeXt101_vd_32x4d, ResNeXt101_vd_64x4d, ResNeXt152_vd_32x4d, ResNeXt152_vd_64x4d
+from .res2net import Res2Net50_48w_2s, Res2Net50_26w_4s, Res2Net50_14w_8s, Res2Net50_48w_2s, Res2Net50_26w_6s, Res2Net50_26w_8s, Res2Net101_26w_4s, Res2Net152_26w_4s, Res2Net200_26w_4s
+from .res2net_vd import Res2Net50_vd_48w_2s, Res2Net50_vd_26w_4s, Res2Net50_vd_14w_8s, Res2Net50_vd_48w_2s, Res2Net50_vd_26w_6s, Res2Net50_vd_26w_8s, Res2Net101_vd_26w_4s, Res2Net152_vd_26w_4s, Res2Net200_vd_26w_4s
+from .se_resnet_vd import SE_ResNet18_vd, SE_ResNet34_vd, SE_ResNet50_vd, SE_ResNet101_vd, SE_ResNet152_vd, SE_ResNet200_vd
+from .se_resnext_vd import SE_ResNeXt50_vd_32x4d, SE_ResNeXt50_vd_32x4d, SENet154_vd
 from .dpn import DPN68
 from .densenet import DenseNet121
-from .hrnet import HRNet_W18_C
\ No newline at end of file
+from .hrnet import HRNet_W18_C
+from .mobilenet_v1 import MobileNetV1_x0_25, MobileNetV1_x0_5, MobileNetV1_x0_75, MobileNetV1
+from .mobilenet_v2 import MobileNetV2_x0_25, MobileNetV2_x0_5, MobileNetV2_x0_75, MobileNetV2, MobileNetV2_x1_5, MobileNetV2_x2_0
+from .mobilenet_v3 import MobileNetV3_small_x0_35, MobileNetV3_small_x0_5, MobileNetV3_small_x0_75, MobileNetV3_small_x1_0, MobileNetV3_small_x1_25, MobileNetV3_large_x0_35, MobileNetV3_large_x0_5, MobileNetV3_large_x0_75, MobileNetV3_large_x1_0, MobileNetV3_large_x1_25
+from .shufflenet_v2 import ShuffleNetV2_x0_25, ShuffleNetV2_x0_33, ShuffleNetV2_x0_5, ShuffleNetV2, ShuffleNetV2_x1_5, ShuffleNetV2_x2_0, ShuffleNetV2_swish

ppcls/modeling/architectures/mobilenet_v1.py

-#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+# copyright (c) 2020 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
+# 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
 #
-#Unless required by applicable law or agreed to in writing, software
-#distributed under the License is distributed on an "AS IS" BASIS,
-#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#See the License for the specific language governing permissions and
-#limitations under the License.
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import numpy as np
+import paddle
 import paddle.fluid as fluid
-from paddle.fluid.initializer import MSRA
 from paddle.fluid.param_attr import ParamAttr
+from paddle.fluid.layer_helper import LayerHelper
+from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear, Dropout
+from paddle.fluid.initializer import MSRA
+import math
 
 __all__ = [
-    'MobileNetV1', 'MobileNetV1_x0_25', 'MobileNetV1_x0_5', 'MobileNetV1_x1_0',
-    'MobileNetV1_x0_75'
+    "MobileNetV1_x0_25", "MobileNetV1_x0_5", "MobileNetV1_x0_75", "MobileNetV1"
 ]
 
 
-class MobileNetV1():
-    def __init__(self, scale=1.0):
-        self.scale = scale
-
-    def net(self, input, class_dim=1000):
-        scale = self.scale
-        # conv1: 112x112
-        input = self.conv_bn_layer(
-            input,
-            filter_size=3,
-            channels=3,
-            num_filters=int(32 * scale),
-            stride=2,
-            padding=1,
-            name="conv1")
-
-        # 56x56
-        input = self.depthwise_separable(
-            input,
-            num_filters1=32,
-            num_filters2=64,
-            num_groups=32,
-            stride=1,
-            scale=scale,
-            name="conv2_1")
-
-        input = self.depthwise_separable(
-            input,
-            num_filters1=64,
-            num_filters2=128,
-            num_groups=64,
-            stride=2,
-            scale=scale,
-            name="conv2_2")
-
-        # 28x28
-        input = self.depthwise_separable(
-            input,
-            num_filters1=128,
-            num_filters2=128,
-            num_groups=128,
-            stride=1,
-            scale=scale,
-            name="conv3_1")
-
-        input = self.depthwise_separable(
-            input,
-            num_filters1=128,
-            num_filters2=256,
-            num_groups=128,
-            stride=2,
-            scale=scale,
-            name="conv3_2")
-
-        # 14x14
-        input = self.depthwise_separable(
-            input,
-            num_filters1=256,
-            num_filters2=256,
-            num_groups=256,
-            stride=1,
-            scale=scale,
-            name="conv4_1")
-
-        input = self.depthwise_separable(
-            input,
-            num_filters1=256,
-            num_filters2=512,
-            num_groups=256,
-            stride=2,
-            scale=scale,
-            name="conv4_2")
+class ConvBNLayer(fluid.dygraph.Layer):
+    def __init__(self,
+                 num_channels,
+                 filter_size,
+                 num_filters,
+                 stride,
+                 padding,
+                 channels=None,
+                 num_groups=1,
+                 act='relu',
+                 use_cudnn=True,
+                 name=None):
+        super(ConvBNLayer, self).__init__()
 
-        # 14x14
-        for i in range(5):
-            input = self.depthwise_separable(
-                input,
-                num_filters1=512,
-                num_filters2=512,
-                num_groups=512,
-                stride=1,
-                scale=scale,
-                name="conv5" + "_" + str(i + 1))
-        # 7x7
-        input = self.depthwise_separable(
-            input,
-            num_filters1=512,
-            num_filters2=1024,
-            num_groups=512,
-            stride=2,
-            scale=scale,
-            name="conv5_6")
-
-        input = self.depthwise_separable(
-            input,
-            num_filters1=1024,
-            num_filters2=1024,
-            num_groups=1024,
-            stride=1,
-            scale=scale,
-            name="conv6")
-
-        input = fluid.layers.pool2d(
-            input=input, pool_type='avg', global_pooling=True)
-
-        output = fluid.layers.fc(input=input,
-                                 size=class_dim,
-                                 param_attr=ParamAttr(
-                                     initializer=MSRA(), name="fc7_weights"),
-                                 bias_attr=ParamAttr(name="fc7_offset"))
-        return output
-
-    def conv_bn_layer(self,
-                      input,
-                      filter_size,
-                      num_filters,
-                      stride,
-                      padding,
-                      channels=None,
-                      num_groups=1,
-                      act='relu',
-                      use_cudnn=True,
-                      name=None):
-        conv = fluid.layers.conv2d(
-            input=input,
+        self._conv = Conv2D(
+            num_channels=num_channels,
             num_filters=num_filters,
             filter_size=filter_size,
             stride=stride,
@@ -161,58 +56,214 @@ class MobileNetV1():
             param_attr=ParamAttr(
                 initializer=MSRA(), name=name + "_weights"),
             bias_attr=False)
-        bn_name = name + "_bn"
-        return fluid.layers.batch_norm(
-            input=conv,
+
+        self._batch_norm = BatchNorm(
+            num_filters,
             act=act,
-            param_attr=ParamAttr(name=bn_name + "_scale"),
-            bias_attr=ParamAttr(name=bn_name + "_offset"),
-            moving_mean_name=bn_name + '_mean',
-            moving_variance_name=bn_name + '_variance')
-
-    def depthwise_separable(self,
-                            input,
-                            num_filters1,
-                            num_filters2,
-                            num_groups,
-                            stride,
-                            scale,
-                            name=None):
-        depthwise_conv = self.conv_bn_layer(
-            input=input,
-            filter_size=3,
+            param_attr=ParamAttr(name + "_bn_scale"),
+            bias_attr=ParamAttr(name + "_bn_offset"),
+            moving_mean_name=name + "_bn_mean",
+            moving_variance_name=name + "_bn_variance")
+
+    def forward(self, inputs):
+        y = self._conv(inputs)
+        y = self._batch_norm(y)
+        return y
+
+
+class DepthwiseSeparable(fluid.dygraph.Layer):
+    def __init__(self,
+                 num_channels,
+                 num_filters1,
+                 num_filters2,
+                 num_groups,
+                 stride,
+                 scale,
+                 name=None):
+        super(DepthwiseSeparable, self).__init__()
+
+        self._depthwise_conv = ConvBNLayer(
+            num_channels=num_channels,
             num_filters=int(num_filters1 * scale),
+            filter_size=3,
             stride=stride,
             padding=1,
             num_groups=int(num_groups * scale),
             use_cudnn=False,
             name=name + "_dw")
 
-        pointwise_conv = self.conv_bn_layer(
-            input=depthwise_conv,
+        self._pointwise_conv = ConvBNLayer(
+            num_channels=int(num_filters1 * scale),
             filter_size=1,
             num_filters=int(num_filters2 * scale),
             stride=1,
             padding=0,
             name=name + "_sep")
-        return pointwise_conv
+
+    def forward(self, inputs):
+        y = self._depthwise_conv(inputs)
+        y = self._pointwise_conv(y)
+        return y
+
+
+class MobileNet(fluid.dygraph.Layer):
+    def __init__(self, scale=1.0, class_dim=1000):
+        super(MobileNet, self).__init__()
+        self.scale = scale
+        self.block_list = []
+
+        self.conv1 = ConvBNLayer(
+            num_channels=3,
+            filter_size=3,
+            channels=3,
+            num_filters=int(32 * scale),
+            stride=2,
+            padding=1,
+            name="conv1")
+
+        conv2_1 = self.add_sublayer(
+            "conv2_1",
+            sublayer=DepthwiseSeparable(
+                num_channels=int(32 * scale),
+                num_filters1=32,
+                num_filters2=64,
+                num_groups=32,
+                stride=1,
+                scale=scale,
+                name="conv2_1"))
+        self.block_list.append(conv2_1)
+
+        conv2_2 = self.add_sublayer(
+            "conv2_2",
+            sublayer=DepthwiseSeparable(
+                num_channels=int(64 * scale),
+                num_filters1=64,
+                num_filters2=128,
+                num_groups=64,
+                stride=2,
+                scale=scale,
+                name="conv2_2"))
+        self.block_list.append(conv2_2)
+
+        conv3_1 = self.add_sublayer(
+            "conv3_1",
+            sublayer=DepthwiseSeparable(
+                num_channels=int(128 * scale),
+                num_filters1=128,
+                num_filters2=128,
+                num_groups=128,
+                stride=1,
+                scale=scale,
+                name="conv3_1"))
+        self.block_list.append(conv3_1)
+
+        conv3_2 = self.add_sublayer(
+            "conv3_2",
+            sublayer=DepthwiseSeparable(
+                num_channels=int(128 * scale),
+                num_filters1=128,
+                num_filters2=256,
+                num_groups=128,
+                stride=2,
+                scale=scale,
+                name="conv3_2"))
+        self.block_list.append(conv3_2)
+
+        conv4_1 = self.add_sublayer(
+            "conv4_1",
+            sublayer=DepthwiseSeparable(
+                num_channels=int(256 * scale),
+                num_filters1=256,
+                num_filters2=256,
+                num_groups=256,
+                stride=1,
+                scale=scale,
+                name="conv4_1"))
+        self.block_list.append(conv4_1)
+
+        conv4_2 = self.add_sublayer(
+            "conv4_2",
+            sublayer=DepthwiseSeparable(
+                num_channels=int(256 * scale),
+                num_filters1=256,
+                num_filters2=512,
+                num_groups=256,
+                stride=2,
+                scale=scale,
+                name="conv4_2"))
+        self.block_list.append(conv4_2)
+
+        for i in range(5):
+            conv5 = self.add_sublayer(
+                "conv5_" + str(i + 1),
+                sublayer=DepthwiseSeparable(
+                    num_channels=int(512 * scale),
+                    num_filters1=512,
+                    num_filters2=512,
+                    num_groups=512,
+                    stride=1,
+                    scale=scale,
+                    name="conv5_" + str(i + 1)))
+            self.block_list.append(conv5)
+
+        conv5_6 = self.add_sublayer(
+            "conv5_6",
+            sublayer=DepthwiseSeparable(
+                num_channels=int(512 * scale),
+                num_filters1=512,
+                num_filters2=1024,
+                num_groups=512,
+                stride=2,
+                scale=scale,
+                name="conv5_6"))
+        self.block_list.append(conv5_6)
+
+        conv6 = self.add_sublayer(
+            "conv6",
+            sublayer=DepthwiseSeparable(
+                num_channels=int(1024 * scale),
+                num_filters1=1024,
+                num_filters2=1024,
+                num_groups=1024,
+                stride=1,
+                scale=scale,
+                name="conv6"))
+        self.block_list.append(conv6)
+
+        self.pool2d_avg = Pool2D(pool_type='avg', global_pooling=True)
+
+        self.out = Linear(
+            int(1024 * scale),
+            class_dim,
+            param_attr=ParamAttr(
+                initializer=MSRA(), name="fc7_weights"),
+            bias_attr=ParamAttr(name="fc7_offset"))
+
+    def forward(self, inputs):
+        y = self.conv1(inputs)
+        for block in self.block_list:
+            y = block(y)
+        y = self.pool2d_avg(y)
+        y = fluid.layers.reshape(y, shape=[-1, int(1024 * self.scale)])
+        y = self.out(y)
+        return y
 
 
-def MobileNetV1_x0_25():
-    model = MobileNetV1(scale=0.25)
+def MobileNetV1_x0_25(**args):
+    model = MobileNet(scale=0.25, **args)
     return model
 
 
-def MobileNetV1_x0_5():
-    model = MobileNetV1(scale=0.5)
+def MobileNetV1_x0_5(**args):
+    model = MobileNet(scale=0.5, **args)
     return model
 
 
-def MobileNetV1_x1_0():
-    model = MobileNetV1(scale=1.0)
+def MobileNetV1_x0_75(**args):
+    model = MobileNet(scale=0.75, **args)
     return model
 
 
-def MobileNetV1_x0_75():
-    model = MobileNetV1(scale=0.75)
+def MobileNetV1(**args):
+    model = MobileNet(scale=1.0, **args)
     return model

ppcls/modeling/architectures/mobilenet_v2.py

-#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+# copyright (c) 2020 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
+# 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
 #
-#Unless required by applicable law or agreed to in writing, software
-#distributed under the License is distributed on an "AS IS" BASIS,
-#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#See the License for the specific language governing permissions and
-#limitations under the License.
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
+
+import numpy as np
+import paddle
 import paddle.fluid as fluid
-from paddle.fluid.initializer import MSRA
 from paddle.fluid.param_attr import ParamAttr
+from paddle.fluid.layer_helper import LayerHelper
+from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear, Dropout
+
+import math
 
 __all__ = [
-    'MobileNetV2_x0_25', 'MobileNetV2_x0_5'
-    'MobileNetV2_x0_75', 'MobileNetV2_x1_0', 'MobileNetV2_x1_5',
-    'MobileNetV2_x2_0', 'MobileNetV2'
+    "MobileNetV2_x0_25", "MobileNetV2_x0_5", "MobileNetV2_x0_75",
+    "MobileNetV2", "MobileNetV2_x1_5", "MobileNetV2_x2_0"
 ]
 
 
-class MobileNetV2():
-    def __init__(self, scale=1.0):
-        self.scale = scale
-
-    def net(self, input, class_dim=1000):
-        scale = self.scale
-        bottleneck_params_list = [
-            (1, 16, 1, 1),
-            (6, 24, 2, 2),
-            (6, 32, 3, 2),
-            (6, 64, 4, 2),
-            (6, 96, 3, 1),
-            (6, 160, 3, 2),
-            (6, 320, 1, 1),
-        ]
-
-        #conv1 
-        input = self.conv_bn_layer(
-            input,
-            num_filters=int(32 * scale),
-            filter_size=3,
-            stride=2,
-            padding=1,
-            if_act=True,
-            name='conv1_1')
-
-        # bottleneck sequences
-        i = 1
-        in_c = int(32 * scale)
-        for layer_setting in bottleneck_params_list:
-            t, c, n, s = layer_setting
-            i += 1
-            input = self.invresi_blocks(
-                input=input,
-                in_c=in_c,
-                t=t,
-                c=int(c * scale),
-                n=n,
-                s=s,
-                name='conv' + str(i))
-            in_c = int(c * scale)
-        #last_conv
-        input = self.conv_bn_layer(
-            input=input,
-            num_filters=int(1280 * scale) if scale > 1.0 else 1280,
-            filter_size=1,
-            stride=1,
-            padding=0,
-            if_act=True,
-            name='conv9')
-
-        input = fluid.layers.pool2d(
-            input=input, pool_type='avg', global_pooling=True)
-
-        output = fluid.layers.fc(input=input,
-                                 size=class_dim,
-                                 param_attr=ParamAttr(name='fc10_weights'),
-                                 bias_attr=ParamAttr(name='fc10_offset'))
-        return output
-
-    def conv_bn_layer(self,
-                      input,
-                      filter_size,
-                      num_filters,
-                      stride,
-                      padding,
-                      channels=None,
-                      num_groups=1,
-                      if_act=True,
-                      name=None,
-                      use_cudnn=True):
-        conv = fluid.layers.conv2d(
-            input=input,
+class ConvBNLayer(fluid.dygraph.Layer):
+    def __init__(self,
+                 num_channels,
+                 filter_size,
+                 num_filters,
+                 stride,
+                 padding,
+                 channels=None,
+                 num_groups=1,
+                 name=None,
+                 use_cudnn=True):
+        super(ConvBNLayer, self).__init__()
+
+        self._conv = Conv2D(
+            num_channels=num_channels,
             num_filters=num_filters,
             filter_size=filter_size,
             stride=stride,
@@ -106,125 +53,197 @@ class MobileNetV2():
             groups=num_groups,
             act=None,
             use_cudnn=use_cudnn,
-            param_attr=ParamAttr(name=name + '_weights'),
+            param_attr=ParamAttr(name=name + "_weights"),
             bias_attr=False)
-        bn_name = name + '_bn'
-        bn = fluid.layers.batch_norm(
-            input=conv,
-            param_attr=ParamAttr(name=bn_name + "_scale"),
-            bias_attr=ParamAttr(name=bn_name + "_offset"),
-            moving_mean_name=bn_name + '_mean',
-            moving_variance_name=bn_name + '_variance')
+
+        self._batch_norm = BatchNorm(
+            num_filters,
+            param_attr=ParamAttr(name=name + "_bn_scale"),
+            bias_attr=ParamAttr(name=name + "_bn_offset"),
+            moving_mean_name=name + "_bn_mean",
+            moving_variance_name=name + "_bn_variance")
+
+    def forward(self, inputs, if_act=True):
+        y = self._conv(inputs)
+        y = self._batch_norm(y)
         if if_act:
-            return fluid.layers.relu6(bn)
-        else:
-            return bn
-
-    def shortcut(self, input, data_residual):
-        return fluid.layers.elementwise_add(input, data_residual)
-
-    def inverted_residual_unit(self,
-                               input,
-                               num_in_filter,
-                               num_filters,
-                               ifshortcut,
-                               stride,
-                               filter_size,
-                               padding,
-                               expansion_factor,
-                               name=None):
-        num_expfilter = int(round(num_in_filter * expansion_factor))
+            y = fluid.layers.relu6(y)
+        return y
 
-        channel_expand = self.conv_bn_layer(
-            input=input,
+
+class InvertedResidualUnit(fluid.dygraph.Layer):
+    def __init__(self, num_channels, num_in_filter, num_filters, stride,
+                 filter_size, padding, expansion_factor, name):
+        super(InvertedResidualUnit, self).__init__()
+        num_expfilter = int(round(num_in_filter * expansion_factor))
+        self._expand_conv = ConvBNLayer(
+            num_channels=num_channels,
             num_filters=num_expfilter,
             filter_size=1,
             stride=1,
             padding=0,
             num_groups=1,
-            if_act=True,
-            name=name + '_expand')
+            name=name + "_expand")
 
-        bottleneck_conv = self.conv_bn_layer(
-            input=channel_expand,
+        self._bottleneck_conv = ConvBNLayer(
+            num_channels=num_expfilter,
             num_filters=num_expfilter,
             filter_size=filter_size,
             stride=stride,
             padding=padding,
             num_groups=num_expfilter,
-            if_act=True,
-            name=name + '_dwise',
-            use_cudnn=False)
+            use_cudnn=False,
+            name=name + "_dwise")
 
-        linear_out = self.conv_bn_layer(
-            input=bottleneck_conv,
+        self._linear_conv = ConvBNLayer(
+            num_channels=num_expfilter,
             num_filters=num_filters,
             filter_size=1,
             stride=1,
             padding=0,
             num_groups=1,
-            if_act=False,
-            name=name + '_linear')
+            name=name + "_linear")
+
+    def forward(self, inputs, ifshortcut):
+        y = self._expand_conv(inputs, if_act=True)
+        y = self._bottleneck_conv(y, if_act=True)
+        y = self._linear_conv(y, if_act=False)
         if ifshortcut:
-            out = self.shortcut(input=input, data_residual=linear_out)
-            return out
-        else:
-            return linear_out
-
-    def invresi_blocks(self, input, in_c, t, c, n, s, name=None):
-        first_block = self.inverted_residual_unit(
-            input=input,
+            y = fluid.layers.elementwise_add(inputs, y)
+        return y
+
+
+class InvresiBlocks(fluid.dygraph.Layer):
+    def __init__(self, in_c, t, c, n, s, name):
+        super(InvresiBlocks, self).__init__()
+
+        self._first_block = InvertedResidualUnit(
+            num_channels=in_c,
             num_in_filter=in_c,
             num_filters=c,
-            ifshortcut=False,
             stride=s,
             filter_size=3,
             padding=1,
             expansion_factor=t,
-            name=name + '_1')
-
-        last_residual_block = first_block
-        last_c = c
+            name=name + "_1")
 
+        self._block_list = []
         for i in range(1, n):
-            last_residual_block = self.inverted_residual_unit(
-                input=last_residual_block,
-                num_in_filter=last_c,
-                num_filters=c,
-                ifshortcut=True,
-                stride=1,
-                filter_size=3,
-                padding=1,
-                expansion_factor=t,
-                name=name + '_' + str(i + 1))
-        return last_residual_block
-
-
-def MobileNetV2_x0_25():
-    model = MobileNetV2(scale=0.25)
+            block = self.add_sublayer(
+                name + "_" + str(i + 1),
+                sublayer=InvertedResidualUnit(
+                    num_channels=c,
+                    num_in_filter=c,
+                    num_filters=c,
+                    stride=1,
+                    filter_size=3,
+                    padding=1,
+                    expansion_factor=t,
+                    name=name + "_" + str(i + 1)))
+            self._block_list.append(block)
+
+    def forward(self, inputs):
+        y = self._first_block(inputs, ifshortcut=False)
+        for block in self._block_list:
+            y = block(y, ifshortcut=True)
+        return y
+
+
+class MobileNet(fluid.dygraph.Layer):
+    def __init__(self, class_dim=1000, scale=1.0):
+        super(MobileNet, self).__init__()
+        self.scale = scale
+        self.class_dim = class_dim
+
+        bottleneck_params_list = [
+            (1, 16, 1, 1),
+            (6, 24, 2, 2),
+            (6, 32, 3, 2),
+            (6, 64, 4, 2),
+            (6, 96, 3, 1),
+            (6, 160, 3, 2),
+            (6, 320, 1, 1),
+        ]
+
+        self.conv1 = ConvBNLayer(
+            num_channels=3,
+            num_filters=int(32 * scale),
+            filter_size=3,
+            stride=2,
+            padding=1,
+            name="conv1_1")
+
+        self.block_list = []
+        i = 1
+        in_c = int(32 * scale)
+        for layer_setting in bottleneck_params_list:
+            t, c, n, s = layer_setting
+            i += 1
+            block = self.add_sublayer(
+                "conv" + str(i),
+                sublayer=InvresiBlocks(
+                    in_c=in_c,
+                    t=t,
+                    c=int(c * scale),
+                    n=n,
+                    s=s,
+                    name="conv" + str(i)))
+            self.block_list.append(block)
+            in_c = int(c * scale)
+
+        self.out_c = int(1280 * scale) if scale > 1.0 else 1280
+        self.conv9 = ConvBNLayer(
+            num_channels=in_c,
+            num_filters=self.out_c,
+            filter_size=1,
+            stride=1,
+            padding=0,
+            name="conv9")
+
+        self.pool2d_avg = Pool2D(pool_type="avg", global_pooling=True)
+
+        self.out = Linear(
+            self.out_c,
+            class_dim,
+            param_attr=ParamAttr(name="fc10_weights"),
+            bias_attr=ParamAttr(name="fc10_offset"))
+
+    def forward(self, inputs):
+        y = self.conv1(inputs, if_act=True)
+        for block in self.block_list:
+            y = block(y)
+        y = self.conv9(y, if_act=True)
+        y = self.pool2d_avg(y)
+        y = fluid.layers.reshape(y, shape=[-1, self.out_c])
+        y = self.out(y)
+        return y
+
+
+def MobileNetV2_x0_25(**args):
+    model = MobileNet(scale=0.25, **args)
     return model
 
 
-def MobileNetV2_x0_5():
-    model = MobileNetV2(scale=0.5)
+def MobileNetV2_x0_5(**args):
+    model = MobileNet(scale=0.5, **args)
     return model
 
 
-def MobileNetV2_x0_75():
-    model = MobileNetV2(scale=0.75)
+def MobileNetV2_x0_75(**args):
+    model = MobileNet(scale=0.75, **args)
     return model
 
 
-def MobileNetV2_x1_0():
-    model = MobileNetV2(scale=1.0)
+def MobileNetV2(**args):
+    model = MobileNet(scale=1.0, **args)
     return model
 
 
-def MobileNetV2_x1_5():
-    model = MobileNetV2(scale=1.5)
+def MobileNetV2_x1_5(**args):
+    model = MobileNet(scale=1.5, **args)
     return model
 
 
-def MobileNetV2_x2_0():
-    model = MobileNetV2(scale=2.0)
+def MobileNetV2_x2_0(**args):
+    model = MobileNet(scale=2.0, **args)
     return model

ppcls/modeling/architectures/mobilenet_v3.py

@@ -16,320 +16,342 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import numpy as np
+import paddle
 import paddle.fluid as fluid
 from paddle.fluid.param_attr import ParamAttr
+from paddle.fluid.layer_helper import LayerHelper
+from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear, Dropout
+
+import math
 
 __all__ = [
-    'MobileNetV3', 'MobileNetV3_small_x0_35', 'MobileNetV3_small_x0_5',
-    'MobileNetV3_small_x0_75', 'MobileNetV3_small_x1_0',
-    'MobileNetV3_small_x1_25', 'MobileNetV3_large_x0_35',
-    'MobileNetV3_large_x0_5', 'MobileNetV3_large_x0_75',
-    'MobileNetV3_large_x1_0', 'MobileNetV3_large_x1_25'
+    "MobileNetV3_small_x0_35", "MobileNetV3_small_x0_5",
+    "MobileNetV3_small_x0_75", "MobileNetV3_small_x1_0",
+    "MobileNetV3_small_x1_25", "MobileNetV3_large_x0_35",
+    "MobileNetV3_large_x0_5", "MobileNetV3_large_x0_75",
+    "MobileNetV3_large_x1_0", "MobileNetV3_large_x1_25"
 ]
 
 
-class MobileNetV3():
-    def __init__(self,
-                 scale=1.0,
-                 model_name='small',
-                 lr_mult_list=[1.0, 1.0, 1.0, 1.0, 1.0]):
-        self.scale = scale
-        self.inplanes = 16
-
-        self.lr_mult_list = lr_mult_list
-        assert len(self.lr_mult_list) == 5, \
-            "lr_mult_list length in MobileNetV3 must be 5 but got {}!!".format(
-            len(self.lr_mult_list))
-        self.curr_stage = 0
+def make_divisible(v, divisor=8, min_value=None):
+    if min_value is None:
+        min_value = divisor
+    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
+    if new_v < 0.9 * v:
+        new_v += divisor
+    return new_v
+
 
+class MobileNetV3(fluid.dygraph.Layer):
+    def __init__(self, scale=1.0, model_name="small", class_dim=1000):
+        super(MobileNetV3, self).__init__()
+
+        inplanes = 16
         if model_name == "large":
             self.cfg = [
                 # k, exp, c,  se,     nl,  s,
-                [3, 16, 16, False, 'relu', 1],
-                [3, 64, 24, False, 'relu', 2],
-                [3, 72, 24, False, 'relu', 1],
-                [5, 72, 40, True, 'relu', 2],
-                [5, 120, 40, True, 'relu', 1],
-                [5, 120, 40, True, 'relu', 1],
-                [3, 240, 80, False, 'hard_swish', 2],
-                [3, 200, 80, False, 'hard_swish', 1],
-                [3, 184, 80, False, 'hard_swish', 1],
-                [3, 184, 80, False, 'hard_swish', 1],
-                [3, 480, 112, True, 'hard_swish', 1],
-                [3, 672, 112, True, 'hard_swish', 1],
-                [5, 672, 160, True, 'hard_swish', 2],
-                [5, 960, 160, True, 'hard_swish', 1],
-                [5, 960, 160, True, 'hard_swish', 1],
+                [3, 16, 16, False, "relu", 1],
+                [3, 64, 24, False, "relu", 2],
+                [3, 72, 24, False, "relu", 1],
+                [5, 72, 40, True, "relu", 2],
+                [5, 120, 40, True, "relu", 1],
+                [5, 120, 40, True, "relu", 1],
+                [3, 240, 80, False, "hard_swish", 2],
+                [3, 200, 80, False, "hard_swish", 1],
+                [3, 184, 80, False, "hard_swish", 1],
+                [3, 184, 80, False, "hard_swish", 1],
+                [3, 480, 112, True, "hard_swish", 1],
+                [3, 672, 112, True, "hard_swish", 1],
+                [5, 672, 160, True, "hard_swish", 2],
+                [5, 960, 160, True, "hard_swish", 1],
+                [5, 960, 160, True, "hard_swish", 1],
             ]
             self.cls_ch_squeeze = 960
             self.cls_ch_expand = 1280
-            self.lr_interval = 3
         elif model_name == "small":
             self.cfg = [
                 # k, exp, c,  se,     nl,  s,
-                [3, 16, 16, True, 'relu', 2],
-                [3, 72, 24, False, 'relu', 2],
-                [3, 88, 24, False, 'relu', 1],
-                [5, 96, 40, True, 'hard_swish', 2],
-                [5, 240, 40, True, 'hard_swish', 1],
-                [5, 240, 40, True, 'hard_swish', 1],
-                [5, 120, 48, True, 'hard_swish', 1],
-                [5, 144, 48, True, 'hard_swish', 1],
-                [5, 288, 96, True, 'hard_swish', 2],
-                [5, 576, 96, True, 'hard_swish', 1],
-                [5, 576, 96, True, 'hard_swish', 1],
+                [3, 16, 16, True, "relu", 2],
+                [3, 72, 24, False, "relu", 2],
+                [3, 88, 24, False, "relu", 1],
+                [5, 96, 40, True, "hard_swish", 2],
+                [5, 240, 40, True, "hard_swish", 1],
+                [5, 240, 40, True, "hard_swish", 1],
+                [5, 120, 48, True, "hard_swish", 1],
+                [5, 144, 48, True, "hard_swish", 1],
+                [5, 288, 96, True, "hard_swish", 2],
+                [5, 576, 96, True, "hard_swish", 1],
+                [5, 576, 96, True, "hard_swish", 1],
             ]
             self.cls_ch_squeeze = 576
             self.cls_ch_expand = 1280
-            self.lr_interval = 2
         else:
             raise NotImplementedError(
                 "mode[{}_model] is not implemented!".format(model_name))
 
-    def net(self, input, class_dim=1000):
-        scale = self.scale
-        inplanes = self.inplanes
-        cfg = self.cfg
-        cls_ch_squeeze = self.cls_ch_squeeze
-        cls_ch_expand = self.cls_ch_expand
-        # conv1
-        conv = self.conv_bn_layer(
-            input,
+        self.conv1 = ConvBNLayer(
+            in_c=3,
+            out_c=make_divisible(inplanes * scale),
             filter_size=3,
-            num_filters=self.make_divisible(inplanes * scale),
             stride=2,
             padding=1,
             num_groups=1,
             if_act=True,
-            act='hard_swish',
-            name='conv1')
+            act="hard_swish",
+            name="conv1")
+
+        self.block_list = []
         i = 0
-        inplanes = self.make_divisible(inplanes * scale)
-        for layer_cfg in cfg:
-            conv = self.residual_unit(
-                input=conv,
-                num_in_filter=inplanes,
-                num_mid_filter=self.make_divisible(scale * layer_cfg[1]),
-                num_out_filter=self.make_divisible(scale * layer_cfg[2]),
-                act=layer_cfg[4],
-                stride=layer_cfg[5],
-                filter_size=layer_cfg[0],
-                use_se=layer_cfg[3],
-                name='conv' + str(i + 2))
-            inplanes = self.make_divisible(scale * layer_cfg[2])
+        inplanes = make_divisible(inplanes * scale)
+        for (k, exp, c, se, nl, s) in self.cfg:
+            self.block_list.append(
+                ResidualUnit(
+                    in_c=inplanes,
+                    mid_c=make_divisible(scale * exp),
+                    out_c=make_divisible(scale * c),
+                    filter_size=k,
+                    stride=s,
+                    use_se=se,
+                    act=nl,
+                    name="conv" + str(i + 2)))
+            self.add_sublayer(
+                sublayer=self.block_list[-1], name="conv" + str(i + 2))
+            inplanes = make_divisible(scale * c)
             i += 1
-            self.curr_stage = i
 
-        conv = self.conv_bn_layer(
-            input=conv,
+        self.last_second_conv = ConvBNLayer(
+            in_c=inplanes,
+            out_c=make_divisible(scale * self.cls_ch_squeeze),
             filter_size=1,
-            num_filters=self.make_divisible(scale * cls_ch_squeeze),
             stride=1,
             padding=0,
             num_groups=1,
             if_act=True,
-            act='hard_swish',
-            name='conv_last')
-        conv = fluid.layers.pool2d(
-            input=conv, pool_type='avg', global_pooling=True, use_cudnn=False)
-        conv = fluid.layers.conv2d(
-            input=conv,
-            num_filters=cls_ch_expand,
+            act="hard_swish",
+            name="conv_last")
+
+        self.pool = Pool2D(
+            pool_type="avg", global_pooling=True, use_cudnn=False)
+
+        self.last_conv = Conv2D(
+            num_channels=make_divisible(scale * self.cls_ch_squeeze),
+            num_filters=self.cls_ch_expand,
             filter_size=1,
             stride=1,
             padding=0,
             act=None,
-            param_attr=ParamAttr(name='last_1x1_conv_weights'),
+            param_attr=ParamAttr(name="last_1x1_conv_weights"),
             bias_attr=False)
-        conv = fluid.layers.hard_swish(conv)
-        drop = fluid.layers.dropout(x=conv, dropout_prob=0.2)
-        out = fluid.layers.fc(input=drop,
-                              size=class_dim,
-                              param_attr=ParamAttr(name='fc_weights'),
-                              bias_attr=ParamAttr(name='fc_offset'))
-        return out
-
-    def conv_bn_layer(self,
-                      input,
-                      filter_size,
-                      num_filters,
-                      stride,
-                      padding,
-                      num_groups=1,
-                      if_act=True,
-                      act=None,
-                      name=None,
-                      use_cudnn=True,
-                      res_last_bn_init=False):
-        lr_idx = self.curr_stage // self.lr_interval
-        lr_idx = min(lr_idx, len(self.lr_mult_list) - 1)
-        lr_mult = self.lr_mult_list[lr_idx]
-
-        conv = fluid.layers.conv2d(
-            input=input,
-            num_filters=num_filters,
+
+        self.out = Linear(
+            input_dim=self.cls_ch_expand,
+            output_dim=class_dim,
+            param_attr=ParamAttr("fc_weights"),
+            bias_attr=ParamAttr(name="fc_offset"))
+
+    def forward(self, inputs, label=None, dropout_prob=0.2):
+        x = self.conv1(inputs)
+        for block in self.block_list:
+            x = block(x)
+        x = self.last_second_conv(x)
+        x = self.pool(x)
+        x = self.last_conv(x)
+        x = fluid.layers.hard_swish(x)
+        x = fluid.layers.dropout(x=x, dropout_prob=dropout_prob)
+        x = fluid.layers.reshape(x, shape=[x.shape[0], x.shape[1]])
+        x = self.out(x)
+
+        return x
+
+
+class ConvBNLayer(fluid.dygraph.Layer):
+    def __init__(self,
+                 in_c,
+                 out_c,
+                 filter_size,
+                 stride,
+                 padding,
+                 num_groups=1,
+                 if_act=True,
+                 act=None,
+                 use_cudnn=True,
+                 name=""):
+        super(ConvBNLayer, self).__init__()
+        self.if_act = if_act
+        self.act = act
+        self.conv = fluid.dygraph.Conv2D(
+            num_channels=in_c,
+            num_filters=out_c,
             filter_size=filter_size,
             stride=stride,
             padding=padding,
             groups=num_groups,
-            act=None,
+            param_attr=ParamAttr(name=name + "_weights"),
+            bias_attr=False,
             use_cudnn=use_cudnn,
+            act=None)
+        self.bn = fluid.dygraph.BatchNorm(
+            num_channels=out_c,
+            act=None,
             param_attr=ParamAttr(
-                name=name + '_weights', learning_rate=lr_mult),
-            bias_attr=False)
-        bn_name = name + '_bn'
-        bn = fluid.layers.batch_norm(
-            input=conv,
-            param_attr=ParamAttr(
-                name=bn_name + "_scale",
+                name=name + "_bn_scale",
                 regularizer=fluid.regularizer.L2DecayRegularizer(
                     regularization_coeff=0.0)),
             bias_attr=ParamAttr(
-                name=bn_name + "_offset",
+                name=name + "_bn_offset",
                 regularizer=fluid.regularizer.L2DecayRegularizer(
                     regularization_coeff=0.0)),
-            moving_mean_name=bn_name + '_mean',
-            moving_variance_name=bn_name + '_variance')
-        if if_act:
-            if act == 'relu':
-                bn = fluid.layers.relu(bn)
-            elif act == 'hard_swish':
-                bn = fluid.layers.hard_swish(bn)
-        return bn
-
-    def make_divisible(self, v, divisor=8, min_value=None):
-        if min_value is None:
-            min_value = divisor
-        new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
-        if new_v < 0.9 * v:
-            new_v += divisor
-        return new_v
-
-    def se_block(self, input, num_out_filter, ratio=4, name=None):
-        lr_idx = self.curr_stage // self.lr_interval
-        lr_idx = min(lr_idx, len(self.lr_mult_list) - 1)
-        lr_mult = self.lr_mult_list[lr_idx]
-
-        num_mid_filter = num_out_filter // ratio
-        pool = fluid.layers.pool2d(
-            input=input, pool_type='avg', global_pooling=True, use_cudnn=False)
-        conv1 = fluid.layers.conv2d(
-            input=pool,
-            filter_size=1,
-            num_filters=num_mid_filter,
-            act='relu',
-            param_attr=ParamAttr(
-                name=name + '_1_weights', learning_rate=lr_mult),
-            bias_attr=ParamAttr(
-                name=name + '_1_offset', learning_rate=lr_mult))
-        conv2 = fluid.layers.conv2d(
-            input=conv1,
-            filter_size=1,
-            num_filters=num_out_filter,
-            act='hard_sigmoid',
-            param_attr=ParamAttr(
-                name=name + '_2_weights', learning_rate=lr_mult),
-            bias_attr=ParamAttr(
-                name=name + '_2_offset', learning_rate=lr_mult))
-        scale = fluid.layers.elementwise_mul(x=input, y=conv2, axis=0)
-        return scale
-
-    def residual_unit(self,
-                      input,
-                      num_in_filter,
-                      num_mid_filter,
-                      num_out_filter,
-                      stride,
-                      filter_size,
-                      act=None,
-                      use_se=False,
-                      name=None):
-
-        conv0 = self.conv_bn_layer(
-            input=input,
+            moving_mean_name=name + "_bn_mean",
+            moving_variance_name=name + "_bn_variance")
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        if self.if_act:
+            if self.act == "relu":
+                x = fluid.layers.relu(x)
+            elif self.act == "hard_swish":
+                x = fluid.layers.hard_swish(x)
+            else:
+                print("The activation function is selected incorrectly.")
+                exit()
+        return x
+
+
+class ResidualUnit(fluid.dygraph.Layer):
+    def __init__(self,
+                 in_c,
+                 mid_c,
+                 out_c,
+                 filter_size,
+                 stride,
+                 use_se,
+                 act=None,
+                 name=''):
+        super(ResidualUnit, self).__init__()
+        self.if_shortcut = stride == 1 and in_c == out_c
+        self.if_se = use_se
+
+        self.expand_conv = ConvBNLayer(
+            in_c=in_c,
+            out_c=mid_c,
             filter_size=1,
-            num_filters=num_mid_filter,
             stride=1,
             padding=0,
             if_act=True,
             act=act,
-            name=name + '_expand')
-
-        conv1 = self.conv_bn_layer(
-            input=conv0,
+            name=name + "_expand")
+        self.bottleneck_conv = ConvBNLayer(
+            in_c=mid_c,
+            out_c=mid_c,
             filter_size=filter_size,
-            num_filters=num_mid_filter,
             stride=stride,
             padding=int((filter_size - 1) // 2),
+            num_groups=mid_c,
             if_act=True,
             act=act,
-            num_groups=num_mid_filter,
-            use_cudnn=False,
-            name=name + '_depthwise')
-        if use_se:
-            conv1 = self.se_block(
-                input=conv1, num_out_filter=num_mid_filter, name=name + '_se')
-
-        conv2 = self.conv_bn_layer(
-            input=conv1,
+            name=name + "_depthwise")
+        if self.if_se:
+            self.mid_se = SEModule(mid_c, name=name + "_se")
+        self.linear_conv = ConvBNLayer(
+            in_c=mid_c,
+            out_c=out_c,
             filter_size=1,
-            num_filters=num_out_filter,
             stride=1,
             padding=0,
             if_act=False,
-            name=name + '_linear',
-            res_last_bn_init=True)
-        if num_in_filter != num_out_filter or stride != 1:
-            return conv2
-        else:
-            return fluid.layers.elementwise_add(x=input, y=conv2, act=None)
+            act=None,
+            name=name + "_linear")
+
+    def forward(self, inputs):
+        x = self.expand_conv(inputs)
+        x = self.bottleneck_conv(x)
+        if self.if_se:
+            x = self.mid_se(x)
+        x = self.linear_conv(x)
+        if self.if_shortcut:
+            x = fluid.layers.elementwise_add(inputs, x)
+        return x
+
+
+class SEModule(fluid.dygraph.Layer):
+    def __init__(self, channel, reduction=4, name=""):
+        super(SEModule, self).__init__()
+        self.avg_pool = fluid.dygraph.Pool2D(
+            pool_type="avg", global_pooling=True, use_cudnn=False)
+        self.conv1 = fluid.dygraph.Conv2D(
+            num_channels=channel,
+            num_filters=channel // reduction,
+            filter_size=1,
+            stride=1,
+            padding=0,
+            act="relu",
+            param_attr=ParamAttr(name=name + "_1_weights"),
+            bias_attr=ParamAttr(name=name + "_1_offset"))
+        self.conv2 = fluid.dygraph.Conv2D(
+            num_channels=channel // reduction,
+            num_filters=channel,
+            filter_size=1,
+            stride=1,
+            padding=0,
+            act=None,
+            param_attr=ParamAttr(name + "_2_weights"),
+            bias_attr=ParamAttr(name=name + "_2_offset"))
+
+    def forward(self, inputs):
+        outputs = self.avg_pool(inputs)
+        outputs = self.conv1(outputs)
+        outputs = self.conv2(outputs)
+        outputs = fluid.layers.hard_sigmoid(outputs)
+        return fluid.layers.elementwise_mul(x=inputs, y=outputs, axis=0)
 
 
-def MobileNetV3_small_x0_35():
-    model = MobileNetV3(model_name='small', scale=0.35)
+def MobileNetV3_small_x0_35(**args):
+    model = MobileNetV3(model_name="small", scale=0.35, **args)
     return model
 
 
-def MobileNetV3_small_x0_5():
-    model = MobileNetV3(model_name='small', scale=0.5)
+def MobileNetV3_small_x0_5(**args):
+    model = MobileNetV3(model_name="small", scale=0.5, **args)
     return model
 
 
-def MobileNetV3_small_x0_75():
-    model = MobileNetV3(model_name='small', scale=0.75)
+def MobileNetV3_small_x0_75(**args):
+    model = MobileNetV3(model_name="small", scale=0.75, **args)
     return model
 
 
 def MobileNetV3_small_x1_0(**args):
-    model = MobileNetV3(model_name='small', scale=1.0, **args)
+    model = MobileNetV3(model_name="small", scale=1.0, **args)
     return model
 
 
-def MobileNetV3_small_x1_25():
-    model = MobileNetV3(model_name='small', scale=1.25)
+def MobileNetV3_small_x1_25(**args):
+    model = MobileNetV3(model_name="small", scale=1.25, **args)
     return model
 
 
-def MobileNetV3_large_x0_35():
-    model = MobileNetV3(model_name='large', scale=0.35)
+def MobileNetV3_large_x0_35(**args):
+    model = MobileNetV3(model_name="large", scale=0.35, **args)
     return model
 
 
-def MobileNetV3_large_x0_5():
-    model = MobileNetV3(model_name='large', scale=0.5)
+def MobileNetV3_large_x0_5(**args):
+    model = MobileNetV3(model_name="large", scale=0.5, **args)
     return model
 
 
-def MobileNetV3_large_x0_75():
-    model = MobileNetV3(model_name='large', scale=0.75)
+def MobileNetV3_large_x0_75(**args):
+    model = MobileNetV3(model_name="large", scale=0.75, **args)
     return model
 
 
 def MobileNetV3_large_x1_0(**args):
-    model = MobileNetV3(model_name='large', scale=1.0, **args)
+    model = MobileNetV3(model_name="large", scale=1.0, **args)
     return model
 
 
-def MobileNetV3_large_x1_25():
-    model = MobileNetV3(model_name='large', scale=1.25)
+def MobileNetV3_large_x1_25(**args):
+    model = MobileNetV3(model_name="large", scale=1.25, **args)
     return model

ppcls/modeling/architectures/res2net.py

-#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+# copyright (c) 2020 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
+# 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
 #
-#Unless required by applicable law or agreed to in writing, software
-#distributed under the License is distributed on an "AS IS" BASIS,
-#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#See the License for the specific language governing permissions and
-#limitations under the License.
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import numpy as np
 import paddle
 import paddle.fluid as fluid
-import math
 from paddle.fluid.param_attr import ParamAttr
+from paddle.fluid.layer_helper import LayerHelper
+from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear, Dropout
+
+import math
 
 __all__ = [
-    "Res2Net", "Res2Net50_48w_2s", "Res2Net50_26w_4s", "Res2Net50_14w_8s",
-    "Res2Net50_26w_6s", "Res2Net50_26w_8s", "Res2Net101_26w_4s",
-    "Res2Net152_26w_4s"
+    "Res2Net50_48w_2s", "Res2Net50_26w_4s", "Res2Net50_14w_8s",
+    "Res2Net50_48w_2s", "Res2Net50_26w_6s", "Res2Net50_26w_8s",
+    "Res2Net101_26w_4s", "Res2Net152_26w_4s", "Res2Net200_26w_4s"
 ]
 
 
-class Res2Net():
-    def __init__(self, layers=50, scales=4, width=26):
+class ConvBNLayer(fluid.dygraph.Layer):
+    def __init__(
+            self,
+            num_channels,
+            num_filters,
+            filter_size,
+            stride=1,
+            groups=1,
+            act=None,
+            name=None, ):
+        super(ConvBNLayer, self).__init__()
+
+        self._conv = Conv2D(
+            num_channels=num_channels,
+            num_filters=num_filters,
+            filter_size=filter_size,
+            stride=stride,
+            padding=(filter_size - 1) // 2,
+            groups=groups,
+            act=None,
+            param_attr=ParamAttr(name=name + "_weights"),
+            bias_attr=False)
+        if name == "conv1":
+            bn_name = "bn_" + name
+        else:
+            bn_name = "bn" + name[3:]
+        self._batch_norm = BatchNorm(
+            num_filters,
+            act=act,
+            param_attr=ParamAttr(name=bn_name + '_scale'),
+            bias_attr=ParamAttr(bn_name + '_offset'),
+            moving_mean_name=bn_name + '_mean',
+            moving_variance_name=bn_name + '_variance')
+
+    def forward(self, inputs):
+        y = self._conv(inputs)
+        y = self._batch_norm(y)
+        return y
+
+
+class BottleneckBlock(fluid.dygraph.Layer):
+    def __init__(self,
+                 num_channels1,
+                 num_channels2,
+                 num_filters,
+                 stride,
+                 scales,
+                 shortcut=True,
+                 if_first=False,
+                 name=None):
+        super(BottleneckBlock, self).__init__()
+        self.stride = stride
+        self.scales = scales
+        self.conv0 = ConvBNLayer(
+            num_channels=num_channels1,
+            num_filters=num_filters,
+            filter_size=1,
+            act='relu',
+            name=name + "_branch2a")
+        self.conv1_list = []
+        for s in range(scales - 1):
+            conv1 = self.add_sublayer(
+                name + '_branch2b_' + str(s + 1),
+                ConvBNLayer(
+                    num_channels=num_filters // scales,
+                    num_filters=num_filters // scales,
+                    filter_size=3,
+                    stride=stride,
+                    act='relu',
+                    name=name + '_branch2b_' + str(s + 1)))
+            self.conv1_list.append(conv1)
+        self.pool2d_avg = Pool2D(
+            pool_size=3, pool_stride=stride, pool_padding=1, pool_type='avg')
+
+        self.conv2 = ConvBNLayer(
+            num_channels=num_filters,
+            num_filters=num_channels2,
+            filter_size=1,
+            act=None,
+            name=name + "_branch2c")
+
+        if not shortcut:
+            self.short = ConvBNLayer(
+                num_channels=num_channels1,
+                num_filters=num_channels2,
+                filter_size=1,
+                stride=stride,
+                name=name + "_branch1")
+
+        self.shortcut = shortcut
+
+    def forward(self, inputs):
+        y = self.conv0(inputs)
+        xs = fluid.layers.split(y, self.scales, 1)
+        ys = []
+        for s, conv1 in enumerate(self.conv1_list):
+            if s == 0 or self.stride == 2:
+                ys.append(conv1(xs[s]))
+            else:
+                ys.append(conv1(xs[s] + ys[-1]))
+        if self.stride == 1:
+            ys.append(xs[-1])
+        else:
+            ys.append(self.pool2d_avg(xs[-1]))
+        conv1 = fluid.layers.concat(ys, axis=1)
+        conv2 = self.conv2(conv1)
+
+        if self.shortcut:
+            short = inputs
+        else:
+            short = self.short(inputs)
+        y = fluid.layers.elementwise_add(x=short, y=conv2)
+        layer_helper = LayerHelper(self.full_name(), act='relu')
+        return layer_helper.append_activation(y)
+
+
+class Res2Net(fluid.dygraph.Layer):
+    def __init__(self, layers=50, scales=4, width=26, class_dim=1000):
+        super(Res2Net, self).__init__()
+
         self.layers = layers
         self.scales = scales
         self.width = width
-
-    def net(self, input, class_dim=1000):
-        layers = self.layers
-        supported_layers = [50, 101, 152]
-        assert layers in supported_layers, \
-            "supported layers are {} but input layer is {}".format(supported_layers, layers)
         basic_width = self.width * self.scales
-        num_filters1 = [basic_width * t for t in [1, 2, 4, 8]]
-        num_filters2 = [256 * t for t in [1, 2, 4, 8]]
+        supported_layers = [50, 101, 152, 200]
+        assert layers in supported_layers, \
+            "supported layers are {} but input layer is {}".format(
+                supported_layers, layers)
 
         if layers == 50:
             depth = [3, 4, 6, 3]
@@ -49,22 +167,25 @@ class Res2Net():
             depth = [3, 4, 23, 3]
         elif layers == 152:
             depth = [3, 8, 36, 3]
-        conv = self.conv_bn_layer(
-            input=input,
+        elif layers == 200:
+            depth = [3, 12, 48, 3]
+        num_channels = [64, 256, 512, 1024]
+        num_channels2 = [256, 512, 1024, 2048]
+        num_filters = [basic_width * t for t in [1, 2, 4, 8]]
+
+        self.conv1 = ConvBNLayer(
+            num_channels=3,
             num_filters=64,
             filter_size=7,
             stride=2,
             act='relu',
             name="conv1")
+        self.pool2d_max = Pool2D(
+            pool_size=3, pool_stride=2, pool_padding=1, pool_type='max')
 
-        conv = fluid.layers.pool2d(
-            input=conv,
-            pool_size=3,
-            pool_stride=2,
-            pool_padding=1,
-            pool_type='max')
-
+        self.block_list = []
         for block in range(len(depth)):
+            shortcut = False
             for i in range(depth[block]):
                 if layers in [101, 152] and block == 2:
                     if i == 0:
@@ -73,153 +194,87 @@ class Res2Net():
                         conv_name = "res" + str(block + 2) + "b" + str(i)
                 else:
                     conv_name = "res" + str(block + 2) + chr(97 + i)
-                conv = self.bottleneck_block(
-                    input=conv,
-                    num_filters1=num_filters1[block],
-                    num_filters2=num_filters2[block],
-                    stride=2 if i == 0 and block != 0 else 1,
-                    name=conv_name)
-        pool = fluid.layers.pool2d(
-            input=conv,
-            pool_size=7,
-            pool_stride=1,
-            pool_type='avg',
-            global_pooling=True)
-
-        stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
-        out = fluid.layers.fc(
-            input=pool,
-            size=class_dim,
-            param_attr=fluid.param_attr.ParamAttr(
+                bottleneck_block = self.add_sublayer(
+                    'bb_%d_%d' % (block, i),
+                    BottleneckBlock(
+                        num_channels1=num_channels[block]
+                        if i == 0 else num_channels2[block],
+                        num_channels2=num_channels2[block],
+                        num_filters=num_filters[block],
+                        stride=2 if i == 0 and block != 0 else 1,
+                        scales=scales,
+                        shortcut=shortcut,
+                        if_first=block == i == 0,
+                        name=conv_name))
+                self.block_list.append(bottleneck_block)
+                shortcut = True
+
+        self.pool2d_avg = Pool2D(
+            pool_size=7, pool_type='avg', global_pooling=True)
+
+        self.pool2d_avg_channels = num_channels[-1] * 2
+
+        stdv = 1.0 / math.sqrt(self.pool2d_avg_channels * 1.0)
+
+        self.out = Linear(
+            self.pool2d_avg_channels,
+            class_dim,
+            param_attr=ParamAttr(
                 initializer=fluid.initializer.Uniform(-stdv, stdv),
-                name='fc_weights'),
-            bias_attr=fluid.param_attr.ParamAttr(name='fc_offset'))
-        return out
-
-    def conv_bn_layer(self,
-                      input,
-                      num_filters,
-                      filter_size,
-                      stride=1,
-                      groups=1,
-                      act=None,
-                      name=None):
-        conv = fluid.layers.conv2d(
-            input=input,
-            num_filters=num_filters,
-            filter_size=filter_size,
-            stride=stride,
-            padding=(filter_size - 1) // 2,
-            groups=groups,
-            act=None,
-            param_attr=ParamAttr(name=name + "_weights"),
-            bias_attr=False)
-
-        if name == "conv1":
-            bn_name = "bn_" + name
-        else:
-            bn_name = "bn" + name[3:]
-
-        return fluid.layers.batch_norm(
-            input=conv,
-            act=act,
-            param_attr=ParamAttr(name=bn_name + '_scale'),
-            bias_attr=ParamAttr(bn_name + '_offset'),
-            moving_mean_name=bn_name + '_mean',
-            moving_variance_name=bn_name + '_variance')
-
-    def shortcut(self, input, ch_out, stride, name):
-        ch_in = input.shape[1]
-        if ch_in != ch_out or stride != 1:
-            return self.conv_bn_layer(input, ch_out, 1, stride, name=name)
-        else:
-            return input
-
-    def bottleneck_block(self, input, num_filters1, num_filters2, stride,
-                         name):
-        conv0 = self.conv_bn_layer(
-            input=input,
-            num_filters=num_filters1,
-            filter_size=1,
-            stride=1,
-            act='relu',
-            name=name + '_branch2a')
-        xs = fluid.layers.split(conv0, self.scales, 1)
-        ys = []
-        for s in range(self.scales - 1):
-            if s == 0 or stride == 2:
-                ys.append(
-                    self.conv_bn_layer(
-                        input=xs[s],
-                        num_filters=num_filters1 // self.scales,
-                        stride=stride,
-                        filter_size=3,
-                        act='relu',
-                        name=name + '_branch2b_' + str(s + 1)))
-            else:
-                ys.append(
-                    self.conv_bn_layer(
-                        input=xs[s] + ys[-1],
-                        num_filters=num_filters1 // self.scales,
-                        stride=stride,
-                        filter_size=3,
-                        act='relu',
-                        name=name + '_branch2b_' + str(s + 1)))
-        if stride == 1:
-            ys.append(xs[-1])
-        else:
-            ys.append(
-                fluid.layers.pool2d(
-                    input=xs[-1],
-                    pool_size=3,
-                    pool_stride=stride,
-                    pool_padding=1,
-                    pool_type='avg'))
-
-        conv1 = fluid.layers.concat(ys, axis=1)
-        conv2 = self.conv_bn_layer(
-            input=conv1,
-            num_filters=num_filters2,
-            filter_size=1,
-            act=None,
-            name=name + "_branch2c")
+                name="fc_weights"),
+            bias_attr=ParamAttr(name="fc_offset"))
+
+    def forward(self, inputs):
+        y = self.conv1(inputs)
+        y = self.pool2d_max(y)
+        for block in self.block_list:
+            y = block(y)
+        y = self.pool2d_avg(y)
+        y = fluid.layers.reshape(y, shape=[-1, self.pool2d_avg_channels])
+        y = self.out(y)
+        return y
+
+
+def Res2Net50_48w_2s(**args):
+    model = Res2Net(layers=50, scales=2, width=48, **args)
+    return model
 
-        short = self.shortcut(
-            input, num_filters2, stride, name=name + "_branch1")
 
-        return fluid.layers.elementwise_add(x=short, y=conv2, act='relu')
+def Res2Net50_26w_4s(**args):
+    model = Res2Net(layers=50, scales=4, width=26, **args)
+    return model
 
 
-def Res2Net50_48w_2s():
-    model = Res2Net(layers=50, scales=2, width=48)
+def Res2Net50_14w_8s(**args):
+    model = Res2Net(layers=50, scales=8, width=14, **args)
     return model
 
 
-def Res2Net50_26w_4s():
-    model = Res2Net(layers=50, scales=4, width=26)
+def Res2Net50_48w_2s(**args):
+    model = Res2Net(layers=50, scales=2, width=48, **args)
     return model
 
 
-def Res2Net50_14w_8s():
-    model = Res2Net(layers=50, scales=8, width=14)
+def Res2Net50_26w_6s(**args):
+    model = Res2Net(layers=50, scales=6, width=26, **args)
     return model
 
 
-def Res2Net50_26w_6s():
-    model = Res2Net(layers=50, scales=6, width=26)
+def Res2Net50_26w_8s(**args):
+    model = Res2Net(layers=50, scales=8, width=26, **args)
     return model
 
 
-def Res2Net50_26w_8s():
-    model = Res2Net(layers=50, scales=8, width=26)
+def Res2Net101_26w_4s(**args):
+    model = Res2Net(layers=101, scales=4, width=26, **args)
     return model
 
 
-def Res2Net101_26w_4s():
-    model = Res2Net(layers=101, scales=4, width=26)
+def Res2Net152_26w_4s(**args):
+    model = Res2Net(layers=152, scales=4, width=26, **args)
     return model
 
 
-def Res2Net152_26w_4s():
-    model = Res2Net(layers=152, scales=4, width=26)
+def Res2Net200_26w_4s(**args):
+    model = Res2Net(layers=200, scales=4, width=26, **args)
     return model

ppcls/modeling/architectures/res2net_vd.py

@@ -16,33 +16,158 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import math
-
+import numpy as np
+import paddle
 import paddle.fluid as fluid
 from paddle.fluid.param_attr import ParamAttr
+from paddle.fluid.layer_helper import LayerHelper
+from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear, Dropout
+
+import math
 
 __all__ = [
-    "Res2Net_vd", "Res2Net50_vd_48w_2s", "Res2Net50_vd_26w_4s",
-    "Res2Net50_vd_14w_8s", "Res2Net50_vd_26w_6s", "Res2Net50_vd_26w_8s",
+    "Res2Net50_vd_48w_2s", "Res2Net50_vd_26w_4s", "Res2Net50_vd_14w_8s",
+    "Res2Net50_vd_48w_2s", "Res2Net50_vd_26w_6s", "Res2Net50_vd_26w_8s",
     "Res2Net101_vd_26w_4s", "Res2Net152_vd_26w_4s", "Res2Net200_vd_26w_4s"
 ]
 
 
-class Res2Net_vd():
-    def __init__(self, layers=50, scales=4, width=26):
+class ConvBNLayer(fluid.dygraph.Layer):
+    def __init__(
+            self,
+            num_channels,
+            num_filters,
+            filter_size,
+            stride=1,
+            groups=1,
+            is_vd_mode=False,
+            act=None,
+            name=None, ):
+        super(ConvBNLayer, self).__init__()
+
+        self.is_vd_mode = is_vd_mode
+        self._pool2d_avg = Pool2D(
+            pool_size=2, pool_stride=2, pool_padding=0, pool_type='avg', ceil_mode=True)
+        self._conv = Conv2D(
+            num_channels=num_channels,
+            num_filters=num_filters,
+            filter_size=filter_size,
+            stride=stride,
+            padding=(filter_size - 1) // 2,
+            groups=groups,
+            act=None,
+            param_attr=ParamAttr(name=name + "_weights"),
+            bias_attr=False)
+        if name == "conv1":
+            bn_name = "bn_" + name
+        else:
+            bn_name = "bn" + name[3:]
+        self._batch_norm = BatchNorm(
+            num_filters,
+            act=act,
+            param_attr=ParamAttr(name=bn_name + '_scale'),
+            bias_attr=ParamAttr(bn_name + '_offset'),
+            moving_mean_name=bn_name + '_mean',
+            moving_variance_name=bn_name + '_variance')
+
+    def forward(self, inputs):
+        if self.is_vd_mode:
+            inputs = self._pool2d_avg(inputs)
+        y = self._conv(inputs)
+        y = self._batch_norm(y)
+        return y
+
+
+class BottleneckBlock(fluid.dygraph.Layer):
+    def __init__(self,
+                 num_channels1,
+                 num_channels2,
+                 num_filters,
+                 stride,
+                 scales,
+                 shortcut=True,
+                 if_first=False,
+                 name=None):
+        super(BottleneckBlock, self).__init__()
+        self.stride = stride
+        self.scales = scales
+        self.conv0 = ConvBNLayer(
+            num_channels=num_channels1,
+            num_filters=num_filters,
+            filter_size=1,
+            act='relu',
+            name=name + "_branch2a")
+        self.conv1_list = []
+        for s in range(scales - 1):
+            conv1 = self.add_sublayer(
+                name + '_branch2b_' + str(s + 1),
+                ConvBNLayer(
+                    num_channels=num_filters // scales,
+                    num_filters=num_filters // scales,
+                    filter_size=3,
+                    stride=stride,
+                    act='relu',
+                    name=name + '_branch2b_' + str(s + 1)))
+            self.conv1_list.append(conv1)
+        self.pool2d_avg = Pool2D(
+            pool_size=3, pool_stride=stride, pool_padding=1, pool_type='avg')
+
+        self.conv2 = ConvBNLayer(
+            num_channels=num_filters,
+            num_filters=num_channels2,
+            filter_size=1,
+            act=None,
+            name=name + "_branch2c")
+
+        if not shortcut:
+            self.short = ConvBNLayer(
+                num_channels=num_channels1,
+                num_filters=num_channels2,
+                filter_size=1,
+                stride=1,
+                is_vd_mode=False if if_first else True,
+                name=name + "_branch1")
+
+        self.shortcut = shortcut
+
+    def forward(self, inputs):
+        y = self.conv0(inputs)
+        xs = fluid.layers.split(y, self.scales, 1)
+        ys = []
+        for s, conv1 in enumerate(self.conv1_list):
+            if s == 0 or self.stride == 2:
+                ys.append(conv1(xs[s]))
+            else:
+                ys.append(conv1(xs[s] + ys[-1]))
+        if self.stride == 1:
+            ys.append(xs[-1])
+        else:
+            ys.append(self.pool2d_avg(xs[-1]))
+        conv1 = fluid.layers.concat(ys, axis=1)
+        conv2 = self.conv2(conv1)
+
+        if self.shortcut:
+            short = inputs
+        else:
+            short = self.short(inputs)
+        y = fluid.layers.elementwise_add(x=short, y=conv2)
+        layer_helper = LayerHelper(self.full_name(), act='relu')
+        return layer_helper.append_activation(y)
+
+
+class Res2Net_vd(fluid.dygraph.Layer):
+    def __init__(self, layers=50, scales=4, width=26, class_dim=1000):
+        super(Res2Net_vd, self).__init__()
+
         self.layers = layers
         self.scales = scales
         self.width = width
-
-    def net(self, input, class_dim=1000):
-        layers = self.layers
+        basic_width = self.width * self.scales
         supported_layers = [50, 101, 152, 200]
         assert layers in supported_layers, \
             "supported layers are {} but input layer is {}".format(
                 supported_layers, layers)
-        basic_width = self.width * self.scales
-        num_filters1 = [basic_width * t for t in [1, 2, 4, 8]]
-        num_filters2 = [256 * t for t in [1, 2, 4, 8]]
+
         if layers == 50:
             depth = [3, 4, 6, 3]
         elif layers == 101:
@@ -51,35 +176,37 @@ class Res2Net_vd():
             depth = [3, 8, 36, 3]
         elif layers == 200:
             depth = [3, 12, 48, 3]
-        conv = self.conv_bn_layer(
-            input=input,
+        num_channels = [64, 256, 512, 1024]
+        num_channels2 = [256, 512, 1024, 2048]
+        num_filters = [basic_width * t for t in [1, 2, 4, 8]]
+
+        self.conv1_1 = ConvBNLayer(
+            num_channels=3,
             num_filters=32,
             filter_size=3,
             stride=2,
             act='relu',
-            name='conv1_1')
-        conv = self.conv_bn_layer(
-            input=conv,
+            name="conv1_1")
+        self.conv1_2 = ConvBNLayer(
+            num_channels=32,
             num_filters=32,
             filter_size=3,
             stride=1,
             act='relu',
-            name='conv1_2')
-        conv = self.conv_bn_layer(
-            input=conv,
+            name="conv1_2")
+        self.conv1_3 = ConvBNLayer(
+            num_channels=32,
             num_filters=64,
             filter_size=3,
             stride=1,
             act='relu',
-            name='conv1_3')
-
-        conv = fluid.layers.pool2d(
-            input=conv,
-            pool_size=3,
-            pool_stride=2,
-            pool_padding=1,
-            pool_type='max')
+            name="conv1_3")
+        self.pool2d_max = Pool2D(
+            pool_size=3, pool_stride=2, pool_padding=1, pool_type='max')
+
+        self.block_list = []
         for block in range(len(depth)):
+            shortcut = False
             for i in range(depth[block]):
                 if layers in [101, 152, 200] and block == 2:
                     if i == 0:
@@ -88,207 +215,89 @@ class Res2Net_vd():
                         conv_name = "res" + str(block + 2) + "b" + str(i)
                 else:
                     conv_name = "res" + str(block + 2) + chr(97 + i)
-                conv = self.bottleneck_block(
-                    input=conv,
-                    num_filters1=num_filters1[block],
-                    num_filters2=num_filters2[block],
-                    stride=2 if i == 0 and block != 0 else 1,
-                    if_first=block == i == 0,
-                    name=conv_name)
-        pool = fluid.layers.pool2d(
-            input=conv,
-            pool_size=7,
-            pool_stride=1,
-            pool_type='avg',
-            global_pooling=True)
-
-        stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
-        out = fluid.layers.fc(
-            input=pool,
-            size=class_dim,
-            param_attr=fluid.param_attr.ParamAttr(
+                bottleneck_block = self.add_sublayer(
+                    'bb_%d_%d' % (block, i),
+                    BottleneckBlock(
+                        num_channels1=num_channels[block]
+                        if i == 0 else num_channels2[block],
+                        num_channels2=num_channels2[block],
+                        num_filters=num_filters[block],
+                        stride=2 if i == 0 and block != 0 else 1,
+                        scales=scales,
+                        shortcut=shortcut,
+                        if_first=block == i == 0,
+                        name=conv_name))
+                self.block_list.append(bottleneck_block)
+                shortcut = True
+
+        self.pool2d_avg = Pool2D(
+            pool_size=7, pool_type='avg', global_pooling=True)
+
+        self.pool2d_avg_channels = num_channels[-1] * 2
+
+        stdv = 1.0 / math.sqrt(self.pool2d_avg_channels * 1.0)
+
+        self.out = Linear(
+            self.pool2d_avg_channels,
+            class_dim,
+            param_attr=ParamAttr(
                 initializer=fluid.initializer.Uniform(-stdv, stdv),
-                name='fc_weights'),
-            bias_attr=fluid.param_attr.ParamAttr(name='fc_offset'))
-        return out
-
-    def conv_bn_layer(self,
-                      input,
-                      num_filters,
-                      filter_size,
-                      stride=1,
-                      groups=1,
-                      act=None,
-                      name=None):
-        conv = fluid.layers.conv2d(
-            input=input,
-            num_filters=num_filters,
-            filter_size=filter_size,
-            stride=stride,
-            padding=(filter_size - 1) // 2,
-            groups=groups,
-            act=None,
-            param_attr=ParamAttr(name=name + "_weights"),
-            bias_attr=False)
-        if name == "conv1":
-            bn_name = "bn_" + name
-        else:
-            bn_name = "bn" + name[3:]
-        return fluid.layers.batch_norm(
-            input=conv,
-            act=act,
-            param_attr=ParamAttr(name=bn_name + '_scale'),
-            bias_attr=ParamAttr(bn_name + '_offset'),
-            moving_mean_name=bn_name + '_mean',
-            moving_variance_name=bn_name + '_variance')
-
-    def conv_bn_layer_new(self,
-                          input,
-                          num_filters,
-                          filter_size,
-                          stride=1,
-                          groups=1,
-                          act=None,
-                          name=None):
-        pool = fluid.layers.pool2d(
-            input=input,
-            pool_size=2,
-            pool_stride=2,
-            pool_padding=0,
-            pool_type='avg',
-            ceil_mode=True)
-
-        conv = fluid.layers.conv2d(
-            input=pool,
-            num_filters=num_filters,
-            filter_size=filter_size,
-            stride=1,
-            padding=(filter_size - 1) // 2,
-            groups=groups,
-            act=None,
-            param_attr=ParamAttr(name=name + "_weights"),
-            bias_attr=False)
-        if name == "conv1":
-            bn_name = "bn_" + name
-        else:
-            bn_name = "bn" + name[3:]
-        return fluid.layers.batch_norm(
-            input=conv,
-            act=act,
-            param_attr=ParamAttr(name=bn_name + '_scale'),
-            bias_attr=ParamAttr(bn_name + '_offset'),
-            moving_mean_name=bn_name + '_mean',
-            moving_variance_name=bn_name + '_variance')
-
-    def shortcut(self, input, ch_out, stride, name, if_first=False):
-        ch_in = input.shape[1]
-        if ch_in != ch_out or stride != 1:
-            if if_first:
-                return self.conv_bn_layer(input, ch_out, 1, stride, name=name)
-            else:
-                return self.conv_bn_layer_new(
-                    input, ch_out, 1, stride, name=name)
-        elif if_first:
-            return self.conv_bn_layer(input, ch_out, 1, stride, name=name)
-        else:
-            return input
-
-    def bottleneck_block(self, input, num_filters1, num_filters2, stride, name,
-                         if_first):
-        conv0 = self.conv_bn_layer(
-            input=input,
-            num_filters=num_filters1,
-            filter_size=1,
-            stride=1,
-            act='relu',
-            name=name + '_branch2a')
-
-        xs = fluid.layers.split(conv0, self.scales, 1)
-        ys = []
-        for s in range(self.scales - 1):
-            if s == 0 or stride == 2:
-                ys.append(
-                    self.conv_bn_layer(
-                        input=xs[s],
-                        num_filters=num_filters1 // self.scales,
-                        stride=stride,
-                        filter_size=3,
-                        act='relu',
-                        name=name + '_branch2b_' + str(s + 1)))
-            else:
-                ys.append(
-                    self.conv_bn_layer(
-                        input=xs[s] + ys[-1],
-                        num_filters=num_filters1 // self.scales,
-                        stride=stride,
-                        filter_size=3,
-                        act='relu',
-                        name=name + '_branch2b_' + str(s + 1)))
-
-        if stride == 1:
-            ys.append(xs[-1])
-        else:
-            ys.append(
-                fluid.layers.pool2d(
-                    input=xs[-1],
-                    pool_size=3,
-                    pool_stride=stride,
-                    pool_padding=1,
-                    pool_type='avg'))
-
-        conv1 = fluid.layers.concat(ys, axis=1)
-        conv2 = self.conv_bn_layer(
-            input=conv1,
-            num_filters=num_filters2,
-            filter_size=1,
-            act=None,
-            name=name + "_branch2c")
-
-        short = self.shortcut(
-            input,
-            num_filters2,
-            stride,
-            if_first=if_first,
-            name=name + "_branch1")
-
-        return fluid.layers.elementwise_add(x=short, y=conv2, act='relu')
+                name="fc_weights"),
+            bias_attr=ParamAttr(name="fc_offset"))
+
+    def forward(self, inputs):
+        y = self.conv1_1(inputs)
+        y = self.conv1_2(y)
+        y = self.conv1_3(y)
+        y = self.pool2d_max(y)
+        for block in self.block_list:
+            y = block(y)
+        y = self.pool2d_avg(y)
+        y = fluid.layers.reshape(y, shape=[-1, self.pool2d_avg_channels])
+        y = self.out(y)
+        return y
+
+
+def Res2Net50_vd_48w_2s(**args):
+    model = Res2Net_vd(layers=50, scales=2, width=48, **args)
+    return model
 
 
-def Res2Net50_vd_48w_2s():
-    model = Res2Net_vd(layers=50, scales=2, width=48)
+def Res2Net50_vd_26w_4s(**args):
+    model = Res2Net_vd(layers=50, scales=4, width=26, **args)
     return model
 
 
-def Res2Net50_vd_26w_4s():
-    model = Res2Net_vd(layers=50, scales=4, width=26)
+def Res2Net50_vd_14w_8s(**args):
+    model = Res2Net_vd(layers=50, scales=8, width=14, **args)
     return model
 
 
-def Res2Net50_vd_14w_8s():
-    model = Res2Net_vd(layers=50, scales=8, width=14)
+def Res2Net50_vd_48w_2s(**args):
+    model = Res2Net_vd(layers=50, scales=2, width=48, **args)
     return model
 
 
-def Res2Net50_vd_26w_6s():
-    model = Res2Net_vd(layers=50, scales=6, width=26)
+def Res2Net50_vd_26w_6s(**args):
+    model = Res2Net_vd(layers=50, scales=6, width=26, **args)
     return model
 
 
-def Res2Net50_vd_26w_8s():
-    model = Res2Net_vd(layers=50, scales=8, width=26)
+def Res2Net50_vd_26w_8s(**args):
+    model = Res2Net_vd(layers=50, scales=8, width=26, **args)
     return model
 
 
-def Res2Net101_vd_26w_4s():
-    model = Res2Net_vd(layers=101, scales=4, width=26)
+def Res2Net101_vd_26w_4s(**args):
+    model = Res2Net_vd(layers=101, scales=4, width=26, **args)
     return model
 
 
-def Res2Net152_vd_26w_4s():
-    model = Res2Net_vd(layers=152, scales=4, width=26)
+def Res2Net152_vd_26w_4s(**args):
+    model = Res2Net_vd(layers=152, scales=4, width=26, **args)
     return model
 
 
-def Res2Net200_vd_26w_4s():
-    model = Res2Net_vd(layers=200, scales=4, width=26)
+def Res2Net200_vd_26w_4s(**args):
+    model = Res2Net_vd(layers=200, scales=4, width=26, **args)
     return model

ppcls/modeling/architectures/resnet.py

@@ -12,19 +12,20 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+import paddle
 import paddle.fluid as fluid
+from paddle.fluid.param_attr import ParamAttr
 from paddle.fluid.layer_helper import LayerHelper
-from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear
+from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear, Dropout
 
 import math
 
-__all__ = [
-    "ResNet18",
-    "ResNet34",
-    "ResNet50",
-    "ResNet101",
-    "ResNet152",
-]
+__all__ = ["ResNet18", "ResNet34", "ResNet50", "ResNet101", "ResNet152"]
 
 
 class ConvBNLayer(fluid.dygraph.Layer):
@@ -34,7 +35,8 @@ class ConvBNLayer(fluid.dygraph.Layer):
                  filter_size,
                  stride=1,
                  groups=1,
-                 act=None):
+                 act=None,
+                 name=None):
         super(ConvBNLayer, self).__init__()
 
         self._conv = Conv2D(
@@ -45,37 +47,54 @@ class ConvBNLayer(fluid.dygraph.Layer):
             padding=(filter_size - 1) // 2,
             groups=groups,
             act=None,
+            param_attr=ParamAttr(name=name + "_weights"),
             bias_attr=False)
-
-        self._batch_norm = BatchNorm(num_filters, act=act)
+        if name == "conv1":
+            bn_name = "bn_" + name
+        else:
+            bn_name = "bn" + name[3:]
+        self._batch_norm = BatchNorm(
+            num_filters,
+            act=act,
+            param_attr=ParamAttr(name=bn_name + "_scale"),
+            bias_attr=ParamAttr(bn_name + "_offset"),
+            moving_mean_name=bn_name + "_mean",
+            moving_variance_name=bn_name + "_variance")
 
     def forward(self, inputs):
         y = self._conv(inputs)
         y = self._batch_norm(y)
-
         return y
 
 
 class BottleneckBlock(fluid.dygraph.Layer):
-    def __init__(self, num_channels, num_filters, stride, shortcut=True):
+    def __init__(self,
+                 num_channels,
+                 num_filters,
+                 stride,
+                 shortcut=True,
+                 name=None):
         super(BottleneckBlock, self).__init__()
 
         self.conv0 = ConvBNLayer(
             num_channels=num_channels,
             num_filters=num_filters,
             filter_size=1,
-            act='relu')
+            act="relu",
+            name=name + "_branch2a")
         self.conv1 = ConvBNLayer(
             num_channels=num_filters,
             num_filters=num_filters,
             filter_size=3,
             stride=stride,
-            act='relu')
+            act="relu",
+            name=name + "_branch2b")
         self.conv2 = ConvBNLayer(
             num_channels=num_filters,
             num_filters=num_filters * 4,
             filter_size=1,
-            act=None)
+            act=None,
+            name=name + "_branch2c")
 
         self.shortcut = shortcut
 
@@ -84,7 +103,8 @@ class BottleneckBlock(fluid.dygraph.Layer):
                 num_channels=num_channels,
                 num_filters=num_filters * 4,
                 filter_size=1,
-                stride=stride)
+                stride=stride,
+                name=name + "_branch1")
 
         self._num_channels_out = num_filters * 4
 
@@ -100,7 +120,54 @@ class BottleneckBlock(fluid.dygraph.Layer):
 
         y = fluid.layers.elementwise_add(x=short, y=conv2)
 
-        layer_helper = LayerHelper(self.full_name(), act='relu')
+        layer_helper = LayerHelper(self.full_name(), act="relu")
+        return layer_helper.append_activation(y)
+
+
+class BasicBlock(fluid.dygraph.Layer):
+    def __init__(self,
+                 num_channels,
+                 num_filters,
+                 stride,
+                 shortcut=True,
+                 name=None):
+        super(BasicBlock, self).__init__()
+        self.stride = stride
+        self.conv0 = ConvBNLayer(
+            num_channels=num_channels,
+            num_filters=num_filters,
+            filter_size=3,
+            stride=stride,
+            act="relu",
+            name=name + "_branch2a")
+        self.conv1 = ConvBNLayer(
+            num_channels=num_filters,
+            num_filters=num_filters,
+            filter_size=3,
+            act=None,
+            name=name + "_branch2b")
+
+        if not shortcut:
+            self.short = ConvBNLayer(
+                num_channels=num_channels,
+                num_filters=num_filters,
+                filter_size=1,
+                stride=stride,
+                name=name + "_branch1")
+
+        self.shortcut = shortcut
+
+    def forward(self, inputs):
+        y = self.conv0(inputs)
+        conv1 = self.conv1(y)
+
+        if self.shortcut:
+            short = inputs
+        else:
+            short = self.short(inputs)
+        y = fluid.layers.elementwise_add(x=short, y=conv1)
+
+        layer_helper = LayerHelper(self.full_name(), act="relu")
         return layer_helper.append_activation(y)
 
 
@@ -108,9 +175,15 @@ class ResNet(fluid.dygraph.Layer):
     def __init__(self, layers=50, class_dim=1000):
         super(ResNet, self).__init__()
 
+        self.layers = layers
+        supported_layers = [18, 34, 50, 101, 152]
+        assert layers in supported_layers, \
+            "supported layers are {} but input layer is {}".format(
+                supported_layers, layers)
+
         if layers == 18:
             depth = [2, 2, 2, 2]
-        elif layers == 18 or layers == 50:
+        elif layers == 34 or layers == 50:
             depth = [3, 4, 6, 3]
         elif layers == 101:
             depth = [3, 4, 23, 3]
@@ -118,77 +191,105 @@ class ResNet(fluid.dygraph.Layer):
             depth = [3, 8, 36, 3]
         else:
             raise ValueError('Input layer is not supported')
-        num_channels = [64, 256, 512, 1024]
-        num_filters = [64, 128, 256, 512]
+        num_channels = [64, 256, 512,
+                        1024] if layers >= 50 else [64, 64, 128, 256]
 
         self.conv = ConvBNLayer(
             num_channels=3,
             num_filters=64,
             filter_size=7,
             stride=2,
-            act='relu')
+            act="relu",
+            name="conv1")
         self.pool2d_max = Pool2D(
-            pool_size=3, pool_stride=2, pool_padding=1, pool_type='max')
-
-        self.bottleneck_block_list = []
-        for block in range(len(depth)):
-            shortcut = False
-            for i in range(depth[block]):
-                bottleneck_block = self.add_sublayer(
-                    'bb_%d_%d' % (block, i),
-                    BottleneckBlock(
-                        num_channels=num_channels[block]
-                        if i == 0 else num_filters[block] * 4,
-                        num_filters=num_filters[block],
-                        stride=2 if i == 0 and block != 0 else 1,
-                        shortcut=shortcut))
-                self.bottleneck_block_list.append(bottleneck_block)
-                shortcut = True
+            pool_size=3, pool_stride=2, pool_padding=1, pool_type="max")
+
+        self.block_list = []
+        if layers >= 50:
+            for block in range(len(depth)):
+                shortcut = False
+                for i in range(depth[block]):
+                    if layers in [101, 152] and block == 2:
+                        if i == 0:
+                            conv_name = "res" + str(block + 2) + "a"
+                        else:
+                            conv_name = "res" + str(block + 2) + "b" + str(i)
+                    else:
+                        conv_name = "res" + str(block + 2) + chr(97 + i)
+                    bottleneck_block = self.add_sublayer(
+                        conv_name,
+                        BottleneckBlock(
+                            num_channels=num_channels[block]
+                            if i == 0 else num_filters[block] * 4,
+                            num_filters=num_filters[block],
+                            stride=2 if i == 0 and block != 0 else 1,
+                            shortcut=shortcut,
+                            name=conv_name))
+                    self.block_list.append(bottleneck_block)
+                    shortcut = True
+        else:
+            for block in range(len(depth)):
+                shortcut = False
+                for i in range(depth[block]):
+                    conv_name = "res" + str(block + 2) + chr(97 + i)
+                    basic_block = self.add_sublayer(
+                        conv_name,
+                        BasicBlock(
+                            num_channels=num_channels[block]
+                            if i == 0 else num_filters[block],
+                            num_filters=num_filters[block],
+                            stride=2 if i == 0 and block != 0 else 1,
+                            shortcut=shortcut,
+                            name=conv_name))
+                    self.block_list.append(basic_block)
+                    shortcut = True
 
         self.pool2d_avg = Pool2D(
             pool_size=7, pool_type='avg', global_pooling=True)
 
-        self.pool2d_avg_output = num_filters[len(num_filters) - 1] * 4 * 1 * 1
+        self.pool2d_avg_channels = num_channels[-1] * 2
 
-        stdv = 1.0 / math.sqrt(2048 * 1.0)
+        stdv = 1.0 / math.sqrt(self.pool2d_avg_channels * 1.0)
 
         self.out = Linear(
-            self.pool2d_avg_output,
+            self.pool2d_avg_channels,
             class_dim,
-            param_attr=fluid.param_attr.ParamAttr(
-                initializer=fluid.initializer.Uniform(-stdv, stdv)))
+            param_attr=ParamAttr(
+                initializer=fluid.initializer.Uniform(-stdv, stdv),
+                name="fc_0.w_0"),
+            bias_attr=ParamAttr(name="fc_0.b_0"))
 
     def forward(self, inputs):
         y = self.conv(inputs)
         y = self.pool2d_max(y)
-        for bottleneck_block in self.bottleneck_block_list:
-            y = bottleneck_block(y)
+        for block in self.block_list:
+            y = block(y)
         y = self.pool2d_avg(y)
-        y = fluid.layers.reshape(y, shape=[-1, self.pool2d_avg_output])
+        y = fluid.layers.reshape(y, shape=[-1, self.pool2d_avg_channels])
         y = self.out(y)
         return y
 
 
-def ResNet18(**kwargs):
-    model = ResNet(layers=18, **kwargs)
+def ResNet18(**args):
+    model = ResNet(layers=18, **args)
     return model
 
 
-def ResNet34(**kwargs):
-    model = ResNet(layers=34, **kwargs)
+def ResNet34(**args):
+    model = ResNet(layers=34, **args)
     return model
 
 
-def ResNet50(**kwargs):
-    model = ResNet(layers=50, **kwargs)
+def ResNet50(**args):
+    model = ResNet(layers=50, **args)
     return model
 
 
-def ResNet101(**kwargs):
-    model = ResNet(layers=101, **kwargs)
+def ResNet101(**args):
+    model = ResNet(layers=101, **args)
     return model
 
 
-def ResNet152(**kwargs):
-    model = ResNet(layers=152, **kwargs)
+def ResNet152(**args):
+    model = ResNet(layers=152, **args)
     return model

ppcls/modeling/architectures/resnet_vc.py

-#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+# copyright (c) 2020 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
+# 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
 #
-#Unless required by applicable law or agreed to in writing, software
-#distributed under the License is distributed on an "AS IS" BASIS,
-#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#See the License for the specific language governing permissions and
-#limitations under the License.
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import math
-
+import numpy as np
 import paddle
 import paddle.fluid as fluid
 from paddle.fluid.param_attr import ParamAttr
+from paddle.fluid.layer_helper import LayerHelper
+from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear, Dropout
 
-__all__ = ["ResNet", "ResNet50_vc", "ResNet101_vc", "ResNet152_vc"]
-
-train_parameters = {
-    "input_size": [3, 224, 224],
-    "input_mean": [0.485, 0.456, 0.406],
-    "input_std": [0.229, 0.224, 0.225],
-    "learning_strategy": {
-        "name": "piecewise_decay",
-        "batch_size": 256,
-        "epochs": [30, 60, 90],
-        "steps": [0.1, 0.01, 0.001, 0.0001]
-    }
-}
-
+import math
 
-class ResNet():
-    def __init__(self, layers=50):
-        self.params = train_parameters
-        self.layers = layers
+__all__ = [
+    "ResNet18_vc", "ResNet34_vc", "ResNet50_vc", "ResNet101_vc", "ResNet152_vc"
+]
 
-    def net(self, input, class_dim=1000):
-        layers = self.layers
-        supported_layers = [50, 101, 152]
-        assert layers in supported_layers, \
-            "supported layers are {} but input layer is {}".format(supported_layers, layers)
 
-        if layers == 50:
-            depth = [3, 4, 6, 3]
-        elif layers == 101:
-            depth = [3, 4, 23, 3]
-        elif layers == 152:
-            depth = [3, 8, 36, 3]
-        num_filters = [64, 128, 256, 512]
+class ConvBNLayer(fluid.dygraph.Layer):
+    def __init__(self,
+                 num_channels,
+                 num_filters,
+                 filter_size,
+                 stride=1,
+                 groups=1,
+                 act=None,
+                 name=None):
+        super(ConvBNLayer, self).__init__()
 
-        conv = self.conv_bn_layer(
-            input=input,
-            num_filters=32,
-            filter_size=3,
-            stride=2,
-            act='relu',
-            name='conv1_1')
-        conv = self.conv_bn_layer(
-            input=conv,
-            num_filters=32,
-            filter_size=3,
-            stride=1,
-            act='relu',
-            name='conv1_2')
-        conv = self.conv_bn_layer(
-            input=conv,
-            num_filters=64,
-            filter_size=3,
-            stride=1,
-            act='relu',
-            name='conv1_3')
-
-        conv = fluid.layers.pool2d(
-            input=conv,
-            pool_size=3,
-            pool_stride=2,
-            pool_padding=1,
-            pool_type='max')
-
-        for block in range(len(depth)):
-            for i in range(depth[block]):
-                if layers in [101, 152] and block == 2:
-                    if i == 0:
-                        conv_name = "res" + str(block + 2) + "a"
-                    else:
-                        conv_name = "res" + str(block + 2) + "b" + str(i)
-                else:
-                    conv_name = "res" + str(block + 2) + chr(97 + i)
-                conv = self.bottleneck_block(
-                    input=conv,
-                    num_filters=num_filters[block],
-                    stride=2 if i == 0 and block != 0 else 1,
-                    name=conv_name)
-
-        pool = fluid.layers.pool2d(
-            input=conv, pool_type='avg', global_pooling=True)
-        stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
-        out = fluid.layers.fc(input=pool,
-                              size=class_dim,
-                              param_attr=fluid.param_attr.ParamAttr(
-                                  name="fc_0.w_0",
-                                  initializer=fluid.initializer.Uniform(-stdv,
-                                                                        stdv)),
-                              bias_attr=ParamAttr(name="fc_0.b_0"))
-        return out
-
-    def conv_bn_layer(self,
-                      input,
-                      num_filters,
-                      filter_size,
-                      stride=1,
-                      groups=1,
-                      act=None,
-                      name=None):
-        conv = fluid.layers.conv2d(
-            input=input,
+        self._conv = Conv2D(
+            num_channels=num_channels,
             num_filters=num_filters,
             filter_size=filter_size,
             stride=stride,
@@ -129,66 +50,264 @@ class ResNet():
             groups=groups,
             act=None,
             param_attr=ParamAttr(name=name + "_weights"),
-            bias_attr=False,
-            name=name + '.conv2d.output.1')
+            bias_attr=False)
         if name == "conv1":
             bn_name = "bn_" + name
         else:
             bn_name = "bn" + name[3:]
-        return fluid.layers.batch_norm(
-            input=conv,
+        self._batch_norm = BatchNorm(
+            num_filters,
             act=act,
-            name=bn_name + '.output.1',
             param_attr=ParamAttr(name=bn_name + '_scale'),
             bias_attr=ParamAttr(bn_name + '_offset'),
             moving_mean_name=bn_name + '_mean',
-            moving_variance_name=bn_name + '_variance', )
+            moving_variance_name=bn_name + '_variance')
 
-    def shortcut(self, input, ch_out, stride, name):
-        ch_in = input.shape[1]
-        if ch_in != ch_out or stride != 1:
-            return self.conv_bn_layer(input, ch_out, 1, stride, name=name)
-        else:
-            return input
+    def forward(self, inputs):
+        y = self._conv(inputs)
+        y = self._batch_norm(y)
+        return y
+
+
+class BottleneckBlock(fluid.dygraph.Layer):
+    def __init__(self,
+                 num_channels,
+                 num_filters,
+                 stride,
+                 shortcut=True,
+                 name=None):
+        super(BottleneckBlock, self).__init__()
 
-    def bottleneck_block(self, input, num_filters, stride, name):
-        conv0 = self.conv_bn_layer(
-            input=input,
+        self.conv0 = ConvBNLayer(
+            num_channels=num_channels,
             num_filters=num_filters,
             filter_size=1,
             act='relu',
             name=name + "_branch2a")
-        conv1 = self.conv_bn_layer(
-            input=conv0,
+        self.conv1 = ConvBNLayer(
+            num_channels=num_filters,
             num_filters=num_filters,
             filter_size=3,
             stride=stride,
             act='relu',
             name=name + "_branch2b")
-        conv2 = self.conv_bn_layer(
-            input=conv1,
+        self.conv2 = ConvBNLayer(
+            num_channels=num_filters,
             num_filters=num_filters * 4,
             filter_size=1,
             act=None,
             name=name + "_branch2c")
 
-        short = self.shortcut(
-            input, num_filters * 4, stride, name=name + "_branch1")
+        if not shortcut:
+            self.short = ConvBNLayer(
+                num_channels=num_channels,
+                num_filters=num_filters * 4,
+                filter_size=1,
+                stride=stride,
+                name=name + "_branch1")
+
+        self.shortcut = shortcut
+
+        self._num_channels_out = num_filters * 4
+
+    def forward(self, inputs):
+        y = self.conv0(inputs)
+        conv1 = self.conv1(y)
+        conv2 = self.conv2(conv1)
+
+        if self.shortcut:
+            short = inputs
+        else:
+            short = self.short(inputs)
+
+        y = fluid.layers.elementwise_add(x=short, y=conv2)
+
+        layer_helper = LayerHelper(self.full_name(), act='relu')
+        return layer_helper.append_activation(y)
+
+
+class BasicBlock(fluid.dygraph.Layer):
+    def __init__(self,
+                 num_channels,
+                 num_filters,
+                 stride,
+                 shortcut=True,
+                 name=None):
+        super(BasicBlock, self).__init__()
+        self.stride = stride
+        self.conv0 = ConvBNLayer(
+            num_channels=num_channels,
+            num_filters=num_filters,
+            filter_size=3,
+            stride=stride,
+            act='relu',
+            name=name + "_branch2a")
+        self.conv1 = ConvBNLayer(
+            num_channels=num_filters,
+            num_filters=num_filters,
+            filter_size=3,
+            act=None,
+            name=name + "_branch2b")
+
+        if not shortcut:
+            self.short = ConvBNLayer(
+                num_channels=num_channels,
+                num_filters=num_filters,
+                filter_size=1,
+                stride=stride,
+                name=name + "_branch1")
+
+        self.shortcut = shortcut
+
+    def forward(self, inputs):
+        y = self.conv0(inputs)
+        conv1 = self.conv1(y)
+
+        if self.shortcut:
+            short = inputs
+        else:
+            short = self.short(inputs)
+        y = fluid.layers.elementwise_add(x=short, y=conv1)
+
+        layer_helper = LayerHelper(self.full_name(), act='relu')
+        return layer_helper.append_activation(y)
+
+
+class ResNet_vc(fluid.dygraph.Layer):
+    def __init__(self, layers=50, class_dim=1000):
+        super(ResNet_vc, self).__init__()
+
+        self.layers = layers
+        supported_layers = [18, 34, 50, 101, 152]
+        assert layers in supported_layers, \
+            "supported layers are {} but input layer is {}".format(
+                supported_layers, layers)
+
+        if layers == 18:
+            depth = [2, 2, 2, 2]
+        elif layers == 34 or layers == 50:
+            depth = [3, 4, 6, 3]
+        elif layers == 101:
+            depth = [3, 4, 23, 3]
+        elif layers == 152:
+            depth = [3, 8, 36, 3]
+        num_channels = [64, 256, 512,
+                        1024] if layers >= 50 else [64, 64, 128, 256]
+        num_filters = [64, 128, 256, 512]
 
-        return fluid.layers.elementwise_add(
-            x=short, y=conv2, act='relu', name=name + ".add.output.5")
+        self.conv1_1 = ConvBNLayer(
+            num_channels=3,
+            num_filters=32,
+            filter_size=3,
+            stride=2,
+            act='relu',
+            name="conv1_1")
+        self.conv1_2 = ConvBNLayer(
+            num_channels=32,
+            num_filters=32,
+            filter_size=3,
+            stride=1,
+            act='relu',
+            name="conv1_2")
+        self.conv1_3 = ConvBNLayer(
+            num_channels=32,
+            num_filters=64,
+            filter_size=3,
+            stride=1,
+            act='relu',
+            name="conv1_3")
+
+        self.pool2d_max = Pool2D(
+            pool_size=3, pool_stride=2, pool_padding=1, pool_type='max')
+
+        self.block_list = []
+        if layers >= 50:
+            for block in range(len(depth)):
+                shortcut = False
+                for i in range(depth[block]):
+                    if layers in [101, 152] and block == 2:
+                        if i == 0:
+                            conv_name = "res" + str(block + 2) + "a"
+                        else:
+                            conv_name = "res" + str(block + 2) + "b" + str(i)
+                    else:
+                        conv_name = "res" + str(block + 2) + chr(97 + i)
+                    bottleneck_block = self.add_sublayer(
+                        'bb_%d_%d' % (block, i),
+                        BottleneckBlock(
+                            num_channels=num_channels[block]
+                            if i == 0 else num_filters[block] * 4,
+                            num_filters=num_filters[block],
+                            stride=2 if i == 0 and block != 0 else 1,
+                            shortcut=shortcut,
+                            name=conv_name))
+                    self.block_list.append(bottleneck_block)
+                    shortcut = True
+        else:
+            for block in range(len(depth)):
+                shortcut = False
+                for i in range(depth[block]):
+                    conv_name = "res" + str(block + 2) + chr(97 + i)
+                    basic_block = self.add_sublayer(
+                        'bb_%d_%d' % (block, i),
+                        BasicBlock(
+                            num_channels=num_channels[block]
+                            if i == 0 else num_filters[block],
+                            num_filters=num_filters[block],
+                            stride=2 if i == 0 and block != 0 else 1,
+                            shortcut=shortcut,
+                            name=conv_name))
+                    self.block_list.append(basic_block)
+                    shortcut = True
+
+        self.pool2d_avg = Pool2D(
+            pool_size=7, pool_type='avg', global_pooling=True)
+
+        self.pool2d_avg_channels = num_channels[-1] * 2
+
+        stdv = 1.0 / math.sqrt(self.pool2d_avg_channels * 1.0)
+
+        self.out = Linear(
+            self.pool2d_avg_channels,
+            class_dim,
+            param_attr=ParamAttr(
+                initializer=fluid.initializer.Uniform(-stdv, stdv),
+                name="fc_0.w_0"),
+            bias_attr=ParamAttr(name="fc_0.b_0"))
+
+    def forward(self, inputs):
+        y = self.conv1_1(inputs)
+        y = self.conv1_2(y)
+        y = self.conv1_3(y)
+        y = self.pool2d_max(y)
+        for block in self.block_list:
+            y = block(y)
+        y = self.pool2d_avg(y)
+        y = fluid.layers.reshape(y, shape=[-1, self.pool2d_avg_channels])
+        y = self.out(y)
+        return y
+
+
+def ResNet18_vc(**args):
+    model = ResNet_vc(layers=18, **args)
+    return model
+
+
+def ResNet34_vc(**args):
+    model = ResNet_vc(layers=34, **args)
+    return model
 
 
-def ResNet50_vc():
-    model = ResNet(layers=50)
+def ResNet50_vc(**args):
+    model = ResNet_vc(layers=50, **args)
     return model
 
 
-def ResNet101_vc():
-    model = ResNet(layers=101)
+def ResNet101_vc(**args):
+    model = ResNet_vc(layers=101, **args)
     return model
 
 
-def ResNet152_vc():
-    model = ResNet(layers=152)
+def ResNet152_vc(**args):
+    model = ResNet_vc(layers=152, **args)
     return model

ppcls/modeling/architectures/resnet_vd.py

-#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+# copyright (c) 2020 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
+# 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
 #
-#Unless required by applicable law or agreed to in writing, software
-#distributed under the License is distributed on an "AS IS" BASIS,
-#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#See the License for the specific language governing permissions and
-#limitations under the License.
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import math
-
+import numpy as np
 import paddle
 import paddle.fluid as fluid
 from paddle.fluid.param_attr import ParamAttr
+from paddle.fluid.layer_helper import LayerHelper
+from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear, Dropout
+
+import math
 
 __all__ = [
-    "ResNet", "ResNet18_vd", "ResNet34_vd", "ResNet50_vd", "ResNet101_vd",
-    "ResNet152_vd", "ResNet200_vd"
+    "ResNet18_vd", "ResNet34_vd", "ResNet50_vd", "ResNet101_vd", "ResNet152_vd"
 ]
 
 
-class ResNet():
-    def __init__(self,
-                 layers=50,
-                 is_3x3=False,
-                 postfix_name="",
-                 lr_mult_list=[1.0, 1.0, 1.0, 1.0, 1.0]):
-        self.layers = layers
-        self.is_3x3 = is_3x3
-        self.postfix_name = "" if postfix_name is None else postfix_name
-        self.lr_mult_list = lr_mult_list
-        assert len(
-            self.lr_mult_list
-        ) == 5, "lr_mult_list length in ResNet must be 5 but got {}!!".format(
-            len(self.lr_mult_list))
-        self.curr_stage = 0
-
-    def net(self, input, class_dim=1000):
-        is_3x3 = self.is_3x3
-        layers = self.layers
-        supported_layers = [18, 34, 50, 101, 152, 200]
-        assert layers in supported_layers, \
-            "supported layers are {} but input layer is {}".format(supported_layers, layers)
-
-        if layers == 18:
-            depth = [2, 2, 2, 2]
-        elif layers == 34 or layers == 50:
-            depth = [3, 4, 6, 3]
-        elif layers == 101:
-            depth = [3, 4, 23, 3]
-        elif layers == 152:
-            depth = [3, 8, 36, 3]
-        elif layers == 200:
-            depth = [3, 12, 48, 3]
-        num_filters = [64, 128, 256, 512]
-        if is_3x3 == False:
-            conv = self.conv_bn_layer(
-                input=input,
-                num_filters=64,
-                filter_size=7,
-                stride=2,
-                act='relu')
-        else:
-            conv = self.conv_bn_layer(
-                input=input,
-                num_filters=32,
-                filter_size=3,
-                stride=2,
-                act='relu',
-                name='conv1_1')
-            conv = self.conv_bn_layer(
-                input=conv,
-                num_filters=32,
-                filter_size=3,
-                stride=1,
-                act='relu',
-                name='conv1_2')
-            conv = self.conv_bn_layer(
-                input=conv,
-                num_filters=64,
-                filter_size=3,
-                stride=1,
-                act='relu',
-                name='conv1_3')
-
-        conv = fluid.layers.pool2d(
-            input=conv,
-            pool_size=3,
-            pool_stride=2,
-            pool_padding=1,
-            pool_type='max')
-
-        if layers >= 50:
-            for block in range(len(depth)):
-                self.curr_stage += 1
-                for i in range(depth[block]):
-                    if layers in [101, 152, 200] and block == 2:
-                        if i == 0:
-                            conv_name = "res" + str(block + 2) + "a"
-                        else:
-                            conv_name = "res" + str(block + 2) + "b" + str(i)
-                    else:
-                        conv_name = "res" + str(block + 2) + chr(97 + i)
-                    conv = self.bottleneck_block(
-                        input=conv,
-                        num_filters=num_filters[block],
-                        stride=2 if i == 0 and block != 0 else 1,
-                        if_first=block == i == 0,
-                        name=conv_name)
-        else:
-            for block in range(len(depth)):
-                self.curr_stage += 1
-                for i in range(depth[block]):
-                    conv_name = "res" + str(block + 2) + chr(97 + i)
-                    conv = self.basic_block(
-                        input=conv,
-                        num_filters=num_filters[block],
-                        stride=2 if i == 0 and block != 0 else 1,
-                        if_first=block == i == 0,
-                        name=conv_name)
-
-        pool = fluid.layers.pool2d(
-            input=conv, pool_type='avg', global_pooling=True)
-        stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
-
-        out = fluid.layers.fc(
-            input=pool,
-            size=class_dim,
-            param_attr=fluid.param_attr.ParamAttr(
-                name="fc_0.w_0" + self.postfix_name,
-                initializer=fluid.initializer.Uniform(-stdv, stdv)),
-            bias_attr=ParamAttr(name="fc_0.b_0" + self.postfix_name))
-
-        return out
-
-    def conv_bn_layer(self,
-                      input,
-                      num_filters,
-                      filter_size,
-                      stride=1,
-                      groups=1,
-                      act=None,
-                      name=None):
-        lr_mult = self.lr_mult_list[self.curr_stage]
-        conv = fluid.layers.conv2d(
-            input=input,
-            num_filters=num_filters,
-            filter_size=filter_size,
-            stride=stride,
-            padding=(filter_size - 1) // 2,
-            groups=groups,
+class ConvBNLayer(fluid.dygraph.Layer):
+    def __init__(
+            self,
+            num_channels,
+            num_filters,
+            filter_size,
+            stride=1,
+            groups=1,
+            is_vd_mode=False,
             act=None,
-            param_attr=ParamAttr(name=name + "_weights" + self.postfix_name),
-            bias_attr=False)
-        if name == "conv1":
-            bn_name = "bn_" + name
-        else:
-            bn_name = "bn" + name[3:]
-        return fluid.layers.batch_norm(
-            input=conv,
-            act=act,
-            param_attr=ParamAttr(name=bn_name + '_scale' + self.postfix_name),
-            bias_attr=ParamAttr(bn_name + '_offset' + self.postfix_name),
-            moving_mean_name=bn_name + '_mean' + self.postfix_name,
-            moving_variance_name=bn_name + '_variance' + self.postfix_name)
-
-    def conv_bn_layer_new(self,
-                          input,
-                          num_filters,
-                          filter_size,
-                          stride=1,
-                          groups=1,
-                          act=None,
-                          name=None):
-        lr_mult = self.lr_mult_list[self.curr_stage]
-        pool = fluid.layers.pool2d(
-            input=input,
+            name=None, ):
+        super(ConvBNLayer, self).__init__()
+
+        self.is_vd_mode = is_vd_mode
+        self._pool2d_avg = Pool2D(
             pool_size=2,
             pool_stride=2,
             pool_padding=0,
             pool_type='avg',
             ceil_mode=True)
-
-        conv = fluid.layers.conv2d(
-            input=pool,
+        self._conv = Conv2D(
+            num_channels=num_channels,
             num_filters=num_filters,
             filter_size=filter_size,
-            stride=1,
+            stride=stride,
             padding=(filter_size - 1) // 2,
             groups=groups,
             act=None,
-            param_attr=ParamAttr(
-                name=name + "_weights" + self.postfix_name,
-                learning_rate=lr_mult),
+            param_attr=ParamAttr(name=name + "_weights"),
             bias_attr=False)
         if name == "conv1":
             bn_name = "bn_" + name
         else:
             bn_name = "bn" + name[3:]
-        return fluid.layers.batch_norm(
-            input=conv,
+        self._batch_norm = BatchNorm(
+            num_filters,
             act=act,
-            param_attr=ParamAttr(
-                name=bn_name + '_scale' + self.postfix_name,
-                learning_rate=lr_mult),
-            bias_attr=ParamAttr(
-                bn_name + '_offset' + self.postfix_name,
-                learning_rate=lr_mult),
-            moving_mean_name=bn_name + '_mean' + self.postfix_name,
-            moving_variance_name=bn_name + '_variance' + self.postfix_name)
-
-    def shortcut(self, input, ch_out, stride, name, if_first=False):
-        ch_in = input.shape[1]
-        if ch_in != ch_out or stride != 1:
-            if if_first:
-                return self.conv_bn_layer(input, ch_out, 1, stride, name=name)
-            else:
-                return self.conv_bn_layer_new(
-                    input, ch_out, 1, stride, name=name)
-        elif if_first:
-            return self.conv_bn_layer(input, ch_out, 1, stride, name=name)
-        else:
-            return input
+            param_attr=ParamAttr(name=bn_name + '_scale'),
+            bias_attr=ParamAttr(bn_name + '_offset'),
+            moving_mean_name=bn_name + '_mean',
+            moving_variance_name=bn_name + '_variance')
 
-    def bottleneck_block(self, input, num_filters, stride, name, if_first):
-        conv0 = self.conv_bn_layer(
-            input=input,
+    def forward(self, inputs):
+        if self.is_vd_mode:
+            inputs = self._pool2d_avg(inputs)
+        y = self._conv(inputs)
+        y = self._batch_norm(y)
+        return y
+
+
+class BottleneckBlock(fluid.dygraph.Layer):
+    def __init__(self,
+                 num_channels,
+                 num_filters,
+                 stride,
+                 shortcut=True,
+                 if_first=False,
+                 name=None):
+        super(BottleneckBlock, self).__init__()
+
+        self.conv0 = ConvBNLayer(
+            num_channels=num_channels,
             num_filters=num_filters,
             filter_size=1,
             act='relu',
             name=name + "_branch2a")
-        conv1 = self.conv_bn_layer(
-            input=conv0,
+        self.conv1 = ConvBNLayer(
+            num_channels=num_filters,
             num_filters=num_filters,
             filter_size=3,
             stride=stride,
             act='relu',
             name=name + "_branch2b")
-        conv2 = self.conv_bn_layer(
-            input=conv1,
+        self.conv2 = ConvBNLayer(
+            num_channels=num_filters,
             num_filters=num_filters * 4,
             filter_size=1,
             act=None,
             name=name + "_branch2c")
 
-        short = self.shortcut(
-            input,
-            num_filters * 4,
-            stride,
-            if_first=if_first,
-            name=name + "_branch1")
+        if not shortcut:
+            self.short = ConvBNLayer(
+                num_channels=num_channels,
+                num_filters=num_filters * 4,
+                filter_size=1,
+                stride=1,
+                is_vd_mode=False if if_first else True,
+                name=name + "_branch1")
+
+        self.shortcut = shortcut
+
+    def forward(self, inputs):
+        y = self.conv0(inputs)
+        conv1 = self.conv1(y)
+        conv2 = self.conv2(conv1)
+
+        if self.shortcut:
+            short = inputs
+        else:
+            short = self.short(inputs)
+        y = fluid.layers.elementwise_add(x=short, y=conv2)
 
-        return fluid.layers.elementwise_add(x=short, y=conv2, act='relu')
+        layer_helper = LayerHelper(self.full_name(), act='relu')
+        return layer_helper.append_activation(y)
 
-    def basic_block(self, input, num_filters, stride, name, if_first):
-        conv0 = self.conv_bn_layer(
-            input=input,
+
+class BasicBlock(fluid.dygraph.Layer):
+    def __init__(self,
+                 num_channels,
+                 num_filters,
+                 stride,
+                 shortcut=True,
+                 if_first=False,
+                 name=None):
+        super(BasicBlock, self).__init__()
+        self.stride = stride
+        self.conv0 = ConvBNLayer(
+            num_channels=num_channels,
             num_filters=num_filters,
             filter_size=3,
-            act='relu',
             stride=stride,
+            act='relu',
             name=name + "_branch2a")
-        conv1 = self.conv_bn_layer(
-            input=conv0,
+        self.conv1 = ConvBNLayer(
+            num_channels=num_filters,
             num_filters=num_filters,
             filter_size=3,
             act=None,
             name=name + "_branch2b")
-        short = self.shortcut(
-            input,
-            num_filters,
-            stride,
-            if_first=if_first,
-            name=name + "_branch1")
-        return fluid.layers.elementwise_add(x=short, y=conv1, act='relu')
 
+        if not shortcut:
+            self.short = ConvBNLayer(
+                num_channels=num_channels,
+                num_filters=num_filters,
+                filter_size=1,
+                stride=1,
+                is_vd_mode=False if if_first else True,
+                name=name + "_branch1")
+
+        self.shortcut = shortcut
+
+    def forward(self, inputs):
+        y = self.conv0(inputs)
+        conv1 = self.conv1(y)
+
+        if self.shortcut:
+            short = inputs
+        else:
+            short = self.short(inputs)
+        y = fluid.layers.elementwise_add(x=short, y=conv1)
+
+        layer_helper = LayerHelper(self.full_name(), act='relu')
+        return layer_helper.append_activation(y)
+
+
+class ResNet_vd(fluid.dygraph.Layer):
+    def __init__(self, layers=50, class_dim=1000):
+        super(ResNet_vd, self).__init__()
+
+        self.layers = layers
+        supported_layers = [18, 34, 50, 101, 152, 200]
+        assert layers in supported_layers, \
+            "supported layers are {} but input layer is {}".format(
+                supported_layers, layers)
+
+        if layers == 18:
+            depth = [2, 2, 2, 2]
+        elif layers == 34 or layers == 50:
+            depth = [3, 4, 6, 3]
+        elif layers == 101:
+            depth = [3, 4, 23, 3]
+        elif layers == 152:
+            depth = [3, 8, 36, 3]
+        elif layers == 200:
+            depth = [3, 12, 48, 3]
+        num_channels = [64, 256, 512,
+                        1024] if layers >= 50 else [64, 64, 128, 256]
+        num_filters = [64, 128, 256, 512]
+
+        self.conv1_1 = ConvBNLayer(
+            num_channels=3,
+            num_filters=32,
+            filter_size=3,
+            stride=2,
+            act='relu',
+            name="conv1_1")
+        self.conv1_2 = ConvBNLayer(
+            num_channels=32,
+            num_filters=32,
+            filter_size=3,
+            stride=1,
+            act='relu',
+            name="conv1_2")
+        self.conv1_3 = ConvBNLayer(
+            num_channels=32,
+            num_filters=64,
+            filter_size=3,
+            stride=1,
+            act='relu',
+            name="conv1_3")
+        self.pool2d_max = Pool2D(
+            pool_size=3, pool_stride=2, pool_padding=1, pool_type='max')
 
-def ResNet18_vd():
-    model = ResNet(layers=18, is_3x3=True)
+        self.block_list = []
+        if layers >= 50:
+            for block in range(len(depth)):
+                shortcut = False
+                for i in range(depth[block]):
+                    if layers in [101, 152] and block == 2:
+                        if i == 0:
+                            conv_name = "res" + str(block + 2) + "a"
+                        else:
+                            conv_name = "res" + str(block + 2) + "b" + str(i)
+                    else:
+                        conv_name = "res" + str(block + 2) + chr(97 + i)
+                    bottleneck_block = self.add_sublayer(
+                        'bb_%d_%d' % (block, i),
+                        BottleneckBlock(
+                            num_channels=num_channels[block]
+                            if i == 0 else num_filters[block] * 4,
+                            num_filters=num_filters[block],
+                            stride=2 if i == 0 and block != 0 else 1,
+                            shortcut=shortcut,
+                            if_first=block == i == 0,
+                            name=conv_name))
+                    self.block_list.append(bottleneck_block)
+                    shortcut = True
+        else:
+            for block in range(len(depth)):
+                shortcut = False
+                for i in range(depth[block]):
+                    conv_name = "res" + str(block + 2) + chr(97 + i)
+                    basic_block = self.add_sublayer(
+                        'bb_%d_%d' % (block, i),
+                        BasicBlock(
+                            num_channels=num_channels[block]
+                            if i == 0 else num_filters[block],
+                            num_filters=num_filters[block],
+                            stride=2 if i == 0 and block != 0 else 1,
+                            shortcut=shortcut,
+                            if_first=block == i == 0,
+                            name=conv_name))
+                    self.block_list.append(basic_block)
+                    shortcut = True
+
+        self.pool2d_avg = Pool2D(
+            pool_size=7, pool_type='avg', global_pooling=True)
+
+        self.pool2d_avg_channels = num_channels[-1] * 2
+
+        stdv = 1.0 / math.sqrt(self.pool2d_avg_channels * 1.0)
+
+        self.out = Linear(
+            self.pool2d_avg_channels,
+            class_dim,
+            param_attr=ParamAttr(
+                initializer=fluid.initializer.Uniform(-stdv, stdv),
+                name="fc_0.w_0"),
+            bias_attr=ParamAttr(name="fc_0.b_0"))
+
+    def forward(self, inputs):
+        y = self.conv1_1(inputs)
+        y = self.conv1_2(y)
+        y = self.conv1_3(y)
+        y = self.pool2d_max(y)
+        for block in self.block_list:
+            y = block(y)
+        y = self.pool2d_avg(y)
+        y = fluid.layers.reshape(y, shape=[-1, self.pool2d_avg_channels])
+        y = self.out(y)
+        return y
+
+
+def ResNet18_vd(**args):
+    model = ResNet_vd(layers=18, **args)
     return model
 
 
-def ResNet34_vd():
-    model = ResNet(layers=34, is_3x3=True)
+def ResNet34_vd(**args):
+    model = ResNet_vd(layers=34, **args)
     return model
 
 
 def ResNet50_vd(**args):
-    model = ResNet(layers=50, is_3x3=True, **args)
+    model = ResNet_vd(layers=50, **args)
     return model
 
 
-def ResNet101_vd():
-    model = ResNet(layers=101, is_3x3=True)
+def ResNet101_vd(**args):
+    model = ResNet_vd(layers=101, **args)
     return model
 
 
-def ResNet152_vd():
-    model = ResNet(layers=152, is_3x3=True)
+def ResNet152_vd(**args):
+    model = ResNet_vd(layers=152, **args)
     return model
 
 
-def ResNet200_vd():
-    model = ResNet(layers=200, is_3x3=True)
+def ResNet200_vd(**args):
+    model = ResNet_vd(layers=200, **args)
     return model

ppcls/modeling/architectures/resnext.py

-#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+# copyright (c) 2020 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
+# 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
 #
-#Unless required by applicable law or agreed to in writing, software
-#distributed under the License is distributed on an "AS IS" BASIS,
-#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#See the License for the specific language governing permissions and
-#limitations under the License.
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import math
-
+import numpy as np
 import paddle
 import paddle.fluid as fluid
 from paddle.fluid.param_attr import ParamAttr
+from paddle.fluid.layer_helper import LayerHelper
+from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear, Dropout
+
+import math
 
 __all__ = [
-    "ResNeXt", "ResNeXt50_64x4d", "ResNeXt101_64x4d", "ResNeXt152_64x4d",
-    "ResNeXt50_32x4d", "ResNeXt101_32x4d", "ResNeXt152_32x4d"
+    "ResNeXt50_32x4d", "ResNeXt50_64x4d", "ResNeXt101_32x4d",
+    "ResNeXt101_64x4d", "ResNeXt152_32x4d", "ResNeXt152_64x4d"
 ]
 
 
-class ResNeXt():
-    def __init__(self, layers=50, cardinality=64):
-        self.layers = layers
-        self.cardinality = cardinality
-
-    def net(self, input, class_dim=1000):
-        layers = self.layers
-        cardinality = self.cardinality
-        supported_layers = [50, 101, 152]
-        assert layers in supported_layers, \
-            "supported layers are {} but input layer is {}".format(supported_layers, layers)
-
-        if layers == 50:
-            depth = [3, 4, 6, 3]
-        elif layers == 101:
-            depth = [3, 4, 23, 3]
-        elif layers == 152:
-            depth = [3, 8, 36, 3]
-
-        num_filters1 = [256, 512, 1024, 2048]
-        num_filters2 = [128, 256, 512, 1024]
+class ConvBNLayer(fluid.dygraph.Layer):
+    def __init__(self,
+                 num_channels,
+                 num_filters,
+                 filter_size,
+                 stride=1,
+                 groups=1,
+                 act=None,
+                 name=None):
+        super(ConvBNLayer, self).__init__()
 
-        conv = self.conv_bn_layer(
-            input=input,
-            num_filters=64,
-            filter_size=7,
-            stride=2,
-            act='relu',
-            name="res_conv1")  #debug
-        conv = fluid.layers.pool2d(
-            input=conv,
-            pool_size=3,
-            pool_stride=2,
-            pool_padding=1,
-            pool_type='max')
-
-        for block in range(len(depth)):
-            for i in range(depth[block]):
-                if layers in [101, 152] and block == 2:
-                    if i == 0:
-                        conv_name = "res" + str(block + 2) + "a"
-                    else:
-                        conv_name = "res" + str(block + 2) + "b" + str(i)
-                else:
-                    conv_name = "res" + str(block + 2) + chr(97 + i)
-                conv = self.bottleneck_block(
-                    input=conv,
-                    num_filters=num_filters1[block]
-                    if cardinality == 64 else num_filters2[block],
-                    stride=2 if i == 0 and block != 0 else 1,
-                    cardinality=cardinality,
-                    name=conv_name)
-
-        pool = fluid.layers.pool2d(
-            input=conv, pool_type='avg', global_pooling=True)
-        stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
-        out = fluid.layers.fc(
-            input=pool,
-            size=class_dim,
-            param_attr=fluid.param_attr.ParamAttr(
-                initializer=fluid.initializer.Uniform(-stdv, stdv),
-                name='fc_weights'),
-            bias_attr=fluid.param_attr.ParamAttr(name='fc_offset'))
-        return out
-
-    def conv_bn_layer(self,
-                      input,
-                      num_filters,
-                      filter_size,
-                      stride=1,
-                      groups=1,
-                      act=None,
-                      name=None):
-        conv = fluid.layers.conv2d(
-            input=input,
+        self._conv = Conv2D(
+            num_channels=num_channels,
             num_filters=num_filters,
             filter_size=filter_size,
             stride=stride,
@@ -110,86 +51,192 @@ class ResNeXt():
             groups=groups,
             act=None,
             param_attr=ParamAttr(name=name + "_weights"),
-            bias_attr=False,
-            name=name + '.conv2d.output.1')
+            bias_attr=False)
         if name == "conv1":
             bn_name = "bn_" + name
         else:
             bn_name = "bn" + name[3:]
-        return fluid.layers.batch_norm(
-            input=conv,
+        self._batch_norm = BatchNorm(
+            num_filters,
             act=act,
-            name=bn_name + '.output.1',
             param_attr=ParamAttr(name=bn_name + '_scale'),
             bias_attr=ParamAttr(bn_name + '_offset'),
             moving_mean_name=bn_name + '_mean',
-            moving_variance_name=bn_name + '_variance', )
-
-    def shortcut(self, input, ch_out, stride, name):
-        ch_in = input.shape[1]
-        if ch_in != ch_out or stride != 1:
-            return self.conv_bn_layer(input, ch_out, 1, stride, name=name)
-        else:
-            return input
-
-    def bottleneck_block(self, input, num_filters, stride, cardinality, name):
-        cardinality = self.cardinality
-        conv0 = self.conv_bn_layer(
-            input=input,
+            moving_variance_name=bn_name + '_variance')
+
+    def forward(self, inputs):
+        y = self._conv(inputs)
+        y = self._batch_norm(y)
+        return y
+
+
+class BottleneckBlock(fluid.dygraph.Layer):
+    def __init__(self,
+                 num_channels,
+                 num_filters,
+                 stride,
+                 cardinality,
+                 shortcut=True,
+                 name=None):
+        super(BottleneckBlock, self).__init__()
+
+        self.conv0 = ConvBNLayer(
+            num_channels=num_channels,
             num_filters=num_filters,
             filter_size=1,
             act='relu',
             name=name + "_branch2a")
-        conv1 = self.conv_bn_layer(
-            input=conv0,
+        self.conv1 = ConvBNLayer(
+            num_channels=num_filters,
             num_filters=num_filters,
             filter_size=3,
-            stride=stride,
             groups=cardinality,
+            stride=stride,
             act='relu',
             name=name + "_branch2b")
-        conv2 = self.conv_bn_layer(
-            input=conv1,
-            num_filters=num_filters if cardinality == 64 else num_filters * 2,
+        self.conv2 = ConvBNLayer(
+            num_channels=num_filters,
+            num_filters=num_filters * 2 if cardinality == 32 else num_filters,
             filter_size=1,
             act=None,
             name=name + "_branch2c")
 
-        short = self.shortcut(
-            input,
-            num_filters if cardinality == 64 else num_filters * 2,
-            stride,
-            name=name + "_branch1")
+        if not shortcut:
+            self.short = ConvBNLayer(
+                num_channels=num_channels,
+                num_filters=num_filters * 2
+                if cardinality == 32 else num_filters,
+                filter_size=1,
+                stride=stride,
+                name=name + "_branch1")
+
+        self.shortcut = shortcut
+
+    def forward(self, inputs):
+        y = self.conv0(inputs)
+        conv1 = self.conv1(y)
+        conv2 = self.conv2(conv1)
+
+        if self.shortcut:
+            short = inputs
+        else:
+            short = self.short(inputs)
+
+        y = fluid.layers.elementwise_add(x=short, y=conv2)
+
+        layer_helper = LayerHelper(self.full_name(), act='relu')
+        return layer_helper.append_activation(y)
+
 
-        return fluid.layers.elementwise_add(
-            x=short, y=conv2, act='relu', name=name + ".add.output.5")
+class ResNeXt(fluid.dygraph.Layer):
+    def __init__(self, layers=50, class_dim=1000, cardinality=32):
+        super(ResNeXt, self).__init__()
+
+        self.layers = layers
+        self.cardinality = cardinality
+        supported_layers = [50, 101, 152]
+        assert layers in supported_layers, \
+            "supported layers are {} but input layer is {}".format(
+                supported_layers, layers)
+        supported_cardinality = [32, 64]
+        assert cardinality in supported_cardinality, \
+            "supported cardinality is {} but input cardinality is {}" \
+            .format(supported_cardinality, cardinality)
+        if layers == 50:
+            depth = [3, 4, 6, 3]
+        elif layers == 101:
+            depth = [3, 4, 23, 3]
+        elif layers == 152:
+            depth = [3, 8, 36, 3]
+        num_channels = [64, 256, 512, 1024]
+        num_filters = [128, 256, 512,
+                       1024] if cardinality == 32 else [256, 512, 1024, 2048]
 
+        self.conv = ConvBNLayer(
+            num_channels=3,
+            num_filters=64,
+            filter_size=7,
+            stride=2,
+            act='relu',
+            name="res_conv1")
+        self.pool2d_max = Pool2D(
+            pool_size=3, pool_stride=2, pool_padding=1, pool_type='max')
 
-def ResNeXt50_64x4d():
-    model = ResNeXt(layers=50, cardinality=64)
+        self.block_list = []
+        for block in range(len(depth)):
+            shortcut = False
+            for i in range(depth[block]):
+                if layers in [101, 152] and block == 2:
+                    if i == 0:
+                        conv_name = "res" + str(block + 2) + "a"
+                    else:
+                        conv_name = "res" + str(block + 2) + "b" + str(i)
+                else:
+                    conv_name = "res" + str(block + 2) + chr(97 + i)
+                bottleneck_block = self.add_sublayer(
+                    'bb_%d_%d' % (block, i),
+                    BottleneckBlock(
+                        num_channels=num_channels[block] if i == 0 else
+                        num_filters[block] * int(64 // self.cardinality),
+                        num_filters=num_filters[block],
+                        stride=2 if i == 0 and block != 0 else 1,
+                        cardinality=self.cardinality,
+                        shortcut=shortcut,
+                        name=conv_name))
+                self.block_list.append(bottleneck_block)
+                shortcut = True
+
+        self.pool2d_avg = Pool2D(
+            pool_size=7, pool_type='avg', global_pooling=True)
+
+        self.pool2d_avg_channels = num_channels[-1] * 2
+
+        stdv = 1.0 / math.sqrt(self.pool2d_avg_channels * 1.0)
+
+        self.out = Linear(
+            self.pool2d_avg_channels,
+            class_dim,
+            param_attr=ParamAttr(
+                initializer=fluid.initializer.Uniform(-stdv, stdv),
+                name="fc_weights"),
+            bias_attr=ParamAttr(name="fc_offset"))
+
+    def forward(self, inputs):
+        y = self.conv(inputs)
+        y = self.pool2d_max(y)
+        for block in self.block_list:
+            y = block(y)
+        y = self.pool2d_avg(y)
+        y = fluid.layers.reshape(y, shape=[-1, self.pool2d_avg_channels])
+        y = self.out(y)
+        return y
+
+
+def ResNeXt50_32x4d(**args):
+    model = ResNeXt(layers=50, cardinality=32, **args)
     return model
 
 
-def ResNeXt50_32x4d():
-    model = ResNeXt(layers=50, cardinality=32)
+def ResNeXt50_64x4d(**args):
+    model = ResNeXt(layers=50, cardinality=64, **args)
     return model
 
 
-def ResNeXt101_64x4d():
-    model = ResNeXt(layers=101, cardinality=64)
+def ResNeXt101_32x4d(**args):
+    model = ResNeXt(layers=101, cardinality=32, **args)
     return model
 
 
-def ResNeXt101_32x4d():
-    model = ResNeXt(layers=101, cardinality=32)
+def ResNeXt101_64x4d(**args):
+    model = ResNeXt(layers=101, cardinality=64, **args)
     return model
 
 
-def ResNeXt152_64x4d():
-    model = ResNeXt(layers=152, cardinality=64)
+def ResNeXt152_32x4d(**args):
+    model = ResNeXt(layers=152, cardinality=32, **args)
     return model
 
 
-def ResNeXt152_32x4d():
-    model = ResNeXt(layers=152, cardinality=32)
+def ResNeXt152_64x4d(**args):
+    model = ResNeXt(layers=152, cardinality=64, **args)
     return model

ppcls/modeling/architectures/resnext_vd.py

-#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+# copyright (c) 2020 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
+# 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
 #
-#Unless required by applicable law or agreed to in writing, software
-#distributed under the License is distributed on an "AS IS" BASIS,
-#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#See the License for the specific language governing permissions and
-#limitations under the License.
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
 import paddle
 import paddle.fluid as fluid
 from paddle.fluid.param_attr import ParamAttr
+from paddle.fluid.layer_helper import LayerHelper
+from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear, Dropout
+
 import math
 
 __all__ = [
-    "ResNeXt", "ResNeXt50_vd_64x4d", "ResNeXt101_vd_64x4d",
-    "ResNeXt152_vd_64x4d", "ResNeXt50_vd_32x4d", "ResNeXt101_vd_32x4d",
-    "ResNeXt152_vd_32x4d"
+    "ResNeXt50_vd_32x4d", "ResNeXt50_vd_64x4d", "ResNeXt101_vd_32x4d",
+    "ResNeXt101_vd_64x4d", "ResNeXt152_vd_32x4d", "ResNeXt152_vd_64x4d"
 ]
 
 
-class ResNeXt():
-    def __init__(self, layers=50, is_3x3=False, cardinality=64):
-        self.layers = layers
-        self.is_3x3 = is_3x3
-        self.cardinality = cardinality
-
-    def net(self, input, class_dim=1000):
-        is_3x3 = self.is_3x3
-        layers = self.layers
-        cardinality = self.cardinality
-        supported_layers = [50, 101, 152]
-        assert layers in supported_layers, \
-            "supported layers are {} but input layer is {}".format(supported_layers, layers)
-
-        if layers == 50:
-            depth = [3, 4, 6, 3]
-        elif layers == 101:
-            depth = [3, 4, 23, 3]
-        elif layers == 152:
-            depth = [3, 8, 36, 3]
-        num_filters1 = [256, 512, 1024, 2048]
-        num_filters2 = [128, 256, 512, 1024]
-
-        if is_3x3 == False:
-            conv = self.conv_bn_layer(
-                input=input,
-                num_filters=64,
-                filter_size=7,
-                stride=2,
-                act='relu')
-        else:
-            conv = self.conv_bn_layer(
-                input=input,
-                num_filters=32,
-                filter_size=3,
-                stride=2,
-                act='relu',
-                name='conv1_1')
-            conv = self.conv_bn_layer(
-                input=conv,
-                num_filters=32,
-                filter_size=3,
-                stride=1,
-                act='relu',
-                name='conv1_2')
-            conv = self.conv_bn_layer(
-                input=conv,
-                num_filters=64,
-                filter_size=3,
-                stride=1,
-                act='relu',
-                name='conv1_3')
-
-        conv = fluid.layers.pool2d(
-            input=conv,
-            pool_size=3,
-            pool_stride=2,
-            pool_padding=1,
-            pool_type='max')
+class ConvBNLayer(fluid.dygraph.Layer):
+    def __init__(
+            self,
+            num_channels,
+            num_filters,
+            filter_size,
+            stride=1,
+            groups=1,
+            is_vd_mode=False,
+            act=None,
+            name=None, ):
+        super(ConvBNLayer, self).__init__()
 
-        for block in range(len(depth)):
-            for i in range(depth[block]):
-                if layers in [101, 152, 200] and block == 2:
-                    if i == 0:
-                        conv_name = "res" + str(block + 2) + "a"
-                    else:
-                        conv_name = "res" + str(block + 2) + "b" + str(i)
-                else:
-                    conv_name = "res" + str(block + 2) + chr(97 + i)
-                conv = self.bottleneck_block(
-                    input=conv,
-                    num_filters=num_filters1[block]
-                    if cardinality == 64 else num_filters2[block],
-                    stride=2 if i == 0 and block != 0 else 1,
-                    cardinality=cardinality,
-                    if_first=block == 0,
-                    name=conv_name)
-
-        pool = fluid.layers.pool2d(
-            input=conv, pool_type='avg', global_pooling=True)
-        stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
-        out = fluid.layers.fc(
-            input=pool,
-            size=class_dim,
-            param_attr=fluid.param_attr.ParamAttr(
-                initializer=fluid.initializer.Uniform(-stdv, stdv),
-                name='fc_weights'),
-            bias_attr=fluid.param_attr.ParamAttr(name='fc_offset'))
-
-        return out
-
-    def conv_bn_layer(self,
-                      input,
-                      num_filters,
-                      filter_size,
-                      stride=1,
-                      groups=1,
-                      act=None,
-                      name=None):
-        conv = fluid.layers.conv2d(
-            input=input,
+        self.is_vd_mode = is_vd_mode
+        self._pool2d_avg = Pool2D(
+            pool_size=2, pool_stride=2, pool_padding=0, pool_type='avg', ceil_mode=True)
+        self._conv = Conv2D(
+            num_channels=num_channels,
             num_filters=num_filters,
             filter_size=filter_size,
             stride=stride,
@@ -137,121 +61,209 @@ class ResNeXt():
             bn_name = "bn_" + name
         else:
             bn_name = "bn" + name[3:]
-        return fluid.layers.batch_norm(
-            input=conv,
+        self._batch_norm = BatchNorm(
+            num_filters,
             act=act,
             param_attr=ParamAttr(name=bn_name + '_scale'),
             bias_attr=ParamAttr(bn_name + '_offset'),
             moving_mean_name=bn_name + '_mean',
             moving_variance_name=bn_name + '_variance')
 
-    def conv_bn_layer_new(self,
-                          input,
-                          num_filters,
-                          filter_size,
-                          stride=1,
-                          groups=1,
-                          act=None,
-                          name=None):
-        pool = fluid.layers.pool2d(
-            input=input,
-            pool_size=2,
-            pool_stride=2,
-            pool_padding=0,
-            pool_type='avg',
-            ceil_mode=True)
-
-        conv = fluid.layers.conv2d(
-            input=pool,
-            num_filters=num_filters,
-            filter_size=filter_size,
-            stride=1,
-            padding=(filter_size - 1) // 2,
-            groups=groups,
-            act=None,
-            param_attr=ParamAttr(name=name + "_weights"),
-            bias_attr=False)
-        if name == "conv1":
-            bn_name = "bn_" + name
-        else:
-            bn_name = "bn" + name[3:]
-        return fluid.layers.batch_norm(
-            input=conv,
-            act=act,
-            param_attr=ParamAttr(name=bn_name + '_scale'),
-            bias_attr=ParamAttr(bn_name + '_offset'),
-            moving_mean_name=bn_name + '_mean',
-            moving_variance_name=bn_name + '_variance')
+    def forward(self, inputs):
+        if self.is_vd_mode:
+            inputs = self._pool2d_avg(inputs)
+        y = self._conv(inputs)
+        y = self._batch_norm(y)
+        return y
 
-    def shortcut(self, input, ch_out, stride, name, if_first=False):
-        ch_in = input.shape[1]
-        if ch_in != ch_out or stride != 1:
-            if if_first:
-                return self.conv_bn_layer(input, ch_out, 1, stride, name=name)
-            else:
-                return self.conv_bn_layer_new(
-                    input, ch_out, 1, stride, name=name)
-        else:
-            return input
 
-    def bottleneck_block(self, input, num_filters, stride, cardinality, name,
-                         if_first):
-        conv0 = self.conv_bn_layer(
-            input=input,
+class BottleneckBlock(fluid.dygraph.Layer):
+    def __init__(self,
+                 num_channels,
+                 num_filters,
+                 stride,
+                 cardinality,
+                 shortcut=True,
+                 if_first=False,
+                 name=None):
+        super(BottleneckBlock, self).__init__()
+
+        self.conv0 = ConvBNLayer(
+            num_channels=num_channels,
             num_filters=num_filters,
             filter_size=1,
             act='relu',
             name=name + "_branch2a")
-        conv1 = self.conv_bn_layer(
-            input=conv0,
+        self.conv1 = ConvBNLayer(
+            num_channels=num_filters,
             num_filters=num_filters,
             filter_size=3,
+            groups=cardinality,
             stride=stride,
             act='relu',
-            groups=cardinality,
             name=name + "_branch2b")
-        conv2 = self.conv_bn_layer(
-            input=conv1,
-            num_filters=num_filters if cardinality == 64 else num_filters * 2,
+        self.conv2 = ConvBNLayer(
+            num_channels=num_filters,
+            num_filters=num_filters * 2 if cardinality == 32 else num_filters,
             filter_size=1,
             act=None,
             name=name + "_branch2c")
 
-        short = self.shortcut(
-            input,
-            num_filters if cardinality == 64 else num_filters * 2,
-            stride,
-            if_first=if_first,
-            name=name + "_branch1")
+        if not shortcut:
+            self.short = ConvBNLayer(
+                num_channels=num_channels,
+                num_filters=num_filters * 2
+                if cardinality == 32 else num_filters,
+                filter_size=1,
+                stride=1,
+                is_vd_mode=False if if_first else True,
+                name=name + "_branch1")
+
+        self.shortcut = shortcut
+
+    def forward(self, inputs):
+        y = self.conv0(inputs)
+        conv1 = self.conv1(y)
+        conv2 = self.conv2(conv1)
+
+        if self.shortcut:
+            short = inputs
+        else:
+            short = self.short(inputs)
+
+        y = fluid.layers.elementwise_add(x=short, y=conv2)
+
+        layer_helper = LayerHelper(self.full_name(), act='relu')
+        return layer_helper.append_activation(y)
+
+
+class ResNeXt(fluid.dygraph.Layer):
+    def __init__(self, layers=50, class_dim=1000, cardinality=32):
+        super(ResNeXt, self).__init__()
+
+        self.layers = layers
+        self.cardinality = cardinality
+        supported_layers = [50, 101, 152]
+        assert layers in supported_layers, \
+            "supported layers are {} but input layer is {}".format(
+                supported_layers, layers)
+        supported_cardinality = [32, 64]
+        assert cardinality in supported_cardinality, \
+            "supported cardinality is {} but input cardinality is {}" \
+            .format(supported_cardinality, cardinality)
+        if layers == 50:
+            depth = [3, 4, 6, 3]
+        elif layers == 101:
+            depth = [3, 4, 23, 3]
+        elif layers == 152:
+            depth = [3, 8, 36, 3]
+        num_channels = [64, 256, 512, 1024]
+        num_filters = [128, 256, 512,
+                       1024] if cardinality == 32 else [256, 512, 1024, 2048]
+
+        self.conv1_1 = ConvBNLayer(
+            num_channels=3,
+            num_filters=32,
+            filter_size=3,
+            stride=2,
+            act='relu',
+            name="conv1_1")
+        self.conv1_2 = ConvBNLayer(
+            num_channels=32,
+            num_filters=32,
+            filter_size=3,
+            stride=1,
+            act='relu',
+            name="conv1_2")
+        self.conv1_3 = ConvBNLayer(
+            num_channels=32,
+            num_filters=64,
+            filter_size=3,
+            stride=1,
+            act='relu',
+            name="conv1_3")
+
+        self.pool2d_max = Pool2D(
+            pool_size=3, pool_stride=2, pool_padding=1, pool_type='max')
+
+        self.block_list = []
+        for block in range(len(depth)):
+            shortcut = False
+            for i in range(depth[block]):
+                if layers in [101, 152] and block == 2:
+                    if i == 0:
+                        conv_name = "res" + str(block + 2) + "a"
+                    else:
+                        conv_name = "res" + str(block + 2) + "b" + str(i)
+                else:
+                    conv_name = "res" + str(block + 2) + chr(97 + i)
+                bottleneck_block = self.add_sublayer(
+                    'bb_%d_%d' % (block, i),
+                    BottleneckBlock(
+                        num_channels=num_channels[block] if i == 0 else
+                        num_filters[block] * int(64 // self.cardinality),
+                        num_filters=num_filters[block],
+                        stride=2 if i == 0 and block != 0 else 1,
+                        cardinality=self.cardinality,
+                        shortcut=shortcut,
+                        if_first=block == i == 0,
+                        name=conv_name))
+                self.block_list.append(bottleneck_block)
+                shortcut = True
+
+        self.pool2d_avg = Pool2D(
+            pool_size=7, pool_type='avg', global_pooling=True)
+
+        self.pool2d_avg_channels = num_channels[-1] * 2
+
+        stdv = 1.0 / math.sqrt(self.pool2d_avg_channels * 1.0)
+
+        self.out = Linear(
+            self.pool2d_avg_channels,
+            class_dim,
+            param_attr=ParamAttr(
+                initializer=fluid.initializer.Uniform(-stdv, stdv),
+                name="fc_weights"),
+            bias_attr=ParamAttr(name="fc_offset"))
 
-        return fluid.layers.elementwise_add(x=short, y=conv2, act='relu')
+    def forward(self, inputs):
+        y = self.conv1_1(inputs)
+        y = self.conv1_2(y)
+        y = self.conv1_3(y)
+        y = self.pool2d_max(y)
+        for block in self.block_list:
+            y = block(y)
+        y = self.pool2d_avg(y)
+        y = fluid.layers.reshape(y, shape=[-1, self.pool2d_avg_channels])
+        y = self.out(y)
+        return y
 
 
-def ResNeXt50_vd_64x4d():
-    model = ResNeXt(layers=50, is_3x3=True)
+def ResNeXt50_vd_32x4d(**args):
+    model = ResNeXt(layers=50, cardinality=32, **args)
     return model
 
 
-def ResNeXt50_vd_32x4d():
-    model = ResNeXt(layers=50, cardinality=32, is_3x3=True)
+def ResNeXt50_vd_64x4d(**args):
+    model = ResNeXt(layers=50, cardinality=64, **args)
     return model
 
 
-def ResNeXt101_vd_64x4d():
-    model = ResNeXt(layers=101, is_3x3=True)
+def ResNeXt101_vd_32x4d(**args):
+    model = ResNeXt(layers=101, cardinality=32, **args)
     return model
 
 
-def ResNeXt101_vd_32x4d():
-    model = ResNeXt(layers=101, cardinality=32, is_3x3=True)
+def ResNeXt101_vd_64x4d(**args):
+    model = ResNeXt(layers=101, cardinality=64, **args)
     return model
 
 
-def ResNeXt152_vd_64x4d():
-    model = ResNeXt(layers=152, is_3x3=True)
+def ResNeXt152_vd_32x4d(**args):
+    model = ResNeXt(layers=152, cardinality=32, **args)
     return model
 
 
-def ResNeXt152_vd_32x4d():
-    model = ResNeXt(layers=152, cardinality=32, is_3x3=True)
+def ResNeXt152_vd_64x4d(**args):
+    model = ResNeXt(layers=152, cardinality=64, **args)
     return model

ppcls/modeling/architectures/vgg.py

@@ -106,7 +106,7 @@ class VGGNet(fluid.dygraph.Layer):
         x = self._conv_block_4(x)
         x = self._conv_block_5(x)
 
-        x = fluid.layers.flatten(x, axis=0)
+        x = fluid.layers.reshape(x, [0,-1])
         x = self._fc1(x)
         x = self._drop(x)
         x = self._fc2(x)

tools/eval.py

@@ -19,13 +19,10 @@ from __future__ import print_function
 import os
 import argparse
 
-import paddle.fluid as fluid
-
-import program
-
 from ppcls.data import Reader
 from ppcls.utils.config import get_config
 from ppcls.utils.save_load import init_model
+from ppcls.utils import logger
 
 from paddle.fluid.incubate.fleet.collective import fleet
 from paddle.fluid.incubate.fleet.base import role_maker
@@ -45,37 +42,25 @@ def parse_args():
         action='append',
         default=[],
         help='config options to be overridden')
-
     args = parser.parse_args()
     return args
 
 
 def main(args):
-    role = role_maker.PaddleCloudRoleMaker(is_collective=True)
-    fleet.init(role)
-
-    config = get_config(args.config, overrides=args.override, show=True)
-    gpu_id = int(os.environ.get('FLAGS_selected_gpus', 0))
+    # assign the place
+    gpu_id = fluid.dygraph.parallel.Env().dev_id
     place = fluid.CUDAPlace(gpu_id)
-
-    startup_prog = fluid.Program()
-    valid_prog = fluid.Program()
-    valid_dataloader, valid_fetchs = program.build(
-        config, valid_prog, startup_prog, is_train=False)
-    valid_prog = valid_prog.clone(for_test=True)
-
-    exe = fluid.Executor(place)
-    exe.run(startup_prog)
-
-    init_model(config, valid_prog, exe)
-
-    valid_reader = Reader(config, 'valid')()
-    valid_dataloader.set_sample_list_generator(valid_reader, place)
-
-    compiled_valid_prog = program.compile(config, valid_prog)
-    program.run(valid_dataloader, exe, compiled_valid_prog, valid_fetchs, -1,
-                'eval')
-
+    with fluid.dygraph.guard(place):
+        pre_weights_dict = fluid.dygraph.load_dygraph(config.pretrained_model)[0]
+        strategy = fluid.dygraph.parallel.prepare_context()
+        net = program.create_model(config.ARCHITECTURE, config.classes_num)
+        net = fluid.dygraph.parallel.DataParallel(net, strategy)
+        net.set_dict(pre_weights_dict)
+        valid_dataloader = program.create_dataloader()
+        valid_reader = Reader(config, 'valid')()
+        valid_dataloader.set_sample_list_generator(valid_reader, place)
+        net.eval()
+        top1_acc = program.run(valid_dataloader, config, net, None, 0, 'valid')
 
 if __name__ == '__main__':
     args = parse_args()

tools/feature_maps_visualization/download_resnet50_pretrained.sh

+wget https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_pretrained.tar
+tar -xf ResNet50_pretrained.tar
\ No newline at end of file

tools/feature_maps_visualization/fm_vis.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from resnet import ResNet50 
+import paddle.fluid as fluid
+
+import numpy as np 
+import cv2 
+import utils
+import argparse
+
+def parse_args():
+    def str2bool(v):
+        return v.lower() in ("true", "t", "1")
+    parser = argparse.ArgumentParser()
+    parser.add_argument("-i", "--image_file", type=str)
+    parser.add_argument("-c", "--channel_num", type=int)
+    parser.add_argument("-p", "--pretrained_model", type=str)
+    parser.add_argument("--show", type=str2bool, default=False)
+    parser.add_argument("--interpolation", type=int, default=1)
+    parser.add_argument("--save_path", type=str)
+    parser.add_argument("--use_gpu", type=str2bool, default=True)
+    
+    return parser.parse_args()
+
+def create_operators(interpolation=1):
+    size = 224
+    img_mean = [0.485, 0.456, 0.406]
+    img_std = [0.229, 0.224, 0.225]
+    img_scale = 1.0 / 255.0
+
+    decode_op = utils.DecodeImage()
+    resize_op = utils.ResizeImage(resize_short=256, interpolation=interpolation)
+    crop_op = utils.CropImage(size=(size, size))
+    normalize_op = utils.NormalizeImage(
+        scale=img_scale, mean=img_mean, std=img_std)
+    totensor_op = utils.ToTensor()
+
+    return [decode_op, resize_op, crop_op, normalize_op, totensor_op]
+
+
+def preprocess(fname, ops):
+    data = open(fname, 'rb').read()
+    for op in ops:
+        data = op(data)
+
+    return data
+
+def main():
+    args = parse_args()
+    operators = create_operators(args.interpolation)
+    # assign the place
+    if args.use_gpu:
+        gpu_id = fluid.dygraph.parallel.Env().dev_id
+        place = fluid.CUDAPlace(gpu_id)
+    else:
+        place = fluid.CPUPlace()
+
+    #pre_weights_dict = fluid.load_program_state(args.pretrained_model)
+    with fluid.dygraph.guard(place):
+        net = ResNet50()
+        data = preprocess(args.image_file, operators)
+        data = np.expand_dims(data, axis=0)
+        data = fluid.dygraph.to_variable(data)
+        dy_weights_dict = net.state_dict()
+        pre_weights_dict_new = {}
+        for key in dy_weights_dict:
+            weights_name = dy_weights_dict[key].name
+            pre_weights_dict_new[key] = pre_weights_dict[weights_name]
+        net.set_dict(pre_weights_dict_new)
+        net.eval()
+        _, fm = net(data)
+        assert args.channel_num >= 0 and args.channel_num <= fm.shape[1], "the channel is out of the range, should be in {} but got {}".format([0, fm.shape[1]], args.channel_num)
+        fm = (np.squeeze(fm[0][args.channel_num].numpy())*255).astype(np.uint8)
+    if fm is not None:
+        if args.save:
+            cv2.imwrite(args.save_path, fm)
+        if args.show:
+            cv2.show(fm)
+            cv2.waitKey(0)
+        
+if __name__ == "__main__":
+    main()

tools/feature_maps_visualization/utils.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import cv2
+import numpy as np
+
+
+class DecodeImage(object):
+    def __init__(self, to_rgb=True):
+        self.to_rgb = to_rgb
+
+    def __call__(self, img):
+        data = np.frombuffer(img, dtype='uint8')
+        img = cv2.imdecode(data, 1)
+        if self.to_rgb:
+            assert img.shape[2] == 3, 'invalid shape of image[%s]' % (
+                img.shape)
+            img = img[:, :, ::-1]
+
+        return img
+
+
+class ResizeImage(object):
+    def __init__(self, resize_short=None, interpolation=1):
+        self.resize_short = resize_short
+        self.interpolation = interpolation
+
+    def __call__(self, img):
+        img_h, img_w = img.shape[:2]
+        percent = float(self.resize_short) / min(img_w, img_h)
+        w = int(round(img_w * percent))
+        h = int(round(img_h * percent))
+        return cv2.resize(img, (w, h), interpolation=self.interpolation)
+
+
+class CropImage(object):
+    def __init__(self, size):
+        if type(size) is int:
+            self.size = (size, size)
+        else:
+            self.size = size
+
+    def __call__(self, img):
+        w, h = self.size
+        img_h, img_w = img.shape[:2]
+        w_start = (img_w - w) // 2
+        h_start = (img_h - h) // 2
+
+        w_end = w_start + w
+        h_end = h_start + h
+        return img[h_start:h_end, w_start:w_end, :]
+
+
+class NormalizeImage(object):
+    def __init__(self, scale=None, mean=None, std=None):
+        self.scale = np.float32(scale if scale is not None else 1.0 / 255.0)
+        mean = mean if mean is not None else [0.485, 0.456, 0.406]
+        std = std if std is not None else [0.229, 0.224, 0.225]
+
+        shape = (1, 1, 3)
+        self.mean = np.array(mean).reshape(shape).astype('float32')
+        self.std = np.array(std).reshape(shape).astype('float32')
+
+    def __call__(self, img):
+        return (img.astype('float32') * self.scale - self.mean) / self.std
+
+
+class ToTensor(object):
+    def __init__(self):
+        pass
+
+    def __call__(self, img):
+        img = img.transpose((2, 0, 1))
+        return img

tools/program.py

@@ -329,9 +329,13 @@ def run(dataloader, config, net, optimizer=None, epoch=0, mode='train'):
         feeds = create_feeds(batch, use_mix)
         fetchs = create_fetchs(feeds, net, config, mode)
         if mode == 'train':
-            avg_loss = net.scale_loss(fetchs['loss'])
-            avg_loss.backward()
-            net.apply_collective_grads()
+            if config["use_data_parallel"]:
+                avg_loss = net.scale_loss(fetchs['loss'])
+                avg_loss.backward()
+                net.apply_collective_grads()
+            else:
+                avg_loss = fetchs['loss']
+                avg_loss.backward()
 
             optimizer.minimize(avg_loss)
             net.clear_gradients()