Update se_resnext.py

ppcls/modeling/architectures/se_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 numpy as np
+import paddle
+from paddle import ParamAttr
+import paddle.nn as nn
+from paddle.nn import Conv2d, BatchNorm, Linear, Dropout
+from paddle.nn import AdaptiveAvgPool2d, MaxPool2d, AvgPool2d
+from paddle.nn.initializer import Uniform
+
 import math
 
-import paddle
-import paddle.fluid as fluid
-from paddle.fluid.param_attr import ParamAttr
+__all__ = ["SE_ResNeXt50_32x4d", "SE_ResNeXt101_32x4d", "SE_ResNeXt152_64x4d"]
 
-__all__ = [
-    "SE_ResNeXt", "SE_ResNeXt50_32x4d", "SE_ResNeXt101_32x4d",
-    "SE_ResNeXt152_32x4d"
-]
 
+class ConvBNLayer(nn.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(
+            in_channels=num_channels,
+            out_channels=num_filters,
+            kernel_size=filter_size,
+            stride=stride,
+            padding=(filter_size - 1) // 2,
+            groups=groups,
+            weight_attr=ParamAttr(name=name + "_weights"),
+            bias_attr=False)
+        bn_name = name + '_bn'
+        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(nn.Layer):
+    def __init__(self,
+                 num_channels,
+                 num_filters,
+                 stride,
+                 cardinality,
+                 reduction_ratio,
+                 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='conv' + name + '_x1')
+        self.conv1 = ConvBNLayer(
+            num_channels=num_filters,
+            num_filters=num_filters,
+            filter_size=3,
+            groups=cardinality,
+            stride=stride,
+            act='relu',
+            name='conv' + name + '_x2')
+        self.conv2 = ConvBNLayer(
+            num_channels=num_filters,
+            num_filters=num_filters * 2 if cardinality == 32 else num_filters,
+            filter_size=1,
+            act=None,
+            name='conv' + name + '_x3')
+        self.scale = SELayer(
+            num_channels=num_filters * 2 if cardinality == 32 else num_filters,
+            num_filters=num_filters * 2 if cardinality == 32 else num_filters,
+            reduction_ratio=reduction_ratio,
+            name='fc' + name)
+
+        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='conv' + name + '_prj')
+
+        self.shortcut = shortcut
+
+    def forward(self, inputs):
+        y = self.conv0(inputs)
+        conv1 = self.conv1(y)
+        conv2 = self.conv2(conv1)
+        scale = self.scale(conv2)
+
+        if self.shortcut:
+            short = inputs
+        else:
+            short = self.short(inputs)
+        y = paddle.elementwise_add(x=short, y=scale, act='relu')
+        return y
+
+
+class SELayer(nn.Layer):
+    def __init__(self, num_channels, num_filters, reduction_ratio, name=None):
+        super(SELayer, self).__init__()
+
+        self.pool2d_gap = AdaptiveAvgPool2d(1)
+
+        self._num_channels = num_channels
+
+        med_ch = int(num_channels / reduction_ratio)
+        stdv = 1.0 / math.sqrt(num_channels * 1.0)
+        self.squeeze = Linear(
+            num_channels,
+            med_ch,
+            weight_attr=ParamAttr(
+               initializer=Uniform(-stdv, stdv),
+               name=name + "_sqz_weights"),
+            bias_attr=ParamAttr(name=name + '_sqz_offset'))
+        self.relu = nn.ReLU()
+        stdv = 1.0 / math.sqrt(med_ch * 1.0)
+        self.excitation = Linear(
+            med_ch,
+            num_filters,
+            weight_attr=ParamAttr(
+                initializer=Uniform(-stdv, stdv),
+                name=name + "_exc_weights"),
+            bias_attr=ParamAttr(name=name + '_exc_offset'))
+        self.sigmoid = nn.Sigmoid()
+        
+    def forward(self, input):
+        pool = self.pool2d_gap(input)
+        pool = paddle.reshape(pool, shape=[-1, self._num_channels])
+        squeeze = self.squeeze(pool)
+        squeeze = self.relu(squeeze)
+        excitation = self.excitation(squeeze)
+        excitation = self.sigmoid(excitation)
+        excitation = paddle.reshape(
+            excitation, shape=[-1, self._num_channels, 1, 1])
+        out = input * excitation
+        return out
 
-class SE_ResNeXt():
-    def __init__(self, layers=50):
-        self.layers = layers
 
-    def net(self, input, class_dim=1000):
-        layers = self.layers
+class ResNeXt(nn.Layer):
+    def __init__(self, layers=50, class_dim=1000, cardinality=32):
+        super(ResNeXt, self).__init__()
+
+        self.layers = layers
+        self.cardinality = cardinality
+        self.reduction_ratio = 16
         supported_layers = [50, 101, 152]
         assert layers in supported_layers, \
-            "supported layers are {} but input layer is {}".format(supported_layers, 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:
-            cardinality = 32
-            reduction_ratio = 16
             depth = [3, 4, 6, 3]
-            num_filters = [128, 256, 512, 1024]
-
-            conv = self.conv_bn_layer(
-                input=input,
-                num_filters=64,
-                filter_size=7,
-                stride=2,
-                act='relu',
-                name='conv1', )
-            conv = fluid.layers.pool2d(
-                input=conv,
-                pool_size=3,
-                pool_stride=2,
-                pool_padding=1,
-                pool_type='max',
-                use_cudnn=False)
         elif layers == 101:
-            cardinality = 32
-            reduction_ratio = 16
             depth = [3, 4, 23, 3]
-            num_filters = [128, 256, 512, 1024]
-
-            conv = self.conv_bn_layer(
-                input=input,
+        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]
+        if layers < 152:
+            self.conv = ConvBNLayer(
+                num_channels=3,
                 num_filters=64,
                 filter_size=7,
                 stride=2,
                 act='relu',
-                name="conv1", )
-            conv = fluid.layers.pool2d(
-                input=conv,
-                pool_size=3,
-                pool_stride=2,
-                pool_padding=1,
-                pool_type='max',
-                use_cudnn=False)
-        elif layers == 152:
-            cardinality = 64
-            reduction_ratio = 16
-            depth = [3, 8, 36, 3]
-            num_filters = [128, 256, 512, 1024]
-
-            conv = self.conv_bn_layer(
-                input=input,
+                name="conv1")
+        else:
+            self.conv1_1 = ConvBNLayer(
+                num_channels=3,
                 num_filters=64,
                 filter_size=3,
                 stride=2,
                 act='relu',
-                name='conv1')
-            conv = self.conv_bn_layer(
-                input=conv,
+                name="conv1")
+            self.conv1_2 = ConvBNLayer(
+                num_channels=64,
                 num_filters=64,
                 filter_size=3,
                 stride=1,
                 act='relu',
-                name='conv2')
-            conv = self.conv_bn_layer(
-                input=conv,
+                name="conv2")
+            self.conv1_3 = ConvBNLayer(
+                num_channels=64,
                 num_filters=128,
                 filter_size=3,
                 stride=1,
                 act='relu',
-                name='conv3')
-            conv = fluid.layers.pool2d(
-                input=conv, pool_size=3, pool_stride=2, pool_padding=1, \
-                pool_type='max', use_cudnn=False)
+                name="conv3")
+
+        self.pool2d_max = MaxPool2d(kernel_size=3, stride=2, padding=1)
+
+        self.block_list = []
         n = 1 if layers == 50 or layers == 101 else 3
         for block in range(len(depth)):
             n += 1
+            shortcut = False
             for i in range(depth[block]):
-                conv = self.bottleneck_block(
-                    input=conv,
+                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=cardinality,
-                    reduction_ratio=reduction_ratio,
-                    name=str(n) + '_' + str(i + 1))
-
-        pool = fluid.layers.pool2d(
-            input=conv, pool_type='avg', global_pooling=True, use_cudnn=False)
-        drop = fluid.layers.dropout(x=pool, dropout_prob=0.5)
-        stdv = 1.0 / math.sqrt(drop.shape[1] * 1.0)
-        out = fluid.layers.fc(
-            input=drop,
-            size=class_dim,
-            param_attr=ParamAttr(
-                initializer=fluid.initializer.Uniform(-stdv, stdv),
-                name='fc6_weights'),
-            bias_attr=ParamAttr(name='fc6_offset'))
-        return out
+                        cardinality=self.cardinality,
+                        reduction_ratio=self.reduction_ratio,
+                        shortcut=shortcut,
+                        if_first=block == 0,
+                        name=str(n) + '_' + str(i + 1)))
+                self.block_list.append(bottleneck_block)
+                shortcut = True
 
-    def shortcut(self, input, ch_out, stride, name):
-        ch_in = input.shape[1]
-        if ch_in != ch_out or stride != 1:
-            filter_size = 1
-            return self.conv_bn_layer(
-                input,
-                ch_out,
-                filter_size,
-                stride,
-                name='conv' + name + '_prj')
-        else:
-            return input
+        self.pool2d_avg = AdaptiveAvgPool2d(1)
 
-    def bottleneck_block(self,
-                         input,
-                         num_filters,
-                         stride,
-                         cardinality,
-                         reduction_ratio,
-                         name=None):
-        conv0 = self.conv_bn_layer(
-            input=input,
-            num_filters=num_filters,
-            filter_size=1,
-            act='relu',
-            name='conv' + name + '_x1')
-        conv1 = self.conv_bn_layer(
-            input=conv0,
-            num_filters=num_filters,
-            filter_size=3,
-            stride=stride,
-            groups=cardinality,
-            act='relu',
-            name='conv' + name + '_x2')
-        conv2 = self.conv_bn_layer(
-            input=conv1,
-            num_filters=num_filters * 2,
-            filter_size=1,
-            act=None,
-            name='conv' + name + '_x3')
-        scale = self.squeeze_excitation(
-            input=conv2,
-            num_channels=num_filters * 2,
-            reduction_ratio=reduction_ratio,
-            name='fc' + name)
+        self.pool2d_avg_channels = num_channels[-1] * 2
 
-        short = self.shortcut(input, num_filters * 2, stride, name=name)
+        stdv = 1.0 / math.sqrt(self.pool2d_avg_channels * 1.0)
 
-        return fluid.layers.elementwise_add(x=short, y=scale, act='relu')
+        self.out = Linear(
+            self.pool2d_avg_channels,
+            class_dim,
+            weight_attr=ParamAttr(
+                initializer=Uniform(-stdv, stdv),
+                name="fc6_weights"),
+            bias_attr=ParamAttr(name="fc6_offset"))
 
-    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,
-            bias_attr=False,
-            param_attr=ParamAttr(name=name + '_weights'), )
-        bn_name = name + "_bn"
-        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.layers < 152:
+            y = self.conv(inputs)
+        else:
+            y = self.conv1_1(inputs)
+            y = self.conv1_2(y)
+            y = self.conv1_3(y)
+        y = self.pool2d_max(y)
            
-    def squeeze_excitation(self,
-                           input,
-                           num_channels,
-                           reduction_ratio,
-                           name=None):
-        pool = fluid.layers.pool2d(
-            input=input, pool_type='avg', global_pooling=True, use_cudnn=False)
-        stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
-        squeeze = fluid.layers.fc(
-            input=pool,
-            size=num_channels // reduction_ratio,
-            act='relu',
-            param_attr=fluid.param_attr.ParamAttr(
-                initializer=fluid.initializer.Uniform(-stdv, stdv),
-                name=name + '_sqz_weights'),
-            bias_attr=ParamAttr(name=name + '_sqz_offset'))
-        stdv = 1.0 / math.sqrt(squeeze.shape[1] * 1.0)
-        excitation = fluid.layers.fc(
-            input=squeeze,
-            size=num_channels,
-            act='sigmoid',
-            param_attr=fluid.param_attr.ParamAttr(
-                initializer=fluid.initializer.Uniform(-stdv, stdv),
-                name=name + '_exc_weights'),
-            bias_attr=ParamAttr(name=name + '_exc_offset'))
-        scale = fluid.layers.elementwise_mul(x=input, y=excitation, axis=0)
-        return scale
+        for block in self.block_list:
+            y = block(y)
+        y = self.pool2d_avg(y)
+        y = paddle.reshape(y, shape=[-1, self.pool2d_avg_channels])
+        y = self.out(y)
+        return y
 
 
-def SE_ResNeXt50_32x4d():
-    model = SE_ResNeXt(layers=50)
+def SE_ResNeXt50_32x4d(**args):
+    model = ResNeXt(layers=50, cardinality=32, **args)
     return model
 
 
-def SE_ResNeXt101_32x4d():
-    model = SE_ResNeXt(layers=101)
+def SE_ResNeXt101_32x4d(**args):
+    model = ResNeXt(layers=101, cardinality=32, **args)
     return model
 
 
-def SE_ResNeXt152_32x4d():
-    model = SE_ResNeXt(layers=152)
+def SE_ResNeXt152_64x4d(**args):
+    model = ResNeXt(layers=152, cardinality=64, **args)
     return model