add resnet

ppcls/modeling/architectures/resnet.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 paddle.fluid as fluid
+from paddle.fluid.layer_helper import LayerHelper
+from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear
 
 import math
 
-import paddle
-import paddle.fluid as fluid
-from paddle.fluid.param_attr import ParamAttr
-
 __all__ = [
-    "ResNet", "ResNet18", "ResNet34", "ResNet50", "ResNet101", "ResNet152"
+    "ResNet18",
+    "ResNet34",
+    "ResNet50",
+    "ResNet101",
+    "ResNet152",
 ]
 
 
-class ResNet():
-    def __init__(self, layers=50):
-        self.layers = layers
+class ConvBNLayer(fluid.dygraph.Layer):
+    def __init__(self,
+                 num_channels,
+                 num_filters,
+                 filter_size,
+                 stride=1,
+                 groups=1,
+                 act=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,
+            bias_attr=False)
+
+        self._batch_norm = BatchNorm(num_filters, act=act)
+
+    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):
+        super(BottleneckBlock, self).__init__()
+
+        self.conv0 = ConvBNLayer(
+            num_channels=num_channels,
+            num_filters=num_filters,
+            filter_size=1,
+            act='relu')
+        self.conv1 = ConvBNLayer(
+            num_channels=num_filters,
+            num_filters=num_filters,
+            filter_size=3,
+            stride=stride,
+            act='relu')
+        self.conv2 = ConvBNLayer(
+            num_channels=num_filters,
+            num_filters=num_filters * 4,
+            filter_size=1,
+            act=None)
+
+        if not shortcut:
+            self.short = ConvBNLayer(
+                num_channels=num_channels,
+                num_filters=num_filters * 4,
+                filter_size=1,
+                stride=stride)
+
+        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)
+
 
-    def net(self, input, class_dim=1000, data_format="NCHW"):
-        layers = self.layers
-        supported_layers = [18, 34, 50, 101, 152]
+class ResNet(fluid.dygraph.Layer):
+    def __init__(self, layers=50, class_dim=1000):
+        super(ResNet, self).__init__()
+
+        self.layers = layers
+        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)
 
-        if layers == 18:
-            depth = [2, 2, 2, 2]
-        elif layers == 34 or layers == 50:
+        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 = [64, 128, 256, 512]
 
-        conv = self.conv_bn_layer(
-            input=input,
+        self.conv = ConvBNLayer(
+            num_channels=3,
             num_filters=64,
             filter_size=7,
             stride=2,
-            act='relu',
-            name="conv1",
-            data_format=data_format)
-        conv = fluid.layers.pool2d(
-            input=conv,
-            pool_size=3,
-            pool_stride=2,
-            pool_padding=1,
-            pool_type='max',
-            data_format=data_format)
-        if layers >= 50:
-            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,
+            act='relu')
+        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,
-                        name=conv_name,
-                        data_format=data_format)
+                        shortcut=shortcut))
+                self.bottleneck_block_list.append(bottleneck_block)
+                shortcut = True
 
-        else:
-            for block in range(len(depth)):
-                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,
-                        is_first=block == i == 0,
-                        name=conv_name,
-                        data_format=data_format)
-
-        pool = fluid.layers.pool2d(
-            input=conv,
-            pool_type='avg',
-            global_pooling=True,
-            data_format=data_format)
-        stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
-        out = fluid.layers.fc(
-            input=pool,
-            size=class_dim,
+        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
+
+        stdv = 1.0 / math.sqrt(2048 * 1.0)
+
+        self.out = Linear(
+            self.pool2d_avg_output,
+            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,
-                      data_format='NCHW'):
-        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,
-            name=name + '.conv2d.output.1',
-            data_format=data_format)
+                initializer=fluid.initializer.Uniform(-stdv, stdv)))
 
-        if name == "conv1":
-            bn_name = "bn_" + name
-        else:
-            bn_name = "bn" + name[3:]
-        return fluid.layers.batch_norm(
-            input=conv,
-            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',
-            data_layout=data_format)
-
-    def shortcut(self, input, ch_out, stride, is_first, name, data_format):
-        if data_format == 'NCHW':
-            ch_in = input.shape[1]
-        else:
-            ch_in = input.shape[-1]
-        if ch_in != ch_out or stride != 1 or is_first == True:
-            return self.conv_bn_layer(
-                input, ch_out, 1, stride, name=name, data_format=data_format)
-        else:
-            return input
+    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)
+        y = self.pool2d_avg(y)
+        y = fluid.layers.reshape(y, shape=[-1, self.pool2d_avg_output])
+        y = self.out(y)
+        return y
 
-    def bottleneck_block(self, input, num_filters, stride, name, data_format):
-        conv0 = self.conv_bn_layer(
-            input=input,
-            num_filters=num_filters,
-            filter_size=1,
-            act='relu',
-            name=name + "_branch2a",
-            data_format=data_format)
-        conv1 = self.conv_bn_layer(
-            input=conv0,
-            num_filters=num_filters,
-            filter_size=3,
-            stride=stride,
-            act='relu',
-            name=name + "_branch2b",
-            data_format=data_format)
-        conv2 = self.conv_bn_layer(
-            input=conv1,
-            num_filters=num_filters * 4,
-            filter_size=1,
-            act=None,
-            name=name + "_branch2c",
-            data_format=data_format)
-
-        short = self.shortcut(
-            input,
-            num_filters * 4,
-            stride,
-            is_first=False,
-            name=name + "_branch1",
-            data_format=data_format)
-
-        return fluid.layers.elementwise_add(
-            x=short, y=conv2, act='relu', name=name + ".add.output.5")
-
-    def basic_block(self, input, num_filters, stride, is_first, name,
-                    data_format):
-        conv0 = self.conv_bn_layer(
-            input=input,
-            num_filters=num_filters,
-            filter_size=3,
-            act='relu',
-            stride=stride,
-            name=name + "_branch2a",
-            data_format=data_format)
-        conv1 = self.conv_bn_layer(
-            input=conv0,
-            num_filters=num_filters,
-            filter_size=3,
-            act=None,
-            name=name + "_branch2b",
-            data_format=data_format)
-        short = self.shortcut(
-            input,
-            num_filters,
-            stride,
-            is_first,
-            name=name + "_branch1",
-            data_format=data_format)
-        return fluid.layers.elementwise_add(x=short, y=conv1, act='relu')
-
-
-def ResNet18():
-    model = ResNet(layers=18)
+
+def ResNet18(**kwargs):
+    model = ResNet(layers=18, **kwargs)
     return model
 
 
-def ResNet34():
-    model = ResNet(layers=34)
+def ResNet34(**kwargs):
+    model = ResNet(layers=34, **kwargs)
     return model
 
 
-def ResNet50():
-    model = ResNet(layers=50)
+def ResNet50(**kwargs):
+    model = ResNet(layers=50, **kwargs)
     return model
 
 
-def ResNet101():
-    model = ResNet(layers=101)
+def ResNet101(**kwargs):
+    model = ResNet(layers=101, **kwargs)
     return model
 
 
-def ResNet152():
-    model = ResNet(layers=152)
+def ResNet152(class_dim=1000):
+    model = ResNet(layers=152, class_dim=class_dim)
     return model