# 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 # # 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 __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__ = ["ResNet18", "ResNet34", "ResNet50", "ResNet101", "ResNet152"] 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_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", name=name + "_branch2a") self.conv1 = ConvBNLayer( num_channels=num_filters, num_filters=num_filters, filter_size=3, stride=stride, act="relu", name=name + "_branch2b") self.conv2 = ConvBNLayer( num_channels=num_filters, num_filters=num_filters * 4, filter_size=1, act=None, name=name + "_branch2c") 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(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 == 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] self.conv = 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") 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_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.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 ResNet18(**args): model = ResNet(layers=18, **args) return model def ResNet34(**args): model = ResNet(layers=34, **args) return model def ResNet50(**args): model = ResNet(layers=50, **args) return model def ResNet101(**args): model = ResNet(layers=101, **args) return model def ResNet152(**args): model = ResNet(layers=152, **args) return model