# 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 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 __all__ = [ "ResNeXt50_vd_32x4d", "ResNeXt50_vd_64x4d", "ResNeXt101_vd_32x4d", "ResNeXt101_vd_64x4d", "ResNeXt152_vd_32x4d", "ResNeXt152_vd_64x4d" ] class ConvBNLayer(nn.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 = AvgPool2d( kernel_size=2, stride=2, padding=0, ceil_mode=True) 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) 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(nn.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") self.conv1 = ConvBNLayer( num_channels=num_filters, num_filters=num_filters, filter_size=3, groups=cardinality, stride=stride, act='relu', name=name + "_branch2b") 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") 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 = paddle.elementwise_add(x=short, y=conv2, act='relu') return y class ResNeXt(nn.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 = MaxPool2d(kernel_size=3, stride=2, padding=1) 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 = AdaptiveAvgPool2d(1) 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, weight_attr=ParamAttr( initializer=Uniform(-stdv, stdv), 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 = paddle.reshape(y, shape=[-1, self.pool2d_avg_channels]) y = self.out(y) return y def ResNeXt50_vd_32x4d(**args): model = ResNeXt(layers=50, cardinality=32, **args) return model def ResNeXt50_vd_64x4d(**args): model = ResNeXt(layers=50, cardinality=64, **args) return model def ResNeXt101_vd_32x4d(**args): model = ResNeXt(layers=101, cardinality=32, **args) return model def ResNeXt101_vd_64x4d(**args): model = ResNeXt(layers=101, cardinality=64, **args) return model def ResNeXt152_vd_32x4d(**args): model = ResNeXt(layers=152, cardinality=32, **args) return model def ResNeXt152_vd_64x4d(**args): model = ResNeXt(layers=152, cardinality=64, **args) return model