# copyright (c) 2022 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 import paddle.nn.functional as F from paddle.nn import Conv2D, BatchNorm, Linear, Dropout from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D from paddle.nn.initializer import Uniform import math from paddle.vision.ops import DeformConv2D from paddle.regularizer import L2Decay from paddle.nn.initializer import Normal, Constant, XavierUniform from .det_resnet_vd import DeformableConvV2, ConvBNLayer class BottleneckBlock(nn.Layer): def __init__(self, num_channels, num_filters, stride, shortcut=True, is_dcn=False): super(BottleneckBlock, self).__init__() self.conv0 = ConvBNLayer( in_channels=num_channels, out_channels=num_filters, kernel_size=1, act="relu", ) self.conv1 = ConvBNLayer( in_channels=num_filters, out_channels=num_filters, kernel_size=3, stride=stride, act="relu", is_dcn=is_dcn, dcn_groups=1, ) self.conv2 = ConvBNLayer( in_channels=num_filters, out_channels=num_filters * 4, kernel_size=1, act=None, ) if not shortcut: self.short = ConvBNLayer( in_channels=num_channels, out_channels=num_filters * 4, kernel_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 = paddle.add(x=short, y=conv2) y = F.relu(y) return y class BasicBlock(nn.Layer): def __init__(self, num_channels, num_filters, stride, shortcut=True, name=None): super(BasicBlock, self).__init__() self.stride = stride self.conv0 = ConvBNLayer( in_channels=num_channels, out_channels=num_filters, kernel_size=3, stride=stride, act="relu") self.conv1 = ConvBNLayer( in_channels=num_filters, out_channels=num_filters, kernel_size=3, act=None) if not shortcut: self.short = ConvBNLayer( in_channels=num_channels, out_channels=num_filters, kernel_size=1, stride=stride) 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 = paddle.add(x=short, y=conv1) y = F.relu(y) return y class ResNet(nn.Layer): def __init__(self, in_channels=3, layers=50, out_indices=None, dcn_stage=None): super(ResNet, self).__init__() self.layers = layers self.input_image_channel = in_channels 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.dcn_stage = dcn_stage if dcn_stage is not None else [ False, False, False, False ] self.out_indices = out_indices if out_indices is not None else [ 0, 1, 2, 3 ] self.conv = ConvBNLayer( in_channels=self.input_image_channel, out_channels=64, kernel_size=7, stride=2, act="relu", ) self.pool2d_max = MaxPool2D( kernel_size=3, stride=2, padding=1, ) self.stages = [] self.out_channels = [] if layers >= 50: for block in range(len(depth)): shortcut = False block_list = [] is_dcn = self.dcn_stage[block] 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, is_dcn=is_dcn)) block_list.append(bottleneck_block) shortcut = True if block in self.out_indices: self.out_channels.append(num_filters[block] * 4) self.stages.append(nn.Sequential(*block_list)) else: for block in range(len(depth)): shortcut = False block_list = [] # is_dcn = self.dcn_stage[block] 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)) block_list.append(basic_block) shortcut = True if block in self.out_indices: self.out_channels.append(num_filters[block]) self.stages.append(nn.Sequential(*block_list)) def forward(self, inputs): y = self.conv(inputs) y = self.pool2d_max(y) out = [] for i, block in enumerate(self.stages): y = block(y) if i in self.out_indices: out.append(y) return out