#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 from paddle import nn, ParamAttr from paddle.nn import functional as F import paddle import numpy as np __all__ = ["ResNetFPN"] class ResNetFPN(nn.Layer): def __init__(self, in_channels=1, layers=50, **kwargs): super(ResNetFPN, self).__init__() supported_layers = { 18: { 'depth': [2, 2, 2, 2], 'block_class': BasicBlock }, 34: { 'depth': [3, 4, 6, 3], 'block_class': BasicBlock }, 50: { 'depth': [3, 4, 6, 3], 'block_class': BottleneckBlock }, 101: { 'depth': [3, 4, 23, 3], 'block_class': BottleneckBlock }, 152: { 'depth': [3, 8, 36, 3], 'block_class': BottleneckBlock } } stride_list = [(2, 2), (2, 2), (1, 1), (1, 1)] num_filters = [64, 128, 256, 512] self.depth = supported_layers[layers]['depth'] self.F = [] self.conv = ConvBNLayer( in_channels=in_channels, out_channels=64, kernel_size=7, stride=2, act="relu", name="conv1") self.block_list = [] in_ch = 64 if layers >= 50: for block in range(len(self.depth)): for i in range(self.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) block_list = self.add_sublayer( "bottleneckBlock_{}_{}".format(block, i), BottleneckBlock( in_channels=in_ch, out_channels=num_filters[block], stride=stride_list[block] if i == 0 else 1, name=conv_name)) in_ch = num_filters[block] * 4 self.block_list.append(block_list) self.F.append(block_list) else: for block in range(len(self.depth)): for i in range(self.depth[block]): conv_name = "res" + str(block + 2) + chr(97 + i) if i == 0 and block != 0: stride = (2, 1) else: stride = (1, 1) basic_block = self.add_sublayer( conv_name, BasicBlock( in_channels=in_ch, out_channels=num_filters[block], stride=stride_list[block] if i == 0 else 1, is_first=block == i == 0, name=conv_name)) in_ch = basic_block.out_channels self.block_list.append(basic_block) out_ch_list = [in_ch // 4, in_ch // 2, in_ch] self.base_block = [] self.conv_trans = [] self.bn_block = [] for i in [-2, -3]: in_channels = out_ch_list[i + 1] + out_ch_list[i] self.base_block.append( self.add_sublayer( "F_{}_base_block_0".format(i), nn.Conv2D( in_channels=in_channels, out_channels=out_ch_list[i], kernel_size=1, weight_attr=ParamAttr(trainable=True), bias_attr=ParamAttr(trainable=True)))) self.base_block.append( self.add_sublayer( "F_{}_base_block_1".format(i), nn.Conv2D( in_channels=out_ch_list[i], out_channels=out_ch_list[i], kernel_size=3, padding=1, weight_attr=ParamAttr(trainable=True), bias_attr=ParamAttr(trainable=True)))) self.base_block.append( self.add_sublayer( "F_{}_base_block_2".format(i), nn.BatchNorm( num_channels=out_ch_list[i], act="relu", param_attr=ParamAttr(trainable=True), bias_attr=ParamAttr(trainable=True)))) self.base_block.append( self.add_sublayer( "F_{}_base_block_3".format(i), nn.Conv2D( in_channels=out_ch_list[i], out_channels=512, kernel_size=1, bias_attr=ParamAttr(trainable=True), weight_attr=ParamAttr(trainable=True)))) self.out_channels = 512 def __call__(self, x): x = self.conv(x) fpn_list = [] F = [] for i in range(len(self.depth)): fpn_list.append(np.sum(self.depth[:i + 1])) for i, block in enumerate(self.block_list): x = block(x) for number in fpn_list: if i + 1 == number: F.append(x) base = F[-1] j = 0 for i, block in enumerate(self.base_block): if i % 3 == 0 and i < 6: j = j + 1 b, c, w, h = F[-j - 1].shape if [w, h] == list(base.shape[2:]): base = base else: base = self.conv_trans[j - 1](base) base = self.bn_block[j - 1](base) base = paddle.concat([base, F[-j - 1]], axis=1) base = block(base) return base class ConvBNLayer(nn.Layer): def __init__(self, in_channels, out_channels, kernel_size, stride=1, groups=1, act=None, name=None): super(ConvBNLayer, self).__init__() self.conv = nn.Conv2D( in_channels=in_channels, out_channels=out_channels, kernel_size=2 if stride == (1, 1) else kernel_size, dilation=2 if stride == (1, 1) else 1, stride=stride, padding=(kernel_size - 1) // 2, groups=groups, weight_attr=ParamAttr(name=name + '.conv2d.output.1.w_0'), bias_attr=False, ) if name == "conv1": bn_name = "bn_" + name else: bn_name = "bn" + name[3:] self.bn = nn.BatchNorm( num_channels=out_channels, act=act, param_attr=ParamAttr(name=name + '.output.1.w_0'), bias_attr=ParamAttr(name=name + '.output.1.b_0'), moving_mean_name=bn_name + "_mean", moving_variance_name=bn_name + "_variance") def __call__(self, x): x = self.conv(x) x = self.bn(x) return x class ShortCut(nn.Layer): def __init__(self, in_channels, out_channels, stride, name, is_first=False): super(ShortCut, self).__init__() self.use_conv = True if in_channels != out_channels or stride != 1 or is_first == True: if stride == (1, 1): self.conv = ConvBNLayer( in_channels, out_channels, 1, 1, name=name) else: # stride==(2,2) self.conv = ConvBNLayer( in_channels, out_channels, 1, stride, name=name) else: self.use_conv = False def forward(self, x): if self.use_conv: x = self.conv(x) return x class BottleneckBlock(nn.Layer): def __init__(self, in_channels, out_channels, stride, name): super(BottleneckBlock, self).__init__() self.conv0 = ConvBNLayer( in_channels=in_channels, out_channels=out_channels, kernel_size=1, act='relu', name=name + "_branch2a") self.conv1 = ConvBNLayer( in_channels=out_channels, out_channels=out_channels, kernel_size=3, stride=stride, act='relu', name=name + "_branch2b") self.conv2 = ConvBNLayer( in_channels=out_channels, out_channels=out_channels * 4, kernel_size=1, act=None, name=name + "_branch2c") self.short = ShortCut( in_channels=in_channels, out_channels=out_channels * 4, stride=stride, is_first=False, name=name + "_branch1") self.out_channels = out_channels * 4 def forward(self, x): y = self.conv0(x) y = self.conv1(y) y = self.conv2(y) y = y + self.short(x) y = F.relu(y) return y class BasicBlock(nn.Layer): def __init__(self, in_channels, out_channels, stride, name, is_first): super(BasicBlock, self).__init__() self.conv0 = ConvBNLayer( in_channels=in_channels, out_channels=out_channels, kernel_size=3, act='relu', stride=stride, name=name + "_branch2a") self.conv1 = ConvBNLayer( in_channels=out_channels, out_channels=out_channels, kernel_size=3, act=None, name=name + "_branch2b") self.short = ShortCut( in_channels=in_channels, out_channels=out_channels, stride=stride, is_first=is_first, name=name + "_branch1") self.out_channels = out_channels def forward(self, x): y = self.conv0(x) y = self.conv1(y) y = y + self.short(x) return F.relu(y)