# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # 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. import math import os import paddle from paddle import ParamAttr import paddle.nn as nn import paddle.nn.functional as F from paddle.nn import SyncBatchNorm as BatchNorm from paddle.nn import Conv2d, Linear from paddle.nn import AdaptiveAvgPool2d, MaxPool2d, AvgPool2d from paddleseg.cvlibs import manager from paddleseg.utils import utils from paddleseg.cvlibs import param_init from paddleseg.models.common import layer_libs __all__ = [ "HRNet_W18_Small_V1", "HRNet_W18_Small_V2", "HRNet_W18", "HRNet_W30", "HRNet_W32", "HRNet_W40", "HRNet_W44", "HRNet_W48", "HRNet_W60", "HRNet_W64" ] class HRNet(nn.Layer): """ HRNet：Deep High-Resolution Representation Learning for Visual Recognition https://arxiv.org/pdf/1908.07919.pdf. Args: backbone_pretrained (str): the path of pretrained model. stage1_num_modules (int): number of modules for stage1. Default 1. stage1_num_blocks (list): number of blocks per module for stage1. Default [4]. stage1_num_channels (list): number of channels per branch for stage1. Default [64]. stage2_num_modules (int): number of modules for stage2. Default 1. stage2_num_blocks (list): number of blocks per module for stage2. Default [4, 4] stage2_num_channels (list): number of channels per branch for stage2. Default [18, 36]. stage3_num_modules (int): number of modules for stage3. Default 4. stage3_num_blocks (list): number of blocks per module for stage3. Default [4, 4, 4] stage3_num_channels (list): number of channels per branch for stage3. Default [18, 36, 72]. stage4_num_modules (int): number of modules for stage4. Default 3. stage4_num_blocks (list): number of blocks per module for stage4. Default [4, 4, 4, 4] stage4_num_channels (list): number of channels per branch for stage4. Default [18, 36, 72. 144]. has_se (bool): whether to use Squeeze-and-Excitation module. Default False. """ def __init__(self, stage1_num_modules=1, stage1_num_blocks=[4], stage1_num_channels=[64], stage2_num_modules=1, stage2_num_blocks=[4, 4], stage2_num_channels=[18, 36], stage3_num_modules=4, stage3_num_blocks=[4, 4, 4], stage3_num_channels=[18, 36, 72], stage4_num_modules=3, stage4_num_blocks=[4, 4, 4, 4], stage4_num_channels=[18, 36, 72, 144], has_se=False): super(HRNet, self).__init__() self.stage1_num_modules = stage1_num_modules self.stage1_num_blocks = stage1_num_blocks self.stage1_num_channels = stage1_num_channels self.stage2_num_modules = stage2_num_modules self.stage2_num_blocks = stage2_num_blocks self.stage2_num_channels = stage2_num_channels self.stage3_num_modules = stage3_num_modules self.stage3_num_blocks = stage3_num_blocks self.stage3_num_channels = stage3_num_channels self.stage4_num_modules = stage4_num_modules self.stage4_num_blocks = stage4_num_blocks self.stage4_num_channels = stage4_num_channels self.has_se = has_se self.conv_layer1_1 = layer_libs.ConvBNReLU( in_channels=3, out_channels=64, kernel_size=3, stride=2, padding='same', bias_attr=False) self.conv_layer1_2 = layer_libs.ConvBNReLU( in_channels=64, out_channels=64, kernel_size=3, stride=2, padding='same', bias_attr=False) self.la1 = Layer1( num_channels=64, num_blocks=self.stage1_num_blocks[0], num_filters=self.stage1_num_channels[0], has_se=has_se, name="layer2") self.tr1 = TransitionLayer( in_channels=[self.stage1_num_channels[0] * 4], out_channels=self.stage2_num_channels, name="tr1") self.st2 = Stage( num_channels=self.stage2_num_channels, num_modules=self.stage2_num_modules, num_blocks=self.stage2_num_blocks, num_filters=self.stage2_num_channels, has_se=self.has_se, name="st2") self.tr2 = TransitionLayer( in_channels=self.stage2_num_channels, out_channels=self.stage3_num_channels, name="tr2") self.st3 = Stage( num_channels=self.stage3_num_channels, num_modules=self.stage3_num_modules, num_blocks=self.stage3_num_blocks, num_filters=self.stage3_num_channels, has_se=self.has_se, name="st3") self.tr3 = TransitionLayer( in_channels=self.stage3_num_channels, out_channels=self.stage4_num_channels, name="tr3") self.st4 = Stage( num_channels=self.stage4_num_channels, num_modules=self.stage4_num_modules, num_blocks=self.stage4_num_blocks, num_filters=self.stage4_num_channels, has_se=self.has_se, name="st4") def forward(self, x, label=None, mode='train'): input_shape = x.shape[2:] conv1 = self.conv_layer1_1(x) conv2 = self.conv_layer1_2(conv1) la1 = self.la1(conv2) tr1 = self.tr1([la1]) st2 = self.st2(tr1) tr2 = self.tr2(st2) st3 = self.st3(tr2) tr3 = self.tr3(st3) st4 = self.st4(tr3) x0_h, x0_w = st4[0].shape[2:] x1 = F.resize_bilinear(st4[1], out_shape=(x0_h, x0_w)) x2 = F.resize_bilinear(st4[2], out_shape=(x0_h, x0_w)) x3 = F.resize_bilinear(st4[3], out_shape=(x0_h, x0_w)) x = paddle.concat([st4[0], x1, x2, x3], axis=1) return [x] class Layer1(nn.Layer): def __init__(self, num_channels, num_filters, num_blocks, has_se=False, name=None): super(Layer1, self).__init__() self.bottleneck_block_list = [] for i in range(num_blocks): bottleneck_block = self.add_sublayer( "bb_{}_{}".format(name, i + 1), BottleneckBlock( num_channels=num_channels if i == 0 else num_filters * 4, num_filters=num_filters, has_se=has_se, stride=1, downsample=True if i == 0 else False, name=name + '_' + str(i + 1))) self.bottleneck_block_list.append(bottleneck_block) def forward(self, input): conv = input for block_func in self.bottleneck_block_list: conv = block_func(conv) return conv class TransitionLayer(nn.Layer): def __init__(self, in_channels, out_channels, name=None): super(TransitionLayer, self).__init__() num_in = len(in_channels) num_out = len(out_channels) self.conv_bn_func_list = [] for i in range(num_out): residual = None if i < num_in: if in_channels[i] != out_channels[i]: residual = self.add_sublayer( "transition_{}_layer_{}".format(name, i + 1), layer_libs.ConvBNReLU( in_channels=in_channels[i], out_channels=out_channels[i], kernel_size=3, padding='same', bias_attr=False)) else: residual = self.add_sublayer( "transition_{}_layer_{}".format(name, i + 1), layer_libs.ConvBNReLU( in_channels=in_channels[-1], out_channels=out_channels[i], kernel_size=3, stride=2, padding='same', bias_attr=False)) self.conv_bn_func_list.append(residual) def forward(self, input): outs = [] for idx, conv_bn_func in enumerate(self.conv_bn_func_list): if conv_bn_func is None: outs.append(input[idx]) else: if idx < len(input): outs.append(conv_bn_func(input[idx])) else: outs.append(conv_bn_func(input[-1])) return outs class Branches(nn.Layer): def __init__(self, num_blocks, in_channels, out_channels, has_se=False, name=None): super(Branches, self).__init__() self.basic_block_list = [] for i in range(len(out_channels)): self.basic_block_list.append([]) for j in range(num_blocks[i]): in_ch = in_channels[i] if j == 0 else out_channels[i] basic_block_func = self.add_sublayer( "bb_{}_branch_layer_{}_{}".format(name, i + 1, j + 1), BasicBlock( num_channels=in_ch, num_filters=out_channels[i], has_se=has_se, name=name + '_branch_layer_' + str(i + 1) + '_' + str(j + 1))) self.basic_block_list[i].append(basic_block_func) def forward(self, inputs): outs = [] for idx, input in enumerate(inputs): conv = input for basic_block_func in self.basic_block_list[idx]: conv = basic_block_func(conv) outs.append(conv) return outs class BottleneckBlock(nn.Layer): def __init__(self, num_channels, num_filters, has_se, stride=1, downsample=False, name=None): super(BottleneckBlock, self).__init__() self.has_se = has_se self.downsample = downsample self.conv1 = layer_libs.ConvBNReLU( in_channels=num_channels, out_channels=num_filters, kernel_size=1, padding='same', bias_attr=False) self.conv2 = layer_libs.ConvBNReLU( in_channels=num_filters, out_channels=num_filters, kernel_size=3, stride=stride, padding='same', bias_attr=False) self.conv3 = layer_libs.ConvBN( in_channels=num_filters, out_channels=num_filters * 4, kernel_size=1, padding='same', bias_attr=False) if self.downsample: self.conv_down = layer_libs.ConvBN( in_channels=num_channels, out_channels=num_filters * 4, kernel_size=1, padding='same', bias_attr=False) if self.has_se: self.se = SELayer( num_channels=num_filters * 4, num_filters=num_filters * 4, reduction_ratio=16, name=name + '_fc') def forward(self, input): residual = input conv1 = self.conv1(input) conv2 = self.conv2(conv1) conv3 = self.conv3(conv2) if self.downsample: residual = self.conv_down(input) if self.has_se: conv3 = self.se(conv3) y = conv3 + residual y = F.relu(y) return y class BasicBlock(nn.Layer): def __init__(self, num_channels, num_filters, stride=1, has_se=False, downsample=False, name=None): super(BasicBlock, self).__init__() self.has_se = has_se self.downsample = downsample self.conv1 = layer_libs.ConvBNReLU( in_channels=num_channels, out_channels=num_filters, kernel_size=3, stride=stride, padding='same', bias_attr=False) self.conv2 = layer_libs.ConvBN( in_channels=num_filters, out_channels=num_filters, kernel_size=3, padding='same', bias_attr=False) if self.downsample: self.conv_down = layer_libs.ConvBNReLU( in_channels=num_channels, out_channels=num_filters, kernel_size=1, padding='same', bias_attr=False) if self.has_se: self.se = SELayer( num_channels=num_filters, num_filters=num_filters, reduction_ratio=16, name=name + '_fc') def forward(self, input): residual = input conv1 = self.conv1(input) conv2 = self.conv2(conv1) if self.downsample: residual = self.conv_down(input) if self.has_se: conv2 = self.se(conv2) y = conv2 + residual y = F.relu(y) return y class SELayer(nn.Layer): def __init__(self, num_channels, num_filters, reduction_ratio, name=None): super(SELayer, self).__init__() self.pool2d_gap = AdaptiveAvgPool2d(1) self._num_channels = num_channels med_ch = int(num_channels / reduction_ratio) stdv = 1.0 / math.sqrt(num_channels * 1.0) self.squeeze = Linear( num_channels, med_ch, act="relu", param_attr=ParamAttr( initializer=nn.initializer.Uniform(-stdv, stdv))) stdv = 1.0 / math.sqrt(med_ch * 1.0) self.excitation = Linear( med_ch, num_filters, act="sigmoid", param_attr=ParamAttr( initializer=nn.initializer.Uniform(-stdv, stdv))) def forward(self, input): pool = self.pool2d_gap(input) pool = paddle.reshape(pool, shape=[-1, self._num_channels]) squeeze = self.squeeze(pool) excitation = self.excitation(squeeze) excitation = paddle.reshape( excitation, shape=[-1, self._num_channels, 1, 1]) out = input * excitation return out class Stage(nn.Layer): def __init__(self, num_channels, num_modules, num_blocks, num_filters, has_se=False, multi_scale_output=True, name=None): super(Stage, self).__init__() self._num_modules = num_modules self.stage_func_list = [] for i in range(num_modules): if i == num_modules - 1 and not multi_scale_output: stage_func = self.add_sublayer( "stage_{}_{}".format(name, i + 1), HighResolutionModule( num_channels=num_channels, num_blocks=num_blocks, num_filters=num_filters, has_se=has_se, multi_scale_output=False, name=name + '_' + str(i + 1))) else: stage_func = self.add_sublayer( "stage_{}_{}".format(name, i + 1), HighResolutionModule( num_channels=num_channels, num_blocks=num_blocks, num_filters=num_filters, has_se=has_se, name=name + '_' + str(i + 1))) self.stage_func_list.append(stage_func) def forward(self, input): out = input for idx in range(self._num_modules): out = self.stage_func_list[idx](out) return out class HighResolutionModule(nn.Layer): def __init__(self, num_channels, num_blocks, num_filters, has_se=False, multi_scale_output=True, name=None): super(HighResolutionModule, self).__init__() self.branches_func = Branches( num_blocks=num_blocks, in_channels=num_channels, out_channels=num_filters, has_se=has_se, name=name) self.fuse_func = FuseLayers( in_channels=num_filters, out_channels=num_filters, multi_scale_output=multi_scale_output, name=name) def forward(self, input): out = self.branches_func(input) out = self.fuse_func(out) return out class FuseLayers(nn.Layer): def __init__(self, in_channels, out_channels, multi_scale_output=True, name=None): super(FuseLayers, self).__init__() self._actual_ch = len(in_channels) if multi_scale_output else 1 self._in_channels = in_channels self.residual_func_list = [] for i in range(self._actual_ch): for j in range(len(in_channels)): residual_func = None if j > i: residual_func = self.add_sublayer( "residual_{}_layer_{}_{}".format(name, i + 1, j + 1), layer_libs.ConvBN( in_channels=in_channels[j], out_channels=out_channels[i], kernel_size=1, padding='same', bias_attr=False)) self.residual_func_list.append(residual_func) elif j < i: pre_num_filters = in_channels[j] for k in range(i - j): if k == i - j - 1: residual_func = self.add_sublayer( "residual_{}_layer_{}_{}_{}".format( name, i + 1, j + 1, k + 1), layer_libs.ConvBN( in_channels=pre_num_filters, out_channels=out_channels[i], kernel_size=3, stride=2, padding='same', bias_attr=False)) pre_num_filters = out_channels[i] else: residual_func = self.add_sublayer( "residual_{}_layer_{}_{}_{}".format( name, i + 1, j + 1, k + 1), layer_libs.ConvBNReLU( in_channels=pre_num_filters, out_channels=out_channels[j], kernel_size=3, stride=2, padding='same', bias_attr=False)) pre_num_filters = out_channels[j] self.residual_func_list.append(residual_func) def forward(self, input): outs = [] residual_func_idx = 0 for i in range(self._actual_ch): residual = input[i] residual_shape = residual.shape[-2:] for j in range(len(self._in_channels)): if j > i: y = self.residual_func_list[residual_func_idx](input[j]) residual_func_idx += 1 y = F.resize_bilinear(input=y, out_shape=residual_shape) residual = residual + y elif j < i: y = input[j] for k in range(i - j): y = self.residual_func_list[residual_func_idx](y) residual_func_idx += 1 residual = residual + y residual = F.relu(residual) outs.append(residual) return outs @manager.BACKBONES.add_component def HRNet_W18_Small_V1(**kwargs): model = HRNet( stage1_num_modules=1, stage1_num_blocks=[1], stage1_num_channels=[32], stage2_num_modules=1, stage2_num_blocks=[2, 2], stage2_num_channels=[16, 32], stage3_num_modules=1, stage3_num_blocks=[2, 2, 2], stage3_num_channels=[16, 32, 64], stage4_num_modules=1, stage4_num_blocks=[2, 2, 2, 2], stage4_num_channels=[16, 32, 64, 128], **kwargs) return model @manager.BACKBONES.add_component def HRNet_W18_Small_V2(**kwargs): model = HRNet( stage1_num_modules=1, stage1_num_blocks=[2], stage1_num_channels=[64], stage2_num_modules=1, stage2_num_blocks=[2, 2], stage2_num_channels=[18, 36], stage3_num_modules=1, stage3_num_blocks=[2, 2, 2], stage3_num_channels=[18, 36, 72], stage4_num_modules=1, stage4_num_blocks=[2, 2, 2, 2], stage4_num_channels=[18, 36, 72, 144], **kwargs) return model @manager.BACKBONES.add_component def HRNet_W18(**kwargs): model = HRNet( stage1_num_modules=1, stage1_num_blocks=[4], stage1_num_channels=[64], stage2_num_modules=1, stage2_num_blocks=[4, 4], stage2_num_channels=[18, 36], stage3_num_modules=4, stage3_num_blocks=[4, 4, 4], stage3_num_channels=[18, 36, 72], stage4_num_modules=3, stage4_num_blocks=[4, 4, 4, 4], stage4_num_channels=[18, 36, 72, 144], **kwargs) return model @manager.BACKBONES.add_component def HRNet_W30(**kwargs): model = HRNet( stage1_num_modules=1, stage1_num_blocks=[4], stage1_num_channels=[64], stage2_num_modules=1, stage2_num_blocks=[4, 4], stage2_num_channels=[30, 60], stage3_num_modules=4, stage3_num_blocks=[4, 4, 4], stage3_num_channels=[30, 60, 120], stage4_num_modules=3, stage4_num_blocks=[4, 4, 4, 4], stage4_num_channels=[30, 60, 120, 240], **kwargs) return model @manager.BACKBONES.add_component def HRNet_W32(**kwargs): model = HRNet( stage1_num_modules=1, stage1_num_blocks=[4], stage1_num_channels=[64], stage2_num_modules=1, stage2_num_blocks=[4, 4], stage2_num_channels=[32, 64], stage3_num_modules=4, stage3_num_blocks=[4, 4, 4], stage3_num_channels=[32, 64, 128], stage4_num_modules=3, stage4_num_blocks=[4, 4, 4, 4], stage4_num_channels=[32, 64, 128, 256], **kwargs) return model @manager.BACKBONES.add_component def HRNet_W40(**kwargs): model = HRNet( stage1_num_modules=1, stage1_num_blocks=[4], stage1_num_channels=[64], stage2_num_modules=1, stage2_num_blocks=[4, 4], stage2_num_channels=[40, 80], stage3_num_modules=4, stage3_num_blocks=[4, 4, 4], stage3_num_channels=[40, 80, 160], stage4_num_modules=3, stage4_num_blocks=[4, 4, 4, 4], stage4_num_channels=[40, 80, 160, 320], **kwargs) return model @manager.BACKBONES.add_component def HRNet_W44(**kwargs): model = HRNet( stage1_num_modules=1, stage1_num_blocks=[4], stage1_num_channels=[64], stage2_num_modules=1, stage2_num_blocks=[4, 4], stage2_num_channels=[44, 88], stage3_num_modules=4, stage3_num_blocks=[4, 4, 4], stage3_num_channels=[44, 88, 176], stage4_num_modules=3, stage4_num_blocks=[4, 4, 4, 4], stage4_num_channels=[44, 88, 176, 352], **kwargs) return model @manager.BACKBONES.add_component def HRNet_W48(**kwargs): model = HRNet( stage1_num_modules=1, stage1_num_blocks=[4], stage1_num_channels=[64], stage2_num_modules=1, stage2_num_blocks=[4, 4], stage2_num_channels=[48, 96], stage3_num_modules=4, stage3_num_blocks=[4, 4, 4], stage3_num_channels=[48, 96, 192], stage4_num_modules=3, stage4_num_blocks=[4, 4, 4, 4], stage4_num_channels=[48, 96, 192, 384], **kwargs) return model @manager.BACKBONES.add_component def HRNet_W60(**kwargs): model = HRNet( stage1_num_modules=1, stage1_num_blocks=[4], stage1_num_channels=[64], stage2_num_modules=1, stage2_num_blocks=[4, 4], stage2_num_channels=[60, 120], stage3_num_modules=4, stage3_num_blocks=[4, 4, 4], stage3_num_channels=[60, 120, 240], stage4_num_modules=3, stage4_num_blocks=[4, 4, 4, 4], stage4_num_channels=[60, 120, 240, 480], **kwargs) return model @manager.BACKBONES.add_component def HRNet_W64(**kwargs): model = HRNet( stage1_num_modules=1, stage1_num_blocks=[4], stage1_num_channels=[64], stage2_num_modules=1, stage2_num_blocks=[4, 4], stage2_num_channels=[64, 128], stage3_num_modules=4, stage3_num_blocks=[4, 4, 4], stage3_num_channels=[64, 128, 256], stage4_num_modules=3, stage4_num_blocks=[4, 4, 4, 4], stage4_num_channels=[64, 128, 256, 512], **kwargs) return model