# 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 math 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 from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D from paddle.nn.initializer import Uniform from ppcls.arch.backbone.base.theseus_layer import TheseusLayer __all__ = [ "HRNet_W18_C", "HRNet_W30_C", "HRNet_W32_C", "HRNet_W40_C", "HRNet_W44_C", "HRNet_W48_C", "HRNet_W60_C", "HRNet_W64_C", "SE_HRNet_W18_C", "SE_HRNet_W30_C", "SE_HRNet_W32_C", "SE_HRNet_W40_C", "SE_HRNet_W44_C", "SE_HRNet_W48_C", "SE_HRNet_W60_C", "SE_HRNet_W64_C", ] class ConvBNLayer(TheseusLayer): def __init__(self, num_channels, num_filters, filter_size, stride=1, groups=1, act="relu", name=None): super(ConvBNLayer, self).__init__() 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) bn_name = name + '_bn' 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, x, res_dict=None): y = self._conv(x) y = self._batch_norm(y) return y class Layer1(TheseusLayer): def __init__(self, num_channels, has_se=False, name=None): super(Layer1, self).__init__() self.bottleneck_block_list = [] for i in range(4): bottleneck_block = self.add_sublayer( "bb_{}_{}".format(name, i + 1), BottleneckBlock( num_channels=num_channels if i == 0 else 256, num_filters=64, 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, x, res_dict=None): y = x for block_func in self.bottleneck_block_list: y = block_func(y) return y class TransitionLayer(TheseusLayer): def __init__(self, in_channels, out_channels, name=None): super(TransitionLayer, self).__init__() num_in = len(in_channels) num_out = len(out_channels) out = [] 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), ConvBNLayer( num_channels=in_channels[i], num_filters=out_channels[i], filter_size=3, name=name + '_layer_' + str(i + 1))) else: residual = self.add_sublayer( "transition_{}_layer_{}".format(name, i + 1), ConvBNLayer( num_channels=in_channels[-1], num_filters=out_channels[i], filter_size=3, stride=2, name=name + '_layer_' + str(i + 1))) self.conv_bn_func_list.append(residual) def forward(self, x, res_dict=None): outs = [] for idx, conv_bn_func in enumerate(self.conv_bn_func_list): if conv_bn_func is None: outs.append(x[idx]) else: if idx < len(x): outs.append(conv_bn_func(x[idx])) else: outs.append(conv_bn_func(x[-1])) return outs class Branches(TheseusLayer): def __init__(self, block_num, 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(block_num): 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, x, res_dict=None): outs = [] for idx, xi in enumerate(x): conv = xi basic_block_list = self.basic_block_list[idx] for basic_block_func in basic_block_list: conv = basic_block_func(conv) outs.append(conv) return outs class BottleneckBlock(TheseusLayer): 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 = ConvBNLayer( num_channels=num_channels, num_filters=num_filters, filter_size=1, act="relu", name=name + "_conv1", ) self.conv2 = ConvBNLayer( num_channels=num_filters, num_filters=num_filters, filter_size=3, stride=stride, act="relu", name=name + "_conv2") self.conv3 = ConvBNLayer( num_channels=num_filters, num_filters=num_filters * 4, filter_size=1, act=None, name=name + "_conv3") if self.downsample: self.conv_down = ConvBNLayer( num_channels=num_channels, num_filters=num_filters * 4, filter_size=1, act=None, name=name + "_downsample") if self.has_se: self.se = SELayer( num_channels=num_filters * 4, num_filters=num_filters * 4, reduction_ratio=16, name='fc' + name) def forward(self, x, res_dict=None): residual = x conv1 = self.conv1(x) conv2 = self.conv2(conv1) conv3 = self.conv3(conv2) if self.downsample: residual = self.conv_down(x) if self.has_se: conv3 = self.se(conv3) y = paddle.add(x=residual, y=conv3) y = F.relu(y) return y class BasicBlock(TheseusLayer): 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 = ConvBNLayer( num_channels=num_channels, num_filters=num_filters, filter_size=3, stride=stride, act="relu", name=name + "_conv1") self.conv2 = ConvBNLayer( num_channels=num_filters, num_filters=num_filters, filter_size=3, stride=1, act=None, name=name + "_conv2") if self.downsample: self.conv_down = ConvBNLayer( num_channels=num_channels, num_filters=num_filters * 4, filter_size=1, act="relu", name=name + "_downsample") if self.has_se: self.se = SELayer( num_channels=num_filters, num_filters=num_filters, reduction_ratio=16, name='fc' + name) def forward(self, input, res_dict=None): 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 = paddle.add(x=residual, y=conv2) y = F.relu(y) return y class SELayer(TheseusLayer): 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, weight_attr=ParamAttr( initializer=Uniform(-stdv, stdv), name=name + "_sqz_weights"), bias_attr=ParamAttr(name=name + '_sqz_offset')) stdv = 1.0 / math.sqrt(med_ch * 1.0) self.excitation = Linear( med_ch, num_filters, weight_attr=ParamAttr( initializer=Uniform(-stdv, stdv), name=name + "_exc_weights"), bias_attr=ParamAttr(name=name + '_exc_offset')) def forward(self, input, res_dict=None): pool = self.pool2d_gap(input) pool = paddle.squeeze(pool, axis=[2, 3]) squeeze = self.squeeze(pool) squeeze = F.relu(squeeze) excitation = self.excitation(squeeze) excitation = F.sigmoid(excitation) excitation = paddle.unsqueeze(excitation, axis=[2, 3]) out = input * excitation return out class Stage(TheseusLayer): def __init__(self, num_channels, num_modules, 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_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_filters=num_filters, has_se=has_se, name=name + '_' + str(i + 1))) self.stage_func_list.append(stage_func) def forward(self, input, res_dict=None): out = input for idx in range(self._num_modules): out = self.stage_func_list[idx](out) return out class HighResolutionModule(TheseusLayer): def __init__(self, num_channels, num_filters, has_se=False, multi_scale_output=True, name=None): super(HighResolutionModule, self).__init__() self.branches_func = Branches( block_num=4, 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, res_dict=None): out = self.branches_func(input) out = self.fuse_func(out) return out class FuseLayers(TheseusLayer): 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), ConvBNLayer( num_channels=in_channels[j], num_filters=out_channels[i], filter_size=1, stride=1, act=None, name=name + '_layer_' + str(i + 1) + '_' + str(j + 1))) 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), ConvBNLayer( num_channels=pre_num_filters, num_filters=out_channels[i], filter_size=3, stride=2, act=None, name=name + '_layer_' + str(i + 1) + '_' + str(j + 1) + '_' + str(k + 1))) pre_num_filters = out_channels[i] else: residual_func = self.add_sublayer( "residual_{}_layer_{}_{}_{}".format( name, i + 1, j + 1, k + 1), ConvBNLayer( num_channels=pre_num_filters, num_filters=out_channels[j], filter_size=3, stride=2, act="relu", name=name + '_layer_' + str(i + 1) + '_' + str(j + 1) + '_' + str(k + 1))) pre_num_filters = out_channels[j] self.residual_func_list.append(residual_func) def forward(self, input, res_dict=None): outs = [] residual_func_idx = 0 for i in range(self._actual_ch): residual = input[i] 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.upsample(y, scale_factor=2**(j - i), mode="nearest") residual = paddle.add(x=residual, y=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 = paddle.add(x=residual, y=y) residual = F.relu(residual) outs.append(residual) return outs class LastClsOut(TheseusLayer): def __init__(self, num_channel_list, has_se, num_filters_list=[32, 64, 128, 256], name=None): super(LastClsOut, self).__init__() self.func_list = [] for idx in range(len(num_channel_list)): func = self.add_sublayer( "conv_{}_conv_{}".format(name, idx + 1), BottleneckBlock( num_channels=num_channel_list[idx], num_filters=num_filters_list[idx], has_se=has_se, downsample=True, name=name + 'conv_' + str(idx + 1))) self.func_list.append(func) def forward(self, inputs, res_dict=None): outs = [] for idx, input in enumerate(inputs): out = self.func_list[idx](input) outs.append(out) return outs class HRNet(TheseusLayer): def __init__(self, width=18, has_se=False, class_dim=1000): super(HRNet, self).__init__() self.width = width self.has_se = has_se self.channels = { 18: [[18, 36], [18, 36, 72], [18, 36, 72, 144]], 30: [[30, 60], [30, 60, 120], [30, 60, 120, 240]], 32: [[32, 64], [32, 64, 128], [32, 64, 128, 256]], 40: [[40, 80], [40, 80, 160], [40, 80, 160, 320]], 44: [[44, 88], [44, 88, 176], [44, 88, 176, 352]], 48: [[48, 96], [48, 96, 192], [48, 96, 192, 384]], 60: [[60, 120], [60, 120, 240], [60, 120, 240, 480]], 64: [[64, 128], [64, 128, 256], [64, 128, 256, 512]] } self._class_dim = class_dim channels_2, channels_3, channels_4 = self.channels[width] num_modules_2, num_modules_3, num_modules_4 = 1, 4, 3 self.conv_layer1_1 = ConvBNLayer( num_channels=3, num_filters=64, filter_size=3, stride=2, act='relu', name="layer1_1") self.conv_layer1_2 = ConvBNLayer( num_channels=64, num_filters=64, filter_size=3, stride=2, act='relu', name="layer1_2") self.la1 = Layer1(num_channels=64, has_se=has_se, name="layer2") self.tr1 = TransitionLayer( in_channels=[256], out_channels=channels_2, name="tr1") self.st2 = Stage( num_channels=channels_2, num_modules=num_modules_2, num_filters=channels_2, has_se=self.has_se, name="st2") self.tr2 = TransitionLayer( in_channels=channels_2, out_channels=channels_3, name="tr2") self.st3 = Stage( num_channels=channels_3, num_modules=num_modules_3, num_filters=channels_3, has_se=self.has_se, name="st3") self.tr3 = TransitionLayer( in_channels=channels_3, out_channels=channels_4, name="tr3") self.st4 = Stage( num_channels=channels_4, num_modules=num_modules_4, num_filters=channels_4, has_se=self.has_se, name="st4") # classification num_filters_list = [32, 64, 128, 256] self.last_cls = LastClsOut( num_channel_list=channels_4, has_se=self.has_se, num_filters_list=num_filters_list, name="cls_head", ) last_num_filters = [256, 512, 1024] self.cls_head_conv_list = [] for idx in range(3): self.cls_head_conv_list.append( self.add_sublayer( "cls_head_add{}".format(idx + 1), ConvBNLayer( num_channels=num_filters_list[idx] * 4, num_filters=last_num_filters[idx], filter_size=3, stride=2, name="cls_head_add" + str(idx + 1)))) self.conv_last = ConvBNLayer( num_channels=1024, num_filters=2048, filter_size=1, stride=1, name="cls_head_last_conv") self.pool2d_avg = AdaptiveAvgPool2D(1) stdv = 1.0 / math.sqrt(2048 * 1.0) self.out = Linear( 2048, class_dim, weight_attr=ParamAttr( initializer=Uniform(-stdv, stdv), name="fc_weights"), bias_attr=ParamAttr(name="fc_offset")) def forward(self, input, res_dict=None): conv1 = self.conv_layer1_1(input) 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) last_cls = self.last_cls(st4) y = last_cls[0] for idx in range(3): y = paddle.add(last_cls[idx + 1], self.cls_head_conv_list[idx](y)) y = self.conv_last(y) y = self.pool2d_avg(y) y = paddle.reshape(y, shape=[-1, y.shape[1]]) y = self.out(y) return y def HRNet_W18_C(**args): model = HRNet(width=18, **args) return model def HRNet_W30_C(**args): model = HRNet(width=30, **args) return model def HRNet_W32_C(**args): model = HRNet(width=32, **args) return model def HRNet_W40_C(**args): model = HRNet(width=40, **args) return model def HRNet_W44_C(**args): model = HRNet(width=44, **args) return model def HRNet_W48_C(**args): model = HRNet(width=48, **args) return model def HRNet_W60_C(**args): model = HRNet(width=60, **args) return model def HRNet_W64_C(**args): model = HRNet(width=64, **args) return model def SE_HRNet_W18_C(**args): model = HRNet(width=18, has_se=True, **args) return model def SE_HRNet_W30_C(**args): model = HRNet(width=30, has_se=True, **args) return model def SE_HRNet_W32_C(**args): model = HRNet(width=32, has_se=True, **args) return model def SE_HRNet_W40_C(**args): model = HRNet(width=40, has_se=True, **args) return model def SE_HRNet_W44_C(**args): model = HRNet(width=44, has_se=True, **args) return model def SE_HRNet_W48_C(**args): model = HRNet(width=48, has_se=True, **args) return model def SE_HRNet_W60_C(**args): model = HRNet(width=60, has_se=True, **args) return model def SE_HRNet_W64_C(**args): model = HRNet(width=64, has_se=True, **args) return model