# coding: utf8 # copyright (c) 2019 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 paddle import paddle.fluid as fluid from paddle.fluid.initializer import MSRA from paddle.fluid.param_attr import ParamAttr from utils.config import cfg def conv_bn_layer(input, filter_size, num_filters, stride=1, padding=1, num_groups=1, if_act=True, name=None): conv = fluid.layers.conv2d( input=input, num_filters=num_filters, filter_size=filter_size, stride=stride, padding=(filter_size - 1) // 2, groups=num_groups, act=None, param_attr=ParamAttr(initializer=MSRA(), name=name + '_weights'), bias_attr=False) bn_name = name + '_bn' bn = fluid.layers.batch_norm(input=conv, param_attr=ParamAttr(name=bn_name + "_scale", initializer=fluid.initializer.Constant(1.0)), bias_attr=ParamAttr(name=bn_name + "_offset", initializer=fluid.initializer.Constant(0.0)), moving_mean_name=bn_name + '_mean', moving_variance_name=bn_name + '_variance') if if_act: bn = fluid.layers.relu(bn) return bn def basic_block(input, num_filters, stride=1, downsample=False, name=None): residual = input conv = conv_bn_layer(input=input, filter_size=3, num_filters=num_filters, stride=stride, name=name + '_conv1') conv = conv_bn_layer(input=conv, filter_size=3, num_filters=num_filters, if_act=False, name=name + '_conv2') if downsample: residual = conv_bn_layer(input=input, filter_size=1, num_filters=num_filters, if_act=False, name=name + '_downsample') return fluid.layers.elementwise_add(x=residual, y=conv, act='relu') def bottleneck_block(input, num_filters, stride=1, downsample=False, name=None): residual = input conv = conv_bn_layer(input=input, filter_size=1, num_filters=num_filters, name=name + '_conv1') conv = conv_bn_layer(input=conv, filter_size=3, num_filters=num_filters, stride=stride, name=name + '_conv2') conv = conv_bn_layer(input=conv, filter_size=1, num_filters=num_filters * 4, if_act=False, name=name + '_conv3') if downsample: residual = conv_bn_layer(input=input, filter_size=1, num_filters=num_filters * 4, if_act=False, name=name + '_downsample') return fluid.layers.elementwise_add(x=residual, y=conv, act='relu') def fuse_layers(x, channels, multi_scale_output=True, name=None): out = [] for i in range(len(channels) if multi_scale_output else 1): residual = x[i] shape = residual.shape width = shape[-1] height = shape[-2] for j in range(len(channels)): if j > i: y = conv_bn_layer(x[j], filter_size=1, num_filters=channels[i], if_act=False, name=name + '_layer_' + str(i + 1) + '_' + str(j + 1)) y = fluid.layers.resize_bilinear(input=y, out_shape=[height, width]) residual = fluid.layers.elementwise_add(x=residual, y=y, act=None) elif j < i: y = x[j] for k in range(i - j): if k == i - j - 1: y = conv_bn_layer(y, filter_size=3, num_filters=channels[i], stride=2, if_act=False, name=name + '_layer_' + str(i + 1) + '_' + str(j + 1) + '_' + str(k + 1)) else: y = conv_bn_layer(y, filter_size=3, num_filters=channels[j], stride=2, name=name + '_layer_' + str(i + 1) + '_' + str(j + 1) + '_' + str(k + 1)) residual = fluid.layers.elementwise_add(x=residual, y=y, act=None) residual = fluid.layers.relu(residual) out.append(residual) return out def branches(x, block_num, channels, name=None): out = [] for i in range(len(channels)): residual = x[i] for j in range(block_num): residual = basic_block(residual, channels[i], name=name + '_branch_layer_' + str(i + 1) + '_' + str(j + 1)) out.append(residual) return out def high_resolution_module(x, channels, multi_scale_output=True, name=None): residual = branches(x, 4, channels, name=name) out = fuse_layers(residual, channels, multi_scale_output=multi_scale_output, name=name) return out def transition_layer(x, in_channels, out_channels, name=None): num_in = len(in_channels) num_out = len(out_channels) out = [] for i in range(num_out): if i < num_in: if in_channels[i] != out_channels[i]: residual = conv_bn_layer(x[i], filter_size=3, num_filters=out_channels[i], name=name + '_layer_' + str(i + 1)) out.append(residual) else: out.append(x[i]) else: residual = conv_bn_layer(x[-1], filter_size=3, num_filters=out_channels[i], stride=2, name=name + '_layer_' + str(i + 1)) out.append(residual) return out def stage(x, num_modules, channels, multi_scale_output=True, name=None): out = x for i in range(num_modules): if i == num_modules - 1 and multi_scale_output == False: out = high_resolution_module(out, channels, multi_scale_output=False, name=name + '_' + str(i + 1)) else: out = high_resolution_module(out, channels, name=name + '_' + str(i + 1)) return out def layer1(input, name=None): conv = input for i in range(4): conv = bottleneck_block(conv, num_filters=64, downsample=True if i == 0 else False, name=name + '_' + str(i + 1)) return conv def high_resolution_net(input, num_classes): channels_2 = cfg.MODEL.HRNET.STAGE2.NUM_CHANNELS channels_3 = cfg.MODEL.HRNET.STAGE3.NUM_CHANNELS channels_4 = cfg.MODEL.HRNET.STAGE4.NUM_CHANNELS num_modules_2 = cfg.MODEL.HRNET.STAGE2.NUM_MODULES num_modules_3 = cfg.MODEL.HRNET.STAGE3.NUM_MODULES num_modules_4 = cfg.MODEL.HRNET.STAGE4.NUM_MODULES x = conv_bn_layer(input=input, filter_size=3, num_filters=64, stride=2, if_act=True, name='layer1_1') x = conv_bn_layer(input=x, filter_size=3, num_filters=64, stride=2, if_act=True, name='layer1_2') la1 = layer1(x, name='layer2') tr1 = transition_layer([la1], [256], channels_2, name='tr1') st2 = stage(tr1, num_modules_2, channels_2, name='st2') tr2 = transition_layer(st2, channels_2, channels_3, name='tr2') st3 = stage(tr2, num_modules_3, channels_3, name='st3') tr3 = transition_layer(st3, channels_3, channels_4, name='tr3') st4 = stage(tr3, num_modules_4, channels_4, name='st4') # upsample shape = st4[0].shape height, width = shape[-2], shape[-1] st4[1] = fluid.layers.resize_bilinear( st4[1], out_shape=[height, width]) st4[2] = fluid.layers.resize_bilinear( st4[2], out_shape=[height, width]) st4[3] = fluid.layers.resize_bilinear( st4[3], out_shape=[height, width]) out = fluid.layers.concat(st4, axis=1) last_channels = sum(channels_4) out = conv_bn_layer(input=out, filter_size=1, num_filters=last_channels, stride=1, if_act=True, name='conv-2') out= fluid.layers.conv2d( input=out, num_filters=num_classes, filter_size=1, stride=1, padding=0, act=None, param_attr=ParamAttr(initializer=MSRA(), name='conv-1_weights'), bias_attr=False) out = fluid.layers.resize_bilinear(out, input.shape[2:]) return out def hrnet(input, num_classes): logit = high_resolution_net(input, num_classes) return logit if __name__ == '__main__': image_shape = [3, 769, 769] image = fluid.layers.data(name='image', shape=image_shape, dtype='float32') logit = hrnet(image, 4) print("logit:", logit.shape)