#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 paddle.fluid as fluid from paddle.fluid.param_attr import ParamAttr __all__ = ["ResNet"] class ResNet(object): def __init__(self, params): """ the Resnet backbone network for detection module. Args: params(dict): the super parameters for network build """ self.layers = params['layers'] supported_layers = [18, 34, 50, 101, 152] assert self.layers in supported_layers, \ "supported layers are {} but input layer is {}".format(supported_layers, self.layers) self.is_3x3 = True def __call__(self, input): layers = self.layers is_3x3 = self.is_3x3 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] elif layers == 200: depth = [3, 12, 48, 3] num_filters = [64, 128, 256, 512] outs = [] if is_3x3 == False: conv = self.conv_bn_layer( input=input, num_filters=64, filter_size=7, stride=2, act='relu') else: conv = self.conv_bn_layer( input=input, num_filters=32, filter_size=3, stride=2, act='relu', name='conv1_1') conv = self.conv_bn_layer( input=conv, num_filters=32, filter_size=3, stride=1, act='relu', name='conv1_2') conv = self.conv_bn_layer( input=conv, num_filters=64, filter_size=3, stride=1, act='relu', name='conv1_3') conv = fluid.layers.pool2d( input=conv, pool_size=3, pool_stride=2, pool_padding=1, pool_type='max') if layers >= 50: for block in range(len(depth)): for i in range(depth[block]): if layers in [101, 152, 200] 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) conv = self.bottleneck_block( input=conv, num_filters=num_filters[block], stride=2 if i == 0 and block != 0 else 1, if_first=block == i == 0, name=conv_name) outs.append(conv) else: for block in range(len(depth)): for i in range(depth[block]): conv_name = "res" + str(block + 2) + chr(97 + i) conv = self.basic_block( input=conv, num_filters=num_filters[block], stride=2 if i == 0 and block != 0 else 1, if_first=block == i == 0, name=conv_name) outs.append(conv) return outs def conv_bn_layer(self, input, num_filters, filter_size, stride=1, groups=1, act=None, name=None): conv = fluid.layers.conv2d( input=input, num_filters=num_filters, filter_size=filter_size, stride=stride, padding=(filter_size - 1) // 2, groups=groups, act=None, param_attr=ParamAttr(name=name + "_weights"), bias_attr=False) if name == "conv1": bn_name = "bn_" + name else: bn_name = "bn" + name[3:] return fluid.layers.batch_norm( input=conv, 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 conv_bn_layer_new(self, input, num_filters, filter_size, stride=1, groups=1, act=None, name=None): pool = fluid.layers.pool2d( input=input, pool_size=2, pool_stride=2, pool_padding=0, pool_type='avg', ceil_mode=True) conv = fluid.layers.conv2d( input=pool, num_filters=num_filters, filter_size=filter_size, stride=1, padding=(filter_size - 1) // 2, groups=groups, act=None, param_attr=ParamAttr(name=name + "_weights"), bias_attr=False) if name == "conv1": bn_name = "bn_" + name else: bn_name = "bn" + name[3:] return fluid.layers.batch_norm( input=conv, 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 shortcut(self, input, ch_out, stride, name, if_first=False): ch_in = input.shape[1] if ch_in != ch_out or stride != 1: if if_first: return self.conv_bn_layer(input, ch_out, 1, stride, name=name) else: return self.conv_bn_layer_new( input, ch_out, 1, stride, name=name) elif if_first: return self.conv_bn_layer(input, ch_out, 1, stride, name=name) else: return input def bottleneck_block(self, input, num_filters, stride, name, if_first): conv0 = self.conv_bn_layer( input=input, num_filters=num_filters, filter_size=1, act='relu', name=name + "_branch2a") conv1 = self.conv_bn_layer( input=conv0, num_filters=num_filters, filter_size=3, stride=stride, act='relu', name=name + "_branch2b") conv2 = self.conv_bn_layer( input=conv1, num_filters=num_filters * 4, filter_size=1, act=None, name=name + "_branch2c") short = self.shortcut( input, num_filters * 4, stride, if_first=if_first, name=name + "_branch1") return fluid.layers.elementwise_add(x=short, y=conv2, act='relu') def basic_block(self, input, num_filters, stride, name, if_first): conv0 = self.conv_bn_layer( input=input, num_filters=num_filters, filter_size=3, act='relu', stride=stride, name=name + "_branch2a") conv1 = self.conv_bn_layer( input=conv0, num_filters=num_filters, filter_size=3, act=None, name=name + "_branch2b") short = self.shortcut( input, num_filters, stride, if_first=if_first, name=name + "_branch1") return fluid.layers.elementwise_add(x=short, y=conv1, act='relu')