#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 math import paddle import paddle.fluid as fluid from paddle.fluid.param_attr import ParamAttr __all__ = ["ResNet", "ResNet50_vd", "ResNet101_vd", "ResNet152_vd", "ResNet200_vd"] train_parameters = { "input_size": [3, 224, 224], "input_mean": [0.485, 0.456, 0.406], "input_std": [0.229, 0.224, 0.225], "learning_strategy": { "name": "piecewise_decay", "batch_size": 256, "epochs": [30, 60, 90], "steps": [0.1, 0.01, 0.001, 0.0001] } } class ResNet(): def __init__(self, layers=50, is_3x3=False): self.params = train_parameters self.layers = layers self.is_3x3 = is_3x3 def net(self, input, class_dim=1000): is_3x3 = self.is_3x3 layers = self.layers supported_layers = [50, 101, 152, 200] assert layers in supported_layers, \ "supported layers are {} but input layer is {}".format(supported_layers, layers) if 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] 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') 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 == 0, name=conv_name) pool = fluid.layers.pool2d(input=conv, pool_size=7, pool_type='avg', global_pooling=True) stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0) out = fluid.layers.fc( input=pool, size=class_dim, param_attr=fluid.param_attr.ParamAttr(initializer=fluid.initializer.Uniform(-stdv, stdv))) return out, pool 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') 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) 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 ResNet50_vd(): model = ResNet(layers=50, is_3x3=True) return model def ResNet101_vd(): model = ResNet(layers=101, is_3x3=True) return model def ResNet152_vd(): model = ResNet(layers=152, is_3x3=True) return model def ResNet200_vd(): model = ResNet(layers=200, is_3x3=True) return model