#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 paddle import paddle.fluid as fluid from paddle.fluid.param_attr import ParamAttr __all__ = ["ResNet", "ResNet18", "ResNet34", "ResNet50", "ResNet101", "ResNet152"] Trainable = True w_nolr = fluid.ParamAttr( trainable = Trainable) 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, params): self.layers = params['layers'] self.params = train_parameters def __call__(self, input): layers = self.layers supported_layers = [18, 34, 50, 101, 152] assert layers in supported_layers, \ "supported layers are {} but input layer is {}".format(supported_layers, layers) 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] stride_list = [(2,2),(2,2),(1,1),(1,1)] num_filters = [64, 128, 256, 512] conv = self.conv_bn_layer( input=input, num_filters=64, filter_size=7, stride=2, act='relu', name="conv1") F = [] if layers >= 50: for block in range(len(depth)): for i in range(depth[block]): if layers in [101, 152] 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=stride_list[block] if i == 0 else 1, name=conv_name) F.append(conv) else: for block in range(len(depth)): for i in range(depth[block]): conv_name = "res" + str(block + 2) + chr(97 + i) if i == 0 and block != 0: stride = (2, 1) else: stride = (1, 1) conv = self.basic_block( input=conv, num_filters=num_filters[block], stride=stride, if_first=block == i == 0, name=conv_name) F.append(conv) base = F[-1] for i in [-2, -3]: b, c, w, h = F[i].shape if (w,h) == base.shape[2:]: base = base else: base = fluid.layers.conv2d_transpose( input=base, num_filters=c,filter_size=4, stride=2, padding=1,act=None, param_attr=w_nolr, bias_attr=w_nolr) base = fluid.layers.batch_norm(base, act = "relu", param_attr=w_nolr, bias_attr=w_nolr) base = fluid.layers.concat([base, F[i]], axis=1) base = fluid.layers.conv2d(base, num_filters=c, filter_size=1, param_attr=w_nolr, bias_attr=w_nolr) base = fluid.layers.conv2d(base, num_filters=c, filter_size=3,padding = 1, param_attr=w_nolr, bias_attr=w_nolr) base = fluid.layers.batch_norm(base, act = "relu", param_attr=w_nolr, bias_attr=w_nolr) base = fluid.layers.conv2d(base, num_filters=512, filter_size=1,bias_attr=w_nolr,param_attr=w_nolr) return base 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= 2 if stride==(1,1) else filter_size, dilation = 2 if stride==(1,1) else 1, stride=stride, padding=(filter_size - 1) // 2, groups=groups, act=None, param_attr=ParamAttr(name=name + "_weights",trainable = Trainable), bias_attr=False, name=name + '.conv2d.output.1') if name == "conv1": bn_name = "bn_" + name else: bn_name = "bn" + name[3:] return fluid.layers.batch_norm(input=conv, act=act, name=bn_name + '.output.1', param_attr=ParamAttr(name=bn_name + '_scale',trainable = Trainable), bias_attr=ParamAttr(bn_name + '_offset',trainable = Trainable), moving_mean_name=bn_name + '_mean', moving_variance_name=bn_name + '_variance', ) def shortcut(self, input, ch_out, stride, is_first, name): ch_in = input.shape[1] if ch_in != ch_out or stride != 1 or is_first == True: if stride == (1,1): return self.conv_bn_layer(input, ch_out, 1, 1, name=name) else: #stride == (2,2) return self.conv_bn_layer(input, ch_out, 1, stride, name=name) else: return input def bottleneck_block(self, input, num_filters, stride, name): 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, is_first=False, name=name + "_branch1") return fluid.layers.elementwise_add(x=short, y=conv2, act='relu', name=name + ".add.output.5") def basic_block(self, input, num_filters, stride, is_first, name): 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, is_first, name=name + "_branch1") return fluid.layers.elementwise_add(x=short, y=conv1, act='relu')