# 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 numpy as np import paddle from paddle import ParamAttr import paddle.nn as nn from paddle.nn import Conv2D, BatchNorm, Linear from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D from paddle.nn.initializer import Uniform import math from ppcls.arch.backbone.base.theseus_layer import TheseusLayer NET_CONFIG = { "18": { "block_type": "BasicBlock", "block_depth": [2, 2, 2, 2], "num_channels": [64, 64, 128, 256]}, "34": { "block_type": "BasicBlock", "block_depth": [3, 4, 6, 3], "num_channels": [64, 64, 128, 256]}, "50": { "block_type": "BottleneckBlock", "block_depth": [3, 4, 6, 3], "num_channels": [64, 256, 512, 1024]}, "101": { "block_type": "BottleneckBlock", "block_depth": [3, 4, 23, 3], "num_channels": [64, 256, 512, 1024]}, "152": { "block_type": "BottleneckBlock", "block_depth": [3, 8, 36, 3], "num_channels": [64, 256, 512, 1024]}, "200": { "block_type": "BottleneckBlock", "block_depth": [3, 12, 48, 3], "num_channels": [64, 256, 512, 1024]}, } class ConvBNLayer(TheseusLayer): def __init__(self, num_channels, num_filters, filter_size, stride=1, groups=1, is_vd_mode=False, act=None, lr_mult=1.0): super(ConvBNLayer, self).__init__() self.is_vd_mode = is_vd_mode self.act = act self.avgpool = AvgPool2D( kernel_size=2, stride=2, padding=0, ceil_mode=True) 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(learning_rate=lr_mult), bias_attr=False) self.bn = BatchNorm( num_filters, act=act, param_attr=ParamAttr(learning_rate=lr_mult), bias_attr=ParamAttr(learning_rate=lr_mult)) self.relu = nn.ReLU() def forward(self, x): if self.is_vd_mode: x = self.avgpool(x) x = self.conv(x) x = self.bn(x) if self.act: x = self.relu(x) return x class BottleneckBlock(TheseusLayer): def __init__(self, num_channels, num_filters, stride, shortcut=True, if_first=False, lr_mult=1.0, ): super(BottleneckBlock, self).__init__() self.conv0 = ConvBNLayer( num_channels=num_channels, num_filters=num_filters, filter_size=1, act='relu', lr_mult=lr_mult) self.conv1 = ConvBNLayer( num_channels=num_filters, num_filters=num_filters, filter_size=3, stride=stride, act='relu', lr_mult=lr_mult) self.conv2 = ConvBNLayer( num_channels=num_filters, num_filters=num_filters * 4, filter_size=1, act=None, lr_mult=lr_mult) if not shortcut: self.short = ConvBNLayer( num_channels=num_channels, num_filters=num_filters * 4, filter_size=1, stride=stride if if_first else 1, is_vd_mode=False if if_first else True, lr_mult=lr_mult) self.relu = nn.ReLU() self.shortcut = shortcut def forward(self, x): identity = x x = self.conv0(x) x = self.conv1(x) x = self.conv2(x) if self.shortcut: short = identity else: short = self.short(identity) x = paddle.add(x=x, y=short) x = self.relu(x) return x class BasicBlock(TheseusLayer): def __init__(self, num_channels, num_filters, stride, shortcut=True, if_first=False, lr_mult=1.0): super(BasicBlock, self).__init__() self.stride = stride self.conv0 = ConvBNLayer( num_channels=num_channels, num_filters=num_filters, filter_size=3, stride=stride, act='relu', lr_mult=lr_mult) self.conv1 = ConvBNLayer( num_channels=num_filters, num_filters=num_filters, filter_size=3, act=None, lr_mult=lr_mult) if not shortcut: self.short = ConvBNLayer( num_channels=num_channels, num_filters=num_filters, filter_size=1, stride=stride if if_first else 1, is_vd_mode=False if if_first else True, lr_mult=lr_mult) self.shortcut = shortcut self.relu = nn.ReLU() def forward(self, x): identity = x x = self.conv0(x) x = self.conv1(x) if self.shortcut: short = identity else: short = self.short(identity) x = paddle.add(x=x, y=short) x = self.relu(x) return x class ResNet(TheseusLayer): """ResNet model from `"Deep Residual Learning for Image Recognition" `_ paper. Parameters ---------- config : dict of string and list Information of whole model. version : str, "vb" and "vd" Different version of ResNet, version vd can perform better. class_dim : int, default 1000 Number of classification classes. lr_mult_list : list of float Control the learning rate of different stages """ def __init__(self, config, version="vd", class_dim=1000, lr_mult_list=[1.0, 1.0, 1.0, 1.0, 1.0]): super(ResNet, self).__init__() self.cfg = config self.lr_mult_list = lr_mult_list self.is_vd_mode = version == "vd" assert isinstance(self.lr_mult_list, ( list, tuple )), "lr_mult_list should be in (list, tuple) but got {}".format( type(self.lr_mult_list)) assert len( self.lr_mult_list ) == 5, "lr_mult_list length should be 5 but got {}".format( len(self.lr_mult_list)) self.num_filters = [64, 128, 256, 512] self.channels_mult = 1 if self.cfg["num_channels"][-1] == 256 else 4 self.stem_cfg = { "vb": [[3, 64, 7, 2]], "vd": [[3, 32, 3, 2], [32, 32, 3, 1], [32, 64, 3, 1]]} self.stem = nn.Sequential(*[ ConvBNLayer( num_channels=in_c, num_filters=out_c, filter_size=k, stride=s, act='relu', lr_mult=self.lr_mult_list[0]) for in_c, out_c, k, s in self.stem_cfg[version] ]) self.maxpool = MaxPool2D(kernel_size=3, stride=2, padding=1) self.block_list = [] for block in range(len(self.cfg["block_depth"])): shortcut = False for i in range(self.cfg["block_depth"][block]): self.block_list.append( globals()[self.cfg["block_type"]]( num_channels=self.cfg["num_channels"][block] if i == 0 else self.num_filters[block] * self.channels_mult, num_filters=self.num_filters[block], stride=2 if i == 0 and block != 0 else 1, shortcut=shortcut, if_first=block == i == 0 if version == "vd" else True, lr_mult=self.lr_mult_list[block + 1])) shortcut = True self.blocks = nn.Sequential(*self.block_list) self.avgpool = AdaptiveAvgPool2D(1) self.avgpool_channels = self.cfg["num_channels"][-1] * 2 stdv = 1.0 / math.sqrt(self.avgpool_channels * 1.0) self.out = Linear( self.avgpool_channels, class_dim, weight_attr=ParamAttr( initializer=Uniform(-stdv, stdv))) def forward(self, x): x = self.stem(x) x = self.maxpool(x) x = self.blocks(x) x = self.avgpool(x) x = paddle.reshape(x, shape=[-1, self.avgpool_channels]) x = self.out(x) return x def ResNet18(**args): model = ResNet(config=NET_CONFIG["18"], version="vb", **args) return model def ResNet18_vd(**args): model = ResNet(config=NET_CONFIG["18"], version="vd", **args) return model def ResNet50(**args): model = ResNet(config=NET_CONFIG["50"], version="vb", **args) return model def ResNet50_vd(**args): model = ResNet(config=NET_CONFIG["50"], version="vd", **args) return model def ResNet101(**args): model = ResNet(config=NET_CONFIG["101"], version="vb", **args) return model def ResNet101_vd(**args): model = ResNet(config=NET_CONFIG["101"], version="vd", **args) return model def ResNet152(**args): model = ResNet(config=NET_CONFIG["152"], version="vb", **args) return model def ResNet152_vd(**args): model = ResNet(config=NET_CONFIG["152"], version="vd", **args) return model def ResNet200(**args): model = ResNet(config=NET_CONFIG["200"], version="vb", **args) return model def ResNet200_vd(**args): model = ResNet(config=NET_CONFIG["200"], version="vd", **args) return model