# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # # 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. import paddle import paddle.nn as nn from typing import Type, Any, Callable, Union, List, Optional def conv3x3(in_planes: int, out_planes: int, stride: int=1, groups: int=1, dilation: int=1) ->paddle.nn.Conv2D: """3x3 convolution with padding""" return nn.Conv2D(in_planes, out_planes, kernel_size=3, stride=stride, padding=dilation, groups=groups, dilation=dilation, bias_attr=False) def conv1x1(in_planes: int, out_planes: int, stride: int=1) ->paddle.nn.Conv2D: """1x1 convolution""" return nn.Conv2D(in_planes, out_planes, kernel_size=1, stride=stride, bias_attr=False) class BasicBlock(nn.Layer): expansion: int = 1 def __init__(self, inplanes: int, planes: int, stride: int=1, downsample: Optional[nn.Layer]=None, groups: int=1, base_width: int=64, dilation: int=1, norm_layer: Optional[Callable[..., paddle. nn.Layer]]=None) ->None: super(BasicBlock, self).__init__() if norm_layer is None: norm_layer = nn.BatchNorm2D if groups != 1 or base_width != 64: raise ValueError( 'BasicBlock only supports groups=1 and base_width=64') if dilation > 1: raise NotImplementedError( 'Dilation > 1 not supported in BasicBlock') self.conv1 = conv3x3(inplanes, planes, stride) self.bn1 = norm_layer(planes) self.relu = paddle.nn.ReLU() self.conv2 = conv3x3(planes, planes) self.bn2 = norm_layer(planes) self.downsample = downsample self.stride = stride def forward(self, x: paddle.Tensor) -> paddle.Tensor: identity = x out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) if self.downsample is not None: identity = self.downsample(x) out += identity out = self.relu(out) return out class Bottleneck(nn.Layer): expansion: int = 4 def __init__(self, inplanes: int, planes: int, stride: int=1, downsample: Optional[nn.Layer]=None, groups: int=1, base_width: int=64, dilation: int=1, norm_layer: Optional[Callable[..., paddle. nn.Layer]]=None) ->None: super(Bottleneck, self).__init__() if norm_layer is None: norm_layer = nn.BatchNorm2D width = int(planes * (base_width / 64.0)) * groups self.conv1 = conv1x1(inplanes, width) self.bn1 = norm_layer(width) self.conv2 = conv3x3(width, width, stride, groups, dilation) self.bn2 = norm_layer(width) self.conv3 = conv1x1(width, planes * self.expansion) self.bn3 = norm_layer(planes * self.expansion) self.relu = paddle.nn.ReLU() self.downsample = downsample self.stride = stride def forward(self, x: paddle.Tensor) -> paddle.Tensor: identity = x out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) out = self.relu(out) out = self.conv3(out) out = self.bn3(out) if self.downsample is not None: identity = self.downsample(x) out += identity out = self.relu(out) return out class ResNet(nn.Layer): def __init__(self, block: Type[Union[BasicBlock, Bottleneck]], layers: List[int], num_classes: int=1000, zero_init_residual: bool=False, groups: int=1, width_per_group: int=64, replace_stride_with_dilation: Optional[List[bool]]=None, norm_layer: Optional[Callable[..., paddle.nn.Layer]]=None) ->None: super(ResNet, self).__init__() if norm_layer is None: norm_layer = nn.BatchNorm2D self._norm_layer = norm_layer self.inplanes = 64 self.dilation = 1 if replace_stride_with_dilation is None: replace_stride_with_dilation = [False, False, False] if len(replace_stride_with_dilation) != 3: raise ValueError( 'replace_stride_with_dilation should be None or a 3-element tuple, got {}' .format(replace_stride_with_dilation)) self.groups = groups self.base_width = width_per_group self.conv1 = nn.Conv2D(3, self.inplanes, kernel_size=7, stride=2, padding=3, bias_attr=False) self.bn1 = norm_layer(self.inplanes) self.relu = paddle.nn.ReLU() self.maxpool = nn.MaxPool2D(kernel_size=3, stride=2, padding=1) self.layer1 = self._make_layer(block, 64, layers[0]) self.layer2 = self._make_layer(block, 128, layers[1], stride=2, dilate=replace_stride_with_dilation[0]) self.layer3 = self._make_layer(block, 256, layers[2], stride=2, dilate=replace_stride_with_dilation[1]) self.layer4 = self._make_layer(block, 512, layers[3], stride=2, dilate=replace_stride_with_dilation[2]) self.avgpool = nn.AdaptiveAvgPool2D((1, 1)) self.fc = nn.Linear(512 * block.expansion, num_classes) def _make_layer(self, block: Type[Union[BasicBlock, Bottleneck]], planes: int, blocks: int, stride: int=1, dilate: bool=False ) ->paddle.nn.Sequential: norm_layer = self._norm_layer downsample = None previous_dilation = self.dilation if dilate: self.dilation *= stride stride = 1 if stride != 1 or self.inplanes != planes * block.expansion: downsample = nn.Sequential(conv1x1(self.inplanes, planes * block.expansion, stride), norm_layer(planes * block.expansion)) layers = [] layers.append(block(self.inplanes, planes, stride, downsample, self .groups, self.base_width, previous_dilation, norm_layer)) self.inplanes = planes * block.expansion for _ in range(1, blocks): layers.append(block(self.inplanes, planes, groups=self.groups, base_width=self.base_width, dilation=self.dilation, norm_layer=norm_layer)) return nn.Sequential(*layers) def _forward_impl(self, x: paddle.Tensor) ->paddle.Tensor: x = self.conv1(x) x = self.bn1(x) x = self.relu(x) x = self.maxpool(x) x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) x = self.layer4(x) x = self.avgpool(x) x= paddle.flatten(x,1) x = self.fc(x) return x def forward(self, x: paddle.Tensor) -> paddle.Tensor: return self._forward_impl(x) def _resnet(arch: str, block: Type[Union[BasicBlock, Bottleneck]], layers: List[int], pretrained: bool, progress: bool, **kwargs: Any) ->ResNet: model = ResNet(block, layers, **kwargs) return model def resnet34(pretrained: bool=False, progress: bool=True, **kwargs: Any ) ->ResNet: """ResNet-34 model from `"Deep Residual Learning for Image Recognition" `_. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the download to stderr """ return _resnet('resnet34', BasicBlock, [3, 4, 6, 3], pretrained, progress, **kwargs)