| # |
| # Licensed to the Apache Software Foundation (ASF) under one |
| # or more contributor license agreements. See the NOTICE file |
| # distributed with this work for additional information |
| # regarding copyright ownership. The ASF licenses this file |
| # to you 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. |
| # |
| |
| # the code is modified from |
| # https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py |
| |
| from singa import layer |
| from singa import model |
| |
| |
| def conv3x3(in_planes, out_planes, stride=1): |
| """3x3 convolution with padding""" |
| return layer.Conv2d( |
| in_planes, |
| out_planes, |
| 3, |
| stride=stride, |
| padding=1, |
| bias=False, |
| ) |
| |
| |
| class BasicBlock(layer.Layer): |
| expansion = 1 |
| |
| def __init__(self, inplanes, planes, stride=1, downsample=None): |
| super(BasicBlock, self).__init__() |
| self.conv1 = conv3x3(inplanes, planes, stride) |
| self.bn1 = layer.BatchNorm2d(planes) |
| self.conv2 = conv3x3(planes, planes) |
| self.bn2 = layer.BatchNorm2d(planes) |
| self.relu1 = layer.ReLU() |
| self.add = layer.Add() |
| self.relu2 = layer.ReLU() |
| self.downsample = downsample |
| self.stride = stride |
| |
| def forward(self, x): |
| residual = x |
| |
| out = self.conv1(x) |
| out = self.bn1(out) |
| out = self.relu1(out) |
| |
| out = self.conv2(out) |
| out = self.bn2(out) |
| |
| if self.downsample is not None: |
| residual = self.downsample(x) |
| |
| out = self.add(out, residual) |
| out = self.relu2(out) |
| |
| return out |
| |
| |
| class Bottleneck(layer.Layer): |
| expansion = 4 |
| |
| def __init__(self, inplanes, planes, stride=1, downsample=None): |
| super(Bottleneck, self).__init__() |
| self.conv1 = layer.Conv2d(inplanes, planes, 1, bias=False) |
| self.bn1 = layer.BatchNorm2d(planes) |
| self.relu1 = layer.ReLU() |
| self.conv2 = layer.Conv2d(planes, |
| planes, |
| 3, |
| stride=stride, |
| padding=1, |
| bias=False) |
| self.bn2 = layer.BatchNorm2d(planes) |
| self.relu2 = layer.ReLU() |
| self.conv3 = layer.Conv2d(planes, |
| planes * self.expansion, |
| 1, |
| bias=False) |
| self.bn3 = layer.BatchNorm2d(planes * self.expansion) |
| |
| self.add = layer.Add() |
| self.relu3 = layer.ReLU() |
| |
| self.downsample = downsample |
| self.stride = stride |
| |
| def forward(self, x): |
| residual = x |
| |
| out = self.conv1(x) |
| out = self.bn1(out) |
| out = self.relu1(out) |
| |
| out = self.conv2(out) |
| out = self.bn2(out) |
| out = self.relu2(out) |
| |
| out = self.conv3(out) |
| out = self.bn3(out) |
| |
| if self.downsample is not None: |
| residual = self.downsample(x) |
| |
| out = self.add(out, residual) |
| out = self.relu3(out) |
| |
| return out |
| |
| |
| __all__ = [ |
| 'ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152' |
| ] |
| |
| |
| class ResNet(model.Model): |
| |
| def __init__(self, block, layers, num_classes=10, num_channels=3): |
| self.inplanes = 64 |
| super(ResNet, self).__init__() |
| self.num_classes = num_classes |
| self.input_size = 224 |
| self.dimension = 4 |
| self.conv1 = layer.Conv2d(num_channels, |
| 64, |
| 7, |
| stride=2, |
| padding=3, |
| bias=False) |
| self.bn1 = layer.BatchNorm2d(64) |
| self.relu = layer.ReLU() |
| self.maxpool = layer.MaxPool2d(kernel_size=3, stride=2, padding=1) |
| self.layer1, layers1 = self._make_layer(block, 64, layers[0]) |
| self.layer2, layers2 = self._make_layer(block, 128, layers[1], stride=2) |
| self.layer3, layers3 = self._make_layer(block, 256, layers[2], stride=2) |
| self.layer4, layers4 = self._make_layer(block, 512, layers[3], stride=2) |
| self.avgpool = layer.AvgPool2d(7, stride=1) |
| self.flatten = layer.Flatten() |
| self.fc = layer.Linear(num_classes) |
| self.softmax_cross_entropy = layer.SoftMaxCrossEntropy() |
| |
| self.register_layers(*layers1, *layers2, *layers3, *layers4) |
| |
| def _make_layer(self, block, planes, blocks, stride=1): |
| downsample = None |
| if stride != 1 or self.inplanes != planes * block.expansion: |
| conv = layer.Conv2d( |
| self.inplanes, |
| planes * block.expansion, |
| 1, |
| stride=stride, |
| bias=False, |
| ) |
| bn = layer.BatchNorm2d(planes * block.expansion) |
| |
| def _downsample(x): |
| return bn(conv(x)) |
| |
| downsample = _downsample |
| |
| layers = [] |
| layers.append(block(self.inplanes, planes, stride, downsample)) |
| self.inplanes = planes * block.expansion |
| for i in range(1, blocks): |
| layers.append(block(self.inplanes, planes)) |
| |
| def forward(x): |
| for layer in layers: |
| x = layer(x) |
| return x |
| |
| return forward, layers |
| |
| def forward(self, x): |
| 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 = self.flatten(x) |
| x = self.fc(x) |
| |
| return x |
| |
| def train_one_batch(self, x, y, dist_option, spars): |
| out = self.forward(x) |
| loss = self.softmax_cross_entropy(out, y) |
| |
| if dist_option == 'plain': |
| self.optimizer(loss) |
| elif dist_option == 'half': |
| self.optimizer.backward_and_update_half(loss) |
| elif dist_option == 'partialUpdate': |
| self.optimizer.backward_and_partial_update(loss) |
| elif dist_option == 'sparseTopK': |
| self.optimizer.backward_and_sparse_update(loss, |
| topK=True, |
| spars=spars) |
| elif dist_option == 'sparseThreshold': |
| self.optimizer.backward_and_sparse_update(loss, |
| topK=False, |
| spars=spars) |
| return out, loss |
| |
| def set_optimizer(self, optimizer): |
| self.optimizer = optimizer |
| |
| |
| def resnet18(pretrained=False, **kwargs): |
| """Constructs a ResNet-18 model. |
| |
| Args: |
| pretrained (bool): If True, returns a model pre-trained on ImageNet. |
| |
| Returns: |
| The created ResNet-18 model. |
| """ |
| model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs) |
| |
| return model |
| |
| |
| def resnet34(pretrained=False, **kwargs): |
| """Constructs a ResNet-34 model. |
| |
| Args: |
| pretrained (bool): If True, returns a model pre-trained on ImageNet. |
| |
| Returns: |
| The created ResNet-34 model. |
| """ |
| model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs) |
| |
| return model |
| |
| |
| def resnet50(pretrained=False, **kwargs): |
| """Constructs a ResNet-50 model. |
| |
| Args: |
| pretrained (bool): If True, returns a model pre-trained on ImageNet. |
| |
| Returns: |
| The created ResNet-50 model. |
| """ |
| model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs) |
| |
| return model |
| |
| |
| def resnet101(pretrained=False, **kwargs): |
| """Constructs a ResNet-101 model. |
| |
| Args: |
| pretrained (bool): If True, returns a model pre-trained on ImageNet. |
| |
| Returns: |
| The created ResNet-101 model. |
| """ |
| model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs) |
| |
| return model |
| |
| |
| def resnet152(pretrained=False, **kwargs): |
| """Constructs a ResNet-152 model. |
| |
| Args: |
| pretrained (bool): If True, returns a model pre-trained on ImageNet. |
| |
| Returns: |
| The created ResNet-152 model. |
| """ |
| model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs) |
| |
| return model |
| |
| |
| __all__ = [ |
| 'ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152' |
| ] |
