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Posted to dev@singa.apache.org by GitBox <gi...@apache.org> on 2019/07/05 06:32:45 UTC
[GitHub] [incubator-singa] xuewanqi commented on a change in pull request
#468: Distributted module
xuewanqi commented on a change in pull request #468: Distributted module
URL: https://github.com/apache/incubator-singa/pull/468#discussion_r300556121
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File path: examples/autograd/resnet_dist.py
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@@ -0,0 +1,295 @@
+#
+# 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 autograd
+from singa import tensor
+from singa import device
+from singa import dist_opt
+
+import numpy as np
+from tqdm import trange
+
+
+__all__ = [
+ "ResNet",
+ "resnet18",
+ "resnet34",
+ "resnet50",
+ "resnet101",
+ "resnet152",
+]
+
+
+def conv3x3(in_planes, out_planes, stride=1):
+ """3x3 convolution with padding"""
+ return autograd.Conv2d(
+ in_planes,
+ out_planes,
+ kernel_size=3,
+ stride=stride,
+ padding=1,
+ bias=False,
+ )
+
+
+class BasicBlock(autograd.Layer):
+ expansion = 1
+
+ def __init__(self, inplanes, planes, stride=1, downsample=None):
+ super(BasicBlock, self).__init__()
+ self.conv1 = conv3x3(inplanes, planes, stride)
+ self.bn1 = autograd.BatchNorm2d(planes)
+ self.conv2 = conv3x3(planes, planes)
+ self.bn2 = autograd.BatchNorm2d(planes)
+ self.downsample = downsample
+ self.stride = stride
+
+ def __call__(self, x):
+ residual = x
+
+ out = self.conv1(x)
+ out = self.bn1(out)
+ out = autograd.relu(out)
+
+ out = self.conv2(out)
+ out = self.bn2(out)
+
+ if self.downsample is not None:
+ residual = self.downsample(x)
+
+ out = autograd.add(out, residual)
+ out = autograd.relu(out)
+
+ return out
+
+
+class Bottleneck(autograd.Layer):
+ expansion = 4
+
+ def __init__(self, inplanes, planes, stride=1, downsample=None):
+ super(Bottleneck, self).__init__()
+ self.conv1 = autograd.Conv2d(
+ inplanes, planes, kernel_size=1, bias=False
+ )
+ self.bn1 = autograd.BatchNorm2d(planes)
+ self.conv2 = autograd.Conv2d(
+ planes, planes, kernel_size=3, stride=stride, padding=1, bias=False
+ )
+ self.bn2 = autograd.BatchNorm2d(planes)
+ self.conv3 = autograd.Conv2d(
+ planes, planes * self.expansion, kernel_size=1, bias=False
+ )
+ self.bn3 = autograd.BatchNorm2d(planes * self.expansion)
+
+ self.downsample = downsample
+ self.stride = stride
+
+ def __call__(self, x):
+ residual = x
+
+ out = self.conv1(x)
+ out = self.bn1(out)
+ out = autograd.relu(out)
+
+ out = self.conv2(out)
+ out = self.bn2(out)
+ out = autograd.relu(out)
+
+ out = self.conv3(out)
+ out = self.bn3(out)
+
+ if self.downsample is not None:
+ residual = self.downsample(x)
+
+ out = autograd.add(out, residual)
+ out = autograd.relu(out)
+
+ return out
+
+
+class ResNet(autograd.Layer):
+ def __init__(self, block, layers, num_classes=1000):
+ self.inplanes = 64
+ super(ResNet, self).__init__()
+ self.conv1 = autograd.Conv2d(
+ 3, 64, kernel_size=7, stride=2, padding=3, bias=False
+ )
+ self.bn1 = autograd.BatchNorm2d(64)
+ self.maxpool = autograd.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)
+ self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
+ self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
+ self.avgpool = autograd.AvgPool2d(7, stride=1)
+ self.fc = autograd.Linear(512 * block.expansion, num_classes)
+
+ def _make_layer(self, block, planes, blocks, stride=1):
+ downsample = None
+ if stride != 1 or self.inplanes != planes * block.expansion:
+ conv = autograd.Conv2d(
+ self.inplanes,
+ planes * block.expansion,
+ kernel_size=1,
+ stride=stride,
+ bias=False,
+ )
+ bn = autograd.BatchNorm2d(planes * block.expansion)
+
+ def downsample(x):
+ return bn(conv(x))
+
+ 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
+
+ def __call__(self, x):
+ x = self.conv1(x)
+ x = self.bn1(x)
+ x = autograd.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 = autograd.flatten(x)
+ x = self.fc(x)
+
+ return x
+
+
+def resnet18(pretrained=False, **kwargs):
+ """Constructs a ResNet-18 model.
+
+ Args:
+ pretrained (bool): If True, returns a model pre-trained on ImageNet
+ """
+ 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
+ """
+ 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
+ """
+ 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
+ """
+ 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
+ """
+ model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
+
+ return model
+
+
Review comment:
yes, will import the resnet function from resnet.py.
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