nudles commented on a change in pull request #468: Distributted module URL: https://github.com/apache/incubator-singa/pull/468#discussion_r300913404
########## File path: examples/autograd/resnet_dist.py ########## @@ -23,229 +23,19 @@ from singa import autograd from singa import tensor from singa import device +from singa import opt 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 - - if __name__ == "__main__": - sgd = dist_opt.Dist_SGD(lr=0.1) + sgd = opt.SGD(lr=0.1, momentum=0.9, weight_decay=1e-5) + sgd = dist_opt.DistOpt(sgd) Review comment: merge dist_opt code into opt? then you can call `sgd = opt.DistOpt(sgd)` ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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