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From GitBox <...@apache.org>
Subject [GitHub] [incubator-singa] xuewanqi commented on a change in pull request #468: Distributted module
Date Mon, 15 Jul 2019 06:35:08 GMT
xuewanqi commented on a change in pull request #468: Distributted module
URL: https://github.com/apache/incubator-singa/pull/468#discussion_r303299477
 
 

 ##########
 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:
   have done it.

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