singa-dev mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From GitBox <...@apache.org>
Subject [GitHub] [incubator-singa] nudles commented on a change in pull request #468: Distributted module
Date Wed, 03 Jul 2019 14:03:18 GMT
nudles commented on a change in pull request #468: Distributted module
URL: https://github.com/apache/incubator-singa/pull/468#discussion_r299961915
 
 

 ##########
 File path: examples/autograd/resnet_dist.py
 ##########
 @@ -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:
   can we reuse the code in resnet.py?

----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
users@infra.apache.org


With regards,
Apache Git Services

Mime
View raw message