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From GitBox <...@apache.org>
Subject [GitHub] [singa] XJDKC commented on a change in pull request #697: New Model Layer Operator API
Date Tue, 02 Jun 2020 02:32:32 GMT

XJDKC commented on a change in pull request #697:
URL: https://github.com/apache/singa/pull/697#discussion_r433577123



##########
File path: examples/mlp/module.py
##########
@@ -56,10 +56,9 @@ def forward(self, inputs):
         x = autograd.add_bias(x, self.b1)
         return x
 
-    def loss(self, out, ty):
-        return autograd.softmax_cross_entropy(out, ty)
-
-    def optim(self, loss, dist_option, spars):
+    def train_one_batch(self, x, y, dist_option, spars):
+        out = self.forward(x)
+        loss = autograd.softmax_cross_entropy(out, y)

Review comment:
       Yes. In this way, users only needs to use layers to define their model.

##########
File path: examples/cnn/model/resnet.py
##########
@@ -21,30 +21,31 @@
 # https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
 
 from singa import autograd
-from singa import module
+from singa import layer
+from singa import model
 
 
 def conv3x3(in_planes, out_planes, stride=1):
     """3x3 convolution with padding"""
-    return autograd.Conv2d(
+    return layer.Conv2d(
         in_planes,
         out_planes,
-        kernel_size=3,
+        3,
         stride=stride,
         padding=1,
         bias=False,
     )
 
 
-class BasicBlock(autograd.Layer):
+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 = autograd.BatchNorm2d(planes)
+        self.bn1 = layer.BatchNorm2d(planes)

Review comment:
       Are there other operators besides ReLU that need to be encapsulated by Layer?




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