XJDKC commented on a change in pull request #697: URL: https://github.com/apache/singa/pull/697#discussion_r433576758
########## 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: So we need to use Layer to encapsulate ReLU operator? ---------------------------------------------------------------- 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: us...@infra.apache.org
