paddle复现pytorch踩坑(一）：Resnet复现

paddlepaddle中关于Resnet复现的实际解决代码

1. 遍历模块初始化部分

pytorch代码：

for m in self.modules():
    if isinstance(m, nn.Conv2d):
        nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
    elif isinstance(m, nn.BatchNorm2d):
        nn.init.constant_(m.weight, 1)
        nn.init.constant_(m.bias, 0)

paddle代码：

for m in self.modules():
    if isinstance(m, nn.Conv2d):
        n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
        m.weight.data.normal_(0, math.sqrt(2. / n))
    elif isinstance(m, BatchNorm):
        m.weight.data.fill_(1)
        m.bias.data.zero_()

或者：

for m in self.modules():
    if isinstance(m, nn.Conv2d):
        n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
        m.weight.data.normal_(0, math.sqrt(2. / n))
    elif isinstance(m, BatchNorm):
        m.weight.data.fill_(1)
        m.bias.data.zero_()

2. 模块初始化

pytorch版本：

for n, m in self.layer4.named_modules():
    if 'conv2' in n:  # conv1 for resnet34
        m.dilation, m.padding, m.stride = (2, 2), (2, 2), (1, 1)
    elif 'downsample.0' in n:
        m.stride = (1, 1)

paddlepaddle版本：

for n, m in self.layer4.named_sublayers():
    if 'conv2' in n:  # conv1 for resnet34
        m.dilation = fluid.initializer.ConstantInitializer(value=[2,2])
        m.padding = fluid.initializer.ConstantInitializer(value=[2,2])
        m.stride = fluid.initializer.ConstantInitializer(value=[1,1])
    elif 'downsample.0' in n:
        m.stride = fluid.initializer.ConstantInitializer(value=[1,1])