Hi.
I want to change the code
        for s in setting:
            self.features.append(nn.Sequential(
                nn.AdaptiveAvgPool2d(s),
                nn.Conv2d(in_dim, reduction_dim, kernel_size=1, bias=False),
                nn.BatchNorm2d(reduction_dim, momentum=.95),
                nn.ReLU(inplace=True)
            ))

to
        for s in setting:
            self.features.append(nn.Sequential(
                nn.AvgPool2d(60 / s, stride=(60 / s)),
                nn.Conv2d(in_dim, reduction_dim, kernel_size=1, bias=False),
                nn.BatchNorm2d(reduction_dim, momentum=.95),
                nn.ReLU(inplace=True)
            ))

so that the AvgPool2d can be converted to caffe's pooling  layer as caffe has no AdaptiveAvgPool2d.
caffe can handle the cases where kernel sizes larger than the feature map sizes, which I think is the reason why pytorch has AdaptiveAvgPool2d.