tchaton commented on issue #9648: BatchNorm Evaluation Mode Backward Fails with cudnn Enabled URL: https://github.com/apache/incubator-mxnet/issues/9648#issuecomment-522504996 ``` import torch import torch.nn as nn import torch.nn.functional as F import math import numpy as np def _upsample(x): h, w = x.shape[2:] return F.upsample_bilinear(x, size=(h * 2, w * 2)) def upsample_conv(x, conv): return conv(_upsample(x)) class genBlock(nn.Module): def __init__(self, in_channels, out_channels, activation=F.relu, hidden_channels=None, ksize=3, pad=1, upsample=False, n_classes=0): super(genBlock, self).__init__() self.activation = activation self.upsample = upsample self.learnable_sc = in_channels != out_channels or upsample hidden_channels = out_channels if hidden_channels is None else hidden_channels self.n_classes = n_classes self.c1 = nn.Conv2d(in_channels, hidden_channels, kernel_size=ksize, padding=pad) #nn.init.xavier_uniform_(self.c1.weight.data, math.sqrt(2)) self.c2 = nn.Conv2d(hidden_channels, out_channels, kernel_size=ksize, padding=pad) #nn.init.xavier_uniform_(self.c2.weight.data, math.sqrt(2)) self.b1 = nn.BatchNorm2d(in_channels) self.b2 = nn.BatchNorm2d(hidden_channels) if self.learnable_sc: self.c_sc = nn.Conv2d(in_channels, out_channels, kernel_size=ksize, padding=pad) def residual(self, x): h = x h = self.b1(h) h = self.activation(h) h = upsample_conv(h, self.c1) if self.upsample else self.c1(h) h = self.b2(h) h = self.activation(h) h = self.c2(h) return h def shortcut(self, x): if self.learnable_sc: x = upsample_conv(x, self.c_sc) if self.upsample else self.c_sc(x) return x else: return x def forward(self, input): return self.residual(input) + self.shortcut(input) if __name__== "__main__": noise = torch.randn(1,256, 4, 4).cuda() g = genBlock(256, 256, activation=F.relu, upsample=True).cuda() #g.apply(weights_init) out = g(noise) print(out.shape) ```
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