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BNInception architecture

See original GitHub issue

Seems like there is a mistake in BNInception architecture after the 29th Oct commit. I try to use its convolutional part as a pretrained model for transfer learning and get this during the forward pass:

RuntimeError: given groups=1, weight of size [64, 192, 1, 1], expected input[1, 64, 8, 8] to have 192 channels, but got 64 channels instead

Issue Analytics

State:
Created 5 years ago
Comments:5 (1 by maintainers)

Top GitHub Comments

1reaction

Bonsencommented, Dec 28, 2018

@Cadene He maybe uses fastai…

0reactions

jaideep11061982commented, Feb 6, 2019

please ignore above layers here is error self.inception_3a_3x3_bn = nn.BatchNorm2d(64, affine=True) self.inception_3a_relu_3x3 = nn.ReLU (inplace) self.inception_3a_double_3x3_reduce = nn.Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1))

Batch norm would return only 64 channels of certain sizes ,reduce layer needs 192 channels

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