forked from mrq/DL-Art-School
13 lines
429 B
Python
13 lines
429 B
Python
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import torch.nn as nn
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def f_conv2d_bias(in_channels, out_channels):
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def padding_same(kernel, stride):
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return [((k - 1) * s + 1) // 2 for k, s in zip(kernel, stride)]
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padding = padding_same([3, 3], [1, 1])
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assert padding == [1, 1], padding
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return nn.Sequential(
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nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=[3, 3], stride=1, padding=1,
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bias=True))
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