forked from mrq/DL-Art-School
86 lines
2.9 KiB
Python
86 lines
2.9 KiB
Python
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import torch
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import torch.nn as nn
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from trainer.networks import register_model
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from utils.util import opt_get
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class WideKernelVgg(nn.Module):
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def __init__(self, nf=64, num_classes=2):
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super().__init__()
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self.net = nn.Sequential(
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# [64, 128, 128]
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nn.Conv2d(6, nf, 7, 1, 3, bias=True),
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nn.BatchNorm2d(nf, affine=True),
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nn.ReLU(),
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nn.Conv2d(nf, nf, 7, 1, 3, bias=False),
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nn.BatchNorm2d(nf, affine=True),
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nn.ReLU(),
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nn.Conv2d(nf, nf, 5, 2, 2, bias=False),
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nn.BatchNorm2d(nf, affine=True),
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nn.ReLU(),
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# [64, 64, 64]
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nn.Conv2d(nf, nf * 2, 3, 1, 1, bias=False),
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nn.BatchNorm2d(nf * 2, affine=True),
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nn.ReLU(),
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nn.Conv2d(nf * 2, nf * 2, 4, 2, 1, bias=False),
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nn.BatchNorm2d(nf * 2, affine=True),
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nn.ReLU(),
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# [128, 32, 32]
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nn.Conv2d(nf * 2, nf * 4, 3, 1, 1, bias=False),
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nn.BatchNorm2d(nf * 4, affine=True),
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nn.ReLU(),
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nn.Conv2d(nf * 4, nf * 4, 4, 2, 1, bias=False),
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nn.BatchNorm2d(nf * 4, affine=True),
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nn.ReLU(),
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# [256, 16, 16]
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nn.Conv2d(nf * 4, nf * 8, 3, 1, 1, bias=False),
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nn.BatchNorm2d(nf * 8, affine=True),
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nn.ReLU(),
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nn.Conv2d(nf * 8, nf * 8, 4, 2, 1, bias=False),
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nn.BatchNorm2d(nf * 8, affine=True),
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nn.ReLU(),
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# [512, 8, 8]
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nn.Conv2d(nf * 8, nf * 8, 3, 1, 1, bias=False),
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nn.BatchNorm2d(nf * 8, affine=True),
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nn.ReLU(),
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nn.Conv2d(nf * 8, nf * 8, 4, 2, 1, bias=False),
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nn.BatchNorm2d(nf * 8, affine=True),
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nn.ReLU(),
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nn.MaxPool2d(kernel_size=2),
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nn.Flatten(),
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nn.Linear(nf * 8 * 4 * 2, 100),
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nn.ReLU(),
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nn.Linear(100, num_classes)
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)
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# These normalization constants should be derived experimentally.
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self.log_fft_mean = torch.tensor([-3.5184, -4.071]).view(1,1,1,2)
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self.log_fft_std = torch.tensor([3.1660, 3.8042]).view(1,1,1,2)
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def forward(self, x):
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b,c,h,w = x.shape
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x_c = x.view(c*b, h, w)
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x_c = torch.view_as_real(torch.fft.rfft(x_c))
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# Log-normalize spectrogram
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x_c = (x_c.abs() ** 2).clip(min=1e-8, max=1e16)
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x_c = torch.log(x_c)
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x_c = (x_c - self.log_fft_mean.to(x.device)) / self.log_fft_std.to(x.device)
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# Return to expected input shape (b,c,h,w)
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x_c = x_c.permute(0, 3, 1, 2).reshape(b, c * 2, h, w // 2 + 1)
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return self.net(x_c)
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@register_model
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def register_wide_kernel_vgg(opt_net, opt):
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""" return a ResNet 18 object
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"""
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return WideKernelVgg(**opt_get(opt_net, ['kwargs'], {}))
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if __name__ == '__main__':
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vgg = WideKernelVgg()
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vgg(torch.randn(1,3,256,256))
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