30 lines
1.2 KiB
Python
30 lines
1.2 KiB
Python
# This file contains modules common to various models
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import torch
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import torch.nn as nn
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from models.common import Conv
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class surrogate_focus(nn.Module):
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# surrogate_focus wh information into c-space
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def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
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super(surrogate_focus, self).__init__()
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self.conv = Conv(c1 * 4, c2, k, s, p, g, act=act)
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with torch.no_grad():
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self.convsp = nn.Conv2d(3, 12, (2, 2), groups=1, bias=False, stride=(2, 2))
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self.convsp.weight.data = torch.zeros(self.convsp.weight.shape).float()
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for i in range(4):
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for j in range(3):
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ch = i*3 + j
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if ch>=0 and ch<3:
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self.convsp.weight[ch:ch+1, j:j+1, 0, 0] = 1
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elif ch>=3 and ch<6:
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self.convsp.weight[ch:ch+1, j:j+1, 1, 0] = 1
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elif ch>=6 and ch<9:
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self.convsp.weight[ch:ch+1, j:j+1, 0, 1] = 1
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elif ch>=9 and ch<12:
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self.convsp.weight[ch:ch+1, j:j+1, 1, 1] = 1
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def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2)
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return self.conv(self.convsp(x)) |