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import torch
import numpy as np
import torch.nn.functional as F
def transI_fusebn(kernel, bn):
gamma = bn.weight
std = (bn.running_var + bn.eps).sqrt()
return kernel * ((gamma / std).reshape(-1, 1, 1, 1)), bn.bias - bn.running_mean * gamma / std
def transII_addbranch(kernels, biases):
return sum(kernels), sum(biases)
def transIII_1x1_kxk(k1, b1, k2, b2, groups):
if groups == 1:
k = F.conv2d(k2, k1.permute(1, 0, 2, 3)) #
b_hat = (k2 * b1.reshape(1, -1, 1, 1)).sum((1, 2, 3))
else:
k_slices = []
b_slices = []
k1_T = k1.permute(1, 0, 2, 3)
k1_group_width = k1.size(0) // groups
k2_group_width = k2.size(0) // groups
for g in range(groups):
k1_T_slice = k1_T[:, g*k1_group_width:(g+1)*k1_group_width, :, :]
k2_slice = k2[g*k2_group_width:(g+1)*k2_group_width, :, :, :]
k_slices.append(F.conv2d(k2_slice, k1_T_slice))
b_slices.append((k2_slice * b1[g*k1_group_width:(g+1)*k1_group_width].reshape(1, -1, 1, 1)).sum((1, 2, 3)))
k, b_hat = transIV_depthconcat(k_slices, b_slices)
return k, b_hat + b2
def transIV_depthconcat(kernels, biases):
return torch.cat(kernels, dim=0), torch.cat(biases)
def transV_avg(channels, kernel_size, groups):
input_dim = channels // groups
k = torch.zeros((channels, input_dim, kernel_size, kernel_size))
k[np.arange(channels), np.tile(np.arange(input_dim), groups), :, :] = 1.0 / kernel_size ** 2
return k
# This has not been tested with non-square kernels (kernel.size(2) != kernel.size(3)) nor even-size kernels
def transVI_multiscale(kernel, target_kernel_size):
H_pixels_to_pad = (target_kernel_size - kernel.size(2)) // 2
W_pixels_to_pad = (target_kernel_size - kernel.size(3)) // 2
return F.pad(kernel, [H_pixels_to_pad, H_pixels_to_pad, W_pixels_to_pad, W_pixels_to_pad])
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