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import math
import torch
import torch.nn as nn
def default_conv(in_channels, out_channels, kernel_size, bias=True, groups=1):
return nn.Conv2d(
in_channels, out_channels, kernel_size,
padding=(kernel_size // 2), bias=bias, groups=groups)
def default_norm(n_feats):
return nn.BatchNorm2d(n_feats)
def default_act():
return nn.ReLU(True)
def empty_h(x, n_feats):
'''
create an empty hidden state
input
x: B x T x 3 x H x W
output
h: B x C x H/4 x W/4
'''
b = x.size(0)
h, w = x.size()[-2:]
return x.new_zeros((b, n_feats, h//4, w//4))
class Normalization(nn.Conv2d):
"""Normalize input tensor value with convolutional layer"""
def __init__(self, mean=(0, 0, 0), std=(1, 1, 1)):
super(Normalization, self).__init__(3, 3, kernel_size=1)
tensor_mean = torch.Tensor(mean)
tensor_inv_std = torch.Tensor(std).reciprocal()
self.weight.data = torch.eye(3).mul(tensor_inv_std).view(3, 3, 1, 1)
self.bias.data = torch.Tensor(-tensor_mean.mul(tensor_inv_std))
for params in self.parameters():
params.requires_grad = False
class BasicBlock(nn.Sequential):
"""Convolution layer + Activation layer"""
def __init__(
self, in_channels, out_channels, kernel_size, bias=True,
conv=default_conv, norm=False, act=default_act):
modules = []
modules.append(
conv(in_channels, out_channels, kernel_size, bias=bias))
if norm: modules.append(norm(out_channels))
if act: modules.append(act())
super(BasicBlock, self).__init__(*modules)
class ResBlock(nn.Module):
def __init__(
self, n_feats, kernel_size, bias=True,
conv=default_conv, norm=False, act=default_act):
super(ResBlock, self).__init__()
modules = []
for i in range(2):
modules.append(conv(n_feats, n_feats, kernel_size, bias=bias))
if norm: modules.append(norm(n_feats))
if act and i == 0: modules.append(act())
self.body = nn.Sequential(*modules)
def forward(self, x):
res = self.body(x)
res += x
return res
class ResBlock_mobile(nn.Module):
def __init__(
self, n_feats, kernel_size, bias=True,
conv=default_conv, norm=False, act=default_act, dropout=False):
super(ResBlock_mobile, self).__init__()
modules = []
for i in range(2):
modules.append(conv(n_feats, n_feats, kernel_size, bias=False, groups=n_feats))
modules.append(conv(n_feats, n_feats, 1, bias=False))
if dropout and i == 0: modules.append(nn.Dropout2d(dropout))
if norm: modules.append(norm(n_feats))
if act and i == 0: modules.append(act())
self.body = nn.Sequential(*modules)
def forward(self, x):
res = self.body(x)
res += x
return res
class Upsampler(nn.Sequential):
def __init__(
self, scale, n_feats, bias=True,
conv=default_conv, norm=False, act=False):
modules = []
if (scale & (scale - 1)) == 0: # Is scale = 2^n?
for _ in range(int(math.log(scale, 2))):
modules.append(conv(n_feats, 4 * n_feats, 3, bias))
modules.append(nn.PixelShuffle(2))
if norm: modules.append(norm(n_feats))
if act: modules.append(act())
elif scale == 3:
modules.append(conv(n_feats, 9 * n_feats, 3, bias))
modules.append(nn.PixelShuffle(3))
if norm: modules.append(norm(n_feats))
if act: modules.append(act())
else:
raise NotImplementedError
super(Upsampler, self).__init__(*modules)
# Only support 1 / 2
class PixelSort(nn.Module):
"""The inverse operation of PixelShuffle
Reduces the spatial resolution, increasing the number of channels.
Currently, scale 0.5 is supported only.
Later, torch.nn.functional.pixel_sort may be implemented.
Reference:
http://pytorch.org/docs/0.3.0/_modules/torch/nn/modules/pixelshuffle.html#PixelShuffle
http://pytorch.org/docs/0.3.0/_modules/torch/nn/functional.html#pixel_shuffle
"""
def __init__(self, upscale_factor=0.5):
super(PixelSort, self).__init__()
self.upscale_factor = upscale_factor
def forward(self, x):
b, c, h, w = x.size()
x = x.view(b, c, 2, 2, h // 2, w // 2)
x = x.permute(0, 1, 5, 3, 2, 4).contiguous()
x = x.view(b, 4 * c, h // 2, w // 2)
return x
class Downsampler(nn.Sequential):
def __init__(
self, scale, n_feats, bias=True,
conv=default_conv, norm=False, act=False):
modules = []
if scale == 0.5:
modules.append(PixelSort())
modules.append(conv(4 * n_feats, n_feats, 3, bias))
if norm: modules.append(norm(n_feats))
if act: modules.append(act())
else:
raise NotImplementedError
super(Downsampler, self).__init__(*modules)
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