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import torch.nn as nn
from lvdm.basics import avg_pool_nd, conv_nd
class Downsample(nn.Module):
"""
A downsampling layer with an optional convolution.
:param channels: channels in the inputs and outputs.
:param use_conv: a bool determining if a convolution is applied.
:param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
downsampling occurs in the inner-two dimensions.
"""
def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1):
super().__init__()
self.channels = channels
self.out_channels = out_channels or channels
self.use_conv = use_conv
self.dims = dims
stride = 2 if dims != 3 else (1, 2, 2)
if use_conv:
self.op = conv_nd(
dims, self.channels, self.out_channels, 3, stride=stride, padding=padding
)
else:
assert self.channels == self.out_channels
self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)
def forward(self, x):
assert x.shape[1] == self.channels
return self.op(x)
class ResnetBlock(nn.Module):
def __init__(self, in_c, out_c, down, ksize=3, sk=False, use_conv=True):
super().__init__()
ps = ksize // 2
if in_c != out_c or sk == False:
self.in_conv = nn.Conv2d(in_c, out_c, ksize, 1, ps)
else:
# print('n_in')
self.in_conv = None
self.block1 = nn.Conv2d(out_c, out_c, 3, 1, 1)
self.act = nn.ReLU()
self.block2 = nn.Conv2d(out_c, out_c, ksize, 1, ps)
if sk == False:
# self.skep = nn.Conv2d(in_c, out_c, ksize, 1, ps) # edit by zhouxiawang
self.skep = nn.Conv2d(out_c, out_c, ksize, 1, ps)
else:
self.skep = None
self.down = down
if self.down == True:
self.down_opt = Downsample(in_c, use_conv=use_conv)
def forward(self, x):
if self.down == True:
x = self.down_opt(x)
if self.in_conv is not None: # edit
x = self.in_conv(x)
h = self.block1(x)
h = self.act(h)
h = self.block2(h)
if self.skep is not None:
return h + self.skep(x)
else:
return h + x
class Adapter(nn.Module):
def __init__(self, channels=[320, 640, 1280, 1280], nums_rb=3, cin=64, ksize=3, sk=False, use_conv=True):
super(Adapter, self).__init__()
self.unshuffle = nn.PixelUnshuffle(8)
self.channels = channels
self.nums_rb = nums_rb
self.body = []
for i in range(len(channels)):
for j in range(nums_rb):
if (i != 0) and (j == 0):
self.body.append(
ResnetBlock(channels[i - 1], channels[i], down=True, ksize=ksize, sk=sk, use_conv=use_conv))
else:
self.body.append(
ResnetBlock(channels[i], channels[i], down=False, ksize=ksize, sk=sk, use_conv=use_conv))
self.body = nn.ModuleList(self.body)
self.conv_in = nn.Conv2d(cin, channels[0], 3, 1, 1)
def forward(self, x):
# unshuffle
x = self.unshuffle(x)
# extract features
features = []
x = self.conv_in(x)
for i in range(len(self.channels)):
for j in range(self.nums_rb):
idx = i * self.nums_rb + j
x = self.body[idx](x)
features.append(x)
return features
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