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| import torch | |
| import torch.nn as nn | |
| from collections import OrderedDict | |
| from lvdm.models.modules.util import ( | |
| zero_module, | |
| conv_nd, | |
| avg_pool_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) | |
| 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 |