| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| import models.modules.module_util as mutil | |
| from basicsr.archs.arch_util import flow_warp, ResidualBlockNoBN | |
| from models.modules.module_util import initialize_weights_xavier | |
| class DenseBlock(nn.Module): | |
| def __init__(self, channel_in, channel_out, init='xavier', gc=32, bias=True): | |
| super(DenseBlock, self).__init__() | |
| self.conv1 = nn.Conv2d(channel_in, gc, 3, 1, 1, bias=bias) | |
| self.conv2 = nn.Conv2d(channel_in + gc, gc, 3, 1, 1, bias=bias) | |
| self.conv3 = nn.Conv2d(channel_in + 2 * gc, gc, 3, 1, 1, bias=bias) | |
| self.conv4 = nn.Conv2d(channel_in + 3 * gc, gc, 3, 1, 1, bias=bias) | |
| self.conv5 = nn.Conv2d(channel_in + 4 * gc, channel_out, 3, 1, 1, bias=bias) | |
| self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) | |
| self.H = None | |
| if init == 'xavier': | |
| mutil.initialize_weights_xavier([self.conv1, self.conv2, self.conv3, self.conv4], 0.1) | |
| else: | |
| mutil.initialize_weights([self.conv1, self.conv2, self.conv3, self.conv4], 0.1) | |
| mutil.initialize_weights(self.conv5, 0) | |
| def forward(self, x): | |
| if isinstance(x, list): | |
| x = x[0] | |
| x1 = self.lrelu(self.conv1(x)) | |
| x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1))) | |
| x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1))) | |
| x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1))) | |
| x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1)) | |
| return x5 | |
| class DenseBlock_v2(nn.Module): | |
| def __init__(self, channel_in, channel_out, groups, init='xavier', gc=32, bias=True): | |
| super(DenseBlock_v2, self).__init__() | |
| self.conv1 = nn.Conv2d(channel_in, gc, 3, 1, 1, bias=bias) | |
| self.conv2 = nn.Conv2d(channel_in + gc, gc, 3, 1, 1, bias=bias) | |
| self.conv3 = nn.Conv2d(channel_in + 2 * gc, gc, 3, 1, 1, bias=bias) | |
| self.conv4 = nn.Conv2d(channel_in + 3 * gc, gc, 3, 1, 1, bias=bias) | |
| self.conv5 = nn.Conv2d(channel_in + 4 * gc, channel_out, 3, 1, 1, bias=bias) | |
| self.conv_final = nn.Conv2d(channel_out*groups, channel_out, 3, 1, 1, bias=bias) | |
| self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) | |
| if init == 'xavier': | |
| mutil.initialize_weights_xavier([self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1) | |
| else: | |
| mutil.initialize_weights([self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1) | |
| mutil.initialize_weights(self.conv_final, 0) | |
| def forward(self, x): | |
| res = [] | |
| for xi in x: | |
| x1 = self.lrelu(self.conv1(xi)) | |
| x2 = self.lrelu(self.conv2(torch.cat((xi, x1), 1))) | |
| x3 = self.lrelu(self.conv3(torch.cat((xi, x1, x2), 1))) | |
| x4 = self.lrelu(self.conv4(torch.cat((xi, x1, x2, x3), 1))) | |
| x5 = self.lrelu(self.conv5(torch.cat((xi, x1, x2, x3, x4), 1))) | |
| res.append(x5) | |
| res = torch.cat(res, dim=1) | |
| res = self.conv_final(res) | |
| return res | |
| def subnet(net_structure, init='xavier'): | |
| def constructor(channel_in, channel_out, groups=None): | |
| if net_structure == 'DBNet': | |
| if init == 'xavier': | |
| return DenseBlock(channel_in, channel_out, init) | |
| elif init == 'xavier_v2': | |
| return DenseBlock_v2(channel_in, channel_out, groups, 'xavier') | |
| else: | |
| return DenseBlock(channel_in, channel_out) | |
| else: | |
| return None | |
| return constructor | |