| import math | |
| import torch | |
| import torch.nn as nn | |
| import ESRGAN.block as B | |
| class RRDB_Net(nn.Module): | |
| def __init__(self, in_nc, out_nc, nf, nb, gc=32, upscale=4, norm_type=None, act_type='leakyrelu', \ | |
| mode='CNA', res_scale=1, upsample_mode='upconv'): | |
| super(RRDB_Net, self).__init__() | |
| n_upscale = int(math.log(upscale, 2)) | |
| if upscale == 3: | |
| n_upscale = 1 | |
| fea_conv = B.conv_block(in_nc, nf, kernel_size=3, norm_type=None, act_type=None) | |
| rb_blocks = [B.RRDB(nf, kernel_size=3, gc=32, stride=1, bias=True, pad_type='zero', \ | |
| norm_type=norm_type, act_type=act_type, mode='CNA') for _ in range(nb)] | |
| LR_conv = B.conv_block(nf, nf, kernel_size=3, norm_type=norm_type, act_type=None, mode=mode) | |
| if upsample_mode == 'upconv': | |
| upsample_block = B.upconv_blcok | |
| elif upsample_mode == 'pixelshuffle': | |
| upsample_block = B.pixelshuffle_block | |
| else: | |
| raise NotImplementedError('upsample mode [%s] is not found' % upsample_mode) | |
| if upscale == 3: | |
| upsampler = upsample_block(nf, nf, 3, act_type=act_type) | |
| else: | |
| upsampler = [upsample_block(nf, nf, act_type=act_type) for _ in range(n_upscale)] | |
| HR_conv0 = B.conv_block(nf, nf, kernel_size=3, norm_type=None, act_type=act_type) | |
| HR_conv1 = B.conv_block(nf, out_nc, kernel_size=3, norm_type=None, act_type=None) | |
| self.model = B.sequential(fea_conv, B.ShortcutBlock(B.sequential(*rb_blocks, LR_conv)),\ | |
| *upsampler, HR_conv0, HR_conv1) | |
| def forward(self, x): | |
| x = self.model(x) | |
| return x | |