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| import math | |
| import torch.nn as nn | |
| import models.basicblock as B | |
| """ | |
| # -------------------------------------------- | |
| # SR network with Residual in Residual Dense Block (RRDB) | |
| # "ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks" | |
| # -------------------------------------------- | |
| """ | |
| class RRDB(nn.Module): | |
| """ | |
| gc: number of growth channels | |
| nb: number of RRDB | |
| """ | |
| def __init__(self, in_nc=3, out_nc=3, nc=64, nb=23, gc=32, upscale=4, act_mode='L', upsample_mode='upconv'): | |
| super(RRDB, self).__init__() | |
| assert 'R' in act_mode or 'L' in act_mode, 'Examples of activation function: R, L, BR, BL, IR, IL' | |
| n_upscale = int(math.log(upscale, 2)) | |
| if upscale == 3: | |
| n_upscale = 1 | |
| m_head = B.conv(in_nc, nc, mode='C') | |
| m_body = [B.RRDB(nc, gc=32, mode='C'+act_mode) for _ in range(nb)] | |
| m_body.append(B.conv(nc, nc, mode='C')) | |
| if upsample_mode == 'upconv': | |
| upsample_block = B.upsample_upconv | |
| elif upsample_mode == 'pixelshuffle': | |
| upsample_block = B.upsample_pixelshuffle | |
| elif upsample_mode == 'convtranspose': | |
| upsample_block = B.upsample_convtranspose | |
| else: | |
| raise NotImplementedError('upsample mode [{:s}] is not found'.format(upsample_mode)) | |
| if upscale == 3: | |
| m_uper = upsample_block(nc, nc, mode='3'+act_mode) | |
| else: | |
| m_uper = [upsample_block(nc, nc, mode='2'+act_mode) for _ in range(n_upscale)] | |
| H_conv0 = B.conv(nc, nc, mode='C'+act_mode) | |
| H_conv1 = B.conv(nc, out_nc, mode='C') | |
| m_tail = B.sequential(H_conv0, H_conv1) | |
| self.model = B.sequential(m_head, B.ShortcutBlock(B.sequential(*m_body)), *m_uper, m_tail) | |
| def forward(self, x): | |
| x = self.model(x) | |
| return x | |