67 lines
2.5 KiB
Python
67 lines
2.5 KiB
Python
import math
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import torch.nn as nn
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import models.basicblock as B
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"""
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# --------------------------------------------
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# simplified information multi-distillation
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# network (IMDN) for SR
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# --------------------------------------------
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References:
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@inproceedings{hui2019lightweight,
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title={Lightweight Image Super-Resolution with Information Multi-distillation Network},
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author={Hui, Zheng and Gao, Xinbo and Yang, Yunchu and Wang, Xiumei},
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booktitle={Proceedings of the 27th ACM International Conference on Multimedia (ACM MM)},
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pages={2024--2032},
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year={2019}
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}
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@inproceedings{zhang2019aim,
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title={AIM 2019 Challenge on Constrained Super-Resolution: Methods and Results},
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author={Kai Zhang and Shuhang Gu and Radu Timofte and others},
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booktitle={IEEE International Conference on Computer Vision Workshops},
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year={2019}
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}
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# --------------------------------------------
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"""
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# --------------------------------------------
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# modified version, https://github.com/Zheng222/IMDN
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# first place solution for AIM 2019 challenge
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# --------------------------------------------
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class IMDN(nn.Module):
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def __init__(self, in_nc=3, out_nc=3, nc=64, nb=8, upscale=4, act_mode='L', upsample_mode='pixelshuffle', negative_slope=0.05):
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"""
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in_nc: channel number of input
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out_nc: channel number of output
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nc: channel number
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nb: number of residual blocks
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upscale: up-scale factor
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act_mode: activation function
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upsample_mode: 'upconv' | 'pixelshuffle' | 'convtranspose'
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"""
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super(IMDN, self).__init__()
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assert 'R' in act_mode or 'L' in act_mode, 'Examples of activation function: R, L, BR, BL, IR, IL'
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m_head = B.conv(in_nc, nc, mode='C')
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m_body = [B.IMDBlock(nc, nc, mode='C'+act_mode, negative_slope=negative_slope) for _ in range(nb)]
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m_body.append(B.conv(nc, nc, mode='C'))
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if upsample_mode == 'upconv':
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upsample_block = B.upsample_upconv
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elif upsample_mode == 'pixelshuffle':
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upsample_block = B.upsample_pixelshuffle
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elif upsample_mode == 'convtranspose':
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upsample_block = B.upsample_convtranspose
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else:
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raise NotImplementedError('upsample mode [{:s}] is not found'.format(upsample_mode))
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m_uper = upsample_block(nc, out_nc, mode=str(upscale))
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self.model = B.sequential(m_head, B.ShortcutBlock(B.sequential(*m_body)), *m_uper)
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def forward(self, x):
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x = self.model(x)
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return x
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