| import torch.nn as nn |
| from basicsr.archs.mair_arch import MaIR |
|
|
| def buildMaIR_Small(upscale=2): |
| return MaIR(img_size=64, |
| patch_size=1, |
| in_chans=3, |
| embed_dim=60, |
| depths=(6, 6, 6, 6), |
| mlp_ratio=1.6, |
| ssm_ratio=1.4, |
| drop_rate=0., |
| norm_layer=nn.LayerNorm, |
| patch_norm=True, |
| use_checkpoint=False, |
| upscale=upscale, |
| img_range=1., |
| upsampler='pixelshuffledirect', |
| resi_connection='1conv') |
|
|
| def buildMaIR_Tiny(upscale=2): |
| return MaIR(img_size=64, |
| patch_size=1, |
| in_chans=3, |
| embed_dim=60, |
| depths=(6, 6, 6, 6), |
| mlp_ratio=1.6, |
| ssm_ratio=1.1, |
| d_state=1, |
| drop_rate=0., |
| norm_layer=nn.LayerNorm, |
| patch_norm=True, |
| use_checkpoint=False, |
| upscale=upscale, |
| img_range=1., |
| upsampler='pixelshuffledirect', |
| resi_connection='1conv') |
|
|
|
|
| def buildMaIR(upscale=2): |
| return MaIR(img_size=64, |
| patch_size=1, |
| in_chans=3, |
| embed_dim=180, |
| depths=(6, 6, 6, 6, 6, 6), |
| mlp_ratio=2., |
| drop_rate=0., |
| norm_layer=nn.LayerNorm, |
| patch_norm=True, |
| use_checkpoint=False, |
| upscale=upscale, |
| img_range=1., |
| upsampler='pixelshuffle', |
| resi_connection='1conv') |
|
|
| def buildMaIR_SR(upscale=2): |
| return MaIR(img_size=64, |
| patch_size=1, |
| in_chans=3, |
| embed_dim=180, |
| depths=(6, 6, 6, 6, 6, 6), |
| drop_rate=0., |
| d_state=16, |
| ssm_ratio=2.0, |
| mlp_ratio=2.5, |
|
|
| drop_path_rate=0.1, |
| norm_layer=nn.LayerNorm, |
| patch_norm=True, |
| use_checkpoint=False, |
| upscale=upscale, |
| img_range=1., |
| upsampler='pixelshuffle', |
| resi_connection='1conv', |
| dynamic_ids=False, |
| scan_len=4, |
| batch_size=1, |
| ) |
|
|
