File size: 2,544 Bytes
6362e9e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 |
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,
)
|