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9c116e0 029f910 9c116e0 | 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 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 | import torch
from torch.nn import functional as F
from basicsr.utils.registry import MODEL_REGISTRY
from basicsr.models.sr_model import SRModel
@MODEL_REGISTRY.register()
class DATModel(SRModel):
def test(self):
self.use_chop = self.opt['val']['use_chop'] if 'use_chop' in self.opt['val'] else False
if not self.use_chop:
if hasattr(self, 'net_g_ema'):
self.net_g_ema.eval()
with torch.no_grad():
self.output = self.net_g_ema(self.lq)
else:
self.net_g.eval()
with torch.no_grad():
self.output = self.net_g(self.lq)
self.net_g.train()
# test by partitioning
else:
_, C, h, w = self.lq.size()
split_token_h = h // 200 + 1 # number of horizontal cut sections
split_token_w = w // 200 + 1 # number of vertical cut sections
patch_size_tmp_h = split_token_h
patch_size_tmp_w = split_token_w
# padding
mod_pad_h, mod_pad_w = 0, 0
if h % patch_size_tmp_h != 0:
mod_pad_h = patch_size_tmp_h - h % patch_size_tmp_h
if w % patch_size_tmp_w != 0:
mod_pad_w = patch_size_tmp_w - w % patch_size_tmp_w
img = self.lq
img = torch.cat([img, torch.flip(img, [2])], 2)[:, :, :h+mod_pad_h, :]
img = torch.cat([img, torch.flip(img, [3])], 3)[:, :, :, :w+mod_pad_w]
_, _, H, W = img.size()
split_h = H // split_token_h # height of each partition
split_w = W // split_token_w # width of each partition
# overlapping
shave_h = 16
shave_w = 16
scale = self.opt.get('scale', 1)
ral = H // split_h
row = W // split_w
slices = [] # list of partition borders
for i in range(ral):
for j in range(row):
if i == 0 and i == ral - 1:
top = slice(i * split_h, (i + 1) * split_h)
elif i == 0:
top = slice(i*split_h, (i+1)*split_h+shave_h)
elif i == ral - 1:
top = slice(i*split_h-shave_h, (i+1)*split_h)
else:
top = slice(i*split_h-shave_h, (i+1)*split_h+shave_h)
if j == 0 and j == row - 1:
left = slice(j*split_w, (j+1)*split_w)
elif j == 0:
left = slice(j*split_w, (j+1)*split_w+shave_w)
elif j == row - 1:
left = slice(j*split_w-shave_w, (j+1)*split_w)
else:
left = slice(j*split_w-shave_w, (j+1)*split_w+shave_w)
temp = (top, left)
slices.append(temp)
img_chops = [] # list of partitions
for temp in slices:
top, left = temp
img_chops.append(img[..., top, left])
if hasattr(self, 'net_g_ema'):
self.net_g_ema.eval()
with torch.no_grad():
outputs = []
for chop in img_chops:
out = self.net_g_ema(chop) # image processing of each partition
outputs.append(out)
_img = torch.zeros(1, C, H * scale, W * scale)
# merge
for i in range(ral):
for j in range(row):
top = slice(i * split_h * scale, (i + 1) * split_h * scale)
left = slice(j * split_w * scale, (j + 1) * split_w * scale)
if i == 0:
_top = slice(0, split_h * scale)
else:
_top = slice(shave_h*scale, (shave_h+split_h)*scale)
if j == 0:
_left = slice(0, split_w*scale)
else:
_left = slice(shave_w*scale, (shave_w+split_w)*scale)
_img[..., top, left] = outputs[i * row + j][..., _top, _left]
self.output = _img
else:
self.net_g.eval()
with torch.no_grad():
outputs = []
for chop in img_chops:
out = self.net_g(chop) # image processing of each partition
outputs.append(out)
_img = torch.zeros(1, C, H * scale, W * scale)
# merge
for i in range(ral):
for j in range(row):
top = slice(i * split_h * scale, (i + 1) * split_h * scale)
left = slice(j * split_w * scale, (j + 1) * split_w * scale)
if i == 0:
_top = slice(0, split_h * scale)
else:
_top = slice(shave_h * scale, (shave_h + split_h) * scale)
if j == 0:
_left = slice(0, split_w * scale)
else:
_left = slice(shave_w * scale, (shave_w + split_w) * scale)
_img[..., top, left] = outputs[i * row + j][..., _top, _left]
self.output = _img
self.net_g.train()
_, _, h, w = self.output.size()
self.output = self.output[:, :, 0:h - mod_pad_h * scale, 0:w - mod_pad_w * scale]
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