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authorliamkerr <liamkerr@users.noreply.github.com>2022-10-02 14:02:38 +0000
committerGitHub <noreply@github.com>2022-10-02 14:02:38 +0000
commit7308aeefd9d25212398068c1a35f0b879b9ba06f (patch)
tree60f81d876522c772f85911ec4065ab3f448a8479 /modules/swinir_model.py
parent3c6a049fc3c6b54ada3736710a7e86663ea7f3d9 (diff)
parent4e72a1aab6d1b3a8d8c09fadc81843a07c05cc18 (diff)
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Merge branch 'master' into token_updates
Diffstat (limited to 'modules/swinir_model.py')
-rw-r--r--modules/swinir_model.py27
1 files changed, 15 insertions, 12 deletions
diff --git a/modules/swinir_model.py b/modules/swinir_model.py
index 41fda5a7..9bd454c6 100644
--- a/modules/swinir_model.py
+++ b/modules/swinir_model.py
@@ -5,6 +5,7 @@ import numpy as np
import torch
from PIL import Image
from basicsr.utils.download_util import load_file_from_url
+from tqdm import tqdm
from modules import modelloader
from modules.paths import models_path
@@ -122,18 +123,20 @@ def inference(img, model, tile, tile_overlap, window_size, scale):
E = torch.zeros(b, c, h * sf, w * sf, dtype=torch.half, device=device).type_as(img)
W = torch.zeros_like(E, dtype=torch.half, device=device)
- for h_idx in h_idx_list:
- for w_idx in w_idx_list:
- in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile]
- out_patch = model(in_patch)
- out_patch_mask = torch.ones_like(out_patch)
-
- E[
- ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
- ].add_(out_patch)
- W[
- ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
- ].add_(out_patch_mask)
+ with tqdm(total=len(h_idx_list) * len(w_idx_list), desc="SwinIR tiles") as pbar:
+ for h_idx in h_idx_list:
+ for w_idx in w_idx_list:
+ in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile]
+ out_patch = model(in_patch)
+ out_patch_mask = torch.ones_like(out_patch)
+
+ E[
+ ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
+ ].add_(out_patch)
+ W[
+ ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
+ ].add_(out_patch_mask)
+ pbar.update(1)
output = E.div_(W)
return output