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Disable refs and centers altogether in single_image_dataset

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I suspect that this might be a cause of failures on parallel datasets.
Plus it is unnecessary computation.
This commit is contained in:
James Betker 2020-12-31 10:13:24 -07:00
parent 8f0984cacf
commit 1de1fa30ac
4 changed files with 18 additions and 14 deletions
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13
codes/data/chunk_with_reference.py
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@ -4,11 +4,15 @@ import torch
import numpy as np
# Iterable that reads all the images in a directory that contains a reference image, tile images and center coordinates.
from utils.util import opt_get
class ChunkWithReference:
def __init__(self, opt, path):
self.path = path.path
self.tiles, _ = util.get_image_paths('img', self.path)
self.strict = opt['strict'] if 'strict' in opt.keys() else True
self.need_metadata = opt_get(opt, ['strict'], False) or opt_get(opt, ['needs_metadata'], False)
self.need_ref = opt_get(opt, ['need_ref'], False)
if 'ignore_first' in opt.keys():
self.tiles = self.tiles[opt['ignore_first']:]
@ -21,13 +25,14 @@ class ChunkWithReference:
def __getitem__(self, item):
tile = self.read_image_or_get_zero(self.tiles[item])
if osp.exists(osp.join(self.path, "ref.jpg")):
if self.need_ref and osp.exists(osp.join(self.path, "ref.jpg")):
tile_id = int(osp.splitext(osp.basename(self.tiles[item]))[0])
centers = torch.load(osp.join(self.path, "centers.pt"))
ref = self.read_image_or_get_zero(osp.join(self.path, "ref.jpg"))
if self.need_metadata:
centers = torch.load(osp.join(self.path, "centers.pt"))
if tile_id in centers.keys():
center, tile_width = centers[tile_id]
elif self.strict:
else:
print("Could not find the given tile id in the accompanying centers.pt. This generally means that "
"centers.pt was overwritten at some point e.g. by duplicate data. If you don't care about tile "
"centers, consider passing strict=false to the dataset options. (Note: you must re-build your"

2
codes/data/image_corruptor.py
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@ -119,7 +119,7 @@ class ImageCorruptor:
raise NotImplementedError("specified jpeg corruption doesn't exist")
# JPEG compression
qf = (rand_int % range + lo)
# cv2's jpeg compression is "odd". It introduces artifacts. Use PIL instead.
# Use PIL to perform a mock compression to a data buffer, then swap back to cv2.
img = (img * 255).astype(np.uint8)
img = Image.fromarray(img)
buffer = BytesIO()

1
codes/data/single_image_dataset.py
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@ -50,7 +50,6 @@ if __name__ == '__main__':
'force_multiple': 32,
'scale': 2,
'eval': False,
'strict': False,
'fixed_corruptions': ['jpeg-broad', 'gaussian_blur'],
'random_corruptions': ['noise-5', 'none'],
'num_corrupts_per_image': 1,

2
codes/train.py
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@ -293,7 +293,7 @@ class Trainer:
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_mi1_rrdb4x_23bl_opt.yml')
parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_xx_faces_glean.yml')
parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
parser.add_argument('--local_rank', type=int, default=0)
args = parser.parse_args()