Add GTLQ dataset
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@ -7,14 +7,14 @@ import torch.utils.data as data
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import data.util as util
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import data.util as util
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class LQGTDataset(data.Dataset):
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class GTLQDataset(data.Dataset):
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"""
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"""
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Read LQ (Low Quality, e.g. LR (Low Resolution), blurry, etc) and GT image pairs.
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Reads unpaired high-resolution and low resolution images. Downsampled, LR images matching the provided high res
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If only GT images are provided, generate LQ images on-the-fly.
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images are produced and fed to the downstream model, which can be used in a pixel loss.
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"""
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"""
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def __init__(self, opt):
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def __init__(self, opt):
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super(LQGTDataset, self).__init__()
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super(GTLQDataset, self).__init__()
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self.opt = opt
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self.opt = opt
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self.data_type = self.opt['data_type']
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self.data_type = self.opt['data_type']
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self.paths_LQ, self.paths_GT = None, None
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self.paths_LQ, self.paths_GT = None, None
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@ -43,7 +43,7 @@ class LQGTDataset(data.Dataset):
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self._init_lmdb()
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self._init_lmdb()
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GT_path, LQ_path = None, None
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GT_path, LQ_path = None, None
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scale = self.opt['scale']
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scale = self.opt['scale']
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GT_size = self.opt['GT_size']
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GT_size = self.opt['target_size']
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# get GT image
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# get GT image
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GT_path = self.paths_GT[index]
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GT_path = self.paths_GT[index]
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@ -32,6 +32,8 @@ def create_dataset(dataset_opt):
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from data.LQ_dataset import LQDataset as D
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from data.LQ_dataset import LQDataset as D
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elif mode == 'LQGT':
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elif mode == 'LQGT':
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from data.LQGT_dataset import LQGTDataset as D
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from data.LQGT_dataset import LQGTDataset as D
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elif mode == 'GTLQ':
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from data.GTLQ_dataset import GTLQDataset as D
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# datasets for video restoration
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# datasets for video restoration
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elif mode == 'REDS':
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elif mode == 'REDS':
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from data.REDS_dataset import REDSDataset as D
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from data.REDS_dataset import REDSDataset as D
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