from data.base_unsupervised_image_dataset import BaseUnsupervisedImageDataset import numpy as np import torch from bisect import bisect_left import os.path as osp class PairedFrameDataset(BaseUnsupervisedImageDataset): def __init__(self, opt): super(PairedFrameDataset, self).__init__(opt) def get_pair(self, chunk_index, chunk_offset): imname = osp.basename(self.chunks[chunk_index].path) if '_left' in imname: chunks = [chunk_index, chunk_index+1] else: chunks = [chunk_index-1, chunk_index] hqs, refs, masks, centers = [], [], [], [] for i in chunks: h, r, c, m, p = self.chunks[i][chunk_offset] hqs.append(h) refs.append(r) masks.append(m) centers.append(c) path = p return hqs, refs, masks, centers, path def __getitem__(self, item): chunk_ind = bisect_left(self.starting_indices, item) chunk_ind = chunk_ind if chunk_ind < len(self.starting_indices) and self.starting_indices[chunk_ind] == item else chunk_ind-1 hqs, refs, masks, centers, path = self.get_pair(chunk_ind, item-self.starting_indices[chunk_ind]) hs, hrs, hms, hcs = self.resize_hq(hqs, refs, masks, centers) ls, lrs, lms, lcs = self.synthesize_lq(hs, hrs, hms, hcs) # Convert to torch tensor hq = torch.from_numpy(np.ascontiguousarray(np.transpose(np.stack(hs), (0, 3, 1, 2)))).float() hq_ref = torch.from_numpy(np.ascontiguousarray(np.transpose(np.stack(hrs), (0, 3, 1, 2)))).float() hq_mask = torch.from_numpy(np.ascontiguousarray(np.stack(hms))).squeeze().unsqueeze(dim=1) hq_ref = torch.cat([hq_ref, hq_mask], dim=1) lq = torch.from_numpy(np.ascontiguousarray(np.transpose(np.stack(ls), (0, 3, 1, 2)))).float() lq_ref = torch.from_numpy(np.ascontiguousarray(np.transpose(np.stack(lrs), (0, 3, 1, 2)))).float() lq_mask = torch.from_numpy(np.ascontiguousarray(np.stack(lms))).squeeze().unsqueeze(dim=1) lq_ref = torch.cat([lq_ref, lq_mask], dim=1) return {'GT_path': path, 'lq': lq, 'hq': hq, 'gt_fullsize_ref': hq_ref, 'lq_fullsize_ref': lq_ref, 'lq_center': torch.tensor(lcs, dtype=torch.long), 'gt_center': torch.tensor(hcs, dtype=torch.long)} if __name__ == '__main__': opt = { 'name': 'amalgam', 'paths': ['F:\\4k6k\\datasets\\ns_images\\vr\\validation'], 'weights': [1], #'target_size': 128, 'force_multiple': 32, 'scale': 2, 'eval': False, 'fixed_corruptions': ['jpeg-medium'], 'random_corruptions': [], 'num_corrupts_per_image': 0, 'num_frames': 10 } ds = PairedFrameDataset(opt) import os os.makedirs("debug", exist_ok=True) bs = 0 batch = None for i in range(len(ds)): import random k = 'lq' element = ds[random.randint(0,len(ds))] base_file = osp.basename(element["GT_path"]) o = element[k].unsqueeze(0) if bs < 2: if batch is None: batch = o else: batch = torch.cat([batch, o], dim=0) bs += 1 continue if 'path' not in k and 'center' not in k: b, fr, f, h, w = batch.shape for j in range(fr): import torchvision base=osp.basename(base_file) torchvision.utils.save_image(batch[:, j], "debug/%i_%s_%i__%s.png" % (i, k, j, base)) bs = 0 batch = None