Implement a few changes to support training BYOL networks
This commit is contained in:
James Betker
parent
2437b33e74
commit
1bbcb96ee8
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@ -35,6 +35,9 @@ class ByolDatasetWrapper(Dataset):
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super().__init__()
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self.wrapped_dataset = create_dataset(opt['dataset'])
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self.cropped_img_size = opt['crop_size']
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self.key1 = opt_get(opt, ['key1'], 'hq')
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self.key2 = opt_get(opt, ['key2'], 'lq')
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augmentations = [ \
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RandomApply(augs.ColorJitter(0.8, 0.8, 0.8, 0.2), p=0.8),
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augs.RandomGrayscale(p=0.2),
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@ -49,7 +52,7 @@ class ByolDatasetWrapper(Dataset):
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def __getitem__(self, item):
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item = self.wrapped_dataset[item]
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item.update({'aug1': self.aug(item['hq']).squeeze(dim=0), 'aug2': self.aug(item['lq']).squeeze(dim=0)})
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item.update({'aug1': self.aug(item[self.key1]).squeeze(dim=0), 'aug2': self.aug(item[self.key2]).squeeze(dim=0)})
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return item
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def __len__(self):
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@ -22,6 +22,9 @@ class ImageFolderDataset:
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self.scale = opt['scale']
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self.paths = opt['paths']
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self.corrupt_before_downsize = opt['corrupt_before_downsize'] if 'corrupt_before_downsize' in opt.keys() else False
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self.fetch_alt_image = opt['fetch_alt_image'] # If specified, this dataset will attempt to find a second image
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# from the same video source. Search for 'fetch_alt_image' for more info.
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self.skip_lq = opt['skip_lq']
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assert (self.target_hq_size // self.scale) % self.multiple == 0 # If we dont throw here, we get some really obscure errors.
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if not isinstance(self.paths, list):
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self.paths = [self.paths]
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@ -110,13 +113,56 @@ class ImageFolderDataset:
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dim = hq.shape[0]
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hs = self.resize_hq([hq])
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ls = self.synthesize_lq(hs)
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if not self.skip_lq:
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ls = self.synthesize_lq(hs)
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# Convert to torch tensor
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hq = torch.from_numpy(np.ascontiguousarray(np.transpose(hs[0], (2, 0, 1)))).float()
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lq = torch.from_numpy(np.ascontiguousarray(np.transpose(ls[0], (2, 0, 1)))).float()
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if not self.skip_lq:
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lq = torch.from_numpy(np.ascontiguousarray(np.transpose(ls[0], (2, 0, 1)))).float()
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out_dict = {'hq': hq, 'LQ_path': self.image_paths[item], 'HQ_path': self.image_paths[item]}
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if not self.skip_lq:
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out_dict['lq'] = lq
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if self.fetch_alt_image:
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# This works by assuming a specific filename structure as would produced by ffmpeg. ex:
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# 'Candied Walnutsxjktqhr_SYc.webm_00000478.jpg` and
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# 'Candied Walnutsxjktqhr_SYc.webm_00000479.jpg` and
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# 'Candied Walnutsxjktqhr_SYc.webm_00000480.jpg`
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# The basic format is `<anything>%08d.<extension>`. This logic parses off that 8 digit number. If it is
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# not found, the 'alt_image' returned is just the current image. If it is found, the algorithm searches for
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# an image one number higher. If it is found - it is returned in the 'alt_hq' and 'alt_lq' keys, else the
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# current image is put in those keys.
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imname_parts = self.image_paths[item]
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while '.jpg.jpg' in imname_parts:
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imname_parts = imname_parts.replace(".jpg.jpg", ".jpg") # Hack workaround to my own bug.
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imname_parts = imname_parts.split('.')
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if len(imname_parts) >= 2 and len(imname_parts[-2]) > 8:
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try:
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imnumber = int(imname_parts[-2][-8:])
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# When we're dealing with images in the 1M range, it's straight up faster to attempt to just open
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# the file rather than searching the path list. Let the exception handler below do its work.
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next_img = self.image_paths[item].replace(str(imnumber), str(imnumber+1))
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alt_hq = util.read_img(None, next_img, rgb=True)
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alt_hq = self.resize_hq([alt_hq])
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alt_hq = torch.from_numpy(np.ascontiguousarray(np.transpose(alt_hq[0], (2, 0, 1)))).float()
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if not self.skip_lq:
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alt_lq = self.synthesize_lq(alt_hq)
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alt_lq = torch.from_numpy(np.ascontiguousarray(np.transpose(alt_lq[0], (2, 0, 1)))).float()
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except:
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alt_hq = hq
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if not self.skip_lq:
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alt_lq = lq
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else:
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alt_hq = hq
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if not self.skip_lq:
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alt_lq = lq
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out_dict['alt_hq'] = alt_hq
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if not self.skip_lq:
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out_dict['alt_lq'] = alt_lq
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out_dict = {'lq': lq, 'hq': hq, 'LQ_path': self.image_paths[item], 'HQ_path': self.image_paths[item]}
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if self.labeler:
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base_file = self.image_paths[item].replace(self.paths[0], "")
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while base_file.startswith("\\"):
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@ -131,19 +177,20 @@ class ImageFolderDataset:
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if __name__ == '__main__':
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opt = {
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'name': 'amalgam',
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'paths': ['F:\\4k6k\\datasets\\ns_images\\512_unsupervised\\'],
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'paths': ['F:\\4k6k\\datasets\\images\\youtube\\4k_quote_unquote\\images'],
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'weights': [1],
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'target_size': 512,
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'target_size': 256,
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'force_multiple': 32,
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'scale': 2,
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'fixed_corruptions': ['jpeg-broad', 'gaussian_blur'],
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'random_corruptions': ['noise-5', 'none'],
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'num_corrupts_per_image': 1,
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'corrupt_before_downsize': True,
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'labeler': {
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'type': 'patch_labels',
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'label_file': 'F:\\4k6k\\datasets\\ns_images\\512_unsupervised\\categories_new.json'
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}
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'fetch_alt_image': True,
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#'labeler': {
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# 'type': 'patch_labels',
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# 'label_file': 'F:\\4k6k\\datasets\\ns_images\\512_unsupervised\\categories_new.json'
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#}
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}
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ds = ImageFolderDataset(opt)
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@ -152,11 +199,11 @@ if __name__ == '__main__':
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for i in range(0, len(ds)):
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o = ds[random.randint(0, len(ds)-1)]
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hq = o['hq']
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masked = (o['labels_mask'] * .5 + .5) * hq
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#masked = (o['labels_mask'] * .5 + .5) * hq
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import torchvision
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torchvision.utils.save_image(hq.unsqueeze(0), "debug/%i_hq.png" % (i,))
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#torchvision.utils.save_image(masked.unsqueeze(0), "debug/%i_masked.png" % (i,))
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if len(o['labels'].unique()) > 1:
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randlbl = np.random.choice(o['labels'].unique()[1:])
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moremask = hq * ((1*(o['labels'] == randlbl))*.5+.5)
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torchvision.utils.save_image(moremask.unsqueeze(0), "debug/%i_%s.png" % (i, o['label_strings'][randlbl]))
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torchvision.utils.save_image(o['alt_hq'].unsqueeze(0), "debug/%i_hq_alt.png" % (i,))
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#if len(o['labels'].unique()) > 1:
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# randlbl = np.random.choice(o['labels'].unique()[1:])
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# moremask = hq * ((1*(o['labels'] == randlbl))*.5+.5)
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# torchvision.utils.save_image(moremask.unsqueeze(0), "debug/%i_%s.png" % (i, o['label_strings'][randlbl]))
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@ -113,7 +113,10 @@ def read_img(env, path, size=None, rgb=False):
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"""read image by cv2 or from lmdb or from a buffer (in which case path=buffer)
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return: Numpy float32, HWC, BGR, [0,1]"""
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if env is None: # img
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img = cv2.imread(path, cv2.IMREAD_UNCHANGED)
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# Indirect open then process to support unicode files.
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stream = open(path, "rb")
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bytes = bytearray(stream.read())
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img = cv2.imdecode(np.asarray(bytes, dtype=np.uint8), cv2.IMREAD_UNCHANGED)
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elif env is 'lmdb':
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img = _read_img_lmdb(env, path, size)
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elif env is 'buffer':
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@ -148,7 +148,7 @@ class NetWrapper(nn.Module):
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if self.structural_mlp:
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projector = StructuralMLP(hidden.shape, self.projection_size, self.projection_hidden_size)
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else:
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_, dim = hidden.shape
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_, dim = hidden.flatten(1,-1).shape
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projector = MLP(dim, self.projection_size, self.projection_hidden_size)
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return projector.to(hidden)
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@ -13,15 +13,16 @@ import torch
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def main():
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split_img = False
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opt = {}
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opt['n_thread'] = 10
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opt['n_thread'] = 8
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opt['compression_level'] = 90 # JPEG compression quality rating.
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# CV_IMWRITE_PNG_COMPRESSION from 0 to 9. A higher value means a smaller size and longer
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# compression time. If read raw images during training, use 0 for faster IO speed.
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opt['dest'] = 'file'
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opt['input_folder'] = ['F:\\4k6k\\datasets\\ns_images\\other_ns']
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opt['save_folder'] = 'F:\\4k6k\\datasets\\ns_images\\512_unsupervised'
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opt['imgsize'] = 512
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opt['input_folder'] = ['F:\\4k6k\\datasets\\ns_images\\512_unsupervised']
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opt['save_folder'] = 'F:\\4k6k\\datasets\\ns_images\\256_unsupervised'
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opt['imgsize'] = 256
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#opt['bottom_crop'] = 120
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save_folder = opt['save_folder']
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if not osp.exists(save_folder):
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@ -36,6 +37,7 @@ class TiledDataset(data.Dataset):
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self.opt = opt
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input_folder = opt['input_folder']
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self.images = data_util.get_image_paths('img', input_folder)[0]
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print("Found %i images" % (len(self.images),))
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def __getitem__(self, index):
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return self.get(index)
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@ -43,7 +45,7 @@ class TiledDataset(data.Dataset):
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def get(self, index):
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path = self.images[index]
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basename = osp.basename(path)
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img = cv2.imread(path, cv2.IMREAD_UNCHANGED)
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img = data_util.read_img(None, path)
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# Greyscale not supported.
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if img is None:
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@ -51,6 +53,12 @@ class TiledDataset(data.Dataset):
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return None
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if len(img.shape) == 2:
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return None
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# Perform explicit crops first. These are generally used to get rid of watermarks so we dont even want to
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# consider these areas of the image.
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if 'bottom_crop' in self.opt.keys():
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img = img[:-self.opt['bottom_crop'], :, :]
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h, w, c = img.shape
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# Uncomment to filter any image that doesnt meet a threshold size.
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if min(h,w) < 512:
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@ -61,7 +69,14 @@ class TiledDataset(data.Dataset):
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# Crop the image so that only the center is left, since this is often the most salient part of the image.
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img = img[(h - dim) // 2:dim + (h - dim) // 2, (w - dim) // 2:dim + (w - dim) // 2, :]
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img = cv2.resize(img, (self.opt['imgsize'], self.opt['imgsize']), interpolation=cv2.INTER_AREA)
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cv2.imwrite(osp.join(self.opt['save_folder'], basename + ".jpg"), img, [cv2.IMWRITE_JPEG_QUALITY, self.opt['compression_level']])
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# I was having some issues with unicode filenames with cv2. Hence using PIL.
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# cv2.imwrite(osp.join(self.opt['save_folder'], basename + ".jpg"), img, [cv2.IMWRITE_JPEG_QUALITY, self.opt['compression_level']])
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img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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img = (img * 255).astype(np.uint8)
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img = Image.fromarray(img)
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img.save(osp.join(self.opt['save_folder'], basename + ".jpg"), "JPEG", quality=self.opt['compression_level'], optimize=True)
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return None
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def __len__(self):
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@ -293,7 +293,7 @@ class Trainer:
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_faces_glean.yml')
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parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_byol_resnet_sameimage.yml')
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parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
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parser.add_argument('--local_rank', type=int, default=0)
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args = parser.parse_args()
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@ -125,6 +125,8 @@ def define_G(opt, opt_net, scale=None):
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from models.spinenet_arch import SpinenetWithLogits
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netG = SpinenetWithLogits(str(opt_net['arch']), opt_net['output_to_attach'], opt_net['num_labels'],
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in_channels=3, use_input_norm=opt_net['use_input_norm'])
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elif which_model == 'resnet52':
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netG = torchvision.models.resnet50(pretrained=opt_net['pretrained'])
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elif which_model == 'glean':
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from models.glean.glean import GleanGenerator
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netG = GleanGenerator(opt_net['nf'], opt_net['pretrained_stylegan'])
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