forked from mrq/DL-Art-School
Mods to support labeled datasets & random augs for those datasets
This commit is contained in:
James Betker
parent
e5a3e6b9b5
commit
8e0e883050
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@ -51,6 +51,8 @@ def create_dataset(dataset_opt):
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from data.byol_attachment import ByolDatasetWrapper as D
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elif mode == 'byol_structured_dataset':
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from data.byol_attachment import StructuredCropDatasetWrapper as D
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elif mode == 'random_aug_wrapper':
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from data.byol_attachment import DatasetRandomAugWrapper as D
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elif mode == 'random_dataset':
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from data.random_dataset import RandomDataset as D
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else:
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@ -1,10 +1,11 @@
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import random
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from time import time
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import numpy as np
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import torch
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import torchvision
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from torch.utils.data import Dataset
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from kornia import augmentation as augs, kornia
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from kornia import augmentation as augs, kornia, Resample
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from kornia import filters
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import torch.nn as nn
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import torch.nn.functional as F
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@ -41,7 +42,8 @@ class ByolDatasetWrapper(Dataset):
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RandomApply(filters.GaussianBlur2d((3, 3), (1.5, 1.5)), p=0.1),
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augs.RandomResizedCrop((self.cropped_img_size, self.cropped_img_size))]
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if opt['normalize']:
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# The paper calls for normalization. Recommend setting true if you want exactly like the paper.
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# The paper calls for normalization. Most datasets/models in this repo don't use this.
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# Recommend setting true if you want to train exactly like the paper.
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augmentations.append(augs.Normalize(mean=torch.tensor([0.485, 0.456, 0.406]), std=torch.tensor([0.229, 0.224, 0.225])))
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self.aug = nn.Sequential(*augmentations)
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@ -54,6 +56,90 @@ class ByolDatasetWrapper(Dataset):
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return len(self.wrapped_dataset)
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# Basically the same as ByolDatasetWrapper except only produces 1 augmentation and stores in the 'lr' key. Also applies
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# crop&resize to 2D tensors in the state dict with the word "label" in them.
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class DatasetRandomAugWrapper(Dataset):
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def __init__(self, opt):
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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.includes_labels = opt['includes_labels']
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augmentations = [ \
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RandomApply(augs.ColorJitter(0.4, 0.4, 0.4, 0.2), p=0.8),
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augs.RandomGrayscale(p=0.2),
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RandomApply(filters.GaussianBlur2d((3, 3), (1.5, 1.5)), p=0.1)]
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self.aug = nn.Sequential(*augmentations)
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self.rrc = nn.Sequential(*[
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augs.RandomHorizontalFlip(),
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augs.RandomResizedCrop((self.cropped_img_size, self.cropped_img_size))])
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def __getitem__(self, item):
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item = self.wrapped_dataset[item]
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hq = self.aug(item['hq'].unsqueeze(0)).squeeze(0)
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labels = []
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dtypes = []
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for k in item.keys():
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if 'label' in k and isinstance(item[k], torch.Tensor) and len(item[k].shape) == 3:
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assert item[k].shape[0] == 1 # Only supports a channel dim of 1.
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labels.append(k)
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dtypes.append(item[k].dtype)
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hq = torch.cat([hq, item[k].type(torch.float)], dim=0)
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hq = self.rrc(hq.unsqueeze(0)).squeeze(0)
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for i, k in enumerate(labels):
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# Strip out any label values that are not whole numbers.
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item[k] = hq[3+i:3+i+1,:,:]
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whole = (item[k].round() == item[k])
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item[k] = item[k] * whole
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item[k] = item[k].type(dtypes[i])
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item['lq'] = hq[:3,:,:]
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item['hq'] = item['lq']
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return item
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def __len__(self):
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return len(self.wrapped_dataset)
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def test_dataset_random_aug_wrapper():
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opt = {
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'dataset': {
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'mode': 'imagefolder',
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'name': 'amalgam',
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'paths': ['F:\\4k6k\\datasets\\ns_images\\512_unsupervised'],
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'weights': [1],
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'target_size': 512,
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'force_multiple': 1,
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'scale': 1,
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'fixed_corruptions': ['jpeg-broad'],
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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': False,
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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.json'
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}
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},
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'crop_size': 512,
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'includes_labels': True,
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}
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ds = DatasetRandomAugWrapper(opt)
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import os
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os.makedirs("debug", exist_ok=True)
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for i in tqdm(range(0, len(ds))):
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o = ds[random.randint(0, len(ds)-1)]
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for k, v in o.items():
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# 'lq', 'hq', 'aug1', 'aug2',
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if k in ['hq']:
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torchvision.utils.save_image(v.unsqueeze(0), "debug/%i_%s.png" % (i, k))
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masked = v * (o['labels_mask'] * .5 + .5)
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#torchvision.utils.save_image(masked.unsqueeze(0), "debug/%i_%s_masked.png" % (i, k))
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# Pick a random (non-zero) label and spit it out with the textual label.
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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 = v * ((1*(o['labels'] == randlbl))*.5+.5)
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torchvision.utils.save_image(moremask.unsqueeze(0), "debug/%i_%s_%s.png" % (i, k, o['label_strings'][randlbl]))
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def no_batch_interpolate(i, size, mode):
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i = i.unsqueeze(0)
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i = F.interpolate(i, size=size, mode=mode)
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@ -235,7 +321,7 @@ class StructuredCropDatasetWrapper(Dataset):
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# For testing this dataset.
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if __name__ == '__main__':
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def test_structured_crop_dataset_wrapper():
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opt = {
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'dataset':
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{
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@ -270,3 +356,7 @@ if __name__ == '__main__':
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rc1, rc2 = reconstructed_shared_regions(pixun(o['aug1'].unsqueeze(0)), pixun(o['aug2'].unsqueeze(0)), rcpkg.unsqueeze(0))
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#torchvision.utils.save_image(pixsh(rc1), "debug/%i_rc1.png" % (i,))
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#torchvision.utils.save_image(pixsh(rc2), "debug/%i_rc2.png" % (i,))
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if __name__ == '__main__':
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test_dataset_random_aug_wrapper()
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@ -111,10 +111,13 @@ class ImageFolderDataset:
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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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base_file = base_file[1:]
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assert dim % hq.shape[1] == 0
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lbls, lbl_masks = self.labeler.get_labels_as_tensor(hq, base_file, dim // hq.shape[1])
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lbls, lbl_masks, lblstrings = self.labeler.get_labels_as_tensor(hq, base_file, dim // hq.shape[1])
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out_dict['labels'] = lbls
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out_dict['labels_mask'] = lbl_masks
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out_dict['label_strings'] = lblstrings
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return out_dict
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if __name__ == '__main__':
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@ -122,7 +125,7 @@ if __name__ == '__main__':
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'name': 'amalgam',
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'paths': ['F:\\4k6k\\datasets\\ns_images\\512_unsupervised\\'],
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'weights': [1],
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'target_size': 128,
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'target_size': 512,
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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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@ -144,4 +147,8 @@ if __name__ == '__main__':
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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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#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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@ -33,7 +33,7 @@ class VsNetImageLabeler:
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# Build the label values, from [1,inf]
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label_value_dict = {}
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for i, l in enumerate(available_labels):
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label_value_dict[l] = i+1
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label_value_dict[l] = i
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# Insert "labelValue" for each entry.
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for v in labeled_images.values():
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@ -41,13 +41,15 @@ class VsNetImageLabeler:
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l['labelValue'] = label_value_dict[l['label']]
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self.labeled_images = labeled_images
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self.str_labels = available_labels
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def get_labeled_paths(self, base_path):
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return [os.path.join(base_path, pth) for pth in self.labeled_images]
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def get_labels_as_tensor(self, hq, img_key, resize_factor):
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labels = torch.zeros(hq.shape, dtype=torch.long)
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mask = torch.zeros_like(hq)
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_, h, w = hq.shape
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labels = torch.zeros((1,h,w), dtype=torch.long)
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mask = torch.zeros((1,h,w), dtype=torch.float)
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lbl_list = self.labeled_images[img_key]
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for patch_lbl in lbl_list:
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t, l, h, w = patch_lbl['patch_top'] // resize_factor, patch_lbl['patch_left'] // resize_factor, \
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@ -55,4 +57,4 @@ class VsNetImageLabeler:
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val = patch_lbl['labelValue']
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labels[:,t:t+h,l:l+w] = val
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mask[:,t:t+h,l:l+w] = 1.0
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return labels, mask
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return labels, mask, self.str_labels
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