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| import torch | |
| from PIL import Image | |
| import random | |
| import logging | |
| import torchvision | |
| import torchvision.transforms as T | |
| from torchvision.transforms.functional import InterpolationMode | |
| IMAGENET_MEAN = (0.485, 0.456, 0.406) | |
| IMAGENET_STD = (0.229, 0.224, 0.225) | |
| def build_transform(input_size): | |
| MEAN, STD = IMAGENET_MEAN, IMAGENET_STD | |
| transform = T.Compose([ | |
| T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img), | |
| T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC), | |
| T.ToTensor(), | |
| T.Normalize(mean=MEAN, std=STD) | |
| ]) | |
| return transform | |
| def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size): | |
| best_ratio_diff = float('inf') | |
| best_ratio = (1, 1) | |
| area = width * height | |
| for ratio in target_ratios: | |
| target_aspect_ratio = ratio[0] / ratio[1] | |
| ratio_diff = abs(aspect_ratio - target_aspect_ratio) | |
| if ratio_diff < best_ratio_diff: | |
| best_ratio_diff = ratio_diff | |
| best_ratio = ratio | |
| elif ratio_diff == best_ratio_diff: | |
| if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]: | |
| best_ratio = ratio | |
| return best_ratio | |
| def dynamic_preprocess(image, min_num=1, max_num=8, image_size=448, use_thumbnail=False): | |
| orig_width, orig_height = image.size | |
| aspect_ratio = orig_width / orig_height | |
| # calculate the existing image aspect ratio | |
| target_ratios = set( | |
| (i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if | |
| i * j <= max_num and i * j >= min_num) | |
| target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1]) | |
| # find the closest aspect ratio to the target | |
| target_aspect_ratio = find_closest_aspect_ratio( | |
| aspect_ratio, target_ratios, orig_width, orig_height, image_size) | |
| # calculate the target width and height | |
| target_width = image_size * target_aspect_ratio[0] | |
| target_height = image_size * target_aspect_ratio[1] | |
| blocks = target_aspect_ratio[0] * target_aspect_ratio[1] | |
| # resize the image | |
| resized_img = image.resize((target_width, target_height)) | |
| processed_images = [] | |
| for i in range(blocks): | |
| box = ( | |
| (i % (target_width // image_size)) * image_size, | |
| (i // (target_width // image_size)) * image_size, | |
| ((i % (target_width // image_size)) + 1) * image_size, | |
| ((i // (target_width // image_size)) + 1) * image_size | |
| ) | |
| # split the image | |
| split_img = resized_img.crop(box) | |
| processed_images.append(split_img) | |
| assert len(processed_images) == blocks | |
| if use_thumbnail and len(processed_images) != 1: | |
| thumbnail_img = image.resize((image_size, image_size)) | |
| processed_images.append(thumbnail_img) | |
| return processed_images | |
| def load_image(image_file, pil_image=None, input_size=224,): | |
| if not pil_image: | |
| pil_image = Image.open(image_file) | |
| image = pil_image.convert('RGB') | |
| transform = build_transform(input_size=input_size) | |
| # images = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num) | |
| pixel_values = [transform(image) for image in [image]] | |
| pixel_values = torch.stack(pixel_values) | |
| return pixel_values | |
| def my_collate(batch): | |
| try: | |
| targets = torch.stack([s['target'] for s in batch]) | |
| samples = torch.stack([s['samples'] for s in batch]) | |
| # targets = torch.stack([s['target'] for s in batch if s is not None]) | |
| # samples = torch.stack([s['samples'] for s in batch if s is not None]) | |
| except Exception as e: | |
| logging.warning('my_collate issue ', e) | |
| return None | |
| return samples, targets | |
| class ImageFolderSample(torchvision.datasets.ImageFolder): | |
| def __init__(self, data_path, k, processor): | |
| super().__init__(data_path) | |
| self.k = k | |
| self.processor = processor | |
| def safe_getitem(self, index): | |
| try: | |
| target_path, class_type = self.samples[index] | |
| target = torch.from_numpy(self.processor(self.loader(target_path)).data['pixel_values'][0]) | |
| input_paths = random.choices([p[0] for p in self.samples if p != target_path and class_type in p], k=self.k) | |
| assert len(input_paths) == self.k # I think it may do this by default... | |
| samples = torch.stack([torch.from_numpy(self.processor(self.loader(i)).data['pixel_values'][0]) for i in input_paths]) | |
| except Exception as e: | |
| logging.warning('getitem issue ', e) | |
| samples, target = None, None | |
| drop_mask = torch.rand(samples.shape[0],) < .2 | |
| samples[drop_mask] = 0 | |
| drop_whole_set_mask = torch.rand(1,) < .1 | |
| if drop_whole_set_mask: | |
| samples = torch.zeros_like(samples) | |
| return {'samples': samples[:, :3], 'target': target[:3]} | |
| def __getitem__(self, index: int): | |
| return self.safe_getitem(index) | |
| # https://data.mendeley.com/datasets/fs4k2zc5j5/3 | |
| # Gomez, J. C., Ibarra-Manzano, M. A., & Almanza-Ojeda, D. L. (2017). User Identification in Pinterest Through the Refinement of Cascade Fusion of Text and Images. Research in Computing Science, 144, 41-52. | |
| def get_dataset(data_path, processor): | |
| return ImageFolderSample(data_path, 8, processor) | |
| def get_dataloader(data_path, batch_size, num_workers, processor): | |
| dataloader = torch.utils.data.DataLoader( | |
| get_dataset(data_path, processor=processor), | |
| num_workers=num_workers, | |
| collate_fn=my_collate, | |
| batch_size=batch_size, | |
| shuffle=True, | |
| drop_last=True | |
| ) | |
| return dataloader | |