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Running
on
Zero
| # -*- coding: utf-8 -*- | |
| # Copyright (c) Alibaba, Inc. and its affiliates. | |
| import torch | |
| import torchvision.transforms as T | |
| from PIL import Image | |
| 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=12, | |
| 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, input_size=448, max_num=12): | |
| if isinstance(image_file, str): | |
| image = Image.open(image_file).convert('RGB') | |
| else: | |
| image = image_file | |
| 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 images] | |
| pixel_values = torch.stack(pixel_values) | |
| return pixel_values | |