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54 lines
1.5 KiB
54 lines
1.5 KiB
6 years ago
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import torch
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from torchvision import transforms
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from PIL import Image
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import math
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import numpy as np
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from data.random_erasing import RandomErasingNumpy
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DEFAULT_CROP_PCT = 0.875
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IMAGENET_DPN_MEAN = [124 / 255, 117 / 255, 104 / 255]
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IMAGENET_DPN_STD = [1 / (.0167 * 255)] * 3
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IMAGENET_INCEPTION_MEAN = [0.5, 0.5, 0.5]
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IMAGENET_INCEPTION_STD = [0.5, 0.5, 0.5]
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IMAGENET_DEFAULT_MEAN = [0.485, 0.456, 0.406]
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IMAGENET_DEFAULT_STD = [0.229, 0.224, 0.225]
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class AsNumpy:
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def __call__(self, pil_img):
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np_img = np.array(pil_img, dtype=np.uint8)
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if np_img.ndim < 3:
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np_img = np.expand_dims(np_img, axis=-1)
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np_img = np.rollaxis(np_img, 2) # HWC to CHW
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return np_img
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def transforms_imagenet_train(
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img_size=224,
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scale=(0.1, 1.0),
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color_jitter=(0.4, 0.4, 0.4),
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random_erasing=0.4):
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tfl = [
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transforms.RandomResizedCrop(img_size, scale=scale),
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transforms.RandomHorizontalFlip(),
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transforms.ColorJitter(*color_jitter),
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AsNumpy(),
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]
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#if random_erasing > 0.:
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# tfl.append(RandomErasingNumpy(random_erasing, per_pixel=True))
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return transforms.Compose(tfl)
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def transforms_imagenet_eval(img_size=224, crop_pct=None):
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crop_pct = crop_pct or DEFAULT_CROP_PCT
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scale_size = int(math.floor(img_size / crop_pct))
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return transforms.Compose([
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transforms.Resize(scale_size, Image.BICUBIC),
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transforms.CenterCrop(img_size),
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AsNumpy(),
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])
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