327 lines
10 KiB
Python
327 lines
10 KiB
Python
from __future__ import division, print_function, absolute_import
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import math
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import random
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from collections import deque
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import torch
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from PIL import Image
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from torchvision.transforms import (
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Resize, Compose, ToTensor, Normalize, ColorJitter, RandomHorizontalFlip
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)
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class Random2DTranslation(object):
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"""Randomly translates the input image with a probability.
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Specifically, given a predefined shape (height, width), the input is first
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resized with a factor of 1.125, leading to (height*1.125, width*1.125), then
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a random crop is performed. Such operation is done with a probability.
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Args:
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height (int): target image height.
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width (int): target image width.
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p (float, optional): probability that this operation takes place.
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Default is 0.5.
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interpolation (int, optional): desired interpolation. Default is
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``PIL.Image.BILINEAR``
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"""
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def __init__(self, height, width, p=0.5, interpolation=Image.BILINEAR):
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self.height = height
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self.width = width
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self.p = p
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self.interpolation = interpolation
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def __call__(self, img):
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if random.uniform(0, 1) > self.p:
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return img.resize((self.width, self.height), self.interpolation)
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new_width, new_height = int(round(self.width * 1.125)
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), int(round(self.height * 1.125))
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resized_img = img.resize((new_width, new_height), self.interpolation)
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x_maxrange = new_width - self.width
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y_maxrange = new_height - self.height
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x1 = int(round(random.uniform(0, x_maxrange)))
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y1 = int(round(random.uniform(0, y_maxrange)))
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croped_img = resized_img.crop(
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(x1, y1, x1 + self.width, y1 + self.height)
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)
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return croped_img
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class RandomErasing(object):
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"""Randomly erases an image patch.
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Origin: `<https://github.com/zhunzhong07/Random-Erasing>`_
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Reference:
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Zhong et al. Random Erasing Data Augmentation.
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Args:
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probability (float, optional): probability that this operation takes place.
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Default is 0.5.
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sl (float, optional): min erasing area.
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sh (float, optional): max erasing area.
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r1 (float, optional): min aspect ratio.
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mean (list, optional): erasing value.
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"""
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def __init__(
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self,
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probability=0.5,
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sl=0.02,
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sh=0.4,
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r1=0.3,
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mean=[0.4914, 0.4822, 0.4465]
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):
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self.probability = probability
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self.mean = mean
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self.sl = sl
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self.sh = sh
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self.r1 = r1
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def __call__(self, img):
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if random.uniform(0, 1) > self.probability:
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return img
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for attempt in range(100):
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area = img.size()[1] * img.size()[2]
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target_area = random.uniform(self.sl, self.sh) * area
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aspect_ratio = random.uniform(self.r1, 1 / self.r1)
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h = int(round(math.sqrt(target_area * aspect_ratio)))
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w = int(round(math.sqrt(target_area / aspect_ratio)))
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if w < img.size()[2] and h < img.size()[1]:
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x1 = random.randint(0, img.size()[1] - h)
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y1 = random.randint(0, img.size()[2] - w)
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if img.size()[0] == 3:
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img[0, x1:x1 + h, y1:y1 + w] = self.mean[0]
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img[1, x1:x1 + h, y1:y1 + w] = self.mean[1]
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img[2, x1:x1 + h, y1:y1 + w] = self.mean[2]
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else:
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img[0, x1:x1 + h, y1:y1 + w] = self.mean[0]
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return img
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return img
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class ColorAugmentation(object):
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"""Randomly alters the intensities of RGB channels.
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Reference:
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Krizhevsky et al. ImageNet Classification with Deep ConvolutionalNeural
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Networks. NIPS 2012.
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Args:
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p (float, optional): probability that this operation takes place.
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Default is 0.5.
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"""
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def __init__(self, p=0.5):
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self.p = p
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self.eig_vec = torch.Tensor(
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[
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[0.4009, 0.7192, -0.5675],
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[-0.8140, -0.0045, -0.5808],
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[0.4203, -0.6948, -0.5836],
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]
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)
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self.eig_val = torch.Tensor([[0.2175, 0.0188, 0.0045]])
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def _check_input(self, tensor):
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assert tensor.dim() == 3 and tensor.size(0) == 3
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def __call__(self, tensor):
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if random.uniform(0, 1) > self.p:
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return tensor
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alpha = torch.normal(mean=torch.zeros_like(self.eig_val)) * 0.1
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quatity = torch.mm(self.eig_val * alpha, self.eig_vec)
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tensor = tensor + quatity.view(3, 1, 1)
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return tensor
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class RandomPatch(object):
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"""Random patch data augmentation.
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There is a patch pool that stores randomly extracted pathces from person images.
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For each input image, RandomPatch
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1) extracts a random patch and stores the patch in the patch pool;
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2) randomly selects a patch from the patch pool and pastes it on the
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input (at random position) to simulate occlusion.
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Reference:
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- Zhou et al. Omni-Scale Feature Learning for Person Re-Identification. ICCV, 2019.
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- Zhou et al. Learning Generalisable Omni-Scale Representations
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for Person Re-Identification. arXiv preprint, 2019.
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"""
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def __init__(
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self,
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prob_happen=0.5,
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pool_capacity=50000,
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min_sample_size=100,
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patch_min_area=0.01,
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patch_max_area=0.5,
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patch_min_ratio=0.1,
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prob_rotate=0.5,
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prob_flip_leftright=0.5,
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):
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self.prob_happen = prob_happen
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self.patch_min_area = patch_min_area
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self.patch_max_area = patch_max_area
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self.patch_min_ratio = patch_min_ratio
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self.prob_rotate = prob_rotate
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self.prob_flip_leftright = prob_flip_leftright
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self.patchpool = deque(maxlen=pool_capacity)
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self.min_sample_size = min_sample_size
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def generate_wh(self, W, H):
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area = W * H
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for attempt in range(100):
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target_area = random.uniform(
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self.patch_min_area, self.patch_max_area
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) * area
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aspect_ratio = random.uniform(
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self.patch_min_ratio, 1. / self.patch_min_ratio
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)
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h = int(round(math.sqrt(target_area * aspect_ratio)))
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w = int(round(math.sqrt(target_area / aspect_ratio)))
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if w < W and h < H:
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return w, h
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return None, None
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def transform_patch(self, patch):
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if random.uniform(0, 1) > self.prob_flip_leftright:
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patch = patch.transpose(Image.FLIP_LEFT_RIGHT)
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if random.uniform(0, 1) > self.prob_rotate:
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patch = patch.rotate(random.randint(-10, 10))
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return patch
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def __call__(self, img):
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W, H = img.size # original image size
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# collect new patch
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w, h = self.generate_wh(W, H)
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if w is not None and h is not None:
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x1 = random.randint(0, W - w)
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y1 = random.randint(0, H - h)
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new_patch = img.crop((x1, y1, x1 + w, y1 + h))
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self.patchpool.append(new_patch)
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if len(self.patchpool) < self.min_sample_size:
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return img
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if random.uniform(0, 1) > self.prob_happen:
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return img
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# paste a randomly selected patch on a random position
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patch = random.sample(self.patchpool, 1)[0]
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patchW, patchH = patch.size
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x1 = random.randint(0, W - patchW)
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y1 = random.randint(0, H - patchH)
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patch = self.transform_patch(patch)
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img.paste(patch, (x1, y1))
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return img
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def build_transforms(
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height,
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width,
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transforms='random_flip',
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norm_mean=[0.485, 0.456, 0.406],
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norm_std=[0.229, 0.224, 0.225],
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**kwargs
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):
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"""Builds train and test transform functions.
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Args:
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height (int): target image height.
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width (int): target image width.
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transforms (str or list of str, optional): transformations applied to model training.
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Default is 'random_flip'.
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norm_mean (list or None, optional): normalization mean values. Default is ImageNet means.
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norm_std (list or None, optional): normalization standard deviation values. Default is
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ImageNet standard deviation values.
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"""
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if transforms is None:
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transforms = []
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if isinstance(transforms, str):
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transforms = [transforms]
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if not isinstance(transforms, list):
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raise ValueError(
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'transforms must be a list of strings, but found to be {}'.format(
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type(transforms)
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)
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)
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if len(transforms) > 0:
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transforms = [t.lower() for t in transforms]
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if norm_mean is None or norm_std is None:
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norm_mean = [0.485, 0.456, 0.406] # imagenet mean
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norm_std = [0.229, 0.224, 0.225] # imagenet std
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normalize = Normalize(mean=norm_mean, std=norm_std)
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print('Building train transforms ...')
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transform_tr = []
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print('+ resize to {}x{}'.format(height, width))
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transform_tr += [Resize((height, width))]
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if 'random_flip' in transforms:
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print('+ random flip')
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transform_tr += [RandomHorizontalFlip()]
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if 'random_crop' in transforms:
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print(
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'+ random crop (enlarge to {}x{} and '
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'crop {}x{})'.format(
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int(round(height * 1.125)), int(round(width * 1.125)), height,
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width
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)
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)
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transform_tr += [Random2DTranslation(height, width)]
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if 'random_patch' in transforms:
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print('+ random patch')
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transform_tr += [RandomPatch()]
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if 'color_jitter' in transforms:
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print('+ color jitter')
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transform_tr += [
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ColorJitter(brightness=0.2, contrast=0.15, saturation=0, hue=0)
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]
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print('+ to torch tensor of range [0, 1]')
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transform_tr += [ToTensor()]
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print('+ normalization (mean={}, std={})'.format(norm_mean, norm_std))
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transform_tr += [normalize]
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if 'random_erase' in transforms:
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print('+ random erase')
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transform_tr += [RandomErasing(mean=norm_mean)]
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transform_tr = Compose(transform_tr)
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print('Building test transforms ...')
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print('+ resize to {}x{}'.format(height, width))
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print('+ to torch tensor of range [0, 1]')
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print('+ normalization (mean={}, std={})'.format(norm_mean, norm_std))
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transform_te = Compose([
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Resize((height, width)),
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ToTensor(),
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normalize,
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])
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return transform_tr, transform_te
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