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functional.py
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functional.py
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# encoding: utf-8
"""
@author: liaoxingyu
@contact: sherlockliao01@gmail.com
"""
import numpy as np
import torch
from PIL import Image, ImageOps, ImageEnhance
def to_tensor(pic):
"""Convert a ``PIL Image`` or ``numpy.ndarray`` to tensor.
See ``ToTensor`` for more details.
Args:
pic (PIL Image or numpy.ndarray): Image to be converted to tensor.
Returns:
Tensor: Converted image.
"""
if isinstance(pic, np.ndarray):
assert len(pic.shape) in (2, 3)
# handle numpy array
if pic.ndim == 2:
pic = pic[:, :, None]
img = torch.from_numpy(pic.transpose((2, 0, 1)))
# backward compatibility
if isinstance(img, torch.ByteTensor):
return img.float()
else:
return img
# handle PIL Image
if pic.mode == 'I':
img = torch.from_numpy(np.array(pic, np.int32, copy=False))
elif pic.mode == 'I;16':
img = torch.from_numpy(np.array(pic, np.int16, copy=False))
elif pic.mode == 'F':
img = torch.from_numpy(np.array(pic, np.float32, copy=False))
elif pic.mode == '1':
img = 255 * torch.from_numpy(np.array(pic, np.uint8, copy=False))
else:
img = torch.ByteTensor(torch.ByteStorage.from_buffer(pic.tobytes()))
# PIL image mode: L, LA, P, I, F, RGB, YCbCr, RGBA, CMYK
if pic.mode == 'YCbCr':
nchannel = 3
elif pic.mode == 'I;16':
nchannel = 1
else:
nchannel = len(pic.mode)
img = img.view(pic.size[1], pic.size[0], nchannel)
# put it from HWC to CHW format
# yikes, this transpose takes 80% of the loading time/CPU
img = img.transpose(0, 1).transpose(0, 2).contiguous()
if isinstance(img, torch.ByteTensor):
return img.float()
else:
return img
def int_parameter(level, maxval):
"""Helper function to scale `val` between 0 and maxval .
Args:
level: Level of the operation that will be between [0, `PARAMETER_MAX`].
maxval: Maximum value that the operation can have. This will be scaled to
level/PARAMETER_MAX.
Returns:
An int that results from scaling `maxval` according to `level`.
"""
return int(level * maxval / 10)
def float_parameter(level, maxval):
"""Helper function to scale `val` between 0 and maxval.
Args:
level: Level of the operation that will be between [0, `PARAMETER_MAX`].
maxval: Maximum value that the operation can have. This will be scaled to
level/PARAMETER_MAX.
Returns:
A float that results from scaling `maxval` according to `level`.
"""
return float(level) * maxval / 10.
def sample_level(n):
return np.random.uniform(low=0.1, high=n)
def autocontrast(pil_img, *args):
return ImageOps.autocontrast(pil_img)
def equalize(pil_img, *args):
return ImageOps.equalize(pil_img)
def posterize(pil_img, level, *args):
level = int_parameter(sample_level(level), 4)
return ImageOps.posterize(pil_img, 4 - level)
def rotate(pil_img, level, *args):
degrees = int_parameter(sample_level(level), 30)
if np.random.uniform() > 0.5:
degrees = -degrees
return pil_img.rotate(degrees, resample=Image.BILINEAR)
def solarize(pil_img, level, *args):
level = int_parameter(sample_level(level), 256)
return ImageOps.solarize(pil_img, 256 - level)
def shear_x(pil_img, level, image_size):
level = float_parameter(sample_level(level), 0.3)
if np.random.uniform() > 0.5:
level = -level
return pil_img.transform(image_size,
Image.AFFINE, (1, level, 0, 0, 1, 0),
resample=Image.BILINEAR)
def shear_y(pil_img, level, image_size):
level = float_parameter(sample_level(level), 0.3)
if np.random.uniform() > 0.5:
level = -level
return pil_img.transform(image_size,
Image.AFFINE, (1, 0, 0, level, 1, 0),
resample=Image.BILINEAR)
def translate_x(pil_img, level, image_size):
level = int_parameter(sample_level(level), image_size[0] / 3)
if np.random.random() > 0.5:
level = -level
return pil_img.transform(image_size,
Image.AFFINE, (1, 0, level, 0, 1, 0),
resample=Image.BILINEAR)
def translate_y(pil_img, level, image_size):
level = int_parameter(sample_level(level), image_size[1] / 3)
if np.random.random() > 0.5:
level = -level
return pil_img.transform(image_size,
Image.AFFINE, (1, 0, 0, 0, 1, level),
resample=Image.BILINEAR)
# operation that overlaps with ImageNet-C's test set
def color(pil_img, level, *args):
level = float_parameter(sample_level(level), 1.8) + 0.1
return ImageEnhance.Color(pil_img).enhance(level)
# operation that overlaps with ImageNet-C's test set
def contrast(pil_img, level, *args):
level = float_parameter(sample_level(level), 1.8) + 0.1
return ImageEnhance.Contrast(pil_img).enhance(level)
# operation that overlaps with ImageNet-C's test set
def brightness(pil_img, level, *args):
level = float_parameter(sample_level(level), 1.8) + 0.1
return ImageEnhance.Brightness(pil_img).enhance(level)
# operation that overlaps with ImageNet-C's test set
def sharpness(pil_img, level, *args):
level = float_parameter(sample_level(level), 1.8) + 0.1
return ImageEnhance.Sharpness(pil_img).enhance(level)
augmentations_reid = [
autocontrast, equalize, posterize, shear_x, shear_y,
color, contrast, brightness, sharpness
]
augmentations = [
autocontrast, equalize, posterize, rotate, solarize, shear_x, shear_y,
translate_x, translate_y
]
augmentations_all = [
autocontrast, equalize, posterize, rotate, solarize, shear_x, shear_y,
translate_x, translate_y, color, contrast, brightness, sharpness
]