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dataprocess.py
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dataprocess.py
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import numpy as np
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
import torch
from utils import *
from PIL import Image, ImageEnhance, ImageOps
import random
# Improved Regularization of Convolutional Neural Networks with Cutout.
# code from https://github.com/uoguelph-mlrg/Cutout/blob/master/util/cutout.py
class Cutout(object):
def __init__(self, n_holes, length):
self.n_holes = n_holes
self.length = length
def __call__(self, img):
h = img.size(1)
w = img.size(2)
mask = np.ones((h, w), np.float32)
for n in range(self.n_holes):
y = np.random.randint(h)
x = np.random.randint(w)
y1 = np.clip(y - self.length // 2, 0, h)
y2 = np.clip(y + self.length // 2, 0, h)
x1 = np.clip(x - self.length // 2, 0, w)
x2 = np.clip(x + self.length // 2, 0, w)
mask[y1: y2, x1: x2] = 0.
mask = torch.from_numpy(mask)
mask = mask.expand_as(img)
img = img * mask
return img
class SubPolicy(object):
def __init__(self, p1, operation1, magnitude_idx1, p2, operation2, magnitude_idx2, fillcolor=(128, 128, 128)):
ranges = {
"shearX": np.linspace(0, 0.3, 10),
"shearY": np.linspace(0, 0.3, 10),
"translateX": np.linspace(0, 150 / 331, 10),
"translateY": np.linspace(0, 150 / 331, 10),
"rotate": np.linspace(0, 30, 10),
"color": np.linspace(0.0, 0.9, 10),
"posterize": np.round(np.linspace(8, 4, 10), 0).astype(np.int32),
"solarize": np.linspace(256, 0, 10),
"contrast": np.linspace(0.0, 0.9, 10),
"sharpness": np.linspace(0.0, 0.9, 10),
"brightness": np.linspace(0.0, 0.9, 10),
"autocontrast": [0] * 10,
"equalize": [0] * 10,
"invert": [0] * 10
}
def rotate_with_fill(img, magnitude):
rot = img.convert("RGBA").rotate(magnitude)
return Image.composite(rot, Image.new("RGBA", rot.size, (128,) * 4), rot).convert(img.mode)
func = {
"shearX": lambda img, magnitude: img.transform(
img.size, Image.AFFINE, (1, magnitude *
random.choice([-1, 1]), 0, 0, 1, 0),
Image.BICUBIC, fillcolor=fillcolor),
"shearY": lambda img, magnitude: img.transform(
img.size, Image.AFFINE, (1, 0, 0, magnitude *
random.choice([-1, 1]), 1, 0),
Image.BICUBIC, fillcolor=fillcolor),
"translateX": lambda img, magnitude: img.transform(
img.size, Image.AFFINE, (1, 0, magnitude *
img.size[0] * random.choice([-1, 1]), 0, 1, 0),
fillcolor=fillcolor),
"translateY": lambda img, magnitude: img.transform(
img.size, Image.AFFINE, (1, 0, 0, 0, 1, magnitude *
img.size[1] * random.choice([-1, 1])),
fillcolor=fillcolor),
"rotate": lambda img, magnitude: rotate_with_fill(img, magnitude),
"color": lambda img, magnitude: ImageEnhance.Color(img).enhance(1 + magnitude * random.choice([-1, 1])),
"posterize": lambda img, magnitude: ImageOps.posterize(img, magnitude),
"solarize": lambda img, magnitude: ImageOps.solarize(img, magnitude),
"contrast": lambda img, magnitude: ImageEnhance.Contrast(img).enhance(
1 + magnitude * random.choice([-1, 1])),
"sharpness": lambda img, magnitude: ImageEnhance.Sharpness(img).enhance(
1 + magnitude * random.choice([-1, 1])),
"brightness": lambda img, magnitude: ImageEnhance.Brightness(img).enhance(
1 + magnitude * random.choice([-1, 1])),
"autocontrast": lambda img, magnitude: ImageOps.autocontrast(img),
"equalize": lambda img, magnitude: ImageOps.equalize(img),
"invert": lambda img, magnitude: ImageOps.invert(img)
}
self.p1 = p1
self.operation1 = func[operation1]
self.magnitude1 = ranges[operation1][magnitude_idx1]
self.p2 = p2
self.operation2 = func[operation2]
self.magnitude2 = ranges[operation2][magnitude_idx2]
def __call__(self, img):
if random.random() < self.p1:
img = self.operation1(img, self.magnitude1)
if random.random() < self.p2:
img = self.operation2(img, self.magnitude2)
return img
class CIFAR10Policy(object):
def __init__(self, fillcolor=(128, 128, 128)):
self.policies = [
SubPolicy(0.1, "invert", 7, 0.2, "contrast", 6, fillcolor),
SubPolicy(0.7, "rotate", 2, 0.3, "translateX", 9, fillcolor),
SubPolicy(0.8, "sharpness", 1, 0.9, "sharpness", 3, fillcolor),
SubPolicy(0.5, "shearY", 8, 0.7, "translateY", 9, fillcolor),
SubPolicy(0.5, "autocontrast", 8, 0.9, "equalize", 2, fillcolor),
SubPolicy(0.2, "shearY", 7, 0.3, "posterize", 7, fillcolor),
SubPolicy(0.4, "color", 3, 0.6, "brightness", 7, fillcolor),
SubPolicy(0.3, "sharpness", 9, 0.7, "brightness", 9, fillcolor),
SubPolicy(0.6, "equalize", 5, 0.5, "equalize", 1, fillcolor),
SubPolicy(0.6, "contrast", 7, 0.6, "sharpness", 5, fillcolor),
SubPolicy(0.7, "color", 7, 0.5, "translateX", 8, fillcolor),
SubPolicy(0.3, "equalize", 7, 0.4, "autocontrast", 8, fillcolor),
SubPolicy(0.4, "translateY", 3, 0.2, "sharpness", 6, fillcolor),
SubPolicy(0.9, "brightness", 6, 0.2, "color", 8, fillcolor),
SubPolicy(0.5, "solarize", 2, 0.0, "invert", 3, fillcolor),
SubPolicy(0.2, "equalize", 0, 0.6, "autocontrast", 0, fillcolor),
SubPolicy(0.2, "equalize", 8, 0.8, "equalize", 4, fillcolor),
SubPolicy(0.9, "color", 9, 0.6, "equalize", 6, fillcolor),
SubPolicy(0.8, "autocontrast", 4, 0.2, "solarize", 8, fillcolor),
SubPolicy(0.1, "brightness", 3, 0.7, "color", 0, fillcolor),
SubPolicy(0.4, "solarize", 5, 0.9, "autocontrast", 3, fillcolor),
SubPolicy(0.9, "translateY", 9, 0.7, "translateY", 9, fillcolor),
SubPolicy(0.9, "autocontrast", 2, 0.8, "solarize", 3, fillcolor),
SubPolicy(0.8, "equalize", 8, 0.1, "invert", 3, fillcolor),
SubPolicy(0.7, "translateY", 9, 0.9, "autocontrast", 1, fillcolor)
]
def __call__(self, img):
policy_idx = random.randint(0, len(self.policies) - 1)
return self.policies[policy_idx](img)
def PreProcess_Cifar10(data_dir, batch_size):
trans_t = transforms.Compose([transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
CIFAR10Policy(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
Cutout(n_holes=1, length=16)
])
trans = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))])
train_data = datasets.CIFAR10(data_dir, train=True, transform=trans_t, download=True)
test_data = datasets.CIFAR10(data_dir, train=False, transform=trans, download=True)
train_dataloader = DataLoader(train_data, batch_size=batch_size, shuffle=True, num_workers=2)
test_dataloader = DataLoader(test_data, batch_size=batch_size, shuffle=False, num_workers=2)
return train_dataloader, test_dataloader
def PreProcess_Cifar100(data_dir, batch_size):
trans_t = transforms.Compose([transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
CIFAR10Policy(),
transforms.ToTensor(),
transforms.Normalize(mean=[n/255. for n in [129.3, 124.1, 112.4]], std=[n/255. for n in [68.2, 65.4, 70.4]]),
Cutout(n_holes=1, length=8)
])
trans = transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean=[n/255. for n in [129.3, 124.1, 112.4]], std=[n/255. for n in [68.2, 65.4, 70.4]])])
train_data = datasets.CIFAR100(data_dir, train=True, transform=trans_t, download=True)
test_data = datasets.CIFAR100(data_dir, train=False, transform=trans, download=True)
train_dataloader = DataLoader(train_data, batch_size=batch_size, shuffle=True, num_workers=2)
test_dataloader = DataLoader(test_data, batch_size=batch_size, shuffle=False, num_workers=2)
return train_dataloader, test_dataloader
def PreProcess_ImageNet(data_dir, batch_size, train_dir, test_dir):
trans_t = transforms.Compose([transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
trans = transforms.Compose([transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
train_data = datasets.ImageFolder(root = train_dir,transform=trans_t)
test_data = datasets.ImageFolder(root = test_dir ,transform=trans)
train_dataloader = DataLoader(train_data, batch_size=batch_size, shuffle=True, num_workers=8)
test_dataloader = DataLoader(test_data, batch_size=batch_size, shuffle=False, num_workers=2)
return train_dataloader, test_dataloader
def PreProcess_MNIST(data_dir, batch_size):
train_dataset = datasets.MNIST(
root=data_dir,
train=True,
transform=transforms.ToTensor(),
download=True
)
test_dataset = datasets.MNIST(
root=data_dir,
train=False,
transform=transforms.ToTensor(),
download=True
)
train_dataloader = DataLoader(
dataset=train_dataset,
batch_size=batch_size,
shuffle=True,
drop_last=True
)
test_dataloader = DataLoader(
dataset=test_dataset,
batch_size=batch_size,
shuffle=False,
drop_last=False
)
return train_dataloader, test_dataloader