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e0c75d6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 | import os
import torch
from torchvision import transforms, datasets
from albumentations import (
HorizontalFlip,
VerticalFlip,
ShiftScaleRotate,
CLAHE,
RandomRotate90,
Transpose,
ShiftScaleRotate,
HueSaturationValue,
GaussNoise,
Sharpen,
Emboss,
RandomBrightnessContrast,
OneOf,
Compose,
)
import numpy as np
from PIL import Image
def strong_aug(p=0.5):
return Compose(
[
RandomRotate90(p=0.2),
Transpose(p=0.2),
HorizontalFlip(p=0.5),
VerticalFlip(p=0.5),
OneOf(
[
GaussNoise(),
],
p=0.2,
),
ShiftScaleRotate(p=0.2),
OneOf(
[
CLAHE(clip_limit=2),
Sharpen(),
Emboss(),
RandomBrightnessContrast(),
],
p=0.2,
),
HueSaturationValue(p=0.2),
],
p=p,
)
def augment(aug, image):
return aug(image=image)["image"]
class Aug(object):
def __call__(self, img):
aug = strong_aug(p=0.9)
return Image.fromarray(augment(aug, np.array(img)))
def normalize_data():
mean = [0.485, 0.456, 0.406]
std = [0.229, 0.224, 0.225]
return {
"train": transforms.Compose(
[Aug(), transforms.ToTensor(), transforms.Normalize(mean, std)]
),
"valid": transforms.Compose(
[transforms.ToTensor(), transforms.Normalize(mean, std)]
),
"test": transforms.Compose(
[transforms.ToTensor(), transforms.Normalize(mean, std)]
),
"vid": transforms.Compose([transforms.Normalize(mean, std)]),
}
def load_data(data_dir="sample/", batch_size=4):
data_dir = data_dir
image_datasets = {
x: datasets.ImageFolder(os.path.join(data_dir, x), normalize_data()[x])
for x in ["train", "valid", "test"]
}
# dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size,
# shuffle=True, num_workers=0, pin_memory=True)
# for x in ['train', 'validation', 'test']}
dataset_sizes = {x: len(image_datasets[x]) for x in ["train", "valid", "test"]}
train_dataloaders = torch.utils.data.DataLoader(
image_datasets["train"],
batch_size,
shuffle=True,
num_workers=0,
pin_memory=True,
)
validation_dataloaders = torch.utils.data.DataLoader(
image_datasets["valid"],
batch_size,
shuffle=False,
num_workers=0,
pin_memory=True,
)
test_dataloaders = torch.utils.data.DataLoader(
image_datasets["test"],
batch_size,
shuffle=False,
num_workers=0,
pin_memory=True,
)
dataloaders = {
"train": train_dataloaders,
"validation": validation_dataloaders,
"test": test_dataloaders,
}
return dataloaders, dataset_sizes
def load_checkpoint(model, optimizer, filename=None):
start_epoch = 0
log_loss = 0
if os.path.isfile(filename):
print("=> loading checkpoint '{}'".format(filename))
checkpoint = torch.load(filename)
start_epoch = checkpoint["epoch"]
model.load_state_dict(checkpoint["state_dict"])
optimizer.load_state_dict(checkpoint["optimizer"])
log_loss = checkpoint["min_loss"]
print(
"=> loaded checkpoint '{}' (epoch {})".format(filename, checkpoint["epoch"])
)
else:
print("=> no checkpoint found at '{}'".format(filename))
return model, optimizer, start_epoch, log_loss
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