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d32e728 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 | import torch
import tensorflow as tf
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
from pathlib import Path
CLASSES = ["buildings", "forest", "glacier", "mountain", "sea", "street"]
IMAGE_SIZE = (150, 150)
# PYTORCH DATA LOADER
def get_pytorch_data(data_dir="data", batch_size=64):
data_dir = Path(data_dir)
train_path = data_dir / "seg_train"
test_path = data_dir / "seg_test"
train_transform = transforms.Compose([
transforms.Resize(IMAGE_SIZE),
transforms.RandomHorizontalFlip(),
transforms.RandomVerticalFlip(p=0.1),
transforms.RandomRotation(15),
transforms.ColorJitter(brightness=0.3, contrast=0.3, saturation=0.2),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]
)
])
test_transform = transforms.Compose([
transforms.Resize(IMAGE_SIZE),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]
)
])
full_train = datasets.ImageFolder(
str(train_path),
transform=train_transform
)
test_dataset = datasets.ImageFolder(
str(test_path),
transform=test_transform
)
print("Class mapping:", full_train.class_to_idx)
# Split train / validation
val_size = int(0.2 * len(full_train))
train_size = len(full_train) - val_size
train_dataset, val_dataset = torch.utils.data.random_split(
full_train,
[train_size, val_size],
generator=torch.Generator().manual_seed(42)
)
train_loader = DataLoader(
train_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=2
)
val_loader = DataLoader(
val_dataset,
batch_size=batch_size,
shuffle=False,
num_workers=2
)
test_loader = DataLoader(
test_dataset,
batch_size=batch_size,
shuffle=False,
num_workers=2
)
return train_loader, val_loader, test_loader
# TENSORFLOW DATA PIPELINE
def get_tensorflow_data(data_dir="data", batch_size=64):
data_dir = Path(data_dir)
train_dir = data_dir / "seg_train"
test_dir = data_dir / "seg_test"
train_ds = tf.keras.utils.image_dataset_from_directory(
str(train_dir),
image_size=IMAGE_SIZE,
batch_size=batch_size,
shuffle=True,
seed=42,
validation_split=0.2,
subset="training",
label_mode="int",
class_names=CLASSES
)
val_ds = tf.keras.utils.image_dataset_from_directory(
str(train_dir),
image_size=IMAGE_SIZE,
batch_size=batch_size,
shuffle=True,
seed=42,
validation_split=0.2,
subset="validation",
label_mode="int",
class_names=CLASSES
)
test_ds = tf.keras.utils.image_dataset_from_directory(
str(test_dir),
image_size=IMAGE_SIZE,
batch_size=batch_size,
shuffle=False,
label_mode="int",
class_names=CLASSES
)
# AUGMENTATION
augmentation = tf.keras.Sequential([
tf.keras.layers.RandomFlip("horizontal"),
tf.keras.layers.RandomRotation(0.1),
tf.keras.layers.RandomZoom(0.1),
tf.keras.layers.RandomContrast(0.2),
])
normalization = tf.keras.layers.Rescaling(1.0 / 255)
train_ds = (
train_ds
.map(lambda x, y: (augmentation(x, training=True), y),
num_parallel_calls=tf.data.AUTOTUNE)
.map(lambda x, y: (normalization(x), y),
num_parallel_calls=tf.data.AUTOTUNE)
.cache()
.shuffle(1000)
.prefetch(tf.data.AUTOTUNE)
)
val_ds = (
val_ds
.map(lambda x, y: (normalization(x), y),
num_parallel_calls=tf.data.AUTOTUNE)
.cache()
.prefetch(tf.data.AUTOTUNE)
)
test_ds = (
test_ds
.map(lambda x, y: (normalization(x), y),
num_parallel_calls=tf.data.AUTOTUNE)
.cache()
.prefetch(tf.data.AUTOTUNE)
)
return train_ds, val_ds, test_ds |