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a18e884 | 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 | import os
import numpy as np
from pathlib import Path
import torch
from torch.utils.data import DataLoader, random_split
from torchvision import datasets, transforms
import tensorflow as tf
IMAGE_SIZE = (150, 150)
BATCH_SIZE = 32
VAL_SPLIT = 0.2
SEED = 42
CLASS_NAMES = ["buildings", "forest", "glacier", "mountain", "sea", "street"]
# PyTorch Data Pipeline
def get_pytorch_loaders(train_dir: str, test_dir: str):
mean = [0.485, 0.456, 0.406]
std = [0.229, 0.224, 0.225]
train_transform = transforms.Compose([
transforms.Resize(IMAGE_SIZE),
transforms.RandomHorizontalFlip(p=0.5),
transforms.RandomRotation(degrees=15),
transforms.RandomResizedCrop(IMAGE_SIZE,scale=(0.8, 1.0)),
transforms.ColorJitter(brightness=0.2,contrast=0.2),
transforms.ToTensor(),
transforms.Normalize(mean, std),
])
eval_transform = transforms.Compose([
transforms.Resize(IMAGE_SIZE),
transforms.ToTensor(),
transforms.Normalize(mean, std),
])
full_train = datasets.ImageFolder(root=train_dir,transform=train_transform)
# Split into train / validation
n_val = int(len(full_train) * VAL_SPLIT)
n_train = len(full_train) - n_val
train_ds, val_ds = random_split(
full_train, [n_train, n_val],
generator=torch.Generator().manual_seed(SEED)
)
val_ds.dataset = datasets.ImageFolder(root=train_dir,transform=eval_transform)
test_ds = datasets.ImageFolder(root=test_dir, transform=eval_transform)
train_loader = DataLoader(train_ds, batch_size=BATCH_SIZE, shuffle=True, num_workers=2, pin_memory=True)
val_loader = DataLoader(val_ds, batch_size=BATCH_SIZE, shuffle=False, num_workers=2, pin_memory=True)
test_loader = DataLoader(test_ds, batch_size=BATCH_SIZE, shuffle=False, num_workers=2, pin_memory=True)
print(f"[PyTorch] Train: {n_train} | Val: {n_val} | Test: {len(test_ds)}")
return train_loader, val_loader, test_loader, full_train.classes
# TensorFlow Data Pipeline
def get_tensorflow_datasets(train_dir: str, test_dir: str):
img_h, img_w = IMAGE_SIZE
raw_train = tf.keras.utils.image_dataset_from_directory(
train_dir,
validation_split=VAL_SPLIT,
subset="training",
seed=SEED,
image_size=IMAGE_SIZE,
batch_size=BATCH_SIZE,
label_mode="categorical",
)
raw_val = tf.keras.utils.image_dataset_from_directory(
train_dir,
validation_split=VAL_SPLIT,
subset="validation",
seed=SEED,
image_size=IMAGE_SIZE,
batch_size=BATCH_SIZE,
label_mode="categorical",
)
raw_test = tf.keras.utils.image_dataset_from_directory(
test_dir,
image_size=IMAGE_SIZE,
batch_size=BATCH_SIZE,
label_mode="categorical",
shuffle=False,
)
class_names = raw_train.class_names
normalization = tf.keras.layers.Rescaling(1.0 / 255)
augmentation = tf.keras.Sequential([
tf.keras.layers.RandomFlip("horizontal"),
tf.keras.layers.RandomRotation(0.1),
tf.keras.layers.RandomZoom(0.2),
tf.keras.layers.RandomContrast(0.1),
])
def preprocess_train(images, labels):
images = normalization(images)
images = augmentation(images, training=True)
return images, labels
def preprocess_eval(images, labels):
images = normalization(images)
return images, labels
AUTOTUNE = tf.data.AUTOTUNE
train_ds = (raw_train
.map(preprocess_train, num_parallel_calls=AUTOTUNE)
.cache()
.shuffle(1000)
.prefetch(AUTOTUNE))
val_ds = (raw_val
.map(preprocess_eval, num_parallel_calls=AUTOTUNE)
.cache()
.prefetch(AUTOTUNE))
test_ds = (raw_test
.map(preprocess_eval, num_parallel_calls=AUTOTUNE)
.prefetch(AUTOTUNE))
print(f"[TensorFlow] Classes: {class_names}")
return train_ds, val_ds, test_ds, class_names
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