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eef8873 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 | import logging
from PIL import Image
from torch.utils.data import Dataset, DataLoader
from transformers import ConvNextImageProcessor
from src.config import (
BATCH_SIZE,
NUM_WORKERS
)
from src.data.ingestion import collect_image_paths
from src.data.preprocessing import split_dataset
from src.data.augmentation import (
get_resnet_train_transforms,
get_resnet_val_transforms,
get_fusion_train_transforms,
get_fusion_val_transforms
)
logger = logging.getLogger(__name__)
class ResNetDataset(Dataset):
def __init__(self, samples, transforms=None):
self.samples = samples
self.transforms = transforms
def __len__(self):
return len(self.samples)
def __getitem__(self, idx):
image_path, label = self.samples[idx]
image = Image.open(image_path).convert("RGB")
if self.transforms:
image = self.transforms(image)
return image, label
class FusionDataset(Dataset):
def __init__(
self,
samples,
transforms=None,
convnext_model_name="facebook/convnext-small-224"
):
self.samples = samples
self.transforms = transforms
logger.info("Loading ConvNeXt processor...")
self.processor = ConvNextImageProcessor.from_pretrained(
convnext_model_name
)
def __len__(self):
return len(self.samples)
def __getitem__(self, idx):
image_path, label = self.samples[idx]
image = Image.open(image_path).convert("RGB")
if self.transforms:
eff_tensor = self.transforms(image)
else:
raise ValueError("Fusion transforms are required.")
convnext_inputs = self.processor(
images=image,
return_tensors="pt"
)
convnext_tensor = convnext_inputs["pixel_values"].squeeze(0)
return {
"pixel_values_eff": eff_tensor,
"pixel_values_cnx": convnext_tensor,
"labels": label
}
def create_resnet_dataloaders():
logger.info("Creating ResNet dataloaders...")
samples = collect_image_paths()
train_data, val_data = split_dataset(samples)
train_dataset = ResNetDataset(
train_data,
transforms=get_resnet_train_transforms()
)
val_dataset = ResNetDataset(
val_data,
transforms=get_resnet_val_transforms()
)
train_loader = DataLoader(
train_dataset,
batch_size=BATCH_SIZE,
shuffle=True,
num_workers=NUM_WORKERS
)
val_loader = DataLoader(
val_dataset,
batch_size=BATCH_SIZE,
shuffle=False,
num_workers=NUM_WORKERS
)
logger.info("ResNet dataloaders created successfully.")
return train_loader, val_loader
def create_fusion_dataloaders():
logger.info("Creating Fusion dataloaders...")
samples = collect_image_paths()
train_data, val_data = split_dataset(samples)
train_dataset = FusionDataset(
train_data,
transforms=get_fusion_train_transforms()
)
val_dataset = FusionDataset(
val_data,
transforms=get_fusion_val_transforms()
)
train_loader = DataLoader(
train_dataset,
batch_size=BATCH_SIZE,
shuffle=True,
num_workers=NUM_WORKERS
)
val_loader = DataLoader(
val_dataset,
batch_size=BATCH_SIZE,
shuffle=False,
num_workers=NUM_WORKERS
)
logger.info("Fusion dataloaders created successfully.")
return train_loader, val_loader
if __name__ == "__main__":
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s - %(levelname)s - %(message)s"
)
print("\nTesting ResNet dataloaders...\n")
train_loader, val_loader = create_resnet_dataloaders()
images, labels = next(iter(train_loader))
print("ResNet batch shape:", images.shape)
print("ResNet labels shape:", labels.shape)
print("\nTesting Fusion dataloaders...\n")
train_loader, val_loader = create_fusion_dataloaders()
batch = next(iter(train_loader))
print(
"Fusion EfficientNet batch shape:",
batch["pixel_values_eff"].shape
)
print(
"Fusion ConvNeXt batch shape:",
batch["pixel_values_cnx"].shape
)
print(
"Fusion labels shape:",
batch["labels"].shape
) |