identification_benchmark/classification/data_loading.py

import torch
from torch.utils.data import TensorDataset
import tensorflow as tf
import tensorflow_datasets as tfds
import jax.numpy as jnp


def get_datasets(
        features_path='../big_model_inference/resnet18_embeddings.pt', 
        labels_path='../big_model_inference/all_cow_ids.pt'
    ):
    embeddings_np = torch.load(features_path)
    all_cow_ids =  torch.load(labels_path) - 1


    # Set the seed for reproducibility
    seed = 42
    torch.manual_seed(seed)

    # Assume embeddings_np and all_cow_ids are already loaded as PyTorch tensors
    num_samples = len(embeddings_np)
    indices = torch.randperm(num_samples)

    # Calculate split indices for 70/20/10 split
    train_end = int(0.001 * num_samples)
    val_end = int(0.2 * num_samples)
    train_indices = indices[:train_end]
    val_indices = indices[train_end:val_end]
    test_indices = indices[val_end:]

    # print(train_indices[:10])
    # print(val_indices[:10])
    # print(test_indices[:10])

#     assert torch.equal(train_indices[:10], torch.tensor([292622,  37548,  42432, 353497, 379054, 301165,  47066, 353666, 409458,
#             454581]))
#     assert torch.equal(val_indices[:10], torch.tensor([219340, 495317, 522025,  36026, 490924, 179563, 533196, 263518, 139048,
#             72363]))
#     assert torch.equal(test_indices[:10], torch.tensor([192226, 477583, 210506, 265639,  82907, 246325, 335726, 395405, 497690,
#             388675]))
    # Create datasets for each split
    train_dataset = TensorDataset(embeddings_np[train_indices], all_cow_ids[train_indices])
    val_dataset   = TensorDataset(embeddings_np[val_indices], all_cow_ids[val_indices])
    test_dataset  = TensorDataset(embeddings_np[test_indices], all_cow_ids[test_indices])

    print(f"Train set: {len(train_dataset)} samples")
    print(f"Validation set: {len(val_dataset)} samples")
    print(f"Test set: {len(test_dataset)} samples")
    return train_dataset, val_dataset, test_dataset



def get_time_series(
        features_path='../big_model_inference/resnet18_embeddings.pt', 
        labels_path='../big_model_inference/all_cow_ids.pt'
    ):
    embeddings_np = torch.load(features_path)
    all_cow_ids =  torch.load(labels_path) - 1


    num_samples = len(embeddings_np)

    train_end = int(0.33 * num_samples)
    val_end = int(0.66 * num_samples)

    # Create datasets for each split
    train_dataset = TensorDataset(embeddings_np[:train_end], all_cow_ids[:train_end])
    val_dataset   = TensorDataset(embeddings_np[train_end:val_end], all_cow_ids[train_end:val_end])
    test_dataset  = TensorDataset(embeddings_np[val_end:], all_cow_ids[val_end:])

    print(f"Train set: {len(train_dataset)} samples")
    print(f"Validation set: {len(val_dataset)} samples")
    print(f"Test set: {len(test_dataset)} samples")
    return train_dataset, val_dataset, test_dataset



def get_time_series_tf(
        features_path='../big_model_inference/resnet18_embeddings.pt', 
        labels_path='../big_model_inference/all_cow_ids.pt'
    ):
    embeddings_np = torch.load(features_path)
    all_cow_ids =  torch.load(labels_path) - 1
    embeddings_np = embeddings_np.numpy()
    all_cow_ids = all_cow_ids.numpy()

    num_samples = len(embeddings_np)

    train_end = int(0.33 * num_samples)
    val_end = int(0.66 * num_samples)

    # Create datasets for each split
    train_dataset = tf.data.Dataset.from_tensor_slices((embeddings_np[:train_end], all_cow_ids[:train_end]))
    val_dataset   = tf.data.Dataset.from_tensor_slices((embeddings_np[train_end:val_end], all_cow_ids[train_end:val_end]))
    test_dataset  = tf.data.Dataset.from_tensor_slices((embeddings_np[val_end:], all_cow_ids[val_end:]))

    print(f"Train set: {len(train_dataset)} samples")
    print(f"Validation set: {len(val_dataset)} samples")
    print(f"Test set: {len(test_dataset)} samples")
    
    batch_size = 32
    
    train_dataset = train_dataset.shuffle(len(train_dataset)).batch(
        batch_size,
        num_parallel_calls=tf.data.AUTOTUNE
    ).prefetch(tf.data.AUTOTUNE)
    
    val_dataset = val_dataset.batch(
        batch_size,
        num_parallel_calls=tf.data.AUTOTUNE
    ).prefetch(tf.data.AUTOTUNE)
    
    test_dataset = test_dataset.batch(
        batch_size,
        num_parallel_calls=tf.data.AUTOTUNE
    ).prefetch(tf.data.AUTOTUNE)
    
    train_dataset = tfds.as_numpy(train_dataset)
    val_dataset = tfds.as_numpy(val_dataset)
    test_dataset = tfds.as_numpy(test_dataset)
    return train_dataset, val_dataset, test_dataset, len(embeddings_np[0])

if __name__ == "__main__":
    train_dataset, val_dataset, test_dataset, in_features = get_time_series_tf(features_path='../big_model_inference/facebook_dinov2_base_embeddings.pt')
    print(f"in features : {in_features}")
    for batch in train_dataset:
        batch = {
            'feature' : jnp.array(batch[0]),
            "label" : jnp.array(batch[1])
        }
        print(batch)
        break
    for batch in val_dataset:
        print(batch)
        break
    for batch in test_dataset:
        print(batch)
        break