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Data utility functions for managing logits, labels, features, and data loaders
"""
import os
import numpy as np
import torch
from tqdm import tqdm
def get_logit_paths(dataset_name, model_name, seed_value, valid_size=0.2, loss_fn='CE', corruption_type=None, severity=None, train_loss=None):
"""
Generate file paths for saving/loading logits with specific parameters
Args:
dataset_name: Name of the dataset
model_name: Name of the model
seed_value: Random seed used
valid_size: Validation set size
loss_fn: Loss function used for calibration (only affects smart model path)
corruption_type: Type of corruption (for ImageNet-C)
severity: Severity level (for ImageNet-C)
train_loss: Training loss type (for CIFAR models)
Returns:
Dictionary of file paths for val/test logits and labels
"""
# Create configuration-specific directory
if dataset_name.startswith('imagenet'):
config_dir = f"{dataset_name}_{model_name}_seed{seed_value}_vs{valid_size}"
elif dataset_name.startswith('cifar'):
# Always include train_loss for CIFAR datasets
# Default to cross_entropy if not provided
train_loss = train_loss or 'cross_entropy'
config_dir = f"{dataset_name}_{model_name}_{train_loss}_seed{seed_value}"
else:
config_dir = f"{dataset_name}_{model_name}_seed{seed_value}"
# For ImageNet-C, add corruption type and severity to the directory name
if dataset_name == 'imagenet_c' and corruption_type is not None and severity is not None:
config_dir = f"{dataset_name}_{corruption_type}_s{severity}_{model_name}_seed{seed_value}_vs{valid_size}"
# Try multiple cache directories (search order: LogitsGap/cache, then cache)
# Get the project root directory (parent of utils directory)
current_file_dir = os.path.dirname(os.path.abspath(__file__))
project_root = os.path.dirname(current_file_dir)
base_dirs = [
os.path.join(project_root, "LogitsGap", "cache"),
os.path.join(project_root, "cache"),
"LogitsGap/cache",
"cache"
]
cache_dir = None
for base_dir in base_dirs:
potential_cache_dir = os.path.join(base_dir, config_dir)
# Check if this directory exists and has the required files
if os.path.exists(potential_cache_dir):
cache_dir = potential_cache_dir
break
# If no existing cache found, use the default cache directory
if cache_dir is None:
cache_dir = os.path.join("cache", config_dir)
os.makedirs(cache_dir, exist_ok=True)
paths = {
'val_logits': os.path.join(cache_dir, "val_logits.npy"),
'val_labels': os.path.join(cache_dir, "val_labels.npy"),
'val_features': os.path.join(cache_dir, "val_features.npy"),
'test_logits': os.path.join(cache_dir, "test_logits.npy"),
'test_labels': os.path.join(cache_dir, "test_labels.npy"),
'test_features': os.path.join(cache_dir, "test_features.npy"),
'logitsgap_values': os.path.join(cache_dir, "logitsgap_values.json"),
'test_logitsgap_values': os.path.join(cache_dir, "test_logitsgap_values.json"),
'smart_model': os.path.join(cache_dir, f"smart_model_{loss_fn}.pth")
}
return paths
def logits_exist(dataset_name, model_name, seed_value, valid_size=0.2, loss_fn='CE', corruption_type=None, severity=None, train_loss=None):
"""
Check if logits already exist for the given parameters
Args:
dataset_name: Name of the dataset
model_name: Name of the model
seed_value: Random seed used
valid_size: Validation set size
loss_fn: Loss function used for calibration (not relevant for logits)
corruption_type: Type of corruption (for ImageNet-C)
severity: Severity level (for ImageNet-C)
train_loss: Training loss type (for CIFAR models)
Returns:
Boolean indicating whether all required files exist
"""
# Get paths
paths = get_logit_paths(dataset_name, model_name, seed_value, valid_size, loss_fn, corruption_type, severity, train_loss)
# Check if files exist
files_exist = (os.path.exists(paths['val_logits']) and
os.path.exists(paths['val_labels']) and
os.path.exists(paths['val_features']) and
os.path.exists(paths['test_logits']) and
os.path.exists(paths['test_labels']) and
os.path.exists(paths['test_features']))
return files_exist
def load_logits(dataset_name, model_name, seed_value, valid_size=0.2, loss_fn='CE', corruption_type=None, severity=None, train_loss=None):
"""
Load logits, labels, and features for the given parameters
Args:
dataset_name: Name of the dataset
model_name: Name of the model
seed_value: Random seed used
valid_size: Validation set size
loss_fn: Loss function used for calibration (not relevant for logits)
corruption_type: Type of corruption (for ImageNet-C)
severity: Severity level (for ImageNet-C)
train_loss: Training loss type (for CIFAR models)
Returns:
Tuple of (val_logits, val_labels, test_logits, test_labels, val_features, test_features)
"""
# Get paths
paths = get_logit_paths(dataset_name, model_name, seed_value, valid_size, loss_fn, corruption_type, severity, train_loss)
# Check if files exist
files_exist = (os.path.exists(paths['val_logits']) and
os.path.exists(paths['val_labels']) and
os.path.exists(paths['val_features']) and
os.path.exists(paths['test_logits']) and
os.path.exists(paths['test_labels']) and
os.path.exists(paths['test_features']))
if not files_exist:
dataset_info = f"{dataset_name}"
if dataset_name == 'imagenet_c' and corruption_type is not None and severity is not None:
dataset_info = f"{dataset_name} (corruption: {corruption_type}, severity: {severity})"
raise FileNotFoundError(f"Logits not found for {dataset_info}, {model_name}, seed {seed_value}, valid_size {valid_size}")
# Load the logits, labels, and features
val_logits = np.load(paths['val_logits'])
val_labels = np.load(paths['val_labels'])
val_features = np.load(paths['val_features'])
test_logits = np.load(paths['test_logits'])
test_labels = np.load(paths['test_labels'])
test_features = np.load(paths['test_features'])
dataset_info = f"{dataset_name}"
if dataset_name == 'imagenet_c' and corruption_type is not None and severity is not None:
dataset_info = f"{dataset_name} (corruption: {corruption_type}, severity: {severity})"
print(f"Loaded logits and features for {dataset_info}, {model_name}, seed {seed_value}, valid_size {valid_size}")
return val_logits, val_labels, test_logits, test_labels, val_features, test_features
def save_logits(val_logits, val_labels, test_logits, test_labels, val_features, test_features,
dataset_name, model_name, seed_value, valid_size=0.2, loss_fn='CE', corruption_type=None, severity=None, train_loss=None):
"""
Save logits, labels, and features with parameter-specific filenames
Args:
val_logits: Validation set logits
val_labels: Validation set labels
test_logits: Test set logits
test_labels: Test set labels
val_features: Validation set features
test_features: Test set features
dataset_name: Name of the dataset
model_name: Name of the model
seed_value: Random seed used
valid_size: Validation set size
loss_fn: Loss function used for calibration
corruption_type: Type of corruption (for ImageNet-C)
severity: Severity level (for ImageNet-C)
train_loss: Training loss type (for CIFAR models)
"""
paths = get_logit_paths(dataset_name, model_name, seed_value, valid_size, loss_fn, corruption_type, severity, train_loss)
# Save the logits, labels, and features
np.save(paths['val_logits'], val_logits)
np.save(paths['val_labels'], val_labels)
np.save(paths['val_features'], val_features)
np.save(paths['test_logits'], test_logits)
np.save(paths['test_labels'], test_labels)
np.save(paths['test_features'], test_features)
dataset_info = f"{dataset_name}"
if dataset_name == 'imagenet_c' and corruption_type is not None and severity is not None:
dataset_info = f"{dataset_name} (corruption: {corruption_type}, severity: {severity})"
print(f"Saved logits and features for {dataset_info}, {model_name}, seed {seed_value}, valid_size {valid_size}")
def create_train_loader_for_dac(args, dataset_name):
"""
Create a training dataloader for DAC feature extraction.
According to the DAC paper, they use 1% of the training set for ImageNet.
Since ImageNet training set is often not available (150GB+), we use:
- For ImageNet-C/Sketch/LT: Original uncorrupted ImageNet validation set
- For CIFAR: Actual training set
This provides in-distribution reference data for KNN density estimation.
Args:
args: Command line arguments
dataset_name: Name of the dataset
Returns:
DataLoader for training/reference data
"""
from utils import dataset_loader
if dataset_name == 'imagenet':
# For ImageNet, try training set first, fall back to validation set
try:
train_loader = dataset_loader['imagenet'].get_data_loader(
root=args.dataset_root,
split='train',
batch_size=args.test_batch_size,
shuffle=True,
num_workers=16,
pin_memory=True
)
except FileNotFoundError:
print("ImageNet training set not found, using validation set as reference for KNN")
train_loader = dataset_loader['imagenet'].get_data_loader(
root=args.dataset_root,
split='val',
batch_size=args.test_batch_size,
shuffle=True,
num_workers=16,
pin_memory=True
)
elif dataset_name in ['cifar10', 'cifar100']:
train_loader, _ = dataset_loader[dataset_name].get_train_valid_loader(
root=args.dataset_root,
batch_size=args.train_batch_size,
shuffle=True,
random_seed=args.random_seed,
augment=False # No augmentation for feature extraction
)
elif dataset_name in ['imagenet_c', 'imagenet_sketch', 'imagenet_lt', 'imagenet_original_val']:
# For ImageNet variants, use the ORIGINAL UNCORRUPTED ImageNet validation set
# as reference in-distribution data for KNN density estimation
# Note: The corrupted validation set is split into calibration (20%) and test (80%)
print(f"Using original ImageNet validation set as reference data for {dataset_name} KNN")
train_loader = dataset_loader['imagenet'].get_data_loader(
root=args.dataset_root,
split='val',
batch_size=args.test_batch_size,
shuffle=True,
num_workers=16,
pin_memory=True
)
else:
raise ValueError(f"Dataset {dataset_name} not supported for DAC")
return train_loader
def extract_train_features_for_dac(model, train_loader, device, max_samples=10000):
"""
Extract features from training set for DAC KNN density estimation.
According to the DAC paper, they use 1% of the training set for ImageNet (~12.8k samples).
This function extracts features from up to max_samples training samples.
Args:
model: The trained model with return_features support
train_loader: DataLoader for training data
device: Device to run inference on
max_samples: Maximum number of samples to extract (default 10000)
Returns:
torch.Tensor: Training features of shape (N, feature_dim)
"""
model.eval()
train_features = []
total_samples = 0
with torch.no_grad():
for inputs, _ in tqdm(train_loader, desc="Extracting train features for DAC"):
if total_samples >= max_samples:
break
inputs = inputs.to(device)
# Get features from model
_, features = model(inputs, return_features=True)
train_features.append(features.cpu())
total_samples += features.shape[0]
train_features = torch.cat(train_features, dim=0)[:max_samples]
print(f"Extracted {train_features.shape[0]} training samples for DAC")
return train_features
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