Spaces:
Running
Running
File size: 15,213 Bytes
9b896f5 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 |
import math
import os
import sys
import torch.cuda
import sparsification.utils
sys.path.append('')
import numpy as np
import torch as ch
from torch.utils.data import Subset
from tqdm import tqdm
# From glm_saga
def get_features_batch(batch, model, device='cuda'):
if not torch.cuda.is_available():
device = "cpu"
ims, targets = batch
output, latents = model(ims.to(device), with_final_features=True )
return latents, targets
def compute_features(loader, model, dataset_type, pooled_output,
batch_size, num_workers,
shuffle=False, device='cpu', n_epoch=1,
filename=None, chunk_threshold=20000, balance=False):
"""Compute deep features for a given dataset using a modeln and returnss
them as a pytorch dataset and loader.
Args:
loader : Torch data loader
model: Torch model
dataset_type (str): One of vision or language
pooled_output (bool): Whether or not to pool outputs
(only relevant for some language models)
batch_size (int): Batch size for output loader
num_workers (int): Number of workers to use for output loader
shuffle (bool): Whether or not to shuffle output data loaoder
device (str): Device on which to keep the model
filename (str):Optional file to cache computed feature. Recommended
for large dataset_classes like ImageNet.
chunk_threshold (int): Size of shard while caching
balance (bool): Whether or not to balance output data loader
(only relevant for some language models)
Returns:
feature_dataset: Torch dataset with deep features
feature_loader: Torch data loader with deep features
"""
if torch.cuda.is_available():
device = "cuda"
print("mem_get_info before", torch.cuda.mem_get_info())
torch.cuda.empty_cache()
print("mem_get_info after", torch.cuda.mem_get_info())
model = model.to(device)
if filename is None or not os.path.exists(os.path.join(filename, f'0_features.npy')):
model.eval()
all_latents, all_targets, all_images = [], [], []
Nsamples, chunk_id = 0, 0
for idx_epoch in range(n_epoch):
for batch_idx, batch in tqdm(enumerate(loader), total=len(loader)):
with ch.no_grad():
latents, targets = get_features_batch(batch, model,
device=device)
if batch_idx == 0:
print("Latents shape", latents.shape)
Nsamples += latents.size(0)
all_latents.append(latents.cpu())
if len(targets.shape) > 1:
targets = targets[:, 0]
all_targets.append(targets.cpu())
# all_images.append(batch[0])
if filename is not None and Nsamples > chunk_threshold:
if not os.path.exists(filename): os.makedirs(filename)
np.save(os.path.join(filename, f'{chunk_id}_features.npy'), ch.cat(all_latents).numpy())
np.save(os.path.join(filename, f'{chunk_id}_labels.npy'), ch.cat(all_targets).numpy())
all_latents, all_targets, Nsamples = [], [], 0
chunk_id += 1
if filename is not None and Nsamples > 0:
if not os.path.exists(filename): os.makedirs(filename)
np.save(os.path.join(filename, f'{chunk_id}_features.npy'), ch.cat(all_latents).numpy())
np.save(os.path.join(filename, f'{chunk_id}_labels.npy'), ch.cat(all_targets).numpy())
# np.save(os.path.join(filename, f'{chunk_id}_images.npy'), ch.cat(all_images).numpy())
feature_dataset = load_features(filename) if filename is not None else \
ch.utils.data.TensorDataset(ch.cat(all_latents), ch.cat(all_targets))
if balance:
feature_dataset = balance_dataset(feature_dataset)
feature_loader = ch.utils.data.DataLoader(feature_dataset,
num_workers=num_workers,
batch_size=batch_size,
shuffle=shuffle)
return feature_dataset, feature_loader
def load_feature_loader(out_dir_feats, val_frac, batch_size, num_workers, random_seed):
feature_loaders = {}
for mode in ['train', 'test']:
print(f"For {mode} set...")
sink_path = f"{out_dir_feats}/features_{mode}"
metadata_path = f"{out_dir_feats}/metadata_{mode}.pth"
feature_ds = load_features(sink_path)
feature_loader = ch.utils.data.DataLoader(feature_ds,
num_workers=num_workers,
batch_size=batch_size)
if mode == 'train':
metadata = calculate_metadata(feature_loader,
num_classes=2048,
filename=metadata_path)
split_datasets, split_loaders = split_dataset(feature_ds,
len(feature_ds),
val_frac=val_frac,
batch_size=batch_size,
num_workers=num_workers,
random_seed=random_seed,
shuffle=True)
feature_loaders.update({mm: sparsification.utils.add_index_to_dataloader(split_loaders[mi])
for mi, mm in enumerate(['train', 'val'])})
else:
feature_loaders[mode] = feature_loader
return feature_loaders, metadata
def balance_dataset(dataset):
"""Balances a given dataset to have the same number of samples/class.
Args:
dataset : Torch dataset
Returns:
Torch dataset with equal number of samples/class
"""
print("Balancing dataset...")
n = len(dataset)
labels = ch.Tensor([dataset[i][1] for i in range(n)]).int()
n0 = sum(labels).item()
I_pos = labels == 1
idx = ch.arange(n)
idx_pos = idx[I_pos]
ch.manual_seed(0)
I = ch.randperm(n - n0)[:n0]
idx_neg = idx[~I_pos][I]
idx_bal = ch.cat([idx_pos, idx_neg], dim=0)
return Subset(dataset, idx_bal)
def load_metadata(feature_path):
return ch.load(os.path.join(feature_path, f'metadata_train.pth'))
def get_mean_std(feature_path):
metadata = load_metadata(feature_path)
return metadata["X"]["mean"], metadata["X"]["std"]
def load_features_dataset_mode(feature_path, mode='test',
num_workers=10, batch_size=128):
"""Loads precomputed deep features corresponding to the
train/test set along with normalization statitic.
Args:
feature_path (str): Path to precomputed deep features
mode (str): One of train or tesst
num_workers (int): Number of workers to use for output loader
batch_size (int): Batch size for output loader
Returns:
features (np.array): Recovered deep features
feature_mean: Mean of deep features
feature_std: Standard deviation of deep features
"""
feature_dataset = load_features(os.path.join(feature_path, f'features_{mode}'))
feature_loader = ch.utils.data.DataLoader(feature_dataset,
num_workers=num_workers,
batch_size=batch_size,
shuffle=False)
feature_metadata = ch.load(os.path.join(feature_path, f'metadata_train.pth'))
feature_mean, feature_std = feature_metadata['X']['mean'], feature_metadata['X']['std']
return feature_loader, feature_mean, feature_std
def load_joint_dataset(feature_path, mode='test',
num_workers=10, batch_size=128):
feature_dataset = load_features(os.path.join(feature_path, f'features_{mode}'))
feature_loader = ch.utils.data.DataLoader(feature_dataset,
num_workers=num_workers,
batch_size=batch_size,
shuffle=False)
features = []
labels = []
for _, (feature, label) in tqdm(enumerate(feature_loader), total=len(feature_loader)):
features.append(feature)
labels.append(label)
features = np.concatenate(features)
labels = np.concatenate(labels)
dataset = ch.utils.data.TensorDataset(torch.tensor(features), torch.tensor(labels))
return dataset
def load_features_mode(feature_path, mode='test',
num_workers=10, batch_size=128):
"""Loads precomputed deep features corresponding to the
train/test set along with normalization statitic.
Args:
feature_path (str): Path to precomputed deep features
mode (str): One of train or tesst
num_workers (int): Number of workers to use for output loader
batch_size (int): Batch size for output loader
Returns:
features (np.array): Recovered deep features
feature_mean: Mean of deep features
feature_std: Standard deviation of deep features
"""
feature_dataset = load_features(os.path.join(feature_path, f'features_{mode}'))
feature_loader = ch.utils.data.DataLoader(feature_dataset,
num_workers=num_workers,
batch_size=batch_size,
shuffle=False)
feature_metadata = ch.load(os.path.join(feature_path, f'metadata_train.pth'))
feature_mean, feature_std = feature_metadata['X']['mean'], feature_metadata['X']['std']
features = []
for _, (feature, _) in tqdm(enumerate(feature_loader), total=len(feature_loader)):
features.append(feature)
features = ch.cat(features).numpy()
return features, feature_mean, feature_std
def load_features(feature_path):
"""Loads precomputed deep features.
Args:
feature_path (str): Path to precomputed deep features
Returns:
Torch dataset with recovered deep features.
"""
if not os.path.exists(os.path.join(feature_path, f"0_features.npy")):
raise ValueError(f"The provided location {feature_path} does not contain any representation files")
ds_list, chunk_id = [], 0
while os.path.exists(os.path.join(feature_path, f"{chunk_id}_features.npy")):
features = ch.from_numpy(np.load(os.path.join(feature_path, f"{chunk_id}_features.npy"))).float()
labels = ch.from_numpy(np.load(os.path.join(feature_path, f"{chunk_id}_labels.npy"))).long()
ds_list.append(ch.utils.data.TensorDataset(features, labels))
chunk_id += 1
print(f"==> loaded {chunk_id} files of representations...")
return ch.utils.data.ConcatDataset(ds_list)
def calculate_metadata(loader, num_classes=None, filename=None):
"""Calculates mean and standard deviation of the deep features over
a given set of images.
Args:
loader : torch data loader
num_classes (int): Number of classes in the dataset
filename (str): Optional filepath to cache metadata. Recommended
for large dataset_classes like ImageNet.
Returns:
metadata (dict): Dictionary with desired statistics.
"""
if filename is not None and os.path.exists(filename):
print("loading Metadata from ", filename)
return ch.load(filename)
# Calculate number of classes if not given
if num_classes is None:
num_classes = 1
for batch in loader:
y = batch[1]
print(y)
num_classes = max(num_classes, y.max().item() + 1)
eye = ch.eye(num_classes)
X_bar, y_bar, y_max, n = 0, 0, 0, 0
# calculate means and maximum
print("Calculating means")
for ans in tqdm(loader, total=len(loader)):
X, y = ans[:2]
X_bar += X.sum(0)
y_bar += eye[y].sum(0)
y_max = max(y_max, y.max())
n += y.size(0)
X_bar = X_bar.float() / n
y_bar = y_bar.float() / n
# calculate std
X_std, y_std = 0, 0
print("Calculating standard deviations")
for ans in tqdm(loader, total=len(loader)):
X, y = ans[:2]
X_std += ((X - X_bar) ** 2).sum(0)
y_std += ((eye[y] - y_bar) ** 2).sum(0)
X_std = ch.sqrt(X_std.float() / n)
y_std = ch.sqrt(y_std.float() / n)
# calculate maximum regularization
inner_products = 0
print("Calculating maximum lambda")
for ans in tqdm(loader, total=len(loader)):
X, y = ans[:2]
y_map = (eye[y] - y_bar) / y_std
inner_products += X.t().mm(y_map) * y_std
inner_products_group = inner_products.norm(p=2, dim=1)
metadata = {
"X": {
"mean": X_bar,
"std": X_std,
"num_features": X.size()[1:],
"num_examples": n
},
"y": {
"mean": y_bar,
"std": y_std,
"num_classes": y_max + 1
},
"max_reg": {
"group": inner_products_group.abs().max().item() / n,
"nongrouped": inner_products.abs().max().item() / n
}
}
if filename is not None:
ch.save(metadata, filename)
return metadata
def split_dataset(dataset, Ntotal, val_frac,
batch_size, num_workers,
random_seed=0, shuffle=True, balance=False):
"""Splits a given dataset into train and validation
Args:
dataset : Torch dataset
Ntotal: Total number of dataset samples
val_frac: Fraction to reserve for validation
batch_size (int): Batch size for output loader
num_workers (int): Number of workers to use for output loader
random_seed (int): Random seed
shuffle (bool): Whether or not to shuffle output data loaoder
balance (bool): Whether or not to balance output data loader
(only relevant for some language models)
Returns:
split_datasets (list): List of dataset_classes (one each for train and val)
split_loaders (list): List of loaders (one each for train and val)
"""
Nval = math.floor(Ntotal * val_frac)
train_ds, val_ds = ch.utils.data.random_split(dataset,
[Ntotal - Nval, Nval],
generator=ch.Generator().manual_seed(random_seed))
if balance:
val_ds = balance_dataset(val_ds)
split_datasets = [train_ds, val_ds]
split_loaders = []
for ds in split_datasets:
split_loaders.append(ch.utils.data.DataLoader(ds,
num_workers=num_workers,
batch_size=batch_size,
shuffle=shuffle))
return split_datasets, split_loaders
|