Spaces:
Runtime error
Runtime error
File size: 16,143 Bytes
e73da9c |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 |
from multiprocessing.sharedctypes import Value
import torch
import torch.distributed.nn
from torch import distributed as dist, nn as nn
from torch.nn import functional as F
import numpy as np
from sklearn.metrics import average_precision_score, roc_auc_score, accuracy_score
try:
import horovod.torch as hvd
except ImportError:
hvd = None
def gather_features(
audio_features,
text_features,
audio_features_mlp=None,
text_features_mlp=None,
local_loss=False,
gather_with_grad=False,
rank=0,
world_size=1,
use_horovod=False,
mlp_loss=False,
):
if use_horovod:
assert hvd is not None, "Please install horovod"
if gather_with_grad:
all_audio_features = hvd.allgather(audio_features)
all_text_features = hvd.allgather(text_features)
if mlp_loss:
all_audio_features_mlp = hvd.allgather(audio_features_mlp)
all_text_features_mlp = hvd.allgather(text_features_mlp)
else:
with torch.no_grad():
all_audio_features = hvd.allgather(audio_features)
all_text_features = hvd.allgather(text_features)
if mlp_loss:
all_audio_features_mlp = hvd.allgather(audio_features_mlp)
all_text_features_mlp = hvd.allgather(text_features_mlp)
if not local_loss:
# ensure grads for local rank when all_* features don't have a gradient
gathered_audio_features = list(
all_audio_features.chunk(world_size, dim=0)
)
gathered_text_features = list(
all_text_features.chunk(world_size, dim=0)
)
gathered_audio_features[rank] = audio_features
gathered_text_features[rank] = text_features
all_audio_features = torch.cat(gathered_audio_features, dim=0)
all_text_features = torch.cat(gathered_text_features, dim=0)
if mlp_loss:
gathered_audio_features_mlp = list(
all_audio_features_mlp.chunk(world_size, dim=0)
)
gathered_text_features_mlp = list(
all_text_features_mlp.chunk(world_size, dim=0)
)
gathered_audio_features_mlp[rank] = audio_features_mlp
gathered_text_features_mlp[rank] = text_features_mlp
all_audio_features_mlp = torch.cat(
gathered_audio_features_mlp, dim=0
)
all_text_features_mlp = torch.cat(gathered_text_features_mlp, dim=0)
else:
# We gather tensors from all gpus
if gather_with_grad:
all_audio_features = torch.cat(
torch.distributed.nn.all_gather(audio_features), dim=0
)
all_text_features = torch.cat(
torch.distributed.nn.all_gather(text_features), dim=0
)
if mlp_loss:
all_audio_features_mlp = torch.cat(
torch.distributed.nn.all_gather(audio_features_mlp), dim=0
)
all_text_features_mlp = torch.cat(
torch.distributed.nn.all_gather(text_features_mlp), dim=0
)
else:
gathered_audio_features = [
torch.zeros_like(audio_features) for _ in range(world_size)
]
gathered_text_features = [
torch.zeros_like(text_features) for _ in range(world_size)
]
dist.all_gather(gathered_audio_features, audio_features)
dist.all_gather(gathered_text_features, text_features)
if mlp_loss:
gathered_audio_features_mlp = [
torch.zeros_like(audio_features_mlp) for _ in range(world_size)
]
gathered_text_features_mlp = [
torch.zeros_like(text_features_mlp) for _ in range(world_size)
]
dist.all_gather(gathered_audio_features_mlp, audio_features_mlp)
dist.all_gather(gathered_text_features_mlp, text_features_mlp)
if not local_loss:
# ensure grads for local rank when all_* features don't have a gradient
gathered_audio_features[rank] = audio_features
gathered_text_features[rank] = text_features
if mlp_loss:
gathered_audio_features_mlp[rank] = audio_features_mlp
gathered_text_features_mlp[rank] = text_features_mlp
all_audio_features = torch.cat(gathered_audio_features, dim=0)
all_text_features = torch.cat(gathered_text_features, dim=0)
if mlp_loss:
all_audio_features_mlp = torch.cat(gathered_audio_features_mlp, dim=0)
all_text_features_mlp = torch.cat(gathered_text_features_mlp, dim=0)
if mlp_loss:
return (
all_audio_features,
all_text_features,
all_audio_features_mlp,
all_text_features_mlp,
)
else:
return all_audio_features, all_text_features
class ClipLoss(nn.Module):
def __init__(
self,
local_loss=False,
gather_with_grad=False,
cache_labels=False,
rank=0,
world_size=1,
use_horovod=False,
mlp_loss=False,
weight_loss_kappa=0,
):
super().__init__()
self.local_loss = local_loss
self.gather_with_grad = gather_with_grad
self.cache_labels = cache_labels
self.rank = rank
self.world_size = world_size
self.use_horovod = use_horovod
self.mlp_loss = mlp_loss
self.weighted_loss = bool(weight_loss_kappa != 0)
self.weight_loss_kappa = weight_loss_kappa
# cache state
self.prev_num_logits = 0
self.labels = {}
def forward(
self,
audio_features,
text_features,
logit_scale_a,
logit_scale_t=None,
audio_features_mlp=None,
text_features_mlp=None,
):
device = audio_features.device
if self.mlp_loss:
if self.world_size > 1:
(
all_audio_features,
all_text_features,
all_audio_features_mlp,
all_text_features_mlp,
) = gather_features(
audio_features=audio_features,
text_features=text_features,
audio_features_mlp=audio_features_mlp,
text_features_mlp=text_features_mlp,
local_loss=self.local_loss,
gather_with_grad=self.gather_with_grad,
rank=self.rank,
world_size=self.world_size,
use_horovod=self.use_horovod,
mlp_loss=self.mlp_loss,
)
if self.local_loss:
a_logits_per_audio = (
logit_scale_a * audio_features @ all_text_features_mlp.T
)
a_logits_per_text = (
logit_scale_a * text_features_mlp @ all_audio_features.T
)
t_logits_per_audio = (
logit_scale_t * audio_features_mlp @ all_text_features.T
)
t_logits_per_text = (
logit_scale_t * text_features @ all_audio_features_mlp.T
)
else:
a_logits_per_audio = (
logit_scale_a * all_audio_features @ all_text_features_mlp.T
)
a_logits_per_text = a_logits_per_audio.T
t_logits_per_audio = (
logit_scale_t * all_audio_features_mlp @ all_text_features.T
)
t_logits_per_text = t_logits_per_audio.T
else:
a_logits_per_audio = (
logit_scale_a * audio_features @ text_features_mlp.T
)
a_logits_per_text = logit_scale_a * text_features_mlp @ audio_features.T
t_logits_per_audio = (
logit_scale_t * audio_features_mlp @ text_features.T
)
t_logits_per_text = logit_scale_t * text_features @ audio_features_mlp.T
# calculated ground-truth and cache if enabled
num_logits = a_logits_per_audio.shape[0]
if self.prev_num_logits != num_logits or device not in self.labels:
labels = torch.arange(num_logits, device=device, dtype=torch.long)
if self.world_size > 1 and self.local_loss:
labels = labels + num_logits * self.rank
if self.cache_labels:
self.labels[device] = labels
self.prev_num_logits = num_logits
else:
labels = self.labels[device]
if not self.weighted_loss:
total_loss = (
F.cross_entropy(a_logits_per_audio, labels)
+ F.cross_entropy(a_logits_per_text, labels)
+ F.cross_entropy(t_logits_per_audio, labels)
+ F.cross_entropy(t_logits_per_text, labels)
) / 4
else:
audio_weight = (audio_features @ audio_features.T).detach()
audio_weight = (
torch.exp(
torch.sum(audio_weight, axis=1)
/ (self.weight_loss_kappa * len(audio_weight))
)
).detach()
text_weight = (text_features @ text_features.T).detach()
text_weight = (
torch.exp(
torch.sum(text_weight, axis=1)
/ (self.weight_loss_kappa * len(text_features))
)
).detach()
total_loss = (
F.cross_entropy(a_logits_per_audio, labels, weight=audio_weight)
+ F.cross_entropy(a_logits_per_text, labels, weight=audio_weight)
+ F.cross_entropy(t_logits_per_audio, labels, weight=text_weight)
+ F.cross_entropy(t_logits_per_text, labels, weight=text_weight)
) / 4
else:
if self.world_size > 1:
all_audio_features, all_text_features = gather_features(
audio_features=audio_features,
text_features=text_features,
local_loss=self.local_loss,
gather_with_grad=self.gather_with_grad,
rank=self.rank,
world_size=self.world_size,
use_horovod=self.use_horovod,
mlp_loss=self.mlp_loss,
)
if self.local_loss:
logits_per_audio = (
logit_scale_a * audio_features @ all_text_features.T
)
logits_per_text = (
logit_scale_a * text_features @ all_audio_features.T
)
else:
logits_per_audio = (
logit_scale_a * all_audio_features @ all_text_features.T
)
logits_per_text = logits_per_audio.T
else:
logits_per_audio = logit_scale_a * audio_features @ text_features.T
logits_per_text = logit_scale_a * text_features @ audio_features.T
# calculated ground-truth and cache if enabled
num_logits = logits_per_audio.shape[0]
if self.prev_num_logits != num_logits or device not in self.labels:
labels = torch.arange(num_logits, device=device, dtype=torch.long)
if self.world_size > 1 and self.local_loss:
labels = labels + num_logits * self.rank
if self.cache_labels:
self.labels[device] = labels
self.prev_num_logits = num_logits
else:
labels = self.labels[device]
if not self.weighted_loss:
total_loss = (
F.cross_entropy(logits_per_audio, labels)
+ F.cross_entropy(logits_per_text, labels)
) / 2
else:
audio_weight = (all_audio_features @ all_audio_features.T).detach()
audio_weight = (
torch.exp(
torch.sum(audio_weight, axis=1)
/ (self.weight_loss_kappa * len(all_audio_features))
)
).detach()
text_weight = (all_text_features @ all_text_features.T).detach()
text_weight = (
torch.exp(
torch.sum(text_weight, axis=1)
/ (self.weight_loss_kappa * len(all_text_features))
)
).detach()
total_loss = (
F.cross_entropy(logits_per_audio, labels, weight=text_weight)
+ F.cross_entropy(logits_per_text, labels, weight=audio_weight)
) / 2
return total_loss
def lp_gather_features(pred, target, world_size=1, use_horovod=False):
if use_horovod:
assert hvd is not None, "Please install horovod"
with torch.no_grad():
all_preds = hvd.allgather(pred)
all_targets = hvd.allgath(target)
else:
gathered_preds = [torch.zeros_like(pred) for _ in range(world_size)]
gathered_targets = [torch.zeros_like(target) for _ in range(world_size)]
dist.all_gather(gathered_preds, pred)
dist.all_gather(gathered_targets, target)
all_preds = torch.cat(gathered_preds, dim=0)
all_targets = torch.cat(gathered_targets, dim=0)
return all_preds, all_targets
def get_map(pred, target):
pred = torch.sigmoid(pred).numpy()
target = target.numpy()
return np.mean(average_precision_score(target, pred, average=None))
def get_acc(pred, target):
pred = torch.argmax(pred, 1).numpy()
target = torch.argmax(target, 1).numpy()
return accuracy_score(target, pred)
def get_mauc(pred, target):
pred = torch.sigmoid(pred).numpy()
target = target.numpy()
return np.mean(roc_auc_score(target, pred, average=None))
class LPMetrics(object):
def __init__(self, metric_names=["map", "acc", "mauc"]):
self.metrics = []
for name in metric_names:
self.metrics.append(self.get_metric(name))
self.metric_names = metric_names
def get_metric(self, name):
if name == "map":
return get_map
elif name == "acc":
return get_acc
elif name == "mauc":
return get_mauc
else:
raise ValueError(f"the metric should be at least one of [map, acc, mauc]")
def evaluate_mertics(self, pred, target):
metric_dict = {}
for i in range(len(self.metric_names)):
metric_dict[self.metric_names[i]] = self.metrics[i](pred, target)
return metric_dict
def calc_celoss(pred, target):
target = torch.argmax(target, 1).long()
return nn.CrossEntropyLoss()(pred, target)
class LPLoss(nn.Module):
def __init__(self, loss_name):
super().__init__()
if loss_name == "bce":
self.loss_func = nn.BCEWithLogitsLoss()
elif loss_name == "ce":
self.loss_func = calc_celoss
elif loss_name == "mse":
self.loss_func = nn.MSELoss()
else:
raise ValueError(f"the loss func should be at least one of [bce, ce, mse]")
def forward(self, pred, target):
loss = self.loss_func(pred, target)
return loss
|