Spaces:
Runtime error
Runtime error
File size: 10,607 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 |
import json
import logging
import math
import os
import time
from contextlib import suppress
import numpy as np
import torch
import torch.nn.functional as F
try:
import wandb
except ImportError:
wandb = None
from open_clip import LPLoss, LPMetrics, lp_gather_features
from open_clip.utils import do_mixup, get_mix_lambda
from .distributed import is_master
from .zero_shot import zero_shot_eval
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
def unwrap_model(model):
if hasattr(model, "module"):
return model.module
else:
return model
def train_one_epoch(
model,
data,
epoch,
optimizer,
scaler,
scheduler,
args,
tb_writer=None,
extra_suffix="",
):
device = torch.device(args.device)
autocast = torch.cuda.amp.autocast if args.precision == "amp" else suppress
model.train()
loss = LPLoss(args.lp_loss)
dataloader, sampler = data["train"].dataloader, data["train"].sampler
if args.distributed and sampler is not None:
sampler.set_epoch(epoch)
num_batches_per_epoch = dataloader.num_batches
sample_digits = math.ceil(math.log(dataloader.num_samples + 1, 10))
# for toy dataset
if args.dataset_type == "toy":
dataloader.dataset.generate_queue()
loss_m = AverageMeter()
batch_time_m = AverageMeter()
data_time_m = AverageMeter()
end = time.time()
for i, batch in enumerate(dataloader):
step = num_batches_per_epoch * epoch + i
if isinstance(scheduler, dict):
for s in scheduler.values():
s(step)
else:
scheduler(step)
audio = batch # contains mel_spec, wavform, and longer list
class_label = batch["class_label"]
# audio = audio.to(device=device, non_blocking=True)
class_label = class_label.to(device=device, non_blocking=True)
if args.mixup:
# https://github.com/RetroCirce/HTS-Audio-Transformer/blob/main/utils.py#L146
mix_lambda = torch.from_numpy(
get_mix_lambda(0.5, len(audio["waveform"]))
).to(device)
class_label = do_mixup(class_label, mix_lambda)
else:
mix_lambda = None
data_time_m.update(time.time() - end)
if isinstance(optimizer, dict):
for o_ in optimizer.values():
o_.zero_grad()
else:
optimizer.zero_grad()
with autocast():
pred = model(audio, mix_lambda=mix_lambda, device=device)
total_loss = loss(pred, class_label)
if isinstance(optimizer, dict):
if scaler is not None:
scaler.scale(total_loss).backward()
for o_ in optimizer.values():
if args.horovod:
o_.synchronize()
scaler.unscale_(o_)
with o_.skip_synchronize():
scaler.step(o_)
else:
scaler.step(o_)
scaler.update()
else:
total_loss.backward()
for o_ in optimizer.values():
o_.step()
else:
if scaler is not None:
scaler.scale(total_loss).backward()
if args.horovod:
optimizer.synchronize()
scaler.unscale_(optimizer)
with optimizer.skip_synchronize():
scaler.step(optimizer)
else:
scaler.step(optimizer)
scaler.update()
else:
total_loss.backward()
optimizer.step()
# Note: we clamp to 4.6052 = ln(100), as in the original paper.
with torch.no_grad():
unwrap_model(model).clap_model.logit_scale_a.clamp_(0, math.log(100))
unwrap_model(model).clap_model.logit_scale_t.clamp_(0, math.log(100))
batch_time_m.update(time.time() - end)
end = time.time()
batch_count = i + 1
if is_master(args) and (i % 100 == 0 or batch_count == num_batches_per_epoch):
if isinstance(audio, dict):
batch_size = len(audio["waveform"])
else:
batch_size = len(audio)
num_samples = batch_count * batch_size * args.world_size
samples_per_epoch = dataloader.num_samples
percent_complete = 100.0 * batch_count / num_batches_per_epoch
# NOTE loss is coarsely sampled, just master node and per log update
loss_m.update(total_loss.item(), batch_size)
if isinstance(optimizer, dict):
logging.info(
f"Train Epoch: {epoch} [{num_samples:>{sample_digits}}/{samples_per_epoch} ({percent_complete:.0f}%)] "
f"Loss: {loss_m.val:#.5g} ({loss_m.avg:#.4g}) "
f"Data (t): {data_time_m.avg:.3f} "
f"Batch (t): {batch_time_m.avg:.3f} "
f"LR: {[o_.param_groups[0]['lr'] for o_ in optimizer.values()]}"
)
log_data = {
"loss": loss_m.val,
"data_time": data_time_m.val,
"batch_time": batch_time_m.val,
"lr": [o_.param_groups[0]["lr"] for o_ in optimizer.values()],
}
else:
logging.info(
f"Train Epoch: {epoch} [{num_samples:>{sample_digits}}/{samples_per_epoch} ({percent_complete:.0f}%)] "
f"Loss: {loss_m.val:#.5g} ({loss_m.avg:#.4g}) "
f"Data (t): {data_time_m.avg:.3f} "
f"Batch (t): {batch_time_m.avg:.3f} "
f"LR: {optimizer.param_groups[0]['lr']:5f} "
)
# Save train loss / etc. Using non avg meter values as loggers have their own smoothing
log_data = {
"loss": loss_m.val,
"data_time": data_time_m.val,
"batch_time": batch_time_m.val,
"lr": optimizer.param_groups[0]["lr"],
}
for name, val in log_data.items():
name = f"train{extra_suffix}/{name}"
if tb_writer is not None:
tb_writer.add_scalar(name, val, step)
if args.wandb:
assert wandb is not None, "Please install wandb."
wandb.log({name: val, "step": step})
# resetting batch / data time meters per log window
batch_time_m.reset()
data_time_m.reset()
# end for
def evaluate(model, data, epoch, args, tb_writer=None, extra_suffix=""):
metrics = {}
if not args.parallel_eval:
if not is_master(args):
return metrics
device = torch.device(args.device)
model.eval()
# CHANGE
# zero_shot_metrics = zero_shot_eval(model, data, epoch, args)
# metrics.update(zero_shot_metrics)
if is_master(args):
print("Evaluating...")
metric_names = args.lp_metrics.split(",")
eval_tool = LPMetrics(metric_names=metric_names)
autocast = torch.cuda.amp.autocast if args.precision == "amp" else suppress
if "val" in data and (
args.val_frequency
and ((epoch % args.val_frequency) == 0 or epoch == args.epochs)
):
if args.parallel_eval:
dataloader, sampler = data["val"].dataloader, data["val"].sampler
if args.distributed and sampler is not None:
sampler.set_epoch(epoch)
samples_per_val = dataloader.num_samples
else:
dataloader = data["val"].dataloader
num_samples = 0
samples_per_val = dataloader.num_samples
eval_info = {"pred": [], "target": []}
with torch.no_grad():
for i, batch in enumerate(dataloader):
audio = batch # contains mel_spec, wavform, and longer list
class_label = batch["class_label"]
# audio = audio.to(device=device, non_blocking=True)
class_label = class_label.to(device=device, non_blocking=True)
with autocast():
pred = model(audio, device=device)
if args.parallel_eval:
pred, class_label = lp_gather_features(
pred, class_label, args.world_size, args.horovod
)
eval_info["pred"].append(pred)
eval_info["target"].append(class_label)
num_samples += class_label.shape[0]
if (i % 100) == 0: # and i != 0:
logging.info(
f"Eval Epoch: {epoch} [{num_samples} / {samples_per_val}]"
)
if is_master(args):
eval_info["pred"] = torch.cat(eval_info["pred"], 0).cpu()
eval_info["target"] = torch.cat(eval_info["target"], 0).cpu()
metric_dict = eval_tool.evaluate_mertics(
eval_info["pred"], eval_info["target"]
)
metrics.update(metric_dict)
if "epoch" not in metrics.keys():
metrics.update({"epoch": epoch})
if is_master(args):
if not metrics:
return metrics
logging.info(
f"Eval Epoch: {epoch} "
+ "\n".join(
["\t".join([f"{m}: {round(metrics[m], 4):.4f}"]) for m in metrics]
)
)
if args.save_logs:
for name, val in metrics.items():
if tb_writer is not None:
tb_writer.add_scalar(f"val{extra_suffix}/{name}", val, epoch)
with open(os.path.join(args.checkpoint_path, "results.jsonl"), "a+") as f:
f.write(json.dumps(metrics))
f.write("\n")
if args.wandb:
assert wandb is not None, "Please install wandb."
for name, val in metrics.items():
wandb.log({f"val{extra_suffix}/{name}": val, "epoch": epoch})
return metrics
else:
return metrics
|