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from gyraudio.audio_separation.experiment_tracking.experiments import get_experience
from gyraudio.audio_separation.parser import shared_parser
from gyraudio.audio_separation.properties import (
TRAIN, TEST, EPOCHS, OPTIMIZER, NAME, MAX_STEPS_PER_EPOCH,
SIGNAL, NOISE, TOTAL, SNR, SCHEDULER, SCHEDULER_CONFIGURATION,
LOSS, LOSS_L2, LOSS_L1, LOSS_TYPE, COEFFICIENT
)
from gyraudio.default_locations import EXPERIMENT_STORAGE_ROOT
from torch.optim.lr_scheduler import ReduceLROnPlateau
from gyraudio.audio_separation.experiment_tracking.storage import get_output_folder, save_checkpoint
from gyraudio.audio_separation.metrics import Costs, snr
# from gyraudio.audio_separation.experiment_tracking.storage import load_checkpoint
from pathlib import Path
from gyraudio.io.dump import Dump
import sys
import torch
from tqdm import tqdm
from copy import deepcopy
import wandb
import logging
def launch_training(exp: int, wandb_flag: bool = True, device: str = "cuda", save_dir: Path = None, override=False):
short_name, model, config, dl = get_experience(exp)
exists, output_folder = get_output_folder(config, root_dir=save_dir, override=override)
if not exists:
logging.warning(f"Skipping experiment {short_name}")
return False
else:
logging.info(f"Experiment {short_name} saved in {output_folder}")
print(short_name)
print(config)
logging.info(f"Starting training for {short_name}")
logging.info(f"Config: {config}")
if wandb_flag:
wandb.init(
project="audio-separation",
entity="teammd",
name=short_name,
tags=["debug"],
config=config
)
training_loop(model, config, dl, wandb_flag=wandb_flag, device=device, exp_dir=output_folder)
if wandb_flag:
wandb.finish()
return True
def update_metrics(metrics, phase, pred, gt, pred_noise, gt_noise):
metrics[phase].update(pred, gt, SIGNAL)
metrics[phase].update(pred_noise, gt_noise, NOISE)
metrics[phase].update(pred, gt, SNR)
loss = metrics[phase].finish_step()
return loss
def training_loop(model: torch.nn.Module, config: dict, dl, device: str = "cuda", wandb_flag: bool = False,
exp_dir: Path = None):
optim_params = deepcopy(config[OPTIMIZER])
optim_name = optim_params[NAME]
optim_params.pop(NAME)
if optim_name == "adam":
optimizer = torch.optim.Adam(model.parameters(), **optim_params)
scheduler = None
scheduler_config = config.get(SCHEDULER_CONFIGURATION, {})
scheduler_name = config.get(SCHEDULER, False)
if scheduler_name:
if scheduler_name == "ReduceLROnPlateau":
scheduler = ReduceLROnPlateau(optimizer, mode='max', verbose=True, **scheduler_config)
logging.info(f"Using scheduler {scheduler_name} with config {scheduler_config}")
else:
raise NotImplementedError(f"Scheduler {scheduler_name} not implemented")
max_steps = config.get(MAX_STEPS_PER_EPOCH, None)
chosen_loss = config.get(LOSS, LOSS_L2)
if chosen_loss == LOSS_L2:
costs = {TRAIN: Costs(TRAIN), TEST: Costs(TEST)}
elif chosen_loss == LOSS_L1:
cost_init = {
SIGNAL: {
COEFFICIENT: 0.5,
LOSS_TYPE: torch.nn.functional.l1_loss
},
NOISE: {
COEFFICIENT: 0.5,
LOSS_TYPE: torch.nn.functional.l1_loss
},
SNR: {
LOSS_TYPE: snr
}
}
costs = {
TRAIN: Costs(TRAIN, costs=cost_init),
TEST: Costs(TEST)
}
for epoch in range(config[EPOCHS]):
costs[TRAIN].reset_epoch()
costs[TEST].reset_epoch()
model.to(device)
# Training loop
# -----------------------------------------------------------
metrics = {TRAIN: {}, TEST: {}}
for step_index, (batch_mix, batch_signal, batch_noise) in tqdm(
enumerate(dl[TRAIN]), desc=f"Epoch {epoch}", total=len(dl[TRAIN])):
if max_steps is not None and step_index >= max_steps:
break
batch_mix, batch_signal, batch_noise = batch_mix.to(device), batch_signal.to(device), batch_noise.to(device)
model.zero_grad()
batch_output_signal, batch_output_noise = model(batch_mix)
loss = update_metrics(
costs, TRAIN,
batch_output_signal, batch_signal,
batch_output_noise, batch_noise
)
# costs[TRAIN].update(batch_output_signal, batch_signal, SIGNAL)
# costs[TRAIN].update(batch_output_noise, batch_noise, NOISE)
# loss = costs[TRAIN].finish_step()
loss.backward()
optimizer.step()
costs[TRAIN].finish_epoch()
# Validation loop
# -----------------------------------------------------------
model.eval()
torch.cuda.empty_cache()
with torch.no_grad():
for step_index, (batch_mix, batch_signal, batch_noise) in tqdm(
enumerate(dl[TEST]), desc=f"Epoch {epoch}", total=len(dl[TEST])):
if max_steps is not None and step_index >= max_steps:
break
batch_mix, batch_signal, batch_noise = batch_mix.to(
device), batch_signal.to(device), batch_noise.to(device)
batch_output_signal, batch_output_noise = model(batch_mix)
loss = update_metrics(
costs, TEST,
batch_output_signal, batch_signal,
batch_output_noise, batch_noise
)
costs[TEST].finish_epoch()
if scheduler is not None and isinstance(scheduler, ReduceLROnPlateau):
scheduler.step(costs[TEST].total_metric[SNR])
print(f"epoch {epoch}:\n{costs[TRAIN]}\n{costs[TEST]}")
wandblogs = {}
if wandb_flag:
for phase in [TRAIN, TEST]:
wandblogs[f"{phase} loss signal"] = costs[phase].total_metric[SIGNAL]
wandblogs[f"debug loss/{phase} loss signal"] = costs[phase].total_metric[SIGNAL]
wandblogs[f"debug loss/{phase} loss total"] = costs[phase].total_metric[TOTAL]
wandblogs[f"debug loss/{phase} loss noise"] = costs[phase].total_metric[NOISE]
wandblogs[f"{phase} snr"] = costs[phase].total_metric[SNR]
wandblogs["learning rate"] = optimizer.param_groups[0]['lr']
wandb.log(wandblogs)
metrics[TRAIN] = costs[TRAIN].total_metric
metrics[TEST] = costs[TEST].total_metric
Dump.save_json(metrics, exp_dir/f"metrics_{epoch:04d}.json")
save_checkpoint(model, exp_dir, optimizer, config=config, epoch=epoch)
torch.cuda.empty_cache()
def main(argv):
default_device = "cuda" if torch.cuda.is_available() else "cpu"
parser_def = shared_parser(help="Launch training \nCheck results at: https://wandb.ai/teammd/audio-separation"
+ ("\n<<<Cuda available>>>" if default_device == "cuda" else ""))
parser_def.add_argument("-nowb", "--no-wandb", action="store_true")
parser_def.add_argument("-o", "--output-dir", type=str, default=EXPERIMENT_STORAGE_ROOT)
parser_def.add_argument("-f", "--force", action="store_true", help="Override existing experiment")
parser_def.add_argument("-d", "--device", type=str, default=default_device,
help="Training device", choices=["cpu", "cuda"])
args = parser_def.parse_args(argv)
for exp in args.experiments:
launch_training(
exp, wandb_flag=not args.no_wandb, save_dir=Path(args.output_dir),
override=args.force,
device=args.device
)
if __name__ == "__main__":
main(sys.argv[1:])
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