File size: 3,221 Bytes
affcd23 |
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 |
import argparse
import collections
import warnings
import numpy as np
import torch
import hw_asr.loss as module_loss
import hw_asr.metric as module_metric
import hw_asr.model as module_arch
from hw_asr.trainer import Trainer
from hw_asr.utils import prepare_device
from hw_asr.utils.object_loading import get_dataloaders
from hw_asr.utils.parse_config import ConfigParser
warnings.filterwarnings("ignore", category=UserWarning)
# fix random seeds for reproducibility
SEED = 123
torch.manual_seed(SEED)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
np.random.seed(SEED)
def main(config):
logger = config.get_logger("train")
# text_encoder
text_encoder = config.get_text_encoder()
# setup data_loader instances
dataloaders = get_dataloaders(config, text_encoder)
# build model architecture, then print to console
model = config.init_obj(config["arch"], module_arch, n_class=len(text_encoder))
logger.info(model)
# prepare for (multi-device) GPU training
device, device_ids = prepare_device(config["n_gpu"])
model = model.to(device)
if len(device_ids) > 1:
model = torch.nn.DataParallel(model, device_ids=device_ids)
# get function handles of loss and metrics
loss_module = config.init_obj(config["loss"], module_loss).to(device)
metrics = [
config.init_obj(metric_dict, module_metric, text_encoder=text_encoder)
for metric_dict in config["metrics"]
]
# build optimizer, learning rate scheduler. delete every line containing lr_scheduler for
# disabling scheduler
trainable_params = filter(lambda p: p.requires_grad, model.parameters())
optimizer = config.init_obj(config["optimizer"], torch.optim, trainable_params)
lr_scheduler = config.init_obj(config["lr_scheduler"], torch.optim.lr_scheduler, optimizer)
trainer = Trainer(
model,
loss_module,
metrics,
optimizer,
text_encoder=text_encoder,
config=config,
device=device,
dataloaders=dataloaders,
lr_scheduler=lr_scheduler,
len_epoch=config["trainer"].get("len_epoch", None)
)
trainer.train()
if __name__ == "__main__":
args = argparse.ArgumentParser(description="PyTorch Template")
args.add_argument(
"-c",
"--config",
default=None,
type=str,
help="config file path (default: None)",
)
args.add_argument(
"-r",
"--resume",
default=None,
type=str,
help="path to latest checkpoint (default: None)",
)
args.add_argument(
"-d",
"--device",
default=None,
type=str,
help="indices of GPUs to enable (default: all)",
)
# custom cli options to modify configuration from default values given in json file.
CustomArgs = collections.namedtuple("CustomArgs", "flags type target")
options = [
CustomArgs(["--lr", "--learning_rate"], type=float, target="optimizer;args;lr"),
CustomArgs(
["--bs", "--batch_size"], type=int, target="data_loader;args;batch_size"
),
]
config = ConfigParser.from_args(args, options)
main(config)
|