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train_d2.py
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train_d2.py
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import argparse
import os
import sys
import yaml
import numpy as np
import torch as T
from tqdm.auto import tqdm
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from model_d2 import DiacritizerD2
from data_utils import DatasetUtils
from dataloader import DataRetriever
SEED = 1337
T.random.manual_seed(SEED)
T.manual_seed(SEED)
np.random.seed(SEED)
class Trainer:
def __init__(self, config, device=T.device('cuda')):
self.device = device
self.config = config
self.debug = config["debug"]
self.run_title = config["run-title"]
self.save_path = config["paths"]["save"]
self.embs_path = config["paths"]["word-embs"]
self.lr_factor = config["train"]["lr-factor"]
self.lr_patience = config["train"]["lr-patience"]
self.min_lr = config["train"]["lr-min"]
self.init_lr = config["train"]["lr-init"]
self.optimizer_name = config['train'].get('optimizer', 'adam').lower()
self.weight_decay_ = config["train"]["weight-decay"]
self.best_loss = np.inf
self.stopping_counter = 0
self.stopping_delta = config["train"]["stopping-delta"]
self.stopping_patience = config["train"]["stopping-patience"]
self.data_utils = DatasetUtils(config)
vocab_size = len(self.data_utils.letter_list)
word_embeddings = self.data_utils.embeddings
self.model = DiacritizerD2(config, self.device)
self.model.build(word_embeddings, vocab_size)
self.resume = config['train']['resume']
self.start_epoch = 0
self.epochs = config["train"]["epochs"]
if self.resume:
model_data = T.load(config["paths"]["resume"], map_location=T.device(self.device))
state_dict = model_data['state_dict']
self.start_epoch = model_data['last_epoch'] + 1
if config["train"]["resume-lr"]:
self.init_lr = model_data["last_lr"]
self.best_loss = model_data["val_loss"]
self.model.load_state_dict(state_dict)
print(f"> Loading Checkpoint {config['paths']['resume']} with Best Val-Loss: {self.best_loss:.4f} and Stopping Counter {self.stopping_counter}")
print(f"> Optimizer {self.optimizer_name} with initial LR {self.init_lr}")
if self.optimizer_name == 'adamw':
self.optimizer = T.optim.AdamW(self.model.parameters(), lr=self.init_lr, weight_decay=self.weight_decay_)
elif self.optimizer_name == 'rmsprop':
self.optimizer = T.optim.RMSprop(self.model.parameters(), lr=self.init_lr, weight_decay=self.weight_decay_)
else:
self.optimizer = T.optim.Adam(self.model.parameters(), lr=self.init_lr, weight_decay=self.weight_decay_)
print("> Creating Dataloaders")
train_set = "train-small" if "small" in self.embs_path else "train"
self.train_loader = DataLoader(
DataRetriever(train_set, self.data_utils),
batch_size=config["train"]["batch-size"],
shuffle=True,
num_workers=config["loader"]["num-workers"]
)
self.val_loader = DataLoader(
DataRetriever("val", self.data_utils),
batch_size=min(config["train"]["batch-size"], 128),
shuffle=False,
num_workers=config["loader"]["num-workers"]
)
self.model.to(self.device)
print(self.model)
def train(self, epoch):
n_batches = len(self.train_loader)
train_loss = np.zeros(n_batches)
progress = tqdm(range(n_batches), ascii=False, dynamic_ncols=True, unit_scale=True)
self.model.train()
for idx, (inputs, outputs) in enumerate(self.train_loader):
self.optimizer.zero_grad()
loss = self.model.step(inputs, outputs, None)
loss.backward()
self.optimizer.step()
train_loss[idx] = loss.item()
progress.update()
progress.set_description(
f"Training: Epoch [{epoch}/{self.epochs}], Loss: {train_loss[:idx+1].mean():.4f} | ({idx}/{n_batches})")
return train_loss.mean()
def validate(self, epoch):
n_batches = len(self.val_loader)
val_loss = np.zeros(n_batches)
progress = tqdm(range(n_batches), ascii=False, dynamic_ncols=True, unit_scale=True)
self.model.eval()
for idx, (inputs, outputs) in enumerate(self.val_loader):
loss = self.model.step(inputs, outputs, None)
val_loss[idx] = loss.item()
progress.update()
progress.set_description(
f"Validating: Epoch [{epoch}/{self.epochs}], Loss: {val_loss[:idx+1].mean():.4f} | ({idx}/{n_batches})")
return val_loss.mean()
def run(self):
filepath = os.path.join(self.save_path, self.run_title, self.run_title+"_{epoch:02d}_{val_loss:.4f}.pt")
best_filepath = os.path.join(self.save_path, self.run_title, self.run_title+".best.pt")
reduce_lr = T.optim.lr_scheduler.ReduceLROnPlateau(self.optimizer, factor=self.lr_factor, patience=self.lr_patience, min_lr=self.min_lr)
writer = SummaryWriter(log_dir=os.path.join("logs", self.run_title))
print("[INFO] Training Model:", self.run_title)
for epi in range(self.start_epoch, self.epochs):
train_loss = self.train(epi)
writer.add_scalar("loss/train", train_loss, epi)
val_loss = self.validate(epi)
writer.add_scalar("loss/val", val_loss, epi)
reduce_lr.step(val_loss)
last_lr = reduce_lr._last_lr[0] if hasattr(reduce_lr, "_last_lr") else self.init_lr
writer.add_scalar("lr", last_lr, epi)
if (val_loss < self.best_loss) and self.save_path and not self.debug:
model_data = {
'last_epoch': epi,
'last_lr': last_lr,
'val_loss': val_loss,
'stopping_counter': self.stopping_counter,
'state_dict': self.model.state_dict()
}
T.save(model_data, filepath.format(epoch=epi, val_loss=val_loss))
if os.path.isfile(best_filepath):
os.remove(best_filepath)
T.save(model_data, best_filepath)
self.best_loss = val_loss
self.stopping_counter = 0
else:
self.stopping_counter += 1
if self.stopping_counter > self.stopping_patience:
break
print(f"\nEpoch [{epi}/{self.epochs}] | Training Loss: {train_loss:.4f} | Val Loss: {val_loss:.4f} | Best Val Loss: {self.best_loss:.4f} | LR: {last_lr} | DDO: {self.model.diac_dropout_p}")
writer.flush()
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Paramaters')
parser.add_argument('-c', '--config', type=str,
default="config.yaml", help='path of config file')
parser.add_argument('--skip', action='store_true',
help='Skip existing run')
parser.add_argument('--force', action='store_true',
help='Force reruns')
args = parser.parse_args()
with open(args.config, 'r', encoding="utf-8") as file:
config = yaml.load(file, Loader=yaml.FullLoader)
run_title = config["run-title"]
exp_path = os.path.join(config["paths"]["save"], config["run-title"])
if not os.path.isdir(exp_path): os.mkdir(exp_path)
if not args.force:
if os.path.isfile(config["paths"]["load"]) and not config["train"]["resume"] and not config["debug"]:
if not args.skip:
print('[WARNING] Found existing run with name: ', run_title)
cont = input('Continue?')
if cont.lower() not in ['y', 'yes']:
sys.exit(0)
else:
sys.exit(0)
with open(os.path.join(exp_path, "config.yaml"), 'w', encoding="utf-8") as fout:
yaml.dump(config, fout)
model = Trainer(config)
model.run()