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"""Training script for PyTorch models."""
from datetime import datetime
from pathlib import Path
from typing import Callable, Dict, Optional
from loguru import logger
import numpy as np
import torch
from tqdm import tqdm, trange
from training.util import RunningAverage
torch.backends.cudnn.benchmark = True
np.random.seed(4711)
torch.manual_seed(4711)
torch.cuda.manual_seed(4711)
EXPERIMENTS_DIRNAME = Path(__file__).parents[0].resolve() / "experiments"
class Trainer:
"""Trainer for training PyTorch models."""
# TODO implement wandb.
def __init__(
self,
model: Callable,
epochs: int,
val_metric: str = "accuracy",
checkpoint_path: Optional[Path] = None,
) -> None:
"""Initialization of the Trainer.
Args:
model (Callable): A model object.
epochs (int): Number of epochs to train.
val_metric (str): The validation metric to evaluate the model on. Defaults to "accuracy".
checkpoint_path (Optional[Path]): The path to a previously trained model. Defaults to None.
"""
self.model = model
self.epochs = epochs
self.checkpoint_path = checkpoint_path
self.start_epoch = 0
if self.checkpoint_path is not None:
self.start_epoch = self.model.load_checkpoint(self.checkpoint_path)
self.val_metric = val_metric
self.best_val_metric = 0.0
logger.add(self.model.name + "_{time}.log")
def train(self) -> None:
"""Training loop."""
# TODO add summary
# Set model to traning mode.
self.model.train()
# Running average for the loss.
loss_avg = RunningAverage()
data_loader = self.model.data_loaders["train"]
with tqdm(
total=len(data_loader),
leave=False,
unit="step",
bar_format="{n_fmt}/{total_fmt} {bar} {remaining} {rate_inv_fmt}{postfix}",
) as t:
for data, targets in data_loader:
data, targets = (
data.to(self.model.device),
targets.to(self.model.device),
)
# Forward pass.
# Get the network prediction.
output = self.model.predict(data)
# Compute the loss.
loss = self.model.criterion(output, targets)
# Backward pass.
# Clear the previous gradients.
self.model.optimizer.zero_grad()
# Compute the gradients.
loss.backward()
# Perform updates using calculated gradients.
self.model.optimizer.step()
# Update the learning rate scheduler.
if self.model.lr_scheduler is not None:
self.model.lr_scheduler.step()
# Compute metrics.
loss_avg.update(loss.item())
output = output.data.cpu()
targets = targets.data.cpu()
metrics = {
metric: round(self.model.metrics[metric](output, targets), 4)
for metric in self.model.metrics
}
metrics["loss"] = round(loss_avg(), 4)
# Update Tqdm progress bar.
t.set_postfix(**metrics)
t.update()
def evaluate(self) -> Dict:
"""Evaluation loop.
Returns:
Dict: A dictionary of evaluation metrics.
"""
# Set model to eval mode.
self.model.eval()
# Running average for the loss.
data_loader = self.model.data_loaders["val"]
# Running average for the loss.
loss_avg = RunningAverage()
# Summary for the current eval loop.
summary = []
with tqdm(
total=len(data_loader),
leave=False,
unit="step",
bar_format="{n_fmt}/{total_fmt} {bar} {remaining} {rate_inv_fmt}{postfix}",
) as t:
for data, targets in data_loader:
data, targets = (
data.to(self.model.device),
targets.to(self.model.device),
)
# Forward pass.
# Get the network prediction.
output = self.model.predict(data)
# Compute the loss.
loss = self.model.criterion(output, targets)
# Compute metrics.
loss_avg.update(loss.item())
output = output.data.cpu()
targets = targets.data.cpu()
metrics = {
metric: round(self.model.metrics[metric](output, targets), 4)
for metric in self.model.metrics
}
metrics["loss"] = round(loss.item(), 4)
summary.append(metrics)
# Update Tqdm progress bar.
t.set_postfix(**metrics)
t.update()
# Compute mean of all metrics.
metrics_mean = {
metric: np.mean(x[metric] for x in summary) for metric in summary[0]
}
metrics_str = " - ".join(f"{k}: {v}" for k, v in metrics_mean.items())
logger.debug(metrics_str)
return metrics_mean
def run(self) -> None:
"""Training and evaluation loop."""
# Create new experiment.
EXPERIMENTS_DIRNAME.mkdir(parents=True, exist_ok=True)
experiment = datetime.now().strftime("%m%d_%H%M%S")
experiment_dir = EXPERIMENTS_DIRNAME / self.model.network.__name__ / experiment
# Create log and model directories.
log_dir = experiment_dir / "log"
model_dir = experiment_dir / "model"
# Make sure the log directory exists.
log_dir.mkdir(parents=True, exist_ok=True)
logger.add(
str(log_dir / "train.log"),
format="{time:YYYY-MM-DD at HH:mm:ss} | {level} | {message}",
)
logger.debug(
f"Running an experiment called {self.model.network.__name__}/{experiment}."
)
# Pŕints a summary of the network in terminal.
self.model.summary()
# Run the training loop.
for epoch in trange(
total=self.epochs,
initial=self.start_epoch,
leave=True,
bar_format="{desc}: {n_fmt}/{total_fmt} {bar} {remaining}{postfix}",
desc="Epoch",
):
# Perform one training pass over the training set.
self.train()
# Evaluate the model on the validation set.
val_metrics = self.evaluate()
# If the model has a learning rate scheduler, compute a step.
if self.model.lr_scheduler is not None:
self.model.lr_scheduler.step()
# The validation metric to evaluate the model on, e.g. accuracy.
val_metric = val_metrics[self.val_metric]
is_best = val_metric >= self.best_val_metric
# Save checkpoint.
self.model.save_checkpoint(model_dir, is_best, epoch, self.val_metric)
if self.start_epoch > 0 and epoch + self.start_epoch == self.epochs:
logger.debug(f"Trained the model for {self.epochs} number of epochs.")
break
|
