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"""Training script for PyTorch models."""
from pathlib import Path
import time
from typing import Dict, List, Optional, Tuple, Type
from loguru import logger
import numpy as np
import torch
from tqdm import tqdm, trange
from training.callbacks import Callback, CallbackList
from training.util import RunningAverage
import wandb
from text_recognizer.models import Model
torch.backends.cudnn.benchmark = True
np.random.seed(4711)
torch.manual_seed(4711)
torch.cuda.manual_seed(4711)
class Trainer:
"""Trainer for training PyTorch models."""
def __init__(
self,
model: Type[Model],
model_dir: Path,
train_args: Dict,
callbacks: CallbackList,
checkpoint_path: Optional[Path] = None,
) -> None:
"""Initialization of the Trainer.
Args:
model (Type[Model]): A model object.
model_dir (Path): Path to the model directory.
train_args (Dict): The training arguments.
callbacks (CallbackList): List of callbacks to be called.
checkpoint_path (Optional[Path]): The path to a previously trained model. Defaults to None.
"""
self.model = model
self.model_dir = model_dir
self.checkpoint_path = checkpoint_path
self.start_epoch = 1
self.epochs = train_args["epochs"] + self.start_epoch
self.callbacks = callbacks
if self.checkpoint_path is not None:
self.start_epoch = self.model.load_checkpoint(self.checkpoint_path) + 1
# Parse the name of the experiment.
experiment_dir = str(self.model_dir.parents[1]).split("/")
self.experiment_name = experiment_dir[-2] + "/" + experiment_dir[-1]
def training_step(
self,
batch: int,
samples: Tuple[torch.Tensor, torch.Tensor],
loss_avg: Type[RunningAverage],
) -> Dict:
"""Performs the training step."""
# Pass the tensor to the device for computation.
data, targets = samples
data, targets = (
data.to(self.model.device),
targets.to(self.model.device),
)
# Forward pass.
# Get the network prediction.
output = self.model.network(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()
# Compute metrics.
loss_avg.update(loss.item())
output = output.data.cpu()
targets = targets.data.cpu()
metrics = {
metric: self.model.metrics[metric](output, targets)
for metric in self.model.metrics
}
metrics["loss"] = loss_avg()
return metrics
def train(self) -> None:
"""Runs the training loop for one epoch."""
# 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:30}| {remaining} {rate_inv_fmt}{postfix}",
) as t:
for batch, samples in enumerate(data_loader):
self.callbacks.on_train_batch_begin(batch)
metrics = self.training_step(batch, samples, loss_avg)
self.callbacks.on_train_batch_end(batch, logs=metrics)
# Update Tqdm progress bar.
t.set_postfix(**metrics)
t.update()
def validation_step(
self,
batch: int,
samples: Tuple[torch.Tensor, torch.Tensor],
loss_avg: Type[RunningAverage],
) -> Dict:
"""Performs the validation step."""
# Pass the tensor to the device for computation.
data, targets = samples
data, targets = (
data.to(self.model.device),
targets.to(self.model.device),
)
# Forward pass.
# Get the network prediction.
output = self.model.network(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: self.model.metrics[metric](output, targets)
for metric in self.model.metrics
}
metrics["loss"] = loss.item()
return metrics
def validate(self, epoch: Optional[int] = None) -> Dict:
"""Runs the validation loop for one epoch."""
# 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:30}| {remaining} {rate_inv_fmt}{postfix}",
) as t:
with torch.no_grad():
for batch, samples in enumerate(data_loader):
self.callbacks.on_validation_batch_begin(batch)
metrics = self.validation_step(batch, samples, loss_avg)
self.callbacks.on_validation_batch_end(batch, logs=metrics)
summary.append(metrics)
# Update Tqdm progress bar.
t.set_postfix(**metrics)
t.update()
# Compute mean of all metrics.
metrics_mean = {
"val_" + metric: np.mean([x[metric] for x in summary])
for metric in summary[0]
}
if epoch:
logger.debug(
f"Validation metrics at epoch {epoch} - "
+ " - ".join(f"{k}: {v:.4f}" for k, v in metrics_mean.items())
)
else:
logger.debug(
"Validation metrics - "
+ " - ".join(f"{k}: {v:.4f}" for k, v in metrics_mean.items())
)
return metrics_mean
def fit(self) -> None:
"""Runs the training and evaluation loop."""
logger.debug(f"Running an experiment called {self.experiment_name}.")
t_start = time.time()
self.callbacks.on_fit_begin()
# TODO: fix progress bar as callback.
# Run the training loop.
for epoch in trange(
self.start_epoch,
self.epochs,
leave=False,
bar_format="{desc}: {n_fmt}/{total_fmt} |{bar:30}| {remaining}{postfix}",
desc="Epoch",
):
self.callbacks.on_epoch_begin(epoch)
# Perform one training pass over the training set.
self.train()
# Evaluate the model on the validation set.
val_metrics = self.validate(epoch)
self.callbacks.on_epoch_end(epoch, logs=val_metrics)
if self.model.stop_training:
break
# Calculate the total training time.
t_end = time.time()
t_training = t_end - t_start
self.callbacks.on_fit_end()
logger.info(f"Training took {t_training:.2f} s.")
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