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from tqdm import tqdm
import torch
from utils import save_model, save_pred, get_pred_prefix, get_model_prefix, detach_and_clone, collate_list
def run_epoch(algorithm, dataset, general_logger, epoch, config, train):
if dataset['verbose']:
general_logger.write(f"\n{dataset['name']}:\n")
if train:
algorithm.train()
torch.set_grad_enabled(True)
else:
algorithm.eval()
torch.set_grad_enabled(False)
# Not preallocating memory is slower
# but makes it easier to handle different types of data loaders
# (which might not return exactly the same number of examples per epoch)
epoch_y_true = []
epoch_y_pred = []
epoch_metadata = []
# Using enumerate(iterator) can sometimes leak memory in some environments (!)
# so we manually increment batch_idx
batch_idx = 0
iterator = tqdm(dataset['loader']) if config.progress_bar else dataset['loader']
for batch in iterator:
if train:
batch_results = algorithm.update(batch)
else:
batch_results = algorithm.evaluate(batch)
# These tensors are already detached, but we need to clone them again
# Otherwise they don't get garbage collected properly in some versions
# The extra detach is just for safety
# (they should already be detached in batch_results)
epoch_y_true.append(detach_and_clone(batch_results['y_true']))
y_pred = detach_and_clone(batch_results['y_pred'])
if config.process_outputs_function is not None:
y_pred = process_outputs_functions[config.process_outputs_function](y_pred)
epoch_y_pred.append(y_pred)
epoch_metadata.append(detach_and_clone(batch_results['metadata']))
if train and (batch_idx + 1) % config.log_every == 0:
log_results(algorithm, dataset, general_logger, epoch, batch_idx)
batch_idx += 1
epoch_y_pred = collate_list(epoch_y_pred)
epoch_y_true = collate_list(epoch_y_true)
epoch_metadata = collate_list(epoch_metadata)
results, results_str = dataset['dataset'].eval(
epoch_y_pred,
epoch_y_true,
epoch_metadata)
if config.scheduler_metric_split == dataset['split']:
algorithm.step_schedulers(
is_epoch=True,
metrics=results,
log_access=(not train))
# log after updating the scheduler in case it needs to access the internal logs
log_results(algorithm, dataset, general_logger, epoch, batch_idx)
results['epoch'] = epoch
dataset['eval_logger'].log(results)
if dataset['verbose']:
general_logger.write('Epoch eval:\n')
general_logger.write(results_str)
return results, epoch_y_pred
def train(algorithm, datasets, general_logger, result_logger, config, epoch_offset, best_val_metric):
for epoch in range(epoch_offset, config.n_epochs):
general_logger.write('\nEpoch [%d]:\n' % epoch)
# First run training
run_epoch(algorithm, datasets['train'], general_logger, epoch, config, train=True)
# Then run val
val_results, y_pred = run_epoch(algorithm, datasets['val'], general_logger, epoch, config, train=False)
curr_val_metric = val_results[config.val_metric]
general_logger.write(f'Validation {config.val_metric}: {curr_val_metric:.3f}\n')
if best_val_metric is None:
is_best = True
else:
if config.val_metric_decreasing:
is_best = curr_val_metric < best_val_metric
else:
is_best = curr_val_metric > best_val_metric
if is_best:
best_val_metric = curr_val_metric
general_logger.write(f'Epoch {epoch} has the best validation performance so far.\n')
save_model_if_needed(algorithm, datasets['val'], epoch, config, is_best, best_val_metric)
save_pred_if_needed(y_pred, datasets['val'], epoch, config, is_best)
# Then run everything else
if config.evaluate_all_splits:
additional_splits = [split for split in datasets.keys() if split not in ['train', 'val']]
else:
additional_splits = config.eval_splits
for split in additional_splits:
_, y_pred = run_epoch(algorithm, datasets[split], general_logger, epoch, config, train=False)
save_pred_if_needed(y_pred, datasets[split], epoch, config, is_best)
general_logger.write('\n')
def evaluate(algorithm, datasets, epoch, general_logger, result_logger, config, is_best):
algorithm.eval()
torch.set_grad_enabled(False)
for split, dataset in datasets.items():
if (not config.evaluate_all_splits) and (split not in config.eval_splits):
continue
epoch_y_true = []
epoch_y_pred = []
epoch_metadata = []
iterator = tqdm(dataset['loader']) if config.progress_bar else dataset['loader']
for batch in iterator:
batch_results = algorithm.evaluate(batch)
epoch_y_true.append(detach_and_clone(batch_results['y_true']))
y_pred = detach_and_clone(batch_results['y_pred'])
if config.process_outputs_function is not None:
y_pred = process_outputs_functions[config.process_outputs_function](y_pred)
epoch_y_pred.append(y_pred)
epoch_metadata.append(detach_and_clone(batch_results['metadata']))
epoch_y_pred = collate_list(epoch_y_pred)
epoch_y_true = collate_list(epoch_y_true)
epoch_metadata = collate_list(epoch_metadata)
results, results_str = dataset['dataset'].eval(
epoch_y_pred,
epoch_y_true,
epoch_metadata)
results['epoch'] = epoch
dataset['eval_logger'].log(results)
general_logger.write(f'Eval split {split} at epoch {epoch}:\n')
general_logger.write(results_str)
# Skip saving train preds, since the train loader generally shuffles the data
if split != 'train':
save_pred_if_needed(epoch_y_pred, dataset, epoch, config, is_best, force_save=True)
def log_results(algorithm, dataset, general_logger, epoch, batch_idx):
if algorithm.has_log:
log = algorithm.get_log()
log['epoch'] = epoch
log['batch'] = batch_idx
dataset['algo_logger'].log(log)
if dataset['verbose']:
general_logger.write(algorithm.get_pretty_log_str())
algorithm.reset_log()
def save_pred_if_needed(y_pred, dataset, epoch, config, is_best, force_save=False):
if config.save_pred:
prefix = get_pred_prefix(dataset, config)
if force_save or (config.save_step is not None and (epoch + 1) % config.save_step == 0):
save_pred(y_pred, prefix, f'epoch-{epoch}_pred')
if (not force_save) and config.save_last:
save_pred(y_pred, prefix, f'epoch-last_pred')
if config.save_best and is_best:
save_pred(y_pred, prefix, f'epoch-best_pred')
def save_model_if_needed(algorithm, dataset, epoch, config, is_best, best_val_metric):
prefix = get_model_prefix(dataset, config)
if config.save_step is not None and (epoch + 1) % config.save_step == 0:
save_model(algorithm, epoch, best_val_metric, prefix, f'epoch-{epoch}_model.pth')
if config.save_last:
save_model(algorithm, epoch, best_val_metric, prefix, 'epoch-last_model.pth')
if config.save_best and is_best:
save_model(algorithm, epoch, best_val_metric, prefix, 'epoch-best_model.pth')
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