def _initial_performance(self, session):
val_start = dt.now()
validation_performance = session.test_model(self.val_loader)
target_performance = Evaluator.means_over_subsets(
validation_performance)['distance']
val_time = dt.now() - val_start
Evaluator.print_result_summary_flat(validation_performance, '\t')
print('\t\tThat took {} milliseconds.'.format(val_time.microseconds //
1000))
return target_performance, copy.deepcopy(session.model.state_dict())
def _logging(self, log, loss, train_performance, val_performance, i):
for key, value in train_performance['DEFAULT'].items():
log['train'][key].append(value)
for subset_key, subset_performance in val_performance.items():
for key, value in subset_performance.items():
log['val'][subset_key][key].append(value)
if self.config['verbose']:
print('Iteration {}/{}:'.format(i, self.iters_per_epoch - 1, loss))
print('Loss: {:.2e}'.format(loss))
Evaluator.print_result_summary_flat(train_performance,
'\tTraining : ')
Evaluator.print_result_summary_flat(val_performance,
'\tValidation: ')
print()
