def batch_metrics_step(self, dto: UnetDto, epoch):
batch_metrics = MetricMeasuresDtoInit.init_dto()
batch_metrics.core = metrics.binary_measures_torch(dto.outputs.core,
dto.given_variables.core, self.is_cuda)
batch_metrics.penu = metrics.binary_measures_torch(dto.outputs.penu,
dto.given_variables.penu, self.is_cuda)
return batch_metrics
def batch_metrics_step(self, dto: CaeDto, epoch):
batch_metrics = MetricMeasuresDtoInit.init_dto()
batch_metrics.lesion = metrics.binary_measures_torch(dto.reconstructions.gtruth.interpolation,
dto.given_variables.gtruth.lesion, self.is_cuda)
batch_metrics.core = metrics.binary_measures_torch(dto.reconstructions.gtruth.core,
dto.given_variables.gtruth.core, self.is_cuda)
batch_metrics.penu = metrics.binary_measures_torch(dto.reconstructions.gtruth.penu,
dto.given_variables.gtruth.penu, self.is_cuda)
return batch_metrics
def batch_metrics_step(self, dto: Dto):
return MetricMeasuresDtoInit.init_dto()
def run_training(self):
min_loss = self.get_start_min_loss()
for epoch in range(self.get_start_epoch(), self._n_epochs):
self.adapt_lr(epoch)
self.adapt_betas(epoch)
# ---------------------------- (1) TRAINING ---------------------------- #
self._model.train()
epoch_metrics = MetricMeasuresDtoInit.init_dto()
for batch in self._dataloader_training:
epoch_metrics.add(self.train_batch(batch, epoch))
epoch_metrics.div(len(self._dataloader_training))
del batch
self.print_epoch(epoch, 'training', epoch_metrics)
self._metric_dtos['training'].append(epoch_metrics)
del epoch_metrics
# ---------------------------- (2) VALIDATE ---------------------------- #
self._model.eval()
if self._dataloader_validation is None:
epoch_metrics = MetricMeasuresDtoInit.init_dto(
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0)
else:
epoch_metrics = MetricMeasuresDtoInit.init_dto()
for batch in self._dataloader_validation:
epoch_metrics.add(self.validate_batch(batch, epoch))
epoch_metrics.div(len(self._dataloader_validation))
del batch
self.print_epoch(epoch, 'validate', epoch_metrics)
self._metric_dtos['validate'].append(epoch_metrics)
del epoch_metrics
# ------------ (3) SAVE MODEL / VISUALIZE (if new optimum) ------------ #
if self._metric_dtos['validate'] and self._metric_dtos['validate'][
-1].loss < min_loss:
min_loss = self._metric_dtos['validate'][-1].loss
self.save_model()
self.save_training(
) # allows to continue if training has been interrupted
print('(New optimum: Training saved)', end=' ')
self.visualize_epoch(epoch)
if epoch % 50 == 0:
self.visualize_epoch(epoch)
# ----------------- (4) PLOT / SAVE EVALUATION METRICS ---------------- #
if epoch > 0:
fig, plot = plt.subplots()
self.plot_epoch(plot, range(1, epoch + 2))
fig.savefig(self._path_outputs_base + self.FN_VIS_BASE +
'plots.png',
bbox_inches='tight',
dpi=300)
del plot
del fig
# ------------ (5) SAVE FINAL MODEL / VISUALIZE ------------- #
self.save_model('_final')
self.visualize_epoch(epoch)