def _save_images(self):
for i, (image, box_coord, true_box) in enumerate(
predict_on_batch_multi_output(self.model,
self.validation_datagen)):
image_with_box = overlay_box(image, box_coord, true_box,
self.bins_nr)
filepath = os.path.join(
self.img_dir,
'epoch{}_batch{}_idx{}.png'.format(self.epoch_id,
self.batch_id, i))
plt.imsave(filepath, image_with_box)
def on_epoch_end(self, *args, **kwargs):
epoch_avg_loss = self.epoch_loss_averager.value
epoch_avg_acc = self.epoch_acc_averager.value
self.epoch_loss_averager.reset()
self.epoch_acc_averager.reset()
self.model.eval()
val_loss, val_acc = score_model_multi_output(self.model,
self.loss_function,
self.validation_datagen)
self.model.train()
logs = {
'epoch_id': self.epoch_id,
'batch_id': self.batch_id,
'epoch_loss': epoch_avg_loss,
'epoch_acc': epoch_avg_acc,
'epoch_val_loss': val_loss,
'epoch_val_acc': val_acc
}
self._send_numeric_channels(logs)
for i, (image, y_pred, y_true) in enumerate(
predict_on_batch_multi_output(self.model,
self.validation_datagen)):
image_with_box = overlay_box(image, y_pred, y_true, self.bins_nr)
pill_image = Image.fromarray(
(image_with_box * 255.).astype(np.uint8))
self.ctx.channel_send(
"plotted bbox",
neptune.Image(name='epoch{}_batch{}_idx{}'.format(
self.epoch_id, self.batch_id, i),
description="true and prediction bbox",
data=pill_image))
if i == self.img_nr:
break
self.epoch_id += 1
