def on_epoch_end(self, epoch, logs=None):
if logs:
for key in logs.keys():
if epoch == 0:
self.history[key] = []
self.history[key].append(logs[key])
if epoch % 5 == 0:
plot_history_for_callback(model_folder + '/train_history.png',
self.history)
save_history_for_callback(model_folder, self.history)
img_vols, gt_vols, pr_vols = [], [], []
for i in range(0, len(valid_dataset),
int(len(valid_dataset) / 64)):
img_vols.append(np.load(
valid_dataloader.dataset.images_fps[i]))
gt_vols.append(valid_dataloader[i][1])
pr_vols.append(self.model.predict(valid_dataloader[i]))
img_vols = np.stack(img_vols, axis=0)
gt_vols = np.concatenate(gt_vols, axis=0)
pr_vols = np.concatenate(pr_vols, axis=0)
save_images(model_folder + '/epoch-{}-img.png'.format(epoch),
np.uint8(img_vols))
save_images(model_folder + '/epoch-{}-gt.png'.format(epoch),
gt_vols / args.scale * 255)
save_images(model_folder + '/epoch-{}-pr.png'.format(epoch),
pr_vols / args.scale * 255)
def on_epoch_end(self, epoch, logs=None):
if logs:
for key in logs.keys():
if epoch == 0:
self.history[key] = []
self.history[key].append(logs[key])
if epoch % 5 == 0:
plot_history_for_callback(model_folder + '/train_history.png',
self.history)
save_history_for_callback(model_folder, self.history)
pr_vols = []
for i in range(0, len(valid_dataset),
int(len(valid_dataset) / 36)):
pr_vols.append(self.model.predict(valid_dataloader[i]))
pr_vols = np.concatenate(pr_vols, axis=0)
pr_map = map2rgb(np.argmax(pr_vols, axis=-1))
save_images(model_folder + '/pr-{}.png'.format(epoch), pr_map)
if epoch == 0:
gt_vols, ph_vols = [], []
for i in range(0, len(valid_dataset),
int(len(valid_dataset) / 36)):
ph_vols.append(valid_dataloader[i][0])
gt_vols.append(valid_dataloader[i][1])
gt_vols = np.concatenate(gt_vols, axis=0)
gt_map = map2rgb(np.argmax(gt_vols, axis=-1))
ph_vols = np.concatenate(ph_vols, axis=0)
ph_vols = ph_vols.squeeze()
save_images(model_folder + '/gt.png'.format(epoch), gt_map)
save_images(model_folder + '/img.png'.format(epoch), ph_vols)
def on_epoch_end(self, epoch, logs=None):
if logs:
for key in logs.keys():
if epoch == 0:
self.history[key] = []
self.history[key].append(logs[key])
if epoch % 10 == 0:
plot_history_for_callback(model_folder + '/train_history.png',
self.history)
save_history_for_callback(model_folder, self.history)
ph_vols, fl_gts, fl_prs = [], [], []
for i in range(len(valid_dataloader)):
ph_vol, fl_gt = valid_dataloader[i]
fl_pr = self.model.predict(ph_vol)
ph_vols.append(ph_vol)
fl_gts.append(fl_gt)
fl_prs.append(fl_pr)
ph_vols = np.stack(ph_vols).squeeze()
fl_gts = np.stack(fl_gts).squeeze()
fl_prs = np.stack(fl_prs).squeeze()
print('\n max voxel value:{:.2f}'.format(fl_prs.max()))
print(ph_vols.shape)
save_images(model_folder + '/epoch-{}_ph.png'.format(epoch),
ph_vols)
save_images(model_folder + '/epoch-{}_gt.png'.format(epoch),
fl_gts * 255. / scale)
save_images(model_folder + '/epoch-{}_pr.png'.format(epoch),
fl_prs * 255. / scale)
def on_epoch_end(self, epoch, logs=None):
if logs:
for key in logs.keys():
if epoch == 0:
self.history[key] = []
self.history[key].append(logs[key])
if epoch % 5 == 0:
plot_history_for_callback(model_folder + '/train_history.png',
self.history)
save_history_for_callback(model_folder, self.history)
img_vols, gt_vols, pr_vols = [], [], []
for i in range(0, len(valid_dataset),
int(len(valid_dataset) / 36)):
img = valid_dataloader[i][0]
img_vols.append(
np.uint8(255 * (img - img.min()) /
(img.max() - img.min())))
gt_vols.append(valid_dataloader[i][1])
pr_vols.append(self.model.predict(valid_dataloader[i]))
img_vols = np.concatenate(img_vols, axis=0)
gt_vols = np.concatenate(gt_vols, axis=0)
gt_map = map2rgb(np.argmax(gt_vols, axis=-1))
pr_vols = np.concatenate(pr_vols, axis=0)
pr_map = map2rgb(np.argmax(pr_vols, axis=-1))
save_images(model_folder + '/epoch-{}-img.png'.format(epoch),
img_vols)
save_images(model_folder + '/epoch-{}-gt.png'.format(epoch),
gt_map)
save_images(model_folder + '/epoch-{}-pr.png'.format(epoch),
pr_map)
def on_epoch_end(self, epoch, logs=None):
if logs:
for key in logs.keys():
if epoch == 0:
self.history[key] = []
self.history[key].append(logs[key])
if epoch % 5 == 0:
plot_history_for_callback(model_folder + '/train_history.png',
self.history)
save_history_for_callback(model_folder, self.history)
