def on_test_epoch_end(self):
if self.epoch % 10 == 0:
self._per_dataset_hausdorff_distance_gauge.reset()
self._class_dice_gauge_on_reconstructed_iseg_images.reset()
self._class_dice_gauge_on_reconstructed_mrbrains_images.reset()
self._class_dice_gauge_on_reconstructed_abide_images.reset()
self._hausdorff_distance_gauge_on_reconstructed_iseg_images.reset()
self._hausdorff_distance_gauge_on_reconstructed_mrbrains_images.reset()
self._hausdorff_distance_gauge_on_reconstructed_abide_images.reset()
img_input = self._input_reconstructor.reconstruct_from_patches_3d()
img_gt = self._gt_reconstructor.reconstruct_from_patches_3d()
img_seg = self._segmentation_reconstructor.reconstruct_from_patches_3d()
save_rebuilt_image(self._current_epoch, self._save_folder, self._dataset_configs.keys(), img_input,
"Input")
save_rebuilt_image(self._current_epoch, self._save_folder, self._dataset_configs.keys(), img_gt,
"Ground_Truth")
save_rebuilt_image(self._current_epoch, self._save_folder, self._dataset_configs.keys(), img_seg,
"Segmented")
if self._training_config.build_augmented_images:
img_augmented_input = self._augmented_input_reconstructor.reconstruct_from_patches_3d()
img_augmented_normalized = self._augmented_normalized_reconstructor.reconstruct_from_patches_3d()
save_augmented_rebuilt_images(self._current_epoch, self._save_folder, self._dataset_configs.keys(),
img_augmented_input, img_augmented_normalized)
mean_mhd = []
for dataset in self._dataset_configs.keys():
self.custom_variables[
"Reconstructed Segmented {} Image".format(dataset)] = self._seg_slicer.get_colored_slice(
SliceType.AXIAL, np.expand_dims(img_seg[dataset], 0), 160).squeeze(0)
self.custom_variables[
"Reconstructed Ground Truth {} Image".format(dataset)] = self._seg_slicer.get_colored_slice(
SliceType.AXIAL, np.expand_dims(img_gt[dataset], 0), 160).squeeze(0)
self.custom_variables[
"Reconstructed Input {} Image".format(dataset)] = self._slicer.get_slice(
SliceType.AXIAL, np.expand_dims(img_input[dataset], 0), 160)
if self._training_config.build_augmented_images:
self.custom_variables[
"Reconstructed Augmented Input {} Image".format(dataset)] = self._slicer.get_slice(
SliceType.AXIAL, np.expand_dims(np.expand_dims(img_augmented_input[dataset], 0), 0), 160)
self.custom_variables[
"Reconstructed Augmented {} After Normalization".format(
dataset)] = self._seg_slicer.get_colored_slice(
SliceType.AXIAL,
np.expand_dims(np.expand_dims(img_augmented_normalized[dataset], 0), 0), 160).squeeze(0)
else:
self.custom_variables["Reconstructed Augmented Input {} Image".format(
dataset)] = np.zeros((224, 192))
self.custom_variables[
"Reconstructed Initial Noise {} Image".format(
dataset)] = np.zeros((224, 192))
self.custom_variables[
"Reconstructed Augmented {} After Normalization".format(
dataset)] = np.zeros((224, 192))
mean_mhd.append(mean_hausdorff_distance(
to_onehot(torch.tensor(img_gt[dataset], dtype=torch.long), num_classes=4),
to_onehot(torch.tensor(img_seg[dataset], dtype=torch.long), num_classes=4))[-3:].mean())
metric = self._model_trainers[0].compute_metrics(
to_onehot(torch.tensor(img_seg[dataset]).unsqueeze(0).long(), num_classes=4),
torch.tensor(img_gt[dataset]).unsqueeze(0).long())
self._class_dice_gauge_on_reconstructed_images.update(np.array(metric["Dice"]))
self._per_dataset_hausdorff_distance_gauge.update(np.array(mean_mhd))
if "iSEG" in img_seg:
metric = self._model_trainers[0].compute_metrics(
to_onehot(torch.tensor(img_seg["iSEG"]).unsqueeze(0).long(), num_classes=4),
torch.tensor(img_gt["iSEG"]).unsqueeze(0).long())
self._class_dice_gauge_on_reconstructed_iseg_images.update(np.array(metric["Dice"]))
self._hausdorff_distance_gauge_on_reconstructed_iseg_images.update(mean_hausdorff_distance(
to_onehot(torch.tensor(img_gt["iSEG"], dtype=torch.long), num_classes=4),
to_onehot(torch.tensor(img_seg["iSEG"], dtype=torch.long), num_classes=4))[-3:])
else:
self._class_dice_gauge_on_reconstructed_iseg_images.update(np.array([0.0, 0.0, 0.0]))
self._hausdorff_distance_gauge_on_reconstructed_iseg_images.update(np.array([0.0, 0.0, 0.0]))
if "MRBrainS" in img_seg:
metric = self._model_trainers[0].compute_metrics(
to_onehot(torch.tensor(img_seg["MRBrainS"]).unsqueeze(0).long(), num_classes=4),
torch.tensor(img_gt["MRBrainS"]).unsqueeze(0).long())
self._class_dice_gauge_on_reconstructed_mrbrains_images.update(np.array(metric["Dice"]))
self._hausdorff_distance_gauge_on_reconstructed_mrbrains_images.update(mean_hausdorff_distance(
to_onehot(torch.tensor(img_gt["MRBrainS"], dtype=torch.long), num_classes=4),
to_onehot(torch.tensor(img_seg["MRBrainS"], dtype=torch.long), num_classes=4))[-3:])
else:
self._class_dice_gauge_on_reconstructed_mrbrains_images.update(np.array([0.0, 0.0, 0.0]))
self._hausdorff_distance_gauge_on_reconstructed_mrbrains_images.update(np.array([0.0, 0.0, 0.0]))
if "ABIDE" in img_seg:
metric = self._model_trainers[0].compute_metrics(
to_onehot(torch.tensor(img_seg["ABIDE"]).unsqueeze(0).long(), num_classes=4),
torch.tensor(img_gt["ABIDE"]).unsqueeze(0).long())
self._class_dice_gauge_on_reconstructed_abide_images.update(np.array(metric["Dice"]))
self._hausdorff_distance_gauge_on_reconstructed_abide_images.update(mean_hausdorff_distance(
to_onehot(torch.tensor(img_gt["ABIDE"], dtype=torch.long), num_classes=4),
to_onehot(torch.tensor(img_seg["ABIDE"], dtype=torch.long), num_classes=4))[-3:])
else:
self._class_dice_gauge_on_reconstructed_abide_images.update(np.array([0.0, 0.0, 0.0]))
self._hausdorff_distance_gauge_on_reconstructed_abide_images.update(np.array([0.0, 0.0, 0.0]))
if "ABIDE" not in self._dataset_configs.keys():
self.custom_variables["Reconstructed Segmented ABIDE Image"] = np.zeros((224, 192))
self.custom_variables["Reconstructed Ground Truth ABIDE Image"] = np.zeros((224, 192))
self.custom_variables["Reconstructed Input ABIDE Image"] = np.zeros((224, 192))
if "iSEG" not in self._dataset_configs.keys():
self.custom_variables["Reconstructed Segmented iSEG Image"] = np.zeros((224, 192))
self.custom_variables["Reconstructed Ground Truth iSEG Image"] = np.zeros((224, 192))
self.custom_variables["Reconstructed Input iSEG Image"] = np.zeros((224, 192))
if "MRBrainS" not in self._dataset_configs.keys():
self.custom_variables["Reconstructed Segmented MRBrainS Image"] = np.zeros((224, 192))
self.custom_variables["Reconstructed Ground Truth MRBrainS Image"] = np.zeros((224, 192))
self.custom_variables["Reconstructed Input MRBrainS Image"] = np.zeros((224, 192))
self.custom_variables["Runtime"] = to_html_time(timedelta(seconds=time.time() - self._start_time))
if self._general_confusion_matrix_gauge._num_examples != 0:
self.custom_variables["Confusion Matrix"] = np.array(
np.fliplr(self._general_confusion_matrix_gauge.compute().cpu().detach().numpy()))
else:
self.custom_variables["Confusion Matrix"] = np.zeros((4, 4))
if self._iSEG_confusion_matrix_gauge._num_examples != 0:
self.custom_variables["iSEG Confusion Matrix"] = np.array(
np.fliplr(self._iSEG_confusion_matrix_gauge.compute().cpu().detach().numpy()))
else:
self.custom_variables["iSEG Confusion Matrix"] = np.zeros((4, 4))
if self._MRBrainS_confusion_matrix_gauge._num_examples != 0:
self.custom_variables["MRBrainS Confusion Matrix"] = np.array(
np.fliplr(self._MRBrainS_confusion_matrix_gauge.compute().cpu().detach().numpy()))
else:
self.custom_variables["MRBrainS Confusion Matrix"] = np.zeros((4, 4))
if self._ABIDE_confusion_matrix_gauge._num_examples != 0:
self.custom_variables["ABIDE Confusion Matrix"] = np.array(
np.fliplr(self._ABIDE_confusion_matrix_gauge.compute().cpu().detach().numpy()))
else:
self.custom_variables["ABIDE Confusion Matrix"] = np.zeros((4, 4))
self.custom_variables["Metric Table"] = to_html(["CSF", "Grey Matter", "White Matter"],
["DSC", "HD"],
[
self._class_dice_gauge_on_patches.compute() if self._class_dice_gauge_on_patches.has_been_updated() else np.array(
[0.0, 0.0, 0.0]),
self._class_hausdorff_distance_gauge.compute() if self._class_hausdorff_distance_gauge.has_been_updated() else np.array(
[0.0, 0.0, 0.0])
])
self.custom_variables[
"Dice score per class per epoch"] = self._class_dice_gauge_on_patches.compute() if self._class_dice_gauge_on_patches.has_been_updated() else np.array(
[0.0, 0.0, 0.0])
self.custom_variables[
"Dice score per class per epoch on reconstructed image"] = self._class_dice_gauge_on_reconstructed_images.compute() if self._class_dice_gauge_on_reconstructed_images.has_been_updated() else np.array(
[0.0, 0.0, 0.0])
self.custom_variables[
"Dice score per class per epoch on reconstructed iSEG image"] = self._class_dice_gauge_on_reconstructed_iseg_images.compute() if self._class_dice_gauge_on_reconstructed_iseg_images.has_been_updated() else np.array(
[0.0, 0.0, 0.0])
self.custom_variables[
"Dice score per class per epoch on reconstructed MRBrainS image"] = self._class_dice_gauge_on_reconstructed_mrbrains_images.compute() if self._class_dice_gauge_on_reconstructed_mrbrains_images.has_been_updated() else np.array(
[0.0, 0.0, 0.0])
self.custom_variables[
"Dice score per class per epoch on reconstructed ABIDE image"] = self._class_dice_gauge_on_reconstructed_abide_images.compute() if self._class_dice_gauge_on_reconstructed_abide_images.has_been_updated() else np.array(
[0.0, 0.0, 0.0])
self.custom_variables[
"Hausdorff Distance per class per epoch on reconstructed iSEG image"] = self._hausdorff_distance_gauge_on_reconstructed_iseg_images.compute() if self._hausdorff_distance_gauge_on_reconstructed_iseg_images.has_been_updated() else np.array(
[0.0, 0.0, 0.0])
self.custom_variables[
"Hausdorff Distance per class per epoch on reconstructed MRBrainS image"] = self._hausdorff_distance_gauge_on_reconstructed_mrbrains_images.compute() if self._hausdorff_distance_gauge_on_reconstructed_mrbrains_images.has_been_updated() else np.array(
[0.0, 0.0, 0.0])
self.custom_variables[
"Hausdorff Distance per class per epoch on reconstructed ABIDE image"] = self._hausdorff_distance_gauge_on_reconstructed_abide_images.compute() if self._hausdorff_distance_gauge_on_reconstructed_abide_images.has_been_updated() else np.array(
[0.0, 0.0, 0.0])
if self._valid_dice_gauge.compute() > self._previous_mean_dice:
new_table = to_html_per_dataset(
["CSF", "Grey Matter", "White Matter"],
["DSC", "HD"],
[
[
self._iSEG_dice_gauge.compute() if self._iSEG_dice_gauge.has_been_updated() else np.array(
[0.0, 0.0, 0.0]),
self._iSEG_hausdorff_gauge.compute() if self._iSEG_hausdorff_gauge.has_been_updated() else np.array(
[0.0, 0.0, 0.0])],
[
self._MRBrainS_dice_gauge.compute() if self._MRBrainS_dice_gauge.has_been_updated() else np.array(
[0.0, 0.0, 0.0]),
self._MRBrainS_hausdorff_gauge.compute() if self._MRBrainS_hausdorff_gauge.has_been_updated() else np.array(
[0.0, 0.0, 0.0])],
[
self._ABIDE_dice_gauge.compute() if self._ABIDE_dice_gauge.has_been_updated() else np.array(
[0.0, 0.0, 0.0]),
self._ABIDE_hausdorff_gauge.compute() if self._ABIDE_hausdorff_gauge.has_been_updated() else np.array(
[0.0, 0.0, 0.0])]],
["iSEG", "MRBrainS", "ABIDE"])
self.custom_variables["Per-Dataset Metric Table"] = new_table
self._previous_mean_dice = self._valid_dice_gauge.compute()
self._previous_per_dataset_table = new_table
else:
self.custom_variables["Per-Dataset Metric Table"] = self._previous_per_dataset_table
self._valid_dice_gauge.reset()
self.custom_variables["Mean Hausdorff Distance"] = [
self._class_hausdorff_distance_gauge.compute().mean() if self._class_hausdorff_distance_gauge.has_been_updated() else np.array(
[0.0])]
self.custom_variables[
"Per Dataset Mean Hausdorff Distance"] = self._per_dataset_hausdorff_distance_gauge.compute()
def on_test_epoch_end(self):
if self.epoch % 20 == 0:
self._per_dataset_hausdorff_distance_gauge.reset()
self._class_dice_gauge_on_reconstructed_iseg_images.reset()
self._class_dice_gauge_on_reconstructed_mrbrains_images.reset()
self._class_dice_gauge_on_reconstructed_abide_images.reset()
self._hausdorff_distance_gauge_on_reconstructed_iseg_images.reset()
self._hausdorff_distance_gauge_on_reconstructed_mrbrains_images.reset()
self._hausdorff_distance_gauge_on_reconstructed_abide_images.reset()
all_patches, ground_truth_patches = get_all_patches(self._reconstruction_datasets, self._is_sliced)
img_input = rebuild_image(self._dataset_configs.keys(), all_patches, self._input_reconstructors)
img_gt = rebuild_image(self._dataset_configs.keys(), ground_truth_patches, self._gt_reconstructors)
img_norm = rebuild_image(self._dataset_configs.keys(), all_patches, self._normalize_reconstructors)
img_seg = rebuild_image(self._dataset_configs.keys(), all_patches, self._segmentation_reconstructors)
save_rebuilt_image(self._current_epoch, self._save_folder, self._dataset_configs.keys(), img_input, "Input")
save_rebuilt_image(self._current_epoch, self._save_folder, self._dataset_configs.keys(), img_gt,
"Ground_Truth")
save_rebuilt_image(self._current_epoch, self._save_folder, self._dataset_configs.keys(), img_norm,
"Normalized")
save_rebuilt_image(self._current_epoch, self._save_folder, self._dataset_configs.keys(), img_seg,
"Segmented")
if self._training_config.build_augmented_images:
img_augmented = rebuild_image(self._dataset_configs.keys(), all_patches, self._augmented_reconstructors)
augmented_minus_inputs, normalized_minus_inputs = rebuild_augmented_images(img_augmented, img_input,
img_gt, img_norm, img_seg)
save_augmented_rebuilt_images(self._current_epoch, self._save_folder, self._dataset_configs.keys(),
img_augmented, augmented_minus_inputs, normalized_minus_inputs)
mean_mhd = []
for dataset in self._dataset_configs.keys():
self.custom_variables[
"Reconstructed Normalized {} Image".format(dataset)] = self._slicer.get_slice(
SliceType.AXIAL, np.expand_dims(np.expand_dims(img_norm[dataset], 0), 0), 160)
self.custom_variables[
"Reconstructed Segmented {} Image".format(dataset)] = self._seg_slicer.get_colored_slice(
SliceType.AXIAL, np.expand_dims(np.expand_dims(img_seg[dataset], 0), 0), 160).squeeze(0)
self.custom_variables[
"Reconstructed Ground Truth {} Image".format(dataset)] = self._seg_slicer.get_colored_slice(
SliceType.AXIAL, np.expand_dims(np.expand_dims(img_gt[dataset], 0), 0), 160).squeeze(0)
self.custom_variables[
"Reconstructed Input {} Image".format(dataset)] = self._slicer.get_slice(
SliceType.AXIAL, np.expand_dims(np.expand_dims(img_input[dataset], 0), 0), 160)
if self._training_config.build_augmented_images:
self.custom_variables[
"Reconstructed Augmented Input {} Image".format(dataset)] = self._slicer.get_slice(
SliceType.AXIAL, np.expand_dims(np.expand_dims(img_augmented[dataset], 0), 0), 160)
self.custom_variables[
"Reconstructed Initial Noise {} Image".format(
dataset)] = self._seg_slicer.get_colored_slice(
SliceType.AXIAL,
np.expand_dims(np.expand_dims(augmented_minus_inputs[dataset], 0), 0), 160).squeeze(0)
self.custom_variables[
"Reconstructed Noise {} After Normalization".format(
dataset)] = self._seg_slicer.get_colored_slice(
SliceType.AXIAL,
np.expand_dims(np.expand_dims(normalized_minus_inputs[dataset], 0), 0), 160).squeeze(0)
else:
self.custom_variables["Reconstructed Augmented Input {} Image".format(
dataset)] = np.zeros((224, 192))
self.custom_variables[
"Reconstructed Initial Noise {} Image".format(
dataset)] = np.zeros((224, 192))
self.custom_variables[
"Reconstructed Noise {} After Normalization".format(
dataset)] = np.zeros((224, 192))
mean_mhd.append(mean_hausdorff_distance(
to_onehot(torch.tensor(img_gt[dataset], dtype=torch.long), num_classes=4),
to_onehot(torch.tensor(img_seg[dataset], dtype=torch.long), num_classes=4))[-3:].mean())
metric = self._model_trainers[SEGMENTER].compute_metrics(
to_onehot(torch.tensor(img_seg[dataset]).unsqueeze(0).long(), num_classes=4),
torch.tensor(img_gt[dataset]).unsqueeze(0).long())
self._class_dice_gauge_on_reconstructed_images.update(np.array(metric["Dice"]))
self._per_dataset_hausdorff_distance_gauge.update(np.array(mean_mhd))
if "iSEG" in img_seg:
metric = self._model_trainers[SEGMENTER].compute_metrics(
to_onehot(torch.tensor(img_seg["iSEG"]).unsqueeze(0).long(), num_classes=4),
torch.tensor(img_gt["iSEG"]).unsqueeze(0).long())
self._class_dice_gauge_on_reconstructed_iseg_images.update(np.array(metric["Dice"]))
self._hausdorff_distance_gauge_on_reconstructed_iseg_images.update(mean_hausdorff_distance(
to_onehot(torch.tensor(img_gt["iSEG"], dtype=torch.long), num_classes=4),
to_onehot(torch.tensor(img_seg["iSEG"], dtype=torch.long), num_classes=4))[-3:])
else:
self._class_dice_gauge_on_reconstructed_iseg_images.update(np.array([0.0, 0.0, 0.0]))
self._hausdorff_distance_gauge_on_reconstructed_iseg_images.update(np.array([0.0, 0.0, 0.0]))
if "MRBrainS" in img_seg:
metric = self._model_trainers[SEGMENTER].compute_metrics(
to_onehot(torch.tensor(img_seg["MRBrainS"]).unsqueeze(0).long(), num_classes=4),
torch.tensor(img_gt["MRBrainS"]).unsqueeze(0).long())
self._class_dice_gauge_on_reconstructed_mrbrains_images.update(np.array(metric["Dice"]))
self._hausdorff_distance_gauge_on_reconstructed_mrbrains_images.update(mean_hausdorff_distance(
to_onehot(torch.tensor(img_gt["MRBrainS"], dtype=torch.long), num_classes=4),
to_onehot(torch.tensor(img_seg["MRBrainS"], dtype=torch.long), num_classes=4))[-3:])
else:
self._class_dice_gauge_on_reconstructed_mrbrains_images.update(np.array([0.0, 0.0, 0.0]))
self._hausdorff_distance_gauge_on_reconstructed_mrbrains_images.update(np.array([0.0, 0.0, 0.0]))
if "ABIDE" in img_seg:
metric = self._model_trainers[SEGMENTER].compute_metrics(
to_onehot(torch.tensor(img_seg["ABIDE"]).unsqueeze(0).long(), num_classes=4),
torch.tensor(img_gt["ABIDE"]).unsqueeze(0).long())
self._class_dice_gauge_on_reconstructed_abide_images.update(np.array(metric["Dice"]))
self._hausdorff_distance_gauge_on_reconstructed_abide_images.update(mean_hausdorff_distance(
to_onehot(torch.tensor(img_gt["ABIDE"], dtype=torch.long), num_classes=4),
to_onehot(torch.tensor(img_seg["ABIDE"], dtype=torch.long), num_classes=4))[-3:])
else:
self._class_dice_gauge_on_reconstructed_abide_images.update(np.array([0.0, 0.0, 0.0]))
self._hausdorff_distance_gauge_on_reconstructed_abide_images.update(np.array([0.0, 0.0, 0.0]))
if len(img_input) == 3:
self.custom_variables["Reconstructed Images Histograms"] = cv2.imread(
construct_triple_histrogram(img_norm["iSEG"],
img_input["iSEG"],
img_norm["MRBrainS"],
img_input["MRBrainS"],
img_norm["ABIDE"],
img_input["ABIDE"])).transpose((2, 0, 1))
elif len(img_input) == 2:
self.custom_variables["Reconstructed Images Histograms"] = cv2.imread(
construct_double_histrogram(img_norm["iSEG"],
img_input["iSEG"],
img_norm["MRBrainS"],
img_input["MRBrainS"])).transpose((2, 0, 1))
elif len(img_input) == 1:
self.custom_variables["Reconstructed Images Histograms"] = cv2.imread(
construct_single_histogram(img_norm[list(self._dataset_configs.keys())[0]],
img_input[list(self._dataset_configs.keys())[0]],
)).transpose((2, 0, 1))
if "ABIDE" not in self._dataset_configs.keys():
self.custom_variables["Reconstructed Normalized ABIDE Image"] = np.zeros((224, 192))
self.custom_variables["Reconstructed Segmented ABIDE Image"] = np.zeros((224, 192))
self.custom_variables["Reconstructed Ground Truth ABIDE Image"] = np.zeros((224, 192))
self.custom_variables["Reconstructed Input ABIDE Image"] = np.zeros((224, 192))
self.custom_variables["Reconstructed Initial Noise ABIDE Image"] = np.zeros((224, 192))
self.custom_variables["Reconstructed Noise ABIDE After Normalization"] = np.zeros((224, 192))
if "iSEG" not in self._dataset_configs.keys():
self.custom_variables["Reconstructed Normalized iSEG Image"] = np.zeros((224, 192))
self.custom_variables["Reconstructed Segmented iSEG Image"] = np.zeros((224, 192))
self.custom_variables["Reconstructed Ground Truth iSEG Image"] = np.zeros((224, 192))
self.custom_variables["Reconstructed Input iSEG Image"] = np.zeros((224, 192))
self.custom_variables["Reconstructed Initial Noise iSEG Image"] = np.zeros((224, 192))
self.custom_variables["Reconstructed Noise iSEG After Normalization"] = np.zeros((224, 192))
if "MRBrainS" not in self._dataset_configs.keys():
self.custom_variables["Reconstructed Normalized MRBrainS Image"] = np.zeros((224, 192))
self.custom_variables["Reconstructed Segmented MRBrainS Image"] = np.zeros((224, 192))
self.custom_variables["Reconstructed Ground Truth MRBrainS Image"] = np.zeros((224, 192))
self.custom_variables["Reconstructed Input MRBrainS Image"] = np.zeros((224, 192))
self.custom_variables["Reconstructed Initial Noise MRBrainS Image"] = np.zeros((224, 192))
self.custom_variables["Reconstructed Noise MRBrainS After Normalization"] = np.zeros((224, 192))
self.custom_variables["Runtime"] = to_html_time(timedelta(seconds=time.time() - self._start_time))
if self._general_confusion_matrix_gauge._num_examples != 0:
self.custom_variables["Confusion Matrix"] = np.array(
np.fliplr(self._general_confusion_matrix_gauge.compute().cpu().detach().numpy()))
else:
self.custom_variables["Confusion Matrix"] = np.zeros((4, 4))
if self._iSEG_confusion_matrix_gauge._num_examples != 0:
self.custom_variables["iSEG Confusion Matrix"] = np.array(
np.fliplr(self._iSEG_confusion_matrix_gauge.compute().cpu().detach().numpy()))
else:
self.custom_variables["iSEG Confusion Matrix"] = np.zeros((4, 4))
if self._MRBrainS_confusion_matrix_gauge._num_examples != 0:
self.custom_variables["MRBrainS Confusion Matrix"] = np.array(
np.fliplr(self._MRBrainS_confusion_matrix_gauge.compute().cpu().detach().numpy()))
else:
self.custom_variables["MRBrainS Confusion Matrix"] = np.zeros((4, 4))
if self._ABIDE_confusion_matrix_gauge._num_examples != 0:
self.custom_variables["ABIDE Confusion Matrix"] = np.array(
np.fliplr(self._ABIDE_confusion_matrix_gauge.compute().cpu().detach().numpy()))
else:
self.custom_variables["ABIDE Confusion Matrix"] = np.zeros((4, 4))
self.custom_variables["Metric Table"] = to_html(["CSF", "Grey Matter", "White Matter"],
["DSC", "HD"],
[
self._class_dice_gauge_on_patches.compute() if self._class_dice_gauge_on_patches.has_been_updated() else np.array(
[0.0, 0.0, 0.0]),
self._class_hausdorff_distance_gauge.compute() if self._class_hausdorff_distance_gauge.has_been_updated() else np.array(
[0.0, 0.0, 0.0])
])
self.custom_variables[
"Dice score per class per epoch"] = self._class_dice_gauge_on_patches.compute() if self._class_dice_gauge_on_patches.has_been_updated() else np.array(
[0.0, 0.0, 0.0])
self.custom_variables[
"Dice score per class per epoch on reconstructed image"] = self._class_dice_gauge_on_reconstructed_images.compute() if self._class_dice_gauge_on_reconstructed_images.has_been_updated() else np.array(
[0.0, 0.0, 0.0])
self.custom_variables[
"Dice score per class per epoch on reconstructed iSEG image"] = self._class_dice_gauge_on_reconstructed_iseg_images.compute() if self._class_dice_gauge_on_reconstructed_iseg_images.has_been_updated() else np.array(
[0.0, 0.0, 0.0])
self.custom_variables[
"Dice score per class per epoch on reconstructed MRBrainS image"] = self._class_dice_gauge_on_reconstructed_mrbrains_images.compute() if self._class_dice_gauge_on_reconstructed_mrbrains_images.has_been_updated() else np.array(
[0.0, 0.0, 0.0])
self.custom_variables[
"Dice score per class per epoch on reconstructed ABIDE image"] = self._class_dice_gauge_on_reconstructed_abide_images.compute() if self._class_dice_gauge_on_reconstructed_abide_images.has_been_updated() else np.array(
[0.0, 0.0, 0.0])
self.custom_variables[
"Hausdorff Distance per class per epoch on reconstructed iSEG image"] = self._hausdorff_distance_gauge_on_reconstructed_iseg_images.compute() if self._hausdorff_distance_gauge_on_reconstructed_iseg_images.has_been_updated() else np.array(
[0.0, 0.0, 0.0])
self.custom_variables[
"Hausdorff Distance per class per epoch on reconstructed MRBrainS image"] = self._hausdorff_distance_gauge_on_reconstructed_mrbrains_images.compute() if self._hausdorff_distance_gauge_on_reconstructed_mrbrains_images.has_been_updated() else np.array(
[0.0, 0.0, 0.0])
self.custom_variables[
"Hausdorff Distance per class per epoch on reconstructed ABIDE image"] = self._hausdorff_distance_gauge_on_reconstructed_abide_images.compute() if self._hausdorff_distance_gauge_on_reconstructed_abide_images.has_been_updated() else np.array(
[0.0, 0.0, 0.0])
if self._valid_dice_gauge.compute() > self._previous_mean_dice:
new_table = to_html_per_dataset(
["CSF", "Grey Matter", "White Matter"],
["DSC", "HD"],
[
[
self._iSEG_dice_gauge.compute() if self._iSEG_dice_gauge.has_been_updated() else np.array(
[0.0, 0.0, 0.0]),
self._iSEG_hausdorff_gauge.compute() if self._iSEG_hausdorff_gauge.has_been_updated() else np.array(
[0.0, 0.0, 0.0])],
[
self._MRBrainS_dice_gauge.compute() if self._MRBrainS_dice_gauge.has_been_updated() else np.array(
[0.0, 0.0, 0.0]),
self._MRBrainS_hausdorff_gauge.compute() if self._MRBrainS_hausdorff_gauge.has_been_updated() else np.array(
[0.0, 0.0, 0.0])],
[
self._ABIDE_dice_gauge.compute() if self._ABIDE_dice_gauge.has_been_updated() else np.array(
[0.0, 0.0, 0.0]),
self._ABIDE_hausdorff_gauge.compute() if self._ABIDE_hausdorff_gauge.has_been_updated() else np.array(
[0.0, 0.0, 0.0])]],
["iSEG", "MRBrainS", "ABIDE"])
self.custom_variables["Per-Dataset Metric Table"] = new_table
self._previous_mean_dice = self._valid_dice_gauge.compute()
self._previous_per_dataset_table = new_table
else:
self.custom_variables["Per-Dataset Metric Table"] = self._previous_per_dataset_table
self._valid_dice_gauge.reset()
self.custom_variables["Jensen-Shannon Table"] = to_html_JS(["Input data", "Generated Data"],
["JS Divergence"],
[
self._js_div_inputs_gauge.compute() if self._js_div_gen_gauge.has_been_updated() else np.array(
[0.0]),
self._js_div_gen_gauge.compute() if self._js_div_gen_gauge.has_been_updated() else np.array(
[0.0])])
self.custom_variables["Jensen-Shannon Divergence"] = [
self._js_div_inputs_gauge.compute(),
self._js_div_gen_gauge.compute()]
self.custom_variables["Mean Hausdorff Distance"] = [
self._class_hausdorff_distance_gauge.compute().mean() if self._class_hausdorff_distance_gauge.has_been_updated() else np.array(
[0.0])]
self.custom_variables[
"Per Dataset Mean Hausdorff Distance"] = self._per_dataset_hausdorff_distance_gauge.compute()