web_tool/ModelSessionPyTorchExample.py [131:170]:
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            return {
                "message": "Fine-tuning accuracy on data: %0.2f" % (score),
                "success": True
            }
        except Exception as e:
            return {
                "message": "Error in 'retrain()': %s" % (e),
                "success": False
            }
    
    def undo(self):
        if len(self.augment_y_train) > 0:
            self.augment_x_train.pop()
            self.augment_y_train.pop()
            return {
                "message": "Undid training sample",
                "success": True
            }
        else:
            return {
                "message": "Nothing to undo",
                "success": False
            }

    def add_sample_point(self, row, col, class_idx):
        if self._last_tile is not None:
            self.augment_x_train.append(self._last_tile[row, col, :].copy())
            self.augment_y_train.append(class_idx)
            return {
                "message": "Training sample for class %d added" % (class_idx),
                "success": True
            }
        else:
            return {
                "message": "Must run model before adding a training sample",
                "success": False
            }
        
    def reset(self):
        self._init_model()
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web_tool/ModelSessionPyTorchSegmentationModel.py [128:167]:
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            return {
                "message": "Fine-tuning accuracy on data: %0.2f" % (score),
                "success": True
            }
        except Exception as e:
            return {
                "message": "Error in 'retrain()': %s" % (e),
                "success": False
            }
    
    def undo(self):
        if len(self.augment_y_train) > 0:
            self.augment_x_train.pop()
            self.augment_y_train.pop()
            return {
                "message": "Undid training sample",
                "success": True
            }
        else:
            return {
                "message": "Nothing to undo",
                "success": False
            }

    def add_sample_point(self, row, col, class_idx):
        if self._last_tile is not None:
            self.augment_x_train.append(self._last_tile[row, col, :].copy())
            self.augment_y_train.append(class_idx)
            return {
                "message": "Training sample for class %d added" % (class_idx),
                "success": True
            }
        else:
            return {
                "message": "Must run model before adding a training sample",
                "success": False
            }
        
    def reset(self):
        self._init_model()
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