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Configuration error
| # | |
| # Copyright (c) 2022 Intel Corporation | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| # | |
| # SPDX-License-Identifier: Apache-2.0 | |
| # | |
| import yaml | |
| import argparse | |
| import os | |
| import sys | |
| from os import path | |
| root_folder = path.dirname(path.abspath(__file__)) | |
| sys.path.insert(0, path.join(root_folder, "../../../")) | |
| print(sys.path) | |
| from vision_wl import train_vision_wl, run_inference, collect_class_labels, load_model, run_inference_per_patient | |
| def main(): | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("--config_file", type=str, required=True) | |
| args = parser.parse_args() | |
| with open(args.config_file, "r") as f: | |
| config = yaml.safe_load(f) | |
| dataset_dir = config['args']['dataset_dir'] | |
| train_dataset_dir = os.path.join(dataset_dir, "train") | |
| test_dataset_dir = os.path.join(dataset_dir, "test") | |
| # output_dir = config['args']['output_dir'] | |
| output_dir = config['training_args']['output_dir'] | |
| batch_size = config['training_args']['batch_size'] | |
| epochs = config['training_args']['epochs'] | |
| bf16 = config['training_args']['bf16'] | |
| model_name = config['args']['model'] | |
| # do_predict = config['training_args']['do_predict'] | |
| do_predict = config['args']['inference'] | |
| do_predict_per_patient = config['args']['inference_per_patient'] | |
| # do_train = config['training_args']['do_train'] | |
| do_train = config['args']['finetune'] | |
| saved_model_dir = config['args']['saved_model_dir'] | |
| # output_file_test = config['args']['output_file_test_dir'] | |
| output_file_test = config['args']['inference_output'] | |
| # output_file_train = config['args']['output_file_train_dir'] | |
| output_file_train = config['args']['finetune_output'] | |
| # this is one is used for place holder | |
| vision_int8_inference = 'vision_int_8.yaml' # config['inference_args']['int8_inference'] | |
| class_labels = collect_class_labels(train_dataset_dir) | |
| if (do_train): | |
| model, history, dict_metrics = train_vision_wl(train_dataset_dir, | |
| output_dir, model_name, | |
| batch_size, epochs, bf16=bf16) | |
| run_inference(train_dataset_dir, saved_model_dir, class_labels, | |
| model_name, vision_int8_inference, output_file_train) | |
| if (do_predict): | |
| run_inference(test_dataset_dir, saved_model_dir, class_labels, | |
| model_name, vision_int8_inference, output_file_test) | |
| if (do_predict_per_patient): | |
| model = load_model(model_name,saved_model_dir) | |
| # Sample dict | |
| patient_dict = {'106L':[os.path.join(train_dataset_dir,"Malignant/P106_L_CM_MLO1.jpg")],\ | |
| '106R':[os.path.join(train_dataset_dir,"Benign/P106_R_CM_CC1.jpg"),\ | |
| os.path.join(train_dataset_dir,"Benign/P106_R_CM_CC2.jpg")]} | |
| results = run_inference_per_patient(model, patient_dict,class_labels) | |
| if __name__ == "__main__": | |
| main() | |