Spaces:
Configuration error
Configuration error
File size: 3,483 Bytes
a01ef8c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 |
#
# 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()
|