def download_base_model(): print("\nDownloading AlexNet base model...") base_folder = os.path.dirname(os.path.abspath(__file__)) sys.path.append( os.path.join(base_folder, "..", "..", "..", "PretrainedModels")) from download_model import download_model_by_name download_model_by_name("AlexNet_ImageNet_Caffe")
def prepare_model(cfg): from download_model import download_model_by_name print('[INFO] ==================================') print('[INFO] STAGE 1: Download pretrained model') if not os.path.exists(cfg.PRETRAINED_MODEL): os.mkdir(cfg.PRETRAINED_MODEL) download_model_by_name("VGG16_ImageNet_Caffe", cfg.PRETRAINED_MODEL)
cleanup_trained_data = True def download_model(model_file_name, model_url): model_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), "PretrainedModels") filename = os.path.join(model_dir, model_file_name) if not os.path.exists(filename): print('Downloading model from ' + model_url + ', may take a while...') urlretrieve(model_url, filename) print('Saved model as ' + filename) else: print('CNTK model already available at ' + filename) print(" !!! Generating mappings !!! STARTED") create_mappings() print(" !!! Generating mappings !!! FINISHED") print(" !!! DOwnloading model !!! STARTED") download_model_by_name("AlexNet_ImageNet_Caffe") print(" !!! DOwnloading model !!! FINISHED") # download_model_by_name("AlexNet") if cleanup_trained_data: print(" !!! Cleaning Output !!! STARTED") fileList = [ f for f in os.listdir("Output") if f.endswith(".model") ] for fileName in fileList: modelFileName = os.path.join("Output", fileName) print("Deleting already trained " + modelFileName) os.remove(modelFileName) print(" !!! Cleaning Output !!! FINISHED")
# Copyright (c) Microsoft. All rights reserved. # Licensed under the MIT license. See LICENSE.md file in the project root # for full license information. # ============================================================================== from __future__ import print_function import zipfile import os, sys import os.path base_folder = os.path.dirname(os.path.abspath(__file__)) sys.path.append(os.path.join(base_folder, "..", "DataSets", "Grocery")) from install_grocery import download_grocery_data download_grocery_data() sys.path.append(os.path.join(base_folder, "..", "..", "..", "PretrainedModels")) from download_model import download_model_by_name download_model_by_name("ResNet18_ImageNet_CNTK")
# Licensed under the MIT license. See LICENSE.md file in the project root # for full license information. # ============================================================================== from __future__ import print_function import zipfile import os, sys def create_grocery_mappings(grocery_folder): sys.path.append(os.path.join(grocery_folder, "..", "..", "Detection", "utils", "annotations")) from annotations_helper import create_class_dict, create_map_files abs_path = os.path.dirname(os.path.abspath(__file__)) data_set_path = os.path.join(abs_path, "..", "..", "DataSets", "Grocery") class_dict = create_class_dict(data_set_path) create_map_files(data_set_path, class_dict, training_set=True) create_map_files(data_set_path, class_dict, training_set=False) if __name__ == '__main__': base_folder = os.path.dirname(os.path.abspath(__file__)) sys.path.append(os.path.join(base_folder, "..", "..", "DataSets", "Grocery")) from install_grocery import download_grocery_data download_grocery_data() sys.path.append(os.path.join(base_folder, "..", "..", "..", "..", "PretrainedModels")) from download_model import download_model_by_name download_model_by_name("AlexNet_ImageNet_Caffe") print("Creating mapping files for Grocery data set..") create_grocery_mappings(base_folder)
# Copyright (c) Microsoft. All rights reserved. # Licensed under the MIT license. See LICENSE.md file in the project root # for full license information. # ============================================================================== from __future__ import print_function import zipfile import os, sys base_folder = os.path.dirname(os.path.abspath(__file__)) sys.path.append(os.path.join(base_folder, "..", "..", "DataSets", "Grocery")) from install_grocery import download_grocery_data download_grocery_data() sys.path.append( os.path.join(base_folder, "..", "..", "..", "..", "PretrainedModels")) from download_model import download_model_by_name download_model_by_name("AlexNet_ImageNet_CNTK")
"annotations")) from annotations_helper import create_class_dict, create_map_files abs_path = os.path.dirname(os.path.abspath(__file__)) data_set_path = os.path.join(abs_path, "..", "..", "DataSets", "Grocery") class_dict = create_class_dict(data_set_path) create_map_files(data_set_path, class_dict, training_set=True) create_map_files(data_set_path, class_dict, training_set=False) if __name__ == '__main__': base_folder = os.path.dirname(os.path.abspath(__file__)) sys.path.append(os.path.join(base_folder, "..", "..", "DataSets", "Pascal")) from install_pascalvoc import download_pascal_data download_pascal_data() sys.path.append( os.path.join(base_folder, "..", "..", "..", "..", "PretrainedModels")) from download_model import download_model_by_name download_model_by_name("VGG16_ImageNet_Caffe") sys.path.append( os.path.join(base_folder, "..", "..", "DataSets", "Pascal", "mappings")) from mapping import create_pascal_mappings print("Creating mapping files for Pascal data set..") create_pascal_mappings( os.path.join(base_folder, "..", "..", "DataSets", "Pascal", "mappings"))