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"))
