from torch.autograd import Variable, grad
import GAN
import utils
import visdom
import numpy as np
import sys
import os
import pickle
torch.manual_seed(1)
#============ PARSE ARGUMENTS =============
args = utils.setup_args()
args.save_name = args.save_file + args.env
print(args)
#============ GRADIENT PENALTY (for discriminator) ================
def calc_gradient_penalty(netD, real_data, fake_data):
alpha = torch.rand(real_data.size(0), 1)
alpha = alpha.expand(real_data.size())
if torch.cuda.is_available():
alpha = alpha.cuda()
interpolates = alpha * real_data + ((1 - alpha) * fake_data)
}
if test_summary['test_acc'] > best_accuracy:
best_accuracy = test_summary['test_acc']
current_state['best_acc'] = best_accuracy
save_checkpoint(current_state=current_state,
filename=os.path.join(args.save_dir,
"models/best.pth"))
logger.info("Best accuracy: %s" % best_accuracy)
if epoch % args.save_freq == 0:
save_checkpoint(current_state=current_state,
filename=os.path.join(
args.save_dir, "models/epoch_%s.pth" % epoch))
save_checkpoint(current_state=current_state,
filename=os.path.join(args.save_dir,
"models/last.pth"))
if args.use_gpu:
net.cuda()
if __name__ == "__main__":
args = setup_args()
os.makedirs(os.path.join(args.save_dir, "models"), exist_ok=True)
logger = setup_logger(name="training_log", save_dir=args.save_dir)
logger.info(args)
run_training(args, logger)
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.
"""
import utils
import json
assets = utils.setup_args('Utility script to extract namespaces from MC: PE/W10/EDU JSON files.')
namespaces = list()
print('\nAnalyzing \'_ui_defs.json\'...\n')
ui_defs = json.loads(utils.get_clean_json(assets + '/ui/_ui_defs.json'))
for ui_file in ui_defs['ui_defs']:
print('Analyzing \'{}\'...'.format(ui_file))
ui_json = json.loads(utils.get_clean_json(assets + '/' + ui_file))
namespaces.append(ui_json['namespace'])
print('\nRemoving duplicates...')
for ns in namespaces:
i = namespaces.count(ns)
while i > 1: