def main():
with open(config_filename) as handle:
model_config = yaml.load(handle, Loader=yaml.FullLoader)
data_name = []
data_name.append(
os.path.join(os.path.abspath('..'), model_config['category']))
data_name.append(
os.path.join(os.path.abspath('..'), model_config['category_valid']))
data_name.append(
os.path.join(os.path.abspath('..'), model_config['category_test']))
if model_config['mask_name'] == 'Original':
mask_name = None
else:
mask_name = os.path.join(os.path.abspath('..'),
model_config['category_mask'],
model_config['mask_name'])
dataset_name = (data_name, mask_name)
tf_config = tf.ConfigProto()
# os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2" # ignore waring
# os.environ["CUDA_VISIBLE_DEVICES"] = "0"
os.environ["CUDA_VISIBLE_DEVICES"] = model_config['GPU']
# tf.device("GPU:1")
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
with tf.Session(config=tf_config) as sess:
Cube_Decoder = Decoder_Handler(dataset_name=dataset_name,
model_config=model_config,
sess=sess,
is_training=True)
Cube_Decoder.train()
def main():
ave_folder, ave_config = 'TSA-Model', 'config_188.yaml'
folder_id, config_id = ave_folder, ave_config
with open(config_filename) as handle:
model_config = yaml.load(handle, Loader=yaml.FullLoader)
data_dir = []
data_dir.append(model_config['category_train'])
data_dir.append(model_config['category_valid'])
data_dir.append(model_config['category_test'])
mask_dir = model_config['category_mask']
log_dir = os.path.join(os.path.abspath('.'), model_config['result_dir'],
model_config['result_model'], folder_id)
with open(os.path.join(log_dir, config_id)) as handle:
model_config = yaml.load(handle, Loader=yaml.FullLoader)
dataset_dir = (data_dir, mask_dir)
tf_config = tf.ConfigProto()
os.environ["CUDA_VISIBLE_DEVICES"] = "1"
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
with tf.Session(config=tf_config) as sess:
Cube_Decoder = Decoder_Handler(dataset_dir=dataset_dir,
model_config=model_config,
sess=sess,
is_training=False)
Cube_Decoder.test()
def main():
folder_id, config_id = 'NBA-Decoder-TNN', 'config-nba.yaml'
with open(config_filename) as handle:
model_config = yaml.load(handle)
log_dir = os.path.join(os.path.abspath('.'), model_config['result_dir'],
model_config['result_model'], folder_id)
with open(os.path.join(log_dir, config_id)) as handle:
model_config = yaml.load(handle)
data_name = os.path.join(os.path.abspath('.'), 'Data',
model_config['category'],
model_config['data_name'])
if model_config['mask_name'] == 'Original':
mask_name = None
else:
mask_name = os.path.join(os.path.abspath('.'), 'Data',
model_config['category'],
model_config['mask_name'])
dataset_name = (data_name, mask_name)
tf_config = tf.ConfigProto()
os.environ[
"CUDA_VISIBLE_DEVICES"] = "2" # Please change the id of GPU in your local server accordingly
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
with tf.Session(config=tf_config) as sess:
Cube_Decoder = Decoder_Handler(dataset_name=dataset_name,
model_config=model_config,
sess=sess,
is_training=False)
Cube_Decoder.test()
def main():
with open(config_filename) as handle:
model_config = yaml.load(handle, Loader=yaml.FullLoader)
data_name = []
data_name.append(
os.path.join(os.path.abspath('.'), model_config['category'],
model_config['data_name']))
data_name.append(
os.path.join(os.path.abspath('.'), model_config['category_valid'],
model_config['data_name']))
data_name.append(
os.path.join(os.path.abspath('.'), model_config['category_test'],
model_config['data_name']))
mask_name = os.path.join(os.path.abspath('.'), model_config['category'],
model_config['mask_name'])
dataset_name = (data_name, mask_name)
tf_config = tf.ConfigProto()
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
# tf.device("GPU:1")
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
with tf.Session(config=tf_config) as sess:
Cube_Decoder = Decoder_Handler(dataset_name=dataset_name,
model_config=model_config,
sess=sess,
is_training=True)
Cube_Decoder.train()
def main():
### pre-trainded model
# pre_model_name [modify]
pre_model_name = 'binary_mask_256_8f_original_model/models-0.0744-256404'
pre_model_dir = 'Result/Model-Config'
model_filename = os.path.join(os.path.abspath('.'), pre_model_dir,
pre_model_name)
model_config = {
'model_filename': model_filename,
'result_data': 'Validation-Result',
'result_dir': 'Result',
'compressive_ratio': 8, # [modify]
'batch_size': 1
} # [modify]
### test set
# test_data_dir [modify]
test_data_dir = os.path.join(os.path.abspath('..'),
'data_simu/testing_truth/')
# mask_name [modify]
# mask_name = 'mask/binary_mask_256_8f_original_model'
# mask_name = 'mask/combine_binary_mask_256_10f'
mask_name = 'mask/mask_256'
## test_data
data_name = []
data_name.append('') # placeholder
data_name.append('')
data_name.append(test_data_dir)
## mask
# used to initialize the network input
mask_name = os.path.join(os.path.abspath('..'), 'data_simu', mask_name)
## test set
dataset_name = (data_name, mask_name)
### inference
## tf config
tf_config = tf.ConfigProto()
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
## run
with tf.Session(config=tf_config) as sess:
# Cube_Decoder = Decoder_Handler_meas(dataset_name=dataset_name, model_config=model_config, sess = sess, is_training=False,Cr=Cr)
Cube_Decoder = Decoder_Handler(dataset_name=dataset_name,
model_config=model_config,
sess=sess,
is_training=False,
is_testing_meas=False)
Cube_Decoder.test()
def main():
Cr = 10
test_data_name = 'waterBalloon'
if Cr == 10:
ave_folder, ave_config = 'Cr10_model', 'config_Cr10_model.yaml'
elif Cr == 20:
ave_folder, ave_config = 'Cr20_model', 'config_Cr20_model.yaml'
else:
ave_folder, ave_config = 'Cr30_model', 'config_Cr30_model.yaml'
folder_id, config_id = ave_folder, ave_config
with open(config_filename) as handle:
model_config = yaml.load(handle, Loader=yaml.FullLoader)
data_name = []
data_name.append(
os.path.join(os.path.abspath('.'), model_config['category'],
model_config['data_name']))
data_name.append(
os.path.join(os.path.abspath('.'), model_config['category_valid'],
model_config['data_name']))
data_name.append(
os.path.join(os.path.abspath('..'), 'dataset',
'meas_' + test_data_name))
log_dir = os.path.join(os.path.abspath('.'), model_config['result_dir'],
model_config['result_model'], folder_id)
with open(os.path.join(log_dir, config_id)) as handle:
model_config = yaml.load(handle, Loader=yaml.FullLoader)
mask_name = os.path.join(os.path.abspath('..'), 'dataset', 'mask')
dataset_name = (data_name, mask_name)
tf_config = tf.ConfigProto()
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
with tf.Session(config=tf_config) as sess:
Cube_Decoder = Decoder_Handler(dataset_name=dataset_name,
model_config=model_config,
sess=sess,
is_training=False,
Cr=Cr)
Cube_Decoder.test(test_data_name)
def main():
with open(config_filename) as handle:
model_config = yaml.load(handle, Loader=yaml.FullLoader)
data_dir = []
data_dir.append(model_config['category_train'])
data_dir.append(model_config['category_valid'])
data_dir.append(model_config['category_test'])
mask_dir = model_config['category_mask']
dataset_dir = (data_dir, mask_dir)
tf_config = tf.ConfigProto()
os.environ["CUDA_VISIBLE_DEVICES"] = "1"
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
with tf.Session(config=tf_config) as sess:
Cube_Decoder = Decoder_Handler(dataset_dir=dataset_dir,
model_config=model_config,
sess=sess,
is_training=True)
Cube_Decoder.train()
def main():
ave_folder, ave_config = 'Decoder-T0427184230-D0.10L0.010-RMSE/', 'config_45.yaml'
# ave_folder,ave_config = 'Decoder-T0510154141-D0.10L0.010-RMSE/','config_311.yaml'
folder_id, config_id = ave_folder, ave_config
with open(config_filename) as handle:
model_config = yaml.load(handle, Loader=yaml.FullLoader)
data_name = []
data_name.append(
os.path.join(os.path.abspath('..'), model_config['category']))
data_name.append(
os.path.join(os.path.abspath('..'), model_config['category_valid']))
data_name.append(
os.path.join(os.path.abspath('..'), model_config['category_test']))
log_dir = os.path.join(os.path.abspath('.'), model_config['result_dir'],
model_config['result_model'], folder_id)
with open(os.path.join(log_dir, config_id)) as handle:
model_config = yaml.load(handle, Loader=yaml.FullLoader)
if model_config['mask_name'] == 'Original':
mask_name = None
else:
mask_name = os.path.join(os.path.abspath('..'),
model_config['category_mask'],
model_config['mask_name'])
dataset_name = (data_name, mask_name)
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
with tf.Session(config=tf_config) as sess:
Cube_Decoder = Decoder_Handler(dataset_name=dataset_name,
model_config=model_config,
sess=sess,
is_training=False)
Cube_Decoder.test()