# TensorBoard related parameters.
max_images=8, # Maximum number of images to save.
shrink_scale=1, # Scale to shrink output image size.
# Channel Attention.
use_ca=False,
reduction=8,
use_gap=False,
use_gmp=False,
# Learning rate scheduling.
lr_red_epochs=[20, 25],
lr_red_rate=0.1,
# Variables that change frequently.
use_slice_metrics=True,
num_epochs=30,
gpu=0, # Set to None for CPU mode.
num_workers=4,
init_lr=2E-4,
max_to_keep=1,
prev_model_ckpt='',
sample_rate_train=1,
start_slice_train=0,
sample_rate_val=1,
start_slice_val=0,
)
arguments = create_arg_parser(**settings).parse_args()
train_cmg_to_img(arguments)
# TensorBoard related parameters.
max_images=8, # Maximum number of images to save.
shrink_scale=1, # Scale to shrink output image size.
# # Channel Attention.
# use_ca=True,
# reduction=8,
# use_gap=True,
# use_gmp=False,
# Learning rate scheduling.
lr_red_epochs=[20, 40],
lr_red_rate=0.25,
# Variables that change frequently.
use_slice_metrics=True,
num_epochs=50,
gpu=0, # Set to None for CPU mode.
num_workers=2,
init_lr=1E-4,
max_to_keep=1,
# prev_model_ckpt='',
sample_rate_train=0.1,
start_slice_train=0,
sample_rate_val=1,
start_slice_val=0,
)
options = create_arg_parser(**settings).parse_args()
train_xnet(options)
dilation_value=2,
pool_stride=1,
use_ca=True,
use_sa=True)
train_model(my_model34, args=args)
train_model(my_model50, args=args)
if __name__ == '__main__':
defaults = dict(
batch_size=12,
num_workers=1,
init_lr=0.001,
gamma=0.1, # Factor by which to reduce lr.
step_size=20,
gpu=0, # Set to None for CPU mode.
num_epochs=30,
verbose=False,
save_best_only=True,
max_to_keep=1,
data_root='/home/veritas/PycharmProjects/PA1/data',
ckpt_root='/home/veritas/PycharmProjects/PA1/checkpoints',
log_root='/home/veritas/PycharmProjects/PA1/logs')
parser = create_arg_parser(**defaults).parse_args()
# models34(parser)
# models50(parser)
my_model(parser)