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main_lth_save_activations.py
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main_lth_save_activations.py
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import argparse
import os
import sys
import yaml
import utils
from lth_pruning import lth_save_activations
sys.path.append(os.path.abspath("/ocean/projects/asc170022p/shg121/PhD/Project_Pruning"))
def run_mnist(args):
print("Save act of BB using pruning by LTH")
# parse arguments
main_dir = args.main_dir
# run config
with open(os.path.join(main_dir, args.config)) as config_file:
config = yaml.safe_load(config_file)
_bb_layers = config["bb_layers_for_concepts"]
_img_size = config["img_size"]
_seed = config["seed"]
_dataset_name = config["dataset_name"]
_data_root = config["data_root"]
_json_root = config["json_root"]
_model_arch = config["model_arch"]
_pretrained = config["pretrained"]
_transfer_learning = config["transfer_learning"]
_lr = config["lr"]
_logs = config["logs"]
_num_classes = config["num_classes"]
_epochs = config["epochs"]
_device = utils.get_device()
_prune_type = config["prune_type"]
_prune_iterations = config["prune_iterations"]
_prune_percent = config["prune_percent"]
_start_iter = config["start_iter"]
_end_iter = config["end_iter"]
_resample = config["resample"]
_epsilon = config["epsilon"]
_attribute_file_name = config["attribute_file_name"]
_batch_size = config["batch_size"]
lth_save_activations.save_activations_with_Pruning(
_seed,
_num_classes,
_pretrained,
_transfer_learning,
_prune_type,
_dataset_name,
_data_root,
_json_root,
_batch_size,
_img_size,
_start_iter,
_prune_iterations,
_logs,
_model_arch,
_bb_layers,
_attribute_file_name,
_device
)
def run_cubs(args):
print("Save act CUBS-200, of BB using pruning by LTH")
# parse arguments
main_dir = args.main_dir
# run config
with open(os.path.join(main_dir, args.config)) as config_file:
config = yaml.safe_load(config_file)
_bb_layers = config["bb_layers_for_concepts"]
_img_size = config["img_size"]
_seed = config["seed"]
_dataset_name = config["dataset_name"]
_data_root = config["data_root"]
_json_root = config["json_root"]
_model_arch = config["model_arch"]
_pretrained = config["pretrained"]
_transfer_learning = config["transfer_learning"]
_logs = config["logs"]
_num_classes = config["num_classes"]
_device = utils.get_device()
_prune_type = config["prune_type"]
_prune_iterations = config["prune_iterations"]
_start_iter = config["start_iter"]
_attribute_file_name = config["attribute_file_name"]
_batch_size = config["batch_size"]
lth_save_activations.save_activations_with_Pruning(
_seed,
_num_classes,
_pretrained,
_transfer_learning,
_prune_type,
_dataset_name,
_data_root,
_json_root,
_batch_size,
_img_size,
_start_iter,
_prune_iterations,
_logs,
_model_arch,
_bb_layers,
_attribute_file_name,
_device
)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("--dataset", "-c", default="mnist")
parser.add_argument(
"--main_dir", "-m", default="/ocean/projects/asc170022p/shg121/PhD/Project_Pruning/")
args = parser.parse_args()
if args.dataset == "cub":
args.config = "config/BB_cub.yaml"
run_cubs(args)
elif args.dataset == "mnist":
args.config = "config/BB_mnist.yaml"
run_mnist(args)