def exec_vgg16(exec_name, pruning_params=None, exec_params=None, dataset_params=None, debug_params=None):
print("*** ", exec_name)
if exec_params.best_result_save_path is not None and os.path.isfile(exec_params.best_result_save_path):
model = torch.load(exec_params.best_result_save_path)
else:
model = models.vgg16(pretrained=True)
model.cuda()
history, test_score = common_training_code(model,
pruned_save_path="../saved/{}/Pruned.pth".format(exec_name),
pruned_best_result_save_path="../saved/{}/pruned_best.pth".format(exec_name),
sample_run=torch.zeros([1, 3, 224, 224]),
pruning_params=pruning_params,
exec_params=exec_params,
dataset_params=dataset_params,
debug_params=debug_params)
return history, test_score
def exec_resnet50(exec_name, pruning_params=None, exec_params=None, dataset_params=None, out_count=1000, debug_params=None):
print("*** ", exec_name)
if exec_params.best_result_save_path is not None and os.path.isfile(exec_params.best_result_save_path):
model = torch.load(exec_params.best_result_save_path)
else:
model = models.resnet50(pretrained=True)
num_ftrs = model.fc.in_features
model.fc = nn.Linear(num_ftrs, 10)
model.cuda()
history, test_score = common_training_code(model,
pruned_save_path="../saved/{}/Pruned.pth".format(exec_name),
pruned_best_result_save_path="../saved/{}/pruned_best.pth".format(exec_name),
sample_run=torch.zeros([1, 3, 224, 224]),
pruning_params=pruning_params,
exec_params=exec_params,
dataset_params=dataset_params,
debug_params=debug_params)
return history, test_score
def exec_dense_net(exec_name, pruning_params=None, exec_params=None, dataset_params=None, debug_params=None):
print("*** ", exec_name)
if exec_params.best_result_save_path is not None and os.path.isfile(exec_params.best_result_save_path):
model = torch.load(exec_params.best_result_save_path)
else:
model = models.densenet121(pretrained=True)
model.cuda()
if exec_params is not None:
exec_params.force_forward_view = True
exec_params.ignore_last_conv = True
history, test_score = common_training_code(model,
pruned_save_path="../saved/{}/Pruned.pth".format(exec_name),
pruned_best_result_save_path="../saved/{}/pruned_best.pth".format(exec_name),
sample_run=torch.zeros([1, 3, 224, 224]),
pruning_params=pruning_params,
exec_params=exec_params,
dataset_params=dataset_params,
debug_params=debug_params)
return history, test_score