import torch
import torch.nn as nn
import torch.nn.functional as F
import modnet_torchscript
if name == 'main':
define cmd arguments
parser = argparse.ArgumentParser()
parser.add_argument('--ckpt-path', type=str, required=False, help='D:/MODNet-master/pretrained')
parser.add_argument('--output-path', type=str, required=False, help='D:/MODNet-master/output-path')
args = parser.parse_args()
print(type("ckpt_path"))
# check input arguments
if not os.path.exists(args.ckpt_path):
print(args.ckpt_path)
print('Cannot find checkpoint path: {0}'.format(args.ckpt_path))
exit()
# create MODNet and load the pre-trained ckpt
modnet = modnet_torchscript.MODNet(backbone_pretrained=False)
modnet = nn.DataParallel(modnet).cuda()
state_dict = torch.load(args.ckpt_path)
modnet.load_state_dict(state_dict)
modnet.eval()
# export to TorchScript model
scripted_model = torch.jit.script(modnet.module)
torch.jit.save(scripted_model, os.path.join(args.output_path))
Traceback (most recent call last):
File "D:/MODNet-master/torchscript/export_torchscript.py", line 32, in
if not os.path.exists(args.ckpt_path):
File "D:\Anaconda3\envs\pytorch\lib\genericpath.py", line 19, in exists
os.stat(path)
TypeError: stat: path should be string, bytes, os.PathLike or integer, not NoneType
import os import argparse
import torch import torch.nn as nn import torch.nn.functional as F import modnet_torchscript
if name == 'main':
define cmd arguments
Traceback (most recent call last): File "D:/MODNet-master/torchscript/export_torchscript.py", line 32, in
if not os.path.exists(args.ckpt_path):
File "D:\Anaconda3\envs\pytorch\lib\genericpath.py", line 19, in exists
os.stat(path)
TypeError: stat: path should be string, bytes, os.PathLike or integer, not NoneType