Initial clean project import

python/MODNet-master/torchscript/export_torchscript.py

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+"""
+Export TorchScript model of MODNet
+
+Arguments:
+    --ckpt-path: path of the checkpoint that will be converted
+    --output-path: path for saving the TorchScript model
+
+Example:
+    python export_torchscript.py \
+        --ckpt-path=modnet_photographic_portrait_matting.ckpt \
+        --output-path=modnet_photographic_portrait_matting.torchscript
+"""
+
+import os
+import argparse
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from . import modnet_torchscript
+
+
+if __name__ == '__main__':
+    # define cmd arguments
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--ckpt-path', type=str, required=True, help='path of the checkpoint that will be converted')
+    parser.add_argument('--output-path', type=str, required=True, help='path for saving the TorchScript model')
+    args = parser.parse_args()
+
+    # 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))