Initial clean project import

python/MODNet-master/onnx/export_onnx.py

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+"""
+Export ONNX model of MODNet with:
+    input shape: (batch_size, 3, height, width)
+    output shape: (batch_size, 1, height, width)  
+
+Arguments:
+    --ckpt-path: path of the checkpoint that will be converted
+    --output-path: path for saving the ONNX model
+
+Example:
+    python export_onnx.py \
+        --ckpt-path=modnet_photographic_portrait_matting.ckpt \
+        --output-path=modnet_photographic_portrait_matting.onnx
+"""
+
+import os
+import argparse
+
+import torch
+import torch.nn as nn
+from torch.autograd import Variable
+
+from . import modnet_onnx
+
+
+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 ONNX model')
+    args = parser.parse_args()
+
+    # check input arguments
+    if not os.path.exists(args.ckpt_path):
+        print('Cannot find checkpoint path: {0}'.format(args.ckpt_path))
+        exit()
+
+    # define model & load checkpoint
+    modnet = modnet_onnx.MODNet(backbone_pretrained=False)
+    modnet = nn.DataParallel(modnet).cuda()
+    state_dict = torch.load(args.ckpt_path)
+    modnet.load_state_dict(state_dict)
+    modnet.eval()
+
+    # prepare dummy_input
+    batch_size = 1
+    height = 512
+    width = 512
+    dummy_input = Variable(torch.randn(batch_size, 3, height, width)).cuda()
+
+    # export to onnx model
+    torch.onnx.export(
+        modnet.module, dummy_input, args.output_path, export_params = True, 
+        input_names = ['input'], output_names = ['output'], 
+        dynamic_axes = {'input': {0:'batch_size', 2:'height', 3:'width'}, 'output': {0: 'batch_size', 2: 'height', 3: 'width'}})