Spaces:
Running
on
A10G
Running
on
A10G
| import numpy as np | |
| import onnx | |
| import torch | |
| def convert_onnx(net, path_module, output, opset=11, simplify=False): | |
| assert isinstance(net, torch.nn.Module) | |
| img = np.random.randint(0, 255, size=(112, 112, 3), dtype=np.int32) | |
| img = img.astype(np.float) | |
| img = (img / 255. - 0.5) / 0.5 # torch style norm | |
| img = img.transpose((2, 0, 1)) | |
| img = torch.from_numpy(img).unsqueeze(0).float() | |
| weight = torch.load(path_module) | |
| net.load_state_dict(weight) | |
| net.eval() | |
| torch.onnx.export(net, img, output, keep_initializers_as_inputs=False, verbose=False, opset_version=opset) | |
| model = onnx.load(output) | |
| graph = model.graph | |
| graph.input[0].type.tensor_type.shape.dim[0].dim_param = 'None' | |
| if simplify: | |
| from onnxsim import simplify | |
| model, check = simplify(model) | |
| assert check, "Simplified ONNX model could not be validated" | |
| onnx.save(model, output) | |
| if __name__ == '__main__': | |
| import os | |
| import argparse | |
| from backbones import get_model | |
| parser = argparse.ArgumentParser(description='ArcFace PyTorch to onnx') | |
| parser.add_argument('input', type=str, help='input backbone.pth file or path') | |
| parser.add_argument('--output', type=str, default=None, help='output onnx path') | |
| parser.add_argument('--network', type=str, default=None, help='backbone network') | |
| parser.add_argument('--simplify', type=bool, default=False, help='onnx simplify') | |
| args = parser.parse_args() | |
| input_file = args.input | |
| if os.path.isdir(input_file): | |
| input_file = os.path.join(input_file, "backbone.pth") | |
| assert os.path.exists(input_file) | |
| model_name = os.path.basename(os.path.dirname(input_file)).lower() | |
| params = model_name.split("_") | |
| if len(params) >= 3 and params[1] in ('arcface', 'cosface'): | |
| if args.network is None: | |
| args.network = params[2] | |
| assert args.network is not None | |
| print(args) | |
| backbone_onnx = get_model(args.network, dropout=0) | |
| output_path = args.output | |
| if output_path is None: | |
| output_path = os.path.join(os.path.dirname(__file__), 'onnx') | |
| if not os.path.exists(output_path): | |
| os.makedirs(output_path) | |
| assert os.path.isdir(output_path) | |
| output_file = os.path.join(output_path, "%s.onnx" % model_name) | |
| convert_onnx(backbone_onnx, input_file, output_file, simplify=args.simplify) | |