| import argparse |
| import os |
| import torch |
| import numpy as np |
| from concern.config import Configurable, Config |
|
|
|
|
| def main(): |
| parser = argparse.ArgumentParser(description='Convert model to ONNX') |
| parser.add_argument('exp', type=str) |
| parser.add_argument('resume', type=str, help='Resume from checkpoint') |
| parser.add_argument('output', type=str, help='Output ONNX path') |
|
|
| args = parser.parse_args() |
| args = vars(args) |
| args = {k: v for k, v in args.items() if v is not None} |
|
|
| conf = Config() |
| experiment_args = conf.compile(conf.load(args['exp']))['Experiment'] |
| experiment_args.update(cmd=args) |
| experiment = Configurable.construct_class_from_config(experiment_args) |
|
|
| Demo(experiment, experiment_args, cmd=args).inference() |
|
|
|
|
| class Demo: |
| def __init__(self, experiment, args, cmd=dict()): |
| self.RGB_MEAN = np.array([122.67891434, 116.66876762, 104.00698793]) |
| self.experiment = experiment |
| experiment.load('evaluation', **args) |
| self.args = cmd |
| self.structure = experiment.structure |
| self.model_path = self.args['resume'] |
| self.output_path = self.args['output'] |
|
|
| def init_torch_tensor(self): |
| |
| if torch.cuda.is_available(): |
| self.device = torch.device('cuda') |
| torch.set_default_tensor_type('torch.cuda.FloatTensor') |
| else: |
| self.device = torch.device('cpu') |
| torch.set_default_tensor_type('torch.FloatTensor') |
|
|
| def init_model(self): |
| model = self.structure.builder.build(self.device) |
| return model |
|
|
| def resume(self, model, path): |
| if not os.path.exists(path): |
| print("Checkpoint not found: " + path) |
| return |
| states = torch.load(path, map_location=self.device) |
| model.load_state_dict(states, strict=False) |
| print("Resumed from " + path) |
|
|
| def inference(self): |
| self.init_torch_tensor() |
| model = self.init_model() |
| self.resume(model, self.model_path) |
| model.eval() |
|
|
| img = np.random.randint(0, 255, size=(960, 960, 3), dtype=np.uint8) |
| img = img.astype(np.float32) |
| img = (img / 255. - 0.5) / 0.5 |
| img = img.transpose((2, 0, 1)) |
| img = torch.from_numpy(img).unsqueeze(0).float() |
| dynamic_axes = {'input': {0: 'batch_size', 2: 'height', 3: 'width'}, |
| 'output': {0: 'batch_size', 2: 'height', 3: 'width'}} |
| with torch.no_grad(): |
| img = img.to(self.device) |
| torch.onnx.export(model.model.module, img, self.output_path, input_names=['input'], |
| output_names=['output'], dynamic_axes=dynamic_axes, keep_initializers_as_inputs=False, |
| verbose=False, opset_version=12) |
|
|
|
|
| if __name__ == '__main__': |
| main() |
|
|
