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