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from argparse import ArgumentParser |
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from io import BytesIO |
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import matplotlib.pyplot as plt |
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import mmcv |
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import requests |
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from mmseg.apis import inference_model, init_model |
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def parse_args(): |
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parser = ArgumentParser( |
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description='Compare result of torchserve and pytorch,' |
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'and visualize them.') |
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parser.add_argument('img', help='Image file') |
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parser.add_argument('config', help='Config file') |
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parser.add_argument('checkpoint', help='Checkpoint file') |
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parser.add_argument('model_name', help='The model name in the server') |
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parser.add_argument( |
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'--inference-addr', |
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default='127.0.0.1:8080', |
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help='Address and port of the inference server') |
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parser.add_argument( |
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'--result-image', |
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type=str, |
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default=None, |
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help='save server output in result-image') |
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parser.add_argument( |
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'--device', default='cuda:0', help='Device used for inference') |
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args = parser.parse_args() |
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return args |
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def main(args): |
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url = 'http://' + args.inference_addr + '/predictions/' + args.model_name |
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with open(args.img, 'rb') as image: |
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tmp_res = requests.post(url, image) |
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content = tmp_res.content |
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if args.result_image: |
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with open(args.result_image, 'wb') as out_image: |
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out_image.write(content) |
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plt.imshow(mmcv.imread(args.result_image, 'grayscale')) |
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plt.show() |
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else: |
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plt.imshow(plt.imread(BytesIO(content))) |
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plt.show() |
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model = init_model(args.config, args.checkpoint, args.device) |
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image = mmcv.imread(args.img) |
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result = inference_model(model, image) |
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plt.imshow(result[0]) |
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plt.show() |
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if __name__ == '__main__': |
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args = parse_args() |
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main(args) |
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