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# Copyright (c) OpenMMLab. All rights reserved.
from argparse import ArgumentParser
import numpy as np
import requests
from mmocr.apis import init_detector, model_inference
def parse_args():
parser = ArgumentParser()
parser.add_argument('img', help='Image file')
parser.add_argument('config', help='Config file')
parser.add_argument('checkpoint', help='Checkpoint file')
parser.add_argument('model_name', help='The model name in the server')
parser.add_argument(
'--inference-addr',
default='127.0.0.1:8080',
help='Address and port of the inference server')
parser.add_argument(
'--device', default='cuda:0', help='Device used for inference')
parser.add_argument(
'--score-thr', type=float, default=0.5, help='bbox score threshold')
args = parser.parse_args()
return args
def main(args):
# build the model from a config file and a checkpoint file
model = init_detector(args.config, args.checkpoint, device=args.device)
# test a single image
model_results = model_inference(model, args.img)
model.show_result(
args.img,
model_results,
win_name='model_results',
show=True,
score_thr=args.score_thr)
url = 'http://' + args.inference_addr + '/predictions/' + args.model_name
with open(args.img, 'rb') as image:
response = requests.post(url, image)
serve_results = response.json()
model.show_result(
args.img,
serve_results,
show=True,
win_name='serve_results',
score_thr=args.score_thr)
assert serve_results.keys() == model_results.keys()
for key in serve_results.keys():
for model_result, serve_result in zip(model_results[key],
serve_results[key]):
if isinstance(model_result[0], (int, float)):
assert np.allclose(model_result, serve_result)
elif isinstance(model_result[0], str):
assert model_result == serve_result
else:
raise TypeError
if __name__ == '__main__':
args = parse_args()
main(args)
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