def main(args):
# Inference single image by native apis.
model = init_model(args.config, args.checkpoint, device=args.device)
model_result = inference_model(model, args.img)
show_result_pyplot(model, args.img, model_result, title='pytorch_result')
# Inference single image by torchserve engine.
url = 'http://' + args.inference_addr + '/predictions/' + args.model_name
with open(args.img, 'rb') as image:
response = requests.post(url, image)
server_result = response.json()
show_result_pyplot(model, args.img, server_result, title='server_result')
assert np.allclose(model_result['pred_score'], server_result['pred_score'])
print('Test complete, the results of PyTorch and TorchServe are the same.')
def main():
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('--device',
default='cuda:0',
help='Device used for inference')
args = parser.parse_args()
# build the model from a config file and a checkpoint file
model = init_model(args.config, args.checkpoint, device=args.device)
# test a single image
result = inference_model(model, args.img)
# print result on terminal
print(result)
def inference(self, data, *args, **kwargs):
results = []
for image in data:
results.append(inference_model(self.model, image))
return results