Method _base_predict

tests/test_tools/test_predict.py:32–65

(self, model_type, model_path, dist=False)

        super().tearDown()

    def _base_predict(self, model_type, model_path, dist=False):
        input_file = tempfile.NamedTemporaryFile('w').name
        input_line_num = 10
        with open(input_file, 'w') as ofile:
            for _ in range(input_line_num):
                ofile.write(
                    os.path.join(TEST_IMAGES_DIR, '000000289059.jpg') + '\n')
        output_file = tempfile.NamedTemporaryFile('w').name

        if dist:
            cmd = f'PYTHONPATH=. python -m torch.distributed.launch --nproc_per_node=2 --master_port=29527 \
                    tools/predict.py \
                    --input_file {input_file} \
                    --output_file {output_file} \
                    --model_type {model_type} \
                    --model_path {model_path} \
                    --launcher pytorch'

        else:
            cmd = f'PYTHONPATH=. python tools/predict.py \
                    --input_file {input_file} \
                    --output_file {output_file} \
                    --model_type {model_type} \
                    --model_path {model_path} '

        logging.info('run command: %s' % cmd)
        run_in_subprocess(cmd)

        with open(output_file, 'r') as infile:
            output_line_num = len(infile.readlines())
        self.assertEqual(input_line_num, output_line_num)

        io.remove(input_file)
        io.remove(output_file)

    def test_predict(self):
        model_type = 'YoloXPredictor'