nanodet model conerted from pytorch to coreml problem

[minushuang]

Hi, I converted a object detection model from pytorch to coreml with the code

def main(config, model_path, output_path, input_shape=(320, 320)):
    logger = Logger(-1, config.save_dir, False)
    model = build_model(config.model)
    checkpoint = torch.load(model_path, map_location=lambda storage, loc: storage)
    load_model_weight(model, checkpoint, logger)

    dummy_input = torch.autograd.Variable(
        torch.randn(1, 3, input_shape[0], input_shape[1])
    )

    traced_model = torch.jit.trace(model, dummy_input)

    logging.info("convert coreml start.")
    core_model = ct.convert(
        traced_model,
        inputs=[ct.ImageType(shape=dummy_input.shape, name='input', scale=0.017429, bias=(-103.53 * 0.017429, -116.28 * 0.017507, -123.675 * 0.017125))],
        outputs=[ct.TensorType(name="output")],
        debug=True
    )
    core_model.save(output_path)
    logging.info("finish convert coreml.")

the infer code with nms

image = Image.open(img_path).resize((320, 320)).convert('RGB')
model = ct.models.MLModel(mlmodel_path)
preds = model.predict({'input': image})
#post-process nms for preds

and the coreml result seems not correct as below the pytorch results the coreml results

could you please give me some advice about hot to get a correct coreml model, and it is so much better if you can convert the model for me if it is convenient, many thanks.

[minushuang]

all the model and the code are in https://github.com/minushuang/nanodet-for-coreml now. please let me know if I can provide you with any further assistance for reproduce the problem.

[minushuang]

sorry, i made a stupid mistake, i forgot to set the model to eval mode before converting, and it's ok now after model.eval()