Function eval

tools/program.py:709–818

(
    model,
    valid_dataloader,
    post_process_class,
    eval_class,
    model_type=None,
    extra_input=False,
    scaler=None,
    amp_level="O2",
    amp_custom_black_list=[],
    amp_custom_white_list=[],
    amp_dtype="float16",
)

def eval(
    model,
    valid_dataloader,
    post_process_class,
    eval_class,
    model_type=None,
    extra_input=False,
    scaler=None,
    amp_level="O2",
    amp_custom_black_list=[],
    amp_custom_white_list=[],
    amp_dtype="float16",
):
    model.eval()
    with paddle.no_grad():
        total_frame = 0.0
        total_time = 0.0
        pbar = tqdm(
            total=len(valid_dataloader), desc="eval model:", position=0, leave=True
        )
        max_iter = (
            len(valid_dataloader) - 1
            if platform.system() == "Windows"
            else len(valid_dataloader)
        )
        sum_images = 0
        for idx, batch in enumerate(valid_dataloader):
            if idx >= max_iter:
                break
            images = batch[0]
            start = time.time()

            # use amp
            if scaler:
                with paddle.amp.auto_cast(
                    level=amp_level,
                    custom_black_list=amp_custom_black_list,
                    dtype=amp_dtype,
                ):
                    if model_type == "table" or extra_input:
                        preds = model(images, data=batch[1:])
                    elif model_type in ["kie"]:
                        preds = model(batch)
                    elif model_type in ["can"]:
                        preds = model(batch[:3])
                    elif model_type in ["latexocr"]:
                        preds = model(batch)
                    elif model_type in ["sr"]:
                        preds = model(batch)
                        sr_img = preds["sr_img"]
                        lr_img = preds["lr_img"]
                    else:
                        preds = model(images)
                preds = to_float32(preds)
            else:
                if model_type == "table" or extra_input:
                    preds = model(images, data=batch[1:])
                elif model_type in ["kie"]: