image_classification/infer_imagenet.py [90:115]:
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def get_data_rec(rec_val, rec_val_idx, batch_size, num_workers):
    rec_val = os.path.expanduser(rec_val)
    rec_val_idx = os.path.expanduser(rec_val_idx)
    mean_rgb = [123.68, 116.779, 103.939]
    
    def batch_fn(batch, ctx):
        data = gluon.utils.split_and_load(batch.data[0], ctx_list=ctx, batch_axis=0)
        label = gluon.utils.split_and_load(batch.label[0], ctx_list=ctx, batch_axis=0)
        return data, label
    
    val_data = mx.io.ImageRecordIter(
        path_imgrec         = rec_val,
        path_imgidx         = rec_val_idx,
        preprocess_threads  = num_workers,
        shuffle             = False,
        batch_size          = batch_size,
        resize              = 256,
        label_width         = 1,
        rand_crop           = False,
        rand_mirror         = False,
        data_shape          = (3, 224, 224),
        mean_r              = mean_rgb[0],
        mean_g              = mean_rgb[1],
        mean_b              = mean_rgb[2]
    )
    return val_data, batch_fn
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image_classification/infer_imagenet_gpu.py [77:106]:
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def get_data_rec(rec_val, rec_val_idx, batch_size, num_workers):
    """
       Creates and returns data MXNet Data Iterator object and a function that splits data into batches
       (if using image record iter for input)
    """
    rec_val = os.path.expanduser(rec_val)
    rec_val_idx = os.path.expanduser(rec_val_idx)
    mean_rgb = [123.68, 116.779, 103.939]
    
    def batch_fn(batch, ctx):
        data = gluon.utils.split_and_load(batch.data[0], ctx_list=ctx, batch_axis=0)
        label = gluon.utils.split_and_load(batch.label[0], ctx_list=ctx, batch_axis=0)
        return data, label
    
    val_data = mx.io.ImageRecordIter(
        path_imgrec         = rec_val,
        path_imgidx         = rec_val_idx,
        preprocess_threads  = num_workers,
        shuffle             = False,
        batch_size          = batch_size,
        resize              = 256,
        label_width         = 1,
        rand_crop           = False,
        rand_mirror         = False,
        data_shape          = (3, 224, 224),
        mean_r              = mean_rgb[0],
        mean_g              = mean_rgb[1],
        mean_b              = mean_rgb[2]
    )
    return val_data, batch_fn
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