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@ -2748,8 +2748,8 @@ def generate_proposals(scores,
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represents the differece between predicted box locatoin and
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anchor location. The data type must be float32.
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im_info(Variable): A 2-D Tensor with shape [N, 3] represents origin
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image information for N batch. Info contains height, width and scale
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between origin image size and the size of feature map.
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image information for N batch. Height and width are the input sizes
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and scale is the ratio of network input size and original size.
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The data type must be int32.
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anchors(Variable): A 4-D Tensor represents the anchors with a layout
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of [H, W, A, 4]. H and W are height and width of the feature map,
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@ -2842,7 +2842,7 @@ def box_clip(input, im_info, name=None):
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the last dimension is 4 and data type is float32 or float64.
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im_info(Variable): The 2-D Tensor with shape [N, 3] with layout
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(height, width, scale) represeting the information of image.
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height and width is the input size and scale is the ratio of input
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Height and width are the input sizes and scale is the ratio of network input
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size and original size. The data type is float32 or float64.
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name(str, optional): For detailed information, please refer
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to :ref:`api_guide_Name`. Usually name is no need to set and
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