Resize - 10 vs 11

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  1. Resize10 → Resize11 +74 -9
Resize10 → Resize11 RENAMED
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- Resize the input tensor.
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+ Resize the input tensor. In general, it calculates every value in the output tensor as a weighted average of neighborhood (a.k.a. sampling locations) in the input tensor.
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  Each dimension value of the output tensor is:
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- output_dimension = floor(input_dimension * scale).
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+ output_dimension = floor(input_dimension * (roi_end - roi_start) * scale) if input "sizes" is not specified.
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  **Attributes**
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+ * **coordinate_transformation_mode**:
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+ This attribute describes how to transform the coordinate in the
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+ resized tensor to the coordinate in the original tensor. <br/> The
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+ coordinate of each dimension is transformed individually. Let's
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+ describe a case using axis x as an example. Denote x_resized as the
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+ coordinate of axis x in the resized tensor, x_original as the
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+ coordinate of axis x in the original tensor, length_original as the
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+ length of the original tensor in axis x, length_resized as the
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+ length of the resized tensor in axis x, roi_x = (start_x, end_x) of
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+ the axis x in input "roi", scale = length_resized / length_original,
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+ <br/> if coordinate_transformation_mode is "half_pixel", <br/>
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+ x_original = (x_resized + 0.5) / scale - 0.5, <br/> if
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+ coordinate_transformation_mode is "pytorch_half_pixel", <br/>
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+ x_original = length_resized > 1 ? (x_resized + 0.5) / scale - 0.5 :
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+ 0, <br/> if coordinate_transformation_mode is "align_corners",
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+ <br/> x_original = x_resized * (length_original - 1) /
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+ (length_resized - 1), <br/> if coordinate_transformation_mode is
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+ "asymmetric", <br/> x_original = x_resized / scale, <br/> if
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+ coordinate_transformation_mode is "tf_half_pixel_for_nn", <br/>
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+ x_original = (x_resized + 0.5) / scale, <br/> if
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+ coordinate_transformation_mode is "tf_crop_and_resize", <br/>
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+ x_original = length_resized > 1 ? start_x * (length_original - 1) +
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+ x_resized * (end_x - start_x) * (length_original - 1) /
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+ (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original -
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+ 1).
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+ * **cubic_coeff_a**:
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+ The coefficient 'a' used in cubic interpolation. Two common choice
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+ are -0.5 (in some cases of TensorFlow) and -0.75 (in PyTorch). Check
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+ out Equation (4) in https://ieeexplore.ieee.org/document/1163711 for
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+ the details. This attribute is valid only if "mode" is "cubic".
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+ * **exclude_outside**:
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+ If set to 1, the weight of sampling locations outside the tensor
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+ will be set to 0 and the weight will be renormalized so that their
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+ sum is 1.0. The default value is 0.
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+ * **extrapolation_value**:
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+ When coordinate_transformation_mode is "tf_crop_and_resize" and
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+ x_original is outside the range [0, length_original - 1], this value
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+ is used as the corresponding output value. Default is 0.0f.
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  * **mode**:
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- Two interpolation modes: nearest (default), and linear (including
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+ Three interpolation modes: nearest (default), linear and cubic. The
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+ "linear" mode includes linear interpolation for 1D tensor and
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+ N-linear interpolation for N-D tensor (for example, bilinear
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+ interpolation for 2D tensor). The "cubic" mode includes cubic
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+ interpolation for 1D tensor and N-cubic interpolation for N-D tensor
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+ (for example, bicubic interpolation for 2D tensor).
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- bilinear, trilinear, etc)
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+ * **nearest_mode**:
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+ Four modes: round_prefer_floor (default, as known as round half
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+ down), round_prefer_ceil (as known as round half up), floor, ceil.
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+ Only used by nearest interpolation. It indicates how to get
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+ "nearest" pixel in input tensor from x_original, so this attribute
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+ is valid only if "mode" is "nearest".
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  **Inputs**
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+ Between 3 and 4 inputs.
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+
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- * **X** (heterogeneous) - **T**:
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+ * **X** (heterogeneous) - **T1**:
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  N-D tensor
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+ * **roi** (heterogeneous) - **T2**:
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+ 1-D tensor given as [start1, ..., startN, end1, ..., endN], where N
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+ is the rank of X. The RoIs' coordinates are normalized in the
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+ coordinate system of the input image. It only takes effect when
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+ coordinate_transformation_mode is "tf_crop_and_resize"
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  * **scales** (heterogeneous) - **tensor(float)**:
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  The scale array along each dimension. It takes value greater than 0.
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  If it's less than 1, it's sampling down, otherwise, it's upsampling.
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  The number of elements of 'scales' should be the same as the rank of
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+ input 'X'. If 'size' is needed, the user must set 'scales' to an
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- input 'X'.
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+ empty tensor.
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+ * **sizes** (optional, heterogeneous) - **tensor(int64)**:
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+ The size of the output tensor. The number of elements of 'sizes'
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+ should be the same as the rank of input 'X'. May only be set if
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+ 'scales' is set to an empty tensor.
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  **Outputs**
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- * **Y** (heterogeneous) - **T**:
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+ * **Y** (heterogeneous) - **T1**:
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  N-D tensor after resizing
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  **Type Constraints**
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- * **T** in (
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+ * **T1** in (
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  tensor(bool),
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  tensor(complex128),
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  tensor(complex64),
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  tensor(double),
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  tensor(float),
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  tensor(float16),
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  tensor(int16),
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  tensor(int32),
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  tensor(int64),
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  tensor(int8),
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  tensor(string),
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  tensor(uint16),
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  tensor(uint32),
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  tensor(uint64),
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  tensor(uint8)
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  ):
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+ * **T2** in (
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+ tensor(double),
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+ tensor(float),
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+ tensor(float16)
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+ ):
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- Constrain input 'X' and output 'Y' to all tensor types.+ Constrain input 'X' and output 'Y' to all tensor types.
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+ Constrain roi type to float or double.