Upsample - 1 vs 7

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  1. Upsample1 → Upsample7 +21 -30
Upsample1 → Upsample7 RENAMED
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  Upsample 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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- The width and height of the output tensor are:
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- output_width = floor(input_width * width_scale),
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- output_height = floor(input_height * height_scale).
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- Example:
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- Given data tensor, width_scale, height_scale, mode,
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- Upsample the input 4-D tensor in nearest mode:
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- data = [[[
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- [1, 2],
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- [3, 4]
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- ]]]
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- width_scale = 2
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- height_scale = 2
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- mode = "nearest"
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- output = [[[
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- [1, 1, 2, 2],
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- [1, 1, 2, 2],
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- [3, 3, 4, 4],
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- [3, 3, 4, 4]
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- ]]]
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  **Attributes**
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- * **height_scale** (required):
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- The scale along height dimension. It takes value greater than or
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- equal to 1.
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  * **mode**:
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- Two interpolation modes: nearest(default), bilinear
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+ Two interpolation modes: nearest (default), and linear (including
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+ bilinear, trilinear, etc)
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- * **width_scale** (required):
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+ * **scales** (required):
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- The scale along width dimension. It takes value greater than or
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+ The scale array along each dimension. It takes value greater than or
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+ equal to 1. The number of elements of 'scales' should be the same as
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- equal to 1.
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+ the rank of input 'X'.
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  **Inputs**
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  * **X** (heterogeneous) - **T**:
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- 4-D tensor, [N,C,H,W]
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+ N-D tensor
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  **Outputs**
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  * **Y** (heterogeneous) - **T**:
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- 4-D tensor after resizing, [N,C,H,W]
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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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  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(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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+ Constrain input and output types to all tensor types.- Constrain output types to bool, int32, int64, float16, float, double
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- tensors.