Supported Ops

Operator	Known limitations
Add	Element by element addition is supported.
AveragePool	- Pooling size 3 x 3, with stride 1, 1 and SAME padding is supported. - "Mean" average pooling is supported where the pooling size is 7 or 8, and the input width and height are equal to the pool size.
Concat	- Output quantization scale smaller than the input quantization scale divided by 128 is not supported. - If concatenating along the channel dimension, the channel dimension of every input tensor must be a multiple of 16.
Constant
Conv2D	- HWIO format weights are supported. - The supported kernel heights and widths (the kernel does not have to be square) are: { 1, 2, 3, 5, 7, 9 }. - The supported strides (the height and width stride have to match) are: { 1, 2 }. - For kernels with height or width >7, only a stride of 1 is supported. - SAME and VALID padding are supported. - I*W/O must be between 0 and 65536, where:     - I is the input quantization scale.     - W is the weight quantization scale.     - O is the output quantization scale.
DepthToSpace	- A block size of 2 is supported. - Depth must be a multiple of the square of the block size.
DepthwiseConvolution2D	- HWIM format weights are supported. - The supported kernel heights and widths (the kernel does not have to be square) are: { 1, 2, 3, 5, 7, 9 }. - The supported strides (the height and width stride have to match) are: { 1, 2 }. - For kernels with height or width >7, only a stride of 1 is supported. - SAME and VALID padding are supported. - A channel multiplier of 1 is supported. A channel multiplier >1 is not supported. - I*W/O must be between 0 and 65536, where:     - I is the input quantization scale.     - W is the weight quantization scale.     - O is the output quantization scale.
FullyConnected	- HWIO format weights are supported, H and W must be 1. - I*W/O must be between 0 and 65536, where:     - I is the input quantization scale.     - W is the weight quantization scale.     - O is the output quantization scale.
LeakyReLU	Alpha must be less than 1 and greater than 0.
MaxPool	- Supported configurations:     - 1 x 1 pooling size, 2, 2 stride (equivalent to downsample 2 x 2).     - 2 x 2 pooling size, 2, 2 stride, VALID padding, input sizes must be even.     - 2 x 2 pooling size, 2, 2 stride, SAME padding, input sizes must be odd.     - 3 x 3 pooling size, 2, 2 stride, VALID padding, input sizes must be even, maximum tensor width is 417.     - 3 x 3 pooling size, 2, 2 stride, SAME padding, input sizes must be odd, maximum tensor width is 417.     - 1, 1 stride with pooling sizes up to 9x9 for VALID padding.     - 1, 1 stride with pooling sizes up to 17x17 for SAME padding. - Input size must not be smaller than the pooling size.
MeanXY	- Supports mean reduction of H x W dimensions to 1 x 1. - Only supports:     - N x 7 x 7 x C input with N x 1 x 1 x C output.     - N x 8 x 8 x C input with N x 1 x 1 x C output.
Mul	- The multiplication of a tensor with a constant tensor is supported when the constant shape is 1 x 1 x 1 x C. - The multiplication of a variable with a scalar constant is supported when the quantized values in the output are the same as the input.
Pad	- Only zero padding in the H and W dimension is supported. - Padding of up to 7 each side of the tensor in those dimensions is supported. - Padding amounts can differ per side, e.g. pad of 1 before the tensor in the H dimension and a pad of 3 after the tensor in the H dimension. - Quantization for input and output tensors must be identical.
Reinterpret quantization
ReLU	Lower bound must be less than the upper bound.
Requantize	- Output quantization scale smaller than the input quantization scale divided by 128 is not supported. - Requantize with different input/output type is supported.
Reshape
Resize	- The resized height or width must be 2n or 2n-1 where n is the original height or width. - If resized height and width are not both odd or both even, the result might be less accurate. - Some Resize Bilinear configurations (align_corners=True, half_pixel_centres=True when heights and widths are not both even or both odd) produce inaccurate results.
Sigmoid	The output for sigmoid always has a quantization zero point equal to the minimum value of the quantized data type and a quantization scale of 1/256.
Split	If splitting along the channel dimension, the channel dimension of every output tensor must be a multiple of 16.
Tanh	The output for tanh always has a quantization zero point equal to the middle value of the quantized data type and a quantization scale of 1/128.
Transpose	Transpose is allowed for height, width, and channel dimensions only.
TransposeConvolution2D	- HWIO format weights are supported. - The supported kernel heights and widths (the kernel does not have to be square) are: { 1, 2, 3, 5, 7, 9 }. - Only a stride of 2 is supported. - SAME and VALID padding are supported. - I*W/O must be between 0 and 65536, where:     - I is the input quantization scale.     - W is the weight quantization scale.     - O is the output quantization scale.