.. _op_ai_onnx_QLinearConv:

QLinearConv
===========

- **Domain**: ``ai.onnx``
- **Since version**: 10

The convolution operator consumes a quantized input tensor, its scale and zero point,
a quantized filter, its scale and zero point, and output's scale and zero point,
and computes the quantized output. Each scale and zero-point pair must have same shape.
It means they must be either scalars (per tensor) or 1-D tensors (per output channel).
Each input or output and its related zero point must have same type.
When bias is present it must be quantized using scale = input scale \* weight scale and
zero point as 0.

**Inputs**

- **x** (*T1*): Input data tensor from previous layer; has size (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and width. Note that this is for the 2D image. Otherwise the size is (N x C x D1 x D2 ... x Dn). Optionally, if dimension denotation is in effect, the operation expects input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...].
- **x_scale** (*tensor(float)*): Scale tensor for input 'x'. It's a scalar, which means a per-tensor/layer quantization.
- **x_zero_point** (*T1*): Zero point tensor for input 'x'. It's a scalar, which means a per-tensor/layer quantization.
- **w** (*T2*): The weight tensor that will be used in the convolutions; has size (M x C/group x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel, and M is the number of feature maps. For more than 2 dimensions, the kernel shape will be (M x C/group x k1 x k2 x ... x kn), where (k1 x k2 x ... kn) is the dimension of the kernel. Optionally, if dimension denotation is in effect, the operation expects the weight tensor to arrive with the dimension denotation of [FILTER_OUT_CHANNEL, FILTER_IN_CHANNEL, FILTER_SPATIAL, FILTER_SPATIAL ...]. X.shape[1] == (W.shape[1] \* group) == C (assuming zero based indices for the shape array). Or in other words FILTER_IN_CHANNEL should be equal to DATA_CHANNEL.
- **w_scale** (*tensor(float)*): Scale tensor for input 'w'. It could be a scalar or a 1-D tensor, which means a per-tensor/layer or per output channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of output channels (M).
- **w_zero_point** (*T2*): Zero point tensor for input 'w'. It could be a scalar or a 1-D tensor, which means a per-tensor/layer or per output channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of output channels (M).
- **y_scale** (*tensor(float)*): Scale tensor for output 'y'. It's a scalar, which means a per-tensor/layer quantization.
- **y_zero_point** (*T3*): Zero point tensor for output 'y'. It's a scalar, which means a per-tensor/layer quantization.
- **B** (*T4*): Optional 1D bias to be added to the convolution, has size of M. Bias must be quantized using scale = x_scale \* w_scale and zero_point = 0

**Outputs**

- **y** (*T3*): Output data tensor that contains the result of the convolution. The output dimensions are functions of the kernel size, stride size, and pad lengths.

**Type Constraints**

- **T1**: Constrain input type to 8-bit integer tensor.
  Allowed types: tensor(int8), tensor(uint8).
- **T2**: Constrain filter type to 8-bit integer tensor.
  Allowed types: tensor(int8), tensor(uint8).
- **T3**: Constrain output type to 8-bit integer tensor.
  Allowed types: tensor(int8), tensor(uint8).
- **T4**: Constrain bias type to 32-bit integer tensor.
  Allowed types: tensor(int32).

Examples
--------

**test_cc_qlinearconv**

.. code-block:: text

    Node:
      QLinearConv(x, x_scale, x_zero_point, w, w_scale, w_zero_point, y_scale, y_zero_point) -> (y)

.. code-block:: text

    Inputs:
      x: shape=(1, 1, 7, 7), dtype=uint8
        [[[[  0,   4,   8,  12,  16,  20,  24],
           [ 28,  32,  36,  40,  44,  48,  52],
           [ 56,  60,  64,  68,  72,  76,  80],
           [ 84,  88,  92,  96, 100, 104, 108],
           [112, 116, 120, 124, 128, 132, 136],
           [140, 144, 148, 152, 156, 160, 164],
           [168, 172, 176, 180, 184, 188, 192]]]]
      x_scale: shape=(), dtype=float32
        0.00369205
      x_zero_point: shape=(), dtype=uint8
        132
      w: shape=(1, 1, 1, 1), dtype=uint8
        [[[[0]]]]
      w_scale: shape=(1,), dtype=float32
        [0.00172795]
      w_zero_point: shape=(1,), dtype=uint8
        [255]
      y_scale: shape=(), dtype=float32
        0.00162681
      y_zero_point: shape=(), dtype=uint8
        123

    Outputs:
      y: shape=(1, 1, 7, 7), dtype=uint8
        [[[[255, 251, 247, 243, 239, 235, 231],
           [227, 223, 219, 215, 211, 207, 203],
           [199, 195, 191, 187, 183, 179, 175],
           [171, 167, 163, 159, 155, 151, 147],
           [143, 139, 135, 131, 127, 123, 119],
           [115, 111, 107, 103,  99,  95,  91],
           [ 87,  83,  79,  75,  71,  67,  63]]]]

**test_cc_qlinearconv_int8**

.. code-block:: text

    Node:
      QLinearConv(x, x_scale, x_zero_point, w, w_scale, w_zero_point, y_scale, y_zero_point) -> (y)

.. code-block:: text

    Inputs:
      x: shape=(1, 1, 2, 2), dtype=int8
        [[[[10, 20],
           [30, 40]]]]
      x_scale: shape=(), dtype=float32
        0.1
      x_zero_point: shape=(), dtype=int8
        5
      w: shape=(1, 1, 2, 2), dtype=int8
        [[[[1, 1],
           [1, 1]]]]
      w_scale: shape=(1,), dtype=float32
        [1.]
      w_zero_point: shape=(1,), dtype=int8
        [0]
      y_scale: shape=(), dtype=float32
        1.
      y_zero_point: shape=(), dtype=int8
        -10

    Outputs:
      y: shape=(1, 1, 1, 1), dtype=int8
        [[[[-2]]]]