### QNN Conv2D operator
#### Tensorflow
~~~
tf.nn.quantized_conv2d(
input,
filter,
min_input,
max_input,
min_filter,
max_filter,
strides,
padding,
out_type=tf.dtypes.qint32,
dilations=[1, 1, 1, 1],
name=None
)
~~~
#### MxNet
~~~
mxnet.symbol.contrib.quantized_conv(
data=None,
weight=None,
bias=None,
min_data=None,
max_data=None,
min_weight=None,
max_weight=None,
min_bias=None,
max_bias=None,
kernel=_Null,
stride=_Null,
dilate=_Null,
pad=_Null,
num_filter=_Null,
num_group=_Null,
workspace=_Null,
no_bias=_Null,
cudnn_tune=_Null,
cudnn_off=_Null,
layout=_Null,
name=None,
attr=None,
out=None, **kwargs)
~~~
#### QNN Proposal
~~~
def conv2d(data,
weight,
input_zero_point,
kernel_zero_point,
strides=(1, 1),
padding=(0, 0),
dilation=(1, 1),
groups=1,
channels=None,
kernel_size=None,
data_layout="NCHW",
kernel_layout="OIHW",
out_layout="",
out_dtype="int32"):
r"""Quantized 2D convolution.
This operator convolves quantized weight with quantized data. The scale of
the output quantized tensor is the product of the weight_scale and
input_scale of the input quantized tensors. The zero point of the output
quantized tensor is 0. By default, the dtype of output is int32. Please also
refer to Requantize operator to understand how to scale back the int32
output to (u)int8.
Parameters
----------
data : tvm.relay.Expr
The input data to the operator.
weight : tvm.relay.Expr
The weight expressions.
input_zero_point: int
The zero point of the data distribution.
kernel_zero_point: int
The zero point of the quantized_kernel distribution.
strides : tuple of int, optional
The strides of convolution.
padding : tuple of int, optional
The padding of convolution on both sides of inputs before convolution.
dilation : tuple of int, optional
Specifies the dilation rate to be used for dilated convolution.
groups : int, optional
Number of groups for grouped convolution.
channels : int, optional
Number of output channels of this convolution.
kernel_size : tuple of int, optional
The spatial of the convolution weight.
data_layout : str, optional
Layout of the input.
kernel_layout : str, optional
Layout of the weight.
out_layout : str, optional
Layout of the output, by default, out_layout is the same as data_layout
out_dtype : str, optional
Specifies the output data type for mixed precision conv2d.
Returns
-------
result : tvm.relay.Expr
The computed result.
"""
~~~
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