conv1d

class torch.nn.quantized.functional.conv1d(input, weight, bias, stride=1, padding=0, dilation=1, groups=1, padding_mode='zeros', scale=1.0, zero_point=0, dtype=torch.quint8)

Applies a 1D convolution over a quantized 1D input composed of several input planes.

See Conv1d for details and output shape.

Parameters

• input – quantized input tensor of shape $(\text{minibatch} , \text{in\_channels} , iW)$

• weight – quantized filters of shape $(\text{out\_channels} , \frac{\text{in\_channels}}{\text{groups}} , iW)$

• bias – non-quantized bias tensor of shape $(\text{out\_channels})$. The tensor type must be torch.float.

• stride – the stride of the convolving kernel. Can be a single number or a tuple (sW,). Default: 1

• padding – implicit paddings on both sides of the input. Can be a single number or a tuple (padW,). Default: 0

• dilation – the spacing between kernel elements. Can be a single number or a tuple (dW,). Default: 1

• groups – split input into groups, $\text{in\_channels}$ should be divisible by the number of groups. Default: 1

• padding_mode – the padding mode to use. Only “zeros” is supported for quantized convolution at the moment. Default: “zeros”

• scale – quantization scale for the output. Default: 1.0

• zero_point – quantization zero_point for the output. Default: 0

• dtype – quantization data type to use. Default: torch.quint8

Examples:

>>> from torch.nn.quantized import functional as qF
>>> filters = torch.randn(33, 16, 3, dtype=torch.float)
>>> inputs = torch.randn(20, 16, 50, dtype=torch.float)
>>> bias = torch.randn(33, dtype=torch.float)
>>>
>>> scale, zero_point = 1.0, 0
>>> dtype_inputs = torch.quint8
>>> dtype_filters = torch.qint8
>>>
>>> q_filters = torch.quantize_per_tensor(filters, scale, zero_point, dtype_filters)
>>> q_inputs = torch.quantize_per_tensor(inputs, scale, zero_point, dtype_inputs)
>>> qF.conv1d(q_inputs, q_filters, bias, padding=1, scale=scale, zero_point=zero_point)