conv1d#

classtorch.ao.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)[source]#

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

SeeConv1d for details and output shape.

Parameters

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

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

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

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

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

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

• groups – split input into groups,$\text{in\_channels}$in_channels should be divisible by thenumber 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:

>>>fromtorch.ao.nn.quantizedimportfunctionalasqF>>>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)