Conv3d#

classtorch.ao.nn.quantized.Conv3d(in_channels,out_channels,kernel_size,stride=1,padding=0,dilation=1,groups=1,bias=True,padding_mode='zeros',device=None,dtype=None)[source]#

Applies a 3D convolution over a quantized input signal composed ofseveral quantized input planes.

For details on input arguments, parameters, and implementation seeConv3d.

Note

Onlyzeros is supported for thepadding_mode argument.

Note

Onlytorch.quint8 is supported for the input data type.

Variables

• weight (Tensor) – packed tensor derived from the learnable weightparameter.

• scale (Tensor) – scalar for the output scale

• zero_point (Tensor) – scalar for the output zero point

SeeConv3d for other attributes.

Examples:

>>># With square kernels and equal stride>>>m=nn.quantized.Conv3d(16,33,3,stride=2)>>># non-square kernels and unequal stride and with padding>>>m=nn.quantized.Conv3d(16,33,(3,5,5),stride=(1,2,2),padding=(1,2,2))>>># non-square kernels and unequal stride and with padding and dilation>>>m=nn.quantized.Conv3d(16,33,(3,5,5),stride=(1,2,2),padding=(1,2,2),dilation=(1,2,2))>>>input=torch.randn(20,16,56,56,56)>>># quantize input to quint8>>>q_input=torch.quantize_per_tensor(input,scale=1.0,zero_point=0,dtype=torch.quint8)>>>output=m(q_input)

classmethodfrom_float(mod,use_precomputed_fake_quant=False)[source]#

Creates a quantized module from a float module or qparams_dict.

Parameters

mod (Module) – a float module, either produced by torch.ao.quantizationutilities or provided by the user