linear# class torch.ao.nn.quantized.functional.linear(input, weight, bias=None, scale=None, zero_point=None)[source]# Applies a linear transformation to the incoming quantized data: y=xAT+by = xA^T + by=xAT+b. See Linear Note Current implementation packs weights on every call, which has penalty on performance. If you want to avoid the overhead, use Linear. Parameters input (Tensor) – Quantized input of type torch.quint8 weight (Tensor) – Quantized weight of type torch.qint8 bias (Tensor) – None or fp32 bias of type torch.float scale (double) – output scale. If None, derived from the input scale zero_point (python:long) – output zero point. If None, derived from the input zero_point Return type Tensor Shape: Input: (N,∗,in_features)(N, *, in\_features)(N,∗,in_features) where * means any number of additional dimensions Weight: (out_features,in_features)(out\_features, in\_features)(out_features,in_features) Bias: (out_features)(out\_features)(out_features) Output: (N,∗,out_features)(N, *, out\_features)(N,∗,out_features)
