torch.nn.intrinsic.quantized

This module implements the quantized implementations of fused operations like conv + relu.

ConvReLU2d

class torch.nn.intrinsic.quantized.ConvReLU2d(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, padding_mode='zeros') [source]

A ConvReLU2d module is a fused module of Conv2d and ReLU

We adopt the same interface as torch.nn.quantized.Conv2d.

Variables

as torch.nn.quantized.Conv2d (Same) –

ConvReLU3d

class torch.nn.intrinsic.quantized.ConvReLU3d(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, padding_mode='zeros') [source]

A ConvReLU3d module is a fused module of Conv3d and ReLU

We adopt the same interface as torch.nn.quantized.Conv3d.

Attributes: Same as torch.nn.quantized.Conv3d

LinearReLU

class torch.nn.intrinsic.quantized.LinearReLU(in_features, out_features, bias=True, dtype=torch.qint8) [source]

A LinearReLU module fused from Linear and ReLU modules

We adopt the same interface as torch.nn.quantized.Linear.

Variables

as torch.nn.quantized.Linear (Same) –

Examples:

>>> m = nn.intrinsic.LinearReLU(20, 30)
>>> input = torch.randn(128, 20)
>>> output = m(input)
>>> print(output.size())
torch.Size([128, 30])