Conv2d

class torch.ao.nn.quantized.dynamic.modules.conv.Conv2d(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, padding_mode='zeros', device=None, dtype=None)

A dynamically quantized conv module with floating point tensors as inputs and outputs.

For details on input arguments, parameters, and implementation see Conv2d and Conv2d and

Variables:

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

• scale (Tensor) – scalar for the output scale

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

See Conv2d for other attributes.

Examples:

>>> # With square kernels and equal stride
>>> m = nn.quantized.dynamic.Conv2d(16, 33, 3, stride=2)
>>> # non-square kernels and unequal stride and with padding
>>> m = nn.quantized.dynamic.Conv2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2))
>>> # non-square kernels and unequal stride and with padding and dilation
>>> m = nn.quantized.dynamic.Conv2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2), dilation=(3, 1))
>>> input = torch.randn(20, 16, 50, 100)
>>> output = m(input)