Conv2d

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

Applies a 2D convolution over a quantized input signal composed of several quantized input planes.

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

Note

Only zeros is supported for the padding_mode argument.

Note

Only torch.quint8 is supported for the input data type.

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.Conv2d(16, 33, 3, stride=2)
>>> # non-square kernels and unequal stride and with padding
>>> m = nn.quantized.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.Conv2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2), dilation=(3, 1))
>>> input = torch.randn(20, 16, 50, 100)
>>> # quantize input to quint8
>>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, dtype=torch.quint8)
>>> output = m(q_input)

classmethod from_float(mod)

Creates a quantized module from a float module or qparams_dict.

Parameters:

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