Conv2d¶
-
class
torch.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)[source]¶ 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
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)