conv3d

class torch.ao.nn.quantized.functional.conv3d(input, weight, bias, stride=1, padding=0, dilation=1, groups=1, padding_mode='zeros', scale=1.0, zero_point=0, dtype=torch.quint8)

Apply a 3D convolution over a quantized 3D input composed of several input planes。

See Conv3d for details and output shape。

参数

• input – quantized input tensor of shape $(\text{minibatch} , \text{in\_channels} , iD , iH , iW)$

• weight – quantized filters of shape $(\text{out\_channels} , \frac{\text{in\_channels}}{\text{groups}} , kD , kH , kW)$

• bias – non-quantized bias tensor of shape $(\text{out\_channels})$. The tensor type must be torch.float.

• stride – the stride of the convolving kernel. Can be a single number or a tuple (sD, sH, sW). Default: 1

• padding – implicit paddings on both sides of the input. Can be a single number or a tuple (padD, padH, padW). Default: 0

• dilation – the spacing between kernel elements. Can be a single number or a tuple (dD, dH, dW). Default: 1

• groups – split input into groups, $\text{in\_channels}$ should be divisible by the number of groups. Default: 1

• padding_mode – 要使用的填充模式。目前量化卷积仅支持“zeros”。默认值：“zeros”

• scale – 输出的量化尺度。默认值：1.0

• zero_point – 输出的量化零点。默认值：0

• dtype – 要使用的量化数据类型。默认值：torch.quint8

示例

>>> from torch.ao.nn.quantized import functional as qF
>>> filters = torch.randn(8, 4, 3, 3, 3, dtype=torch.float)
>>> inputs = torch.randn(1, 4, 5, 5, 5, dtype=torch.float)
>>> bias = torch.randn(8, dtype=torch.float)
>>>
>>> scale, zero_point = 1.0, 0
>>> dtype_inputs = torch.quint8
>>> dtype_filters = torch.qint8
>>>
>>> q_filters = torch.quantize_per_tensor(filters, scale, zero_point, dtype_filters)
>>> q_inputs = torch.quantize_per_tensor(inputs, scale, zero_point, dtype_inputs)
>>> qF.conv3d(q_inputs, q_filters, bias, padding=1, scale=scale, zero_point=zero_point)