tensorplay.nn.functional.conv2d

tensorplay.nn.functional.conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1)[source]

Applies a 2D convolution over an input image composed of several input planes.

See Conv2d for details and output shape.

Parameters:

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

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

• bias – optional bias tensor of shape $(\text{out\_channels})$. Default: None

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

• padding – implicit paddings on both sides of the input. Can be a single number, a tuple (padH, padW), 'valid' for no padding, or 'same' to keep every output position (unit stride only). Default: 0

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

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

Examples:

>>> # With square kernels and equal stride
>>> filters = tp.randn(8, 4, 3, 3)
>>> inputs = tp.randn(1, 4, 5, 5)
>>> F.conv2d(inputs, filters, padding=1)