Pytorch学习——常用函数

Pytorch

torch.Tensor

torch.Tensor is a multi-dimensional matrix containing elements of a single data type.
torch.Tensor is an alias for the default tensor type (torch.FloatTensor).

torch.nn.Conv2d

class torch.nn.Conv2d(
    in_channels, 
    out_channels, 
    kernel_size, 
    stride=1, 
    padding=0, 
    dilation=1, 
    groups=1, 
    bias=True)

In the simplest case, the output value of the layer with input size (N,Cin,H,W) and output (N,Cout,Hout,Wout) can be precisely described as:

• ⋆ is the valid 2D cross-correlation operator,
• N is a batch size,
• C denotes a number of channels,
• H is a height of input planes in pixels,
• W is width in pixels.

torch.nn.functional.conv2d

torch.nn.functional.conv2d(
    input, 
    weight, 
    bias=None, 
    stride=1, 
    padding=0, 
    dilation=1, 
    groups=1) → Tensor

• input – input tensor of shape
• weight – filters of shape
• bias – optional bias tensor of shape (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 zero paddings on both sides of the input. Can be a single number or a tuple (padH, padW). 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, in_channels
• should be divisible by the number of groups. Default: 1

MaxPool2d

class torch.nn.MaxPool2d(
    kernel_size, 
    stride=None, 
    padding=0, 
    dilation=1, 
    return_indices=False, 
    ceil_mode=False)

torch.tensor permute

>>> x = torch.randn(2, 3, 5)
>>> x.size()
torch.Size([2, 3, 5])
>>> x.permute(2, 0, 1).size()
torch.Size([5, 2, 3])