Source code for aitemplate.frontend.nn.conv2d.conv2d_bias

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"""
conv2d bias module
"""
from aitemplate.frontend.nn.conv2d.common_conv2d_bias_act import Conv2dBiasAct


[docs]class Conv2dBias(Conv2dBiasAct): r"""Applies 2D convolution with bias. Args: in_channels (int): Number of channels in the input image out_channels (int): Number of channels produced by the convolution kernel_size (int): Size of the convolving kernel stride (int): Stride of the convolution padding (int, optional): Padding added to all four sides of the input. Default: 0 dilation (int, optional): Spacing between kernel elements. Default: 1 groups (int, optional): Number of blocked connections from input channels to output channels. Default: 1 dtype (string, optional): Data type. Default: "float16" Shape: - Input: :math:`(N, H_{in}, W_{in}, C_{in})` - Output: :math:`(N, H_{out}, W_{out}, C_{out})`, where .. math:: H_{out} = \left\lfloor\frac{H_{in} + 2 \times \text{padding} - \text{dilation} \times (\text{kernel_size} - 1) - 1}{\text{stride}} + 1\right\rfloor .. math:: W_{out} = \left\lfloor\frac{W_{in} + 2 \times \text{padding} - \text{dilation} \times (\text{kernel_size} - 1) - 1}{\text{stride}} + 1\right\rfloor Attributes: weight (Tensor): the learnable weights of the module of shape :math:`(\text{out_channels}, \text{kernel_size}, \text{kernel_size}, ` :math:`\frac{\text{in_channels}}{\text{groups}})`. bias (Tensor): the learnable bias of the module of shape (out_channels). Examples:: >>> m = nn.Conv2d(16, 33, 3, 2) >>> input = Tensor(shape=[20, 50, 100, 16]) >>> output = m(input) """ def __init__( self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, dtype="float16", ): super().__init__( "conv2d_bias", in_channels, out_channels, kernel_size, stride, padding, dilation, groups, dtype, )