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

#  Copyright (c) Meta Platforms, Inc. and affiliates.
#
#  Licensed under the Apache License, Version 2.0 (the "License");
#  you may not use this file except in compliance with the License.
#  You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
#  Unless required by applicable law or agreed to in writing, software
#  distributed under the License is distributed on an "AS IS" BASIS,
#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#  See the License for the specific language governing permissions and
#  limitations under the License.
#
"""
conv2d Module.
"""
from aitemplate.compiler.ops import conv2d
from aitemplate.frontend.nn.module import Module
from aitemplate.frontend.nn.parameter import Parameter

# pylint: disable=C0103


[docs]class Conv2d(Module): r"""Applies a 2D convolution over an input signal composed of several input planes. In the simplest case, the output value of the layer with input size :math:`(N, H, W, C_{\text{in}})` and output :math:`(N, H_{\text{out}}, W_{\text{out}}, C_{\text{out}})` can be precisely described as: .. math:: \text{out}(N_i, C_{\text{out}_j}) = \text{bias}(C_{\text{out}_j}) + \sum_{k = 0}^{C_{\text{in}} - 1} \text{weight}(C_{\text{out}_j}, k) \star \text{input}(N_i, k) where :math:`\star` is the valid 2D `cross-correlation`_ operator, :math:`N` is a batch size, :math:`H` is a height of input planes in pixels, :math:`W` is width in pixels, and :math:`C` denotes a number of channels. * :attr:`stride` controls the stride for the cross-correlation. * :attr:`padding` controls the amount of padding applied to the input. * :attr:`dilation` controls the spacing between the kernel points; also known as the à trous algorithm. It is harder to describe, but this `link`_ has a nice visualization of what :attr:`dilation` does. 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}})`. Examples:: >>> m = nn.Conv2d(16, 33, 3, 2) >>> input = Tensor(shape=[20, 50, 100, 16]) >>> output = m(input) .. _cross-correlation: https://en.wikipedia.org/wiki/Cross-correlation .. _link: https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md """ def __init__( self, in_channels, out_channels, kernel_size, stride, padding=0, dilation=1, groups=1, dtype="float16", ): super().__init__() self.weight = Parameter( shape=[out_channels, kernel_size, kernel_size, in_channels // groups], dtype=dtype, ) self.op = conv2d(stride=stride, pad=padding, dilate=dilation, group=groups)
[docs] def forward(self, *args): """Applies Conv2d on the input tensor.""" assert len(args) == 1 x = args[0] return self.op(x, self.weight.tensor())