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您正在阅读 MMSelfSup 0.x 版本的文档，而 MMSelfSup 0.x 版本将会在 2022 年末开始逐步停止维护。我们建议您及时升级到 MMSelfSup 1.0.0rc 版本，享受由 OpenMMLab 2.0 带来的更多新特性和更佳的性能表现。阅读 MMSelfSup 1.0.0rc 的发版日志, 代码和文档获取更多信息。

mmselfsup.models.utils.sobel 源代码

# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from mmcv.runner import BaseModule


[文档]class Sobel(BaseModule):
    """Sobel layer."""

    def __init__(self):
        super(Sobel, self).__init__()
        grayscale = nn.Conv2d(3, 1, kernel_size=1, stride=1, padding=0)
        grayscale.weight.data.fill_(1.0 / 3.0)
        grayscale.bias.data.zero_()
        sobel_filter = nn.Conv2d(1, 2, kernel_size=3, stride=1, padding=1)
        sobel_filter.weight.data[0, 0].copy_(
            torch.FloatTensor([[1, 0, -1], [2, 0, -2], [1, 0, -1]]))
        sobel_filter.weight.data[1, 0].copy_(
            torch.FloatTensor([[1, 2, 1], [0, 0, 0], [-1, -2, -1]]))
        sobel_filter.bias.data.zero_()
        self.sobel = nn.Sequential(grayscale, sobel_filter)
        for p in self.sobel.parameters():
            p.requires_grad = False

[文档]    def forward(self, x):
        return self.sobel(x)