Function get_conv2d

model/replknet.py:17–34

(in_channels, out_channels, kernel_size, stride, padding, dilation, groups, bias)

import os

def get_conv2d(in_channels, out_channels, kernel_size, stride, padding, dilation, groups, bias):
    if type(kernel_size) is int:
        use_large_impl = kernel_size > 5
    else:
        assert len(kernel_size) == 2 and kernel_size[0] == kernel_size[1]
        use_large_impl = kernel_size[0] > 5
    has_large_impl = 'LARGE_KERNEL_CONV_IMPL' in os.environ
    if has_large_impl and in_channels == out_channels and out_channels == groups and use_large_impl and stride == 1 and padding == kernel_size // 2 and dilation == 1:
        sys.path.append(os.environ['LARGE_KERNEL_CONV_IMPL'])
        #   Please follow the instructions https://github.com/DingXiaoH/RepLKNet-pytorch/blob/main/README.md
        #   export LARGE_KERNEL_CONV_IMPL=absolute_path_to_where_you_cloned_the_example (i.e., depthwise_conv2d_implicit_gemm.py)
        # TODO more efficient PyTorch implementations of large-kernel convolutions. Pull requests are welcomed.
        # Or you may try MegEngine. We have integrated an efficient implementation into MegEngine and it will automatically use it.
        from depthwise_conv2d_implicit_gemm import DepthWiseConv2dImplicitGEMM
        return DepthWiseConv2dImplicitGEMM(in_channels, kernel_size, bias=bias)
    else:
        return nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride,
                         padding=padding, dilation=dilation, groups=groups, bias=bias)

use_sync_bn = False