pytorch-image-models/timm/layers/create_conv2d.py

""" Create Conv2d Factory Method

Hacked together by / Copyright 2020 Ross Wightman
"""

from .mixed_conv2d import MixedConv2d
from .cond_conv2d import CondConv2d
from .conv2d_same import create_conv2d_pad


def create_conv2d(in_channels, out_channels, kernel_size, **kwargs):
    """ Select a 2d convolution implementation based on arguments
    Creates and returns one of torch.nn.Conv2d, Conv2dSame, MixedConv2d, or CondConv2d.

    Used extensively by EfficientNet, MobileNetv3 and related networks.
    """
    if isinstance(kernel_size, list):
        assert 'num_experts' not in kwargs  # MixNet + CondConv combo not supported currently
        if 'groups' in kwargs:
            groups = kwargs.pop('groups')
            if groups == in_channels:
                kwargs['depthwise'] = True
            else:
                assert groups == 1
        # We're going to use only lists for defining the MixedConv2d kernel groups,
        # ints, tuples, other iterables will continue to pass to normal conv and specify h, w.
        m = MixedConv2d(in_channels, out_channels, kernel_size, **kwargs)
    else:
        depthwise = kwargs.pop('depthwise', False)
        # for DW out_channels must be multiple of in_channels as must have out_channels % groups == 0
        groups = in_channels if depthwise else kwargs.pop('groups', 1)
        if 'num_experts' in kwargs and kwargs['num_experts'] > 0:
            m = CondConv2d(in_channels, out_channels, kernel_size, groups=groups, **kwargs)
        else:
            m = create_conv2d_pad(in_channels, out_channels, kernel_size, groups=groups, **kwargs)
    return m