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| """ 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 | |
| assert 'groups' not in kwargs # MixedConv groups are defined by kernel list | |
| # 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 | |