timm/layers/mlp.py [198:228]:
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class ConvMlp(nn.Module):
    """ MLP using 1x1 convs that keeps spatial dims (for 2D NCHW tensors)
    """
    def __init__(
            self,
            in_features,
            hidden_features=None,
            out_features=None,
            act_layer=nn.ReLU,
            norm_layer=None,
            bias=True,
            drop=0.,
    ):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        bias = to_2tuple(bias)

        self.fc1 = nn.Conv2d(in_features, hidden_features, kernel_size=1, bias=bias[0])
        self.norm = norm_layer(hidden_features) if norm_layer else nn.Identity()
        self.act = act_layer()
        self.drop = nn.Dropout(drop)
        self.fc2 = nn.Conv2d(hidden_features, out_features, kernel_size=1, bias=bias[1])

    def forward(self, x):
        x = self.fc1(x)
        x = self.norm(x)
        x = self.act(x)
        x = self.drop(x)
        x = self.fc2(x)
        return x
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timm/models/inception_next.py [61:93]:
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class ConvMlp(nn.Module):
    """ MLP using 1x1 convs that keeps spatial dims
    copied from timm: https://github.com/huggingface/pytorch-image-models/blob/v0.6.11/timm/models/layers/mlp.py
    """

    def __init__(
            self,
            in_features,
            hidden_features=None,
            out_features=None,
            act_layer=nn.ReLU,
            norm_layer=None,
            bias=True,
            drop=0.,
    ):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        bias = to_2tuple(bias)

        self.fc1 = nn.Conv2d(in_features, hidden_features, kernel_size=1, bias=bias[0])
        self.norm = norm_layer(hidden_features) if norm_layer else nn.Identity()
        self.act = act_layer()
        self.drop = nn.Dropout(drop)
        self.fc2 = nn.Conv2d(hidden_features, out_features, kernel_size=1, bias=bias[1])

    def forward(self, x):
        x = self.fc1(x)
        x = self.norm(x)
        x = self.act(x)
        x = self.drop(x)
        x = self.fc2(x)
        return x
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