Implement Adaptive Kernel selection

When channel size is given,
calculate adaptive kernel size according to original paper.
Otherwise use the given kernel size(k_size), which defaults to 3

timm/models/EcaModule.py

@@ -31,6 +31,7 @@ LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 SOFTWARE.
 '''
+import math
 from torch import nn
 import torch.nn.functional as F
 
@@ -38,15 +39,25 @@ class EcaModule(nn.Module):
     """Constructs a ECA module.
 
     Args:
-        channel: Number of channels of the input feature map
+        channel: Number of channels of the input feature map for use in adaptive kernel sizes
-        k_size: Adaptive selection of kernel size
+            for actual calculations according to channel.
+            gamma, beta: when channel is given parameters of mapping function
+            refer to original paper https://arxiv.org/pdf/1910.03151.pdf
+            (default=None. if channel size not given, use k_size given for kernel size.)
+        k_size: Adaptive selection of kernel size (default=3)
     """
-    def __init__(self, channel, k_size=3):
+    def __init__(self, channel=None, k_size=3, gamma=2, beta=1):
         super(EcaModule, self).__init__()
         assert k_size % 2 == 1
+
+        if channel is not None:
+            t = int(abs(math.log(channel, 2)+beta) / gamma)
+            k_size = t if t % 2 else t + 1
+
         self.avg_pool = nn.AdaptiveAvgPool2d(1)
         self.conv = nn.Conv1d(1, 1, kernel_size=k_size, padding=(k_size - 1) // 2, bias=False)
         self.sigmoid = nn.Sigmoid()
+
     def forward(self, x):
         # feature descriptor on the global spatial information
         y = self.avg_pool(x)
@@ -58,25 +69,6 @@ class EcaModule(nn.Module):
         y = self.sigmoid(y.view(x.shape[0], -1, 1, 1))
         return x * y.expand_as(x)
 
-
-    '''original implementation
-    def forward(self, x):
-        # x: input features with shape [b, c, h, w]
-        b, c, h, w = x.size()
-
-        # feature descriptor on the global spatial information
-        y = self.avg_pool(x)
-
-        # Two different branches of ECA module
-        y = self.conv(y.squeeze(-1).transpose(-1, -2)).transpose(-1, -2).unsqueeze(-1)
-
-        # Multi-scale information fusion
-        y = self.sigmoid(y)
-        return x * y.expand_as(x)
-    '''
-
-
-
 class CecaModule(nn.Module):
     """Constructs a circular ECA module.
     the primary difference is that the conv uses a circular padding rather than zero padding.
@@ -88,15 +80,26 @@ class CecaModule(nn.Module):
     (parameter size, throughput,latency, etc)
 
     Args:
-        channel: Number of channels of the input feature map
+        channel: Number of channels of the input feature map for use in adaptive kernel sizes
-        k_size: Adaptive selection of kernel size
+            for actual calculations according to channel.
+            gamma, beta: when channel is given parameters of mapping function
+            refer to original paper https://arxiv.org/pdf/1910.03151.pdf
+            (default=None. if channel size not given, use k_size given for kernel size.)
+        k_size: Adaptive selection of kernel size (default=3)
     """
-    def __init__(self, channel, k_size=3):
+
+    def __init__(self, channel=None, k_size=3, gamma=2, beta=1):
         super(CecaModule, self).__init__()
         assert k_size % 2 == 1
+
+        if channel is not None:
+            t = int(abs(math.log(channel, 2)+beta) / gamma)
+            k_size = t if t % 2 else t + 1
+
         self.avg_pool = nn.AdaptiveAvgPool2d(1)
         #pytorch circular padding mode is bugged as of pytorch 1.4
-        # see https://github.com/pytorch/pytorch/pull/17240
+        #see https://github.com/pytorch/pytorch/pull/17240
+
         #implement manual circular padding
         self.conv = nn.Conv1d(1, 1, kernel_size=k_size, padding=0, bias=False)
         self.padding = (k_size - 1) // 2