Fix: construct our Hardswish activation

src/digitizer/yolov5/models/common.py

@@ -3,6 +3,48 @@ import math
 
 import torch
 import torch.nn as nn
+import torch.nn.functional as F
+
+# Avoid the use of original Hardswish at torch to avoid the error:
+class Hardswish(nn.Module):
+    r"""Applies the hardswish function, element-wise, as described in the paper:
+
+    `Searching for MobileNetV3`_.
+
+    .. math::
+        \text{Hardswish}(x) = \begin{cases}
+            0 & \text{if~} x \le -3, \\
+            x & \text{if~} x \ge +3, \\
+            x \cdot (x + 3) /6 & \text{otherwise}
+        \end{cases}
+
+    Args:
+        inplace: can optionally do the operation in-place. Default: ``False``
+
+    Shape:
+        - Input: :math:`(N, *)` where `*` means, any number of additional
+          dimensions
+        - Output: :math:`(N, *)`, same shape as the input
+
+    Examples::
+
+        >>> m = nn.Hardswish()
+        >>> input = torch.randn(2)
+        >>> output = m(input)
+
+    .. _`Searching for MobileNetV3`:
+        https://arxiv.org/abs/1905.02244
+    """
+    __constants__ = ['inplace']
+
+    inplace: bool
+
+    def __init__(self, inplace : bool = False) -> None:
+        super(Hardswish, self).__init__()
+        self.inplace = inplace
+
+    def forward(self, input: Tensor) -> Tensor:
+        return F.hardswish(input)
 
 
 def autopad(k, p=None):  # kernel, padding
@@ -23,7 +65,7 @@ class Conv(nn.Module):
         super(Conv, self).__init__()
         self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False)
         self.bn = nn.BatchNorm2d(c2)
-        self.act = nn.Hardswish() if act else nn.Identity()
+        self.act = Hardswish() if act else nn.Identity()
 
     def forward(self, x):
         return self.act(self.bn(self.conv(x)))