[Enhance]enhance hsigmoid (#657)

* enhance hsigmoid

* delete int

mmcv/cnn/bricks/hsigmoid.py

@@ -6,16 +6,28 @@ from .registry import ACTIVATION_LAYERS
 @ACTIVATION_LAYERS.register_module()
 class HSigmoid(nn.Module):
     """Hard Sigmoid Module. Apply the hard sigmoid function:
-    Hsigmoid(x) = min(max((x + 1) / 2, 0), 1)
+    Hsigmoid(x) = min(max((x + bias) / divisor, min_value), max_value)
+    Default: Hsigmoid(x) = min(max((x + 1) / 2, 0), 1)
+
+    Args:
+        bias (float): Bias of the input feature map. Default: 1.0.
+        divisor (float): Divisor of the input feature map. Default: 2.0.
+        min_value (float): Lower bound value. Default: 0.0.
+        max_value (float): Upper bound value. Default: 1.0.
 
     Returns:
         Tensor: The output tensor.
     """
 
-    def __init__(self):
+    def __init__(self, bias=1.0, divisor=2.0, min_value=0.0, max_value=1.0):
         super(HSigmoid, self).__init__()
+        self.bias = bias
+        self.divisor = divisor
+        assert self.divisor != 0
+        self.min_value = min_value
+        self.max_value = max_value
 
     def forward(self, x):
-        x = (x + 1) / 2
+        x = (x + self.bias) / self.divisor
 
-        return x.clamp_(0, 1)
+        return x.clamp_(self.min_value, self.max_value)

tests/test_cnn/test_hsigmoid.py

@@ -1,9 +1,15 @@
+import pytest
 import torch
 
 from mmcv.cnn.bricks import HSigmoid
 
 
 def test_hsigmoid():
+    # test assertion divisor can not be zero
+    with pytest.raises(AssertionError):
+        HSigmoid(divisor=0)
+
+    # test with default parameters
     act = HSigmoid()
     input_shape = torch.Size([1, 3, 64, 64])
     input = torch.randn(input_shape)
@@ -15,3 +21,16 @@ def test_hsigmoid():
     assert output.shape == expected_output.shape
     # test output value
     assert torch.equal(output, expected_output)
+
+    # test with designated parameters
+    act = HSigmoid(3, 6, 0, 1)
+    input_shape = torch.Size([1, 3, 64, 64])
+    input = torch.randn(input_shape)
+    output = act(input)
+    expected_output = torch.min(
+        torch.max((input + 3) / 6, torch.zeros(input_shape)),
+        torch.ones(input_shape))
+    # test output shape
+    assert output.shape == expected_output.shape
+    # test output value
+    assert torch.equal(output, expected_output)