[Feature] Add clamp activation layer. (#685)

* add clamp without unittest

* add clamp-act with unit test

* fix name bug

* use logical and

* fix logical_and

* fix linting

* rename ClampLayer to Clamp

* rename ClampLayer to Clamp

Co-authored-by: nbei <631557085@qq.com>

mmcv/cnn/bricks/activation.py

@@ -1,3 +1,4 @@
+import torch
 import torch.nn as nn
 
 from mmcv.utils import build_from_cfg
@@ -10,6 +11,38 @@ for module in [
     ACTIVATION_LAYERS.register_module(module=module)
 
 
+@ACTIVATION_LAYERS.register_module(name='Clip')
+@ACTIVATION_LAYERS.register_module()
+class Clamp(nn.Module):
+    """Clamp activation layer.
+
+    This activation function is to clamp the feature map value within
+    :math:`[min, max]`. More details can be found in ``torch.clamp()``.
+
+    Args:
+        min (Number | optional): Lower-bound of the range to be clamped to.
+            Default to -1.
+        max (Number | optional): Upper-bound of the range to be clamped to.
+            Default to 1.
+    """
+
+    def __init__(self, min=-1., max=1.):
+        super(Clamp, self).__init__()
+        self.min = min
+        self.max = max
+
+    def forward(self, x):
+        """Forward function.
+
+        Args:
+            x (torch.Tensor): The input tensor.
+
+        Returns:
+            torch.Tensor: Clamped tensor.
+        """
+        return torch.clamp(x, min=self.min, max=self.max)
+
+
 def build_activation_layer(cfg):
     """Build activation layer.
 

tests/test_cnn/test_build_layers.py

@@ -1,3 +1,4 @@
+import numpy as np
 import pytest
 import torch
 import torch.nn as nn
@@ -182,6 +183,18 @@ def test_build_activation_layer():
         layer = build_activation_layer(cfg)
         assert isinstance(layer, module)
 
+    # sanity check for Clamp
+    act = build_activation_layer(dict(type='Clamp'))
+    x = torch.randn(10) * 1000
+    y = act(x)
+    assert np.logical_and((y >= -1).numpy(), (y <= 1).numpy()).all()
+    act = build_activation_layer(dict(type='Clip', min=0))
+    y = act(x)
+    assert np.logical_and((y >= 0).numpy(), (y <= 1).numpy()).all()
+    act = build_activation_layer(dict(type='Clamp', max=0))
+    y = act(x)
+    assert np.logical_and((y >= -1).numpy(), (y <= 0).numpy()).all()
+
 
 def test_build_padding_layer():
     with pytest.raises(TypeError):