Activations

PyTorch/sparseconvnet/__init__.py

@@ -6,6 +6,7 @@
 
 forward_pass_multiplyAdd_count = 0
 forward_pass_hidden_states = 0
+from .activations import Tanh, Sigmoid, ReLU, ELU
 from .averagePooling import AveragePooling
 from .batchNormalization import BatchNormalization, BatchNormReLU, BatchNormLeakyReLU
 from .classificationTrainValidate import ClassificationTrainValidate

PyTorch/sparseconvnet/activations.py

+# Copyright 2016-present, Facebook, Inc.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import sparseconvnet
+import torch.nn.functional as F
+from torch.autograd import Function, Variable
+from torch.nn import Module, Parameter
+from .utils import *
+from .sparseConvNetTensor import SparseConvNetTensor
+from .batchNormalization import BatchNormalization
+
+class Sigmoid(Module):
+    def forward(self, input):
+        output = SparseConvNetTensor()
+        output.features = F.sigmoid(input.features)
+        output.metadata = input.metadata
+        output.spatial_size = input.spatial_size
+        return output
+
+class Tanh(Module):
+    def forward(self, input):
+        output = SparseConvNetTensor()
+        output.features = F.tanh(input.features)
+        output.metadata = input.metadata
+        output.spatial_size = input.spatial_size
+        return output
+
+class ReLU(Module):
+    def forward(self, input):
+        output = SparseConvNetTensor()
+        output.features = F.relu(input.features)
+        output.metadata = input.metadata
+        output.spatial_size = input.spatial_size
+        return output
+
+class ELU(Module):
+    def forward(self, input):
+        output = SparseConvNetTensor()
+        output.features = F.elu(input.features)
+        output.metadata = input.metadata
+        output.spatial_size = input.spatial_size
+        return output