tidy

PyTorch/sparseconvnet/legacy/__init__.py

@@ -10,7 +10,7 @@ from .inputBatch import InputBatch
 from .sparseConvNetTensor import SparseConvNetTensor
 from .sparseModule import SparseModule
 from .averagePooling import AveragePooling
-from .batchNormalization import BatchNormReLU, BatchNormLeakyReLU, BatchNormalizationInTensor
+from .batchNormalization import BatchNormalization, BatchNormReLU, BatchNormLeakyReLU, BatchNormalizationInTensor
 from .concatTable import ConcatTable
 from .convolution import Convolution
 from .cAddTable import CAddTable
@@ -18,7 +18,7 @@ from .deconvolution import Deconvolution
 from .denseToSparse import DenseToSparse
 from .identity import Identity
 from .joinTable import JoinTable
-from .leakyReLU import LeakyReLU, Tanh
+from .leakyReLU import LeakyReLU
 from .maxPooling import MaxPooling
 from .networkInNetwork import NetworkInNetwork
 from .reLU import ReLU
@@ -27,3 +27,4 @@ from .sparseToDense import SparseToDense
 from .validConvolution import ValidConvolution
 from .networkArchitectures import *
 from .classificationTrainValidate import ClassificationTrainValidate
+from .misc import *

PyTorch/sparseconvnet/legacy/batchNormalization.py

@@ -21,7 +21,6 @@ from . import SparseModule
 from ..utils import toLongTensor, typed_fn, optionalTensor, nullptr
 from .sparseConvNetTensor import SparseConvNetTensor
 
-
 class BatchNormalization(SparseModule):
     def __init__(
             self,
@@ -122,7 +121,6 @@ class BatchNormLeakyReLU(BatchNormalization):
             ',momentum=' + str(self.momentum) + ',affine=' + str(self.affine) + ')'
         return s
 
-
 class BatchNormalizationInTensor(BatchNormalization):
     def __init__(
             self,

PyTorch/sparseconvnet/legacy/leakyReLU.py

@@ -42,28 +42,3 @@ class LeakyReLU(SparseModule):
         if t:
             self.output.type(t)
             self.gradInput = self.gradInput.type(t)
-
-
-class Tanh(SparseModule):
-    def __init__(self):
-        SparseModule.__init__(self)
-        self.output = SparseConvNetTensor(torch.Tensor())
-        #self.gradInput = None if ip else torch.Tensor()
-        self.gradInput = torch.Tensor()
-
-    def updateOutput(self, input):
-        self.output.metadata = input.metadata
-        self.output.spatial_size = input.spatial_size
-        self.output.features=torch.tanh(input.features)
-        return self.output
-
-    def updateGradInput(self, input, gradOutput):
-        self.gradInput.resize_as_(gradOutput).copy_(gradOutput)
-        self.gradInput.mul(1+self.output.features)
-        self.gradInput.mul(1-self.output.features)
-        return self.gradInput
-
-    def type(self, t, tensorCache=None):
-        if t:
-            self.output.type(t)
-            self.gradInput = self.gradInput.type(t)

PyTorch/sparseconvnet/legacy/misc.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 torch.legacy.nn as nn
+from .sequential import Sequential
+from .sparseModule import SparseModule
+
+class Tanh(SparseModule):
+    def __init__(self):
+        SparseModule.__init__(self)
+        self.module=nn.Tanh()
+        self.output = SparseConvNetTensor()
+        self.output.features=self.module.output
+        self.gradInput = self.module.gradInput
+
+    def updateOutput(self, input):
+        self.output.metadata = input.metadata
+        self.output.spatial_size = input.spatial_size
+        self.module.forward(input.features)
+        return self.output
+
+    def updateGradInput(self, input, gradOutput):
+        self.module.updateGradInput(input.features,gradOutput)
+        return self.gradInput
+
+    def type(self, t, tensorCache=None):
+        if t:
+            self.module.type(t,tensorCache)
+            self.output.features=self.module.output
+            self.gradInput = self.module.gradInput
+
+class ELU(SparseModule):
+    def __init__(self):
+        SparseModule.__init__(self)
+        self.module=nn.ELU()
+        self.output = SparseConvNetTensor()
+        self.gradInput = self.module.gradInput
+
+    def updateOutput(self, input):
+        self.output.metadata = input.metadata
+        self.output.spatial_size = input.spatial_size
+        self.module.forward(input.features)
+        return self.output
+
+    def updateGradInput(self, input, gradOutput):
+        self.module.updateGradInput(input.features,gradOutput)
+        return self.gradInput
+
+    def type(self, t, tensorCache=None):
+        if t:
+            self.module.type(t,tensorCache)
+            self.output.features=self.module.output
+            self.gradInput = self.module.gradInput
+
+def BatchNormELU(nPlanes, eps=1e-4, momentum=0.9):
+    return Sequential().add(BatchNormalization(nPlanes,eps,momentum)).add(ELU())