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Commit 954223a2 authored by Benjamin Thomas Graham's avatar Benjamin Thomas Graham
Browse files

make SCN proper submodule

parent 19aca522
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Showing with 53 additions and 53 deletions
setup.py
View file @ 954223a2
......@@ -23,13 +23,13 @@ setup(
url='https://github.com/facebookresearch/SparseConvNet',
packages=['sparseconvnet','sparseconvnet.SCN'],
ext_modules=[
CUDAExtension('sparseconvnet_SCN',
CUDAExtension('sparseconvnet.SCN',
[
'sparseconvnet/SCN/cuda.cu', 'sparseconvnet/SCN/sparseconvnet_cuda.cpp', 'sparseconvnet/SCN/pybind.cpp'],
include_dirs=[conda_include_dir, this_dir+'/sparseconvnet/SCN/'],
extra_compile_args=extra)
if torch.cuda.is_available() else
CppExtension('sparseconvnet_SCN',
CppExtension('sparseconvnet.SCN',
['sparseconvnet/SCN/pybind.cpp', 'sparseconvnet/SCN/sparseconvnet_cpu.cpp'],
include_dirs=[conda_include_dir, this_dir+'/sparseconvnet/SCN/'],
extra_compile_args=extra['cxx'])],
......
sparseconvnet/averagePooling.py
View file @ 954223a2
......@@ -4,7 +4,7 @@
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import sparseconvnet_SCN
import sparseconvnet.SCN
from torch.autograd import Function
from torch.nn import Module
from .utils import *
......@@ -82,7 +82,7 @@ class AveragePoolingFunction(Function):
ctx.nFeaturesToDrop = nFeaturesToDrop
output_features = input_features.new()
sparseconvnet_SCN.AveragePooling_updateOutput(
sparseconvnet.SCN.AveragePooling_updateOutput(
input_spatial_size,
output_spatial_size,
pool_size,
......@@ -109,7 +109,7 @@ class AveragePoolingFunction(Function):
pool_size,\
pool_stride = ctx.saved_tensors
grad_input = grad_output.new()
sparseconvnet_SCN.AveragePooling_updateGradInput(
sparseconvnet.SCN.AveragePooling_updateGradInput(
input_spatial_size,
output_spatial_size,
pool_size,
......
sparseconvnet/batchNormalization.py
View file @ 954223a2
......@@ -4,7 +4,7 @@
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import sparseconvnet_SCN
import sparseconvnet.SCN
from torch.autograd import Function
from torch.nn import Module, Parameter
from .utils import *
......@@ -107,7 +107,7 @@ class BatchNormalizationFunction(Function):
output_features = input_features.new()
saveMean = input_features.new().resize_(ctx.nPlanes)
saveInvStd = runningMean.clone().resize_(ctx.nPlanes)
sparseconvnet_SCN.BatchNormalization_updateOutput(
sparseconvnet.SCN.BatchNormalization_updateOutput(
input_features,
output_features,
saveMean,
......@@ -144,7 +144,7 @@ class BatchNormalizationFunction(Function):
grad_input = grad_output.new()
grad_weight = torch.zeros_like(weight)
grad_bias = torch.zeros_like(bias)
sparseconvnet_SCN.BatchNormalization_backward(
sparseconvnet.SCN.BatchNormalization_backward(
input_features,
grad_input,
output_features,
......
sparseconvnet/convolution.py
View file @ 954223a2
......@@ -4,7 +4,7 @@
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import sparseconvnet, sparseconvnet_SCN
import sparseconvnet, sparseconvnet.SCN
from torch.autograd import Function
from torch.nn import Module, Parameter
from .utils import *
......@@ -91,7 +91,7 @@ class ConvolutionFunction(Function):
filter_size,
filter_stride)
sparseconvnet.forward_pass_multiplyAdd_count +=\
sparseconvnet_SCN.Convolution_updateOutput(
sparseconvnet.SCN.Convolution_updateOutput(
input_spatial_size,
output_spatial_size,
filter_size,
......@@ -110,7 +110,7 @@ class ConvolutionFunction(Function):
grad_input = grad_output.new()
grad_weight = torch.zeros_like(weight)
grad_bias = torch.zeros_like(bias)
sparseconvnet_SCN.Convolution_backward(
sparseconvnet.SCN.Convolution_backward(
input_spatial_size,
output_spatial_size,
filter_size,
......
sparseconvnet/deconvolution.py
View file @ 954223a2
......@@ -4,7 +4,7 @@
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import sparseconvnet, sparseconvnet_SCN
import sparseconvnet, sparseconvnet.SCN
from torch.autograd import Function
from torch.nn import Module, Parameter
from .utils import *
......@@ -105,7 +105,7 @@ class DeconvolutionFunction(Function):
ctx.dimension = dimension
sparseconvnet.forward_pass_multiplyAdd_count +=\
sparseconvnet_SCN.Deconvolution_updateOutput(
sparseconvnet.SCN.Deconvolution_updateOutput(
input_spatial_size,
output_spatial_size,
filter_size,
......@@ -139,7 +139,7 @@ class DeconvolutionFunction(Function):
grad_input = grad_output.new()
grad_weight = torch.zeros_like(weight)
grad_bias = torch.zeros_like(bias)
sparseconvnet_SCN.Deconvolution_backward(
sparseconvnet.SCN.Deconvolution_backward(
input_spatial_size,
output_spatial_size,
filter_size,
......
sparseconvnet/fullConvolution.py
View file @ 954223a2
......@@ -4,7 +4,7 @@
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import sparseconvnet, sparseconvnet_SCN
import sparseconvnet, sparseconvnet.SCN
from torch.autograd import Function, Variable
from torch.nn import Module, Parameter
from .utils import *
......@@ -115,7 +115,7 @@ class FullConvolutionFunction(Function):
filter_size,
filter_stride)
sparseconvnet.forward_pass_multiplyAdd_count +=\
sparseconvnet_SCN.FullConvolution_updateOutput(
sparseconvnet.SCN.FullConvolution_updateOutput(
input_spatial_size,
output_spatial_size,
filter_size,
......@@ -134,7 +134,7 @@ class FullConvolutionFunction(Function):
grad_input = grad_output.new()
grad_weight = torch.zeros_like(weight)
grad_bias = torch.zeros_like(bias)
sparseconvnet_SCN.FullConvolution_backward(
sparseconvnet.SCN.FullConvolution_backward(
input_spatial_size,
output_spatial_size,
filter_size,
......
sparseconvnet/ioLayers.py
View file @ 954223a2
......@@ -4,7 +4,7 @@
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import sparseconvnet_SCN
import sparseconvnet.SCN
from torch.autograd import Function
from torch.nn import Module, Parameter
from .utils import *
......@@ -165,7 +165,7 @@ class InputLayerFunction(Function):
output_features = input_features.new()
ctx.dimension = dimension
ctx.metadata_ = metadata
sparseconvnet_SCN.InputLayer_updateOutput(
sparseconvnet.SCN.InputLayer_updateOutput(
metadata,
spatial_size,
coords,
......@@ -179,7 +179,7 @@ class InputLayerFunction(Function):
@staticmethod
def backward(ctx, grad_output):
grad_input = grad_output.new()
sparseconvnet_SCN.InputLayer_updateGradInput(
sparseconvnet.SCN.InputLayer_updateGradInput(
ctx.metadata_,
grad_input,
grad_output.contiguous())
......@@ -196,7 +196,7 @@ class OutputLayerFunction(Function):
output_features = input_features.new()
ctx.metadata_ = metadata
ctx.dimension = dimension
sparseconvnet_SCN.OutputLayer_updateOutput(
sparseconvnet.SCN.OutputLayer_updateOutput(
metadata,
input_features.contiguous(),
output_features
......@@ -207,7 +207,7 @@ class OutputLayerFunction(Function):
def backward(ctx, grad_output):
grad_input = grad_output.new()
grad_output=grad_output.contiguous()
sparseconvnet_SCN.OutputLayer_updateGradInput(
sparseconvnet.SCN.OutputLayer_updateGradInput(
ctx.metadata_,
grad_input,
grad_output.contiguous())
......@@ -227,7 +227,7 @@ class BLInputLayerFunction(Function):
output_features = input_features.new()
ctx.metadata_ = metadata
ctx.dimension = dimension
sparseconvnet_SCN.BLInputLayer_updateOutput(
sparseconvnet.SCN.BLInputLayer_updateOutput(
metadata,
spatial_size,
coords,
......@@ -240,7 +240,7 @@ class BLInputLayerFunction(Function):
@staticmethod
def backward(ctx, grad_output):
grad_input = grad_output.new()
sparseconvnet_SCN.BLInputLayer_updateGradInput(
sparseconvnet.SCN.BLInputLayer_updateGradInput(
ctx.metadata_,
grad_input,
grad_output.contiguous())
......@@ -257,7 +257,7 @@ class BLOutputLayerFunction(Function):
output_features = input_features.new()
ctx.metadata_ = metadata
ctx.dimension = dimension
sparseconvnet_SCN.BLOutputLayer_updateOutput(
sparseconvnet.SCN.BLOutputLayer_updateOutput(
metadata,
input_features.contiguous(),
output_features
......@@ -267,7 +267,7 @@ class BLOutputLayerFunction(Function):
@staticmethod
def backward(ctx, grad_output):
grad_input = grad_output.new()
sparseconvnet_SCN.BLOutputLayer_updateGradInput(
sparseconvnet.SCN.BLOutputLayer_updateGradInput(
ctx.metadata_,
grad_input,
grad_output.contiguous())
......
sparseconvnet/maxPooling.py
View file @ 954223a2
......@@ -4,7 +4,7 @@
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import sparseconvnet_SCN
import sparseconvnet.SCN
from torch.autograd import Function
from torch.nn import Module
from .utils import *
......@@ -27,7 +27,7 @@ class MaxPoolingFunction(Function):
ctx.dimension = dimension
ctx.nFeaturesToDrop = nFeaturesToDrop
output_features = input_features.new()
sparseconvnet_SCN.MaxPooling_updateOutput(
sparseconvnet.SCN.MaxPooling_updateOutput(
input_spatial_size,
output_spatial_size,
pool_size,
......@@ -54,7 +54,7 @@ class MaxPoolingFunction(Function):
pool_size,\
pool_stride = ctx.saved_tensors
grad_input = grad_output.new()
sparseconvnet_SCN.MaxPooling_updateGradInput(
sparseconvnet.SCN.MaxPooling_updateGradInput(
input_spatial_size,
output_spatial_size,
pool_size,
......
sparseconvnet/metadata.py
View file @ 954223a2
......@@ -11,7 +11,7 @@ all coexist within the same MetaData object as long as each spatial size
only occurs once.
"""
import sparseconvnet_SCN
import sparseconvnet.SCN
def Metadata(dim):
return getattr(sparseconvnet_SCN, 'Metadata_%d'%dim)()
return getattr(sparseconvnet.SCN, 'Metadata_%d'%dim)()
sparseconvnet/networkInNetwork.py
View file @ 954223a2
......@@ -4,7 +4,7 @@
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import sparseconvnet, sparseconvnet_SCN
import sparseconvnet, sparseconvnet.SCN
from torch.autograd import Function
from torch.nn import Module, Parameter
from .utils import *
......@@ -24,7 +24,7 @@ class NetworkInNetworkFunction(Function):
weight,
bias)
sparseconvnet.forward_pass_multiplyAdd_count +=\
sparseconvnet_SCN.NetworkInNetwork_updateOutput(
sparseconvnet.SCN.NetworkInNetwork_updateOutput(
input_features,
output_features,
weight,
......@@ -44,11 +44,11 @@ class NetworkInNetworkFunction(Function):
grad_bias = None
else:
grad_bias = torch.zeros_like(bias)
sparseconvnet_SCN.NetworkInNetwork_updateGradInput(
sparseconvnet.SCN.NetworkInNetwork_updateGradInput(
grad_input,
grad_output,
weight)
sparseconvnet_SCN.NetworkInNetwork_accGradParameters(
sparseconvnet.SCN.NetworkInNetwork_accGradParameters(
input_features,
grad_output,
grad_weight,
......
sparseconvnet/permutohedralSubmanifoldConvolution.py
View file @ 954223a2
......@@ -5,7 +5,7 @@
# LICENSE file in the root directory of this source tree.
import sparseconvnet
import sparseconvnet_SCN
import sparseconvnet.SCN
from torch.autograd import Function
from torch.nn import Module, Parameter
from .utils import *
......@@ -82,7 +82,7 @@ class PermutohedralSubmanifoldConvolutionFunction(Function):
bias)
sparseconvnet.forward_pass_multiplyAdd_count +=\
sparseconvnet_SCN.PermutohedralSubmanifoldConvolution_updateOutput(
sparseconvnet.SCN.PermutohedralSubmanifoldConvolution_updateOutput(
spatial_size,
input_metadata,
input_features,
......@@ -98,7 +98,7 @@ class PermutohedralSubmanifoldConvolutionFunction(Function):
grad_input = grad_output.new()
grad_weight = torch.zeros_like(weight)
grad_bias = torch.zeros_like(bias)
sparseconvnet_SCN.PermutohedralSubmanifoldConvolution_backward(
sparseconvnet.SCN.PermutohedralSubmanifoldConvolution_backward(
spatial_size,
ctx.input_metadata,
input_features,
......
sparseconvnet/randomizedStrideConvolution.py
View file @ 954223a2
......@@ -4,7 +4,7 @@
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import sparseconvnet, sparseconvnet_SCN
import sparseconvnet, sparseconvnet.SCN
from torch.autograd import Function
from torch.nn import Module, Parameter
from .utils import *
......@@ -104,7 +104,7 @@ class RandomizedStrideConvolutionFunction(Function):
filter_size,
filter_stride)
sparseconvnet.forward_pass_multiplyAdd_count +=\
sparseconvnet_SCN.RandomizedStrideConvolution_updateOutput(
sparseconvnet.SCN.RandomizedStrideConvolution_updateOutput(
input_spatial_size,
output_spatial_size,
filter_size,
......@@ -124,7 +124,7 @@ class RandomizedStrideConvolutionFunction(Function):
grad_input = grad_output.new()
grad_weight = torch.zeros_like(weight)
grad_bias = torch.zeros_like(bias)
sparseconvnet_SCN.RandomizedStrideConvolution_backward(
sparseconvnet.SCN.RandomizedStrideConvolution_backward(
input_spatial_size,
output_spatial_size,
filter_size,
......
sparseconvnet/randomizedStrideMaxPooling.py
View file @ 954223a2
......@@ -4,7 +4,7 @@
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import sparseconvnet_SCN
import sparseconvnet.SCN
from torch.autograd import Function
from torch.nn import Module
from .utils import *
......@@ -27,7 +27,7 @@ class RandomizedStrideMaxPoolingFunction(Function):
ctx.dimension = dimension
ctx.nFeaturesToDrop = nFeaturesToDrop
output_features = input_features.new()
sparseconvnet_SCN.RandomizedStrideMaxPooling_updateOutput(
sparseconvnet.SCN.RandomizedStrideMaxPooling_updateOutput(
input_spatial_size,
output_spatial_size,
pool_size,
......@@ -54,7 +54,7 @@ class RandomizedStrideMaxPoolingFunction(Function):
pool_size,\
pool_stride = ctx.saved_tensors
grad_input = grad_output.new()
sparseconvnet_SCN.RandomizedStrideMaxPooling_updateGradInput(
sparseconvnet.SCN.RandomizedStrideMaxPooling_updateGradInput(
input_spatial_size,
output_spatial_size,
pool_size,
......
sparseconvnet/sparseToDense.py
View file @ 954223a2
......@@ -15,7 +15,7 @@ Parameters:
dimension : of the input field,
"""
import sparseconvnet_SCN
import sparseconvnet.SCN
from torch.autograd import Function
from torch.nn import Module
from .utils import *
......@@ -35,7 +35,7 @@ class SparseToDenseFunction(Function):
ctx.dimension = dimension
ctx.save_for_backward(input_features, spatial_size)
output = input_features.new()
sparseconvnet_SCN.SparseToDense_updateOutput(
sparseconvnet.SCN.SparseToDense_updateOutput(
spatial_size,
input_metadata,
input_features,
......@@ -47,7 +47,7 @@ class SparseToDenseFunction(Function):
def backward(ctx, grad_output):
grad_input = grad_output.new()
input_features, spatial_size = ctx.saved_tensors
sparseconvnet_SCN.SparseToDense_updateGradInput(
sparseconvnet.SCN.SparseToDense_updateGradInput(
spatial_size,
ctx.input_metadata,
input_features,
......
sparseconvnet/submanifoldConvolution.py
View file @ 954223a2
......@@ -7,7 +7,7 @@
# 'SubmanifoldConvolution == SubmanifoldConvolution'
import sparseconvnet
import sparseconvnet_SCN
import sparseconvnet.SCN
from torch.autograd import Function
from torch.nn import Module, Parameter
from .utils import *
......@@ -84,7 +84,7 @@ class SubmanifoldConvolutionFunction(Function):
filter_size)
sparseconvnet.forward_pass_multiplyAdd_count +=\
sparseconvnet_SCN.SubmanifoldConvolution_updateOutput(
sparseconvnet.SCN.SubmanifoldConvolution_updateOutput(
spatial_size,
filter_size,
input_metadata,
......@@ -101,7 +101,7 @@ class SubmanifoldConvolutionFunction(Function):
grad_input = grad_output.new()
grad_weight = torch.zeros_like(weight)
grad_bias = torch.zeros_like(bias)
sparseconvnet_SCN.SubmanifoldConvolution_backward(
sparseconvnet.SCN.SubmanifoldConvolution_backward(
spatial_size,
filter_size,
ctx.input_metadata,
......
sparseconvnet/unPooling.py
View file @ 954223a2
......@@ -4,7 +4,7 @@
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import sparseconvnet_SCN
import sparseconvnet.SCN
from torch.autograd import Function, Variable
from torch.nn import Module
from .utils import *
......@@ -31,7 +31,7 @@ class UnPoolingFunction(Function):
ctx.pool_stride = pool_stride
ctx.nFeaturesToDrop = nFeaturesToDrop
output_features = input_features.new()
sparseconvnet_SCN.UnPooling_updateOutput(
sparseconvnet.SCN.UnPooling_updateOutput(
input_spatial_size,
output_spatial_size,
pool_size,
......@@ -45,7 +45,7 @@ class UnPoolingFunction(Function):
@staticmethod
def backward(ctx, grad_output):
grad_input=Variable(grad_output.data.new())
sparseconvnet_SCN.UnPooling_updateGradInput(
sparseconvnet.SCN.UnPooling_updateGradInput(
ctx.input_spatial_size,
ctx.output_spatial_size,
ctx.pool_size,
......
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