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Commit 1df7b845 authored by Benjamin Thomas Graham's avatar Benjamin Thomas Graham
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3d segmantation

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.gitignore
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...@@ -10,4 +10,4 @@ pickle ...@@ -10,4 +10,4 @@ pickle
*.pyc *.pyc
PyTorch/sparseconvnet.egg-info/ PyTorch/sparseconvnet.egg-info/
PyTorch/sparseconvnet/SCN/__init__.py PyTorch/sparseconvnet/SCN/__init__.py
sparseconvnet.egg-info
CODE_OF_CONDUCT.md 0 → 100644
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# Code of Conduct
Facebook has adopted a Code of Conduct that we expect project participants to adhere to. Please read the [full text](https://code.facebook.com/pages/876921332402685/open-source-code-of-conduct) so that you can understand what actions will and will not be tolerated.
CONTRIBUTING.md 0 → 100644
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# Contributing to SparseConvNet
We want to make contributing to this project as easy and transparent as
possible.
## Pull Requests
We actively welcome your pull requests.
1. Fork the repo and create your branch from `master`.
2. Ensure the examples still run.
3. If you haven't already, complete the Contributor License Agreement ("CLA").
## Contributor License Agreement ("CLA")
In order to accept your pull request, we need you to submit a CLA. You only need
to do this once to work on any of Facebook's open source projects.
Complete your CLA here: <https://code.facebook.com/cla>
## Issues
We use GitHub issues to track public bugs. Please ensure your description is
clear and has sufficient instructions to be able to reproduce the issue.
Facebook has a [bounty program](https://www.facebook.com/whitehat/) for the safe
disclosure of security bugs. In those cases, please go through the process
outlined on that page and do not file a public issue.
## Coding Style
We try to follow the PEP style guidelines and encourage you to as well.
## License
By contributing to SparseConvNet, you agree that your contributions will be licensed
under the LICENSE file in the root directory of this source tree.
PyTorch/sparseconvnet/legacy/__init__.py deleted 100644 → 0
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# 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.
from ..utils import *
from ..metadata import Metadata
from ..inputBatch import InputBatch
from ..sparseConvNetTensor import SparseConvNetTensor
from .sparseModule import SparseModule
from .averagePooling import AveragePooling
from .batchNormalization import BatchNormalization, BatchNormReLU, BatchNormLeakyReLU, BatchNormalizationInTensor
from .batchwiseDropout import BatchwiseDropout, BatchwiseDropoutInTensor
from .concatTable import ConcatTable
from .convolution import Convolution
from .cAddTable import CAddTable
from .deconvolution import Deconvolution
from .denseToSparse import DenseToSparse
from .identity import Identity
from .joinTable import JoinTable
from .leakyReLU import LeakyReLU
from .maxPooling import MaxPooling
from .networkInNetwork import NetworkInNetwork
from .reLU import ReLU
from .sequential import Sequential
from .sparseToDense import SparseToDense
from .submanifoldConvolution import SubmanifoldConvolution
from .networkArchitectures import *
from .classificationTrainValidate import ClassificationTrainValidate
from .misc import *
PyTorch/sparseconvnet/legacy/affineReLUTrivialConvolution.py deleted 100644 → 0
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# 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.
"""
Affine transformation (i.e. the second half of a typical batchnormalization layer)
Parameters:
nPlanes : number of input planes
noise : add multiplicative and additive noise during training if >0.
leakiness : Apply activation function inplace: 0<=leakiness<=1.
0 for ReLU, values in (0,1) for LeakyReLU, 1 for no activation function.
"""
import torch
import sparseconvnet as s
from . import SparseModule
from ..utils import toLongTensor, typed_fn, optionalTensor, nullptr
from ..sparseConvNetTensor import SparseConvNetTensor
import math
class AffineReLUTrivialConvolution(SparseModule):
def __init__(self, nIn, nOut, additiveGrad=False):
SparseModule.__init__(self)
self.nIn = nIn
self.nOut = nOut
self.affineWeight = torch.Tensor(nIn).fill_(1)
self.affineBias = torch.Tensor(nIn).zero_()
std = math.sqrt(2.0 / nIn)
self.convWeight = torch.Tensor(nIn, nOut).normal_(0, std)
self.gradAffineWeight = torch.Tensor(nIn).fill_(0)
self.gradAffineBias = torch.Tensor(nIn).zero_(0.333)
self.gradConvWeight = torch.Tensor(nIn, nOut).fill_(std)
self.additiveGrad = additiveGrad
self.output = SparseConvNetTensor(torch.Tensor())
self.gradInput = torch.Tensor()
def parameters(self):
return [self.affineWeight, self.affineBias, self.convWeight], [
self.gradAffineWeight, self.gradAffineBias, self.gradConvWeight]
def updateOutput(self, input):
self.output.metadata = input.metadata
self.output.spatial_size = input.spatial_size
typed_fn(input.features, 'AffineReluTrivialConvolution_updateOutput')(
input.features,
self.output.features,
self.affineWeight,
self.affineBias,
self.convWeight)
s.forward_pass_multiplyAdd_count += input.features.size(
0) * self.nIn * self.nOut
s.forward_pass_hidden_states += self.output.features.nelement()
return self.output
def backward(self, input, gradOutput, scale=1):
assert scale == 1
typed_fn(input.features, 'AffineReluTrivialConvolution_backward')(
input.features,
self.gradInput,
gradOutput,
self.affineWeight,
self.gradAffineWeight,
self.affineBias,
self.gradAffineBias,
self.convWeight,
self.gradConvWeight,
self.additiveGrad)
return self.gradInput
def updateGradInput(self, input, gradOutput):
assert false # just call backward
def accGradParameters(input, gradOutput, scale):
assert false # just call backward
def __repr__(self):
s = 'AffineReluTrivialConvolution ' + \
str(self.nIn) + '->' + str(self.nOut)
return s
def type(self, t=None, tensorCache=None):
if t is None:
return self._type
self._type = t
self.affineWeight = self.affineWeight.type(t)
self.affineBias = self.affineBias.type(t)
self.convWeight = self.convWeight.type(t)
self.gradAffineWeight = self.gradAffineWeight.type(t)
self.gradAffineBias = self.gradAffineBias.type(t)
self.gradConvWeight = self.gradConvWeight.type(t)
self.gradInput = self.gradInput.type(t)
self.output.type(t)
PyTorch/sparseconvnet/legacy/averagePooling.py deleted 100644 → 0
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# 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
import sparseconvnet
from . import SparseModule
from ..utils import toLongTensor, dim_typed_fn, optionalTensor, nullptr
from ..sparseConvNetTensor import SparseConvNetTensor
class AveragePooling(SparseModule):
def __init__(self, dimension, pool_size, pool_stride, nFeaturesToDrop=0):
SparseModule.__init__(self)
self.dimension = dimension
self.pool_size = toLongTensor(dimension, pool_size)
self.pool_stride = toLongTensor(dimension, pool_stride)
self.nFeaturesToDrop = nFeaturesToDrop
self.output = SparseConvNetTensor(torch.Tensor())
self.gradInput = torch.Tensor()
def updateOutput(self, input):
self.output.metadata = input.metadata
self.output.spatial_size =\
(input.spatial_size - self.pool_size) / self.pool_stride + 1
dim_typed_fn(
self.dimension,
input.features,
'AveragePooling_updateOutput')(
input.spatial_size,
self.output.spatial_size,
self.pool_size,
self.pool_stride,
input.metadata.ffi,
input.features,
self.output.features,
self.nFeaturesToDrop,
torch.cuda.IntTensor() if input.features.is_cuda else nullptr)
return self.output
def updateGradInput(self, input, gradOutput):
dim_typed_fn(
self.dimension, input.features, 'AveragePooling_updateGradInput')(
input.spatial_size,
self.output.spatial_size,
self.pool_size,
self.pool_stride,
input.metadata.ffi,
input.features,
self.gradInput,
gradOutput,
self.nFeaturesToDrop,
torch.cuda.IntTensor() if input.features.is_cuda else nullptr)
return self.gradInput
def type(self, t=None, tensorCache=None):
if t is None:
return self._type
self.output.type(t)
self.gradInput = self.gradInput.type(t)
def __repr__(self):
s = 'AveragePooling'
if self.pool_size.max() == self.pool_size.min() and\
self.pool_stride.max() == self.pool_stride.min():
s = s + str(self.pool_size[0]) + '/' + str(self.pool_stride[0])
else:
s = s + '(' + str(self.pool_size[0])
for i in self.pool_size[1:]:
s = s + ',' + str(i)
s = s + ')/(' + str(self.pool_stride[0])
for i in self.pool_stride[1:]:
s = s + ',' + str(i)
s = s + ')'
if self.nFeaturesToDrop > 0:
s = s + ' nFeaturesToDrop = ' + self.nFeaturesToDrop
return s
def suggestInputSize(self, out_size):
return (out_size - 1) * self.pool_stride + self.pool_size
PyTorch/sparseconvnet/legacy/batchNormalization.py deleted 100644 → 0
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# 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.
"""
Parameters:
nPlanes : number of input planes
eps : small number used to stabilise standard deviation calculation
momentum : for calculating running average for testing (default 0.9)
affine : only 'true' is supported at present (default 'true')
noise : add multiplicative and additive noise during training if >0.
leakiness : Apply activation def inplace: 0<=leakiness<=1.
0 for ReLU, values in (0,1) for LeakyReLU, 1 for no activation def.
"""
import torch
import sparseconvnet
from . import SparseModule
from ..utils import toLongTensor, typed_fn, optionalTensor, nullptr
from ..sparseConvNetTensor import SparseConvNetTensor
class BatchNormalization(SparseModule):
def __init__(
self,
nPlanes,
eps=1e-4,
momentum=0.9,
affine=True,
leakiness=1):
SparseModule.__init__(self)
self.nPlanes = nPlanes
self.eps = eps
self.momentum = momentum
self.affine = affine
self.leakiness = leakiness
self.saveMean = torch.Tensor(nPlanes).fill_(0)
self.saveInvStd = torch.Tensor(nPlanes).fill_(1)
self.runningMean = torch.Tensor(nPlanes).fill_(0)
self.runningVar = torch.Tensor(nPlanes).fill_(1)
if affine:
self.weight = torch.Tensor(nPlanes).fill_(1)
self.bias = torch.Tensor(nPlanes).fill_(0)
self.gradWeight = torch.Tensor(nPlanes).fill_(0)
self.gradBias = torch.Tensor(nPlanes).fill_(0.333)
self.output = SparseConvNetTensor(torch.Tensor())
self.gradInput = torch.Tensor()
def updateOutput(self, input):
assert input.features.ndimension() == 0 or input.features.size(1) == self.nPlanes
self.output.metadata = input.metadata
self.output.spatial_size = input.spatial_size
typed_fn(input.features, 'BatchNormalization_updateOutput')(
input.features,
self.output.features,
self.saveMean,
self.saveInvStd,
self.runningMean,
self.runningVar,
optionalTensor(self, 'weight'),
optionalTensor(self, 'bias'),
self.eps,
self.momentum,
self.train,
self.leakiness)
return self.output
def backward(self, input, gradOutput, scale=1):
assert scale == 1
assert self.train
typed_fn(input.features, 'BatchNormalization_backward')(
input.features,
self.gradInput,
self.output.features,
gradOutput,
self.saveMean,
self.saveInvStd,
self.runningMean,
self.runningVar,
optionalTensor(self, 'weight'),
optionalTensor(self, 'bias'),
optionalTensor(self, 'gradWeight'),
optionalTensor(self, 'gradBias'),
self.leakiness)
return self.gradInput
def updateGradInput(self, input, gradOutput):
assert false # just call backward
def accGradParameters(self, input, gradOutput, scale):
assert false # just call backward
def type(self, t=None, tensorCache=None):
self.output.type(t)
SparseModule.type(self, t, tensorCache)
def __repr__(self):
s = 'BatchNorm(' + str(self.nPlanes) + ',eps=' + str(self.eps) + \
',momentum=' + str(self.momentum) + ',affine=' + str(self.affine)
if self.leakiness > 0:
s = s + ',leakiness=' + str(self.leakiness)
s = s + ')'
return s
class BatchNormReLU(BatchNormalization):
def __init__(self, nPlanes, eps=1e-4, momentum=0.9):
BatchNormalization.__init__(self, nPlanes, eps, momentum, True, 0)
def __repr__(self):
s = 'BatchNormReLU(' + str(self.nPlanes) + ',eps=' + str(self.eps) + \
',momentum=' + str(self.momentum) + ',affine=' + str(self.affine) + ')'
return s
class BatchNormLeakyReLU(BatchNormalization):
def __init__(self, nPlanes, eps=1e-4, momentum=0.9):
BatchNormalization.__init__(self, nPlanes, eps, momentum, True, 0.333)
def __repr__(self):
s = 'BatchNormReLU(' + str(self.nPlanes) + ',eps=' + str(self.eps) + \
',momentum=' + str(self.momentum) + ',affine=' + str(self.affine) + ')'
return s
class BatchNormalizationInTensor(BatchNormalization):
def __init__(
self,
nPlanes,
eps=1e-4,
momentum=0.9,
output_column_offset=0):
BatchNormalization.__init__(self, nPlanes, eps, momentum, False, 1)
self.output_column_offset = output_column_offset
def updateOutput(self, input):
o = self.output.features.narrow(
1, self.output_column_offset, self.nPlanes)
self.output.metadata = input.metadata
self.output.spatial_size = input.spatial_size
typed_fn(input.features, 'BatchNormalizationInTensor_updateOutput')(
input.features,
o,
self.saveMean,
self.saveInvStd,
self.runningMean,
self.runningVar,
optionalTensor(self, 'weight'),
optionalTensor(self, 'bias'),
self.eps,
self.momentum,
self.train,
self.leakiness)
return self.output
def backward(self, input, gradOutput, scale=1):
assert scale == 1
assert self.train
o = self.output.features.narrow(
1, self.output_column_offset, self.nPlanes)
d_o = gradOutput.narrow(1, self.output_column_offset, self.nPlanes)
typed_fn(input.features, 'BatchNormalization_backward')(
input.features,
self.gradInput,
o,
d_o,
self.saveMean,
self.saveInvStd,
self.runningMean,
self.runningVar,
optionalTensor(self, 'weight'),
optionalTensor(self, 'bias'),
optionalTensor(self, 'gradWeight'),
optionalTensor(self, 'gradBias'),
self.leakiness)
return self.gradInput
PyTorch/sparseconvnet/legacy/batchwiseDropout.py deleted 100644 → 0
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# 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.
"""
Implementation of batchwise dropout, optionally followed by LeakyReLU
Parameters:
nPlanes: number of input planes
p : dropout probability in the range [0,1]
ip : perform dropout inplace (default true)
leaky : in the range [0,1]. Set to zero to do ReLU after the dropout. Set to one
just to do dropout. Set to 1/3 for LeakyReLU after the dropout, etc. (default 1)
"""
import torch
import sparseconvnet
from . import SparseModule
from ..utils import toLongTensor, typed_fn
from ..sparseConvNetTensor import SparseConvNetTensor
class BatchwiseDropout(SparseModule):
def __init__(
self,
nPlanes,
p,
ip=True,
leaky=1):
self.inplace = ip
self.p = p
self.leakiness = leaky
self.noise = torch.Tensor(nPlanes)
self.nPlanes = nPlanes
self.output = None if ip else SparseConvNetTensor(torch.Tensor())
self.gradInput = None if ip else torch.Tensor()
def updateOutput(self, input):
if self.train:
self.noise.bernoulli_(1 - self.p)
else:
self.noise.fill_(1 - self.p)
if self.inplace:
self.output = input
else:
self.output.metadata = input.metadata
self.output.spatialSize = input.spatialSize
typed_fn(input.features, 'BatchwiseMultiplicativeDropout_updateOutput')(
input.features, self.output.features, self.noise, self.leakiness)
return self.output
def updateGradInput(self, input, gradOutput):
if self.inplace:
self.gradInput = gradOutput
typed_fn(input.features, 'BatchwiseMultiplicativeDropout_updateGradInput')(
input.features,
self.gradInput,
gradOutput,
self.noise,
self.leakiness
)
return self.gradInput
def type(self, t, tensorCache=None):
self.noise.type(t)
if not self.inplace:
self.output.features.type(t)
self.gradInput.type(t)
SparseModule.type(self, t, tensorCache)
def clearState(self):
if self.inPlace:
self.output = None
self.gradOutput = None
else:
SparseModule.clearState(self)
def __repr__(self):
s = 'BatchwiseDropout(' + str(self.nPlanes) + ',p=' + str(self.p) + \
',ip=' + str(self.inplace)
if self.leakiness > 0:
s = s + ',leakiness=' + str(self.leakiness)
s = s + ')'
return s
class BatchwiseDropoutInTensor(BatchwiseDropout):
def __init__(
self,
nPlanes,
p,
output_column_offset=0,
leaky=1):
BatchwiseDropout.__init__(self, nPlanes, p, False, leaky)
self.output_column_offset = output_column_offset
def updateOutput(self, input):
if self.train:
self.noise.bernoulli_(1 - self.p)
else:
self.noise.fill_(1 - self.p)
self.output.metadata = input.metadata
self.output.spatial_size = input.spatial_size
o = self.output.features.narrow(
1, self.output_column_offset, self.nPlanes)
typed_fn(
input.features,
'BatchwiseMultiplicativeDropout_updateOutput')(
input.features,
o,
self.noise,
self.leakiness)
return self.output
def updateGradInput(self, input, gradOutput):
assert self.train
d_o = gradOutput.narrow(1, self.output_column_offset, self.nPlanes)
typed_fn(input.features, 'BatchwiseMultiplicativeDropout_updateGradInput')(
input.features, self.gradInput, d_o, self.noise, self.leakiness)
return self.gradInput
def __repr__(self):
s = 'BatchwiseDropoutInTensor(' + str(self.nPlanes) + ',p=' + str(
self.p) + ',column_offset=' + str(self.output_column_offset)
if self.leakiness > 0:
s = s + ',leakiness=' + str(self.leakiness)
s = s + ')'
return s
PyTorch/sparseconvnet/legacy/cAddTable.py deleted 100644 → 0
View file @ f2e3800b
# 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.
"""
Assume all the inputs have identical SparseGrids and input[i].nActive
Assume input[0].nPlanes >= input[i].nPlanes for all i=1,#input
output.submanifoldRules is taken from input[0].submanifoldRules (could do set union?)
(for resnets, make sure the residual link is input[1])
"""
import torch
import sparseconvnet
from . import SparseModule
from ..utils import toLongTensor, dim_typed_fn, optionalTensor, nullptr, set
from ..sparseConvNetTensor import SparseConvNetTensor
class CAddTable(SparseModule):
def __init__(self, ip=False):
SparseModule.__init__(self)
self.inplace = ip
if ip:
self.output = None
else:
self.output = SparseConvNetTensor(torch.Tensor())
def updateOutput(self, input):
if self.inplace:
self.output = input[0]
else:
self.output.features.resize_as_(
input[0].features).copy_(
input[0].features)
self.output.metadata = input[0].metadata
self.output.spatial_size = input[0].spatial_size
for i in input[1:]:
self.output.features.narrow(
1, 0, i.features.size(1)).add_(
i.features)
return self.output
def updateGradInput(self, input, gradOutput):
self.gradInput = []
n = input[0].features.size(1)
for i in input:
n_ = i.features.size(1)
if self.inplace and n_ == n:
self.gradInput.append(gradOutput)
else:
self.gradInput.append(gradOutput.narrow(1, 0, n_).clone())
return self.gradInput
def type(self, t, tensorCache=None):
if t and not self.inplace:
self.output.type(t)
def clearState(self):
if self.inplace:
self.output = None
else:
set(self.output)
self.gradInput = None
PyTorch/sparseconvnet/legacy/classificationTrainValidate.py deleted 100644 → 0
View file @ f2e3800b
# 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.
from torch.legacy import nn, optim
import sparseconvnet as s
import time
import os
import torch
import math
def updateStats(stats, output, target, loss):
batchSize = output.size(0)
stats['n'] = stats['n'] + batchSize
stats['nll'] = stats['nll'] + loss * batchSize
_, predictions = output.float().sort(1, True)
correct = predictions.eq(
target.long().view(batchSize, 1).expand_as(output))
# Top-1 score
stats['top1'] += correct.narrow(1, 0, 1).sum()
# Top-5 score
l = min(5, correct.size(1))
stats['top5'] += correct.narrow(1, 0, l).sum()
def ClassificationTrainValidate(model, dataset, p):
t = model.type()
if 'nEpochs' not in p:
p['nEpochs'] = 100
if 'initial_LR' not in p:
p['initial_LR'] = 1e-1
if 'LR_decay' not in p:
p['LR_decay'] = 4e-2
if 'weightDecay' not in p:
p['weightDecay'] = 1e-4
if 'momentum' not in p:
p['momentum'] = 0.9
if 'checkPoint' not in p:
p['checkPoint'] = False
optimState = {
'learningRate': p['initial_LR'],
'learningRateDecay': 0.0,
'momentum': p['momentum'],
'nesterov': True,
'dampening': 0.0,
'weightDecay': p['weightDecay'],
'epoch': 1
}
if p['checkPoint'] and os.path.isfile('epoch.pth'):
optimState['epoch'] = torch.load('epoch.pth') + 1
print('Restarting at epoch ' +
str(optimState['epoch']) +
' from model.pth ..')
model = torch.load('model.pth')
print(p)
criterion = nn.CrossEntropyCriterion()
criterion.type(model.type())
params, gradParams = model.flattenParameters()
print('#parameters', params.nelement())
for epoch in range(optimState['epoch'], p['nEpochs'] + 1):
model.training()
stats = {'top1': 0, 'top5': 0, 'n': 0, 'nll': 0}
optimState['learningRate'] = p['initial_LR'] * \
math.exp((1 - epoch) * p['LR_decay'])
start = time.time()
for batch in dataset['train']():
batch['input'].type(t)
batch['target'] = batch['target'].type(t)
model.forward(batch['input'])
criterion.forward(model.output, batch['target'])
updateStats(stats, model.output, batch['target'], criterion.output)
gradParams.zero_() # model:zeroGradParameters()
criterion.backward(model.output, batch['target'])
model.backward(batch['input'], criterion.gradInput)
def feval(x):
return criterion.output, gradParams
optim.sgd(feval, params, optimState)
print(epoch, 'train: top1=%.2f%% top5=%.2f%% nll:%.2f time:%.1fs' %
(100 *
(1 -
1.0 * stats['top1'] /
stats['n']), 100 *
(1 -
1.0 * stats['top5'] /
stats['n']), stats['nll'] /
stats['n'], time.time() -
start))
if p['checkPoint']:
model.modules[0].clearState()
torch.save(model, 'model.pth')
torch.save(epoch, 'epoch.pth')
model.evaluate()
s.forward_pass_multiplyAdd_count = 0
s.forward_pass_hidden_states = 0
stats = {'top1': 0, 'top5': 0, 'n': 0, 'nll': 0}
start = time.time()
for batch in dataset['val']():
batch['input'].type(t)
batch['target'] = batch['target'].type(t)
model.forward(batch['input'])
criterion.forward(model.output, batch['target'])
updateStats(stats, model.output, batch['target'], criterion.output)
print(epoch, 'test: top1=%.2f%% top5=%.2f%% nll:%.2f time:%.1fs' %
(100 *
(1 -
1.0 * stats['top1'] /
stats['n']), 100 *
(1 -
1.0 * stats['top5'] /
stats['n']), stats['nll'] /
stats['n'], time.time() -
start))
print(
'%.3e MultiplyAdds/sample %.3e HiddenStates/sample' %
(s.forward_pass_multiplyAdd_count /
stats['n'],
s.forward_pass_hidden_states /
stats['n']))
PyTorch/sparseconvnet/legacy/concatTable.py deleted 100644 → 0
View file @ f2e3800b
# 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
import sparseconvnet
from torch.legacy.nn import ConcatTable as C
from ..utils import toLongTensor, dim_typed_fn, optionalTensor, nullptr, set
from ..sparseConvNetTensor import SparseConvNetTensor
class ConcatTable(C):
def __init__(self):
C.__init__(self)
self.gradInput = torch.Tensor()
def updateOutput(self, input):
self.output = []
for m in self.modules:
self.output.append(m.forward(input))
return self.output
def backward(self, input, gradOutput, scale=1):
self.gradInput.resize_as_(input.features).zero_()
for m, g in zip(self.modules, gradOutput):
self.gradInput.add_(m.backward(input, g, scale))
return self.gradInput
def clearState(self):
self.output = None
set(self.gradInput)
for m in self.modules:
m.clearState()
def suggestInputSize(self, nOut):
return self.modules[0].suggestInputSize(nOut)
PyTorch/sparseconvnet/legacy/convolution.py deleted 100644 → 0
View file @ f2e3800b
# 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
from . import SparseModule
import sparseconvnet as s
from ..utils import toLongTensor, dim_typed_fn, optionalTensor, nullptr
from ..sparseConvNetTensor import SparseConvNetTensor
class Convolution(SparseModule):
def __init__(self, dimension, nIn, nOut, filter_size, filter_stride, bias):
SparseModule.__init__(self)
self.dimension = dimension
self.nIn = nIn
self.nOut = nOut
self.filter_size = toLongTensor(dimension, filter_size)
self.filter_volume = self.filter_size.prod().item()
self.filter_stride = toLongTensor(dimension, filter_stride)
std = (2.0 / nIn / self.filter_volume)**0.5
self.weight = torch.Tensor(
nIn *
self.filter_volume,
nOut).normal_(
0,
std)
self.gradWeight = torch.Tensor(
nIn * self.filter_volume, nOut).fill_(std)
if bias:
self.bias = torch.Tensor(nOut).zero_()
self.gradBias = torch.Tensor(nOut).zero_()
self.output = SparseConvNetTensor(torch.Tensor())
self.gradInput = torch.Tensor()
def updateOutput(self, input):
assert input.features.ndimension() == 0 or input.features.size(1) == self.nIn
self.output.metadata = input.metadata
self.output.spatial_size =\
(input.spatial_size - self.filter_size) / self.filter_stride + 1
s.forward_pass_multiplyAdd_count +=\
dim_typed_fn(
self.dimension, input.features, 'Convolution_updateOutput')(
input.spatial_size,
self.output.spatial_size,
self.filter_size,
self.filter_stride,
input.metadata.ffi,
input.features,
self.output.features,
self.weight,
optionalTensor(self, 'bias'),
self.filter_volume,
torch.cuda.IntTensor() if input.features.is_cuda else nullptr)
s.forward_pass_hidden_states += self.output.features.nelement()
return self.output
def backward(self, input, gradOutput, scale=1):
assert scale == 1
dim_typed_fn(
self.dimension, input.features, 'Convolution_backward')(
input.spatial_size,
self.output.spatial_size,
self.filter_size,
self.filter_stride,
input.metadata.ffi,
input.features,
self.gradInput,
gradOutput,
self.weight,
self.gradWeight,
optionalTensor(self, 'gradBias'),
self.filter_volume,
torch.cuda.IntTensor() if input.features.is_cuda else nullptr)
return self.gradInput
def type(self, t=None, tensorCache=None):
if t is None:
return self._type
self._type = t
self.weight = self.weight.type(t)
self.gradWeight = self.gradWeight.type(t)
self.output.type(t)
self.gradInput = self.gradInput.type(t)
if hasattr(self, 'bias'):
self.bias = self.bias.type(t)
self.gradBias = self.gradBias.type(t)
def __repr__(self):
s = 'Convolution ' + str(self.nIn) + '->' + str(self.nOut) + ' C'
if self.filter_size.max() == self.filter_size.min() and\
self.filter_stride.max() == self.filter_stride.min():
s = s + str(self.filter_size[0].item()) + '/' + str(self.filter_stride[0].item())
else:
s = s + '(' + str(self.filter_size[0].item())
for i in self.filter_size[1:]:
s = s + ',' + str(i.item())
s = s + ')/(' + str(self.filter_stride[0].item())
for i in self.filter_stride[1:]:
s = s + ',' + str(i.item())
s = s + ')'
return s
def suggestInputSize(self, out_size):
return (out_size - 1) * self.filter_stride + self.filter_size
PyTorch/sparseconvnet/legacy/deconvolution.py deleted 100644 → 0
View file @ f2e3800b
# 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
from torch.legacy.nn import Module
import sparseconvnet as s
from . import SparseModule
from ..utils import toLongTensor, dim_typed_fn, optionalTensor, nullptr
from ..sparseConvNetTensor import SparseConvNetTensor
class Deconvolution(SparseModule):
def __init__(self, dimension, nIn, nOut,
filter_size, filter_stride, bias):
SparseModule.__init__(self)
self.dimension = dimension
self.nIn = nIn
self.nOut = nOut
self.filter_size = toLongTensor(dimension, filter_size)
self.filter_stride = toLongTensor(dimension, filter_stride)
self.filter_volume = self.filter_size.prod().item()
std = (2.0 / nIn / self.filter_volume)**0.5
self.weight = torch.Tensor(
nIn * self.filter_volume, nOut
).normal_(0, std)
self.gradWeight = torch.Tensor(
nIn * self.filter_volume, nOut).fill_(std)
if bias:
self.bias = torch.Tensor(nOut).zero_()
self.gradBias = torch.Tensor(nOut).zero_()
self.output = SparseConvNetTensor(torch.Tensor())
self.gradInput = torch.Tensor()
def updateOutput(self, input):
assert input.features.ndimension() == 0 or input.features.size(1) == self.nIn
self.output.metadata = input.metadata
self.output.spatial_size =\
(input.spatial_size - 1) * self.filter_stride + self.filter_size
s.forward_pass_multiplyAdd_count +=\
dim_typed_fn(
self.dimension, input.features, 'Deconvolution_updateOutput')(
input.spatial_size,
self.output.spatial_size,
self.filter_size,
self.filter_stride,
input.metadata.ffi,
input.features,
self.output.features,
self.weight,
optionalTensor(self, 'bias'),
self.filter_volume,
torch.cuda.IntTensor() if input.features.is_cuda else nullptr)
s.forward_pass_hidden_states += self.output.features.nelement()
return self.output
def backward(self, input, gradOutput, scale=1):
assert scale == 1
dim_typed_fn(
self.dimension, input.features, 'Deconvolution_backward')(
input.spatial_size,
self.output.spatial_size,
self.filter_size,
self.filter_stride,
input.metadata.ffi,
input.features,
self.gradInput,
gradOutput,
self.weight,
self.gradWeight,
optionalTensor(self, 'gradBias'),
self.filter_volume,
torch.cuda.IntTensor() if input.features.is_cuda else nullptr)
return self.gradInput
def type(self, t=None, tensorCache=None):
if t is None:
return self._type
self._type = t
self.weight = self.weight.type(t)
self.gradWeight = self.gradWeight.type(t)
self.output.type(t)
self.gradInput = self.gradInput.type(t)
if hasattr(self, 'bias'):
self.bias = self.bias.type(t)
self.gradBias = self.gradBias.type(t)
def __repr__(self):
s = 'Deconvolution ' + str(self.nIn) + '->' + str(self.nOut) + ' C'
if self.filter_size.max() == self.filter_size.min() and\
self.filter_stride.max() == self.filter_stride.min():
s = s + str(self.filter_size[0].item()) + '/' + str(self.filter_stride[0].item())
else:
s = s + '(' + str(self.filter_size[0].item())
for i in self.filter_size[1:]:
s = s + ',' + str(i.item())
s = s + ')/(' + str(self.filter_stride[0].item())
for i in self.filter_stride[1:]:
s = s + ',' + str(i.item())
s = s + ')'
return s
def suggestInputSize(self, out_size):
return (out_size - self.filter_size) / self.filter_stride + 1
PyTorch/sparseconvnet/legacy/denseNetBlock.py deleted 100644 → 0
View file @ f2e3800b
# 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
import sparseconvnet as s
from torch.legacy.nn import Container
from ..utils import toLongTensor, typed_fn, optionalTensor, nullptr, set
from ..sparseConvNetTensor import SparseConvNetTensor
from .batchNormalization import *
from .affineReLUTrivialConvolution import AffineReLUTrivialConvolution
from .submanifoldConvolution import SubmanifoldConvolution
import math
class DenseNetBlock(Container):
def __init__(self, dimension, nInputPlanes, nExtraLayers=2, growthRate=16):
Container.__init__(self)
self.dimension = dimension
self.nInputPlanes = nInputPlanes
self.nExtraLayers = nExtraLayers
self.growthRate = growthRate
assert(self.nExtraLayers >= 1)
self.nOutputPlanes = nInputPlanes + nExtraLayers * growthRate
self.output = SparseConvNetTensor(torch.Tensor())
# Module 1: Batchnorm the input into the start of self.output
self.add(
BatchNormalizationInTensor(
nInputPlanes,
output_column_offset=0))
self.modules[0].output = self.output
self.gradInput = self.modules[0].gradInput
for i in range(nExtraLayers):
nFeatures = self.nInputPlanes + i * growthRate
nFeaturesB = 4 * growthRate
# Modules 4*i+1
self.add(AffineReLUTrivialConvolution(nFeatures, nFeaturesB, True))
# Module 4*i+2
self.add(BatchNormalization(nFeaturesB))
# Module 4*i+3
self.add(
SubmanifoldConvolution(
dimension,
nFeaturesB,
growthRate,
3,
False))
# Module 4*i+4
self.add(
BatchNormalizationInTensor(
growthRate,
output_column_offset=self.nInputPlanes +
i *
growthRate))
self.modules[4 * i + 4].output = self.output
def updateOutput(self, input):
assert input.features.size(1) == self.nInputPlanes
self.output.spatial_size = input.spatial_size
self.output.metadata = input.metadata
self.output.features.resize_(
input.features.size(0), self.nOutputPlanes)
i = input
for m in self.modules:
i = m.updateOutput(i)
return self.output
def backward(self, input, gradOutput, scale=1):
assert scale == 1
g = gradOutput
for i in range(self.nExtraLayers):
self.modules[4 * i + 1].gradInput = gradOutput
for m, m_ in zip(self.modules[:0:-1],
self.modules[len(self.modules) - 2::-1]):
g = m.backward(m_.output, g)
self.modules[0].backward(input, g)
return self.gradInput
def type(self, type, tensorCache=None):
self._type = type
self.output.features = self.output.features.type(type)
for x in self.modules:
x.type(type)
self.gradInput = self.modules[0].gradInput
def __repr__(self):
s = 'DenseNetBlock(' + str(self.nInputPlanes) + '->' + str(self.nInputPlanes) + '+' + str(
self.nExtraLayers) + '*' + str(self.growthRate) + '=' + str(self.nOutputPlanes) + ')'
return s
def clearState(self):
for _, m in ipairs(self.modules):
m.clearState()
set(self.output)
set(self.gradInput)
def suggestInputSize(self, out_size):
return out_size
PyTorch/sparseconvnet/legacy/denseToSparse.py deleted 100644 → 0
View file @ f2e3800b
# 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.
"""
Function to convert a Dense Input into a sparse input.
If possible, avoid using this module; build the hidden layer using InputBatch.
Parameters:
dimension : of the input field
"""
import torch
from . import SparseModule
from ..utils import dim_fn, nullptr
from ..sparseConvNetTensor import SparseConvNetTensor
from ..metadata import Metadata
class DenseToSparse(SparseModule):
def __init__(self, dimension):
SparseModule.__init__(self)
self.dimension = dimension
self.output = SparseConvNetTensor(torch.Tensor(), Metadata(dimension))
self.gradInput = torch.Tensor()
def updateOutput(self, input):
a = input
aa = a.permute(
*([0, ] + list(range(2, 2 + self.dimension)) + [1, ])).clone()
self.aas = aa.size()
nz = aa.abs().sum(self.dimension + 1).view(aa.size()[0:-1])
s = torch.LongTensor(nz.stride()).view(1, self.dimension + 1)
nz = nz.nonzero()
s = s.type_as(nz)
aa = aa.view(-1, a.size(1))
self.aas2 = aa.size()
self.r = (nz * s.expand_as(nz)).sum(1).view(-1)
self.output.features = aa.index_select(0, self.r)
self.output.spatial_size = torch.LongTensor(list(input.size()[2:]))
dim_fn(self.dimension, 'createMetadataForDenseToSparse')(
self.output.metadata.ffi,
self.output.spatial_size,
nz.cpu(),
input.size(0))
return self.output
def updateGradInput(self, input, gradOutput):
self.gradInput.resize_(self.aas2).zero_()
self.gradInput.index_copy_(0, self.r, gradOutput)
self.gradInput = self.gradInput.view(self.aas).permute(
*([0, self.dimension + 1] + list(range(1, self.dimension + 1))))
return self.gradInput
def clearState(self):
SparseModule.clearState(self)
self.aas = None
self.r = None
def __repr__(self):
return 'DenseToSparse(' + str(self.dimension) + ')'
PyTorch/sparseconvnet/legacy/identity.py deleted 100644 → 0
View file @ f2e3800b
# 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.
from torch.legacy.nn import Identity as I
from .sparseModule import SparseModule
class Identity(SparseModule):
def forward(self, input):
self.output = input
return self.output
def backward(self, input, gradOutput, scale=1):
self.gradInput = gradOutput
return self.gradInput
def clearState(self):
self.output = None
self.gradInput = None
def suggestInputSize(self, out_size):
return out_size
PyTorch/sparseconvnet/legacy/joinTable.py deleted 100644 → 0
View file @ f2e3800b
# 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
import sparseconvnet
from . import SparseModule
from ..utils import toLongTensor, dim_typed_fn, optionalTensor, nullptr, set
from ..sparseConvNetTensor import SparseConvNetTensor
class JoinTable(SparseModule):
def __init__(self, nPlanes):
SparseModule.__init__(self)
self.nPlanes = nPlanes
self.gradInput = [torch.Tensor() for p in nPlanes]
self.nOutputPlanes = sum(nPlanes)
self.output = SparseConvNetTensor(torch.Tensor())
def updateOutput(self, input):
self.output.features.resize_(
input[0].features.size(0),
self.nOutputPlanes)
self.output.metadata = input[0].metadata
self.output.spatial_size = input[0].spatial_size
offset = 0
for i, n in zip(input, self.nPlanes):
self.output.features.narrow(1, offset, n).copy_(i.features)
offset += n
return self.output
def updateGradInput(self, input, gradOutput):
offset = 0
a = input[0].features.size(0)
for g, n in zip(self.gradInput, self.nPlanes):
g.resize_(a, n).copy_(gradOutput.narrow(1, offset, n))
offset += n
return self.gradInput
def type(self, t, tensorCache=None):
if t:
self.output.type(t)
self.gradInput = [g.type(t) for g in self.gradInput]
def clearState(self):
set(self.output)
for g in self.gradInput:
set(g)
def __repr__(self):
s = 'JoinTable: ' + str(self.nPlanes[0])
for n in self.nPlanes[1:]:
s = s + ' + ' + str(n)
s = s + ' -> ' + str(self.nOutputPlanes)
return s
PyTorch/sparseconvnet/legacy/leakyReLU.py deleted 100644 → 0
View file @ f2e3800b
# 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
import sparseconvnet
from . import SparseModule
from ..utils import toLongTensor, typed_fn, optionalTensor, nullptr
from ..sparseConvNetTensor import SparseConvNetTensor
class LeakyReLU(SparseModule):
def __init__(self, leakage=0.333, ip=True):
SparseModule.__init__(self)
self.inplace = ip
self.leakage = leakage
self.output = SparseConvNetTensor(torch.Tensor())
self.gradInput = None if ip else torch.Tensor()
def updateOutput(self, input):
self.output.metadata = input.metadata
self.output.spatial_size = input.spatial_size
typed_fn(input.features, 'LeakyReLU_updateOutput')(
input.features,
self.output.features,
self.leakage)
return self.output
def updateGradInput(self, input, gradOutput):
if self.inplace:
self.gradInput = gradOutput
typed_fn(input.features, 'LeakyReLU_updateGradInput')(
input.features,
self.gradInput,
gradOutput,
self.leakage)
return self.gradInput
def type(self, t, tensorCache=None):
if t:
self.output.type(t)
self.gradInput = self.gradInput.type(t)
PyTorch/sparseconvnet/legacy/maxPooling.py deleted 100644 → 0
View file @ f2e3800b
# 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
import sparseconvnet
from . import SparseModule
from ..utils import toLongTensor, dim_typed_fn, optionalTensor, nullptr
from ..sparseConvNetTensor import SparseConvNetTensor
class MaxPooling(SparseModule):
def __init__(self, dimension, pool_size, pool_stride, nFeaturesToDrop=0):
SparseModule.__init__(self)
self.dimension = dimension
self.pool_size = toLongTensor(dimension, pool_size)
self.pool_stride = toLongTensor(dimension, pool_stride)
self.pool_volume = self.pool_size.prod()
self.nFeaturesToDrop = nFeaturesToDrop or 0
self.output = SparseConvNetTensor(torch.Tensor())
self.gradInput = torch.Tensor()
def updateOutput(self, input):
self.output.metadata = input.metadata
self.output.spatial_size =\
(input.spatial_size - self.pool_size) / self.pool_stride + 1
dim_typed_fn(
self.dimension,
input.features,
'MaxPooling_updateOutput')(
input.spatial_size,
self.output.spatial_size,
self.pool_size,
self.pool_stride,
input.metadata.ffi,
input.features,
self.output.features,
self.nFeaturesToDrop,
torch.cuda.IntTensor() if input.features.is_cuda else nullptr)
return self.output
def updateGradInput(self, input, gradOutput):
dim_typed_fn(
self.dimension,
input.features,
'MaxPooling_updateGradInput')(
input.spatial_size,
self.output.spatial_size,
self.pool_size,
self.pool_stride,
input.metadata.ffi,
input.features,
self.gradInput,
self.output.features,
gradOutput,
self.nFeaturesToDrop,
torch.cuda.IntTensor() if input.features.is_cuda else nullptr)
return self.gradInput
def type(self, t=None, tensorCache=None):
if t is None:
return self._type
self.output.type(t)
self.gradInput = self.gradInput.type(t)
def __repr__(self):
s = 'MaxPooling'
if self.pool_size.max() == self.pool_size.min() and\
self.pool_stride.max() == self.pool_stride.min():
s = s + str(self.pool_size[0]) + '/' + str(self.pool_stride[0])
else:
s = s + '(' + str(self.pool_size[0])
for i in self.pool_size[1:]:
s = s + ',' + str(i)
s = s + ')/(' + str(self.pool_stride[0])
for i in self.pool_stride[1:]:
s = s + ',' + str(i)
s = s + ')'
if self.nFeaturesToDrop > 0:
s = s + ' nFeaturesToDrop = ' + self.nFeaturesToDrop
return s
def suggestInputSize(self, out_size):
return (out_size - 1) * self.pool_stride + self.pool_size
PyTorch/sparseconvnet/legacy/misc.py deleted 100644 → 0
View file @ f2e3800b
# 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
from ..sparseConvNetTensor import SparseConvNetTensor
from .batchNormalization import BatchNormalization
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())
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