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Commit fdee4456 authored by Ed Ng's avatar Ed Ng Committed by GitHub
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Merge branch 'master' into get_locations

parents 94a39536 fc961107
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Showing with 116 additions and 84 deletions
PyTorch/sparseconvnet/SCN/generic/Geometry/Metadata.cpp
View file @ fdee4456
......@@ -23,65 +23,81 @@ extern "C" void scn_D_(batchAddSample)(void **m) {
_m.inputSGs->resize(_m.inputSGs->size() + 1);
_m.inputSG = &_m.inputSGs->back();
}
extern "C" void scn_D_(setInputSpatialLocation)(void **m,
void scn_D_(addPointToSparseGridMapAndFeatures)(SparseGridMap<Dimension> &mp,
Point<Dimension> p,
uInt &nActive, long nPlanes,
THFloatTensor *features,
THLongTensor *location,
THFloatTensor *vec,
bool overwrite) {
SCN_INITIALIZE_AND_REFERENCE(Metadata<Dimension>, m)
auto p = LongTensorToPoint<Dimension>(location);
auto &mp = _m.inputSG->mp;
auto &nActive = *_m.inputNActive;
float *vec, bool overwrite) {
auto iter = mp.find(p);
auto nPlanes = vec->size[0];
if (iter == mp.end()) {
iter = mp.insert(std::make_pair(p, nActive++)).first;
THFloatTensor_resize2d(features, nActive, nPlanes);
std::memcpy(THFloatTensor_data(features) + (nActive - 1) * nPlanes,
THFloatTensor_data(vec), sizeof(float) * nPlanes);
std::memcpy(THFloatTensor_data(features) + (nActive - 1) * nPlanes, vec,
sizeof(float) * nPlanes);
} else if (overwrite) {
std::memcpy(THFloatTensor_data(features) + iter->second * nPlanes,
THFloatTensor_data(vec), sizeof(float) * nPlanes);
std::memcpy(THFloatTensor_data(features) + iter->second * nPlanes, vec,
sizeof(float) * nPlanes);
}
}
extern "C" void scn_D_(setInputSpatialLocations)(void **m,
extern "C" void scn_D_(setInputSpatialLocation)(void **m,
THFloatTensor *features,
THLongTensor *locations,
THFloatTensor *vecs,
THLongTensor *location,
THFloatTensor *vec,
bool overwrite) {
assert(locations->size[0] == vecs->size[0] &&
"Location and vec length must be identical!");
SCN_INITIALIZE_AND_REFERENCE(Metadata<Dimension>, m)
auto p = LongTensorToPoint<Dimension>(location);
auto &mp = _m.inputSG->mp;
auto &nActive = *_m.inputNActive;
auto nSamples = locations->size[0];
auto isMpEmpty = mp.empty();
auto nPlanes = vec->size[0];
scn_D_(addPointToSparseGridMapAndFeatures)(
mp, p, nActive, nPlanes, features, THFloatTensor_data(vec), overwrite);
}
extern "C" void scn_D_(setInputSpatialLocations)(void **m,
THFloatTensor *features,
THLongTensor *locations,
THFloatTensor *vecs,
bool overwrite) {
assert(locations->size[0] == vecs->size[0] and
"Location.size(0) and vecs.size(0) must be equal!");
assert((locations->size[1] == Dimension or
locations->size[1] == 1 + Dimension) and
"locations.size(0) must be either Dimension or Dimension+1");
if (isMpEmpty) {
auto nPlanes = vecs->size[1];
SCN_INITIALIZE_AND_REFERENCE(Metadata<Dimension>, m)
THFloatTensor_resize2d(features, nSamples, nPlanes);
std::memcpy(THFloatTensor_data(features),
THFloatTensor_data(vecs), sizeof(float) * nSamples * nPlanes);
Point<Dimension> p;
auto &nActive = *_m.inputNActive;
auto nPlanes = vecs->size[1];
auto l = THLongTensor_data(locations);
auto v = THFloatTensor_data(vecs);
mp.resize(nSamples);
if (locations->size[1] == Dimension) {
assert(_m.inputSG); // add points to current sample
auto &mp = _m.inputSG->mp;
for (uInt idx = 0; idx < locations->size[0]; ++idx) {
for (int d = 0; d < Dimension; ++d)
p[d] = *l++;
scn_D_(addPointToSparseGridMapAndFeatures)(mp, p, nActive, nPlanes,
features, v, overwrite);
v += nPlanes;
}
}
for(unsigned int i = 0; i < nSamples; ++i) {
THLongTensor *location = THLongTensor_newSelect(locations, 0, i);
THFloatTensor *vec = THFloatTensor_newSelect(vecs, 0, i);
if (isMpEmpty) {
auto p = LongTensorToPoint<Dimension>(location);
mp.insert(std::make_pair(p, nActive++));
} else {
scn_D_(setInputSpatialLocation)(m, features, location, vec, overwrite);
if (locations->size[1] == Dimension + 1) {
// add new samples to batch as necessary
auto &SGs = *_m.inputSGs;
for (uInt idx = 0; idx < locations->size[0]; ++idx) {
for (int d = 0; d < Dimension; ++d)
p[d] = *l++;
auto batch = *l++;
if (batch >= SGs.size()) {
SGs.resize(batch + 1);
}
auto &mp = SGs[batch].mp;
scn_D_(addPointToSparseGridMapAndFeatures)(mp, p, nActive, nPlanes,
features, v, overwrite);
v += nPlanes;
}
THLongTensor_free(location);
THFloatTensor_free(vec);
}
}
extern "C" void scn_D_(getSpatialLocations)(void **m,
......
PyTorch/sparseconvnet/SCN/generic/SparseConvNet.h
View file @ fdee4456
......@@ -32,7 +32,7 @@
template <uInt dimension>
using SparseGridMap =
google::dense_hash_map<Point<dimension>, int, IntArrayHash<dimension>,
google::dense_hash_map<Point<dimension>, uInt, IntArrayHash<dimension>,
std::equal_to<Point<dimension>>>;
template <uInt dimension> class SparseGrid {
......
PyTorch/sparseconvnet/legacy/inputBatch.py
View file @ fdee4456
......@@ -33,8 +33,8 @@ class InputBatch(SparseConvNetTensor):
self.metadata.ffi, self.features, location, vector, overwrite)
def setLocations(self, locations, vectors, overwrite=False):
assert locations.min() >= 0 and (self.spatial_size.expand_as(locations) - locations).min() > 0
l =locations.narrow(1,0,self.dimension)
assert l.min() >= 0 and (self.spatial_size.expand_as(l) - l).min() > 0
dim_fn(self.dimension, 'setInputSpatialLocations')(
self.metadata.ffi, self.features, locations, vectors, overwrite)
......
Torch/InputBatch.lua
View file @ fdee4456
......@@ -15,7 +15,7 @@ return function(sparseconvnet)
self.spatialSize = type(spatialSize)=='number' and torch.LongTensor(
dimension):fill(spatialSize) or spatialSize
C.dimensionFn(self.dimension,'setInputSpatialSize')(self.metadata.ffi,
self.spatialSize:cdata())
self.spatialSize:cdata())
end
function InputBatch:addSample()
C.dimensionFn(self.dimension, 'batchAddSample')(self.metadata.ffi)
......@@ -28,7 +28,7 @@ return function(sparseconvnet)
end
function InputBatch:setLocation(location, vector, overwrite)
--[[location is a self.dimensional length set of coordinates:
torch.LongStorage or a table]]
torch.LongStorage or a table]]
if type(location)=='table' then
local l=torch.LongStorage(self.dimension)
for i,x in ipairs(location) do
......@@ -38,19 +38,20 @@ return function(sparseconvnet)
end
assert(location:min()>=0 and (self.spatialSize-location):min()>0)
C.dimensionFn(self.dimension,'setInputSpatialLocation')(self.metadata.ffi,
self.features:cdata(), location:cdata(), vector:cdata(), overwrite)
self.features:cdata(), location:cdata(), vector:cdata(), overwrite)
end
function InputBatch:setLocations(locations, vectors, overwrite)
--[[locations is a n_locations x self.dimensional length set of coordinates:
torch.LongStorage or a 2-D table]]
torch.LongStorage or a 2-D table]]
if type(locations)=='table' then
locations = torch.LongStorage(locations)
end
assert(locations:min()>=0 and (self.spatialSize:view(1, self.dimension):expandAs(locations)-locations):min()>0)
local l = locations:narrow(2,1,self.dimension)
assert(l:min()>=0 and (self.spatialSize:view(1, self.dimension):expandAs(l)-l):min()>0)
C.dimensionFn(self.dimension,'setInputSpatialLocations')(self.metadata.ffi,
self.features:cdata(), locations:cdata(), vectors:cdata(), overwrite)
self.features:cdata(), locations:cdata(), vectors:cdata(), overwrite)
end
function InputBatch:precomputeMetadata(stride)
if stride==2 then
......
examples/hello-world.lua
View file @ fdee4456
......@@ -10,26 +10,26 @@ tensorType = scn.cutorch and 'torch.CudaTensor' or 'torch.FloatTensor'
model = scn.Sequential()
:add(scn.SparseVggNet(2,1,{ --dimension 2, 1 input plane
{'C', 8}, -- 3x3 VSC convolution, 8 output planes, batchnorm, ReLU
{'C', 8}, -- and another
{'MP', 3, 2}, --max pooling, size 3, stride 2
{'C', 16}, -- etc
{'C', 16},
{'MP', 3, 2},
{'C', 24},
{'C', 24},
{'MP', 3, 2}}))
:add(scn.Convolution(2,24,32,3,1,false)) --an SC convolution on top
:add(scn.BatchNormReLU(32))
:add(scn.SparseToDense(2))
:type(tensorType)
:add(scn.SparseVggNet(2,1,{ --dimension 2, 1 input plane
{'C', 8}, -- 3x3 VSC convolution, 8 output planes, batchnorm, ReLU
{'C', 8}, -- and another
{'MP', 3, 2}, --max pooling, size 3, stride 2
{'C', 16}, -- etc
{'C', 16},
{'MP', 3, 2},
{'C', 24},
{'C', 24},
{'MP', 3, 2}}))
:add(scn.Convolution(2,24,32,3,1,false)) --an SC convolution on top
:add(scn.BatchNormReLU(32))
:add(scn.SparseToDense(2))
:type(tensorType)
--[[
To use the network we must create an scn.InputBatch with right dimensionality.
If we want the output to have spatial size 10x10, we can find the appropriate
input size, give that we uses three layers of MP3/2 max-pooling, and finish
with a SC convoluton
To use the network we must create an scn.InputBatch with right dimensionality.
If we want the output to have spatial size 10x10, we can find the appropriate
input size, give that we uses three layers of MP3/2 max-pooling, and finish
with a SC convoluton
]]
inputSpatialSize=model:suggestInputSize(torch.LongTensor{10,10}) --103x103
......@@ -43,12 +43,21 @@ msg={
" O O O O O O O O O O O O O O O O O O ",
" O O OOO OOO OOO OO O O OO O O OOO OOO ",
}
input:addSample()
for y,line in ipairs(msg) do
for x = 1,string.len(line) do
if string.sub(line,x,x) == 'O' then
local location = torch.LongTensor{x,y}
local featureVector = torch.FloatTensor{1}
input:setLocation(location,featureVector,0)
end
end
end
--We can also use setLocations
input:addSample()
local locations = {}
local featureVectors = {}
for y,line in ipairs(msg) do
for x = 1,string.len(line) do
if string.sub(line,x,x) == 'O' then
......@@ -57,19 +66,18 @@ for y,line in ipairs(msg) do
end
end
end
input:setLocations(
torch.LongTensor(locations),
torch.FloatTensor(featureVectors),
0)
--[[
Optional: allow metadata preprocessing to be done in batch preparation threads
to improve GPU utilization.
Optional: allow metadata preprocessing to be done in batch preparation threads
to improve GPU utilization.
Parameter:
3 if using MP3/2 or size-3 stride-2 convolutions for downsizeing,
2 if using MP2
Parameter:
3 if using MP3/2 or size-3 stride-2 convolutions for downsizeing,
2 if using MP2
]]
input:precomputeMetadata(3)
......@@ -78,7 +86,7 @@ input:type(tensorType)
output = model:forward(input)
--[[
Output is 1x32x10x10: our minibatch has 1 sample, the network has 32 output
feature planes, and 10x10 is the spatial size of the output.
Output is 2x32x10x10: our minibatch has 2 samples, the network has 32 output
feature planes, and 10x10 is the spatial size of the output.
]]
print(output:size(), output:type())
examples/hello-world.py
View file @ fdee4456
......@@ -13,9 +13,9 @@ dtype = 'torch.cuda.FloatTensor' if torch.cuda.is_available() else 'torch.FloatT
model = scn.Sequential().add(
scn.SparseVggNet(2, 1,
[['C', 8], ['C', 8], ['MP', 3, 2],
[['C', 8], ['C', 8], ['MP', 3, 2],
['C', 16], ['C', 16], ['MP', 3, 2],
['C', 24], ['C', 24], ['MP', 3, 2]])
['C', 24], ['C', 24], ['MP', 3, 2]])
).add(
scn.ValidConvolution(2, 24, 32, 3, False)
).add(
......@@ -34,20 +34,27 @@ msg = [
" XXXXX XX X X X X X X X X X XXX X X X ",
" X X X X X X X X X X X X X X X X X X ",
" X X XXX XXX XXX XX X X XX X X XXX XXX "]
#Add a sample using setLocation
input.addSample()
for y, line in enumerate(msg):
for x, c in enumerate(line):
if c == 'X':
location = torch.LongTensor([x, y])
featureVector = torch.FloatTensor([1])
input.setLocation(location, featureVector, 0)
#Add a sample using setLocations
input.addSample()
locations = []
features = []
for y, line in enumerate(msg):
for x, c in enumerate(line):
if c == 'X':
locations.append([x,y])
features.append([1])
locations = torch.LongTensor(locations)
features = torch.FloatTensor(features)
input.setLocations(locations, features, 0)
# Optional: allow metadata preprocessing to be done in batch preparation threads
......@@ -62,6 +69,6 @@ model.evaluate()
input.type(dtype)
output = model.forward(input)
# Output is 1x32x10x10: our minibatch has 1 sample, the network has 32 output
# Output is 2x32x10x10: our minibatch has 2 samples, the network has 32 output
# feature planes, and 10x10 is the spatial size of the output.
print(output.size(), output.type())
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