diff options
| -rw-r--r-- | SpatialRecursiveFovea.lua (renamed from todo/RecursiveFovea.lua) | 114 | ||||
| -rw-r--r-- | init.lua | 1 | ||||
| -rw-r--r-- | nnx-1.0-1.rockspec | 1 |
3 files changed, 51 insertions, 65 deletions
diff --git a/todo/RecursiveFovea.lua b/SpatialRecursiveFovea.lua index 69513fc..28a534a 100644 --- a/todo/RecursiveFovea.lua +++ b/SpatialRecursiveFovea.lua @@ -1,7 +1,6 @@ -local RecursiveFovea, parent = torch.class('nn.RecursiveFovea', 'nn.Module') +local SpatialRecursiveFovea, parent = torch.class('nn.SpatialRecursiveFovea', 'nn.Module') -local help_desc = -[[ +local help_desc = [[ From a given image, generates a pyramid of scales, and process each scale with the given list of preprocessors and processors. The result of each module/scale is upsampled to feed the next stage of recursion. @@ -30,41 +29,27 @@ the current error with respect to some target vectors. If criterions are provided, then target vectors can be provided as well to the forward and backward functions.]] -function RecursiveFovea:__init(...) +function SpatialRecursiveFovea:__init(...) parent.__init(self) + -- check args xlua.unpack_class( self, {...}, - 'nn.RecursiveFovea', + 'nn.SpatialRecursiveFovea', help_desc, - {arg='nInputPlane', type='number', - help='number of input planes', - req=true}, - {arg='nRecursivePlane', type='number', - help='number of recursive planes (e.g. nb of output planes used by next input stage', req=true}, - {arg='ratios', type='table', - help='list of downsampling ratios, in decreasing order', - req=true}, - {arg='processors', type='table', - help='list of processors (each processor sees a single scale)', - req=true}, - {arg='preProcessors', type='table', - help='list of preprocessors (applied before padding)'}, - {arg='postProcessors', type='table', - help='list of postprocessors (applied before criterions, and on last stage)'}, - {arg='criterions', type='table', - help='list of criterions (applied to each intermediate output)'}, - {arg='fov', type='number', - help='field of view (== processors\' receptive field)', default=1}, - {arg='batchSize', type='number', - help='size of mini-batch used when computing gradient [default = fov/sub]'}, - {arg='sub', type='number', - help='global subsampling (== processors\' subsampling ratio)', default=1}, - {arg='scale_target_values', type='boolean', - help='scale the target values as well as dimension',default=false}, - {arg='verbose', type='boolean', - help='prints a lot of information', default=true} + {arg='nInputPlane', type='number', help='number of input planes', req=true}, + {arg='nRecursivePlane', type='number', help='number of recursive planes (e.g. nb of output planes used by next input stage', req=true}, + {arg='ratios', type='table', help='list of downsampling ratios, in decreasing order', req=true}, + {arg='processors', type='table', help='list of processors (each processor sees a single scale)', req=true}, + {arg='preProcessors', type='table', help='list of preprocessors (applied before padding)'}, + {arg='postProcessors', type='table', help='list of postprocessors (applied before criterions, and on last stage)'}, + {arg='criterions', type='table', help='list of criterions (applied to each intermediate output)'}, + {arg='fov', type='number', help='field of view (== processors\' receptive field)', default=1}, + {arg='batchSize', type='number', help='size of mini-batch used when computing gradient [default = fov/sub]'}, + {arg='sub', type='number', help='global subsampling (== processors\' subsampling ratio)', default=1}, + {arg='scaleTargetValues', type='boolean', help='scale the target values as well as dimension', default=false}, + {arg='verbose', type='boolean', help='prints a lot of information', default=true} ) -- batchSize ? @@ -112,15 +97,15 @@ function RecursiveFovea:__init(...) -- check preprocessors/processors/criterions if #self.processors ~= #self.ratios then xerror('the number of processors provided should == the number of ratios (scales): ' .. #self.ratios, - 'nn.RecursiveFovea') + 'nn.SpatialRecursiveFovea') end if self.preProcessors[1] and #self.preProcessors ~= #self.ratios then xerror('the number of preProcessors provided should == the number of ratios (scales): ' .. #self.ratios, - 'nn.RecursiveFovea') + 'nn.SpatialRecursiveFovea') end if self.criterions[1] and #self.criterions ~= #self.ratios then xerror('the number of criterions provided should == the number of ratios (scales): ' .. #self.ratios, - 'nn.RecursiveFovea') + 'nn.SpatialRecursiveFovea') end -- sort scales, in decreasing order @@ -132,7 +117,7 @@ function RecursiveFovea:__init(...) end end -function RecursiveFovea:configure(fov, sub, input_w, input_h) +function SpatialRecursiveFovea:configure(fov, sub, input_w, input_h) -- don't reconfigure if params have not changed if fov == self.fov and input_w == self.input_w and input_h == self.input_h then return @@ -156,7 +141,7 @@ function RecursiveFovea:configure(fov, sub, input_w, input_h) self.bilinear = false for idx = 1,nscales do if ratios[idx] < 1 then - xerror('ratios should be >= 1','nn.RecursiveFovea') + xerror('ratios should be >= 1','nn.SpatialRecursiveFovea') elseif ratios[idx] ~= math.floor(ratios[idx]) then self.bilinear = true end @@ -167,7 +152,7 @@ function RecursiveFovea:configure(fov, sub, input_w, input_h) -- check order if idx > 1 and self.ratios[idx] > self.ratios[idx-1] then xerror('downsampling ratios should be provided in decreasing order, for proper coarse-to-fine recursion', - 'nn.RecursiveFovea') + 'nn.SpatialRecursiveFovea') end -- pyramid size pyramid[idx] = {w = math.floor(input_w / ratios[idx]), h = math.floor(input_h / ratios[idx])} @@ -194,7 +179,7 @@ function RecursiveFovea:configure(fov, sub, input_w, input_h) -- downsamplers (for pyramid) local r = ratios[idx] if self.bilinear then - self.downsamplers[idx] = nn.SpatialReSampling(pyramid[idx].w, pyramid[idx].h) + self.downsamplers[idx] = nn.SpatialReSampling(1/r, 1/r) else self.downsamplers[idx] = nn.SpatialSubSampling(self.nInputPlane, r, r, r, r) self.downsamplers[idx].weight:fill(1/(r^2)) @@ -216,9 +201,9 @@ function RecursiveFovea:configure(fov, sub, input_w, input_h) local upw = (ratios[idx] / ratios[idx+1]) * sub local uph = (ratios[idx] / ratios[idx+1]) * sub if self.bilinear then - self.upsamplers[idx] = nn.SpatialReSampling(pyramid[idx+1].w, pyramid[idx+1].h) + self.upsamplers[idx] = nn.SpatialReSampling(upw, uph) else - self.upsamplers[idx] = nn.SpatialUpSampling(self.nInputPlane, upw, uph) + self.upsamplers[idx] = nn.SpatialUpSampling(upw, uph) end end end @@ -232,7 +217,7 @@ function RecursiveFovea:configure(fov, sub, input_w, input_h) -- info if self.debug then print('') - xprint('reconfig complete:','nn.RecursiveFovea') + xprint('reconfig complete:','nn.SpatialRecursiveFovea') for idx = 1,nscales do print('scale ' .. idx .. ' :') print(' + pyramid > ' .. pyramid[idx].w .. 'x' .. pyramid[idx].h) @@ -244,8 +229,8 @@ function RecursiveFovea:configure(fov, sub, input_w, input_h) end end -function RecursiveFovea:__tostring__() - local str = 'nn.RecursiveFovea:\n' +function SpatialRecursiveFovea:__tostring__() + local str = 'nn.SpatialRecursiveFovea:\n' str = str .. ' + number of recursion stages : '..(#self.ratios) .. '\n' str = str .. ' + downsampling ratios (scales) :' for idx = 1,#self.ratios do @@ -261,7 +246,7 @@ function RecursiveFovea:__tostring__() return str end -function RecursiveFovea:focus(x,y) +function SpatialRecursiveFovea:focus(x,y) -- fprop and bprop sizes must be different -- * frop must create an output which will be upsampled to batchsize+fov -- * bprop creates a batchsize+fov sized input around center of focus @@ -290,10 +275,10 @@ function RecursiveFovea:focus(x,y) self.corners = corners end -function RecursiveFovea:forward(input,target,x,y) +function SpatialRecursiveFovea:forward(input,target,x,y) -- input must be 3D if input:nDimension() ~= 3 or input:size(3) ~= self.nInputPlane then - xerror('input must be 3d and have ' .. self.nInputPlane .. ' input planes','nn.RecursiveFovea') + xerror('input must be 3d and have ' .. self.nInputPlane .. ' input planes','nn.SpatialRecursiveFovea') end -- focus ? @@ -331,24 +316,23 @@ function RecursiveFovea:forward(input,target,x,y) -- (4) is fovea focused ? self.narrowed[idx] - = self.padded[idx]:narrow(1,1,self.narrowed_size[idx].w):narrow(2,1,self.narrowed_size[idx].h) + = self.padded[idx]:narrow(3,1,self.narrowed_size[idx].w):narrow(2,1,self.narrowed_size[idx].h) -- (5) concatenate current input and upsampled result from previous stage in the recursion self.concatenated[idx] = self.concatenated[idx] or torch.Tensor() - self.concatenated[idx]:resize(self.narrowed[idx]:size(1), self.narrowed[idx]:size(2), - self.narrowed[idx]:size(3) + self.nRecursivePlane) - self.concatenated[idx]:narrow(3,1,self.narrowed[idx]:size(3)):copy(self.narrowed[idx]) + self.concatenated[idx]:resize(self.narrowed[idx]:size(1) + self.nRecursivePlane, + self.narrowed[idx]:size(2), self.narrowed[idx]:size(3)) + self.concatenated[idx]:narrow(1,1,self.narrowed[idx]:size(1)):copy(self.narrowed[idx]) if idx > 1 then - local p = self.concatenated[idx]:narrow(3,self.narrowed[idx]:size(3)+1, - self.nRecursivePlane) + local p = self.concatenated[idx]:narrow(1,self.narrowed[idx]:size(1)+1, self.nRecursivePlane) p:copy(self.upsampledNarrowed[idx-1]) - if self.scale_target_values then + if self.scaleTargetValues then local r = self.ratios[idx-1]/self.ratios[idx] p:mul(r) end else - self.concatenated[idx]:narrow(3,self.narrowed[idx]:size(3)+1,self.nRecursivePlane):zero() + self.concatenated[idx]:narrow(1,self.narrowed[idx]:size(1)+1,self.nRecursivePlane):zero() end -- (6) apply processors to pyramid @@ -364,7 +348,7 @@ function RecursiveFovea:forward(input,target,x,y) -- (7.c) self.upsampledNarrowed[idx] - = self.upsampledPadded[idx]:narrow(1,1,self.narrowed_size[idx+1].w):narrow(2,1,self.narrowed_size[idx+1].h) + = self.upsampledPadded[idx]:narrow(3,1,self.narrowed_size[idx+1].w):narrow(2,1,self.narrowed_size[idx+1].h) end end @@ -408,7 +392,7 @@ function RecursiveFovea:forward(input,target,x,y) -- 20px shift at scale 1 is a 10px shift at scale 2, this -- changes the dimension of the target. Need some sensibly -- named tag for this - if self.scale_target_values then + if self.scaleTargetValues then local ts = self.targets_scaled[idx] ts:mul(1/self.ratios[idx]) end @@ -435,7 +419,7 @@ function RecursiveFovea:forward(input,target,x,y) -- DEBUG if self.debug then print('') - xprint('adjusted focus points','nn.RecursiveFovea') + xprint('adjusted focus points','nn.SpatialRecursiveFovea') for idx = 1,nscales do if self.focused then print(' + at scale ' .. idx .. ', focused on corner: ' .. corners[idx].x .. ',' .. corners[idx].y .. @@ -450,7 +434,7 @@ function RecursiveFovea:forward(input,target,x,y) return self.output, error end -function RecursiveFovea:backward(input) +function SpatialRecursiveFovea:backward(input) -- local params local nscales = #self.ratios local fov = self.fov @@ -525,31 +509,31 @@ function RecursiveFovea:backward(input) return self.gradInput end -function RecursiveFovea:reset(stdv) +function SpatialRecursiveFovea:reset(stdv) for idx = 1,#self.processors do self.processors[idx]:reset(stdv) end end -function RecursiveFovea:zeroGradParameters(momentum) +function SpatialRecursiveFovea:zeroGradParameters(momentum) for idx = 1,#self.processors do self.processors[idx]:zeroGradParameters(momentum) end end -function RecursiveFovea:updateParameters(learningRate) +function SpatialRecursiveFovea:updateParameters(learningRate) for idx = 1,#self.processors do self.processors[idx]:updateParameters(learningRate) end end -function RecursiveFovea:decayParameters(decay) +function SpatialRecursiveFovea:decayParameters(decay) for idx = 1,#self.processors do self.processors[idx]:decayParameters(decay) end end -function RecursiveFovea:write(file) +function SpatialRecursiveFovea:write(file) parent.write(self, file) -- params file:writeInt(self.nInputPlane) @@ -580,7 +564,7 @@ function RecursiveFovea:write(file) file:writeObject(self.predicted) end -function RecursiveFovea:read(file) +function SpatialRecursiveFovea:read(file) parent.read(self, file) -- params self.nInputPlane = file:readInt() @@ -77,6 +77,7 @@ torch.include('nnx', 'SpatialPadding.lua') torch.include('nnx', 'SpatialNormalization.lua') torch.include('nnx', 'SpatialUpSampling.lua') torch.include('nnx', 'SpatialReSampling.lua') +torch.include('nnx', 'SpatialRecursiveFovea.lua') torch.include('nnx', 'SpatialFovea.lua') torch.include('nnx', 'SpatialGraph.lua') diff --git a/nnx-1.0-1.rockspec b/nnx-1.0-1.rockspec index 284dc11..20fe9e5 100644 --- a/nnx-1.0-1.rockspec +++ b/nnx-1.0-1.rockspec @@ -89,6 +89,7 @@ build = { install_files(/lua/nnx SparseCriterion.lua) install_files(/lua/nnx SpatialSparseCriterion.lua) install_files(/lua/nnx SpatialGraph.lua) + install_files(/lua/nnx SpatialRecursiveFovea.lua) add_subdirectory (test) install_targets(/lib nnx) ]], |