X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?p=dagnn.git;a=blobdiff_plain;f=dagnn.lua;h=b82398c0de429bd19875776fa465222a84504bbe;hp=0073e395f039be4a853920e712b26e0627d5547d;hb=HEAD;hpb=e1575dfc09f7a8fe97f74acfe4cbed1e8a617dff diff --git a/dagnn.lua b/dagnn.lua index 0073e39..b82398c 100755 --- a/dagnn.lua +++ b/dagnn.lua @@ -61,10 +61,15 @@ function DAG:putInOrder() end local distance = {} - self:nestedApply(function(m) distance[m] = 1 end, self.inputModules) + self:nestedApply( + function(m) distance[m] = 1 end, + self.inputModules + ) local nc + local nl = 0 repeat + assert(nl < #self.modules, 'Cycle detected in the graph.') nc = 0 for nnma, node in pairs(self.node) do for _, nnmb in pairs(node.succ) do @@ -74,8 +79,13 @@ function DAG:putInOrder() end end end + nl = nl + 1 until nc == 0 + for _, nnm in pairs(self.modules) do + assert(distance[nnm], 'Some modules are not connected to inputs.') + end + self.sorted = {} for m, d in pairs(distance) do table.insert(self.sorted, { distance = d, nnm = m }) @@ -86,14 +96,17 @@ function DAG:putInOrder() for i, a in ipairs(self.sorted) do self.sorted[i] = a.nnm end end --- This accumulate x in a where they are both nested tables of --- tensors. If first is true, set a = x. +-- This accumulates x in a, where they are both nested tables of +-- tensors with same structures / keys. If first is true, set a = x +-- (in which case a can be nil) otherwise a = a + x. The behavior is +-- undefined if a and x do not have the exact same structure. function DAG:nestedAccTensor(a, x, first) if torch.type(x) == 'table' then - a = a or {} + local b = {} for i in pairs(x) do - a[i] = self:nestedAccTensor(a[i], x[i], first) + b[i] = self:nestedAccTensor(a[i], x[i], first) end + a = b else if first then if a then @@ -135,17 +148,17 @@ function DAG:connect(...) end end +function DAG:setLabel(nnm, label) + self.node[nnm].label = label +end + function DAG:setInput(i) self.sorted = nil self.inputModules = i self:nestedApply( function(nnm) - if #self.node[nnm].succ == 0 then - error('Input modules must have outgoing edges.') - end - if #self.node[nnm].pred > 0 then - error('Input modules cannot have incoming edges.') - end + assert(#self.node[nnm].succ > 0, 'Input modules must have outgoing edges.') + assert(#self.node[nnm].pred == 0, 'Input modules cannot have incoming edges.') end, self.inputModules ) @@ -156,12 +169,8 @@ function DAG:setOutput(o) self.outputModules = o self:nestedApply( function(nnm) - if #self.node[nnm].pred == 0 then - error('Output module must have incoming edges.') - end - if #self.node[nnm].succ > 0 then - error('Output module cannot have outgoing edges.') - end + assert(#self.node[nnm].pred > 0, 'Output module must have incoming edges.') + assert(#self.node[nnm].succ == 0, 'Output module cannot have outgoing edges.') end, self.outputModules ) @@ -171,7 +180,11 @@ function DAG:print() self:putInOrder() for i, d in ipairs(self.sorted) do - print('#' .. i .. ' -> ' .. torch.type(d)) + local decoration = '' + if self.node[d].label then + decoration = ' [' .. self.node[d].label .. ']' + end + print('#' .. i .. ' -> ' .. torch.type(d) .. decoration) end end @@ -180,15 +193,33 @@ end function DAG:saveDot(filename) local file = (filename and io.open(filename, 'w')) or io.stdout + local function writeNestedCluster(prefix, list, indent) + local indent = indent or '' + if torch.type(list) == 'table' then + file:write(indent .. ' subgraph cluster_' .. prefix .. ' {\n'); + for k, x in pairs(list) do + writeNestedCluster(prefix .. '_' .. k, x, ' ' .. indent) + end + file:write(indent .. ' }\n'); + else + file:write(indent .. ' ' .. self.node[list].index .. ' [color=red]\n') + end + end + file:write('digraph {\n') file:write('\n') + writeNestedCluster('input', self.inputModules) + writeNestedCluster('output', self.outputModules) + + file:write('\n') + for nnmb, node in pairs(self.node) do file:write( ' ' .. node.index - .. ' [shape=box,label=\"' .. torch.type(nnmb) .. '\"]' + .. ' [shape=box,label=\"' .. (self.node[nnmb].label or torch.type(nnmb)) .. '\"]' .. '\n' ) @@ -222,8 +253,9 @@ function DAG:updateOutput(input) self:nestedApply( function(nnm, i) - self.node[nnm].input = i - self:rethrowErrors(nnm, self.node[nnm].index, 'updateOutput', i) + local node = self.node[nnm] + node.input = i + self:rethrowErrors(nnm, node.index, 'updateOutput', i) end, self.inputModules, input @@ -231,18 +263,19 @@ function DAG:updateOutput(input) for _, nnm in ipairs(self.sorted) do local node = self.node[nnm] - if #node.pred > 0 then + local pred = node.pred + if #pred > 0 then local i - if #node.pred == 1 then - i = node.pred[1].output - elseif #node.pred > 1 then + if #pred == 1 then + i = pred[1].output + elseif #pred > 1 then i = {} - for k = 1, #node.pred do - i[k] = node.pred[k].output + for k = 1, #pred do + i[k] = pred[k].output end end node.input = i - self:rethrowErrors(nnm, self.node[nnm].index, 'updateOutput', i) + self:rethrowErrors(nnm, node.index, 'updateOutput', i) end end @@ -255,13 +288,13 @@ function DAG:updateOutput(input) end function DAG:updateGradInput(input, gradOutput) - assert(self.sorted, 'There has been a DAG structure change before a DAG:updateGradInput') + assert(self.sorted, 'There has been a structure change before a DAG:updateGradInput.') self:nestedApply( function(nnm, go) local node = self.node[nnm] node.gradOutput = go - self:rethrowErrors(nnm, node.index, 'updateGradInput', self.node[nnm].input, go) + self:rethrowErrors(nnm, node.index, 'updateGradInput', node.input, go) end, self.outputModules, gradOutput ) @@ -282,31 +315,31 @@ function DAG:updateGradInput(input, gradOutput) if #node.gradInputSucc > 0 then self:updateGradOutput(node) - self:rethrowErrors(nnm, self.node[nnm].index, 'updateGradInput', node.input, node.gradOutput) + self:rethrowErrors(nnm, node.index, 'updateGradInput', node.input, node.gradOutput) end -- We fill the gradInputSucc of our predecessors if #pred == 1 then table.insert(self.node[pred[1]].gradInputSucc, nnm.gradInput) elseif #pred > 1 then - if not torch.type(nnm.gradInput) == 'table' then - error('Should have a table gradInput since it has multiple predecessors') - end + assert(torch.type(nnm.gradInput) == 'table', + 'Should have a table gradInput since it has multiple predecessors.') for n = 1, #pred do - table.insert(self.node[node.pred[n]].gradInputSucc, nnm.gradInput[n]) + table.insert(self.node[pred[n]].gradInputSucc, nnm.gradInput[n]) end end end - self.gradInput = self:nestedApply(function(m) return m.gradInput end, self.inputModules) + self.gradInput = self:nestedApply( + function(m) return m.gradInput end, + self.inputModules + ) return self.gradInput end function DAG:accGradParameters(input, gradOutput, scale) - scale = scale or 1 - - assert(self.sorted, 'There has been a DAG structure change before a DAG:accGradParameters') + assert(self.sorted, 'There has been a structure change before a DAG:accGradParameters.') self:nestedApply( function(nnm, go) self.node[nnm].gradOutput = go end, @@ -328,8 +361,8 @@ end function DAG:clearState() self.sorted = nil for _, node in pairs(self.node) do - node.gradInputSucc = nil node.input = nil + node.gradInputSucc = nil node.gradOutput = nil end return parent.clearState(self)