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nn.utils = {}
-- oops; someone forgot to add torch.Storage.type
-- TODO replace with torch.Storage.type when implemented
local function torch_Storage_type(self, type)
local current = torch.typename(self)
if not type then return current end
if type ~= current then
local new = torch.getmetatable(type).new()
if self:size() > 0 then
new:resize(self:size()):copy(self)
end
return new
else
return self
end
end
-- tensorCache maintains a list of all tensors and storages that have been
-- converted (recursively) by calls to recursiveType() and type().
-- It caches conversions in order to preserve sharing semantics
-- i.e. if two tensors share a common storage, then type conversion
-- should preserve that.
--
-- You can preserve sharing semantics across multiple networks by
-- passing tensorCache between the calls to type, e.g.
--
-- > tensorCache = {}
-- > net1:type('torch.CudaTensor', tensorCache)
-- > net2:type('torch.CudaTensor', tensorCache)
-- > nn.utils.recursiveType(anotherTensor, 'torch.CudaTensor', tensorCache)
--
-- Implementation note: to make Lua table lookup behave correctly,
-- tensor keys are stored as actual tensor objects, while storage
-- keys are stored as the pointers themselves (as numbers).
function nn.utils.recursiveType(param, type, tensorCache)
tensorCache = tensorCache or {}
if torch.type(param) == 'table' then
for k, v in pairs(param) do
param[k] = nn.utils.recursiveType(v, type, tensorCache)
end
elseif torch.isTypeOf(param, 'nn.Module') or
torch.isTypeOf(param, 'nn.Criterion') then
param:type(type, tensorCache)
elseif torch.isTensor(param) then
if torch.typename(param) ~= type then
local newparam
if tensorCache[param] then
newparam = tensorCache[param]
else
newparam = torch.Tensor():type(type)
local storageType = type:gsub('Tensor','Storage')
if param:storage() then
local storage_key = torch.pointer(param:storage())
if not tensorCache[storage_key] then
tensorCache[storage_key] = torch_Storage_type(
param:storage(), storageType)
end
assert(torch.type(tensorCache[storage_key]) == storageType)
newparam:set(
tensorCache[storage_key],
param:storageOffset(),
param:size(),
param:stride()
)
end
tensorCache[param] = newparam
end
assert(torch.type(newparam) == type)
param = newparam
end
end
return param
end
function nn.utils.recursiveResizeAs(t1,t2)
if torch.type(t2) == 'table' then
t1 = (torch.type(t1) == 'table') and t1 or {t1}
for key,_ in pairs(t2) do
t1[key], t2[key] = nn.utils.recursiveResizeAs(t1[key], t2[key])
end
for key,_ in pairs(t1) do
if not t2[key] then
t1[key] = nil
end
end
elseif torch.isTensor(t2) then
t1 = torch.isTensor(t1) and t1 or t2.new()
t1:resize(t2:size())
else
error("expecting nested tensors or tables. Got "..
torch.type(t1).." and "..torch.type(t2).." instead")
end
return t1, t2
end
function nn.utils.recursiveFill(t2, val)
if torch.type(t2) == 'table' then
for key,_ in pairs(t2) do
t2[key] = nn.utils.recursiveFill(t2[key], val)
end
elseif torch.isTensor(t2) then
t2:fill(val)
else
error("expecting tensor or table thereof. Got "
..torch.type(t2).." instead")
end
return t2
end
function nn.utils.recursiveAdd(t1, val, t2)
if not t2 then
assert(val, "expecting at least two arguments")
t2 = val
val = 1
end
val = val or 1
if torch.type(t2) == 'table' then
t1 = (torch.type(t1) == 'table') and t1 or {t1}
for key,_ in pairs(t2) do
t1[key], t2[key] = nn.utils.recursiveAdd(t1[key], val, t2[key])
end
elseif torch.isTensor(t1) and torch.isTensor(t2) then
t1:add(val, t2)
else
error("expecting nested tensors or tables. Got "..
torch.type(t1).." and "..torch.type(t2).." instead")
end
return t1, t2
end
function nn.utils.recursiveCopy(t1,t2,async)
if torch.type(t2) == 'table' then
t1 = (torch.type(t1) == 'table') and t1 or {t1}
for key,_ in pairs(t2) do
t1[key], t2[key] = nn.utils.recursiveCopy(t1[key], t2[key], async)
end
elseif torch.isTensor(t2) then
t1 = torch.isTensor(t1) and t1 or t2.new()
t1:resize(t2:size())
if async then
t1:copyAsync(t2)
else
t1:copy(t2)
end
else
error("expecting nested tensors or tables. Got "..
torch.type(t1).." and "..torch.type(t2).." instead")
end
return t1, t2
end
function nn.utils.addSingletonDimension(...)
local view, t, dim
if select('#',...) < 3 then
t, dim = select(1,...)
else
view, t, dim = select(1,...)
assert(torch.isTensor(view),
"output tensor expected, got " .. type(view))
end
assert(torch.isTensor(t), "input tensor expected")
dim = dim or 1
assert(dim > 0 and dim <= (t:dim() + 1), "invalid dimension: " .. dim
.. '. Tensor is of ' .. t:dim() .. ' dimensions.')
view = view or t.new()
local size = torch.LongStorage(t:dim() + 1)
local stride = torch.LongStorage(t:dim() + 1)
for d = 1, dim - 1 do
size[d] = t:size(d)
stride[d] = t:stride(d)
end
size[dim] = 1
stride[dim] = 1
for d = dim + 1, t:dim() + 1 do
size[d] = t:size(d - 1)
stride[d] = t:stride(d - 1)
end
view:set(t:storage(), t:storageOffset(), size, stride)
return view
end
function nn.utils.contiguousView(output, input, ...)
output = output or input.new()
if input:isContiguous() then
output:view(input, ...)
else
output:resize(input:size())
output:copy(input)
output:view(output, ...)
end
return output
end
-- go over specified fields and clear them. accepts
-- nn.utils.clearState(self, {'_buffer', '_buffer2'}) and
-- nn.utils.clearState(self, '_buffer', '_buffer2')
function nn.utils.clear(self, ...)
local arg = {...}
if #arg > 0 and type(arg[1]) == 'table' then
arg = arg[1]
end
local function clear(f)
if self[f] then
if torch.isTensor(self[f]) then
self[f]:set()
elseif type(self[f]) == 'table' then
self[f] = {}
else
self[f] = nil
end
end
end
for i,v in ipairs(arg) do clear(v) end
return self
end
table.unpack = table.unpack or unpack
|
