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checkpoints.lua
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checkpoints.lua
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--
-- Copyright (c) 2016, Facebook, Inc.
-- All rights reserved.
--
-- This source code is licensed under the BSD-style license found in the
-- LICENSE file in the root directory of this source tree. An additional grant
-- of patent rights can be found in the PATENTS file in the same directory.
--
local checkpoint = {}
local function deepCopy(tbl)
-- creates a copy of a network with new modules and the same tensors
local copy = {}
for k, v in pairs(tbl) do
if type(v) == 'table' then
copy[k] = deepCopy(v)
else
copy[k] = v
end
end
if torch.typename(tbl) then
torch.setmetatable(copy, torch.typename(tbl))
end
return copy
end
function checkpoint.latest(opt)
if opt.resume == 'none' then
return nil
end
local latestPath = paths.concat(opt.resume, 'latest.t7')
if not paths.filep(latestPath) then
return nil
end
print('=> Loading checkpoint ' .. latestPath)
local latest = torch.load(latestPath)
local optimState = torch.load(paths.concat(opt.resume, latest.optimFile))
return latest, optimState
end
function checkpoint.best(opt)
if opt.resume == 'none' then
return nil
end
local latestPath = paths.concat(opt.resume, 'latest.t7')
if not paths.filep(latestPath) then
return nil
end
print('=> Loading checkpoint ' .. latestPath)
local latest = torch.load(latestPath)
latest.modelFile = 'model_best.t7'
latest.epoch = 15
local optimState = torch.load(paths.concat(opt.resume, latest.optimFile))
print(latest)
return latest, optimState
end
function checkpoint.saveInit(model, optimState, opt)
if torch.type(model) == 'nn.DataParallelTable' then
model = model:get(1)
end
local epoch = 0
-- create a clean copy on the CPU without modifying the original network
model = deepCopy(model):float():clearState()
local modelFile = 'model_' .. epoch .. '.t7'
local optimFile = 'optimState_' .. epoch .. '.t7'
torch.save(paths.concat(opt.save, modelFile), model)
torch.save(paths.concat(opt.save, optimFile), optimState)
end
function checkpoint.save(epoch, model, optimState, isBestModel, opt)
-- don't save the DataParallelTable for easier loading on other machines
if torch.type(model) == 'nn.DataParallelTable' then
model = model:get(1)
end
-- create a clean copy on the CPU without modifying the original network
model = deepCopy(model):float():clearState()
local modelFile = 'model_' .. epoch .. '.t7'
local optimFile = 'optimState_' .. epoch .. '.t7'
torch.save(paths.concat(opt.save, modelFile), model)
torch.save(paths.concat(opt.save, optimFile), optimState)
torch.save(paths.concat(opt.save, 'latest.t7'), {
epoch = epoch,
modelFile = modelFile,
optimFile = optimFile,
})
if isBestModel then
torch.save(paths.concat(opt.save, 'model_best.t7'), model)
end
if epoch > 1 then
if opt.remainLatest then
local prevModelFile = 'model_' .. (epoch-1) .. '.t7'
local prevOptimFile = 'optimState_' .. (epoch-1) .. '.t7'
os.remove(paths.concat(opt.save, prevModelFile))
os.remove(paths.concat(opt.save, prevOptimFile))
end
end
end
return checkpoint