-
Notifications
You must be signed in to change notification settings - Fork 5
/
checkpoints.lua
79 lines (68 loc) · 2.37 KB
/
checkpoints.lua
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
--
-- 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.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'
if opt.checkpoint == 'true' then
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,
})
end
if isBestModel then
local bestModelFile = 'model_best.t7'
local bestOptimFile = 'model_best_optimState.t7'
torch.save(paths.concat(opt.save, bestModelFile), model)
torch.save(paths.concat(opt.save, bestOptimFile), optimState)
torch.save(paths.concat(opt.save, 'latest.t7'), {
epoch = epoch,
modelFile = bestModelFile,
optimFile = bestOptimFile,
})
end
end
return checkpoint