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train.lua
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train.lua
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--
-- Copyright (c) 2016-present, 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.
--
require 'torch'
require 'cutorch'
require 'nn'
require 'cunn'
require 'cudnn'
require 'nngraph'
require 'logroll'
require 'xlua'
local framework = require 'train.rl_framework.infra.framework'
local rl = require 'train.rl_framework.infra.env'
local pl = require 'pl.import_into'()
require 'train.rl_framework.infra.bundle'
require 'train.rl_framework.infra.agent'
local tnt = require 'torchnet'
cutorch.setDevice(3)
-- Build simple models.
function build_policy_model(opt)
local network_maker = require('train.rl_framework.examples.go.models.' .. opt.model_name)
local network, crit, outputdim, monitor_list = network_maker({1, 25, 19, 19}, opt)
--network = torch.load("df2.bin")
print(network)
return network:cuda(), crit:cuda()
end
local opt = pl.lapp[[
--actor (default "policy")
--sampling (default "replay")
--optim (default "supervised")
--loss (default 'policy')
--alpha (default 0.1)
--nthread (default 8)
--batchsize (default 256)
--num_forward_models (default 4096) Number of forward models.
--progress Whether to print the progress
--epoch_size (default 12800) Epoch size
--epoch_size_test (default 128000) Epoch size for test.
--data_augmentation Whether to use data_augmentation
--nGPU (default 1) Number of GPUs to use.
--nstep (default 3) Number of steps.
--model_name (default 'model-12-parallel-384-n-output-bn')
--datasource (default 'kgs')
--feature_type (default 'extended')
]]
if not paths.dirp('experiments') then
paths.mkdir('experiments')
end
paths.mkdir(paths.concat('experiments', opt.feature_type))
local flog = logroll.file_logger(paths.concat('experiments', opt.feature_type,'_log.txt'))
local plog = logroll.print_logger()
log = logroll.combine(flog, plog)
opt.userank = true
opt.intermediate_step = opt.epoch_size / opt.batchsize / 10
print(pl.pretty.write(opt))
local model, crits = build_policy_model(opt)
local bundle = rl.Bundle{
models = {
policy = model,
},
crits = crits
}
local agent = rl.Agent{
bundle = bundle,
opt = opt
}
local stats = {
sgf_idx = { },
board_freq = torch.FloatTensor(19, 19):zero(),
ply = { },
count = 0
}
local callbacks = {
thread_init = function()
require 'train.rl_framework.examples.go.ParallelCriterion2'
end,
forward_model_init = function(partition)
local tnt = require 'torchnet'
return tnt.IndexedDataset{
fields = { opt.datasource .. "_" .. partition },
path = './dataset'
}
end,
forward_model_generator = function(dataset, partition)
local fm_go = require 'train.rl_framework.examples.go.fm_go'
return fm_go.FMGo(dataset, partition, opt)
end,
onSample = function(state)
-- Compute the stats.
--[[
if state.signature == 'train' then return end
for i = 1, state.sample.sgf_idx:size(1) do
local idx = state.sample.sgf_idx[i]
if stats.sgf_idx[idx] == nil then stats.sgf_idx[idx] = 0 end
stats.sgf_idx[idx] = stats.sgf_idx[idx] + 1
local xy = state.sample.xy[i]
local x = xy[1]
local y = xy[2]
stats.board_freq[x][y] = stats.board_freq[x][y] + 1
stats.count = stats.count + 1
local ply = state.sample.ply[i]
if stats.ply[ply] == nil then stats.ply[ply] = 0 end
stats.ply[ply] = stats.ply[ply] + 1
end
if stats.count % (2000 * opt.batchsize) == 0 then
print(stats.board_freq:clone():mul(1.0 / stats.count))
require 'fb.debugger'.enter()
end
]]
end,
--[[
onStartEpoch = function()
print("In onStartEpoch")
end,
onStart = function()
print("In onStart")
end,
onSample = function()
print("In onSample")
end,
onUpdate = function()
print("In onUpdate")
end,
onEndEpoch = function()
print("In onEndEpoch")
end
]]
}
-- callbacks:
-- forward_model_generator
-- checkpoint_filename(state, err): Get checkpoint filename
-- tune_lr(state): tune the learning rate
-- print(log, state): print the current state
-- (All the remaining functions take state as input)
-- onStartEpoch
-- onStart
-- onSample
-- onUpdate
-- For now just shortcut the trainloss/testloss.
framework.run_rl(agent, callbacks, opt)