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export_msgpack.lua
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export_msgpack.lua
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-- --------------------------------------------------
-- Convert context-encoder-gan torch model into message pack
--
-- Written by June Xie
-- Date: 05/24/17
-- Copyright (c) 2017
-- --------------------------------------------------
require 'torch'
require 'nn'
-- require 'cudnn'
-- require 'bnn'
require 'paths'
mp = require 'MessagePack'
mp.set_array'without_hole'
-- torch.setdefaulttensortype it is a good answer
torch.setdefaulttensortype('torch.FloatTensor')
-- save convolution layer parameters
local save_conv = function(layer, name, save_path)
print('save_conv'..name..'start')
th_weight = layer.weight:float()
th_bias = layer.bias:float()
weight = {}
for i = 1, th_weight:size(1) do
weight[i] = {}
for j = 1, th_weight:size(2) do
weight[i][j] = {}
for k = 1, th_weight:size(3) do
weight[i][j][k] = {}
for l = 1, th_weight:size(4) do
weight[i][j][k][l] = th_weight[i][j][k][l]
end
end
end
end
bias = {}
for i = 1 , th_bias:size(1) do
bias[i] = th_bias[i]
end
mp_w = mp.pack(weight)
mp_b = mp.pack(bias)
file = io.open(save_path..name..".msg" , "w")
file:write(mp_w)
file:write(mp_b)
file:close()
print('save_conv'..name..'done')
end
-- save batch normalization layer parameters
local save_batch_norm = function(layer, name, save_path)
print('save_batch_norm'..name..'start')
th_weight = layer.weight:float() -- gamma
th_bias = layer.bias:float() -- beta
th_mean = layer.running_mean:float()
th_var = layer.running_var:float()
gamma = {}
beta = {}
mean = {}
var = {}
for i = 1, th_weight:size(1) do
gamma[i] = th_weight[i]
beta[i] = th_bias[i]
mean[i] = th_mean[i]
var[i] = th_var[i]
end
mp_w = mp.pack(gamma)
mp_b = mp.pack(beta)
mp_m = mp.pack(mean)
mp_v = mp.pack(var)
file = io.open(save_path..name..".msg", "w")
file:write(mp_w)
file:write(mp_b)
file:write(mp_m)
file:write(mp_v)
file:close()
print('save_conv'..name..'done')
end
-- relu, leakyrelu and tanh donnot need parameters, only slope=0.2 in leakyrelu
-- save full convolution layer parameters
local save_fullconv = function(layer, name, save_path)
print('save_fullconv'..name..'start')
th_weight = layer.weight:float()
th_bias = layer.bias:float()
weight = {}
for i = 1, th_weight:size(1) do
weight[i] = {}
for j = 1, th_weight:size(2) do
weight[i][j] = {}
for k = 1, th_weight:size(3) do
weight[i][j][k] = {}
for l = 1, th_weight:size(4) do
weight[i][j][k][l] = th_weight[i][j][k][l]
end
end
end
end
bias = {}
for i = 1 , th_bias:size(1) do
bias[i] = th_bias[i]
end
mp_w = mp.pack(weight)
mp_b = mp.pack(bias)
file = io.open(save_path..name..".msg" , "w")
file:write(mp_w)
file:write(mp_b)
file:close()
print('save_conv'..name..'done')
end
-- export models
local export_model = function(model_path, save_path)
local model = {}
model = torch.load(model_path)
os.execute('mkdir -p ' .. save_path)
model:evaluate()
print(model)
save_conv(model:get(1):get(1), "l1_conv", save_path)
save_conv(model:get(1):get(3), "l3_conv", save_path)
save_conv(model:get(1):get(6), "l6_conv", save_path)
save_conv(model:get(1):get(9), "l9_conv", save_path)
save_conv(model:get(1):get(12), "l12_conv", save_path)
save_conv(model:get(1):get(15), "l15_conv", save_path)
save_batch_norm(model:get(1):get(4), "l4_bn", save_path)
save_batch_norm(model:get(1):get(7), "l7_bn", save_path)
save_batch_norm(model:get(1):get(10), "l10_bn", save_path)
save_batch_norm(model:get(1):get(13), "l13_bn", save_path)
save_batch_norm(model:get(2), "l16_bn", save_path)
save_batch_norm(model:get(5), "l19_bn", save_path)
save_batch_norm(model:get(8), "l22_bn", save_path)
save_batch_norm(model:get(11), "l25_bn", save_path)
save_batch_norm(model:get(14), "l28_bn", save_path)
save_fullconv(model:get(4), "l18_conv", save_path)
save_fullconv(model:get(7), "l21_conv", save_path)
save_fullconv(model:get(10), "l24_conv", save_path)
save_fullconv(model:get(13), "l27_conv", save_path)
save_fullconv(model:get(16), "l30_conv", save_path)
print('parameters are saved in: '..save_path)
end
local model_path = './models/imagenet_inpaintCenter.t7'
local save_path = './msgpack/'
if paths.filep(model_path) then
export_model(model_path, save_path)
else
print('Modwl not exists')
end