-
Notifications
You must be signed in to change notification settings - Fork 341
/
main.lua
272 lines (241 loc) · 9.39 KB
/
main.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
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
require 'torch'
require 'nn'
require 'optim'
opt = {
dataset = 'folder', -- imagenet / lsun / folder
batchSize = 64,
loadSize = 129,
fineSize = 128,
netG = '',
netD = '',
nz = 100, -- # of dim for Z
ngf = 160, -- # of gen filters in first conv layer
ndf = 40, -- # of discrim filters in first conv layer
nThreads = 4, -- # of data loading threads to use
niter = 100, -- # of iter at starting learning rate
saveIter = 100, -- # of epochs per save
lr = 0.0002, -- initial learning rate for adam
beta1 = 0.5, -- momentum term of adam
ntrain = math.huge, -- # of examples per epoch. math.huge for full dataset
display = 1, -- display samples while training. 0 = false
display_id = 10, -- display window id.
gpu = 1, -- gpu = 0 is CPU mode. gpu=X is GPU mode on GPU X
name = 'experiment1',
noise = 'normal', -- uniform / normal
}
if opt.gpu > 0 then
require 'cunn'
require 'cudnn'
end
-- one-line argument parser. parses enviroment variables to override the defaults
for k,v in pairs(opt) do opt[k] = tonumber(os.getenv(k)) or os.getenv(k) or opt[k] end
print(opt)
if opt.display == 0 then opt.display = false end
opt.manualSeed = torch.random(1, 10000) -- fix seed
print("Random Seed: " .. opt.manualSeed)
torch.manualSeed(opt.manualSeed)
torch.setnumthreads(1)
torch.setdefaulttensortype('torch.FloatTensor')
-- create data loader
local DataLoader = paths.dofile('data/data.lua')
local data = DataLoader.new(opt.nThreads, opt.dataset, opt)
print("Dataset: " .. opt.dataset, " Size: ", data:size())
----------------------------------------------------------------------------
local function weights_init(m)
local name = torch.type(m)
if name:find('Convolution') then
m.weight:normal(0.0, 0.02)
m:noBias()
elseif name:find('BatchNormalization') then
if m.weight then m.weight:normal(1.0, 0.02) end
if m.bias then m.bias:fill(0) end
end
end
local nc = 3
local nz = opt.nz
local ndf = opt.ndf
local ngf = opt.ngf
local real_label = 1
local fake_label = 0
local SpatialBatchNormalization = nn.SpatialBatchNormalization
local SpatialConvolution = nn.SpatialConvolution
local SpatialFullConvolution = nn.SpatialFullConvolution
if (opt.netG ~= '') then
print('Initializing generator network from ' .. opt.netG)
netG = torch.load(opt.netG)
else
netG = nn.Sequential()
-- input is Z, going into a convolution
netG:add(SpatialFullConvolution(nz, ngf * 16, 4, 4))
netG:add(SpatialBatchNormalization(ngf * 16)):add(nn.ReLU(true))
-- state size: (ngf*16) x 4 x 4
netG:add(SpatialFullConvolution(ngf * 16, ngf * 8, 4, 4, 2, 2, 1, 1))
netG:add(SpatialBatchNormalization(ngf * 8)):add(nn.ReLU(true))
-- state size: (ngf*8) x 8 x 8
netG:add(SpatialFullConvolution(ngf * 8, ngf * 4, 4, 4, 2, 2, 1, 1))
netG:add(SpatialBatchNormalization(ngf * 4)):add(nn.ReLU(true))
-- state size: (ngf*4) x 16 x 16
netG:add(SpatialFullConvolution(ngf * 4, ngf * 2, 4, 4, 2, 2, 1, 1))
netG:add(SpatialBatchNormalization(ngf * 2)):add(nn.ReLU(true))
-- state size: (ngf * 2) x 32 x 32
netG:add(SpatialFullConvolution(ngf * 2, ngf, 4, 4, 2, 2, 1, 1))
netG:add(SpatialBatchNormalization(ngf)):add(nn.ReLU(true))
-- state size: (ngf) x 64 x 64
netG:add(SpatialFullConvolution(ngf, nc, 4, 4, 2, 2, 1, 1))
netG:add(nn.Tanh())
-- state size: (nc) x 128 x 128
netG:apply(weights_init)
end
if (opt.netD ~= '') then
print('Initializing discriminator network from ' .. opt.netD)
netD = torch.load(opt.netD)
else
netD = nn.Sequential()
-- input is (nc) x 128 x 128
netD:add(SpatialConvolution(nc, ndf, 4, 4, 2, 2, 1, 1))
netD:add(nn.LeakyReLU(0.2, true))
-- state size: (ndf) x 64 x 64
netD:add(SpatialConvolution(ndf, ndf * 2, 4, 4, 2, 2, 1, 1))
netD:add(SpatialBatchNormalization(ndf * 2)):add(nn.LeakyReLU(0.2, true))
-- state size: (ndf*2) x 32 x 32
netD:add(SpatialConvolution(ndf * 2, ndf * 4, 4, 4, 2, 2, 1, 1))
netD:add(SpatialBatchNormalization(ndf * 4)):add(nn.LeakyReLU(0.2, true))
-- state size: (ndf*4) x 16 x 16
netD:add(SpatialConvolution(ndf * 4, ndf * 8, 4, 4, 2, 2, 1, 1))
netD:add(SpatialBatchNormalization(ndf * 8)):add(nn.LeakyReLU(0.2, true))
-- state size: (ndf*8) x 8 x 8
netD:add(SpatialConvolution(ndf * 8, ndf * 16, 4, 4, 2, 2, 1, 1))
netD:add(SpatialBatchNormalization(ndf * 16)):add(nn.LeakyReLU(0.2, true))
-- state size: (ndf*16) x 4 x 4
netD:add(SpatialConvolution(ndf * 16, 1, 4, 4))
netD:add(nn.Sigmoid())
-- state size: 1 x 1 x 1
netD:add(nn.View(1):setNumInputDims(3))
-- state size: 1
netD:apply(weights_init)
end
local criterion = nn.BCECriterion()
---------------------------------------------------------------------------
optimStateG = {
learningRate = opt.lr,
beta1 = opt.beta1,
}
optimStateD = {
learningRate = opt.lr,
beta1 = opt.beta1,
}
----------------------------------------------------------------------------
local input = torch.Tensor(opt.batchSize, 3, opt.fineSize, opt.fineSize)
local noise = torch.Tensor(opt.batchSize, nz, 1, 1)
local label = torch.Tensor(opt.batchSize)
local errD, errG
local epoch_tm = torch.Timer()
local tm = torch.Timer()
local data_tm = torch.Timer()
----------------------------------------------------------------------------
if opt.gpu > 0 then
require 'cunn'
cutorch.setDevice(opt.gpu)
input = input:cuda(); noise = noise:cuda(); label = label:cuda()
if pcall(require, 'cudnn') then
require 'cudnn'
cudnn.benchmark = true
cudnn.convert(netG, cudnn)
cudnn.convert(netD, cudnn)
end
netD:cuda(); netG:cuda(); criterion:cuda()
end
local parametersD, gradParametersD = netD:getParameters()
local parametersG, gradParametersG = netG:getParameters()
if opt.display then disp = require 'display' end
noise_vis = noise:clone()
if opt.noise == 'uniform' then
noise_vis:uniform(-1, 1)
elseif opt.noise == 'normal' then
noise_vis:normal(0, 1)
end
-- create closure to evaluate f(X) and df/dX of discriminator
local fDx = function(x)
gradParametersD:zero()
-- train with real
data_tm:reset(); data_tm:resume()
local real = data:getBatch()
data_tm:stop()
input:copy(real)
label:fill(real_label)
local output = netD:forward(input)
local errD_real = criterion:forward(output, label)
local df_do = criterion:backward(output, label)
netD:backward(input, df_do)
-- train with fake
if opt.noise == 'uniform' then -- regenerate random noise
noise:uniform(-1, 1)
elseif opt.noise == 'normal' then
noise:normal(0, 1)
end
local fake = netG:forward(noise)
input:copy(fake)
label:fill(fake_label)
local output = netD:forward(input)
local errD_fake = criterion:forward(output, label)
local df_do = criterion:backward(output, label)
netD:backward(input, df_do)
errD = errD_real + errD_fake
return errD, gradParametersD
end
-- create closure to evaluate f(X) and df/dX of generator
local fGx = function(x)
gradParametersG:zero()
--[[ the three lines below were already executed in fDx, so save computation
noise:uniform(-1, 1) -- regenerate random noise
local fake = netG:forward(noise)
input:copy(fake) ]]--
label:fill(real_label) -- fake labels are real for generator cost
local output = netD.output -- netD:forward(input) was already executed in fDx, so save computation
errG = criterion:forward(output, label)
local df_do = criterion:backward(output, label)
local df_dg = netD:updateGradInput(input, df_do)
netG:backward(noise, df_dg)
return errG, gradParametersG
end
-- train
for epoch = 1, opt.niter do
epoch_tm:reset()
local counter = 0
for i = 1, math.min(data:size(), opt.ntrain), opt.batchSize do
tm:reset()
-- (1) Update D network: maximize log(D(x)) + log(1 - D(G(z)))
optim.adam(fDx, parametersD, optimStateD)
-- (2) Update G network: maximize log(D(G(z)))
optim.adam(fGx, parametersG, optimStateG)
-- display
counter = counter + 1
if counter % 10 == 0 and opt.display then
local fake = netG:forward(noise_vis)
local real = data:getBatch()
disp.image(fake, {win=opt.display_id, title=opt.name})
disp.image(real, {win=opt.display_id * 3, title=opt.name})
end
-- logging
if ((i-1) / opt.batchSize) % 1 == 0 then
print(('Epoch: [%d][%8d / %8d]\t Time: %.3f DataTime: %.3f '
.. ' Err_G: %.4f Err_D: %.4f'):format(
epoch, ((i-1) / opt.batchSize),
math.floor(math.min(data:size(), opt.ntrain) / opt.batchSize),
tm:time().real, data_tm:time().real,
errG and errG or -1, errD and errD or -1))
end
end
if epoch % opt.saveIter == 0 then
paths.mkdir('checkpoints')
parametersD, gradParametersD = nil, nil -- nil them to avoid spiking memory
parametersG, gradParametersG = nil, nil
torch.save('checkpoints/' .. opt.name .. '_' .. epoch .. '_net_G.t7', netG:clearState())
torch.save('checkpoints/' .. opt.name .. '_' .. epoch .. '_net_D.t7', netD:clearState())
parametersD, gradParametersD = netD:getParameters() -- reflatten the params and get them
parametersG, gradParametersG = netG:getParameters()
print('Saved checkpoint: '..epoch)
end
print(('End of epoch %d / %d \t Time Taken: %.3f'):format(
epoch, opt.niter, epoch_tm:time().real))
end