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@Newmu
Created February 3, 2016 20:43
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import sys
sys.path.append('..')
import os
import json
from time import time
import numpy as np
from tqdm import tqdm
from matplotlib import pyplot as plt
from sklearn.externals import joblib
import theano
import theano.tensor as T
from theano.sandbox.cuda.dnn import dnn_conv
from lib import activations
from lib import updates
from lib import inits
from lib.vis import color_grid_vis
from lib.rng import py_rng, np_rng
from lib.ops import batchnorm, conv_cond_concat, deconv, dropout, l2normalize
from lib.metrics import nnc_score, nnd_score
from lib.theano_utils import floatX, sharedX
from lib.data_utils import OneHot, shuffle, iter_data, center_crop, patch
from lib.cv2_utils import min_resize
from load import imagenet
def transform(X):
X = [min_resize(x, npx) for x in X]
X = [center_crop(x, npx) for x in X]
return floatX(X).transpose(0, 3, 1, 2)/127.5 - 1.
def inverse_transform(X):
X = (X.reshape(-1, nc, npx, npx).transpose(0, 2, 3, 1)+1.)/2.
return X
nvis = 400
b1 = 0.5
nc = 3
ny = 10
nbatch = 128
npx = 32
nz = 256
ndf = 128
ngf = 128
nx = nc*npx*npx
niter = 30
niter_decay = 30
lr = 0.0002
# ntrain = 100000
ntrain = 1281167
tr_data, te_data, tr_stream, val_stream, te_stream = imagenet(ntrain=ntrain)
te_handle = te_data.open()
vaX, vaY = te_data.get_data(te_handle, slice(0, 10000))
vaX = transform(vaX)
desc = 'imagenet_gan_pretrain_128f_relu_lrelu_7l_3x3_256z'
model_dir = 'models/%s'%desc
samples_dir = 'samples/%s'%desc
if not os.path.exists(model_dir):
os.makedirs(model_dir)
if not os.path.exists(samples_dir):
os.makedirs(samples_dir)
relu = activations.Rectify()
sigmoid = activations.Sigmoid()
lrelu = activations.LeakyRectify()
tanh = activations.Tanh()
bce = T.nnet.binary_crossentropy
gifn = inits.Normal(scale=0.02)
difn = inits.Normal(scale=0.02)
gain_ifn = inits.Normal(loc=1., scale=0.02)
bias_ifn = inits.Constant(c=0.)
gw = gifn((nz, ngf*4*4*4), 'gw')
gg = gain_ifn((ngf*4*4*4), 'gg')
gb = bias_ifn((ngf*4*4*4), 'gb')
gw2 = gifn((ngf*4, ngf*4, 3, 3), 'gw2')
gg2 = gain_ifn((ngf*4), 'gg2')
gb2 = bias_ifn((ngf*4), 'gb2')
gw3 = gifn((ngf*4, ngf*2, 3, 3), 'gw3')
gg3 = gain_ifn((ngf*2), 'gg3')
gb3 = bias_ifn((ngf*2), 'gb3')
gw4 = gifn((ngf*2, ngf*2, 3, 3), 'gw4')
gg4 = gain_ifn((ngf*2), 'gg4')
gb4 = bias_ifn((ngf*2), 'gb4')
gw5 = gifn((ngf*2, ngf, 3, 3), 'gw5')
gg5 = gain_ifn((ngf), 'gg5')
gb5 = bias_ifn((ngf), 'gb5')
gw6 = gifn((ngf, ngf, 3, 3), 'gwx')
gg6 = gain_ifn((ngf), 'gg6')
gb6 = bias_ifn((ngf), 'gb6')
gwx = gifn((ngf, nc, 3, 3), 'gwx')
dw = difn((ndf, nc, 3, 3), 'dw')
dw2 = difn((ndf, ndf, 3, 3), 'dw2')
dg2 = gain_ifn((ndf), 'dg2')
db2 = bias_ifn((ndf), 'db2')
dw3 = difn((ndf*2, ndf, 3, 3), 'dw3')
dg3 = gain_ifn((ndf*2), 'dg3')
db3 = bias_ifn((ndf*2), 'db3')
dw4 = difn((ndf*2, ndf*2, 3, 3), 'dw4')
dg4 = gain_ifn((ndf*2), 'dg4')
db4 = bias_ifn((ndf*2), 'db4')
dw5 = difn((ndf*4, ndf*2, 3, 3), 'dw5')
dg5 = gain_ifn((ndf*4), 'dg5')
db5 = bias_ifn((ndf*4), 'db5')
dw6 = difn((ndf*4, ndf*4, 3, 3), 'dw6')
dg6 = gain_ifn((ndf*4), 'dg6')
db6 = bias_ifn((ndf*4), 'db6')
dwy = difn((ndf*4*4*4, 1), 'dwy')
gen_params = [gw, gg, gb, gw2, gg2, gb2, gw3, gg3, gb3, gw4, gg4, gb4, gw5, gg5, gb5, gw6, gg6, gb6, gwx]
discrim_params = [dw, dw2, dg2, db2, dw3, dg3, db3, dw4, dg4, db4, dw5, dg5, db5, dw6, dg6, db6, dwy]
def gen(Z, w, g, b, w2, g2, b2, w3, g3, b3, w4, g4, b4, w5, g5, b5, w6, g6, b6, wx):
h = relu(batchnorm(T.dot(Z, w), g=g, b=b))
h = h.reshape((h.shape[0], ngf*4, 4, 4))
h2 = relu(batchnorm(deconv(h, w2, subsample=(2, 2), border_mode=(1, 1)), g=g2, b=b2))
h3 = relu(batchnorm(deconv(h2, w3, subsample=(1, 1), border_mode=(1, 1)), g=g3, b=b3))
h4 = relu(batchnorm(deconv(h3, w4, subsample=(2, 2), border_mode=(1, 1)), g=g4, b=b4))
h5 = relu(batchnorm(deconv(h4, w5, subsample=(1, 1), border_mode=(1, 1)), g=g5, b=b5))
h6 = relu(batchnorm(deconv(h5, w6, subsample=(2, 2), border_mode=(1, 1)), g=g6, b=b6))
x = tanh(deconv(h6, wx, subsample=(1, 1), border_mode=(1, 1)))
return x
def discrim(X, w, w2, g2, b2, w3, g3, b3, w4, g4, b4, w5, g5, b5, w6, g6, b6, wy):
h = lrelu(dnn_conv(X, w, subsample=(1, 1), border_mode=(1, 1)))
h2 = lrelu(batchnorm(dnn_conv(h, w2, subsample=(2, 2), border_mode=(1, 1)), g=g2, b=b2))
h3 = lrelu(batchnorm(dnn_conv(h2, w3, subsample=(1, 1), border_mode=(1, 1)), g=g3, b=b3))
h4 = lrelu(batchnorm(dnn_conv(h3, w4, subsample=(2, 2), border_mode=(1, 1)), g=g4, b=b4))
h5 = lrelu(batchnorm(dnn_conv(h4, w5, subsample=(1, 1), border_mode=(1, 1)), g=g5, b=b5))
h6 = lrelu(batchnorm(dnn_conv(h5, w6, subsample=(2, 2), border_mode=(1, 1)), g=g6, b=b6))
h6 = T.flatten(h6, 2)
y = sigmoid(T.dot(h6, wy))
return y
X = T.tensor4()
Z = T.matrix()
gX = gen(Z, *gen_params)
p_real = discrim(X, *discrim_params)
p_gen = discrim(gX, *discrim_params)
d_cost_real = bce(p_real, T.ones(p_real.shape)).mean()
d_cost_gen = bce(p_gen, T.zeros(p_gen.shape)).mean()
g_cost_d = bce(p_gen, T.ones(p_gen.shape)).mean()
d_cost = d_cost_real + d_cost_gen
g_cost = g_cost_d
cost = [g_cost, d_cost, g_cost_d, d_cost_real, d_cost_gen]
lrt = sharedX(lr)
d_updater = updates.Adam(lr=lrt, b1=b1)
g_updater = updates.Adam(lr=lrt, b1=b1)
d_updates = d_updater(discrim_params, d_cost)
g_updates = g_updater(gen_params, g_cost)
updates = d_updates + g_updates
print 'COMPILING'
t = time()
_train_g = theano.function([X, Z], cost, updates=g_updates)
_train_d = theano.function([X, Z], cost, updates=d_updates)
_gen = theano.function([Z], gX)
print '%.2f seconds to compile theano functions'%(time()-t)
vis_idxs = py_rng.sample(np.arange(len(vaX)), nvis)
vaX_vis = inverse_transform(vaX[vis_idxs])
color_grid_vis(vaX_vis, (20, 20), 'samples/%s_etl_test.png'%desc)
sample_zmb = floatX(np_rng.uniform(-1., 1., size=(nvis, nz)))
def gen_samples(n, nbatch=128):
samples = []
n_gen = 0
for i in range(n/nbatch):
zmb = floatX(np_rng.uniform(-1., 1., size=(nbatch, nz)))
xmb = _gen(zmb)
samples.append(xmb)
n_gen += len(xmb)
n_left = n-n_gen
zmb = floatX(np_rng.uniform(-1., 1., size=(n_left, nz)))
xmb = _gen(zmb)
samples.append(xmb)
return np.concatenate(samples, axis=0)
f_log = open('logs/%s.ndjson'%desc, 'wb')
log_fields = [
'n_epochs',
'n_updates',
'n_examples',
'n_seconds',
'1k_va_nnd',
'10k_va_nnd',
'100k_va_nnd',
'g_cost',
'd_cost',
]
vaX = vaX.reshape(len(vaX), -1)
print desc.upper()
k = 1
n_updates = 0
n_check = 0
n_epochs = 0
n_updates = 0
n_examples = 0
t = time()
for epoch in range(1, niter+niter_decay+1):
for imb, ymb in tqdm(tr_stream.get_epoch_iterator(), total=ntrain/nbatch):
imb = transform(imb)
zmb = floatX(np_rng.uniform(-1., 1., size=(len(imb), nz)))
if n_updates % (k+1) == 0:
cost = _train_g(imb, zmb)
else:
cost = _train_d(imb, zmb)
n_updates += 1
n_examples += len(imb)
if (epoch-1) % 5 == 0:
g_cost = float(cost[0])
d_cost = float(cost[1])
gX = gen_samples(100000)
gX = gX.reshape(len(gX), -1)
va_nnd_1k = nnd_score(gX[:1000], vaX, metric='euclidean')
va_nnd_10k = nnd_score(gX[:10000], vaX, metric='euclidean')
va_nnd_100k = nnd_score(gX[:100000], vaX, metric='euclidean')
log = [n_epochs, n_updates, n_examples, time()-t, va_nnd_1k, va_nnd_10k, va_nnd_100k, g_cost, d_cost]
print '%.0f %.2f %.2f %.2f %.4f %.4f'%(epoch, va_nnd_1k, va_nnd_10k, va_nnd_100k, g_cost, d_cost)
f_log.write(json.dumps(dict(zip(log_fields, log)))+'\n')
f_log.flush()
samples = np.asarray(_gen(sample_zmb))
color_grid_vis(inverse_transform(samples), (20, 20), 'samples/%s/%d.png'%(desc, n_epochs))
n_epochs += 1
if n_epochs > niter:
lrt.set_value(floatX(lrt.get_value() - lr/niter_decay))
if n_epochs in [1, 2, 3, 4, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 60, 70, 80, 90, 100]:
joblib.dump([p.get_value() for p in gen_params], 'models/%s/%d_gen_params.jl'%(desc, n_epochs))
joblib.dump([p.get_value() for p in discrim_params], 'models/%s/%d_discrim_params.jl'%(desc, n_epochs))
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