import theano
import theano.tensor as T
from lib import activations
from lib import updates
from lib import inits
from lib.rng import py_rng, np_rng
from lib.ops import batchnorm, conv_cond_concat, conv, dropout
from lib.theano_utils import floatX, sharedX
from lib.data_utils import OneHot, shuffle, iter_data
from lib.metrics import nnc_score, nnd_score
from load import load_shapenet_train, load_shapenet_test
relu = activations.Rectify()
sigmoid = activations.Sigmoid()
lrelu = activations.LeakyRectify()
bce = T.nnet.binary_crossentropy
parameters = {
'objectNumber': 2,
'Nz': 200,
'Channel': (1, 64, 128, 256, 512),
'kernal': (4, 4, 4, 4),
'batchsize': 50,
'Convlayersize': (64, 32, 16, 8, 4),
'Genlrt': 0.001,
'Discrimlrt': 0.00001,
'beta': 0.5,
'l2': 2.5e-5,
def mnistGANcond():
"""
This example loads the 32x32 imagenet model used in the paper,
generates 400 random samples, and sorts them according to the
discriminator's probability of being real and renders them to
the file samples.png
"""
nc = 1
npx = 28
ngf = 64 # # of gen filters in first conv layer
ndf = 128
ny = 10 # # of classes
nz = 100 # # of dim for Z
k = 1 # # of discrim updates for each gen update
l2 = 2.5e-5 # l2 weight decay
b1 = 0.5 # momentum term of adam
nc = 1 # # of channels in image
ny = 10 # # of classes
nbatch = 128 # # of examples in batch
npx = 28 # # of pixels width/height of images
nz = 100 # # of dim for Z
ngfc = 1024 # # of gen units for fully connected layers
ndfc = 1024 # # of discrim units for fully connected layers
ngf = 64 # # of gen filters in first conv layer
ndf = 64 # # of discrim filters in first conv layer
nx = npx * npx * nc # # of dimensions in X
niter = 100 # # of iter at starting learning rate
niter_decay = 100 # # of iter to linearly decay learning rate to zero
lr = 0.0002
relu = activations.Rectify()
sigmoid = activations.Sigmoid()
lrelu = activations.LeakyRectify()
tanh = activations.Tanh()
model_path = 'dcgan_code-master/mnist/models/cond_dcgan/'
gen_params = [
sharedX(p) for p in joblib.load(model_path + '200_gen_params.jl')
]
discrim_params = [
sharedX(p) for p in joblib.load(model_path + '200_discrim_params.jl')
]
def gen(Z, Y, w, w2, w3, wx):
yb = Y.dimshuffle(0, 1, 'x', 'x')
Z = T.concatenate([Z, Y], axis=1)
h = relu(batchnorm(T.dot(Z, w)))
h = T.concatenate([h, Y], axis=1)
h2 = relu(batchnorm(T.dot(h, w2)))
h2 = h2.reshape((h2.shape[0], ngf * 2, 7, 7))
h2 = conv_cond_concat(h2, yb)
h3 = relu(
batchnorm(deconv(h2, w3, subsample=(2, 2), border_mode=(2, 2))))
h3 = conv_cond_concat(h3, yb)
x = sigmoid(deconv(h3, wx, subsample=(2, 2), border_mode=(2, 2)))
return x
def discrim(X, Y, w, w2, w3, wy):
yb = Y.dimshuffle(0, 1, 'x', 'x')
X = conv_cond_concat(X, yb)
h = lrelu(dnn_conv(X, w, subsample=(2, 2), border_mode=(2, 2)))
h = conv_cond_concat(h, yb)
h2 = lrelu(
batchnorm(dnn_conv(h, w2, subsample=(2, 2), border_mode=(2, 2))))
h2 = T.flatten(h2, 2)
h2 = T.concatenate([h2, Y], axis=1)
h3 = lrelu(batchnorm(T.dot(h2, w3)))
h3 = T.concatenate([h3, Y], axis=1)
y = sigmoid(T.dot(h3, wy))
return y
def inverse_transform(X):
X = (X.reshape(-1, nc, npx, npx).transpose(0, 2, 3, 1) + 1.) / 2.
return X
Z = T.matrix()
X = T.tensor4()
Y = T.matrix()
gX = gen(Z, Y, *gen_params)
dX = discrim(X, Y, *discrim_params)
_gen = theano.function([Z, Y], gX)
_discrim = theano.function([X, Y], dX)
sample_zmb = floatX(np_rng.uniform(-1., 1., size=(200, nz)))
sample_ymb = floatX(
OneHot(
np.asarray([[i for _ in range(20)] for i in range(10)]).flatten(),
ny))
samples = _gen(sample_zmb, sample_ymb)
scores = _discrim(samples, sample_ymb)
print(scores[1:10])
sort = np.argsort(scores.flatten())[::-1]
samples = samples[sort]
print(np.shape(inverse_transform(samples)))
print(min(scores))
print(max(scores))
color_grid_vis(inverse_transform(samples), (20, 20), 'samples.png')
return inverse_transform(samples), sample_ymb
