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GenSample_obj.py
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import sys
sys.path.append('..')
import os
import json
from time import time
import numpy as np
from sklearn.externals import joblib
import scipy
from scipy import io
# from matplotlib import pyplot as plt
# from sklearn.externals import joblib
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, 'Genk' : 2 , 'niter':50, 'niter_decay' : 150}
for p in parameters:
tmp = p + " = parameters[p]"
exec(tmp)
# print conditional,type(batchsize),Channel[-1],kernal
gifn = inits.Normal(scale=0.02)
difn = inits.Normal(scale=0.02)
## filter_shape: (output channels, input channels, filter height, filter width, filter depth)
## load the parameters
# gen_params = [gw1, gw2, gw3, gw4, gw5, gwx]
# discrim_params = [dw1, dw2, dw3, dw4, dw5, dwy]
temp = joblib.load('models%d/50_gen_params.jl'%objectNumber)
gw1 = sharedX(temp[0])
gg1 = sharedX(temp[1])
gb1 = sharedX(temp[2])
gw2 = sharedX(temp[3])
gg2 = sharedX(temp[4])
gb2 = sharedX(temp[5])
gw3 = sharedX(temp[6])
gg3 = sharedX(temp[7])
gb3 = sharedX(temp[8])
gw4 = sharedX(temp[9])
gg4 = sharedX(temp[10])
gb4 = sharedX(temp[11])
gwx = sharedX(temp[12])
gen_params = [gw1, gg1, gb1, gw2, gg2, gb2, gw3, gg3, gb3, gw4 ,gg4, gb4, gwx]
##
def gen(Z, w1, g1, b1, w2, g2, b2, w3, g3, b3, w4, g4, b4, wx):
Gl1 = relu(batchnorm(T.dot(Z, w1), g=g1, b=b1))
Gl1 = Gl1.reshape((Gl1.shape[0],Channel[-1],Convlayersize[-1],Convlayersize[-1],Convlayersize[-1]))
input_shape = (None , None,Convlayersize[-1],Convlayersize[-1],Convlayersize[-1])
filter_shape = (Channel[-1] , Channel[-2], kernal[-1], kernal[-1], kernal[-1])
Gl2 = relu(batchnorm(conv(Gl1,w2,filter_shape = filter_shape, input_shape = input_shape, conv_mode = 'deconv'),g = g2, b = b2))
input_shape = (None , None,Convlayersize[-2],Convlayersize[-2],Convlayersize[-2])
filter_shape = (Channel[-2] , Channel[-3], kernal[-2], kernal[-2], kernal[-2])
Gl3 = relu(batchnorm(conv(Gl2,w3,filter_shape = filter_shape, input_shape = input_shape, conv_mode = 'deconv'),g = g3, b = b3))
input_shape = (None , None,Convlayersize[-3],Convlayersize[-3],Convlayersize[-3])
filter_shape = (Channel[-3] , Channel[-4], kernal[-3], kernal[-3], kernal[-3])
Gl4 = relu(batchnorm(conv(Gl3,w4,filter_shape = filter_shape, input_shape = input_shape, conv_mode = 'deconv'),g = g4, b= b4))
input_shape = (None, None, Convlayersize[-4],Convlayersize[-4],Convlayersize[-4])
filter_shape = (Channel[-4], Channel[-5], kernal[-4], kernal[-4], kernal[-4])
GlX = sigmoid(conv(Gl4,wx,filter_shape = filter_shape, input_shape = input_shape, conv_mode = 'deconv'))
return GlX
X = T.tensor5()
Z = T.matrix()
gX = gen(Z, *gen_params)
print 'COMPILING'
t = time()
# _train_g = theano.function([X, Z, Y], cost, updates=g_updates)
# _train_d = theano.function([X, Z, Y], cost, updates=d_updates)
_gen = theano.function([Z], gX)
print '%.2f seconds to compile theano functions'%(time()-t)
# trX, trY, ntrain = load_shapenet_train()
n = 10
nbatch = 10
rng = np.random.RandomState(int(time()))
# sample_ymb = floatX(np.asarray(np.eye(3)))
z_dist = scipy.io.loadmat('Z_dist_class2.mat')
z_mean = z_dist['mean']
z_mean = np.reshape(z_mean,(Nz,1))
z_std = z_dist['std']
z_std = np.reshape(z_std,(Nz,1))
def gen_z(z_dist,nbatch):
ret = np.zeros((nbatch,Nz))
for j in xrange(Nz):
z_tmp = np_rng.normal(z_mean[j],z_std[j],nbatch)
ret[:,j] = z_tmp
# print ret
return ret
try:
os.mkdir('Gen_models%d'%objectNumber)
except:
pass
for j in xrange(n/nbatch):
sample_zmb = floatX(gen_z(z_dist,nbatch))
samples = np.asarray(_gen(sample_zmb))
for i in xrange(nbatch):
io.savemat('Gen_models%d/Gen_example_%d.mat'%(objectNumber,nbatch*j+i),{'instance':samples[i,:,:,:],'Z':sample_zmb[i,:]})
# niter = 1
# niter_decay = 1