adding imagenet pretrained model and demo code to load it

imagenet/load_pretrained.py

@@ -0,0 +1,84 @@
+import sys
+sys.path.append('..')
+
+import numpy as np
+import theano
+import theano.tensor as T
+from theano.sandbox.cuda.dnn import dnn_conv
+
+from lib import costs
+from lib import inits
+from lib import updates
+from lib import activations
+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, intX
+from lib.data_utils import OneHot, shuffle, iter_data, center_crop, patch
+
+from sklearn.externals import joblib
+
+"""
+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
+"""
+
+nz = 256
+nc = 3
+npx = 32
+ngf = 128
+ndf = 128
+
+relu = activations.Rectify()
+sigmoid = activations.Sigmoid()
+lrelu = activations.LeakyRectify()
+tanh = activations.Tanh()
+
+model_path = '../models/imagenet_gan_pretrain_128f_relu_lrelu_7l_3x3_256z/'
+gen_params = [sharedX(p) for p in joblib.load(model_path+'30_gen_params.jl')]
+discrim_params = [sharedX(p) for p in joblib.load(model_path+'30_discrim_params.jl')]
+
+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
+
+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()
+
+gX = gen(Z, *gen_params)
+dX = discrim(X, *discrim_params)
+
+_gen = theano.function([Z], gX)
+_discrim = theano.function([X], dX)
+
+sample_zmb = floatX(np_rng.uniform(-1., 1., size=(400, 256)))
+samples = _gen(sample_zmb)
+scores = _discrim(samples)
+sort = np.argsort(scores.flatten())[::-1]
+samples = samples[sort]
+color_grid_vis(inverse_transform(samples), (20, 20), 'samples.png')