Add adapted code from OpenAI ebs

imagenet_demo.py

@@ -0,0 +1,73 @@
+from models import ResNet128
+import numpy as np
+import os.path as osp
+from tensorflow.python.platform import flags
+import tensorflow as tf
+import imageio
+from utils import optimistic_restore
+
+
+flags.DEFINE_string('logdir', 'cachedir', 'location where log of experiments will be stored')
+flags.DEFINE_integer('num_steps', 200, 'num of steps for conditional imagenet sampling')
+flags.DEFINE_float('step_lr', 180., 'step size for Langevin dynamics')
+flags.DEFINE_integer('batch_size', 16, 'number of steps to run')
+flags.DEFINE_string('exp', 'default', 'name of experiments')
+flags.DEFINE_integer('resume_iter', -1, 'iteration to resume training from')
+flags.DEFINE_bool('spec_norm', True, 'whether to use spectral normalization in weights in a model')
+flags.DEFINE_bool('cclass', True, 'conditional models')
+flags.DEFINE_bool('use_attention', False, 'using attention')
+
+FLAGS = flags.FLAGS
+
+def rescale_im(im):
+    return np.clip(im * 256, 0, 255).astype(np.uint8)
+
+
+if __name__ == "__main__":
+    model = ResNet128(num_filters=64)
+    X_NOISE = tf.placeholder(shape=(None, 128, 128, 3), dtype=tf.float32)
+    LABEL = tf.placeholder(shape=(None, 1000), dtype=tf.float32)
+
+    sess = tf.InteractiveSession()
+    weights = model.construct_weights("context_0")
+
+    x_mod = X_NOISE
+    x_mod = x_mod + tf.random_normal(tf.shape(x_mod),
+                                     mean=0.0,
+                                     stddev=0.005)
+
+    energy_noise = energy_start = model.forward(x_mod, weights, label=LABEL,
+            reuse=True, stop_at_grad=False, stop_batch=True)
+
+    x_grad = tf.gradients(energy_noise, [x_mod])[0]
+    energy_noise_old = energy_noise
+
+    lr = FLAGS.step_lr
+
+    x_last = x_mod - (lr) * x_grad
+
+    x_mod = x_last
+    x_mod = tf.clip_by_value(x_mod, 0, 1)
+    x_output = x_mod
+
+    sess.run(tf.global_variables_initializer())
+    saver = loader = tf.train.Saver()
+
+    logdir = osp.join(FLAGS.logdir, FLAGS.exp)
+    model_file = osp.join(logdir, 'model_{}'.format(FLAGS.resume_iter))
+    saver.restore(sess, model_file)
+
+    lx = np.random.permutation(1000)[:16]
+    ims = []
+
+    # What to initialize sampling with. 
+    x_mod = np.random.uniform(0, 1, size=(FLAGS.batch_size, 128, 128, 3))
+    labels = np.eye(1000)[lx]
+
+    for i in range(FLAGS.num_steps):
+        e, x_mod = sess.run([energy_noise, x_output], {X_NOISE:x_mod, LABEL:labels})
+        ims.append(rescale_im(x_mod).reshape((4, 4, 128, 128, 3)).transpose((0, 2, 1, 3, 4)).reshape((512, 512, 3)))
+
+    imageio.mimwrite('sample.gif', ims)
+
+