two layers of feature loss

2 files changed, 1010 insertions, 0 deletions

diff --git a/inversion/image_inversion_inception.py b/inversion/image_inversion_inception.py
new file mode 100644
index 0000000..00d5e4d
--- /dev/null
+++ b/inversion/image_inversion_inception.py
@@ -0,0 +1,509 @@
+# ------------------------------------------------------------------------------
+# Implementation of the inverse of Generator by Gradient descent w.r.t.
+# generator's inputs, for many intermediate layers.
+# ------------------------------------------------------------------------------
+
+import glob
+import h5py
+import itertools
+import numpy as np
+import os
+import params
+import PIL
+import scipy
+import sys
+import tensorflow as tf
+import tensorflow_probability as tfp
+import tensorflow_hub as hub
+import time
+import visualize as vs
+tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
+
+# --------------------------
+# Hyper-parameters.
+# --------------------------
+# Expected parameters:
+#  generator_path: path to generator module.
+#  generator_fixed_inputs: dictionary of fixed generator's input parameters.
+#  dataset: name of the dataset (hdf5 file).
+#  dataset_out: name for the output inverted dataset (hdf5 file).
+# General parameters:
+#  batch_size: number of images inverted at the same time.
+#  inv_it: number of iterations to invert an image.
+#  inv_layer: 'latent' or name of the tensor of the custom layer to be inverted.
+#  lr: learning rate.
+#  decay_lr: exponential decay on the learning rate.
+#  decay_n: number of exponential decays on the learning rate.
+#  custom_grad_relu: replace relus with custom gradient.
+# Logging:
+#  sample_size: number of images included in sampled images.
+#  save_progress: whether to save intermediate images during optimization.
+#  log_z_norm: log the norm of different sections of z.
+#  log_activation_layer: log the percentage of active neurons in this layer.
+# Losses:
+#  mse: use the mean squared error on pixels for image comparison.
+#  features: use features extracted by a feature extractor for image comparison.
+#  feature_extractor_path: path to feature extractor module.
+#  feature_extractor_output: output name from feature extractor.
+#  likeli_loss: regularization loss on the log likelihood of encodings.
+#  norm_loss: regularization loss on the norm of encodings.
+#  dist_loss: whether to include a loss on the dist between g1(z) and enc.
+#  lambda_mse: coefficient for mse loss.
+#  lambda_feat: coefficient for features loss.
+#  lambda_reg: coefficient for regularization loss on latent.
+#  lambda_dist: coefficient for l1 regularization on delta.
+# Latent:
+#  clipping: whether to clip encoding values after every update.
+#  stochastic_clipping: whether to consider stochastic clipping.
+#  clip: clipping bound.
+#  pretrained_latent: load pre trained fixed latent.
+#  fixed_z: do not train the latent vector.
+# Initialization:
+#  init_gen_dist: initialize encodings from the generated distribution.
+#  init_lo: init min value.
+#  init_hi: init max value.
+if len(sys.argv) < 2:
+  sys.exit('Must provide a configuration file.')
+params = params.Params(sys.argv[1])
+
+# --------------------------
+# Global directories.
+# --------------------------
+LATENT_TAG = 'latent' if params.inv_layer == 'latent' else 'dense'
+BATCH_SIZE = params.batch_size
+SAMPLE_SIZE = params.sample_size
+LOGS_DIR = os.path.join('inverses', params.tag, LATENT_TAG, 'logs')
+SAMPLES_DIR = os.path.join('inverses', params.tag, LATENT_TAG, 'samples')
+INVERSES_DIR = os.path.join('inverses', params.tag)
+if not os.path.exists(LOGS_DIR):
+  os.makedirs(LOGS_DIR)
+if not os.path.exists(SAMPLES_DIR):
+  os.makedirs(SAMPLES_DIR)
+if not os.path.exists(INVERSES_DIR):
+  os.makedirs(INVERSES_DIR)
+
+# --------------------------
+# Util functions.
+# --------------------------
+# One hot encoding for classes.
+def one_hot(values):
+  return np.eye(N_CLASS)[values]
+
+# --------------------------
+# Logging.
+# --------------------------
+summary_writer = tf.summary.FileWriter(LOGS_DIR)
+def log_stats(name, val, it):
+  summary = tf.Summary(value=[tf.Summary.Value(tag=name, simple_value=val)])
+  summary_writer.add_summary(summary, it)
+
+# --------------------------
+# Load Graph.
+# --------------------------
+generator = hub.Module(str(params.generator_path))
+
+gen_signature = 'generator'
+if 'generator' not in generator.get_signature_names():
+  gen_signature = 'default'
+
+input_info = generator.get_input_info_dict(gen_signature)
+COND_GAN = 'y' in input_info
+
+if COND_GAN:
+  Z_DIM = input_info['z'].get_shape().as_list()[1]
+  latent = tf.get_variable(name='latent', dtype=tf.float32,
+                           shape=[BATCH_SIZE, Z_DIM])
+  N_CLASS = input_info['y'].get_shape().as_list()[1]
+  label = tf.get_variable(name='label', dtype=tf.float32,
+                          shape=[BATCH_SIZE, N_CLASS])
+  gen_in = dict(params.generator_fixed_inputs)
+  gen_in['z'] = latent
+  gen_in['y'] = label
+  gen_img = generator(gen_in, signature=gen_signature)
+else:
+  Z_DIM = input_info['default'].get_shape().as_list()[1]
+  latent = tf.get_variable(name='latent', dtype=tf.float32,
+                           shape=[BATCH_SIZE, Z_DIM])
+  if (params.generator_fixed_inputs):
+    gen_in = dict(params.generator_fixed_inputs)
+    gen_in['z'] = latent
+    gen_img = generator(gen_in, signature=gen_signature)
+  else:
+    gen_img = generator(latent, signature=gen_signature)
+
+# Convert generated image to channels_first.
+gen_img = tf.transpose(gen_img, [0, 3, 1, 2])
+
+# Override intermediate layer.
+if params.inv_layer == 'latent':
+  encoding = latent
+  ENC_SHAPE = [Z_DIM]
+else:
+  layer_name = 'module_apply_' + gen_signature + '/' + params.inv_layer
+  gen_encoding = tf.get_default_graph().get_tensor_by_name(layer_name)
+  ENC_SHAPE = gen_encoding.get_shape().as_list()[1:]
+  encoding = tf.get_variable(name='encoding', dtype=tf.float32,
+                             shape=[BATCH_SIZE,] + ENC_SHAPE)
+  tf.contrib.graph_editor.swap_ts(gen_encoding, tf.convert_to_tensor(encoding))
+
+# Step counter.
+inv_step = tf.get_variable('inv_step', initializer=0, trainable=False)
+
+# Define target image.
+IMG_SHAPE = gen_img.get_shape().as_list()[1:]
+target = tf.get_variable(name='target', dtype=tf.float32,  # normally this is the real [0-255]image
+                         shape=[BATCH_SIZE,] + IMG_SHAPE)
+# target_img = (tf.cast(target, tf.float32) / 255.) * 2.0 - 1. # Norm to [-1, 1].
+target_img = target
+
+# Custom Gradient for Relus.
+if params.custom_grad_relu:
+  grad_lambda = tf.train.exponential_decay(0.1, inv_step, params.inv_it / 5,
+                                           0.1, staircase=False)
+  @tf.custom_gradient
+  def relu_custom_grad(x):
+    def grad(dy):
+      return tf.where(x >= 0, dy,
+          grad_lambda*tf.where(dy < 0, dy, tf.zeros_like(dy)))
+    return tf.nn.relu(x), grad
+
+  gen_scope = 'module_apply_' + gen_signature + '/'
+  for op in tf.get_default_graph().get_operations():
+    if 'Relu' in op.name and gen_scope in op.name:
+      assert len(op.inputs) == 1
+      assert len(op.outputs) == 1
+      new_out = relu_custom_grad(op.inputs[0])
+      tf.contrib.graph_editor.swap_ts(op.outputs[0], new_out)
+
+# Operations to clip the values of the encodings.
+if params.clipping or params.stochastic_clipping:
+  assert params.clip >= 0
+  if params.stochastic_clipping:
+    new_enc = tf.where(tf.abs(latent) >= params.clip,
+        tf.random.uniform([BATCH_SIZE, Z_DIM], minval=-params.clip,
+                          maxval=params.clip), latent)
+  else:
+    new_enc = tf.clip_by_value(latent, -params.clip, params.clip)
+  clip_latent = tf.assign(latent, new_enc)
+
+# Monitor relu's activation.
+if params.log_activation_layer:
+  gen_scope = 'module_apply_' + gen_signature + '/'
+  activation_rate = 1.0 - tf.nn.zero_fraction(tf.get_default_graph()\
+      .get_tensor_by_name(gen_scope + params.log_activation_layer))
+
+# --------------------------
+# Reconstruction losses.
+# --------------------------
+# Mse loss for image comparison.
+if params.mse:
+  pix_square_diff = tf.square((target_img - gen_img) / 2.0)
+  mse_loss = tf.reduce_mean(pix_square_diff)
+  img_mse_err = tf.reduce_mean(pix_square_diff, axis=[1,2,3])
+else:
+  mse_loss = tf.constant(0.0)
+  img_mse_err = tf.constant(0.0)
+
+# Use custom features for image comparison.
+if params.features:
+  feature_extractor = hub.Module(str(params.feature_extractor_path))
+
+  # Convert images from range [-1, 1] channels_first to [0, 1] channels_last.
+  gen_img_1 = tf.transpose(gen_img / 2.0 + 0.5, [0, 2, 3, 1])
+  target_img_1 = tf.transpose(target_img / 2.0 + 0.5, [0, 2, 3, 1])
+
+  # Convert images to appropriate size for feature extraction.
+  height, width = hub.get_expected_image_size(feature_extractor)
+  gen_img_1 = tf.image.resize_images(gen_img_1, [height, width])
+  target_img_1 = tf.image.resize_images(target_img_1, [height, width])
+
+  gen_feat_ex = feature_extractor(dict(images=gen_img_1), as_dict=True, signature='image_feature_vector')
+  target_feat_ex = feature_extractor(dict(images=target_img_1), as_dict=True, signature='image_feature_vector')
+
+  gen_feat = gen_feat_ex["InceptionV3/Mixed_7a"]
+  target_feat = target_feat_ex["InceptionV3/Mixed_7a"]
+  feat_square_diff = tf.reshape(tf.square(gen_feat - target_feat), [BATCH_SIZE, -1])
+  feat_loss = tf.reduce_mean(feat_square_diff)
+  img_feat_err = tf.reduce_mean(feat_square_diff, axis=1)
+
+  gen_feat = gen_feat_ex["InceptionV3/Mixed_6a"]
+  target_feat = target_feat_ex["InceptionV3/Mixed_6a"]
+  feat_square_diff = tf.reshape(tf.square(gen_feat - target_feat), [BATCH_SIZE, -1])
+  feat_loss += tf.reduce_mean(feat_square_diff)
+  img_feat_err += tf.reduce_mean(feat_square_diff, axis=1)
+
+  # gen_feat = gen_feat_ex["InceptionV3/Mixed_5a"]
+  # target_feat = target_feat_ex["InceptionV3/Mixed_5a"]
+  # feat_square_diff = tf.reshape(tf.square(gen_feat - target_feat), [BATCH_SIZE, -1])
+  # feat_loss += tf.reduce_mean(feat_square_diff)
+  # img_feat_err += tf.reduce_mean(feat_square_diff, axis=1)
+
+else:
+  feat_loss = tf.constant(0.0)
+  img_feat_err = tf.constant(0.0)
+
+# --------------------------
+# Regularization losses.
+# --------------------------
+# Loss on the norm of the encoding.
+if params.norm_loss:
+  dim = 20
+  chi2_dist = tfp.distributions.Chi2(dim)
+  mode = dim - 2
+  mode_log_prob = chi2_dist.log_prob(mode)
+  norm_loss = 0.0
+  for i in range(int(Z_DIM / dim)):
+    squared_l2 = tf.reduce_sum(tf.square(latent[:,i*dim:(i+1)*dim]), axis=1)
+    over_mode = tf.nn.relu(squared_l2 - mode)
+    norm_loss -= tf.reduce_mean(chi2_dist.log_prob(mode + over_mode))
+    norm_loss += mode_log_prob
+else:
+  norm_loss = tf.constant(0.0)
+
+# Loss on the likelihood of the encoding.
+if params.likeli_loss:
+  norm_dist = tfp.distributions.Normal(0.0, 1.0)
+  likeli_loss = - tf.reduce_mean(norm_dist.log_prob(latent))
+  mode_log_prob = norm_dist.log_prob(0.0)
+  likeli_loss += mode_log_prob
+else:
+  likeli_loss = tf.constant(0.0)
+
+# Regularization loss.
+reg_loss = norm_loss + likeli_loss
+
+# Loss on the l1 distance between gen_encoding and inverted encoding.
+if params.dist_loss:
+  dist_loss = tf.reduce_mean(tf.abs(encoding - gen_encoding))
+else:
+  dist_loss = tf.constant(0.0)
+
+# Per image reconstruction error.
+img_rec_err = params.lambda_mse * img_mse_err\
+    + params.lambda_feat * img_feat_err
+
+# Batch reconstruction error.
+rec_loss = params.lambda_mse * mse_loss + params.lambda_feat * feat_loss
+
+# Total inversion loss.
+inv_loss = rec_loss + params.lambda_reg * reg_loss\
+    + params.lambda_dist * dist_loss
+
+# --------------------------
+# Optimizer.
+# --------------------------
+if params.decay_lr:
+  lrate = tf.train.exponential_decay(params.lr, inv_step,
+      params.inv_it / params.decay_n, 0.1, staircase=True)
+else:
+  lrate = tf.constant(params.lr)
+trained_params = [encoding] if params.fixed_z else [latent, encoding]
+optimizer = tf.train.AdamOptimizer(learning_rate=lrate, beta1=0.9, beta2=0.999)
+inv_train_op = optimizer.minimize(inv_loss, var_list=trained_params,
+                                  global_step=inv_step)
+reinit_optimizer = tf.variables_initializer(optimizer.variables())
+
+# --------------------------
+# Noise source.
+# --------------------------
+def noise_sampler():
+  return np.random.normal(size=[BATCH_SIZE, Z_DIM])
+
+def small_init(shape=[BATCH_SIZE, Z_DIM]):
+  return np.random.uniform(low=params.init_lo, high=params.init_hi, size=shape)
+
+# --------------------------
+# Dataset.
+# --------------------------
+if params.dataset.endswith('.hdf5'):
+  in_file = h5py.File(params.dataset, 'r')
+  sample_images = in_file['xtrain'][()]
+  if COND_GAN:
+    sample_labels = in_file['ytrain'][()]
+  sample_fns = in_file['fn'][()]
+  NUM_IMGS = sample_images.shape[0] # number of images to be inverted.
+  print("Number of images: {}".format(NUM_IMGS))
+  print("Batch size: {}".format(BATCH_SIZE))
+  def sample_images_gen():
+    for i in range(int(NUM_IMGS / BATCH_SIZE)):
+      i_1, i_2 = i*BATCH_SIZE, (i+1)*BATCH_SIZE
+      if COND_GAN:
+        yield sample_images[i_1:i_2], sample_labels[i_1:i_2]
+      else:
+        yield sample_images[i_1:i_2], np.zeros(BATCH_SIZE)
+  image_gen = sample_images_gen()
+  if 'latent' in in_file:
+    sample_latents = in_file['latent']
+    def sample_latent_gen():
+      for i in range(int(NUM_IMGS / BATCH_SIZE)):
+        i_1, i_2 = i*BATCH_SIZE, (i+1)*BATCH_SIZE
+        yield sample_latents[i_1:i_2]
+    latent_gen = sample_latent_gen()
+  if NUM_IMGS % BATCH_SIZE != 0:
+    REMAINDER = BATCH_SIZE - (NUM_IMGS % BATCH_SIZE)
+    NUM_IMGS += REMAINDER
+    sample_images = np.append(sample_images, sample_images[-REMAINDER:,...], axis=0)
+    sample_labels = np.append(sample_labels, sample_labels[-REMAINDER:,...], axis=0)
+    sample_fns = np.append(sample_fns, sample_fns[-REMAINDER:], axis=0)
+  assert(NUM_IMGS % BATCH_SIZE == 0)
+else:
+  sys.exit('Unknown dataset {}.'.format(params.dataset))
+
+# --------------------------
+# Training.
+# --------------------------
+# Start session.
+sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
+sess.run(tf.global_variables_initializer())
+sess.run(tf.tables_initializer())
+
+if params.max_batches > 0:
+  NUM_IMGS_TO_PROCESS = params.max_batches * BATCH_SIZE
+else:
+  NUM_IMGS_TO_PROCESS = NUM_IMGS
+
+# Output file.
+out_file = h5py.File(os.path.join(INVERSES_DIR, params.out_dataset), 'w')
+out_images = out_file.create_dataset('xtrain', [NUM_IMGS_TO_PROCESS,] + IMG_SHAPE, dtype='float32')
+out_enc = out_file.create_dataset('encoding', [NUM_IMGS_TO_PROCESS,] + ENC_SHAPE, dtype='float32')
+out_lat = out_file.create_dataset('latent', [NUM_IMGS_TO_PROCESS, Z_DIM], dtype='float32')
+out_fns = out_file.create_dataset('fn', [NUM_IMGS_TO_PROCESS], dtype=h5py.string_dtype())
+if COND_GAN:
+  out_labels = out_file.create_dataset('ytrain', (NUM_IMGS_TO_PROCESS, N_CLASS,), dtype='float32')
+out_err = out_file.create_dataset('err', (NUM_IMGS_TO_PROCESS,))
+
+out_fns[:] = sample_fns[:NUM_IMGS_TO_PROCESS]
+
+# Gradient descent w.r.t. generator's inputs.
+it = 0
+out_pos = 0
+start_time = time.time()
+
+for image_batch, label_batch in image_gen:
+
+  # Save target.
+  sess.run(target.assign(image_batch))
+  if COND_GAN:
+    sess.run(label.assign(label_batch))
+
+  # Initialize encodings to random values.
+  if params.pre_trained_latent:
+    sess.run(latent.assign(next(latent_gen)))
+    if params.inv_layer != 'latent':
+      sess.run(encoding.assign(gen_encoding))
+  else:
+    if params.init_gen_dist:
+      sess.run(latent.assign(noise_sampler()))
+      if params.inv_layer != 'latent':
+        sess.run(encoding.assign(gen_encoding))
+    else:
+      sess.run(latent.assign(small_init()))
+      if params.inv_layer != 'latent':
+        sess.run(encoding.assign(small_init(shape=[BATCH_SIZE,] + ENC_SHAPE)))
+
+  # Init optimizer.
+  sess.run(inv_step.assign(0))
+  sess.run(reinit_optimizer)
+
+  # Main optimization loop.
+  print("Total iterations: {}".format(params.inv_it))
+  for _ in range(params.inv_it):
+
+    _inv_loss, _mse_loss, _feat_loss, _rec_loss, _reg_loss, _dist_loss,\
+        _lrate, _ = sess.run([inv_loss, mse_loss, feat_loss,
+        rec_loss, reg_loss, dist_loss, lrate, inv_train_op])
+
+    if params.clipping or params.stochastic_clipping:
+      sess.run(clip_latent)
+
+    # Save logs with training information.
+    if it % 500 == 0:
+      # Log losses.
+      etime = time.time() - start_time
+      print('It [{:8d}] time [{:5.1f}] total [{:.4f}] mse [{:.4f}] '
+            'feat [{:.4f}] rec [{:.4f}] reg [{:.4f}] dist [{:.4f}] '
+            'lr [{:.4f}]'.format(it, etime, _inv_loss, _mse_loss,
+            _feat_loss, _rec_loss, _reg_loss, _dist_loss, _lrate))
+
+      if params.log_z_norm:
+        _lat = sess.run(latent)
+        dim = 20 if Z_DIM == 120 else Z_DIM
+        for i in range(int(Z_DIM/dim)):
+          _subset = _lat[:,i*dim:(i+1)*dim]
+          print('section {:1d}: norm={:.4f} (exp={:.4f}) min={:.4f} max={:.4f}'\
+                .format(i, np.mean(np.linalg.norm(_subset, axis=1)),
+                        np.sqrt(dim-2), np.min(_subset), np.max(_subset)))
+
+      if params.log_activation_layer:
+        _act_rate = sess.run(activation_rate)
+        print('activation_rate={:.4f}'.format(_act_rate))
+        log_stats('activation rate', _act_rate, it)
+
+      sys.stdout.flush()
+
+      # Log tensorboard's statistics.
+      log_stats('total loss', _inv_loss, it)
+      log_stats('mse loss', _mse_loss, it)
+      log_stats('feat loss', _feat_loss, it)
+      log_stats('rec loss', _rec_loss, it)
+      log_stats('reg loss', _reg_loss, it)
+      log_stats('dist loss', _dist_loss, it)
+      log_stats('out pos', out_pos, it)
+      log_stats('lrate', _lrate, it)
+      summary_writer.flush()
+
+      # Save target images and reconstructions.
+      if params.save_progress:
+        assert SAMPLE_SIZE <= BATCH_SIZE
+        gen_images  = sess.run(gen_img)
+        inv_batch = vs.interleave(vs.data2img(image_batch[BATCH_SIZE - SAMPLE_SIZE:]),
+                        vs.data2img(gen_images[BATCH_SIZE - SAMPLE_SIZE:]))
+        inv_batch = vs.grid_transform(inv_batch)
+        vs.save_image('{}/progress_{}_{}.png'.format(SAMPLES_DIR, params.tag, it), inv_batch)
+
+    # It counter.
+    it += 1
+
+  if params.save_progress:
+    # Save linear interpolation between the actual and generated encodings.
+    if params.dist_loss:
+      enc_batch, gen_enc = sess.run([encoding, gen_encoding])
+      for j in range(10):
+        custom_enc =  gen_enc * (1-(j/10.0)) + enc_batch * (j/10.0)
+        sess.run(encoding.assign(custom_enc))
+        gen_images = sess.run(gen_img)
+        inv_batch = vs.interleave(vs.data2img(image_batch[BATCH_SIZE - SAMPLE_SIZE:]),
+            vs.data2img(gen_images[BATCH_SIZE - SAMPLE_SIZE:]))
+        inv_batch = vs.grid_transform(inv_batch)
+        vs.save_image('{}/progress_{}_{}_lat_{}.png'.format(SAMPLES_DIR,params.tag,it,j),
+                      inv_batch)
+      sess.run(encoding.assign(enc_batch))
+
+  # Save samples of inverted images.
+  if SAMPLE_SIZE > 0:
+    assert SAMPLE_SIZE <= BATCH_SIZE
+    gen_images  = sess.run(gen_img)
+    inv_batch = vs.interleave(vs.data2img(image_batch[BATCH_SIZE - SAMPLE_SIZE:]),
+                    vs.data2img(gen_images[BATCH_SIZE - SAMPLE_SIZE:]))
+    inv_batch = vs.grid_transform(inv_batch)
+    vs.save_image('{}/{}_{}.png'.format(SAMPLES_DIR, params.tag, out_pos), inv_batch)
+    print('Saved samples for out_pos: {}.'.format(out_pos))
+
+  # Save images that are ready.
+  latent_batch, enc_batch, rec_err_batch =\
+      sess.run([latent, encoding, img_rec_err])
+  out_lat[out_pos:out_pos+BATCH_SIZE] = latent_batch
+  out_enc[out_pos:out_pos+BATCH_SIZE] = enc_batch
+  out_images[out_pos:out_pos+BATCH_SIZE] = image_batch
+  if COND_GAN:
+    out_labels[out_pos:out_pos+BATCH_SIZE] = label_batch
+  out_err[out_pos:out_pos+BATCH_SIZE] = rec_err_batch
+  out_pos += BATCH_SIZE
+  if params.max_batches > 0 and (out_pos / BATCH_SIZE) >= params.max_batches:
+    break
+
+print('Mean reconstruction error: {}'.format(np.mean(out_err)))
+print('Stdev reconstruction error: {}'.format(np.std(out_err)))
+print('End of inversion.')
+out_file.close()
+sess.close()
diff --git a/inversion/image_inversion_vgg.py b/inversion/image_inversion_vgg.py
new file mode 100644
index 0000000..1ee4c1a
--- /dev/null
+++ b/inversion/image_inversion_vgg.py
@@ -0,0 +1,501 @@
+# ------------------------------------------------------------------------------
+# Implementation of the inverse of Generator by Gradient descent w.r.t.
+# generator's inputs, for many intermediate layers.
+# ------------------------------------------------------------------------------
+
+import glob
+import h5py
+import itertools
+import numpy as np
+import os
+import params
+import PIL
+import scipy
+import sys
+import tensorflow as tf
+import tensorflow_probability as tfp
+import tensorflow_hub as hub
+import time
+import visualize as vs
+tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
+
+# --------------------------
+# Hyper-parameters.
+# --------------------------
+# Expected parameters:
+#  generator_path: path to generator module.
+#  generator_fixed_inputs: dictionary of fixed generator's input parameters.
+#  dataset: name of the dataset (hdf5 file).
+#  dataset_out: name for the output inverted dataset (hdf5 file).
+# General parameters:
+#  batch_size: number of images inverted at the same time.
+#  inv_it: number of iterations to invert an image.
+#  inv_layer: 'latent' or name of the tensor of the custom layer to be inverted.
+#  lr: learning rate.
+#  decay_lr: exponential decay on the learning rate.
+#  decay_n: number of exponential decays on the learning rate.
+#  custom_grad_relu: replace relus with custom gradient.
+# Logging:
+#  sample_size: number of images included in sampled images.
+#  save_progress: whether to save intermediate images during optimization.
+#  log_z_norm: log the norm of different sections of z.
+#  log_activation_layer: log the percentage of active neurons in this layer.
+# Losses:
+#  mse: use the mean squared error on pixels for image comparison.
+#  features: use features extracted by a feature extractor for image comparison.
+#  feature_extractor_path: path to feature extractor module.
+#  feature_extractor_output: output name from feature extractor.
+#  likeli_loss: regularization loss on the log likelihood of encodings.
+#  norm_loss: regularization loss on the norm of encodings.
+#  dist_loss: whether to include a loss on the dist between g1(z) and enc.
+#  lambda_mse: coefficient for mse loss.
+#  lambda_feat: coefficient for features loss.
+#  lambda_reg: coefficient for regularization loss on latent.
+#  lambda_dist: coefficient for l1 regularization on delta.
+# Latent:
+#  clipping: whether to clip encoding values after every update.
+#  stochastic_clipping: whether to consider stochastic clipping.
+#  clip: clipping bound.
+#  pretrained_latent: load pre trained fixed latent.
+#  fixed_z: do not train the latent vector.
+# Initialization:
+#  init_gen_dist: initialize encodings from the generated distribution.
+#  init_lo: init min value.
+#  init_hi: init max value.
+if len(sys.argv) < 2:
+  sys.exit('Must provide a configuration file.')
+params = params.Params(sys.argv[1])
+
+# --------------------------
+# Global directories.
+# --------------------------
+LATENT_TAG = 'latent' if params.inv_layer == 'latent' else 'dense'
+BATCH_SIZE = params.batch_size
+SAMPLE_SIZE = params.sample_size
+LOGS_DIR = os.path.join('inverses', params.tag, LATENT_TAG, 'logs')
+SAMPLES_DIR = os.path.join('inverses', params.tag, LATENT_TAG, 'samples')
+INVERSES_DIR = os.path.join('inverses', params.tag)
+if not os.path.exists(LOGS_DIR):
+  os.makedirs(LOGS_DIR)
+if not os.path.exists(SAMPLES_DIR):
+  os.makedirs(SAMPLES_DIR)
+if not os.path.exists(INVERSES_DIR):
+  os.makedirs(INVERSES_DIR)
+
+# --------------------------
+# Util functions.
+# --------------------------
+# One hot encoding for classes.
+def one_hot(values):
+  return np.eye(N_CLASS)[values]
+
+# --------------------------
+# Logging.
+# --------------------------
+summary_writer = tf.summary.FileWriter(LOGS_DIR)
+def log_stats(name, val, it):
+  summary = tf.Summary(value=[tf.Summary.Value(tag=name, simple_value=val)])
+  summary_writer.add_summary(summary, it)
+
+# --------------------------
+# Load Graph.
+# --------------------------
+generator = hub.Module(str(params.generator_path))
+
+gen_signature = 'generator'
+if 'generator' not in generator.get_signature_names():
+  gen_signature = 'default'
+
+input_info = generator.get_input_info_dict(gen_signature)
+COND_GAN = 'y' in input_info
+
+if COND_GAN:
+  Z_DIM = input_info['z'].get_shape().as_list()[1]
+  latent = tf.get_variable(name='latent', dtype=tf.float32,
+                           shape=[BATCH_SIZE, Z_DIM])
+  N_CLASS = input_info['y'].get_shape().as_list()[1]
+  label = tf.get_variable(name='label', dtype=tf.float32,
+                          shape=[BATCH_SIZE, N_CLASS])
+  gen_in = dict(params.generator_fixed_inputs)
+  gen_in['z'] = latent
+  gen_in['y'] = label
+  gen_img = generator(gen_in, signature=gen_signature)
+else:
+  Z_DIM = input_info['default'].get_shape().as_list()[1]
+  latent = tf.get_variable(name='latent', dtype=tf.float32,
+                           shape=[BATCH_SIZE, Z_DIM])
+  if (params.generator_fixed_inputs):
+    gen_in = dict(params.generator_fixed_inputs)
+    gen_in['z'] = latent
+    gen_img = generator(gen_in, signature=gen_signature)
+  else:
+    gen_img = generator(latent, signature=gen_signature)
+
+# Convert generated image to channels_first.
+gen_img = tf.transpose(gen_img, [0, 3, 1, 2])
+
+# Override intermediate layer.
+if params.inv_layer == 'latent':
+  encoding = latent
+  ENC_SHAPE = [Z_DIM]
+else:
+  layer_name = 'module_apply_' + gen_signature + '/' + params.inv_layer
+  gen_encoding = tf.get_default_graph().get_tensor_by_name(layer_name)
+  ENC_SHAPE = gen_encoding.get_shape().as_list()[1:]
+  encoding = tf.get_variable(name='encoding', dtype=tf.float32,
+                             shape=[BATCH_SIZE,] + ENC_SHAPE)
+  tf.contrib.graph_editor.swap_ts(gen_encoding, tf.convert_to_tensor(encoding))
+
+# Step counter.
+inv_step = tf.get_variable('inv_step', initializer=0, trainable=False)
+
+# Define target image.
+IMG_SHAPE = gen_img.get_shape().as_list()[1:]
+target = tf.get_variable(name='target', dtype=tf.float32,  # normally this is the real [0-255]image
+                         shape=[BATCH_SIZE,] + IMG_SHAPE)
+# target_img = (tf.cast(target, tf.float32) / 255.) * 2.0 - 1. # Norm to [-1, 1].
+target_img = target
+
+# Custom Gradient for Relus.
+if params.custom_grad_relu:
+  grad_lambda = tf.train.exponential_decay(0.1, inv_step, params.inv_it / 5,
+                                           0.1, staircase=False)
+  @tf.custom_gradient
+  def relu_custom_grad(x):
+    def grad(dy):
+      return tf.where(x >= 0, dy,
+          grad_lambda*tf.where(dy < 0, dy, tf.zeros_like(dy)))
+    return tf.nn.relu(x), grad
+
+  gen_scope = 'module_apply_' + gen_signature + '/'
+  for op in tf.get_default_graph().get_operations():
+    if 'Relu' in op.name and gen_scope in op.name:
+      assert len(op.inputs) == 1
+      assert len(op.outputs) == 1
+      new_out = relu_custom_grad(op.inputs[0])
+      tf.contrib.graph_editor.swap_ts(op.outputs[0], new_out)
+
+# Operations to clip the values of the encodings.
+if params.clipping or params.stochastic_clipping:
+  assert params.clip >= 0
+  if params.stochastic_clipping:
+    new_enc = tf.where(tf.abs(latent) >= params.clip,
+        tf.random.uniform([BATCH_SIZE, Z_DIM], minval=-params.clip,
+                          maxval=params.clip), latent)
+  else:
+    new_enc = tf.clip_by_value(latent, -params.clip, params.clip)
+  clip_latent = tf.assign(latent, new_enc)
+
+# Monitor relu's activation.
+if params.log_activation_layer:
+  gen_scope = 'module_apply_' + gen_signature + '/'
+  activation_rate = 1.0 - tf.nn.zero_fraction(tf.get_default_graph()\
+      .get_tensor_by_name(gen_scope + params.log_activation_layer))
+
+# --------------------------
+# Reconstruction losses.
+# --------------------------
+# Mse loss for image comparison.
+if params.mse:
+  pix_square_diff = tf.square((target_img - gen_img) / 2.0)
+  mse_loss = tf.reduce_mean(pix_square_diff)
+  img_mse_err = tf.reduce_mean(pix_square_diff, axis=[1,2,3])
+else:
+  mse_loss = tf.constant(0.0)
+  img_mse_err = tf.constant(0.0)
+
+# Use custom features for image comparison.
+if params.features:
+  # feature_extractor = hub.Module(str(params.feature_extractor_path))
+  feature_extractor = tf.keras.applications.VGG16(
+    include_top=False,
+    weights='imagenet')
+  feature_extractor.trainable = False
+
+  # Convert images from range [-1, 1] channels_first to [0, 1] channels_last.
+  gen_img_1 = tf.transpose(gen_img / 2.0 + 0.5, [0, 2, 3, 1])
+  target_img_1 = tf.transpose(target_img / 2.0 + 0.5, [0, 2, 3, 1])
+
+  # Convert images to appropriate size for feature extraction.
+  # height, width = hub.get_expected_image_size(feature_extractor)
+  height = 224
+  width = 224
+  gen_img_1 = tf.image.resize_images(gen_img_1, [height, width])
+  target_img_1 = tf.image.resize_images(target_img_1, [height, width])
+
+  gen_feat = feature_extractor(gen_img_1)
+  # target_feat = feature_extractor(dict(images=target_img_1), as_dict=True,
+  #     signature='image_feature_vector')[params.feature_extractor_output]
+  feat_square_diff = tf.reshape(tf.square(gen_feat - target_feat),
+                                [BATCH_SIZE, -1])
+  feat_loss = tf.reduce_mean(feat_square_diff)
+  img_feat_err = tf.reduce_mean(feat_square_diff, axis=1)
+else:
+  feat_loss = tf.constant(0.0)
+  img_feat_err = tf.constant(0.0)
+
+# --------------------------
+# Regularization losses.
+# --------------------------
+# Loss on the norm of the encoding.
+if params.norm_loss:
+  dim = 20
+  chi2_dist = tfp.distributions.Chi2(dim)
+  mode = dim - 2
+  mode_log_prob = chi2_dist.log_prob(mode)
+  norm_loss = 0.0
+  for i in range(int(Z_DIM / dim)):
+    squared_l2 = tf.reduce_sum(tf.square(latent[:,i*dim:(i+1)*dim]), axis=1)
+    over_mode = tf.nn.relu(squared_l2 - mode)
+    norm_loss -= tf.reduce_mean(chi2_dist.log_prob(mode + over_mode))
+    norm_loss += mode_log_prob
+else:
+  norm_loss = tf.constant(0.0)
+
+# Loss on the likelihood of the encoding.
+if params.likeli_loss:
+  norm_dist = tfp.distributions.Normal(0.0, 1.0)
+  likeli_loss = - tf.reduce_mean(norm_dist.log_prob(latent))
+  mode_log_prob = norm_dist.log_prob(0.0)
+  likeli_loss += mode_log_prob
+else:
+  likeli_loss = tf.constant(0.0)
+
+# Regularization loss.
+reg_loss = norm_loss + likeli_loss
+
+# Loss on the l1 distance between gen_encoding and inverted encoding.
+if params.dist_loss:
+  dist_loss = tf.reduce_mean(tf.abs(encoding - gen_encoding))
+else:
+  dist_loss = tf.constant(0.0)
+
+# Per image reconstruction error.
+img_rec_err = params.lambda_mse * img_mse_err\
+    + params.lambda_feat * img_feat_err
+
+# Batch reconstruction error.
+rec_loss = params.lambda_mse * mse_loss + params.lambda_feat * feat_loss
+
+# Total inversion loss.
+inv_loss = rec_loss + params.lambda_reg * reg_loss\
+    + params.lambda_dist * dist_loss
+
+# --------------------------
+# Optimizer.
+# --------------------------
+if params.decay_lr:
+  lrate = tf.train.exponential_decay(params.lr, inv_step,
+      params.inv_it / params.decay_n, 0.1, staircase=True)
+else:
+  lrate = tf.constant(params.lr)
+trained_params = [encoding] if params.fixed_z else [latent, encoding]
+optimizer = tf.train.AdamOptimizer(learning_rate=lrate, beta1=0.9, beta2=0.999)
+inv_train_op = optimizer.minimize(inv_loss, var_list=trained_params,
+                                  global_step=inv_step)
+reinit_optimizer = tf.variables_initializer(optimizer.variables())
+
+# --------------------------
+# Noise source.
+# --------------------------
+def noise_sampler():
+  return np.random.normal(size=[BATCH_SIZE, Z_DIM])
+
+def small_init(shape=[BATCH_SIZE, Z_DIM]):
+  return np.random.uniform(low=params.init_lo, high=params.init_hi, size=shape)
+
+# --------------------------
+# Dataset.
+# --------------------------
+if params.dataset.endswith('.hdf5'):
+  in_file = h5py.File(params.dataset, 'r')
+  sample_images = in_file['xtrain'][()]
+  if COND_GAN:
+    sample_labels = in_file['ytrain'][()]
+  sample_fns = in_file['fn'][()]
+  NUM_IMGS = sample_images.shape[0] # number of images to be inverted.
+  print("Number of images: {}".format(NUM_IMGS))
+  print("Batch size: {}".format(BATCH_SIZE))
+  def sample_images_gen():
+    for i in range(int(NUM_IMGS / BATCH_SIZE)):
+      i_1, i_2 = i*BATCH_SIZE, (i+1)*BATCH_SIZE
+      if COND_GAN:
+        yield sample_images[i_1:i_2], sample_labels[i_1:i_2]
+      else:
+        yield sample_images[i_1:i_2], np.zeros(BATCH_SIZE)
+  image_gen = sample_images_gen()
+  if 'latent' in in_file:
+    sample_latents = in_file['latent']
+    def sample_latent_gen():
+      for i in range(int(NUM_IMGS / BATCH_SIZE)):
+        i_1, i_2 = i*BATCH_SIZE, (i+1)*BATCH_SIZE
+        yield sample_latents[i_1:i_2]
+    latent_gen = sample_latent_gen()
+  if NUM_IMGS % BATCH_SIZE != 0:
+    REMAINDER = BATCH_SIZE - (NUM_IMGS % BATCH_SIZE)
+    NUM_IMGS += REMAINDER
+    sample_images = np.append(sample_images, sample_images[-REMAINDER:,...], axis=0)
+    sample_labels = np.append(sample_labels, sample_labels[-REMAINDER:,...], axis=0)
+    sample_fns = np.append(sample_fns, sample_fns[-REMAINDER:], axis=0)
+  assert(NUM_IMGS % BATCH_SIZE == 0)
+else:
+  sys.exit('Unknown dataset {}.'.format(params.dataset))
+
+# --------------------------
+# Training.
+# --------------------------
+# Start session.
+sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
+sess.run(tf.global_variables_initializer())
+sess.run(tf.tables_initializer())
+
+if params.max_batches > 0:
+  NUM_IMGS_TO_PROCESS = params.max_batches * BATCH_SIZE
+else:
+  NUM_IMGS_TO_PROCESS = NUM_IMGS
+
+# Output file.
+out_file = h5py.File(os.path.join(INVERSES_DIR, params.out_dataset), 'w')
+out_images = out_file.create_dataset('xtrain', [NUM_IMGS_TO_PROCESS,] + IMG_SHAPE, dtype='float32')
+out_enc = out_file.create_dataset('encoding', [NUM_IMGS_TO_PROCESS,] + ENC_SHAPE, dtype='float32')
+out_lat = out_file.create_dataset('latent', [NUM_IMGS_TO_PROCESS, Z_DIM], dtype='float32')
+out_fns = out_file.create_dataset('fn', [NUM_IMGS_TO_PROCESS], dtype=h5py.string_dtype())
+if COND_GAN:
+  out_labels = out_file.create_dataset('ytrain', (NUM_IMGS_TO_PROCESS, N_CLASS,), dtype='float32')
+out_err = out_file.create_dataset('err', (NUM_IMGS_TO_PROCESS,))
+
+out_fns[:] = sample_fns[:NUM_IMGS_TO_PROCESS]
+
+# Gradient descent w.r.t. generator's inputs.
+it = 0
+out_pos = 0
+start_time = time.time()
+
+for image_batch, label_batch in image_gen:
+
+  # Save target.
+  sess.run(target.assign(image_batch))
+  if COND_GAN:
+    sess.run(label.assign(label_batch))
+
+  # Initialize encodings to random values.
+  if params.pre_trained_latent:
+    sess.run(latent.assign(next(latent_gen)))
+    if params.inv_layer != 'latent':
+      sess.run(encoding.assign(gen_encoding))
+  else:
+    if params.init_gen_dist:
+      sess.run(latent.assign(noise_sampler()))
+      if params.inv_layer != 'latent':
+        sess.run(encoding.assign(gen_encoding))
+    else:
+      sess.run(latent.assign(small_init()))
+      if params.inv_layer != 'latent':
+        sess.run(encoding.assign(small_init(shape=[BATCH_SIZE,] + ENC_SHAPE)))
+
+  # Init optimizer.
+  sess.run(inv_step.assign(0))
+  sess.run(reinit_optimizer)
+
+  # Main optimization loop.
+  print("Total iterations: {}".format(params.inv_it))
+  for _ in range(params.inv_it):
+
+    _inv_loss, _mse_loss, _feat_loss, _rec_loss, _reg_loss, _dist_loss,\
+        _lrate, _ = sess.run([inv_loss, mse_loss, feat_loss,
+        rec_loss, reg_loss, dist_loss, lrate, inv_train_op])
+
+    if params.clipping or params.stochastic_clipping:
+      sess.run(clip_latent)
+
+    # Save logs with training information.
+    if it % 500 == 0:
+      # Log losses.
+      etime = time.time() - start_time
+      print('It [{:8d}] time [{:5.1f}] total [{:.4f}] mse [{:.4f}] '
+            'feat [{:.4f}] rec [{:.4f}] reg [{:.4f}] dist [{:.4f}] '
+            'lr [{:.4f}]'.format(it, etime, _inv_loss, _mse_loss,
+            _feat_loss, _rec_loss, _reg_loss, _dist_loss, _lrate))
+
+      if params.log_z_norm:
+        _lat = sess.run(latent)
+        dim = 20 if Z_DIM == 120 else Z_DIM
+        for i in range(int(Z_DIM/dim)):
+          _subset = _lat[:,i*dim:(i+1)*dim]
+          print('section {:1d}: norm={:.4f} (exp={:.4f}) min={:.4f} max={:.4f}'\
+                .format(i, np.mean(np.linalg.norm(_subset, axis=1)),
+                        np.sqrt(dim-2), np.min(_subset), np.max(_subset)))
+
+      if params.log_activation_layer:
+        _act_rate = sess.run(activation_rate)
+        print('activation_rate={:.4f}'.format(_act_rate))
+        log_stats('activation rate', _act_rate, it)
+
+      sys.stdout.flush()
+
+      # Log tensorboard's statistics.
+      log_stats('total loss', _inv_loss, it)
+      log_stats('mse loss', _mse_loss, it)
+      log_stats('feat loss', _feat_loss, it)
+      log_stats('rec loss', _rec_loss, it)
+      log_stats('reg loss', _reg_loss, it)
+      log_stats('dist loss', _dist_loss, it)
+      log_stats('out pos', out_pos, it)
+      log_stats('lrate', _lrate, it)
+      summary_writer.flush()
+
+      # Save target images and reconstructions.
+      if params.save_progress:
+        assert SAMPLE_SIZE <= BATCH_SIZE
+        gen_images  = sess.run(gen_img)
+        inv_batch = vs.interleave(vs.data2img(image_batch[BATCH_SIZE - SAMPLE_SIZE:]),
+                        vs.data2img(gen_images[BATCH_SIZE - SAMPLE_SIZE:]))
+        inv_batch = vs.grid_transform(inv_batch)
+        vs.save_image('{}/progress_{}_{}.png'.format(SAMPLES_DIR, params.tag, it), inv_batch)
+
+    # It counter.
+    it += 1
+
+  if params.save_progress:
+    # Save linear interpolation between the actual and generated encodings.
+    if params.dist_loss:
+      enc_batch, gen_enc = sess.run([encoding, gen_encoding])
+      for j in range(10):
+        custom_enc =  gen_enc * (1-(j/10.0)) + enc_batch * (j/10.0)
+        sess.run(encoding.assign(custom_enc))
+        gen_images = sess.run(gen_img)
+        inv_batch = vs.interleave(vs.data2img(image_batch[BATCH_SIZE - SAMPLE_SIZE:]),
+            vs.data2img(gen_images[BATCH_SIZE - SAMPLE_SIZE:]))
+        inv_batch = vs.grid_transform(inv_batch)
+        vs.save_image('{}/progress_{}_{}_lat_{}.png'.format(SAMPLES_DIR,params.tag,it,j),
+                      inv_batch)
+      sess.run(encoding.assign(enc_batch))
+
+  # Save samples of inverted images.
+  if SAMPLE_SIZE > 0:
+    assert SAMPLE_SIZE <= BATCH_SIZE
+    gen_images  = sess.run(gen_img)
+    inv_batch = vs.interleave(vs.data2img(image_batch[BATCH_SIZE - SAMPLE_SIZE:]),
+                    vs.data2img(gen_images[BATCH_SIZE - SAMPLE_SIZE:]))
+    inv_batch = vs.grid_transform(inv_batch)
+    vs.save_image('{}/{}_{}.png'.format(SAMPLES_DIR, params.tag, out_pos), inv_batch)
+    print('Saved samples for out_pos: {}.'.format(out_pos))
+
+  # Save images that are ready.
+  latent_batch, enc_batch, rec_err_batch =\
+      sess.run([latent, encoding, img_rec_err])
+  out_lat[out_pos:out_pos+BATCH_SIZE] = latent_batch
+  out_enc[out_pos:out_pos+BATCH_SIZE] = enc_batch
+  out_images[out_pos:out_pos+BATCH_SIZE] = image_batch
+  if COND_GAN:
+    out_labels[out_pos:out_pos+BATCH_SIZE] = label_batch
+  out_err[out_pos:out_pos+BATCH_SIZE] = rec_err_batch
+  out_pos += BATCH_SIZE
+  if params.max_batches > 0 and (out_pos / BATCH_SIZE) >= params.max_batches:
+    break
+
+print('Mean reconstruction error: {}'.format(np.mean(out_err)))
+print('Stdev reconstruction error: {}'.format(np.std(out_err)))
+print('End of inversion.')
+out_file.close()
+sess.close()