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# ------------------------------------------------------------------------------
# Cluster the attention map of inverted images for unsupervised segmentation.
# ------------------------------------------------------------------------------
import h5py
import numpy as np
import os
import params
import scipy
import scipy.cluster.hierarchy
from sklearn.cluster import AgglomerativeClustering
import sys
import tensorflow as tf
import tensorflow_hub as hub
import time
import visualize as vs
if len(sys.argv) < 2:
sys.exit('Must provide a configuration file.')
params = params.Params(sys.argv[1])
params.batch_size = 1
params.sample_size = 1
# --------------------------
# Global directories.
# --------------------------
BATCH_SIZE = params.batch_size
SAMPLE_SIZE = params.sample_size
SAMPLES_DIR = 'attention'
INVERSES_DIR = 'inverses'
if not os.path.exists(SAMPLES_DIR):
os.makedirs(SAMPLES_DIR)
# --------------------------
# Util functions.
# --------------------------
# One hot encoding for classes.
def one_hot(values):
return np.eye(N_CLASS)[values]
def segment_img(diss_matrix, n_clusters):
# Cluster image based on the information from the attention map.
clustering = AgglomerativeClustering(n_clusters=n_clusters,
affinity='precomputed', linkage='average')
clustering.fit(diss_matrix)
labels = clustering.labels_
# Upsample segmentation (from 64x64 to 128x128) and create an image where each
# segment has the average color of its members.
labels = np.broadcast_to(labels.reshape(64, 1, 64, 1), (64, 2, 64, 2))\
.reshape(128*128)
labels = np.eye(labels.max() + 1)[labels]
cluster_col = np.matmul(labels.T,
np.transpose(_gen_img, [0, 2, 3, 1]).reshape(128*128, 3))
cluster_count = labels.T.sum(axis=1).reshape(-1, 1)
labels_img = np.matmul(labels, cluster_col) / np.matmul(labels, cluster_count)
labels_img = np.transpose(labels_img, [1, 0]).reshape(1,3,128,128)
return vs.data2img(labels_img)
# --------------------------
# 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))
# Get attention map.
att_map_name = 'module_apply_' + gen_signature + '/' + params.attention_map_layer
att_map = tf.get_default_graph().get_tensor_by_name(att_map_name)
# Define image shape.
IMG_SHAPE = gen_img.get_shape().as_list()[1:]
# --------------------------
# Dataset.
# --------------------------
if params.out_dataset.endswith('.hdf5'):
in_file = h5py.File(os.path.join(INVERSES_DIR, params.out_dataset), 'r')
sample_images = in_file['xtrain']
if COND_GAN:
sample_labels = in_file['ytrain']
sample_latents = in_file['latent']
sample_encodings = in_file['encoding']
NUM_IMGS = sample_images.shape[0] # number of images.
def sample_images_gen():
for i in xrange(NUM_IMGS / BATCH_SIZE):
i_1, i_2 = i*BATCH_SIZE, (i+1)*BATCH_SIZE
if COND_GAN:
label_batch = sample_labels[i_1:i_2]
else:
label_batch = np.zeros(BATCH_SIZE)
yield sample_images[i_1:i_2], label_batch, sample_latents[i_1:i_2],\
sample_encodings[i_1:i_2]
image_gen = sample_images_gen()
else:
sys.exit('Unknown dataset {}.'.format(params.out_dataset))
NUM_IMGS -= NUM_IMGS % BATCH_SIZE
# --------------------------
# Training.
# --------------------------
# Start session.
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
sess.run(tf.global_variables_initializer())
sess.run(tf.tables_initializer())
# Export attention map for reconstructed images.
it = 0
out_pos = 0
start_time = time.time()
for image_batch, label_batch, lat_batch, enc_batch in image_gen:
# Set target label.
if COND_GAN:
sess.run(label.assign(one_hot(label_batch)))
# Initialize encodings.
sess.run(latent.assign(lat_batch))
sess.run(encoding.assign(enc_batch))
# Get attention map.
_att_map, _gen_img = sess.run([att_map, gen_img])
# Upsampling (from 32x32 to 64x64).
_att_map = np.broadcast_to(_att_map.reshape(64,64,32,1,32,1),
(64,64,32,2,32,2)).reshape(4096,4096)
# Define dissimilarity matrix.
dissimilarity = 1.0 - (_att_map + _att_map.T) / 2.0
dissimilarity *= (np.ones((4096,4096)) - np.identity(4096))
# Segment the image with different number of clusters.
seg_img_8 = segment_img(dissimilarity, 8)
seg_img_20 = segment_img(dissimilarity, 20)
seg_img_40 = segment_img(dissimilarity, 40)
# Save segmentation.
out_batch_1 = vs.interleave(image_batch, seg_img_20)
out_batch_2 = vs.interleave(seg_img_8, seg_img_40)
out_batch = vs.interleave(out_batch_1, out_batch_2)
out_batch = vs.seq_transform(out_batch)
vs.save_image('{}/segmented_img_{}.png'.format(SAMPLES_DIR, it), out_batch)
it += 1
sess.close()
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