[tf.sparse_placeholder(tf.float32)
for _ in range(num_supports)], # graph structure in the third block
'faces':
[tf.placeholder(tf.int32, shape=(None, 4))
for _ in range(num_blocks)], # helper for face loss (not used)
'edges':
[tf.placeholder(tf.int32, shape=(None, 2))
for _ in range(num_blocks)], # helper for normal loss
'lape_idx':
[tf.placeholder(tf.int32, shape=(None, 10))
for _ in range(num_blocks)], # helper for laplacian regularization
'pool_idx':
[tf.placeholder(tf.int32, shape=(None, 2))
for _ in range(num_blocks - 1)] # helper for graph unpooling
}
model = GCN(placeholders, logging=True)
def f_score(label, predict, dist_label, dist_pred, threshold):
num_label = label.shape[0]
num_predict = predict.shape[0]
f_scores = []
for i in range(len(threshold)):
num = len(np.where(dist_label <= threshold[i])[0])
recall = 100.0 * num / num_label
num = len(np.where(dist_pred <= threshold[i])[0])
precision = 100.0 * num / num_predict
f_scores.append((2 * precision * recall) / (precision + recall + 1e-8))
return np.array(f_scores)
# Define placeholders(dict) and model
num_blocks = 3
num_supports = 2
placeholders = {
'features': tf.placeholder(tf.float32, shape=(None, 3)),
'img_inp': tf.placeholder(tf.float32, shape=(224, 224, 3)),
'labels': tf.placeholder(tf.float32, shape=(None, 6)),
'support1': [tf.sparse_placeholder(tf.float32) for _ in range(num_supports)],
'support2': [tf.sparse_placeholder(tf.float32) for _ in range(num_supports)],
'support3': [tf.sparse_placeholder(tf.float32) for _ in range(num_supports)],
'faces': [tf.placeholder(tf.int32, shape=(None, 4)) for _ in range(num_blocks)], #for face loss, not used.
'edges': [tf.placeholder(tf.int32, shape=(None, 2)) for _ in range(num_blocks)],
'lape_idx': [tf.placeholder(tf.int32, shape=(None, 10)) for _ in range(num_blocks)], #for laplace term
'pool_idx': [tf.placeholder(tf.int32, shape=(None, 2)) for _ in range(num_blocks-1)] #for unpooling
}
model = GCN(placeholders, logging=True)
# Load data, initialize session
data = DataFetcher(FLAGS.data_list)
data.setDaemon(True) ####
data.start()
config=tf.ConfigProto()
#config.gpu_options.allow_growth=True
config.allow_soft_placement=True
sess = tf.Session(config=config)
sess.run(tf.global_variables_initializer())
#model.load(sess)
# Train graph model
train_loss = open('record_train_loss.txt', 'a')
train_loss.write('Start training, lr = %f\n'%(FLAGS.learning_rate))