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train_shape.py
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train_shape.py
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import tensorflow as tf
import numpy as np
import os
import sys
sys.path.append('./utils')
sys.path.append('./models')
import dataset as dataset
import model_shape as model
FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_string('train_dir', './train_shape',
"""Directory where to write summaries and checkpoint.""")
tf.app.flags.DEFINE_string('base_dir', './data/ShapeNetCore_im2avatar',
"""The path containing all the samples.""")
tf.app.flags.DEFINE_string('cat_id', '02958343',
"""The category id for each category: 02958343, 03001627, 03467517, 04379243""")
tf.app.flags.DEFINE_string('data_list_path', './data_list',
"""The path containing data lists.""")
tf.app.flags.DEFINE_integer('train_epochs', 501, """Training epochs.""")
tf.app.flags.DEFINE_integer('batch_size', 60, """Batch size.""")
tf.app.flags.DEFINE_integer('gpu', 0, """""")
tf.app.flags.DEFINE_float('learning_rate', 0.0003, """""")
tf.app.flags.DEFINE_float('wd', 0.00001, """""")
tf.app.flags.DEFINE_integer('epochs_to_save',20, """""")
tf.app.flags.DEFINE_integer('decay_step',20000, """for lr""")
tf.app.flags.DEFINE_float('decay_rate', 0.7, """for lr""")
IM_DIM = 128
VOL_DIM = 64
BATCH_SIZE = FLAGS.batch_size
TRAIN_EPOCHS = FLAGS.train_epochs
GPU_INDEX = FLAGS.gpu
BASE_LEARNING_RATE = FLAGS.learning_rate
DECAY_STEP = FLAGS.decay_step
DECAY_RATE = FLAGS.decay_rate
BN_INIT_DECAY = 0.5
BN_DECAY_DECAY_RATE = 0.5
BN_DECAY_DECAY_STEP = float(DECAY_STEP)
BN_DECAY_CLIP = 0.99
os.environ["CUDA_VISIBLE_DEVICES"] = str(GPU_INDEX)
TRAIN_DIR = os.path.join(FLAGS.train_dir, FLAGS.cat_id)
if not os.path.exists(TRAIN_DIR):
os.makedirs(TRAIN_DIR)
LOG_FOUT = open(os.path.join(TRAIN_DIR, 'log_train.txt'), 'w')
LOG_FOUT.write(str(tf.flags._global_parser.parse_args())+'\n')
def log_string(out_str):
LOG_FOUT.write(out_str+'\n')
LOG_FOUT.flush()
print(out_str)
def get_learning_rate(batch):
learning_rate = tf.train.exponential_decay(
BASE_LEARNING_RATE, # Base learning rate.
batch * BATCH_SIZE, # Current index into the dataset.
DECAY_STEP, # Decay step.
DECAY_RATE, # Decay rate.
staircase=True)
learning_rate = tf.maximum(learning_rate, 0.00001) # CLIP THE LEARNING RATE!
return learning_rate
def get_bn_decay(batch):
bn_momentum = tf.train.exponential_decay(
BN_INIT_DECAY,
batch*BATCH_SIZE,
BN_DECAY_DECAY_STEP,
BN_DECAY_DECAY_RATE,
staircase=True)
bn_decay = tf.minimum(BN_DECAY_CLIP, 1 - bn_momentum)
return bn_decay
def train(dataset_):
with tf.Graph().as_default():
with tf.device('/gpu:'+str(GPU_INDEX)):
is_train_pl = tf.placeholder(tf.bool)
img_pl, vol_pl = model.placeholder_inputs(BATCH_SIZE, IM_DIM, VOL_DIM)
global_step = tf.Variable(0)
bn_decay = get_bn_decay(global_step)
tf.summary.scalar('bn_decay', bn_decay)
pred = model.get_model(img_pl, is_train_pl, weight_decay=FLAGS.wd, bn_decay=bn_decay)
loss = model.get_MSFE_cross_entropy_loss(pred, vol_pl)
tf.summary.scalar('loss', loss)
learning_rate = get_learning_rate(global_step)
tf.summary.scalar('learning_rate', learning_rate)
optimizer = tf.train.AdamOptimizer(learning_rate)
train_op = optimizer.minimize(loss, global_step=global_step)
summary_op = tf.summary.merge_all()
saver = tf.train.Saver()
config = tf.ConfigProto()
config.gpu_options.allocator_type = 'BFC'
config.gpu_options.allow_growth = True
config.allow_soft_placement = True
with tf.Session(config=config) as sess:
model_path = os.path.join(TRAIN_DIR, "trained_models")
if tf.gfile.Exists(os.path.join(model_path, "checkpoint")):
ckpt = tf.train.get_checkpoint_state(model_path)
restorer = tf.train.Saver()
restorer.restore(sess, ckpt.model_checkpoint_path)
print ("Load parameters from checkpoint.")
else:
sess.run(tf.global_variables_initializer())
train_summary_writer = tf.summary.FileWriter(model_path, graph=sess.graph)
train_sample_size = dataset_.getTrainSampleSize()
train_batches = train_sample_size // BATCH_SIZE # The number of batches per epoch
val_sample_size = dataset_.getValSampleSize()
val_batches = val_sample_size // BATCH_SIZE
for epoch in range(TRAIN_EPOCHS):
####################
# For training
####################
dataset_.shuffleIds()
for batch_idx in range(train_batches):
imgs, vols_clr = dataset_.next_batch(batch_idx * BATCH_SIZE, BATCH_SIZE, vol_dim=VOL_DIM)
vols_occu = np.prod(vols_clr > -0.5, axis=-1, keepdims=True) # (batch, vol_dim, vol_dim, vol_dim, 1)
vols_occu = vols_occu.astype(np.float32)
feed_dict = {img_pl: imgs, vol_pl: vols_occu, is_train_pl: True}
step = sess.run(global_step)
_, loss_val = sess.run([train_op, loss], feed_dict=feed_dict)
log_string("<TRAIN> Epoch {} - Batch {}: loss: {}.".format(epoch, batch_idx, loss_val))
#####################
# For validation
#####################
loss_sum = 0.0
for batch_idx in range(val_batches):
imgs, vols_clr = dataset_.next_batch(batch_idx * BATCH_SIZE, BATCH_SIZE, vol_dim=VOL_DIM, process="val")
vols_occu = np.prod(vols_clr > -0.5, axis=-1, keepdims=True) # (batch, vol_dim, vol_dim, vol_dim, 1)
vols_occu = vols_occu.astype(np.float32)
feed_dict = {img_pl: imgs, vol_pl: vols_occu, is_train_pl: False}
loss_val = sess.run(loss, feed_dict=feed_dict)
loss_sum += loss_val
log_string("<VAL> Epoch {}: loss: {}.".format(epoch, loss_sum/val_batches))
#####################
# Save model parameters.
#####################
if epoch % FLAGS.epochs_to_save == 0:
checkpoint_path = os.path.join(model_path, 'model.ckpt')
saver.save(sess, checkpoint_path, global_step=epoch)
def main():
train_dataset = dataset.Dataset(base_path=FLAGS.base_dir,
cat_id=FLAGS.cat_id,
data_list_path=FLAGS.data_list_path)
train(train_dataset)
if __name__ == '__main__':
main()