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train_pool54.py
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train_pool54.py
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import os
import time
import numpy
from six.moves import xrange # pylint: disable=redefined-builtin
import tensorflow as tf
import input_train_data
import input_test_data
import c3d_model
import fcn_model
import numpy as np
from utils import placeholder_inputs, _variable_with_weight_decay, fcn_model_loss, tower_acc
flags = tf.app.flags
flags.DEFINE_float('learning_rate',1e-4, 'Initial learning rate.')
flags.DEFINE_integer('max_steps',2000, 'Number of steps to run trainer.')
flags.DEFINE_integer('batch_size',16 , 'Batch size.')
FLAGS = flags.FLAGS
MOVING_AVERAGE_DECAY = 0.9999
model_save_dir = './models/SGD_pool54'
def run_training():
if not os.path.exists(model_save_dir):
os.makedirs(model_save_dir)
use_pretrained_model = True
model_filename = "./sports1m_finetuning_ucf101.model"
with tf.Graph().as_default():
global_step = tf.get_variable(
'global_step',
[],
initializer=tf.constant_initializer(0),
trainable=False
)
with tf.variable_scope('var_name') as var_scope:
weights = {
'wc1': _variable_with_weight_decay('wc1', [3, 3, 3, 3, 64], 0.005),
'wc2': _variable_with_weight_decay('wc2', [3, 3, 3, 64, 128], 0.005),
'wc3a': _variable_with_weight_decay('wc3a', [3, 3, 3, 128, 256], 0.005),
'wc3b': _variable_with_weight_decay('wc3b', [3, 3, 3, 256, 256], 0.005),
'wc4a': _variable_with_weight_decay('wc4a', [3, 3, 3, 256, 512], 0.005),
'wc4b': _variable_with_weight_decay('wc4b', [3, 3, 3, 512, 512], 0.005),
'wc5a': _variable_with_weight_decay('wc5a', [3, 3, 3, 512, 512], 0.005),
'wc5b': _variable_with_weight_decay('wc5b', [3, 3, 3, 512, 512], 0.005),
#'wd1': _variable_with_weight_decay('wd1', [8192, 4096], 0.005),
#'wd2': _variable_with_weight_decay('wd2', [4096, 4096], 0.005),
#'out': _variable_with_weight_decay('wout', [4096, c3d_model.NUM_CLASSES], 0.005)
}
biases = {
'bc1': _variable_with_weight_decay('bc1', [64], 0.000),
'bc2': _variable_with_weight_decay('bc2', [128], 0.000),
'bc3a': _variable_with_weight_decay('bc3a', [256], 0.000),
'bc3b': _variable_with_weight_decay('bc3b', [256], 0.000),
'bc4a': _variable_with_weight_decay('bc4a', [512], 0.000),
'bc4b': _variable_with_weight_decay('bc4b', [512], 0.000),
'bc5a': _variable_with_weight_decay('bc5a', [512], 0.000),
'bc5b': _variable_with_weight_decay('bc5b', [512], 0.000),
#'bd1': _variable_with_weight_decay('bd1', [4096], 0.000),
#'bd2': _variable_with_weight_decay('bd2', [4096], 0.000),
#'out': _variable_with_weight_decay('bout', [c3d_model.NUM_CLASSES], 0.000),
}
fcn_weights = {
'wconv6': _variable_with_weight_decay('conv6', [1, 4, 4, 512, 512], 0.005),
'wconv7': _variable_with_weight_decay('conv7', [1, 7, 7, 512, 512], 0.005),
'wup6': _variable_with_weight_decay('up6', [2, 1, 1, 4096, 512], 0.005),
'wup7': _variable_with_weight_decay('up7', [2, 1, 1, 4096, 4096], 0.005),
'wup8': _variable_with_weight_decay('up8', [2, 1, 1, fcn_model.NUM_CLASSES, 4096], 0.005),
}
fcn_biases = {
'bconv6': _variable_with_weight_decay('bconv6', [512], 0.000),
'bconv7': _variable_with_weight_decay('bconv7', [512], 0.000),
'bup6': _variable_with_weight_decay('bup6', [4096], 0.000),
'bup7': _variable_with_weight_decay('bup7', [4096], 0.000),
'bup8': _variable_with_weight_decay('bup8', [fcn_model.NUM_CLASSES], 0.000),
}
with tf.name_scope('inputs'):
images_placeholder, labels_placeholder, keep_pro = placeholder_inputs( FLAGS.batch_size )
varlist1 = list( set(fcn_weights.values() + fcn_biases.values()) )
varlist2 = list( set(weights.values() + biases.values()) )
feature_map = c3d_model.inference_c3d(
images_placeholder,
keep_pro,
FLAGS.batch_size,
weights,
biases
)
logit=fcn_model.inference_pool54(
feature_map,
keep_pro,
FLAGS.batch_size,
fcn_weights,
fcn_biases
)
loss = fcn_model_loss(
logit,
labels_placeholder,
FLAGS.batch_size
)
SGD_cdc = tf.train.GradientDescentOptimizer(1e-4).minimize(loss, var_list = varlist1)
SGD_c3d = tf.train.GradientDescentOptimizer(1e-5).minimize(loss, var_list = varlist2)
accuracy = tower_acc(logit, labels_placeholder, FLAGS.batch_size)
tf.summary.scalar('accuracy', accuracy)
variable_averages = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY)
variables_averages_op = variable_averages.apply(tf.trainable_variables())
train_op = tf.group(SGD_cdc, SGD_c3d, variables_averages_op)
null_op = tf.no_op()
# Create a saver for writing training checkpoints.
saver = tf.train.Saver(weights.values() + biases.values())
new_saver = tf.train.Saver(weights.values() + biases.values()+ fcn_weights.values() + fcn_biases.values())
init = tf.global_variables_initializer()
# Create a session for running Ops on the Graph.
sess = tf.Session(
config=tf.ConfigProto(allow_soft_placement=True)
)
sess.run(init)
merged = tf.summary.merge_all()
if os.path.isfile(model_filename) and use_pretrained_model:
print 'loading pretrained_model....'
saver.restore(sess, model_filename)
print 'complete!'
# Create summary writter
train_writer = tf.summary.FileWriter('./visual_logs/SGD_pool54_visual_logs/train', sess.graph)
test_writer = tf.summary.FileWriter('./visual_logs/SGD_pool54_visual_logs/test', sess.graph)
video_list = []
position = -1
for step in xrange(FLAGS.max_steps+1):
start_time = time.time()
train_images, train_labels, _, _, video_list, position = input_train_data.read_clip_and_label(
filename='annotation/train.list',
batch_size=FLAGS.batch_size,
start_pos=position,
num_frames_per_clip=c3d_model.NUM_FRAMES_PER_CLIP,
crop_size=c3d_model.CROP_SIZE,
video_list=video_list
)
sess.run(train_op, feed_dict={
images_placeholder: train_images,
labels_placeholder: train_labels,
keep_pro: 0.5
})
duration = time.time() - start_time
print('Batchnum %d: %.3f sec' % (step, duration))
if (step) %2 == 0 or (step + 1) == FLAGS.max_steps:
print('Step %d/%d: %.3f sec' % (step, FLAGS.max_steps, duration))
print('Training Data Eval:')
summary,loss_train,acc = sess.run(
[merged, loss, accuracy],
feed_dict={
images_placeholder: train_images,
labels_placeholder: train_labels,
keep_pro: 1
})
print 'loss: %f' % np.mean(loss_train)
print ("accuracy: " + "{:.5f}".format(acc))
train_writer.add_summary(summary, step)
if (step) %10 == 0 or (step + 1) == FLAGS.max_steps:
print('Validation Data Eval:')
val_images, val_labels, _, _, _, _ = input_train_data.read_clip_and_label(
filename='annotation/test.list',
batch_size=FLAGS.batch_size,
start_pos=-1,
num_frames_per_clip=c3d_model.NUM_FRAMES_PER_CLIP,
crop_size=c3d_model.CROP_SIZE,
video_list=[]
)
summary,loss_val, acc = sess.run(
[merged, loss, accuracy],
feed_dict={
images_placeholder: val_images,
labels_placeholder: val_labels,
keep_pro: 1
})
print 'loss: %f' % np.mean(loss_val)
print ("accuracy: " + "{:.5f}".format(acc))
test_writer.add_summary(summary, step)
# Save the model checkpoint periodically.
if step > 1 and step % 200 == 0:
checkpoint_path = os.path.join('./models/SGD_pool54', 'model.ckpt')
new_saver.save(sess, checkpoint_path, global_step=global_step)
print("done")
def main(_):
run_training()
if __name__ == '__main__':
tf.app.run()