def testModel(self, video_id, out_dir="rolo_out_train"):
with tf.Session(config=self.config) as sess:
self.saver = tf.train.Saver()
self.saver.restore(sess, self.rolo_weights_dir)
#self.saver.restore(sess, "/home/sharat/Documents/assignment2_new/ROLO/weights/train/model_step6_exp1.ckpt")
img_w, img_h, video_seq, _, test_iters = helper.getVideoSequenceMetadata(
video_id)
x_dir = os.path.join("DATA", video_seq, "yolo_out")
y_dir = os.path.join("DATA", video_seq, "groundtruth_rect.txt")
out_dir = os.path.join("DATA", video_seq, out_dir)
if not os.path.exists(out_dir):
os.mkdir(out_dir)
total_loss = 0
index = 0
while index < test_iters - self.time_steps:
inputs_x = helper.loadYoloPredictions(index, self.time_steps,
self.batch_size,
self.yolo_output_size,
x_dir)
inputs_x = np.reshape(
inputs_x,
[self.batch_size, self.time_steps, self.yolo_output_size])
inputs_y = helper.loadRoloGroundTruth(index, self.time_steps,
self.batch_size, y_dir)
inputs_y = helper.getNormalizedCoordinates(
img_w, img_h, inputs_y)
inputs_y = np.reshape(inputs_y,
[self.batch_size, self.gt_size])
input_dict = {
self.x:
inputs_x,
self.y:
inputs_y,
self.state:
np.zeros((self.batch_size, 2 * self.yolo_output_size))
}
location_pred = sess.run(self.box_prediction,
feed_dict=input_dict)
location_pred = helper.getRealCoordinates(
img_w, img_h, location_pred)
location_pred = np.reshape(location_pred,
[self.batch_size, self.gt_size])
helper.dumpRoloPredictions(location_pred, index,
self.time_steps, self.batch_size,
out_dir)
if index % self.display_count == 0:
loss = sess.run(self.loss, feed_dict=input_dict)
print(
"Index: {}, Loss: {}, Predictions: {}, Video Sequence Name: {}"
.format(index, "{:.6f}".format(loss), location_pred,
video_seq))
total_loss += loss
index += 1
avg_loss = total_loss / index
print("Video Name: {}, Average Loss: {}".format(
video_seq, avg_loss))
def trainModel(self):
self.merged = tf.summary.merge_all()
with tf.variable_scope(tf.get_variable_scope(), reuse=tf.AUTO_REUSE):
self.optimizer = tf.train.AdamOptimizer(
learning_rate=self.lr).minimize(self.loss)
initialize = tf.global_variables_initializer()
with tf.Session(config=self.config) as sess:
self.saver = tf.train.Saver(max_to_keep=1)
self.saver_intermediate = tf.train.Saver(max_to_keep=0)
self.train_writer = tf.summary.FileWriter(self.summary_dir,
sess.graph)
sess.run(initialize)
if self.use_weights:
self.saver.restore(sess, self.rolo_weights_dir)
iterator = 1
for epoch in xrange(self.num_epoches):
video_id = epoch % self.num_videos + self.offset
img_w, img_h, video_seq, train_iters, _ = helper.getVideoSequenceMetadata(
video_id)
x_dir = os.path.join("DATA", video_seq, "yolo_out")
y_dir = os.path.join("DATA", video_seq, "groundtruth_rect.txt")
out_dir = os.path.join("DATA", video_seq, "rolo_out_train")
if not os.path.exists(out_dir):
os.mkdir(out_dir)
total_loss = 0
index = 0
while index < train_iters - self.time_steps:
inputs_x = helper.loadYoloPredictions(
index, self.time_steps, self.batch_size,
self.yolo_output_size, x_dir)
inputs_x = np.reshape(inputs_x, [
self.batch_size, self.time_steps, self.yolo_output_size
])
inputs_y = helper.loadRoloGroundTruth(
index, self.time_steps, self.batch_size, y_dir)
inputs_y = helper.getNormalizedCoordinates(
img_w, img_h, inputs_y)
inputs_y = np.reshape(inputs_y,
[self.batch_size, self.gt_size])
input_dict = {
self.x:
inputs_x,
self.y:
inputs_y,
self.state:
np.zeros((self.batch_size, 2 * self.yolo_output_size))
}
sess.run(self.optimizer, feed_dict=input_dict)
if index % self.display_count == 0:
loss = sess.run(self.loss, feed_dict=input_dict)
print(
"Epoch: {}, Index: {}, Loss: {}, Video Sequence Name: {}"
.format(epoch, index, "{:.6f}".format(loss),
video_seq))
total_loss += loss
if index % self.summary_count == 0:
summary = sess.run(self.merged, feed_dict=input_dict)
self.train_writer.add_summary(summary, iterator)
iterator += 1
index += 1
avg_loss = total_loss / index
print(
"Epoch: {}, Video Name: {}, Average Loss: {}, Sequence Name: {}"
.format(epoch, video_seq, avg_loss, video_seq))
model_save_path = self.saver.save(sess,
self.rolo_weights_dir,
global_step=epoch)
print("Model Checkpoint files dumped at {}:{}".format(
epoch, model_save_path))
if epoch % 50:
save_path = self.saver_intermediate.save(
sess,
self.rolo_weights_intermediate_dir,
global_step=epoch)
print(
"Intermediate Model Checkpoint files dumped at {}:{}".
format(epoch, save_path))
def main(): video_id = 21 time_steps = 6 out_dump_dir = "output/frames_new/" demoDumpImages(out_dump_dir, time_steps, helper.getVideoSequenceMetadata(video_id), imshow=False)
