def get_placeholders(batch_size=1, c3d_depth=0):
'''
Define the placeholders used by the network.
-c3d_depth: the depth at which the c3d activation map should be drawn
'''
return {
# imported placeholders
"c3d_in":
c3d.get_input_placeholder(batch_size),
"itr_in":
itr.get_input_placeholder(batch_size, c3d_depth),
"aud_in":
aud.get_input_placeholder(batch_size),
# system specific placeholders
"pt_in":
tf.placeholder(tf.float32, shape=(batch_size, 1), name="pt_ph"),
"system_out":
tf.placeholder(tf.float32,
shape=(batch_size, NUM_LABEL),
name="out_ph")
}
def identify_min_maxes(filenames, records):
placeholders = c3d.get_input_placeholder(batch_size)
weights, biases = c3d.get_variables()
variable_name_dict = list( set(weights.values() + biases.values()))
sem = Semaphore(4)
for c3d_depth in range(1):#5):
max_vals, min_vals = RawArray('d', 64), RawArray('d', 64)
for i in range(64):
max_vals[i] = float("-inf")
min_vals[i] = float("inf")
# define model
c3d_model = c3d.generate_activation_map(placeholders, weights, biases, depth=c3d_depth)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
saver = tf.train.Saver(variable_name_dict)
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
saver.restore(sess, C3D_NETWORK_VARIABLE_FILE)
#setup file io
tf_records = input_pipeline(filenames, batch_size=batch_size)
sess.graph.finalize()
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord, sess=sess)
#process files
for i in range(len(filenames)):
if(i %1000 == 0 ):
print("Converted "+str(i)+" files")
ph_values, info_values = generate_model_input(placeholders, tf_records, sess)
all_procs = []
if(ph_values != 0):
#generate activation map from 3D-CNN
c3d_activation_map = sess.run(c3d_model, feed_dict=ph_values)
# try to acquire a ticket, if ticket is available converting activation map to IAD
# have to use Semaphores here because activation maps are save on GPU. ATTempting to start multiple threads
# means that the GPU never relesaes the memory used for activation maps.
sem.acquire()
p = Thread(target=get_row_min_max, args=(c3d_activation_map, info_values,sem, max_vals, min_vals, records))
p.start()
all_procs.append(p)
for p in all_procs:
p.join()
coord.request_stop()
coord.join(threads)
return max_vals, min_vals
info_values["example_id"][0], c3d_depth,
compression_method["value"])
print("write to: ", video_name)
writer = tf.python_io.TFRecordWriter(video_name)
writer.write(ex.SerializeToString())
writer.close()
if __name__ == '__main__':
# open the files
compression_method = {"type": "peaks", "value": 10, "num_channels": 10}
# setup variables
placeholders = c3d.get_input_placeholder(1)
weights, biases = c3d.get_variables()
variable_name_dict = list(set(weights.values() + biases.values()))
cur_dir = "../one_person_tfrecords"
filenames = read_files_in_dir(cur_dir)
for f in filenames:
print(f)
for c3d_depth in range(5):
new_dir = "../iad_3d_tfrecords/" + str(c3d_depth) + "/"
# define model
c3d_model = c3d.generate_activation_map(placeholders,
weights,
biases,
def convert_videos_to_IAD(filenames, c3d_depth, records=None):
'''
opens an unthreshodled IAD and thresholds given the new values
- records - providing a records variable indicates that the function is
meant to be run as global_norm not local_norm
'''
max_vals, min_vals = None, None
if (records):
max_vals, min_vals = RawArray('d', 64), RawArray('d', 64)
for i in range(64):
max_vals[i] = float("-inf")
min_vals[i] = float("inf")
# define model
placeholders = c3d.get_input_placeholder(BATCH_SIZE)
weights, biases = c3d.get_variables()
variable_name_dict = list(set(weights.values() + biases.values()))
c3d_model = c3d.generate_activation_map(placeholders,
weights,
biases,
depth=c3d_depth)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
saver = tf.train.Saver(variable_name_dict)
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
saver.restore(sess, C3D_NETWORK_VARIABLE_FILE)
#setup file io
tf_records = input_pipeline(filenames, batch_size=BATCH_SIZE)
sess.graph.finalize()
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord, sess=sess)
#limit the number of threads running at once
sem = Semaphore(NUM_THREADS)
#process files
for i in range(len(filenames)):
if (i % 1000 == 0):
print("Converted " + str(i) + " files")
ph_values, info_values = generate_model_input(
placeholders, tf_records, sess)
all_procs = []
if (ph_values != 0):
#generate activation map from 3D-CNN
c3d_activation_map = sess.run(c3d_model, feed_dict=ph_values)
# try to acquire a ticket, if ticket is available converting activation map to IAD
# have to use Semaphores here because activation maps are save on GPU. ATTempting to start multiple threads
# means that the GPU never relesaes the memory used for activation maps.
sem.acquire()
p = None
if (records):
p = Thread(target=get_row_min_max,
args=(c3d_activation_map, info_values, sem,
max_vals, min_vals, records))
else:
p = Thread(target=threshold_iad,
args=(
c3d_activation_map,
info_values,
sem,
))
p.start()
all_procs.append(p)
else:
print("ph_value is 0, file generation failed")
for p in all_procs:
p.join()
coord.request_stop()
coord.join(threads)
if (records):
return max_vals, min_vals
return None