def run_training():
# Get the sets of images and labels for training, validation, and
# Tell TensorFlow that the model will be built into the default Graph.
pre_model_save_dir = "./models/rgb_imagenet_10000_6_64_0.0001_decay"
test_list_file = '../../list/hmdb_list/test_flow.list'
file = list(open(test_list_file, 'r'))
num_test_videos = len(file)
print("Number of test videos={}".format(num_test_videos))
with tf.Graph().as_default():
rgb_images_placeholder, _, labels_placeholder, is_training = placeholder_inputs(
FLAGS.batch_size * gpu_num, FLAGS.num_frame_per_clib,
FLAGS.crop_size, FLAGS.rgb_channels)
with tf.variable_scope('RGB'):
logit, _ = InceptionI3d(num_classes=FLAGS.classics,
spatial_squeeze=True,
final_endpoint='Logits',
name='inception_i3d')(
rgb_images_placeholder, is_training)
norm_score = tf.nn.softmax(logit)
# Create a saver for writing training checkpoints.
saver = tf.train.Saver()
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)
ckpt = tf.train.get_checkpoint_state(pre_model_save_dir)
if ckpt and ckpt.model_checkpoint_path:
print("loading checkpoint %s,waiting......" %
ckpt.model_checkpoint_path)
saver.restore(sess, ckpt.model_checkpoint_path)
print("load complete!")
all_steps = num_test_videos
top1_list = []
for step in xrange(all_steps):
start_time = time.time()
s_index = 0
predicts = []
top1 = False
while True:
val_images, _, val_labels, s_index, is_end = input_test.read_clip_and_label(
filename=file[step],
batch_size=FLAGS.batch_size * gpu_num,
s_index=s_index,
num_frames_per_clip=FLAGS.num_frame_per_clib,
crop_size=FLAGS.crop_size,
)
predict = sess.run(norm_score,
feed_dict={
rgb_images_placeholder: val_images,
labels_placeholder: val_labels,
is_training: False
})
predicts.append(
np.array(predict).astype(np.float32).reshape(FLAGS.classics))
if is_end:
avg_pre = np.mean(predicts, axis=0).tolist()
top1 = (avg_pre.index(max(avg_pre)) == val_labels)
top1_list.append(top1)
break
duration = time.time() - start_time
print('TOP_1_ACC in test: %f , time use: %.3f' % (top1, duration))
print(len(top1_list))
print('TOP_1_ACC in test: %f' % np.mean(top1_list))
print("done")
def run_training():
# Get the sets of images and labels for training, validation, and
# Tell TensorFlow that the model will be built into the default Graph.
rgb_pre_model_save_dir = "/media/senilab/DATA/Master/I3D-Tensorflow/experiments/ucf-101/models/rgb_imagenet_30000_101_5_64_0.0001_decay"
flow_pre_model_save_dir = "/media/senilab/DATA/Master/I3D-Tensorflow/experiments/ucf-101/models/flow_imagenet_101_20000_5_64_0.0001_decay"
test_list_file = '/media/senilab/DATA/Master/I3D-Tensorflow/list/ucf_list/test.list'
file = list(open(test_list_file, 'r'))
num_test_videos = len(file)
print("Number of test videos={}".format(num_test_videos))
with tf.Graph().as_default():
rgb_images_placeholder, flow_images_placeholder, labels_placeholder, is_training = placeholder_inputs(
FLAGS.batch_size * gpu_num, FLAGS.num_frame_per_clib,
FLAGS.crop_size, FLAGS.rgb_channels)
with tf.variable_scope('RGB'):
rgb_logit, _ = InceptionI3d(
num_classes=FLAGS.classics,
spatial_squeeze=True,
final_endpoint='Logits',
name='inception_i3d')(rgb_images_placeholder, is_training)
with tf.variable_scope('Flow'):
flow_logit, _ = InceptionI3d(
num_classes=FLAGS.classics,
spatial_squeeze=True,
final_endpoint='Logits',
name='inception_i3d')(flow_images_placeholder, is_training)
norm_score = tf.nn.softmax(tf.add(rgb_logit, flow_logit))
# Create a saver for writing training checkpoints.
rgb_variable_map = {}
flow_variable_map = {}
for variable in tf.global_variables():
if variable.name.split('/')[
0] == 'RGB' and 'Adam' not in variable.name.split('/')[-1]:
rgb_variable_map[variable.name.replace(':0', '')] = variable
rgb_saver = tf.train.Saver(var_list=rgb_variable_map, reshape=True)
for variable in tf.global_variables():
if variable.name.split(
'/')[0] == 'Flow' and 'Adam' not in variable.name.split(
'/')[-1]:
flow_variable_map[variable.name.replace(':0', '')] = variable
flow_saver = tf.train.Saver(var_list=flow_variable_map, reshape=True)
saver = tf.train.Saver()
init = tf.global_variables_initializer()
# Create a session for running Ops on the Graph
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
sess.run(init)
# load pre_train models
ckpt = tf.train.get_checkpoint_state(rgb_pre_model_save_dir)
if ckpt and ckpt.model_checkpoint_path:
print("loading checkpoint %s,waiting......" %
ckpt.model_checkpoint_path)
rgb_saver.restore(sess, ckpt.model_checkpoint_path)
print("load complete!")
ckpt = tf.train.get_checkpoint_state(flow_pre_model_save_dir)
if ckpt and ckpt.model_checkpoint_path:
print("loading checkpoint %s,waiting......" %
ckpt.model_checkpoint_path)
flow_saver.restore(sess, ckpt.model_checkpoint_path)
print("load complete!")
all_steps = num_test_videos
top1_list = []
start_time_all = time.time()
for step in xrange(all_steps):
start_time = time.time()
s_index = 0
predicts = []
top1 = False
while True:
rgb_images, flow_images, val_labels, s_index, is_end = input_test.read_clip_and_label(
filename=file[step],
batch_size=FLAGS.batch_size * gpu_num,
s_index=s_index,
num_frames_per_clip=FLAGS.num_frame_per_clib,
crop_size=FLAGS.crop_size,
)
predict = sess.run(norm_score,
feed_dict={
rgb_images_placeholder: rgb_images,
flow_images_placeholder: flow_images,
labels_placeholder: val_labels,
is_training: False
})
predicts.append(
np.array(predict).astype(np.float32).reshape(FLAGS.classics))
# print ('predict', predict)
if is_end:
avg_pre = np.mean(predicts, axis=0).tolist()
# print ('avg_pred',avg_pre)
print(avg_pre.index(max(avg_pre)))
print('val_label', val_labels)
top1 = (avg_pre.index(max(avg_pre)) == val_labels)
top1_list.append(top1)
break
duration = time.time() - start_time
print('TOP_1_ACC in test: %f , time use: %.3f' % (top1, duration))
print(len(top1_list))
dur_time_all = time.time() - start_time_all
print('TOP_1_ACC in test_all: %f, time use: %.3f' %
(np.mean(top1_list), dur_time_all))
print("done")
