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test_ts_resnet.py
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test_ts_resnet.py
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from __future__ import print_function
from __future__ import absolute_import
from __future__ import division
import tensorflow as tf
from model import i3d , Res3D , TS_resnet , ts_inception_v2 , ts_inception_v3 , ts_inception_v1 , ts_SE
import os
import time
import numpy as np
import pickle
import ucf_ts,hmdb_ts
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
gpu_options = tf.GPUOptions (per_process_gpu_memory_fraction=0.98, allow_growth=True)
file_save_path = '/mnt/zhujian/action_recognition'
log_dir = 'log_dir/'
tf.app.flags.DEFINE_string ('video_dir', './UCF-101/', 'the orginal and optical video directory')
tf.app.flags.DEFINE_integer ('image_size', 224, 'the uniform input video size')
tf.app.flags.DEFINE_integer ('num_classes', 101, 'the classes number of the video dataset')
tf.app.flags.DEFINE_integer ('frame_counts', 25, 'the input video frame counts')
tf.app.flags.DEFINE_integer ('batch_size', 1, 'the inputed batch size ')
tf.app.flags.DEFINE_integer ('gpu_nums', 2, 'the inputed batch size ')
tf.app.flags.DEFINE_string ('dataset', 'UCF101', 'the training dataset')
tf.app.flags.DEFINE_string ('eval_type', 'rgb', 'rgb flow or joint')
tf.app.flags.DEFINE_boolean ('reboot', True, 'reboot traing process if True else False')
tf.app.flags.DEFINE_integer ('split', 0, 'split number ')
tf.app.flags.DEFINE_integer ('num_segments', 3, 'num segments for TSN')
tf.app.flags.DEFINE_boolean ('use_pbn', False, 'use partial batch norm')
tf.app.flags.DEFINE_string ('model_type', 'resnet50', 'use which model vgg,inception,resnet and so on')
tf.app.flags.DEFINE_integer('video_split',10,'video split')
tf.app.flags.DEFINE_string ('test_crop', 'center', 'use which test_crop center or multiscale')
tf.app.flags.DEFINE_integer ('new_length', 10, 'optical flow length for TSN')
Flags = tf.app.flags.FLAGS
tf.logging.set_verbosity (tf.logging.INFO)
video_split = Flags.video_split
split = Flags.split
use_pbn = Flags.use_pbn
num_segments = Flags.num_segments
eval_type = Flags.eval_type
gpu_nums = Flags.gpu_nums
reboot = Flags.reboot
model_type = Flags.model_type
test_crop = Flags.test_crop
new_length = Flags.new_length
print(model_type)
if test_crop == 'multi':
factor = 10
else:
factor = 2
class model ():
def __init__(self,
video_dir='./UCF-101/',
image_size=224,
num_classes=101,
frame_counts=64,
batch_size=3,
learning_rate=1e-4,
num_segments=3,
TRAINING_STEP=10000,
epoch=10,
video_split = 10,
dataset='UCF101',
reboot=False):
self._video_dir = video_dir
self._IMAGE_SIZE = image_size
self._NUM_CLASSES = num_classes
self._FRAME_COUNTS = frame_counts
self.batch_size = batch_size
self.learning_rate = learning_rate
self.TRAINING_STEP = TRAINING_STEP
self.num_segments = num_segments
self.epoch = epoch
self.dataset_name = dataset
self.reboot = reboot
self.video_split = video_split
self.init_config ()
self.init_type ()
self.init_dataset ()
self.init_model ()
self.train ()
self._init_sess ()
self._restore_model ()
def init_config(self):
if eval_type in ['rgb', 'joint']:
self.rgb_train = tf.placeholder (tf.float32,
[None,self._FRAME_COUNTS * factor, self._IMAGE_SIZE, self._IMAGE_SIZE, 3])
else:
self.rgb_train = None
if eval_type in ['flow', 'joint']:
self.flow_train = tf.placeholder (tf.float32,
[None, self._FRAME_COUNTS * factor, self._IMAGE_SIZE, self._IMAGE_SIZE, new_length * 2])
else:
self.flow_train = None
# init label
self.y_ = tf.placeholder (tf.float32, [None, self._NUM_CLASSES])
def init_dataset(self):
dataset = Flags.dataset
if dataset == 'UCF101':
self.dataset = ucf_ts.ucf_dataset (split_number=split, is_training_split=False,
batch_size=self.batch_size, epoch=1,test_crop=test_crop,
eval_type=eval_type,video_split=self.video_split,new_length=new_length,
image_size=self._IMAGE_SIZE,frame_counts=self._FRAME_COUNTS,
prefetch_buffer_size=self.batch_size).test_dataset ()
elif dataset == 'hmdb51':
self.dataset = hmdb_ts.hmdb_dataset (split_number=split, is_training_split=False,
batch_size=self.batch_size, epoch=1,test_crop=test_crop,
eval_type=eval_type,video_split=self.video_split,new_length=new_length,
image_size=self._IMAGE_SIZE,frame_counts=self._FRAME_COUNTS,
prefetch_buffer_size=self.batch_size).test_dataset ()
iter = tf.data.Iterator.from_structure (self.dataset.output_types, self.dataset.output_shapes)
self.next_element = iter.get_next ()
self.training_init_op = iter.make_initializer (self.dataset)
def init_type(self):
if model_type == 'resnet50':
self.base_net = 'TS_resnet50'
elif model_type == 'se_resnet50':
self.base_net = 'ts_se_resnet50'
elif model_type == 'resnet101':
self.base_net = 'ts_resnet101'
elif model_type == 'inception_v1':
self.base_net = 'ts_inception_v1'
elif model_type == 'inception_v3':
self.base_net = 'ts_inceptionv3'
def init_model(self):
if eval_type in ['rgb', 'joint']:
with tf.variable_scope ('RGB', reuse=tf.AUTO_REUSE):
if model_type == 'resnet50':
self.rgb_model = TS_resnet.Resnet(num_classes=self._NUM_CLASSES,final_endpoint='logits',name='resnet_v1_50')
if model_type == 'se_resnet50':
self.rgb_model = ts_SE.SE_Resnet(num_classes=self._NUM_CLASSES,final_endpoint='logits',name='resnet_v1_50')
elif model_type == 'resnet101':
self.rgb_model = TS_resnet.Resnet(num_classes=self._NUM_CLASSES,final_endpoint='logits',name='resnet_v1_101',unit_num=[3, 4, 23, 3])
elif model_type == 'inception_v1':
self.rgb_model = ts_inception_v1.InceptionV1(num_classes=self._NUM_CLASSES)
elif model_type == 'inception_v3':
self.rgb_model = ts_inception_v3.InceptionV3(num_classes=self._NUM_CLASSES)
if eval_type in ['flow', 'joint']:
with tf.variable_scope ('Flow', reuse=tf.AUTO_REUSE):
if model_type == 'resnet50':
self.flow_model = TS_resnet.Resnet(num_classes=self._NUM_CLASSES,final_endpoint='logits',name='resnet_v1_50')
if model_type == 'se_resnet50':
self.flow_model = ts_SE.SE_Resnet(num_classes=self._NUM_CLASSES,final_endpoint='logits',name='resnet_v1_50')
if model_type == 'resnet101':
self.flow_model = TS_resnet.Resnet(num_classes=self._NUM_CLASSES,final_endpoint='logits',name='resnet_v1_101',unit_num=[3, 4, 23, 3])
elif model_type == 'inception_v1':
self.flow_model = ts_inception_v1.InceptionV1(num_classes=self._NUM_CLASSES)
elif model_type == 'inception_v3':
self.flow_model = ts_inception_v3.InceptionV3(num_classes=self._NUM_CLASSES)
@staticmethod
def Multigpu_train(model_fn,num_gpus,rgb_input,flow_input):
in_splits = {}
in_splits['rgb'] = tf.split(rgb_input,num_gpus) if rgb_input is not None else None
in_splits['flow'] = tf.split(flow_input,num_gpus) if flow_input is not None else None
out_split = []
for i in range(num_gpus):
if tf.test.is_built_with_cuda():
device_type = 'GPU'
else:
device_type = 'CPU'
with tf.device(tf.DeviceSpec(device_type=device_type,device_index=i)):
with tf.variable_scope(tf.get_variable_scope(),reuse=tf.AUTO_REUSE):
if in_splits['flow'] is None:
out_split.append(model_fn(in_splits['rgb'][i],None))
elif in_splits['rgb'] is None:
out_split.append(model_fn(None,in_splits['flow'][i]))
else:
out_split.append (model_fn (in_splits['rgb'][i], in_splits['flow'][i]))
tf.get_variable_scope().reuse_variables()
out = tf.concat(out_split,axis=0)
return out
def train_model(self,rgb_input,flow_input):
if eval_type in ['rgb', 'joint'] and rgb_input is not None:
rgb_logits, rgb_endpoints = self.rgb_model (rgb_input, is_training=False,
dropout_keep_prob=1.0)
if eval_type in ['flow', 'joint'] and flow_input is not None:
flow_logits, flow_endpoints = self.flow_model (flow_input, is_training=False,
dropout_keep_prob=1.0)
if eval_type == 'rgb':
model_logits = rgb_logits
elif eval_type == 'flow':
model_logits = flow_logits
elif eval_type == 'joint':
model_logits = rgb_logits + 1.5 * flow_logits
return model_logits
def get_saver(self):
try:
self.rgb_saver = tf.train.Saver(reshape=True,var_list=tf.global_variables(scope='RGB'))
except:
print('no rgb modality')
try:
self.flow_saver = tf.train.Saver(reshape=True,var_list=tf.global_variables(scope='Flow'))
except:
print('no flow modality')
self.saver = tf.train.Saver (max_to_keep=15, reshape=True)
def train(self):
self.model_logits = self.train_model (self.rgb_train,self.flow_train)
self.get_saver()
self.model_predictions = tf.nn.softmax (self.model_logits)
correct_prediction = tf.equal (tf.argmax (self.model_predictions, 1), tf.argmax (self.y_, 1))
self.train_accuracy = tf.reduce_mean (tf.cast (correct_prediction, tf.float32))
def _init_sess(self):
self.init_op = tf.global_variables_initializer ()
self.merge_summary = tf.summary.merge_all ()
self.sess = tf.Session (config=tf.ConfigProto (gpu_options=gpu_options))
self.sess.run (self.init_op)
log_path = os.path.join (file_save_path, log_dir, self.dataset_name, str (split), self.base_net,
eval_type)
if os.path.exists (log_path) is False:
os.makedirs (log_path)
self.train_writer = tf.summary.FileWriter (
log_path, self.sess.graph)
self.train_log_path = os.path.join (log_dir, self.dataset_name, str (split), self.base_net, eval_type)
if os.path.exists (self.train_log_path) is False:
os.makedirs (self.train_log_path)
def _restore_model(self):
if reboot and eval_type == 'joint':
rgb_path = os.path.join ('/mnt/zhujian/action_recognition/two_stream/', self.dataset_name, str (split),
self.base_net,'rgb')
rgb_ckpt = tf.train.get_checkpoint_state(rgb_path)
if rgb_ckpt is not None:
self.rgb_saver.restore(self.sess,rgb_ckpt.model_checkpoint_path)
print('rgb reboot successfully')
flow_path = os.path.join ('/mnt/zhujian/action_recognition/two_stream/', self.dataset_name, str (split),
self.base_net,'flow')
flow_ckpt = tf.train.get_checkpoint_state(flow_path)
if flow_ckpt is not None:
self.flow_saver.restore(self.sess,flow_ckpt.model_checkpoint_path)
print('flow reboot successfully')
else:
path = os.path.join ('/mnt/zhujian/action_recognition/two_stream/', self.dataset_name, str (split),
self.base_net,eval_type)
ckpt = tf.train.get_checkpoint_state (path)
print(ckpt)
if ckpt is not None:
self.saver.restore (self.sess, ckpt.model_checkpoint_path)
def one_epoch_test(self, epoch):
self.sess.run (self.training_init_op)
feed_dict = {}
test_batch_accuracy = []
valid_accuracy = []
video_predict = []
k = 1
while True:
try:
t = time.time()
if eval_type == 'rgb':
test_rgb, test_label = self.sess.run (self.next_element)
test_rgb = np.reshape(test_rgb,[self.batch_size,1,-1,224,224,3])
elif eval_type == 'flow':
test_flow, test_label = self.sess.run (self.next_element)
test_flow = np.reshape(test_flow,[self.batch_size,1,-1,224,224,20])
else:
test_rgb, test_flow, test_label = self.sess.run (self.next_element)
test_rgb = np.reshape(test_rgb,[self.batch_size,1,-1,224,224,3])
test_flow = np.reshape(test_flow,[self.batch_size,1,-1,224,224,20])
logits = []
feed_dict[self.y_] = test_label
duratio = time.time() - t
for i in range(self.batch_size):
if eval_type in ['rgb','joint']:
feed_dict[self.rgb_train] = test_rgb[i,...]
if eval_type in ['flow','joint']:
feed_dict[self.flow_train] = test_flow[i,...]
out_logits = self.sess.run(self.model_logits,feed_dict=feed_dict)
# out_logits = sess.run([model_predictions],feed_dict=feed_dict)
out_logits = np.sum(out_logits,axis=0)
logits.append(out_logits)
logits = np.array(logits)
for i in range(logits.shape[0]):
video_predict.append((test_label[i],logits[i]))
out_pred = (np.equal (np.argmax (logits, 1), np.argmax (test_label, 1))).sum() / self.batch_size
test_batch_accuracy.append(out_pred)
end_t = time.time() - t
print ('after %d step with %.2fs duratio,the test accuracy is %f,use time %.2fs' %
(k, duratio,np.mean (np.array (test_batch_accuracy)),end_t - duratio))
k +=1
except tf.errors.OutOfRangeError:
with open(os.path.join(self.train_log_path,'test_result_%s.txt' % test_crop),'a') as f:
f.write(str(np.mean (np.array (test_batch_accuracy)))+'\n')
with open(os.path.join(self.train_log_path,'video_predict_%s.pickle' % test_crop),'wb') as f:
pickle.dump(video_predict,f)
print('video predict result save successfully')
break
def main(self):
self.one_epoch_test (0)
def main(argv):
m = model (video_dir=Flags.video_dir,
image_size=Flags.image_size,
num_classes=Flags.num_classes,
frame_counts=Flags.frame_counts,
batch_size=Flags.batch_size,
video_split=Flags.video_split,
dataset=Flags.dataset,
reboot=Flags.reboot)
m.main ()
if __name__ == '__main__':
tf.app.run (main)