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test_2d.py
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test_2d.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 net, resnet, inception_v1, inception_v2, vgg ,SE,inception_v3
from model import resnet_attention , bn_inception , inception_resnet_v2 , SE
import os
import time
import numpy as np
import pickle
import ucf_2d,hmdb_2d
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', 8, 'the inputed batch size ')
tf.app.flags.DEFINE_integer ('gpu_nums', 2, 'the inputed batch size ')
tf.app.flags.DEFINE_float ('learning_rate', 1e-3, 'the learning rate of optimizer')
tf.app.flags.DEFINE_float ('weight_decay', 5e-4, 'the learning rate of optimizer')
tf.app.flags.DEFINE_integer ('training_step', 100000, 'the training steps of networks')
tf.app.flags.DEFINE_integer ('epoch', 100, 'the training epoches')
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_string ('lr_step', '[150,200]', 'epochs to decay learning rate by 10')
tf.app.flags.DEFINE_boolean ('is_training', True, 'is training or not')
tf.app.flags.DEFINE_integer ('split', 0, 'split number ')
tf.app.flags.DEFINE_float ('rgb_dr', 1.0, 'rgb dropout ratio')
tf.app.flags.DEFINE_float ('flow_dr', 1.0, 'flow dropout ratio')
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', 'inception_v2', 'use which model vgg,inception,resnet and so on')
tf.app.flags.DEFINE_string ('test_crop', 'center', 'use which test_crop center or multiscale')
tf.app.flags.DEFINE_integer ('new_length',10,'length for flow modality')
tf.app.flags.DEFINE_string ('preprocess_name', 'pytorch', 'use which model vgg,inception,resnet and so on')
Flags = tf.app.flags.FLAGS
tf.logging.set_verbosity (tf.logging.INFO)
rgb_dr = Flags.rgb_dr
flow_dr = Flags.flow_dr
split = Flags.split
use_pbn = Flags.use_pbn
num_segments = Flags.num_segments
eval_type = Flags.eval_type
lr_step = eval (Flags.lr_step)
gpu_nums = Flags.gpu_nums
# is_training = Flags.is_training
model_type = Flags.model_type
new_length = Flags.new_length
test_crop = Flags.test_crop
preprocess_name = Flags.preprocess_name
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,
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.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._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._IMAGE_SIZE, self._IMAGE_SIZE, new_length * 2])
else:
self.flow_train = None
# init learning rate
# 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_2d.ucf_dataset (split_number=split, is_training_split=False,new_length=new_length,
batch_size=self.batch_size, epoch=1,preprocess_name=preprocess_name,
eval_type=eval_type,frame_counts=self._FRAME_COUNTS,
image_size=self._IMAGE_SIZE,test_crop=test_crop,
prefetch_buffer_size=self.batch_size).test_dataset ()
elif dataset == 'hmdb51':
self.dataset = hmdb_2d.hmdb_dataset (split_number=split, is_training_split=False,
batch_size=self.batch_size, epoch=1,
eval_type=eval_type,test_crop=test_crop,
image_size=self._IMAGE_SIZE,
prefetch_buffer_size=self.batch_size).test_dataset ()
self.iter = self.dataset.make_initializable_iterator ()
self.next_element = self.iter.get_next ()
def init_type(self):
if model_type == 'resnet50':
self.base_net = 'resnet_v1_50'
if model_type == 'resnet50_attention':
self.base_net = 'resnet_v1_50_attention'
elif model_type == 'inception_v1':
self.base_net = 'inceptionv1'
elif model_type == 'resnet152':
self.base_net = 'resnet_v1_152'
elif model_type == 'resnet101':
self.base_net = 'resnet_v1_101'
elif model_type == 'inception_v2':
self.base_net = 'inceptionv2'
elif model_type == 'se_resnet50':
self.base_net = 'se_resnet50'
elif model_type == 'inception_v3':
self.base_net = 'inceptionv3'
elif model_type == 'inception_resnet_v2':
self.base_net = 'inception_resnet_v2'
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 = resnet.Resnet (num_classes=self._NUM_CLASSES, name='resnet_v1_50')
print ('resnet50 process successfully')
elif model_type == 'resnet50_attention':
self.rgb_model = resnet_attention.Resnet (num_classes=self._NUM_CLASSES, name='resnet_v1_50')
elif model_type == 'inception_v1':
self.rgb_model = inception_v1.InceptionV1 (num_classes=self._NUM_CLASSES)
elif model_type == 'vgg_16':
self.rgb_model = vgg.vgg (num_classes=self._NUM_CLASSES)
elif model_type == 'resnet101':
self.rgb_model = resnet.Resnet (num_classes=self._NUM_CLASSES, name='resnet_v1_101',
unit_num=[3, 4, 23, 3])
elif model_type == 'resnet152':
self.rgb_model = resnet.Resnet (num_classes=self._NUM_CLASSES, name='resnet_v1_152',
unit_num=[3, 8, 36, 3])
elif model_type == 'inception_v2':
self.rgb_model = inception_v2.InceptionV2 (num_classes=self._NUM_CLASSES)
elif model_type == 'se_resnet50':
self.rgb_model = SE.SE_Resnet(num_classes=self._NUM_CLASSES, name='resnet_v1_50')
elif model_type == 'inception_v3':
self.rgb_model = inception_v3.InceptionV3 (num_classes=self._NUM_CLASSES)
elif model_type == 'inception_resnet_v2':
self.rgb_model = inception_resnet_v2.InceptionResnetV2(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 = resnet.Resnet (num_classes=self._NUM_CLASSES, name='resnet_v1_50')
print ('resnet50 process successfully')
elif model_type == 'resnet50_attention':
self.flow_model = resnet_attention.Resnet (num_classes=self._NUM_CLASSES, name='resnet_v1_50')
elif model_type == 'inception_v1':
self.flow_model = inception_v1.InceptionV1 (num_classes=self._NUM_CLASSES)
elif model_type == 'vgg_16':
self.flow_model = vgg.vgg (num_classes=self._NUM_CLASSES)
elif model_type == 'resnet101':
self.flow_model = resnet.Resnet (num_classes=self._NUM_CLASSES, name='resnet_v1_101',
unit_num=[3, 4, 23, 3])
elif model_type == 'resnet152':
self.flow_model = resnet.Resnet (num_classes=self._NUM_CLASSES, name='resnet_v1_152',
unit_num=[3, 8, 36, 3])
elif model_type == 'inception_v2':
self.flow_model = inception_v2.InceptionV2 (num_classes=self._NUM_CLASSES)
elif model_type == 'se_resnet50':
self.flow_model = SE.SE_Resnet(num_classes=self._NUM_CLASSES, name='resnet_v1_50')
elif model_type == 'inception_v3':
self.flow_model = inception_v3.InceptionV3 (num_classes=self._NUM_CLASSES)
elif model_type == 'inception_resnet_v2':
self.flow_model = inception_resnet_v2.InceptionResnetV2(num_classes=self._NUM_CLASSES)
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 + flow_logits
return model_logits
def get_saver(self):
self.saver = tf.train.Saver (max_to_keep=15, reshape=True)
def train(self):
# if gpu_nums == 1:
# self.model_logits = self.train_model (self.rgb_train,self.flow_train)
# else:
# self.model_logits = self.Multigpu_train(self.train_model,gpu_nums,self.rgb_train,self.flow_train)
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.sess = tf.Session (config=tf.ConfigProto (gpu_options=gpu_options,allow_soft_placement=True))
self.sess.run (self.init_op)
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):
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.iter.initializer)
feed_dict = {}
test_batch_accuracy = []
valid_accuracy = []
k = 1
video_predict = []
while True:
try:
t = time.time()
if eval_type == 'rgb':
test_rgb, test_label = self.sess.run (self.next_element)
test_len = test_rgb.shape[1]
elif eval_type == 'flow':
test_flow, test_label = self.sess.run (self.next_element)
test_len = test_flow.shape[1]
else:
test_rgb, test_flow, test_label = self.sess.run (self.next_element)
test_len = test_rgb.shape[1]
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 = self.sess.run(self.model_predictions,feed_dict=feed_dict)
out_logits = np.sum(out_logits,axis=0)
logits.append(out_logits)
end_t = time.time()
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))
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,
learning_rate=Flags.learning_rate,
TRAINING_STEP=Flags.training_step,
epoch=Flags.epoch,
dataset=Flags.dataset,
reboot=Flags.reboot)
m.main ()
if __name__ == '__main__':
tf.app.run (main)