使用QThread在Tensorflow上出现图形错误，我正在使用这段代码https://github.com/FingerRec/real_time_video_action_recognition.git在TF中运行人类动作识别，代码可以正常工作，但是我在尝试对其进行线程处理时遇到问题
我试图从主线程中分离推理代码以提高速度。
这是代码
"""
build c3d model
:return:
norm_score:
sess:
"""
#model_name = "pretrained_model/c3d_ucf101_finetune_whole_iter_20000_TF.model.mdlp"
#model_name = "pretrained_model/conv3d_deepnetA_sport1m_iter_1900000_TF.model"
model_name = "pretrained_model/sports1m_finetuning_ucf101.model"
# model_name = "new_model2/c3d_ucf_model-499"
# Get the sets of images and labels for training, validation, and
with tf.variable_scope('var_name') as var_scope:
weights = {
'wc1': _variable_with_weight_decay('wc1', [3, 3, 3, 3, 64], 0.04, 0.00),
'wc2': _variable_with_weight_decay('wc2', [3, 3, 3, 64, 128], 0.04, 0.00),
'wc3a': _variable_with_weight_decay('wc3a', [3, 3, 3, 128, 256], 0.04, 0.00),
'wc3b': _variable_with_weight_decay('wc3b', [3, 3, 3, 256, 256], 0.04, 0.00),
'wc4a': _variable_with_weight_decay('wc4a', [3, 3, 3, 256, 512], 0.04, 0.00),
'wc4b': _variable_with_weight_decay('wc4b', [3, 3, 3, 512, 512], 0.04, 0.00),
'wc5a': _variable_with_weight_decay('wc5a', [3, 3, 3, 512, 512], 0.04, 0.00),
'wc5b': _variable_with_weight_decay('wc5b', [3, 3, 3, 512, 512], 0.04, 0.00),
'wd1': _variable_with_weight_decay('wd1', [8192, 4096], 0.04, 0.001),
'wd2': _variable_with_weight_decay('wd2', [4096, 4096], 0.04, 0.002),
'out': _variable_with_weight_decay('wout', [4096, c3d_model.NUM_CLASSES], 0.04, 0.005)
}
biases = {
'bc1': _variable_with_weight_decay('bc1', [64], 0.04, 0.0),
'bc2': _variable_with_weight_decay('bc2', [128], 0.04, 0.0),
'bc3a': _variable_with_weight_decay('bc3a', [256], 0.04, 0.0),
'bc3b': _variable_with_weight_decay('bc3b', [256], 0.04, 0.0),
'bc4a': _variable_with_weight_decay('bc4a', [512], 0.04, 0.0),
'bc4b': _variable_with_weight_decay('bc4b', [512], 0.04, 0.0),
'bc5a': _variable_with_weight_decay('bc5a', [512], 0.04, 0.0),
'bc5b': _variable_with_weight_decay('bc5b', [512], 0.04, 0.0),
'bd1': _variable_with_weight_decay('bd1', [4096], 0.04, 0.0),
'bd2': _variable_with_weight_decay('bd2', [4096], 0.04, 0.0),
'out': _variable_with_weight_decay('bout', [c3d_model.NUM_CLASSES], 0.04, 0.0),
}
logits = []
# tf.reset_default_graph()
for gpu_index in range(0, gpu_num):
with tf.device('/gpu:%d' % gpu_index):
logit = c3d_model.inference_c3d(
images_placeholder[0 * FLAGS.batch_size:(0 + 1) * FLAGS.batch_size,:,:,:,:], 0.6,
FLAGS.batch_size, weights, biases)
logits.append(logit)
logits = tf.concat(logits, 0)
norm_score = tf.nn.softmax(logits)
saver = tf.train.Saver()
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
init = tf.global_variables_initializer()
sess.run(init)
# Create a saver for writing training checkpoints.
saver.restore(sess, model_name)
# saver = tf.train.import_meta_graph('pretrained_model/c3d_ucf_model-9.meta')
# saver.restore(sess, 'pretrained_model/c3d_ucf_moel-9')
return norm_score, sess
class DetectionThread(QThread):
signal = pyqtSignal(tuple)
def __init__(self):
super(DetectionThread, self).__init__()
self._stopped = True
# self.img = None
self.camera_name = ''
self.video_imgs = list()
self.prediction_result = tuple()
def __del__(self):
self.wait()
def stop(self):
self._stopped = True
# run method gets called when we start the thread
def run(self):
# self._stopped = False
# ''' actual prediction code'''
norm_score, sess = build_c3d_model()
这是我得到的结果
ValueError: Tensor("var_name/wc1:0", shape=(3, 3, 3, 3, 64), dtype=float32_ref, device=/device:CPU:0) must be from the same graph as Tensor("strided_slice:0", shape=(1, 16, 112, 112, 3), dtype=float32).
Aborted (core dumped)
我需要帮助对该代码进行线程化，谢谢