我将使用以下代码进行测试，以在实时网络摄像头上测试基于Tensorflow的E-Net。
但是，在第78行代码中会出现占位符错误。
如果使用以下代码对图像进行前缀处理，则占位符将发生错误。
该实验的视频文件是（480，360）尺寸的彩色图像（360,480,3）和MJPG编解码器的avi文件。
占位符是什么？ 我该怎么解决？
请帮助我。
这是错误代码。
True
HERE (1, 360, 480)
WARNING:tensorflow:From predict_video.py:102: Supervisor.__init__ (from tensorflow.python.training.supervisor) is deprecated and will be removed in a future version.
Instructions for updating:
Please switch to tf.train.MonitoredTrainingSession
2018-09-13 14:11:41.510826: I c:\users\user\source\repos\tensorflow\tensorflow\core\platform\cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2
2018-09-13 14:11:42.140975: I c:\users\user\source\repos\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:1356] Found device 0 with properties:
name: GeForce GTX 960M major: 5 minor: 0 memoryClockRate(GHz): 1.176
pciBusID: 0000:01:00.0
totalMemory: 4.00GiB freeMemory: 3.34GiB
2018-09-13 14:11:42.162310: I c:\users\user\source\repos\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:1435] Adding visible gpu devices: 0
2018-09-13 14:11:43.001599: I c:\users\user\source\repos\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:923] Device interconnect StreamExecutor with strength 1 edge matrix:
2018-09-13 14:11:43.013639: I c:\users\user\source\repos\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:929] 0
2018-09-13 14:11:43.020308: I c:\users\user\source\repos\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:942] 0: N
2018-09-13 14:11:43.029139: I c:\users\user\source\repos\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:1053] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 3075 MB memory) -> physical GPU (device: 0, name: GeForce GTX 960M, pci bus id: 0000:01:00.0, compute capability: 5.0)
WARNING:tensorflow:Standard services need a 'logdir' passed to the SessionManager
Traceback (most recent call last):
File "C:\Users\zmqp1\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\client\session.py", line 1322, in _do_call
return fn(*args)
File "C:\Users\zmqp1\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\client\session.py", line 1307, in _run_fn
options, feed_dict, fetch_list, target_list, run_metadata)
File "C:\Users\zmqp1\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\client\session.py", line 1409, in _call_tf_sessionrun
run_metadata)
**tensorflow.python.framework.errors_impl.InvalidArgumentError: You must feed a value for placeholder tensor 'Placeholder' with dtype float and shape [360,480,3]**
[[Node: Placeholder = Placeholder[dtype=DT_FLOAT, shape=[360,480,3], _device="/job:localhost/replica:0/task:0/device:GPU:0"]()]]
[[Node: Placeholder/_2837 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_4_Placeholder", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "predict_video.py", line 113, in <module>
print('~~~~~~~~~~~~~~',sess.run(image_tensor))
File "C:\Users\zmqp1\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\client\session.py", line 900, in run
run_metadata_ptr)
File "C:\Users\zmqp1\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\client\session.py", line 1135, in _run
feed_dict_tensor, options, run_metadata)
File "C:\Users\zmqp1\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\client\session.py", line 1316, in _do_run
run_metadata)
File "C:\Users\zmqp1\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\client\session.py", line 1335, in _do_call
raise type(e)(node_def, op, message)
**tensorflow.python.framework.errors_impl.InvalidArgumentError: You must feed a value for placeholder tensor 'Placeholder' with dtype float and shape [360,480,3]**
[[Node: Placeholder = Placeholder[dtype=DT_FLOAT, shape=[360,480,3], _device="/job:localhost/replica:0/task:0/device:GPU:0"]()]]
[[Node: Placeholder/_2837 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_4_Placeholder", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]
Caused by op 'Placeholder', defined at:
File "predict_video.py", line 78, in <module>
image_tensor = tf.placeholder(tf.float32, shape=[H,W,3])
File "C:\Users\zmqp1\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\ops\array_ops.py", line 1808, in placeholder
return gen_array_ops.placeholder(dtype=dtype, shape=shape, name=name)
File "C:\Users\zmqp1\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\ops\gen_array_ops.py", line 5835, in placeholder
"Placeholder", dtype=dtype, shape=shape, name=name)
File "C:\Users\zmqp1\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\framework\op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "C:\Users\zmqp1\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\framework\ops.py", line 3392, in create_op
op_def=op_def)
File "C:\Users\zmqp1\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\framework\ops.py", line 1718, in __init__
self._traceback = self._graph._extract_stack() # pylint: disable=protected-access
**InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'Placeholder' with dtype float and shape [360,480,3]**
[[Node: Placeholder = Placeholder[dtype=DT_FLOAT, shape=[360,480,3], _device="/job:localhost/replica:0/task:0/device:GPU:0"]()]]
[[Node: Placeholder/_2837 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_4_Placeholder", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]
这是我的代码。
import tensorflow as tf
import os
import matplotlib.pyplot as plt
from enet import ENet, ENet_arg_scope
from preprocessing import preprocess
from scipy.misc import imsave
import numpy as np
import cv2
import time
slim = tf.contrib.slim
os.environ["CUDA_VISIBLE_DEVICES"]="0"
W = 480
H = 360
batch_size = 1
num_classes = 7
video_dir = './data/'
fname = 'bottom.avi'
checkpoint_dir = "./log/original/"
checkpoint = tf.train.latest_checkpoint(checkpoint_dir)
cap = cv2.VideoCapture(video_dir+fname)
print(cap.isOpened())
num_initial_blocks = 1
skip_connections = False
stage_two_repeat = 2
label_to_colours = {0: [0,255,0],
1: [0,255,255],
2: [0,0,255],
3: [0,150,150],
4: [255,255,0],
5: [255,0,0],
6: [255,255,255]}
#Create the photo directory
photo_dir = checkpoint_dir + "/test_images"
if not os.path.exists(photo_dir):
os.mkdir(photo_dir)
def vis_segmentation(image, seg_map):
"""Visualizes input image, segmentation map and overlay view."""
image_width, image_height = image.size
colored_label = label_to_color_image(seg_map).astype(np.uint8)
image_empty = np.zeros((image_height,2*image_width,3),np.uint8)
image_empty[:image_height,:image_width] = image.copy()
image_empty[:image_height,image_width:] = colored_label.copy()
image_empty[:image_height,:image_width] = image.copy()
image_empty[:image_height,image_width:] = colored_label.copy()
alpha = 0.35
beta = 1-alpha
gamma = 0
img_add = cv2.addWeighted(np.array(image), alpha, seg_map, beta, gamma)
return img_add
#Create a function to convert each pixel label to colour.
def grayscale_to_colour(image):
print('Converting image...')
image = image.reshape((H, W, 1))
image = np.repeat(image, 3, axis=-1)
for i in range(image.shape[0]):
for j in range(image.shape[1]):
label = int(image[i][j][0])
image[i][j] = np.array(label_to_colours[label])
return image
def model_run(image):
return predictions
with tf.Graph().as_default() as graph:
image_tensor = tf.placeholder(tf.float32, shape=[H,W,3])
#image_tensor = tf.reshape(image_tensor, shape=(H,W,3))
images = tf.expand_dims(image_tensor, 0)
#Create the model inference
with slim.arg_scope(ENet_arg_scope()):
logits, probabilities = ENet(images,
num_classes=num_classes,
batch_size=batch_size,
is_training=False,
reuse=None,
num_initial_blocks=num_initial_blocks,
stage_two_repeat=stage_two_repeat,
skip_connections=skip_connections)
variables_to_restore = slim.get_variables_to_restore()
saver = tf.train.Saver(variables_to_restore)
def restore_fn(sess):
return saver.restore(sess, checkpoint)
predictions = tf.argmax(probabilities, -1)
predictions = tf.cast(predictions, tf.float32)
print('HERE', predictions.get_shape())
sv = tf.train.Supervisor(logdir=None, init_fn=restore_fn)
with sv.managed_session() as sess:
now = 0.0
while(cap.isOpened()):
ret, frame = cap.read()
if (ret == False):
print('~~~~~~~~~~~~~~~~~~did not get any frame~~~~~~~~~~~~~~~~~~')
break
image = frame.copy()
image = np.asarray(image, np.float32)/255
print('~~~~~~~~~~~~~~',sess.run(image_tensor))
segmentations = sess.run(predictions, feed_dict={image_tensor:image})
cv2.imshow('pre',segmentations[0])
cv2.waitKey(0)
#T = time.time() - now
#print(int(1/T))
#now = time.time()
cap.release()
cv2.destroyAllWindows()
答案 0 :(得分：0)
错误是您违反了在TensorFlow中使用占位符的基本原则。
占位符用于填充，不像其他操作那样进行评估。
清除错误代码很容易，因为它会转到代码中的以下行：
print('~~~~~~~~~~~~~~',sess.run(image_tensor))
这始终是不一致的，因为您可以直接打印image。