读取保存有多个样例的TFRecord文件时报错：
InvalidArgumentError (see above for traceback): Input to reshape is a tensor with 14410143 values, but the requested shape has 230400 [[Node: Reshape = Reshape[T=DT_UINT8, Tshape=DT_INT32, _device="/job:localhost/replica:0/task:0/device:CPU:0"](DecodeRaw, Reshape/shape)]]
从报错信息来看，与reshape有关。
先生成保存有两张图片的TFRecord文件
1 #!coding:utf8
2
3 import tensorflow as tf
4 import numpy as np
5 from PIL import Image
6
7 INPUT_DATA = [‘/home/error/tt/cat.jpg‘, ‘/home/error/tt/5605502523_05acb00ae7_n.jpg‘] # 输入文件
8 OUTPUT_DATA = ‘/home/error/tt.tfrecords‘ # 输出文件
9
10 def _int64_feature(value):
11 return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))
12
13
14 def _bytes_feature(value):
15 return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
16
17
18 def create_image_lists(sess):
19 writer1 = tf.python_io.TFRecordWriter(OUTPUT_DATA)
20
21 # 处理图片数据
22 for f in INPUT_DATA:
23 print(f)
24 image = Image.open(f)
25 image_value = np.asarray(image, np.uint8)
26
27 height, width, channles = image_value.shape
28 print(height, width, channles, height*width*channles)
29 label = 0
30
31 example = tf.train.Example(features=tf.train.Features(feature={
32 ‘name‘: _bytes_feature(f.encode(‘utf8‘)),
33 ‘image‘: _bytes_feature(image_value.tostring()),
34 ‘label‘: _int64_feature(label),
35 ‘height‘: _int64_feature(height),
36 ‘width‘: _int64_feature(width),
37 ‘channels‘: _int64_feature(channles)
38 }))
39 serialized_example = example.SerializeToString()
40 writer1.write(serialized_example)
41
42 writer1.close()
43
44
45 with tf.Session() as sess:
46 create_image_lists(sess)
47
48
49 # /home/error/tt/cat.jpg
50 # 1797 2673 3 14410143
51 # /home/error/tt/5605502523_05acb00ae7_n.jpg
52 # 240 320 3 230400
错误读取：
1 #!coding:utf8
2 import tensorflow as tf
3 import matplotlib.pyplot as plt
4 import numpy as np
5
6 OUTPUT_DATA = ‘/home/error/tt.tfrecords‘ # 输出文件
7 train_queue = tf.train.string_input_producer([OUTPUT_DATA])
8
9
10 def read_file(file_queue, sess):
11 reader = tf.TFRecordReader()
12 _, serialized_example = reader.read(file_queue)
13 features = tf.parse_single_example(
14 serialized_example,
15 features={
16 ‘image‘: tf.FixedLenFeature([], tf.string),
17 ‘label‘: tf.FixedLenFeature([], tf.int64),
18 ‘height‘: tf.FixedLenFeature([], tf.int64),
19 ‘width‘: tf.FixedLenFeature([], tf.int64),
20 ‘channels‘: tf.FixedLenFeature([], tf.int64),
21 })
22
23 image, label = features[‘image‘], features[‘label‘]
24 height, width = features[‘height‘], features[‘width‘]
25 channels = features[‘channels‘]
26 decoded_image = tf.decode_raw(image, tf.uint8)
27
28
29 print(sess.run(decoded_image).shape) # (14410143,)
30 # 为啥打印时，会影响reshape?? sess.run造成的，因为可视化时也会出现这个问题，但不知道原因;
31 # 原因是执行sess.run()时会从队列中重新取一个样例导致样例不同。
32
33 height_val, width_val, channels_val = sess.run([height, width, channels])
34 print(height_val, width_val, channels_val, height_val*width_val*channels_val) # 240 320 3 230400
35 reshaped_decoded_image = tf.reshape(decoded_image, [height_val, width_val, channels_val])
36 # print(reshaped_decoded_image.shape) # (240, 320, 3)
37
38 reshaped_decoded_image_val = sess.run(reshaped_decoded_image) # reshape时不会报错，当执行运算时才会报错
39 # plt.imshow(sess.run(reshaped_decoded_image))
40 # plt.show()
41
42 with tf.Session() as sess:
43 tf.local_variables_initializer().run()
44
45 coord = tf.train.Coordinator()
46 threads = tf.train.start_queue_runners(sess=sess, coord=coord)
47
48 for _ in range(2):
49 read_file(train_queue, sess)
50
51 coord.request_stop()
52 coord.join(threads)
53
54 # 从保存有多个样例的tfrecord文件中读取数据会报错
55 # 报错日志：
56 # InvalidArgumentError (see above for traceback): Input to reshape is a tensor with 14410143 values, but the requested shape has 230400
57 # [[Node: Reshape = Reshape[T=DT_UINT8, Tshape=DT_INT32, _device="/job:localhost/replica:0/task:0/device:CPU:0"](DecodeRaw, Reshape/shape)]]
正确读取：
#!coding:utf8
import tensorflow as tf
import matplotlib.pyplot as plt
import numpy as np
OUTPUT_DATA = ‘/home/error/tt.tfrecords‘ # 输出文件
train_queue = tf.train.string_input_producer([OUTPUT_DATA])
def read_file(file_queue, sess):
reader = tf.TFRecordReader()
_, serialized_example = reader.read(file_queue)
features = tf.parse_single_example(
serialized_example,
features={
‘image‘: tf.FixedLenFeature([], tf.string),
‘label‘: tf.FixedLenFeature([], tf.int64),
‘height‘: tf.FixedLenFeature([], tf.int64),
‘width‘: tf.FixedLenFeature([], tf.int64),
‘channels‘: tf.FixedLenFeature([], tf.int64),
})
image, label = features[‘image‘], features[‘label‘]
height, width = features[‘height‘], features[‘width‘]
channels = features[‘channels‘]
decoded_image = tf.decode_raw(image, tf.uint8)
return decoded_image, label, height, width, channels
# decoded_image_val, label_val, height_val, width_val, channels_val = sess.run([decoded_image, label, height, width, channels])
# # print(decoded_image.shape) # (230400,)
# # 为啥打印时，会影响reshape?? sess.run造成的，因为可视化时也会出现这个问题，但不知道原因; 原因是会从队列中重新取一个样例导致样例不同。
with tf.Session() as sess:
tf.local_variables_initializer().run()
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
decoded_image, label, height, width, channels = read_file(train_queue, sess)
for _ in range(6):
# 迭代`sess.run`本身，能够保证在同一时刻处理的是同一个样例
decoded_image_val, label_val, height_val, width_val, channels_val = sess.run([decoded_image, label, height, width, channels])
print(height_val)
reshaped_decoded_image = np.reshape(decoded_image_val, [height_val, width_val, channels_val])
plt.imshow(reshaped_decoded_image)
plt.show()
coord.request_stop()
coord.join(threads)
InvalidArgumentError (see above for traceback): Input to reshape is a tensor with 14410143 values, but the requested shape has 230400
[[Node: Reshape = Reshape[T=DT_UINT8, Tshape=DT_INT32, _device="/job:localhost/replica:0/task:0/device:CPU:0"](DecodeRaw, Reshape/shape)]]
原文地址：https://www.cnblogs.com/yangxiaoling/p/9629831.html
时间： 2024-10-08 18:29:35