我遇到了另一个无效的参数错误而且不确定这次是什么原因。
我用形状[299,299]的图像(我知道的混合扩展)创建了一个TFRecord。
我试图分批加载图片,但我遇到了这个错误:
'InvalidArgumentError (see above for traceback): Input to reshape is a tensor with 178802 values, but the requested shape has 89401
[[Node: Reshape = Reshape[T=DT_FLOAT, Tshape=DT_INT32, _device="/job:localhost/replica:0/task:0/cpu:0"](DecodeRaw, Reshape/shape)]]
这是我的代码:
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import os
IMAGE_DIR =r'C:\Users\Moondra\Desktop\TF_FISH_PROJECT\FINAL_FISHES'
data_path = r'E:\TFRECORDS\normal_fish_conversion_2.tfrecords'
with tf.Session() as sess:
feature = {'train/image': tf.FixedLenFeature([], tf.string),
'train/label': tf.FixedLenFeature([], tf.int64),
'rows': tf.FixedLenFeature([], tf.int64),
'columns': tf.FixedLenFeature([], tf.int64)}
# Create a list of filenames and pass it to a queue
filename_queue = tf.train.string_input_producer([data_path], num_epochs=1000)
# Define a reader and read the next record
reader = tf.TFRecordReader()
_, serialized_example = reader.read(filename_queue)
# Decode the record read by the reader
features = tf.parse_single_example(serialized_example, features=feature)
# Convert the image data from string back to the numbers
image = tf.decode_raw(features['train/image'], tf.float32)
# Cast label data into int32
label = tf.cast(features['train/label'], tf.int32)
# Reshape image data into the original shape
image = tf.reshape(image, [299, 299])
print(image.shape) #shape is printing out correctly
# Creates batches by randomly shuffling tensors
#images, labels = tf.train.shuffle_batch([image, label], batch_size=50, capacity=10000, num_threads=3, min_after_dequeue=2000)
init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())
sess.run(init_op)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
for batch_index in range(5):
img = sess.run([image])
img = img.astype(np.uint8)
print(img.shape)
coord.request_stop()
coord.join(threads)
sess.close()
我不确定如何调试这个..
第一个打印语句(reshaped_image.shape)正在打印出来 (299,299)形状,所以不确定是什么问题。
谢谢。
答案 0 :(得分:2)
我需要做的是将图像解码为JPEG,将其转换为浮点,扩展其尺寸,然后使用双线性插值来调整其大小,如下所示:
image = tf.image.decode_jpeg(features['train/image'], channels=3)
image = tf.image.convert_image_dtype(image, dtype=tf.float32)
image = tf.expand_dims(image, 0)
image = tf.image.resize_bilinear(image, [299, 299], align_corners=False)
注意:
channels
设置为1,或者在TFRecords中保存每个图像的通道数,然后从那里动态获取(每个图像都不同)。