def tensorflow_reader(list_0, list_1, flow_list, shuffle_all, batchs):
"""Average endpoint error between prediction and groundtruth
Keyword arguments:
list_0 -- source list of first of img pair
list_1 -- source list of second of img pair
flow_list -- source list of optical flow between first and second img
shuffle_all -- boolean if list should be shuffled
batchs -- batchsize
"""
assert len(list_0) == len(list_1) == len(
flow_list) != 0, ('Input Lengths not correct')
print("Number of inputs: " + str(len(list_0)))
if shuffle_all == True:
p = np.random.permutation(len(list_0))
else:
p = np.arange(len(list_0))
list_0 = [list_0[i] for i in p]
list_1 = [list_1[i] for i in p]
flow_list = [flow_list[i] for i in p]
input_queue = tf.train.slice_input_producer(
[list_0, list_1],
shuffle=False) # shuffled before
# image reader
content_0 = tf.read_file(input_queue[0])
content_1 = tf.read_file(input_queue[1])
imgs_0 = tf.image.decode_image(content_0, channels=3)
imgs_1 = tf.image.decode_image(content_1, channels=3)
# convert to [0, 1] images
imgs_0 = tf.image.convert_image_dtype(imgs_0, dtype=tf.float32)
imgs_1 = tf.image.convert_image_dtype(imgs_1, dtype=tf.float32)
# flow reader
filename_queue = tf.train.string_input_producer(flow_list, shuffle=False)
record_bytes = FLAGS.record_bytes # 1572876
reader = tf.FixedLengthRecordReader(record_bytes=record_bytes)
key, value = reader.read(filename_queue)
record_bytes = tf.decode_raw(value, tf.float32)
magic = tf.slice(record_bytes, [0], [1]) # .flo number 202021.25
size = tf.slice(record_bytes, [1], [2]) # size of flow / image
flows = tf.slice(record_bytes, [3], [np.prod(FLAGS.flow_shape)])
flows = tf.reshape(flows, FLAGS.flow_shape)
# set shape
imgs_0.set_shape(FLAGS.img_shape)
imgs_1.set_shape(FLAGS.img_shape)
flows.set_shape(FLAGS.flow_shape)
return tf.train.batch([imgs_0, imgs_1, flows],
batch_size=batchs
#,num_threads=1
)
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