nvidia.dali.plugin.tf makes my label incorrect

[wangguangyuan]

When I use pipe.build() and pipe.run() to enter the result, the label output is correct, but when I use nvidia.dali.plugin.tf and then use sess.run() to output the result, label is all zero.
my test file format: (image_path, label)
/n01704323/n01704323_1569.JPEG 0
/n01704323/n01704323_5833.JPEG 0
/n01704323/n01704323_2956.JPEG 0
/n01704323/n01704323_4976.JPEG 0
/n01704323/n01704323_8911.JPEG 0
/n01704323/n01704323_9292.JPEG 0
/n01704323/n01704323_1379.JPEG 0
/n01704323/n01704323_8648.JPEG 0
/n01704323/n01704323_8497.JPEG 0
/n01704323/n01704323_2732.JPEG 0
/n02017213/n02017213_3476.JPEG 1
/n02017213/n02017213_6601.JPEG 1
/n02017213/n02017213_3582.JPEG 1
/n02017213/n02017213_765.JPEG 1
/n02017213/n02017213_3461.JPEG 1
/n02017213/n02017213_3894.JPEG 1
/n02017213/n02017213_513.JPEG 1
/n02017213/n02017213_6029.JPEG 1
/n02017213/n02017213_43.JPEG 1
/n02017213/n02017213_5432.JPEG 1
/n02017213/n02017213_7325.JPEG 1
/n02017213/n02017213_7622.JPEG 1
/n02017213/n02017213_7330.JPEG 1

my test code：
`
#First test procedure：
pipe = FileReadPipeline(batch_size=32, num_threads=1, device_id=0, random_shuffle=True,
file_list=save_file_path, initial_fill=4096)
pipe.build()
for _ in range(1):
pipe_out = pipe.run()
images, labels = pipe_out
labels_tensor = labels.asCPU().as_tensor()
images = images.asCPU()
print(np.array(labels_tensor))
# print(images.at(9))

output:
[[1]
[0]
[1]
[0]
[1]
[1]
[1]
[0]
[1]
[1]
[0]
[1]
[1]
[0]
[0]
[0]
[1]
[0]
[0]
[0]
[1]
[1]
[0]
[0]
[0]
[1]
[0]
[1]
[0]
[1]
[0]
[0]]
#--------------------------------------#
#Second test procedure:
device_id = 0
pipe = FileReadPipeline(batch_size=32, num_threads=1, device_id=0, random_shuffle=True,
file_list=save_file_path, initial_fill=4096)
seri_pipe = pipe.serialize()
daliop = dali_tf.DALIIterator()
with tf.device('/gpu:%i' % device_id):
image, label = daliop(serialized_pipeline=seri_pipe,
batch_size=32,
height=224,
width=224,
device_id=device_id)
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.5)
config = tf.ConfigProto(gpu_options=gpu_options)
with tf.Session(config=config) as sess:
ims, labels = sess.run([image, label])
print(labels)
`
output:
[[ 0.00000000e+00]
[ 0.00000000e+00]
[ 1.40129846e-45]
[ 1.40129846e-45]
[ 1.40129846e-45]
[ 1.40129846e-45]
[ 0.00000000e+00]
[ 0.00000000e+00]
[ 1.40129846e-45]
[ 0.00000000e+00]
[ 0.00000000e+00]
[ 1.40129846e-45]
[ 0.00000000e+00]
[ 0.00000000e+00]
[ 1.40129846e-45]
[ 1.40129846e-45]
[ 1.40129846e-45]
[ 1.40129846e-45]
[ 0.00000000e+00]
[ 0.00000000e+00]
[ 1.40129846e-45]
[ 1.40129846e-45]
[ 1.40129846e-45]
[ 0.00000000e+00]
[ 0.00000000e+00]
[ 1.40129846e-45]
[ 0.00000000e+00]
[ 1.40129846e-45]
[ 0.00000000e+00]
[ 0.00000000e+00]
[ 0.00000000e+00]
[ 0.00000000e+00]]

`
class CommonPipeline(Pipeline):
def init(self, batch_size, num_threads, device_id, size=(224, 224), crop_size=(224, 224),
mean=IMAGE_MEAN,
std=IMAGE_STD,
channel_format=types.NHWC,
probability=0.5,
device='gpu',
decode_method='in_gpu'):
super(CommonPipeline, self).init(batch_size, num_threads, device_id)
assert device == 'gpu' and decode_method == 'in_gpu'
if decode_method == 'in_gpu':
self.decode = ops.nvJPEGDecoder(device="mixed", output_type=types.RGB)
else:
self.decode = ops.HostDecoder(output_type=types.RGB)

    self.rrc = ops.RandomResizedCrop(device=device, size=size)
    self.cmnp = ops.CropMirrorNormalize(device=device,
                                        output_dtype=types.FLOAT,
                                        output_layout=channel_format,
                                        image_type=types.RGB,
                                        crop=crop_size,
                                        mean=mean,
                                        std=std)
    self.coin = ops.CoinFlip(probability=probability)
    # self.cast = ops.Cast(device=device, dtype=types.FLOAT16)

def base_define_graph(self, inputs, labels):
    rng = self.coin()
    images = self.decode(inputs)
    images = self.rrc(images)
    images = self.cmnp(images, mirror=rng)
    # images = self.cast(images)
    return images, labels.gpu()

class FileReadPipeline(CommonPipeline):
def init(self,
batch_size,
num_threads,
device_id,
random_shuffle,
file_root='',
file_list='',
size=(224, 224),
crop_size=(224, 224),
mean=IMAGE_MEAN,
std=IMAGE_STD,
channel_format=types.NHWC,
probability=0.5,
device='gpu',
decode_method='in_gpu',
initial_fill=4096):
super(FileReadPipeline, self).init(batch_size=batch_size,
num_threads=num_threads,
device_id=device_id,
size=size,
crop_size=crop_size,
mean=mean,
std=std,
channel_format=channel_format,
probability=probability,
device=device,
decode_method=decode_method)
self.input = ops.FileReader(file_root=file_root, file_list=file_list,
random_shuffle=random_shuffle, initial_fill=initial_fill)

def define_graph(self):
    images, labels = self.input()
    return self.base_define_graph(images, labels)`

[wangguangyuan]