tensorflow / tensorflow/models

ValueError: The two structures don't have the same nested structure.

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@pkulzc is already working on this.

Since Aug 5, 2021.

models:research:odapi type:support
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Description

Hello

The efficient D2 model is being used.
I'm going to run the assessment with that code.
An error has occurred.

code:

python model_main_tf2.py --model_dir=woo/dev3/Data/train/d2/model_663 --pipeline_config_path=woo/dev3/Data/train/d2/pipeline.config --checkpoint_dir=woo/dev3/Data/train/d2/model_663

error message :

2021-07-30 16:30:04.240837: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
WARNING:tensorflow:Forced number of epochs for all eval validations to be 1.
W0730 16:30:05.285229 139928160339776 model_lib_v2.py:1064] Forced number of epochs for all eval validations to be 1.
INFO:tensorflow:Maybe overwriting sample_1_of_n_eval_examples: None
I0730 16:30:05.285332 139928160339776 config_util.py:552] Maybe overwriting sample_1_of_n_eval_examples: None
INFO:tensorflow:Maybe overwriting use_bfloat16: False
I0730 16:30:05.285376 139928160339776 config_util.py:552] Maybe overwriting use_bfloat16: False
INFO:tensorflow:Maybe overwriting eval_num_epochs: 1
I0730 16:30:05.285417 139928160339776 config_util.py:552] Maybe overwriting eval_num_epochs: 1
WARNING:tensorflow:Expected number of evaluation epochs is 1, but instead encountered eval_on_train_input_config.num_epochs = 0. Overwriting num_epochs to 1.
W0730 16:30:05.285473 139928160339776 model_lib_v2.py:1085] Expected number of evaluation epochs is 1, but instead encountered eval_on_train_input_config.num_epochs = 0. Overwriting num_epochs to 1.
2021-07-30 16:30:05.291736: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcuda.so.1
2021-07-30 16:30:05.318661: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-30 16:30:05.318955: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: NVIDIA GeForce GTX 1080 Ti computeCapability: 6.1
coreClock: 1.683GHz coreCount: 28 deviceMemorySize: 10.91GiB deviceMemoryBandwidth: 451.17GiB/s
2021-07-30 16:30:05.318972: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
2021-07-30 16:30:05.320037: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10
2021-07-30 16:30:05.321033: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10
2021-07-30 16:30:05.321188: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10
2021-07-30 16:30:05.322310: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10
2021-07-30 16:30:05.322876: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.10
2021-07-30 16:30:05.325164: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7
2021-07-30 16:30:05.325246: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-30 16:30:05.325570: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-30 16:30:05.325832: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0
2021-07-30 16:30:05.326069: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN)to use the following CPU instructions in performance-critical operations: AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2021-07-30 16:30:05.330362: I tensorflow/core/platform/profile_utils/cpu_utils.cc:104] CPU Frequency: 3600000000 Hz
2021-07-30 16:30:05.330595: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x55a3e2502090 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2021-07-30 16:30:05.330610: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version
2021-07-30 16:30:05.378194: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-30 16:30:05.378557: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x55a3e24b1130 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2021-07-30 16:30:05.378574: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce GTX 1080 Ti, Compute Capability 6.1
2021-07-30 16:30:05.378732: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-30 16:30:05.379045: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: NVIDIA GeForce GTX 1080 Ti computeCapability: 6.1
coreClock: 1.683GHz coreCount: 28 deviceMemorySize: 10.91GiB deviceMemoryBandwidth: 451.17GiB/s
2021-07-30 16:30:05.379067: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
2021-07-30 16:30:05.379091: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10
2021-07-30 16:30:05.379103: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10
2021-07-30 16:30:05.379113: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10
2021-07-30 16:30:05.379122: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10
2021-07-30 16:30:05.379133: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.10
2021-07-30 16:30:05.379143: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7
2021-07-30 16:30:05.379230: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-30 16:30:05.379762: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-30 16:30:05.380020: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0
2021-07-30 16:30:05.380041: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
2021-07-30 16:30:05.631085: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1257] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-07-30 16:30:05.631115: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1263] 0
2021-07-30 16:30:05.631121: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1276] 0: N
2021-07-30 16:30:05.631271: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-30 16:30:05.631583: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-30 16:30:05.631847: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1402] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9230 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce GTX 1080 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1)
I0730 16:30:05.719444 139928160339776 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b2
I0730 16:30:05.719544 139928160339776 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 112
I0730 16:30:05.719586 139928160339776 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 5
I0730 16:30:05.725369 139928160339776 efficientnet_model.py:147] round_filter input=32 output=32
I0730 16:30:05.752268 139928160339776 efficientnet_model.py:147] round_filter input=32 output=32
I0730 16:30:05.752362 139928160339776 efficientnet_model.py:147] round_filter input=16 output=16
I0730 16:30:05.851547 139928160339776 efficientnet_model.py:147] round_filter input=16 output=16
I0730 16:30:05.851643 139928160339776 efficientnet_model.py:147] round_filter input=24 output=24
I0730 16:30:06.045676 139928160339776 efficientnet_model.py:147] round_filter input=24 output=24
I0730 16:30:06.045773 139928160339776 efficientnet_model.py:147] round_filter input=40 output=48
I0730 16:30:06.279940 139928160339776 efficientnet_model.py:147] round_filter input=40 output=48
I0730 16:30:06.280038 139928160339776 efficientnet_model.py:147] round_filter input=80 output=88
I0730 16:30:06.544079 139928160339776 efficientnet_model.py:147] round_filter input=80 output=88
I0730 16:30:06.544173 139928160339776 efficientnet_model.py:147] round_filter input=112 output=120
I0730 16:30:06.805504 139928160339776 efficientnet_model.py:147] round_filter input=112 output=120
I0730 16:30:06.805598 139928160339776 efficientnet_model.py:147] round_filter input=192 output=208
I0730 16:30:07.134595 139928160339776 efficientnet_model.py:147] round_filter input=192 output=208
I0730 16:30:07.134699 139928160339776 efficientnet_model.py:147] round_filter input=320 output=352
I0730 16:30:07.263487 139928160339776 efficientnet_model.py:147] round_filter input=1280 output=1408
I0730 16:30:07.290740 139928160339776 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=1.1, depth_coefficient=1.2, resolution=260, dropout_rate=0.3, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')
INFO:tensorflow:Reading unweighted datasets: ['woo/dev3/Data/train/dataset_82.record']
I0730 16:30:07.344820 139928160339776 dataset_builder.py:163] Reading unweighted datasets: ['woo/dev3/Data/train/dataset_82.record']
INFO:tensorflow:Reading record datasets for input file: ['woo/dev3/Data/train/dataset_82.record']
I0730 16:30:07.345714 139928160339776 dataset_builder.py:80] Reading record datasets for input file: ['woo/dev3/Data/train/dataset_82.record']
INFO:tensorflow:Number of filenames to read: 1
I0730 16:30:07.345799 139928160339776 dataset_builder.py:81] Number of filenames to read: 1
WARNING:tensorflow:num_readers has been reduced to 1 to match input file shards.
W0730 16:30:07.345847 139928160339776 dataset_builder.py:88] num_readers has been reduced to 1 to match input file shards.
WARNING:tensorflow:From /home/leeyongseong/models/학습용/models-master/research/object_detection/builders/dataset_builder.py:105: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future vion.
Instructions for updating:
Use tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.experimental.AUTOTUNE) instead. If sloppy execution is desired, use tf.data.Options.experimental_deterministic.
W0730 16:30:07.347270 139928160339776 deprecation.py:323] From /home/leeyongseong/models/학습용/models-master/research/object_detection/builders/dataset_builder.py:105: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is depreed and will be removed in a future version.
Instructions for updating:
Use tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.experimental.AUTOTUNE) instead. If sloppy execution is desired, use tf.data.Options.experimental_deterministic.
WARNING:tensorflow:From /home/leeyongseong/models/학습용/models-master/research/object_detection/builders/dataset_builder.py:237: DatasetV1.map_with_legacy_function (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future veon.
Instructions for updating:
Use tf.data.Dataset.map() W0730 16:30:07.361915 139928160339776 deprecation.py:323] From /home/leeyongseong/models/학습용/models-master/research/object_detection/builders/dataset_builder.py:237: DatasetV1.map_with_legacy_function (from tensorflow.python.data.ops.dataset_ops) is deprecd and will be removed in a future version. Instructions for updating: Use tf.data.Dataset.map()
WARNING:tensorflow:From /home/leeyongseong/다운로드/myanaconda/envs/models-master-20210528T081613Z-001/lib/python3.6/site-packages/tensorflow/python/util/dispatch.py:201: sparse_to_dense (from tensorflow.python.ops.sparse_ops) is deprecated and will be remove a future version.
Instructions for updating:
Create a tf.sparse.SparseTensor and use tf.sparse.to_dense instead.
W0730 16:30:09.792060 139928160339776 deprecation.py:323] From /home/leeyongseong/다운로드/myanaconda/envs/models-master-20210528T081613Z-001/lib/python3.6/site-packages/tensorflow/python/util/dispatch.py:201: sparse_to_dense (from tensorflow.python.ops.sparss) is deprecated and will be removed in a future version.
Instructions for updating:
Create a tf.sparse.SparseTensor and use tf.sparse.to_dense instead.
WARNING:tensorflow:From /home/leeyongseong/models/학습용/models-master/research/object_detection/inputs.py:282: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.cast instead.
W0730 16:30:10.683436 139928160339776 deprecation.py:323] From /home/leeyongseong/models/학습용/models-master/research/object_detection/inputs.py:282: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.cast instead.
INFO:tensorflow:Waiting for new checkpoint at woo/dev3/Data/train/d2/model_663
I0730 16:30:12.394732 139928160339776 checkpoint_utils.py:125] Waiting for new checkpoint at woo/dev3/Data/train/d2/model_663
INFO:tensorflow:Found new checkpoint at woo/dev3/Data/train/d2/model_663/ckpt-30
I0730 16:30:12.395892 139928160339776 checkpoint_utils.py:134] Found new checkpoint at woo/dev3/Data/train/d2/model_663/ckpt-30
WARNING:tensorflow:From /home/leeyongseong/models/학습용/models-master/research/object_detection/model_lib_v2.py:845: set_learning_phase (from tensorflow.python.keras.backend) is deprecated and will be removed after 2020-10-11.
Instructions for updating:
Simply pass a True/False value to the training argument of the __call__ method of your layer or model.
W0730 16:30:17.353836 139928160339776 deprecation.py:323] From /home/leeyongseong/models/학습용/models-master/research/object_detection/model_lib_v2.py:845: set_learning_phase (from tensorflow.python.keras.backend) is deprecated and will be removed after 2020-11.
Instructions for updating:
Simply pass a True/False value to the training argument of the __call__ method of your layer or model.
2021-07-30 16:30:34.613273: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10
2021-07-30 16:30:34.713459: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7
WARNING:tensorflow:From /home/leeyongseong/models/학습용/models-master/research/object_detection/eval_util.py:929: to_int64 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.cast instead.
W0730 16:30:35.632518 139928160339776 deprecation.py:323] From /home/leeyongseong/models/학습용/models-master/research/object_detection/eval_util.py:929: to_int64 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.cast instead.
INFO:tensorflow:Finished eval step 0
I0730 16:30:35.637181 139928160339776 model_lib_v2.py:940] Finished eval step 0
WARNING:tensorflow:From /home/leeyongseong/models/학습용/models-master/research/object_detection/utils/visualization_utils.py:617: py_func (from tensorflow.python.ops.script_ops) is deprecated and will be removed in a future version.
Instructions for updating:
tf.py_func is deprecated in TF V2. Instead, there are two
options available in V2.
- tf.py_function takes a python function which manipulates tf eager
tensors instead of numpy arrays. It's easy to convert a tf eager tensor to
an ndarray (just call tensor.numpy()) but having access to eager tensors
means tf.py_functions can use accelerators such as GPUs as well as
being differentiable using a gradient tape.
- tf.numpy_function maintains the semantics of the deprecated tf.py_func
(it is not differentiable, and manipulates numpy arrays). It drops the
stateful argument making all functions stateful.

W0730 16:30:35.728933 139928160339776 deprecation.py:323] From /home/leeyongseong/models/학습용/models-master/research/object_detection/utils/visualization_utils.py:617: py_func (from tensorflow.python.ops.script_ops) is deprecated and will be removed in a fue version.
Instructions for updating:
tf.py_func is deprecated in TF V2. Instead, there are two
options available in V2.
- tf.py_function takes a python function which manipulates tf eager
tensors instead of numpy arrays. It's easy to convert a tf eager tensor to
an ndarray (just call tensor.numpy()) but having access to eager tensors
means tf.py_functions can use accelerators such as GPUs as well as
being differentiable using a gradient tape.
- tf.numpy_function maintains the semantics of the deprecated tf.py_func
(it is not differentiable, and manipulates numpy arrays). It drops the
stateful argument making all functions stateful.

Traceback (most recent call last):
File "/home/leeyongseong/다운로드/myanaconda/envs/models-master-20210528T081613Z-001/lib/python3.6/site-packages/tensorflow/python/util/nest.py", line 395, in assert_same_structure
expand_composites)
ValueError: The two structures don't have the same nested structure.

First structure: type=DType str=<dtype: 'uint8'>

Second structure: type=list str=[<tf.Tensor: shape=(600, 600, 3), dtype=uint8, numpy=
array([[[254, 254, 254],
[254, 254, 254],
[254, 254, 254],
...,
[254, 254, 254],
[254, 254, 254],
[254, 254, 254]],

   [[254, 254, 254],
    [254, 254, 254],
    [254, 254, 254],
    ...,
    [254, 254, 254],
    [254, 254, 254],
    [254, 254, 254]],

   [[254, 254, 254],
    [254, 254, 254],
    [254, 254, 254],
    ...,
    [254, 254, 254],
    [254, 254, 254],
    [254, 254, 254]],

   ...,

   [[254, 254, 254],
    [254, 254, 254],
    [254, 254, 254],
    ...,
    [254, 254, 254],
    [254, 254, 254],
    [254, 254, 254]],

   [[254, 254, 254],
    [254, 254, 254],
    [254, 254, 254],
    ...,
    [254, 254, 254],
    [254, 254, 254],
    [254, 254, 254]],

   [[254, 254, 254],
    [254, 254, 254],
    [254, 254, 254],
    ...,
    [254, 254, 254],
    [254, 254, 254],
    [254, 254, 254]]], dtype=uint8)>, <tf.Tensor: shape=(), dtype=int32, numpy=102441>, <tf.Tensor: shape=(), dtype=int32, numpy=227339>, <tf.Tensor: shape=(), dtype=int32, numpy=144315>, <tf.Tensor: shape=(), dtype=int32, numpy=246055>, <tf.Tensor: shape=(), dtype=int32, numpy=102372>, <tf.Tensor: shape=(), dtype=int32, numpy=190468>, <tf.Tensor: shape=(), dtype=int32, numpy=144929>, <tf.Tensor: shape=(), dtype=int32, numpy=209832>, <tf.Tensor: shape=(), dtype=int32, numpy=217558>, <tf.Tensor: shape=(), dtype=int32, numpy=227925>, <tf.Tensor: shape=(), dtype=int32, numpy=259601>, <tf.Tensor: shape=(), dtype=int32, numpy=246571>, <tf.Tensor: shape=(), dtype=int32, numpy=102827>, <tf.Tensor: shape=(), dtype=int32, numpy=208662>, <tf.Tensor: shape=(), dtype=int32, numpy=144846>, <tf.Tensor: shape=(), dtype=int32, numpy=227246>, <tf.Tensor: shape=(), dtype=int32, numpy=102889>, <tf.Tensor: shape=(), dtype=int32, numpy=170202>, <tf.Tensor: shape=(), dtype=int32, numpy=145150>, <tf.Tensor: shape=(), dtype=int32, numpy=190190>, <tf.Tensor: shape=(), dtype=int32, numpy=218468>, <tf.Tensor: shape=(), dtype=int32, numpy=209628>, <tf.Tensor: shape=(), dtype=int32, numpy=260118>, <tf.Tensor: shape=(), dtype=int32, numpy=228065>, <tf.Tensor: shape=(), dtype=int32, numpy=217536>, <tf.Tensor: shape=(), dtype=int32, numpy=170533>, <tf.Tensor: shape=(), dtype=int32, numpy=260163>, <tf.Tensor: shape=(), dtype=int32, numpy=190970>, <tf.Tensor: shape=(), dtype=int32, numpy=217527>, <tf.Tensor: shape=(), dtype=int32, numpy=191056>, <tf.Tensor: shape=(), dtype=int32, numpy=260223>, <tf.Tensor: shape=(), dtype=int32, numpy=210530>, <tf.Tensor: shape=(), dtype=int32, numpy=102056>, <tf.Tensor: shape=(), dtype=int32, numpy=245077>, <tf.Tensor: shape=(), dtype=int32, numpy=145052>, <tf.Tensor: shape=(), dtype=int32, numpy=265519>, <tf.Tensor: shape=(), dtype=int32, numpy=219341>, <tf.Tensor: shape=(), dtype=int32, numpy=245055>, <tf.Tensor: shape=(), dtype=int32, numpy=258944>, <tf.Tensor: shape=(), dtype=int32, numpy=264574>, <tf.Tensor: shape=(), dtype=int32, numpy=163408>, <tf.Tensor: shape=(), dtype=int32, numpy=90431>, <tf.Tensor: shape=(), dtype=int32, numpy=199535>, <tf.Tensor: shape=(), dtype=int32, numpy=164714>, <tf.Tensor: shape=(), dtype=string, numpy=b'2'>, <tf.Tensor: shape=(), dtype=string, numpy=b'2'>, <tf.Tensor: shape=(), dtype=string, numpy=b'2'>, <tf.Tensor: shape=(), dtype=string, numpy=b'2'>, <tf.Tensor: shape=(), dtype=string, numpy=b'2'>, <tf.Tensor: shape=(), dtype=string, numpy=b'2'>, <tf.Tensor: shape=(), dtype=string, numpy=b'2'>, <tf.Tensor: shape=(), dtype=string, numpy=b'2'>, <tf.Tensor: shape=(), dtype=string, numpy=b'2'>, <tf.Tensor: shape=(), dtype=string, numpy=b'2'>, <tf.Tensor: shape=(), dtype=string, numpy=b'2'>, <tf.Tensor: shape=(), dtype=float32, numpy=0.94149244>, <tf.Tensor: shape=(), dtype=float32, numpy=0.92466325>, <tf.Tensor: shape=(), dtype=float32, numpy=0.9234351>, <tf.Tensor: shape=(), dtype=float32, numpy=0.91964996>, <tf.Tensor: shape=(), dtype=float32, numpy=0.9171783>, <tf.Tensor: shape=(), dtype=float32, numpy=0.9043603>, <tf.Tensor: shape=(), dtype=float32, numpy=0.89829016>, <tf.Tensor: shape=(), dtype=float32, numpy=0.89090973>, <tf.Tensor: shape=(), dtype=float32, numpy=0.823297>, <tf.Tensor: shape=(), dtype=float32, numpy=0.7679921>, <tf.Tensor: shape=(), dtype=float32, numpy=0.6440104>]

More specifically: Substructure "type=list str=[<tf.Tensor: shape=(600, 600, 3), dtype=uint8, numpy=
array([[[254, 254, 254],
[254, 254, 254],
[254, 254, 254],
...,
[254, 254, 254],
[254, 254, 254],
[254, 254, 254]],

   [[254, 254, 254],
    [254, 254, 254],
    [254, 254, 254],
    ...,
    [254, 254, 254],
    [254, 254, 254],
    [254, 254, 254]],

   [[254, 254, 254],
    [254, 254, 254],
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    ...,
    [254, 254, 254],
    [254, 254, 254],
    [254, 254, 254]],

   ...,

   [[254, 254, 254],
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    [254, 254, 254],
    ...,
    [254, 254, 254],
    [254, 254, 254],
    [254, 254, 254]],

   [[254, 254, 254],
    [254, 254, 254],
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    ...,
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    [254, 254, 254]],

   [[254, 254, 254],
    [254, 254, 254],
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    [254, 254, 254],
    [254, 254, 254],
    [254, 254, 254]]], dtype=uint8)>, <tf.Tensor: shape=(), dtype=int32, numpy=102441>, <tf.Tensor: shape=(), dtype=int32, numpy=227339>, <tf.Tensor: shape=(), dtype=int32, numpy=144315>, <tf.Tensor: shape=(), dtype=int32, numpy=246055>, <tf.Tensor: shape=(), dtype=int32, numpy=102372>, <tf.Tensor: shape=(), dtype=int32, numpy=190468>, <tf.Tensor: shape=(), dtype=int32, numpy=144929>, <tf.Tensor: shape=(), dtype=int32, numpy=209832>, <tf.Tensor: shape=(), dtype=int32, numpy=217558>, <tf.Tensor: shape=(), dtype=int32, numpy=227925>, <tf.Tensor: shape=(), dtype=int32, numpy=259601>, <tf.Tensor: shape=(), dtype=int32, numpy=246571>, <tf.Tensor: shape=(), dtype=int32, numpy=102827>, <tf.Tensor: shape=(), dtype=int32, numpy=208662>, <tf.Tensor: shape=(), dtype=int32, numpy=144846>, <tf.Tensor: shape=(), dtype=int32, numpy=227246>, <tf.Tensor: shape=(), dtype=int32, numpy=102889>, <tf.Tensor: shape=(), dtype=int32, numpy=170202>, <tf.Tensor: shape=(), dtype=int32, numpy=145150>, <tf.Tensor: shape=(), dtype=int32, numpy=190190>, <tf.Tensor: shape=(), dtype=int32, numpy=218468>, <tf.Tensor: shape=(), dtype=int32, numpy=209628>, <tf.Tensor: shape=(), dtype=int32, numpy=260118>, <tf.Tensor: shape=(), dtype=int32, numpy=228065>, <tf.Tensor: shape=(), dtype=int32, numpy=217536>, <tf.Tensor: shape=(), dtype=int32, numpy=170533>, <tf.Tensor: shape=(), dtype=int32, numpy=260163>, <tf.Tensor: shape=(), dtype=int32, numpy=190970>, <tf.Tensor: shape=(), dtype=int32, numpy=217527>, <tf.Tensor: shape=(), dtype=int32, numpy=191056>, <tf.Tensor: shape=(), dtype=int32, numpy=260223>, <tf.Tensor: shape=(), dtype=int32, numpy=210530>, <tf.Tensor: shape=(), dtype=int32, numpy=102056>, <tf.Tensor: shape=(), dtype=int32, numpy=245077>, <tf.Tensor: shape=(), dtype=int32, numpy=145052>, <tf.Tensor: shape=(), dtype=int32, numpy=265519>, <tf.Tensor: shape=(), dtype=int32, numpy=219341>, <tf.Tensor: shape=(), dtype=int32, numpy=245055>, <tf.Tensor: shape=(), dtype=int32, numpy=258944>, <tf.Tensor: shape=(), dtype=int32, numpy=264574>, <tf.Tensor: shape=(), dtype=int32, numpy=163408>, <tf.Tensor: shape=(), dtype=int32, numpy=90431>, <tf.Tensor: shape=(), dtype=int32, numpy=199535>, <tf.Tensor: shape=(), dtype=int32, numpy=164714>, <tf.Tensor: shape=(), dtype=string, numpy=b'2'>, <tf.Tensor: shape=(), dtype=string, numpy=b'2'>, <tf.Tensor: shape=(), dtype=string, numpy=b'2'>, <tf.Tensor: shape=(), dtype=string, numpy=b'2'>, <tf.Tensor: shape=(), dtype=string, numpy=b'2'>, <tf.Tensor: shape=(), dtype=string, numpy=b'2'>, <tf.Tensor: shape=(), dtype=string, numpy=b'2'>, <tf.Tensor: shape=(), dtype=string, numpy=b'2'>, <tf.Tensor: shape=(), dtype=string, numpy=b'2'>, <tf.Tensor: shape=(), dtype=string, numpy=b'2'>, <tf.Tensor: shape=(), dtype=string, numpy=b'2'>, <tf.Tensor: shape=(), dtype=float32, numpy=0.94149244>, <tf.Tensor: shape=(), dtype=float32, numpy=0.92466325>, <tf.Tensor: shape=(), dtype=float32, numpy=0.9234351>, <tf.Tensor: shape=(), dtype=float32, numpy=0.91964996>, <tf.Tensor: shape=(), dtype=float32, numpy=0.9171783>, <tf.Tensor: shape=(), dtype=float32, numpy=0.9043603>, <tf.Tensor: shape=(), dtype=float32, numpy=0.89829016>, <tf.Tensor: shape=(), dtype=float32, numpy=0.89090973>, <tf.Tensor: shape=(), dtype=float32, numpy=0.823297>, <tf.Tensor: shape=(), dtype=float32, numpy=0.7679921>, <tf.Tensor: shape=(), dtype=float32, numpy=0.6440104>]" is a sequence, while substructure "type=DType str=<dtype: 'uint8'>" is not

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "model_main_tf2.py", line 113, in
tf.compat.v1.app.run()
File "/home/leeyongseong/다운로드/myanaconda/envs/models-master-20210528T081613Z-001/lib/python3.6/site-packages/tensorflow/python/platform/app.py", line 40, in run
_run(main=main, argv=argv, flags_parser=_parse_flags_tolerate_undef)
File "/home/leeyongseong/다운로드/myanaconda/envs/models-master-20210528T081613Z-001/lib/python3.6/site-packages/absl/app.py", line 303, in run
_run_main(main, args)
File "/home/leeyongseong/다운로드/myanaconda/envs/models-master-20210528T081613Z-001/lib/python3.6/site-packages/absl/app.py", line 251, in _run_main
sys.exit(main(argv))
File "model_main_tf2.py", line 88, in main
wait_interval=300, timeout=FLAGS.eval_timeout)
File "/home/leeyongseong/models/학습용/models-master/research/object_detection/model_lib_v2.py", line 1139, in eval_continuously
global_step=global_step,
File "/home/leeyongseong/models/학습용/models-master/research/object_detection/model_lib_v2.py", line 950, in eager_eval_loop
keypoint_edges=keypoint_edges or None)
File "/home/leeyongseong/models/학습용/models-master/research/object_detection/utils/visualization_utils.py", line 733, in draw_side_by_side_evaluation_image
use_normalized_coordinates=use_normalized_coordinates)
File "/home/leeyongseong/models/학습용/models-master/research/object_detection/utils/visualization_utils.py", line 621, in draw_bounding_boxes_on_image_tensors
images = tf.map_fn(draw_boxes, elems, dtype=tf.uint8, back_prop=False)
File "/home/leeyongseong/다운로드/myanaconda/envs/models-master-20210528T081613Z-001/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py", line 507, in new_func
return func(*args, **kwargs)
File "/home/leeyongseong/다운로드/myanaconda/envs/models-master-20210528T081613Z-001/lib/python3.6/site-packages/tensorflow/python/ops/map_fn.py", line 499, in map_fn
maximum_iterations=n)
File "/home/leeyongseong/다운로드/myanaconda/envs/models-master-20210528T081613Z-001/lib/python3.6/site-packages/tensorflow/python/ops/control_flow_ops.py", line 2735, in while_loop
loop_vars = body(*loop_vars)
File "/home/leeyongseong/다운로드/myanaconda/envs/models-master-20210528T081613Z-001/lib/python3.6/site-packages/tensorflow/python/ops/control_flow_ops.py", line 2726, in
body = lambda i, lv: (i + 1, orig_body(*lv))
File "/home/leeyongseong/다운로드/myanaconda/envs/models-master-20210528T081613Z-001/lib/python3.6/site-packages/tensorflow/python/ops/map_fn.py", line 484, in compute
nest.assert_same_structure(fn_output_signature or elems, result_value)
File "/home/leeyongseong/다운로드/myanaconda/envs/models-master-20210528T081613Z-001/lib/python3.6/site-packages/tensorflow/python/util/nest.py", line 402, in assert_same_structure
% (str(e), str1, str2))
ValueError: The two structures don't have the same nested structure.

First structure: type=DType str=<dtype: 'uint8'>

Second structure: type=list str=[<tf.Tensor: shape=(600, 600, 3), dtype=uint8, numpy=
array([[[254, 254, 254],
[254, 254, 254],
[254, 254, 254],
...,
[254, 254, 254],
[254, 254, 254],
[254, 254, 254]],

   [[254, 254, 254],
    [254, 254, 254],
    [254, 254, 254],
    ...,
    [254, 254, 254],
    [254, 254, 254],
    [254, 254, 254]],

   [[254, 254, 254],
    [254, 254, 254],
    [254, 254, 254],
    ...,
    [254, 254, 254],
    [254, 254, 254],
    [254, 254, 254]],

   ...,

   [[254, 254, 254],
    [254, 254, 254],
    [254, 254, 254],
    ...,
    [254, 254, 254],
    [254, 254, 254],
    [254, 254, 254]],

   [[254, 254, 254],
    [254, 254, 254],
    [254, 254, 254],
    ...,
    [254, 254, 254],
    [254, 254, 254],
    [254, 254, 254]],

   [[254, 254, 254],
    [254, 254, 254],
    [254, 254, 254],
    ...,
    [254, 254, 254],
    [254, 254, 254],
    [254, 254, 254]]], dtype=uint8)>, <tf.Tensor: shape=(), dtype=int32, numpy=102441>, <tf.Tensor: shape=(), dtype=int32, numpy=227339>, <tf.Tensor: shape=(), dtype=int32, numpy=144315>, <tf.Tensor: shape=(), dtype=int32, numpy=246055>, <tf.Tensor: shape=(), dtype=int32, numpy=102372>, <tf.Tensor: shape=(), dtype=int32, numpy=190468>, <tf.Tensor: shape=(), dtype=int32, numpy=144929>, <tf.Tensor: shape=(), dtype=int32, numpy=209832>, <tf.Tensor: shape=(), dtype=int32, numpy=217558>, <tf.Tensor: shape=(), dtype=int32, numpy=227925>, <tf.Tensor: shape=(), dtype=int32, numpy=259601>, <tf.Tensor: shape=(), dtype=int32, numpy=246571>, <tf.Tensor: shape=(), dtype=int32, numpy=102827>, <tf.Tensor: shape=(), dtype=int32, numpy=208662>, <tf.Tensor: shape=(), dtype=int32, numpy=144846>, <tf.Tensor: shape=(), dtype=int32, numpy=227246>, <tf.Tensor: shape=(), dtype=int32, numpy=102889>, <tf.Tensor: shape=(), dtype=int32, numpy=170202>, <tf.Tensor: shape=(), dtype=int32, numpy=145150>, <tf.Tensor: shape=(), dtype=int32, numpy=190190>, <tf.Tensor: shape=(), dtype=int32, numpy=218468>, <tf.Tensor: shape=(), dtype=int32, numpy=209628>, <tf.Tensor: shape=(), dtype=int32, numpy=260118>, <tf.Tensor: shape=(), dtype=int32, numpy=228065>, <tf.Tensor: shape=(), dtype=int32, numpy=217536>, <tf.Tensor: shape=(), dtype=int32, numpy=170533>, <tf.Tensor: shape=(), dtype=int32, numpy=260163>, <tf.Tensor: shape=(), dtype=int32, numpy=190970>, <tf.Tensor: shape=(), dtype=int32, numpy=217527>, <tf.Tensor: shape=(), dtype=int32, numpy=191056>, <tf.Tensor: shape=(), dtype=int32, numpy=260223>, <tf.Tensor: shape=(), dtype=int32, numpy=210530>, <tf.Tensor: shape=(), dtype=int32, numpy=102056>, <tf.Tensor: shape=(), dtype=int32, numpy=245077>, <tf.Tensor: shape=(), dtype=int32, numpy=145052>, <tf.Tensor: shape=(), dtype=int32, numpy=265519>, <tf.Tensor: shape=(), dtype=int32, numpy=219341>, <tf.Tensor: shape=(), dtype=int32, numpy=245055>, <tf.Tensor: shape=(), dtype=int32, numpy=258944>, <tf.Tensor: shape=(), dtype=int32, numpy=264574>, <tf.Tensor: shape=(), dtype=int32, numpy=163408>, <tf.Tensor: shape=(), dtype=int32, numpy=90431>, <tf.Tensor: shape=(), dtype=int32, numpy=199535>, <tf.Tensor: shape=(), dtype=int32, numpy=164714>, <tf.Tensor: shape=(), dtype=string, numpy=b'2'>, <tf.Tensor: shape=(), dtype=string, numpy=b'2'>, <tf.Tensor: shape=(), dtype=string, numpy=b'2'>, <tf.Tensor: shape=(), dtype=string, numpy=b'2'>, <tf.Tensor: shape=(), dtype=string, numpy=b'2'>, <tf.Tensor: shape=(), dtype=string, numpy=b'2'>, <tf.Tensor: shape=(), dtype=string, numpy=b'2'>, <tf.Tensor: shape=(), dtype=string, numpy=b'2'>, <tf.Tensor: shape=(), dtype=string, numpy=b'2'>, <tf.Tensor: shape=(), dtype=string, numpy=b'2'>, <tf.Tensor: shape=(), dtype=string, numpy=b'2'>, <tf.Tensor: shape=(), dtype=float32, numpy=0.94149244>, <tf.Tensor: shape=(), dtype=float32, numpy=0.92466325>, <tf.Tensor: shape=(), dtype=float32, numpy=0.9234351>, <tf.Tensor: shape=(), dtype=float32, numpy=0.91964996>, <tf.Tensor: shape=(), dtype=float32, numpy=0.9171783>, <tf.Tensor: shape=(), dtype=float32, numpy=0.9043603>, <tf.Tensor: shape=(), dtype=float32, numpy=0.89829016>, <tf.Tensor: shape=(), dtype=float32, numpy=0.89090973>, <tf.Tensor: shape=(), dtype=float32, numpy=0.823297>, <tf.Tensor: shape=(), dtype=float32, numpy=0.7679921>, <tf.Tensor: shape=(), dtype=float32, numpy=0.6440104>]

More specifically: Substructure "type=list str=[<tf.Tensor: shape=(600, 600, 3), dtype=uint8, numpy=
array([[[254, 254, 254],
[254, 254, 254],
[254, 254, 254],
...,
[254, 254, 254],
[254, 254, 254],
[254, 254, 254]],

   [[254, 254, 254],
    [254, 254, 254],
    [254, 254, 254],
    ...,
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    [254, 254, 254],
    [254, 254, 254]],

   [[254, 254, 254],
    [254, 254, 254],
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    ...,
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    [254, 254, 254],
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   ...,

   [[254, 254, 254],
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    [254, 254, 254],
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    ...,
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    ...,
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Entire first structure:
.
Entire second structure:
[., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., ., .]

config file :

model {
ssd {
num_classes: 3
image_resizer {
keep_aspect_ratio_resizer {
min_dimension: 768
max_dimension: 768
pad_to_max_dimension: true
}
}
feature_extractor {
type: "ssd_efficientnet-b2_bifpn_keras"
conv_hyperparams {
regularizer {
l2_regularizer {
weight: 3.9999998989515007e-05
}
}
initializer {
truncated_normal_initializer {
mean: 0.0
stddev: 0.029999999329447746
}
}
activation: SWISH
batch_norm {
decay: 0.9900000095367432
scale: true
epsilon: 0.0010000000474974513
}
force_use_bias: true
}
bifpn {
min_level: 3
max_level: 7
num_iterations: 5
num_filters: 112
}
}
box_coder {
faster_rcnn_box_coder {
y_scale: 1.0
x_scale: 1.0
height_scale: 1.0
width_scale: 1.0
}
}
matcher {
argmax_matcher {
matched_threshold: 0.5
unmatched_threshold: 0.5
ignore_thresholds: false
negatives_lower_than_unmatched: true
force_match_for_each_row: true
use_matmul_gather: true
}
}
similarity_calculator {
iou_similarity {
}
}
box_predictor {
weight_shared_convolutional_box_predictor {
conv_hyperparams {
regularizer {
l2_regularizer {
weight: 3.9999998989515007e-05
}
}
initializer {
random_normal_initializer {
mean: 0.0
stddev: 0.009999999776482582
}
}
activation: SWISH
batch_norm {
decay: 0.9900000095367432
scale: true
epsilon: 0.0010000000474974513
}
force_use_bias: true
}
depth: 112
num_layers_before_predictor: 3
kernel_size: 3
class_prediction_bias_init: -4.599999904632568
use_depthwise: true
}
}
anchor_generator {
multiscale_anchor_generator {
min_level: 3
max_level: 7
anchor_scale: 4.0
aspect_ratios: 1.0
aspect_ratios: 2.0
aspect_ratios: 0.5
scales_per_octave: 3
}
}
post_processing {
batch_non_max_suppression {
score_threshold: 9.99999993922529e-09
iou_threshold: 0.5
max_detections_per_class: 100
max_total_detections: 100
}
score_converter: SIGMOID
}
normalize_loss_by_num_matches: true
loss {
localization_loss {
weighted_smooth_l1 {
}
}
classification_loss {
weighted_sigmoid_focal {
gamma: 1.5
alpha: 0.25
}
}
classification_weight: 1.0
localization_weight: 1.0
}
encode_background_as_zeros: true
normalize_loc_loss_by_codesize: true
inplace_batchnorm_update: true
freeze_batchnorm: false
add_background_class: false
}
}
train_config {
batch_size: 3
data_augmentation_options {
random_horizontal_flip {
}
}
data_augmentation_options {
random_scale_crop_and_pad_to_square {
output_size: 768
scale_min: 0.10000000149011612
scale_max: 2.0
}
}
sync_replicas: true
optimizer {
momentum_optimizer {
learning_rate {
cosine_decay_learning_rate {
learning_rate_base: 0.07999999821186066
total_steps: 150000
warmup_learning_rate: 0.0010000000474974513
warmup_steps: 2500
}
}
momentum_optimizer_value: 0.8999999761581421
}
use_moving_average: false
}
fine_tune_checkpoint: ""
num_steps: 67000
startup_delay_steps: 0.0
replicas_to_aggregate: 8
max_number_of_boxes: 100
unpad_groundtruth_tensors: false
fine_tune_checkpoint_type: "classification"
use_bfloat16: true
fine_tune_checkpoint_version: V2
}
train_input_reader: {
label_map_path: "woo/dev3/Data/train/label_map_elc.pbtxt"
tf_record_input_reader {
input_path: "woo/dev3/Data/train/dataset_663.record"
}
}

eval_config: {
metrics_set: "coco_detection_metrics"
use_moving_averages: false
batch_size: 1;
}

eval_input_reader: {
label_map_path: "woo/dev3/Data/train/label_map_elc.pbtxt"
shuffle: false
num_epochs: 1
tf_record_input_reader {
input_path: "woo/dev3/Data/train/dataset_82.record"
}
}

I tried changing the record file, but it didn't work.
Is there a way?

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