vighneshbirodkar
commented
3 years ago Can you share the config file and error message as a github gist ? It is hard to read them in github comments.
Open deeprine opened 3 years ago
vighneshbirodkar
commented
3 years ago Can you share the config file and error message as a github gist ? It is hard to read them in github comments.
deeprine
commented
3 years ago @vighneshbirodkar
https://gist.github.com/YongSeongLee25/d97e9e5f67d3692ecae81075521102d2
Sorry, I'm giving you a link because I don't know how to upload the code itself to issues.
google-ml-butler[bot]
commented
3 years ago
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. Overwritingnum_epochsto 1. W0730 16:30:05.285473 139928160339776 model_lib_v2.py:1085] Expected number of evaluation epochs is 1, but instead encounteredeval_on_train_input_config.num_epochs= 0. Overwritingnum_epochsto 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: Usetf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.experimental.AUTOTUNE)instead. If sloppy execution is desired, usetf.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: Usetf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.experimental.AUTOTUNE)instead. If sloppy execution is desired, usetf.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: Usetf.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: Usetf.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 atf.sparse.SparseTensorand usetf.sparse.to_denseinstead. 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 atf.sparse.SparseTensorand usetf.sparse.to_denseinstead. 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: Usetf.castinstead. 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: Usetf.castinstead. 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 thetrainingargument 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 thetrainingargument 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: Usetf.castinstead. 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: Usetf.castinstead. 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_functions can use accelerators such as GPUs as well as being differentiable using a gradient tape.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_functions can use accelerators such as GPUs as well as being differentiable using a gradient tape.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]],
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]],
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]],
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]],
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?