model_main_tf2.py -> UnicodeDecodeError: 'utf-8' codec can't decode byte 0xbf in position 142: invalid start byte

Prerequisites

Please answer the following questions for yourself before submitting an issue.

• [X ] I am using the latest TensorFlow Model Garden release and TensorFlow 2.
• [ X] I am reporting the issue to the correct repository. (Model Garden official or research directory)
• [ X] I checked to make sure that this issue has not been filed already.

System information

• Have I written custom code (as opposed to using a stock example script provided in TensorFlow): example script
• OS Platform and Distribution : win10
• Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device:
• TensorFlow installed from (source or binary): bin
• TensorFlow version (use command below): tensorflow-cpu 2.4.1
• Python version: 3.8.8
• Bazel version (if compiling from source):
• GCC/Compiler version (if compiling from source):
• CUDA/cuDNN version:
• GPU model and memory: intel integrated

Describe the current behavior

(tensorflow) C:\Users\kacpe\Desktop\tensorflow\models\research\object_detection>python model_main_tf2.py --pipeline_config_path=training/pipeline.config --model_dir=training --alsologtostderr
2021-04-06 14:21:49.605417: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cudart64_110.dll'; dlerror: cudart64_110.dll not found
2021-04-06 14:21:49.605814: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
2021-04-06 14:22:08.223915: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set
2021-04-06 14:22:08.229736: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'nvcuda.dll'; dlerror: nvcuda.dll not found
2021-04-06 14:22:08.230068: W tensorflow/stream_executor/cuda/cuda_driver.cc:326] failed call to cuInit: UNKNOWN ERROR (303)
2021-04-06 14:22:08.242115: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: DESKTOP-HO16S6U
2021-04-06 14:22:08.242701: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: DESKTOP-HO16S6U
2021-04-06 14:22:08.244472: 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
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2021-04-06 14:22:08.245897: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set
WARNING:tensorflow:There are non-GPU devices in tf.distribute.Strategy, not using nccl allreduce.
W0406 14:22:08.247780 9044 cross_device_ops.py:1321] There are non-GPU devices in tf.distribute.Strategy, not using nccl allreduce.
INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:CPU:0',)
I0406 14:22:08.247780 9044 mirrored_strategy.py:350] Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:CPU:0',)
INFO:tensorflow:Maybe overwriting train_steps: None
I0406 14:22:08.263412 9044 config_util.py:552] Maybe overwriting train_steps: None
INFO:tensorflow:Maybe overwriting use_bfloat16: False
I0406 14:22:08.263412 9044 config_util.py:552] Maybe overwriting use_bfloat16: False
I0406 14:22:08.325909 9044 ssd_efficientnet_bifpn_feature_extractor.py:142] EfficientDet EfficientNet backbone version: efficientnet-b1
I0406 14:22:08.325909 9044 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 88
I0406 14:22:08.325909 9044 ssd_efficientnet_bifpn_feature_extractor.py:145] EfficientDet BiFPN num iterations: 4
I0406 14:22:08.357155 9044 efficientnet_model.py:147] round_filter input=32 output=32
I0406 14:22:08.510153 9044 efficientnet_model.py:147] round_filter input=32 output=32
I0406 14:22:08.510153 9044 efficientnet_model.py:147] round_filter input=16 output=16
I0406 14:22:09.333078 9044 efficientnet_model.py:147] round_filter input=16 output=16
I0406 14:22:09.333078 9044 efficientnet_model.py:147] round_filter input=24 output=24
I0406 14:22:10.936553 9044 efficientnet_model.py:147] round_filter input=24 output=24
I0406 14:22:10.936553 9044 efficientnet_model.py:147] round_filter input=40 output=40
I0406 14:22:12.487046 9044 efficientnet_model.py:147] round_filter input=40 output=40
I0406 14:22:12.487046 9044 efficientnet_model.py:147] round_filter input=80 output=80
I0406 14:22:14.495594 9044 efficientnet_model.py:147] round_filter input=80 output=80
I0406 14:22:14.495594 9044 efficientnet_model.py:147] round_filter input=112 output=112
I0406 14:22:16.817428 9044 efficientnet_model.py:147] round_filter input=112 output=112
I0406 14:22:16.817428 9044 efficientnet_model.py:147] round_filter input=192 output=192
I0406 14:22:19.744267 9044 efficientnet_model.py:147] round_filter input=192 output=192
I0406 14:22:19.744267 9044 efficientnet_model.py:147] round_filter input=320 output=320
I0406 14:22:20.961132 9044 efficientnet_model.py:147] round_filter input=1280 output=1280
I0406 14:22:21.257415 9044 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=1.0, depth_coefficient=1.1, resolution=240, dropout_rate=0.2, 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')
WARNING:tensorflow:From C:\Users\kacpe\anaconda3\envs\tensorflow\lib\site-packages\object_detection\model_lib_v2.py:545: StrategyBase.experimental_distribute_datasets_from_function (from tensorflow.python.distribute.distribute_lib) is deprecated and will be removed in a future version.
Instructions for updating:
rename to distribute_datasets_from_function
W0406 14:22:21.567980 9044 deprecation.py:333] From C:\Users\kacpe\anaconda3\envs\tensorflow\lib\site-packages\object_detection\model_lib_v2.py:545: StrategyBase.experimental_distribute_datasets_from_function (from tensorflow.python.distribute.distribute_lib) is deprecated and will be removed in a future version.
Instructions for updating:
rename to distribute_datasets_from_function
Traceback (most recent call last):
File "model_main_tf2.py", line 113, in
tf.compat.v1.app.run()
File "C:\Users\kacpe\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\platform\app.py", line 40, in run
_run(main=main, argv=argv, flags_parser=_parse_flags_tolerate_undef)
File "C:\Users\kacpe\anaconda3\envs\tensorflow\lib\site-packages\absl\app.py", line 303, in run
_run_main(main, args)
File "C:\Users\kacpe\anaconda3\envs\tensorflow\lib\site-packages\absl\app.py", line 251, in _run_main
sys.exit(main(argv))
File "model_main_tf2.py", line 104, in main
model_lib_v2.train_loop(
File "C:\Users\kacpe\anaconda3\envs\tensorflow\lib\site-packages\object_detection\model_lib_v2.py", line 545, in train_loop
train_input = strategy.experimental_distribute_datasets_from_function(
File "C:\Users\kacpe\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\util\deprecation.py", line 340, in new_func
return func(*args, **kwargs)
File "C:\Users\kacpe\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\distribute\distribute_lib.py", line 1143, in experimental_distribute_datasets_from_function
return self.distribute_datasets_from_function(dataset_fn, options)
File "C:\Users\kacpe\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\distribute\distribute_lib.py", line 1134, in distribute_datasets_from_function
return self._extended._distribute_datasets_from_function( # pylint: disable=protected-access
File "C:\Users\kacpe\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\distribute\mirrored_strategy.py", line 545, in _distribute_datasets_from_function
return input_lib.get_distributed_datasets_from_function(
File "C:\Users\kacpe\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\distribute\input_lib.py", line 161, in get_distributed_datasets_from_function
return DistributedDatasetsFromFunction(dataset_fn, input_workers,
File "C:\Users\kacpe\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\distribute\input_lib.py", line 1272, in init
_create_datasets_from_function_with_input_context(
File "C:\Users\kacpe\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\distribute\input_lib.py", line 1936, in _create_datasets_from_function_with_input_context
dataset = dataset_fn(ctx)
File "C:\Users\kacpe\anaconda3\envs\tensorflow\lib\site-packages\object_detection\model_lib_v2.py", line 536, in train_dataset_fn
train_input = inputs.train_input(
File "C:\Users\kacpe\anaconda3\envs\tensorflow\lib\site-packages\object_detection\inputs.py", line 893, in train_input
dataset = INPUT_BUILDER_UTIL_MAP['dataset_build'](
File "C:\Users\kacpe\anaconda3\envs\tensorflow\lib\site-packages\object_detection\builders\dataset_builder.py", line 210, in build
decoder = decoder_builder.build(input_reader_config)
File "C:\Users\kacpe\anaconda3\envs\tensorflow\lib\site-packages\object_detection\builders\decoder_builder.py", line 52, in build
decoder = tf_example_decoder.TfExampleDecoder(
File "C:\Users\kacpe\anaconda3\envs\tensorflow\lib\site-packages\object_detection\data_decoders\tf_example_decoder.py", line 414, in init
_ClassTensorHandler(
File "C:\Users\kacpe\anaconda3\envs\tensorflow\lib\site-packages\object_detection\data_decoders\tf_example_decoder.py", line 88, in init
name_to_id = label_map_util.get_label_map_dict(
File "C:\Users\kacpe\anaconda3\envs\tensorflow\lib\site-packages\object_detection\utils\label_map_util.py", line 201, in get_label_map_dict
label_map = load_labelmap(label_map_path_or_proto)
File "C:\Users\kacpe\anaconda3\envs\tensorflow\lib\site-packages\object_detection\utils\label_map_util.py", line 168, in load_labelmap
label_map_string = fid.read()
File "C:\Users\kacpe\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\lib\io\file_io.py", line 117, in read
self._preread_check()
File "C:\Users\kacpe\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\lib\io\file_io.py", line 79, in _preread_check
self._read_buf = _pywrap_file_io.BufferedInputStream(
UnicodeDecodeError: 'utf-8' codec can't decode byte 0xbf in position 142: invalid start byte

Describe the expected behavior

train the model

Standalone code to reproduce the issue
Provide a reproducible test case that is the bare minimum necessary to generate
the problem. If possible, please share a link to Colab/Jupyter/any notebook.

pipeline.config :

`model {
ssd {
num_classes: 1
image_resizer {
keep_aspect_ratio_resizer {
min_dimension: 640
max_dimension: 640
pad_to_max_dimension: true
}
}
feature_extractor {
type: "ssd_efficientnet-b1_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: 4
num_filters: 88
}
}
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: 88
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: 8
data_augmentation_options {
random_horizontal_flip {
}
}
data_augmentation_options {
random_scale_crop_and_pad_to_square {
output_size: 640
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: 300000
warmup_learning_rate: 0.0010000000474974513
warmup_steps: 2500
}
}
momentum_optimizer_value: 0.8999999761581421
}
use_moving_average: false
}
fine_tune_checkpoint: "C:/Users/kacpe/Desktop/tensorflow/models/research/object_detection/efficientdet_d1_coco17_tpu-32/checkpoint/ckpt-0"
num_steps: 300000
startup_delay_steps: 0.0
replicas_to_aggregate: 8
max_number_of_boxes: 100
unpad_groundtruth_tensors: false
fine_tune_checkpoint_type: "detection"
use_bfloat16: true
fine_tune_checkpoint_version: V2
}
train_input_reader: {
label_map_path: "C:/Users/kacpe/Desktop/tensorflow/models/research/object_detection/training/labelmap.bptxt"
tf_record_input_reader {
input_path: "C:/Users/kacpe/Desktop/tensorflow/models/research/object_detection/images/train.record"
}
}

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

eval_input_reader: {
label_map_path: "C:/Users/kacpe/Desktop/tensorflow/models/research/object_detection/training/labelmap.bptxt"
shuffle: false
num_epochs: 1
tf_record_input_reader {
input_path: "C:/Users/kacpe/Desktop/tensorflow/models/research/object_detection/images/test.record"
}
}
`

Other info / logs Include any logs or source code that would be helpful to
diagnose the problem. If including tracebacks, please include the full
traceback. Large logs and files should be attached.