haf*_*031 3 python invalid-argument assertion tensorflow google-colaboratory
在使用来自 Google Colab 的 TensorFlow Object Detection API 进行训练时，我收到以下错误（以下详细内容中有两个类似的错误......其中一个在它的末尾）：
WARNING:tensorflow:Forced number of epochs for all eval validations to be 1.
W0528 21:13:21.113062 140292083513216 model_lib.py:717] Forced number of epochs for all eval validations to be 1.
INFO:tensorflow:Maybe overwriting train_steps: 200000
I0528 21:13:21.113316 140292083513216 config_util.py:523] Maybe overwriting train_steps: 200000
INFO:tensorflow:Maybe overwriting use_bfloat16: False
I0528 21:13:21.113430 140292083513216 config_util.py:523] Maybe overwriting use_bfloat16: False
INFO:tensorflow:Maybe overwriting sample_1_of_n_eval_examples: 1
I0528 21:13:21.113519 140292083513216 config_util.py:523] Maybe overwriting sample_1_of_n_eval_examples: 1
INFO:tensorflow:Maybe overwriting eval_num_epochs: 1
I0528 21:13:21.113614 140292083513216 config_util.py:523] Maybe overwriting eval_num_epochs: 1
INFO:tensorflow:Maybe overwriting load_pretrained: True
I0528 21:13:21.113696 140292083513216 config_util.py:523] Maybe overwriting load_pretrained: True
INFO:tensorflow:Ignoring config override key: load_pretrained
I0528 21:13:21.113776 140292083513216 config_util.py:533] Ignoring config override key: load_pretrained
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.
W0528 21:13:21.114626 140292083513216 model_lib.py:733] Expected number of evaluation epochs is 1, but instead encountered `eval_on_train_input_config.num_epochs` = 0. Overwriting `num_epochs` to 1.
INFO:tensorflow:create_estimator_and_inputs: use_tpu False, export_to_tpu False
I0528 21:13:21.114744 140292083513216 model_lib.py:768] create_estimator_and_inputs: use_tpu False, export_to_tpu False
INFO:tensorflow:Using config: {'_model_dir': 'training/', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true
graph_options {
rewrite_options {
meta_optimizer_iterations: ONE
}
}
, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_service': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f97ed4dd128>, '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}
I0528 21:13:21.115245 140292083513216 estimator.py:212] Using config: {'_model_dir': 'training/', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true
graph_options {
rewrite_options {
meta_optimizer_iterations: ONE
}
}
, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_service': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f97ed4dd128>, '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}
WARNING:tensorflow:Estimator's model_fn (<function create_model_fn.<locals>.model_fn at 0x7f97d328dbf8>) includes params argument, but params are not passed to Estimator.
W0528 21:13:21.115487 140292083513216 model_fn.py:630] Estimator's model_fn (<function create_model_fn.<locals>.model_fn at 0x7f97d328dbf8>) includes params argument, but params are not passed to Estimator.
INFO:tensorflow:Not using Distribute Coordinator.
I0528 21:13:21.116259 140292083513216 estimator_training.py:186] Not using Distribute Coordinator.
INFO:tensorflow:Running training and evaluation locally (non-distributed).
I0528 21:13:21.116456 140292083513216 training.py:612] Running training and evaluation locally (non-distributed).
INFO:tensorflow:Start train and evaluate loop. The evaluate will happen after every checkpoint. Checkpoint frequency is determined based on RunConfig arguments: save_checkpoints_steps None or save_checkpoints_secs 600.
I0528 21:13:21.116694 140292083513216 training.py:700] Start train and evaluate loop. The evaluate will happen after every checkpoint. Checkpoint frequency is determined based on RunConfig arguments: save_checkpoints_steps None or save_checkpoints_secs 600.
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/training/training_util.py:236: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.
Instructions for updating:
Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.
W0528 21:13:21.124795 140292083513216 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/training/training_util.py:236: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.
Instructions for updating:
Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.
WARNING:tensorflow:num_readers has been reduced to 1 to match input file shards.
W0528 21:13:21.162153 140292083513216 dataset_builder.py:84] num_readers has been reduced to 1 to match input file shards.
WARNING:tensorflow:From /content/models/research/object_detection/builders/dataset_builder.py:101: parallel_interleave (from tensorflow.contrib.data.python.ops.interleave_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.data.experimental.parallel_interleave(...)`.
W0528 21:13:21.167545 140292083513216 deprecation.py:323] From /content/models/research/object_detection/builders/dataset_builder.py:101: parallel_interleave (from tensorflow.contrib.data.python.ops.interleave_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.data.experimental.parallel_interleave(...)`.
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/contrib/data/python/ops/interleave_ops.py:77: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated 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_determinstic`.
W0528 21:13:21.167754 140292083513216 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow_core/contrib/data/python/ops/interleave_ops.py:77: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated 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_determinstic`.
2020-05-28 21:13:22.910301: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1
2020-05-28 21:13:22.953259: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-05-28 21:13:22.953875: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties:
name: Tesla T4 major: 7 minor: 5 memoryClockRate(GHz): 1.59
pciBusID: 0000:00:04.0
2020-05-28 21:13:22.960996: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.0
2020-05-28 21:13:22.967688: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10.0
2020-05-28 21:13:22.977811: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10.0
2020-05-28 21:13:22.985131: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10.0
2020-05-28 21:13:22.995549: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10.0
2020-05-28 21:13:23.004617: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10.0
2020-05-28 21:13:23.025234: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2020-05-28 21:13:23.025382: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-05-28 21:13:23.026101: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-05-28 21:13:23.026693: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0
WARNING:tensorflow:From /content/models/research/object_detection/inputs.py:77: sparse_to_dense (from tensorflow.python.ops.sparse_ops) 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.
W0528 21:13:33.109247 140292083513216 deprecation.py:323] From /content/models/research/object_detection/inputs.py:77: sparse_to_dense (from tensorflow.python.ops.sparse_ops) 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 /content/models/research/object_detection/utils/ops.py:493: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.where in 2.0, which has the same broadcast rule as np.where
W0528 21:13:33.221111 140292083513216 deprecation.py:323] From /content/models/research/object_detection/utils/ops.py:493: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.where in 2.0, which has the same broadcast rule as np.where
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/autograph/operators/control_flow.py:1004: sample_distorted_bounding_box (from tensorflow.python.ops.image_ops_impl) is deprecated and will be removed in a future version.
Instructions for updating:
`seed2` arg is deprecated.Use sample_distorted_bounding_box_v2 instead.
W0528 21:13:39.145547 140292083513216 api.py:332] From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/autograph/operators/control_flow.py:1004: sample_distorted_bounding_box (from tensorflow.python.ops.image_ops_impl) is deprecated and will be removed in a future version.
Instructions for updating:
`seed2` arg is deprecated.Use sample_distorted_bounding_box_v2 instead.
WARNING:tensorflow:From /content/models/research/object_detection/inputs.py:259: 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.
W0528 21:13:42.865469 140292083513216 deprecation.py:323] From /content/models/research/object_detection/inputs.py:259: 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.
WARNING:tensorflow:From /content/models/research/object_detection/builders/dataset_builder.py:174: batch_and_drop_remainder (from tensorflow.contrib.data.python.ops.batching) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.data.Dataset.batch(..., drop_remainder=True)`.
W0528 21:13:46.217640 140292083513216 deprecation.py:323] From /content/models/research/object_detection/builders/dataset_builder.py:174: batch_and_drop_remainder (from tensorflow.contrib.data.python.ops.batching) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.data.Dataset.batch(..., drop_remainder=True)`.
INFO:tensorflow:Calling model_fn.
I0528 21:13:46.233859 140292083513216 estimator.py:1148] Calling model_fn.
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tf_slim/layers/layers.py:1089: Layer.apply (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.
Instructions for updating:
Please use `layer.__call__` method instead.
W0528 21:13:46.430602 140292083513216 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tf_slim/layers/layers.py:1089: Layer.apply (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.
Instructions for updating:
Please use `layer.__call__` method instead.
INFO:tensorflow:depth of additional conv before box predictor: 0
I0528 21:13:49.101978 140292083513216 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0
INFO:tensorflow:depth of additional conv before box predictor: 0
I0528 21:13:49.133970 140292083513216 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0
INFO:tensorflow:depth of additional conv before box predictor: 0
I0528 21:13:49.165436 140292083513216 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0
INFO:tensorflow:depth of additional conv before box predictor: 0
I0528 21:13:49.343221 140292083513216 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0
INFO:tensorflow:depth of additional conv before box predictor: 0
I0528 21:13:49.377842 140292083513216 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0
INFO:tensorflow:depth of additional conv before box predictor: 0
I0528 21:13:49.414346 140292083513216 convolutional_box_predictor.py:156] depth of additional conv before box predictor: 0
W0528 21:13:49.456603 140292083513216 variables_helper.py:161] Variable [FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_2_3x3_s2_512/weights] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 256, 512]], model variable shape: [[3, 3, 256, 512]]. This variable will not be initialized from the checkpoint.
W0528 21:13:49.456816 140292083513216 variables_helper.py:161] Variable [FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_3_3x3_s2_256/weights] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 128, 256]], model variable shape: [[3, 3, 128, 256]]. This variable will not be initialized from the checkpoint.
W0528 21:13:49.456997 140292083513216 variables_helper.py:161] Variable [FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_4_3x3_s2_256/weights] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 128, 256]], model variable shape: [[3, 3, 128, 256]]. This variable will not be initialized from the checkpoint.
W0528 21:13:49.457174 140292083513216 variables_helper.py:161] Variable [FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_5_3x3_s2_128/weights] is available in checkpoint, but has an incompatible shape with model variable. Checkpoint shape: [[1, 1, 64, 128]], model variable shape: [[3, 3, 64, 128]]. This variable will not be initialized from the checkpoint.
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/training/rmsprop.py:119: calling Ones.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
W0528 21:13:54.449208 140292083513216 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/training/rmsprop.py:119: calling Ones.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
INFO:tensorflow:Done calling model_fn.
I0528 21:14:00.871218 140292083513216 estimator.py:1150] Done calling model_fn.
INFO:tensorflow:Create CheckpointSaverHook.
I0528 21:14:00.872715 140292083513216 basic_session_run_hooks.py:541] Create CheckpointSaverHook.
INFO:tensorflow:Graph was finalized.
I0528 21:14:04.557027 140292083513216 monitored_session.py:240] Graph was finalized.
2020-05-28 21:14:04.557485: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 AVX512F FMA
2020-05-28 21:14:04.562729: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2000165000 Hz
2020-05-28 21:14:04.563012: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1771800 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2020-05-28 21:14:04.563048: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version
2020-05-28 21:14:04.666903: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-05-28 21:14:04.667672: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1770d80 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2020-05-28 21:14:04.667705: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Tesla T4, Compute Capability 7.5
2020-05-28 21:14:04.668018: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-05-28 21:14:04.668594: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties:
name: Tesla T4 major: 7 minor: 5 memoryClockRate(GHz): 1.59
pciBusID: 0000:00:04.0
2020-05-28 21:14:04.668682: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.0
2020-05-28 21:14:04.668724: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10.0
2020-05-28 21:14:04.668747: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10.0
2020-05-28 21:14:04.668769: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10.0
2020-05-28 21:14:04.668796: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10.0
2020-05-28 21:14:04.668819: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10.0
2020-05-28 21:14:04.668842: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2020-05-28 21:14:04.668951: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS h
好吧！正式的回答可能会有所帮助。我分两步解决了这个问题。首先，在创建时CSV你应该确保没有无效的条目。我的意思是，没有无效图像和/或图像外没有边界框，即首先检查xmin, ymin, xmax,ymax是否都在图像的分辨率范围内，并且它们不是底片。还要检查width和height是阳性。
其次，在制作的同时，tf_example我执行了一些额外的检查以确保所有坐标仍在图像内。tfrecord想要将坐标按比例缩放[0, 1]。虽然，从逻辑上讲，如果我们做了第一步，就没有必要再次检查它。但是我发现，可能是由于一些浮点精度问题，这些缩放坐标有时会大于1.0或小于0.0并再次产生此错误。因此，我对以下函数进行了一些额外的检查，以确保在将每个条目写入到tfrecord. 万一他们是> 1.0我做的1.0，如果< 0.0我做的0.0。以下是代码：
def create_tf_example(group, path):
with tf.gfile.GFile(os.path.join(path, '{}'.format(group.filename)), 'rb') as fid:
encoded_jpg = fid.read()
encoded_jpg_io = io.BytesIO(encoded_jpg)
image = Image.open(encoded_jpg_io)
width, height = image.size
filename = group.filename.encode('utf8')
image_format = b'jpg'
xmins = []
xmaxs = []
ymins = []
ymaxs = []
classes_text = []
classes = []
for index, row in group.object.iterrows():
########### ADDITIONAL CHECKS START HERE ###################
xmn = row['xmin'] / width
if xmn < 0.0:
xmn = 0.0
elif xmn > 1.0:
xmn = 1.0
xmins.append(xmn)
xmx = row['xmax'] / width
if xmx < 0.0:
xmx = 0.0
elif xmx > 1.0:
xmx = 1.0
xmaxs.append(xmx)
ymn = row['ymin'] / height
if ymn < 0.0:
ymn = 0.0
elif ymn > 1.0:
ymn = 1.0
ymins.append(ymn)
ymx = row['ymax'] / height
if ymx < 0.0:
ymx = 0.0
elif ymx > 1.0:
ymx = 1.0
ymaxs.append(ymx)
############ ADDITIONAL CHECKS END HERE ####################
classes_text.append(row['class'].encode('utf8'))
classes.append(class_text_to_int(row['class']))
tf_example = tf.train.Example(features=tf.train.Features(feature={
'image/height': dataset_util.int64_feature(height),
'image/width': dataset_util.int64_feature(width),
'image/filename': dataset_util.bytes_feature(filename),
'image/source_id': dataset_util.bytes_feature(filename),
'image/encoded': dataset_util.bytes_feature(encoded_jpg),
'image/format': dataset_util.bytes_feature(image_format),
'image/object/bbox/xmin': dataset_util.float_list_feature(xmins),
'image/object/bbox/xmax': dataset_util.float_list_feature(xmaxs),
'image/object/bbox/ymin': dataset_util.float_list_feature(ymins),
'image/object/bbox/ymax': dataset_util.float_list_feature(ymaxs),
'image/object/class/text': dataset_util.bytes_list_feature(classes_text),
'image/object/class/label': dataset_util.int64_list_feature(classes),
}))
return tf_example
还有另一个极端情况可能是造成这种情况的原因。这与您注释边界框的方式有关。首先，我描述了正确的注释方式。剩下的你自己会明白的。在绘制这些注释框时，如果您将鼠标从 拖动left-top到right-bottom注释器工具，则会将该left-top点视为第一个点 ie，(xmin, ymin)将该right-bottom点视为第二个点 ie, (xmax, ymax)。这是完全可以的，因为在这种情况下自动的条件xmin < xmax和ymin < ymax保持。但是当你做一些不同的事情时会发生什么？就像，如果您从一个right-bottom点拖到left-top另一个点，因此注释器工具可能会将该right-bottom点作为(xmin, ymin)和left-top点为(xmax, ymax). 这是完全错误的。由于在这种情况下xmax是不是越来越少xmin和出现同样的问题ymax和ymin。因此，通过观察您拖动鼠标的方式，确保您的注释器软件能够处理这些类型的情况。
所以，如果你找到边框注释确实有这个问题，那么你就可以很容易地修正CSV通过更新的值xmin，xmax，ymin和ymax为如下：
import numpy as np
xmin_new = np.min(xmin, xmax)
xmax_new = np.max(xmin, xmax)
ymin_new = np.min(ymin, ymax)
ymax_new = np.max(ymin, ymax)
另请注意，我使用了不同的变量来获取新值，替换旧变量的值（xmin、xmax、ymin、ymax）将使进一步的计算出错，因为在获取np.max()或np.min()表达式期望它们之前的值时，而不是我们刚刚在此过程中更新的值。
小智 4
在构建自己的 tfrecords 来重新训练模型时，我遇到了完全相同的错误。问题在于其中一个标记框的高度为负值。我建议检查您的数据的完整性。