我正在尝试使用我使用 Label-img 标记的样本来训练对象检测算法。我的图像尺寸为 1100 x 1100 像素。我使用的算法是 Faster R-CNN Inception ResNet V2 1024x1024，在TensorFlow 2 Detection Model Zoo 上找到。我的操作规范如下：
.config 文件如下：
# Faster R-CNN with Inception Resnet v2 (no atrous)
# Sync-trained on COCO (with 8 GPUs) with batch size 16 (800x1333 resolution)
# Initialized from Imagenet classification checkpoint
# TF2-Compatible, *Not* TPU-Compatible
#
# Achieves 39.6 mAP on COCO
model {
faster_rcnn {
num_classes: 1
image_resizer {
keep_aspect_ratio_resizer {
min_dimension: 800
max_dimension: 1333
pad_to_max_dimension: true
}
}
feature_extractor {
type: 'faster_rcnn_inception_resnet_v2_keras'
}
first_stage_anchor_generator {
grid_anchor_generator {
scales: [0.25, 0.5, 1.0, 2.0]
aspect_ratios: [0.5, 1.0, 2.0]
height_stride: 16
width_stride: 16
}
}
first_stage_box_predictor_conv_hyperparams {
op: CONV
regularizer {
l2_regularizer {
weight: 0.0
}
}
initializer {
truncated_normal_initializer {
stddev: 0.01
}
}
}
first_stage_nms_score_threshold: 0.0
first_stage_nms_iou_threshold: 0.7
first_stage_max_proposals: 300
first_stage_localization_loss_weight: 2.0
first_stage_objectness_loss_weight: 1.0
initial_crop_size: 17
maxpool_kernel_size: 1
maxpool_stride: 1
second_stage_box_predictor {
mask_rcnn_box_predictor {
use_dropout: false
dropout_keep_probability: 1.0
fc_hyperparams {
op: FC
regularizer {
l2_regularizer {
weight: 0.0
}
}
initializer {
variance_scaling_initializer {
factor: 1.0
uniform: true
mode: FAN_AVG
}
}
}
}
}
second_stage_post_processing {
batch_non_max_suppression {
score_threshold: 0.0
iou_threshold: 0.6
max_detections_per_class: 100
max_total_detections: 100
}
score_converter: SOFTMAX
}
second_stage_localization_loss_weight: 2.0
second_stage_classification_loss_weight: 1.0
}
}
train_config: {
batch_size: 1
num_steps: 200000
optimizer {
momentum_optimizer: {
learning_rate: {
cosine_decay_learning_rate {
learning_rate_base: 0.008
total_steps: 200000
warmup_learning_rate: 0.0
warmup_steps: 5000
}
}
momentum_optimizer_value: 0.9
}
use_moving_average: false
}
gradient_clipping_by_norm: 10.0
fine_tune_checkpoint_version: V2
fine_tune_checkpoint: "pre-trained-models/faster_rcnn_inception_resnet_v2_1024x1024_coco17_tpu-8/checkpoint/ckpt-0"
fine_tune_checkpoint_type: "detection"
data_augmentation_options {
random_horizontal_flip {
}
}
data_augmentation_options {
random_adjust_hue {
}
}
data_augmentation_options {
random_adjust_contrast {
}
}
data_augmentation_options {
random_adjust_saturation {
}
}
data_augmentation_options {
random_square_crop_by_scale {
scale_min: 0.6
scale_max: 1.3
}
}
}
train_input_reader: {
label_map_path: "annotations/label_map.pbtxt"
tf_record_input_reader {
input_path: "annotations/train.record"
}
}
eval_config: {
metrics_set: "coco_detection_metrics"
use_moving_averages: false
batch_size: 1;
}
eval_input_reader: {
label_map_path: "annotations/label_map.pbtxt"
shuffle: false
num_epochs: 1
tf_record_input_reader {
input_path: "annotations/test.record"
}
}
运行约5分钟后抛出以下错误：
2020-11-16 16:52:14.415133: W tensorflow/core/framework/op_kernel.cc:1767] OP_REQUIRES failed at conv_ops.cc:539 : Resource exhausted: OOM when allocating tensor with shape[64,288,9,9] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
Traceback (most recent call last):
File "model_main_tf2.py", line 113, in <module>
tf.compat.v1.app.run()
File "C:\Users\user\anaconda3\envs\object_detection_api\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\user\anaconda3\envs\object_detection_api\lib\site-packages\absl\app.py", line 303, in run
_run_main(main, args)
File "C:\Users\user\anaconda3\envs\object_detection_api\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\user\anaconda3\envs\object_detection_api\lib\site-packages\object_detection\model_lib_v2.py", line 639, in train_loop
loss = _dist_train_step(train_input_iter)
File "C:\Users\user\anaconda3\envs\object_detection_api\lib\site-packages\tensorflow\python\eager\def_function.py", line 780, in __call__
result = self._call(*args, **kwds)
File "C:\Users\user\anaconda3\envs\object_detection_api\lib\site-packages\tensorflow\python\eager\def_function.py", line 840, in _call
return self._stateless_fn(*args, **kwds)
File "C:\Users\user\anaconda3\envs\object_detection_api\lib\site-packages\tensorflow\python\eager\function.py", line 2829, in __call__
return graph_function._filtered_call(args, kwargs) # pylint: disable=protected-access
File "C:\Users\user\anaconda3\envs\object_detection_api\lib\site-packages\tensorflow\python\eager\function.py", line 1843, in _filtered_call
return self._call_flat(
File "C:\Users\user\anaconda3\envs\object_detection_api\lib\site-packages\tensorflow\python\eager\function.py", line 1923, in _call_flat
return self._build_call_outputs(self._inference_function.call(
File "C:\Users\user\anaconda3\envs\object_detection_api\lib\site-packages\tensorflow\python\eager\function.py", line 545, in call
outputs = execute.execute(
File "C:\Users\user\anaconda3\envs\object_detection_api\lib\site-packages\tensorflow\python\eager\execute.py", line 59, in quick_execute
tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
tensorflow.python.framework.errors_impl.ResourceExhaustedError: 2 root error(s) found.
(0) Resource exhausted: OOM when allocating tensor with shape[64,256,17,17] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
[[node functional_3/conv2d_160/Conv2D (defined at \site-packages\object_detection\meta_architectures\faster_rcnn_meta_arch.py:1149) ]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.
[[Identity_1/_432]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.
(1) Resource exhausted: OOM when allocating tensor with shape[64,256,17,17] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
[[node functional_3/conv2d_160/Conv2D (defined at \site-packages\object_detection\meta_architectures\faster_rcnn_meta_arch.py:1149) ]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.
0 successful operations.
0 derived errors ignored. [Op:__inference__dist_train_step_79248]
Errors may have originated from an input operation.
Input Source operations connected to node functional_3/conv2d_160/Conv2D:
MaxPool2D/MaxPool (defined at \site-packages\object_detection\meta_architectures\faster_rcnn_meta_arch.py:1973)
Input Source operations connected to node functional_3/conv2d_160/Conv2D:
MaxPool2D/MaxPool (defined at \site-packages\object_detection\meta_architectures\faster_rcnn_meta_arch.py:1973)
Function call stack:
_dist_train_step -> _dist_train_step
这个问题的一个常见解决方案是减少你的批量大小，但我已经把它减少到 1。是我处理内存不足的问题，还是有其他方法可以解决这个问题？
注意：这是在抛出异常之前给出的输出：
2020-11-16 16:52:14.409101: I tensorflow/core/common_runtime/bfc_allocator.cc:1046] Stats:
Limit: 4817616896
InUse: 4809875456
MaxInUse: 4817131776
NumAllocs: 11104
MaxAllocSize: 4129325056
Reserved: 0
PeakReserved: 0
LargestFreeBlock: 0
2020-11-16 16:52:14.413310: W tensorflow/core/common_runtime/bfc_allocator.cc:439] ****************************************************************************************************
小智 2
看看这个帖子（通过你的帖子，我认为你已经读过它）：
两种可能的解决方案是将 config.gpu_options.per_process_gpu_memory_fraction 更改为更大的数字。
其他解决方案是重新安装cuda。
您可以使用 nvidia docker。然后您就可以在版本之间快速切换。
https://github.com/NVIDIA/nvidia-docker
您可以更改cuda版本并查看错误是否仍然存在。
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