自定义对象检测train.py-错误：在https://pastebin.com/raw/EtkkfiDX处进行分块
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我对tensorflow对象检测还很陌生。当前正在运行最新版本1.13.1。我的CUDA版本是10.0，cudnn版本也相应。我已经正确设置了模型设置，labelmap很好。但是，当我开始训练时，它会显示此信息，而不是训练进度。
我尝试了不同的模型进行训练，还几次重新安装了tensorflow
这是我用于训练的命令：
python train.py --logtostderr --train_dir = Training_dir / --pipeline_config_path = Training / faster_rcnn_inception_v2_pets.config
这是我的.config文件：
model
ssd
num_classes: 1
box_coder
faster_rcnn_box_coder
y_scale: 10.0
x_scale: 10.0
height_scale: 5.0
width_scale: 5.0
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
similarity_calculator
iou_similarity
anchor_generator
ssd_anchor_generator
num_layers: 6
min_scale: 0.2
max_scale: 0.95
aspect_ratios: 1.0
aspect_ratios: 2.0
aspect_ratios: 0.5
aspect_ratios: 3.0
aspect_ratios: 0.3333
image_resizer
fixed_shape_resizer
height: 300
width: 300
box_predictor
convolutional_box_predictor
min_depth: 0
max_depth: 0
num_layers_before_predictor: 0
use_dropout: false
dropout_keep_probability: 0.8
kernel_size: 1
box_code_size: 4
apply_sigmoid_to_scores: false
conv_hyperparams
activation: RELU_6,
regularizer
l2_regularizer
weight: 0.00004
initializer
truncated_normal_initializer
stddev: 0.03
mean: 0.0
batch_norm
train: true,
scale: true,
center: true,
decay: 0.9997,
epsilon: 0.001,
feature_extractor
type: 'ssd_mobilenet_v2'
min_depth: 16
depth_multiplier: 1.0
conv_hyperparams
activation: RELU_6,
regularizer
l2_regularizer
weight: 0.00004
initializer
truncated_normal_initializer
stddev: 0.03
mean: 0.0
batch_norm
train: true,
scale: true,
center: true,
decay: 0.9997,
epsilon: 0.001,
loss
classification_loss
weighted_sigmoid
localization_loss
weighted_smooth_l1
hard_example_miner
num_hard_examples: 3000
iou_threshold: 0.99
loss_type: CLASSIFICATION
max_negatives_per_positive: 3
min_negatives_per_image: 3
classification_weight: 1.0
localization_weight: 1.0
normalize_loss_by_num_matches: true
post_processing
batch_non_max_suppression
score_threshold: 1e-8
iou_threshold: 0.6
max_detections_per_class: 100
max_total_detections: 100
score_converter: SIGMOID
train_config:
batch_size: 1
optimizer
rms_prop_optimizer:
learning_rate:
exponential_decay_learning_rate
initial_learning_rate: 0.004
decay_steps: 800720
decay_factor: 0.95
momentum_optimizer_value: 0.9
decay: 0.9
epsilon: 1.0
fine_tune_checkpoint: "D:/tensorflow/models-master/research/object_detection/ssd_mobilenet_v2_coco_2018_03_29/model.ckpt"
fine_tune_checkpoint_type: "detection"
num_steps: 200000
data_augmentation_options
random_horizontal_flip
data_augmentation_options
ssd_random_crop
train_input_reader:
tf_record_input_reader
input_path: "D:/tensorflow/models-master/research/object_detection/Images/train.record"
label_map_path: "D:/tensorflow/models-master/research/object_detection/Training/labelmap.pbtxt"
eval_config:
num_examples: 8000
max_evals: 10
eval_input_reader:
tf_record_input_reader
input_path: "D:/tensorflow/models-master/research/object_detection/Images/test.record"
label_map_path: "D:/tensorflow/models-master/research/object_detection/Training/labelmap.pbtxt"
shuffle: false
num_readers: 1
这是我每次训练时日志的样子（缩短了-完整日志在这里https://pastebin.com/raw/EtkkfiDX）：
2019-05-04 15:49:49.681406: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0000000715B7F200 of size 256
...
2019-05-04 15:49:49.915674: I tensorflow/core/common_runtime/bfc_allocator.cc:641] 2 Chunks of size 718848000 totalling 1.34GiB
2019-05-04 15:49:49.918760: I tensorflow/core/common_runtime/bfc_allocator.cc:645] Sum Total of in-use chunks: 1.77GiB
2019-05-04 15:49:49.921284: I tensorflow/core/common_runtime/bfc_allocator.cc:647] Stats:
Limit: 3006477107
InUse: 1902062848
MaxInUse: 1902062848
NumAllocs: 12819
MaxAllocSize: 718848000
2019-05-04 15:49:49.928506: W tensorflow/core/common_runtime/bfc_allocator.cc:271] ________________**********************************************************__________________
2019-05-04 15:49:49.931977: W tensorflow/core/framework/op_kernel.cc:1401] OP_REQUIRES failed at conv_ops.cc:446 : Resource exhausted: OOM when allocating tensor with shape[52,64,480,300] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
INFO:tensorflow:Error reported to Coordinator: OOM when allocating tensor with shape[52,64,480,300] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
[[node FirstStageFeatureExtractor/InceptionV2/InceptionV2/Conv2d_1a_7x7/separable_conv2d (defined at D:\tensorflow\models-master\research\slim\nets\inception_v2.py:117) ]]
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.
[[node BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/Reshape (defined at D:\tensorflow\models-master\research\object_detection\core\post_processing.py:136) ]]
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.
Caused by op 'FirstStageFeatureExtractor/InceptionV2/InceptionV2/Conv2d_1a_7x7/separable_conv2d', defined at:
File "train.py", line 184, in
tf.app.run()
File "D:\Program Files\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\platform\app.py", line 125, in run
_sys.exit(main(argv))
File "D:\Program Files\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\util\deprecation.py", line 324, in new_func
return func(*args, **kwargs)
File "train.py", line 180, in main
graph_hook_fn=graph_rewriter_fn)
File "D:\tensorflow\models-master\research\object_detection\legacy\trainer.py", line 291, in train
clones = model_deploy.create_clones(deploy_config, model_fn, [input_queue])
File "D:\tensorflow\models-master\research\slim\deployment\model_deploy.py", line 193, in create_clones
outputs = model_fn(*args, **kwargs)
File "D:\tensorflow\models-master\research\object_detection\legacy\trainer.py", line 204, in _create_losses
prediction_dict = detection_model.predict(images, true_image_shapes)
File "D:\tensorflow\models-master\research\object_detection\meta_architectures\faster_rcnn_meta_arch.py", line 647, in predict
image_shape) = self._extract_rpn_feature_maps(preprocessed_inputs)
File "D:\tensorflow\models-master\research\object_detection\meta_architectures\faster_rcnn_meta_arch.py", line 978, in _extract_rpn_feature_maps
scope=self.first_stage_feature_extractor_scope))
File "D:\tensorflow\models-master\research\object_detection\meta_architectures\faster_rcnn_meta_arch.py", line 163, in extract_proposal_features
return self._extract_proposal_features(preprocessed_inputs, scope)
File "D:\tensorflow\models-master\research\object_detection\models\faster_rcnn_inception_v2_feature_extractor.py", line 138, in _extract_proposal_features
scope=scope)
File "D:\tensorflow\models-master\research\slim\nets\inception_v2.py", line 117, in inception_v2_base
scope=end_point)
File "D:\Program Files\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\contrib\framework\python\ops\arg_scope.py", line 182, in func_with_args
return func(*args, **current_args)
File "D:\Program Files\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\contrib\layers\python\layers\layers.py", line 2778, in separable_convolution2d
outputs = layer.apply(inputs)
File "D:\Program Files\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\keras\engine\base_layer.py", line 1227, in apply
return self.call(inputs, *args, **kwargs)
File "D:\Program Files\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\layers\base.py", line 530, in call
outputs = super(Layer, self).call(inputs, *args, **kwargs)
File "D:\Program Files\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\keras\engine\base_layer.py", line 554, in call
outputs = self.call(inputs, *args, **kwargs)
File "D:\Program Files\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\keras\layers\convolutional.py", line 1691, in call
data_format=conv_utils.convert_data_format(self.data_format, ndim=4))
File "D:\Program Files\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\ops\nn_impl.py", line 681, in separable_conv2d
name=name)
File "D:\Program Files\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\ops\gen_nn_ops.py", line 1113, in conv2d
data_format=data_format, dilations=dilations, name=name)
File "D:\Program Files\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 788, in _apply_op_helper
op_def=op_def)
File "D:\Program Files\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\util\deprecation.py", line 507, in new_func
return func(*args, **kwargs)
File "D:\Program Files\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\framework\ops.py", line 3300, in create_op
op_def=op_def)
File "D:\Program Files\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\framework\ops.py", line 1801, in init
self._traceback = tf_stack.extract_stack()
ResourceExhaustedError (see above for traceback): OOM when allocating tensor with shape[52,64,480,300] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
[[node FirstStageFeatureExtractor/InceptionV2/InceptionV2/Conv2d_1a_7x7/separable_conv2d (defined at D:\tensorflow\models-master\research\slim\nets\inception_v2.py:117) ]]
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.
[[node BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/Reshape (defined at D:\tensorflow\models-master\research\object_detection\core\post_processing.py:136) ]]
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.
答案
我可以通过玩“ batch_size”配置参数来解决OOM问题（batch_size：10对我有用）。
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