症状：没有任何报错，训练一开始跑起来，立马断电关机
硬件： GTX 1080 单GPU。机型：联想拯救者
原因：电源功率不足，需要更换更大功率电源。如 450W或500W
关机显示的信息（仅供参考）
D:\Projects\learnpy\labeling_train\venv\Scripts\python.exe D:/Projects/learnpy/labeling_train/SSD-Tensorflow-master/train_ssd_network.py --train_dir=D:\Projects\learnpy\labeling_train\house_model\ --dataset_dir=D:\Projects\learnpy\labeling_train\house_tfrecord\ --dataset_name=pascalvoc_2007 --dataset_split_name=train --model_name=ssd_300_vgg --checkpoint_path=D:\Projects\learnpy\labeling_train\model_pre_train\VGG_VOC0712_SSD_300x300_ft_iter_120000.ckpt\VGG_VOC0712_SSD_300x300_ft_iter_120000.ckpt --save_summaries_secs=60 --save_interval_secs=600 --weight_decay=0.0005 --optimizer=adam --learning_rate=0.0001 --batch_size=8
WARNING:tensorflow:From D:/Projects/learnpy/labeling_train/SSD-Tensorflow-master/train_ssd_network.py:201: create_global_step (from tensorflow.contrib.framework.python.ops.variables) is deprecated and will be removed in a future version.
Instructions for updating:
Please switch to tf.train.create_global_step
# =========================================================================== #
# Training | Evaluation flags:
# =========================================================================== #
{'adadelta_rho': <absl.flags._flag.Flag object at 0x000002D497FDBC50>,
'adagrad_initial_accumulator_value': <absl.flags._flag.Flag object at 0x000002D497FDBCF8>,
'adam_beta1': <absl.flags._flag.Flag object at 0x000002D497FDBDA0>,
'adam_beta2': <absl.flags._flag.Flag object at 0x000002D497FDBE48>,
'batch_size': <absl.flags._flag.Flag object at 0x000002D497FE0C18>,
'checkpoint_exclude_scopes': <absl.flags._flag.Flag object at 0x000002D497FE0E80>,
'checkpoint_model_scope': <absl.flags._flag.Flag object at 0x000002D497FE0E10>,
'checkpoint_path': <absl.flags._flag.Flag object at 0x000002D497FE0DA0>,
'clone_on_cpu': <absl.flags._flag.BooleanFlag object at 0x000002D497FDB630>,
'dataset_dir': <absl.flags._flag.Flag object at 0x000002D497FE0A20>,
'dataset_name': <absl.flags._flag.Flag object at 0x000002D497FE0860>,
'dataset_split_name': <absl.flags._flag.Flag object at 0x000002D497FE0978>,
'end_learning_rate': <absl.flags._flag.Flag object at 0x000002D497FE0588>,
'ftrl_initial_accumulator_value': <absl.flags._flag.Flag object at 0x000002D497FE0080>,
'ftrl_l1': <absl.flags._flag.Flag object at 0x000002D497FE0128>,
'ftrl_l2': <absl.flags._flag.Flag object at 0x000002D497FE01D0>,
'ftrl_learning_rate_power': <absl.flags._flag.Flag object at 0x000002D497FDBF98>,
'gpu_memory_fraction': <absl.flags._flag.Flag object at 0x000002D497FDBA90>,
'h': <tensorflow.python.platform.app._HelpFlag object at 0x000002D497FE0F98>,
'help': <tensorflow.python.platform.app._HelpFlag object at 0x000002D497FE0F98>,
'helpfull': <tensorflow.python.platform.app._HelpfullFlag object at 0x000002D497FE2048>,
'helpshort': <tensorflow.python.platform.app._HelpshortFlag object at 0x000002D497FE20B8>,
'ignore_missing_vars': <absl.flags._flag.BooleanFlag object at 0x000002D497FE0F28>,
'label_smoothing': <absl.flags._flag.Flag object at 0x000002D497FE05F8>,
'labels_offset': <absl.flags._flag.Flag object at 0x000002D497FE0A90>,
'learning_rate': <absl.flags._flag.Flag object at 0x000002D497FE04E0>,
'learning_rate_decay_factor': <absl.flags._flag.Flag object at 0x000002D497FE06A0>,
'learning_rate_decay_type': <absl.flags._flag.Flag object at 0x000002D497FE0470>,
'log_every_n_steps': <absl.flags._flag.Flag object at 0x000002D497FDB898>,
'loss_alpha': <absl.flags._flag.Flag object at 0x000002D482B28BE0>,
'match_threshold': <absl.flags._flag.Flag object at 0x000002D497FDB4A8>,
'max_number_of_steps': <absl.flags._flag.Flag object at 0x000002D497FE0D30>,
'model_name': <absl.flags._flag.Flag object at 0x000002D497FE0B38>,
'momentum': <absl.flags._flag.Flag object at 0x000002D497FE0278>,
'moving_average_decay': <absl.flags._flag.Flag object at 0x000002D497FE07F0>,
'negative_ratio': <absl.flags._flag.Flag object at 0x000002D497FDB400>,
'num_classes': <absl.flags._flag.Flag object at 0x000002D497FE08D0>,
'num_clones': <absl.flags._flag.Flag object at 0x000002D497FDB5F8>,
'num_epochs_per_decay': <absl.flags._flag.Flag object at 0x000002D497FE0748>,
'num_preprocessing_threads': <absl.flags._flag.Flag object at 0x000002D497FDB7F0>,
'num_readers': <absl.flags._flag.Flag object at 0x000002D497FDB748>,
'opt_epsilon': <absl.flags._flag.Flag object at 0x000002D497FDBEF0>,
'optimizer': <absl.flags._flag.Flag object at 0x000002D497FDBBE0>,
'preprocessing_name': <absl.flags._flag.Flag object at 0x000002D497FE0BA8>,
'rmsprop_decay': <absl.flags._flag.Flag object at 0x000002D497FE03C8>,
'rmsprop_momentum': <absl.flags._flag.Flag object at 0x000002D497FE0320>,
'save_interval_secs': <absl.flags._flag.Flag object at 0x000002D497FDB9E8>,
'save_summaries_secs': <absl.flags._flag.Flag object at 0x000002D497FDB940>,
'train_dir': <absl.flags._flag.Flag object at 0x000002D497FDB550>,
'train_image_size': <absl.flags._flag.Flag object at 0x000002D497FE0CC0>,
'trainable_scopes': <absl.flags._flag.Flag object at 0x000002D497FE0EF0>,
'weight_decay': <absl.flags._flag.Flag object at 0x000002D497FDBB38>}
# =========================================================================== #
# SSD net parameters:
# =========================================================================== #
{'anchor_offset': 0.5,
'anchor_ratios': [[2, 0.5],
[2, 0.5, 3, 0.3333333333333333],
[2, 0.5, 3, 0.3333333333333333],
[2, 0.5, 3, 0.3333333333333333],
[2, 0.5],
[2, 0.5]],
'anchor_size_bounds': [0.15, 0.9],
'anchor_sizes': [(21.0, 45.0),
(45.0, 99.0),
(99.0, 153.0),
(153.0, 207.0),
(207.0, 261.0),
(261.0, 315.0)],
'anchor_steps': [8, 16, 32, 64, 100, 300],
'feat_layers': ['block4', 'block7', 'block8', 'block9', 'block10', 'block11'],
'feat_shapes': [(38, 38), (19, 19), (10, 10), (5, 5), (3, 3), (1, 1)],
'img_shape': (300, 300),
'no_annotation_label': 21,
'normalizations': [20, -1, -1, -1, -1, -1],
'num_classes': 21,
'prior_scaling': [0.1, 0.1, 0.2, 0.2]}
# =========================================================================== #
# Training | Evaluation dataset files:
# =========================================================================== #
['D:\\Projects\\learnpy\\labeling_train\\house_tfrecord\\voc_2007_train_000.tfrecord']
WARNING:tensorflow:From C:\Users\Lenovo\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\contrib\slim\python\slim\data\parallel_reader.py:242: string_input_producer (from tensorflow.python.training.input) is deprecated and will be removed in a future version.
Instructions for updating:
Queue-based input pipelines have been replaced by `tf.data`. Use `tf.data.Dataset.from_tensor_slices(string_tensor).shuffle(tf.shape(input_tensor, out_type=tf.int64)[0]).repeat(num_epochs)`. If `shuffle=False`, omit the `.shuffle(...)`.
WARNING:tensorflow:From C:\Users\Lenovo\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\training\input.py:276: input_producer (from tensorflow.python.training.input) is deprecated and will be removed in a future version.
Instructions for updating:
Queue-based input pipelines have been replaced by `tf.data`. Use `tf.data.Dataset.from_tensor_slices(input_tensor).shuffle(tf.shape(input_tensor, out_type=tf.int64)[0]).repeat(num_epochs)`. If `shuffle=False`, omit the `.shuffle(...)`.
WARNING:tensorflow:From C:\Users\Lenovo\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\training\input.py:188: limit_epochs (from tensorflow.python.training.input) is deprecated and will be removed in a future version.
Instructions for updating:
Queue-based input pipelines have been replaced by `tf.data`. Use `tf.data.Dataset.from_tensors(tensor).repeat(num_epochs)`.
WARNING:tensorflow:From C:\Users\Lenovo\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\training\input.py:197: QueueRunner.__init__ (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.
Instructions for updating:
To construct input pipelines, use the `tf.data` module.
WARNING:tensorflow:From C:\Users\Lenovo\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\training\input.py:197: add_queue_runner (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.
Instructions for updating:
To construct input pipelines, use the `tf.data` module.
WARNING:tensorflow:From C:\Users\Lenovo\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\contrib\slim\python\slim\data\parallel_reader.py:94: TFRecordReader.__init__ (from tensorflow.python.ops.io_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Queue-based input pipelines have been replaced by `tf.data`. Use `tf.data.TFRecordDataset`.
WARNING:tensorflow:From C:\Users\Lenovo\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\ops\sparse_ops.py:1165: 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 D:/Projects/learnpy/labeling_train/SSD-Tensorflow-master/train_ssd_network.py:251: batch (from tensorflow.python.training.input) is deprecated and will be removed in a future version.
Instructions for updating:
Queue-based input pipelines have been replaced by `tf.data`. Use `tf.data.Dataset.batch(batch_size)` (or `padded_batch(...)` if `dynamic_pad=True`).
INFO:tensorflow:Fine-tuning from D:\Projects\learnpy\labeling_train\model_pre_train\VGG_VOC0712_SSD_300x300_ft_iter_120000.ckpt\VGG_VOC0712_SSD_300x300_ft_iter_120000.ckpt. Ignoring missing vars: False
WARNING:tensorflow:From C:\Users\Lenovo\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\contrib\slim\python\slim\learning.py:737: Supervisor.__init__ (from tensorflow.python.training.supervisor) is deprecated and will be removed in a future version.
Instructions for updating:
Please switch to tf.train.MonitoredTrainingSession
2019-04-06 17:49:38.366689: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2
2019-04-06 17:49:38.665692: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Found device 0 with properties:
name: GeForce GTX 1080 major: 6 minor: 1 memoryClockRate(GHz): 1.7335
pciBusID: 0000:01:00.0
totalMemory: 8.00GiB freeMemory: 6.60GiB
2019-04-06 17:49:38.666003: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1511] Adding visible gpu devices: 0
2019-04-06 17:49:40.302000: I tensorflow/core/common_runtime/gpu/gpu_device.cc:982] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-04-06 17:49:40.302212: I tensorflow/core/common_runtime/gpu/gpu_device.cc:988] 0
2019-04-06 17:49:40.302284: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1001] 0: N
2019-04-06 17:49:40.303136: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6553 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1080, pci bus id: 0000:01:00.0, compute capability: 6.1)
INFO:tensorflow:Restoring parameters from D:\Projects\learnpy\labeling_train\model_pre_train\VGG_VOC0712_SSD_300x300_ft_iter_120000.ckpt\VGG_VOC0712_SSD_300x300_ft_iter_120000.ckpt
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Starting Session.
INFO:tensorflow:Saving checkpoint to path D:\Projects\learnpy\labeling_train\house_model\model.ckpt
INFO:tensorflow:Starting Queues.
INFO:tensorflow:global_step/sec: 0
