SSD-Tensorflow
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InvalidArgumentError: Restoring from checkpoint failed.This is most likely due to a mismatch
I have problems when i run the eval_ssd_network.py in my PC.(python3.7)
my eval_ssd_network.sh is presented as follows: python eval_ssd_network.py --eval_dir=C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\eval_result\eval_1\ --dataset_dir=C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\ --dataset_name=pascalvoc_2007 --dataset_split_name=test --model_name=ssd_300_vgg --checkpoint_path=C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\checkpoints\ssd_300_vgg.ckpt --batch_size=1
problem: C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master>python eval_ssd_network.py --eval_dir=C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\eval_result\eval_1\ --dataset_dir=C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\ --dataset_name=pascalvoc_2007 --dataset_split_name=test --model_name=ssd_300_vgg --checkpoint_path=C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\checkpoints\ssd_300_vgg.ckpt --batch_size=1 WARNING:tensorflow:From eval_ssd_network.py:123: get_or_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.get_or_create_global_step WARNING:tensorflow:From C:\Users\Alice\Anaconda3\lib\site-packages\tensorflow\python\framework\op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version. Instructions for updating: Colocations handled automatically by placer.
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Training | Evaluation flags:
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{'batch_size': <absl.flags._flag.Flag object at 0x00000204FE4B94A8>, 'checkpoint_path': <absl.flags._flag.Flag object at 0x00000204FE4B9668>, 'dataset_dir': <absl.flags._flag.Flag object at 0x00000204FE4B9908>, 'dataset_name': <absl.flags._flag.Flag object at 0x00000204FE4B97F0>, 'dataset_split_name': <absl.flags._flag.Flag object at 0x00000204FE4B9860>, 'eval_dir': <absl.flags._flag.Flag object at 0x00000204FE4B96D8>, 'eval_image_size': <absl.flags._flag.Flag object at 0x00000204FE4B92E8>, 'eval_resize': <absl.flags._flag.Flag object at 0x00000204FE4B9240>, 'gpu_memory_fraction': <absl.flags._flag.Flag object at 0x00000204FE4B9AC8>, 'h': <tensorflow.python.platform.app._HelpFlag object at 0x00000204FE4B9BA8>, 'help': <tensorflow.python.platform.app._HelpFlag object at 0x00000204FE4B9BA8>, 'helpfull': <tensorflow.python.platform.app._HelpfullFlag object at 0x00000204FE4B9C18>, 'helpshort': <tensorflow.python.platform.app._HelpshortFlag object at 0x00000204FE4B9C88>, 'keep_top_k': <absl.flags._flag.Flag object at 0x00000204FE4AF668>, 'master': <absl.flags._flag.Flag object at 0x00000204FE4B95C0>, 'matching_threshold': <absl.flags._flag.Flag object at 0x00000204FE4B9198>, 'max_num_batches': <absl.flags._flag.Flag object at 0x00000204FE4B9550>, 'model_name': <absl.flags._flag.Flag object at 0x00000204FE4B9978>, 'moving_average_decay': <absl.flags._flag.Flag object at 0x00000204FE4B9A58>, 'nms_threshold': <absl.flags._flag.Flag object at 0x00000204FE4B90F0>, 'num_classes': <absl.flags._flag.Flag object at 0x00000204FE4B9400>, 'num_preprocessing_threads': <absl.flags._flag.Flag object at 0x00000204FE4B9748>, 'preprocessing_name': <absl.flags._flag.Flag object at 0x00000204FE4B99E8>, 'remove_difficult': <absl.flags._flag.BooleanFlag object at 0x00000204FE4B9320>, 'select_threshold': <absl.flags._flag.Flag object at 0x00000204F43E75F8>, 'select_top_k': <absl.flags._flag.Flag object at 0x00000204FE483C50>, 'wait_for_checkpoints': <absl.flags._flag.BooleanFlag object at 0x00000204FE4B9B00>}
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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': 2, 'normalizations': [20, -1, -1, -1, -1, -1], 'num_classes': 2, 'prior_scaling': [0.1, 0.1, 0.2, 0.2]}
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Training | Evaluation dataset files:
===========================================================================
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'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_153.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_154.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_155.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_156.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_157.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_158.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_159.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_160.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_161.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_162.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_163.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_164.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_165.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_166.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_167.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_168.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_169.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_170.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_171.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_172.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_173.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_174.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_175.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_176.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_177.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_178.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_179.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_180.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_181.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_182.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_183.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_184.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_185.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_186.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_187.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_188.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_189.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_190.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_191.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_192.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_193.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_194.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_195.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_196.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_197.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_198.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_199.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_200.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_201.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_202.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_203.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_204.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_205.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_206.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_207.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_208.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_209.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_210.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_211.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_212.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_213.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_214.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_215.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_216.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_217.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_218.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_219.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_220.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_221.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_222.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_223.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_224.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_225.tfrecord', 'C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\test_tf\voc_2007_test_226.tfrecord']
WARNING:tensorflow:From C:\Users\Alice\Anaconda3\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\Alice\Anaconda3\lib\site-packages\tensorflow\python\training\input.py:278: 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\Alice\Anaconda3\lib\site-packages\tensorflow\python\training\input.py:190: 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\Alice\Anaconda3\lib\site-packages\tensorflow\python\training\input.py:199: 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\Alice\Anaconda3\lib\site-packages\tensorflow\python\training\input.py:199: 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\Alice\Anaconda3\lib\site-packages\tensorflow\python\training\input.py:202: 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 C:\Users\Alice\Anaconda3\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\Alice\Desktop\Program\SSD-Tensorflow-master\nets\ssd_common.py:77: div (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Deprecated in favor of operator or tf.math.divide.
WARNING:tensorflow:From eval_ssd_network.py:186: 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
).
WARNING:tensorflow:From C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\nets\ssd_vgg_300.py:476: dropout (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.
Instructions for updating:
Use keras.layers.dropout instead.
WARNING:tensorflow:From eval_ssd_network.py:236: streaming_mean (from tensorflow.contrib.metrics.python.ops.metric_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Please switch to tf.metrics.mean
WARNING:tensorflow:From C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\tf_extended\metrics.py:159: to_int64 (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 eval_ssd_network.py:284: Print (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2018-08-20.
Instructions for updating:
Use tf.print instead of tf.Print. Note that tf.print returns a no-output operator that directly prints the output. Outside of defuns or eager mode, this operator will not be executed unless it is directly specified in session.run or used as a control dependency for other operators. This is only a concern in graph mode. Below is an example of how to ensure tf.print executes in graph mode:
sess = tf.Session()
with sess.as_default():
tensor = tf.range(10)
print_op = tf.print(tensor)
with tf.control_dependencies([print_op]):
out = tf.add(tensor, tensor)
sess.run(out)
```
Additionally, to use tf.print in python 2.7, users must make sure to import
the following:
`from __future__ import print_function`
INFO:tensorflow:Evaluating C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\checkpoints\ssd_300_vgg.ckpt
INFO:tensorflow:Starting evaluation at 2019-05-05T09:22:18Z
INFO:tensorflow:Graph was finalized.
2019-05-05 17:22:18.343015: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
WARNING:tensorflow:From C:\Users\Alice\Anaconda3\lib\site-packages\tensorflow\python\training\saver.py:1266: checkpoint_exists (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.
Instructions for updating:
Use standard file APIs to check for files with this prefix.
INFO:tensorflow:Restoring parameters from C:\Users\Alice\Desktop\Program\SSD-Tensorflow-master\checkpoints\ssd_300_vgg.ckpt
Traceback (most recent call last):
File "C:\Users\Alice\Anaconda3\lib\site-packages\tensorflow\python\client\session.py", line 1334, in _do_call
return fn(*args)
File "C:\Users\Alice\Anaconda3\lib\site-packages\tensorflow\python\client\session.py", line 1319, in _run_fn
options, feed_dict, fetch_list, target_list, run_metadata)
File "C:\Users\Alice\Anaconda3\lib\site-packages\tensorflow\python\client\session.py", line 1407, in _call_tf_sessionrun
run_metadata)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Assign requires shapes of both tensors to match. lhs shape= [8] rhs shape= [84]
[[{{node save/Assign_5}}]]
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "C:\Users\Alice\Anaconda3\lib\site-packages\tensorflow\python\training\saver.py", line 1276, in restore
{self.saver_def.filename_tensor_name: save_path})
File "C:\Users\Alice\Anaconda3\lib\site-packages\tensorflow\python\client\session.py", line 929, in run
run_metadata_ptr)
File "C:\Users\Alice\Anaconda3\lib\site-packages\tensorflow\python\client\session.py", line 1152, in _run
feed_dict_tensor, options, run_metadata)
File "C:\Users\Alice\Anaconda3\lib\site-packages\tensorflow\python\client\session.py", line 1328, in _do_run
run_metadata)
File "C:\Users\Alice\Anaconda3\lib\site-packages\tensorflow\python\client\session.py", line 1348, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Assign requires shapes of both tensors to match. lhs shape= [8] rhs shape= [84]
[[node save/Assign_5 (defined at eval_ssd_network.py:332) ]]
Caused by op 'save/Assign_5', defined at:
File "eval_ssd_network.py", line 358, in <module>
tf.app.run()
File "C:\Users\Alice\Anaconda3\lib\site-packages\tensorflow\python\platform\app.py", line 125, in run
_sys.exit(main(argv))
File "eval_ssd_network.py", line 332, in main
session_config=config)
File "C:\Users\Alice\Anaconda3\lib\site-packages\tensorflow\contrib\slim\python\slim\evaluation.py", line 205, in evaluate_once
saver = tf_saver.Saver(variables_to_restore)
File "C:\Users\Alice\Anaconda3\lib\site-packages\tensorflow\python\training\saver.py", line 832, in __init__
self.build()
File "C:\Users\Alice\Anaconda3\lib\site-packages\tensorflow\python\training\saver.py", line 844, in build
self._build(self._filename, build_save=True, build_restore=True)
File "C:\Users\Alice\Anaconda3\lib\site-packages\tensorflow\python\training\saver.py", line 881, in _build
build_save=build_save, build_restore=build_restore)
File "C:\Users\Alice\Anaconda3\lib\site-packages\tensorflow\python\training\saver.py", line 513, in _build_internal
restore_sequentially, reshape)
File "C:\Users\Alice\Anaconda3\lib\site-packages\tensorflow\python\training\saver.py", line 354, in _AddRestoreOps
assign_ops.append(saveable.restore(saveable_tensors, shapes))
File "C:\Users\Alice\Anaconda3\lib\site-packages\tensorflow\python\training\saving\saveable_object_util.py", line 73, in restore
self.op.get_shape().is_fully_defined())
File "C:\Users\Alice\Anaconda3\lib\site-packages\tensorflow\python\ops\state_ops.py", line 223, in assign
validate_shape=validate_shape)
File "C:\Users\Alice\Anaconda3\lib\site-packages\tensorflow\python\ops\gen_state_ops.py", line 67, in assign
use_locking=use_locking, name=name)
File "C:\Users\Alice\Anaconda3\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 788, in _apply_op_helper
op_def=op_def)
File "C:\Users\Alice\Anaconda3\lib\site-packages\tensorflow\python\util\deprecation.py", line 507, in new_func
return func(*args, **kwargs)
File "C:\Users\Alice\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py", line 3300, in create_op
op_def=op_def)
File "C:\Users\Alice\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py", line 1801, in __init__
self._traceback = tf_stack.extract_stack()
InvalidArgumentError (see above for traceback): Assign requires shapes of both tensors to match. lhs shape= [8] rhs shape= [84]
[[node save/Assign_5 (defined at eval_ssd_network.py:332) ]]
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "eval_ssd_network.py", line 358, in <module>
tf.app.run()
File "C:\Users\Alice\Anaconda3\lib\site-packages\tensorflow\python\platform\app.py", line 125, in run
_sys.exit(main(argv))
File "eval_ssd_network.py", line 332, in main
session_config=config)
File "C:\Users\Alice\Anaconda3\lib\site-packages\tensorflow\contrib\slim\python\slim\evaluation.py", line 217, in evaluate_once
config=session_config)
File "C:\Users\Alice\Anaconda3\lib\site-packages\tensorflow\python\training\evaluation.py", line 271, in _evaluate_once
session_creator=session_creator, hooks=hooks) as session:
File "C:\Users\Alice\Anaconda3\lib\site-packages\tensorflow\python\training\monitored_session.py", line 934, in __init__
stop_grace_period_secs=stop_grace_period_secs)
File "C:\Users\Alice\Anaconda3\lib\site-packages\tensorflow\python\training\monitored_session.py", line 648, in __init__
self._sess = _RecoverableSession(self._coordinated_creator)
File "C:\Users\Alice\Anaconda3\lib\site-packages\tensorflow\python\training\monitored_session.py", line 1122, in __init__
_WrappedSession.__init__(self, self._create_session())
File "C:\Users\Alice\Anaconda3\lib\site-packages\tensorflow\python\training\monitored_session.py", line 1127, in _create_session
return self._sess_creator.create_session()
File "C:\Users\Alice\Anaconda3\lib\site-packages\tensorflow\python\training\monitored_session.py", line 805, in create_session
self.tf_sess = self._session_creator.create_session()
File "C:\Users\Alice\Anaconda3\lib\site-packages\tensorflow\python\training\monitored_session.py", line 571, in create_session
init_fn=self._scaffold.init_fn)
File "C:\Users\Alice\Anaconda3\lib\site-packages\tensorflow\python\training\session_manager.py", line 281, in prepare_session
config=config)
File "C:\Users\Alice\Anaconda3\lib\site-packages\tensorflow\python\training\session_manager.py", line 195, in _restore_checkpoint
saver.restore(sess, checkpoint_filename_with_path)
File "C:\Users\Alice\Anaconda3\lib\site-packages\tensorflow\python\training\saver.py", line 1312, in restore
err, "a mismatch between the current graph and the graph")
tensorflow.python.framework.errors_impl.InvalidArgumentError: Restoring from checkpoint failed. This is most likely due to a mismatch between the current graph and the graph from the checkpoint. Please ensure that you have not altered the graph expected based on the checkpoint. Original error:
Assign requires shapes of both tensors to match. lhs shape= [8] rhs shape= [84]
[[node save/Assign_5 (defined at eval_ssd_network.py:332) ]]
Caused by op 'save/Assign_5', defined at:
File "eval_ssd_network.py", line 358, in <module>
tf.app.run()
File "C:\Users\Alice\Anaconda3\lib\site-packages\tensorflow\python\platform\app.py", line 125, in run
_sys.exit(main(argv))
File "eval_ssd_network.py", line 332, in main
session_config=config)
File "C:\Users\Alice\Anaconda3\lib\site-packages\tensorflow\contrib\slim\python\slim\evaluation.py", line 205, in evaluate_once
saver = tf_saver.Saver(variables_to_restore)
File "C:\Users\Alice\Anaconda3\lib\site-packages\tensorflow\python\training\saver.py", line 832, in __init__
self.build()
File "C:\Users\Alice\Anaconda3\lib\site-packages\tensorflow\python\training\saver.py", line 844, in build
self._build(self._filename, build_save=True, build_restore=True)
File "C:\Users\Alice\Anaconda3\lib\site-packages\tensorflow\python\training\saver.py", line 881, in _build
build_save=build_save, build_restore=build_restore)
File "C:\Users\Alice\Anaconda3\lib\site-packages\tensorflow\python\training\saver.py", line 513, in _build_internal
restore_sequentially, reshape)
File "C:\Users\Alice\Anaconda3\lib\site-packages\tensorflow\python\training\saver.py", line 354, in _AddRestoreOps
assign_ops.append(saveable.restore(saveable_tensors, shapes))
File "C:\Users\Alice\Anaconda3\lib\site-packages\tensorflow\python\training\saving\saveable_object_util.py", line 73, in restore
self.op.get_shape().is_fully_defined())
File "C:\Users\Alice\Anaconda3\lib\site-packages\tensorflow\python\ops\state_ops.py", line 223, in assign
validate_shape=validate_shape)
File "C:\Users\Alice\Anaconda3\lib\site-packages\tensorflow\python\ops\gen_state_ops.py", line 67, in assign
use_locking=use_locking, name=name)
File "C:\Users\Alice\Anaconda3\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 788, in _apply_op_helper
op_def=op_def)
File "C:\Users\Alice\Anaconda3\lib\site-packages\tensorflow\python\util\deprecation.py", line 507, in new_func
return func(*args, **kwargs)
File "C:\Users\Alice\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py", line 3300, in create_op
op_def=op_def)
File "C:\Users\Alice\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py", line 1801, in __init__
self._traceback = tf_stack.extract_stack()
InvalidArgumentError (see above for traceback): Restoring from checkpoint failed. This is most likely due to a mismatch between the current graph and the graph from the checkpoint. Please ensure that you have not altered the graph expected based on the checkpoint. Original error:
Assign requires shapes of both tensors to match. lhs shape= [8] rhs shape= [84]
[[node save/Assign_5 (defined at eval_ssd_network.py:332) ]]
I tried a lot but i can't solve it. Wo can help me ?
I met the same problem with you. Did you solve the problem?
Can you tell me the version of python? I need a good python environment
I got the same problem with you...if you have solved this, please help me, thank you very much.
i had gave up it
------------------ 原始邮件 ------------------ 发件人: "cbyAIybh"[email protected]; 发送时间: 2019年7月24日(星期三) 晚上8:25 收件人: "balancap/SSD-Tensorflow"[email protected]; 抄送: "山木"[email protected]; "Comment"[email protected]; 主题: Re: [balancap/SSD-Tensorflow] InvalidArgumentError: Restoring fromcheckpoint failed.This is most likely due to a mismatch (#345)
I got the same problem with you...if you have solved this, please help me, thank you very much.
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@mathuse Fortunately,I have solved this problem through inputing the parameter '--checkpoint_exclude_scopes' which means the layers we want to retrain. Maybe just the parameter that caused the problem. So you can try this "--checkpoint_exclude_scopes=ssd_300_vgg/conv6,ssd_300_vgg/conv7,ssd_300_vgg/block8,ssd_300_vgg/block9,ssd_300_vgg/block10,ssd_300_vgg/block11,ssd_300_vgg/block4_box,ssd_300_vgg/block7_box,ssd_300_vgg/block8_box,ssd_300_vgg/block9_box,ssd_300_vgg/block10_box,ssd_300_vgg/block11_box" and "--trainable_scopes=ssd_300_vgg/conv6,ssd_300_vgg/conv7,ssd_300_vgg/block8,ssd_300_vgg/block9,ssd_300_vgg/block10,ssd_300_vgg/block11,ssd_300_vgg/block4_box,ssd_300_vgg/block7_box,ssd_300_vgg/block8_box,ssd_300_vgg/block9_box,ssd_300_vgg/block10_box,ssd_300_vgg/block11_box". I hope it can help you, come on!
Maybe your do not understand my question. i need to know the environment of this SSD,such as the version of python.I run this program in the Ubuntu that is installed in the VMstation,unreal PC.When i run the program,many function is discarded or out of style. I do not trust whether the training file is right.I use the training file to test my sample,and some error is happen.So i gave up.I am a noob,and now i am lenrning CNN.I hope I can do it rightly in the soon future,by the way,my English is pool,i wish you can understand my thinking.
------------------ 原始邮件 ------------------ 发件人: "cbyAIybh"[email protected]; 发送时间: 2019年7月25日(星期四) 下午3:52 收件人: "balancap/SSD-Tensorflow"[email protected]; 抄送: "山木"[email protected]; "Mention"[email protected]; 主题: Re: [balancap/SSD-Tensorflow] InvalidArgumentError: Restoring fromcheckpoint failed.This is most likely due to a mismatch (#345)
@mathuse Fortunately,I have solved this problem through inputing the parameter '--checkpoint_exclude_scopes' which means the layers we want to retrain. Maybe just the parameter that caused the problem. So you can try this "--checkpoint_exclude_scopes=ssd_300_vgg/conv6,ssd_300_vgg/conv7,ssd_300_vgg/block8,ssd_300_vgg/block9,ssd_300_vgg/block10,ssd_300_vgg/block11,ssd_300_vgg/block4_box,ssd_300_vgg/block7_box,ssd_300_vgg/block8_box,ssd_300_vgg/block9_box,ssd_300_vgg/block10_box,ssd_300_vgg/block11_box" and "--trainable_scopes=ssd_300_vgg/conv6,ssd_300_vgg/conv7,ssd_300_vgg/block8,ssd_300_vgg/block9,ssd_300_vgg/block10,ssd_300_vgg/block11,ssd_300_vgg/block4_box,ssd_300_vgg/block7_box,ssd_300_vgg/block8_box,ssd_300_vgg/block9_box,ssd_300_vgg/block10_box,ssd_300_vgg/block11_box". I hope it can help you, come on!
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@mathuse i'm so sorry. i commented about one day ago, meant that i met the same question with yuchanWang, then i saw your comment and thought you got the same problem with me by mistake, so i replied you without scanning your problem carefully. i have got many errors when i study this SSD-Tensorflow project, but i am still making my effort to solve them and making progress at the same time. so ,do not give up. come on!
Any update on this please?