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shape mismatch when optimize resnet_v1_50.pb

Dongjie_S_Intel
Employee
2,078 Views

Hi, 

I downloaded resnet_v1_50_2016_08_28.tar.gz from  http://download.tensorflow.org/models/resnet_v1_50_2016_08_28.tar.gz, and use dldt 2018_R5 to optimize it. Get shape mismatch error. Please help.

Commands:

python3 tf_models/research/slim/export_inference_graph.py \
    --model_name resnet_v1_50 \
    --output_file ov_irs/resnet_v1_50.pb

python3 ~/dldt/model-optimizer/mo/utils/summarize_graph.py --input_model ov_irs/resnet_v1_50.pb

wget http://download.tensorflow.org/models/resnet_v1_50_2016_08_28.tar.gz
tar xvf resnet_v1_50_2016_08_28.tar.gz -C ov_irs

python3 ~/dldt/model-optimizer/mo_tf.py --input_model ./ov_irs/resnet_v1_50.pb --input_checkpoint ./ov_irs/resnet_v1_50.ckpt -b 1 --mean_value [103.94,116.78,123.68] --scale 1

output:

Model Optimizer arguments:
Common parameters:
        - Path to the Input Model:      /home/glorysdj/./ov_irs/resnet_v1_50.pb
        - Path for generated IR:        /home/glorysdj/.
        - IR output name:       resnet_v1_50
        - Log level:    ERROR
        - Batch:        1
        - Input layers:         Not specified, inherited from the model
        - Output layers:        Not specified, inherited from the model
        - Input shapes:         Not specified, inherited from the model
        - Mean values:  [103.94,116.78,123.68]
        - Scale values:         Not specified
        - Scale factor:         1.0
        - Precision of IR:      FP32
        - Enable fusing:        True
        - Enable grouped convolutions fusing:   True
        - Move mean values to preprocess section:       False
        - Reverse input channels:       False
TensorFlow specific parameters:
        - Input model in text protobuf format:  False
        - Offload unsupported operations:       False
        - Path to model dump for TensorBoard:   None
        - List of shared libraries with TensorFlow custom layers implementation:        None
        - Update the configuration file with input/output node names:   None
        - Use configuration file used to generate the model with Object Detection API:  None
        - Operations to offload:        None
        - Patterns to offload:  None
        - Use the config file:  None
Model Optimizer version:        1.5.12.49d067a0
[ FRAMEWORK ERROR ]  Cannot load input model: 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= [1,1,2048,1001] rhs shape= [1,1,2048,1000]
         [[node save/Assign_266 (defined at /home/glorysdj/dldt/model-optimizer/mo/front/tf/loader.py:105)  = Assign[T=DT_FLOAT, _class=["loc:@resnet_v1_50/logits/weights"], use_locking=true, validate_shape=true, _device="/job:localhost/replica:0/task:0/device:CPU:0"](resnet_v1_50/logits/weights, save/RestoreV2:266)]]

Caused by op 'save/Assign_266', defined at:
  File "/home/glorysdj/dldt/model-optimizer/mo_tf.py", line 31, in <module>
    sys.exit(main(get_tf_cli_parser(), 'tf'))
  File "/home/glorysdj/dldt/model-optimizer/mo/main.py", line 325, in main
    return driver(argv)
  File "/home/glorysdj/dldt/model-optimizer/mo/main.py", line 267, in driver
    mean_scale_values=mean_scale)
  File "/home/glorysdj/dldt/model-optimizer/mo/pipeline/tf.py", line 127, in tf2nx
    saved_model_tags=argv.saved_model_tags)
  File "/home/glorysdj/dldt/model-optimizer/mo/front/tf/loader.py", line 208, in load_tf_graph_def
    graph_def = freeze_checkpoint(graph_def=graph_def, checkpoint=checkpoint, output_node_names=outputs)
  File "/home/glorysdj/dldt/model-optimizer/mo/front/tf/loader.py", line 105, in freeze_checkpoint
    tf.train.Saver(var_list=var_list).restore(sess, checkpoint)
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/training/saver.py", line 1102, in __init__
    self.build()
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/training/saver.py", line 1114, in build
    self._build(self._filename, build_save=True, build_restore=True)
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/training/saver.py", line 1151, in _build
    build_save=build_save, build_restore=build_restore)
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/training/saver.py", line 795, in _build_internal
    restore_sequentially, reshape)
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/training/saver.py", line 428, in _AddRestoreOps
    assign_ops.append(saveable.restore(saveable_tensors, shapes))
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/training/saver.py", line 119, in restore
    self.op.get_shape().is_fully_defined())
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/state_ops.py", line 221, in assign
    validate_shape=validate_shape)
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/gen_state_ops.py", line 61, in assign
    use_locking=use_locking, name=name)
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
    op_def=op_def)
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/util/deprecation.py", line 488, in new_func
    return func(*args, **kwargs)
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 3274, in create_op
    op_def=op_def)
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 1770, 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= [1,1,2048,1001] rhs shape= [1,1,2048,1000]
         [[node save/Assign_266 (defined at /home/glorysdj/dldt/model-optimizer/mo/front/tf/loader.py:105)  = Assign[T=DT_FLOAT, _class=["loc:@resnet_v1_50/logits/weights"], use_locking=true, validate_shape=true, _device="/job:localhost/replica:0/task:0/device:CPU:0"](resnet_v1_50/logits/weights, save/RestoreV2:266)]]

 

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10 Replies
Shubha_R_Intel
Employee
2,078 Views

Dear Dongjie S,

Please make sure your Tensorflow model is a frozen protobuf first before you use Model Optimizer.  Below are instructions on using Model Optimizer to convert a Tensorflow model:

https://docs.openvinotoolkit.org/latest/_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_TensorFlow.html

Also you are using an older version of OpenVino (though not too old). I strongly encourage you to download the latest 2019 R1 release.

Thanks !

Shubha

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Dan_L_Intel
Employee
2,078 Views

Dear Dongjie S,

I encourted exactly the same error as you described "Assign requires shapes of both tensors to match. lhs shape= [1,1,2048,1001] rhs shape= [1,1,2048,1000]" when modifying resnet_v1_50.pb model using OpenVINO 2019 R1. 

I also downloaded resnet_v1_50_2016_08_28.tar.gz from  http://download.tensorflow.org/models/resnet_v1_50_2016_08_28.tar.gz, from where our guide https://docs.openvinotoolkit.org/latest/_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_TensorFlow.html suggests.

So did you solve this issue?  Looking forward to your reply.

Thanks,

Una

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Shubha_R_Intel
Employee
2,078 Views

Dear everyone,

By now we have 2019R2. In fact it was just released last week. Please update to the latest release and try again.

Thanks,

Shubha

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Bin_X_Intel
Employee
2,078 Views
Hi Shubha, I'm using 2019R2, I still suffer same issue, any idea how to solve this? Thanks
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Shubha_R_Intel
Employee
2,078 Views

Dear Bin X,

Can you kindly be more explicit about your error ? This thread is pretty old. Is it a mismatch error using Model Optimizer ? And are you using the resnet_v1_50 model just like the Original Poster ? What is your exact model optimizer command ?

Thanks,

Shubha

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Bin_X_Intel
Employee
2,078 Views

Hi Shubha, I tried 2019R2 and internal 2019R3, all fail with same issue as Dongjie mentioned:

1. wget http://download.tensorflow.org/models/ resnet_v1_50_2016_08_28.tar.gz

2. tar xvf resnet_v1_50_2016_08_28.tar.gz

3. python3 research/slim/export_inference_graph.py     --model_name resnet_v1_50     --output_file resnet_v1_50_inference_graph.pb

4. python3 /opt/intel/openvino_2019.1.144/deployment_tools/model_optimizer/mo/utils/summarize_graph.py --input_model ./resnet_v1_50_inference_graph.pb

5. python3 /usr/lib/python3.6/site-packages/tensorflow/python/tools/freeze_graph.py --input_graph ./resnet_v1_50_inference_graph.pb --input_checkpoint resnet_v1_50.ckpt --input_binary=true --output_graph frozen_resnet_v1_50.pb --output_node_names=resnet_v1_50/predictions/Reshape_1

then error showed as attachment

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Shubha_R_Intel
Employee
2,078 Views

Dear Bin X.,

Why do you think that this is an OpenVino problem ? In looking at your *.jpg it's clearly a Tensorflow (freeze_graph.py) error. In fact I found this Stack Overflow link about Tensorflow Assign error about the very same error you're getting.

This is the Model Optimizer Documentation for Tensorflow Slim .  It looks like you followed the steps in this document, based on what I see in your post.  But in your steps, you decided to use  freeze_graph.py while the MO document does not recommend this step. Instead it jumps immediately to (keep in mind it's a different model than yours):

<MODEL_OPTIMIZER_INSTALL_DIR>/mo_tf.py --input_model ./inception_v1_inference_graph.pb --input_checkpoint ./inception_v1.ckpt -b 1 --mean_value [127.5,127.5,127.5] --scale 127.5

Hope it helps,

Thanks,

Shubha

 

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Bin_X_Intel
Employee
2,078 Views

Hi Shubha, I run tf_mo.py directly as you pointed out, but still encounter similar issue, are you able to duplicate this problem?

[root@localhost 00464629]# python3.6 /opt/intel/openvino_2019.2.245/deployment_tools/model_optimizer/mo_tf.py --input_model ./resnet_v1_50_inference_graph.pb --input_checkpoint ./resnet_v1_50.ckpt -b 1 --mean_value [127.5,127.5,127.5] --scale 127.5
Model Optimizer arguments:
Common parameters:
    - Path to the Input Model:     /home/xiebin/00464629/./resnet_v1_50_inference_graph.pb
    - Path for generated IR:     /home/xiebin/00464629/.
    - IR output name:     resnet_v1_50_inference_graph
    - Log level:     ERROR
    - Batch:     1
    - Input layers:     Not specified, inherited from the model
    - Output layers:     Not specified, inherited from the model
    - Input shapes:     Not specified, inherited from the model
    - Mean values:     [127.5,127.5,127.5]
    - Scale values:     Not specified
    - Scale factor:     127.5
    - Precision of IR:     FP32
    - Enable fusing:     True
    - Enable grouped convolutions fusing:     True
    - Move mean values to preprocess section:     False
    - Reverse input channels:     False
TensorFlow specific parameters:
    - Input model in text protobuf format:     False
    - Path to model dump for TensorBoard:     None
    - List of shared libraries with TensorFlow custom layers implementation:     None
    - Update the configuration file with input/output node names:     None
    - Use configuration file used to generate the model with Object Detection API:     None
    - Operations to offload:     None
    - Patterns to offload:     None
    - Use the config file:     None
Model Optimizer version:     2019.2.0-436-gf5827d4
[ FRAMEWORK ERROR ]  Cannot load input model: Assign requires shapes of both tensors to match. lhs shape= [1001] rhs shape= [1000]
     [[Node: save/Assign_265 = Assign[T=DT_FLOAT, _class=["loc:@resnet_v1_50/logits/biases"], use_locking=true, validate_shape=true, _device="/job:localhost/replica:0/task:0/device:CPU:0"](resnet_v1_50/logits/biases, save/RestoreV2:265)]]

Caused by op 'save/Assign_265', defined at:
  File "/opt/intel/openvino_2019.2.245/deployment_tools/model_optimizer/mo_tf.py", line 31, in <module>
    sys.exit(main(get_tf_cli_parser(), 'tf'))
  File "/opt/intel/openvino_2019.2.245/deployment_tools/model_optimizer/mo/main.py", line 302, in main
    return driver(argv)
  File "/opt/intel/openvino_2019.2.245/deployment_tools/model_optimizer/mo/main.py", line 251, in driver
    is_binary=not argv.input_model_is_text)
  File "/opt/intel/openvino_2019.2.245/deployment_tools/model_optimizer/mo/pipeline/tf.py", line 82, in tf2nx
    saved_model_tags=argv.saved_model_tags)
  File "/opt/intel/openvino_2019.2.245/deployment_tools/model_optimizer/mo/front/tf/loader.py", line 206, in load_tf_graph_def
    graph_def = freeze_checkpoint(graph_def=graph_def, checkpoint=checkpoint, output_node_names=outputs)
  File "/opt/intel/openvino_2019.2.245/deployment_tools/model_optimizer/mo/front/tf/loader.py", line 103, in freeze_checkpoint
    tf.train.Saver(var_list=var_list).restore(sess, checkpoint)
  File "/usr/lib/python3.6/site-packages/tensorflow/python/training/saver.py", line 1284, in __init__
    self.build()
  File "/usr/lib/python3.6/site-packages/tensorflow/python/training/saver.py", line 1296, in build
    self._build(self._filename, build_save=True, build_restore=True)
  File "/usr/lib/python3.6/site-packages/tensorflow/python/training/saver.py", line 1333, in _build
    build_save=build_save, build_restore=build_restore)
  File "/usr/lib/python3.6/site-packages/tensorflow/python/training/saver.py", line 781, in _build_internal
    restore_sequentially, reshape)
  File "/usr/lib/python3.6/site-packages/tensorflow/python/training/saver.py", line 422, in _AddRestoreOps
    assign_ops.append(saveable.restore(saveable_tensors, shapes))
  File "/usr/lib/python3.6/site-packages/tensorflow/python/training/saver.py", line 113, in restore
    self.op.get_shape().is_fully_defined())
  File "/usr/lib/python3.6/site-packages/tensorflow/python/ops/state_ops.py", line 219, in assign
    validate_shape=validate_shape)
  File "/usr/lib/python3.6/site-packages/tensorflow/python/ops/gen_state_ops.py", line 60, in assign
    use_locking=use_locking, name=name)
  File "/usr/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
    op_def=op_def)
  File "/usr/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 3414, in create_op
    op_def=op_def)
  File "/usr/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 1740, in __init__
    self._traceback = self._graph._extract_stack()  # pylint: disable=protected-access

InvalidArgumentError (see above for traceback): Assign requires shapes of both tensors to match. lhs shape= [1001] rhs shape= [1000]
     [[Node: save/Assign_265 = Assign[T=DT_FLOAT, _class=["loc:@resnet_v1_50/logits/biases"], use_locking=true, validate_shape=true, _device="/job:localhost/replica:0/task:0/device:CPU:0"](resnet_v1_50/logits/biases, save/RestoreV2:265)]]

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Shubha_R_Intel
Employee
2,078 Views

Dear Bin X.

Thanks for posting the detailed exception log. But it's clear that it's still not a Model Optimizer issue.  Please take a look. Model Optimizer is simply passing Tensorflow's error along:

  File "/opt/intel/openvino_2019.2.245/deployment_tools/model_optimizer/mo/front/tf/loader.py", line 206, in load_tf_graph_def
    graph_def = freeze_checkpoint(graph_def=graph_def, checkpoint=checkpoint, output_node_names=outputs)
  File "/opt/intel/openvino_2019.2.245/deployment_tools/model_optimizer/mo/front/tf/loader.py", line 103, in freeze_checkpoint
    tf.train.Saver(var_list=var_list).restore(sess, checkpoint)
  File "/usr/lib/python3.6/site-packages/tensorflow/python/training/saver.py", line 1284, in __init__
    self.build()
  File "/usr/lib/python3.6/site-packages/tensorflow/python/training/saver.py", line 1296, in build
    self._build(self._filename, build_save=True, build_restore=True)
  File "/usr/lib/python3.6/site-packages/tensorflow/python/training/saver.py", line 1333, in _build
    build_save=build_save, build_restore=build_restore)
  File "/usr/lib/python3.6/site-packages/tensorflow/python/training/saver.py", line 781, in _build_internal
    restore_sequentially, reshape)
  File "/usr/lib/python3.6/site-packages/tensorflow/python/training/saver.py", line 422, in _AddRestoreOps
    assign_ops.append(saveable.restore(saveable_tensors, shapes))

Hope it helps.  It looks like your Tensorflow Model is encountering difficulties getting frozen. Why that is, I don't know.

Thanks,

Shubha

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yu__jia
Beginner
2,078 Views

Hi Bin X.

Can you solve this problem? I have the same problem as you. If you have already solved it,can you share how you solved it.

Thanks,

Yujia

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