OpenVino model optimizer error(FusedBatchNormV3)

若如初见. 提交于 2020-03-02 12:22:11

问题


I ask the question because I wanted to solve the error I experienced.

I want to use 'SSD lite Mobilenet V2' in Raspberry Pi 3 B+ and NCS(not 2, it is NCS1).

So I installed OpenVINO 2019_R3 on my Pi(Raspbian stretch) and Laptop(Linux, not all programs, just Model optimizer).

When I optimize SSD lite mobilenet v2(trained zoo model), it was fine.

So, i trained my model in Google Colab using Tensorflow object detection api.

But when I optimize my own SSD lite model, here is log and what I typed to shell.

sudo python3 mo_tf.py --input_model frozen_inference_graph.pb --tensorflow_use_custom_operations_config ssd_support_api_v1.14.json --tensorflow_object_detection_api_pipeline_config pipeline.config --reverse_input_channels --data_type FP16 --keep_shape_ops

[ WARNING ]  Use of deprecated cli option --disable_fusing detected. Option use in the following releases will be fatal. Please use --finegrain_fusing cli option instead
Model Optimizer arguments:
Common parameters:
        - Path to the Input Model:      /opt/intel/openvino_2019.3.334/deployment_tools/model_optimizer/frozen_inference_graph.pb
        - Path for generated IR:        /opt/intel/openvino_2019.3.334/deployment_tools/model_optimizer/.
        - IR output name:       frozen_inference_graph
        - Log level:    ERROR
        - Batch:        Not specified, inherited from the model
        - 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:  Not specified
        - Scale values:         Not specified
        - Scale factor:         Not specified
        - Precision of IR:      FP16
        - Enable fusing:        False
        - Enable grouped convolutions fusing:   True
        - Move mean values to preprocess section:       False
        - Reverse input channels:       True
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:  /opt/intel/openvino_2019.3.334/deployment_tools/model_optimizer/pipeline.config
        - Operations to offload:        None
        - Patterns to offload:  None
        - Use the config file:  /opt/intel/openvino_2019.3.334/deployment_tools/model_optimizer/ssd_support_api_v1.14.json
Model Optimizer version:        2019.3.0-375-g332562022
The Preprocessor block has been removed. Only nodes performing mean value subtraction and scaling (if applicable) are kept.
[ ERROR ]  List of operations that cannot be converted to Inference Engine IR:
[ ERROR ]      FusedBatchNormV3 (76)
[ ERROR ]          FeatureExtractor/MobilenetV2/Conv/BatchNorm/FusedBatchNormV3
[ ERROR ]          FeatureExtractor/MobilenetV2/expanded_conv/depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ]          FeatureExtractor/MobilenetV2/expanded_conv/project/BatchNorm/FusedBatchNormV3
[ ERROR ]          FeatureExtractor/MobilenetV2/expanded_conv_1/expand/BatchNorm/FusedBatchNormV3
[ ERROR ]          FeatureExtractor/MobilenetV2/expanded_conv_1/depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ]          FeatureExtractor/MobilenetV2/expanded_conv_1/project/BatchNorm/FusedBatchNormV3
[ ERROR ]          FeatureExtractor/MobilenetV2/expanded_conv_2/expand/BatchNorm/FusedBatchNormV3
[ ERROR ]          FeatureExtractor/MobilenetV2/expanded_conv_2/depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ]          FeatureExtractor/MobilenetV2/expanded_conv_2/project/BatchNorm/FusedBatchNormV3
[ ERROR ]          FeatureExtractor/MobilenetV2/expanded_conv_3/expand/BatchNorm/FusedBatchNormV3
[ ERROR ]          FeatureExtractor/MobilenetV2/expanded_conv_3/depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ]          FeatureExtractor/MobilenetV2/expanded_conv_3/project/BatchNorm/FusedBatchNormV3
[ ERROR ]          FeatureExtractor/MobilenetV2/expanded_conv_4/expand/BatchNorm/FusedBatchNormV3
[ ERROR ]          FeatureExtractor/MobilenetV2/expanded_conv_4/depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ]          FeatureExtractor/MobilenetV2/expanded_conv_4/project/BatchNorm/FusedBatchNormV3
[ ERROR ]          FeatureExtractor/MobilenetV2/expanded_conv_5/expand/BatchNorm/FusedBatchNormV3
[ ERROR ]          FeatureExtractor/MobilenetV2/expanded_conv_5/depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ]          FeatureExtractor/MobilenetV2/expanded_conv_5/project/BatchNorm/FusedBatchNormV3
[ ERROR ]          FeatureExtractor/MobilenetV2/expanded_conv_6/expand/BatchNorm/FusedBatchNormV3
[ ERROR ]          FeatureExtractor/MobilenetV2/expanded_conv_6/depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ]          FeatureExtractor/MobilenetV2/expanded_conv_6/project/BatchNorm/FusedBatchNormV3
[ ERROR ]          FeatureExtractor/MobilenetV2/expanded_conv_7/expand/BatchNorm/FusedBatchNormV3
[ ERROR ]          FeatureExtractor/MobilenetV2/expanded_conv_7/depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ]          FeatureExtractor/MobilenetV2/expanded_conv_7/project/BatchNorm/FusedBatchNormV3
[ ERROR ]          FeatureExtractor/MobilenetV2/expanded_conv_8/expand/BatchNorm/FusedBatchNormV3
[ ERROR ]          FeatureExtractor/MobilenetV2/expanded_conv_8/depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ]          FeatureExtractor/MobilenetV2/expanded_conv_8/project/BatchNorm/FusedBatchNormV3
[ ERROR ]          FeatureExtractor/MobilenetV2/expanded_conv_9/expand/BatchNorm/FusedBatchNormV3
[ ERROR ]          FeatureExtractor/MobilenetV2/expanded_conv_9/depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ]          FeatureExtractor/MobilenetV2/expanded_conv_9/project/BatchNorm/FusedBatchNormV3
[ ERROR ]          FeatureExtractor/MobilenetV2/expanded_conv_10/expand/BatchNorm/FusedBatchNormV3
[ ERROR ]          FeatureExtractor/MobilenetV2/expanded_conv_10/depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ]          FeatureExtractor/MobilenetV2/expanded_conv_10/project/BatchNorm/FusedBatchNormV3
[ ERROR ]          FeatureExtractor/MobilenetV2/expanded_conv_11/expand/BatchNorm/FusedBatchNormV3
[ ERROR ]          FeatureExtractor/MobilenetV2/expanded_conv_11/depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ]          FeatureExtractor/MobilenetV2/expanded_conv_11/project/BatchNorm/FusedBatchNormV3
[ ERROR ]          FeatureExtractor/MobilenetV2/expanded_conv_12/expand/BatchNorm/FusedBatchNormV3
[ ERROR ]          FeatureExtractor/MobilenetV2/expanded_conv_12/depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ]          FeatureExtractor/MobilenetV2/expanded_conv_12/project/BatchNorm/FusedBatchNormV3
[ ERROR ]          FeatureExtractor/MobilenetV2/expanded_conv_13/expand/BatchNorm/FusedBatchNormV3
[ ERROR ]          BoxPredictor_0/BoxEncodingPredictor_depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ]          FeatureExtractor/MobilenetV2/expanded_conv_13/depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ]          FeatureExtractor/MobilenetV2/expanded_conv_13/project/BatchNorm/FusedBatchNormV3
[ ERROR ]          FeatureExtractor/MobilenetV2/expanded_conv_14/expand/BatchNorm/FusedBatchNormV3
[ ERROR ]          FeatureExtractor/MobilenetV2/expanded_conv_14/depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ]          FeatureExtractor/MobilenetV2/expanded_conv_14/project/BatchNorm/FusedBatchNormV3
[ ERROR ]          FeatureExtractor/MobilenetV2/expanded_conv_15/expand/BatchNorm/FusedBatchNormV3
[ ERROR ]          FeatureExtractor/MobilenetV2/expanded_conv_15/depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ]          FeatureExtractor/MobilenetV2/expanded_conv_15/project/BatchNorm/FusedBatchNormV3
[ ERROR ]          FeatureExtractor/MobilenetV2/expanded_conv_16/expand/BatchNorm/FusedBatchNormV3
[ ERROR ]          FeatureExtractor/MobilenetV2/expanded_conv_16/depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ]          FeatureExtractor/MobilenetV2/expanded_conv_16/project/BatchNorm/FusedBatchNormV3
[ ERROR ]          FeatureExtractor/MobilenetV2/Conv_1/BatchNorm/FusedBatchNormV3
[ ERROR ]          BoxPredictor_1/BoxEncodingPredictor_depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ]          FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_2_1x1_256/BatchNorm/FusedBatchNormV3
[ ERROR ]          FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_2_3x3_s2_512_depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ]          FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_2_3x3_s2_512/BatchNorm/FusedBatchNormV3
[ ERROR ]          BoxPredictor_2/BoxEncodingPredictor_depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ]          FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_3_1x1_128/BatchNorm/FusedBatchNormV3
[ ERROR ]          FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_3_3x3_s2_256_depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ]          FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_3_3x3_s2_256/BatchNorm/FusedBatchNormV3
[ ERROR ]          BoxPredictor_3/BoxEncodingPredictor_depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ]          FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_4_1x1_128/BatchNorm/FusedBatchNormV3
[ ERROR ]          FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_4_3x3_s2_256_depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ]          FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_4_3x3_s2_256/BatchNorm/FusedBatchNormV3
[ ERROR ]          BoxPredictor_4/BoxEncodingPredictor_depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ]          FeatureExtractor/MobilenetV2/layer_19_1_Conv2d_5_1x1_64/BatchNorm/FusedBatchNormV3
[ ERROR ]          FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_5_3x3_s2_128_depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ]          FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_5_3x3_s2_128/BatchNorm/FusedBatchNormV3
[ ERROR ]          BoxPredictor_5/BoxEncodingPredictor_depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ]          BoxPredictor_0/ClassPredictor_depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ]          BoxPredictor_1/ClassPredictor_depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ]          BoxPredictor_2/ClassPredictor_depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ]          BoxPredictor_3/ClassPredictor_depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ]          BoxPredictor_4/ClassPredictor_depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ]          BoxPredictor_5/ClassPredictor_depthwise/BatchNorm/FusedBatchNormV3
[ ERROR ]  Part of the nodes was not converted to IR. Stopped.
 For more information please refer to Model Optimizer FAQ (https://docs.openvinotoolkit.org/latest/_docs_MO_DG_prepare_model_Model_...), question #24.

I guessed there are problems in using 'FusedBatchNormV3'. Tensorflow 1.15.0-rc0 is installed in laptop and colab.

So I changed Tensorflow versions both colab and laptop(from 1.15 to 1.14), but still problem remained.

Is there any method that I turn off the FusedBatchNormV3?(I think OpenVINO R3 doesn't support it)

and here is pipeline.config file.

model {
  ssd {
    num_classes: 7
    image_resizer {
      fixed_shape_resizer {
        height: 300
        width: 300
      }
    }
    feature_extractor {
      type: "ssd_mobilenet_v2"
      depth_multiplier: 1.0
      min_depth: 16
      conv_hyperparams {
        regularizer {
          l2_regularizer {
            weight: 3.99999989895e-05
          }
        }
        initializer {
          truncated_normal_initializer {
            mean: 0.0
            stddev: 0.0299999993294
          }
        }
        activation: RELU_6
        batch_norm {
          decay: 0.999700009823
          center: true
          scale: true
          epsilon: 0.0010000000475
          train: true
        }
      }
      use_depthwise: true
    }
    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 {
      }
    }
    box_predictor {
      convolutional_box_predictor {
        conv_hyperparams {
          regularizer {
            l2_regularizer {
              weight: 3.99999989895e-05
            }
          }
          initializer {
            truncated_normal_initializer {
              mean: 0.0
              stddev: 0.0299999993294
            }
          }
          activation: RELU_6
          batch_norm {
            decay: 0.999700009823
            center: true
            scale: true
            epsilon: 0.0010000000475
            train: true
          }
        }
        min_depth: 0
        max_depth: 0
        num_layers_before_predictor: 0
        use_dropout: false
        dropout_keep_probability: 0.800000011921
        kernel_size: 3
        box_code_size: 4
        apply_sigmoid_to_scores: false
        use_depthwise: true
      }
    }
    anchor_generator {
      ssd_anchor_generator {
        num_layers: 6
        min_scale: 0.20000000298
        max_scale: 0.949999988079
        aspect_ratios: 1.0
        aspect_ratios: 2.0
        aspect_ratios: 0.5
        aspect_ratios: 3.0
        aspect_ratios: 0.333299994469
      }
    }
    post_processing {
      batch_non_max_suppression {
        score_threshold: 0.300000011921
        iou_threshold: 0.600000023842
        max_detections_per_class: 100
        max_total_detections: 100
      }
      score_converter: SIGMOID
    }
    normalize_loss_by_num_matches: true
    loss {
      localization_loss {
        weighted_smooth_l1 {
        }
      }
      classification_loss {
        weighted_sigmoid {
        }
      }
      hard_example_miner {
        num_hard_examples: 3000
        iou_threshold: 0.990000009537
        loss_type: CLASSIFICATION
        max_negatives_per_positive: 3
        min_negatives_per_image: 3
      }
      classification_weight: 1.0
      localization_weight: 1.0
    }
  }
}
train_config {
  batch_size: 32
  data_augmentation_options {
    random_horizontal_flip {
    }
  }
  data_augmentation_options {
    ssd_random_crop {
    }
  }
  optimizer {
    rms_prop_optimizer {
      learning_rate {
        exponential_decay_learning_rate {
          initial_learning_rate: 0.00400000018999
          decay_steps: 800720
          decay_factor: 0.949999988079
        }
      }
      momentum_optimizer_value: 0.899999976158
      decay: 0.899999976158
      epsilon: 1.0
    }
  }
  fine_tune_checkpoint: "/content/confg_ssd2/model.ckpt"
  num_steps: 200000
  fine_tune_checkpoint_type: "detection"
}
train_input_reader {
  label_map_path: "/content/confg_ssd2/mscoco_label_map.pbtxt"
  tf_record_input_reader {
    input_path: "/content/confg_ssd2/mscoco_train.record"
  }
}
eval_config {
  num_examples: 8000
  max_evals: 10
  use_moving_averages: false
}
eval_input_reader {
  label_map_path: "/content/confg_ssd2/mscoco_label_map.pbtxt"
  shuffle: false
  num_readers: 1
  tf_record_input_reader {
    input_path: "/content/confg_ssd2/mscoco_val.record"
  }
}

回答1:


I finally solved it! Downgrade tensorflow from 1.15(or 1.14) to 1.13. It worked for me!



来源:https://stackoverflow.com/questions/58584797/openvino-model-optimizer-errorfusedbatchnormv3

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