About TRT-object-detecction

AI & Data Science Deep Learning (Training & Inference) TensorRT
1

Description

[TensorRT] ERROR: UFFParser: Validator error: FeatureExtractor/MobilenetV2/layer_19_2_Conv2d_5_3x3_s2_128_depthwise/BatchNorm/FusedBatchNormV3: Unsupported operation _FusedBatchNormV3
[TensorRT] ERROR: Network must have at least one output
Traceback (most recent call last):
File “main.py”, line 43, in
buf = engine.serialize()
AttributeError: ‘NoneType’ object has no attribute ‘serialize’

There was no error when the TRT-object-detection script was executed as it was.
So, I updated coco.py(add the item ‘face’ and delete the rest) and added my retrained new model to the model directory. (checkpoint, frozen_inference_graph.pb, model.ckpt-20000.data-00000-of 00001, model.ckpt-20000.index, model.ckpt-20000.meta, pipeline.config )

That will produce such an error.
Both the example and my model are ssd_mobilenet_v2_coco_2018_03_29 and my model is retrained with that model.
Tell me exactly what should be fixed in the TRT-object-detection example.
Or tell me how to detect through the TRT process.

Environment

TensorRT Version:
GPU Type:
Nvidia Driver Version:
CUDA Version:
CUDNN Version:
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Relevant Files

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Steps To Reproduce

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  • Exact steps/commands to run your repro
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2

Hi,
FusedBatchNormV3 operation is currently not supported by UFF parser.

You can try to convert your model to ONNX instead of UFF using tf2onnx:

tf2onnx supports converting this op to BatchNormalization op in ONNX:

And BatchNormalization op is supported by the TensorRT ONNX parser:
https://github.com/onnx/onnx-tensorrt/blob/master/operators.md

I will also recommend you to use latest TRT version.

Thanks

3

I’m not asking about errors. I’m asking why running the script as it is doesn’t make an error, but running it as my model does.

4

Your model might be having the “FusedBatchNormV3” op which is not supported by UFF parser.

Thanks

5

Can you do that even though you trained with the same model?

6

Can you share your model file so we can help better?

Thanks

7

My retrained model is ssd_mobilenet_v2_coco_2018_03_29, and using model in TRT-object-detecion is ssd_mobilenet_v2_coco_2018_03_29, too.

model {
ssd {
num_classes: 1
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
  }
}
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: 24
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: “pre-trained-model/model.ckpt”
num_steps: 200000
fine_tune_checkpoint_type: “detection”
}
train_input_reader {
label_map_path: “annotations/label_map.pbtxt”
tf_record_input_reader {
input_path: “annotations/train.record”
}
}
eval_config {
num_examples: 8000
max_evals: 10
use_moving_averages: false
}
eval_input_reader {
label_map_path: “annotations/label_map.pbtxt”
shuffle: false
num_readers: 1
tf_record_input_reader {
input_path: “annotations/test.record”
}
}

It is my retrained model’s config file.

8

Just update the config.py

def preprocess(dynamic_graph):
        ...
        # Rename the FusedBatchNormV3 op to FusedBatchNorm 
        fusedbatchnorm_nodes = dynamic_graph.find_nodes_by_op("FusedBatchNormV3")
        for node in fusedbatchnorm_nodes:
            node.op = "FusedBatchNorm"

Or you can try latest TRT 7.1 release.

Thanks