github.com/tensorflow/models
  

  
    
  
	  
  

  
    
      ssd_mobilenet_v2_320x320_coco17_tpu-8 for object detection on GPU using Tensorflow 2 "INFO:tensorflow:depth of additional conv before box predictor: 0" and "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.meta_architectures.ssd_meta_arch.SSDMetaArch object at 0x7f51187a02b0>, because it is not built. W0423 10:47:57.954887 139987999496000 save_impl.py:77] Skipping full serialization of Keras layer <object_detection.meta_architectures.ssd_meta_arch.SSDMetaArch object at 0x7f51187a02b0>, because it is not built." 56
    

    
      
        opened 
        
          
        
        Apr 23, 2021
      
      


      
        
          
          thongvhoang
        
      
    

    
        models:research:odapi
        type:bug
    
  


  
    # Prerequisites

- [x] I am using the ssd_mobilenet_v2_320x320_coco17_tpu-8 an…d TensorFlow 2:  2.3.0 
- [x] I am reporting the issue to the correct repository. (research directory) https://github.com/tensorflow/models/blob/master/research/object_detection/configs/tf2/ssd_mobilenet_v2_320x320_coco17_tpu-8.config
- [x] I checked to make sure that this issue has not already been filed.

## 1. The entire URL of the file you are using

https://github.com/tensorflow/models/tree/master/research/object_detection

## 2. Describe the bug

Was the error caused by INFO:tensorflow:depth of additional conv before box predictor: 0 and the model after being exported does not use the latest checkpoint (checkpoint3)?

**Error messages:**
2021-04-23 11:14:58.273174: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
2021-04-23 11:14:59.464301: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcuda.so.1
2021-04-23 11:14:59.486935: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA n
ode zero
2021-04-23 11:14:59.487311: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce GTX 1070 computeCapability: 6.1
coreClock: 1.7085GHz coreCount: 15 deviceMemorySize: 7.93GiB deviceMemoryBandwidth: 238.66GiB/s
2021-04-23 11:14:59.487332: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
2021-04-23 11:14:59.488443: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10
2021-04-23 11:14:59.489511: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10
2021-04-23 11:14:59.489687: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10
2021-04-23 11:14:59.490804: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10
2021-04-23 11:14:59.491509: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.10
2021-04-23 11:14:59.494130: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7
2021-04-23 11:14:59.494262: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returni[38/1972]
ode zero
2021-04-23 11:14:59.494699: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA n
ode zero
2021-04-23 11:14:59.495014: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0
2021-04-23 11:14:59.495262: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN)to use the following CPU instructions in perfo
rmance-critical operations: AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2021-04-23 11:14:59.499752: I tensorflow/core/platform/profile_utils/cpu_utils.cc:104] CPU Frequency: 3600000000 Hz
2021-04-23 11:14:59.500040: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x54c78b0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2021-04-23 11:14:59.500051: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version
2021-04-23 11:14:59.561614: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA n
ode zero
2021-04-23 11:14:59.561986: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x555b8e0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2021-04-23 11:14:59.562000: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): GeForce GTX 1070, Compute Capability 6.1
2021-04-23 11:14:59.562205: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA n
ode zero
2021-04-23 11:14:59.562522: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce GTX 1070 computeCapability: 6.1
coreClock: 1.7085GHz coreCount: 15 deviceMemorySize: 7.93GiB deviceMemoryBandwidth: 238.66GiB/s
2021-04-23 11:14:59.562539: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1 [18/1972]
2021-04-23 11:14:59.562557: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10
2021-04-23 11:14:59.562583: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10
2021-04-23 11:14:59.562593: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10
2021-04-23 11:14:59.562615: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10
2021-04-23 11:14:59.562639: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.10
2021-04-23 11:14:59.562650: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7
2021-04-23 11:14:59.562718: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA n
ode zero
2021-04-23 11:14:59.563031: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA n
ode zero
2021-04-23 11:14:59.563370: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0
2021-04-23 11:14:59.563389: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
2021-04-23 11:14:59.921326: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1257] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-04-23 11:14:59.921355: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1263] 0
2021-04-23 11:14:59.921361: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1276] 0: N
2021-04-23 11:14:59.921533: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA n
ode zero
2021-04-23 11:14:59.921864: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA n
ode zero
2021-04-23 11:14:59.922150: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1402] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 7014 MB memory) -> physical GPU (device: 0, name:
GeForce GTX 1070, pci bus id: 0000:01:00.0, compute capability: 6.1)
WARNING:tensorflow:From /home/nguyen_truong/.local/lib/python3.8/site-packages/object_detection/exporter_lib_v2.py:106: calling map_fn_v2 (from tensorflow.python.ops.map_fn) with back_prop=False is deprecated an
d will be removed in a future version.
Instructions for updating:
back_prop=False is deprecated. Consider using tf.stop_gradient instead.
Instead of:
results = tf.map_fn(fn, elems, back_prop=False)
Use:
results = tf.nest.map_structure(tf.stop_gradient, tf.map_fn(fn, elems))
W0423 11:15:00.141795 140359799744320 deprecation.py:568] From /home/nguyen_truong/.local/lib/python3.8/site-packages/object_detection/exporter_lib_v2.py:106: calling map_fn_v2 (from tensorflow.python.ops.map_fn
) with back_prop=False is deprecated and will be removed in a future version.
Instructions for updating:
back_prop=False is deprecated. Consider using tf.stop_gradient instead.
Instead of:
results = tf.map_fn(fn, elems, back_prop=False)
Use:
results = tf.nest.map_structure(tf.stop_gradient, tf.map_fn(fn, elems))
INFO:tensorflow:depth of additional conv before box predictor: 0 [1/1993]
I0423 11:15:03.574930 140359799744320 convolutional_keras_box_predictor.py:153] depth of additional conv before box predictor: 0
INFO:tensorflow:depth of additional conv before box predictor: 0
I0423 11:15:03.575145 140359799744320 convolutional_keras_box_predictor.py:153] depth of additional conv before box predictor: 0
INFO:tensorflow:depth of additional conv before box predictor: 0
I0423 11:15:03.575302 140359799744320 convolutional_keras_box_predictor.py:153] depth of additional conv before box predictor: 0
INFO:tensorflow:depth of additional conv before box predictor: 0
I0423 11:15:03.575448 140359799744320 convolutional_keras_box_predictor.py:153] depth of additional conv before box predictor: 0
INFO:tensorflow:depth of additional conv before box predictor: 0
I0423 11:15:03.575573 140359799744320 convolutional_keras_box_predictor.py:153] depth of additional conv before box predictor: 0
INFO:tensorflow:depth of additional conv before box predictor: 0
I0423 11:15:03.575695 140359799744320 convolutional_keras_box_predictor.py:153] depth of additional conv before box predictor: 0
WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.meta_architectures.ssd_meta_arch.SSDMetaArch object at 0x7fa7b84d5580>, because it is not built.
W0423 11:15:08.253181 140359799744320 save_impl.py:77] Skipping full serialization of Keras layer <object_detection.meta_architectures.ssd_meta_arch.SSDMetaArch object at 0x7fa7b84d5580>, because it is not built
.
2021-04-23 11:15:15.046676: W tensorflow/python/util/util.cc:348] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.
INFO:tensorflow:Assets written to: inference_graph/saved_model/assets
I0423 11:15:26.415863 140359799744320 builder_impl.py:774] Assets written to: inference_graph/saved_model/assets
INFO:tensorflow:Writing pipeline config file to inference_graph/pipeline.config
I0423 11:15:26.901574 140359799744320 config_util.py:253] Writing pipeline config file to inference_graph/pipeline.config

## 3. Steps to reproduce
Command 1: python model_main_tf2.py --model_dir=ssd_mobilenet_v2_320x320_coco17_tpu-8 --pipeline_config_path=ssd_mobilenet_v2_320x320_coco17_tpu-8/pipeline.config

Command 2: python models/research/object_detection/exporter_main_v2.py --trained_checkpoint_dir ssd_mobilenet_v2_320x320_coco17_tpu-8 --output_directory inference_graph --pipeline_config_path ssd_mobilenet_v2_320x320_coco17_tpu-8/ssd_mobilenet_v2_320x320_coco17_tpu-8.config

## 4. Expected behavior

Folder inference_graph contained model exported lasted with checkpoint3 and detected images clearly. But the bug exported the model using checkpoint1 has not updated so can not detect images with bounding box.

## 5. Additional context

My ssd mobilenet v2 configure:
model {
ssd {
num_classes: 1
image_resizer {
fixed_shape_resizer {
height: 300
width: 300
}
}
feature_extractor {
type: "ssd_mobilenet_v2_keras"
depth_multiplier: 1.0
min_depth: 16
conv_hyperparams {
regularizer {
l2_regularizer {
weight: 3.9999998989515007e-05
}
}
initializer {
truncated_normal_initializer {
mean: 0.0
stddev: 0.029999999329447746
}
}
activation: RELU_6
batch_norm {
decay: 0.9700000286102295
center: true
scale: true
epsilon: 0.0010000000474974513
train: true
}
}
override_base_feature_extractor_hyperparams: 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
use_matmul_gather: true
}
}
similarity_calculator {
iou_similarity {
}
}
box_predictor {
convolutional_box_predictor {
conv_hyperparams {
regularizer {
l2_regularizer {
weight: 3.9999998989515007e-05
}
}
initializer {
random_normal_initializer {
mean: 0.0
stddev: 0.009999999776482582
}
}
activation: RELU_6
batch_norm {
decay: 0.9700000286102295
center: true
scale: true
epsilon: 0.0010000000474974513
train: true
}
}
min_depth: 0
max_depth: 0
num_layers_before_predictor: 0
use_dropout: false
dropout_keep_probability: 0.800000011920929
kernel_size: 1
box_code_size: 4
apply_sigmoid_to_scores: false
class_prediction_bias_init: -4.599999904632568
}
}
anchor_generator {
ssd_anchor_generator {
num_layers: 6
min_scale: 0.20000000298023224
max_scale: 0.949999988079071
aspect_ratios: 1.0
aspect_ratios: 2.0
aspect_ratios: 0.5
aspect_ratios: 3.0
aspect_ratios: 0.33329999446868896
}
}
post_processing {
batch_non_max_suppression {
score_threshold: 9.99999993922529e-09
iou_threshold: 0.6000000238418579
max_detections_per_class: 100
max_total_detections: 100
use_static_shapes: false
}
score_converter: SIGMOID
}
normalize_loss_by_num_matches: true
loss {
localization_loss {
weighted_smooth_l1 {
delta: 1.0
}
}
classification_loss {
weighted_sigmoid_focal {
gamma: 2.0
alpha: 0.75
}
}
classification_weight: 1.0
localization_weight: 1.0
}
encode_background_as_zeros: true
normalize_loc_loss_by_codesize: true
inplace_batchnorm_update: true
freeze_batchnorm: false
}
}
train_config {
batch_size: 12
data_augmentation_options {
random_horizontal_flip {
}
}
data_augmentation_options {
ssd_random_crop {
}
}
sync_replicas: true
optimizer {
momentum_optimizer {
learning_rate {
cosine_decay_learning_rate {
learning_rate_base: 0.03999999910593033
total_steps: 2000
warmup_learning_rate: 0.013333000242710114
warmup_steps: 500
}
}
momentum_optimizer_value: 0.8999999761581421
}
use_moving_average: false
}
fine_tune_checkpoint: "ssd_mobilenet_v2_320x320_coco17_tpu-8/checkpoint0/ckpt-0"
num_steps: 2000
startup_delay_steps: 0.0
replicas_to_aggregate: 8
max_number_of_boxes: 100
unpad_groundtruth_tensors: false
fine_tune_checkpoint_type: "detection"
fine_tune_checkpoint_version: V2
}
train_input_reader {
label_map_path: "data/object-detection.pbxt"
tf_record_input_reader {
input_path: "data/train.record"
}
}
eval_config {
metrics_set: "coco_detection_metrics"
use_moving_averages: false
}
eval_input_reader {
label_map_path: "data/object-detection.pbxt"
shuffle: false
num_epochs: 1
tf_record_input_reader {
input_path: "data/test.record"
}
}

## 6. System information

- OS Platform and Distribution: Linux Ubuntu 20.04
- TensorFlow installed from (source or binary): pip install
- TensorFlow version (use command below): 2.3.0
- Python version: 3.8.5
- CUDA/cuDNN version: Cuda release 10.1, V10.1.243
- GPU model and memory: GeForce GTX 1070

TensorFlow 2.0
`python -c "import tensorflow as tf; print(tf.version.GIT_VERSION, tf.version.VERSION)"`
--> 2.3.0