Converting retrained ssd_resnet50_v1 object detection model to TFLite for use on Coral USB Accelerator

System information

OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Linux Ubuntu 16.04
TensorFlow installed from (source or binary): tf-nightly installed through pip
TensorFlow version (or github SHA if from source): 2.2.0.dev20200401
Nvidia GeForce GTX 1050 Ti Driver Version: 440.64.00
Cuda compilation tools, release 9.0, V9.0.176
CUDNN_MAJOR 7 CUDNN_MINOR 0 CUDNN_PATCHLEVEL 5
Edge TPU Compiler version 2.1.302470888

Command used to run the converter or code if you’re using the Python API
If possible, please share a link to Colab/Jupyter/any notebook.
import tensorflow as tf

BATCH_SIZE = 32
IMG_HEIGHT = 320
IMG_WIDTH = 320
physical_devices = tf.config.list_physical_devices('GPU')
tf.config.set_logical_device_configuration(physical_devices[0],[tf.config.LogicalDeviceConfiguration(memory_limit=500)])
logical_devices = tf.config.list_logical_devices('GPU')

list_ds_Dianthus = tf.data.Dataset.list_files(str('/home/afayed/coral/detection_ag/ag/Dianthus''*/*/*'))
list_ds_DustyMiller = tf.data.Dataset.list_files(str('/home/afayed/coral/detection_ag/ag/DustyMiller''*/*/*'))
list_ds_Pansy = tf.data.Dataset.list_files(str('/home/afayed/coral/detection_ag/ag/Pansy''*/*/*'))
list_all = list_ds_Dianthus.concatenate(list_ds_DustyMiller).concatenate(list_ds_Pansy)
#len(list(list_all))

def get_label(file_path):
  parts = tf.strings.split(file_path, '/')
  class_name = tf.strings.split(parts[-3], '1')
  class_name = tf.strings.split(class_name, '2')
  class_name = tf.strings.split(class_name, '3')
  class_name = tf.strings.split(class_name, '4')
  return class_name

def decode_img(img):
	img = tf.image.decode_jpeg(img, channels=3) #color images
	img = tf.image.convert_image_dtype(img, tf.float32)
	#convert unit8 tensor to floats in the [0,1]range
	return tf.image.resize(img, [IMG_WIDTH, IMG_HEIGHT])

def process_path(file_path):
  label = get_label(file_path)
  img = tf.io.read_file(file_path)
  img = decode_img(img)
  return img, label

num_calibration_steps= 1000

def representative_dataset_gen():
	for _ in range(num_calibration_steps):
	# Get sample input data as a numpy array in a method of your choosing.
		image = list_all.take(1)
		yield [decode_img(image)]

saved_model_obj = tf.saved_model.load(export_dir='/home/afayed/coral/detection_ag/train/export/Servo/1586490086/')
concrete_func = saved_model_obj.signatures['serving_default']
concrete_func.inputs[0].set_shape([])
converter =  tf.lite.TFLiteConverter.from_concrete_functions([concrete_func])
converter.experimental_new_converter = False
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS,
                                       tf.lite.OpsSet.SELECT_TF_OPS]
converter.optimizations =  [tf.lite.Optimize.DEFAULT]
converter.representative_dataset = representative_dataset_gen
tflite_quant_model = converter.convert()

open("/home/afayed/Desktop/output_tflite_graph.tflite", "wb").write(tflite_quant_model)

The output from the converter invocation
TensorFlow Lite currently doesn't support control flow ops: Merge, Switch.
We are working on supporting control flow ops,
please see github issue at https://github.com/tensorflow/tensorflow/issues/28485.
Some of the operators in the model are not supported by the
standard TensorFlow Lite runtime and are not recognized by TensorFlow.
If you have a custom implementation
for them you can disable this error with --allow_custom_ops, or by setting
allow_custom_ops=True when calling tf.lite.TFLiteConverter().
Here is a list of builtin operators you
are using: ADD, CAST, CONCATENATION, CONV_2D, EQUAL, EXP, EXPAND_DIMS, FILL,
GATHER, GREATER, GREATER_EQUAL,
LESS, LOGICAL_OR, LOGISTIC, MAXIMUM, MAX_POOL_2D, MINIMUM, MUL, PACK, PAD,
RANGE, RESHAPE, RESIZE_BILINEAR, SELECT,
SHAPE, SLICE, SPLIT, SQUEEZE, STRIDED_SLICE, SUB, SUM, TOPK_V2, TRANSPOSE, UNPACK,
WHERE.Here is a list of operators for which you will need custom implementations:
DecodeGif, DecodeJpeg, DecodePng, DecodeRaw, NonMaxSuppressionV5, Substr.

Also, please include a link to the saved model or GraphDef
Model ckpt 5 used here as a .tar.
Frozen graph generated here
saved_model.pb used here
Failure details
Conversion not successful...
Any other info / logs
Detailed explanation
I am trying to use custom training data to retrain 'ssd_resnet50_v1_fpn_shared_box_predictor_640x640_coco14_sync_2018_07_03' (model obtained from model zoo here ) following Google's Retrain an object detection model tutorial
I retrained it on custom training data using TensorFlow with GPU devel version 1.12.0-rc2 on a docker container built with this Dockerfile.
I also tried retraining with TensorFlow with GPU version 1.15.2. The pipeline config file I am using is available here.
I have made sure I used a fixed_shape_resizer and included "max_number_of_boxes: 200" in train_input_reader and eval_input_reader and all of that.
My image dimensions as found in pipeline_v2.config are 320,320.
I found the new Experimental Converter here
So, my process is as such:
I start the docker container and start retraining from ckpt 0 that is given by downloading the model from the model zoo:
sudo docker run --gpus all --name edgetpu-detect \
--rm -it --privileged -p 6006:6006 \
--mount type=bind,src=${DETECT_DIR},dst=/tensorflow/models/research/learn_pet \
detect-tutorial

For the sake of testing lets only retrain 5 steps (I've tried up to 20,000 giving the same results):
./retrain_detection_model.sh --num_training_steps 5 --num_eval_steps 100

retrain_detection_model.sh found here
I then use 'saved_model_cli.py' to get any info I need about the model. Text file generated [here](https://drive.google.com/open?id=1nDNSpEyjExg-_L_wpaDJaNlbVrRvfbp8):
python /tensorflow/tensorflow/python/tools/saved_model_cli.py show --dir /tensorflow/models/research/learn_pet/train/export/Servo/1586336368/ --all > /tensorflow/models/research/learn_pet/train/saved_model_cli_output.txt```

I then export a frozen graph using export_tflite_ssd_graph.py (unedited) both through 1.12.0-rc2 and 1.15.2 code shown here is using TF 1.15.2:

Output:
root@d97ce87cb12f:/tensorflow/models/research# python object_detection/export_tflite_ssd_graph.py --pipeline_config_path="/tensorflow/learn_pet/ckpt/pipeline_v2.config" --trained_checkpoint_prefix="/tensorflow/learn_pet/train/model.ckpt-5" --output_directory="/tensorflow/learn_pet/models"
WARNING:tensorflow:
The TensorFlow contrib module will not be included in TensorFlow 2.0.
For more information, please see:
  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md
  * https://github.com/tensorflow/addons
  * https://github.com/tensorflow/io (for I/O related ops)
If you depend on functionality not listed there, please file an issue.

WARNING:tensorflow:From object_detection/export_tflite_ssd_graph.py:143: The name tf.app.run is deprecated. Please use tf.compat.v1.app.run instead.

WARNING:tensorflow:From object_detection/export_tflite_ssd_graph.py:133: The name tf.gfile.GFile is deprecated. Please use tf.io.gfile.GFile instead.

W0410 12:39:17.462405<PHONE_NUMBER>27136 module_wrapper.py:139] From object_detection/export_tflite_ssd_graph.py:133: The name tf.gfile.GFile is deprecated. Please use tf.io.gfile.GFile instead.

WARNING:tensorflow:From /tensorflow/models/research/object_detection/export_tflite_ssd_graph_lib.py:193: The name tf.gfile.MakeDirs is deprecated. Please use tf.io.gfile.makedirs instead.

W0410 12:39:17.465477<PHONE_NUMBER>27136 module_wrapper.py:139] From /tensorflow/models/research/object_detection/export_tflite_ssd_graph_lib.py:193: The name tf.gfile.MakeDirs is deprecated. Please use tf.io.gfile.makedirs instead.

WARNING:tensorflow:From /tensorflow/models/research/object_detection/export_tflite_ssd_graph_lib.py:237: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.

W0410 12:39:17.465742<PHONE_NUMBER>27136 module_wrapper.py:139] From /tensorflow/models/research/object_detection/export_tflite_ssd_graph_lib.py:237: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.

WARNING:tensorflow:From /tensorflow/models/research/object_detection/meta_architectures/ssd_meta_arch.py:597: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead.

W0410 12:39:17.468422<PHONE_NUMBER>27136 module_wrapper.py:139] From /tensorflow/models/research/object_detection/meta_architectures/ssd_meta_arch.py:597: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead.

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/contrib/layers/python/layers/layers.py:1057: Layer.apply (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.
Instructions for updating:
Please use `layer.__call__` method instead.
W0410 12:39:17.471358<PHONE_NUMBER>27136 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow_core/contrib/layers/python/layers/layers.py:1057: Layer.apply (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.
Instructions for updating:
Please use `layer.__call__` method instead.
WARNING:tensorflow:From /tensorflow/models/research/object_detection/core/anchor_generator.py:171: The name tf.assert_equal is deprecated. Please use tf.compat.v1.assert_equal instead.

W0410 12:39:18.826324<PHONE_NUMBER>27136 module_wrapper.py:139] From /tensorflow/models/research/object_detection/core/anchor_generator.py:171: The name tf.assert_equal is deprecated. Please use tf.compat.v1.assert_equal instead.

WARNING:tensorflow:From /tensorflow/models/research/object_detection/predictors/convolutional_box_predictor.py:380: The name tf.AUTO_REUSE is deprecated. Please use tf.compat.v1.AUTO_REUSE instead.

W0410 12:39:19.233271<PHONE_NUMBER>27136 module_wrapper.py:139] From /tensorflow/models/research/object_detection/predictors/convolutional_box_predictor.py:380: The name tf.AUTO_REUSE is deprecated. Please use tf.compat.v1.AUTO_REUSE instead.

WARNING:tensorflow:From /tensorflow/models/research/object_detection/export_tflite_ssd_graph_lib.py:52: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.

W0410 12:39:19.890158<PHONE_NUMBER>27136 module_wrapper.py:139] From /tensorflow/models/research/object_detection/export_tflite_ssd_graph_lib.py:52: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.

2020-04-10 12:39:19.891180: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1
2020-04-10 12:39:19.908694: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-04-10 12:39:19.909025: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Found device 0 with properties:
name: GeForce GTX 1050 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.62
pciBusID: 0000:01:00.0
2020-04-10 12:39:19.909296: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.0
2020-04-10 12:39:19.910343: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10.0
2020-04-10 12:39:19.911197: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10.0
2020-04-10 12:39:19.911425: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10.0
2020-04-10 12:39:19.912640: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10.0
2020-04-10 12:39:19.913612: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10.0
2020-04-10 12:39:19.916425: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2020-04-10 12:39:19.916589: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-04-10 12:39:19.916980: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-04-10 12:39:19.917217: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1767] Adding visible gpu devices: 0
2020-04-10 12:39:19.917530: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2020-04-10 12:39:19.941192: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency:<PHONE_NUMBER> Hz
2020-04-10 12:39:19.941657: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7efacc0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2020-04-10 12:39:19.941674: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
2020-04-10 12:39:19.987579: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-04-10 12:39:19.987958: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7905220 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2020-04-10 12:39:19.987995: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): GeForce GTX 1050 Ti, Compute Capability 6.1
2020-04-10 12:39:19.988151: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-04-10 12:39:19.988386: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Found device 0 with properties:
name: GeForce GTX 1050 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.62
pciBusID: 0000:01:00.0
2020-04-10 12:39:19.988425: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.0
2020-04-10 12:39:19.988441: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10.0
2020-04-10 12:39:19.988454: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10.0
2020-04-10 12:39:19.988469: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10.0
2020-04-10 12:39:19.988485: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10.0
2020-04-10 12:39:19.988499: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10.0
2020-04-10 12:39:19.988516: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2020-04-10 12:39:19.988588: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-04-10 12:39:19.988915: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-04-10 12:39:19.989197: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1767] Adding visible gpu devices: 0
2020-04-10 12:39:19.989246: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.0
2020-04-10 12:39:19.989760: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1180] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-04-10 12:39:19.989773: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1186]      0
2020-04-10 12:39:19.989782: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1199] 0:   N
2020-04-10 12:39:19.989861: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-04-10 12:39:19.990093: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-04-10 12:39:19.990307: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1325] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 2888 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1050 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1)
WARNING:tensorflow:From /tensorflow/models/research/object_detection/export_tflite_ssd_graph_lib.py:267: The name tf.train.get_or_create_global_step is deprecated. Please use tf.compat.v1.train.get_or_create_global_step instead.

W0410 12:39:20.492587<PHONE_NUMBER>27136 module_wrapper.py:139] From /tensorflow/models/research/object_detection/export_tflite_ssd_graph_lib.py:267: The name tf.train.get_or_create_global_step is deprecated. Please use tf.compat.v1.train.get_or_create_global_step instead.

WARNING:tensorflow:From /tensorflow/models/research/object_detection/builders/graph_rewriter_builder.py:41: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.

W0410 12:39:20.495462<PHONE_NUMBER>27136 module_wrapper.py:139] From /tensorflow/models/research/object_detection/builders/graph_rewriter_builder.py:41: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.

INFO:tensorflow:Skipping quantizing WeightSharedConvolutionalBoxPredictor/BoxPredictionTower/conv2d_0/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
I0410 12:39:20.895448<PHONE_NUMBER>27136 quantize.py:588] Skipping quantizing WeightSharedConvolutionalBoxPredictor/BoxPredictionTower/conv2d_0/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
INFO:tensorflow:Skipping quantizing WeightSharedConvolutionalBoxPredictor/BoxPredictionTower/conv2d_1/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
I0410 12:39:20.895938<PHONE_NUMBER>27136 quantize.py:588] Skipping quantizing WeightSharedConvolutionalBoxPredictor/BoxPredictionTower/conv2d_1/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
INFO:tensorflow:Skipping quantizing WeightSharedConvolutionalBoxPredictor/BoxPredictionTower/conv2d_2/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
I0410 12:39:20.896350<PHONE_NUMBER>27136 quantize.py:588] Skipping quantizing WeightSharedConvolutionalBoxPredictor/BoxPredictionTower/conv2d_2/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
INFO:tensorflow:Skipping quantizing WeightSharedConvolutionalBoxPredictor/BoxPredictionTower/conv2d_3/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
I0410 12:39:20.896607<PHONE_NUMBER>27136 quantize.py:588] Skipping quantizing WeightSharedConvolutionalBoxPredictor/BoxPredictionTower/conv2d_3/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
INFO:tensorflow:Skipping quantizing WeightSharedConvolutionalBoxPredictor/ClassPredictionTower/conv2d_0/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
I0410 12:39:20.896919<PHONE_NUMBER>27136 quantize.py:588] Skipping quantizing WeightSharedConvolutionalBoxPredictor/ClassPredictionTower/conv2d_0/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
INFO:tensorflow:Skipping quantizing WeightSharedConvolutionalBoxPredictor/ClassPredictionTower/conv2d_1/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
I0410 12:39:20.897162<PHONE_NUMBER>27136 quantize.py:588] Skipping quantizing WeightSharedConvolutionalBoxPredictor/ClassPredictionTower/conv2d_1/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
INFO:tensorflow:Skipping quantizing WeightSharedConvolutionalBoxPredictor/ClassPredictionTower/conv2d_2/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
I0410 12:39:20.897400<PHONE_NUMBER>27136 quantize.py:588] Skipping quantizing WeightSharedConvolutionalBoxPredictor/ClassPredictionTower/conv2d_2/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
INFO:tensorflow:Skipping quantizing WeightSharedConvolutionalBoxPredictor/ClassPredictionTower/conv2d_3/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
I0410 12:39:20.897668<PHONE_NUMBER>27136 quantize.py:588] Skipping quantizing WeightSharedConvolutionalBoxPredictor/ClassPredictionTower/conv2d_3/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
INFO:tensorflow:Skipping quantizing WeightSharedConvolutionalBoxPredictor_1/BoxPredictionTower/conv2d_0/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
I0410 12:39:20.897969<PHONE_NUMBER>27136 quantize.py:588] Skipping quantizing WeightSharedConvolutionalBoxPredictor_1/BoxPredictionTower/conv2d_0/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
INFO:tensorflow:Skipping quantizing WeightSharedConvolutionalBoxPredictor_1/BoxPredictionTower/conv2d_1/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
I0410 12:39:20.898177<PHONE_NUMBER>27136 quantize.py:588] Skipping quantizing WeightSharedConvolutionalBoxPredictor_1/BoxPredictionTower/conv2d_1/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
INFO:tensorflow:Skipping quantizing WeightSharedConvolutionalBoxPredictor_1/BoxPredictionTower/conv2d_2/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
I0410 12:39:20.898397<PHONE_NUMBER>27136 quantize.py:588] Skipping quantizing WeightSharedConvolutionalBoxPredictor_1/BoxPredictionTower/conv2d_2/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
INFO:tensorflow:Skipping quantizing WeightSharedConvolutionalBoxPredictor_1/BoxPredictionTower/conv2d_3/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
I0410 12:39:20.898632<PHONE_NUMBER>27136 quantize.py:588] Skipping quantizing WeightSharedConvolutionalBoxPredictor_1/BoxPredictionTower/conv2d_3/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
INFO:tensorflow:Skipping quantizing WeightSharedConvolutionalBoxPredictor_1/ClassPredictionTower/conv2d_0/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
I0410 12:39:20.898874<PHONE_NUMBER>27136 quantize.py:588] Skipping quantizing WeightSharedConvolutionalBoxPredictor_1/ClassPredictionTower/conv2d_0/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
INFO:tensorflow:Skipping quantizing WeightSharedConvolutionalBoxPredictor_1/ClassPredictionTower/conv2d_1/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
I0410 12:39:20.899109<PHONE_NUMBER>27136 quantize.py:588] Skipping quantizing WeightSharedConvolutionalBoxPredictor_1/ClassPredictionTower/conv2d_1/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
INFO:tensorflow:Skipping quantizing WeightSharedConvolutionalBoxPredictor_1/ClassPredictionTower/conv2d_2/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
I0410 12:39:20.899347<PHONE_NUMBER>27136 quantize.py:588] Skipping quantizing WeightSharedConvolutionalBoxPredictor_1/ClassPredictionTower/conv2d_2/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
INFO:tensorflow:Skipping quantizing WeightSharedConvolutionalBoxPredictor_1/ClassPredictionTower/conv2d_3/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
I0410 12:39:20.899583<PHONE_NUMBER>27136 quantize.py:588] Skipping quantizing WeightSharedConvolutionalBoxPredictor_1/ClassPredictionTower/conv2d_3/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
INFO:tensorflow:Skipping quantizing WeightSharedConvolutionalBoxPredictor_2/BoxPredictionTower/conv2d_0/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
I0410 12:39:20.899877<PHONE_NUMBER>27136 quantize.py:588] Skipping quantizing WeightSharedConvolutionalBoxPredictor_2/BoxPredictionTower/conv2d_0/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
INFO:tensorflow:Skipping quantizing WeightSharedConvolutionalBoxPredictor_2/BoxPredictionTower/conv2d_1/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
I0410 12:39:20.900119<PHONE_NUMBER>27136 quantize.py:588] Skipping quantizing WeightSharedConvolutionalBoxPredictor_2/BoxPredictionTower/conv2d_1/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
INFO:tensorflow:Skipping quantizing WeightSharedConvolutionalBoxPredictor_2/BoxPredictionTower/conv2d_2/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
I0410 12:39:20.900363<PHONE_NUMBER>27136 quantize.py:588] Skipping quantizing WeightSharedConvolutionalBoxPredictor_2/BoxPredictionTower/conv2d_2/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
INFO:tensorflow:Skipping quantizing WeightSharedConvolutionalBoxPredictor_2/BoxPredictionTower/conv2d_3/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
I0410 12:39:20.900600<PHONE_NUMBER>27136 quantize.py:588] Skipping quantizing WeightSharedConvolutionalBoxPredictor_2/BoxPredictionTower/conv2d_3/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
INFO:tensorflow:Skipping quantizing WeightSharedConvolutionalBoxPredictor_2/ClassPredictionTower/conv2d_0/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
I0410 12:39:20.900838<PHONE_NUMBER>27136 quantize.py:588] Skipping quantizing WeightSharedConvolutionalBoxPredictor_2/ClassPredictionTower/conv2d_0/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
INFO:tensorflow:Skipping quantizing WeightSharedConvolutionalBoxPredictor_2/ClassPredictionTower/conv2d_1/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
I0410 12:39:20.901076<PHONE_NUMBER>27136 quantize.py:588] Skipping quantizing WeightSharedConvolutionalBoxPredictor_2/ClassPredictionTower/conv2d_1/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
INFO:tensorflow:Skipping quantizing WeightSharedConvolutionalBoxPredictor_2/ClassPredictionTower/conv2d_2/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
I0410 12:39:20.901296<PHONE_NUMBER>27136 quantize.py:588] Skipping quantizing WeightSharedConvolutionalBoxPredictor_2/ClassPredictionTower/conv2d_2/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
INFO:tensorflow:Skipping quantizing WeightSharedConvolutionalBoxPredictor_2/ClassPredictionTower/conv2d_3/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
I0410 12:39:20.901502<PHONE_NUMBER>27136 quantize.py:588] Skipping quantizing WeightSharedConvolutionalBoxPredictor_2/ClassPredictionTower/conv2d_3/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
INFO:tensorflow:Skipping quantizing WeightSharedConvolutionalBoxPredictor_3/BoxPredictionTower/conv2d_0/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
I0410 12:39:20.901758<PHONE_NUMBER>27136 quantize.py:588] Skipping quantizing WeightSharedConvolutionalBoxPredictor_3/BoxPredictionTower/conv2d_0/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
INFO:tensorflow:Skipping quantizing WeightSharedConvolutionalBoxPredictor_3/BoxPredictionTower/conv2d_1/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
I0410 12:39:20.901966<PHONE_NUMBER>27136 quantize.py:588] Skipping quantizing WeightSharedConvolutionalBoxPredictor_3/BoxPredictionTower/conv2d_1/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
INFO:tensorflow:Skipping quantizing WeightSharedConvolutionalBoxPredictor_3/BoxPredictionTower/conv2d_2/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
I0410 12:39:20.902171<PHONE_NUMBER>27136 quantize.py:588] Skipping quantizing WeightSharedConvolutionalBoxPredictor_3/BoxPredictionTower/conv2d_2/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
INFO:tensorflow:Skipping quantizing WeightSharedConvolutionalBoxPredictor_3/BoxPredictionTower/conv2d_3/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
I0410 12:39:20.902374<PHONE_NUMBER>27136 quantize.py:588] Skipping quantizing WeightSharedConvolutionalBoxPredictor_3/BoxPredictionTower/conv2d_3/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
INFO:tensorflow:Skipping quantizing WeightSharedConvolutionalBoxPredictor_3/ClassPredictionTower/conv2d_0/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
I0410 12:39:20.902588<PHONE_NUMBER>27136 quantize.py:588] Skipping quantizing WeightSharedConvolutionalBoxPredictor_3/ClassPredictionTower/conv2d_0/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
INFO:tensorflow:Skipping quantizing WeightSharedConvolutionalBoxPredictor_3/ClassPredictionTower/conv2d_1/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
I0410 12:39:20.902798<PHONE_NUMBER>27136 quantize.py:588] Skipping quantizing WeightSharedConvolutionalBoxPredictor_3/ClassPredictionTower/conv2d_1/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
INFO:tensorflow:Skipping quantizing WeightSharedConvolutionalBoxPredictor_3/ClassPredictionTower/conv2d_2/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
I0410 12:39:20.903002<PHONE_NUMBER>27136 quantize.py:588] Skipping quantizing WeightSharedConvolutionalBoxPredictor_3/ClassPredictionTower/conv2d_2/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
INFO:tensorflow:Skipping quantizing WeightSharedConvolutionalBoxPredictor_3/ClassPredictionTower/conv2d_3/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
I0410 12:39:20.903205<PHONE_NUMBER>27136 quantize.py:588] Skipping quantizing WeightSharedConvolutionalBoxPredictor_3/ClassPredictionTower/conv2d_3/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
INFO:tensorflow:Skipping quantizing WeightSharedConvolutionalBoxPredictor_4/BoxPredictionTower/conv2d_0/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
I0410 12:39:20.903466<PHONE_NUMBER>27136 quantize.py:588] Skipping quantizing WeightSharedConvolutionalBoxPredictor_4/BoxPredictionTower/conv2d_0/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
INFO:tensorflow:Skipping quantizing WeightSharedConvolutionalBoxPredictor_4/BoxPredictionTower/conv2d_1/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
I0410 12:39:20.903675<PHONE_NUMBER>27136 quantize.py:588] Skipping quantizing WeightSharedConvolutionalBoxPredictor_4/BoxPredictionTower/conv2d_1/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
INFO:tensorflow:Skipping quantizing WeightSharedConvolutionalBoxPredictor_4/BoxPredictionTower/conv2d_2/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
I0410 12:39:20.903881<PHONE_NUMBER>27136 quantize.py:588] Skipping quantizing WeightSharedConvolutionalBoxPredictor_4/BoxPredictionTower/conv2d_2/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
INFO:tensorflow:Skipping quantizing WeightSharedConvolutionalBoxPredictor_4/BoxPredictionTower/conv2d_3/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
I0410 12:39:20.904086<PHONE_NUMBER>27136 quantize.py:588] Skipping quantizing WeightSharedConvolutionalBoxPredictor_4/BoxPredictionTower/conv2d_3/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
INFO:tensorflow:Skipping quantizing WeightSharedConvolutionalBoxPredictor_4/ClassPredictionTower/conv2d_0/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
I0410 12:39:20.904299<PHONE_NUMBER>27136 quantize.py:588] Skipping quantizing WeightSharedConvolutionalBoxPredictor_4/ClassPredictionTower/conv2d_0/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
INFO:tensorflow:Skipping quantizing WeightSharedConvolutionalBoxPredictor_4/ClassPredictionTower/conv2d_1/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
I0410 12:39:20.904532<PHONE_NUMBER>27136 quantize.py:588] Skipping quantizing WeightSharedConvolutionalBoxPredictor_4/ClassPredictionTower/conv2d_1/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
INFO:tensorflow:Skipping quantizing WeightSharedConvolutionalBoxPredictor_4/ClassPredictionTower/conv2d_2/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
I0410 12:39:20.904742<PHONE_NUMBER>27136 quantize.py:588] Skipping quantizing WeightSharedConvolutionalBoxPredictor_4/ClassPredictionTower/conv2d_2/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
INFO:tensorflow:Skipping quantizing WeightSharedConvolutionalBoxPredictor_4/ClassPredictionTower/conv2d_3/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
I0410 12:39:20.904950<PHONE_NUMBER>27136 quantize.py:588] Skipping quantizing WeightSharedConvolutionalBoxPredictor_4/ClassPredictionTower/conv2d_3/Identity, because it is the output of a conv/fc followed by a identity, feeding a fused batch norm.
INFO:tensorflow:Skipping FeatureExtractor/resnet_v1_50/resnet_v1_50/block1/unit_1/bottleneck_v1/add, because its followed by an activation.
I0410 12:39:20.949430<PHONE_NUMBER>27136 quantize.py:166] Skipping FeatureExtractor/resnet_v1_50/resnet_v1_50/block1/unit_1/bottleneck_v1/add, because its followed by an activation.
INFO:tensorflow:Skipping FeatureExtractor/resnet_v1_50/resnet_v1_50/block1/unit_2/bottleneck_v1/add, because its followed by an activation.
I0410 12:39:20.949680<PHONE_NUMBER>27136 quantize.py:201] Skipping FeatureExtractor/resnet_v1_50/resnet_v1_50/block1/unit_2/bottleneck_v1/add, because its followed by an activation.
INFO:tensorflow:Skipping FeatureExtractor/resnet_v1_50/resnet_v1_50/block1/unit_2/bottleneck_v1/add, because its followed by an activation.
I0410 12:39:20.964820<PHONE_NUMBER>27136 quantize.py:166] Skipping FeatureExtractor/resnet_v1_50/resnet_v1_50/block1/unit_2/bottleneck_v1/add, because its followed by an activation.
INFO:tensorflow:Skipping FeatureExtractor/resnet_v1_50/resnet_v1_50/block1/unit_3/bottleneck_v1/add, because its followed by an activation.
I0410 12:39:20.965119<PHONE_NUMBER>27136 quantize.py:201] Skipping FeatureExtractor/resnet_v1_50/resnet_v1_50/block1/unit_3/bottleneck_v1/add, because its followed by an activation.
INFO:tensorflow:Skipping FeatureExtractor/resnet_v1_50/resnet_v1_50/block2/unit_1/bottleneck_v1/add, because its followed by an activation.
I0410 12:39:20.980214<PHONE_NUMBER>27136 quantize.py:166] Skipping FeatureExtractor/resnet_v1_50/resnet_v1_50/block2/unit_1/bottleneck_v1/add, because its followed by an activation.
INFO:tensorflow:Skipping FeatureExtractor/resnet_v1_50/resnet_v1_50/block2/unit_2/bottleneck_v1/add, because its followed by an activation.
I0410 12:39:20.980386<PHONE_NUMBER>27136 quantize.py:201] Skipping FeatureExtractor/resnet_v1_50/resnet_v1_50/block2/unit_2/bottleneck_v1/add, because its followed by an activation.
INFO:tensorflow:Skipping FeatureExtractor/resnet_v1_50/resnet_v1_50/block2/unit_2/bottleneck_v1/add, because its followed by an activation.
I0410 12:39:20.996045<PHONE_NUMBER>27136 quantize.py:166] Skipping FeatureExtractor/resnet_v1_50/resnet_v1_50/block2/unit_2/bottleneck_v1/add, because its followed by an activation.
INFO:tensorflow:Skipping FeatureExtractor/resnet_v1_50/resnet_v1_50/block2/unit_3/bottleneck_v1/add, because its followed by an activation.
I0410 12:39:20.996264<PHONE_NUMBER>27136 quantize.py:201] Skipping FeatureExtractor/resnet_v1_50/resnet_v1_50/block2/unit_3/bottleneck_v1/add, because its followed by an activation.
INFO:tensorflow:Skipping FeatureExtractor/resnet_v1_50/resnet_v1_50/block2/unit_3/bottleneck_v1/add, because its followed by an activation.
I0410 12:39:21.011528<PHONE_NUMBER>27136 quantize.py:166] Skipping FeatureExtractor/resnet_v1_50/resnet_v1_50/block2/unit_3/bottleneck_v1/add, because its followed by an activation.
INFO:tensorflow:Skipping FeatureExtractor/resnet_v1_50/resnet_v1_50/block2/unit_4/bottleneck_v1/add, because its followed by an activation.
I0410 12:39:21.011718<PHONE_NUMBER>27136 quantize.py:201] Skipping FeatureExtractor/resnet_v1_50/resnet_v1_50/block2/unit_4/bottleneck_v1/add, because its followed by an activation.
INFO:tensorflow:Skipping FeatureExtractor/resnet_v1_50/resnet_v1_50/block3/unit_1/bottleneck_v1/add, because its followed by an activation.
I0410 12:39:21.027328<PHONE_NUMBER>27136 quantize.py:166] Skipping FeatureExtractor/resnet_v1_50/resnet_v1_50/block3/unit_1/bottleneck_v1/add, because its followed by an activation.
INFO:tensorflow:Skipping FeatureExtractor/resnet_v1_50/resnet_v1_50/block3/unit_2/bottleneck_v1/add, because its followed by an activation.
I0410 12:39:21.027498<PHONE_NUMBER>27136 quantize.py:201] Skipping FeatureExtractor/resnet_v1_50/resnet_v1_50/block3/unit_2/bottleneck_v1/add, because its followed by an activation.
INFO:tensorflow:Skipping FeatureExtractor/resnet_v1_50/resnet_v1_50/block3/unit_2/bottleneck_v1/add, because its followed by an activation.
I0410 12:39:21.044524<PHONE_NUMBER>27136 quantize.py:166] Skipping FeatureExtractor/resnet_v1_50/resnet_v1_50/block3/unit_2/bottleneck_v1/add, because its followed by an activation.
INFO:tensorflow:Skipping FeatureExtractor/resnet_v1_50/resnet_v1_50/block3/unit_3/bottleneck_v1/add, because its followed by an activation.
I0410 12:39:21.044679<PHONE_NUMBER>27136 quantize.py:201] Skipping FeatureExtractor/resnet_v1_50/resnet_v1_50/block3/unit_3/bottleneck_v1/add, because its followed by an activation.
INFO:tensorflow:Skipping FeatureExtractor/resnet_v1_50/resnet_v1_50/block3/unit_3/bottleneck_v1/add, because its followed by an activation.
I0410 12:39:21.061121<PHONE_NUMBER>27136 quantize.py:166] Skipping FeatureExtractor/resnet_v1_50/resnet_v1_50/block3/unit_3/bottleneck_v1/add, because its followed by an activation.
INFO:tensorflow:Skipping FeatureExtractor/resnet_v1_50/resnet_v1_50/block3/unit_4/bottleneck_v1/add, because its followed by an activation.
I0410 12:39:21.061372<PHONE_NUMBER>27136 quantize.py:201] Skipping FeatureExtractor/resnet_v1_50/resnet_v1_50/block3/unit_4/bottleneck_v1/add, because its followed by an activation.
INFO:tensorflow:Skipping FeatureExtractor/resnet_v1_50/resnet_v1_50/block3/unit_4/bottleneck_v1/add, because its followed by an activation.
I0410 12:39:21.076568<PHONE_NUMBER>27136 quantize.py:166] Skipping FeatureExtractor/resnet_v1_50/resnet_v1_50/block3/unit_4/bottleneck_v1/add, because its followed by an activation.
INFO:tensorflow:Skipping FeatureExtractor/resnet_v1_50/resnet_v1_50/block3/unit_5/bottleneck_v1/add, because its followed by an activation.
I0410 12:39:21.076805<PHONE_NUMBER>27136 quantize.py:201] Skipping FeatureExtractor/resnet_v1_50/resnet_v1_50/block3/unit_5/bottleneck_v1/add, because its followed by an activation.
INFO:tensorflow:Skipping FeatureExtractor/resnet_v1_50/resnet_v1_50/block3/unit_5/bottleneck_v1/add, because its followed by an activation.
I0410 12:39:21.092691<PHONE_NUMBER>27136 quantize.py:166] Skipping FeatureExtractor/resnet_v1_50/resnet_v1_50/block3/unit_5/bottleneck_v1/add, because its followed by an activation.
INFO:tensorflow:Skipping FeatureExtractor/resnet_v1_50/resnet_v1_50/block3/unit_6/bottleneck_v1/add, because its followed by an activation.
I0410 12:39:21.092893<PHONE_NUMBER>27136 quantize.py:201] Skipping FeatureExtractor/resnet_v1_50/resnet_v1_50/block3/unit_6/bottleneck_v1/add, because its followed by an activation.
INFO:tensorflow:Skipping FeatureExtractor/resnet_v1_50/resnet_v1_50/block4/unit_1/bottleneck_v1/add, because its followed by an activation.
I0410 12:39:21.107756<PHONE_NUMBER>27136 quantize.py:166] Skipping FeatureExtractor/resnet_v1_50/resnet_v1_50/block4/unit_1/bottleneck_v1/add, because its followed by an activation.
INFO:tensorflow:Skipping FeatureExtractor/resnet_v1_50/resnet_v1_50/block4/unit_2/bottleneck_v1/add, because its followed by an activation.
I0410 12:39:21.107955<PHONE_NUMBER>27136 quantize.py:201] Skipping FeatureExtractor/resnet_v1_50/resnet_v1_50/block4/unit_2/bottleneck_v1/add, because its followed by an activation.
INFO:tensorflow:Skipping FeatureExtractor/resnet_v1_50/resnet_v1_50/block4/unit_2/bottleneck_v1/add, because its followed by an activation.
I0410 12:39:21.123640<PHONE_NUMBER>27136 quantize.py:166] Skipping FeatureExtractor/resnet_v1_50/resnet_v1_50/block4/unit_2/bottleneck_v1/add, because its followed by an activation.
INFO:tensorflow:Skipping FeatureExtractor/resnet_v1_50/resnet_v1_50/block4/unit_3/bottleneck_v1/add, because its followed by an activation.
I0410 12:39:21.123864<PHONE_NUMBER>27136 quantize.py:201] Skipping FeatureExtractor/resnet_v1_50/resnet_v1_50/block4/unit_3/bottleneck_v1/add, because its followed by an activation.
INFO:tensorflow:Skipping FeatureExtractor/resnet_v1_50/resnet_v1_50/block1/unit_3/bottleneck_v1/add, because its followed by an activation.
I0410 12:39:21.206912<PHONE_NUMBER>27136 quantize.py:166] Skipping FeatureExtractor/resnet_v1_50/resnet_v1_50/block1/unit_3/bottleneck_v1/add, because its followed by an activation.
INFO:tensorflow:Skipping FeatureExtractor/resnet_v1_50/resnet_v1_50/block2/unit_4/bottleneck_v1/add, because its followed by an activation.
I0410 12:39:21.297351<PHONE_NUMBER>27136 quantize.py:166] Skipping FeatureExtractor/resnet_v1_50/resnet_v1_50/block2/unit_4/bottleneck_v1/add, because its followed by an activation.
INFO:tensorflow:Skipping FeatureExtractor/resnet_v1_50/resnet_v1_50/block3/unit_6/bottleneck_v1/add, because its followed by an activation.
I0410 12:39:21.426005<PHONE_NUMBER>27136 quantize.py:166] Skipping FeatureExtractor/resnet_v1_50/resnet_v1_50/block3/unit_6/bottleneck_v1/add, because its followed by an activation.
INFO:tensorflow:Skipping FeatureExtractor/resnet_v1_50/resnet_v1_50/block4/unit_3/bottleneck_v1/add, because its followed by an activation.
I0410 12:39:21.500694<PHONE_NUMBER>27136 quantize.py:166] Skipping FeatureExtractor/resnet_v1_50/resnet_v1_50/block4/unit_3/bottleneck_v1/add, because its followed by an activation.
INFO:tensorflow:Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block1/unit_1/bottleneck_v1/add
I0410 12:39:22.099278<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block1/unit_1/bottleneck_v1/add
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I0410 12:39:22.099895<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block1/unit_2/bottleneck_v1/add
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I0410 12:39:22.100310<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block1/unit_3/bottleneck_v1/add
INFO:tensorflow:Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block2/unit_1/bottleneck_v1/add
I0410 12:39:22.100712<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block2/unit_1/bottleneck_v1/add
INFO:tensorflow:Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block2/unit_2/bottleneck_v1/add
I0410 12:39:22.101081<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block2/unit_2/bottleneck_v1/add
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I0410 12:39:22.101440<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block2/unit_3/bottleneck_v1/add
INFO:tensorflow:Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block2/unit_4/bottleneck_v1/add
I0410 12:39:22.101804<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block2/unit_4/bottleneck_v1/add
INFO:tensorflow:Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block3/unit_1/bottleneck_v1/add
I0410 12:39:22.102235<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block3/unit_1/bottleneck_v1/add
INFO:tensorflow:Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block3/unit_2/bottleneck_v1/add
I0410 12:39:22.102601<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block3/unit_2/bottleneck_v1/add
INFO:tensorflow:Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block3/unit_3/bottleneck_v1/add
I0410 12:39:22.103026<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block3/unit_3/bottleneck_v1/add
INFO:tensorflow:Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block3/unit_4/bottleneck_v1/add
I0410 12:39:22.103431<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block3/unit_4/bottleneck_v1/add
INFO:tensorflow:Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block3/unit_5/bottleneck_v1/add
I0410 12:39:22.103817<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block3/unit_5/bottleneck_v1/add
INFO:tensorflow:Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block3/unit_6/bottleneck_v1/add
I0410 12:39:22.104173<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block3/unit_6/bottleneck_v1/add
INFO:tensorflow:Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block4/unit_1/bottleneck_v1/add
I0410 12:39:22.104722<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block4/unit_1/bottleneck_v1/add
INFO:tensorflow:Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block4/unit_2/bottleneck_v1/add
I0410 12:39:22.105071<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block4/unit_2/bottleneck_v1/add
INFO:tensorflow:Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block4/unit_3/bottleneck_v1/add
I0410 12:39:22.105430<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block4/unit_3/bottleneck_v1/add
INFO:tensorflow:Inserting fake quant op activation_AddV2_quant after FeatureExtractor/resnet_v1_50/fpn/top_down/add
I0410 12:39:22.105675<PHONE_NUMBER>27136 quantize.py:262] Inserting fake quant op activation_AddV2_quant after FeatureExtractor/resnet_v1_50/fpn/top_down/add
INFO:tensorflow:Inserting fake quant op activation_AddV2_quant after FeatureExtractor/resnet_v1_50/fpn/top_down/add_1
I0410 12:39:22.111881<PHONE_NUMBER>27136 quantize.py:262] Inserting fake quant op activation_AddV2_quant after FeatureExtractor/resnet_v1_50/fpn/top_down/add_1
INFO:tensorflow:Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/conv1/add_fold
I0410 12:39:22.121784<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/conv1/add_fold
INFO:tensorflow:Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block1/unit_1/bottleneck_v1/conv1/add_fold
I0410 12:39:22.122250<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block1/unit_1/bottleneck_v1/conv1/add_fold
INFO:tensorflow:Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block1/unit_1/bottleneck_v1/conv2/add_fold
I0410 12:39:22.122534<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block1/unit_1/bottleneck_v1/conv2/add_fold
INFO:tensorflow:Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block1/unit_2/bottleneck_v1/conv1/add_fold
I0410 12:39:22.122827<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block1/unit_2/bottleneck_v1/conv1/add_fold
INFO:tensorflow:Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block1/unit_2/bottleneck_v1/conv2/add_fold
I0410 12:39:22.123043<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block1/unit_2/bottleneck_v1/conv2/add_fold
INFO:tensorflow:Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block1/unit_3/bottleneck_v1/conv1/add_fold
I0410 12:39:22.123297<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block1/unit_3/bottleneck_v1/conv1/add_fold
INFO:tensorflow:Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block1/unit_3/bottleneck_v1/conv2/add_fold
I0410 12:39:22.123489<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block1/unit_3/bottleneck_v1/conv2/add_fold
INFO:tensorflow:Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block2/unit_1/bottleneck_v1/conv1/add_fold
I0410 12:39:22.123872<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block2/unit_1/bottleneck_v1/conv1/add_fold
INFO:tensorflow:Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block2/unit_1/bottleneck_v1/conv2/add_fold
I0410 12:39:22.124067<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block2/unit_1/bottleneck_v1/conv2/add_fold
INFO:tensorflow:Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block2/unit_2/bottleneck_v1/conv1/add_fold
I0410 12:39:22.124314<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block2/unit_2/bottleneck_v1/conv1/add_fold
INFO:tensorflow:Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block2/unit_2/bottleneck_v1/conv2/add_fold
I0410 12:39:22.124595<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block2/unit_2/bottleneck_v1/conv2/add_fold
INFO:tensorflow:Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block2/unit_3/bottleneck_v1/conv1/add_fold
I0410 12:39:22.124916<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block2/unit_3/bottleneck_v1/conv1/add_fold
INFO:tensorflow:Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block2/unit_3/bottleneck_v1/conv2/add_fold
I0410 12:39:22.125163<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block2/unit_3/bottleneck_v1/conv2/add_fold
INFO:tensorflow:Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block2/unit_4/bottleneck_v1/conv1/add_fold
I0410 12:39:22.125458<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block2/unit_4/bottleneck_v1/conv1/add_fold
INFO:tensorflow:Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block2/unit_4/bottleneck_v1/conv2/add_fold
I0410 12:39:22.125700<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block2/unit_4/bottleneck_v1/conv2/add_fold
INFO:tensorflow:Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block3/unit_1/bottleneck_v1/conv1/add_fold
I0410 12:39:22.126079<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block3/unit_1/bottleneck_v1/conv1/add_fold
INFO:tensorflow:Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block3/unit_1/bottleneck_v1/conv2/add_fold
I0410 12:39:22.126330<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block3/unit_1/bottleneck_v1/conv2/add_fold
INFO:tensorflow:Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block3/unit_2/bottleneck_v1/conv1/add_fold
I0410 12:39:22.126605<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block3/unit_2/bottleneck_v1/conv1/add_fold
INFO:tensorflow:Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block3/unit_2/bottleneck_v1/conv2/add_fold
I0410 12:39:22.126782<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block3/unit_2/bottleneck_v1/conv2/add_fold
INFO:tensorflow:Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block3/unit_3/bottleneck_v1/conv1/add_fold
I0410 12:39:22.127025<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block3/unit_3/bottleneck_v1/conv1/add_fold
INFO:tensorflow:Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block3/unit_3/bottleneck_v1/conv2/add_fold
I0410 12:39:22.127182<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block3/unit_3/bottleneck_v1/conv2/add_fold
INFO:tensorflow:Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block3/unit_4/bottleneck_v1/conv1/add_fold
I0410 12:39:22.127393<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block3/unit_4/bottleneck_v1/conv1/add_fold
INFO:tensorflow:Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block3/unit_4/bottleneck_v1/conv2/add_fold
I0410 12:39:22.127542<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block3/unit_4/bottleneck_v1/conv2/add_fold
INFO:tensorflow:Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block3/unit_5/bottleneck_v1/conv1/add_fold
I0410 12:39:22.127753<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block3/unit_5/bottleneck_v1/conv1/add_fold
INFO:tensorflow:Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block3/unit_5/bottleneck_v1/conv2/add_fold
I0410 12:39:22.127903<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block3/unit_5/bottleneck_v1/conv2/add_fold
INFO:tensorflow:Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block3/unit_6/bottleneck_v1/conv1/add_fold
I0410 12:39:22.128112<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block3/unit_6/bottleneck_v1/conv1/add_fold
INFO:tensorflow:Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block3/unit_6/bottleneck_v1/conv2/add_fold
I0410 12:39:22.128261<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block3/unit_6/bottleneck_v1/conv2/add_fold
INFO:tensorflow:Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block4/unit_1/bottleneck_v1/conv1/add_fold
I0410 12:39:22.128529<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block4/unit_1/bottleneck_v1/conv1/add_fold
INFO:tensorflow:Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block4/unit_1/bottleneck_v1/conv2/add_fold
I0410 12:39:22.128677<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block4/unit_1/bottleneck_v1/conv2/add_fold
INFO:tensorflow:Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block4/unit_2/bottleneck_v1/conv1/add_fold
I0410 12:39:22.128887<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block4/unit_2/bottleneck_v1/conv1/add_fold
INFO:tensorflow:Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block4/unit_2/bottleneck_v1/conv2/add_fold
I0410 12:39:22.129033<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block4/unit_2/bottleneck_v1/conv2/add_fold
INFO:tensorflow:Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block4/unit_3/bottleneck_v1/conv1/add_fold
I0410 12:39:22.129251<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block4/unit_3/bottleneck_v1/conv1/add_fold
INFO:tensorflow:Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block4/unit_3/bottleneck_v1/conv2/add_fold
I0410 12:39:22.129395<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after FeatureExtractor/resnet_v1_50/resnet_v1_50/block4/unit_3/bottleneck_v1/conv2/add_fold
INFO:tensorflow:Skipping quant after FeatureExtractor/resnet_v1_50/fpn/top_down/smoothing_2/add_fold
I0410 12:39:22.129603<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after FeatureExtractor/resnet_v1_50/fpn/top_down/smoothing_2/add_fold
INFO:tensorflow:Skipping quant after FeatureExtractor/resnet_v1_50/fpn/top_down/smoothing_1/add_fold
I0410 12:39:22.129747<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after FeatureExtractor/resnet_v1_50/fpn/top_down/smoothing_1/add_fold
INFO:tensorflow:Skipping quant after FeatureExtractor/resnet_v1_50/fpn/bottom_up_block5/add_fold
I0410 12:39:22.129896<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after FeatureExtractor/resnet_v1_50/fpn/bottom_up_block5/add_fold
INFO:tensorflow:Skipping quant after FeatureExtractor/resnet_v1_50/fpn/bottom_up_block6/add_fold
I0410 12:39:22.130037<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after FeatureExtractor/resnet_v1_50/fpn/bottom_up_block6/add_fold
INFO:tensorflow:Skipping quant after WeightSharedConvolutionalBoxPredictor/BoxPredictionTower/conv2d_0/add_fold
I0410 12:39:22.130177<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after WeightSharedConvolutionalBoxPredictor/BoxPredictionTower/conv2d_0/add_fold
INFO:tensorflow:Skipping quant after WeightSharedConvolutionalBoxPredictor/BoxPredictionTower/conv2d_1/add_fold
I0410 12:39:22.130314<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after WeightSharedConvolutionalBoxPredictor/BoxPredictionTower/conv2d_1/add_fold
INFO:tensorflow:Skipping quant after WeightSharedConvolutionalBoxPredictor/BoxPredictionTower/conv2d_2/add_fold
I0410 12:39:22.130451<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after WeightSharedConvolutionalBoxPredictor/BoxPredictionTower/conv2d_2/add_fold
INFO:tensorflow:Skipping quant after WeightSharedConvolutionalBoxPredictor/BoxPredictionTower/conv2d_3/add_fold
I0410 12:39:22.130587<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after WeightSharedConvolutionalBoxPredictor/BoxPredictionTower/conv2d_3/add_fold
INFO:tensorflow:Skipping quant after WeightSharedConvolutionalBoxPredictor/ClassPredictionTower/conv2d_0/add_fold
I0410 12:39:22.130723<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after WeightSharedConvolutionalBoxPredictor/ClassPredictionTower/conv2d_0/add_fold
INFO:tensorflow:Skipping quant after WeightSharedConvolutionalBoxPredictor/ClassPredictionTower/conv2d_1/add_fold
I0410 12:39:22.130858<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after WeightSharedConvolutionalBoxPredictor/ClassPredictionTower/conv2d_1/add_fold
INFO:tensorflow:Skipping quant after WeightSharedConvolutionalBoxPredictor/ClassPredictionTower/conv2d_2/add_fold
I0410 12:39:22.130995<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after WeightSharedConvolutionalBoxPredictor/ClassPredictionTower/conv2d_2/add_fold
INFO:tensorflow:Skipping quant after WeightSharedConvolutionalBoxPredictor/ClassPredictionTower/conv2d_3/add_fold
I0410 12:39:22.131131<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after WeightSharedConvolutionalBoxPredictor/ClassPredictionTower/conv2d_3/add_fold
INFO:tensorflow:Skipping quant after WeightSharedConvolutionalBoxPredictor_1/BoxPredictionTower/conv2d_0/add_fold
I0410 12:39:22.131264<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after WeightSharedConvolutionalBoxPredictor_1/BoxPredictionTower/conv2d_0/add_fold
INFO:tensorflow:Skipping quant after WeightSharedConvolutionalBoxPredictor_1/BoxPredictionTower/conv2d_1/add_fold
I0410 12:39:22.131427<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after WeightSharedConvolutionalBoxPredictor_1/BoxPredictionTower/conv2d_1/add_fold
INFO:tensorflow:Skipping quant after WeightSharedConvolutionalBoxPredictor_1/BoxPredictionTower/conv2d_2/add_fold
I0410 12:39:22.131577<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after WeightSharedConvolutionalBoxPredictor_1/BoxPredictionTower/conv2d_2/add_fold
INFO:tensorflow:Skipping quant after WeightSharedConvolutionalBoxPredictor_1/BoxPredictionTower/conv2d_3/add_fold
I0410 12:39:22.131731<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after WeightSharedConvolutionalBoxPredictor_1/BoxPredictionTower/conv2d_3/add_fold
INFO:tensorflow:Skipping quant after WeightSharedConvolutionalBoxPredictor_1/ClassPredictionTower/conv2d_0/add_fold
I0410 12:39:22.131886<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after WeightSharedConvolutionalBoxPredictor_1/ClassPredictionTower/conv2d_0/add_fold
INFO:tensorflow:Skipping quant after WeightSharedConvolutionalBoxPredictor_1/ClassPredictionTower/conv2d_1/add_fold
I0410 12:39:22.132046<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after WeightSharedConvolutionalBoxPredictor_1/ClassPredictionTower/conv2d_1/add_fold
INFO:tensorflow:Skipping quant after WeightSharedConvolutionalBoxPredictor_1/ClassPredictionTower/conv2d_2/add_fold
I0410 12:39:22.132216<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after WeightSharedConvolutionalBoxPredictor_1/ClassPredictionTower/conv2d_2/add_fold
INFO:tensorflow:Skipping quant after WeightSharedConvolutionalBoxPredictor_1/ClassPredictionTower/conv2d_3/add_fold
I0410 12:39:22.132382<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after WeightSharedConvolutionalBoxPredictor_1/ClassPredictionTower/conv2d_3/add_fold
INFO:tensorflow:Skipping quant after WeightSharedConvolutionalBoxPredictor_2/BoxPredictionTower/conv2d_0/add_fold
I0410 12:39:22.132542<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after WeightSharedConvolutionalBoxPredictor_2/BoxPredictionTower/conv2d_0/add_fold
INFO:tensorflow:Skipping quant after WeightSharedConvolutionalBoxPredictor_2/BoxPredictionTower/conv2d_1/add_fold
I0410 12:39:22.132689<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after WeightSharedConvolutionalBoxPredictor_2/BoxPredictionTower/conv2d_1/add_fold
INFO:tensorflow:Skipping quant after WeightSharedConvolutionalBoxPredictor_2/BoxPredictionTower/conv2d_2/add_fold
I0410 12:39:22.132832<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after WeightSharedConvolutionalBoxPredictor_2/BoxPredictionTower/conv2d_2/add_fold
INFO:tensorflow:Skipping quant after WeightSharedConvolutionalBoxPredictor_2/BoxPredictionTower/conv2d_3/add_fold
I0410 12:39:22.132973<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after WeightSharedConvolutionalBoxPredictor_2/BoxPredictionTower/conv2d_3/add_fold
INFO:tensorflow:Skipping quant after WeightSharedConvolutionalBoxPredictor_2/ClassPredictionTower/conv2d_0/add_fold
I0410 12:39:22.133126<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after WeightSharedConvolutionalBoxPredictor_2/ClassPredictionTower/conv2d_0/add_fold
INFO:tensorflow:Skipping quant after WeightSharedConvolutionalBoxPredictor_2/ClassPredictionTower/conv2d_1/add_fold
I0410 12:39:22.133264<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after WeightSharedConvolutionalBoxPredictor_2/ClassPredictionTower/conv2d_1/add_fold
INFO:tensorflow:Skipping quant after WeightSharedConvolutionalBoxPredictor_2/ClassPredictionTower/conv2d_2/add_fold
I0410 12:39:22.133402<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after WeightSharedConvolutionalBoxPredictor_2/ClassPredictionTower/conv2d_2/add_fold
INFO:tensorflow:Skipping quant after WeightSharedConvolutionalBoxPredictor_2/ClassPredictionTower/conv2d_3/add_fold
I0410 12:39:22.133538<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after WeightSharedConvolutionalBoxPredictor_2/ClassPredictionTower/conv2d_3/add_fold
INFO:tensorflow:Skipping quant after WeightSharedConvolutionalBoxPredictor_3/BoxPredictionTower/conv2d_0/add_fold
I0410 12:39:22.133684<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after WeightSharedConvolutionalBoxPredictor_3/BoxPredictionTower/conv2d_0/add_fold
INFO:tensorflow:Skipping quant after WeightSharedConvolutionalBoxPredictor_3/BoxPredictionTower/conv2d_1/add_fold
I0410 12:39:22.133875<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after WeightSharedConvolutionalBoxPredictor_3/BoxPredictionTower/conv2d_1/add_fold
INFO:tensorflow:Skipping quant after WeightSharedConvolutionalBoxPredictor_3/BoxPredictionTower/conv2d_2/add_fold
I0410 12:39:22.134034<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after WeightSharedConvolutionalBoxPredictor_3/BoxPredictionTower/conv2d_2/add_fold
INFO:tensorflow:Skipping quant after WeightSharedConvolutionalBoxPredictor_3/BoxPredictionTower/conv2d_3/add_fold
I0410 12:39:22.134214<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after WeightSharedConvolutionalBoxPredictor_3/BoxPredictionTower/conv2d_3/add_fold
INFO:tensorflow:Skipping quant after WeightSharedConvolutionalBoxPredictor_3/ClassPredictionTower/conv2d_0/add_fold
I0410 12:39:22.134354<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after WeightSharedConvolutionalBoxPredictor_3/ClassPredictionTower/conv2d_0/add_fold
INFO:tensorflow:Skipping quant after WeightSharedConvolutionalBoxPredictor_3/ClassPredictionTower/conv2d_1/add_fold
I0410 12:39:22.134491<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after WeightSharedConvolutionalBoxPredictor_3/ClassPredictionTower/conv2d_1/add_fold
INFO:tensorflow:Skipping quant after WeightSharedConvolutionalBoxPredictor_3/ClassPredictionTower/conv2d_2/add_fold
I0410 12:39:22.134625<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after WeightSharedConvolutionalBoxPredictor_3/ClassPredictionTower/conv2d_2/add_fold
INFO:tensorflow:Skipping quant after WeightSharedConvolutionalBoxPredictor_3/ClassPredictionTower/conv2d_3/add_fold
I0410 12:39:22.134763<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after WeightSharedConvolutionalBoxPredictor_3/ClassPredictionTower/conv2d_3/add_fold
INFO:tensorflow:Skipping quant after WeightSharedConvolutionalBoxPredictor_4/BoxPredictionTower/conv2d_0/add_fold
I0410 12:39:22.134897<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after WeightSharedConvolutionalBoxPredictor_4/BoxPredictionTower/conv2d_0/add_fold
INFO:tensorflow:Skipping quant after WeightSharedConvolutionalBoxPredictor_4/BoxPredictionTower/conv2d_1/add_fold
I0410 12:39:22.135031<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after WeightSharedConvolutionalBoxPredictor_4/BoxPredictionTower/conv2d_1/add_fold
INFO:tensorflow:Skipping quant after WeightSharedConvolutionalBoxPredictor_4/BoxPredictionTower/conv2d_2/add_fold
I0410 12:39:22.135166<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after WeightSharedConvolutionalBoxPredictor_4/BoxPredictionTower/conv2d_2/add_fold
INFO:tensorflow:Skipping quant after WeightSharedConvolutionalBoxPredictor_4/BoxPredictionTower/conv2d_3/add_fold
I0410 12:39:22.135301<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after WeightSharedConvolutionalBoxPredictor_4/BoxPredictionTower/conv2d_3/add_fold
INFO:tensorflow:Skipping quant after WeightSharedConvolutionalBoxPredictor_4/ClassPredictionTower/conv2d_0/add_fold
I0410 12:39:22.135435<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after WeightSharedConvolutionalBoxPredictor_4/ClassPredictionTower/conv2d_0/add_fold
INFO:tensorflow:Skipping quant after WeightSharedConvolutionalBoxPredictor_4/ClassPredictionTower/conv2d_1/add_fold
I0410 12:39:22.135572<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after WeightSharedConvolutionalBoxPredictor_4/ClassPredictionTower/conv2d_1/add_fold
INFO:tensorflow:Skipping quant after WeightSharedConvolutionalBoxPredictor_4/ClassPredictionTower/conv2d_2/add_fold
I0410 12:39:22.135709<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after WeightSharedConvolutionalBoxPredictor_4/ClassPredictionTower/conv2d_2/add_fold
INFO:tensorflow:Skipping quant after WeightSharedConvolutionalBoxPredictor_4/ClassPredictionTower/conv2d_3/add_fold
I0410 12:39:22.135844<PHONE_NUMBER>27136 quantize.py:299] Skipping quant after WeightSharedConvolutionalBoxPredictor_4/ClassPredictionTower/conv2d_3/add_fold
WARNING:tensorflow:From /tensorflow/models/research/object_detection/export_tflite_ssd_graph_lib.py:292: The name tf.train.Saver is deprecated. Please use tf.compat.v1.train.Saver instead.

W0410 12:39:22.138879<PHONE_NUMBER>27136 module_wrapper.py:139] From /tensorflow/models/research/object_detection/export_tflite_ssd_graph_lib.py:292: The name tf.train.Saver is deprecated. Please use tf.compat.v1.train.Saver instead.

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/tools/freeze_graph.py:127: 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.
W0410 12:39:22.693765<PHONE_NUMBER>27136 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/tools/freeze_graph.py:127: 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.
2020-04-10 12:39:23.471555: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-04-10 12:39:23.471913: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1639] Found device 0 with properties:
name: GeForce GTX 1050 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.62
pciBusID: 0000:01:00.0
2020-04-10 12:39:23.471967: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.0
2020-04-10 12:39:23.471993: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10.0
2020-04-10 12:39:23.472027: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10.0
2020-04-10 12:39:23.472041: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10.0
2020-04-10 12:39:23.472053: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10.0
2020-04-10 12:39:23.472066: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10.0
2020-04-10 12:39:23.472083: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2020-04-10 12:39:23.472137: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-04-10 12:39:23.472323: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-04-10 12:39:23.472471: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1767] Adding visible gpu devices: 0
2020-04-10 12:39:23.472493: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1180] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-04-10 12:39:23.472501: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1186]      0
2020-04-10 12:39:23.472508: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1199] 0:   N
2020-04-10 12:39:23.472571: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-04-10 12:39:23.472756: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-04-10 12:39:23.472915: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1325] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 2888 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1050 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1)
INFO:tensorflow:Restoring parameters from /tensorflow/learn_pet/train/model.ckpt-5
I0410 12:39:23.473652<PHONE_NUMBER>27136 saver.py:1284] Restoring parameters from /tensorflow/learn_pet/train/model.ckpt-5
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/tools/freeze_graph.py:233: convert_variables_to_constants (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.compat.v1.graph_util.convert_variables_to_constants`
W0410 12:39:25.057172<PHONE_NUMBER>27136 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/tools/freeze_graph.py:233: convert_variables_to_constants (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.compat.v1.graph_util.convert_variables_to_constants`
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/graph_util_impl.py:277: extract_sub_graph (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.compat.v1.graph_util.extract_sub_graph`
W0410 12:39:25.057349<PHONE_NUMBER>27136 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/graph_util_impl.py:277: extract_sub_graph (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.compat.v1.graph_util.extract_sub_graph`
INFO:tensorflow:Froze 923 variables.
I0410 12:39:25.800305<PHONE_NUMBER>27136 graph_util_impl.py:334] Froze 923 variables.
INFO:tensorflow:Converted 923 variables to const ops.
I0410 12:39:25.936274<PHONE_NUMBER>27136 graph_util_impl.py:394] Converted 923 variables to const ops.
2020-04-10 12:39:26.212524: I tensorflow/tools/graph_transforms/transform_graph.cc:317] Applying strip_unused_nodes
root@d97ce87cb12f:/tensorflow/models/research#

Now up until here everything goes relatively fine. However, converting this frozen graph or the resulting saved_model.pb from retraining causes issues.
Method 1: converting a frozen using TFLite Converter (typically following the tutorial)
tflite_convert  --output_file="/tensorflow/models/research/learn_pet/models/output_tflite_graph.tflite"   --graph_def_file="/tensorflow/models/research/learn_pet/models/tflite_graph.pb"   --inference_type=QUANTIZED_UINT8  --input_arrays='normalized_input_image_tensor'   --output_arrays="TFLite_Detection_PostProcess,TFLite_Detection_PostProcess:1,TFLite_Detection_PostProcess:2,TFLite_Detection_PostProcess:3" --mean_values=128 --std_dev_values=128 --input_shapes=1,320,320,3  --change_concat_input_ranges=false --allow_nudging_weights_to_use_fast_gemm_kernel=true --allow_custom_ops

I get this output message:
root@fe9c39ea1f8e:/tensorflow/models/research# tflite_convert  --output_file="/tensorflow/models/research/learn_pet/models/output_tflite_graph.tflite"   --graph_def_file="/tensorflow/models/research/learn_pet/models/tflite_graph.pb"   --inference_type=QUANTIZED_UINT8  --input_arrays='normalized_input_image_tensor'   --output_arrays="TFLite_Detection_PostProcess,TFLite_Detection_PostProcess:1,TFLite_Detection_PostProcess:2,TFLite_Detection_PostProcess:3" --mean_values=128 --std_dev_values=128 --input_shapes=1,320,320,3 \--change_concat_input_ranges=false --allow_nudging_weights_to_use_fast_gemm_kernel=true --allow_custom_ops
2020-04-10 11:05:40.265936: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2020-04-10 11:05:40.330137: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:964] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-04-10 11:05:40.330537: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Found device 0 with properties:
name: GeForce GTX 1050 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.62
pciBusID: 0000:01:00.0
totalMemory: 3.95GiB freeMemory: 3.11GiB
2020-04-10 11:05:40.330577: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1511] Adding visible gpu devices: 0
2020-04-10 11:05:40.571784: I tensorflow/core/common_runtime/gpu/gpu_device.cc:982] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-04-10 11:05:40.571846: I tensorflow/core/common_runtime/gpu/gpu_device.cc:988]      0
2020-04-10 11:05:40.571855: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1001] 0:   N
2020-04-10 11:05:40.571999: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 2822 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1050 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1)
Traceback (most recent call last):
  File "/usr/local/bin/tflite_convert", line 11, in <module>
    sys.exit(main())
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/lite/python/tflite_convert.py", line 412, in main
    app.run(main=run_main, argv=sys.argv[:1])
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/platform/app.py", line 125, in run
    _sys.exit(main(argv))
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/lite/python/tflite_convert.py", line 408, in run_main
    _convert_model(tflite_flags)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/lite/python/tflite_convert.py", line 162, in _convert_model
    output_data = converter.convert()
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/lite/python/lite.py", line 464, in convert
    **converter_kwargs)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/lite/python/convert.py", line 311, in toco_convert_graph_def
    input_data.SerializeToString())
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/lite/python/convert.py", line 135, in toco_convert_protos
    (stdout, stderr))
RuntimeError: TOCO failed see console for info.
2020-04-10 11:05:43.184443: I tensorflow/contrib/lite/toco/import_tensorflow.cc:1080] Converting unsupported operation: TFLite_Detection_PostProcess
2020-04-10 11:05:43.418899: I tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.cc:39] Before Removing unused ops: 1987 operators, 2964 arrays (0 quantized)
2020-04-10 11:05:43.488337: I tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.cc:39] Before general graph transformations: 1987 operators, 2964 arrays (0 quantized)
2020-04-10 11:05:46.741187: I tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.cc:39] After general graph transformations pass 1: 360 operators, 642 arrays (1 quantized)
2020-04-10 11:05:46.750968: I tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.cc:39] Before pre-quantization graph transformations: 360 operators, 642 arrays (1 quantized)
2020-04-10 11:05:46.755164: I tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.cc:39] After pre-quantization graph transformations pass 1: 234 operators, 516 arrays (1 quantized)
2020-04-10 11:05:46.758687: F tensorflow/contrib/lite/toco/tooling_util.cc:1634] Array FeatureExtractor/resnet_v1_50/fpn/top_down/nearest_neighbor_upsampling/mul, which is an input to the Reshape operator producing the output array FeatureExtractor/resnet_v1_50/fpn/top_down/nearest_neighbor_upsampling/Reshape_1, is lacking min/max data, which is necessary for quantization. If accuracy matters, either target a non-quantized output format, or run quantized training with your model from a floating point checkpoint to change the input graph to contain min/max information. If you don't care about accuracy, you can pass --default_ranges_min= and --default_ranges_max= for easy experimentation.
Aborted

None
root@fe9c39ea1f8e:/tensorflow/models/research#

So I try adding  --default_ranges_min=0 and --default_ranges_max=6 following a Relu6 similar issue and it works, but fails at the edgetpu_compiler with:
Code used:
afayed@metecs-0497:~/coral/detection_ag/models$ sudo edgetpu_compiler output_tflite_graph.tflite
Edge TPU Compiler version 2.1.302470888
ERROR: :79 input->params.zero_point != output->params.zero_point (107 != 0)
ERROR: Node number 77 (PACK) failed to prepare.

Internal compiler error. Aborting!
afayed@metecs-0497:~/coral/detection_ag/models$

Trying to convert the saved_model.pb route becomes a lot more interesting! This file gets generated after retraining in dir:
/tensorflow/models/research/learn_pet/train/export/Servo/1586490086/saved_model.pb
There are a few scripts I have found and stitched together. I will paste them each with their respective output: (The following is being run outside the docker container on my tf-nightly 2.2.0.dev20200401 pip install)
Method 2 try 1: (converting a saved_model.pb using TFLite Converter)
import tensorflow as tf
converter = tf.lite.TFLiteConverter.from_saved_model('/home/afayed/coral/detection_ag/train/export/Servo/1586490086/')
converter.experimental_new_converter = **True or False**
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.target_spec.supported_types = [tf.float16]
tflite_quant_model = converter.convert()

I know the above used tf.float16 I was trying to get something... anything out of the converter. I might try GPU inference and drop the Coral USB Accelerator at this point with all this hassle or if its not even possible to use these more powerful models.
Output:
afayed@metecs-0497:~$ python3 ~/Desktop/test.py
2020-04-10 04:52:09.463558: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libnvinfer.so.6'; dlerror: libnvinfer.so.6: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda-9.0/lib64:/home/afayed/torch/install/lib:/home/afayed/kinova-demo/devel/lib:/opt/ros/kinetic/lib::/usr/local/lib:/usr/lib:/usr/aarch64-linux-gnu/lib
2020-04-10 04:52:09.463687: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libnvinfer_plugin.so.6'; dlerror: libnvinfer_plugin.so.6: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda-9.0/lib64:/home/afayed/torch/install/lib:/home/afayed/kinova-demo/devel/lib:/opt/ros/kinetic/lib::/usr/local/lib:/usr/lib:/usr/aarch64-linux-gnu/lib
2020-04-10 04:52:09.463710: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:30] Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
2020-04-10 04:52:11.015862: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1
2020-04-10 04:52:11.033075: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-04-10 04:52:11.033430: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1555] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce GTX 1050 Ti computeCapability: 6.1
coreClock: 1.62GHz coreCount: 6 deviceMemorySize: 3.95GiB deviceMemoryBandwidth: 104.43GiB/s
2020-04-10 04:52:11.033684: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2020-04-10 04:52:11.035302: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2020-04-10 04:52:11.036590: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2020-04-10 04:52:11.036867: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2020-04-10 04:52:11.038115: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2020-04-10 04:52:11.038672: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2020-04-10 04:52:11.040090: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2020-04-10 04:52:11.040214: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-04-10 04:52:11.040514: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-04-10 04:52:11.040744: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1697] Adding visible gpu devices: 0
2020-04-10 04:52:11.041008: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2020-04-10 04:52:11.065286: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency:<PHONE_NUMBER> Hz
2020-04-10 04:52:11.065997: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0xaf50e40 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2020-04-10 04:52:11.066016: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
2020-04-10 04:52:11.105688: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-04-10 04:52:11.106075: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0xaf740d0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2020-04-10 04:52:11.106093: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): GeForce GTX 1050 Ti, Compute Capability 6.1
2020-04-10 04:52:11.106271: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-04-10 04:52:11.106524: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1555] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce GTX 1050 Ti computeCapability: 6.1
coreClock: 1.62GHz coreCount: 6 deviceMemorySize: 3.95GiB deviceMemoryBandwidth: 104.43GiB/s
2020-04-10 04:52:11.106575: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2020-04-10 04:52:11.106590: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2020-04-10 04:52:11.106604: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2020-04-10 04:52:11.106618: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2020-04-10 04:52:11.106632: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2020-04-10 04:52:11.106646: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2020-04-10 04:52:11.106659: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2020-04-10 04:52:11.106705: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-04-10 04:52:11.107028: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-04-10 04:52:11.107216: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1697] Adding visible gpu devices: 0
2020-04-10 04:52:11.107242: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2020-04-10 04:52:11.107840: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1096] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-04-10 04:52:11.107853: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102]      0
2020-04-10 04:52:11.107862: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] 0:   N
2020-04-10 04:52:11.107946: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-04-10 04:52:11.108196: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-04-10 04:52:11.108428: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1241] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 2889 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1050 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1)
WARNING: Logging before flag parsing goes to stderr.
W0410 04:52:11.142004<PHONE_NUMBER>63968 wrap_function.py:214] Unable to create a python object for variable <tf.Variable 'global_step:0' shape=() dtype=int64_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
W0410 04:52:11.142286<PHONE_NUMBER>63968 wrap_function.py:214] Unable to create a python object for variable <tf.Variable 'FeatureExtractor/resnet_v1_50/conv1/weights:0' shape=(7, 7, 3, 64) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
W0410 04:52:11.142424<PHONE_NUMBER>63968 wrap_function.py:214] Unable to create a python object for variable <tf.Variable 'FeatureExtractor/resnet_v1_50/conv1/BatchNorm/gamma:0' shape=(64,) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
W0410 04:52:11.142553<PHONE_NUMBER>63968 wrap_function.py:214] Unable to create a python object for variable <tf.Variable 'FeatureExtractor/resnet_v1_50/conv1/BatchNorm/beta:0' shape=(64,) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
W0410 04:52:11.142632<PHONE_NUMBER>63968 wrap_function.py:214] Unable to create a python object for variable <tf.Variable 'FeatureExtractor/resnet_v1_50/conv1/BatchNorm/moving_mean:0' shape=(64,) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
W0410 04:52:11.659588<PHONE_NUMBER>63968 wrap_function.py:214] Unable to create a python object for variable <tf.Variable 'global_step:0' shape=() dtype=int64_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
W0410 04:52:11.659760<PHONE_NUMBER>63968 wrap_function.py:214] Unable to create a python object for variable <tf.Variable 'FeatureExtractor/resnet_v1_50/conv1/weights:0' shape=(7, 7, 3, 64) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
W0410 04:52:11.659844<PHONE_NUMBER>63968 wrap_function.py:214] Unable to create a python object for variable <tf.Variable 'FeatureExtractor/resnet_v1_50/conv1/BatchNorm/gamma:0' shape=(64,) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
W0410 04:52:11.659930<PHONE_NUMBER>63968 wrap_function.py:214] Unable to create a python object for variable <tf.Variable 'FeatureExtractor/resnet_v1_50/conv1/BatchNorm/beta:0' shape=(64,) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
W0410 04:52:11.660046<PHONE_NUMBER>63968 wrap_function.py:214] Unable to create a python object for variable <tf.Variable 'FeatureExtractor/resnet_v1_50/conv1/BatchNorm/moving_mean:0' shape=(64,) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
W0410 04:52:12.539669<PHONE_NUMBER>63968 wrap_function.py:214] Unable to create a python object for variable <tf.Variable 'global_step:0' shape=() dtype=int64_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
W0410 04:52:12.539836<PHONE_NUMBER>63968 wrap_function.py:214] Unable to create a python object for variable <tf.Variable 'FeatureExtractor/resnet_v1_50/conv1/weights:0' shape=(7, 7, 3, 64) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
W0410 04:52:12.539940<PHONE_NUMBER>63968 wrap_function.py:214] Unable to create a python object for variable <tf.Variable 'FeatureExtractor/resnet_v1_50/conv1/BatchNorm/gamma:0' shape=(64,) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
W0410 04:52:12.540039<PHONE_NUMBER>63968 wrap_function.py:214] Unable to create a python object for variable <tf.Variable 'FeatureExtractor/resnet_v1_50/conv1/BatchNorm/beta:0' shape=(64,) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
W0410 04:52:12.540147<PHONE_NUMBER>63968 wrap_function.py:214] Unable to create a python object for variable <tf.Variable 'FeatureExtractor/resnet_v1_50/conv1/BatchNorm/moving_mean:0' shape=(64,) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
W0410 04:52:13.977087<PHONE_NUMBER>63968 wrap_function.py:214] Unable to create a python object for variable <tf.Variable 'global_step:0' shape=() dtype=int64_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
W0410 04:52:13.977231<PHONE_NUMBER>63968 wrap_function.py:214] Unable to create a python object for variable <tf.Variable 'FeatureExtractor/resnet_v1_50/conv1/weights:0' shape=(7, 7, 3, 64) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
W0410 04:52:13.977302<PHONE_NUMBER>63968 wrap_function.py:214] Unable to create a python object for variable <tf.Variable 'FeatureExtractor/resnet_v1_50/conv1/BatchNorm/gamma:0' shape=(64,) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
W0410 04:52:13.977363<PHONE_NUMBER>63968 wrap_function.py:214] Unable to create a python object for variable <tf.Variable 'FeatureExtractor/resnet_v1_50/conv1/BatchNorm/beta:0' shape=(64,) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
W0410 04:52:13.977421<PHONE_NUMBER>63968 wrap_function.py:214] Unable to create a python object for variable <tf.Variable 'FeatureExtractor/resnet_v1_50/conv1/BatchNorm/moving_mean:0' shape=(64,) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
Traceback (most recent call last):
  File "/home/afayed/Desktop/test.py", line 6, in <module>
    tflite_quant_model = converter.convert()
  File "/home/afayed/.local/lib/python3.5/site-packages/tensorflow_core/lite/python/lite.py", line 418, in convert
    raise ValueError("This converter can only convert a single "
ValueError: This converter can only convert a single ConcreteFunction. Converting multiple functions is under development.
afayed@metecs-0497:~$

Method 2 try 2:
Here I try the 'tf.lite.OpsSet.TFLITE_BUILTINS' and  'tf.lite.OpsSet.SELECT_TF_OPS'
import tensorflow as tf
saved_model_obj = tf.saved_model.load(export_dir='/home/afayed/coral/detection_ag/train/export/Servo/1586490086/')
concrete_func = saved_model_obj.signatures['serving_default']
concrete_func.inputs[0].set_shape([1,320,320,3])
converter =  tf.lite.TFLiteConverter.from_concrete_functions([concrete_func])
converter.experimental_new_converter = True
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS,
                                       tf.lite.OpsSet.SELECT_TF_OPS]
converter.optimizations =  [tf.lite.Optimize.DEFAULT]
tflite_model = converter.convert()

Output:
afayed@metecs-0497:~$ python3 ~/Desktop/test.py
2020-04-10 04:54:40.829681: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libnvinfer.so.6'; dlerror: libnvinfer.so.6: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda-9.0/lib64:/home/afayed/torch/install/lib:/home/afayed/kinova-demo/devel/lib:/opt/ros/kinetic/lib::/usr/local/lib:/usr/lib:/usr/aarch64-linux-gnu/lib
2020-04-10 04:54:40.829826: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libnvinfer_plugin.so.6'; dlerror: libnvinfer_plugin.so.6: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda-9.0/lib64:/home/afayed/torch/install/lib:/home/afayed/kinova-demo/devel/lib:/opt/ros/kinetic/lib::/usr/local/lib:/usr/lib:/usr/aarch64-linux-gnu/lib
2020-04-10 04:54:40.829836: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:30] Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
2020-04-10 04:54:42.415761: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1
2020-04-10 04:54:42.433531: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-04-10 04:54:42.433764: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1555] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce GTX 1050 Ti computeCapability: 6.1
coreClock: 1.62GHz coreCount: 6 deviceMemorySize: 3.95GiB deviceMemoryBandwidth: 104.43GiB/s
2020-04-10 04:54:42.433970: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2020-04-10 04:54:42.435321: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2020-04-10 04:54:42.436558: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2020-04-10 04:54:42.436804: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2020-04-10 04:54:42.438298: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2020-04-10 04:54:42.438873: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2020-04-10 04:54:42.440300: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2020-04-10 04:54:42.440422: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-04-10 04:54:42.440706: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-04-10 04:54:42.440901: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1697] Adding visible gpu devices: 0
2020-04-10 04:54:42.441211: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2020-04-10 04:54:42.465540: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency:<PHONE_NUMBER> Hz
2020-04-10 04:54:42.466703: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0xb492f10 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2020-04-10 04:54:42.466747: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
2020-04-10 04:54:42.513942: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-04-10 04:54:42.514292: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0xb4b61a0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2020-04-10 04:54:42.514310: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): GeForce GTX 1050 Ti, Compute Capability 6.1
2020-04-10 04:54:42.514512: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-04-10 04:54:42.514784: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1555] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce GTX 1050 Ti computeCapability: 6.1
coreClock: 1.62GHz coreCount: 6 deviceMemorySize: 3.95GiB deviceMemoryBandwidth: 104.43GiB/s
2020-04-10 04:54:42.514855: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2020-04-10 04:54:42.514901: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2020-04-10 04:54:42.514913: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2020-04-10 04:54:42.514940: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2020-04-10 04:54:42.514950: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2020-04-10 04:54:42.514960: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2020-04-10 04:54:42.514990: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2020-04-10 04:54:42.515094: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-04-10 04:54:42.515367: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-04-10 04:54:42.515614: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1697] Adding visible gpu devices: 0
2020-04-10 04:54:42.515671: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2020-04-10 04:54:42.516305: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1096] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-04-10 04:54:42.516329: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102]      0
2020-04-10 04:54:42.516336: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] 0:   N
2020-04-10 04:54:42.516450: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-04-10 04:54:42.516728: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-04-10 04:54:42.516989: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1241] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 2889 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1050 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1)
WARNING: Logging before flag parsing goes to stderr.
W0410 04:54:42.553511<PHONE_NUMBER>85152 wrap_function.py:214] Unable to create a python object for variable <tf.Variable 'global_step:0' shape=() dtype=int64_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
W0410 04:54:42.553783<PHONE_NUMBER>85152 wrap_function.py:214] Unable to create a python object for variable <tf.Variable 'FeatureExtractor/resnet_v1_50/conv1/weights:0' shape=(7, 7, 3, 64) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
W0410 04:54:42.553906<PHONE_NUMBER>85152 wrap_function.py:214] Unable to create a python object for variable <tf.Variable 'FeatureExtractor/resnet_v1_50/conv1/BatchNorm/gamma:0' shape=(64,) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
W0410 04:54:42.554021<PHONE_NUMBER>85152 wrap_function.py:214] Unable to create a python object for variable <tf.Variable 'FeatureExtractor/resnet_v1_50/conv1/BatchNorm/beta:0' shape=(64,) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
W0410 04:54:42.554129<PHONE_NUMBER>85152 wrap_function.py:214] Unable to create a python object for variable <tf.Variable 'FeatureExtractor/resnet_v1_50/conv1/BatchNorm/moving_mean:0' shape=(64,) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
W0410 04:54:43.084858<PHONE_NUMBER>85152 wrap_function.py:214] Unable to create a python object for variable <tf.Variable 'global_step:0' shape=() dtype=int64_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
W0410 04:54:43.085023<PHONE_NUMBER>85152 wrap_function.py:214] Unable to create a python object for variable <tf.Variable 'FeatureExtractor/resnet_v1_50/conv1/weights:0' shape=(7, 7, 3, 64) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
W0410 04:54:43.085146<PHONE_NUMBER>85152 wrap_function.py:214] Unable to create a python object for variable <tf.Variable 'FeatureExtractor/resnet_v1_50/conv1/BatchNorm/gamma:0' shape=(64,) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
W0410 04:54:43.085255<PHONE_NUMBER>85152 wrap_function.py:214] Unable to create a python object for variable <tf.Variable 'FeatureExtractor/resnet_v1_50/conv1/BatchNorm/beta:0' shape=(64,) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
W0410 04:54:43.085352<PHONE_NUMBER>85152 wrap_function.py:214] Unable to create a python object for variable <tf.Variable 'FeatureExtractor/resnet_v1_50/conv1/BatchNorm/moving_mean:0' shape=(64,) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
W0410 04:54:44.004590<PHONE_NUMBER>85152 wrap_function.py:214] Unable to create a python object for variable <tf.Variable 'global_step:0' shape=() dtype=int64_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
W0410 04:54:44.004742<PHONE_NUMBER>85152 wrap_function.py:214] Unable to create a python object for variable <tf.Variable 'FeatureExtractor/resnet_v1_50/conv1/weights:0' shape=(7, 7, 3, 64) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
W0410 04:54:44.004844<PHONE_NUMBER>85152 wrap_function.py:214] Unable to create a python object for variable <tf.Variable 'FeatureExtractor/resnet_v1_50/conv1/BatchNorm/gamma:0' shape=(64,) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
W0410 04:54:44.004932<PHONE_NUMBER>85152 wrap_function.py:214] Unable to create a python object for variable <tf.Variable 'FeatureExtractor/resnet_v1_50/conv1/BatchNorm/beta:0' shape=(64,) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
W0410 04:54:44.004984<PHONE_NUMBER>85152 wrap_function.py:214] Unable to create a python object for variable <tf.Variable 'FeatureExtractor/resnet_v1_50/conv1/BatchNorm/moving_mean:0' shape=(64,) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
W0410 04:54:45.414238<PHONE_NUMBER>85152 wrap_function.py:214] Unable to create a python object for variable <tf.Variable 'global_step:0' shape=() dtype=int64_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
W0410 04:54:45.414433<PHONE_NUMBER>85152 wrap_function.py:214] Unable to create a python object for variable <tf.Variable 'FeatureExtractor/resnet_v1_50/conv1/weights:0' shape=(7, 7, 3, 64) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
W0410 04:54:45.414536<PHONE_NUMBER>85152 wrap_function.py:214] Unable to create a python object for variable <tf.Variable 'FeatureExtractor/resnet_v1_50/conv1/BatchNorm/gamma:0' shape=(64,) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
W0410 04:54:45.414678<PHONE_NUMBER>85152 wrap_function.py:214] Unable to create a python object for variable <tf.Variable 'FeatureExtractor/resnet_v1_50/conv1/BatchNorm/beta:0' shape=(64,) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
W0410 04:54:45.414774<PHONE_NUMBER>85152 wrap_function.py:214] Unable to create a python object for variable <tf.Variable 'FeatureExtractor/resnet_v1_50/conv1/BatchNorm/moving_mean:0' shape=(64,) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
Traceback (most recent call last):
  File "/home/afayed/.local/lib/python3.5/site-packages/tensorflow_core/python/framework/ops.py", line 634, in set_shape
    unknown_shape)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Shapes must be equal rank, but are 0 and 4

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/home/afayed/Desktop/test.py", line 4, in <module>
    concrete_func.inputs[0].set_shape([1,320,320,3])
  File "/home/afayed/.local/lib/python3.5/site-packages/tensorflow_core/python/framework/ops.py", line 637, in set_shape
    raise ValueError(str(e))
ValueError: Shapes must be equal rank, but are 0 and 4
afayed@metecs-0497:~$

Method 2 try 3 fixing set_shape() to an empty array:
import tensorflow as tf
physical_devices = tf.config.list_physical_devices('GPU')
tf.config.set_logical_device_configuration(physical_devices[0],[tf.config.LogicalDeviceConfiguration(memory_limit=500)])
logical_devices = tf.config.list_logical_devices('GPU')

saved_model_obj = tf.saved_model.load(export_dir='/home/afayed/coral/detection_ag/train/export/Servo/1586490086/')
concrete_func = saved_model_obj.signatures['serving_default']
concrete_func.inputs[0].set_shape([])
converter =  tf.lite.TFLiteConverter.from_concrete_functions([concrete_func])
converter.experimental_new_converter = True
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS,
                                       tf.lite.OpsSet.SELECT_TF_OPS]
converter.optimizations =  [tf.lite.Optimize.DEFAULT]
tflite_model = converter.convert()

Output:
afayed@metecs-0497:~$ python3 ~/Desktop/test.py
2020-04-10 04:56:49.944995: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libnvinfer.so.6'; dlerror: libnvinfer.so.6: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda-9.0/lib64:/home/afayed/torch/install/lib:/home/afayed/kinova-demo/devel/lib:/opt/ros/kinetic/lib::/usr/local/lib:/usr/lib:/usr/aarch64-linux-gnu/lib
2020-04-10 04:56:49.945132: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libnvinfer_plugin.so.6'; dlerror: libnvinfer_plugin.so.6: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda-9.0/lib64:/home/afayed/torch/install/lib:/home/afayed/kinova-demo/devel/lib:/opt/ros/kinetic/lib::/usr/local/lib:/usr/lib:/usr/aarch64-linux-gnu/lib
2020-04-10 04:56:49.945157: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:30] Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
2020-04-10 04:56:50.596387: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1
2020-04-10 04:56:50.613734: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-04-10 04:56:50.613967: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1555] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce GTX 1050 Ti computeCapability: 6.1
coreClock: 1.62GHz coreCount: 6 deviceMemorySize: 3.95GiB deviceMemoryBandwidth: 104.43GiB/s
2020-04-10 04:56:50.614179: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2020-04-10 04:56:50.615449: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2020-04-10 04:56:50.616734: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2020-04-10 04:56:50.617019: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2020-04-10 04:56:50.618337: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2020-04-10 04:56:50.619126: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2020-04-10 04:56:50.620796: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2020-04-10 04:56:50.620927: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-04-10 04:56:50.621282: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-04-10 04:56:50.621526: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1697] Adding visible gpu devices: 0
2020-04-10 04:56:50.621866: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2020-04-10 04:56:50.645443: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency:<PHONE_NUMBER> Hz
2020-04-10 04:56:50.646094: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x6ca8e80 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2020-04-10 04:56:50.646140: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
2020-04-10 04:56:50.686690: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-04-10 04:56:50.687029: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x6ccc110 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2020-04-10 04:56:50.687046: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): GeForce GTX 1050 Ti, Compute Capability 6.1
2020-04-10 04:56:50.687214: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-04-10 04:56:50.687461: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1555] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce GTX 1050 Ti computeCapability: 6.1
coreClock: 1.62GHz coreCount: 6 deviceMemorySize: 3.95GiB deviceMemoryBandwidth: 104.43GiB/s
2020-04-10 04:56:50.687508: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2020-04-10 04:56:50.687520: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2020-04-10 04:56:50.687531: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2020-04-10 04:56:50.687541: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2020-04-10 04:56:50.687551: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2020-04-10 04:56:50.687561: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2020-04-10 04:56:50.687571: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2020-04-10 04:56:50.687658: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-04-10 04:56:50.687918: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-04-10 04:56:50.688156: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1697] Adding visible gpu devices: 0
2020-04-10 04:56:50.688198: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2020-04-10 04:56:50.688912: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1096] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-04-10 04:56:50.688923: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102]      0
2020-04-10 04:56:50.688947: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] 0:   N
2020-04-10 04:56:50.689028: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-04-10 04:56:50.689310: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-04-10 04:56:50.689592: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1241] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 500 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1050 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1)
WARNING: Logging before flag parsing goes to stderr.
W0410 04:56:51.706341<PHONE_NUMBER>24160 wrap_function.py:214] Unable to create a python object for variable <tf.Variable 'global_step:0' shape=() dtype=int64_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
W0410 04:56:51.706622<PHONE_NUMBER>24160 wrap_function.py:214] Unable to create a python object for variable <tf.Variable 'FeatureExtractor/resnet_v1_50/conv1/weights:0' shape=(7, 7, 3, 64) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().