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import numpy as np
import tensorflow as tf
from ipdb import set_trace
from tensorflow.python.compiler.tensorrt import trt_convert as trt

tf.enable_eager_execution()

INPUT_SAVED_MODEL_DIR = 'tst'
OUTPUT_SAVED_MODEL_DIR = 'tst_out'

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isi@nano:~/Emulsefai$ python tst.py
2019-11-14 16:51:49.211844: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0
2019-11-14 16:52:01.519426: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0
WARNING:tensorflow:From tst.py:6: The name tf.enable_eager_execution is deprecated. Please use tf.compat.v1.enable_eager_execution instead.

2019-11-14 16:52:05.991091: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcuda.so.1
2019-11-14 16:52:06.003322: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:972] ARM64 does not support NUMA - returning NUMA node zero
2019-11-14 16:52:06.003476: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties:
name: NVIDIA Tegra X1 major: 5 minor: 3 memoryClockRate(GHz): 0.9216
pciBusID: 0000:00:00.0

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isi@nano:~/Emulsefai$ python tst.py
2019-11-14 11:18:08.090728: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0
2019-11-14 11:18:16.575729: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0
WARNING:tensorflow:From tst.py:6: The name tf.enable_eager_execution is deprecated. Please use tf.compat.v1.enable_eager_execution instead.

2019-11-14 11:18:21.897651: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcuda.so.1
2019-11-14 11:18:21.910336: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:972] ARM64 does not support NUMA - returning NUMA node zero
2019-11-14 11:18:21.910489: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties:
name: NVIDIA Tegra X1 major: 5 minor: 3 memoryClockRate(GHz): 0.9216
pciBusID: 0000:00:00.0

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# SSDLite with Mobilenet v2 configuration for SANATA dataset.
# Users should configure the fine_tune_checkpoint field in the train config as
# well as the label_map_path and input_path fields in the train_input_reader and
# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
# should be configured.

model {
  ssd {
    # PATH_TO_BE_CONFIGURED
    num_classes: 6

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# SSDLite with Mobilenet v2 configuration for SANATA dataset.
# Users should configure the fine_tune_checkpoint field in the train config as
# well as the label_map_path and input_path fields in the train_input_reader and
# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that
# should be configured.

model {
  ssd {
    # PATH_TO_BE_CONFIGURED
    num_classes: 3

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import tensorflow as tf
import tensorflow.contrib.tensorrt as trt
import numpy as np
import PIL
from timeit import default_timer as timer
from tqdm import tqdm


'''
This script benchmarks how long it takes to run perform inference on a pure Tensorflow (TF) model vs a converted TensorRT model