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Device 0: "Xavier"                                                      
  CUDA Driver Version / Runtime Version          10.0 / 10.0
  CUDA Capability Major/Minor version number:    7.2
  Total amount of global memory:                 15700 MBytes (16462909440 bytes)            
  ( 8) Multiprocessors, ( 64) CUDA Cores/MP:     512 CUDA Cores
  GPU Max Clock rate:                            1500 MHz (1.50 GHz)                                                                             
  Memory Clock rate:                             1377 Mhz
  Memory Bus Width:                              256-bit
  L2 Cache Size:                                 524288 bytes
  Maximum Texture Dimension Size (x,y,z)         1D=(131072), 2D=(131072, 65536), 3D=(16384, 16384, 16384)

r7vme

Device 0: "NVIDIA Tegra X2"                                                                                                               [2/141]
  CUDA Driver Version / Runtime Version          10.0 / 10.0                                                                                     
  CUDA Capability Major/Minor version number:    6.2
  Total amount of global memory:                 7852 MBytes (8233566208 bytes)
  ( 2) Multiprocessors, (128) CUDA Cores/MP:     256 CUDA Cores
  GPU Max Clock rate:                            1020 MHz (1.02 GHz)
  Memory Clock rate:                             1300 Mhz
  Memory Bus Width:                              128-bit
  L2 Cache Size:                                 524288 bytes
  Maximum Texture Dimension Size (x,y,z)         1D=(131072), 2D=(131072, 65536), 3D=(16384, 16384, 16384)

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diff --git a/net/bbox_3D_net.py b/net/bbox_3D_net.py
index 6daafdd..8146caa 100644
--- a/net/bbox_3D_net.py
+++ b/net/bbox_3D_net.py
@@ -18,7 +18,7 @@ def bbox_3D_net(input_shape=(224, 224, 3), vgg_weights=None, freeze_vgg=False, b
         for layer in vgg16_model.layers:
             layer.trainable = False
 
-    x = Flatten()(vgg16_model.output)
+    x = Reshape((25088,))(vgg16_model.output)

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#!/usr/bin/env python3
import graphsurgeon as gs
import tensorflow as tf
import tensorrt as trt
import uff

if __name__ == "__main__":
  data_type = trt.DataType.HALF
  #data_type = trt.DataType.FLOAT
  output_node = "test_model/model/logits/linear/BiasAdd"

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import tensorflow as tf

def get_graph_def_from_file(graph_filepath):
  with tf.Graph().as_default():
    with tf.gfile.GFile(graph_filepath, 'rb') as f:
      graph_def = tf.GraphDef()
      graph_def.ParseFromString(f.read())
      return graph_def

# inputs

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#!/usr/bin/env python3
import math
# input
VFOV=60
H=480
W=856

aspRatio=W/H
VFOV_rad=VFOV*math.pi/180.
HFOV_rad= 2 * math.atan( math.tan(VFOV_rad/2) * aspRatio )

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python3 scripts/convert_plan.py box3d.pb box3d.plan input 224 224 dimension/LeakyRelu 1 0 float
/usr/lib/python3/dist-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters
WARNING:tensorflow:From /usr/lib/python3.6/dist-packages/uff/converters/tensorflow/conversion_helpers.py:185: FastGFile.__init__ (from tensorflow.python.platform.gfile) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.gfile.GFile.
UFF Version 0.5.5
=== Automatically deduced input nodes ===
[name: "input_1"
op: "Placeholder"

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import math
import numpy as np


# camera : x->right, y->down, z->forward
# world: x->forward, y->left, z->up
world_frame_from_cam_frame = np.array([
  [  0.,  0.,  1.],
  [ -1.,  0.,  0.],
  [  0., -1.,  0.]

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#!/usr/bin/env python

'''
  Script to augment data by flopping images and steering angle.
  It will outout new tub into separate directory with "_flopped"
  suffix.

  Usage: ./flop.py <tub directory>
'''

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#!/usr/bin/env python3

import cv2
import time

def write_video(name):
    # first camera
    stream = 0

    # camera