Provide details on the platforms you are using:

Linux distro and version Ubuntu 16.04

GPU type 1080ti

nvidia driver version 396.54

CUDA version 9

CUDNN version 7.1

Python version [if using python] 3.5

Tensorflow version r1.11

TensorRT version 4.0.1.6

If Jetson, OS, hw versions n/a

Describe the problem

```
2018-09-25 17:20:14.600365: I tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc:577] Starting calibration thread on device 0, Calibration Resource @ 0x7f7fb4001730
2018-09-25 17:20:14.600539: I tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc:577] Starting calibration thread on device 0, Calibration Resource @ 0x7f7fac071a40
2018-09-25 17:20:14.973555: I tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc:577] Starting calibration thread on device 0, Calibration Resource @ 0x7f7fa4004fe0
python: helpers.cpp:56: nvinfer1::DimsCHW nvinfer1::getCHW(const nvinfer1::Dims&): Assertion `d.nbDims >= 3' failed.
Aborted (core dumped
```

)

How to reproduce

```
import tensorflow as tf
from tensorflow.contrib import tensorrt as trt
def load_graph(frozen_graph_filename):
# We load the protobuf file from the disk and parse it to retrieve the
# unserialized graph_def
with tf.gfile.GFile(frozen_graph_filename, "rb") as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
# Then, we can use again a convenient built-in function to import a graph_def into the
# current default Graph
with tf.Graph().as_default() as graph:
tf.import_graph_def(
graph_def,
name='', #DEBUG
)
return graph
fid = "model.pb"
output_nodenames = 'out1,out2,out3'
output_node = list(output_nodenames.split(","))
g = load_graph(fid)
with tf.Session(graph=g) as sess:
trt_graph = trt.create_inference_graph(
input_graph_def=tf.get_default_graph().as_graph_def(),
outputs=output_node,
max_batch_size=99999,
max_workspace_size_bytes=1 << 25,
precision_mode="INT8", # TRT Engine precision "FP32","FP16" or "INT8"
minimum_segment_size=2 # minimum number of nodes in an engine
)
with tf.gfile.GFile("trt.pb", "wb") as f:
f.write(trt_graph.SerializeToString())
g2 = load_graph("trt.pb")
with tf.Session(graph=g2) as sess:
"""Run given calibration graph multiple times."""
num_samples = 10
np.random.seed(0)
ip1_data = np.random.rand(num_samples,700,800,6).astype(np.float32)
ip1 = g2.get_tensor_by_name("ip1:0")
ip2_data = np.random.rand(4).astype(np.float32)
ip2 = g2.get_tensor_by_name("ip2:0")
ip3_data = np.random.rand(20000,6).astype(np.float32)
ip3 = g2.get_tensor_by_name("ip3:0")
ip4_data = np.random.rand(20000,4).astype(np.float32)
ip4 = g2.get_tensor_by_name("ip4:0")
out1 = g2.get_tensor_by_name("out1:0")
out2 = g2.get_tensor_by_name("out2:0")
out3 = g2.get_tensor_by_name("out3:0")
# run over real calibration data here, we are mimicking a calibration set of
# 30 different batches. Use as much calibration data as you want
for i in range(num_samples):
val = sess.run([out1, out2, out3], feed_dict={ip1:ip1_data[i], ip2:ip2_data, ip3:ip3_data, ip4:ip4_data})
```