Tony607

import time

times = []

# Run inference 20 times and do the average.
for i in range(20):
    start_time = time.time()
    # Use the API internal call directly.
    results = rknn.rknn_base.inference(
        inputs=[img], data_type="uint8", data_format="nhwc", outputs=None

Tony607

import numpy as np
import cv2
from rknn.api import RKNN

# Create RKNN object
rknn = RKNN()
img_height = 299

# Direct Load RKNN Model
ret = rknn.load_rknn("./inception_v3.rknn")

Tony607

from rknn.api import RKNN


INPUT_NODE = ["input_1"]
OUTPUT_NODE = ["predictions/Softmax"]

img_height = 299

# Create RKNN object
rknn = RKNN()

Tony607

pip3 install -U tensorflow scipy onnx
pip3 install rknn_toolkit-0.9.9-cp36-cp36m-linux_x86_64.whl
# Or if you have Python 3.5
# pip3 install rknn_toolkit-0.9.9-cp35-cp35m-linux_x86_64.whl

Tony607

sudo dnf update -y
sudo dnf install -y cmake gcc gcc-c++ protobuf-devel protobuf-compiler lapack-devel
sudo dnf install -y python3-devel python3-opencv python3-numpy-f2py python3-h5py python3-lmdb
sudo dnf install -y python3-grpcio

sudo pip3 install scipy-1.2.0-cp36-cp36m-linux_aarch64.whl
sudo pip3 install onnx-1.4.1-cp36-cp36m-linux_aarch64.whl
sudo pip3 install tensorflow-1.10.1-cp36-cp36m-linux_aarch64.whl
sudo pip3 install rknn_toolkit-0.9.9-cp36-cp36m-linux_aarch64.whl

Tony607

from IPython.display import Image as DisplayImage

# Boxes unit in pixels (image coordinates).
boxes_pixels = []
for i in range(num_detections):
    # scale box to image coordinates
    box = boxes[i] * np.array([image.shape[0],
                               image.shape[1], image.shape[0], image.shape[1]])
    box = np.round(box).astype(int)
    boxes_pixels.append(box)

Tony607

import cv2
IMAGE_PATH = "./data/dogs.jpg"
image = cv2.imread(IMAGE_PATH)
image = cv2.resize(image, (300, 300))

scores, boxes, classes, num_detections = tf_sess.run([tf_scores, tf_boxes, tf_classes, tf_num_detections], feed_dict={
    tf_input: image[None, ...]
})
boxes = boxes[0]  # index by 0 to remove batch dimension
scores = scores[0]

Tony607

import tensorflow as tf

def get_frozen_graph(graph_file):
    """Read Frozen Graph file from disk."""
    with tf.gfile.FastGFile(graph_file, "rb") as f:
        graph_def = tf.GraphDef()
        graph_def.ParseFromString(f.read())
    return graph_def

# The TensorRT inference graph file downloaded from Colab or your local machine.

Tony607

with open('./data/trt_graph.pb', 'wb') as f:
    f.write(trt_graph.SerializeToString())

# Download the tensorRT graph .pb file from colab to your local machine.
from google.colab import files

files.download('./data/trt_graph.pb')

Tony607

import tensorflow.contrib.tensorrt as trt

trt_graph = trt.create_inference_graph(
    input_graph_def=frozen_graph,
    outputs=output_names,
    max_batch_size=1,
    max_workspace_size_bytes=1 << 25,
    precision_mode='FP16',
    minimum_segment_size=50
)