├── .github
└── ISSUE_TEMPLATE
│ └── issue.md
├── .gitignore
├── LICENSE
├── README.md
├── data
├── input
│ ├── 000003.jpg
│ ├── 000004.jpg
│ ├── 000170.jpg
│ ├── 009649.png
│ ├── ade20k.jpg
│ ├── cityscapes1.png
│ ├── cityscapes2.png
│ ├── my_own_image.jpg
│ ├── testvideo1.mp4
│ ├── testvideo2.mp4
│ └── testvideo3.mp4
└── output
│ ├── result1.MP4
│ ├── result2.MP4
│ ├── result3.MP4
│ └── result_deeplabv3.jpg
├── lib
├── libcpu_extension.so
├── libformat_reader.so
└── libgflags_nothreads.a
├── lrmodelconvscript.txt
├── lrmodels
├── MSCOCOcityscapes
│ ├── FP16
│ │ └── .gitkeep
│ └── FP32
│ │ └── .gitkeep
└── PascalVOC
│ ├── FP16
│ ├── frozen_inference_graph.bin
│ ├── frozen_inference_graph.mapping
│ └── frozen_inference_graph.xml
│ └── FP32
│ ├── frozen_inference_graph.bin
│ ├── frozen_inference_graph.mapping
│ ├── frozen_inference_graph.xml
│ └── inputoutput.txt
├── media
├── 01.jpg
├── 02.jpg
├── 03.jpg
├── sample01.jpg
├── sample02.jpg
├── sample03.jpg
└── sample04.jpg
├── openvino_deeplabv3_test.py
├── pbmodels
├── MSCOCOcityscapes
│ ├── frozen_inference_graph.pb
│ ├── inputoutput.txt
│ ├── model.ckpt.data-00000-of-00001
│ └── model.ckpt.index
└── PascalVOC
│ ├── frozen_inference_graph.pb
│ ├── inputoutput.txt
│ ├── model.ckpt-30000.data-00000-of-00001
│ └── model.ckpt-30000.index
├── savedmodel
└── events.out.tfevents.1546483660.ubuntu
├── tensorboard_output.py
├── tensorboard_start_script.txt
└── tfconverter.py
/.github/ISSUE_TEMPLATE/issue.md:
--------------------------------------------------------------------------------
1 | ---
2 | name: Issue
3 | about: You provide the necessary information for the early resolution of the problem.
4 | title: ''
5 | labels: ''
6 | assignees: ''
7 |
8 | ---
9 |
10 | **[Required]** Your device (RaspberryPi3, LaptopPC, or other device name):
11 |
12 | **[Required]** Your device's CPU architecture (armv7l, x86_64, or other architecture name):
13 |
14 | **[Required]** Your OS (Raspbian, Ubuntu1604, or other os name):
15 |
16 | **[Required]** Details of the work you did before the problem occurred:
17 | 97-usbboot.rules
86 | SUBSYSTEM=="usb", ATTRS{idProduct}=="2150", ATTRS{idVendor}=="03e7", GROUP="users", MODE="0666", ENV{ID_MM_DEVICE_IGNORE}="1"
87 | SUBSYSTEM=="usb", ATTRS{idProduct}=="2485", ATTRS{idVendor}=="03e7", GROUP="users", MODE="0666", ENV{ID_MM_DEVICE_IGNORE}="1"
88 | SUBSYSTEM=="usb", ATTRS{idProduct}=="f63b", ATTRS{idVendor}=="03e7", GROUP="users", MODE="0666", ENV{ID_MM_DEVICE_IGNORE}="1"
89 | EOF
90 |
91 | $ sudo cp 97-usbboot.rules /etc/udev/rules.d/
92 | $ sudo udevadm control --reload-rules
93 | $ sudo udevadm trigger
94 | $ cd /opt/intel/common/mdf/lib64
95 | $ sudo mv igfxcmrt64.so igfxcmrt64.so.org
96 | $ sudo ln -s libigfxcmrt64.so igfxcmrt64.so
97 | $ cd /opt/intel/mediasdk/lib64
98 | $ sudo mv libmfxhw64.so.1 libmfxhw64.so.1.org
99 | $ sudo mv libmfx.so.1 libmfx.so.1.org
100 | $ sudo mv libva-glx.so.2 libva-glx.so.2.org
101 | $ sudo mv libva.so.2 libva.so.2.org
102 | $ sudo mv libigdgmm.so.1 libigdgmm.so.1.org
103 | $ sudo mv libva-drm.so.2 libva-drm.so.2.org
104 | $ sudo mv libva-x11.so.2 libva-x11.so.2.org
105 | $ sudo ln -s libmfxhw64.so.1.28 libmfxhw64.so.1
106 | $ sudo ln -s libmfx.so.1.28 libmfx.so.1
107 | $ sudo ln -s libva-glx.so.2.300.0 libva-glx.so.2
108 | $ sudo ln -s libva.so.2.300.0 libva.so.2
109 | $ sudo ln -s libigdgmm.so.1.0.0 libigdgmm.so.1
110 | $ sudo ln -s libva-drm.so.2.300.0 libva-drm.so.2
111 | $ sudo ln -s libva-x11.so.2.300.0 libva-x11.so.2
112 | $ sudo ldconfig
113 | $ rm 97-usbboot.rules
114 | ```
115 | ##### 1.3.2 【Optional execution】 Additional installation steps for processor graphics (GPU)
116 | ```bash
117 | $ cd /opt/intel/computer_vision_sdk/install_dependencies/
118 | $ sudo -E su
119 | $ uname -r
120 | 4.15.0-42-generic #<--- display kernel version sample
121 |
122 | ### Execute only when the kernel version is older than 4.14
123 | $ ./install_4_14_kernel.sh
124 |
125 | $ ./install_NEO_OCL_driver.sh
126 | $ sudo reboot
127 | ```
128 |
129 | ### 2. Downgrade to stable OpenCV
130 | Since OpenCV 4.0.0-pre introduced by default had bug in Gstreamer and it did not work properly, we will reinstall OpenCV 3.4.3 on our own.
131 | ```bash
132 | $ sudo -H pip3 install opencv-python==3.4.3.18
133 | $ nano ~/.bashrc
134 | export PYTHONPATH=/usr/local/lib/python3.5/dist-packages/cv2:$PYTHONPATH
135 |
136 | $ source ~/.bashrc
137 | ```
138 | ### 3. Upgrade to Tensorflow v1.11.0
139 | Upgrade to old version Tensorflow v1.9.0, introduced by default, to Tensorflow v1.11.0, as subsequent model optimizer processing will fail.
140 | ```bash
141 | $ sudo -H pip3 install pip --upgrade
142 |
143 | $ python3 -c 'import tensorflow as tf; print(tf.__version__)'
144 | 1.9.0 #<--- display Tensorflow version sample
145 |
146 | $ sudo -H pip3 install tensorflow==1.11.0 --upgrade
147 | or
148 | $ sudo -H pip3 install tensorflow-gpu==1.11.0 --upgrade
149 | ```
150 | ### 4. Settings for offloading custom layer behavior to Tensorflow
151 | ```bash
152 | $ sudo apt-get install -y git pkg-config zip g++ zlib1g-dev unzip
153 | $ cd ~
154 | $ wget https://github.com/bazelbuild/bazel/releases/download/0.18.1/bazel-0.18.1-installer-linux-x86_64.sh
155 | $ sudo chmod +x bazel-0.18.1-installer-linux-x86_64.sh
156 | $ ./bazel-0.18.1-installer-linux-x86_64.sh --user
157 | $ echo 'export PATH=$PATH:$HOME/bin' >> ~/.bashrc
158 | $ source ~/.bashrc
159 | $ cd /opt
160 | $ sudo git clone -b v1.11.0 https://github.com/tensorflow/tensorflow.git
161 | $ cd tensorflow
162 | $ sudo git checkout -b v1.11.0
163 | $ echo 'export TF_ROOT_DIR=/opt/tensorflow' >> ~/.bashrc
164 | $ source ~/.bashrc
165 | $ sudo nano /opt/intel/computer_vision_sdk/bin/setupvars.sh
166 |
167 | #Before
168 | INSTALLDIR=/opt/intel//computer_vision_sdk_2018.4.420
169 | ↓
170 | #After
171 | INSTALLDIR=/opt/intel/computer_vision_sdk_2018.4.420
172 |
173 | $ source /opt/intel/computer_vision_sdk/bin/setupvars.sh
174 | $ sudo nano /opt/intel/computer_vision_sdk/deployment_tools/model_optimizer/tf_call_ie_layer/build.sh
175 |
176 | #Before
177 | bazel build --config=monolithic //tensorflow/cc/inference_engine_layer:libtensorflow_call_layer.so
178 | ↓
179 | #After
180 | sudo -H $HOME/bin/bazel build --config monolithic //tensorflow/cc/inference_engine_layer:libtensorflow_call_layer.so
181 | or
182 | sudo -H $HOME/bin/bazel --host_jvm_args=-Xmx512m build --config monolithic --local_resources 1024.0,0.5,0.5 //tensorflow/cc/inference_engine_layer:libtensorflow_call_layer.so
183 |
184 | $ sudo -E /opt/intel/computer_vision_sdk/deployment_tools/model_optimizer/tf_call_ie_layer/build.sh
185 |
186 | $ su -
187 | $ cp /opt/tensorflow/bazel-bin/tensorflow/cc/inference_engine_layer/libtensorflow_call_layer.so /usr/local/lib
188 | $ exit
189 | $ nano ~/.bashrc
190 | export PYTHONPATH=$PYTHONPATH:/usr/local/lib
191 |
192 | $ source ~/.bashrc
193 | $ sudo ldconfig
194 | ```
195 | ### 5. 【Optional execution】 Build sample programs and CPU extension
196 | ```bash
197 | $ cd /opt/intel/computer_vision_sdk/deployment_tools/inference_engine/samples
198 | $ sudo ./build_samples.sh
199 |
200 | ### The prebuilt binary is saved in the following path
201 | ### ~/inference_engine_samples_build/intel64/Release/
202 | ```
203 | ### 6. 【Optional execution】【Example】 Conversion of Tensorflow-DeeplabV3 model to lr format
204 | #### 6-1. Pascal VOC
205 | ```bash
206 | $ cd ~
207 | $ mkdir model
208 | $ wget http://download.tensorflow.org/models/deeplabv3_mnv2_pascal_train_aug_2018_01_29.tar.gz
209 | $ tar -zxvf deeplabv3_mnv2_pascal_train_aug_2018_01_29.tar.gz
210 | $ cp deeplabv3_mnv2_pascal_train_aug/frozen_inference_graph.pb model
211 | $ sudo python3 /opt/intel/computer_vision_sdk/deployment_tools/model_optimizer/mo_tf.py \
212 | --input_model pbmodels/PascalVOC/frozen_inference_graph.pb \
213 | --input 0:MobilenetV2/Conv/Conv2D \
214 | --output ArgMax \
215 | --input_shape [1,513,513,3] \
216 | --output_dir lrmodels/PascalVOC/FP32
217 | ```
218 | #### 6-2. MS-COCO cityscapes
219 | ```bash
220 | $ wget http://download.tensorflow.org/models/deeplabv3_mnv2_cityscapes_train_2018_02_05.tar.gz
221 | $ tar -zxvf deeplabv3_mnv2_cityscapes_train_2018_02_05.tar.gz
222 | ```
223 |
224 | ### 7. Execution (The default is "USB Camera mode")
225 | ```bash
226 | $ cd ~
227 | $ git clone https://github.com/PINTO0309/OpenVINO-DeeplabV3.git
228 | $ cd OpenVINO-DeeplabV3
229 | $ python3 openvino_deeplabv3_test.py
230 | ```
231 |
232 | # How to install Bazel (version 0.17.2, x86_64 only)
233 | ### 1. Bazel introduction command
234 | ```bash
235 | $ cd ~
236 | $ curl -sc /tmp/cookie "https://drive.google.com/uc?export=download&id=1dvR3pdM6vtkTWqeR-DpgVUoDV0EYWil5" > /dev/null
237 | $ CODE="$(awk '/_warning_/ {print $NF}' /tmp/cookie)"
238 | $ curl -Lb /tmp/cookie "https://drive.google.com/uc?export=download&confirm=${CODE}&id=1dvR3pdM6vtkTWqeR-DpgVUoDV0EYWil5" -o bazel
239 | $ sudo cp ./bazel /usr/local/bin
240 | $ rm ./bazel
241 | ```
242 | ### 2. Supplementary information
243 | **https://github.com/PINTO0309/Bazel_bin.git**
244 |
245 | # How to check the graph structure of a ".pb" file [Part.1]
246 | Simple structure analysis.
247 | ### 1. Build and run graph structure analysis program
248 | ```bash
249 | $ cd ~
250 | $ git clone -b v1.11.0 https://github.com/tensorflow/tensorflow.git
251 | $ cd tensorflow
252 | $ git checkout -b v1.11.0
253 | $ bazel build tensorflow/tools/graph_transforms:summarize_graph
254 | $ bazel-bin/tensorflow/tools/graph_transforms/summarize_graph --in_graph=xxxx.pb
255 | ```
256 | ### 2. Sample of display result
257 | ```bash
258 | Found 1 possible inputs: (name=ImageTensor, type=uint8(4), shape=[1,?,?,3])
259 | No variables spotted.
260 | Found 1 possible outputs: (name=SemanticPredictions, op=Slice)
261 | Found 2146325 (2.15M) const parameters, 0 (0) variable parameters, and 4 control_edges
262 | Op types used: 374 Const, 357 Identity, 54 FusedBatchNorm, 38 Conv2D, 34 Relu6, \
263 | 17 DepthwiseConv2dNative, 13 Add, 10 StridedSlice, 10 BatchToSpaceND, 10 \
264 | SpaceToBatchND, 8 Sub, 5 Pack, 4 GreaterEqual, 4 Assert, 4 Shape, 4 ResizeBilinear, \
265 | 3 Cast, 3 Relu, 2 ExpandDims, 2 Squeeze, 2 Maximum, 2 Mul, 1 Slice, 1 LogicalAnd, \
266 | 1 Reshape, 1 Placeholder, 1 Pad, 1 Equal, 1 ConcatV2, 1 BiasAdd, 1 AvgPool, 1 ArgMax
267 | To use with tensorflow/tools/benchmark:benchmark_model try these arguments:
268 | bazel run tensorflow/tools/benchmark:benchmark_model -- \
269 | --graph=xxxx.pb \
270 | --show_flops \
271 | --input_layer=ImageTensor \
272 | --input_layer_type=uint8 \
273 | --input_layer_shape=1,-1,-1,3 \
274 | --output_layer=SemanticPredictions
275 | ```
276 |
277 | # How to check the graph structure of a ".pb" file [Part.2]
278 | Convert to text format.
279 | ### 1. Run graph structure analysis program
280 | ```bash
281 | $ python3 tfconverter.py
282 | ### ".pbtxt" in ProtocolBuffer format is output.
283 | ### The size of the generated text file is huge.
284 | ```
285 |
286 | # How to check the graph structure of a ".pb" file [Part.3]
287 | Use Tensorboard.
288 | ### 1. Build Tensorboard
289 | ```bash
290 | $ cd ~
291 | $ git clone -b v1.11.0 https://github.com/tensorflow/tensorflow.git
292 | $ cd tensorflow
293 | $ git checkout -b v1.11.0
294 | $ bazel build tensorflow/tensorboard:tensorboard
295 | ```
296 | ### 2. Run log output program for Tensorboard
297 | ```python
298 | import tensorflow as tf
299 | from tensorflow.python.platform import gfile
300 |
301 | with tf.Session() as sess:
302 | model_filename ="xxxx.pb"
303 | with gfile.FastGFile(model_filename, "rb") as f:
304 | graph_def = tf.GraphDef()
305 | graph_def.ParseFromString(f.read())
306 | g_in = tf.import_graph_def(graph_def)
307 |
308 | LOGDIR="path/to/logs"
309 | train_writer = tf.summary.FileWriter(LOGDIR)
310 | train_writer.add_graph(sess.graph)
311 | ```
312 | ### 3. Starting Tensorboard
313 | ```bash
314 | $ bazel-bin/tensorflow/tensorboard/tensorboard --logdir=path/to/logs
315 | ```
316 | ### 4. Display of Tensorboard
317 | Access `http://localhost:6006` from the browser.
318 |
319 | # Reference article, thanks
320 | https://github.com/FionaZZ92/OpenVINO.git
321 | https://medium.com/@oleksandrsavsunenko/optimizing-neural-networks-for-production-with-intels-openvino-a7ee3a6883d
322 | https://github.com/tensorflow/models/blob/master/research/deeplab/g3doc/model_zoo.md
323 | https://blogs.yahoo.co.jp/verification_engineer/71450155.html
324 |
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/lrmodelconvscript.txt:
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1 | sudo python3 /opt/intel/computer_vision_sdk/deployment_tools/model_optimizer/mo_tf.py \
2 | --input_model pbmodels/PascalVOC/frozen_inference_graph.pb \
3 | --input 0:MobilenetV2/Conv/Conv2D \
4 | --output ArgMax \
5 | --input_shape [1,513,513,3] \
6 | --output_dir lrmodels/PascalVOC/FP32
7 |
8 | sudo python3 /opt/intel/computer_vision_sdk/deployment_tools/model_optimizer/mo_tf.py \
9 | --input_model pbmodels/PascalVOC/frozen_inference_graph.pb \
10 | --input 0:MobilenetV2/Conv/Conv2D \
11 | --output ResizeBilinear_3 \
12 | --input_shape [1,513,513,3] \
13 | --output_dir lrmodels/PascalVOC/FP16 \
14 | --data_type FP16
15 |
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/lrmodels/MSCOCOcityscapes/FP16/.gitkeep:
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1748 | 960
1749 | 65
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/lrmodels/PascalVOC/FP32/inputoutput.txt:
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1 | INPUT_TENSOR_NAME = 0:MobilenetV2/Conv/Conv2D
2 | OUTPUT_TENSOR_NAME = ArgMax
3 | INPUT_SIZE = [1,513,513,3]
4 | FROZEN_GRAPH_NAME = frozen_inference_graph.pb
5 |
6 |
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/openvino_deeplabv3_test.py:
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1 | import sys
2 | import os
3 | from argparse import ArgumentParser
4 | import numpy as np
5 | import cv2
6 | import time
7 | from PIL import Image
8 | import tensorflow as tf
9 | from tensorflow.python.platform import gfile
10 | try:
11 | from armv7l.openvino.inference_engine import IENetwork, IEPlugin
12 | except:
13 | from openvino.inference_engine import IENetwork, IEPlugin
14 |
15 | class _model_preprocess():
16 | def __init__(self):
17 | graph = tf.Graph()
18 | f_handle = gfile.FastGFile("pbmodels/PascalVOC/frozen_inference_graph.pb", "rb")
19 | graph_def = tf.GraphDef.FromString(f_handle.read())
20 | with graph.as_default():
21 | tf.import_graph_def(graph_def, name='')
22 | self. sess = tf.Session(graph=graph)
23 |
24 | def _pre_process(self, image):
25 | seg_map = self.sess.run("sub_7:0", feed_dict={"ImageTensor:0": [image]})
26 | return seg_map
27 |
28 |
29 | class _model_postprocess():
30 | def __init__(self):
31 | graph = tf.Graph()
32 | f_handle = gfile.FastGFile("pbmodels/PascalVOC/frozen_inference_graph.pb", "rb")
33 | graph_def = tf.GraphDef.FromString(f_handle.read())
34 | with graph.as_default():
35 | new_input = tf.placeholder(tf.int64, shape=(1, 513, 513), name="new_input")
36 | tf.import_graph_def(graph_def, input_map={"ArgMax:0": new_input}, name='')
37 | self.sess = tf.Session(graph=graph)
38 |
39 | def _post_process(self, image_ir, image):
40 | seg_map = self.sess.run("SemanticPredictions:0", feed_dict={"ImageTensor:0": [image], "new_input:0": np.int64(image_ir)})
41 | return seg_map
42 |
43 |
44 | _pre = _model_preprocess()
45 | _post = _model_postprocess()
46 |
47 |
48 | def build_argparser():
49 | parser = ArgumentParser()
50 | parser.add_argument("-pp", "--plugin_dir", help="Path to a plugin folder", type=str, default=None)
51 | parser.add_argument("-d", "--device", help="Specify the target device to infer on; CPU, GPU, FPGA or MYRIAD is acceptable. Sample will look for a suitable plugin for device specified (CPU by default)", default="CPU", type=str)
52 | parser.add_argument("-nt", "--number_top", help="Number of top results", default=10, type=int)
53 | parser.add_argument("-pc", "--performance", help="Enables per-layer performance report", action='store_true')
54 |
55 | return parser
56 |
57 |
58 | def main_IE_infer():
59 | camera_width = 320
60 | camera_height = 240
61 | m_input_size=513
62 | fps = ""
63 | framepos = 0
64 | frame_count = 0
65 | vidfps = 0
66 | skip_frame = 0
67 | elapsedTime = 0
68 |
69 | args = build_argparser().parse_args()
70 | #model_xml = "lrmodels/PascalVOC/FP32/frozen_inference_graph.xml" #<--- CPU
71 | model_xml = "lrmodels/PascalVOC/FP16/frozen_inference_graph.xml" #<--- MYRIAD
72 | model_bin = os.path.splitext(model_xml)[0] + ".bin"
73 |
74 | seg_image = Image.open("data/input/009649.png")
75 | palette = seg_image.getpalette() # Get a color palette
76 |
77 | cap = cv2.VideoCapture(0)
78 | cap.set(cv2.CAP_PROP_FPS, 10)
79 | cap.set(cv2.CAP_PROP_FRAME_WIDTH, camera_width)
80 | cap.set(cv2.CAP_PROP_FRAME_HEIGHT, camera_height)
81 |
82 | #cap = cv2.VideoCapture("data/input/testvideo.mp4")
83 | #camera_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
84 | #camera_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
85 | #frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
86 | #vidfps = int(cap.get(cv2.CAP_PROP_FPS))
87 | #print("videosFrameCount =", str(frame_count))
88 | #print("videosFPS =", str(vidfps))
89 |
90 | time.sleep(1)
91 |
92 | plugin = IEPlugin(device=args.device, plugin_dirs=args.plugin_dir)
93 | if "CPU" in args.device:
94 | plugin.add_cpu_extension("lib/libcpu_extension.so")
95 | if args.performance:
96 | plugin.set_config({"PERF_COUNT": "YES"})
97 | # Read IR
98 | net = IENetwork(model=model_xml, weights=model_bin)
99 | input_blob = next(iter(net.inputs))
100 | exec_net = plugin.load(network=net)
101 |
102 | while cap.isOpened():
103 | t1 = time.time()
104 |
105 | #cap.set(cv2.CAP_PROP_POS_FRAMES, framepos) # Uncomment only when playing video files
106 |
107 | ret, image = cap.read()
108 | if not ret:
109 | break
110 |
111 | ratio = 1.0 * m_input_size / max(image.shape[0], image.shape[1])
112 | shrink_size = (int(ratio * image.shape[1]), int(ratio * image.shape[0]))
113 | image = cv2.resize(image, shrink_size, interpolation=cv2.INTER_CUBIC)
114 |
115 | prepimg = _pre._pre_process(image)
116 | prepimg = prepimg.transpose((0, 3, 1, 2)) #NHWC to NCHW
117 | res = exec_net.infer(inputs={input_blob: prepimg})
118 | result = _post._post_process(res["ArgMax/Squeeze"], image)[0]
119 |
120 | outputimg = Image.fromarray(np.uint8(result), mode="P")
121 | outputimg.putpalette(palette)
122 | outputimg = outputimg.convert("RGB")
123 | outputimg = np.asarray(outputimg)
124 | outputimg = cv2.cvtColor(outputimg, cv2.COLOR_RGB2BGR)
125 | outputimg = cv2.addWeighted(image, 1.0, outputimg, 0.9, 0)
126 | outputimg = cv2.resize(outputimg, (camera_width, camera_height))
127 |
128 | cv2.putText(outputimg, fps, (camera_width-180,15), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (38,0,255), 1, cv2.LINE_AA)
129 | cv2.imshow("Result", outputimg)
130 |
131 | if cv2.waitKey(1)&0xFF == ord('q'):
132 | break
133 | elapsedTime = time.time() - t1
134 | fps = "(Playback) {:.1f} FPS".format(1/elapsedTime)
135 |
136 | # frame skip, video file only
137 | skip_frame = int((vidfps - int(1/elapsedTime)) / int(1/elapsedTime))
138 | framepos += skip_frame
139 |
140 | cv2.destroyAllWindows()
141 | del net
142 | del exec_net
143 | del plugin
144 |
145 |
146 | if __name__ == '__main__':
147 | sys.exit(main_IE_infer() or 0)
148 |
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/pbmodels/MSCOCOcityscapes/frozen_inference_graph.pb:
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https://raw.githubusercontent.com/PINTO0309/OpenVINO-DeeplabV3/b6e59554524494a4f8a35f49ccfe449982f507b3/pbmodels/MSCOCOcityscapes/frozen_inference_graph.pb
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/pbmodels/MSCOCOcityscapes/inputoutput.txt:
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1 | INPUT_TENSOR_NAME = ImageTensor:0
2 | OUTPUT_TENSOR_NAME = SemanticPredictions:0
3 | INPUT_SIZE = [1,-1,-1,3]
4 | FROZEN_GRAPH_NAME = frozen_inference_graph.pb
5 |
6 |
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/pbmodels/PascalVOC/inputoutput.txt:
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1 | INPUT_TENSOR_NAME = ImageTensor:0
2 | OUTPUT_TENSOR_NAME = SemanticPredictions:0
3 | INPUT_SIZE = [1,513,513,3]
4 | FROZEN_GRAPH_NAME = frozen_inference_graph.pb
5 |
6 |
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https://raw.githubusercontent.com/PINTO0309/OpenVINO-DeeplabV3/b6e59554524494a4f8a35f49ccfe449982f507b3/pbmodels/PascalVOC/model.ckpt-30000.index
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/savedmodel/events.out.tfevents.1546483660.ubuntu:
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https://raw.githubusercontent.com/PINTO0309/OpenVINO-DeeplabV3/b6e59554524494a4f8a35f49ccfe449982f507b3/savedmodel/events.out.tfevents.1546483660.ubuntu
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/tensorboard_output.py:
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1 | import tensorflow as tf
2 | from tensorflow.python.platform import gfile
3 |
4 | with tf.Session() as sess:
5 | model_filename ="pbmodels/PascalVOC/frozen_inference_graph.pb"
6 | with gfile.FastGFile(model_filename, "rb") as f:
7 | graph_def = tf.GraphDef()
8 | graph_def.ParseFromString(f.read())
9 | g_in = tf.import_graph_def(graph_def)
10 |
11 | LOGDIR="savedmodel"
12 | train_writer = tf.summary.FileWriter(LOGDIR)
13 | train_writer.add_graph(sess.graph)
14 |
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/tensorboard_start_script.txt:
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1 | tensorboard --logdir=/home/b920405/git/OpenVINO-DeeplabV3/savedmodel
2 |
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/tfconverter.py:
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1 | import tensorflow as tf
2 | from google.protobuf import text_format
3 | from tensorflow.python.platform import gfile
4 |
5 | def pbtxt_to_graphdef(filename):
6 | with open(filename, 'r') as f:
7 | graph_def = tf.GraphDef()
8 | file_content = f.read()
9 | text_format.Merge(file_content, graph_def)
10 | tf.import_graph_def(graph_def, name='')
11 | tf.train.write_graph(graph_def, './', 'protobuf.pb', as_text=False)
12 |
13 | def graphdef_to_pbtxt(filename):
14 | with gfile.FastGFile(filename,'rb') as f:
15 | graph_def = tf.GraphDef()
16 | graph_def.ParseFromString(f.read())
17 | tf.import_graph_def(graph_def, name='')
18 | tf.train.write_graph(graph_def, './', filename+'txt', as_text=True)
19 | return
20 |
21 |
22 | graphdef_to_pbtxt('pbmodels/PascalVOC/frozen_inference_graph.pb') # here you can write the name of the file to be converted
23 |
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](https://youtu.be/CxxDwK7vBAo)
22 |
23 | **【Result 2】 Click the image below to play Youtube video. (Core m3 + CPU only mode. 4.0FPS - 5.0FPS)**
24 | [
](https://youtu.be/-pXB3dDj-rQ)
25 |
26 | **【Result 3】 Click the image below to play Youtube video. (Core m3 + CPU only mode. 4.0FPS - 5.0FPS)**
27 | [
](https://youtu.be/1NLCr5XnVX8)
28 |
29 | **【Result 4】 Click the image below to play Youtube video. (Core i7 + CPU only mode. 11.0FPS - 12.0FPS)**
30 | [
](https://youtu.be/TjiH2dMltl4)
31 |
32 | # Environment
33 | - LattePanda Alpha (Intel 7th Core m3-7y30) or LaptopPC (Intel 8th Core i7-8750H)
34 | - Ubuntu 16.04 x86_64
35 | - OpenVINO toolkit 2018 R4 (2018.4.420)
36 | - Python 3.5
37 | - OpenCV 3.4.3
38 | - PIL
39 | - Tensorflow v1.11.0 or Tensorflow-GPU v1.11.0 (pip install)
40 | - DeeplabV3 + MobilenetV2 (Pascal VOC 2012)
41 | - USB Camera (PlaystationEye) / Movie file (mp4)
42 | - 【option】 Intel Neural Compute Stick / Intel Neural Compute Stick 2 or GPU
43 |
44 | # Benchmark
45 | **https://ncsforum.movidius.com/discussion/1329/lattepanda-alpha-openvino-cpu-core-m3-vs-ncs1-vs-ncs2-performance-comparison**
46 |
47 | # Usage
48 | ### 1. Installation of OpenVINO main unit
49 | #### 1.1 Download
50 | ```bash
51 | $ cd ~/Downloads
52 | $ curl -sc /tmp/cookie "https://drive.google.com/uc?export=download&id=18-TeUzeN34CV-QqM0rO3wpdEGODTWrBc" > /dev/null
53 | $ CODE="$(awk '/_warning_/ {print $NF}' /tmp/cookie)"
54 | $ curl -Lb /tmp/cookie "https://drive.google.com/uc?export=download&confirm=${CODE}&id=18-TeUzeN34CV-QqM0rO3wpdEGODTWrBc" -o l_openvino_toolkit_p_2018.4.420.tgz
55 | $ tar -zxf l_openvino_toolkit_p_2018.4.420.tgz
56 | $ rm l_openvino_toolkit_p_2018.4.420.tgz
57 | $ cd l_openvino_toolkit_p_2018.4.420
58 | ```
59 | #### 1.2 Install basic functions
60 | ```bash
61 | ## GUI version installer
62 | $ sudo ./install_GUI.sh
63 | or
64 | ## CUI version installer
65 | $ sudo ./install.sh
66 | ```
67 | 
68 | 
69 | 
70 |
71 | ```bash
72 | $ cd /opt/intel/computer_vision_sdk/install_dependencies
73 | $ sudo -E ./install_cv_sdk_dependencies.sh
74 | $ nano ~/.bashrc
75 | source /opt/intel/computer_vision_sdk/bin/setupvars.sh
76 |
77 | $ source ~/.bashrc
78 | $ cd /opt/intel/computer_vision_sdk/deployment_tools/model_optimizer/install_prerequisites
79 | $ sudo ./install_prerequisites.sh
80 | ```
81 | #### 1.3 Install optional features
82 | ##### 1.3.1 【Optional execution】 Additional installation steps for the Intel® Movidius™ Neural Compute Stick v1 and Intel® Neural Compute Stick v2
83 | ```bash
84 | $ sudo usermod -a -G users "$(whoami)"
85 | $ cat <