├── LICENSE ├── README.md ├── detect_tf.py ├── detect_trt.py ├── resources_tf ├── data │ └── object-detection.pbtxt └── fine_tuned_model │ ├── checkpoint │ ├── frozen_inference_graph.pb │ ├── model.ckpt.data-00000-of-00001 │ ├── model.ckpt.index │ ├── model.ckpt.meta │ ├── pipeline.config │ └── saved_model │ └── saved_model.pb ├── resources_trt ├── frozen_graph_to_trt.py ├── pb_to_pb_trt_transfere.py ├── read.me └── trt_graph.pb ├── result_output_detect_tf.out ├── result_output_detect_trt.out └── test └── images ├── 85.jpg ├── 86.jpg ├── 87.jpg ├── 88.jpg ├── 89.jpg ├── ddeteccted_result.png └── result.png /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # JetsonNanoInsulatorDetection 2 | Detection insulator with ssd_mobilenet_v1 custom trained network. 3 | *** 4 | After all steps of first steps with NVidia Jetson Nano development board (link - https://developer.nvidia.com/embedded/learn/get-started-jetson-nano-devkit) we need to make all requirement installation for tensorflow inference tests with custom ssd_mobilenet_v1 network (trained to detect insulators of power supply substation). 5 | *** 6 | 1. Installing base dependancies and pip 7 | ``` 8 | sudo apt-get update 9 | sudo apt-get install git cmake 10 | sudo su 11 | apt-get install libatlas-base-dev gfortran 12 | apt-get install libhdf5-serial-dev hdf5-tools 13 | sudo apt-get install python3-dev 14 | 15 | wget https://bootstrap.pypa.io/get-pip.py 16 | sudo python3 get-pip.py 17 | sudo apt install python3-testresources 18 | sudo rm get-pip.py 19 | ``` 20 | 2.tensorflow and others 21 | ``` 22 | sudo pip install numpy 23 | sudo pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/jp/v42 tensorflow-gpu==1.13.1+nv19.3 24 | 25 | sudo pip install scipy 26 | sudo pip install keras 27 | ``` 28 | this libraries need good internet connection and time for installlation ( from 15 to 40 minutes)

29 | 30 | Now any of pre-trained Keras and tensorflow models can run on Jetson Nano board. 31 | *** 32 | 2. installing pretrained models for TensorRT from https://github.com/NVIDIA-AI-IOT/tf_trt_models 33 | ``` 34 | cd ~ 35 | git clone --recursive https://github.com/NVIDIA-Jetson/tf_trt_models.git 36 | cd tf_trt_models 37 | ./install.sh python3 38 | ``` 39 | You have all installed TensorRT models in /tf_trt_models and also in /tf_trt_models/third_party/models all Tensorflow models zoo (https://github.com/tensorflow/models) 40 | 41 | Directory and file structure 42 | - resources_tf - tensorflow trained graph for Insulator detection. 43 | this frozen model transferred to TensorRT graph inside 44 | - resourses_trt directory. Also tis directory has python scripts to prepare TensorRT graph from Tensorflow graph 45 | -frozen_graph_to_trt.py - script to transfere TF frozen graph to TRT 46 | -pb_to_pb_trt_transfere.py - also the script with othe approach to prepare TRT frozen graph 47 | -trt_graph.pb - frozen TRT graph 48 | - test/images - directory for testing images 49 | 50 | -detect_tf.py - script to test tensorflow speed of Inference 51 | -detect_trt.py - script to test TensorRT speed 52 | 53 | *** 54 | results 55 | *** 56 | Testing with tensorflow frozen graph give about 0.07sec per one image (~15FPS) 57 | Testing with TensorRT frozen graph give about 0.09 sec per image (~11FPS) 58 | Loading of TF program to memory - about 15 seconds 59 | Loading TRT program for execution about 200 seconds 60 | 61 | some problems with TRT approach is could be the training of base tensorflow graph which is used for TRT have done with different sized images ( not 224x224 or 300x300 ) - but thisis not definitely - need to understand later 62 | 63 | also recognition of TensorRT graph have bad results with some amount of insulators at the picture and small insulators... 64 | 65 | 66 | example 67 | ![insulators detected](test/images/result.png) 68 | 69 | Update! 70 | with this approach https://github.com/NVIDIA-AI-IOT/tf_trt_models/blob/master/examples/detection/detection.ipynb 71 | I have recieved better result (about 20fps) with TensorRT library.... 72 | ![insulator detected](test/images/ddeteccted_result.png) 73 | -------------------------------------------------------------------------------- /detect_tf.py: -------------------------------------------------------------------------------- 1 | #detect and classify image program test for jetson nano 2 | #python PIL realisation and direct tensorflow graph not TensorRT optimized 3 | #2019-05-25 4 | #checking for library installed with tf_trt_models installation 5 | 6 | import numpy as np 7 | import os 8 | import sys 9 | import tensorflow as tf 10 | # with PIL time to process1 image is about 0,25 sec 11 | #from PIL import Image # choose library for image processing 12 | # with cv2 time to process 1 image is about 0,07 sec 13 | import cv2 # coose which library will process image 14 | import base64 15 | import time 16 | 17 | ################################################################################ 18 | #need to add object detection libraries and code downloaded From 19 | #https://github.com/tensorflow/models and stored in /home/jnano/ directory 20 | # this is important if not installed tt_trt_models with install.sh script 21 | #because it insltall all PATHs with installation and tensorflow models located in 22 | #tf_trt_models/third_party/models directory 23 | ################################################################################ 24 | #sys.path.append('/home/jnano/tf_trt_models/third_party/models/research/') 25 | #sys.path.append('/home/jnano/tf_trt_models/third_part/models/research/slim/') 26 | 27 | from object_detection.utils import ops as utils_ops 28 | from object_detection.utils import label_map_util 29 | from object_detection.utils import visualization_utils as vis_util 30 | 31 | ################################################################################ 32 | #load image to numpy array procedure for wirking with PIL image processing 33 | ################################################################################ 34 | def load_image_into_numpy_array(image): 35 | (im_width, im_height) = image.size 36 | return np.array(image.getdata()).reshape( 37 | (im_height, im_width, 3)).astype(np.uint8) 38 | 39 | ################################################################################ 40 | #MAIN procedure 41 | ################################################################################ 42 | # start timing of processes 43 | time_start = time.time() 44 | 45 | # Path to frozen detection graph. This is the actual model that is used for the object detection. 46 | PATH_TO_CKPT = './resources_tf/fine_tuned_model' + '/frozen_inference_graph.pb' 47 | # List of the strings that is used to add correct label for each box. 48 | PATH_TO_LABELS = os.path.join('./resources_tf/data/', 'object-detection.pbtxt') 49 | #number ofclassesfor classification 50 | NUM_CLASSES = 1 51 | 52 | #for local testing 53 | # If you want to test the code with your images, just add path to the images to the TEST_IMAGE_PATHS. 54 | PATH_TO_TEST_IMAGES_DIR = './test/images/' 55 | TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, '8{}.jpg'.format(i)) for i in range(5, 10) ] 56 | 57 | 58 | 59 | # init graph 60 | detection_graph = tf.Graph() 61 | with detection_graph.as_default(): 62 | od_graph_def = tf.GraphDef() 63 | with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid: 64 | serialized_graph = fid.read() 65 | od_graph_def.ParseFromString(serialized_graph) 66 | tf.import_graph_def(od_graph_def, name='') 67 | 68 | #creat labels 69 | label_map = label_map_util.load_labelmap(PATH_TO_LABELS) 70 | categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True) 71 | category_index = label_map_util.create_category_index(categories) 72 | 73 | #print(categories) 74 | # return [{'name': 'insulator', 'id': 1}] 75 | #print(category_index) 76 | #return {1: {'name': 'insulator', 'id': 1}} 77 | 78 | #init graph all the vars 79 | with detection_graph.as_default(): 80 | with tf.Session() as sess: 81 | # Get handles to input and output tensors 82 | ops = tf.get_default_graph().get_operations() 83 | all_tensor_names = {output.name for op in ops for output in op.outputs} 84 | tensor_dict = {} 85 | for key in [ 86 | 'num_detections', 'detection_boxes', 'detection_scores', 87 | 'detection_classes', 'detection_masks' 88 | ]: 89 | tensor_name = key + ':0' 90 | if tensor_name in all_tensor_names: 91 | tensor_dict[key] = tf.get_default_graph().get_tensor_by_name( 92 | tensor_name) 93 | if 'detection_masks' in tensor_dict: 94 | # The following processing is only for single image 95 | detection_boxes = tf.squeeze(tensor_dict['detection_boxes'], [0]) 96 | detection_masks = tf.squeeze(tensor_dict['detection_masks'], [0]) 97 | # Reframe is required to translate mask from box coordinates to image coordinates and fit the image size. 98 | real_num_detection = tf.cast(tensor_dict['num_detections'][0], tf.int32) 99 | detection_boxes = tf.slice(detection_boxes, [0, 0], [real_num_detection, -1]) 100 | detection_masks = tf.slice(detection_masks, [0, 0, 0], [real_num_detection, -1, -1]) 101 | detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks( 102 | detection_masks, detection_boxes, image.shape[0], image.shape[1]) 103 | detection_masks_reframed = tf.cast( 104 | tf.greater(detection_masks_reframed, 0.5), tf.uint8) 105 | # Follow the convention by adding back the batch dimension 106 | tensor_dict['detection_masks'] = tf.expand_dims( 107 | detection_masks_reframed, 0) 108 | image_tensor = tf.get_default_graph().get_tensor_by_name('image_tensor:0') 109 | 110 | print(time.time()-time_start) 111 | 112 | for counter_ in range(0,10): # average detection time 0.067 per one image ~ 15FPS 113 | for image_path in TEST_IMAGE_PATHS: 114 | #this part is for PIL image processing 115 | """ 116 | image = Image.open(image_path) 117 | # the array based representation of the image will be used later in order to prepare the 118 | # result image with boxes and labels on it. 119 | image_np = load_image_into_numpy_array(image) 120 | #end of part for PIL image processing 121 | """ 122 | #this part is for cv2 image processing 123 | 124 | in_file = open(image_path, "rb") 125 | data = in_file.read() 126 | in_file.close() 127 | encoded_string = base64.standard_b64encode(data) 128 | file_string = str(encoded_string, 'ascii', 'ignore') 129 | 130 | file_bytes = np.asarray(bytearray(base64.b64decode(file_string)), dtype=np.uint8) 131 | image_ = cv2.imdecode(file_bytes, 1) 132 | #convertcolor from BGR to RGB 133 | image_np = cv2.cvtColor(image_, 4) 134 | 135 | # Actual detection. 136 | time_cycle = time.time() 137 | 138 | # Run inference 139 | output_dict = sess.run(tensor_dict, 140 | feed_dict={image_tensor: np.expand_dims(image_np, 0)}) 141 | 142 | # all outputs are float32 numpy arrays, so convert types as appropriate 143 | output_dict['num_detections'] = int(output_dict['num_detections'][0]) 144 | output_dict['detection_classes'] = output_dict[ 145 | 'detection_classes'][0].astype(np.uint8) 146 | output_dict['detection_boxes'] = output_dict['detection_boxes'][0] 147 | output_dict['detection_scores'] = output_dict['detection_scores'][0] 148 | if 'detection_masks' in output_dict: 149 | output_dict['detection_masks'] = output_dict['detection_masks'][0] 150 | print('time of inference:'+str(time.time() - time_cycle)) 151 | print("detected "+str(output_dict['num_detections'])) 152 | #print(output_dict['detection_boxes']) 153 | #print(output_dict['detection_classes']) 154 | #print(output_dict['detection_scores']) 155 | #print(output_dict['detection_classes']) 156 | #print(output_dict.get('detection_masks')) 157 | 158 | # Visualization of the results of a detection. 159 | """ 160 | vis_util.visualize_boxes_and_labels_on_image_array( 161 | image_np, 162 | output_dict['detection_boxes'], 163 | output_dict['detection_classes'], 164 | output_dict['detection_scores'], 165 | category_index, 166 | instance_masks=output_dict.get('detection_masks'), 167 | use_normalized_coordinates=True, 168 | line_thickness=3) 169 | 170 | vis_util.save_image_array_as_png(image_np,'./test/images/result.png') 171 | #make result png string from image numpy array 172 | #result_string = vis_util.encode_image_array_as_png_str(image_np) 173 | #print(result_string) 174 | """ 175 | 176 | print('thats all folks') 177 | #sys.exit(0) 178 | -------------------------------------------------------------------------------- /detect_trt.py: -------------------------------------------------------------------------------- 1 | #2019-05-19 for detection and classification with TensorRT optimized graph (directory resources_trt) 2 | #need specially prepared graph with pb_to_pb_trt_transfere.py script 3 | #very slow loaded to memory ( about 3 min) 4 | import tensorflow as tf 5 | import cv2 6 | import os 7 | import time 8 | 9 | init_time = time.time() 10 | 11 | def get_frozen_graph(graph_file): 12 | """Read Frozen Graph file from disk.""" 13 | with tf.gfile.GFile(graph_file, "rb") as f: 14 | graph_def = tf.GraphDef() 15 | graph_def.ParseFromString(f.read()) 16 | return graph_def 17 | 18 | 19 | PATH_TO_TEST_IMAGES_DIR = './test/images/' 20 | TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, '8{}.jpg'.format(i)) for i in range(5, 10) ] 21 | 22 | # The TensorRT frozen inference graph 23 | pb_fname = "./resources_trt/trt_graph.pb" 24 | trt_graph = get_frozen_graph(pb_fname) 25 | 26 | input_names = ['image_tensor'] 27 | 28 | # Create session and load graph 29 | tf_config = tf.ConfigProto() 30 | tf_config.gpu_options.allow_growth = True 31 | tf_sess = tf.Session(config=tf_config) 32 | tf.import_graph_def(trt_graph, name='') 33 | 34 | tf_input = tf_sess.graph.get_tensor_by_name(input_names[0] + ':0') 35 | tf_scores = tf_sess.graph.get_tensor_by_name('detection_scores:0') 36 | tf_boxes = tf_sess.graph.get_tensor_by_name('detection_boxes:0') 37 | tf_classes = tf_sess.graph.get_tensor_by_name('detection_classes:0') 38 | tf_num_detections = tf_sess.graph.get_tensor_by_name('num_detections:0') 39 | 40 | print('time of loading:'+str(time.time()-init_time)) 41 | 42 | for counter_ in range(0,10): # average detection time 0.087 per one image ~ 11FPS 43 | for image_path in TEST_IMAGE_PATHS: 44 | cycle_time = time.time() 45 | image = cv2.imread(image_path) 46 | #image = cv2.resize(image, (300, 300)) 47 | 48 | scores, boxes, classes, num_detections = tf_sess.run([tf_scores, tf_boxes, tf_classes, tf_num_detections], feed_dict={ 49 | tf_input: image[None, ...] 50 | }) 51 | boxes = boxes[0] # index by 0 to remove batch dimension 52 | scores = scores[0] 53 | classes = classes[0] 54 | num_detections = int(num_detections[0]) 55 | 56 | print('time of inference:'+str(time.time()-cycle_time)) 57 | print("detected " + str(num_detections)) 58 | -------------------------------------------------------------------------------- /resources_tf/data/object-detection.pbtxt: -------------------------------------------------------------------------------- 1 | item { 2 | id: 1 3 | name: 'insulator' 4 | } 5 | -------------------------------------------------------------------------------- /resources_tf/fine_tuned_model/checkpoint: -------------------------------------------------------------------------------- 1 | model_checkpoint_path: "model.ckpt" 2 | all_model_checkpoint_paths: "model.ckpt" 3 | -------------------------------------------------------------------------------- /resources_tf/fine_tuned_model/frozen_inference_graph.pb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/toborobot/JetsonNanoInsulatorDetection/33213261a40de098b9ec5f4507e4cc47ed079c26/resources_tf/fine_tuned_model/frozen_inference_graph.pb -------------------------------------------------------------------------------- /resources_tf/fine_tuned_model/model.ckpt.data-00000-of-00001: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/toborobot/JetsonNanoInsulatorDetection/33213261a40de098b9ec5f4507e4cc47ed079c26/resources_tf/fine_tuned_model/model.ckpt.data-00000-of-00001 -------------------------------------------------------------------------------- /resources_tf/fine_tuned_model/model.ckpt.index: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/toborobot/JetsonNanoInsulatorDetection/33213261a40de098b9ec5f4507e4cc47ed079c26/resources_tf/fine_tuned_model/model.ckpt.index -------------------------------------------------------------------------------- /resources_tf/fine_tuned_model/model.ckpt.meta: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/toborobot/JetsonNanoInsulatorDetection/33213261a40de098b9ec5f4507e4cc47ed079c26/resources_tf/fine_tuned_model/model.ckpt.meta -------------------------------------------------------------------------------- /resources_tf/fine_tuned_model/pipeline.config: -------------------------------------------------------------------------------- 1 | model { 2 | ssd { 3 | num_classes: 1 4 | image_resizer { 5 | fixed_shape_resizer { 6 | height: 300 7 | width: 300 8 | } 9 | } 10 | feature_extractor { 11 | type: "ssd_mobilenet_v1" 12 | depth_multiplier: 1.0 13 | min_depth: 16 14 | conv_hyperparams { 15 | regularizer { 16 | l2_regularizer { 17 | weight: 3.9999998989515007e-05 18 | } 19 | } 20 | initializer { 21 | truncated_normal_initializer { 22 | mean: 0.0 23 | stddev: 0.029999999329447746 24 | } 25 | } 26 | activation: RELU_6 27 | batch_norm { 28 | decay: 0.9997000098228455 29 | center: true 30 | scale: true 31 | epsilon: 0.0010000000474974513 32 | train: true 33 | } 34 | } 35 | } 36 | box_coder { 37 | faster_rcnn_box_coder { 38 | y_scale: 10.0 39 | x_scale: 10.0 40 | height_scale: 5.0 41 | width_scale: 5.0 42 | } 43 | } 44 | matcher { 45 | argmax_matcher { 46 | matched_threshold: 0.5 47 | unmatched_threshold: 0.5 48 | ignore_thresholds: false 49 | negatives_lower_than_unmatched: true 50 | force_match_for_each_row: true 51 | } 52 | } 53 | similarity_calculator { 54 | iou_similarity { 55 | } 56 | } 57 | box_predictor { 58 | convolutional_box_predictor { 59 | conv_hyperparams { 60 | regularizer { 61 | l2_regularizer { 62 | weight: 3.9999998989515007e-05 63 | } 64 | } 65 | initializer { 66 | truncated_normal_initializer { 67 | mean: 0.0 68 | stddev: 0.029999999329447746 69 | } 70 | } 71 | activation: RELU_6 72 | batch_norm { 73 | decay: 0.9997000098228455 74 | center: true 75 | scale: true 76 | epsilon: 0.0010000000474974513 77 | train: true 78 | } 79 | } 80 | min_depth: 0 81 | max_depth: 0 82 | num_layers_before_predictor: 0 83 | use_dropout: false 84 | dropout_keep_probability: 0.800000011920929 85 | kernel_size: 1 86 | box_code_size: 4 87 | apply_sigmoid_to_scores: false 88 | } 89 | } 90 | anchor_generator { 91 | ssd_anchor_generator { 92 | num_layers: 6 93 | min_scale: 0.20000000298023224 94 | max_scale: 0.949999988079071 95 | aspect_ratios: 1.0 96 | aspect_ratios: 2.0 97 | aspect_ratios: 0.5 98 | aspect_ratios: 3.0 99 | aspect_ratios: 0.33329999446868896 100 | } 101 | } 102 | post_processing { 103 | batch_non_max_suppression { 104 | score_threshold: 0.30000001192092896 105 | iou_threshold: 0.6000000238418579 106 | max_detections_per_class: 100 107 | max_total_detections: 100 108 | } 109 | score_converter: SIGMOID 110 | } 111 | normalize_loss_by_num_matches: true 112 | loss { 113 | localization_loss { 114 | weighted_smooth_l1 { 115 | } 116 | } 117 | classification_loss { 118 | weighted_sigmoid { 119 | } 120 | } 121 | hard_example_miner { 122 | num_hard_examples: 3000 123 | iou_threshold: 0.9900000095367432 124 | loss_type: CLASSIFICATION 125 | max_negatives_per_positive: 3 126 | min_negatives_per_image: 0 127 | } 128 | classification_weight: 1.0 129 | localization_weight: 1.0 130 | } 131 | } 132 | } 133 | train_config { 134 | batch_size: 24 135 | data_augmentation_options { 136 | random_horizontal_flip { 137 | } 138 | } 139 | data_augmentation_options { 140 | ssd_random_crop { 141 | } 142 | } 143 | optimizer { 144 | rms_prop_optimizer { 145 | learning_rate { 146 | exponential_decay_learning_rate { 147 | initial_learning_rate: 0.004000000189989805 148 | decay_steps: 800720 149 | decay_factor: 0.949999988079071 150 | } 151 | } 152 | momentum_optimizer_value: 0.8999999761581421 153 | decay: 0.8999999761581421 154 | epsilon: 1.0 155 | } 156 | } 157 | fine_tune_checkpoint: "/content/data_insulators/models/ssd_mobilenet_v1_coco/model.ckpt" 158 | from_detection_checkpoint: true 159 | num_steps: 200000 160 | } 161 | train_input_reader { 162 | label_map_path: "/content/data_insulators/data/object-detection.pbtxt" 163 | tf_record_input_reader { 164 | input_path: "/content/data_insulators/data/train.record" 165 | } 166 | } 167 | eval_config { 168 | num_examples: 8000 169 | max_evals: 10 170 | use_moving_averages: false 171 | } 172 | eval_input_reader { 173 | label_map_path: "/content/data_insulators/data/object-detection.pbtxt" 174 | shuffle: false 175 | num_readers: 1 176 | tf_record_input_reader { 177 | input_path: "/content/data_insulators/data/test.record" 178 | } 179 | } 180 | -------------------------------------------------------------------------------- /resources_tf/fine_tuned_model/saved_model/saved_model.pb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/toborobot/JetsonNanoInsulatorDetection/33213261a40de098b9ec5f4507e4cc47ed079c26/resources_tf/fine_tuned_model/saved_model/saved_model.pb -------------------------------------------------------------------------------- /resources_trt/frozen_graph_to_trt.py: -------------------------------------------------------------------------------- 1 | # Import TensorFlow and TensorRT 2 | # from https://docs.nvidia.com/deeplearning/frameworks/tf-trt-user-guide/index.html 3 | import tensorflow as tf 4 | import tensorflow.contrib.tensorrt as trt 5 | # Inference with TF-TRT frozen graph workflow: 6 | 7 | frozen_graph_path = './resources_tf/fine_tuned_model/frozen_inference_graph.pb' 8 | output_names = ['num_detections','detection_classes','detection_boxes','detection_scores'] 9 | 10 | graph = tf.Graph() 11 | with graph.as_default(): 12 | with tf.Session() as sess: 13 | # First deserialize your frozen graph: 14 | with tf.gfile.GFile(frozen_graph_path, 'rb') as f: 15 | graph_def = tf.GraphDef() 16 | graph_def.ParseFromString(f.read()) 17 | # Now you can create a TensorRT inference graph from your 18 | # frozen graph: 19 | trt_graph = trt.create_inference_graph( 20 | input_graph_def=graph_def, 21 | outputs=output_names, 22 | max_batch_size=1, 23 | max_workspace_size_bytes=1<<25, 24 | precision_mode='FP16') 25 | # Import the TensorRT graph into a new graph and run: 26 | with open('/home/jnano/tf/prog10/resources_trt/trt_graph.pb', 'wb') as f: 27 | f.write(trt_graph.SerializeToString()) 28 | -------------------------------------------------------------------------------- /resources_trt/pb_to_pb_trt_transfere.py: -------------------------------------------------------------------------------- 1 | import tensorflow.contrib.tensorrt as trt 2 | import tensorflow as tf 3 | import os 4 | #from tf_trt_models.detection import build_detection_graph 5 | 6 | ################################################################################ 7 | #need to add object detection libraries and code downloaded From 8 | #https://github.com/tensorflow/models and stored in /home/jnano/ directory 9 | ################################################################################ 10 | #sys.path.append('/home/jnano/tf/models/research/') 11 | #sys.path.append('/home/jnano/tf/models/research/slim/') 12 | config_path = './resources_tf/fine_tuned_model/pipeline.config' 13 | checkpoint_path = './resources_tf/fine_tuned_model/model.ckpt' 14 | frozen_graph_path = './resources_tf/fine_tuned_model' 15 | output_names = ['num_detections','detection_classes','detection_boxes','detection_scores'] 16 | """ 17 | frozen_graph, input_names, output_names = build_detection_graph( 18 | config=config_path, # path to model’s pipeline.config file 19 | checkpoint=checkpoint_path, # path to model.ckpt file 20 | score_threshold=0.3, 21 | #iou_threshold=0.5, 22 | batch_size=1 23 | ) 24 | """ 25 | # init graph 26 | # read frozen graph from file 27 | frozen_graph = tf.GraphDef() 28 | with open(os.path.join(frozen_graph_path, 'frozen_inference_graph.pb'), 'rb') as f: 29 | frozen_graph.ParseFromString(f.read()) 30 | 31 | """ 32 | link https://docs.nvidia.com/deeplearning/frameworks/tf-trt-user-guide/index.html 33 | 34 | def create_inference_graph(input_graph_def, 35 | outputs, 36 | max_batch_size=1, 37 | max_workspace_size_bytes=2 << 20, 38 | precision_mode="fp32", 39 | minimum_segment_size=3, 40 | is_dynamic_op=False, 41 | maximum_cached_engines=1, 42 | cached_engine_batch_sizes=None 43 | use_calibration=True, 44 | rewriter_config=None, 45 | input_saved_model_dir=None, 46 | input_saved_model_tags=None, 47 | output_saved_model_dir=None, 48 | session_config=None): 49 | 50 | Where: 51 | -input_graph_def 52 | This parameter is the GraphDef object that contains the model to be transformed. 53 | -outputs 54 | This parameter lists the output nodes in the graph. Tensors which are not marked 55 | as outputs are considered to be transient values that may be optimized away by 56 | the builder. 57 | -max_batch_size 58 | This parameter is the maximum batch size that specifies the batch size for which 59 | TensorRT will optimize. At runtime, a smaller batch size may be chosen. At runtime, 60 | larger batch size is not supported. 61 | -max_workspace_size_bytes 62 | TensorRT operators often require temporary workspace. This parameter limits the 63 | maximum size that any layer in the network can use. If insufficient scratch is 64 | provided, it is possible that TensorRT may not be able to find an implementation 65 | for a given layer. 66 | -precision_mode 67 | TF-TRT only supports models trained in FP32, in other words all the weights of 68 | the model should be stored in FP32 precision. That being said, TensorRT can 69 | convert tensors and weights to lower precisions during the optimization. 70 | The precision_mode parameter sets the precision mode; which can be one of 71 | fp32, fp16, or int8. Precision lower than FP32, meaning FP16 and INT8, would 72 | improve the performance of inference. The FP16 mode uses Tensor Cores or half 73 | precision hardware instructions, if possible. The INT8 precision mode uses 74 | integer hardware instructions. 75 | -minimum_segment_size 76 | This parameter determines the minimum number of TensorFlow nodes in a TensorRT 77 | engine, which means the TensorFlow subgraphs that have fewer nodes than this 78 | number will not be converted to TensorRT. Therefore, in general smaller numbers 79 | such as 5 are preferred. This can also be used to change the minimum number of 80 | nodes in the optimized INT8 engines to change the final optimized graph to 81 | fine tune result accuracy. 82 | -is_dynamic_op 83 | If this parameter is set to True, the conversion and building the TensorRT 84 | engines will happen during the runtime, which would be necessary if there are 85 | tensors in the graphs with unknown initial shapes or dynamic shapes. For more 86 | information, see index.html#static-dynamic-mode. 87 | Note: Conversion during runtime may increase the latency, depending on your 88 | model and how you use it. 89 | -maximum_cached_engines 90 | In dynamic mode, this sets the maximum number of cached TensorRT engines per 91 | TRTEngineOp. For more information, see index.html#static-dynamic-mode. 92 | -cached_engine_batch_sizes 93 | The list of batch sizes used to create cached engines, only used when 94 | is_dynamic_op is True. The length of the list should be smaller than 95 | maximum_cached_engines, and the dynamic TensorRT op will use this list to 96 | determine the batch sizes of the cached engines, instead of making the decision 97 | while in progress. This is useful when we know the most common batch size(s) 98 | the application is going to generate. 99 | -cached_engine_batches 100 | The batch sizes used to pre-create cached engines. 101 | -use_calibration 102 | This argument is ignored if precision_mode is not INT8. 103 | If set to True, a calibration graph will be created to calibrate the missing 104 | ranges. The calibration graph must be converted to an inference graph using 105 | calib_graph_to_infer_graph() after running calibration. 106 | If set to False, quantization nodes will be expected for every tensor in the 107 | graph (excluding those which will be fused). If a range is missing, an error will occur. 108 | Note: Accuracy may be negatively affected if there is a mismatch between which 109 | tensors TensorRT quantizes and which tensors were trained with fake quantization. 110 | -rewriter_config 111 | A RewriterConfig proto to append the TensorRTOptimizer to. If None, it will 112 | create one with default settings. 113 | -input_saved_model_dir 114 | The directory to load the SavedModel containing the input graph to transform. 115 | Used only when input_graph_def is None. 116 | -input_saved_model_tags 117 | A list of tags used to identify the MetaGraphDef of the SavedModel to load. 118 | -output_saved_model_dir 119 | If not None, construct a SavedModel using the returned GraphDef and save it to 120 | the specified directory. This option only works when the input graph is loaded 121 | from a SavedModel, in other words, when input_saved_model_dir is specified and 122 | input_graph_def is None. 123 | -session_config 124 | The ConfigProto used to create a Session. If not specified, a default ConfigProto 125 | will be used. 126 | 127 | Returns: 128 | New GraphDef with TRTEngineOps placed in graph replacing subgraphs. 129 | Raises: 130 | ValueError: If the provided precision mode is invalid. 131 | RuntimeError: If the returned status message is malformed. 132 | """ 133 | 134 | trt_graph = trt.create_inference_graph( 135 | input_graph_def=frozen_graph, 136 | outputs=output_names, 137 | max_batch_size=1, 138 | max_workspace_size_bytes=1<<25, 139 | precision_mode='FP16', 140 | minimum_segment_size=5) 141 | 142 | with open('/home/jnano/tf/prog10/resources_trt/trt_graph.pb', 'wb') as f: 143 | f.write(trt_graph.SerializeToString()) 144 | -------------------------------------------------------------------------------- /resources_trt/read.me: -------------------------------------------------------------------------------- 1 | #read me file for prepare TensorflowRT model from tensorflow frozen graph 2 | 3 | #article here 4 | https://www.dlology.com/blog/how-to-run-tensorflow-object-detection-model-on-jetson-nano/ 5 | 6 | #first need to clone model from official nvidia github 7 | cd ~ 8 | git clone --recursive https://github.com/NVIDIA-Jetson/tf_trt_models.git 9 | cd tf_trt_models 10 | ./install.sh python3 11 | при этом в директорию tf_trt_models устанавливаются модели tensorflow 12 | 13 | 14 | #create directory resources_trt 15 | #make program for transfere fromtensorflow to trt pb 16 | 17 | #link for tensorflow to tensorrt 18 | https://github.com/NVIDIA-AI-IOT/tf_to_trt_image_classification 19 | -------------------------------------------------------------------------------- /resources_trt/trt_graph.pb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/toborobot/JetsonNanoInsulatorDetection/33213261a40de098b9ec5f4507e4cc47ed079c26/resources_trt/trt_graph.pb -------------------------------------------------------------------------------- /result_output_detect_tf.out: -------------------------------------------------------------------------------- 1 | python3 detect_tf.py 2 | 2019-05-25 20:36:50.318603: W tensorflow/core/platform/profile_utils/cpu_utils.cc:98] Failed to find bogomips in /proc/cpuinfo; cannot determine CPU frequency 3 | 2019-05-25 20:36:50.319259: I tensorflow/compiler/xla/service/service.cc:161] XLA service 0x2c88cd40 executing computations on platform Host. Devices: 4 | 2019-05-25 20:36:50.319322: I tensorflow/compiler/xla/service/service.cc:168] StreamExecutor device (0): , 5 | 2019-05-25 20:36:50.431621: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:965] ARM64 does not support NUMA - returning NUMA node zero 6 | 2019-05-25 20:36:50.432253: I tensorflow/compiler/xla/service/service.cc:161] XLA service 0x242e08f0 executing computations on platform CUDA. Devices: 7 | 2019-05-25 20:36:50.432329: I tensorflow/compiler/xla/service/service.cc:168] StreamExecutor device (0): NVIDIA Tegra X1, Compute Capability 5.3 8 | 2019-05-25 20:36:50.432750: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 0 with properties: 9 | name: NVIDIA Tegra X1 major: 5 minor: 3 memoryClockRate(GHz): 0.9216 10 | pciBusID: 0000:00:00.0 11 | totalMemory: 3.87GiB freeMemory: 1.51GiB 12 | 2019-05-25 20:36:50.432829: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0 13 | 2019-05-25 20:36:51.634978: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix: 14 | 2019-05-25 20:36:51.635065: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990] 0 15 | 2019-05-25 20:36:51.635104: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0: N 16 | 2019-05-25 20:36:51.635286: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 1147 MB memory) -> physical GPU (device: 0, name: NVIDIA Tegra X1, pci bus id: 0000:00:00.0, compute capability: 5.3) 17 | 6.787420272827148 18 | 2019-05-25 20:37:06.781820: W tensorflow/core/common_runtime/bfc_allocator.cc:211] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.05GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 19 | 2019-05-25 20:37:07.483913: W tensorflow/core/common_runtime/bfc_allocator.cc:211] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.07GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 20 | 2019-05-25 20:37:07.938149: W tensorflow/core/common_runtime/bfc_allocator.cc:211] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.13GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 21 | 2019-05-25 20:37:08.132305: W tensorflow/core/common_runtime/bfc_allocator.cc:211] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.13GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 22 | 2019-05-25 20:37:08.185010: W tensorflow/core/common_runtime/bfc_allocator.cc:211] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.26GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 23 | time of inference:18.9060161113739 24 | detected 5 25 | time of inference:0.32711172103881836 26 | detected 2 27 | time of inference:0.11663103103637695 28 | detected 2 29 | time of inference:0.10812783241271973 30 | detected 2 31 | time of inference:0.10611200332641602 32 | detected 2 33 | thats all folks 34 | -------------------------------------------------------------------------------- /result_output_detect_trt.out: -------------------------------------------------------------------------------- 1 | python3 detect_trt.py 2 | 2019-05-25 20:23:16.170712: W tensorflow/core/platform/profile_utils/cpu_utils.cc:98] Failed to find bogomips in /proc/cpuinfo; cannot determine CPU frequency 3 | 2019-05-25 20:23:16.171777: I tensorflow/compiler/xla/service/service.cc:161] XLA service 0x1902fce0 executing computations on platform Host. Devices: 4 | 2019-05-25 20:23:16.172430: I tensorflow/compiler/xla/service/service.cc:168] StreamExecutor device (0): , 5 | 2019-05-25 20:23:16.283447: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:965] ARM64 does not support NUMA - returning NUMA node zero 6 | 2019-05-25 20:23:16.283737: I tensorflow/compiler/xla/service/service.cc:161] XLA service 0x14c17c70 executing computations on platform CUDA. Devices: 7 | 2019-05-25 20:23:16.283795: I tensorflow/compiler/xla/service/service.cc:168] StreamExecutor device (0): NVIDIA Tegra X1, Compute Capability 5.3 8 | 2019-05-25 20:23:16.284193: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 0 with properties: 9 | name: NVIDIA Tegra X1 major: 5 minor: 3 memoryClockRate(GHz): 0.9216 10 | pciBusID: 0000:00:00.0 11 | totalMemory: 3.87GiB freeMemory: 1.41GiB 12 | 2019-05-25 20:23:16.284264: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0 13 | 2019-05-25 20:23:21.062719: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix: 14 | 2019-05-25 20:23:21.062799: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990] 0 15 | 2019-05-25 20:23:21.062836: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0: N 16 | 2019-05-25 20:23:21.063019: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 721 MB memory) -> physical GPU (device: 0, name: NVIDIA Tegra X1, pci bus id: 0000:00:00.0, compute capability: 5.3) 17 | time of loading:199.7234411239624 18 | 2019-05-25 20:24:02.881493: W tensorflow/core/common_runtime/bfc_allocator.cc:211] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.02GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 19 | 2019-05-25 20:24:02.897610: W tensorflow/core/common_runtime/bfc_allocator.cc:211] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.02GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 20 | 2019-05-25 20:24:02.916653: W tensorflow/core/common_runtime/bfc_allocator.cc:211] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.05GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 21 | 2019-05-25 20:24:02.933499: W tensorflow/core/common_runtime/bfc_allocator.cc:211] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.04GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 22 | 2019-05-25 20:24:02.950453: W tensorflow/core/common_runtime/bfc_allocator.cc:211] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.07GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 23 | 2019-05-25 20:24:02.968622: W tensorflow/core/common_runtime/bfc_allocator.cc:211] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.06GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 24 | 2019-05-25 20:24:02.990567: W tensorflow/core/common_runtime/bfc_allocator.cc:211] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.13GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 25 | 2019-05-25 20:24:03.110873: W tensorflow/core/common_runtime/bfc_allocator.cc:211] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.13GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 26 | 2019-05-25 20:24:03.388728: W tensorflow/core/common_runtime/bfc_allocator.cc:211] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.26GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 27 | 2019-05-25 20:24:04.083007: W tensorflow/core/common_runtime/bfc_allocator.cc:211] Allocator (GPU_0_bfc) ran out of memory trying to allocate 579.25MiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available. 28 | time of inference:8.978713750839233 29 | detected 2 30 | time of inference:0.21228814125061035 31 | detected 2 32 | time of inference:0.12187004089355469 33 | detected 2 34 | time of inference:0.09182429313659668 35 | detected 3 36 | time of inference:0.1100008487701416 37 | detected 2 38 | -------------------------------------------------------------------------------- /test/images/85.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/toborobot/JetsonNanoInsulatorDetection/33213261a40de098b9ec5f4507e4cc47ed079c26/test/images/85.jpg -------------------------------------------------------------------------------- /test/images/86.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/toborobot/JetsonNanoInsulatorDetection/33213261a40de098b9ec5f4507e4cc47ed079c26/test/images/86.jpg -------------------------------------------------------------------------------- /test/images/87.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/toborobot/JetsonNanoInsulatorDetection/33213261a40de098b9ec5f4507e4cc47ed079c26/test/images/87.jpg -------------------------------------------------------------------------------- /test/images/88.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/toborobot/JetsonNanoInsulatorDetection/33213261a40de098b9ec5f4507e4cc47ed079c26/test/images/88.jpg -------------------------------------------------------------------------------- /test/images/89.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/toborobot/JetsonNanoInsulatorDetection/33213261a40de098b9ec5f4507e4cc47ed079c26/test/images/89.jpg -------------------------------------------------------------------------------- /test/images/ddeteccted_result.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/toborobot/JetsonNanoInsulatorDetection/33213261a40de098b9ec5f4507e4cc47ed079c26/test/images/ddeteccted_result.png -------------------------------------------------------------------------------- /test/images/result.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/toborobot/JetsonNanoInsulatorDetection/33213261a40de098b9ec5f4507e4cc47ed079c26/test/images/result.png --------------------------------------------------------------------------------