ReLU는 일종의 threshold 함수인데(loss 함수 혹은 손실 함수 등등등..)

0 미만은 0 으로 억제하는 함수이다.

 

그나저나 Rectified를 찾아보니 정정하다(correct) 정류하다 등으로 뜻이 나오는데

수정된 선형 단위 라고 번역을 하면 되려나?

 

[링크 : https://ko.wikipedia.org/wiki/ReLU]

 

retifier는 전자회로에서 "정류기"로 많이 번역되는데, 다이오드 등을 통해서 교류를 직류로 바꾸는 걸 의미한다.

[링크 : https://ko.wikipedia.org/wiki/정류기]

 

그나저나 다이오드의 전압 그래프를 보면 ReLU랑 비슷한 것 같으면서도 아닌것 같기도 하고(...?!)

아무튼 머.. 그렇다고 한다.

[링크 : http://www.ktechno.co.kr/ls_parts/parts04.html]

Posted by 구차니

netron으로 보다보면 softmax라는게 나오는데

그냥 그러려니 하고 넘어가던거에서 조금은 이론적으로 설명이 되는걸 보니 궁금해짐

[링크 : https://m.hanbit.co.kr/store/books/book_view.html?p_code=B7257101308]

 

아무튼 수식으로는 먼가 와닫지 않는데

[링크 : https://syj9700.tistory.com/38]

 

값들의 평균을 내어 합이 1이 되도록 정규화한다고 해야하나..

(1,2,8)을 (0.001, 0.002, 0.997) 로 변환한다.

(1,2,8) 에 e^n 을 하면

(e^1, e^2, e^8) 이 되고

밑은 e^1 +  e^2 + e^8 하면 되니까

(e^1 / (e^1 +  e^2 + e^8), e^2 / (e^1 +  e^2 + e^8), e^8 / (e^1 +  e^2 + e^8)) 로 계산하면

 

(2.71828182845904, 7.38905609893065, 2980.95798704173)

2.71828182845904 + 7.38905609893065 + 2980.95798704173 = 2991.06532496912

(2.71828182845904 / 2991.06532496912, 7.38905609893065 / 2991.06532496912, 2980.95798704173 / 2991.06532496912)

= (0.000908800555363033, 0.00247037603533682, 0.9966208234093)

 

이름과 달리 최댓값(max) 함수를 매끄럽거나 부드럽게 한 것이 아니라, 최댓값의 인수인 원핫 형태의 arg max 함수를 매끄럽게 한 것이다. 그 계산 방법은 입력값을 자연로그의 밑을 밑으로 한 지수 함수를 취한 뒤 그 지수함수의 합으로 나눠주는 것이다.

[링크 : https://ko.wikipedia.org/wiki/소프트맥스_함수]

 

For example, the standard softmax of (1,2,8) is approximately (0.001,0.002,0.997), which amounts to assigning almost all of the total unit weight in the result to the position of the vector's maximal element (of 8).

>>> import numpy as np
>>> a = [1.0, 2.0, 3.0, 4.0, 1.0, 2.0, 3.0]
>>> np.exp(a) / np.sum(np.exp(a)) 
array([0.02364054, 0.06426166, 0.1746813, 0.474833, 0.02364054,
       0.06426166, 0.1746813])

[링크 : https://en.wikipedia.org/wiki/Softmax_function]

 

아무튼 계산에 의한 결과가 true, false로 판별할 수 있는 값이 아닌

사람이 보기 편한 값으로 환산되기 때문에, 에측에는 softmax를 쓰지 말라는게 이해 될 것 같기도, 안 갈 것 같기도..

[링크 : https://velog.io/@francomoon7/예측에-Softmax를-사용하면-안되는-이유]

Posted by 구차니

오래된 글들이라 지금에 와서는 pip 패키지 버전 문제등으로 여전히 쉽지 않다.

학습에 대해서 어렵게 생각했는데.. python과 tensorflow를 통해서 텐서(다차원 행렬)을 곱하는 것이 포인트 이고

그 데이터를 어떻게 구성하고 돌리냐(학습 데이터, 검증 데이터, 라벨)가 전부인 듯.

 

[링크 : https://github.com/abhimanyu1990/SSD-Mobilenet-Custom-Object-Detector-Model-using-Tensorflow-2]

 

[링크 : https://seoftware.tistory.com/108]

[링크 : https://seoftware.tistory.com/109]

[링크 : https://towardsdatascience.com/custom-object-detection-using-tensorflow-from-scratch-e61da2e10087]

    [링크 : https://github.com/bourdakos1/Custom-Object-Detection]

    [링크 : https://github.com/cloud-annotations/cloud-annotations]

 

[링크 : https://blog.roboflow.com/how-to-train-mobilenetv2-on-a-custom-dataset/]

    [링크 : https://colab.research.google.com/drive/1bOzVaDQo8h6Ngstb7AcfzC35OihpHspt]

Posted by 구차니

이게 ssd 인지, ssd + mobilenet v2 쪽인진 모르겠다.

[링크 : https://stackoverflow.com/questions/67868644/post-process-of-tf2-ssd-detection-models]

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Posted by 구차니

어쩌면 당연한데.. python 으로 짠녀석이니 pip로 설치하면 된다.

 

$ pip install notebook
$ jupyter notebook

[링크 : https://jupyter.org/install]

 

주피터 노트북 실행하면 아래와 같이 웹으로 뜨고 ipynb를 더블클릭으로 열면 끝

 

아무튼 Run에 Run All Cells 하면 순차적으로 실행된다.

 

막상실행해보려니 2년전꺼라 패키지가 달라져서 안되는 듯.. 쩝

 

+

numpy 버전 문제인가.. 1.25.0 미만이어야 하는데 1.26.2라서 에러라..

1.17.3 ~ 1.24.x 면 될테니 적당하게 바꾸고 해봐야지

pip show numpy 
pip uninstall numpy
pip install numpy==1.16.4

[링크 : https://reyrei.tistory.com/m/28]

 

+

24.01.03

numpy 손 보니 tensorflow.keras 에서 막히는 마법이.. -_-

Posted by 구차니

ipynb 라는 확장자가 보여서 확인 중

[링크 : https://github.com/saunack/MobileNetv2-SSD]

 

아나콘다 깔고

거기서 jupiter notebook을 설치하면 된다고

[링크 : https://mananacho.tistory.com/31]

[링크 : https://blog.naver.com/tamiel/221956194782]

 

커맨드 라인으로는 먼가 복잡한데, 쥬피터 없이 돌리는것도 아니고 무슨 의미가 있나 싶긴하다.

$ jupyter nbconvert --execute --to notebook lda2.ipynb

[링크 : https://data-scient2st.tistory.com/234]

Posted by 구차니

문서를  찾다가 지쳐서 걍 실행하고 인자를 보는걸로..

root        3019     925 72 06:38 ?        00:00:25 /usr/bin/python3 /home/root/.nxp-demo-experience/scripts/machine_learning/MLDemoLauncher.py detect

 

root@imx8mpevk:~/.nxp-demo-experience/scripts/machine_learning# cat MLDemoLauncher.py 
#!/usr/bin/env python3

"""
Copyright 2021-2023 NXP

SPDX-License-Identifier: BSD-2-Clause

This script launches the NNStreamer ML Demos using a UI to pick settings.
"""

import gi
import os
import sys
import glob
from gi.repository import Gtk, GLib, Gio

gi.require_version("Gtk", "3.0")

sys.path.append("/home/root/.nxp-demo-experience/scripts/")
import utils


class MLLaunch(Gtk.Window):
    """The GUI window for the ML demo launcher"""

    def __init__(self, demo):
        """Creates the UI window"""
        # Initialization
        self.demo = demo
        super().__init__(title=demo)
        self.set_default_size(450, 200)
        self.set_resizable(False)

        # Get platform
        self.platform = os.uname().nodename
     
        # OpenVX graph caching is not available on i.MX 8QuadMax platform.
        if self.platform != "imx8qmmek" :
            os.environ["VIV_VX_CACHE_BINARY_GRAPH_DIR"] = "/home/root/.cache/gopoint"
            os.environ["VIV_VX_ENABLE_CACHE_GRAPH_BINARY"] = "1"

        # Get widget properties
        devices = []
        if self.demo != "brand" and self.demo != "selfie_nn":
            if self.platform != "imx93evk":
                devices.append("Example Video")

        for device in glob.glob("/dev/video*"):
            devices.append(device)

        backends_available = ["CPU"]
        if (
            os.path.exists("/usr/lib/libvx_delegate.so")
            and self.demo != "pose"
            and self.demo != "selfie_nn"
        ):
            backends_available.insert(1, "GPU")
        if os.path.exists("/usr/lib/libneuralnetworks.so") and self.demo != "brand" and self.platform != "imx8qmmek":
            backends_available.insert(0, "NPU")
        if os.path.exists("/usr/lib/libethosu_delegate.so"):
            backends_available.insert(0, "NPU")
            backends_available.pop()

        displays_available = ["Weston"]

        colors_available = ["Red", "Green", "Blue", "Black", "White"]

        demo_modes_available = ["Background Substitution", "Segmentation Mask"]

        # Create widgets
        main_grid = Gtk.Grid.new()
        device_label = Gtk.Label.new("Source")
        self.device_combo = Gtk.ComboBoxText()
        backend_label = Gtk.Label.new("Backend")
        self.backend_combo = Gtk.ComboBoxText()
        self.display_combo = Gtk.ComboBoxText()
        self.launch_button = Gtk.Button.new_with_label("Run")
        self.status_bar = Gtk.Label.new()
        header = Gtk.HeaderBar()
        quit_button = Gtk.Button()
        quit_icon = Gio.ThemedIcon(name="process-stop-symbolic")
        quit_image = Gtk.Image.new_from_gicon(quit_icon, Gtk.IconSize.BUTTON)
        separator = Gtk.Separator.new(0)
        time_title_label = Gtk.Label.new("Video Refresh")
        self.time_label = Gtk.Label.new("--.-- ms")
        self.fps_label = Gtk.Label.new("-- FPS")
        inference_title_label = Gtk.Label.new("Inference Time")
        self.inference_label = Gtk.Label.new("--.-- ms")
        self.ips_label = Gtk.Label.new("-- IPS")
        if self.demo != "selfie_nn":
            self.width_entry = self.r_scale = Gtk.Scale.new_with_range(
                Gtk.Orientation.HORIZONTAL, 300, 1920, 2
            )
            self.height_entry = self.r_scale = Gtk.Scale.new_with_range(
                Gtk.Orientation.HORIZONTAL, 300, 1080, 2
            )
            self.width_label = Gtk.Label.new("Height")
            self.height_label = Gtk.Label.new("Width")
            self.color_label = Gtk.Label.new("Label Color")
        else:
            self.color_label = Gtk.Label.new("Text Color")
            self.demo_mode = Gtk.Label.new("Demo Mode")
            self.mode_combo = Gtk.ComboBoxText()
        self.color_combo = Gtk.ComboBoxText()

        # Organize widgets
        self.add(main_grid)
        self.set_titlebar(header)

        quit_button.add(quit_image)
        header.pack_end(quit_button)

        main_grid.set_row_spacing(10)
        main_grid.set_border_width(10)

        main_grid.attach(device_label, 0, 1, 2, 1)
        device_label.set_hexpand(True)
        main_grid.attach(backend_label, 0, 2, 2, 1)
        # main_grid.attach(display_label, 0, 3, 2, 1)
        if self.demo != "selfie_nn":
            main_grid.attach(self.width_label, 0, 4, 2, 1)
            main_grid.attach(self.height_label, 0, 5, 2, 1)
            main_grid.attach(self.color_label, 0, 6, 2, 1)
        else:
            main_grid.attach(self.demo_mode, 0, 4, 2, 1)
            main_grid.attach(self.color_label, 0, 5, 2, 1)

        main_grid.attach(self.device_combo, 2, 1, 2, 1)
        self.device_combo.set_hexpand(True)
        main_grid.attach(self.backend_combo, 2, 2, 2, 1)
        # main_grid.attach(self.display_combo, 2, 3, 2, 1)
        if self.demo != "selfie_nn":
            main_grid.attach(self.width_entry, 2, 4, 2, 1)
            main_grid.attach(self.height_entry, 2, 5, 2, 1)
            main_grid.attach(self.color_combo, 2, 6, 2, 1)
        else:
            main_grid.attach(self.mode_combo, 2, 4, 2, 1)
            main_grid.attach(self.color_combo, 2, 5, 2, 1)

        main_grid.attach(self.launch_button, 0, 7, 4, 1)
        main_grid.attach(self.status_bar, 0, 8, 4, 1)

        main_grid.attach(separator, 0, 9, 4, 1)

        main_grid.attach(time_title_label, 0, 10, 2, 1)
        main_grid.attach(self.time_label, 0, 11, 1, 1)
        main_grid.attach(self.fps_label, 1, 11, 1, 1)
        main_grid.attach(inference_title_label, 2, 10, 2, 1)
        main_grid.attach(self.inference_label, 2, 11, 1, 1)
        main_grid.attach(self.ips_label, 3, 11, 1, 1)

        # Configure widgets
        for device in devices:
            self.device_combo.append_text(device)
        for backend in backends_available:
            self.backend_combo.append_text(backend)
        for display in displays_available:
            self.display_combo.append_text(display)
        for color in colors_available:
            self.color_combo.append_text(color)
        if self.demo == "selfie_nn":
            for mode in demo_modes_available:
                self.mode_combo.append_text(mode)

        self.device_combo.set_active(0)
        self.backend_combo.set_active(0)
        self.display_combo.set_active(0)
        self.color_combo.set_active(0)
        if self.demo != "selfie_nn":
            self.width_entry.set_value(1920)
            self.height_entry.set_value(1080)
            self.width_entry.set_sensitive(False)
            self.height_entry.set_sensitive(False)
        else:
            self.mode_combo.set_active(0)
        self.device_combo.connect("changed", self.on_source_change)
        self.launch_button.connect("clicked", self.start)
        quit_button.connect("clicked", exit)
        if self.demo == "detect":
            header.set_title("Detection Demo")
        elif self.demo == "id":
            header.set_title("Classification Demo")
        elif self.demo == "pose":
            header.set_title("Pose Demo")
        elif self.demo == "brand":
            header.set_title("Brand Demo")
        elif self.demo == "selfie_nn":
            header.set_title("Selfie Segmenter Demo")
        else:
            header.set_title("NNStreamer Demo")
        header.set_subtitle("NNStreamer Examples")

    def start(self, button):
        """Starts the ML Demo with selected settings"""
        self.update_time = GLib.get_monotonic_time()
        self.launch_button.set_sensitive(False)
        if self.color_combo.get_active_text() == "Red":
            r = 1
            g = 0
            b = 0
        elif self.color_combo.get_active_text() == "Blue":
            r = 0
            g = 0
            b = 1
        elif self.color_combo.get_active_text() == "Green":
            r = 0
            g = 1
            b = 0
        elif self.color_combo.get_active_text() == "Black":
            r = 0
            g = 0
            b = 0
        elif self.color_combo.get_active_text() == "White":
            r = 1
            g = 1
            b = 1
        else:
            r = 1
            g = 0
            b = 0
        if self.demo == "detect":
            if self.platform == "imx93evk":
                model = utils.download_file("mobilenet_ssd_v2_coco_quant_postprocess_vela.tflite")
            else:
                model = utils.download_file("mobilenet_ssd_v2_coco_quant_postprocess.tflite")
            labels = utils.download_file("coco_labels.txt")
            if self.device_combo.get_active_text() == "Example Video":
                device = utils.download_file("detect_example.mov")
            else:
                device = self.device_combo.get_active_text()
            if model == -1 or model == -2 or model == -3:
                if self.platform == "imx93evk":
                    error = "mobilenet_ssd_v2_coco_quant_postprocess_vela.tflite"
                else:
                    error = "mobilenet_ssd_v2_coco_quant_postprocess.tflite"
            elif labels == -1 or labels == -2 or labels == -3:
                error = "coco_labels.txt"
            elif device == -1 or device == -2 or device == -3:
                error = "detect_example.mov"
        if self.demo == "id":
            if self.platform == "imx93evk":
                model = utils.download_file("mobilenet_v1_1.0_224_quant_vela.tflite")
            else:
                model = utils.download_file("mobilenet_v1_1.0_224_quant.tflite")
            labels = utils.download_file("1_1.0_224_labels.txt")
            if self.device_combo.get_active_text() == "Example Video":
                device = utils.download_file("id_example.mov")
            else:
                device = self.device_combo.get_active_text()
            if model == -1 or model == -2 or model == -3:
                if self.platform == "imx93evk":
                    error = "mobilenet_v1_1.0_224_quant_vela.tflite"
                else:
                    error = "mobilenet_v1_1.0_224_quant.tflite"
            elif labels == -1 or labels == -2 or labels == -3:
                error = "1_1.0_224_labels.txt"
            elif device == -1 or device == -2 or device == -3:
                error = "id_example.mov"
        if self.demo == "pose":
            model = utils.download_file("posenet_resnet50_uint8_float32_quant.tflite")
            labels = utils.download_file("key_point_labels.txt")
            if self.device_combo.get_active_text() == "Example Video":
                device = utils.download_file("pose_example.mov")
            else:
                device = self.device_combo.get_active_text()
            if model == -1 or model == -2 or model == -3:
                error = "posenet_resnet50_uint8_float32_quant.tflite"
            elif labels == -1 or labels == -2 or labels == -3:
                error = "key_point_labels.txt"
            elif device == -1 or device == -2 or device == -3:
                error = "pose_example.mov"
        if self.demo == "brand":
            model = utils.download_file("brand_model.tflite")
            labels = utils.download_file("brand_labels.txt")
            if self.device_combo.get_active_text() == "Example Video":
                device = utils.download_file("brand_example.mov")
            else:
                device = self.device_combo.get_active_text()
            if model == -1 or model == -2 or model == -3:
                error = "brand_model.tflite"
            elif labels == -1 or labels == -2 or labels == -3:
                error = "brand_labels.txt"
            elif device == -1 or device == -2 or device == -3:
                error = "brand_example.mov"
        if self.demo == "selfie_nn":
            if self.platform == "imx93evk":
                model = utils.download_file(
                    "selfie_segmenter_landscape_int8_vela.tflite"
                )
            else:
                model = utils.download_file("selfie_segmenter_int8.tflite")
            # Labels refer to background img
            if self.platform == "imx93evk":
                labels = utils.download_file("bg_image_landscape.jpg")
            else:
                labels = utils.download_file("bg_image.jpg")
            if self.device_combo.get_active_text() == "Example Video":
                device = utils.download_file("selfie_example.mov")
            else:
                device = self.device_combo.get_active_text()
            if model == -1 or model == -2 or model == -3:
                if self.platform == "imx93evk":
                    error = "selfie_segmenter_landscape_int8_vela.tflite"
                else:
                    error = "selfie_segmenter_int8.tflite"
            elif labels == -1 or labels == -2 or labels == -3:
                if self.platform == "imx93evk":
                    error = "bg_image_landscape.jpg"
                else:
                    error = "bg_image.jpg"
            elif device == -1 or device == -2 or device == -3:
                error = "selfie_example.mov"
            if self.mode_combo.get_active_text() == "Background Substitution":
                set_mode = 0
            else:
                set_mode = 1

        if model == -1 or labels == -1 or device == -1:
            """
            dialog = Gtk.MessageDialog(
                transient_for=self,
                flags=0,
                message_type=Gtk.MessageType.ERROR,
                buttons=Gtk.ButtonsType.CANCEL,
                text="Cannot find files! The file that you requested" +
                " does not have any metadata that is related to it. " +
                "Please see /home/root/.nxp-demo-experience/downloads.txt" +
                " to see if the requested file exists! \n \n Cannot find:" +
                error)
            dialog.run()
            dialog.destroy()
            """
            self.status_bar.set_text("Cannot find files!")
            self.launch_button.set_sensitive(True)
            return
        if model == -2 or labels == -2 or device == -2:
            """
            dialog = Gtk.MessageDialog(
                transient_for=self,
                flags=0,
                message_type=Gtk.MessageType.ERROR,
                buttons=Gtk.ButtonsType.CANCEL,
                text="Cannot download files! The URL used to download the" +
                " file cannot be reached. If you are connected to the " +
                "internet, please check the /home/root/.nxp-demo-experience" +
                "/downloads.txt for the URL. For some regions, " +
                "these sites may be blocked. To install these manually," +
                " please go to the file listed above and provide the " +
                "path to the file in \"PATH\" \n \n Cannot download " + error)
            dialog.run()
            dialog.destroy()
            """
            self.status_bar.set_text("Download failed!")
            self.launch_button.set_sensitive(True)
            return
        if model == -3 or labels == -3 or device == -4:
            """
            dialog = Gtk.MessageDialog(
                transient_for=self,
                flags=0,
                message_type=Gtk.MessageType.ERROR,
                buttons=Gtk.ButtonsType.CANCEL,
                text="Invalid files! The files where not what we expected." +
                "If you are SURE that the files are correct, delete " +
                "the \"SHA\" value in /home/root/.nxp-demo-experience" +
                "/downloads.txt to bypass the SHA check. \n \n Bad SHA for " +
                error)
            dialog.run()
            dialog.destroy()
            """
            self.status_bar.set_text("Downloaded bad file!")
            self.launch_button.set_sensitive(True)
            return
        if self.demo == "detect":
            import nndetection

            example = nndetection.ObjectDetection(
                self.platform,
                device,
                self.backend_combo.get_active_text(),
                model,
                labels,
                self.display_combo.get_active_text(),
                self.update_stats,
                self.width_entry.get_value(),
                self.height_entry.get_value(),
                r,
                g,
                b,
            )
            example.run()
        if self.demo == "id":
            import nnclassification

            example = nnclassification.NNStreamerExample(
                self.platform,
                device,
                self.backend_combo.get_active_text(),
                model,
                labels,
                self.display_combo.get_active_text(),
                self.update_stats,
                self.width_entry.get_value(),
                self.height_entry.get_value(),
                r,
                g,
                b,
            )
            example.run_example()
        if self.demo == "pose":
            import nnpose

            example = nnpose.NNStreamerExample(
                self.platform,
                device,
                self.backend_combo.get_active_text(),
                model,
                labels,
                self.display_combo.get_active_text(),
                self.update_stats,
                self.width_entry.get_value(),
                self.height_entry.get_value(),
                r,
                g,
                b,
            )
            example.run_example()
        if self.demo == "brand":
            import nnbrand

            example = nnbrand.NNStreamerExample(
                self.platform,
                device,
                self.backend_combo.get_active_text(),
                model,
                labels,
                self.display_combo.get_active_text(),
                self.update_stats,
                self.width_entry.get_value(),
                self.height_entry.get_value(),
                r,
                g,
                b,
            )
            example.run_example()
        if self.demo == "selfie_nn":
            import selfie_segmenter

            example = selfie_segmenter.SelfieSegmenter(
                self.platform,
                device,
                self.backend_combo.get_active_text(),
                model,
                labels,
                self.update_stats,
                set_mode,
                r,
                g,
                b,
            )
            example.run()

        self.launch_button.set_sensitive(True)

    def update_stats(self, time):
        """Callback used the update stats in GUI"""
        interval_time = (GLib.get_monotonic_time() - self.update_time) / 1000000
        if interval_time > 1:
            refresh_time = time.interval_time
            inference_time = time.tensor_filter.get_property("latency")

            if refresh_time != 0 and inference_time != 0:
                # Print pipeline information
                if self.demo == "selfie_nn" or self.demo == "id" or self.demo == "detect":
                    self.time_label.set_text(
                        "{:12.2f} ms".format(1.0 / time.current_framerate * 1000.0)
                    )
                    self.fps_label.set_text(
                        "{:12.2f} FPS".format(time.current_framerate)
                    )
                else:
                    self.time_label.set_text("{:12.2f} ms".format(refresh_time / 1000))
                    self.fps_label.set_text(
                        "{:12.2f} FPS".format(1 / (refresh_time / 1000000))
                    )
                # Print inference information
                self.inference_label.set_text(
                    "{:12.2f} ms".format(inference_time / 1000)
                )
                self.ips_label.set_text(
                    "{:12.2f} FPS".format(1 / (inference_time / 1000000))
                )
            self.update_time = GLib.get_monotonic_time()
        return True

    def on_source_change(self, widget):
        """Callback to lock sliders"""
        if self.demo != "selfie_nn":
            if self.device_combo.get_active_text() == "Example Video":
                self.width_entry.set_value(1920)
                self.height_entry.set_value(1080)
                self.width_entry.set_sensitive(False)
                self.height_entry.set_sensitive(False)
            else:
                self.width_entry.set_sensitive(True)
                self.height_entry.set_sensitive(True)


if __name__ == "__main__":
    if (
        len(sys.argv) != 2
        and sys.argv[1] != "detect"
        and sys.argv[1] != "id"
        and sys.argv[1] != "pose"
        and sys.argv[1] != "selfie_nn"
    ):
        print("Demos available: detect, id, pose, selfie_nn")
    else:
        win = MLLaunch(sys.argv[1])
        win.connect("destroy", Gtk.main_quit)
        win.show_all()
        Gtk.main()

 

아래가 실행되는 녀석인데 nndetection을 import 하니까 그걸 따라가서 보는 중.

그나저나 LGPL 이면 그냥 공개해도 되려나?

root@imx8mpevk:~/.nxp-demo-experience/scripts/machine_learning# find / -name nndetection.py
/run/media/root-mmcblk2p2/home/root/.nxp-demo-experience/scripts/machine_learning/nndetection.py
/home/root/.nxp-demo-experience/scripts/machine_learning/nndetection.py

root@imx8mpevk:~/.nxp-demo-experience/scripts/machine_learning# cat /home/root/.nxp-demo-experience/scripts/machine_learning/nndetection.py
#!/usr/bin/env python3

"""
Copyright SSAFY Team 1 <jangjongha.sw@gmail.com>
Copyright 2021-2023 NXP

SPDX-License-Identifier: LGPL-2.1-only
Original Source: https://github.com/nnstreamer/nnstreamer-example

This demo shows how you can use the NNStreamer to detect objects.

From the original source, this was modified to better work with the a
GUI and to get better performance on the i.MX 8M Plus and i.MX93.
"""

import os
import sys
import gi
import re
import logging
import numpy as np
import cairo

gi.require_version("Gst", "1.0")
gi.require_foreign("cairo")
from gi.repository import Gst, GObject, GLib

DEBUG = False


class ObjectDetection:
    """The class that manages the demo"""

    def __init__(
        self,
        platform,
        device,
        backend,
        model,
        labels,
        display="Weston",
        callback=None,
        width=1920,
        height=1080,
        r=1,
        g=0,
        b=0,
    ):
        """Creates an instance of the demo

        Arguments:
        device -- What camera or video file to use
        backend -- Whether to use NPU or CPU
        model -- the path to the model
        labels -- the path to the labels
        display -- Whether to use X11 or Weston
        callback -- Callback to pass stats to
        width -- Width of output
        height -- Height of output
        r -- Red value for labels
        g -- Green value for labels
        b -- Blue value for labels
        """
        self.loop = None
        self.pipeline = None
        self.running = False
        self.video_caps = None
        self.first_frame = True

        self.BOX_SIZE = 4
        self.LABEL_SIZE = 91
        self.DETECTION_MAX = 20
        self.MAX_OBJECT_DETECTION = 20

        self.Y_SCALE = 10.0
        self.X_SCALE = 10.0
        self.H_SCALE = 5.0
        self.W_SCALE = 5.0

        self.VIDEO_WIDTH = width
        self.VIDEO_HEIGHT = height
        self.MODEL_WIDTH = 300
        self.MODEL_HEIGHT = 300

        self.tflite_model = model
        self.label_path = labels
        self.device = device
        self.backend = backend
        self.display = display
        self.tflite_labels = []
        self.detected_objects = []
        self.callback = callback
        self.r = r
        self.b = b
        self.g = g
        self.platform = platform
        self.current_framerate = 1000

        # Define PXP or GPU2D converter
        if self.platform == "imx93evk":
            self.nxp_converter = "imxvideoconvert_pxp "
        else:
            self.nxp_converter = "imxvideoconvert_g2d "

        if not self.tflite_init():
            raise Exception

        Gst.init(None)

    def run(self):
        """Starts pipeline and run demo"""

        if self.backend == "CPU":
            if self.platform == "imx93evk":
                backend = "true:cpu custom=NumThreads:2"
            else:
                backend = "true:cpu custom=NumThreads:4"
        elif self.backend == "GPU":
            os.environ["USE_GPU_INFERENCE"] = "1"
            backend = (
                "true:gpu custom=Delegate:External," "ExtDelegateLib:libvx_delegate.so"
            )
        else:
            if self.platform == "imx93evk":
                backend = (
                    "true:npu custom=Delegate:External,"
                    "ExtDelegateLib:libethosu_delegate.so"
                )
            else:
                os.environ["USE_GPU_INFERENCE"] = "0"
                backend = (
                    "true:npu custom=Delegate:External,"
                    "ExtDelegateLib:libvx_delegate.so"
                )

        if self.display == "X11":
            display = "ximagesink name=img_tensor "
        elif self.display == "None":
            self.print_time = GLib.get_monotonic_time()
            display = "fakesink "
        else:
            display = "fpsdisplaysink name=img_tensor text-overlay=false video-sink=waylandsink sync=false"

        # main loop
        self.loop = GLib.MainLoop()
        self.old_time = GLib.get_monotonic_time()
        self.update_time = GLib.get_monotonic_time()
        self.reload_time = -1
        self.interval_time = 999999

        # Create decoder for video file
        if self.platform == "imx8qmmek":
            decoder = "h264parse ! v4l2h264dec "
        else:
            decoder = "vpudec "

        if "/dev/video" in self.device:
            gst_launch_cmdline = "v4l2src name=cam_src device=" + self.device
            gst_launch_cmdline += " ! " + self.nxp_converter + "! video/x-raw,width="
            gst_launch_cmdline += str(int(self.VIDEO_WIDTH)) + ",height="
            gst_launch_cmdline += str(int(self.VIDEO_HEIGHT))
            gst_launch_cmdline += ",framerate=30/1,format=BGRx ! tee name=t"
        else:
            gst_launch_cmdline = "filesrc location=" + self.device
            gst_launch_cmdline += " ! qtdemux ! " + decoder + "! tee name=t"

        gst_launch_cmdline += " t. ! " + self.nxp_converter + "!  video/x-raw,"
        gst_launch_cmdline += "width={:d},".format(self.MODEL_WIDTH)
        gst_launch_cmdline += "height={:d},".format(self.MODEL_HEIGHT)
        gst_launch_cmdline += " ! queue max-size-buffers=2 leaky=2 ! "
        gst_launch_cmdline += "videoconvert ! video/x-raw,format=RGB !"
        gst_launch_cmdline += " tensor_converter ! tensor_filter"
        gst_launch_cmdline += " framework=tensorflow-lite model="
        gst_launch_cmdline += self.tflite_model + " accelerator=" + backend
        gst_launch_cmdline += " silent=FALSE name=tensor_filter latency=1 ! "
        gst_launch_cmdline += "tensor_sink name=tensor_sink t. ! "
        gst_launch_cmdline += self.nxp_converter + "! "
        gst_launch_cmdline += "cairooverlay name=tensor_res ! "
        gst_launch_cmdline += "queue max-size-buffers=2 leaky=2 ! "
        gst_launch_cmdline += display

        self.pipeline = Gst.parse_launch(gst_launch_cmdline)

        # bus and message callback
        bus = self.pipeline.get_bus()
        bus.add_signal_watch()
        bus.connect("message", self.on_bus_message)

        self.tensor_filter = self.pipeline.get_by_name("tensor_filter")
        self.wayland_sink = self.pipeline.get_by_name("img_tensor")

        # tensor sink signal : new data callback
        tensor_sink = self.pipeline.get_by_name("tensor_sink")
        tensor_sink.connect("new-data", self.new_data_cb)

        tensor_res = self.pipeline.get_by_name("tensor_res")
        tensor_res.connect("draw", self.draw_overlay_cb)
        tensor_res.connect("caps-changed", self.prepare_overlay_cb)
        if self.callback is not None:
            GObject.timeout_add(500, self.callback, self)

        # start pipeline
        self.pipeline.set_state(Gst.State.PLAYING)
        self.running = True

        self.set_window_title("img_tensor", "NNStreamer Object Detection Example")

        # run main loop
        self.loop.run()

        # quit when received eos or error message
        self.running = False
        self.pipeline.set_state(Gst.State.NULL)

        bus.remove_signal_watch()

    def tflite_init(self):
        """
        :return: True if successfully initialized
        """

        if not os.path.exists(self.tflite_model):
            logging.error("cannot find tflite model [%s]", self.tflite_model)
            return False

        label_path = self.label_path
        try:
            with open(label_path, "r") as label_file:
                for line in label_file.readlines():
                    if line[0].isdigit():
                        while str(len(self.tflite_labels)) not in line:
                            self.tflite_labels.append("Invalid")
                        self.tflite_labels.append(line[line.find(" ") + 1 :])
                    else:
                        self.tflite_labels.append(line)
        except FileNotFoundError:
            logging.error("cannot find tflite label [%s]", label_path)
            return False

        logging.info("finished to load labels, total [%d]", len(self.tflite_labels))
        return True

    # @brief Callback for tensor sink signal.
    def new_data_cb(self, sink, buffer):
        """Callback for tensor sink signal.

        :param sink: tensor sink element
        :param buffer: buffer from element
        :return: None
        """
        if self.running:
            new_time = GLib.get_monotonic_time()
            self.interval_time = new_time - self.old_time
            self.old_time = new_time
            if buffer.n_memory() != 4:
                return False

            #  tensor type is float32.
            # LOCATIONS_IDX:CLASSES_IDX:SCORES_IDX:NUM_DETECTION_IDX
            # 4:20:1:1\,20:1:1:1\,20:1:1:1\,1:1:1:1
            # [0] detection_boxes (default 4th tensor). BOX_SIZE :
            # #MaxDetection, ANY-TYPE
            # [1] detection_classes (default 2nd tensor).
            # #MaxDetection, ANY-TYPE
            # [2] detection_scores (default 3rd tensor)
            # #MaxDetection, ANY-TYPE
            # [3] num_detection (default 1st tensor). 1, ANY-TYPE

            # bytestrings that are based on float32 must be
            # decoded into float list.

            # boxes
            mem_boxes = buffer.peek_memory(0)
            ret, info_boxes = mem_boxes.map(Gst.MapFlags.READ)
            if ret:
                assert info_boxes.size == (
                    self.BOX_SIZE * self.DETECTION_MAX * 4
                ), "Invalid info_box size"
                decoded_boxes = list(
                    np.frombuffer(info_boxes.data, dtype=np.float32)
                )  # decode bytestrings to float list

            # detections
            mem_detections = buffer.peek_memory(1)
            ret, info_detections = mem_detections.map(Gst.MapFlags.READ)
            if ret:
                assert info_detections.size == (
                    self.DETECTION_MAX * 4
                ), "Invalid info_detection size"
                decoded_detections = list(
                    np.frombuffer(info_detections.data, dtype=np.float32)
                )  # decode bytestrings to float list

            # scores
            mem_scores = buffer.peek_memory(2)
            ret, info_scores = mem_scores.map(Gst.MapFlags.READ)
            if ret:
                assert info_scores.size == (
                    self.DETECTION_MAX * 4
                ), "Invalid info_score size"
                decoded_scores = list(
                    np.frombuffer(info_scores.data, dtype=np.float32)
                )  # decode bytestrings to float list

            # num detection
            mem_num = buffer.peek_memory(3)
            ret, info_num = mem_num.map(Gst.MapFlags.READ)
            if ret:
                assert info_num.size == 4, "Invalid info_num size"
                decoded_num = list(
                    np.frombuffer(info_num.data, dtype=np.float32)
                )  # decode bytestrings to float list

            self.get_detected_objects(
                decoded_boxes, decoded_detections, decoded_scores, int(decoded_num[0])
            )

            mem_boxes.unmap(info_boxes)
            mem_detections.unmap(info_detections)
            mem_scores.unmap(info_scores)
            mem_num.unmap(info_num)

            if self.display == "None":
                if (GLib.get_monotonic_time() - self.print_time) > 1000000:
                    inference = self.tensor_filter.get_property("latency")
                    print(
                        "Inference time: "
                        + str(inference / 1000)
                        + " ms ("
                        + "{:5.2f}".format(1 / (inference / 1000000))
                        + " IPS)"
                    )
                    self.print_time = GLib.get_monotonic_time()

    def get_detected_objects(self, boxes, detections, scores, num):
        """Pairs boxes with dectected objects"""
        threshold_score = 0.5
        detected = list()

        for i in range(num):
            score = scores[i]
            if score < threshold_score:
                continue

            c = detections[i]

            box_offset = self.BOX_SIZE * i
            ymin = boxes[box_offset + 0]
            xmin = boxes[box_offset + 1]
            ymax = boxes[box_offset + 2]
            xmax = boxes[box_offset + 3]

            x = xmin * self.MODEL_WIDTH
            y = ymin * self.MODEL_HEIGHT
            width = (xmax - xmin) * self.MODEL_WIDTH
            height = (ymax - ymin) * self.MODEL_HEIGHT

            obj = {
                "class_id": int(c),
                "x": x,
                "y": y,
                "width": width,
                "height": height,
                "prob": score,
            }

            detected.append(obj)

        # update result
        self.detected_objects.clear()

        for d in detected:
            self.detected_objects.append(d)
            if DEBUG:
                print("==============================")
                print("LABEL           : {}".format(self.tflite_labels[d["class_id"]]))
                print("x               : {}".format(d["x"]))
                print("y               : {}".format(d["y"]))
                print("width           : {}".format(d["width"]))
                print("height          : {}".format(d["height"]))
                print("Confidence Score: {}".format(d["prob"]))

    def prepare_overlay_cb(self, overlay, caps):
        """Store the information from the caps that we are interested in."""
        self.video_caps = caps

    def draw_overlay_cb(self, overlay, context, timestamp, duration):
        """Callback to draw the overlay."""
        if self.video_caps is None or not self.running:
            return
        scale_height = self.VIDEO_HEIGHT / 1080
        scale_width = self.VIDEO_WIDTH / 1920
        scale_text = max(scale_height, scale_width)

        # mutex_lock alternative required
        detected = self.detected_objects
        # mutex_unlock alternative needed

        drawed = 0
        context.select_font_face(
            "Sans", cairo.FONT_SLANT_NORMAL, cairo.FONT_WEIGHT_BOLD
        )
        context.set_font_size(int(50.0 * scale_text))
        context.set_source_rgb(self.r, self.g, self.b)

        for obj in detected:
            label = self.tflite_labels[obj["class_id"]][:-1]
            x = obj["x"] * self.VIDEO_WIDTH // self.MODEL_WIDTH
            y = obj["y"] * self.VIDEO_HEIGHT // self.MODEL_HEIGHT
            width = obj["width"] * self.VIDEO_WIDTH // self.MODEL_WIDTH
            height = obj["height"] * self.VIDEO_HEIGHT // self.MODEL_HEIGHT

            # draw rectangle
            context.rectangle(x, y, width, height)
            context.set_line_width(3)
            context.stroke()

            # draw title
            context.move_to(x + 5, y + int(50.0 * scale_text))
            context.show_text(label)

            drawed += 1
            if drawed >= self.MAX_OBJECT_DETECTION:
                break

        inference = self.tensor_filter.get_property("latency")
        # Get current framerate and avg. framerate
        output_wayland = self.wayland_sink.get_property("last-message")
        if output_wayland:
            current_text = re.findall(r"current:\s[\d]+[.\d]*", output_wayland)[0]
            self.current_framerate = float(re.findall(r"[\d]+[.\d]*", current_text)[0])

        context.set_font_size(int(25.0 * scale_text))
        context.move_to(
            int(50 * scale_width), int(self.VIDEO_HEIGHT - (100 * scale_height))
        )
        context.show_text("i.MX NNStreamer Detection Demo")
        if inference == 0:
            context.move_to(
                int(50 * scale_width), int(self.VIDEO_HEIGHT - (75 * scale_height))
            )
            context.show_text("FPS: ")
            context.move_to(
                int(50 * scale_width), int(self.VIDEO_HEIGHT - (50 * scale_height))
            )
            context.show_text("IPS: ")
        elif (
            GLib.get_monotonic_time() - self.reload_time
        ) < 100000 and self.refresh_time != -1:
            context.move_to(
                int(50 * scale_width), int(self.VIDEO_HEIGHT - (75 * scale_height))
            )
            context.show_text(
                "FPS: {:6.2f} ({:6.2f} ms)".format(
                    self.current_framerate, 1.0 / self.current_framerate * 1000
                )
            )
            context.move_to(
                int(50 * scale_width), int(self.VIDEO_HEIGHT - (50 * scale_height))
            )
            context.show_text(
                "IPS: {:6.2f} ({:6.2f} ms)".format(
                    1 / (inference / 1000000), inference / 1000
                )
            )
        else:
            self.reload_time = GLib.get_monotonic_time()
            self.refresh_time = self.interval_time
            self.inference = self.tensor_filter.get_property("latency")
            context.move_to(
                int(50 * scale_width), int(self.VIDEO_HEIGHT - (75 * scale_height))
            )
            context.show_text(
                "FPS: {:6.2f} ({:6.2f} ms)".format(
                    self.current_framerate, 1.0 / self.current_framerate * 1000
                )
            )
            context.move_to(
                int(50 * scale_width), int(self.VIDEO_HEIGHT - (50 * scale_height))
            )
            context.show_text(
                "IPS: {:6.2f} ({:6.2f} ms)".format(
                    1 / (inference / 1000000), inference / 1000
                )
            )
        if self.first_frame:
            context.move_to(int(400 * scale_width), int(600 * scale_height))
            context.set_font_size(int(200.0 * min(scale_width, scale_height)))
            context.show_text("Loading...")
            self.first_frame = False
        context.fill()

    def on_bus_message(self, bus, message):
        """Callback for message.

        :param bus: pipeline bus
        :param message: message from pipeline
        :return: None
        """
        if message.type == Gst.MessageType.EOS:
            logging.info("received eos message")
            self.loop.quit()
        elif message.type == Gst.MessageType.ERROR:
            error, debug = message.parse_error()
            logging.warning("[error] %s : %s", error.message, debug)
            self.loop.quit()
        elif message.type == Gst.MessageType.WARNING:
            error, debug = message.parse_warning()
            logging.warning("[warning] %s : %s", error.message, debug)
        elif message.type == Gst.MessageType.STREAM_START:
            logging.info("received start message")
        elif message.type == Gst.MessageType.QOS:
            data_format, processed, dropped = message.parse_qos_stats()
            format_str = Gst.Format.get_name(data_format)
            logging.debug(
                "[qos] format[%s] processed[%d] dropped[%d]",
                format_str,
                processed,
                dropped,
            )

    def set_window_title(self, name, title):
        """Set window title for X11.

        :param name: GstXImageasink element name
        :param title: window title
        :return: None
        """
        element = self.pipeline.get_by_name(name)
        if element is not None:
            pad = element.get_static_pad("sink")
            if pad is not None:
                tags = Gst.TagList.new_empty()
                tags.add_value(Gst.TagMergeMode.APPEND, "title", title)
                pad.send_event(Gst.Event.new_tag(tags))


if __name__ == "__main__":
    if (
        len(sys.argv) != 7
        and len(sys.argv) != 5
        and len(sys.argv) != 9
        and len(sys.argv) != 12
        and len(sys.argv) != 6
    ):
        print(
            "Usage: python3 nndetection.py <dev/video*/video file>"
            + " <NPU/CPU> <model file> <label file>"
        )
        exit()
    # Get platform
    platform = os.uname().nodename
    if len(sys.argv) == 7:
        example = ObjectDetection(
            platform,
            sys.argv[1],
            sys.argv[2],
            sys.argv[3],
            sys.argv[4],
            sys.argv[5],
            sys.argv[6],
        )
    if len(sys.argv) == 5:
        example = ObjectDetection(
            platform, sys.argv[1], sys.argv[2], sys.argv[3], sys.argv[4]
        )
    if len(sys.argv) == 6:
        example = ObjectDetection(
            platform, sys.argv[1], sys.argv[2], sys.argv[3], sys.argv[4], sys.argv[5]
        )
    if len(sys.argv) == 9:
        example = ObjectDetection(
            platform,
            sys.argv[1],
            sys.argv[2],
            sys.argv[3],
            sys.argv[4],
            sys.argv[5],
            sys.argv[6],
            int(sys.argv[7]),
            int(sys.argv[8]),
        )
    if len(sys.argv) == 12:
        example = ObjectDetection(
            platform,
            sys.argv[1],
            sys.argv[2],
            sys.argv[3],
            sys.argv[4],
            sys.argv[5],
            sys.argv[6],
            int(sys.argv[7]),
            int(sys.argv[8]),
            int(sys.argv[9]),
            int(sys.argv[10]),
            int(sys.argv[11]),
        )
    example.run()

 

        self.pipeline = Gst.parse_launch(
            'v4l2src name=cam_src ! videoconvert ! videoscale ! '
            'video/x-raw,width=640,height=480,format=RGB ! tee name=t_raw '
            't_raw. ! queue leaky=2 max-size-buffers=2 ! videoscale ! video/x-raw,width=300,height=300 ! tensor_converter ! '
            'tensor_transform mode=arithmetic option=typecast:float32,add:-127.5,div:127.5 ! '
            'tensor_filter framework=tensorflow-lite model=' + self.tflite_model + ' ! '
            'tensor_decoder mode=bounding_boxes option1=mobilenet-ssd option2='
            + self.tflite_label + ' option3=' + self.tflite_box_prior + ' option4=640:480 option5=300:300 !'
            'compositor name=mix sink_0::zorder=2 sink_1::zorder=1 ! videoconvert ! ximagesink '
            't_raw. ! queue leaky=2 max-size-buffers=10 ! mix. '
        )

[링크 : https://github.com/nnstreamer/nnstreamer-example/blob/main/native/example_object_detection_tensorflow_lite/nnstreamer_example_object_detection_tflite.py]

 

gst_launch_cmdline 를 출력해보니 아래와 같이 gstreamer 파이프라인이 나온다.

v4l2src name=cam_src device=/dev/video3 ! imxvideoconvert_g2d ! video/x-raw,width=1920,height=1080,framerate=30/1,format=BGRx ! tee name=t t. ! imxvideoconvert_g2d !  video/x-raw,width=300,height=300, ! queue max-size-buffers=2 leaky=2 ! videoconvert ! video/x-raw,format=RGB ! tensor_converter ! tensor_filter framework=tensorflow-lite model=/home/root/.cache/gopoint/mobilenet_ssd_v2_coco_quant_postprocess.tflite accelerator=true:npu custom=Delegate:External,ExtDelegateLib:libvx_delegate.so silent=FALSE name=tensor_filter latency=1 ! tensor_sink name=tensor_sink t. ! imxvideoconvert_g2d ! cairooverlay name=tensor_res ! queue max-size-buffers=2 leaky=2 ! fpsdisplaysink name=img_tensor text-overlay=false video-sink=waylandsink sync=false

 

보기어려우니 엔터로 구분

v4l2src name=cam_src device=/dev/video3 !
imxvideoconvert_g2d !
video/x-raw,width=1920,height=1080,framerate=30/1,format=BGRx !
tee name=t t. !
imxvideoconvert_g2d !
video/x-raw,width=300,height=300, !
queue max-size-buffers=2 leaky=2 !
videoconvert !
video/x-raw,format=RGB !
tensor_converter !
tensor_filter framework=tensorflow-lite model=/home/root/.cache/gopoint/mobilenet_ssd_v2_coco_quant_postprocess.tflite accelerator=true:npu custom=Delegate:External,ExtDelegateLib:libvx_delegate.so silent=FALSE name=tensor_filter latency=1 !
tensor_sink name=tensor_sink t. !
imxvideoconvert_g2d !
cairooverlay name=tensor_res !
queue max-size-buffers=2 leaky=2 !
fpsdisplaysink name=img_tensor text-overlay=false video-sink=waylandsink sync=false

 

+

2024.01.03

# cd /home/root/.nxp-demo-experience/scripts/machine_learning
# python3 nndetection.py /dev/video3 NPU /home/root/.cache/gopoint/mobilenet_ssd_v2_coco_quant_postprocess.tflite /home/root/.cache/gopoint/coco_labels.txt

 

gst-launch 로도 실행은 되는데 callback 처리가 안되서 overlay가 출력이 안되어 동일한 화면을 보여주진 않는다.

gst-launch-1.0 v4l2src name=cam_src device=/dev/video3 ! imxvideoconvert_g2d ! video/x-raw,width=1920,height=1080,framerate=30/1,format=BGRx ! tee name=t t. ! imxvideoconvert_g2d ! video/x-raw,width=300,height=300, ! queue max-size-buffers=2 leaky=2 ! videoconvert ! video/x-raw,format=RGB ! tensor_converter ! tensor_filter framework=tensorflow-lite model=/home/root/.cache/gopoint/mobilenet_ssd_v2_coco_quant_postprocess.tflite accelerator=true:npu custom=Delegate:External,ExtDelegateLib:libvx_delegate.so silent=FALSE name=tensor_filter latency=1 !  tensor_sink name=tensor_sink t. ! imxvideoconvert_g2d ! cairooverlay name=tensor_res ! queue max-size-buffers=2 leaky=2 ! fpsdisplaysink name=img_tensor text-overlay=false video-sink=waylandsink sync=false
Posted by 구차니

자동완성으로 해보니 몇가지 나오는데 mnist 말고는 몰라서 찾아보는 중

>>> tf.keras.datasets.
tf.keras.datasets.boston_housing  tf.keras.datasets.cifar100        tf.keras.datasets.imdb            tf.keras.datasets.reuters         
tf.keras.datasets.cifar10         tf.keras.datasets.fashion_mnist   tf.keras.datasets.mnist

 

imdb는 영화 db

boston_housing은 statlib 사이트에서 정의된 보스톤 주택가격

reuter는 46 주제에 따른 11228 뉴스(로이터 뉴스) 인 듯.

This is a dataset of 11,228 newswires from Reuters, labeled over 46 topics.

[링크 : https://www.tensorflow.org/api_docs/python/tf/keras/datasets/boston_housing/load_data]

[링크 : https://www.tensorflow.org/api_docs/python/tf/keras/datasets]

 

cifar10은 10개 클래스니까.. 결과도 MNIST 처럼 10개로 나올 것 같고..

The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images.

[링크 : https://www.tensorflow.org/datasets/catalog/cifar10?hl=en]

 

This dataset is just like the CIFAR-10, except it has 100 classes containing 600 images each. There are 500 training images and 100 testing images per class. The 100 classes in the CIFAR-100 are grouped into 20 superclasses. Each image comes with a "fine" label (the class to which it belongs) and a "coarse" label (the superclass to which it belongs).

[링크 : https://www.tensorflow.org/datasets/catalog/cifar100?hl=en]

[링크 : https://www.tensorflow.org/datasets/catalog/fashion_mnist?hl=en]

[링크 : https://www.tensorflow.org/datasets/catalog/emnist?hl=en]

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Posted by 구차니

신기하네.. 그냥 알아서 받네?

>>> mnist = tf.keras.datasets.mnist
>>> (train_images, train_labels), (test_images, test_labels) = mnist.load_data()
Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz
11490434/11490434 [==============================] - 1s 0us/step

 

학습하고 tflite 파일로 저장하기

import tensorflow as tf
import numpy as np

mnist = tf.keras.datasets.mnist
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()

train_images = train_images.astype(np.float32) / 255.0
test_images = test_images.astype(np.float32) / 255.0

model = tf.keras.Sequential([
  tf.keras.layers.InputLayer(input_shape=(28, 28)),
  tf.keras.layers.Reshape(target_shape=(28, 28, 1)),
  tf.keras.layers.Conv2D(filters=12, kernel_size=(3, 3), activation='relu'),
  tf.keras.layers.MaxPooling2D(pool_size=(2, 2)),
  tf.keras.layers.Flatten(),
  tf.keras.layers.Dense(10)
])

model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(
                  from_logits=True),
              metrics=['accuracy'])
model.fit(
  train_images,
  train_labels,
  epochs=5,
  validation_data=(test_images, test_labels)
)

# 일반 모델로 변환
converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()

# 요건 차이 없음
# converter = tf.lite.TFLiteConverter.from_keras_model(model)
# converter.optimizations = [tf.lite.Optimize.DEFAULT]
# tflite_model_quant = converter.convert()

# quant 를 하려면 아래 코드 실행해야 함
def representative_data_gen():
  for input_value in tf.data.Dataset.from_tensor_slices(train_images).batch(1).take(100):
    yield [input_value]

converter = tf.lite.TFLiteConverter.from_keras_model(model)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.representative_dataset = representative_data_gen
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
converter.inference_input_type = tf.uint8
converter.inference_output_type = tf.uint8
tflite_model_quant = converter.convert()

# 파일로 저장하기
import pathlib

tflite_models_dir = pathlib.Path("/tmp/mnist_tflite_models/")
tflite_models_dir.mkdir(exist_ok=True, parents=True)

# Save the unquantized/float model:
tflite_model_file = tflite_models_dir/"mnist_model.tflite"
tflite_model_file.write_bytes(tflite_model)

# Save the quantized model:
tflite_model_quant_file = tflite_models_dir/"mnist_model_quant.tflite"
tflite_model_quant_file.write_bytes(tflite_model_quant)

[링크 : https://www.tensorflow.org/lite/performance/post_training_integer_quant?hl=ko]

 

netron을 통해 생성한걸 보는데 quant나 그냥이나 어째 차이가 없냐?

 

 

+

quantization 하면 uint8로 변경된다.

 

그나저나, MNIST에 대해서 오해가 있었다.

출력이 [1,10] 인데 0~9 까지의 숫자에 대한 필기 데이터베이스지 알파벳이 아니란 것 -_-!

그래서 출력이 딱 10개인 건 당연하다는 것..

[링크 : https://en.wikipedia.org/wiki/MNIST_database]

 

+

EMNIST 라고 알파벳 손글씨가 따로 있다.

[링크 : https://www.nist.gov/itl/products-and-services/emnist-dataset]

[링크 : https://www.tensorflow.org/datasets/catalog/emnist?hl=ko]

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Posted by 구차니

왼쪽은 PC(ubuntu 22.04)

오른쪽은 arm에서 소스 받아서 직접 빌드.

동일한 tessdata 를 이용했는데 인식율에 차이가 어마어마하게 난다 -_-

 

$ tesseract --dpi 132 스크린샷\ 2023-12-26\ 17-19-43.png - -v
tesseract 4.1.1
 leptonica-1.82.0
  libgif 5.1.9 : libjpeg 8d (libjpeg-turbo 2.1.1) : libpng 1.6.37 : libtiff 4.3.0 : zlib 1.2.11 : libwebp 1.2.2 : libopenjp2 2.4.0
 Found AVX2
 Found AVX
 Found FMA
 Found SSE
 Found libarchive 3.6.0 zlib/1.2.11 liblzma/5.2.5 bz2lib/1.0.8 liblz4/1.9.3 libzstd/1.4.8
Warning:guessing pitch as xheight on row 1, block 1
Pec ea a

ach Seer

OEE PC LU)

Pee at Pere EEC
Rae ientcee Prete meee ceed

aa MEL ig
Pace
Pace Cra ur ue ecg

feta
Reset

ERTL Ee a Peay



# ./tesseract /home/root/a.png - -v
tesseract 5.3.3
 leptonica-1.84.0
  libjpeg 6b (libjpeg-turbo 2.1.5.1) : libpng 1.6.39 : zlib 1.2.13 : libopenjp2 2.5.0
 Found NEON
 Found libarchive 3.6.2 zlib/1.2.13 liblzma/5.4.4 bz2lib/1.0.8 libzstd/1.5.4
 Found libcurl/8.0.1 OpenSSL/3.1.3 zlib/1.2.13 libidn2/2.3.4
Error in pixReadMemTiff: function not present
Error in pixReadMem: tiff: no pix returned
Error in pixaGenerateFontFromString: pix not made
Error in bmfCreate: font pixa not made
Estimating resolution as 132
Warning:guessing pitch as xheight on row 1, block 1
1.MX8MP Evaluation Kit

.MxeMP 1.50 GHz

2020-06-07 2848 MB RAM

> Console Options wait, 65535 means
Select Language <Standard English> Reset

> Device Manager
> Boot Manager
> Boot Maintenance Manager

Continue
Reset

ay=Move Highlight <Enter>=Select Entry

 

--dpi 옵션 유용..한가?

[링크 : https://simmigyeong.tistory.com/3]

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