아래에서 받아서 빌드하고 설치하고
[링크 : https://github.com/rpp0/gr-lora]
gqrx로 일단 주파수를 보는데 잘 나오는것 확인
몇번의 고생(?) 끝에
설정을 맞춰보려고 했는데 파형은 가끔 보이는데 데이터로 파싱까진 가지 못한다.
너무 짧게 지나가서 동영상으로 촬영한 파형. 오른쪽이 신호가 잡힌 것
 |
 |
| gnuraduio wx gui deprecated (0) | 2025.09.25 |
|---|---|
| qr-lora tutorial (0) | 2025.09.25 |
| osmo sdr (0) | 2025.09.22 |
| gnuradio lora (0) | 2025.09.21 |
| hackrf portapack (portable) (0) | 2025.09.11 |
예전에 본거 같은데 이제야 해보네
| $ apt-cache search osmosdr gr-osmosdr - Gnuradio blocks from the OsmoSDR project libgnuradio-osmosdr0.2.0t64 - Gnuradio blocks from the OsmoSDR project - library libosmosdr-dev - Software defined radio support for OsmoSDR hardware (development files) libosmosdr0 - Software defined radio support for OsmoSDR hardware (library) osmo-sdr - Software defined radio support for OsmoSDR hardware (tools) soapyosmo-common0.8 - Use gr-osmosdr drivers with SoapySDR (common files) soapysdr-module-osmosdr - OsmoSDR device support for SoapySDR (default version) soapysdr0.8-module-osmosdr - OsmoSDR device support for SoapySDR |
[링크 : https://osmocom.org/projects/osmosdr/wiki/OsmoSDR]
[링크 : https://osmocom.org/projects/osmosdr/wiki/Hardware]
원래는 osmosdr 이라는 장치를 위한 녀석이었는데
trl2832u 기반의 sdr도 지원한다고. librtlsdr 이라는 패키지도 있나보다?
| gr-osmosdr - generic gnuradio SDR I/O block While originally being developed for the OsmoSDR hardware, this block has become a generic SDR I/O block for a variety of SDR hardware, including: FUNcube Dongle / Pro+ through gr-funcube RTL2832U based DVB-T dongles through librtlsdr RTL-TCP spectrum server (see librtlsdr project) MSi2500 based DVB-T dongles through libmirisdr SDRplay RSP through SDRplay API library gnuradio .cfile input through libgnuradio-blocks RFSPACE SDR-IQ, SDR-IP, NetSDR (incl. X2 option), Cloud-IQ, and CloudSDR AirSpy Wideband Receiver through libairspy CCCamp 2015 rad1o Badge through libhackrf Great Scott Gadgets HackRF through libhackrf Nuand LLC bladeRF through libbladeRF library Ettus USRP Devices through Ettus UHD library Fairwaves UmTRX through Fairwaves' module for UHD Fairwaves XTRX through libxtrx Red Pitaya SDR transceiver http://bazaar.redpitaya.com FreeSRP through libfreesrp By using the gr-osmosdr block you can take advantage of a common software API in your application(s) independent of the underlying radio hardware. |
[링크 : https://github.com/osmocom/gr-osmosdr]
그러면 빌드 안해도 이걸로 쓸 수 있는건가?
| $ apt-cache search librtlsdr librtlsdr-dev - Software defined radio receiver for Realtek RTL2832U (development) librtlsdr2 - Software defined radio receiver for Realtek RTL2832U (library) |
+
2025.09.24
ubuntu 22.04 에 librtlsdr0 를 설치해서 정상적으로 gqrx 작동하는 것 확인함
| qr-lora tutorial (0) | 2025.09.25 |
|---|---|
| rtlsdr + gnuradio + lora 일단.. 실패 (0) | 2025.09.24 |
| gnuradio lora (0) | 2025.09.21 |
| hackrf portapack (portable) (0) | 2025.09.11 |
| gnuradio LoRa (0) | 2025.09.11 |
설치하려니까 아래 패키지들이 필요했다.
| $ sudo apt-get install gnuradio-dev libliquid-dev liblog4cpp5-dev |
-----
상세로그
| $ cmake ../ -- The CXX compiler identification is GNU 13.3.0 -- The C compiler identification is GNU 13.3.0 -- Detecting CXX compiler ABI info -- Detecting CXX compiler ABI info - done -- Check for working CXX compiler: /usr/bin/c++ - skipped -- Detecting CXX compile features -- Detecting CXX compile features - done -- Detecting C compiler ABI info -- Detecting C compiler ABI info - done -- Check for working C compiler: /usr/bin/cc - skipped -- Detecting C compile features -- Detecting C compile features - done -- Build type not specified: defaulting to release. CMake Error at CMakeLists.txt:87 (find_package): By not providing "FindGnuradio.cmake" in CMAKE_MODULE_PATH this project has asked CMake to find a package configuration file provided by "Gnuradio", but CMake did not find one. Could not find a package configuration file provided by "Gnuradio" (requested version 3.9) with any of the following names: GnuradioConfig.cmake gnuradio-config.cmake Add the installation prefix of "Gnuradio" to CMAKE_PREFIX_PATH or set "Gnuradio_DIR" to a directory containing one of the above files. If "Gnuradio" provides a separate development package or SDK, be sure it has been installed. |
| $ sudo apt-get install gnuradio-dev |
| [ 30%] Building CXX object lib/CMakeFiles/gnuradio-lora.dir/message_socket_sink_impl.cc.o /home/minimonk/src/gr-lora/lib/decoder_impl.cc:28:10: fatal error: liquid/liquid.h: No such file or directory 28 | #include <liquid/liquid.h> | ^~~~~~~~~~~~~~~~~ compilation terminated. make[2]: *** [lib/CMakeFiles/gnuradio-lora.dir/build.make:76: lib/CMakeFiles/gnuradio-lora.dir/decoder_impl.cc.o] Error 1 make[2]: *** Waiting for unfinished jobs.... make[1]: *** [CMakeFiles/Makefile2:234: lib/CMakeFiles/gnuradio-lora.dir/all] Error 2 make: *** [Makefile:146: all] Error 2 |
| $ apt-cache search liquid libliquid-dev - signal processing library for software defined radio (development files) libliquid1 - signal processing library for software defined radio liquidctl - CLI and Python drivers for AIO liquid coolers and other devices liquidprompt - adaptative prompt for bash & zsh liquidsoap - audio streaming language liquidsoap-doc - Documentation for Liquidsoap liquidsoap-mode - Emacs mode for editing Liquidsoap code liquidwar - truly original multiplayer wargame liquidwar-data - data files for Liquid War liquidwar-server - Liquid War server ruby-jekyll-gist - Liquid tag for displaying GitHub Gists in Jekyll sites ruby-jekyll-include-cache - Jekyll plugin to cache the rendering of Liquid includes ruby-liquid - Ruby library for rendering safe HTML and email templates ruby-liquid-c - liquid performance extension in C |
| [ 45%] Linking CXX shared library libgnuradio-lora.so /usr/bin/ld: cannot find -llog4cpp: No such file or directory collect2: error: ld returned 1 exit status make[2]: *** [lib/CMakeFiles/gnuradio-lora.dir/build.make:205: lib/libgnuradio-lora.so.1.0.0.0] Error 1 make[1]: *** [CMakeFiles/Makefile2:234: lib/CMakeFiles/gnuradio-lora.dir/all] Error 2 make: *** [Makefile:146: all] Error 2 |
| $ apt-cache search log4cpp liblog4cpp-doc - C++ library for flexible logging (documentation) liblog4cpp5-dev - C++ library for flexible logging (development) liblog4cpp5v5 - C++ library for flexible logging (runtime) |
| $ sudo apt-get install liblog4cpp5-dev |
+
2025.09.22
gr-lora
| s$ ./lora_receive_file_nogui.py [?] Download test LoRa signal to decode? [y/N] y [+] Downloading https://research.edm.uhasselt.be/probyns/lora/usrp-868.1-sf7-cr4-bw125-crc-0.sigmf-data -> ./example-trace.sigmf-data . . . . . . . . . . . . . . . . . . [+] Downloading https://research.edm.uhasselt.be/probyns/lora/usrp-868.1-sf7-cr4-bw125-crc-0.sigmf-meta -> ./example-trace.sigmf-meta . . [+] Configuration: 868.1 MHz, SF 7, CR 4/8, BW 125 kHz, prlen 8, crc on, implicit off [+] Decoding. You should see a header, followed by 'deadbeef' and a CRC 5 times. Bits (nominal) per symbol: 3.5 Bins per symbol: 128 Samples per symbol: 1024 Decimation: 8 vmcircbuf_prefs::get :info: /home/minimonk/.gnuradio/prefs/vmcircbuf_default_factory failed to open: bad true, fail true, eof true 04 90 40 de ad be ef 70 0d (p) 04 90 40 de ad be ef 70 0d (p) 04 90 40 de ad be ef 70 0d (p) 04 90 40 de ad be ef 70 0d (p) 04 90 40 de ad be ef 70 0d (p) [+] Done |
실행실패
| <<< Welcome to GNU Radio Companion 3.10.9.2 >>> Block paths: /usr/share/gnuradio/grc/blocks /usr/local/share/gnuradio/grc/blocks Loading: "/home/minimonk/src/gr-lora/apps/lora_receive_realtime.grc" >>> Done Generating: "/home/minimonk/src/gr-lora/apps/lora_receive_realtime.py" >>> Warning: This flow graph contains a throttle block and another rate limiting block, e.g. a hardware source or sink. This is usually undesired. Consider removing the throttle block. >>> Warning: The block 'blocks_throttle_0' is deprecated. Executing: /usr/bin/python3 -u /home/minimonk/src/gr-lora/apps/lora_receive_realtime.py qt.qpa.plugin: Could not find the Qt platform plugin "wayland" in "" [INFO] [UHD] linux; GNU C++ version 13.2.0; Boost_108300; UHD_4.6.0.0+ds1-5.1ubuntu0.24.04.1 Traceback (most recent call last): File "/home/minimonk/src/gr-lora/apps/lora_receive_realtime.py", line 234, in <module> main() File "/home/minimonk/src/gr-lora/apps/lora_receive_realtime.py", line 212, in main tb = top_block_cls() ^^^^^^^^^^^^^^^ File "/home/minimonk/src/gr-lora/apps/lora_receive_realtime.py", line 77, in __init__ self.uhd_usrp_source_0 = uhd.usrp_source( ^^^^^^^^^^^^^^^^ RuntimeError: LookupError: KeyError: No devices found for -----> Empty Device Address >>> Done (return code 1) |
귀찮아서(!) wayland에서 x.org로 변경후 실행
[링크 : https://mokhwasomssi.tistory.com/427]
UHD :USRP source에서 RTL-SDR source로 바꾸고 일단 실행하니
먼가 그래프로 나오긴 한데.. LoRa가 없어서 지금은 머가 나오는진 모르겠다.
상단에 Variable 라고 된 것을
LoRa Receiver의 설정(?)에 변수로 연결해서 사용하는데
이전에 qt로 드래그 하는 식으로 하는걸 본거 같긴한디..
| $ wget http://research.edm.uhasselt.be/~probyns/lora/gr-lora-samples.zip $ unzip gr-lora-samples.zip |
파일에서 하는걸로 해서 해보니 그래프에서 두번째 peak가 뜰때 데이터가 하나씩 나온다.
[링크 : https://github.com/rpp0/gr-lora]
[링크 : https://github.com/tapparelj/gr-lora_sdr]
| rtlsdr + gnuradio + lora 일단.. 실패 (0) | 2025.09.24 |
|---|---|
| osmo sdr (0) | 2025.09.22 |
| hackrf portapack (portable) (0) | 2025.09.11 |
| gnuradio LoRa (0) | 2025.09.11 |
| gnuradio dvb-t (0) | 2025.04.02 |
odroid c2 / armbian 에서 빌드 성공, 실행 성공
실행하면 다음과 같이 나온다.
videotestsrc와 waylandsink 를 추가하는데
sink를 추가하니 오른쪽 끝에 스크롤 돌려서 나와서 추가딘지도 모르고 몇번을 눌렀으니 주의
src_0 와 sink_0 중에 골라서 드래그 하면 선으로 이어진다.
그리고 play 버튼을 누르면 하단에 videotestsrc가 실행되고 출력되는걸 확인할 수 있다.
우클릭하면 properties가 나오는데
머 이것저것 나오긴한다.
waylandsink로 프로퍼티를 보면
먼가 잔뜩 나오긴 한다. 머.. 이거야 capfilter를 통해서 설정해야 하는데, 참조하긴 충분하려나?
저장하고 파일을 열어보니 xml로 그래프 로 저장된다.
$ cat test
<?xml version="1.0" encoding="utf-8"?>
<Graph id="0">
<Node name="videotestsrc" id="1" type="Source" pos_x="41.492188" pos_y="111.921875">
<Port name="src_0" id="1" direction="Output" presence="Always">
<Property name="_caps" value="video/x-raw, format=(string){ A444_16LE, A444_16BE, AYUV64, RGBA64_LE, ARGB64, ARGB64_LE, BGRA64_LE, ABGR64_LE, RGBA64_BE, ARGB64_BE, BGRA64_BE, ABGR64_BE, A422_16LE, A422_16BE, A420_16LE, A420_16BE, A444_12LE, GBRA_12LE, A444_12BE, GBRA_12BE, Y412_LE, Y412_BE, A422_12LE, A422_12BE, A420_12LE, A420_12BE, A444_10LE, GBRA_10LE, A444_10BE, GBRA_10BE, A422_10LE, A422_10BE, A420_10LE, A420_10BE, BGR10A2_LE, RGB10A2_LE, Y410, A444, GBRA, AYUV, VUYA, RGBA, RBGA, ARGB, BGRA, ABGR, A422, A420, AV12, Y444_16LE, GBR_16LE, Y444_16BE, GBR_16BE, v216, P016_LE, P016_BE, Y444_12LE, GBR_12LE, Y444_12BE, GBR_12BE, I422_12LE, I422_12BE, Y212_LE, Y212_BE, I420_12LE, I420_12BE, P012_LE, P012_BE, Y444_10LE, GBR_10LE, Y444_10BE, GBR_10BE, r210, I422_10LE, I422_10BE, NV16_10LE32, Y210, UYVP, v210, I420_10LE, I420_10BE, P010_10LE, NV12_10LE40, NV12_10LE32, P010_10BE, MT2110R, MT2110T, NV12_10BE_8L128, NV12_10LE40_4L4, Y444, BGRP, GBR, RGBP, NV24, v308, IYU2, RGBx, xRGB, BGRx, xBGR, RGB, BGR, Y42B, NV16, NV61, YUY2, YVYU, UYVY, VYUY, I420, YV12, NV12, NV21, NV12_16L32S, NV12_32L32, NV12_4L4, NV12_64Z32, NV12_8L128, Y41B, IYU1, YUV9, YVU9, BGR16, RGB16, BGR15, RGB15, RGB8P, GRAY16_LE, GRAY16_BE, GRAY10_LE32, GRAY8 }, width=(int)[ 1, 2147483647 ], height=(int)[ 1, 2147483647 ], framerate=(fraction)[ 0/1, 2147483647/1 ], multiview-mode=(string){ mono, left, right }; video/x-bayer, format=(string){ bggr, rggb, grbg, gbrg, bggr10le, rggb10le, grbg10le, gbrg10le, bggr10be, rggb10be, grbg10be, gbrg10be, bggr12le, rggb12le, grbg12le, gbrg12le, bggr12be, rggb12be, grbg12be, gbrg12be, bggr14le, rggb14le, grbg14le, gbrg14le, bggr14be, rggb14be, grbg14be, gbrg14be, bggr16le, rggb16le, grbg16le, gbrg16le, bggr16be, rggb16be, grbg16be, gbrg16be }, width=(int)[ 1, 2147483647 ], height=(int)[ 1, 2147483647 ], framerate=(fraction)[ 0/1, 2147483647/1 ], multiview-mode=(string){ mono, left, right }" />
</Port>
</Node>
<Node name="waylandsink" id="4" type="Sink" pos_x="360.66797" pos_y="109.390625">
<Port name="sink_0" id="4" direction="Input" presence="Always">
<Property name="_caps" value="video/x-raw, format=(string){ AYUV, RGBA, ARGB, BGRA, ABGR, P010_10LE, v308, RGBx, xRGB, BGRx, xBGR, RGB, BGR, Y42B, NV16, NV61, YUY2, YVYU, UYVY, I420, YV12, NV12, NV21, Y41B, YUV9, YVU9, BGR16, RGB16 }, width=(int)[ 1, 2147483647 ], height=(int)[ 1, 2147483647 ], framerate=(fraction)[ 0/1, 2147483647/1 ]; video/x-raw(memory:DMABuf), format=(string)DMA_DRM, width=(int)[ 1, 2147483647 ], height=(int)[ 1, 2147483647 ], framerate=(fraction)[ 0/1, 2147483647/1 ]" />
</Port>
</Node>
<Link id="2" node_from="1" node_to="4" port_from="1" port_to="4" active="true" />
</Graph>| gstreamer gstnavigation (0) | 2025.09.27 |
|---|---|
| GstPipelineStudio install on armbian (0) | 2025.09.20 |
| GstPipelineStudio install 실패 (0) | 2025.09.20 |
| gstpipelinestudio (0) | 2025.09.11 |
| gstreamer 기초 (0) | 2025.08.27 |
그래도 24.04 라서 그런가 20.04 보단 수월하게 설치되네
| $ cat /etc/lsb-release DISTRIB_ID=Ubuntu DISTRIB_RELEASE=24.04 DISTRIB_CODENAME=noble DISTRIB_DESCRIPTION="Ubuntu 24.04 LTS" |
다만 git readme에 써있는것 보다는 조금 많은 걸 추가로 설치해야 한다.
| sudo apt install python3-pip ninja-build pkg-config meson cmake libgstreamer-plugins-bad1.0-dev sudo apt install libgtk-4-dev libgstreamer1.0-dev libgstreamer-plugins-base1.0-dev sudo apt install appstream-util gettext |
[링크 : https://stackoverflow.com/questions/9500898/compiler-error-msgfmt-command-not-found]
| gstreamer gstnavigation (0) | 2025.09.27 |
|---|---|
| GstPipelineStudio on odroidc2 (0) | 2025.09.21 |
| GstPipelineStudio install 실패 (0) | 2025.09.20 |
| gstpipelinestudio (0) | 2025.09.11 |
| gstreamer 기초 (0) | 2025.08.27 |
odroid c2 / ubuntu mate 20.04
적힌거랑은 다르게 libgtk-4가 없어서 libgtk-3로 깔았는데
libgtk-3-dev
| apt install python3-pip ninja-build pkgconfig pip3 install --user meson apt install libgtk-4-dev libgstreamer1.0-dev libgstreamer-plugins-base1.0-dev meson builddir -Dbuildtype=release ninja -C builddir ./builddir/target/release/gst_pipeline_studio |
2022년에 커밋된(가장 마지막으로) 녀석이라, 2020 년 버전으로는 무리인 듯.
| commit 9e4e915b1d5c4603c312dcb007e0efbe137789af (HEAD -> main, origin/main, origin/HEAD) Author: Stéphane Cerveau <scerveau@collabora.com> Date: Wed Feb 9 11:29:42 2022 +0100 release: mention flathub for a new release |
glibc가 낮으면 포기하는데 편하지
| $ meson builddir -Dbuildtype=release The Meson build system Version: 1.9.0 Source dir: /home/odroid/GstPipelineStudio Build dir: /home/odroid/GstPipelineStudio/builddir Build type: native build WARNING: Failed to load Cargo.lock: Could not find an implementation of tomllib, nor toml2json Project name: gst_pipeline_studio Project version: 0.2.3 Host machine cpu family: aarch64 Host machine cpu: aarch64 Program python3 found: YES (/usr/bin/python3) WARNING: You should add the boolean check kwarg to the run_command call. It currently defaults to false, but it will default to true in meson 2.0. See also: https://github.com/mesonbuild/meson/issues/9300 Found pkg-config: YES (/usr/bin/pkg-config) 0.29.1 Dependency glib-2.0 found: NO. Found 2.64.6 but need: '>= 2.66' Did not find CMake 'cmake' Found CMake: NO Run-time dependency glib-2.0 found: NO meson.build:13:0: ERROR: Dependency lookup for glib-2.0 with method 'pkgconfig' failed: Invalid version, need 'glib-2.0' ['>= 2.66'] found '2.64.6'. A full log can be found at /home/odroid/GstPipelineStudio/builddir/meson-logs/meson-log.txt WARNING: Running the setup command as `meson [options]` instead of `meson setup [options]` is ambiguous and deprecated. |
| GstPipelineStudio on odroidc2 (0) | 2025.09.21 |
|---|---|
| GstPipelineStudio install on armbian (0) | 2025.09.20 |
| gstpipelinestudio (0) | 2025.09.11 |
| gstreamer 기초 (0) | 2025.08.27 |
| gstreamer pipeline (0) | 2025.08.22 |
-A 3 하면 cpu3 에서만 function call interrupts가 많이 올라가고
-A 3-4 나 -A 3/4는 3에서만 올라간다. 정상적으로 구현이 안된건가?
| $ cat /proc/interrupts CPU0 CPU1 CPU2 CPU3 CPU4 CPU5 CPU6 CPU7 CAL: 5824227 4254156 5919589 3149051 2891279 2950553 2439272 2860177 Function call interrupts |
| $ man iperf3 -A, --affinity n/n,m Set the CPU affinity, if possible (Linux, FreeBSD, and Windows only). On both the client and server you can set the local affinity by using the n form of this argument (where n is a CPU number). In addition, on the client side you can override the server's affinity for just that one test, using the n,m form of argument. Note that when using this feature, a process will only be bound to a single CPU (as opposed to a set containing potentialy mul‐ tiple CPUs). |
| $ iperf3 --help Usage: iperf3 [-s|-c host] [options] iperf3 [-h|--help] [-v|--version] Server or Client: -p, --port # server port to listen on/connect to -f, --format [kmgtKMGT] format to report: Kbits, Mbits, Gbits, Tbits -i, --interval # seconds between periodic throughput reports -F, --file name xmit/recv the specified file -A, --affinity n/n,m set CPU affinity -B, --bind <host> bind to the interface associated with the address <host> -V, --verbose more detailed output -J, --json output in JSON format --logfile f send output to a log file --forceflush force flushing output at every interval --timestamps <format> emit a timestamp at the start of each output line (using optional format string as per strftime(3)) -d, --debug emit debugging output -v, --version show version information and quit -h, --help show this message and quit Server specific: -s, --server run in server mode -D, --daemon run the server as a daemon -I, --pidfile file write PID file -1, --one-off handle one client connection then exit --server-bitrate-limit #[KMG][/#] server's total bit rate limit (default 0 = no limit) (optional slash and number of secs interval for averaging total data rate. Default is 5 seconds) --rsa-private-key-path path to the RSA private key used to decrypt authentication credentials --authorized-users-path path to the configuration file containing user credentials Client specific: -c, --client <host> run in client mode, connecting to <host> --sctp use SCTP rather than TCP -X, --xbind <name> bind SCTP association to links --nstreams # number of SCTP streams -u, --udp use UDP rather than TCP --connect-timeout # timeout for control connection setup (ms) -b, --bitrate #[KMG][/#] target bitrate in bits/sec (0 for unlimited) (default 1 Mbit/sec for UDP, unlimited for TCP) (optional slash and packet count for burst mode) --pacing-timer #[KMG] set the timing for pacing, in microseconds (default 1000) --fq-rate #[KMG] enable fair-queuing based socket pacing in bits/sec (Linux only) -t, --time # time in seconds to transmit for (default 10 secs) -n, --bytes #[KMG] number of bytes to transmit (instead of -t) -k, --blockcount #[KMG] number of blocks (packets) to transmit (instead of -t or -n) -l, --length #[KMG] length of buffer to read or write (default 128 KB for TCP, dynamic or 1460 for UDP) --cport <port> bind to a specific client port (TCP and UDP, default: ephemeral port) -P, --parallel # number of parallel client streams to run -R, --reverse run in reverse mode (server sends, client receives) --bidir run in bidirectional mode. Client and server send and receive data. -w, --window #[KMG] set window size / socket buffer size -C, --congestion <algo> set TCP congestion control algorithm (Linux and FreeBSD only) -M, --set-mss # set TCP/SCTP maximum segment size (MTU - 40 bytes) -N, --no-delay set TCP/SCTP no delay, disabling Nagle's Algorithm -4, --version4 only use IPv4 -6, --version6 only use IPv6 -S, --tos N set the IP type of service, 0-255. The usual prefixes for octal and hex can be used, i.e. 52, 064 and 0x34 all specify the same value. --dscp N or --dscp val set the IP dscp value, either 0-63 or symbolic. Numeric values can be specified in decimal, octal and hex (see --tos above). -L, --flowlabel N set the IPv6 flow label (only supported on Linux) -Z, --zerocopy use a 'zero copy' method of sending data -O, --omit N omit the first n seconds -T, --title str prefix every output line with this string --extra-data str data string to include in client and server JSON --get-server-output get results from server --udp-counters-64bit use 64-bit counters in UDP test packets --repeating-payload use repeating pattern in payload, instead of randomized payload (like in iperf2) --username username for authentication --rsa-public-key-path path to the RSA public key used to encrypt authentication credentials [KMG] indicates options that support a K/M/G suffix for kilo-, mega-, or giga- iperf3 homepage at: https://software.es.net/iperf/ Report bugs to: https://github.com/esnet/iperf |
받거나 보내는 건 각각 1개의 프로세스에서만 하도록 되어있나 보다.
| Set the CPU affinity for the sender (-A 2) or the sender, receiver (-A 2,3), where the core numbering starts at 0. This has the same effect as running numactl -C 4 iperf3. iperf3 -c <dst-ip> -A 2,3 |
| iperf3 udp 속도 테스트 on rpi 4 (0) | 2025.09.15 |
|---|---|
| iperf3 jumbo packet? (0) | 2025.02.15 |
| iperf 파일로 바로바로 떨궈서 tail로 보기 (0) | 2025.02.10 |
| iperf3 (0) | 2023.07.28 |
| iperf udp 테스트 (0) | 2022.03.14 |
tflite 에서 weight만 빼서 거기서 부터 학습 시키면 그게 fine tune / transfer learning(전이학습)이 되는건가?
| The conversion from a TensorFlow SaveModel or tf.keras H5 model to .tflite is an irreversible process. Specifically, the original model topology is optimized during the compilation by the TFLite converter, which leads to some loss of information. Also, the original tf.keras model's loss and optimizer configurations are discarded, because those aren't required for inference. However, the .tflite file still contains some information that can help you restore the original trained model. Most importantly, the weight values are available, although they might be quantized, which could lead to some loss in precision. The code example below shows you how to read weight values from a .tflite file after it's created from a simple trained tf.keras.Model. import numpy as np import tensorflow as tf # First, create and train a dummy model for demonstration purposes. model = tf.keras.Sequential([ tf.keras.layers.Dense(10, input_shape=[5], activation="relu"), tf.keras.layers.Dense(1, activation="sigmoid")]) model.compile(loss="binary_crossentropy", optimizer="sgd") xs = np.ones([8, 5]) ys = np.zeros([8, 1]) model.fit(xs, ys, epochs=1) # Convert it to a TFLite model file. converter = tf.lite.TFLiteConverter.from_keras_model(model) tflite_model = converter.convert() open("converted.tflite", "wb").write(tflite_model) # Use `tf.lite.Interpreter` to load the written .tflite back from the file system. interpreter = tf.lite.Interpreter(model_path="converted.tflite") all_tensor_details = interpreter.get_tensor_details() interpreter.allocate_tensors() for tensor_item in all_tensor_details: print("Weight %s:" % tensor_item["name"]) print(interpreter.tensor(tensor_item["index"])()) |
읽어오든.. tf.keras.application.MobileNetV2 해서 만들던 weight의 유무를 제외하면 동일하게 불러오는건가 보다.
| # Create the base model from the pre-trained model MobileNet V2 IMG_SHAPE = IMG_SIZE + (3,) base_model = tf.keras.applications.MobileNetV2(input_shape=IMG_SHAPE, include_top=False, weights='imagenet') #or load your own #base_model= tf.saved_model.load("./pretrained_models/ssd_mobilenet_v2_320x320_coco17_tpu-8/saved_model") |
[링크 : https://www.aranacorp.com/en/training-a-tensorflow2-model-with-keras/]
다시 찾아보니 keras쪽 표현으로
transfer-learning은 기존의 값을 변화시키지 않고 추가 레이어에 학습시키는 것 같고
| The typical transfer-learning workflow This leads us to how a typical transfer learning workflow can be implemented in Keras: Instantiate a base model and load pre-trained weights into it. Freeze all layers in the base model by setting trainable = False. Create a new model on top of the output of one (or several) layers from the base model. Train your new model on your new dataset. Note that an alternative, more lightweight workflow could also be: Instantiate a base model and load pre-trained weights into it. Run your new dataset through it and record the output of one (or several) layers from the base model. This is called feature extraction. Use that output as input data for a new, smaller model. First, instantiate a base model with pre-trained weights. base_model = keras.applications.Xception( weights='imagenet', # Load weights pre-trained on ImageNet. input_shape=(150, 150, 3), include_top=False) # Do not include the ImageNet classifier at the top. Then, freeze the base model. base_model.trainable = False Create a new model on top. inputs = keras.Input(shape=(150, 150, 3)) # We make sure that the base_model is running in inference mode here, # by passing `training=False`. This is important for fine-tuning, as you will # learn in a few paragraphs. x = base_model(inputs, training=False) # Convert features of shape `base_model.output_shape[1:]` to vectors x = keras.layers.GlobalAveragePooling2D()(x) # A Dense classifier with a single unit (binary classification) outputs = keras.layers.Dense(1)(x) model = keras.Model(inputs, outputs) Train the model on new data. model.compile(optimizer=keras.optimizers.Adam(), loss=keras.losses.BinaryCrossentropy(from_logits=True), metrics=[keras.metrics.BinaryAccuracy()]) model.fit(new_dataset, epochs=20, callbacks=..., validation_data=...) |
fine tuning은 모델 자체의 가중치를 변경시킬수 있도록 해서, 천천히 학습 시키는 것 같다.
| Fine-tuning Once your model has converged on the new data, you can try to unfreeze all or part of the base model and retrain the whole model end-to-end with a very low learning rate. This is an optional last step that can potentially give you incremental improvements. It could also potentially lead to quick overfitting – keep that in mind. It is critical to only do this step after the model with frozen layers has been trained to convergence. If you mix randomly-initialized trainable layers with trainable layers that hold pre-trained features, the randomly-initialized layers will cause very large gradient updates during training, which will destroy your pre-trained features. It's also critical to use a very low learning rate at this stage, because you are training a much larger model than in the first round of training, on a dataset that is typically very small. As a result, you are at risk of overfitting very quickly if you apply large weight updates. Here, you only want to readapt the pretrained weights in an incremental way. This is how to implement fine-tuning of the whole base model: # Unfreeze the base model base_model.trainable = True # It's important to recompile your model after you make any changes # to the `trainable` attribute of any inner layer, so that your changes # are take into account model.compile(optimizer=keras.optimizers.Adam(1e-5), # Very low learning rate loss=keras.losses.BinaryCrossentropy(from_logits=True), metrics=[keras.metrics.BinaryAccuracy()]) # Train end-to-end. Be careful to stop before you overfit! model.fit(new_dataset, epochs=10, callbacks=..., validation_data=...) |
[링크 : https://keras.io/guides/transfer_learning/]
+
[링크 : https://89douner.tistory.com/270] vgg16 기준 추가학습
| SavedModel 형식과 비교하여 H5 파일에 포함되지 않은 두 가지가 있습니다. model.add_loss() 및 model.add_metric()을 통해 추가된 외부 손실 및 메트릭은 저장되지 않습니다(SavedModel과 다름). 모델에 이러한 솔실 및 메트릭이 있고 훈련을 다시 시작하려면 모델을 로드한 후 이러한 손실을 다시 추가해야 합니다. 이는 self.add_loss() 및 self.add_metric()을 통해 레이어 내부에서 생성한 손실/메트릭에는 적용되지 않습니다. 이러한 손실 및 메트릭은 레이어가 로드되는 한 레이어의 call 메서드의 일부이기 때문에 계속 유지됩니다. 사용자 정의 레이어와 같은 사용자 정의 객체의 계산 그래프는 저장 파일에 포함되지 않습니다. 로드 시 Keras는 모델을 다시 구성하기 위해 이러한 객체의 Python 클래스/함수에 액세스해야 합니다. 사용자 정의 객체를 참고하세요. |
[링크 : https://www.tensorflow.org/guide/keras/save_and_serialize?hl=ko]
| keras ssd mobilenet (0) | 2025.09.16 |
|---|---|
| mobilenet 학습시키기 with keras, tensorflow (0) | 2025.09.15 |
| ssd mobilenet (0) | 2025.09.11 |
| LLM 시각화 (0) | 2025.09.06 |
| 모델 학습 (0) | 2025.09.05 |
Object Detection with RetinaNet
[링크 : https://keras.io/examples/vision/retinanet/]
MobileNet-SSD300-Keras
| Dependencies Python 2.x or 3.x Numpy TensorFlow 1.x Keras 2.x OpenCV |
[링크 : https://github.com/ManishSoni1908/Mobilenet-ssd-keras]
keras + ssd
| Dependencies Python 3.x Numpy TensorFlow 1.x Keras 2.x OpenCV Beautiful Soup 4.x |
[링크 : https://github.com/pierluigiferrari/ssd_keras]
MobileNetV3-SSD
| 操作系统: Ubuntu18.04 Python: 3.6 PyTorch: 1.1.0 |
[링크 : https://github.com/shaoshengsong/MobileNetV3-SSD]
| keras - transfer learning / fine tuning (0) | 2025.09.17 |
|---|---|
| mobilenet 학습시키기 with keras, tensorflow (0) | 2025.09.15 |
| ssd mobilenet (0) | 2025.09.11 |
| LLM 시각화 (0) | 2025.09.06 |
| 모델 학습 (0) | 2025.09.05 |
대충 버전이 맞았는지 돌아는 간다.
Epoch 1/25 52/755 [=>............................] - ETA: 1:04:39 - loss: 0.3203 - accuracy: 0.8534 |
주요 설치 패키지 버전은 아래와 같고
| keras 2.14.0 mobilenet-v3 0.1.2 numpy 1.24.4 tensorflow 2.14.0 |
수정된 소스는 다음과 같다.
그런데 voc 디렉토리를 통채로 넣었는데 어찌 돌아는 가는데.. 어떤 파일로 학습을 하는거냐.. -_-
| from keras.applications import MobileNet from keras.models import Sequential,Model from keras.layers import Dense,Dropout,Activation,Flatten,GlobalAveragePooling2D from keras.layers import Conv2D,MaxPooling2D,ZeroPadding2D from keras.layers.normalization import BatchNormalization from tensorflow.keras.preprocessing.image import ImageDataGenerator # MobileNet is designed to work with images of dim 224,224 img_rows,img_cols = 224,224 MobileNet = MobileNet(weights='imagenet',include_top=False,input_shape=(img_rows,img_cols,3)) # Here we freeze the last 4 layers # Layers are set to trainable as True by default for layer in MobileNet.layers: layer.trainable = True # Let's print our layers for (i,layer) in enumerate(MobileNet.layers): print(str(i),layer.__class__.__name__,layer.trainable) def addTopModelMobileNet(bottom_model, num_classes): """creates the top or head of the model that will be placed ontop of the bottom layers""" top_model = bottom_model.output top_model = GlobalAveragePooling2D()(top_model) top_model = Dense(1024,activation='relu')(top_model) top_model = Dense(1024,activation='relu')(top_model) top_model = Dense(512,activation='relu')(top_model) top_model = Dense(num_classes,activation='softmax')(top_model) return top_model num_classes = 5 # ['Angry','Happy','Neutral','Sad','Surprise'] FC_Head = addTopModelMobileNet(MobileNet, num_classes) model = Model(inputs = MobileNet.input, outputs = FC_Head) print(model.summary()) train_data_dir = 'VOC2012_train_val/VOC2012_train_val' validation_data_dir = 'VOC2012_test/VOC2012_test' train_datagen = ImageDataGenerator( rescale=1./255, rotation_range=30, width_shift_range=0.3, height_shift_range=0.3, horizontal_flip=True, fill_mode='nearest' ) validation_datagen = ImageDataGenerator(rescale=1./255) batch_size = 32 train_generator = train_datagen.flow_from_directory( train_data_dir, target_size = (img_rows,img_cols), batch_size = batch_size, class_mode = 'categorical' ) validation_generator = validation_datagen.flow_from_directory( validation_data_dir, target_size=(img_rows,img_cols), batch_size=batch_size, class_mode='categorical') from keras.optimizers import RMSprop,Adam from keras.callbacks import ModelCheckpoint,EarlyStopping,ReduceLROnPlateau checkpoint = ModelCheckpoint( 'emotion_face_mobilNet.h5', monitor='val_loss', mode='min', save_best_only=True, verbose=1) earlystop = EarlyStopping( monitor='val_loss', min_delta=0, patience=10, verbose=1,restore_best_weights=True) learning_rate_reduction = ReduceLROnPlateau(monitor='val_acc', patience=5, verbose=1, factor=0.2, min_lr=0.0001) callbacks = [earlystop,checkpoint,learning_rate_reduction] model.compile(loss='categorical_crossentropy', optimizer=Adam(learning_rate=0.001), metrics=['accuracy'] ) nb_train_samples = 24176 nb_validation_samples = 3006 epochs = 25 history = model.fit( train_generator, steps_per_epoch=nb_train_samples//batch_size, epochs=epochs, callbacks=callbacks, validation_data=validation_generator, validation_steps=nb_validation_samples//batch_size) |
돌리다가 에러가 나서 멘붕.. 급 귀찮아짐..
먼가 파일 갯수가 안 맞는건가?
| Epoch 1/25 718/755 [===========================>..] - ETA: 3:28 - loss: 0.1569 - accuracy: 0.9301WARNING:tensorflow:Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches (in this case, 18875 batches). You may need to use the repeat() function when building your dataset. Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/minimonk/.local/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 70, in error_handler raise e.with_traceback(filtered_tb) from None File "/home/minimonk/.local/lib/python3.10/site-packages/tensorflow/python/eager/execute.py", line 60, in quick_execute tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name, tensorflow.python.framework.errors_impl.InvalidArgumentError: Graph execution error: Detected at node categorical_crossentropy/softmax_cross_entropy_with_logits defined at (most recent call last): File "<stdin>", line 1, in <module> File "/home/minimonk/.local/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler File "/home/minimonk/.local/lib/python3.10/site-packages/keras/src/engine/training.py", line 1832, in fit File "/home/minimonk/.local/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py", line 65, in error_handler File "/home/minimonk/.local/lib/python3.10/site-packages/keras/src/engine/training.py", line 2272, in evaluate File "/home/minimonk/.local/lib/python3.10/site-packages/keras/src/engine/training.py", line 4079, in run_step File "/home/minimonk/.local/lib/python3.10/site-packages/keras/src/engine/training.py", line 2042, in test_function File "/home/minimonk/.local/lib/python3.10/site-packages/keras/src/engine/training.py", line 2025, in step_function File "/home/minimonk/.local/lib/python3.10/site-packages/keras/src/engine/training.py", line 2013, in run_step File "/home/minimonk/.local/lib/python3.10/site-packages/keras/src/engine/training.py", line 1895, in test_step File "/home/minimonk/.local/lib/python3.10/site-packages/keras/src/engine/training.py", line 1185, in compute_loss File "/home/minimonk/.local/lib/python3.10/site-packages/keras/src/engine/compile_utils.py", line 277, in __call__ File "/home/minimonk/.local/lib/python3.10/site-packages/keras/src/losses.py", line 143, in __call__ File "/home/minimonk/.local/lib/python3.10/site-packages/keras/src/losses.py", line 270, in call File "/home/minimonk/.local/lib/python3.10/site-packages/keras/src/losses.py", line 2221, in categorical_crossentropy File "/home/minimonk/.local/lib/python3.10/site-packages/keras/src/backend.py", line 5581, in categorical_crossentropy logits and labels must be broadcastable: logits_size=[32,5] labels_size=[32,3] [[{{node categorical_crossentropy/softmax_cross_entropy_with_logits}}]] [Op:__inference_test_function_15346] >>> |
전체 pip 패키지들 버전 정보는 아래와 같다.
| $ pip list Package Version ---------------------------- ---------------- absl-py 2.3.1 appdirs 1.4.4 apturl 0.5.2 astunparse 1.6.3 attrs 21.2.0 bcrypt 3.2.0 beautifulsoup4 4.10.0 beniget 0.4.1 blinker 1.4 Brlapi 0.8.3 Brotli 1.0.9 cachetools 5.5.2 certifi 2020.6.20 chardet 4.0.0 click 8.0.3 colorama 0.4.4 command-not-found 0.3 cryptography 3.4.8 cupshelpers 1.0 cycler 0.11.0 dbus-python 1.2.18 decorator 4.4.2 defer 1.0.6 distro 1.7.0 distro-info 1.1+ubuntu0.2 duplicity 0.8.21 fasteners 0.14.1 flatbuffers 25.2.10 fonttools 4.29.1 fs 2.4.12 future 0.18.2 gast 0.6.0 google-auth 2.40.3 google-auth-oauthlib 1.0.0 google-pasta 0.2.0 grpcio 1.74.0 h5py 3.14.0 html5lib 1.1 httplib2 0.20.2 idna 3.3 importlib-metadata 4.6.4 jeepney 0.7.1 keras 2.14.0 keyring 23.5.0 kiwisolver 1.3.2 language-selector 0.1 launchpadlib 1.10.16 lazr.restfulclient 0.14.4 lazr.uri 1.0.6 libclang 18.1.1 lockfile 0.12.2 louis 3.20.0 lxml 4.8.0 lz4 3.1.3+dfsg macaroonbakery 1.3.1 Mako 1.1.3 Markdown 3.9 markdown-it-py 4.0.0 MarkupSafe 3.0.2 matplotlib 3.5.1 mdurl 0.1.2 meld 3.20.4 ml-dtypes 0.2.0 mobilenet-v3 0.1.2 monotonic 1.6 more-itertools 8.10.0 mpmath 0.0.0 namex 0.1.0 netifaces 0.11.0 numpy 1.24.4 oauthlib 3.2.0 olefile 0.46 opt_einsum 3.4.0 optree 0.17.0 packaging 21.3 paramiko 2.9.3 pexpect 4.8.0 Pillow 9.0.1 pip 22.0.2 Pivy 0.6.5 ply 3.11 protobuf 4.25.8 ptyprocess 0.7.0 pyasn1 0.6.1 pyasn1_modules 0.4.2 pycairo 1.20.1 pycups 2.0.1 Pygments 2.19.2 PyGObject 3.42.1 PyJWT 2.3.0 pymacaroons 0.13.0 PyNaCl 1.5.0 pyparsing 2.4.7 pyRFC3339 1.1 python-apt 2.4.0+ubuntu4 python-dateutil 2.8.1 python-debian 0.1.43+ubuntu1.1 pythran 0.10.0 pytz 2022.1 pyxdg 0.27 PyYAML 5.4.1 reportlab 3.6.8 requests 2.25.1 requests-oauthlib 2.0.0 rich 14.1.0 rsa 4.9.1 scipy 1.15.3 scour 0.38.2 SecretStorage 3.3.1 setuptools 59.6.0 six 1.16.0 soupsieve 2.3.1 ssh-import-id 5.11 sympy 1.9 systemd-python 234 tensorboard 2.14.1 tensorboard-data-server 0.7.2 tensorflow 2.14.0 tensorflow-estimator 2.14.0 tensorflow-io-gcs-filesystem 0.37.1 termcolor 3.1.0 typing_extensions 4.15.0 ubuntu-drivers-common 0.0.0 ubuntu-pro-client 8001 ufoLib2 0.13.1 ufw 0.36.1 unattended-upgrades 0.1 unicodedata2 14.0.0 urllib3 1.26.5 usb-creator 0.3.7 wadllib 1.3.6 webencodings 0.5.1 Werkzeug 3.1.3 wheel 0.37.1 wrapt 1.14.2 xdg 5 xkit 0.0.0 zipp 1.0.0 |
-------- 아래는 참고 안하는게 속 편할지도...?
2020년 3월의 문서를 keras와 tensorflow로 2025년에 다시 시도해봄
일단은 아래처럼 설치하니 어찌 되는 느낌
| $ pip install mobilenet-v3 $ pip install tensorflow $ pip install numpy==1.26.4 |
상세로그
| $ pip install mobilenet-v3 Defaulting to user installation because normal site-packages is not writeable Collecting mobilenet-v3 Downloading mobilenet_v3-0.1.4-py3-none-any.whl (18 kB) Installing collected packages: mobilenet-v3 Successfully installed mobilenet-v3-0.1.4 $ pip install tensorflow Defaulting to user installation because normal site-packages is not writeable Collecting tensorflow Downloading tensorflow-2.20.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (620.4 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 620.4/620.4 MB 2.5 MB/s eta 0:00:00 Collecting google_pasta>=0.1.1 Downloading google_pasta-0.2.0-py3-none-any.whl (57 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 57.5/57.5 KB 9.6 MB/s eta 0:00:00 Collecting absl-py>=1.0.0 Downloading absl_py-2.3.1-py3-none-any.whl (135 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 135.8/135.8 KB 12.8 MB/s eta 0:00:00 Collecting libclang>=13.0.0 Downloading libclang-18.1.1-py2.py3-none-manylinux2010_x86_64.whl (24.5 MB) 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termcolor-3.1.0-py3-none-any.whl (7.7 kB) Collecting typing_extensions>=3.6.6 Downloading typing_extensions-4.15.0-py3-none-any.whl (44 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 44.6/44.6 KB 8.2 MB/s eta 0:00:00 Collecting tensorboard~=2.20.0 Downloading tensorboard-2.20.0-py3-none-any.whl (5.5 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 5.5/5.5 MB 10.9 MB/s eta 0:00:00 Collecting astunparse>=1.6.0 Downloading astunparse-1.6.3-py2.py3-none-any.whl (12 kB) Collecting opt_einsum>=2.3.2 Downloading opt_einsum-3.4.0-py3-none-any.whl (71 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 71.9/71.9 KB 8.4 MB/s eta 0:00:00 Collecting h5py>=3.11.0 Downloading h5py-3.14.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.6 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 4.6/4.6 MB 9.4 MB/s eta 0:00:00 Collecting flatbuffers>=24.3.25 Downloading flatbuffers-25.2.10-py2.py3-none-any.whl (30 kB) Collecting keras>=3.10.0 Downloading keras-3.11.3-py3-none-any.whl (1.4 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.4/1.4 MB 8.0 MB/s eta 0:00:00 Requirement already satisfied: setuptools in /usr/lib/python3/dist-packages (from tensorflow) (59.6.0) Requirement already satisfied: requests<3,>=2.21.0 in /usr/lib/python3/dist-packages (from tensorflow) (2.25.1) Collecting wrapt>=1.11.0 Downloading wrapt-1.17.3-cp310-cp310-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl (81 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 82.0/82.0 KB 7.3 MB/s eta 0:00:00 Collecting grpcio<2.0,>=1.24.3 Downloading grpcio-1.74.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (6.2 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 6.2/6.2 MB 12.3 MB/s eta 0:00:00 Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/lib/python3/dist-packages (from astunparse>=1.6.0->tensorflow) (0.37.1) Collecting optree Downloading optree-0.17.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (387 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 388.0/388.0 KB 11.8 MB/s eta 0:00:00 Collecting rich Downloading rich-14.1.0-py3-none-any.whl (243 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 243.4/243.4 KB 10.4 MB/s eta 0:00:00 Collecting namex Downloading namex-0.1.0-py3-none-any.whl (5.9 kB) Requirement already satisfied: pillow in /usr/lib/python3/dist-packages (from tensorboard~=2.20.0->tensorflow) (9.0.1) Collecting werkzeug>=1.0.1 Downloading werkzeug-3.1.3-py3-none-any.whl (224 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 224.5/224.5 KB 7.7 MB/s eta 0:00:00 Collecting markdown>=2.6.8 Downloading markdown-3.9-py3-none-any.whl (107 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 107.4/107.4 KB 7.1 MB/s eta 0:00:00 Collecting tensorboard-data-server<0.8.0,>=0.7.0 Downloading tensorboard_data_server-0.7.2-py3-none-manylinux_2_31_x86_64.whl (6.6 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 6.6/6.6 MB 9.9 MB/s eta 0:00:00 Collecting MarkupSafe>=2.1.1 Downloading MarkupSafe-3.0.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (20 kB) Collecting markdown-it-py>=2.2.0 Downloading markdown_it_py-4.0.0-py3-none-any.whl (87 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 87.3/87.3 KB 8.2 MB/s eta 0:00:00 Collecting pygments<3.0.0,>=2.13.0 Downloading pygments-2.19.2-py3-none-any.whl (1.2 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.2/1.2 MB 13.2 MB/s eta 0:00:00 Collecting mdurl~=0.1 Downloading mdurl-0.1.2-py3-none-any.whl (10.0 kB) Installing collected packages: namex, libclang, flatbuffers, wrapt, typing_extensions, termcolor, tensorboard-data-server, pygments, protobuf, opt_einsum, numpy, mdurl, MarkupSafe, markdown, grpcio, google_pasta, gast, astunparse, absl-py, werkzeug, optree, ml_dtypes, markdown-it-py, h5py, tensorboard, rich, keras, tensorflow WARNING: The script pygmentize is installed in '/home/minimonk/.local/bin' which is not on PATH. Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location. WARNING: The scripts f2py and numpy-config are installed in '/home/minimonk/.local/bin' which is not on PATH. Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location. WARNING: The script markdown_py is installed in '/home/minimonk/.local/bin' which is not on PATH. Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location. WARNING: The script markdown-it is installed in '/home/minimonk/.local/bin' which is not on PATH. Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location. WARNING: The script tensorboard is installed in '/home/minimonk/.local/bin' which is not on PATH. Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location. WARNING: The scripts import_pb_to_tensorboard, saved_model_cli, tensorboard, tf_upgrade_v2, tflite_convert and toco are installed in '/home/minimonk/.local/bin' which is not on PATH. Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location. Successfully installed MarkupSafe-3.0.2 absl-py-2.3.1 astunparse-1.6.3 flatbuffers-25.2.10 gast-0.6.0 google_pasta-0.2.0 grpcio-1.74.0 h5py-3.14.0 keras-3.11.3 libclang-18.1.1 markdown-3.9 markdown-it-py-4.0.0 mdurl-0.1.2 ml_dtypes-0.5.3 namex-0.1.0 numpy-2.2.6 opt_einsum-3.4.0 optree-0.17.0 protobuf-6.32.1 pygments-2.19.2 rich-14.1.0 tensorboard-2.20.0 tensorboard-data-server-0.7.2 tensorflow-2.20.0 termcolor-3.1.0 typing_extensions-4.15.0 werkzeug-3.1.3 wrapt-1.17.3 minimonk@minimonk-HP-EliteBook-2760p:~$ pip install keras Defaulting to user installation because normal site-packages is not writeable Requirement already satisfied: keras in ./.local/lib/python3.10/site-packages (3.11.3) Requirement already satisfied: absl-py in ./.local/lib/python3.10/site-packages (from keras) (2.3.1) Requirement already satisfied: numpy in ./.local/lib/python3.10/site-packages (from keras) (2.2.6) Requirement already satisfied: rich in ./.local/lib/python3.10/site-packages (from keras) (14.1.0) Requirement already satisfied: ml-dtypes in ./.local/lib/python3.10/site-packages (from keras) (0.5.3) Requirement already satisfied: namex in ./.local/lib/python3.10/site-packages (from keras) (0.1.0) Requirement already satisfied: optree in ./.local/lib/python3.10/site-packages (from keras) (0.17.0) Requirement already satisfied: h5py in ./.local/lib/python3.10/site-packages (from keras) (3.14.0) Requirement already satisfied: packaging in /usr/lib/python3/dist-packages (from keras) (21.3) Requirement already satisfied: typing-extensions>=4.6.0 in ./.local/lib/python3.10/site-packages (from optree->keras) (4.15.0) Requirement already satisfied: markdown-it-py>=2.2.0 in ./.local/lib/python3.10/site-packages (from rich->keras) (4.0.0) Requirement already satisfied: pygments<3.0.0,>=2.13.0 in ./.local/lib/python3.10/site-packages (from rich->keras) (2.19.2) Requirement already satisfied: mdurl~=0.1 in ./.local/lib/python3.10/site-packages (from markdown-it-py>=2.2.0->rich->keras) (0.1.2) $ pip install numpy==1.26.4 |
numpy 1.26.4를 깔게 된 에러메시지
| $ python3 Python 3.10.12 (main, Aug 15 2025, 14:32:43) [GCC 11.4.0] on linux Type "help", "copyright", "credits" or "license" for more information. >>> import tensorflow as tf 2025-09-15 15:28:06.544207: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:31] Could not find cuda drivers on your machine, GPU will not be used. /usr/lib/python3/dist-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 2.2.6 warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}" A module that was compiled using NumPy 1.x cannot be run in NumPy 2.2.6 as it may crash. To support both 1.x and 2.x versions of NumPy, modules must be compiled with NumPy 2.0. Some module may need to rebuild instead e.g. with 'pybind11>=2.12'. If you are a user of the module, the easiest solution will be to downgrade to 'numpy<2' or try to upgrade the affected module. We expect that some modules will need time to support NumPy 2. Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/minimonk/.local/lib/python3.10/site-packages/tensorflow/__init__.py", line 49, in <module> from tensorflow._api.v2 import __internal__ File "/home/minimonk/.local/lib/python3.10/site-packages/tensorflow/_api/v2/__internal__/__init__.py", line 13, in <module> from tensorflow._api.v2.__internal__ import feature_column File "/home/minimonk/.local/lib/python3.10/site-packages/tensorflow/_api/v2/__internal__/feature_column/__init__.py", line 8, in <module> from tensorflow.python.feature_column.feature_column_v2 import DenseColumn # line: 1777 File "/home/minimonk/.local/lib/python3.10/site-packages/tensorflow/python/feature_column/feature_column_v2.py", line 38, in <module> from tensorflow.python.feature_column import feature_column as fc_old File "/home/minimonk/.local/lib/python3.10/site-packages/tensorflow/python/feature_column/feature_column.py", line 41, in <module> from tensorflow.python.layers import base File "/home/minimonk/.local/lib/python3.10/site-packages/tensorflow/python/layers/base.py", line 16, in <module> from tensorflow.python.keras.legacy_tf_layers import base File "/home/minimonk/.local/lib/python3.10/site-packages/tensorflow/python/keras/__init__.py", line 25, in <module> from tensorflow.python.keras import models File "/home/minimonk/.local/lib/python3.10/site-packages/tensorflow/python/keras/models.py", line 25, in <module> from tensorflow.python.keras.engine import training_v1 File "/home/minimonk/.local/lib/python3.10/site-packages/tensorflow/python/keras/engine/training_v1.py", line 46, in <module> from tensorflow.python.keras.engine import training_arrays_v1 File "/home/minimonk/.local/lib/python3.10/site-packages/tensorflow/python/keras/engine/training_arrays_v1.py", line 37, in <module> from scipy.sparse import issparse # pylint: disable=g-import-not-at-top File "/usr/lib/python3/dist-packages/scipy/sparse/__init__.py", line 267, in <module> from ._csr import * File "/usr/lib/python3/dist-packages/scipy/sparse/_csr.py", line 10, in <module> from ._sparsetools import (csr_tocsc, csr_tobsr, csr_count_blocks, AttributeError: _ARRAY_API not found |
[링크 : https://mhui123.tistory.com/143]
그런데 mobilenet이 ssd가 없으면 classification만 되는 놈이었나?
| from keras.applications import MobileNet from keras.layers import Dense,Dropout,Activation, Flatten, GlobalAveragePooling2D from keras.layers import Conv2D, MaxPooling2D, ZeroPadding2D from tensorflow.keras.preprocessing.image import ImageDataGenerator from keras.optimizers import RMSprop, Adam from keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau img_rows,img_cols = 224,224 MobileNet = MobileNet(weights='imagenet', include_top=False, input_shape=(img_rows, img_cols, 3)) 2025-09-15 16:00:19.852870: E external/local_xla/xla/stream_executor/cuda/cuda_platform.cc:51] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: UNKNOWN ERROR (303) Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/mobilenet/mobilenet_1_0_224_tf_no_top.h5 17225924/17225924 ━━━━━━━━━━━━━━━━━━━━ 1s 0us/step >>> for layer in MobileNet.layers: ... layer.trainable = True ... >>> for (i, layer) in enumerate(MobileNet.layers): ... print(str(i), layer.__class__.__name__, layer.trainable) ... 0 InputLayer True 1 Conv2D True 2 BatchNormalization True 3 ReLU True 4 DepthwiseConv2D True 5 BatchNormalization True 6 ReLU True 7 Conv2D True 8 BatchNormalization True 9 ReLU True 10 ZeroPadding2D True 11 DepthwiseConv2D True 12 BatchNormalization True 13 ReLU True 14 Conv2D True 15 BatchNormalization True 16 ReLU True 17 DepthwiseConv2D True 18 BatchNormalization True 19 ReLU True 20 Conv2D True 21 BatchNormalization True 22 ReLU True 23 ZeroPadding2D True 24 DepthwiseConv2D True 25 BatchNormalization True 26 ReLU True 27 Conv2D True 28 BatchNormalization True 29 ReLU True 30 DepthwiseConv2D True 31 BatchNormalization True 32 ReLU True 33 Conv2D True 34 BatchNormalization True 35 ReLU True 36 ZeroPadding2D True 37 DepthwiseConv2D True 38 BatchNormalization True 39 ReLU True 40 Conv2D True 41 BatchNormalization True 42 ReLU True 43 DepthwiseConv2D True 44 BatchNormalization True 45 ReLU True 46 Conv2D True 47 BatchNormalization True 48 ReLU True 49 DepthwiseConv2D True 50 BatchNormalization True 51 ReLU True 52 Conv2D True 53 BatchNormalization True 54 ReLU True 55 DepthwiseConv2D True 56 BatchNormalization True 57 ReLU True 58 Conv2D True 59 BatchNormalization True 60 ReLU True 61 DepthwiseConv2D True 62 BatchNormalization True 63 ReLU True 64 Conv2D True 65 BatchNormalization True 66 ReLU True 67 DepthwiseConv2D True 68 BatchNormalization True 69 ReLU True 70 Conv2D True 71 BatchNormalization True 72 ReLU True 73 ZeroPadding2D True 74 DepthwiseConv2D True 75 BatchNormalization True 76 ReLU True 77 Conv2D True 78 BatchNormalization True 79 ReLU True 80 DepthwiseConv2D True 81 BatchNormalization True 82 ReLU True 83 Conv2D True 84 BatchNormalization True 85 ReLU True >>> MobileNet.output <KerasTensor shape=(None, 7, 7, 1024), dtype=float32, sparse=False, ragged=False, name=keras_tensor_85> >>> MobileNet.input <KerasTensor shape=(None, 224, 224, 3), dtype=float32, sparse=False, ragged=False, name=keras_tensor> >>> MobileNet.summary() Model: "mobilenet_1.00_224" ┏--------------------------------------┳-----------------------------┳-----------------┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡--------------------------------------╇-----------------------------╇-----------------┩ │ input_layer (InputLayer) │ (None, 224, 224, 3) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv1 (Conv2D) │ (None, 112, 112, 32) │ 864 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv1_bn (BatchNormalization) │ (None, 112, 112, 32) │ 128 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv1_relu (ReLU) │ (None, 112, 112, 32) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_1 (DepthwiseConv2D) │ (None, 112, 112, 32) │ 288 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_1_bn (BatchNormalization) │ (None, 112, 112, 32) │ 128 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_1_relu (ReLU) │ (None, 112, 112, 32) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_1 (Conv2D) │ (None, 112, 112, 64) │ 2,048 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_1_bn (BatchNormalization) │ (None, 112, 112, 64) │ 256 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_1_relu (ReLU) │ (None, 112, 112, 64) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pad_2 (ZeroPadding2D) │ (None, 113, 113, 64) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_2 (DepthwiseConv2D) │ (None, 56, 56, 64) │ 576 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_2_bn (BatchNormalization) │ (None, 56, 56, 64) │ 256 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_2_relu (ReLU) │ (None, 56, 56, 64) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_2 (Conv2D) │ (None, 56, 56, 128) │ 8,192 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_2_bn (BatchNormalization) │ (None, 56, 56, 128) │ 512 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_2_relu (ReLU) │ (None, 56, 56, 128) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_3 (DepthwiseConv2D) │ (None, 56, 56, 128) │ 1,152 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_3_bn (BatchNormalization) │ (None, 56, 56, 128) │ 512 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_3_relu (ReLU) │ (None, 56, 56, 128) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_3 (Conv2D) │ (None, 56, 56, 128) │ 16,384 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_3_bn (BatchNormalization) │ (None, 56, 56, 128) │ 512 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_3_relu (ReLU) │ (None, 56, 56, 128) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pad_4 (ZeroPadding2D) │ (None, 57, 57, 128) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_4 (DepthwiseConv2D) │ (None, 28, 28, 128) │ 1,152 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_4_bn (BatchNormalization) │ (None, 28, 28, 128) │ 512 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_4_relu (ReLU) │ (None, 28, 28, 128) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_4 (Conv2D) │ (None, 28, 28, 256) │ 32,768 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_4_bn (BatchNormalization) │ (None, 28, 28, 256) │ 1,024 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_4_relu (ReLU) │ (None, 28, 28, 256) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_5 (DepthwiseConv2D) │ (None, 28, 28, 256) │ 2,304 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_5_bn (BatchNormalization) │ (None, 28, 28, 256) │ 1,024 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_5_relu (ReLU) │ (None, 28, 28, 256) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_5 (Conv2D) │ (None, 28, 28, 256) │ 65,536 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_5_bn (BatchNormalization) │ (None, 28, 28, 256) │ 1,024 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_5_relu (ReLU) │ (None, 28, 28, 256) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pad_6 (ZeroPadding2D) │ (None, 29, 29, 256) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_6 (DepthwiseConv2D) │ (None, 14, 14, 256) │ 2,304 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_6_bn (BatchNormalization) │ (None, 14, 14, 256) │ 1,024 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_6_relu (ReLU) │ (None, 14, 14, 256) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_6 (Conv2D) │ (None, 14, 14, 512) │ 131,072 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_6_bn (BatchNormalization) │ (None, 14, 14, 512) │ 2,048 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_6_relu (ReLU) │ (None, 14, 14, 512) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_7 (DepthwiseConv2D) │ (None, 14, 14, 512) │ 4,608 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_7_bn (BatchNormalization) │ (None, 14, 14, 512) │ 2,048 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_7_relu (ReLU) │ (None, 14, 14, 512) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_7 (Conv2D) │ (None, 14, 14, 512) │ 262,144 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_7_bn (BatchNormalization) │ (None, 14, 14, 512) │ 2,048 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_7_relu (ReLU) │ (None, 14, 14, 512) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_8 (DepthwiseConv2D) │ (None, 14, 14, 512) │ 4,608 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_8_bn (BatchNormalization) │ (None, 14, 14, 512) │ 2,048 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_8_relu (ReLU) │ (None, 14, 14, 512) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_8 (Conv2D) │ (None, 14, 14, 512) │ 262,144 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_8_bn (BatchNormalization) │ (None, 14, 14, 512) │ 2,048 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_8_relu (ReLU) │ (None, 14, 14, 512) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_9 (DepthwiseConv2D) │ (None, 14, 14, 512) │ 4,608 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_9_bn (BatchNormalization) │ (None, 14, 14, 512) │ 2,048 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_9_relu (ReLU) │ (None, 14, 14, 512) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_9 (Conv2D) │ (None, 14, 14, 512) │ 262,144 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_9_bn (BatchNormalization) │ (None, 14, 14, 512) │ 2,048 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_9_relu (ReLU) │ (None, 14, 14, 512) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_10 (DepthwiseConv2D) │ (None, 14, 14, 512) │ 4,608 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_10_bn (BatchNormalization) │ (None, 14, 14, 512) │ 2,048 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_10_relu (ReLU) │ (None, 14, 14, 512) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_10 (Conv2D) │ (None, 14, 14, 512) │ 262,144 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_10_bn (BatchNormalization) │ (None, 14, 14, 512) │ 2,048 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_10_relu (ReLU) │ (None, 14, 14, 512) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_11 (DepthwiseConv2D) │ (None, 14, 14, 512) │ 4,608 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_11_bn (BatchNormalization) │ (None, 14, 14, 512) │ 2,048 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_11_relu (ReLU) │ (None, 14, 14, 512) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_11 (Conv2D) │ (None, 14, 14, 512) │ 262,144 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_11_bn (BatchNormalization) │ (None, 14, 14, 512) │ 2,048 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_11_relu (ReLU) │ (None, 14, 14, 512) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pad_12 (ZeroPadding2D) │ (None, 15, 15, 512) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_12 (DepthwiseConv2D) │ (None, 7, 7, 512) │ 4,608 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_12_bn (BatchNormalization) │ (None, 7, 7, 512) │ 2,048 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_12_relu (ReLU) │ (None, 7, 7, 512) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_12 (Conv2D) │ (None, 7, 7, 1024) │ 524,288 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_12_bn (BatchNormalization) │ (None, 7, 7, 1024) │ 4,096 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_12_relu (ReLU) │ (None, 7, 7, 1024) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_13 (DepthwiseConv2D) │ (None, 7, 7, 1024) │ 9,216 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_13_bn (BatchNormalization) │ (None, 7, 7, 1024) │ 4,096 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_dw_13_relu (ReLU) │ (None, 7, 7, 1024) │ 0 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_13 (Conv2D) │ (None, 7, 7, 1024) │ 1,048,576 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_13_bn (BatchNormalization) │ (None, 7, 7, 1024) │ 4,096 │ ├--------------------------------------┼-----------------------------┼-----------------┤ │ conv_pw_13_relu (ReLU) │ (None, 7, 7, 1024) │ 0 │ └--------------------------------------┴-----------------------------┴-----------------┘ Total params: 3,228,864 (12.32 MB) Trainable params: 3,206,976 (12.23 MB) Non-trainable params: 21,888 (85.50 KB) |
[링크 : https://kau-deeperent.tistory.com/m/59]
| # from keras.preprocessing.image import ImageDataGenerator # 에러났음 from tensorflow.keras.preprocessing.image import ImageDataGenerator |
[링크 : https://sugyeong0425.tistory.com/151]
voc2012 데이터셋설명
[링크 : https://bo-10000.tistory.com/38]
[링크 : https://velog.io/@kyungmin1029/CV-OpenCV]
2024.8 월 이니 한번 시도해볼 만할 듯?
[링크 : https://velog.io/@choonsik_mom/MobileNet-SSD-object-detector-커스텀-데이터-학습하기-m3j5d0xh]
| keras - transfer learning / fine tuning (0) | 2025.09.17 |
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| keras ssd mobilenet (0) | 2025.09.16 |
| ssd mobilenet (0) | 2025.09.11 |
| LLM 시각화 (0) | 2025.09.06 |
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