프로그램 사용/gstreamer2025. 9. 20. 20:55

GstPipelineStudio install 실패

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.

 

[링크  : https://github.com/patrickelectric/GstPipelineStudio]

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Posted by 구차니
프로그램 사용/iperf2025. 9. 19. 15:46

iperf3 affinity

-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 

[링크 : https://docs.nvidia.com/networking-ethernet-software/knowledge-base/Configuration-and-Usage/Monitoring/Throughput-Testing-and-Troubleshooting/]

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Posted by 구차니
프로그램 사용/yolo_tensorflow2025. 9. 17. 14:28

keras - transfer learning / fine tuning

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"])())

[링크 : https://stackoverflow.com/questions/59559289/is-there-any-way-to-convert-a-tensorflow-lite-tflite-file-back-to-a-keras-fil]

 

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]

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Posted by 구차니
프로그램 사용/yolo_tensorflow2025. 9. 16. 18:58

keras ssd mobilenet

 

Object Detection with RetinaNet

 

 

[링크 : https://keras.io/examples/vision/retinanet/]

    [링크 : https://www.reddit.com/r/deeplearning/comments/tk5eju/where_can_i_find_an_end_to_end_keras_based_ssd/?tl=ko]

 

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]

 

저작자표시 (새창열림)

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Posted by 구차니
프로그램 사용/yolo_tensorflow2025. 9. 15. 17:11

mobilenet 학습시키기 with keras, tensorflow

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)
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Collecting google_pasta>=0.1.1
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Collecting libclang>=13.0.0
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Collecting gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1
  Downloading gast-0.6.0-py3-none-any.whl (21 kB)
Collecting protobuf>=5.28.0
  Downloading protobuf-6.32.1-cp39-abi3-manylinux2014_x86_64.whl (322 kB)
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Collecting ml_dtypes<1.0.0,>=0.5.1
  Downloading ml_dtypes-0.5.3-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (4.9 MB)
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Collecting numpy>=1.26.0
  Downloading numpy-2.2.6-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (16.8 MB)
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Requirement already satisfied: packaging in /usr/lib/python3/dist-packages (from tensorflow) (21.3)
Requirement already satisfied: six>=1.12.0 in /usr/lib/python3/dist-packages (from tensorflow) (1.16.0)
Collecting termcolor>=1.1.0
  Downloading 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)
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Collecting tensorboard~=2.20.0
  Downloading tensorboard-2.20.0-py3-none-any.whl (5.5 MB)
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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)
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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)
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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)
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  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.
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  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)
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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)
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$ 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]

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프로그램 사용/iperf2025. 9. 15. 12:45

iperf3 udp 속도 테스트 on rpi 4

라즈베리 4 가 성능이 좋아도..

GbE UDP 테스트 하니 2세대 노트북에서 80% 쳐먹으시고

13세대에서도 25~30% 쳐드시는데

 

이걸 어떻게 해야하나 찾아보는데

이전에 이런 옵션으로 zerocpy 해서 부하를 줄여보려고 했는데

$ iperf3 -c localhost -u -f m -b 1000M -Z

 

pid 작은게 받는 쪽 pid 큰게 보내는 쪽. 대충 17.8%(클라) -> 10.9% (서버)

1988904 minimonk   20   0    8316   3968   3456 S  17.8   0.0   0:01.01 iperf3                                                                                                                       
1988889 minimonk   20   0    8316   3840   3328 S  10.9   0.0   0:01.60 iperf3   

 

-l 64k는 왜 안되는지 모르겠고 63k는 먹는데

$ iperf3 -c localhost -u -f m -b 1000M -Z -l 63K

 

pid 작은게 받는 쪽 pid 큰게 보내는 쪽. 대충 14.7%(클라) -> 8.8% (서버)

1988975 minimonk   20   0    8348   3968   3456 S  14.7   0.0   0:00.61 iperf3                                                                                                                        
1988889 minimonk   20   0    8348   3840   3328 S   8.8   0.0   0:03.46 iperf3   

 

이렇게 옵션 주고 하니 라즈베리 4에서도 UDP로 GbE 잘 뽑아낸다. 휴..

[링크 : https://serverfault.com/questions/813413/how-to-set-the-udp-packet-size-with-iperf3]

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프로그램 사용/yolo_tensorflow2025. 9. 11. 19:09

ssd mobilenet

최종 목표는 ssd-mobilenet 인데

어째 걸려온은건 mobilenet을 이용한 classification 같냐..

 

[링크 : https://keras.io/api/applications/mobilenet/]

[링크 : https://keras.io/api/models/model_training_apis/]

 

[링크 : https://kau-deeperent.tistory.com/m/59] << mobilenet을 이용한 감정 탐지

[링크 : https://wikidocs.net/193031] keras model.fit_generator() - deprecated -> model.fit()

 

전이학습 - 학습된 모델에 추가로 내가 원하는 클래스 추가하기

 FC(Fully conencted) layer

[링크 : https://recipesds.tistory.com/entry/Transfer-Learning을-해-보자-mobilenet을-활용해서요-실습]

 

tensorflow의 python 스크립트로 학습하기

[링크 : https://prod.velog.io/@choonsik_mom/MobileNet-SSD-object-detector-커스텀-데이터-학습하기-m3j5d0xh]

   [링크 : https://github.com/tensorflow/models/tree/master/research/object_detection]

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프로그램 사용/gstreamer2025. 9. 11. 15:21

gstpipelinestudio

2021년 시작해서 2022년 이후로는 수정이 없는 프로젝트

그래도 보면서 할 수 있으면 괜찮아 보이는데, 한번 설치해서 해봐야겠다

[링크 : https://github.com/patrickelectric/GstPipelineStudio?tab=readme-ov-file]

[링크 : https://blogs.igalia.com/scerveau/introducing-gstpipelinestudio-0-3-4/]

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프로그램 사용/rtl-sdr2025. 9. 11. 10:47

hackrf portapack (portable)

hackrf 라서 rx tx 다 되는 sdr이 있는데

그걸 포터블 버전으로 만들어 주는 add-on이 알리에서 보이길래 검색

 

원본(?)

[링크 : https://github.com/sharebrained/portapack-hackrf/]

  [링크 : https://sharebrained.com/portapack/]

 

portapack 을 fork

[링크  : https://github.com/furrtek/portapack-havoc/]

 

havoc을 fork. 그러나 portapack에서 정식으로 인정?

[링크 : https://github.com/portapack-mayhem/mayhem-firmware]

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프로그램 사용/rtl-sdr2025. 9. 11. 10:31

gnuradio LoRa

gnu radio에서 lora의 패킷을 볼 수 있으려나?

 

Functionalities
Sending and receiving LoRa packets between USRP-USRP and USRP-commercial LoRa transceiver (tested with RFM95, SX1276, SX1262).

Parameters available:

Spreading factors: 5-12*
Coding rates: 0-4
Implicit and explicit header mode
Payload length: 1-255 bytes
Sync word selection (network ID)
Verification of payload CRC
Verification of explicit header checksum
Low datarate optimisation mode
Utilisation of soft-decision decoding for improved performances
* Spreading factors 5 and 6 are not compatible with SX126x.

[링크 : https://github.com/tapparelj/gr-lora_sdr]

 

Hardware support
The following LoRa modules and SDRs were tested and work with gr-lora:

Transmitters: Pycom LoPy, Dragino LoRa Raspberry Pi HAT, Adafruit Feather 32u4, Microchip RN 2483 (custom board), SX1276(Custom Board with STM32 Support) Receivers: HackRF One, USRP B201, RTL-SDR, LimeSDR(LMS7002M)-LimeSDR USB.

[링크 : https://github.com/rpp0/gr-lora]

 

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