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No commits in common. "main" and "feature/load-dataset" have entirely different histories.
main
...
feature/lo
5
.gitignore
vendored
5
.gitignore
vendored
@ -1,7 +1,6 @@
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secrets.txt
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.ipynb_checkpoints
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data/
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*.zip
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||||
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# https://github.com/microsoft/vscode-python/blob/main/.gitignore
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.DS_Store
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||||
.huskyrc.json
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@ -52,4 +51,4 @@ dist/**
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||||
# translation files
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*.xlf
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package.nls.*.json
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l10n/
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l10n/
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36
Dockerfile
36
Dockerfile
@ -1,36 +0,0 @@
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FROM ubuntu:22.04
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# Packages
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RUN apt-get update && apt-get upgrade && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \
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curl liblzma-dev python-tk python3-tk tk-dev libssl-dev libffi-dev libncurses5-dev zlib1g zlib1g-dev \
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libreadline-dev libbz2-dev libsqlite3-dev make gcc curl git-all wget python3-openssl gnupg2
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|
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# Setup CUDA
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RUN apt-key del 7fa2af80 && \
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wget https://developer.download.nvidia.com/compute/cuda/repos/wsl-ubuntu/x86_64/cuda-wsl-ubuntu.pin && \
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mv cuda-wsl-ubuntu.pin /etc/apt/preferences.d/cuda-repository-pin-600 && \
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wget https://developer.download.nvidia.com/compute/cuda/12.2.2/local_installers/cuda-repo-wsl-ubuntu-12-2-local_12.2.2-1_amd64.deb && \
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dpkg -i cuda-repo-wsl-ubuntu-12-2-local_12.2.2-1_amd64.deb && \
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cp /var/cuda-repo-wsl-ubuntu-12-2-local/cuda-*-keyring.gpg /usr/share/keyrings/ && \
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apt-get update && \
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apt-get -y install cuda-toolkit-12-2
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# Pyenv
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ENV PYENV_ROOT="$HOME/.pyenv"
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ENV PATH="$PYENV_ROOT/bin:$PYENV_ROOT/versions/3.10.12/bin:$PATH"
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RUN curl https://pyenv.run | bash
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RUN pyenv install 3.10.12 && \
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pyenv global 3.10.12 && \
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echo 'eval "$(pyenv init --path)"' >> ~/.bashrc && \
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echo 'eval "$(pyenv virtualenv-init -)"' >> ~/.bashrc
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SHELL ["/bin/bash", "-c"]
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WORKDIR /app
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ADD ./requirements.txt /app/requirements.txt
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RUN pip install -r requirements.txt
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ENV CUDNN_PATH="/.pyenv/versions/3.10.12/lib/python3.10/site-packages/nvidia/cudnn/"
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ENV LD_LIBRARY_PATH="$CUDNN_PATH/lib":"/usr/local/cuda-12.2/lib64"
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ENV PATH="$PATH":"/usr/local/cuda-12.2/bin"
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21
Makefile
21
Makefile
@ -1,24 +1,7 @@
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.PHONY: download-dataset resize-dataset sobel-dataset
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.PHONY: download-dataset sobel-dataset
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# Use inside docker container
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download-dataset:
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python3 ./file_manager/data_manager.py --download
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resize-dataset:
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python3 ./file_manager/data_manager.py --resize --shape 64 64 --source "original_dataset"
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sobel-dataset:
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python3 ./file_manager/data_manager.py --sobel --source "resized_dataset"
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login:
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wandb login $$(cat "$$API_KEY_SECRET")
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# Outside docker
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docker-run:
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docker-compose run --entrypoint=/bin/bash gpu
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docker-build:
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docker-compose build
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check-gpu:
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python3 ./gpu_check.py
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python3 ./file_manager/data_manager.py --sobel
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99
README.md
99
README.md
@ -12,26 +12,85 @@
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| 15.06.2024 | Prezentacja działania systemu
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| | Prezentacja wyników i skuteczności wybranego modelu
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# Dokumentacja
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# Szczegółowy harmonogram
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[Link do dokumentacji](https://uam-my.sharepoint.com/personal/krzboj_st_amu_edu_pl/_layouts/15/doc.aspx?sourcedoc={dc695bbe-68d1-4947-8c29-1d008f252a3b}&action=edit)
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Spotkania dot. progresu prac - każda niedziela, godzina 18:00-20:00.
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Poniżej, kolumna "działanie" jest w formacie `<osoba/osoby> (<numer_zadania>)`.
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Brak osoby oznacza, że zadanie nie zostało jeszcze przypisane.
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# Setup
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| Data | Działanie
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|----------------------------:|:------------------------------------------------------------|
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| 05.05.2024 | Sergiusz (1), Mateusz (3), Krzysztof (2)
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| 12.05.2024 | Wszyscy (5), (4), (6), (7.1)
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| 19.05.2024 | Wszyscy (5), (7.2)
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| 26.05.2024 | Wszyscy (5), (7.3), (9)
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| 02.06.2024 | (8)
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| 09.06.2024 | Feedback, ewentualne poprawki
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| 15.06.2024 | Finalna prezentacja
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Szczegóły działań:
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1) Przygotowanie danych i modułu do ich przetwarzania
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- Napisanie skryptu, który pobiera dane oraz rozpakowuje je lokalnie.
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- Napisanie szablonu skryptu do przetwarzania danych. Skrypt powinien tworzyć katalogi (struktura katalogowa) z danymi po transformacji. Każda transformacja na oryginalnych danych będzie commit'owana do repozytorium, tak aby reszta zeszpołu mogła ją uruchomić.
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- Napisać jedną przykładową transformację, np. resize i kontury, korzystając z szablonu.
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- Utworzyć README.md z instrukcją tworzenia nowego modułu do przetwarzania.
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2) Modele do przygotowania:
|
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- Przygotować wstępnie 3 modele w formacie WanDB, np. MobileNet, ResNet, ew. custom CNN z klasyfikacją wielozadaniową.
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- Uruchomić modele na WanDB żeby zobaczyć czy się uruchamiają i generują poprawne wykresy.
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- Utworzyć README.md z instrukcją tworzenia nowych modeli.
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3) Moduł do ładowania plików
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- Napisać moduł, który ładuje dane po transformacji. Dane będą wykorzystywane do uczenia i walidacji modelu.
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- Moduł powinien dokonywać podziału zbioru danych na 3 czesci - train, valid, test.
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- Powinno być możliwe zdefiniowanie rozmiaru batch'a, rozmiaru validation set'a, scieżki skąd załadować dane.
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- Dodać możliwość definiowania seed'a, tak aby każdy mógł uzyskać podobne rezultaty w razie potrzeby. Seed powinien być przekierowany na stdout podczas uruchamiania skryptu.
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- Dodać możliwość wyboru rozkładu danych.
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- Dane wyjściowe powinny być w formacie pozwalającym na załadowanie ich bezpośrednio do modelu (binarne, tf record, lub inne).
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- README.md opisujący w jaki sposób parametryzować moduł.
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4) Moduł do obslugi i uruchaminia WanDB Job's
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- Napisać skrypt do ściągania danych z kolejki aby obejść problem uruchamiania agenta na Colab/Kaggle.
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- Napisać skrypt, który uruchamia job'y i wysyła go na kolejkę. Powinien obsługiwać przyjmowanie hiperparamterów, oraz nazwę kolejki, do której zostanie job przesłany.
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- Napisać skrypt, który uruchamia agenta na danej maszynie.
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- Napisać skrypt do tworzenia jobów - powinna być sprecyzowana struktura katalogowa, pozwalająca na zarządzanie nimi i obsługę różnych modeli. Ewentualnie synchronizacja job'ów, między WanDB i środowiskiem lokalnym.
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- README.md opisujący powyższe.
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5) Eksperymenty, dobieranie hiperparametrów, rozkładu danych, testowanie różnych strategii. Jeżeli konieczne, dodanie nowych modeli.
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6) Dodać Heatmap'ę do modelu (CAM).
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||||
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7) Przygotowanie frontu do projektu (https://www.gradio.app/)
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1. Uruchomienie lokalne Frontu do testów.
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2. Obsługa wyświetlania Heatmap.
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||||
3. Deploy frontu na środowisko (lokalne/zdalne, do wyboru).
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||||
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8) Wybór najlepszego modelu.
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9) Modul do obslugi Sweeps - automatycznego dobierania hiperparametrów (opcjonalnie).
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# Źródło danych
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https://www.kaggle.com/datasets/vipoooool/new-plant-diseases-dataset
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# Technologie
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## WanDB
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WanDB built-in features:
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||||
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||||
- Experiments Tracking
|
||||
- Predictions Visualization
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||||
- Scheduling runs through queues & connected agents
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||||
- Model Registry
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||||
- Hyperparamter optimization via Sweeps
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||||
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||||
## Moc obliczeniowa
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||||
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||||
- Radeon 7800XT
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||||
- GeForce RTX 3060TI
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||||
- GeForce RTX 3070
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||||
- GeForce RTX 4050M
|
||||
- [zasoby uczelniane](https://laboratoria.wmi.amu.edu.pl/uslugi/zasoby-dla-projektow/maszyna-gpu/)
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||||
|
||||
1. Install Docker on your local system.
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||||
2. To build docker image and run the shell, use Makefile
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```bash
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make docker-build
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make docker-run
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||||
```
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||||
3. Get your API key from https://wandb.ai/settings#api, and add the key to **secrets.txt** file.
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||||
4. After running the container
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```bash
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make login # to login to WanDB.
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make check-gpu # to verify if GPU works
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||||
```
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||||
5. If needed, to manually run containers, run:
|
||||
```bash
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docker build -t gpu api_key="<wandb_api_key>" .
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||||
docker run --rm -it --gpus all --entrypoint /bin/bash gpu
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||||
```
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||||
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23
compose.yaml
23
compose.yaml
@ -1,23 +0,0 @@
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services:
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gpu:
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||||
image: gpu
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||||
volumes:
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||||
- .:/app
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||||
command: nvidia-smi
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||||
build:
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||||
context: .
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||||
dockerfile: Dockerfile
|
||||
environment:
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||||
API_KEY_SECRET: /run/secrets/api_key_secret
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||||
secrets:
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||||
- api_key_secret
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||||
deploy:
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||||
resources:
|
||||
reservations:
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||||
devices:
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||||
- driver: nvidia
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||||
count: 1
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||||
capabilities: [gpu]
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||||
secrets:
|
||||
api_key_secret:
|
||||
file: ./secrets.txt
|
||||
@ -1,51 +1,38 @@
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import argparse
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||||
import glob
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||||
import os
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import shutil
|
||||
from pathlib import Path
|
||||
import zipfile
|
||||
from tqdm import tqdm
|
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|
||||
import cv2
|
||||
from zipfile import ZipFile
|
||||
import os
|
||||
import wget
|
||||
import argparse
|
||||
from pathlib import Path
|
||||
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||||
main_path = Path("data/")
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path_to_train_and_valid = main_path / "%s/**/*.*"
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path_to_test_dataset = main_path / "test"
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original_dataset_name = "original_dataset"
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||||
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parser = argparse.ArgumentParser()
|
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parser.add_argument("--download", action="store_true",
|
||||
help="Download the data")
|
||||
parser.add_argument("--resize", action="store_true",
|
||||
help="Resize the dataset")
|
||||
parser.add_argument("--shape", type=int, nargs="+", default=(64, 64),
|
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help="Shape of the resized images. Applied only for resize option. Default: (64, 64)")
|
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parser.add_argument("--sobel", action="store_true",
|
||||
help="Apply Sobel filter to the dataset")
|
||||
parser.add_argument("--source", type=str, default="original_dataset",
|
||||
help="Name of the source dataset. Applied for all arguments except download. Default: original_dataset")
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
|
||||
class DataManager:
|
||||
|
||||
def download_data(self):
|
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print("Downloading")
|
||||
if not os.path.isfile("archive.zip"):
|
||||
wget.download("https://storage.googleapis.com/kaggle-data-sets/78313/182633/bundle/archive.zip?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=gcp-kaggle-com%40kaggle-161607.iam.gserviceaccount.com%2F20240512%2Fauto%2Fstorage%2Fgoog4_request&X-Goog-Date=20240512T222712Z&X-Goog-Expires=259200&X-Goog-SignedHeaders=host&X-Goog-Signature=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")
|
||||
wget.download("https://storage.googleapis.com/kaggle-data-sets/78313/182633/bundle/archive.zip?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=gcp-kaggle-com%40kaggle-161607.iam.gserviceaccount.com%2F20240502%2Fauto%2Fstorage%2Fgoog4_request&X-Goog-Date=20240502T181500Z&X-Goog-Expires=259200&X-Goog-SignedHeaders=host&X-Goog-Signature=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")
|
||||
|
||||
def unzip_data(self, file_name, path_to_extract):
|
||||
full_path_to_extract = main_path / path_to_extract
|
||||
old_path = "New Plant Diseases Dataset(Augmented)/New Plant Diseases Dataset(Augmented)"
|
||||
if not os.path.exists(main_path):
|
||||
os.makedirs(main_path)
|
||||
|
||||
with zipfile.ZipFile(file_name) as zf:
|
||||
for member in tqdm(zf.infolist(), desc='Extracting'):
|
||||
try:
|
||||
zf.extract(member, full_path_to_extract)
|
||||
except zipfile.error as e:
|
||||
pass
|
||||
ZipFile(file_name).extractall(full_path_to_extract)
|
||||
# shutil.move("data/test/test",
|
||||
# full_path_to_extract, copy_function=shutil.copytree)
|
||||
shutil.move(full_path_to_extract / old_path / "train",
|
||||
@ -58,61 +45,32 @@ class DataManager:
|
||||
shutil.rmtree(
|
||||
full_path_to_extract / "new plant diseases dataset(augmented)"
|
||||
)
|
||||
shutil.rmtree(full_path_to_extract / "test")
|
||||
self.get_test_ds_from_validation()
|
||||
|
||||
def write_image(self, image, path):
|
||||
os.makedirs(path.rsplit('/', 1)[0], exist_ok=True)
|
||||
cv2.imwrite(path, image)
|
||||
|
||||
def get_test_ds_from_validation(self, files_per_category: int = 2):
|
||||
path_to_extract = main_path / original_dataset_name
|
||||
valid_ds = glob.glob(str(path_to_extract / "valid/*/*"))
|
||||
|
||||
category_dirs = set([category_dir.split("/")[-2]
|
||||
for category_dir in valid_ds])
|
||||
category_lists = {category: [] for category in category_dirs}
|
||||
for file_path in valid_ds:
|
||||
category = file_path.split("/")[-2]
|
||||
category_lists[category].append(file_path)
|
||||
|
||||
test_dir = path_to_extract / "test"
|
||||
if not os.path.exists(test_dir):
|
||||
os.makedirs(test_dir, exist_ok=True)
|
||||
|
||||
for category, files in category_lists.items():
|
||||
os.makedirs(test_dir / category, exist_ok=True)
|
||||
files.sort()
|
||||
for file in files[:files_per_category]:
|
||||
shutil.move(file, test_dir / category)
|
||||
|
||||
def resize_dataset(self, source_dataset_name, shape):
|
||||
def resize_dataset(self, source_dataset_name, width, height):
|
||||
dataset_name = "resized_dataset"
|
||||
if not os.path.exists(main_path / dataset_name):
|
||||
counter=0
|
||||
for file in glob.glob(str(path_to_train_and_valid) % source_dataset_name, recursive=True):
|
||||
counter+=1
|
||||
path_to_file = file.replace("\\", "/")
|
||||
image = cv2.imread(path_to_file)
|
||||
image = cv2.resize(image, shape)
|
||||
image = cv2.resize(image, (width, height))
|
||||
new_path = path_to_file.replace(
|
||||
source_dataset_name, dataset_name)
|
||||
self.write_image(image, new_path)
|
||||
print("Resized %s files" % (counter), end='\r')
|
||||
|
||||
def sobelx(self, source_dataset_name):
|
||||
dataset_name = "sobel_dataset"
|
||||
if not os.path.exists(main_path / dataset_name):
|
||||
counter=0
|
||||
for file in glob.glob(str(path_to_train_and_valid) % source_dataset_name, recursive=True):
|
||||
counter+=1
|
||||
path_to_file = file.replace("\\", "/")
|
||||
image = cv2.imread(path_to_file)
|
||||
sobel = cv2.Sobel(image, cv2.CV_64F, 1, 0, ksize=5)
|
||||
new_path = path_to_file.replace(
|
||||
source_dataset_name, dataset_name)
|
||||
self.write_image(sobel, new_path)
|
||||
print("Sobel processed %s files" % (counter), end='\r')
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
@ -120,7 +78,6 @@ if __name__ == "__main__":
|
||||
if args.download:
|
||||
data_manager.download_data()
|
||||
data_manager.unzip_data("archive.zip", original_dataset_name)
|
||||
if args.resize:
|
||||
data_manager.resize_dataset(args.source, tuple(args.shape))
|
||||
data_manager.resize_dataset(original_dataset_name, 64, 64)
|
||||
if args.sobel:
|
||||
data_manager.sobelx(args.source)
|
||||
data_manager.sobelx("resized_dataset")
|
||||
|
||||
18
gpu_check.py
18
gpu_check.py
@ -1,18 +0,0 @@
|
||||
try:
|
||||
import tensorflow
|
||||
except ImportError:
|
||||
print("Tensorflow is not installed, install requied packages from requirements.txt")
|
||||
exit(1)
|
||||
|
||||
import tensorflow
|
||||
|
||||
print("If you see the tensor result, then the Tensorflow is available.")
|
||||
rs = tensorflow.reduce_sum(tensorflow.random.normal([1000, 1000]))
|
||||
print(rs)
|
||||
|
||||
gpus = tensorflow.config.list_physical_devices('GPU')
|
||||
if len(gpus) == 0:
|
||||
print("No GPU available.")
|
||||
else:
|
||||
print(f"GPUs available: {len(gpus)}")
|
||||
print(gpus)
|
||||
@ -1,10 +0,0 @@
|
||||
max_jobs: 1
|
||||
|
||||
entity: uczenie-maszynowe-projekt
|
||||
|
||||
queues:
|
||||
- GPU queue 1
|
||||
- GPU queue 2
|
||||
|
||||
builder:
|
||||
type: docker
|
||||
15
main.py
15
main.py
@ -1,15 +0,0 @@
|
||||
from model.test_model import TestModel
|
||||
from pathlib import Path
|
||||
from dataset.dataset import Dataset
|
||||
|
||||
if __name__ == "__main__":
|
||||
# Loading dataset
|
||||
train_dataset = Dataset(Path('data/resized_dataset/train'))
|
||||
valid_dataset = Dataset(Path('data/resized_dataset/valid'))
|
||||
for i in train_dataset.take(1):
|
||||
print(i)
|
||||
|
||||
# Training model
|
||||
model = TestModel()
|
||||
history = model.fit()
|
||||
model.save("./src/model/test_model_final.keras")
|
||||
@ -1,55 +0,0 @@
|
||||
import tensorflow as tf
|
||||
|
||||
from wandb_utils.config import Config
|
||||
from wandb.keras import WandbMetricsLogger
|
||||
|
||||
|
||||
class Resnet50Model:
|
||||
def __init__(self):
|
||||
self.config = Config(epoch=8, batch_size=64).config()
|
||||
self.config.learning_rate = 0.01
|
||||
# Define specific configuration below, they will be visible in the W&B interface
|
||||
# Start of config
|
||||
self.config.optimizer = "sgd"
|
||||
self.config.loss = "sparse_categorical_crossentropy"
|
||||
self.config.metrics = ["accuracy"]
|
||||
# End
|
||||
self.model = self.__build_model()
|
||||
self.__compile()
|
||||
self.__load_dataset()
|
||||
|
||||
def __build_model(self):
|
||||
return tf.keras.applications.ResNet50(
|
||||
input_shape=(224, 224, 3), include_top=False, weights='imagenet'
|
||||
# output - odblokować ostatnią warstwę freeze
|
||||
# zobaczyc czy dziala to by default, czy wewn. warstwy są frozen, i czy ost. jest dynamiczna
|
||||
)
|
||||
|
||||
def __compile(self):
|
||||
self.model.compile(
|
||||
optimizer=self.config.optimizer,
|
||||
loss=self.config.loss,
|
||||
metrics=self.config.metrics,
|
||||
)
|
||||
|
||||
def __load_dataset(self):
|
||||
(self.x_train, self.y_train), (self.x_test, self.y_test) = tf.keras.datasets.cifar10.load_data()
|
||||
self.x_train = self.x_train.astype('float32') / 255.0
|
||||
self.x_test = self.x_test.astype('float32') / 255.0
|
||||
|
||||
def fit(self):
|
||||
wandb_callbacks = [
|
||||
WandbMetricsLogger(log_freq=5),
|
||||
# Not supported with Keras >= 3.0.0
|
||||
# WandbModelCheckpoint(filepath="models"),
|
||||
]
|
||||
return self.model.fit(
|
||||
x=self.x_train,
|
||||
y=self.y_train,
|
||||
epochs=self.config.epoch,
|
||||
batch_size=self.config.batch_size,
|
||||
callbacks=wandb_callbacks
|
||||
)
|
||||
|
||||
def save(self, filepath):
|
||||
self.model.save(filepath)
|
||||
@ -1,65 +0,0 @@
|
||||
import random
|
||||
import tensorflow as tf
|
||||
|
||||
from wandb_utils.config import Config
|
||||
from wandb.keras import WandbMetricsLogger
|
||||
|
||||
|
||||
class TestModel:
|
||||
def __init__(self):
|
||||
self.config = Config(epoch=8, batch_size=256).config()
|
||||
self.config.learning_rate = 0.01
|
||||
# Define specific configuration below, they will be visible in the W&B interface
|
||||
# Start of config
|
||||
self.config.layer_1 = 512
|
||||
self.config.activation_1 = "relu"
|
||||
self.config.dropout = random.uniform(0.01, 0.80)
|
||||
self.config.layer_2 = 10
|
||||
self.config.activation_2 = "softmax"
|
||||
self.config.optimizer = "sgd"
|
||||
self.config.loss = "sparse_categorical_crossentropy"
|
||||
self.config.metrics = ["accuracy"]
|
||||
# End
|
||||
self.model = self.__build_model()
|
||||
self.__compile()
|
||||
self.__load_dataset()
|
||||
|
||||
def __build_model(self):
|
||||
return tf.keras.models.Sequential([
|
||||
tf.keras.layers.Flatten(input_shape=(28, 28)),
|
||||
tf.keras.layers.Dense(self.config.layer_1, activation=self.config.activation_1),
|
||||
tf.keras.layers.Dropout(self.config.dropout),
|
||||
tf.keras.layers.Dense(self.config.layer_2, activation=self.config.activation_2)
|
||||
])
|
||||
|
||||
def __compile(self):
|
||||
self.model.compile(
|
||||
optimizer=self.config.optimizer,
|
||||
loss=self.config.loss,
|
||||
metrics=self.config.metrics,
|
||||
)
|
||||
def __load_dataset(self):
|
||||
mnist = tf.keras.datasets.mnist
|
||||
(self.x_train, self.y_train), (self.x_test, self.y_test) = mnist.load_data()
|
||||
self.x_train, self.x_test = self.x_train / 255.0, self.x_test / 255.0
|
||||
self.x_train, self.y_train = self.x_train[::5], self.y_train[::5]
|
||||
self.x_test, self.y_test = self.x_test[::20], self.y_test[::20]
|
||||
|
||||
def fit(self):
|
||||
wandb_callbacks = [
|
||||
WandbMetricsLogger(log_freq=5),
|
||||
# Not supported with Keras >= 3.0.0
|
||||
# WandbModelCheckpoint(filepath="models"),
|
||||
]
|
||||
return self.model.fit(
|
||||
x=self.x_train,
|
||||
y=self.y_train,
|
||||
epochs=self.config.epoch,
|
||||
batch_size=self.config.batch_size,
|
||||
validation_data=(self.x_test, self.y_test),
|
||||
callbacks=wandb_callbacks
|
||||
)
|
||||
|
||||
def save(self, filepath):
|
||||
self.model.save(filepath)
|
||||
|
||||
Binary file not shown.
@ -1,7 +1,4 @@
|
||||
tensorflow[and-cuda]==2.16.1
|
||||
tensorflow-io==0.37.0
|
||||
tensorflow==2.16.1
|
||||
numpy==1.26.4
|
||||
opencv-python==4.9.0.80
|
||||
numpy==1.26.4
|
||||
wget==3.2
|
||||
wandb==0.16.6
|
||||
@ -1 +0,0 @@
|
||||
FILL IN
|
||||
10
test.py
Normal file
10
test.py
Normal file
@ -0,0 +1,10 @@
|
||||
|
||||
from pathlib import Path
|
||||
|
||||
from dataset.dataset import Dataset
|
||||
|
||||
train_dataset = Dataset(Path('data/resized_dataset/train'))
|
||||
valid_dataset = Dataset(Path('data/resized_dataset/valid'))
|
||||
|
||||
for i in train_dataset.take(1):
|
||||
print(i)
|
||||
363
testing.ipynb
Normal file
363
testing.ipynb
Normal file
@ -0,0 +1,363 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"Tracking run with wandb version 0.16.6"
|
||||
],
|
||||
"text/plain": [
|
||||
"<IPython.core.display.HTML object>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"Run data is saved locally in <code>/mnt/c/Users/krzys/OneDrive/Studia/inz-uczenia-maszynowego/Detection-of-plant-diseases/wandb/run-20240416_232247-bfji8amn</code>"
|
||||
],
|
||||
"text/plain": [
|
||||
"<IPython.core.display.HTML object>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"Syncing run <strong><a href='https://wandb.ai/uczenie-maszynowe-projekt/Detection%20of%20plant%20diseases/runs/bfji8amn' target=\"_blank\">floral-energy-3</a></strong> to <a href='https://wandb.ai/uczenie-maszynowe-projekt/Detection%20of%20plant%20diseases' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
|
||||
],
|
||||
"text/plain": [
|
||||
"<IPython.core.display.HTML object>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
" View project at <a href='https://wandb.ai/uczenie-maszynowe-projekt/Detection%20of%20plant%20diseases' target=\"_blank\">https://wandb.ai/uczenie-maszynowe-projekt/Detection%20of%20plant%20diseases</a>"
|
||||
],
|
||||
"text/plain": [
|
||||
"<IPython.core.display.HTML object>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
" View run at <a href='https://wandb.ai/uczenie-maszynowe-projekt/Detection%20of%20plant%20diseases/runs/bfji8amn' target=\"_blank\">https://wandb.ai/uczenie-maszynowe-projekt/Detection%20of%20plant%20diseases/runs/bfji8amn</a>"
|
||||
],
|
||||
"text/plain": [
|
||||
"<IPython.core.display.HTML object>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Epoch 1/8\n",
|
||||
"44/47 [===========================>..] - ETA: 0s - loss: 2.1872 - accuracy: 0.2224INFO:tensorflow:Assets written to: models/assets\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"INFO:tensorflow:Assets written to: models/assets\n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: Adding directory to artifact (./models)... Done. 0.1s\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"47/47 [==============================] - 2s 32ms/step - loss: 2.1734 - accuracy: 0.2344 - val_loss: 1.9111 - val_accuracy: 0.5380\n",
|
||||
"Epoch 2/8\n",
|
||||
"40/47 [========================>.....] - ETA: 0s - loss: 1.7703 - accuracy: 0.5437INFO:tensorflow:Assets written to: models/assets\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"INFO:tensorflow:Assets written to: models/assets\n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: Adding directory to artifact (./models)... Done. 0.1s\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"47/47 [==============================] - 1s 31ms/step - loss: 1.7483 - accuracy: 0.5527 - val_loss: 1.5486 - val_accuracy: 0.6880\n",
|
||||
"Epoch 3/8\n",
|
||||
"46/47 [============================>.] - ETA: 0s - loss: 1.4466 - accuracy: 0.6818INFO:tensorflow:Assets written to: models/assets\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"INFO:tensorflow:Assets written to: models/assets\n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: Adding directory to artifact (./models)... Done. 0.1s\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"47/47 [==============================] - 2s 33ms/step - loss: 1.4444 - accuracy: 0.6829 - val_loss: 1.2824 - val_accuracy: 0.7460\n",
|
||||
"Epoch 4/8\n",
|
||||
"44/47 [===========================>..] - ETA: 0s - loss: 1.2232 - accuracy: 0.7362INFO:tensorflow:Assets written to: models/assets\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"INFO:tensorflow:Assets written to: models/assets\n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: Adding directory to artifact (./models)... Done. 0.1s\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"47/47 [==============================] - 2s 32ms/step - loss: 1.2162 - accuracy: 0.7390 - val_loss: 1.0886 - val_accuracy: 0.7880\n",
|
||||
"Epoch 5/8\n",
|
||||
"44/47 [===========================>..] - ETA: 0s - loss: 1.0583 - accuracy: 0.7694INFO:tensorflow:Assets written to: models/assets\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"INFO:tensorflow:Assets written to: models/assets\n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: Adding directory to artifact (./models)... Done. 0.1s\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"47/47 [==============================] - 1s 28ms/step - loss: 1.0519 - accuracy: 0.7711 - val_loss: 0.9497 - val_accuracy: 0.8020\n",
|
||||
"Epoch 6/8\n",
|
||||
"41/47 [=========================>....] - ETA: 0s - loss: 0.9382 - accuracy: 0.7897INFO:tensorflow:Assets written to: models/assets\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"INFO:tensorflow:Assets written to: models/assets\n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: Adding directory to artifact (./models)... Done. 0.1s\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"47/47 [==============================] - 1s 28ms/step - loss: 0.9339 - accuracy: 0.7902 - val_loss: 0.8484 - val_accuracy: 0.8180\n",
|
||||
"Epoch 7/8\n",
|
||||
"47/47 [==============================] - ETA: 0s - loss: 0.8496 - accuracy: 0.8043INFO:tensorflow:Assets written to: models/assets\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"INFO:tensorflow:Assets written to: models/assets\n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: Adding directory to artifact (./models)... Done. 0.1s\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"47/47 [==============================] - 1s 27ms/step - loss: 0.8496 - accuracy: 0.8043 - val_loss: 0.7735 - val_accuracy: 0.8220\n",
|
||||
"Epoch 8/8\n",
|
||||
"44/47 [===========================>..] - ETA: 0s - loss: 0.7790 - accuracy: 0.8180INFO:tensorflow:Assets written to: models/assets\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"INFO:tensorflow:Assets written to: models/assets\n",
|
||||
"\u001b[34m\u001b[1mwandb\u001b[0m: Adding directory to artifact (./models)... Done. 0.1s\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
|
||||
"47/47 [==============================] - 1s 29ms/step - loss: 0.7779 - accuracy: 0.8183 - val_loss: 0.7165 - val_accuracy: 0.8260\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "316da49b179f47019f8cf5c9c72353fe"
|
||||
}
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<style>\n",
|
||||
" table.wandb td:nth-child(1) { padding: 0 10px; text-align: left ; width: auto;} td:nth-child(2) {text-align: left ; width: 100%}\n",
|
||||
" .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; justify-content: flex-start; width: 100% }\n",
|
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" .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
|
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" </style>\n",
|
||||
"<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>batch/accuracy</td><td>▁▁▁▂▂▄▅▅▅▅▆▆▆▇▇▇▇▇▇▇▇▇▇▇████████████████</td></tr><tr><td>batch/batch_step</td><td>▁▁▁▂▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▆▇▇▇▇▇███</td></tr><tr><td>batch/learning_rate</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>batch/loss</td><td>███▇▇▆▆▆▅▅▅▄▄▄▄▄▃▃▃▃▃▂▂▂▂▂▂▂▂▂▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>epoch/accuracy</td><td>▁▅▆▇▇███</td></tr><tr><td>epoch/epoch</td><td>▁▂▃▄▅▆▇█</td></tr><tr><td>epoch/learning_rate</td><td>▁▁▁▁▁▁▁▁</td></tr><tr><td>epoch/loss</td><td>█▆▄▃▂▂▁▁</td></tr><tr><td>epoch/val_accuracy</td><td>▁▅▆▇▇███</td></tr><tr><td>epoch/val_loss</td><td>█▆▄▃▂▂▁▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>batch/accuracy</td><td>0.81726</td></tr><tr><td>batch/batch_step</td><td>395</td></tr><tr><td>batch/learning_rate</td><td>0.01</td></tr><tr><td>batch/loss</td><td>0.77969</td></tr><tr><td>epoch/accuracy</td><td>0.81825</td></tr><tr><td>epoch/epoch</td><td>7</td></tr><tr><td>epoch/learning_rate</td><td>0.01</td></tr><tr><td>epoch/loss</td><td>0.77791</td></tr><tr><td>epoch/val_accuracy</td><td>0.826</td></tr><tr><td>epoch/val_loss</td><td>0.71648</td></tr></table><br/></div></div>"
|
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],
|
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"text/plain": [
|
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"<IPython.core.display.HTML object>"
|
||||
]
|
||||
},
|
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"metadata": {},
|
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"output_type": "display_data"
|
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},
|
||||
{
|
||||
"data": {
|
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"text/html": [
|
||||
" View run <strong style=\"color:#cdcd00\">floral-energy-3</strong> at: <a href='https://wandb.ai/uczenie-maszynowe-projekt/Detection%20of%20plant%20diseases/runs/bfji8amn' target=\"_blank\">https://wandb.ai/uczenie-maszynowe-projekt/Detection%20of%20plant%20diseases/runs/bfji8amn</a><br/> View project at: <a href='https://wandb.ai/uczenie-maszynowe-projekt/Detection%20of%20plant%20diseases' target=\"_blank\">https://wandb.ai/uczenie-maszynowe-projekt/Detection%20of%20plant%20diseases</a><br/>Synced 5 W&B file(s), 0 media file(s), 42 artifact file(s) and 0 other file(s)"
|
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],
|
||||
"text/plain": [
|
||||
"<IPython.core.display.HTML object>"
|
||||
]
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},
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||||
"metadata": {},
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"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"Find logs at: <code>./wandb/run-20240416_232247-bfji8amn/logs</code>"
|
||||
],
|
||||
"text/plain": [
|
||||
"<IPython.core.display.HTML object>"
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]
|
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},
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"metadata": {},
|
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"output_type": "display_data"
|
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}
|
||||
],
|
||||
"source": [
|
||||
"# This script needs these libraries to be installed:\n",
|
||||
"# tensorflow, numpy\n",
|
||||
"\n",
|
||||
"import wandb\n",
|
||||
"from wandb.keras import WandbMetricsLogger, WandbModelCheckpoint\n",
|
||||
"\n",
|
||||
"import random\n",
|
||||
"import numpy as np\n",
|
||||
"import tensorflow as tf\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Start a run, tracking hyperparameters\n",
|
||||
"wandb.init(\n",
|
||||
" # set the wandb project where this run will be logged\n",
|
||||
" project=\"Detection of plant diseases\",\n",
|
||||
"\n",
|
||||
" # track hyperparameters and run metadata with wandb.config\n",
|
||||
" config={\n",
|
||||
" \"layer_1\": 512,\n",
|
||||
" \"activation_1\": \"relu\",\n",
|
||||
" \"dropout\": random.uniform(0.01, 0.80),\n",
|
||||
" \"layer_2\": 10,\n",
|
||||
" \"activation_2\": \"softmax\",\n",
|
||||
" \"optimizer\": \"sgd\",\n",
|
||||
" \"loss\": \"sparse_categorical_crossentropy\",\n",
|
||||
" \"metric\": \"accuracy\",\n",
|
||||
" \"epoch\": 8,\n",
|
||||
" \"batch_size\": 256\n",
|
||||
" }\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"# [optional] use wandb.config as your config\n",
|
||||
"config = wandb.config\n",
|
||||
"\n",
|
||||
"# get the data\n",
|
||||
"mnist = tf.keras.datasets.mnist\n",
|
||||
"(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
|
||||
"x_train, x_test = x_train / 255.0, x_test / 255.0\n",
|
||||
"x_train, y_train = x_train[::5], y_train[::5]\n",
|
||||
"x_test, y_test = x_test[::20], y_test[::20]\n",
|
||||
"labels = [str(digit) for digit in range(np.max(y_train) + 1)]\n",
|
||||
"\n",
|
||||
"# build a model\n",
|
||||
"model = tf.keras.models.Sequential([\n",
|
||||
" tf.keras.layers.Flatten(input_shape=(28, 28)),\n",
|
||||
" tf.keras.layers.Dense(config.layer_1, activation=config.activation_1),\n",
|
||||
" tf.keras.layers.Dropout(config.dropout),\n",
|
||||
" tf.keras.layers.Dense(config.layer_2, activation=config.activation_2)\n",
|
||||
" ])\n",
|
||||
"\n",
|
||||
"# compile the model\n",
|
||||
"model.compile(optimizer=config.optimizer,\n",
|
||||
" loss=config.loss,\n",
|
||||
" metrics=[config.metric]\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
"# WandbMetricsLogger will log train and validation metrics to wandb\n",
|
||||
"# WandbModelCheckpoint will upload model checkpoints to wandb\n",
|
||||
"history = model.fit(x=x_train, y=y_train,\n",
|
||||
" epochs=config.epoch,\n",
|
||||
" batch_size=config.batch_size,\n",
|
||||
" validation_data=(x_test, y_test),\n",
|
||||
" callbacks=[\n",
|
||||
" WandbMetricsLogger(log_freq=5),\n",
|
||||
" WandbModelCheckpoint(\"models\")\n",
|
||||
" ])\n",
|
||||
"\n",
|
||||
"# [optional] finish the wandb run, necessary in notebooks\n",
|
||||
"wandb.finish()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.12"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@ -1,22 +0,0 @@
|
||||
import wandb
|
||||
|
||||
class Config:
|
||||
def __init__(self, epoch, batch_size):
|
||||
self.epoch = epoch
|
||||
self.batch_size = batch_size
|
||||
|
||||
self.run = wandb.init(
|
||||
project="Detection of plant diseases",
|
||||
config={
|
||||
"epoch": epoch,
|
||||
"batch_size": batch_size,
|
||||
}
|
||||
)
|
||||
|
||||
def config(self):
|
||||
return self.run.config
|
||||
|
||||
def finish(self):
|
||||
self.run.config.finish()
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user