wayandadang
commited on
Commit
•
37e9e4f
1
Parent(s):
5d50376
deploy
Browse files- .gitignore +148 -0
- ROC Curve.jpg +0 -0
- Report Training.jpg +0 -0
- app.py +133 -0
- kan_linear.py +91 -0
- requirements.txt +79 -0
.gitignore
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# Byte-compiled / optimized / DLL files
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+
__pycache__/
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+
*.py[cod]
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*$py.class
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# C extensions
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*.so
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# Local folder
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local_folder
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+
project_demo
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project_demo/
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logs/
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local_folder/
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/demo.py
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demo.py
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runs/
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# Large folders
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weights/
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videos/
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images/
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+
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# Distribution / packaging
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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+
eggs/
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.eggs/
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lib/
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lib64/
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+
parts/
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+
sdist/
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var/
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+
wheels/
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+
pip-wheel-metadata/
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+
share/python-wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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+
MANIFEST
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+
# PyInstaller
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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*.manifest
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*.spec
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+
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.vscode
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+
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# Installer logs
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pip-log.txt
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+
pip-delete-this-directory.txt
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+
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# Unit test / coverage reports
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+
htmlcov/
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.tox/
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.nox/
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.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*.cover
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*.py,cover
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.hypothesis/
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.pytest_cache/
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# Translations
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*.mo
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*.pot
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# Django stuff:
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*.log
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local_settings.py
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db.sqlite3
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db.sqlite3-journal
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# Flask stuff:
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instance/
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.webassets-cache
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# Scrapy stuff:
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.scrapy
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# Sphinx documentation
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docs/_build/
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# PyBuilder
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target/
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# Jupyter Notebook
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.ipynb_checkpoints
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# IPython
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profile_default/
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ipython_config.py
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# pyenv
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.python-version
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# pipenv
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# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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# However, in case of collaboration, if having platform-specific dependencies or dependencies
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# having no cross-platform support, pipenv may install dependencies that don't work, or not
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# install all needed dependencies.
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#Pipfile.lock
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# PEP 582; used by e.g. github.com/David-OConnor/pyflow
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__pypackages__/
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# Celery stuff
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celerybeat-schedule
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celerybeat.pid
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# SageMath parsed files
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*.sage.py
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# Environments
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.env
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.venv
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env/
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venv/
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venv_/
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ENV/
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env.bak/
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venv.bak/
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# Spyder project settings
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.spyderproject
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.spyproject
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# Rope project settings
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.ropeproject
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# mkdocs documentation
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/site
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# mypy
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.mypy_cache/
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.dmypy.json
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dmypy.json
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# Pyre type checker
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.pyre/
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ROC Curve.jpg
ADDED
Report Training.jpg
ADDED
app.py
ADDED
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from torchvision import transforms, models
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from PIL import Image, UnidentifiedImageError
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import streamlit as st
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import numpy as np
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import requests
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from io import BytesIO
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from kan_linear import KANLinear
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import logging
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# Setup logging
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logging.basicConfig(level=logging.INFO)
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# Definisikan model ResNet Anda
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class CustomResNetKAN(nn.Module):
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def __init__(self, num_classes=1): # Set num_classes to 1 for binary classification
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super(CustomResNetKAN, self).__init__()
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self.model = models.resnet50(pretrained=False)
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self.model.fc = KANLinear(self.model.fc.in_features, num_classes)
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def forward(self, x):
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return self.model(x)
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def load_model(weights_path, device):
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model = CustomResNetKAN().to(device)
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state_dict = torch.load(weights_path, map_location=device)
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# Remove 'module.' prefix from keys
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new_state_dict = {}
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for k, v in state_dict.items():
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if k.startswith('module.'):
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new_state_dict[k[len('module.'):]] = v
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else:
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new_state_dict[k] = v
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model.load_state_dict(new_state_dict)
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model.eval()
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return model
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class CustomImageLoadingError(Exception):
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"""Custom exception for image loading errors"""
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pass
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def load_image_from_url(url):
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try:
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logging.info(f"Loading image from URL: {url}")
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response = requests.get(url)
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response.raise_for_status() # Check if the request was successful
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content_type = response.headers['Content-Type']
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logging.info(f"Content-Type: {content_type}")
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# Check if the content type is an image
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if 'image' not in content_type:
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raise CustomImageLoadingError(f"URL does not point to an image: {content_type}")
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img = Image.open(BytesIO(response.content)).convert('RGB')
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logging.info("Image successfully loaded and converted to RGB")
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return img
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except requests.HTTPError as e:
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logging.error(f"HTTPError while loading image: {e}")
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raise CustomImageLoadingError(f"Error loading image from URL: {e}")
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except UnidentifiedImageError as e:
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logging.error(f"UnidentifiedImageError while loading image: {e}")
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raise CustomImageLoadingError(f"Cannot identify image file: {e}")
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except requests.RequestException as e:
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logging.error(f"RequestException while loading image: {e}")
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raise CustomImageLoadingError(f"Error loading image from URL: {e}")
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except Exception as e:
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logging.error(f"Unexpected error while loading image: {e}")
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raise CustomImageLoadingError(f"Error loading image from URL: {e}")
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def preprocess_image(image):
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transform = transforms.Compose([
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transforms.Resize((224, 224)),
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transforms.ToTensor()
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])
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return transform(image).unsqueeze(0)
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# Streamlit app
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st.title("Cat and Dog Classification with ResNet-KAN")
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st.sidebar.title("Upload Images")
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uploaded_file = st.sidebar.file_uploader("Choose an image...", type=["jpg", "jpeg", "png", "webp"])
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image_url = st.sidebar.text_input("Or enter image URL...")
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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model = load_model('weights/best_model_resnet50_KAN.pth', device)
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img = None
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if uploaded_file is not None:
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logging.info("Image uploaded via file uploader")
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img = Image.open(uploaded_file).convert('RGB')
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elif image_url:
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try:
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img = load_image_from_url(image_url)
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except CustomImageLoadingError as e:
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st.sidebar.error(str(e))
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except Exception as e:
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st.sidebar.error(f"Unexpected error: {e}")
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st.sidebar.write("-----")
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# Define your information for the footer
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name = "Wayan Dadang"
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st.sidebar.write("Follow me on:")
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# Create a footer section with links and copyright information
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st.sidebar.markdown(f"""
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[LinkedIn](https://www.linkedin.com/in/wayan-dadang-801757116/)
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[GitHub](https://github.com/Wayan123)
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[Resume](https://wayan123.github.io/)
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© {name} - {2024}
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""", unsafe_allow_html=True)
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if img is not None:
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st.image(np.array(img), caption='Uploaded Image.', use_column_width=True)
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if st.button('Predict'):
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img_tensor = preprocess_image(img).to(device)
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with torch.no_grad():
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output = model(img_tensor)
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prob = torch.sigmoid(output).item()
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st.write(f"Prediction: {prob:.4f}")
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if prob < 0.5:
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st.write("This image is classified as a Cat.")
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else:
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st.write("This image is classified as a Dog.")
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kan_linear.py
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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4 |
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import math
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class KANLinear(nn.Module):
|
7 |
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def __init__(self, in_features, out_features, grid_size=5, spline_order=3, scale_noise=0.1, scale_base=1.0, scale_spline=1.0, enable_standalone_scale_spline=True, base_activation=nn.SiLU, grid_eps=0.02, grid_range=[-1, 1]):
|
8 |
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super(KANLinear, self).__init__()
|
9 |
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self.in_features = in_features
|
10 |
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self.out_features = out_features
|
11 |
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self.grid_size = grid_size
|
12 |
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self.spline_order = spline_order
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13 |
+
|
14 |
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h = (grid_range[1] - grid_range[0]) / grid_size
|
15 |
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grid = ((torch.arange(-spline_order, grid_size + spline_order + 1) * h + grid_range[0]).expand(in_features, -1).contiguous())
|
16 |
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self.register_buffer("grid", grid)
|
17 |
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|
18 |
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self.base_weight = nn.Parameter(torch.Tensor(out_features, in_features))
|
19 |
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self.spline_weight = nn.Parameter(torch.Tensor(out_features, in_features, grid_size + spline_order))
|
20 |
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if enable_standalone_scale_spline:
|
21 |
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self.spline_scaler = nn.Parameter(torch.Tensor(out_features, in_features))
|
22 |
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|
23 |
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self.scale_noise = scale_noise
|
24 |
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self.scale_base = scale_base
|
25 |
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self.scale_spline = scale_spline
|
26 |
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self.enable_standalone_scale_spline = enable_standalone_scale_spline
|
27 |
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self.base_activation = base_activation()
|
28 |
+
self.grid_eps = grid_eps
|
29 |
+
|
30 |
+
self.reset_parameters()
|
31 |
+
|
32 |
+
def reset_parameters(self):
|
33 |
+
nn.init.kaiming_uniform_(self.base_weight, a=math.sqrt(5) * self.scale_base)
|
34 |
+
with torch.no_grad():
|
35 |
+
noise = ((torch.rand(self.grid_size + 1, self.in_features, self.out_features) - 1 / 2) * self.scale_noise / self.grid_size)
|
36 |
+
self.spline_weight.data.copy_((self.scale_spline if not self.enable_standalone_scale_spline else 1.0) * self.curve2coeff(self.grid.T[self.spline_order : -self.spline_order], noise))
|
37 |
+
if self.enable_standalone_scale_spline:
|
38 |
+
nn.init.kaiming_uniform_(self.spline_scaler, a=math.sqrt(5) * self.scale_spline)
|
39 |
+
|
40 |
+
def b_splines(self, x: torch.Tensor):
|
41 |
+
assert x.dim() == 2 and x.size(1) == self.in_features
|
42 |
+
grid = self.grid
|
43 |
+
x = x.unsqueeze(-1)
|
44 |
+
bases = ((x >= grid[:, :-1]) & (x < grid[:, 1:])).to(x.dtype)
|
45 |
+
for k in range(1, self.spline_order + 1):
|
46 |
+
bases = ((x - grid[:, : -(k + 1)]) / (grid[:, k:-1] - grid[:, : -(k + 1)]) * bases[:, :, :-1]) + ((grid[:, k + 1 :] - x) / (grid[:, k + 1 :] - grid[:, 1:(-k)]) * bases[:, :, 1:])
|
47 |
+
assert bases.size() == (x.size(0), self.in_features, self.grid_size + self.spline_order)
|
48 |
+
return bases.contiguous()
|
49 |
+
|
50 |
+
def curve2coeff(self, x: torch.Tensor, y: torch.Tensor):
|
51 |
+
assert x.dim() == 2 and x.size(1) == self.in_features
|
52 |
+
assert y.size() == (x.size(0), self.in_features, self.out_features)
|
53 |
+
A = self.b_splines(x).transpose(0, 1)
|
54 |
+
B = y.transpose(0, 1)
|
55 |
+
solution = torch.linalg.lstsq(A, B).solution
|
56 |
+
result = solution.permute(2, 0, 1)
|
57 |
+
assert result.size() == (self.out_features, self.in_features, self.grid_size + self.spline_order)
|
58 |
+
return result.contiguous()
|
59 |
+
|
60 |
+
@property
|
61 |
+
def scaled_spline_weight(self):
|
62 |
+
return self.spline_weight * (self.spline_scaler.unsqueeze(-1) if self.enable_standalone_scale_spline else 1.0)
|
63 |
+
|
64 |
+
def forward(self, x: torch.Tensor):
|
65 |
+
assert x.dim() == 2 and x.size(1) == self.in_features
|
66 |
+
base_output = F.linear(self.base_activation(x), self.base_weight)
|
67 |
+
spline_output = F.linear(self.b_splines(x).view(x.size(0), -1), self.scaled_spline_weight.view(self.out_features, -1))
|
68 |
+
return base_output + spline_output
|
69 |
+
|
70 |
+
@torch.no_grad()
|
71 |
+
def update_grid(self, x: torch.Tensor, margin=0.01):
|
72 |
+
assert x.dim() == 2 and x.size(1) == self.in_features
|
73 |
+
batch = x.size(0)
|
74 |
+
splines = self.b_splines(x).permute(1, 0, 2)
|
75 |
+
orig_coeff = self.scaled_spline_weight.permute(1, 2, 0)
|
76 |
+
unreduced_spline_output = torch.bmm(splines, orig_coeff).permute(1, 0, 2)
|
77 |
+
x_sorted = torch.sort(x, dim=0)[0]
|
78 |
+
grid_adaptive = x_sorted[torch.linspace(0, batch - 1, self.grid_size + 1, dtype=torch.int64, device=x.device)]
|
79 |
+
uniform_step = (x_sorted[-1] - x_sorted[0] + 2 * margin) / self.grid_size
|
80 |
+
grid_uniform = (torch.arange(self.grid_size + 1, dtype=torch.float32, device=x.device).unsqueeze(1) * uniform_step + x_sorted[0] - margin)
|
81 |
+
grid = self.grid_eps * grid_uniform + (1 - self.grid_eps) * grid_adaptive
|
82 |
+
grid = torch.cat([grid[:1] - uniform_step * torch.arange(self.spline_order, 0, -1, device=x.device).unsqueeze(1), grid, grid[-1:] + uniform_step * torch.arange(1, self.spline_order + 1, device=x.device).unsqueeze(1)], dim=0)
|
83 |
+
self.grid.copy_(grid.T)
|
84 |
+
self.spline_weight.data.copy_(self.curve2coeff(x, unreduced_spline_output))
|
85 |
+
|
86 |
+
def regularization_loss(self, regularize_activation=1.0, regularize_entropy=1.0):
|
87 |
+
l1_fake = self.spline_weight.abs().mean(-1)
|
88 |
+
regularization_loss_activation = l1_fake.sum()
|
89 |
+
p = l1_fake / regularization_loss_activation
|
90 |
+
regularization_loss_entropy = -torch.sum(p * p.log())
|
91 |
+
return regularize_activation * regularization_loss_activation + regularize_entropy * regularization_loss_entropy
|
requirements.txt
ADDED
@@ -0,0 +1,79 @@
|
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|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
altair==5.2.0
|
2 |
+
attrs==23.2.0
|
3 |
+
blinker==1.7.0
|
4 |
+
cachetools==5.3.3
|
5 |
+
certifi==2024.2.2
|
6 |
+
charset-normalizer==3.3.2
|
7 |
+
click==8.1.7
|
8 |
+
contourpy==1.2.0
|
9 |
+
cycler==0.12.1
|
10 |
+
filelock==3.13.1
|
11 |
+
fonttools==4.50.0
|
12 |
+
fsspec==2024.3.1
|
13 |
+
gitdb==4.0.11
|
14 |
+
GitPython==3.1.42
|
15 |
+
idna==3.6
|
16 |
+
Jinja2==3.1.3
|
17 |
+
jsonschema==4.21.1
|
18 |
+
jsonschema-specifications==2023.12.1
|
19 |
+
kiwisolver==1.4.5
|
20 |
+
markdown-it-py==3.0.0
|
21 |
+
MarkupSafe==2.1.5
|
22 |
+
matplotlib==3.8.3
|
23 |
+
mdurl==0.1.2
|
24 |
+
mpmath==1.3.0
|
25 |
+
networkx==3.2.1
|
26 |
+
numpy==1.26.4
|
27 |
+
nvidia-cublas-cu12==12.1.3.1
|
28 |
+
nvidia-cuda-cupti-cu12==12.1.105
|
29 |
+
nvidia-cuda-nvrtc-cu12==12.1.105
|
30 |
+
nvidia-cuda-runtime-cu12==12.1.105
|
31 |
+
nvidia-cudnn-cu12==8.9.2.26
|
32 |
+
nvidia-cufft-cu12==11.0.2.54
|
33 |
+
nvidia-curand-cu12==10.3.2.106
|
34 |
+
nvidia-cusolver-cu12==11.4.5.107
|
35 |
+
nvidia-cusparse-cu12==12.1.0.106
|
36 |
+
nvidia-nccl-cu12==2.19.3
|
37 |
+
nvidia-nvjitlink-cu12==12.4.99
|
38 |
+
nvidia-nvtx-cu12==12.1.105
|
39 |
+
opencv-python==4.9.0.80
|
40 |
+
packaging==23.2
|
41 |
+
pandas==2.2.1
|
42 |
+
pillow==10.2.0
|
43 |
+
protobuf==4.25.3
|
44 |
+
psutil==5.9.8
|
45 |
+
py-cpuinfo==9.0.0
|
46 |
+
pyarrow==15.0.2
|
47 |
+
pydeck==0.8.1b0
|
48 |
+
Pygments==2.17.2
|
49 |
+
pyparsing==3.1.2
|
50 |
+
python-dateutil==2.9.0.post0
|
51 |
+
pytz==2024.1
|
52 |
+
PyYAML==6.0.1
|
53 |
+
referencing==0.34.0
|
54 |
+
requests==2.31.0
|
55 |
+
rich==13.7.1
|
56 |
+
rpds-py==0.18.0
|
57 |
+
scipy==1.12.0
|
58 |
+
seaborn==0.13.2
|
59 |
+
six==1.16.0
|
60 |
+
smmap==5.0.1
|
61 |
+
streamlit==1.32.2
|
62 |
+
sympy==1.12
|
63 |
+
tenacity==8.2.3
|
64 |
+
thop==0.1.1.post2209072238
|
65 |
+
toml==0.10.2
|
66 |
+
toolz==0.12.1
|
67 |
+
torch==2.2.1
|
68 |
+
torchvision==0.17.1
|
69 |
+
tornado==6.4
|
70 |
+
tqdm==4.66.2
|
71 |
+
triton==2.2.0
|
72 |
+
typing_extensions==4.10.0
|
73 |
+
tzdata==2024.1
|
74 |
+
ultralytics==8.1.30
|
75 |
+
urllib3==2.2.1
|
76 |
+
watchdog==4.0.0
|
77 |
+
pafy
|
78 |
+
youtube-dl
|
79 |
+
optuna
|