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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 |
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from PIL import Image |
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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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class CNNKAN(nn.Module): |
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def __init__(self): |
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super(CNNKAN, self).__init__() |
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self.conv1 = nn.Conv2d(3, 32, kernel_size=3, padding=1) |
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self.bn1 = nn.BatchNorm2d(32) |
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self.pool1 = nn.MaxPool2d(2) |
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self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1) |
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self.bn2 = nn.BatchNorm2d(64) |
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self.pool2 = nn.MaxPool2d(2) |
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self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1) |
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self.bn3 = nn.BatchNorm2d(128) |
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self.pool3 = nn.MaxPool2d(2) |
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self.dropout = nn.Dropout(0.5) |
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self.kan1 = KANLinear(128 * 25 * 25, 256) |
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self.kan2 = KANLinear(256, 1) |
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def forward(self, x): |
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x = F.selu(self.bn1(self.conv1(x))) |
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x = self.pool1(x) |
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x = F.selu(self.bn2(self.conv2(x))) |
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x = self.pool2(x) |
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x = F.selu(self.bn3(self.conv3(x))) |
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x = self.pool3(x) |
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x = x.view(x.size(0), -1) |
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x = self.dropout(x) |
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x = self.kan1(x) |
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x = self.dropout(x) |
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x = self.kan2(x) |
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return x |
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def load_model(weights_path, device): |
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model = CNNKAN().to(device) |
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model.load_state_dict(torch.load(weights_path, map_location=device)) |
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model.eval() |
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return model |
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def load_image_from_url(url): |
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response = requests.get(url) |
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img = Image.open(BytesIO(response.content)).convert('RGB') |
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return img |
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def preprocess_image(image): |
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transform = transforms.Compose([ |
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transforms.Resize((200, 200)), |
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transforms.ToTensor() |
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]) |
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return transform(image).unsqueeze(0) |
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st.title("Image Classification with CNN-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_weights_KAN.pth', device) |
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img = None |
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if uploaded_file is not None: |
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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 Exception as e: |
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st.sidebar.error(f"Error loading image from URL: {e}") |
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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 dandelion flower.") |
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else: |
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st.write("This image is classified as grass.") |
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