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| # import streamlit as st | |
| # import torch | |
| # import torchvision.transforms as T | |
| # from PIL import Image | |
| # | |
| # # Assuming the necessary packages (featup, clip, etc.) are installed and accessible | |
| # from featup.util import norm, unnorm | |
| # from featup.plotting import plot_feats | |
| # | |
| # # Setup - ensure the repository content is accessible in the environment | |
| # | |
| # # Streamlit UI | |
| # st.title("Feature Upsampling Demo") | |
| # | |
| # # File uploader | |
| # uploaded_file = st.file_uploader("Choose an image...", type=["png", "jpg", "jpeg"]) | |
| # if uploaded_file is not None: | |
| # image = Image.open(uploaded_file).convert("RGB") | |
| # | |
| # # Image preprocessing | |
| # input_size = 224 | |
| # transform = T.Compose([ | |
| # T.Resize(input_size), | |
| # T.CenterCrop((input_size, input_size)), | |
| # T.ToTensor(), | |
| # norm | |
| # ]) | |
| # | |
| # image_tensor = transform(image).unsqueeze(0) # Assuming CUDA is available, .cuda() | |
| # | |
| # # Model selection | |
| # model_option = st.selectbox( | |
| # 'Choose a model for feature upsampling', | |
| # ('dino16', 'dinov2', 'clip', 'resnet50') | |
| # ) | |
| # | |
| # if st.button('Upsample Features'): | |
| # # Load the selected model | |
| # upsampler = torch.hub.load("mhamilton723/FeatUp", model_option).cuda() | |
| # hr_feats = upsampler(image_tensor) | |
| # lr_feats = upsampler.model(image_tensor) | |
| # | |
| # # Plotting - adjust the plot_feats function or find an alternative to display images in Streamlit | |
| # # This step will likely need customization to display within Streamlit's interface | |
| # plot_feats(unnorm(image_tensor)[0], lr_feats[0], hr_feats[0]) | |
| import streamlit as st | |
| import torch | |
| def check_gpu_status(): | |
| # Check if CUDA (GPU support) is available in PyTorch | |
| cuda_available = torch.cuda.is_available() | |
| gpu_count = torch.cuda.device_count() | |
| gpu_name = torch.cuda.get_device_name(0) if cuda_available else "Not Available" | |
| return cuda_available, gpu_count, gpu_name | |
| # Streamlit page configuration | |
| st.title("PyTorch GPU Availability Test") | |
| # Checking the GPU status | |
| cuda_available, gpu_count, gpu_name = check_gpu_status() | |
| # Displaying the results | |
| if cuda_available: | |
| st.success(f"GPU is available! 🎉") | |
| st.info(f"Number of GPUs available: {gpu_count}") | |
| st.info(f"GPU Name: {gpu_name}") | |
| else: | |
| st.error("GPU is not available. 😢") | |