test

app.py

@@ -1,45 +1,71 @@
-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
+# 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])
 
-# Setup - ensure the repository content is accessible in the environment
 
-# Streamlit UI
-st.title("Feature Upsampling Demo")
+import streamlit as st
+import torch
 
-# 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")
+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"
 
-    # Image preprocessing
-    input_size = 224
-    transform = T.Compose([
-        T.Resize(input_size),
-        T.CenterCrop((input_size, input_size)),
-        T.ToTensor(),
-        norm
-    ])
+    return cuda_available, gpu_count, gpu_name
 
-    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')
-    )
+# Streamlit page configuration
+st.title("PyTorch GPU Availability Test")
 
-    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)
+# Checking the GPU status
+cuda_available, gpu_count, gpu_name = check_gpu_status()
 
-        # 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])
+# 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. 😢")

requirements.txt

@@ -1 +0,0 @@
-git+https://github.com/mhamilton723/FeatUp