import os import subprocess import matplotlib.pyplot as plt import torch import torchvision.transforms as T import gradio as gr from pytorch_lightning import seed_everything import os import requests import csv import spaces def plot_feats(image, lr, hr): from featup.util import pca, remove_axes assert len(image.shape) == len(lr.shape) == len(hr.shape) == 3 seed_everything(0) [lr_feats_pca, hr_feats_pca], _ = pca( [lr.unsqueeze(0), hr.unsqueeze(0)], dim=9) fig, ax = plt.subplots(3, 3, figsize=(15, 15)) ax[0, 0].imshow(image.permute(1, 2, 0).detach().cpu()) ax[1, 0].imshow(image.permute(1, 2, 0).detach().cpu()) ax[2, 0].imshow(image.permute(1, 2, 0).detach().cpu()) ax[0, 0].set_title("Image", fontsize=22) ax[0, 1].set_title("Original", fontsize=22) ax[0, 2].set_title("Upsampled Features", fontsize=22) ax[0, 1].imshow(lr_feats_pca[0, :3].permute(1, 2, 0).detach().cpu()) ax[0, 0].set_ylabel("PCA Components 1-3", fontsize=22) ax[0, 2].imshow(hr_feats_pca[0, :3].permute(1, 2, 0).detach().cpu()) ax[1, 1].imshow(lr_feats_pca[0, 3:6].permute(1, 2, 0).detach().cpu()) ax[1, 0].set_ylabel("PCA Components 4-6", fontsize=22) ax[1, 2].imshow(hr_feats_pca[0, 3:6].permute(1, 2, 0).detach().cpu()) ax[2, 1].imshow(lr_feats_pca[0, 6:9].permute(1, 2, 0).detach().cpu()) ax[2, 0].set_ylabel("PCA Components 7-9", fontsize=22) ax[2, 2].imshow(hr_feats_pca[0, 6:9].permute(1, 2, 0).detach().cpu()) remove_axes(ax) plt.tight_layout() plt.close(fig) # Close plt to avoid additional empty plots return fig def download_image(url, save_path): response = requests.get(url) with open(save_path, 'wb') as file: file.write(response.content) base_url = "https://marhamilresearch4.blob.core.windows.net/feature-upsampling-public/sample_images/" sample_images_urls = { "skate.jpg": base_url + "skate.jpg", "car.jpg": base_url + "car.jpg", "plant.png": base_url + "plant.png", } sample_images_dir = "/tmp/sample_images" # Ensure the directory for sample images exists os.makedirs(sample_images_dir, exist_ok=True) # Download each sample image for filename, url in sample_images_urls.items(): save_path = os.path.join(sample_images_dir, filename) # Download the image if it doesn't already exist if not os.path.exists(save_path): print(f"Downloading {filename}...") download_image(url, save_path) else: print(f"{filename} already exists. Skipping download.") os.environ['TORCH_HOME'] = '/tmp/.cache' os.environ['GRADIO_EXAMPLES_CACHE'] = '/tmp/gradio_cache' csv.field_size_limit(100000000) options = ['dino16', 'vit', 'dinov2', 'clip', 'resnet50'] image_input = gr.Image(label="Choose an image to featurize", height=480, type="pil", image_mode='RGB', sources=['upload', 'webcam', 'clipboard'] ) model_option = gr.Radio(options, value="dino16", label='Choose a backbone to upsample') def find_nvcc(): try: result = subprocess.check_output('find / -name "nvcc" 2>/dev/null', shell=True, text=True) return result.strip() except subprocess.CalledProcessError as e: print(f"Error occurred: {e}") return None @spaces.GPU(duration=120) def upsample_features(image, model_option): with torch.no_grad(): from setuptools import setup, find_packages from torch.utils.cpp_extension import BuildExtension, CUDAExtension, CppExtension print(subprocess.check_output(['ls', '/usr/local/']).decode()) nvcc_path = find_nvcc() if nvcc_path: print(f"CUDA 'nvcc' found at: {nvcc_path}") else: print("CUDA 'nvcc' not found.") setup( name='featup', version='0.1.2', packages=find_packages(), ext_modules=[ CUDAExtension( 'adaptive_conv_cuda_impl', [ 'featup/adaptive_conv_cuda/adaptive_conv_cuda.cpp', 'featup/adaptive_conv_cuda/adaptive_conv_kernel.cu', ]), CppExtension( 'adaptive_conv_cpp_impl', ['featup/adaptive_conv_cuda/adaptive_conv.cpp'], undef_macros=["NDEBUG"]), ], cmdclass={ 'build_ext': BuildExtension } ) from featup.util import norm, unnorm models = {o: torch.hub.load("mhamilton723/FeatUp", o) for o in options} # 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).cuda() # Load the selected model upsampler = models[model_option].cuda() hr_feats = upsampler(image_tensor) lr_feats = upsampler.model(image_tensor) upsampler.cpu() return plot_feats(unnorm(image_tensor)[0], lr_feats[0], hr_feats[0]) demo = gr.Interface(fn=upsample_features, inputs=[image_input, model_option], outputs="plot", title="Feature Upsampling Demo", description="This demo allows you to upsample features of an image using selected models.", examples=[ ["/tmp/sample_images/skate.jpg", "dino16"], ["/tmp/sample_images/car.jpg", "dinov2"], ["/tmp/sample_images/plant.png", "dino16"], ] ) demo.launch(server_name="0.0.0.0", server_port=7860, debug=True)