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Running
on
Zero
| import shlex | |
| import subprocess | |
| import spaces | |
| import torch | |
| # install packages for mamba | |
| def install(): | |
| print("Install personal packages", flush=True) | |
| subprocess.run(shlex.split("pip install causal_conv1d-1.0.0-cp310-cp310-linux_x86_64.whl")) | |
| subprocess.run(shlex.split("pip install mamba_ssm-1.0.1-cp310-cp310-linux_x86_64.whl")) | |
| install() | |
| import torch.nn as nn | |
| import numpy as np | |
| import torch.nn.functional as F | |
| import torchvision.transforms as T | |
| from PIL import Image | |
| from decord import VideoReader | |
| from decord import cpu | |
| from videomamba_image import videomamba_image_tiny | |
| from videomamba_video import videomamba_tiny | |
| from kinetics_class_index import kinetics_classnames | |
| from imagenet_class_index import imagenet_classnames | |
| from transforms import ( | |
| GroupNormalize, GroupScale, GroupCenterCrop, | |
| Stack, ToTorchFormatTensor | |
| ) | |
| import gradio as gr | |
| from huggingface_hub import hf_hub_download | |
| # Device on which to run the model | |
| # Set to cuda to load on GPU | |
| device = "cuda" | |
| model_video_path = hf_hub_download(repo_id="OpenGVLab/VideoMamba", filename="videomamba_t16_k400_f16_res224.pth") | |
| model_image_path = hf_hub_download(repo_id="OpenGVLab/VideoMamba", filename="videomamba_t16_in1k_res224.pth") | |
| # Pick a pretrained model | |
| model_video = videomamba_tiny(num_classes=400, num_frames=16) | |
| video_sd = torch.load(model_video_path, map_location='cpu') | |
| model_video.load_state_dict(video_sd) | |
| model_image = videomamba_image_tiny() | |
| image_sd = torch.load(model_image_path, map_location='cpu') | |
| model_image.load_state_dict(image_sd['model']) | |
| # Set to eval mode and move to desired device | |
| model_video = model_video.to(device).eval() | |
| model_image = model_image.to(device).eval() | |
| # Create an id to label name mapping | |
| kinetics_id_to_classname = {} | |
| for k, v in kinetics_classnames.items(): | |
| kinetics_id_to_classname[k] = v | |
| imagenet_id_to_classname = {} | |
| for k, v in imagenet_classnames.items(): | |
| imagenet_id_to_classname[k] = v[1] | |
| def get_index(num_frames, num_segments=8): | |
| seg_size = float(num_frames - 1) / num_segments | |
| start = int(seg_size / 2) | |
| offsets = np.array([ | |
| start + int(np.round(seg_size * idx)) for idx in range(num_segments) | |
| ]) | |
| return offsets | |
| def load_video(video_path): | |
| vr = VideoReader(video_path, ctx=cpu(0)) | |
| num_frames = len(vr) | |
| frame_indices = get_index(num_frames, 16) | |
| # transform | |
| crop_size = 224 | |
| scale_size = 224 | |
| input_mean = [0.485, 0.456, 0.406] | |
| input_std = [0.229, 0.224, 0.225] | |
| transform = T.Compose([ | |
| GroupScale(int(scale_size)), | |
| GroupCenterCrop(crop_size), | |
| Stack(), | |
| ToTorchFormatTensor(), | |
| GroupNormalize(input_mean, input_std) | |
| ]) | |
| images_group = list() | |
| for frame_index in frame_indices: | |
| img = Image.fromarray(vr[frame_index].asnumpy()) | |
| images_group.append(img) | |
| torch_imgs = transform(images_group) | |
| return torch_imgs | |
| def inference_video(video): | |
| vid = load_video(video) | |
| # The model expects inputs of shape: B x C x H x W | |
| TC, H, W = vid.shape | |
| inputs = vid.reshape(1, TC//3, 3, H, W).permute(0, 2, 1, 3, 4) | |
| with torch.no_grad(): | |
| prediction = model_video(inputs.to(device)) | |
| prediction = F.softmax(prediction, dim=1).flatten() | |
| return {kinetics_id_to_classname[str(i)]: float(prediction[i]) for i in range(400)} | |
| def inference_image(img): | |
| image = img | |
| image_transform = T.Compose( | |
| [ | |
| T.Resize(224), | |
| T.CenterCrop(224), | |
| T.ToTensor(), | |
| T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), | |
| ] | |
| ) | |
| image = image_transform(image) | |
| # The model expects inputs of shape: B x C x H x W | |
| image = image.unsqueeze(0) | |
| with torch.no_grad(): | |
| prediction = model_image(image.to(device)) | |
| prediction = F.softmax(prediction, dim=1).flatten() | |
| return {imagenet_id_to_classname[str(i)]: float(prediction[i]) for i in range(1000)} | |
| demo = gr.Blocks() | |
| with demo: | |
| gr.Markdown( | |
| """ | |
| # VideoMamba-Ti | |
| Gradio demo for <a href='https://github.com/OpenGVLab/VideoMamba' target='_blank'>VideoMamba</a>: To use it, simply upload your video, or click one of the examples to load them. Read more at the links below. | |
| """ | |
| ) | |
| with gr.Tab("Video"): | |
| # with gr.Box(): | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Row(): | |
| input_video = gr.Video(label='Input Video', height=360) | |
| with gr.Row(): | |
| submit_video_button = gr.Button('Submit') | |
| with gr.Column(): | |
| label_video = gr.Label(num_top_classes=5) | |
| with gr.Row(): | |
| gr.Examples(examples=['./videos/hitting_baseball.mp4', './videos/hoverboarding.mp4', './videos/yoga.mp4'], inputs=input_video, outputs=label_video, fn=inference_video, cache_examples=True) | |
| with gr.Tab("Image"): | |
| # with gr.Box(): | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Row(): | |
| input_image = gr.Image(label='Input Image', type='pil', height=360) | |
| with gr.Row(): | |
| submit_image_button = gr.Button('Submit') | |
| with gr.Column(): | |
| label_image = gr.Label(num_top_classes=5) | |
| with gr.Row(): | |
| gr.Examples(examples=['./images/cat.png', './images/dog.png', './images/panda.png'], inputs=input_image, outputs=label_image, fn=inference_image, cache_examples=True) | |
| gr.Markdown( | |
| """ | |
| <p style='text-align: center'><a href='https://arxiv.org/abs/2403.06977' target='_blank'>VideoMamba: State Space Model for Efficient Video Understanding</a> | <a href='https://github.com/OpenGVLab/VideoMamba' target='_blank'>Github Repo</a></p> | |
| """ | |
| ) | |
| submit_video_button.click(fn=inference_video, inputs=input_video, outputs=label_video) | |
| submit_image_button.click(fn=inference_image, inputs=input_image, outputs=label_image) | |
| demo.queue(max_size=20).launch() | |