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| import os | |
| import torch | |
| 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 uniformerv2 import uniformerv2_b16 | |
| from kinetics_class_index import kinetics_classnames | |
| from transforms import ( | |
| GroupNormalize, GroupScale, GroupCenterCrop, | |
| Stack, ToTorchFormatTensor | |
| ) | |
| import gradio as gr | |
| from huggingface_hub import hf_hub_download | |
| class Uniformerv2(nn.Module): | |
| def __init__(self, model): | |
| super().__init__() | |
| self.backbone = model | |
| def forward(self, x): | |
| return self.backbone(x) | |
| # Device on which to run the model | |
| # Set to cuda to load on GPU | |
| device = "cpu" | |
| model_path = hf_hub_download(repo_id="Andy1621/uniformerv2", filename="k400+k710_uniformerv2_b16_8x224.pyth") | |
| # Pick a pretrained model | |
| model = Uniformerv2(uniformerv2_b16(pretrained=False, t_size=8, no_lmhra=True, temporal_downsample=False)) | |
| state_dict = torch.load(model_path, map_location='cpu') | |
| model.load_state_dict(state_dict) | |
| # Set to eval mode and move to desired device | |
| model = model.to(device) | |
| model = model.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 | |
| 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, 8) | |
| # transform | |
| crop_size = 224 | |
| scale_size = 256 | |
| 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): | |
| 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) | |
| prediction = model(inputs) | |
| prediction = F.softmax(prediction, dim=1).flatten() | |
| return {kinetics_id_to_classname[str(i)]: float(prediction[i]) for i in range(400)} | |
| def set_example_video(example: list) -> dict: | |
| return gr.Video.update(value=example[0]) | |
| demo = gr.Blocks() | |
| with demo: | |
| gr.Markdown( | |
| """ | |
| # UniFormerV2-B | |
| Gradio demo for <a href='https://github.com/OpenGVLab/UniFormerV2' target='_blank'>UniFormerV2</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.Box(): | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Row(): | |
| input_video = gr.Video(label='Input Video') | |
| with gr.Row(): | |
| submit_button = gr.Button('Submit') | |
| with gr.Column(): | |
| label = gr.Label(num_top_classes=5) | |
| with gr.Row(): | |
| example_videos = gr.Dataset(components=[input_video], samples=[['hitting_baseball.mp4'], ['hoverboarding.mp4'], ['yoga.mp4']]) | |
| gr.Markdown( | |
| """ | |
| <p style='text-align: center'><a href='https://arxiv.org/abs/2211.09552' target='_blank'>[Arxiv] UniFormerV2: Spatiotemporal Learning by Arming Image ViTs with Video UniFormer</a> | <a href='https://github.com/OpenGVLab/UniFormerV2' target='_blank'>Github Repo</a></p> | |
| """ | |
| ) | |
| submit_button.click(fn=inference, inputs=input_video, outputs=label) | |
| example_videos.click(fn=set_example_video, inputs=example_videos, outputs=example_videos.components) | |
| demo.launch(enable_queue=True) |