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gradio_app.py
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import csv
import os
import tempfile
import gradio as gr
import requests
import torch
import torchvision
import torchvision.transforms as T
from PIL import Image
from featup.util import norm
from torchaudio.functional import resample
from denseav.train import LitAVAligner
from denseav.plotting import plot_attention_video, plot_2head_attention_video, plot_feature_video
from denseav.shared import norm, crop_to_divisor, blur_dim
from os.path import join
if __name__ == "__main__":
mode = "local"
if mode == "local":
sample_videos_dir = "samples"
else:
os.environ['TORCH_HOME'] = '/tmp/.cache'
os.environ['HF_HOME'] = '/tmp/.cache'
os.environ['HF_DATASETS_CACHE'] = '/tmp/.cache'
os.environ['TRANSFORMERS_CACHE'] = '/tmp/.cache'
os.environ['GRADIO_EXAMPLES_CACHE'] = '/tmp/gradio_cache'
sample_videos_dir = "/tmp/samples"
def download_video(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/denseav-public/samples/"
sample_videos_urls = {
"puppies.mp4": base_url + "puppies.mp4",
"peppers.mp4": base_url + "peppers.mp4",
"boat.mp4": base_url + "boat.mp4",
"elephant2.mp4": base_url + "elephant2.mp4",
}
# Ensure the directory for sample videos exists
os.makedirs(sample_videos_dir, exist_ok=True)
# Download each sample video
for filename, url in sample_videos_urls.items():
save_path = os.path.join(sample_videos_dir, filename)
# Download the video if it doesn't already exist
if not os.path.exists(save_path):
print(f"Downloading {filename}...")
download_video(url, save_path)
else:
print(f"{filename} already exists. Skipping download.")
csv.field_size_limit(100000000)
options = ['language', "sound-language", "sound"]
load_size = 224
plot_size = 224
video_input = gr.Video(label="Choose a video to featurize", height=480)
model_option = gr.Radio(options, value="language", label='Choose a model')
video_output1 = gr.Video(label="Audio Video Attention", height=480)
video_output2 = gr.Video(label="Multi-Head Audio Video Attention (Only Availible for sound_and_language)",
height=480)
video_output3 = gr.Video(label="Visual Features", height=480)
models = {o: LitAVAligner.from_pretrained(f"mhamilton723/DenseAV-{o}") for o in options}
def process_video(video, model_option):
model = models[model_option].cuda()
original_frames, audio, info = torchvision.io.read_video(video, end_pts=10, pts_unit='sec')
sample_rate = 16000
if info["audio_fps"] != sample_rate:
audio = resample(audio, info["audio_fps"], sample_rate)
audio = audio[0].unsqueeze(0)
img_transform = T.Compose([
T.Resize(load_size, Image.BILINEAR),
lambda x: crop_to_divisor(x, 8),
lambda x: x.to(torch.float32) / 255,
norm])
frames = torch.cat([img_transform(f.permute(2, 0, 1)).unsqueeze(0) for f in original_frames], axis=0)
plotting_img_transform = T.Compose([
T.Resize(plot_size, Image.BILINEAR),
lambda x: crop_to_divisor(x, 8),
lambda x: x.to(torch.float32) / 255])
frames_to_plot = plotting_img_transform(original_frames.permute(0, 3, 1, 2))
with torch.no_grad():
audio_feats = model.forward_audio({"audio": audio.cuda()})
audio_feats = {k: v.cpu() for k, v in audio_feats.items()}
image_feats = model.forward_image({"frames": frames.unsqueeze(0).cuda()}, max_batch_size=2)
image_feats = {k: v.cpu() for k, v in image_feats.items()}
sim_by_head = model.sim_agg.get_pairwise_sims(
{**image_feats, **audio_feats},
raw=False,
agg_sim=False,
agg_heads=False
).mean(dim=-2).cpu()
sim_by_head = blur_dim(sim_by_head, window=3, dim=-1)
print(sim_by_head.shape)
temp_video_path_1 = tempfile.mktemp(suffix='.mp4')
plot_attention_video(
sim_by_head,
frames_to_plot,
audio,
info["video_fps"],
sample_rate,
temp_video_path_1)
if model_option == "sound_and_language":
temp_video_path_2 = tempfile.mktemp(suffix='.mp4')
plot_2head_attention_video(
sim_by_head,
frames_to_plot,
audio,
info["video_fps"],
sample_rate,
temp_video_path_2)
else:
temp_video_path_2 = None
temp_video_path_3 = tempfile.mktemp(suffix='.mp4')
temp_video_path_4 = tempfile.mktemp(suffix='.mp4')
plot_feature_video(
image_feats["image_feats"].cpu(),
audio_feats['audio_feats'].cpu(),
frames_to_plot,
audio,
info["video_fps"],
sample_rate,
temp_video_path_3,
temp_video_path_4,
)
# return temp_video_path_1, temp_video_path_2, temp_video_path_3, temp_video_path_4
return temp_video_path_1, temp_video_path_2, temp_video_path_3
with gr.Blocks() as demo:
with gr.Column():
gr.Markdown("## Visualizing Sound and Language with DenseAV")
gr.Markdown(
"This demo allows you to explore the inner attention maps of DenseAV's dense multi-head contrastive operator.")
with gr.Row():
with gr.Column(scale=1):
model_option.render()
with gr.Column(scale=3):
video_input.render()
with gr.Row():
submit_button = gr.Button("Submit")
with gr.Row():
gr.Examples(
examples=[
[join(sample_videos_dir, "puppies.mp4"), "sound_and_language"],
[join(sample_videos_dir, "peppers.mp4"), "language"],
[join(sample_videos_dir, "elephant2.mp4"), "language"],
[join(sample_videos_dir, "boat.mp4"), "language"]
],
inputs=[video_input, model_option]
)
with gr.Row():
video_output1.render()
video_output2.render()
video_output3.render()
submit_button.click(fn=process_video, inputs=[video_input, model_option],
outputs=[video_output1, video_output2, video_output3])
if mode == "local":
demo.launch(server_name="0.0.0.0", server_port=6006, debug=True)
else:
demo.launch(server_name="0.0.0.0", server_port=7860, debug=True)