Update app.py

app.py

@@ -1,154 +1,198 @@
-import gradio as gr
-import numpy as np
-import random
+import csv
+import os
+import tempfile
 
-# import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
+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
 
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
 
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
+mode = "hf"
+
+if mode == "local":
+    sample_videos_dir = "samples"
 else:
-    torch_dtype = torch.float32
-
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
-
-# @spaces.GPU #[uncomment to use ZeroGPU]
-def infer(
-    prompt,
-    negative_prompt,
-    seed,
-    randomize_seed,
-    width,
-    height,
-    guidance_scale,
-    num_inference_steps,
-    progress=gr.Progress(track_tqdm=True),
-):
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-
-    generator = torch.Generator().manual_seed(seed)
-
-    image = pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        width=width,
-        height=height,
-        generator=generator,
-    ).images[0]
-
-    return image, seed
-
-
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
-]
-
-css = """
-#col-container {
-    margin: 0 auto;
-    max-width: 640px;
-}
-"""
+    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"
 
-with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
 
-        with gr.Row():
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
+def download_video(url, save_path):
+    response = requests.get(url)
+    with open(save_path, 'wb') as file:
+        file.write(response.content)
 
-            run_button = gr.Button("Run", scale=0, variant="primary")
 
-        result = gr.Image(label="Result", show_label=False)
+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()
+    model = models[model_option]
+
+    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 = model.forward_audio({"audio": audio})
+        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 = model.forward_image({"frames": frames.unsqueeze(0)}, 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.Accordion("Advanced Settings", open=False):
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
-            )
 
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
+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()
 
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-
-            with gr.Row():
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-            with gr.Row():
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,  # Replace with defaults that work for your model
-                )
-
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=2,  # Replace with defaults that work for your model
-                )
-
-        gr.Examples(examples=examples, inputs=[prompt])
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn=infer,
-        inputs=[
-            prompt,
-            negative_prompt,
-            seed,
-            randomize_seed,
-            width,
-            height,
-            guidance_scale,
-            num_inference_steps,
-        ],
-        outputs=[result, seed],
-    )
+    submit_button.click(fn=process_video, inputs=[video_input, model_option],
+                        outputs=[video_output1, video_output2, video_output3])
 
-if __name__ == "__main__":
-    demo.launch()
+
+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)