使用 Sentence Transformers 的多模態嵌入¶

from sentence_transformers import SentenceTransformer

model_id = "google/embeddinggemma-2"
model = SentenceTransformer(model_id)

print(f"device: {model.device}")
print(f"dtype: {model.dtype}")
print(f"Supported modalities: {model.modalities}")
print("Total number of parameters in the model:", sum(p.numel() for p in model.parameters()))

from PIL import Image
from IPython.display import display
import requests

IMAGE_SIZE = 240

img_url = "https://raw.githubusercontent.com/google-gemma/cookbook/refs/heads/main/apps/sample-data/GoldenGate.png"
input_image = Image.open(requests.get(img_url, stream=True).raw).resize((IMAGE_SIZE, IMAGE_SIZE))

print("Input Image:")
display(input_image)

# Embed the image directly from a URL, file path, or PIL Image object
emb_goldengate = model.encode({"image": img_url})

# Compare the image embedding against another image and text candidates
example_img_url = "https://raw.githubusercontent.com/google-gemma/cookbook/refs/heads/main/apps/sample-data/kitchen_painting.jpg"
example_image = Image.open(requests.get(example_img_url, stream=True).raw).resize((IMAGE_SIZE, IMAGE_SIZE))

candidates = [
    {"image": example_img_url},
    "San Francisco",
    "a cute cat walking",
    "a friendly dog sitting in the grass",
]
emb_candidates = model.encode(candidates)
image_similarities = model.similarity(emb_goldengate, emb_candidates)[0]

print("\nSimilarity with candidates:\n"+"-"*80)
for idx, score in enumerate(image_similarities):
    if idx == 0:
        display(example_image)
    else:
        print(candidates[idx])
    print(f" `-> 🤖 score: {score:.3f}\n")

img_processing_args = {
    "image": {
        "max_soft_tokens": 1120,
    }
}

emb_goldengate = model.encode({"image": img_url}, processing_kwargs=img_processing_args)
emb_candidates = model.encode(candidates, processing_kwargs=img_processing_args)
image_similarities = model.similarity(emb_goldengate, emb_candidates)[0]

print("Similarity with candidates:\n"+"-"*80)
for idx, score in enumerate(image_similarities):
    if idx == 0:
        display(example_image)
    else:
        print(candidates[idx])
    print(f" `-> 🤖 score: {score:.3f}\n")

from IPython.display import display, Audio

aud_url = "https://ai.google.dev/gemma/docs/audio/roses-are.wav"

print("Input Audio:")
display(Audio(aud_url))

# Embed audio directly from a URL or file path
emb_speech = model.encode({"audio": aud_url})

# Compare audio embedding with text descriptions
candidates = [
    "a person speaking English",
    "random static white noise",
    "Roses are red, violets are blue.",
]
emb_candidates = model.encode(candidates)
audio_similarities = model.similarity(emb_speech, emb_candidates)[0]

print("\nSimilarity with candidates:\n"+"-"*80)
for idx, score in enumerate(audio_similarities):
    print(candidates[idx])
    print(f" `-> 🤖 score: {score:.3f}\n")

from IPython.display import display, Video

vid_url = "https://github.com/bebechien/gemma/raw/refs/heads/main/videos/cat_dozing.mp4"

print("Input Video:")
display(Video(vid_url))

# Embed video directly from a URL or file path
emb_video = model.encode({"video": vid_url})

# Compare video embedding with text descriptions
candidates = [
    "a cat dozing",
    "a cat playing with a small lizard",
    "a sports car racing on a highway",
]
emb_candidates = model.encode(candidates)
video_similarities = model.similarity(emb_video, emb_candidates)[0]

print("\nSimilarity with candidates:\n"+"-"*80)
for idx, score in enumerate(video_similarities):
    print(candidates[idx])
    print(f" `-> 🤖 score: {score:.3f}\n")

# Override video processing parameters for a specific encode() call
emb_video_custom = model.encode(
    {"video": vid_url},
    processing_kwargs={
        "video": {
            "max_frames": 16,               # Maximum frames to keep (default is 32)
            "fps": None,                    # Frames per second to extract (None keeps all up to max_frames)
            "overflow_strategy": "uniform", # How to sample when total frames exceed max_frames ('uniform' or 'truncate')
            "add_timestamps": True,         # Whether to insert frame timestamps into the multimodal sequence
        }
    },
)

video_similarities = model.similarity(emb_video_custom, emb_candidates)[0]

print("Similarity with customized video processing:\n"+"-"*80)
for idx, score in enumerate(video_similarities):
    print(candidates[idx])
    print(f" `-> 🤖 score: {score:.3f}\n")

img_url = "https://raw.githubusercontent.com/google-gemma/cookbook/refs/heads/main/apps/sample-data/GoldenGate.png"
example_img_url = "https://raw.githubusercontent.com/google-gemma/cookbook/refs/heads/main/apps/sample-data/kitchen_painting.jpg"
aud_url = "https://ai.google.dev/gemma/docs/audio/roses-are.wav"
vid_url = "https://github.com/bebechien/gemma/raw/refs/heads/main/videos/cat_dozing.mp4"

# 1. Multimodal query combining text, multiple images, and audio
query_multimodal = {
    "text": "Landmark and interior photos with spoken narration",
    "image": [img_url, example_img_url],
    "audio": aud_url,
}
emb_multimodal = model.encode(query_multimodal)

# 2. Interleaved query using placeholders to specify media ordering in text
query_interleaved = {
    "text": "Compare the scene in <|video|> with <|image|> and <|image|>.",
    "image": [img_url, example_img_url],
    "video": vid_url,
}
emb_interleaved = model.encode(query_interleaved)

# 3. Media-only interleaving using placeholders to control sequence order
query_media_sequence = {
    "text": "<|image|><|audio|><|image|>",
    "image": [img_url, example_img_url],
    "audio": aud_url,
}
emb_media_sequence = model.encode(query_media_sequence)

print("Multimodal embedding shape:", emb_multimodal.shape)
print("Interleaved embedding shape:", emb_interleaved.shape)
print("Media sequence embedding shape:", emb_media_sequence.shape)