1S-Lab, Nanyang Technological University
2Microsoft Research, Redmond
## 🦦 Simple Code For Otter-9B
Here is an example of multi-modal ICL (in-context learning) with 🦦 Otter. We provide two demo images with corresponding instructions and answers, then we ask the model to generate an answer given our instruct. You may change your instruction and see how the model responds.
``` python
import mimetypes
import os
from io import BytesIO
from typing import Union
import cv2
import requests
import torch
import transformers
from PIL import Image
from torchvision.transforms import Compose, Resize, ToTensor
from tqdm import tqdm
import sys
from otter.modeling_otter import OtterForConditionalGeneration
# Disable warnings
requests.packages.urllib3.disable_warnings()
# ------------------- Utility Functions -------------------
def get_content_type(file_path):
content_type, _ = mimetypes.guess_type(file_path)
return content_type
# ------------------- Image and Video Handling Functions -------------------
def extract_frames(video_path, num_frames=16):
video = cv2.VideoCapture(video_path)
total_frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
frame_step = total_frames // num_frames
frames = []
for i in range(num_frames):
video.set(cv2.CAP_PROP_POS_FRAMES, i * frame_step)
ret, frame = video.read()
if ret:
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frame = Image.fromarray(frame).convert("RGB")
frames.append(frame)
video.release()
return frames
def get_image(url: str) -> Union[Image.Image, list]:
if "://" not in url: # Local file
content_type = get_content_type(url)
else: # Remote URL
content_type = requests.head(url, stream=True, verify=False).headers.get("Content-Type")
if "image" in content_type:
if "://" not in url: # Local file
return Image.open(url)
nne
else: # Remote URL
return Image.open(requests.get(url, stream=True, verify=False).raw)
elif "video" in content_type:
video_path = "temp_video.mp4"
if "://" not in url: # Local file
video_path = url
else: # Remote URL
with open(video_path, "wb") as f:
f.write(requests.get(url, stream=True, verify=False).content)
frames = extract_frames(video_path)
if "://" in url: # Only remove the temporary video file if it was downloaded
os.remove(video_path)
return frames
else:
raise ValueError("Invalid content type. Expected image or video.")
# ------------------- OTTER Prompt and Response Functions -------------------
def get_formatted_prompt(prompt: str, in_context_prompts: list = []) -> str:
in_context_string = ""
for in_context_prompt, in_context_answer in in_context_prompts:
in_context_string += f"
User: {in_context_prompt} GPT: {in_context_answer}<|endofchunk|>"
return f"{in_context_string}User: {prompt} GPT:"
def get_response(image_list, prompt: str, model=None, image_processor=None, in_context_prompts: list = []) -> str:
input_data = image_list
if isinstance(input_data, Image.Image):
vision_x = (
image_processor.preprocess([input_data], return_tensors="pt")["pixel_values"].unsqueeze(1).unsqueeze(0)
)
elif isinstance(input_data, list): # list of video frames
vision_x = (
image_processor.preprocess(input_data, return_tensors="pt")["pixel_values"].unsqueeze(1).unsqueeze(0)
)
else:
raise ValueError("Invalid input data. Expected PIL Image or list of video frames.")
lang_x = model.text_tokenizer(
[
get_formatted_prompt(prompt, in_context_prompts),
],
return_tensors="pt",
)
generated_text = model.generate(
vision_x=vision_x.to(model.device),
lang_x=lang_x["input_ids"].to(model.device),
attention_mask=lang_x["attention_mask"].to(model.device),
max_new_tokens=512,
# num_beams=3,
# no_repeat_ngram_size=3,
)
parsed_output = (
model.text_tokenizer.decode(generated_text[0])
.split("")[-1]
.lstrip()
.rstrip()
.split("<|endofchunk|>")[0]
.lstrip()
.rstrip()
.lstrip('"')
.rstrip('"')
)
return parsed_output
# ------------------- Main Function -------------------
if __name__ == "__main__":
model = OtterForConditionalGeneration.from_pretrained(
"luodian/otter-9b-hf", device_map="auto"
)
model.text_tokenizer.padding_side = "left"
tokenizer = model.text_tokenizer
image_processor = transformers.CLIPImageProcessor()
model.eval()
while True:
urls = [
"https://images.cocodataset.org/train2017/000000339543.jpg",
"https://images.cocodataset.org/train2017/000000140285.jpg",
]
encoded_frames_list = []
for url in urls:
frames = get_image(url)
encoded_frames_list.append(frames)
in_context_prompts = []
in_context_examples = [
"What does the image describe?::A family is taking picture in front of a snow mountain.",
]
for in_context_input in in_context_examples:
in_context_prompt, in_context_answer = in_context_input.split("::")
in_context_prompts.append((in_context_prompt.strip(), in_context_answer.strip()))
# prompts_input = input("Enter the prompts separated by commas (or type 'quit' to exit): ")
prompts_input = "What does the image describe?"
prompts = [prompt.strip() for prompt in prompts_input.split(",")]
for prompt in prompts:
print(f"\nPrompt: {prompt}")
response = get_response(encoded_frames_list, prompt, model, image_processor, in_context_prompts)
print(f"Response: {response}")
if prompts_input.lower() == "quit":
break
``` 