import torch
from .constants import *
from .conversation import conv_templates, SeparatorStyle
from .model.builder import load_pretrained_model
from .utils import disable_torch_init
from .mm_utils import tokenizer_image_token, KeywordsStoppingCriteria
from PIL import Image
import os
from decord import VideoReader, cpu
import numpy as np
class Chat:
def __init__(self, model_path, conv_mode="simple", load_8bit=False, load_4bit=False):
disable_torch_init()
self.tokenizer, self.model, self.image_processor, context_len = load_pretrained_model(model_path, None, model_name="ChatUniVi", load_8bit=load_8bit, load_4bit=load_4bit)
self.model.to("cuda:0")
mm_use_im_start_end = getattr(self.model.config, "mm_use_im_start_end", False)
mm_use_im_patch_token = getattr(self.model.config, "mm_use_im_patch_token", True)
if mm_use_im_patch_token:
self.tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True)
if mm_use_im_start_end:
self.tokenizer.add_tokens([DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], special_tokens=True)
self.model.resize_token_embeddings(len(self.tokenizer))
vision_tower = self.model.get_vision_tower()
if not vision_tower.is_loaded:
vision_tower.load_model()
self.image_processor = vision_tower.image_processor
self.conv_mode = conv_mode
print(self.model)
def get_prompt(self, qs, state):
state.append_message(state.roles[0], qs)
state.append_message(state.roles[1], None)
return state
def _get_rawvideo_dec(self, video_path, image_processor, max_frames=MAX_IMAGE_LENGTH, image_resolution=224,
video_framerate=1, s=None, e=None):
if s is None:
start_time, end_time = None, None
else:
start_time = int(s)
end_time = int(e)
start_time = start_time if start_time >= 0. else 0.
end_time = end_time if end_time >= 0. else 0.
if start_time > end_time:
start_time, end_time = end_time, start_time
elif start_time == end_time:
end_time = start_time + 1
if os.path.exists(video_path):
vreader = VideoReader(video_path, ctx=cpu(0))
else:
print(video_path)
raise FileNotFoundError
fps = vreader.get_avg_fps()
f_start = 0 if start_time is None else int(start_time * fps)
f_end = int(min(1000000000 if end_time is None else end_time * fps, len(vreader) - 1))
num_frames = f_end - f_start + 1
if num_frames > 0:
sample_fps = int(video_framerate)
t_stride = int(round(float(fps) / sample_fps))
all_pos = list(range(f_start, f_end + 1, t_stride))
if len(all_pos) > max_frames:
sample_pos = [all_pos[_] for _ in np.linspace(0, len(all_pos) - 1, num=max_frames, dtype=int)]
else:
sample_pos = all_pos
patch_images = [Image.fromarray(f) for f in vreader.get_batch(sample_pos).asnumpy()]
return patch_images
@torch.inference_mode()
@spaces.GPU
def generate(self, images_tensor: list, prompt: str, first_run: bool, state):
tokenizer, model, image_processor = self.tokenizer, self.model, self.image_processor
state = self.get_prompt(prompt, state)
prompt = state.get_prompt()
print(prompt)
images_tensor = torch.stack(images_tensor, dim=0)
input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).to("cuda:0")
temperature = 0.2
max_new_tokens = 1024
stop_str = conv_templates[self.conv_mode].copy().sep if conv_templates[self.conv_mode].copy().sep_style != SeparatorStyle.TWO else \
conv_templates[self.conv_mode].copy().sep2
keywords = [stop_str]
stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
with torch.inference_mode():
output_ids = model.generate(
input_ids,
images=images_tensor,
do_sample=True,
temperature=temperature,
num_beams=1,
max_new_tokens=max_new_tokens,
use_cache=True,
stopping_criteria=[stopping_criteria])
input_token_len = input_ids.shape[1]
n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item()
if n_diff_input_output > 0:
print(f'[Warning] {n_diff_input_output} output_ids are not the same as the input_ids')
outputs = tokenizer.batch_decode(output_ids[:, input_token_len:], skip_special_tokens=True)[0]
outputs = outputs.strip()
if outputs.endswith(stop_str):
outputs = outputs[:-len(stop_str)]
outputs = outputs.strip()
print('response', outputs)
return outputs, state
title_markdown = ("""
Chat-UniVi: Unified Visual Representation Empowers Large Language Models with Image and Video Understanding
If you like our project, please give us a star ✨ on Github for the latest update.
""")
block_css = """
#buttons button {
min-width: min(120px,100%);
}
"""
tos_markdown = ("""
### Terms of use
By using this service, users are required to agree to the following terms:
The service is a research preview intended for non-commercial use only. It only provides limited safety measures and may generate offensive content. It must not be used for any illegal, harmful, violent, racist, or sexual purposes. The service may collect user dialogue data for future research.
Please click the "Flag" button if you get any inappropriate answer! We will collect those to keep improving our moderator.
For an optimal experience, please use desktop computers for this demo, as mobile devices may compromise its quality.
""")
learn_more_markdown = ("""
### License
The service is a research preview intended for non-commercial use only, subject to the model [License](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) of LLaMA, [Terms of Use](https://openai.com/policies/terms-of-use) of the data generated by OpenAI, and [Privacy Practices](https://chrome.google.com/webstore/detail/sharegpt-share-your-chatg/daiacboceoaocpibfodeljbdfacokfjb) of ShareGPT. Please contact us if you find any potential violation.
""")
