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from PIL import Image
from io import BytesIO
import base64
import torch
from transformers import StoppingCriteria
from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
from llava.conversation import conv_templates, SeparatorStyle
from transformers import TextStreamer
from os.path import join
def load_image_from_base64(image):
return Image.open(BytesIO(base64.b64decode(image)))
def load_image(image_file):
if image_file.startswith('http://') or image_file.startswith('https://'):
response = requests.get(image_file)
image = Image.open(BytesIO(response.content)).convert('RGB')
else:
image = Image.open(image_file).convert('RGB')
return image
def expand2square(pil_img, background_color):
width, height = pil_img.size
if width == height:
return pil_img
elif width > height:
result = Image.new(pil_img.mode, (width, width), background_color)
result.paste(pil_img, (0, (width - height) // 2))
return result
else:
result = Image.new(pil_img.mode, (height, height), background_color)
result.paste(pil_img, ((height - width) // 2, 0))
return result
def process_images(images, image_processor, model_cfg):
image_aspect_ratio = getattr(model_cfg, "image_aspect_ratio", None)
new_images = []
if image_aspect_ratio == 'pad':
for image in images:
image = expand2square(image, tuple(int(x*255) for x in image_processor.image_mean))
image = image_processor.preprocess(image, return_tensors='pt')['pixel_values'][0]
new_images.append(image)
else:
return image_processor(images, return_tensors='pt')['pixel_values']
if all(x.shape == new_images[0].shape for x in new_images):
new_images = torch.stack(new_images, dim=0)
return new_images
def tokenizer_image_token(prompt, tokenizer, image_token_index=IMAGE_TOKEN_INDEX, return_tensors=None):
prompt_chunks = [tokenizer(chunk).input_ids for chunk in prompt.split('<image>')]
def insert_separator(X, sep):
return [ele for sublist in zip(X, [sep]*len(X)) for ele in sublist][:-1]
input_ids = []
offset = 0
if len(prompt_chunks) > 0 and len(prompt_chunks[0]) > 0 and prompt_chunks[0][0] == tokenizer.bos_token_id:
offset = 1
input_ids.append(prompt_chunks[0][0])
for x in insert_separator(prompt_chunks, [image_token_index] * (offset + 1)):
input_ids.extend(x[offset:])
if return_tensors is not None:
if return_tensors == 'pt':
return torch.tensor(input_ids, dtype=torch.long)
raise ValueError(f'Unsupported tensor type: {return_tensors}')
return input_ids
def get_model_name_from_path(model_path):
model_path = model_path.strip("/")
model_paths = model_path.split("/")
if model_paths[-1].startswith('checkpoint-'):
return model_paths[-2] + "_" + model_paths[-1]
else:
return model_paths[-1]
from PIL import Image
from io import BytesIO
import base64
import torch
from transformers import StoppingCriteria
from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
from llava.conversation import conv_templates, SeparatorStyle
from transformers import TextStreamer
from os.path import join
def load_image_from_base64(image):
return Image.open(BytesIO(base64.b64decode(image)))
def load_image(image_file):
if image_file.startswith('http://') or image_file.startswith('https://'):
response = requests.get(image_file)
image = Image.open(BytesIO(response.content)).convert('RGB')
else:
image = Image.open(image_file).convert('RGB')
return image
def expand2square(pil_img, background_color):
width, height = pil_img.size
if width == height:
return pil_img
elif width > height:
result = Image.new(pil_img.mode, (width, width), background_color)
result.paste(pil_img, (0, (width - height) // 2))
return result
else:
result = Image.new(pil_img.mode, (height, height), background_color)
result.paste(pil_img, ((height - width) // 2, 0))
return result
def process_images(images, image_processor, model_cfg):
image_aspect_ratio = getattr(model_cfg, "image_aspect_ratio", None)
new_images = []
if image_aspect_ratio == 'pad':
for image in images:
image = expand2square(image, tuple(int(x*255) for x in image_processor.image_mean))
image = image_processor.preprocess(image, return_tensors='pt')['pixel_values'][0]
new_images.append(image)
else:
return image_processor(images, return_tensors='pt')['pixel_values']
if all(x.shape == new_images[0].shape for x in new_images):
new_images = torch.stack(new_images, dim=0)
return new_images
def tokenizer_image_token(prompt, tokenizer, image_token_index=IMAGE_TOKEN_INDEX, return_tensors=None):
prompt_chunks = [tokenizer(chunk).input_ids for chunk in prompt.split('<image>')]
def insert_separator(X, sep):
return [ele for sublist in zip(X, [sep]*len(X)) for ele in sublist][:-1]
input_ids = []
offset = 0
if len(prompt_chunks) > 0 and len(prompt_chunks[0]) > 0 and prompt_chunks[0][0] == tokenizer.bos_token_id:
offset = 1
input_ids.append(prompt_chunks[0][0])
for x in insert_separator(prompt_chunks, [image_token_index] * (offset + 1)):
input_ids.extend(x[offset:])
if return_tensors is not None:
if return_tensors == 'pt':
return torch.tensor(input_ids, dtype=torch.long)
raise ValueError(f'Unsupported tensor type: {return_tensors}')
return input_ids
def get_model_name_from_path(model_path):
model_path = model_path.strip("/")
model_paths = model_path.split("/")
if model_paths[-1].startswith('checkpoint-'):
return model_paths[-2] + "_" + model_paths[-1]
else:
return model_paths[-1]
def get_independent_context_n_captions(vidframes_path_list, model, image_processor, tokenizer, args, num_captions=3, cond='', agg_type='concat'):
vidframes_path_list = vidframes_path_list[1:-1]
num_captions = int(num_captions)
if num_captions == 1:
img_files = [vidframes_path_list[randint(0, len(vidframes_path_list)-1)]]
elif num_captions >= len(vidframes_path_list):
img_files = vidframes_path_list
else:
L = len(vidframes_path_list)
if num_captions <= 0 or num_captions > L:
return "Invalid value of n. Please provide a valid number."
max_gap = (L - 1) // (num_captions - 1) # Calculate the maximum gap between indices
img_files = [vidframes_path_list[i * max_gap] for i in range(num_captions)]
outputs = []
# for idx, fname in enumerate([first_img_file, last_img_file]):
for idx, fname in enumerate(img_files):
# image load
image = load_image(fname)
# Similar operation in model_worker.py
image_tensor = process_images([image], image_processor, args)
image_tensor = image_tensor.to(model.device, dtype=torch.float16)
conv = conv_templates[args.conv_mode].copy()
tmp_query = 'Considering the following question, \'' + cond + '\', describe this image concisely and shortly.'
question = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + tmp_query
conv.append_message(conv.roles[0], question)
image = None
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()
input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda()
stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
keywords = [stop_str]
stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
with torch.inference_mode():
output_ids = model.generate(
input_ids,
images=image_tensor,
do_sample=True,
temperature=args.temperature,
max_new_tokens=args.max_new_tokens,
streamer=streamer,
use_cache=True,
stopping_criteria=[stopping_criteria])
output = tokenizer.decode(output_ids[0, input_ids.shape[1]:]).strip()
outputs.append(output)
outputs = ' '.join(outputs)
outputs = outputs.replace('</s>', '')
if agg_type == 'summ':
conv = conv_templates[args.conv_mode].copy()
tmp_query = 'Summarize the following sentences, ' + outputs
question = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + tmp_query
conv.append_message(conv.roles[0], question)
image = None
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()
input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda()
stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
keywords = [stop_str]
stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
with torch.inference_mode():
output_ids = model.generate(
input_ids,
images=image_tensor,
do_sample=True,
temperature=args.temperature,
max_new_tokens=args.max_new_tokens,
streamer=streamer,
use_cache=True,
stopping_criteria=[stopping_criteria])
outputs = tokenizer.decode(output_ids[0, input_ids.shape[1]:]).strip()
# outputs.append(output)
outputs = outputs.replace('</s>', '')
return outputs
def get_independent_context_captions(vidframes_path_list, model, image_processor, tokenizer, args, num_captions=3, cond='', agg_type='concat'):
first_img_file = vidframes_path_list[1]
tmp_frm_idx = max(0, int(len(vidframes_path_list)/2))
tmp_frm_idx = min(len(vidframes_path_list)-1, int(len(vidframes_path_list)/2))
center_img_file = vidframes_path_list[tmp_frm_idx]
last_img_file = vidframes_path_list[-2]
outputs = []
for idx, fname in enumerate([first_img_file, center_img_file, last_img_file]):
# image load
image = load_image(fname)
# Similar operation in model_worker.py
image_tensor = process_images([image], image_processor, args)
image_tensor = image_tensor.to(model.device, dtype=torch.float16)
conv = conv_templates[args.conv_mode].copy()
tmp_query = 'Considering the following question, \'' + cond + '\', describe this image concisely and shortly.'
question = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + tmp_query
conv.append_message(conv.roles[0], question)
image = None
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()
input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda()
stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
keywords = [stop_str]
stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
with torch.inference_mode():
output_ids = model.generate(
input_ids,
images=image_tensor,
do_sample=True,
temperature=args.temperature,
max_new_tokens=args.max_new_tokens,
streamer=streamer,
use_cache=True,
stopping_criteria=[stopping_criteria])
output = tokenizer.decode(output_ids[0, input_ids.shape[1]:]).strip()
outputs.append(output)
outputs = ' '.join(outputs)
outputs = outputs.replace('</s>', '')
if agg_type == 'summ':
conv = conv_templates[args.conv_mode].copy()
tmp_query = 'Summarize the following sentences, ' + outputs
question = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + tmp_query
conv.append_message(conv.roles[0], question)
image = None
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()
input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda()
stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
keywords = [stop_str]
stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
with torch.inference_mode():
output_ids = model.generate(
input_ids,
images=image_tensor,
do_sample=True,
temperature=args.temperature,
max_new_tokens=args.max_new_tokens,
streamer=streamer,
use_cache=True,
stopping_criteria=[stopping_criteria])
outputs = tokenizer.decode(output_ids[0, input_ids.shape[1]:]).strip()
# outputs.append(output)
outputs = outputs.replace('</s>', '')
return outputs
def get_dependent_context_captions(vidframes_path_list, model, image_processor, tokenizer, args, num_captions=3, cond='', agg_type='concat'):
first_img_file = vidframes_path_list[1]
tmp_frm_idx = max(0, int(len(vidframes_path_list)/2))
tmp_frm_idx = min(len(vidframes_path_list)-1, int(len(vidframes_path_list)/2))
center_img_file = vidframes_path_list[tmp_frm_idx]
last_img_file = vidframes_path_list[-2]
outputs = []
for idx, fname in enumerate([first_img_file, center_img_file, last_img_file]):
# image load
image = load_image(fname)
# Similar operation in model_worker.py
image_tensor = process_images([image], image_processor, args)
image_tensor = image_tensor.to(model.device, dtype=torch.float16)
conv = conv_templates[args.conv_mode].copy()
if idx == 0:
tmp_query = 'Considering the following question, \'' + cond + '\', describe this image concisely and shortly.'
else:
tmp_query = 'Considering the following question and sentences\'' + cond + ' and ' + output + '\', describe this image concisely and shortly.'
tmp_query = tmp_query.replace('</s>', '')
question = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + tmp_query
conv.append_message(conv.roles[0], question)
image = None
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()
input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda()
stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
keywords = [stop_str]
stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
with torch.inference_mode():
output_ids = model.generate(
input_ids,
images=image_tensor,
do_sample=True,
temperature=args.temperature,
max_new_tokens=args.max_new_tokens,
streamer=streamer,
use_cache=True,
stopping_criteria=[stopping_criteria])
output = tokenizer.decode(output_ids[0, input_ids.shape[1]:]).strip()
output.replace('</s>', '')
outputs.append(output)
outputs = ' '.join(outputs)
outputs = outputs.replace('</s>', '')
if agg_type == 'summ':
conv = conv_templates[args.conv_mode].copy()
tmp_query = 'Summarize the following sentences, ' + outputs
# question = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + tmp_query
conv.append_message(conv.roles[0], tmp_query)
image = None
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()
input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda()
stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
keywords = [stop_str]
stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
with torch.inference_mode():
output_ids = model.generate(
input_ids,
do_sample=True,
temperature=args.temperature,
max_new_tokens=args.max_new_tokens,
streamer=streamer,
use_cache=True,
stopping_criteria=[stopping_criteria])
outputs = tokenizer.decode(output_ids[0, input_ids.shape[1]:]).strip()
# outputs.append(output)
outputs = outputs.replace('</s>', '')
return outputs
def get_dependent_context_n_captions(vidframes_path_list, model, image_processor, tokenizer, args, num_captions=3, cond='', agg_type='concat'):
vidframes_path_list = vidframes_path_list[1:-1]
num_captions = int(num_captions)
if num_captions == 1:
img_files = [vidframes_path_list[randint(0, len(vidframes_path_list)-1)]]
elif num_captions >= len(vidframes_path_list):
img_files = vidframes_path_list
else:
L = len(vidframes_path_list)
if num_captions <= 0 or num_captions > L:
return "Invalid value of n. Please provide a valid number."
max_gap = (L - 1) // (num_captions - 1) # Calculate the maximum gap between indices
img_files = [vidframes_path_list[i * max_gap] for i in range(num_captions)]
# first_img_file = vidframes_path_list[1]
# tmp_frm_idx = max(0, int(len(vidframes_path_list)/2))
# tmp_frm_idx = min(len(vidframes_path_list)-1, int(len(vidframes_path_list)/2))
# center_img_file = vidframes_path_list[tmp_frm_idx]
# last_img_file = vidframes_path_list[-2]
outputs = []
for idx, fname in enumerate([first_img_file, center_img_file, last_img_file]):
# image load
image = load_image(fname)
# Similar operation in model_worker.py
image_tensor = process_images([image], image_processor, args)
image_tensor = image_tensor.to(model.device, dtype=torch.float16)
conv = conv_templates[args.conv_mode].copy()
if idx == 0:
tmp_query = 'Considering the following question, \'' + cond + '\', describe this image concisely and shortly.'
else:
tmp_query = 'Considering the following question and sentences\'' + cond + ' and ' + output + '\', describe this image concisely and shortly.'
tmp_query = tmp_query.replace('</s>', '')
question = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + tmp_query
conv.append_message(conv.roles[0], question)
image = None
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()
input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda()
stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
keywords = [stop_str]
stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
with torch.inference_mode():
output_ids = model.generate(
input_ids,
images=image_tensor,
do_sample=True,
temperature=args.temperature,
max_new_tokens=args.max_new_tokens,
streamer=streamer,
use_cache=True,
stopping_criteria=[stopping_criteria])
output = tokenizer.decode(output_ids[0, input_ids.shape[1]:]).strip()
output.replace('</s>', '')
outputs.append(output)
outputs = ' '.join(outputs)
outputs = outputs.replace('</s>', '')
if agg_type == 'summ':
conv = conv_templates[args.conv_mode].copy()
tmp_query = 'Summarize the following sentences, ' + outputs
# question = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + tmp_query
conv.append_message(conv.roles[0], tmp_query)
image = None
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()
input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda()
stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
keywords = [stop_str]
stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
with torch.inference_mode():
output_ids = model.generate(
input_ids,
do_sample=True,
temperature=args.temperature,
max_new_tokens=args.max_new_tokens,
streamer=streamer,
use_cache=True,
stopping_criteria=[stopping_criteria])
outputs = tokenizer.decode(output_ids[0, input_ids.shape[1]:]).strip()
# outputs.append(output)
outputs = outputs.replace('</s>', '')
return outputs
class KeywordsStoppingCriteria(StoppingCriteria):
def __init__(self, keywords, tokenizer, input_ids):
self.keywords = keywords
self.keyword_ids = []
self.max_keyword_len = 0
for keyword in keywords:
cur_keyword_ids = tokenizer(keyword).input_ids
if len(cur_keyword_ids) > 1 and cur_keyword_ids[0] == tokenizer.bos_token_id:
cur_keyword_ids = cur_keyword_ids[1:]
if len(cur_keyword_ids) > self.max_keyword_len:
self.max_keyword_len = len(cur_keyword_ids)
self.keyword_ids.append(torch.tensor(cur_keyword_ids))
self.tokenizer = tokenizer
self.start_len = input_ids.shape[1]
def __call__(self, output_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
assert output_ids.shape[0] == 1, "Only support batch size 1 (yet)" # TODO
offset = min(output_ids.shape[1] - self.start_len, self.max_keyword_len)
self.keyword_ids = [keyword_id.to(output_ids.device) for keyword_id in self.keyword_ids]
for keyword_id in self.keyword_ids:
if (output_ids[0, -keyword_id.shape[0]:] == keyword_id).all():
return True
outputs = self.tokenizer.batch_decode(output_ids[:, -offset:], skip_special_tokens=True)[0]
for keyword in self.keywords:
if keyword in outputs:
return True
return False |