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from qwen_vl_utils import process_vision_info
from transformers import (
AutoProcessor,
Qwen2VLForConditionalGeneration,
Qwen2_5_VLForConditionalGeneration,
)
from torchvision.transforms import ToPILImage
to_pil = ToPILImage()
Qwen25VL_7b_PREFIX = '''You are an expert image analyst specializing in 3D scene understanding. Your goal is to describe the 3D structure and layout of the scene in the image to aid in depth estimation tasks.
- Focus your description on elements crucial for 3D understanding. Incorporate observations about key **objects**, their **spatial relationships** (like positions, relative distances, and relative sizes using real-world orientation), the overall **layout**, potential **camera perspective**, and visible **depth cues** (like occlusion or perspective lines) into a unified description.
- Be concise but informative, and assume it is a real-world image. Only describe what you are very confident about.
User Prompt:'''
Qwen25VL_7b_PREFIX2 = '''Given a user prompt, generate an "Enhanced prompt" that provides detailed visual descriptions suitable for image generation. Evaluate the level of detail in the user prompt:
- If the prompt is simple, focus on adding specifics about colors, shapes, sizes, textures, and spatial relationships to create vivid and concrete scenes.
- If the prompt is already detailed, refine and enhance the existing details slightly without overcomplicating.\n
Here are examples of how to transform or refine prompts:
- User Prompt: A cat sleeping -> Enhanced: A small, fluffy white cat curled up in a round shape, sleeping peacefully on a warm sunny windowsill, surrounded by pots of blooming red flowers.
- User Prompt: A busy city street -> Enhanced: A bustling city street scene at dusk, featuring glowing street lamps, a diverse crowd of people in colorful clothing, and a double-decker bus passing by towering glass skyscrapers.\n
Please generate only the enhanced description for the prompt below and avoid including any additional commentary or evaluations:
User Prompt:'''
Qwen25VL_7b_PREFIX3 = '''You are a helpful assistant. Describe the image in detail. User Prompt:'''
def split_string(s):
# 将中文引号替换为英文引号
s = s.replace("“", '"').replace("”", '"') # use english quotes
result = []
# 标记是否在引号内
in_quotes = False
temp = ""
# 遍历字符串中的每个字符及其索引
for idx, char in enumerate(s):
# 如果字符是引号且索引大于 155
if char == '"' and idx > 155:
# 将引号添加到临时字符串
temp += char
# 如果不在引号内
if not in_quotes:
# 将临时字符串添加到结果列表
result.append(temp)
# 清空临时字符串
temp = ""
# 切换引号状态
in_quotes = not in_quotes
continue
# 如果在引号内
if in_quotes:
# 如果字符是空格
if char.isspace():
pass # have space token
# 将字符用中文引号包裹后添加到结果列表
result.append("“" + char + "”")
else:
# 将字符添加到临时字符串
temp += char
# 如果临时字符串不为空
if temp:
# 将临时字符串添加到结果列表
result.append(temp)
return result
class Qwen25VL_7b_Embedder(torch.nn.Module):
def __init__(self, model_path, max_length=640, dtype=torch.bfloat16, device="cuda",args=None):
super(Qwen25VL_7b_Embedder, self).__init__()
self.max_length = max_length
self.model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
model_path,
torch_dtype=dtype,
attn_implementation="flash_attention_2",
).to(torch.cuda.current_device())
self.model.requires_grad_(False)
self.processor = AutoProcessor.from_pretrained(
model_path, min_pixels=256 * 28 * 28, max_pixels=324 * 28 * 28
)
self.prefix = Qwen25VL_7b_PREFIX if (args is not None) and (not args.old_prompt) else Qwen25VL_7b_PREFIX2
self.prefix = Qwen25VL_7b_PREFIX3 if (args is not None) and (args.prompt_type == "rgb" or args.prompt_type == "empty") else self.prefix
self.prefix_len = self.calculate_len_of_prefix()
@property
def device(self) -> torch.device:
return next(self.parameters()).device
@property
def dtype(self) -> torch.dtype:
return next(self.parameters()).dtype
def calculate_len_of_prefix(self):
messages_specific = [{"role": "user", "content": []}]
messages_specific[0]["content"].append({"type": "text", "text": f"{self.prefix}"})
text_specific = self.processor.apply_chat_template(
messages_specific, tokenize=False, add_generation_prompt=True, add_vision_id=True
)
inputs_specific = self.processor(
text=[text_specific],
padding=True,
# return_tensors="pt",
)
len_specific = len(inputs_specific["input_ids"][0]) - 6 # 223 - 6 = self.prefix_len
return len_specific
def forward(self, caption, ref_images):
text_list = caption
embs = torch.zeros(
len(text_list),
self.max_length,
self.model.config.hidden_size,
dtype=torch.bfloat16,
device=torch.cuda.current_device(),
)
hidden_states = torch.zeros(
len(text_list),
self.max_length,
self.model.config.hidden_size,
dtype=torch.bfloat16,
device=torch.cuda.current_device(),
)
masks = torch.zeros(
len(text_list),
self.max_length,
dtype=torch.long,
device=torch.cuda.current_device(),
)
input_ids_list = []
attention_mask_list = []
emb_list = []
def split_string(s):
s = s.replace("“", '"').replace("”", '"').replace("'", '''"''') # use english quotes
result = []
in_quotes = False
temp = ""
for idx,char in enumerate(s):
if char == '"' and idx>155:
temp += char
if not in_quotes:
result.append(temp)
temp = ""
in_quotes = not in_quotes
continue
if in_quotes:
if char.isspace():
pass # have space token
result.append("“" + char + "”")
else:
temp += char
if temp:
result.append(temp)
return result
for idx, (txt, imgs) in enumerate(zip(text_list, ref_images)):
messages = [{"role": "user", "content": []}]
messages[0]["content"].append({"type": "text", "text": f"{self.prefix}"})
messages[0]["content"].append({"type": "image", "image": to_pil(imgs)})
# 再添加 text
messages[0]["content"].append({"type": "text", "text": f"{txt}"})
# Preparation for inference
text = self.processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True, add_vision_id=True
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = self.processor(
text=[text],
images=image_inputs,
padding=True,
return_tensors="pt",
)
old_inputs_ids = inputs.input_ids
text_split_list = split_string(text)
token_list = []
for text_each in text_split_list:
txt_inputs = self.processor(
text=text_each,
images=None,
videos=None,
padding=True,
return_tensors="pt",
)
token_each = txt_inputs.input_ids
if token_each[0][0] == 2073 and token_each[0][-1] == 854:
token_each = token_each[:, 1:-1]
token_list.append(token_each)
else:
token_list.append(token_each)
new_txt_ids = torch.cat(token_list, dim=1).to("cuda")
new_txt_ids = new_txt_ids.to(old_inputs_ids.device)
idx1 = (old_inputs_ids == 151653).nonzero(as_tuple=True)[1][0]
idx2 = (new_txt_ids == 151653).nonzero(as_tuple=True)[1][0]
inputs.input_ids = (
torch.cat([old_inputs_ids[0, :idx1], new_txt_ids[0, idx2:]], dim=0)
.unsqueeze(0)
.to("cuda")
)
inputs.attention_mask = (inputs.input_ids > 0).long().to("cuda")
outputs = self.model(
input_ids=inputs.input_ids,
attention_mask=inputs.attention_mask,
pixel_values=inputs.pixel_values.to("cuda"),
image_grid_thw=inputs.image_grid_thw.to("cuda"),
output_hidden_states=True,
)
emb = outputs["hidden_states"][-1]
embs[idx, : min(self.max_length, emb.shape[1] - self.prefix_len)] = emb[0, self.prefix_len:][:self.max_length] #2,640,3584
masks[idx, : min(self.max_length, emb.shape[1] - self.prefix_len)] = torch.ones(
(min(self.max_length, emb.shape[1] - self.prefix_len)),
dtype=torch.long,
device=torch.cuda.current_device(),
)
return embs, masks |