File size: 5,302 Bytes
52a881a 6bfad80 52a881a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 | from __future__ import annotations
from pathlib import Path
from threading import Thread
from typing import List
import torch
from qwen_vl_utils import process_vision_info
from transformers import (
AutoProcessor,
Qwen2_5_VLForConditionalGeneration,
TextIteratorStreamer,
)
DEFAULT_MODEL_PATH = "./checkpoints"
DEFAULT_SYSTEM_PROMPT = "You are a professional AI dermatology assistant."
def resolve_model_path(model_path: str = DEFAULT_MODEL_PATH) -> str:
"""Resolve a model path for both cloned-repo and local-dev layouts."""
raw_path = Path(model_path).expanduser()
repo_root = Path(__file__).resolve().parents[2]
candidates = [raw_path]
if not raw_path.is_absolute():
candidates.append(Path.cwd() / raw_path)
candidates.append(repo_root / raw_path)
if raw_path.parts and raw_path.parts[0] == repo_root.name:
candidates.append(repo_root.joinpath(*raw_path.parts[1:]))
for candidate in candidates:
if candidate.exists():
return str(candidate)
return str(raw_path)
def build_single_turn_messages(
image_path: str,
prompt: str,
system_prompt: str = DEFAULT_SYSTEM_PROMPT,
) -> List[dict]:
return [
{
"role": "user",
"content": [
{"type": "image", "image": image_path},
{"type": "text", "text": f"{system_prompt}\n\n{prompt}"},
],
}
]
class SkinGPTModel:
def __init__(self, model_path: str = DEFAULT_MODEL_PATH, device: str | None = None):
resolved_model_path = resolve_model_path(model_path)
self.model_path = resolved_model_path
self.device = device or ("cuda" if torch.cuda.is_available() else "cpu")
print(f"Loading model from {resolved_model_path} on {self.device}...")
self.model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
resolved_model_path,
torch_dtype=torch.bfloat16 if self.device != "cpu" else torch.float32,
attn_implementation="flash_attention_2" if self.device == "cuda" else None,
device_map="auto" if self.device != "mps" else None,
trust_remote_code=True,
)
if self.device == "mps":
self.model = self.model.to(self.device)
self.processor = AutoProcessor.from_pretrained(
resolved_model_path,
trust_remote_code=True,
min_pixels=256 * 28 * 28,
max_pixels=1280 * 28 * 28,
)
print("Model loaded successfully.")
def generate_response(
self,
messages,
max_new_tokens: int = 1024,
temperature: float = 0.7,
repetition_penalty: float = 1.2,
no_repeat_ngram_size: int = 3,
) -> str:
text = self.processor.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = self.processor(
text=[text],
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt",
).to(self.model.device)
with torch.no_grad():
generated_ids = self.model.generate(
**inputs,
max_new_tokens=max_new_tokens,
temperature=temperature,
repetition_penalty=repetition_penalty,
no_repeat_ngram_size=no_repeat_ngram_size,
top_p=0.9,
do_sample=True,
)
generated_ids_trimmed = [
out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = self.processor.batch_decode(
generated_ids_trimmed,
skip_special_tokens=True,
clean_up_tokenization_spaces=False,
)
return output_text[0]
def generate_response_stream(
self,
messages,
max_new_tokens: int = 1024,
temperature: float = 0.7,
repetition_penalty: float = 1.2,
no_repeat_ngram_size: int = 3,
):
text = self.processor.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = self.processor(
text=[text],
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt",
).to(self.model.device)
streamer = TextIteratorStreamer(
self.processor.tokenizer,
skip_prompt=True,
skip_special_tokens=True,
)
generation_kwargs = {
**inputs,
"max_new_tokens": max_new_tokens,
"temperature": temperature,
"repetition_penalty": repetition_penalty,
"no_repeat_ngram_size": no_repeat_ngram_size,
"top_p": 0.9,
"do_sample": True,
"streamer": streamer,
}
thread = Thread(target=self.model.generate, kwargs=generation_kwargs)
thread.start()
for text_chunk in streamer:
yield text_chunk
thread.join()
|