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from transformers import PreTrainedModel, PretrainedConfig
from .config import MoondreamConfig
from .moondream import MoondreamModel
# Files sometimes don't get loaded without these...
from .image_crops import *
from .vision import *
from .text import *
from .region import *
from .utils import *
def extract_question(text):
prefix = "<image>\n\nQuestion: "
suffix = "\n\nAnswer:"
if text.startswith(prefix) and text.endswith(suffix):
return text[len(prefix) : -len(suffix)]
else:
return None
class HfConfig(PretrainedConfig):
_auto_class = "AutoConfig"
model_type = "moondream1"
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.config = {}
class HfMoondream(PreTrainedModel):
_auto_class = "AutoModelForCausalLM"
config_class = HfConfig
def __init__(self, config):
super().__init__(config)
self.model = MoondreamModel(MoondreamConfig.from_dict(config.config))
@property
def encode_image(self):
return self.model.encode_image
@property
def query(self):
return self.model.query
@property
def caption(self):
return self.model.caption
@property
def detect(self):
return self.model.detect
@property
def point(self):
return self.model.point
@property
def detect_gaze(self):
return self.model.detect_gaze
def answer_question(
self,
image_embeds,
question,
tokenizer=None,
chat_history="",
result_queue=None,
max_new_tokens=256,
**kwargs
):
answer = self.query(image_embeds, question)["answer"].strip()
if result_queue is not None:
result_queue.put(answer)
return answer
def batch_answer(self, images, prompts, tokenizer=None, **kwargs):
answers = []
for image, prompt in zip(images, prompts):
answers.append(self.query(image, prompt)["answer"].strip())
return answers
def _unsupported_exception(self):
raise NotImplementedError(
"This method is not supported in the latest version of moondream. "
"Consider upgrading to the updated API spec, or alternately pin "
"to 'revision=2024-08-26'."
)
def generate(self, image_embeds, prompt, tokenizer, max_new_tokens=128, **kwargs):
"""
Function definition remains unchanged for backwards compatibility.
Be aware that tokenizer, max_new_takens, and kwargs are ignored.
"""
prompt_extracted = extract_question(prompt)
if prompt_extracted is not None:
answer = self.model.query(image=image_embeds, question=prompt_extracted, stream=False)[
"answer"
]
else:
image_embeds = self.encode_image(image_embeds)
prompt_tokens = torch.tensor(
[self.model.tokenizer.encode(prompt).ids],
device=self.device,
)
def generator():
for token in self.model._generate_text(
prompt_tokens, image_embeds.kv_cache, image_embeds.pos, max_new_tokens
):
yield token
answer = "".join(list(generator()))
return [answer]
def get_input_embeddings(self):
return super().get_input_embeddings()
def input_embeds(self, *args, **kwargs):
self._unsupported_exception()
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