diff --git a/examples/vlm/qwen2-5-vl-vision-only.yml b/examples/vlm/qwen2-5-vl-vision-only.yml new file mode 100755 index 00000000..a4950a6b --- /dev/null +++ b/examples/vlm/qwen2-5-vl-vision-only.yml @@ -0,0 +1,28 @@ +nodes: + - id: camera + build: pip install opencv-video-capture + path: opencv-video-capture + inputs: + tick: dora/timer/millis/100 + outputs: + - image + env: + CAPTURE_PATH: 1 + + - id: dora-qwenvl + build: pip install -e ../../node-hub/dora-qwen2-5-vl + path: dora-qwenvl + inputs: + image: camera/image + text: dora/timer/millis/1000 + outputs: + - text + env: + DEFAULT_QUESTION: Describe the image in three words. + + - id: plot + build: pip install dora-rerun + path: dora-rerun + inputs: + image: camera/image + text_qwenvl: dora-qwenvl/text diff --git a/node-hub/dora-qwen2-5-vl/README.md b/node-hub/dora-qwen2-5-vl/README.md new file mode 100644 index 00000000..535ff3c3 --- /dev/null +++ b/node-hub/dora-qwen2-5-vl/README.md @@ -0,0 +1,32 @@ +# Dora QwenVL2 node + +Experimental node for using a VLM within dora. + +## YAML Specification + +This node is supposed to be used as follows: + +```yaml +- id: dora-qwenvl + build: pip install dora-qwenvl + path: dora-qwenvl + inputs: + image: + source: camera/image + queue_size: 1 + text: dora-distil-whisper/text + outputs: + - text + env: + DEFAULT_QUESTION: Describe the image in a very short sentence. +``` + +## Additional documentation + +- Qwenvl: https://github.com/QwenLM/Qwen-VL + +## Examples + +- Vision Language Model + - Github: https://github.com/dora-rs/dora/blob/main/examples/vlm + - Website: https://dora-rs.ai/docs/examples/vlm diff --git a/node-hub/dora-qwen2-5-vl/dora_qwenvl/__init__.py b/node-hub/dora-qwen2-5-vl/dora_qwenvl/__init__.py new file mode 100644 index 00000000..cde7a377 --- /dev/null +++ b/node-hub/dora-qwen2-5-vl/dora_qwenvl/__init__.py @@ -0,0 +1,11 @@ +import os + +# Define the path to the README file relative to the package directory +readme_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "README.md") + +# Read the content of the README file +try: + with open(readme_path, encoding="utf-8") as f: + __doc__ = f.read() +except FileNotFoundError: + __doc__ = "README file not found." diff --git a/node-hub/dora-qwen2-5-vl/dora_qwenvl/main.py b/node-hub/dora-qwen2-5-vl/dora_qwenvl/main.py new file mode 100644 index 00000000..d7846b2a --- /dev/null +++ b/node-hub/dora-qwen2-5-vl/dora_qwenvl/main.py @@ -0,0 +1,220 @@ +import os +from pathlib import Path + +import cv2 +import numpy as np +import pyarrow as pa +from dora import Node +from PIL import Image +from qwen_vl_utils import process_vision_info +from transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration + +DEFAULT_PATH = "Qwen/Qwen2.5-VL-3B-Instruct" + +MODEL_NAME_OR_PATH = os.getenv("MODEL_NAME_OR_PATH", DEFAULT_PATH) + +if bool(os.getenv("USE_MODELSCOPE_HUB") in ["True", "true"]): + from modelscope import snapshot_download + + if not Path(MODEL_NAME_OR_PATH).exists(): + MODEL_NAME_OR_PATH = snapshot_download(MODEL_NAME_OR_PATH) + +SYSTEM_PROMPT = os.getenv( + "SYSTEM_PROMPT", + "You're a very succinct AI assistant, that describes image with a very short sentence.", +) +DEFAULT_QUESTION = os.getenv( + "DEFAULT_QUESTION", + "Describe this image", +) +IMAGE_WIDTH = int( + os.getenv( + "IMAGE_WIDTH", + "320", + ) +) +IMAGE_HEIGHT = int( + os.getenv( + "IMAGE_HEIGHT", + "225", + ) +) +HISTORY = os.getenv("HISTORY", "False") in ["True", "true"] +ADAPTER_PATH = os.getenv("ADAPTER_PATH", "") + +# Check if flash_attn is installed +try: + import flash_attn as _ # noqa + + model = Qwen2_5_VLForConditionalGeneration.from_pretrained( + MODEL_NAME_OR_PATH, + torch_dtype="auto", + device_map="auto", + attn_implementation="flash_attention_2", + ) +except (ImportError, ModuleNotFoundError): + model = Qwen2_5_VLForConditionalGeneration.from_pretrained( + MODEL_NAME_OR_PATH, torch_dtype="auto", device_map="auto" + ) + + +if ADAPTER_PATH != "": + model.load_adapter(ADAPTER_PATH, "dora") + + +# default processor +processor = AutoProcessor.from_pretrained(MODEL_NAME_OR_PATH) + + +def generate(frames: dict, question, history): + """Generate the response to the question given the image using Qwen2 model.""" + messages = [ + { + "role": "user", + "content": [ + { + "type": "image", + "image": image, + } + for image in frames.values() + ] + + [ + {"type": "text", "text": question}, + ], + }, + ] + tmp_history = history + messages + # Preparation for inference + text = processor.apply_chat_template( + tmp_history, + tokenize=False, + add_generation_prompt=True, + ) + image_inputs, video_inputs = process_vision_info(messages) + inputs = processor( + text=[text], + images=image_inputs, + videos=video_inputs, + padding=True, + return_tensors="pt", + ) + + inputs = inputs.to(model.device) + + # Inference: Generation of the output + generated_ids = model.generate(**inputs, max_new_tokens=128) + generated_ids_trimmed = [ + out_ids[len(in_ids) :] + for in_ids, out_ids in zip(inputs.input_ids, generated_ids) + ] + output_text = processor.batch_decode( + generated_ids_trimmed, + skip_special_tokens=True, + clean_up_tokenization_spaces=False, + ) + if HISTORY: + history += [ + { + "role": "user", + "content": [ + {"type": "text", "text": question}, + ], + }, + { + "role": "assistant", + "content": [ + {"type": "text", "text": output_text[0]}, + ], + }, + ] + + return output_text[0], history + + +def main(): + pa.array([]) # initialize pyarrow array + node = Node() + + frames = {} + history = [ + { + "role": "system", + "content": [ + {"type": "text", "text": SYSTEM_PROMPT}, + ], + }, + { + "role": "user", + "content": [ + {"type": "text", "text": DEFAULT_QUESTION}, + ], + }, + ] + + for event in node: + event_type = event["type"] + + if event_type == "INPUT": + event_id = event["id"] + + if "image" in event_id: + storage = event["value"] + metadata = event["metadata"] + encoding = metadata["encoding"] + width = metadata["width"] + height = metadata["height"] + + if ( + encoding == "bgr8" + or encoding == "rgb8" + or encoding in ["jpeg", "jpg", "jpe", "bmp", "webp", "png"] + ): + channels = 3 + storage_type = np.uint8 + else: + raise RuntimeError(f"Unsupported image encoding: {encoding}") + + if encoding == "bgr8": + frame = ( + storage.to_numpy() + .astype(storage_type) + .reshape((height, width, channels)) + ) + frame = frame[:, :, ::-1] # OpenCV image (BGR to RGB) + elif encoding == "rgb8": + frame = ( + storage.to_numpy() + .astype(storage_type) + .reshape((height, width, channels)) + ) + elif encoding in ["jpeg", "jpg", "jpe", "bmp", "webp", "png"]: + storage = storage.to_numpy() + frame = cv2.imdecode(storage, cv2.IMREAD_COLOR) + frame = frame[:, :, ::-1] # OpenCV image (BGR to RGB) + else: + raise RuntimeError(f"Unsupported image encoding: {encoding}") + image = Image.fromarray(frame) + frames[event_id] = image.resize((IMAGE_HEIGHT, IMAGE_WIDTH)) + + elif event_id == "text": + if len(event["value"]) > 0: + text = event["value"][0].as_py() + else: + text = "" + + if len(frames.keys()) == 0: + continue + # set the max number of tiles in `max_num` + response, history = generate(frames, text, history) + node.send_output( + "text", + pa.array([response]), + {}, + ) + + elif event_type == "ERROR": + print("Event Error:" + event["error"]) + + +if __name__ == "__main__": + main() diff --git a/node-hub/dora-qwen2-5-vl/pyproject.toml b/node-hub/dora-qwen2-5-vl/pyproject.toml new file mode 100644 index 00000000..bc0d1f56 --- /dev/null +++ b/node-hub/dora-qwen2-5-vl/pyproject.toml @@ -0,0 +1,37 @@ +[project] +name = "dora-qwenvl" +version = "0.3.8" +authors = [ + { name = "Haixuan Xavier Tao", email = "tao.xavier@outlook.com" }, + { name = "Enzo Le Van", email = "dev@enzo-le-van.fr" }, +] +description = "Dora Node for VLM" +license = { text = "MIT" } +readme = "README.md" +requires-python = ">=3.9" + +dependencies = [ + "dora-rs >= 0.3.6", + "numpy < 2.0.0", + "torch == 2.4.0", + "torchvision >= 0.19", + "torchaudio >= 2.1.0", + "qwen-vl-utils >= 0.0.5", + "opencv-python >= 4.1.1", + "modelscope >= 1.18.1", + "peft == 0.13.2", + "accelerate>=1.3.0", + "transformers", +] + +# flash_attn = "^2.6.1" # Install using: pip install -U flash-attn --no-build-isolation + + +[dependency-groups] +dev = ["pytest >=8.1.1", "ruff >=0.9.1"] + +[tool.uv.sources] +transformers = { git = "https://github.com/huggingface/transformers" } + +[project.scripts] +dora-qwenvl = "dora_qwenvl.main:main" diff --git a/node-hub/dora-qwen2-5-vl/tests/test_dora_qwenvl.py b/node-hub/dora-qwen2-5-vl/tests/test_dora_qwenvl.py new file mode 100644 index 00000000..4105676f --- /dev/null +++ b/node-hub/dora-qwen2-5-vl/tests/test_dora_qwenvl.py @@ -0,0 +1,9 @@ +import pytest + + +def test_import_main(): + from dora_qwenvl.main import main + + # Check that everything is working, and catch dora Runtime Exception as we're not running in a dora dataflow. + with pytest.raises(RuntimeError): + 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