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3243c68 | 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 | # ObsDrive
ObsDrive is a vision-language model designed for multimodal autonomous driving understanding, supporting camera, LiDAR BEV, and RADAR BEV inputs.
---
### 📦 Requirements
```bash
pip install torch transformers accelerate qwen-vl-utils flash-attn
```
## 🚀 Inference
```bash
import torch
from transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration
from qwen_vl_utils import process_vision_info
MODEL_PATH = "russellyq/ObsDrive/XXX"
class ObsDrive:
def __init__(self, model_path=MODEL_PATH):
self.model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
model_path,
torch_dtype=torch.bfloat16,
device_map="auto",
attn_implementation="flash_attention_2",
)
self.MAX_NEW_TOKENS = 4096
self.processor = AutoProcessor.from_pretrained(
model_path,
)
def chat(self, question, image=None, system_prompt=None):
message_content = []
# Handle image input
if isinstance(image, str):
message_content.append({"type": "image", "image": f"file://{image}"})
elif isinstance(image, list):
for img_path in image:
message_content.append({"type": "image", "image": f"file://{img_path}"})
# Add text input
text = system_prompt + "\n" + question if system_prompt is not None else question
message_content.append({"type": "text", "text": text})
messages = [
{
"role": "user",
"content": message_content,
}
]
# Apply chat template
text_prompt = 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_prompt],
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt",
)
inputs = inputs.to("cuda")
# Generate output
generated_ids = self.model.generate(
**inputs,
max_new_tokens=self.MAX_NEW_TOKENS,
do_sample=False,
)
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,
)[0]
return output_text
if __name__ == "__main__":
model = ObsDrive()
question = "Please describe the driving scene."
image = "/path/to/image.png"
answer = model.chat(question, image=image)
print(answer)
```
## 🖼️ Multi-image Inference
```bash
question = "Analyze the scene using all modalities."
images = [
"/path/to/camera.png",
"/path/to/lidar_bev.png",
"/path/to/radar_bev.png",
]
answer = model.chat(question, image=images)
print(answer)
```
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