compile

app.py

@@ -5,13 +5,16 @@ import tempfile
 from PIL import Image, ImageDraw, ImageFont
 from transformers import RTDetrForObjectDetection, RTDetrImageProcessor
 import torch
+import requests
 
 image_processor = RTDetrImageProcessor.from_pretrained("PekingU/rtdetr_r50vd")
 model = RTDetrForObjectDetection.from_pretrained("PekingU/rtdetr_r50vd", torch_dtype=torch.float16).to("cuda")
 model = torch.compile(model, mode="reduce-overhead")
 
 # Compile by running inference
-inputs = image_processor(images="bus.png", return_tensors="pt").to("cuda", torch.float16)
+url = 'http://images.cocodataset.org/val2017/000000039769.jpg' 
+image = Image.open(requests.get(url, stream=True).raw)
+inputs = image_processor(images=image, return_tensors="pt").to("cuda", torch.float16)
 with torch.no_grad():
     outputs = model(**inputs)
 

requirements.txt

@@ -1,4 +1,4 @@
 safetensors==0.4.3
-transformers
+transformers @ git+https://github.com/yonigozlan/transformers@optim-rt-detr
 gradio-client @ git+https://github.com/gradio-app/gradio@66349fe26827e3a3c15b738a1177e95fec7f5554#subdirectory=client/python
 https://gradio-pypi-previews.s3.amazonaws.com/66349fe26827e3a3c15b738a1177e95fec7f5554/gradio-4.42.0-py3-none-any.whl