cuda and docker updates

Dockerfile

@@ -1,4 +1,4 @@
-FROM pytorch/pytorch:2.0.0-cuda11.7-cudnn8-devel
+FROM nvidia/cuda:11.8.0-cudnn8-devel-ubuntu20.04
 
 ARG DEBIAN_FRONTEND=noninteractive
 ENV PYTHONUNBUFFERED=1
@@ -22,6 +22,12 @@ COPY ./requirements.txt /code/requirements.txt
 
 RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
 
+RUN mim install mmdet 
+RUN mim install mmocr
+RUN mim install mmcv==2.0.1
+RUN mim install mmengine 
+
+
 # Set up a new user named "user" with user ID 1000
 RUN useradd -m -u 1000 user
 
@@ -45,4 +51,4 @@ WORKDIR $HOME/app
 # Copy the current directory contents into the container at $HOME/app setting the owner to the user
 COPY --chown=user . $HOME/app
 
-CMD ["python3", "app.py"]
+CMD ["python3", "app.py"]

src/htr_pipeline/models.py

@@ -10,7 +10,7 @@ from mmocr.apis import TextRecInferencer
 
 class HtrModels:
     def __init__(self, local_run=False):
-        self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
         model_folder = "./models"
         self.region_config = f"{model_folder}/RmtDet_regions/rtmdet_m_textregions_2_concat.py"

src/htr_pipeline/utils/filter_segmask.py

@@ -88,7 +88,7 @@ class FilterSegMask:
         yB = min(box_a[1] + box_a[3], box_b[1] + box_b[3])  # min y1
 
         box_a_area = box_a[2] * box_a[3]
-        box_b_area = box_b[2] * box_b[3]
+        # box_b_area = box_b[2] * box_b[3]
 
         intersection_area = max(0, xB - xA + 1) * max(0, yB - yA + 1)
         containment = intersection_area / box_a_area if box_a_area > 0 else 0