Update trt inference

docker/tensorrt/Dockerfile

@@ -1,6 +1,6 @@
 # nVidia TensorRT Base Image
-ARG TRT_CONTAINER_VERSION=21.07
-FROM nvcr.io/nvidia/tensorrt:${TRT_CONTAINER_VERSION}-py3
+ARG TRT_CONTAINER_VERSION=21.12
+FROM nvcr.io/nvidia/pytorch:${TRT_CONTAINER_VERSION}-py3
 
 ENV DEBIAN_FRONTEND noninteractive
 ENV TZ=Europe/Kiev
@@ -22,7 +22,6 @@ RUN apt-get install -y libturbojpeg
 
 RUN pip install --upgrade pip
 RUN pip install pillow==8.0.1
-RUN pip install torch==1.11.0+cu115 torchvision==0.12.0+cu115 -f https://download.pytorch.org/whl/torch_stable.html
 RUN pip install setuptools
 RUN pip install "PyYAML>=5.3"
 RUN pip install "numpy>=1.16.*"

examples/py/benchmark/runtime-test.py

@@ -5,6 +5,10 @@
 
 from _paths import nomeroff_net_dir
 from nomeroff_net import pipeline
+import faulthandler
+
+
+faulthandler.enable()
 
 warnings.filterwarnings("ignore")
 

examples/py/model_convertors/convert_yolo_to_tensorrt.py

@@ -5,6 +5,7 @@
 """
 import sys
 import os
+import pathlib
 import argparse
 import subprocess
 
@@ -16,7 +17,7 @@ def parse_args():
     ap = argparse.ArgumentParser()
     ap.add_argument("-f", "--filepath",
                     default=os.path.join(os.path.abspath(os.getcwd()),
-                                         "../../../data/model_repository/numberplate_options/1/model.onnx"),
+                                         "../../../data/model_repository/yolov5s/1/model.engine"),
                     required=False,
                     type=str,
                     help="Result onnx model filepath")
@@ -28,15 +29,20 @@ def main():
     args = parse_args()
     model_filepath = args["filepath"]
 
+    # make dirs
+    p = pathlib.Path(os.path.dirname(model_filepath))
+    p.mkdir(parents=True, exist_ok=True)
+
     # download and append to path yolo repo
     info = modelhub.download_repo_for_model("yolov5")
     repo_path = info["repo_path"]
     model_info = modelhub.download_model_by_name("yolov5")
     path_to_model = model_info["path"]
+    print(f'python3 ./export.py --weights={path_to_model} --include=engine --device 0 --dynamic;')
     res = path_to_model.replace(".pt", ".engine")
     # python3 ./export.py --weights yolov5s-2021-12-14.pt --include engine --device 0 --dinamic
     subprocess.call([f'cd {repo_path}; '
-                     f'python3 ./export.py --weights={path_to_model} --include=engine --device 0 --dinamic;'
+                     f'python3 ./export.py --weights={path_to_model} --include=engine --device 0;'
                      f'cp {res} {model_filepath}'], shell=True)
 
 

nomeroff_net/pipelines/number_plate_classification_trt.py

@@ -24,6 +24,6 @@ def forward(self, inputs: Any, **forward_parameters: Dict) -> Any:
         model_outputs = []
         for inp in inputs:
             model_output = self.detector.forward([inp])
-            model_output = unzip(model_output)
+            model_output = unzip(model_output)[0]
             model_outputs.append(model_output)
         return model_outputs

nomeroff_net/pipelines/number_plate_localization.py

@@ -41,7 +41,7 @@ def preprocess(self, inputs: Any, **preprocess_parameters: Dict) -> Any:
     @no_grad()
     def forward(self, images: Any, **forward_parameters: Dict) -> Any:
         model_outputs = self.detector.predict(images)
-        return unzip([model_outputs, images])
+        return unzip([images, model_outputs])
 
     def postprocess(self, inputs: Any, **postprocess_parameters: Dict) -> Any:
         return inputs

nomeroff_net/pipelines/number_plate_localization_trt.py

@@ -36,7 +36,7 @@ def preprocess(self, inputs: Any, **preprocess_parameters: Dict) -> Any:
     @no_grad()
     def forward(self, images: Any, **forward_parameters: Dict) -> Any:
         detected_images_bboxs = self.detector.predict(images)
-        return unzip([images, detected_images_bboxs])
+        return unzip([detected_images_bboxs, images])
 
     def postprocess(self, inputs: Any, **postprocess_parameters: Dict) -> Any:
         return inputs

nomeroff_net/pipelines/number_plate_text_reading_trt.py

@@ -27,5 +27,5 @@ def forward(self, inputs: Any, **forward_parameters: Dict) -> Any:
             model_inputs = self.detector.preprocess([image], [label], [line])
             model_output = self.detector.forward(model_inputs)
             model_output = self.detector.postprocess(model_output)
-            model_outputs.append(model_output)
+            model_outputs.append(model_output[0])
         return unzip([images, model_outputs, labels])

nomeroff_net/pipes/number_plate_classificators/options_detector_trt.py

@@ -80,6 +80,7 @@ def run_engine(self, input_image):
                                           self.height,
                                           self.width
                                       ))
+            input_image = np.array(input_image)
             # Allocate host and device buffers
             bindings = []
             outputs = []

nomeroff_net/pipes/number_plate_localizators/yolo_v5_detector.py

@@ -44,4 +44,4 @@ def predict(self, imgs: List[np.ndarray], min_accuracy: float = 0.5) -> List:
                          for item in img_item.to_dict(orient="records")
                          if item["confidence"] > min_accuracy]
                          for img_item in model_outputs.pandas().xyxy]
-        return model_outputs
+        return np.array(model_outputs)