Merge pull request #32 from stefanklut/oom-to-cpu

Inference in API on CPU when encountering OOM

api/flask_app.py

@@ -12,7 +12,7 @@
 from flask import Flask, Response, abort, jsonify, request
 from prometheus_client import Counter, Gauge, generate_latest
 
-sys.path.append(str(Path(__file__).resolve().parent.joinpath("..")))
+sys.path.append(str(Path(__file__).resolve().parent.joinpath("..")))  # noqa: E402
 from datasets.mapper import AugInput
 from main import setup_cfg, setup_logging
 from page_xml.output_pageXML import OutputPageXML
@@ -123,6 +123,31 @@ def setup_model(self, model_name: str, args: DummyArgs):
 exception_predict_counter = Counter("exception_predict", "Exception thrown in predict() function")
 
 
+def safe_predict(data, device):
+    """
+    Attempt to predict on the speci
+
+    Args:
+        data: Data to predict on
+        device: Device to predict on
+
+    Returns:
+        Prediction output
+    """
+
+    try:
+        return predict_gen_page_wrapper.predictor(data, device)
+    except Exception as exception:
+        # Catch CUDA out of memory errors
+        if isinstance(exception, torch.cuda.OutOfMemoryError) or (
+            isinstance(exception, RuntimeError) and "NVML_SUCCESS == r INTERNAL ASSERT FAILED" in str(exception)
+        ):
+            logger.warning("CUDA OOM encountered, falling back to CPU.")
+            torch.cuda.empty_cache()
+            torch.cuda.reset_peak_memory_stats()
+            return predict_gen_page_wrapper.predictor(data, "cpu")
+
+
 def predict_image(
     image: np.ndarray,
     dpi: Optional[int],
@@ -172,7 +197,7 @@ def predict_image(
             manual_dpi=predict_gen_page_wrapper.predictor.cfg.INPUT.DPI.MANUAL_DPI_TEST,
         )
 
-        outputs = predict_gen_page_wrapper.predictor(data)
+        outputs = safe_predict(data, device=predict_gen_page_wrapper.predictor.cfg.MODEL.DEVICE)
 
         output_image = outputs[0]["sem_seg"]
         # output_image = torch.argmax(outputs[0]["sem_seg"], dim=-3).cpu().numpy()

run.py

@@ -133,49 +133,64 @@ def __init__(self, cfg: CfgNode):
 
     #         return predictions, height, width
 
-    def cpu_call(self, data: AugInput) -> tuple[dict, int, int]:
+    def cpu_call(self, data: AugInput, device: str = None) -> tuple[dict, int, int]:
         """
         Run the model on the image with preprocessing on the cpu
 
         Args:
             data (AugInput): image to run the model on
+            device (str): device to run the model on
 
         Returns:
             tuple[dict, int, int]: predictions, height, width
         """
+        logger = logging.getLogger(get_logger_name())
+
+        # Default value of device should be the one in the config
+        if device is None:
+            device = self.cfg.MODEL.DEVICE
+
         with torch.no_grad():  # https://github.com/sphinx-doc/sphinx/issues/4258
             # Apply pre-processing to image.
 
             height, width, channels = data.image.shape
             assert channels == 3, f"Must be a RBG image, found {channels} channels"
             # In place augmentation
             transform = self.aug(data)
-            image = torch.as_tensor(data.image, dtype=torch.float32, device=self.cfg.MODEL.DEVICE).permute(2, 0, 1)
+            image = torch.as_tensor(data.image, dtype=torch.float32, device=device).permute(2, 0, 1)
 
             if self.cfg.INPUT.FORMAT == "BGR":
                 # whether the model expects BGR inputs or RGB
                 image = image[[2, 1, 0], :, :]
 
             inputs = {"image": image, "height": image.shape[1], "width": image.shape[2]}
 
+            # If we predict on CPU, use full precision
+            precision = self.precision if device != "cpu" else torch.float32
+
             with torch.autocast(
-                device_type=self.cfg.MODEL.DEVICE,
+                device_type=device,
                 enabled=self.cfg.MODEL.AMP_TEST.ENABLED,
-                dtype=self.precision,
+                dtype=precision,
             ):
+                if next(self.model.parameters()).device != device:
+                    logger.info(f"Moving model to {device} device")
+                    self.model.to(device)
+
                 predictions = self.model([inputs])[0]
 
             # if torch.isnan(predictions["sem_seg"]).any():
             #     raise ValueError("NaN in predictions")
 
         return predictions, height, width
 
-    def __call__(self, data: AugInput):
+    def __call__(self, data: AugInput, device: str = None) -> tuple[dict, int, int]:
         """
         Run the model on the image with preprocessing
 
         Args:
             data (AugInput): image to run the model on
+            device (str): device to run the model on
         Returns:
             tuple[dict, int, int]: predictions, height, width
         """
@@ -186,7 +201,7 @@ def __call__(self, data: AugInput):
         #     return self.gpu_call(original_image)
         # else:
         #     raise TypeError(f"Unknown image type: {type(original_image)}")
-        return self.cpu_call(data)
+        return self.cpu_call(data, device)
 
 
 class LoadingDataset(Dataset):