Separate out module LogitsDecoder

yolort/runtime/logits_decoder.py

@@ -0,0 +1,59 @@
+# Copyright (c) 2021, yolort team. All rights reserved.
+from typing import List, Tuple
+import torch
+from torch import nn, Tensor
+from yolort.models.box_head import _concat_pred_logits, _decode_pred_logits
+
+
+class LogitsDecoder(nn.Module):
+    """
+    This is a simplified version of post-processing module, we manually remove
+    the ``torchvision::ops::nms``, and it will be used later in the procedure for
+    exporting the ONNX Graph to YOLOTRTModule or others.
+    """
+
+    def __init__(self, strides: List[int]) -> None:
+        """
+        Args:
+            strides (List[int]): Strides of the AnchorGenerator.
+        """
+
+        super().__init__()
+        self.strides = strides
+
+    def forward(
+        self,
+        head_outputs: List[Tensor],
+        grids: List[Tensor],
+        shifts: List[Tensor],
+    ) -> Tuple[Tensor, Tensor]:
+        """
+        Just concat the predict logits, ignore the original ``torchvision::nms`` module
+        from original ``yolort.models.box_head.PostProcess``.
+
+        Args:
+            head_outputs (List[Tensor]): The predicted locations and class/object confidence,
+                shape of the element is (N, A, H, W, K).
+            grids (List[Tensor]): Anchor grids.
+            shifts (List[Tensor]): Anchor shifts.
+        """
+        batch_size = len(head_outputs[0])
+        device = head_outputs[0].device
+        dtype = head_outputs[0].dtype
+        strides = torch.as_tensor(self.strides, dtype=torch.float32, device=device).to(dtype=dtype)
+
+        all_pred_logits = _concat_pred_logits(head_outputs, grids, shifts, strides)
+
+        bbox_regression = []
+        pred_scores = []
+
+        for idx in range(batch_size):  # image idx, image inference
+            pred_logits = all_pred_logits[idx]
+            boxes, scores = _decode_pred_logits(pred_logits)
+            bbox_regression.append(boxes)
+            pred_scores.append(scores)
+
+        # The default boxes tensor has shape [batch_size, number_boxes, 4].
+        boxes = torch.stack(bbox_regression)
+        scores = torch.stack(pred_scores)
+        return boxes, scores

yolort/runtime/trt_helper.py

@@ -11,7 +11,7 @@
 
 import logging
 from pathlib import Path
-from typing import Optional, List, Tuple, Union
+from typing import Optional, Tuple, Union
 
 try:
     import tensorrt as trt
@@ -23,9 +23,10 @@
 from yolort.models import YOLO
 from yolort.models.anchor_utils import AnchorGenerator
 from yolort.models.backbone_utils import darknet_pan_backbone
-from yolort.models.box_head import _concat_pred_logits, _decode_pred_logits
 from yolort.utils import load_from_ultralytics
 
+from .logits_decoder import LogitsDecoder
+
 logging.basicConfig(level=logging.INFO)
 logging.getLogger("TRTHelper").setLevel(logging.INFO)
 logger = logging.getLogger("TRTHelper")
@@ -34,60 +35,6 @@
 __all__ = ["YOLOTRTModule", "EngineBuilder"]
 
 
-class LogitsDecoder(nn.Module):
-    """
-    This is a simplified version of post-processing module, we manually remove
-    the ``torchvision::ops::nms``, and it will be used later in the procedure for
-    exporting the ONNX Graph to YOLOTRTModule or others.
-    """
-
-    def __init__(self, strides: List[int]) -> None:
-        """
-        Args:
-            strides (List[int]): Strides of the AnchorGenerator.
-        """
-
-        super().__init__()
-        self.strides = strides
-
-    def forward(
-        self,
-        head_outputs: List[Tensor],
-        grids: List[Tensor],
-        shifts: List[Tensor],
-    ) -> Tuple[Tensor, Tensor]:
-        """
-        Just concat the predict logits, ignore the original ``torchvision::nms`` module
-        from original ``yolort.models.box_head.PostProcess``.
-
-        Args:
-            head_outputs (List[Tensor]): The predicted locations and class/object confidence,
-                shape of the element is (N, A, H, W, K).
-            grids (List[Tensor]): Anchor grids.
-            shifts (List[Tensor]): Anchor shifts.
-        """
-        batch_size = len(head_outputs[0])
-        device = head_outputs[0].device
-        dtype = head_outputs[0].dtype
-        strides = torch.as_tensor(self.strides, dtype=torch.float32, device=device).to(dtype=dtype)
-
-        all_pred_logits = _concat_pred_logits(head_outputs, grids, shifts, strides)
-
-        bbox_regression = []
-        pred_scores = []
-
-        for idx in range(batch_size):  # image idx, image inference
-            pred_logits = all_pred_logits[idx]
-            boxes, scores = _decode_pred_logits(pred_logits)
-            bbox_regression.append(boxes)
-            pred_scores.append(scores)
-
-        # The default boxes tensor has shape [batch_size, number_boxes, 4].
-        boxes = torch.stack(bbox_regression)
-        scores = torch.stack(pred_scores)
-        return boxes, scores
-
-
 class YOLOTRTModule(nn.Module):
     """
     TensorRT deployment friendly wrapper for YOLO.