update remap_mscoco_category

README.md

@@ -36,6 +36,7 @@ This is the official implementation of the paper "[DETRs Beat YOLOs on Real-time
 
 ## Updates!!!
 ---
+- \[2023.11.05\] upgrade the logic of `remap_mscoco_category` to facilitate training of custom datasets, see detils in [Train custom data](./rtdetr_pytorch/) part.
 - \[2023.10.23\] Add [*discussion for deployments*](https://github.com/lyuwenyu/RT-DETR/issues/95), supported onnxruntime, TensorRT, openVINO
 - \[2023.10.12\] Add tuning code for pytorch version, now you can tuning rtdetr based on pretrained weights
 - \[2023.09.19\] Upload [*pytorch weights*](https://github.com/lyuwenyu/RT-DETR/issues/42) convert from paddle version

rtdetr_pytorch/README.md

@@ -79,7 +79,7 @@ python tools/export_onnx.py -c configs/rtdetr/rtdetr_r18vd_6x_coco.yml -r path/t
 <details open>
 <summary>Train custom data</summary>
 
-1. set `remap_mscoco_category: False`. This variable only works for ms-coco dataset.
+1. set `remap_mscoco_category: False`. This variable only works for ms-coco dataset. If you want to use `remap_mscoco_category` logic on your dataset, please modify variable [`mscoco_category2name`](https://github.com/lyuwenyu/RT-DETR/blob/main/rtdetr_pytorch/src/data/coco/coco_dataset.py) based on your dataset.
 
 2. add `-t path/to/checkpoint` (optinal) to tuning rtdetr based on pretrained checkpoint. see [training script details](./tools/README.md).
 </details>

rtdetr_pytorch/src/data/coco/__init__.py

@@ -1,5 +1,9 @@
-
-from .coco_dataset import *
+from .coco_dataset import (
+    CocoDetection, 
+    mscoco_category2label,
+    mscoco_label2category,
+    mscoco_category2name,
+)
 from .coco_eval import *
 
 from .coco_utils import get_coco_api_from_dataset

rtdetr_pytorch/src/data/coco/coco_dataset.py

@@ -104,7 +104,7 @@ def __call__(self, image, target):
         boxes[:, 1::2].clamp_(min=0, max=h)
 
         if self.remap_mscoco_category:
-            classes = [category2label[obj["category_id"]] - 1 for obj in anno]
+            classes = [mscoco_category2label[obj["category_id"]] for obj in anno]
         else:
             classes = [obj["category_id"] for obj in anno]
 
@@ -151,10 +151,7 @@ def __call__(self, image, target):
         return image, target
 
 
-
-
-names = {
-    0: 'background',
+mscoco_category2name = {
     1: 'person',
     2: 'bicycle',
     3: 'car',
@@ -237,88 +234,5 @@ def __call__(self, image, target):
     90: 'toothbrush'
 }
 
-
-label2category = {
-    1: 1,
-    2: 2,
-    3: 3,
-    4: 4,
-    5: 5,
-    6: 6,
-    7: 7,
-    8: 8,
-    9: 9,
-    10: 10,
-    11: 11,
-    12: 13,
-    13: 14,
-    14: 15,
-    15: 16,
-    16: 17,
-    17: 18,
-    18: 19,
-    19: 20,
-    20: 21,
-    21: 22,
-    22: 23,
-    23: 24,
-    24: 25,
-    25: 27,
-    26: 28,
-    27: 31,
-    28: 32,
-    29: 33,
-    30: 34,
-    31: 35,
-    32: 36,
-    33: 37,
-    34: 38,
-    35: 39,
-    36: 40,
-    37: 41,
-    38: 42,
-    39: 43,
-    40: 44,
-    41: 46,
-    42: 47,
-    43: 48,
-    44: 49,
-    45: 50,
-    46: 51,
-    47: 52,
-    48: 53,
-    49: 54,
-    50: 55,
-    51: 56,
-    52: 57,
-    53: 58,
-    54: 59,
-    55: 60,
-    56: 61,
-    57: 62,
-    58: 63,
-    59: 64,
-    60: 65,
-    61: 67,
-    62: 70,
-    63: 72,
-    64: 73,
-    65: 74,
-    66: 75,
-    67: 76,
-    68: 77,
-    69: 78,
-    70: 79,
-    71: 80,
-    72: 81,
-    73: 82,
-    74: 84,
-    75: 85,
-    76: 86,
-    77: 87,
-    78: 88,
-    79: 89,
-    80: 90
-}
-
-category2label = {v: k for k, v in label2category.items()}
+mscoco_category2label = {k: i for i, k in enumerate(mscoco_category2name.keys())}
+mscoco_label2category = {v: k for k, v in mscoco_category2label.items()}

rtdetr_pytorch/src/zoo/rtdetr/rtdetr_postprocessor.py

@@ -58,7 +58,8 @@ def forward(self, outputs, orig_target_sizes):
 
         # TODO
         if self.remap_mscoco_category:
-            labels = torch.tensor([self.mscoco_label_category_map[int(x.item()) + 1] for x in labels.flatten()])\
+            from ...data.coco import mscoco_label2category
+            labels = torch.tensor([mscoco_label2category[int(x.item())] for x in labels.flatten()])\
                 .to(boxes.device).reshape(labels.shape)
 
         results = []
@@ -77,90 +78,3 @@ def deploy(self, ):
     @property
     def iou_types(self, ):
         return ('bbox', )
-
-
-    @property
-    def mscoco_label_category_map(self, ):
-        return {
-                1: 1,
-                2: 2,
-                3: 3,
-                4: 4,
-                5: 5,
-                6: 6,
-                7: 7,
-                8: 8,
-                9: 9,
-                10: 10,
-                11: 11,
-                12: 13,
-                13: 14,
-                14: 15,
-                15: 16,
-                16: 17,
-                17: 18,
-                18: 19,
-                19: 20,
-                20: 21,
-                21: 22,
-                22: 23,
-                23: 24,
-                24: 25,
-                25: 27,
-                26: 28,
-                27: 31,
-                28: 32,
-                29: 33,
-                30: 34,
-                31: 35,
-                32: 36,
-                33: 37,
-                34: 38,
-                35: 39,
-                36: 40,
-                37: 41,
-                38: 42,
-                39: 43,
-                40: 44,
-                41: 46,
-                42: 47,
-                43: 48,
-                44: 49,
-                45: 50,
-                46: 51,
-                47: 52,
-                48: 53,
-                49: 54,
-                50: 55,
-                51: 56,
-                52: 57,
-                53: 58,
-                54: 59,
-                55: 60,
-                56: 61,
-                57: 62,
-                58: 63,
-                59: 64,
-                60: 65,
-                61: 67,
-                62: 70,
-                63: 72,
-                64: 73,
-                65: 74,
-                66: 75,
-                67: 76,
-                68: 77,
-                69: 78,
-                70: 79,
-                71: 80,
-                72: 81,
-                73: 82,
-                74: 84,
-                75: 85,
-                76: 86,
-                77: 87,
-                78: 88,
-                79: 89,
-                80: 90
-            }
-