Properly handle maskrcnn and keypoints w.r.t. V2 in detection references

references/detection/coco_utils.py

@@ -1,5 +1,4 @@
-import copy
 import os
 
 import torch
 import torch.utils.data
@@ -10,24 +9,6 @@
 from torchvision.datasets import wrap_dataset_for_transforms_v2
 
 
-class FilterAndRemapCocoCategories:
-    def __init__(self, categories, remap=True):
-        self.categories = categories
-        self.remap = remap
-
-    def __call__(self, image, target):
-        anno = target["annotations"]
-        anno = [obj for obj in anno if obj["category_id"] in self.categories]
-        if not self.remap:
-            target["annotations"] = anno
-            return image, target
-        anno = copy.deepcopy(anno)
-        for obj in anno:
-            obj["category_id"] = self.categories.index(obj["category_id"])
-        target["annotations"] = anno
-        return image, target
-
-
 def convert_coco_poly_to_mask(segmentations, height, width):
     masks = []
     for polygons in segmentations:
@@ -219,7 +200,7 @@
         return img, target
 
 
-def get_coco(root, image_set, transforms, mode="instances", use_v2=False):
+def get_coco(root, image_set, transforms, mode="instances", use_v2=False, with_masks=False):
     anno_file_template = "{}_{}2017.json"
     PATHS = {
         "train": ("train2017", os.path.join("annotations", anno_file_template.format(mode, "train"))),
@@ -233,9 +214,12 @@
 
     if use_v2:
         dataset = torchvision.datasets.CocoDetection(img_folder, ann_file, transforms=transforms)
-        # TODO: need to update target_keys to handle masks for segmentation!
-        dataset = wrap_dataset_for_transforms_v2(dataset, target_keys={"boxes", "labels", "image_id"})
+        target_keys = ["boxes", "labels", "image_id"]
+        if with_masks:
+            target_keys += ["masks"]
+        dataset = wrap_dataset_for_transforms_v2(dataset, target_keys=target_keys)
     else:
+        # TODO: handle with_masks for V1?
         t = [ConvertCocoPolysToMask()]
         if transforms is not None:
             t.append(transforms)
@@ -249,9 +233,3 @@
     # dataset = torch.utils.data.Subset(dataset, [i for i in range(500)])
 
     return dataset
-
-
-def get_coco_kp(root, image_set, transforms, use_v2=False):
-    if use_v2:
-        raise ValueError("KeyPoints aren't supported by transforms V2 yet.")
-    return get_coco(root, image_set, transforms, mode="person_keypoints")

references/detection/train.py

@@ -28,7 +28,7 @@
 import torchvision.models.detection
 import torchvision.models.detection.mask_rcnn
 import utils
-from coco_utils import get_coco, get_coco_kp
+from coco_utils import get_coco
 from engine import evaluate, train_one_epoch
 from group_by_aspect_ratio import create_aspect_ratio_groups, GroupedBatchSampler
 from torchvision.transforms import InterpolationMode
@@ -42,10 +42,16 @@ def copypaste_collate_fn(batch):
 
 def get_dataset(is_train, args):
     image_set = "train" if is_train else "val"
-    paths = {"coco": (args.data_path, get_coco, 91), "coco_kp": (args.data_path, get_coco_kp, 2)}
-    p, ds_fn, num_classes = paths[args.dataset]
-
-    ds = ds_fn(p, image_set=image_set, transforms=get_transform(is_train, args), use_v2=args.use_v2)
+    num_classes, mode = {"coco": (91, "instances"), "coco_kp": (2, "person_keypoints")}[args.dataset]
+    with_masks = "mask" in args.model
+    ds = get_coco(
+        root=args.data_path,
+        image_set=image_set,
+        transforms=get_transform(is_train, args),
+        mode=mode,
+        use_v2=args.use_v2,
+        with_masks=with_masks,
+    )
     return ds, num_classes
 
 
@@ -68,7 +74,12 @@ def get_args_parser(add_help=True):
     parser = argparse.ArgumentParser(description="PyTorch Detection Training", add_help=add_help)
 
     parser.add_argument("--data-path", default="/datasets01/COCO/022719/", type=str, help="dataset path")
-    parser.add_argument("--dataset", default="coco", type=str, help="dataset name")
+    parser.add_argument(
+        "--dataset",
+        default="coco",
+        type=str,
+        help="dataset name. Use coco for object detection and instance segmentation and coco_kp for Keypoint detection",
+    )
     parser.add_argument("--model", default="maskrcnn_resnet50_fpn", type=str, help="model name")
     parser.add_argument("--device", default="cuda", type=str, help="device (Use cuda or cpu Default: cuda)")
     parser.add_argument(
@@ -171,6 +182,12 @@ def get_args_parser(add_help=True):
 def main(args):
     if args.backend.lower() == "datapoint" and not args.use_v2:
         raise ValueError("Use --use-v2 if you want to use the datapoint backend.")
+    if args.dataset not in ("coco", "coco_kp"):
+        raise ValueError(f"Dataset should be coco or coco_kp, got {args.dataset}")
+    if "keypoint" in args.model and args.dataset != "coco_kp":
+        raise ValueError("Oops, if you want Keypoint detection, set --dataset coco_kp")
+    if args.dataset == "coco_kp" and args.use_v2:
+        raise ValueError("KeyPoint detection doesn't support V2 transforms yet")
 
     if args.output_dir:
         utils.mkdir(args.output_dir)