removing debug code

happypose/pose_estimators/cosypose/cosypose/datasets/detection_dataset.py

@@ -7,19 +7,6 @@
 
 from happypose.pose_estimators.cosypose.cosypose.config import LOCAL_DATA_DIR
 
-"""
- from .augmentations import (
-    CropResizeToAspectAugmentation,
-    PillowBlur,
-    PillowBrightness,
-    PillowColor,
-    PillowContrast,
-    PillowSharpness,
-    VOCBackgroundAugmentation,
-    to_torch_uint8,
-)
-"""
-
 from happypose.toolbox.datasets.augmentations import (
     CropResizeToAspectTransform,
     PillowBlur,
@@ -33,7 +20,6 @@
 # HappyPose
 from happypose.toolbox.datasets.scene_dataset import (
     IterableSceneDataset,
-    ObjectData,
     SceneDataset,
     SceneObservation,
 )
@@ -43,26 +29,9 @@
 )
 
 from happypose.toolbox.datasets.scene_dataset_wrappers import remove_invisible_objects
-# from happypose.toolbox.datasets.scene_dataset_wrappers import VisibilityWrapper
-"""
-from .augmentations import (
-    CropResizeToAspectAugmentation,
-    PillowBlur,
-    PillowBrightness,
-    PillowColor,
-    PillowContrast,
-    PillowSharpness,
-    VOCBackgroundAugmentation,
-    to_torch_uint8,
-)
-from .wrappers.visibility_wrapper import VisibilityWrapper
-"""
 
 def collate_fn(batch):
-    print("inside collate fn")
     rgbs, targets = zip(*batch)
-    print("rgbs = ", rgbs)
-    print("targets =", targets)
     # Stack the rgbs and convert to a tensor
     rgbs = torch.stack(rgbs, dim=0)
 
@@ -175,25 +144,7 @@ def __init__(
 
         self.label_to_category_id = label_to_category_id
         self.min_area = min_area
-    """
-    def collate_fn(self, list_data: List[DetectionData]) -> BatchDetectionData:
-        batch_data = BatchDetectionData(
-            rgbs=torch.from_numpy(np.stack([d.rgb for d in list_data])).permute(
-                0,
-                3,
-                1,
-                2,
-            ),
-            bboxes=torch.from_numpy(np.stack([d.bboxes for d in list_data])),
-            labels=torch.from_numpy(np.stack([d.labels for d in list_data])),
-            masks=torch.from_numpy(np.stack([d.masks for d in list_data])),
-            #image_id=torch.from_numpy(np.stack([d.image_id for d in list_data])),
-            area=torch.from_numpy(np.stack([d.area for d in list_data])),
-            iscrowd=torch.from_numpy(np.stack([d.iscrowd for d in list_data]))
-            )
 
-        return batch_data
-    """
     def make_data_from_obs(self, obs: SceneObservation, idx):
 
         obs = remove_invisible_objects(obs)
@@ -261,8 +212,6 @@ def make_data_from_obs(self, obs: SceneObservation, idx):
 
         return rgb, target
 
-
-
     def __getitem__(self, index: int):
         assert isinstance(self.scene_ds, SceneDataset)
         obs = self.scene_ds[index]
@@ -285,112 +234,3 @@ def __iter__(self):
         iterator = iter(self.scene_ds)
         while True:
             yield self.find_valid_data(iterator)
-
-
-
-
-
-    def __init___old(
-        self,
-        scene_ds,
-        label_to_category_id,
-        min_area=50,
-        resize=(640, 480),
-        gray_augmentation=False,
-        rgb_augmentation=False,
-        background_augmentation=False,
-    ):
-        self.scene_ds = scene_ds
-
-        self.resize_augmentation = CropResizeToAspectAugmentation(resize=resize)
-
-        self.background_augmentation = background_augmentation
-        self.background_augmentations = VOCBackgroundAugmentation(
-            voc_root=LOCAL_DATA_DIR,
-            p=0.3,
-        )
-
-        self.rgb_augmentation = rgb_augmentation
-        self.rgb_augmentations = [
-            PillowBlur(p=0.4, factor_interval=(1, 3)),
-            PillowSharpness(p=0.3, factor_interval=(0.0, 50.0)),
-            PillowContrast(p=0.3, factor_interval=(0.2, 50.0)),
-            PillowBrightness(p=0.5, factor_interval=(0.1, 6.0)),
-            PillowColor(p=0.3, factor_interval=(0.0, 20.0)),
-        ]
-
-        self.label_to_category_id = label_to_category_id
-        self.min_area = min_area
-
-
-
-    def get_data_old(self, idx):
-
-        print("I am in get_data")
-        rgb, mask, state = self.scene_ds[idx]
-
-        rgb, mask, state = self.resize_augmentation(rgb, mask, state)
-
-        if self.background_augmentation:
-            rgb, mask, state = self.background_augmentations(rgb, mask, state)
-
-        if self.rgb_augmentation and random.random() < 0.8:
-            for augmentation in self.rgb_augmentations:
-                rgb, mask, state = augmentation(rgb, mask, state)
-
-        rgb, mask = to_torch_uint8(rgb), to_torch_uint8(mask)
-
-        categories = torch.tensor(
-            [self.label_to_category_id[obj["name"]] for obj in state["objects"]],
-        )
-        obj_ids = np.array([obj["id_in_segm"] for obj in state["objects"]])
-        boxes = np.array(
-            [torch.as_tensor(obj["bbox"]).tolist() for obj in state["objects"]],
-        )
-        boxes = torch.as_tensor(boxes, dtype=torch.float32).view(-1, 4)
-        area = torch.as_tensor(
-            (boxes[:, 3] - boxes[:, 1]) * (boxes[:, 2] - boxes[:, 0]),
-        )
-        mask = np.array(mask)
-        masks = mask == obj_ids[:, None, None]
-        masks = torch.as_tensor(masks)
-
-        keep = area > self.min_area
-        boxes = boxes[keep]
-        area = area[keep]
-        categories = categories[keep]
-        masks = masks[keep, :, :]
-        num_objs = len(keep)
-
-        num_objs = len(obj_ids)
-        area = torch.as_tensor(area)
-        boxes = torch.as_tensor(boxes)
-        masks = torch.as_tensor(masks, dtype=torch.uint8)
-        image_id = torch.tensor([idx])
-        iscrowd = torch.zeros((num_objs), dtype=torch.int64)
-
-        target = {}
-        target["boxes"] = boxes
-        target["labels"] = categories
-        target["masks"] = masks
-        target["image_id"] = image_id
-        target["area"] = area
-        target["iscrowd"] = iscrowd
-        return rgb, target
-
-    def __getitem__old(self, index):
-        try_index = index
-        valid = False
-        n_attempts = 0
-        while not valid:
-            print("valid =", valid)
-            if n_attempts > 10:
-                msg = "Cannot find valid image in the dataset"
-                raise ValueError(msg)
-            im, target = self.get_data(try_index)
-            valid = len(target["boxes"]) > 0
-            if not valid:
-                try_index = random.randint(0, len(self.scene_ds) - 1)
-                n_attempts += 1
-        return im, target
-