Merge branch 'main' into ddpm_tutorials

detection/generate_transforms.py

@@ -24,21 +24,28 @@
     RandRotated,
     RandScaleIntensityd,
     RandShiftIntensityd,
+    RandCropByPosNegLabeld,
+    RandZoomd,
+    RandFlipd,
+    RandRotate90d,
+    MapTransform,
 )
+from monai.transforms.utility.dictionary import ApplyTransformToPointsd
+from monai.transforms.spatial.dictionary import ConvertBoxToPointsd, ConvertPointsToBoxesd
 from monai.apps.detection.transforms.dictionary import (
     AffineBoxToImageCoordinated,
     AffineBoxToWorldCoordinated,
     BoxToMaskd,
     ClipBoxToImaged,
     ConvertBoxToStandardModed,
     MaskToBoxd,
-    RandCropBoxByPosNegLabeld,
-    RandFlipBoxd,
-    RandRotateBox90d,
-    RandZoomBoxd,
     ConvertBoxModed,
     StandardizeEmptyBoxd,
 )
+from monai.config import KeysCollection
+from monai.utils.type_conversion import convert_data_type
+from monai.data.box_utils import clip_boxes_to_image
+from monai.apps.detection.transforms.box_ops import convert_box_to_mask
 
 
 def generate_detection_train_transform(
@@ -49,6 +56,7 @@ def generate_detection_train_transform(
     intensity_transform,
     patch_size,
     batch_size,
+    point_key="points",
     affine_lps_to_ras=False,
     amp=True,
 ):
@@ -59,6 +67,7 @@ def generate_detection_train_transform(
         image_key: the key to represent images in the input json files
         box_key: the key to represent boxes in the input json files
         label_key: the key to represent box labels in the input json files
+        point_key: the key to represent points to save the box coordinates
         gt_box_mode: ground truth box mode in the input json files
         intensity_transform: transform to scale image intensities,
             usually ScaleIntensityRanged for CT images, and NormalizeIntensityd for MR images.
@@ -87,67 +96,67 @@ def generate_detection_train_transform(
             intensity_transform,
             EnsureTyped(keys=[image_key], dtype=torch.float16),
             ConvertBoxToStandardModed(box_keys=[box_key], mode=gt_box_mode),
+            ConvertBoxToPointsd(keys=[box_key]),
             AffineBoxToImageCoordinated(
                 box_keys=[box_key],
                 box_ref_image_keys=image_key,
                 image_meta_key_postfix="meta_dict",
                 affine_lps_to_ras=affine_lps_to_ras,
             ),
-            RandCropBoxByPosNegLabeld(
-                image_keys=[image_key],
-                box_keys=box_key,
-                label_keys=label_key,
+            # generate box mask based on the input boxes which used for cropping
+            GenerateExtendedBoxMask(
+                keys=box_key,
+                image_key=image_key,
                 spatial_size=patch_size,
                 whole_box=True,
+            ),
+            RandCropByPosNegLabeld(
+                keys=[image_key],
+                label_key="mask_image",
+                spatial_size=patch_size,
                 num_samples=batch_size,
                 pos=1,
                 neg=1,
             ),
-            RandZoomBoxd(
-                image_keys=[image_key],
-                box_keys=[box_key],
-                box_ref_image_keys=[image_key],
+            RandZoomd(
+                keys=[image_key],
                 prob=0.2,
                 min_zoom=0.7,
                 max_zoom=1.4,
                 padding_mode="constant",
                 keep_size=True,
             ),
-            ClipBoxToImaged(
-                box_keys=box_key,
-                label_keys=[label_key],
-                box_ref_image_keys=image_key,
-                remove_empty=True,
-            ),
-            RandFlipBoxd(
-                image_keys=[image_key],
-                box_keys=[box_key],
-                box_ref_image_keys=[image_key],
+            RandFlipd(
+                keys=[image_key],
                 prob=0.5,
                 spatial_axis=0,
             ),
-            RandFlipBoxd(
-                image_keys=[image_key],
-                box_keys=[box_key],
-                box_ref_image_keys=[image_key],
+            RandFlipd(
+                keys=[image_key],
                 prob=0.5,
                 spatial_axis=1,
             ),
-            RandFlipBoxd(
-                image_keys=[image_key],
-                box_keys=[box_key],
-                box_ref_image_keys=[image_key],
+            RandFlipd(
+                keys=[image_key],
                 prob=0.5,
                 spatial_axis=2,
             ),
-            RandRotateBox90d(
-                image_keys=[image_key],
-                box_keys=[box_key],
-                box_ref_image_keys=[image_key],
+            RandRotate90d(
+                keys=[image_key],
                 prob=0.75,
                 max_k=3,
                 spatial_axes=(0, 1),
             ),
+            # apply the same affine matrix which already applied on the images to the points
+            ApplyTransformToPointsd(keys=[point_key], refer_key=image_key, affine_lps_to_ras=affine_lps_to_ras),
+            # convert points back to boxes
+            ConvertPointsToBoxesd(keys=[point_key]),
+            ClipBoxToImaged(
+                box_keys=box_key,
+                label_keys=[label_key],
+                box_ref_image_keys=image_key,
+                remove_empty=True,
+            ),
             BoxToMaskd(
                 box_keys=[box_key],
                 label_keys=[label_key],
@@ -184,7 +193,7 @@ def generate_detection_train_transform(
             RandScaleIntensityd(keys=[image_key], prob=0.15, factors=0.25),
             RandShiftIntensityd(keys=[image_key], prob=0.15, offsets=0.1),
             RandAdjustContrastd(keys=[image_key], prob=0.3, gamma=(0.7, 1.5)),
-            EnsureTyped(keys=[image_key, box_key], dtype=compute_dtype),
+            EnsureTyped(keys=[image_key], dtype=compute_dtype),
             EnsureTyped(keys=[label_key], dtype=torch.long),
         ]
     )
@@ -307,3 +316,73 @@ def generate_detection_inference_transform(
         ]
     )
     return test_transforms, post_transforms
+
+
+class GenerateExtendedBoxMask(MapTransform):
+    """
+    Generate box mask based on the input boxes.
+    """
+
+    def __init__(
+        self,
+        keys: KeysCollection,
+        image_key: str,
+        spatial_size: tuple[int, int, int],
+        whole_box: bool,
+        mask_image_key: str = "mask_image",
+    ) -> None:
+        """
+        Args:
+            keys: keys of the corresponding items to be transformed.
+            image_key: key for the image data in the dictionary.
+            spatial_size: size of the spatial dimensions of the mask.
+            whole_box: whether to use the whole box for generating the mask.
+            mask_image_key: key to store the generated box mask.
+        """
+        super().__init__(keys)
+        self.image_key = image_key
+        self.spatial_size = spatial_size
+        self.whole_box = whole_box
+        self.mask_image_key = mask_image_key
+
+    def generate_fg_center_boxes_np(self, boxes, image_size, whole_box=True):
+        # We don't require crop center to be within the boxes.
+        # As along as the cropped patch contains a box, it is considered as a foreground patch.
+        # Positions within extended_boxes are crop centers for foreground patches
+        spatial_dims = len(image_size)
+        boxes_np, *_ = convert_data_type(boxes, np.ndarray)
+
+        extended_boxes = np.zeros_like(boxes_np, dtype=int)
+        boxes_start = np.ceil(boxes_np[:, :spatial_dims]).astype(int)
+        boxes_stop = np.floor(boxes_np[:, spatial_dims:]).astype(int)
+        for axis in range(spatial_dims):
+            if not whole_box:
+                extended_boxes[:, axis] = boxes_start[:, axis] - self.spatial_size[axis] // 2 + 1
+                extended_boxes[:, axis + spatial_dims] = boxes_stop[:, axis] + self.spatial_size[axis] // 2 - 1
+            else:
+                # extended box start
+                extended_boxes[:, axis] = boxes_stop[:, axis] - self.spatial_size[axis] // 2 - 1
+                extended_boxes[:, axis] = np.minimum(extended_boxes[:, axis], boxes_start[:, axis])
+                # extended box stop
+                extended_boxes[:, axis + spatial_dims] = extended_boxes[:, axis] + self.spatial_size[axis] // 2
+                extended_boxes[:, axis + spatial_dims] = np.maximum(
+                    extended_boxes[:, axis + spatial_dims], boxes_stop[:, axis]
+                )
+        extended_boxes, _ = clip_boxes_to_image(extended_boxes, image_size, remove_empty=True)  # type: ignore
+        return extended_boxes
+
+    def generate_mask_img(self, boxes, image_size, whole_box=True):
+        extended_boxes_np = self.generate_fg_center_boxes_np(boxes, image_size, whole_box)
+        mask_img = convert_box_to_mask(
+            extended_boxes_np, np.ones(extended_boxes_np.shape[0]), image_size, bg_label=0, ellipse_mask=False
+        )
+        mask_img = np.amax(mask_img, axis=0, keepdims=True)[0:1, ...]
+        return mask_img
+
+    def __call__(self, data):
+        d = dict(data)
+        for key in self.key_iterator(d):
+            image = d[self.image_key]
+            boxes = d[key]
+            data[self.mask_image_key] = self.generate_mask_img(boxes, image.shape[1:], whole_box=self.whole_box)
+        return data

detection/luna16_prepare_images.py

@@ -47,8 +47,10 @@ def main():
 
     monai.config.print_config()
 
-    env_dict = json.load(open(args.environment_file, "r"))
-    config_dict = json.load(open(args.config_file, "r"))
+    with open(args.environment_file, "r") as env_file:
+        env_dict = json.load(env_file)
+    with open(args.config_file, "r") as config_file:
+        config_dict = json.load(config_file)
 
     for k, v in env_dict.items():
         setattr(args, k, v)

detection/luna16_training.py

@@ -78,8 +78,10 @@ def main():
     torch.backends.cudnn.benchmark = True
     torch.set_num_threads(4)
 
-    env_dict = json.load(open(args.environment_file, "r"))
-    config_dict = json.load(open(args.config_file, "r"))
+    with open(args.environment_file, "r") as env_file:
+        env_dict = json.load(env_file)
+    with open(args.config_file, "r") as config_file:
+        config_dict = json.load(config_file)
 
     for k, v in env_dict.items():
         setattr(args, k, v)
@@ -103,6 +105,7 @@ def main():
         intensity_transform,
         args.patch_size,
         args.batch_size,
+        point_key="points",
         affine_lps_to_ras=True,
         amp=amp,
     )
@@ -233,7 +236,7 @@ def main():
     )
     after_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=150, gamma=0.1)
     scheduler_warmup = GradualWarmupScheduler(optimizer, multiplier=1, total_epoch=10, after_scheduler=after_scheduler)
-    scaler = torch.cuda.amp.GradScaler() if amp else None
+    scaler = torch.amp.GradScaler("cuda") if amp else None
     optimizer.zero_grad()
     optimizer.step()
 
@@ -279,7 +282,7 @@ def main():
                 param.grad = None
 
             if amp and (scaler is not None):
-                with torch.cuda.amp.autocast():
+                with torch.amp.autocast("cuda"):
                     outputs = detector(inputs, targets)
                     loss = w_cls * outputs[detector.cls_key] + outputs[detector.box_reg_key]
                 scaler.scale(loss).backward()