np.float -> float

agimus-project · Feb 14, 2024 · 242a1ce · 242a1ce
1 parent c3b2c71
commit 242a1ce
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Showing 10 changed files with 13 additions and 13 deletions.
diff --git a/happypose/pose_estimators/cosypose/cosypose/evaluation/eval_runner/detection_eval.py b/happypose/pose_estimators/cosypose/cosypose/evaluation/eval_runner/detection_eval.py
@@ -66,7 +66,7 @@ def make_empty_predictions():
             "view_id": np.empty(0, dtype=int),
             "scene_id": np.empty(0, dtype=int),
             "label": np.empty(0, dtype=np.object),
-            "score": np.empty(0, dtype=np.float),
+            "score": np.empty(0, dtype=float),
         }
         bboxes = torch.empty(0, 4, dtype=torch.float)
         return tc.PandasTensorCollection(infos=pd.DataFrame(infos), bboxes=bboxes)

diff --git a/happypose/pose_estimators/cosypose/cosypose/evaluation/eval_runner/pose_eval.py b/happypose/pose_estimators/cosypose/cosypose/evaluation/eval_runner/pose_eval.py
@@ -66,7 +66,7 @@ def make_empty_predictions():
             "view_id": np.empty(0, dtype=int),
             "scene_id": np.empty(0, dtype=int),
             "label": np.empty(0, dtype=np.object),
-            "score": np.empty(0, dtype=np.float),
+            "score": np.empty(0, dtype=float),
         }
         poses = torch.empty(0, 4, 4, dtype=torch.float)
         return tc.PandasTensorCollection(infos=pd.DataFrame(infos), poses=poses)

diff --git a/happypose/pose_estimators/cosypose/cosypose/evaluation/meters/detection_meters.py b/happypose/pose_estimators/cosypose/cosypose/evaluation/meters/detection_meters.py
@@ -223,7 +223,7 @@ def compute_ap(label_df, label_n_gt):
             label_df = label_df.sort_values("score", ascending=False).reset_index(
                 drop=True,
             )
-            label_df["n_tp"] = np.cumsum(label_df[valid_k].values.astype(np.float))
+            label_df["n_tp"] = np.cumsum(label_df[valid_k].values.astype(float))
             label_df["prec"] = label_df["n_tp"] / (np.arange(len(label_df)) + 1)
             label_df["recall"] = label_df["n_tp"] / label_n_gt
             y_true = label_df[valid_k]

diff --git a/happypose/pose_estimators/cosypose/cosypose/evaluation/meters/pose_meters.py b/happypose/pose_estimators/cosypose/cosypose/evaluation/meters/pose_meters.py
@@ -361,7 +361,7 @@ def compute_ap(label_df, label_n_gt):
             label_df = label_df.sort_values("score", ascending=False).reset_index(
                 drop=True,
             )
-            label_df["n_tp"] = np.cumsum(label_df[valid_k].values.astype(np.float))
+            label_df["n_tp"] = np.cumsum(label_df[valid_k].values.astype(float))
             label_df["prec"] = label_df["n_tp"] / (np.arange(len(label_df)) + 1)
             label_df["recall"] = label_df["n_tp"] / label_n_gt
             y_true = label_df[valid_k]

diff --git a/happypose/pose_estimators/cosypose/cosypose/integrated/icp_refiner.py b/happypose/pose_estimators/cosypose/cosypose/integrated/icp_refiner.py
@@ -28,7 +28,7 @@ def get_normal(
         mask[depth_refine == 0] = 1
         depth_refine = depth_refine.astype(np.float32)
         depth_refine = cv2.inpaint(depth_refine, mask, 2, cv2.INPAINT_NS)
-        depth_refine = depth_refine.astype(np.float)
+        depth_refine = depth_refine.astype(float)
         depth_refine = ndimage.gaussian_filter(depth_refine, 2)
 
     uv_table = np.zeros((res_y, res_x, 2), dtype=np.int16)

diff --git a/happypose/pose_estimators/cosypose/cosypose/recording/bop_recording_scene.py b/happypose/pose_estimators/cosypose/cosypose/recording/bop_recording_scene.py
@@ -151,7 +151,7 @@ def objects_pos_orn_rand_falling(self):
 
     def sample_camera(self):
         assert self.focal_interval.shape == (2, 2)
-        K = np.zeros((3, 3), dtype=np.float)
+        K = np.zeros((3, 3), dtype=float)
         fxfy = self.np_random.uniform(*self.focal_interval)
         W, H = max(self.resolution), min(self.resolution)
         K[0, 0] = fxfy[0]

diff --git a/happypose/pose_estimators/cosypose/cosypose/visualization/multiview.py b/happypose/pose_estimators/cosypose/cosypose/visualization/multiview.py
@@ -179,8 +179,8 @@ def mark_inliers(cand_inputs, cand_matched):
         on=["scene_id", "view_id", "label", "cand_id"],
         how="left",
     )
-    infos["is_inlier"] = infos["is_inlier"].astype(np.float)
-    infos.loc[~np.isfinite(infos.loc[:, "is_inlier"].astype(np.float)), "is_inlier"] = 0
+    infos["is_inlier"] = infos["is_inlier"].astype(float)
+    infos.loc[~np.isfinite(infos.loc[:, "is_inlier"].astype(float)), "is_inlier"] = 0
     infos["is_inlier"] = infos["is_inlier"].astype(np.bool)
     cand_inputs.infos = infos
     return cand_inputs

diff --git a/happypose/pose_estimators/megapose/evaluation/evaluation_runner.py b/happypose/pose_estimators/megapose/evaluation/evaluation_runner.py
@@ -77,7 +77,7 @@ def make_empty_predictions():
             "view_id": np.empty(0, dtype=int),
             "scene_id": np.empty(0, dtype=int),
             "label": np.empty(0, dtype=np.object),
-            "score": np.empty(0, dtype=np.float),
+            "score": np.empty(0, dtype=float),
         }
         poses = torch.empty(0, 4, 4, dtype=torch.float)
         return tc.PandasTensorCollection(infos=pd.DataFrame(infos), poses=poses)

diff --git a/happypose/pose_estimators/megapose/inference/types.py b/happypose/pose_estimators/megapose/inference/types.py
@@ -172,8 +172,8 @@ def from_numpy(
         Args:
         ----
             rgb: [H,W,3] np.uint8
-            depth: [H,W] np.float
-            K: [3,3] np.float
+            depth: [H,W] float
+            K: [3,3] float
 
         """
         assert rgb.dtype == np.uint8

diff --git a/happypose/toolbox/inference/types.py b/happypose/toolbox/inference/types.py
@@ -172,8 +172,8 @@ def from_numpy(
         Args:
         ----
             rgb: [H,W,3] np.uint8
-            depth: [H,W] np.float
-            K: [3,3] np.float
+            depth: [H,W] float
+            K: [3,3] float
 
         """
         assert rgb.dtype == np.uint8