formatting.

Author: B1ueber2y

limap/base/unit_test.py

@@ -34,10 +34,10 @@ def report_error(imagecols_pred, imagecols):
         R_error = (
             imagecols_pred.camimage(img_id).R() - imagecols.camimage(img_id).R()
         )
-        R_error = np.sqrt(np.sum(R_error ** 2))
+        R_error = np.sqrt(np.sum(R_error**2))
         T_error = (
             imagecols_pred.camimage(img_id).T() - imagecols.camimage(img_id).T()
         )
-        T_error = np.sqrt(np.sum(T_error ** 2))
+        T_error = np.sqrt(np.sum(T_error**2))
         pose_errors.append(np.array([R_error, T_error]))
     print("pose_error: (R, T)", np.array(pose_errors).mean(0))

limap/estimators/absolute_pose/_pl_estimate_absolute_pose.py

@@ -110,15 +110,12 @@ def _pl_estimate_absolute_pose(
         ransac_options.data_type_weights_ = np.array(
             [ransac_cfg["weight_point"], ransac_cfg["weight_line"]]
         )
-        ransac_options.data_type_weights_ *= (
-            np.array(
-                [
-                    ransac_options.squared_inlier_thresholds_[1],
-                    ransac_options.squared_inlier_thresholds_[0],
-                ]
-            )
-            / np.sum(ransac_options.squared_inlier_thresholds_)
-        )
+        ransac_options.data_type_weights_ *= np.array(
+            [
+                ransac_options.squared_inlier_thresholds_[1],
+                ransac_options.squared_inlier_thresholds_[0],
+            ]
+        ) / np.sum(ransac_options.squared_inlier_thresholds_)
         ransac_options.min_num_iterations_ = ransac_cfg["min_num_iterations"]
         ransac_options.final_least_squares_ = ransac_cfg["final_least_squares"]
 

limap/features/models/s2dnet.py

@@ -66,7 +66,7 @@ def print_gpu_memory():
     a = torch.cuda.memory_allocated(0)
     f = r - a  # free inside reserved
 
-    print(np.array([t, r, a, f]) / 2 ** 30)
+    print(np.array([t, r, a, f]) / 2**30)
 
 
 class AdapLayers(nn.Module):
@@ -130,7 +130,7 @@ def _init(self, conf):
             if isinstance(layer, torch.nn.MaxPool2d):
                 current_scale += 1
             if i in self.hypercolumn_indices:
-                self.scales.append(2 ** current_scale)
+                self.scales.append(2**current_scale)
 
         self.adaptation_layers = AdapLayers(
             conf.hypercolumn_layers, conf.output_dim

limap/features/models/vggnet.py

@@ -31,7 +31,7 @@ def _init(self, conf=default_conf):
             if isinstance(layer, torch.nn.MaxPool2d):
                 current_scale += 1
             if i in self.hypercolumn_indices:
-                self.scales.append(2 ** current_scale)
+                self.scales.append(2**current_scale)
 
     def _forward(self, data):
         image = data  # data['image']

limap/line2d/LineTR/line_attention.py

@@ -38,7 +38,7 @@ def __init__(self, n_heads: int, d_feature: int, dropout=0.1):
         self.w_vs = nn.Linear(d_feature, n_heads * dim, bias=True)
         self.fc = nn.Linear(n_heads * dim, d_feature, bias=True)
 
-        self.attention = ScaledDotProduct(scale=dim ** 0.5)
+        self.attention = ScaledDotProduct(scale=dim**0.5)
 
         self.dropout = nn.Dropout(dropout)
         self.layer_norm = nn.LayerNorm(d_feature, eps=1e-6)

limap/line2d/LineTR/line_process.py

@@ -54,7 +54,7 @@ def point_on_line(line, dist_px):
     vec = ep - sp
     if vec[0] != 0:
         m = vec[1] / vec[0]
-        x = np.sqrt(dist_px ** 2 / (1 + m ** 2))
+        x = np.sqrt(dist_px**2 / (1 + m**2))
         y = m * x
     else:
         x = 0
@@ -94,10 +94,8 @@ def remove_borders(
         sp = np.floor(klines[:, 0]).astype(int)
         ep = np.floor(klines[:, 1]).astype(int)
         valid_mask_given = (
-            (
-                valid_mask_given[sp[:, 1], sp[:, 0]]
-                + valid_mask_given[ep[:, 1], ep[:, 0]]
-            )
+            valid_mask_given[sp[:, 1], sp[:, 0]]
+            + valid_mask_given[ep[:, 1], ep[:, 0]]
         ).astype(bool)
         valid_mask = valid_mask & valid_mask_given
 
@@ -275,7 +273,7 @@ def change_cv2_T_np(klines_cv):
             kline_ep = [sp_x, sp_y]
 
         # linelength = math.sqrt((kline_ep[0]-kline_sp[0])**2 +(kline_ep[1]-kline_sp[1])**2)
-        linelength = line.lineLength * (2 ** line.octave)
+        linelength = line.lineLength * (2**line.octave)
 
         klines_sp.append(kline_sp)
         klines_ep.append(kline_ep)

limap/line2d/LineTR/line_transformer.py

@@ -186,7 +186,7 @@ def forward(
 def attention(query, key, value):
     dim = query.shape[1]
     scores = (
-        torch.einsum("bdhn,bdhm->bhnm", query, key) / dim ** 0.5
+        torch.einsum("bdhn,bdhm->bhnm", query, key) / dim**0.5
     )  # [3, 64, 4, 512] -> [3, 4, 512, 512]
     prob = torch.nn.functional.softmax(scores, dim=-1)
     return torch.einsum("bhnm,bdhm->bdhn", prob, value), prob

limap/line2d/LineTR/linetr_pipeline.py

@@ -168,7 +168,7 @@ def process_siamese(data, i):
                     pred["keypoints0"],
                     pred["keypoints1"],
                     **data,
-                    pos_th=self.conf.ground_truth.th_positive
+                    pos_th=self.conf.ground_truth.th_positive,
                 )
                 pred["gt_assignment"] = assignment
                 pred["gt_matches0"], pred["gt_matches1"] = m0, m1
@@ -188,7 +188,7 @@ def process_siamese(data, i):
                         pred["lines0"].reshape(b_size, -1, 2),
                         pred["lines1"].reshape(b_size, -1, 2),
                         **data,
-                        pos_th=self.conf.ground_truth.th_positive
+                        pos_th=self.conf.ground_truth.th_positive,
                     )
                     pred["samples_gt_assignment"] = samples_assignment
                     pred["samples_gt_matches0"] = samples_m0
@@ -219,7 +219,7 @@ def process_siamese(data, i):
                     pred["keypoints1"],
                     **data,
                     pos_th=self.conf.ground_truth.th_positive,
-                    neg_th=self.conf.ground_truth.th_negative
+                    neg_th=self.conf.ground_truth.th_negative,
                 )
                 pred["gt_assignment"] = assignment
                 pred["gt_matches0"], pred["gt_matches1"] = m0, m1
@@ -244,10 +244,8 @@ def process_siamese(data, i):
                         pred["lines1"].reshape(b_size, -1, 2),
                         **data,
                         pos_th=self.conf.ground_truth.th_positive,
-                        neg_th=self.conf.ground_truth.th_negative
-                    )[
-                        :3
-                    ]
+                        neg_th=self.conf.ground_truth.th_negative,
+                    )[:3]
                     pred["samples_gt_assignment"] = samples_assignment
                     pred["samples_gt_matches0"] = samples_m0
                     pred["samples_gt_matches1"] = samples_m1
@@ -308,13 +306,13 @@ def process_siamese(data, i):
         assert match_mat.shape[0] == 1
         bool_match_mat = match_mat[0] > 0
         pred["line_matches0"] = np.argmax(bool_match_mat, axis=1)
-        pred["line_matches0"][
-            ~np.any(bool_match_mat, axis=1)
-        ] = UNMATCHED_FEATURE
+        pred["line_matches0"][~np.any(bool_match_mat, axis=1)] = (
+            UNMATCHED_FEATURE
+        )
         pred["line_matches1"] = np.argmax(bool_match_mat, axis=0)
-        pred["line_matches1"][
-            ~np.any(bool_match_mat, axis=0)
-        ] = UNMATCHED_FEATURE
+        pred["line_matches1"][~np.any(bool_match_mat, axis=0)] = (
+            UNMATCHED_FEATURE
+        )
         pred["line_matches0"] = torch.from_numpy(pred["line_matches0"])[None]
         pred["line_matches1"] = torch.from_numpy(pred["line_matches1"])[None]
         lmatch_scores = torch.from_numpy(

limap/line2d/SOLD2/misc/visualize_util.py

@@ -1,4 +1,4 @@
-""" Organize some frequently used visualization functions. """
+"""Organize some frequently used visualization functions."""
 
 import cv2
 import numpy as np

limap/line2d/SOLD2/model/line_detection.py

@@ -178,9 +178,7 @@ def detect(self, junctions, heatmap, device=torch.device("cpu")):
                     dim=-1,
                 )
             )
-            normalized_seg_length = segments_length / (
-                ((H ** 2) + (W ** 2)) ** 0.5
-            )
+            normalized_seg_length = segments_length / (((H**2) + (W**2)) ** 0.5)
 
             # Perform local max search
             num_cand = cand_h.shape[0]
@@ -552,7 +550,7 @@ def detect_local_max(
         """Detection by local maximum search."""
         # Compute the distance threshold
         dist_thresh = (
-            0.5 * (2 ** 0.5) + self.lambda_radius * normalized_seg_length
+            0.5 * (2**0.5) + self.lambda_radius * normalized_seg_length
         )
         # Make it N x 64
         dist_thresh = torch.repeat_interleave(

limap/line2d/SOLD2/model/loss.py

@@ -154,7 +154,7 @@ def space_to_depth(input_tensor, grid_size):
     # (N, bs, bs, C, H//bs, W//bs)
     x = x.permute(0, 3, 5, 1, 2, 4).contiguous()
     # (N, C*bs^2, H//bs, W//bs)
-    x = x.view(N, C * (grid_size ** 2), H // grid_size, W // grid_size)
+    x = x.view(N, C * (grid_size**2), H // grid_size, W // grid_size)
     return x
 
 

limap/line2d/SOLD2/train.py

@@ -373,7 +373,7 @@ def train_single_epoch(
             results = metric_func.metric_results
             average = average_meter.average()
             # Get gpu memory usage in GB
-            gpu_mem_usage = torch.cuda.max_memory_allocated() / (1024 ** 3)
+            gpu_mem_usage = torch.cuda.max_memory_allocated() / (1024**3)
             if compute_descriptors:
                 print(
                     "Epoch [%d / %d] Iter [%d / %d] loss=%.4f (%.4f), junc_loss=%.4f (%.4f), heatmap_loss=%.4f (%.4f), descriptor_loss=%.4f (%.4f), gpu_mem=%.4fGB"
@@ -734,7 +734,7 @@ def record_train_summaries(writer, global_step, scalars, images):
 
     # GPU memory part
     # Get gpu memory usage in GB
-    gpu_mem_usage = torch.cuda.max_memory_allocated() / (1024 ** 3)
+    gpu_mem_usage = torch.cuda.max_memory_allocated() / (1024**3)
     writer.add_scalar("GPU/GPU_memory_usage", gpu_mem_usage, global_step)
 
     # Loss part

limap/line2d/line_utils/merge_lines.py

@@ -104,8 +104,8 @@ def merge_line_cluster(lines):
     if b == 0:
         u = np.array([1, 0]) if a >= c else np.array([0, 1])
     else:
-        m = (c - a + np.sqrt((a - c) ** 2 + 4 * b ** 2)) / (2 * b)
-        u = np.array([1, m]) / np.sqrt(1 + m ** 2)
+        m = (c - a + np.sqrt((a - c) ** 2 + 4 * b**2)) / (2 * b)
+        u = np.array([1, m]) / np.sqrt(1 + m**2)
 
     # Get the center of gravity of all endpoints
     cross = np.mean(points, axis=0)

limap/optimize/line_localization/functions.py

@@ -113,9 +113,10 @@ def reprojection_filter_matches_2to3(
         best_id = None
         for id in track_ids:
             l3d = linetracks[id].line
-            l2d_start, l2d_end = ref_camview.projection(
-                l3d.start
-            ), ref_camview.projection(l3d.end)
+            l2d_start, l2d_end = (
+                ref_camview.projection(l3d.start),
+                ref_camview.projection(l3d.end),
+            )
             l2d = _base.Line2d(l2d_start, l2d_end)
 
             dist = dist_func(ref_line, l2d)

limap/point2d/superglue/superglue.py

@@ -91,7 +91,7 @@ def attention(
     query: torch.Tensor, key: torch.Tensor, value: torch.Tensor
 ) -> Tuple[torch.Tensor, torch.Tensor]:
     dim = query.shape[1]
-    scores = torch.einsum("bdhn,bdhm->bhnm", query, key) / dim ** 0.5
+    scores = torch.einsum("bdhn,bdhm->bhnm", query, key) / dim**0.5
     prob = torch.nn.functional.softmax(scores, dim=-1)
     return torch.einsum("bhnm,bdhm->bdhn", prob, value), prob
 
@@ -336,9 +336,10 @@ def log_optimal_transport(
         return Z
 
     def _get_matches(self, scores_mat):
-        max0, max1 = scores_mat[:, :-1, :-1].max(2), scores_mat[
-            :, :-1, :-1
-        ].max(1)
+        max0, max1 = (
+            scores_mat[:, :-1, :-1].max(2),
+            scores_mat[:, :-1, :-1].max(1),
+        )
         m0, m1 = max0.indices, max1.indices
         mutual0 = arange_like(m0, 1)[None] == m1.gather(1, m0)
         mutual1 = arange_like(m1, 1)[None] == m0.gather(1, m1)

limap/pointsfm/database.py

@@ -38,7 +38,7 @@
 
 IS_PYTHON3 = sys.version_info[0] >= 3
 
-MAX_IMAGE_ID = 2 ** 31 - 1
+MAX_IMAGE_ID = 2**31 - 1
 
 CREATE_CAMERAS_TABLE = """CREATE TABLE IF NOT EXISTS cameras (
     camera_id INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL,
@@ -68,9 +68,7 @@
     prior_tz REAL,
     CONSTRAINT image_id_check CHECK(image_id >= 0 and image_id < {}),
     FOREIGN KEY(camera_id) REFERENCES cameras(camera_id))
-""".format(
-    MAX_IMAGE_ID
-)
+""".format(MAX_IMAGE_ID)
 
 CREATE_TWO_VIEW_GEOMETRIES_TABLE = """
 CREATE TABLE IF NOT EXISTS two_view_geometries (

limap/util/geometry.py

@@ -48,13 +48,13 @@ def rotation_from_quaternion(quad):
     quad = quad / norm
     qr, qi, qj, qk = quad[0], quad[1], quad[2], quad[3]
     rot_mat = np.zeros((3, 3))
-    rot_mat[0, 0] = 1 - 2 * (qj ** 2 + qk ** 2)
+    rot_mat[0, 0] = 1 - 2 * (qj**2 + qk**2)
     rot_mat[0, 1] = 2 * (qi * qj - qk * qr)
     rot_mat[0, 2] = 2 * (qi * qk + qj * qr)
     rot_mat[1, 0] = 2 * (qi * qj + qk * qr)
-    rot_mat[1, 1] = 1 - 2 * (qi ** 2 + qk ** 2)
+    rot_mat[1, 1] = 1 - 2 * (qi**2 + qk**2)
     rot_mat[1, 2] = 2 * (qj * qk - qi * qr)
     rot_mat[2, 0] = 2 * (qi * qk - qj * qr)
     rot_mat[2, 1] = 2 * (qj * qk + qi * qr)
-    rot_mat[2, 2] = 1 - 2 * (qi ** 2 + qj ** 2)
+    rot_mat[2, 2] = 1 - 2 * (qi**2 + qj**2)
     return rot_mat

runners/hypersim/Hypersim.py

@@ -95,8 +95,9 @@ def set_max_dim(cls, max_dim):
 
     @classmethod
     def set_resize_ratio(cls, ratio):
-        cls.h, cls.w = int(round(cls.default_h * ratio)), int(
-            round(cls.default_w * ratio)
+        cls.h, cls.w = (
+            int(round(cls.default_h * ratio)),
+            int(round(cls.default_w * ratio)),
         )
         cls.K[0, :] = cls.default_K[0, :] * cls.w / cls.default_w
         cls.K[1, :] = cls.default_K[1, :] * cls.h / cls.default_h

runners/inloc/localization.py

@@ -98,9 +98,7 @@ def parse_config():
     # Output folder for LIMAP linetracks (in tmp)
     if cfg["output_folder"] is None:
         cfg["output_folder"] = "finaltracks"
-    cfg[
-        "inloc_dataset"
-    ] = (
+    cfg["inloc_dataset"] = (
         args.dataset
     )  # For reading camera poses for estimating 3D lines fron depth
     return cfg, args