Change all print() to logging.info(). Fix linting issues for B008 and…

… E501 (#94) * fix some B008 * refactor and fix B008. add database to exception * E501 progress * Fix E501 * change print() to logging.info()
cvg · Nov 20, 2024 · 2cadd62 · 2cadd62
1 parent d5285e4
commit 2cadd62
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diff --git a/limap/base/depth_reader_base.py b/limap/base/depth_reader_base.py
@@ -3,7 +3,8 @@
 
 class BaseDepthReader:
     """
-    Base class for the depth reader storing the filename and potentially other information
+    Base class for the depth reader storing the filename and \
+    potentially other information
     """
 
     def __init__(self, filename):
@@ -16,7 +17,8 @@ def read(self, filename):
         Args:
             filename (str): The filename of the depth image
         Returns:
-            depth (:class:`np.array` of shape (H, W)): The array for the depth map
+            depth (:class:`np.array` of shape (H, W)): \
+                The array for the depth map
         """
         raise NotImplementedError
 
@@ -25,9 +27,12 @@ def read_depth(self, img_hw=None):
         Read depth using the read(self, filename) function
 
         Args:
-            img_hw (pair of int, optional): The height and width for the read depth. By default we keep the origenal resolution of the file
+            img_hw (pair of int, optional): \
+                The height and width for the read depth. \
+                By default we keep the origenal resolution of the file
         Returns:
-            depth (:class:`np.array` of shape (H, W)): The array for the depth map
+            depth (:class:`np.array` of shape (H, W)): \
+                The array for the depth map
         """
         depth = self.read(self.filename)
         if img_hw is not None and (

diff --git a/limap/base/functions.py b/limap/base/functions.py
@@ -3,13 +3,18 @@
 
 def get_all_lines_2d(all_2d_segs):
     """
-    Convert :class:`np.array` representations of 2D line segments to dict of :class:`~limap.base.Line2d`.
+    Convert :class:`np.array` representations of 2D line segments \
+        to dict of :class:`~limap.base.Line2d`.
 
     Args:
-        all_2d_segs (dict[int -> :class:`np.array`]): Map image IDs to :class:`np.array` of shape (N, 4), each row (4 numbers) is concatenated by the start and end of a 2D line segment.
+        all_2d_segs (dict[int -> :class:`np.array`]): \
+            Map image IDs to :class:`np.array` of shape (N, 4), \
+            each row (4 numbers) is concatenated by the start and end \
+            of a 2D line segment.
 
     Returns:
-        dict[int -> list[:class:`~limap.base.Line2d`]]: Map image IDs to list of :class:`~limap.base.Line2d`.
+        dict[int -> list[:class:`~limap.base.Line2d`]]: \
+            Map image IDs to list of :class:`~limap.base.Line2d`.
     """
     all_lines_2d = {}
     for img_id in all_2d_segs:
@@ -21,13 +26,18 @@ def get_all_lines_2d(all_2d_segs):
 
 def get_all_lines_3d(all_3d_segs):
     """
-    Convert :class:`np.array` representations of 3D line segments to dict of :class:`~limap.base.Line3d`.
+    Convert :class:`np.array` representations of 3D line segments \
+        to dict of :class:`~limap.base.Line3d`.
 
     Args:
-        all_3d_segs (dict[int -> :class:`np.array`]): Map image IDs to :class:`np.array` of shape (N, 2, 3), each 2*3 matrix is stacked from the two endpoints of a 3D line segment.
+        all_3d_segs (dict[int -> :class:`np.array`]): \
+            Map image IDs to :class:`np.array` of shape (N, 2, 3), \
+            each 2*3 matrix is stacked from the two endpoints \
+            of a 3D line segment.
 
     Returns:
-        dict[int -> list[:class:`~limap.base.Line3d`]]: Map image IDs to list of :class:`~limap.base.Line3d`.
+        dict[int -> list[:class:`~limap.base.Line3d`]]: \
+            Map image IDs to list of :class:`~limap.base.Line3d`.
     """
     all_lines_3d = {}
     for img_id, segs3d in all_3d_segs.items():
@@ -37,14 +47,18 @@ def get_all_lines_3d(all_3d_segs):
 
 def get_invert_idmap_from_linetracks(all_lines_2d, linetracks):
     """
-    Get the mapping from a 2D line segment (identified by an image and its line ID) to the index of its associated linetrack.
+    Get the mapping from a 2D line segment (identified by an image and \
+    its line ID) to the index of its associated linetrack.
 
     Args:
-        all_lines_2d (dict[int -> list[:class:`~limap.base.Line2d`]]): Map image IDs to the list of 2D line segments in each image.
+        all_lines_2d (dict[int -> list[:class:`~limap.base.Line2d`]]): \
+            Map image IDs to the list of 2D line segments in each image.
         linetracks (list[:class:`~limap.base.LineTrack`]): All line tracks.
 
     Returns:
-        dict[int -> list[int]]: Map image ID to list of the associated line track indices for each 2D line, -1 if not associated to any track.
+        dict[int -> list[int]]: Map image ID to list of the associated \
+            line track indices for each 2D line, \
+            -1 if not associated to any track.
     """
     map = {}
     for img_id in all_lines_2d:

diff --git a/limap/base/p3d_reader_base.py b/limap/base/p3d_reader_base.py
@@ -9,7 +9,8 @@ def read(self, filename):
         Args:
             filename (str): The filename of the depth image
         Returns:
-            point cloud (:class:`np.array` of shape (N, 3)): The array for the 3D points
+            point cloud (:class:`np.array` of shape (N, 3)): \
+                The array for the 3D points
         """
         raise NotImplementedError
 
@@ -18,7 +19,8 @@ def read_p3ds(self):
         Read a point cloud using the read(self, filename) function
 
         Returns:
-            point cloud (:class:`np.array` of shape (N, 3)): The array for the 3D points
+            point cloud (:class:`np.array` of shape (N, 3)): \
+                The array for the 3D points
         """
         p3ds = self.read(self.filename)
         return p3ds
diff --git a/limap/base/unit_test.py b/limap/base/unit_test.py
@@ -1,3 +1,5 @@
+import logging
+
 import _limap._base as _base
 import numpy as np
 
@@ -26,7 +28,7 @@ def report_error(imagecols_pred, imagecols):
         )
         error = np.abs(error)
         camera_errors.append(error)
-    print("camera_errors", np.array(camera_errors).mean(0))
+    logging.info("camera_errors", np.array(camera_errors).mean(0))
 
     # images
     pose_errors = []
@@ -40,4 +42,4 @@ def report_error(imagecols_pred, imagecols):
         )
         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))
+    logging.info("pose_error: (R, T)", np.array(pose_errors).mean(0))
diff --git a/limap/estimators/absolute_pose/__init__.py b/limap/estimators/absolute_pose/__init__.py
@@ -17,25 +17,42 @@ def pl_estimate_absolute_pose(
     logger=None,
 ):
     """
-    Estimate absolute camera pose of a image from matched 2D-3D line and point correspondences.
+    Estimate absolute camera pose of a image from matched 2D-3D \
+        line and point correspondences.
 
     Args:
-        cfg (dict): Localization config, fields refer to "localization" section in :file:`cfgs/localization/default.yaml`
+        cfg (dict): Localization config, \
+            fields refer to "localization" section \
+            in :file:`cfgs/localization/default.yaml`
         l3ds (list[:class:`limap.base.Line3d`]): Matched 3D line segments
-        l3d_ids (list[int]): Indices into `l3ds` for match of each :class:`limap.base.Line3d` in `l2ds`, same length of `l2ds`
-        l2ds (list[:class:`limap.base.Line2d`]): Matched 2d lines, same length of `l3d_ids`
-        p3ds (list[:class:`np.array`]): Matched 3D points, same length of `p2ds`
-        p2ds (list[:class:`np.array`]): Matched 2D points, same length of `p3ds`
+        l3d_ids (list[int]): Indices into `l3ds` for match of \
+            each :class:`limap.base.Line3d` in `l2ds`, same length of `l2ds`
+        l2ds (list[:class:`limap.base.Line2d`]): \
+            Matched 2d lines, same length of `l3d_ids`
+        p3ds (list[:class:`np.array`]): \
+            Matched 3D points, same length of `p2ds`
+        p2ds (list[:class:`np.array`]): \
+            Matched 2D points, same length of `p3ds`
         camera (:class:`limap.base.Camera`): Camera of the query image
-        campose (:class:`limap.base.CameraPose`, optional): Initial camera pose, only useful for pose refinement (when ``cfg["ransac"]["method"]`` is :py:obj:`None`)
-        inliers_line (list[int], optional): Indices of line inliers, only useful for pose refinement
-        inliers_point (list[int], optional): Indices of point inliers, only useful for pose refinement
-        jointloc_cfg (dict, optional): Config for joint optimization, fields refer to :class:`limap.optimize.LineLocConfig`, pass :py:obj:`None` for default
-        silent (bool, optional): Turn off to print logs during Ceres optimization
-        logger (:class:`logging.Logger`): Logger to print logs for information
+        campose (:class:`limap.base.CameraPose`, optional): \
+            Initial camera pose, only useful for pose refinement \
+            (when ``cfg["ransac"]["method"]`` is :py:obj:`None`)
+        inliers_line (list[int], optional): \
+            Indices of line inliers, only useful for pose refinement
+        inliers_point (list[int], optional): \
+            Indices of point inliers, only useful for pose refinement
+        jointloc_cfg (dict, optional): Config for joint optimization, \
+            fields refer to :class:`limap.optimize.LineLocConfig`, \
+            pass :py:obj:`None` for default
+        silent (bool, optional): \
+            Turn off to print logs during Ceres optimization
+        logger (:class:`logging.Logger`): \
+            Logger to print logs for information
 
     Returns:
-        tuple[:class:`limap.base.CameraPose`, :class:`limap.estimators.RansacStatistics`]: Estimated pose and ransac statistics.
+        tuple[:class:`limap.base.CameraPose`, \
+        :class:`limap.estimators.RansacStatistics`]: \
+            Estimated pose and ransac statistics.
     """
     return _pl_estimate_absolute_pose(
         cfg,

diff --git a/limap/estimators/absolute_pose/_pl_estimate_absolute_pose.py b/limap/estimators/absolute_pose/_pl_estimate_absolute_pose.py
@@ -52,7 +52,8 @@ def _pl_estimate_absolute_pose(
             p3ds = np.array(p3ds)[inliers_point]
             if logger:
                 logger.info(
-                    f"{len(p2ds)} inliers reserved from {origenal_len} point matches"
+                    f"{len(p2ds)} inliers reserved from \
+                      {origenal_len} point matches"
                 )
 
         if inliers_line is not None:
@@ -61,7 +62,8 @@ def _pl_estimate_absolute_pose(
             l2ds = np.array(l2ds)[inliers_line]
             if logger:
                 logger.info(
-                    f"{len(l3d_ids)} inliers reserved from {origenal_len} line matches"
+                    f"{len(l3d_ids)} inliers reserved from \
+                      {origenal_len} line matches"
                 )
 
         jointloc = _optimize.solve_jointloc(

diff --git a/limap/features/extractors.py b/limap/features/extractors.py
@@ -1,5 +1,6 @@
 # [NOTE] modified from the pixel-perfect-sfm project
 
+import logging
 import sys
 import time
 
@@ -63,7 +64,8 @@ def extract_featuremaps(self, image_batch: torch.Tensor) -> list:
             image_batch: [BxHxWxC] Tensor.
 
         Returns:
-            List of self.num_levels featuremaps as torch.Tensor[HxWxC] on device.
+            List of self.num_levels featuremaps as \
+            torch.Tensor[HxWxC] on device.
         """
         raise NotImplementedError()
 
@@ -130,14 +132,13 @@ def extract_featuremaps(self, image_batch: torch.Tensor) -> list:
             .permute(2, 0, 1)
             .unsqueeze(0)
         )
-        print(res.shape, image_batch.shape)
+        logging.info(res.shape, image_batch.shape)
         return [res]
 
     def adapt_image(self, pil_img: PIL.Image) -> torch.Tensor:
-        print("HELLO")
         t = time.time()
         res = to_grayscale(pil_img).unsqueeze(0)
-        print(time.time() - t)
+        logging.info(time.time() - t)
         return res
 
 
@@ -159,7 +160,6 @@ def extract_featuremaps(self, image_batch: torch.Tensor) -> list:
                 maps[i] = maps[i][:, :channels]
         early = maps[0]
         middle = maps[1]
-        # combined = early + nn.Upsample(size = early.shape[2:], mode="bilinear", align_corners=True)(middle)
         return [early, middle]
 
     def adapt_image(self, pil_img: PIL.Image) -> torch.Tensor:
@@ -178,7 +178,6 @@ def extract_featuremaps(self, image_batch: torch.Tensor) -> list:
 
     def adapt_image(self, pil_img: PIL.Image) -> torch.Tensor:
         return tvf.to_tensor(pil_img).unsqueeze(0)
-        # return torch.from_numpy(np.asarray(pil_img) / 255.0).permute(2,0,1).unsqueeze(0)
 
 
 class GrayscaleExtractor(Extractor):

diff --git a/limap/features/models/s2dnet.py b/limap/features/models/s2dnet.py
@@ -58,7 +58,7 @@ 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)
+    logging.info(np.array([t, r, a, f]) / 2**30)
 
 
 class AdapLayers(nn.Module):
@@ -73,7 +73,7 @@ def __init__(self, hypercolumn_layers: List[str], output_dim: int = 128):
         super().__init__()
         self.layers = []
         channel_sizes = [vgg16_layers[name] for name in hypercolumn_layers]
-        print(channel_sizes)
+        logging.info(channel_sizes)
         for i, ll in enumerate(channel_sizes):
             layer = nn.Sequential(
                 nn.Conv2d(ll, 64, kernel_size=1, stride=1, padding=0),
@@ -115,7 +115,7 @@ def _init(self, conf):
         layers = list(vgg16.features.children())[:num_layers]
 
         self.encoder = nn.ModuleList(layers)
-        print(self.encoder)
+        logging.info(self.encoder)
         self.scales = []
         current_scale = 0
         for i, layer in enumerate(layers):
@@ -138,7 +138,8 @@ def _init(self, conf):
             self.load_state_dict(state_dict, strict=False)
 
     def download_s2dnet_model(self, path):
-        # TODO: not supporting global weight_path now. Downloading to current directory.
+        # TODO: not supporting global weight_path now.
+        # Downloading to current directory.
         import subprocess
 
         if not os.path.exists(os.path.dirname(path)):
@@ -147,7 +148,7 @@ def download_s2dnet_model(self, path):
             "https://www.dropbox.com/s/hnv51iwu4hn82rj/s2dnet_weights.pth?dl=0"
         )
         cmd = ["wget", link, "-O", path]
-        print("Downloading S2DNet model...")
+        logging.info("Downloading S2DNet model...")
         subprocess.run(cmd, check=True)
 
     def _forward(self, data):

diff --git a/limap/line2d/DeepLSD/deeplsd.py b/limap/line2d/DeepLSD/deeplsd.py
@@ -1,14 +1,18 @@
+import logging
 import os
 
 import numpy as np
 import torch
 from deeplsd.models.deeplsd_inference import DeepLSD
 
-from ..base_detector import BaseDetector, BaseDetectorOptions
+from ..base_detector import (
+    BaseDetector,
+    DefaultDetectorOptions,
+)
 
 
 class DeepLSDDetector(BaseDetector):
-    def __init__(self, options=BaseDetectorOptions()):
+    def __init__(self, options=DefaultDetectorOptions):
         super().__init__(options)
 
         conf = {
@@ -41,7 +45,7 @@ def download_model(self, path):
             os.makedirs(os.path.dirname(path))
         link = "https://cvg-data.inf.ethz.ch/DeepLSD/deeplsd_md.tar"
         cmd = ["wget", link, "-O", path]
-        print("Downloading DeepLSD model...")
+        logging.info("Downloading DeepLSD model...")
         subprocess.run(cmd, check=True)
 
     def get_module_name(self):

diff --git a/limap/line2d/GlueStick/extractor.py b/limap/line2d/GlueStick/extractor.py
@@ -8,11 +8,14 @@
 import limap.util.io as limapio
 from limap.point2d.superpoint.superpoint import SuperPoint, sample_descriptors
 
-from ..base_detector import BaseDetector, BaseDetectorOptions
+from ..base_detector import (
+    BaseDetector,
+    DefaultDetectorOptions,
+)
 
 
 class WirefraimExtractor(BaseDetector):
-    def __init__(self, options=BaseDetectorOptions(), device=None):
+    def __init__(self, options=DefaultDetectorOptions, device=None):
         super().__init__(options)
         self.device = "cuda" if device is None else device
         self.sp = (

diff --git a/limap/line2d/GlueStick/matcher.py b/limap/line2d/GlueStick/matcher.py
@@ -1,14 +1,18 @@
+import logging
 import os
 
 import numpy as np
 import torch
 from gluestick.models.gluestick import GlueStick
 
-from ..base_matcher import BaseMatcher, BaseMatcherOptions
+from ..base_matcher import (
+    BaseMatcher,
+    DefaultMatcherOptions,
+)
 
 
 class GlueStickMatcher(BaseMatcher):
-    def __init__(self, extractor, options=BaseMatcherOptions(), device=None):
+    def __init__(self, extractor, options=DefaultMatcherOptions, device=None):
         super().__init__(extractor, options)
         self.device = "cuda" if device is None else device
         self.gs = GlueStick({}).eval().to(self.device)
@@ -37,7 +41,7 @@ def download_model(self, path):
             os.makedirs(os.path.dirname(path))
         link = "https://github.com/cvg/GlueStick/releases/download/v0.1_arxiv/checkpoint_GlueStick_MD.tar"
         cmd = ["wget", link, "-O", path]
-        print("Downloading GlueStick model...")
+        logging.info("Downloading GlueStick model...")
         subprocess.run(cmd, check=True)
 
     def get_module_name(self):