Use pre-commit to reformat code

Use pre-commit to reformat code

autonomous_driving/openlane-v2/tools/data_converter/nuscenes_converter.py

@@ -6,14 +6,13 @@ from typing import List, Tuple, Union
 
 import mmcv
 import numpy as np
+from mmdet3d.core.bbox import points_cam2img
+from mmdet3d.datasets import NuScenesDataset
 from nuscenes.nuscenes import NuScenes
 from nuscenes.utils.geometry_utils import view_points
 from pyquaternion import Quaternion
 from shapely.geometry import MultiPoint, box
 
-from mmdet3d.core.bbox import points_cam2img
-from mmdet3d.datasets import NuScenesDataset
-
 nus_categories = ('car', 'truck', 'trailer', 'bus', 'construction_vehicle',
                   'bicycle', 'motorcycle', 'pedestrian', 'traffic_cone',
                   'barrier')
@@ -324,9 +323,9 @@ def obtain_sensor2top(nusc,
     l2e_r_s_mat = Quaternion(l2e_r_s).rotation_matrix
     e2g_r_s_mat = Quaternion(e2g_r_s).rotation_matrix
     R = (l2e_r_s_mat.T @ e2g_r_s_mat.T) @ (
-        np.linalg.inv(e2g_r_mat).T @ np.linalg.inv(l2e_r_mat).T)
+            np.linalg.inv(e2g_r_mat).T @ np.linalg.inv(l2e_r_mat).T)
     T = (l2e_t_s @ e2g_r_s_mat.T + e2g_t_s) @ (
-        np.linalg.inv(e2g_r_mat).T @ np.linalg.inv(l2e_r_mat).T)
+            np.linalg.inv(e2g_r_mat).T @ np.linalg.inv(l2e_r_mat).T)
     T -= e2g_t @ (np.linalg.inv(e2g_r_mat).T @ np.linalg.inv(l2e_r_mat).T
                   ) + l2e_t @ np.linalg.inv(l2e_r_mat).T
     sweep['sensor2lidar_rotation'] = R.T  # points @ R.T + T
@@ -420,8 +419,8 @@ def get_2d_boxes(nusc,
     sd_rec = nusc.get('sample_data', sample_data_token)
 
     assert sd_rec[
-        'sensor_modality'] == 'camera', 'Error: get_2d_boxes only works' \
-        ' for camera sample_data!'
+               'sensor_modality'] == 'camera', 'Error: get_2d_boxes only works' \
+                                               ' for camera sample_data!'
     if not sd_rec['is_key_frame']:
         raise ValueError(
             'The 2D re-projections are available only for keyframes.')
@@ -532,7 +531,7 @@ def get_2d_boxes(nusc,
 
 
 def post_process_coords(
-    corner_coords: List, imsize: Tuple[int, int] = (1600, 900)
+        corner_coords: List, imsize: Tuple[int, int] = (1600, 900)
 ) -> Union[Tuple[float, float, float, float], None]:
     """Get the intersection of the convex hull of the reprojected bbox corners
     and the image canvas, return None if no intersection.

autonomous_driving/openlane-v2/tools/data_converter/s3dis_data_utils.py

@@ -181,7 +181,7 @@ class S3DISSegData(object):
         self.ignore_index = len(self.cat_ids)
 
         self.cat_id2class = np.ones((self.all_ids.shape[0],), dtype=np.int) * \
-            self.ignore_index
+                            self.ignore_index
         for i, cat_id in enumerate(self.cat_ids):
             self.cat_id2class[cat_id] = i
 
@@ -221,7 +221,7 @@ class S3DISSegData(object):
         """
         num_classes = len(self.cat_ids)
         num_point_all = []
-        label_weight = np.zeros((num_classes + 1, ))  # ignore_index
+        label_weight = np.zeros((num_classes + 1,))  # ignore_index
         for data_info in self.data_infos:
             label = self._convert_to_label(
                 osp.join(self.data_root, data_info['pts_semantic_mask_path']))

autonomous_driving/openlane-v2/tools/data_converter/scannet_data_utils.py

@@ -231,7 +231,7 @@ class ScanNetSegData(object):
         self.ignore_index = len(self.cat_ids)
 
         self.cat_id2class = np.ones((self.all_ids.shape[0],), dtype=np.int) * \
-            self.ignore_index
+                            self.ignore_index
         for i, cat_id in enumerate(self.cat_ids):
             self.cat_id2class[cat_id] = i
 
@@ -273,7 +273,7 @@ class ScanNetSegData(object):
         """
         num_classes = len(self.cat_ids)
         num_point_all = []
-        label_weight = np.zeros((num_classes + 1, ))  # ignore_index
+        label_weight = np.zeros((num_classes + 1,))  # ignore_index
         for data_info in self.data_infos:
             label = self._convert_to_label(
                 osp.join(self.data_root, data_info['pts_semantic_mask_path']))

autonomous_driving/openlane-v2/tools/data_converter/sunrgbd_data_utils.py

@@ -47,7 +47,7 @@ class SUNRGBDInstance(object):
         # z_size (height) in our depth coordinate system,
         # l corresponds to the size along the x axis
         self.size = np.array([data[9], data[8], data[10]]) * 2
-        self.orientation = np.zeros((3, ))
+        self.orientation = np.zeros((3,))
         self.orientation[0] = data[11]
         self.orientation[1] = data[12]
         self.heading_angle = np.arctan2(self.orientation[1],
@@ -187,12 +187,12 @@ class SUNRGBDData(object):
                         obj.box2d.reshape(1, 4) for obj in obj_list
                         if obj.classname in self.cat2label.keys()
                     ],
-                                                         axis=0)
+                        axis=0)
                     annotations['location'] = np.concatenate([
                         obj.centroid.reshape(1, 3) for obj in obj_list
                         if obj.classname in self.cat2label.keys()
                     ],
-                                                             axis=0)
+                        axis=0)
                     annotations['dimensions'] = 2 * np.array([
                         [obj.length, obj.width, obj.height] for obj in obj_list
                         if obj.classname in self.cat2label.keys()

autonomous_driving/openlane-v2/tools/data_converter/waymo_converter.py

@@ -141,8 +141,8 @@ class Waymo2KITTI(object):
         """
         for img in frame.images:
             img_path = f'{self.image_save_dir}{str(img.name - 1)}/' + \
-                f'{self.prefix}{str(file_idx).zfill(3)}' + \
-                f'{str(frame_idx).zfill(3)}.jpg'
+                       f'{self.prefix}{str(file_idx).zfill(3)}' + \
+                       f'{str(frame_idx).zfill(3)}.jpg'
             with open(img_path, 'wb') as fp:
                 fp.write(img.image)
 
@@ -171,7 +171,7 @@ class Waymo2KITTI(object):
                 self.cart_to_homo(T_front_cam_to_ref) @ T_vehicle_to_cam
             if camera.name == 1:  # FRONT = 1, see dataset.proto for details
                 self.T_velo_to_front_cam = Tr_velo_to_cam.copy()
-            Tr_velo_to_cam = Tr_velo_to_cam[:3, :].reshape((12, ))
+            Tr_velo_to_cam = Tr_velo_to_cam[:3, :].reshape((12,))
             Tr_velo_to_cams.append([f'{i:e}' for i in Tr_velo_to_cam])
 
             # intrinsic parameters
@@ -189,11 +189,11 @@ class Waymo2KITTI(object):
         # camera 0 is unknown in the proto
         for i in range(5):
             calib_context += 'P' + str(i) + ': ' + \
-                ' '.join(camera_calibs[i]) + '\n'
+                             ' '.join(camera_calibs[i]) + '\n'
         calib_context += 'R0_rect' + ': ' + ' '.join(R0_rect) + '\n'
         for i in range(5):
             calib_context += 'Tr_velo_to_cam_' + str(i) + ': ' + \
-                ' '.join(Tr_velo_to_cams[i]) + '\n'
+                             ' '.join(Tr_velo_to_cams[i]) + '\n'
 
         with open(
                 f'{self.calib_save_dir}/{self.prefix}' +
@@ -253,7 +253,7 @@ class Waymo2KITTI(object):
             (points, intensity, elongation, mask_indices))
 
         pc_path = f'{self.point_cloud_save_dir}/{self.prefix}' + \
-            f'{str(file_idx).zfill(3)}{str(frame_idx).zfill(3)}.bin'
+                  f'{str(file_idx).zfill(3)}{str(frame_idx).zfill(3)}.bin'
         point_cloud.astype(np.float32).tofile(pc_path)
 
     def save_label(self, frame, file_idx, frame_idx):
@@ -321,7 +321,7 @@ class Waymo2KITTI(object):
 
             # project bounding box to the virtual reference frame
             pt_ref = self.T_velo_to_front_cam @ \
-                np.array([x, y, z, 1]).reshape((4, 1))
+                     np.array([x, y, z, 1]).reshape((4, 1))
             x, y, z, _ = pt_ref.flatten().tolist()
 
             rotation_y = -obj.box.heading - np.pi / 2
@@ -333,13 +333,13 @@ class Waymo2KITTI(object):
             alpha = -10
 
             line = my_type + \
-                ' {} {} {} {} {} {} {} {} {} {} {} {} {} {}\n'.format(
-                    round(truncated, 2), occluded, round(alpha, 2),
-                    round(bounding_box[0], 2), round(bounding_box[1], 2),
-                    round(bounding_box[2], 2), round(bounding_box[3], 2),
-                    round(height, 2), round(width, 2), round(length, 2),
-                    round(x, 2), round(y, 2), round(z, 2),
-                    round(rotation_y, 2))
+                   ' {} {} {} {} {} {} {} {} {} {} {} {} {} {}\n'.format(
+                       round(truncated, 2), occluded, round(alpha, 2),
+                       round(bounding_box[0], 2), round(bounding_box[1], 2),
+                       round(bounding_box[2], 2), round(bounding_box[3], 2),
+                       round(height, 2), round(width, 2), round(length, 2),
+                       round(x, 2), round(y, 2), round(z, 2),
+                       round(rotation_y, 2))
 
             if self.save_track_id:
                 line_all = line[:-1] + ' ' + name + ' ' + track_id + '\n'

autonomous_driving/openlane-v2/tools/deployment/mmdet3d2torchserve.py

@@ -13,12 +13,12 @@ except ImportError:
 
 
 def mmdet3d2torchserve(
-    config_file: str,
-    checkpoint_file: str,
-    output_folder: str,
-    model_name: str,
-    model_version: str = '1.0',
-    force: bool = False,
+        config_file: str,
+        checkpoint_file: str,
+        output_folder: str,
+        model_name: str,
+        model_version: str = '1.0',
+        force: bool = False,
 ):
     """Converts MMDetection3D model (config + checkpoint) to TorchServe `.mar`.
 
@@ -83,8 +83,8 @@ def parse_args():
         type=str,
         default=None,
         help='If not None, used for naming the `{model_name}.mar`'
-        'file that will be created under `output_folder`.'
-        'If None, `{Path(checkpoint_file).stem}` will be used.')
+             'file that will be created under `output_folder`.'
+             'If None, `{Path(checkpoint_file).stem}` will be used.')
     parser.add_argument(
         '--model-version',
         type=str,

autonomous_driving/openlane-v2/tools/deployment/mmdet3d_handler.py

@@ -4,10 +4,9 @@ import os
 
 import numpy as np
 import torch
-from ts.torch_handler.base_handler import BaseHandler
-
 from mmdet3d.apis import inference_detector, init_model
 from mmdet3d.core.points import get_points_type
+from ts.torch_handler.base_handler import BaseHandler
 
 
 class MMdet3dHandler(BaseHandler):

autonomous_driving/openlane-v2/tools/deployment/test_torchserver.py

@@ -2,7 +2,6 @@ from argparse import ArgumentParser
 
 import numpy as np
 import requests
-
 from mmdet3d.apis import inference_detector, init_model
 
 

autonomous_driving/openlane-v2/tools/misc/browse_dataset.py

@@ -7,7 +7,6 @@ from pathlib import Path
 import mmcv
 import numpy as np
 from mmcv import Config, DictAction, mkdir_or_exist
-
 from mmdet3d.core.bbox import (Box3DMode, CameraInstance3DBoxes, Coord3DMode,
                                DepthInstance3DBoxes, LiDARInstance3DBoxes)
 from mmdet3d.core.visualizer import (show_multi_modality_result, show_result,
@@ -42,17 +41,17 @@ def parse_args():
         '--online',
         action='store_true',
         help='Whether to perform online visualization. Note that you often '
-        'need a monitor to do so.')
+             'need a monitor to do so.')
     parser.add_argument(
         '--cfg-options',
         nargs='+',
         action=DictAction,
         help='override some settings in the used config, the key-value pair '
-        'in xxx=yyy format will be merged into config file. If the value to '
-        'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
-        'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
-        'Note that the quotation marks are necessary and that no white space '
-        'is allowed.')
+             'in xxx=yyy format will be merged into config file. If the value to '
+             'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
+             'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
+             'Note that the quotation marks are necessary and that no white space '
+             'is allowed.')
     args = parser.parse_args()
     return args
 

autonomous_driving/openlane-v2/tools/misc/fuse_conv_bn.py

@@ -3,9 +3,8 @@ import argparse
 
 import torch
 from mmcv.runner import save_checkpoint
-from torch import nn as nn
-
 from mmdet3d.apis import init_model
+from torch import nn as nn
 
 
 def fuse_conv_bn(conv, bn):

autonomous_driving/openlane-v2/tools/misc/visualize_results.py

@@ -3,7 +3,6 @@ import argparse
 
 import mmcv
 from mmcv import Config
-
 from mmdet3d.datasets import build_dataset
 
 

autonomous_driving/openlane-v2/tools/model_converters/convert_h3dnet_checkpoints.py

@@ -5,7 +5,6 @@ import tempfile
 import torch
 from mmcv import Config
 from mmcv.runner import load_state_dict
-
 from mmdet3d.models import build_detector
 
 
@@ -129,13 +128,13 @@ def main():
 
     EXTRACT_KEYS = {
         'rpn_head.conv_pred.conv_cls.weight':
-        ('rpn_head.conv_pred.conv_out.weight', [(0, 2), (-NUM_CLASSES, -1)]),
+            ('rpn_head.conv_pred.conv_out.weight', [(0, 2), (-NUM_CLASSES, -1)]),
         'rpn_head.conv_pred.conv_cls.bias':
-        ('rpn_head.conv_pred.conv_out.bias', [(0, 2), (-NUM_CLASSES, -1)]),
+            ('rpn_head.conv_pred.conv_out.bias', [(0, 2), (-NUM_CLASSES, -1)]),
         'rpn_head.conv_pred.conv_reg.weight':
-        ('rpn_head.conv_pred.conv_out.weight', [(2, -NUM_CLASSES)]),
+            ('rpn_head.conv_pred.conv_out.weight', [(2, -NUM_CLASSES)]),
         'rpn_head.conv_pred.conv_reg.bias':
-        ('rpn_head.conv_pred.conv_out.bias', [(2, -NUM_CLASSES)])
+            ('rpn_head.conv_pred.conv_out.bias', [(2, -NUM_CLASSES)])
     }
 
     # Delete some useless keys

autonomous_driving/openlane-v2/tools/model_converters/convert_votenet_checkpoints.py

@@ -5,7 +5,6 @@ import tempfile
 import torch
 from mmcv import Config
 from mmcv.runner import load_state_dict
-
 from mmdet3d.models import build_detector
 
 
@@ -105,13 +104,13 @@ def main():
 
     EXTRACT_KEYS = {
         'bbox_head.conv_pred.conv_cls.weight':
-        ('bbox_head.conv_pred.conv_out.weight', [(0, 2), (-NUM_CLASSES, -1)]),
+            ('bbox_head.conv_pred.conv_out.weight', [(0, 2), (-NUM_CLASSES, -1)]),
         'bbox_head.conv_pred.conv_cls.bias':
-        ('bbox_head.conv_pred.conv_out.bias', [(0, 2), (-NUM_CLASSES, -1)]),
+            ('bbox_head.conv_pred.conv_out.bias', [(0, 2), (-NUM_CLASSES, -1)]),
         'bbox_head.conv_pred.conv_reg.weight':
-        ('bbox_head.conv_pred.conv_out.weight', [(2, -NUM_CLASSES)]),
+            ('bbox_head.conv_pred.conv_out.weight', [(2, -NUM_CLASSES)]),
         'bbox_head.conv_pred.conv_reg.bias':
-        ('bbox_head.conv_pred.conv_out.bias', [(2, -NUM_CLASSES)])
+            ('bbox_head.conv_pred.conv_out.bias', [(2, -NUM_CLASSES)])
     }
 
     # Delete some useless keys

autonomous_driving/openlane-v2/tools/test.py

@@ -4,14 +4,13 @@ import os
 import warnings
 
 import mmcv
+import mmdet
 import torch
 from mmcv import Config, DictAction
 from mmcv.cnn import fuse_conv_bn
 from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
 from mmcv.runner import (get_dist_info, init_dist, load_checkpoint,
                          wrap_fp16_model)
-
-import mmdet
 from mmdet3d.apis import single_gpu_test
 from mmdet3d.datasets import build_dataloader, build_dataset
 from mmdet3d.models import build_model
@@ -43,31 +42,31 @@ def parse_args():
         '--fuse-conv-bn',
         action='store_true',
         help='Whether to fuse conv and bn, this will slightly increase'
-        'the inference speed')
+             'the inference speed')
     parser.add_argument(
         '--gpu-ids',
         type=int,
         nargs='+',
         help='(Deprecated, please use --gpu-id) ids of gpus to use '
-        '(only applicable to non-distributed training)')
+             '(only applicable to non-distributed training)')
     parser.add_argument(
         '--gpu-id',
         type=int,
         default=0,
         help='id of gpu to use '
-        '(only applicable to non-distributed testing)')
+             '(only applicable to non-distributed testing)')
     parser.add_argument(
         '--format-only',
         action='store_true',
         help='Format the output results without perform evaluation. It is'
-        'useful when you want to format the result to a specific format and '
-        'submit it to the test server')
+             'useful when you want to format the result to a specific format and '
+             'submit it to the test server')
     parser.add_argument(
         '--eval',
         type=str,
         nargs='+',
         help='evaluation metrics, which depends on the dataset, e.g., "bbox",'
-        ' "segm", "proposal" for COCO, and "mAP", "recall" for PASCAL VOC')
+             ' "segm", "proposal" for COCO, and "mAP", "recall" for PASCAL VOC')
     parser.add_argument('--show', action='store_true', help='show results')
     parser.add_argument(
         '--show-dir', help='directory where results will be saved')
@@ -78,7 +77,7 @@ def parse_args():
     parser.add_argument(
         '--tmpdir',
         help='tmp directory used for collecting results from multiple '
-        'workers, available when gpu-collect is not specified')
+             'workers, available when gpu-collect is not specified')
     parser.add_argument('--seed', type=int, default=0, help='random seed')
     parser.add_argument(
         '--deterministic',
@@ -89,24 +88,24 @@ def parse_args():
         nargs='+',
         action=DictAction,
         help='override some settings in the used config, the key-value pair '
-        'in xxx=yyy format will be merged into config file. If the value to '
-        'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
-        'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
-        'Note that the quotation marks are necessary and that no white space '
-        'is allowed.')
+             'in xxx=yyy format will be merged into config file. If the value to '
+             'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
+             'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
+             'Note that the quotation marks are necessary and that no white space '
+             'is allowed.')
     parser.add_argument(
         '--options',
         nargs='+',
         action=DictAction,
         help='custom options for evaluation, the key-value pair in xxx=yyy '
-        'format will be kwargs for dataset.evaluate() function (deprecate), '
-        'change to --eval-options instead.')
+             'format will be kwargs for dataset.evaluate() function (deprecate), '
+             'change to --eval-options instead.')
     parser.add_argument(
         '--eval-options',
         nargs='+',
         action=DictAction,
         help='custom options for evaluation, the key-value pair in xxx=yyy '
-        'format will be kwargs for dataset.evaluate() function')
+             'format will be kwargs for dataset.evaluate() function')
     parser.add_argument(
         '--launcher',
         choices=['none', 'pytorch', 'slurm', 'mpi'],
@@ -131,7 +130,7 @@ def main():
     args = parse_args()
 
     assert args.out or args.eval or args.format_only or args.show \
-        or args.show_dir, \
+           or args.show_dir, \
         ('Please specify at least one operation (save/eval/format/show the '
          'results / save the results) with the argument "--out", "--eval"'
          ', "--format-only", "--show" or "--show-dir"')
@@ -248,8 +247,8 @@ def main():
             eval_kwargs = cfg.get('evaluation', {}).copy()
             # hard-code way to remove EvalHook args
             for key in [
-                    'interval', 'tmpdir', 'start', 'gpu_collect', 'save_best',
-                    'rule'
+                'interval', 'tmpdir', 'start', 'gpu_collect', 'save_best',
+                'rule'
             ]:
                 eval_kwargs.pop(key, None)
             eval_kwargs.update(dict(metric=args.eval, **kwargs))

autonomous_driving/openlane-v2/tools/train.py

 # Copyright (c) OpenMMLab. All rights reserved.
 from __future__ import division
+
 import argparse
 import copy
 import os
@@ -12,7 +13,6 @@ import torch
 import torch.distributed as dist
 from mmcv import Config, DictAction
 from mmcv.runner import get_dist_info, init_dist
-
 from mmdet import __version__ as mmdet_version
 from mmdet3d import __version__ as mmdet3d_version
 from mmdet3d.apis import init_random_seed, train_model
@@ -49,19 +49,19 @@ def parse_args():
         '--gpus',
         type=int,
         help='(Deprecated, please use --gpu-id) number of gpus to use '
-        '(only applicable to non-distributed training)')
+             '(only applicable to non-distributed training)')
     group_gpus.add_argument(
         '--gpu-ids',
         type=int,
         nargs='+',
         help='(Deprecated, please use --gpu-id) ids of gpus to use '
-        '(only applicable to non-distributed training)')
+             '(only applicable to non-distributed training)')
     group_gpus.add_argument(
         '--gpu-id',
         type=int,
         default=0,
         help='number of gpus to use '
-        '(only applicable to non-distributed training)')
+             '(only applicable to non-distributed training)')
     parser.add_argument('--seed', type=int, default=0, help='random seed')
     parser.add_argument(
         '--diff-seed',
@@ -76,18 +76,18 @@ def parse_args():
         nargs='+',
         action=DictAction,
         help='override some settings in the used config, the key-value pair '
-        'in xxx=yyy format will be merged into config file (deprecate), '
-        'change to --cfg-options instead.')
+             'in xxx=yyy format will be merged into config file (deprecate), '
+             'change to --cfg-options instead.')
     parser.add_argument(
         '--cfg-options',
         nargs='+',
         action=DictAction,
         help='override some settings in the used config, the key-value pair '
-        'in xxx=yyy format will be merged into config file. If the value to '
-        'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
-        'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
-        'Note that the quotation marks are necessary and that no white space '
-        'is allowed.')
+             'in xxx=yyy format will be merged into config file. If the value to '
+             'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
+             'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
+             'Note that the quotation marks are necessary and that no white space '
+             'is allowed.')
     parser.add_argument(
         '--launcher',
         choices=['none', 'pytorch', 'slurm', 'mpi'],

autonomous_driving/openlane-v2/tools/update_data_coords.py

@@ -4,7 +4,6 @@ from os import path as osp
 
 import mmcv
 import numpy as np
-
 from mmdet3d.core.bbox import limit_period
 
 
@@ -61,7 +60,6 @@ def update_outdoor_dbinfos(root_dir, out_dir, pkl_files):
 
 
 def update_nuscenes_or_lyft_infos(root_dir, out_dir, pkl_files):
-
     print(f'{pkl_files} will be modified because '
           f'of the refactor of the LIDAR coordinate system.')
     if root_dir == out_dir:
@@ -89,7 +87,7 @@ def update_nuscenes_or_lyft_infos(root_dir, out_dir, pkl_files):
 
 
 parser = argparse.ArgumentParser(description='Arg parser for data coords '
-                                 'update due to coords sys refactor.')
+                                             'update due to coords sys refactor.')
 parser.add_argument('dataset', metavar='kitti', help='name of the dataset')
 parser.add_argument(
     '--root-dir',

classification/README.md

@@ -3,14 +3,16 @@
 This folder contains the implementation of the InternImage for image classification.
 
 <!-- TOC -->
-* [Install](#install)
-* [Data Preparation](#data-preparation)
-* [Evaluation](#evaluation)
-* [Training from Scratch on ImageNet-1K](#training-from-scratch-on-imagenet-1k)
-* [Manage Jobs with Slurm](#manage-jobs-with-slurm)
-* [Training with Deepspeed](#training-with-deepspeed)
-* [Extracting Intermediate Features](#extracting-intermediate-features)
-* [Export](#export)
+
+- [Install](#install)
+- [Data Preparation](#data-preparation)
+- [Evaluation](#evaluation)
+- [Training from Scratch on ImageNet-1K](#training-from-scratch-on-imagenet-1k)
+- [Manage Jobs with Slurm](#manage-jobs-with-slurm)
+- [Training with Deepspeed](#training-with-deepspeed)
+- [Extracting Intermediate Features](#extracting-intermediate-features)
+- [Export](#export)
+
 <!-- TOC -->
 
 ## Usage
@@ -36,6 +38,7 @@ conda activate internimage
 - Install `PyTorch>=1.10.0` and `torchvision>=0.9.0` with `CUDA>=10.2`:
 
 For examples, to install torch==1.11 with CUDA==11.3:
+
 ```bash
 pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113  -f https://download.pytorch.org/whl/torch_stable.html
 ```
@@ -55,20 +58,24 @@ pip install opencv-python termcolor yacs pyyaml scipy
 ```
 
 - Compiling CUDA operators
+
 ```bash
 cd ./ops_dcnv3
 sh ./make.sh
 # unit test (should see all checking is True)
 python test.py
 ```
+
 - You can also install the operator using .whl files
-[DCNv3-1.0-whl](https://github.com/OpenGVLab/InternImage/releases/tag/whl_files)
+  [DCNv3-1.0-whl](https://github.com/OpenGVLab/InternImage/releases/tag/whl_files)
+
 ### Data Preparation
 
 We use standard ImageNet dataset, you can download it from http://image-net.org/. We provide the following two ways to
 load data:
 
 - For standard folder dataset, move validation images to labeled sub-folders. The file structure should look like:
+
   ```bash
   $ tree data
   imagenet
@@ -90,13 +97,15 @@ load data:
       │   ├── img6.jpeg
       │   └── ...
       └── ...
- 
+
   ```
+
 - To boost the slow speed when reading images from massive small files, we also support zipped ImageNet, which includes
   four files:
-    - `train.zip`, `val.zip`: which store the zipped folder for train and validate splits.
-    - `train.txt`, `val.txt`: which store the relative path in the corresponding zip file and ground truth
-      label. Make sure the data folder looks like this:
+
+  - `train.zip`, `val.zip`: which store the zipped folder for train and validate splits.
+  - `train.txt`, `val.txt`: which store the relative path in the corresponding zip file and ground truth
+    label. Make sure the data folder looks like this:
 
   ```bash
   $ tree data
@@ -106,14 +115,14 @@ load data:
       ├── train.zip
       ├── val_map.txt
       └── val.zip
-  
+
   $ head -n 5 meta_data/val.txt
   ILSVRC2012_val_00000001.JPEG	65
   ILSVRC2012_val_00000002.JPEG	970
   ILSVRC2012_val_00000003.JPEG	230
   ILSVRC2012_val_00000004.JPEG	809
   ILSVRC2012_val_00000005.JPEG	516
-  
+
   $ head -n 5 meta_data/train.txt
   n01440764/n01440764_10026.JPEG	0
   n01440764/n01440764_10027.JPEG	0
@@ -121,6 +130,7 @@ load data:
   n01440764/n01440764_10040.JPEG	0
   n01440764/n01440764_10042.JPEG	0
   ```
+
 - For ImageNet-22K dataset, make a folder named `fall11_whole` and move all images to labeled sub-folders in this
   folder. Then download the train-val split
   file ([ILSVRC2011fall_whole_map_train.txt](https://github.com/SwinTransformer/storage/releases/download/v2.0.1/ILSVRC2011fall_whole_map_train.txt)
@@ -144,7 +154,7 @@ To evaluate a pretrained `InternImage` on ImageNet val, run:
 
 ```bash
 python -m torch.distributed.launch --nproc_per_node <num-of-gpus-to-use> --master_port 12345 main.py --eval \
---cfg <config-file> --resume <checkpoint> --data-path <imagenet-path> 
+--cfg <config-file> --resume <checkpoint> --data-path <imagenet-path>
 ```
 
 For example, to evaluate the `InternImage-B` with a single GPU:
@@ -161,7 +171,7 @@ python -m torch.distributed.launch --nproc_per_node 1 --master_port 12345 main.p
 To train an `InternImage` on ImageNet from scratch, run:
 
 ```bash
-python -m torch.distributed.launch --nproc_per_node <num-of-gpus-to-use> --master_port 12345  main.py \ 
+python -m torch.distributed.launch --nproc_per_node <num-of-gpus-to-use> --master_port 12345  main.py \
 --cfg <config-file> --data-path <imagenet-path> [--batch-size <batch-size-per-gpu> --output <output-directory> --tag <job-tag>]
 ```
 
@@ -187,13 +197,13 @@ GPUS=8 sh train_in1k.sh <partition> <job-name> configs/internimage_s_1k_224.yaml
 GPUS=8 sh train_in1k.sh <partition> <job-name> configs/internimage_xl_22kto1k_384.pth --resume internimage_xl_22kto1k_384.pth --eval
 ```
 
-<!-- 
+<!--
 ### Test pretrained model on ImageNet-22K
 
 For example, to evaluate the `InternImage-L-22k`:
 
 ```bash
-python -m torch.distributed.launch --nproc_per_node <num-of-gpus-to-use> --master_port 12345  main.py \ 
+python -m torch.distributed.launch --nproc_per_node <num-of-gpus-to-use> --master_port 12345  main.py \
 --cfg configs/internimage_xl_22k_192to384.yaml --data-path <imagenet-path> [--batch-size <batch-size-per-gpu> --output <output-directory>] \
 --resume internimage_xl_22k_192to384.pth --eval
 ``` -->
@@ -220,15 +230,15 @@ pip install deepspeed==0.8.3
 
 Then you could launch the training in a slurm system with 8 GPUs as follows (tiny and huge as examples).
 The default zero stage is 1 and it could config via command line args `--zero-stage`.
+
 ```
-GPUS=8 GPUS_PER_NODE=8 sh train_in1k_deepspeed.sh vc_research_4 train configs/internimage_t_1k_224.yaml --batch-size 128 --accumulation-steps 4 
+GPUS=8 GPUS_PER_NODE=8 sh train_in1k_deepspeed.sh vc_research_4 train configs/internimage_t_1k_224.yaml --batch-size 128 --accumulation-steps 4
 GPUS=8 GPUS_PER_NODE=8 sh train_in1k_deepspeed.sh vc_research_4 train configs/internimage_t_1k_224.yaml --batch-size 128 --accumulation-steps 4 --eval --resume ckpt.pth
 GPUS=8 GPUS_PER_NODE=8 sh train_in1k_deepspeed.sh vc_research_4 train configs/internimage_t_1k_224.yaml --batch-size 128 --accumulation-steps 4 --eval --resume deepspeed_ckpt_dir
 GPUS=8 GPUS_PER_NODE=8 sh train_in1k_deepspeed.sh vc_research_4 train configs/internimage_h_22kto1k_640.yaml --batch-size 16 --accumulation-steps 4 --pretrained ckpt/internimage_h_jointto22k_384.pth
 GPUS=8 GPUS_PER_NODE=8 sh train_in1k_deepspeed.sh vc_research_4 train configs/internimage_h_22kto1k_640.yaml --batch-size 16 --accumulation-steps 4 --pretrained ckpt/internimage_h_jointto22k_384.pth --zero-stage 3
 ```
 
-
 🤗 **Huggingface Accelerate Integration of Deepspeed**
 
 Optionally, you could use our [Huggingface accelerate](https://github.com/huggingface/accelerate) integration to use deepspeed.
@@ -250,12 +260,12 @@ Here is the reference GPU memory cost for InternImage-H with 8 GPUs.
 - total batch size = 512, 16 batch size for each GPU, gradient accumulation steps = 4.
 
 | Resolution | Deepspeed | Cpu offloading | Memory |
-| --- | --- | --- | --- |
-| 640 | zero1 | False | 22572 |
-| 640 | zero3 | False | 20000 |
-| 640 | zero3 | True | 19144 |
-| 384 | zero1 | False | 16000 |
-| 384 | zero3 | True | 11928 |
+| ---------- | --------- | -------------- | ------ |
+| 640        | zero1     | False          | 22572  |
+| 640        | zero3     | False          | 20000  |
+| 640        | zero3     | True           | 19144  |
+| 384        | zero1     | False          | 16000  |
+| 384        | zero3     | True           | 11928  |
 
 **Convert Checkpoints**
 
@@ -272,7 +282,7 @@ Then, you could use `best.pth` as usual, e.g., `model.load_state_dict(torch.load
 
 ### Extracting Intermediate Features
 
-To extract the features of an intermediate layer, you could use `extract_feature.py`. 
+To extract the features of an intermediate layer, you could use `extract_feature.py`.
 
 For example, extract features of `b.png` from layers `patch_embed` and `levels.0.downsample` and save them to 'b.pth'.
 
@@ -280,16 +290,16 @@ For example, extract features of `b.png` from layers `patch_embed` and `levels.0
 python extract_feature.py --cfg configs/internimage_t_1k_224.yaml --img b.png --keys patch_embed levels.0.downsample --save --resume internimage_t_1k_224.pth
 ```
 
-
-
 ### Export
 
 To export `InternImage-T` from PyTorch to ONNX, run:
+
 ```shell
 python export.py --model_name internimage_t_1k_224 --ckpt_dir /path/to/ckpt/dir --onnx
 ```
 
 To export `InternImage-T` from PyTorch to TensorRT, run:
+
 ```shell
 python export.py --model_name internimage_t_1k_224 --ckpt_dir /path/to/ckpt/dir --trt
 ```

classification/config.py

@@ -5,6 +5,7 @@
 # --------------------------------------------------------
 
 import os
+
 import yaml
 from yacs.config import CfgNode as CN
 
@@ -82,7 +83,6 @@ _C.MODEL.INTERN_IMAGE.CENTER_FEATURE_SCALE = False
 _C.MODEL.INTERN_IMAGE.REMOVE_CENTER = False
 
 
-
 # -----------------------------------------------------------------------------
 # Training settings
 # -----------------------------------------------------------------------------

classification/configs/accelerate/deepspeed/ds_config_zero1.json

@@ -17,4 +17,4 @@
     "steps_per_print": "inf",
     "train_batch_size": "auto",
     "train_micro_batch_size_per_gpu": "auto"
-}
\ No newline at end of file
+}

classification/configs/accelerate/deepspeed/ds_config_zero1_wo_loss_scale.json

@@ -18,4 +18,4 @@
     "steps_per_print": "inf",
     "train_batch_size": "auto",
     "train_micro_batch_size_per_gpu": "auto"
-}
\ No newline at end of file
+}