[Refactor] migrate File I/O to the newest mmengine (open-mmlab#9709)

Author: zwhus

configs/_base_/datasets/cityscapes_detection.py

@@ -2,8 +2,23 @@
 dataset_type = 'CityscapesDataset'
 data_root = 'data/cityscapes/'
 
+# Example to use different file client
+# Method 1: simply set the data root and let the file I/O module
+# automatically infer from prefix (not support LMDB and Memcache yet)
+
+# data_root = 's3://openmmlab/datasets/segmentation/cityscapes/'
+
+# Method 2: Use `backend_args`, `file_client_args` in versions before 3.0.0rc6
+# backend_args = dict(
+#     backend='petrel',
+#     path_mapping=dict({
+#         './data/': 's3://openmmlab/datasets/segmentation/',
+#          'data/': 's3://openmmlab/datasets/segmentation/'
+#      }))
+backend_args = None
+
 train_pipeline = [
-    dict(type='LoadImageFromFile'),
+    dict(type='LoadImageFromFile', backend_args=backend_args),
     dict(type='LoadAnnotations', with_bbox=True),
     dict(
         type='RandomResize',
@@ -14,7 +29,7 @@
 ]
 
 test_pipeline = [
-    dict(type='LoadImageFromFile'),
+    dict(type='LoadImageFromFile', backend_args=backend_args),
     dict(type='Resize', scale=(2048, 1024), keep_ratio=True),
     # If you don't have a gt annotation, delete the pipeline
     dict(type='LoadAnnotations', with_bbox=True),
@@ -39,7 +54,8 @@
             ann_file='annotations/instancesonly_filtered_gtFine_train.json',
             data_prefix=dict(img='leftImg8bit/train/'),
             filter_cfg=dict(filter_empty_gt=True, min_size=32),
-            pipeline=train_pipeline)))
+            pipeline=train_pipeline,
+            backend_args=backend_args)))
 
 val_dataloader = dict(
     batch_size=1,
@@ -54,13 +70,15 @@
         data_prefix=dict(img='leftImg8bit/val/'),
         test_mode=True,
         filter_cfg=dict(filter_empty_gt=True, min_size=32),
-        pipeline=test_pipeline))
+        pipeline=test_pipeline,
+        backend_args=backend_args))
 
 test_dataloader = val_dataloader
 
 val_evaluator = dict(
     type='CocoMetric',
     ann_file=data_root + 'annotations/instancesonly_filtered_gtFine_val.json',
-    metric='bbox')
+    metric='bbox',
+    backend_args=backend_args)
 
 test_evaluator = val_evaluator

configs/_base_/datasets/cityscapes_instance.py

@@ -2,8 +2,23 @@
 dataset_type = 'CityscapesDataset'
 data_root = 'data/cityscapes/'
 
+# Example to use different file client
+# Method 1: simply set the data root and let the file I/O module
+# automatically infer from prefix (not support LMDB and Memcache yet)
+
+# data_root = 's3://openmmlab/datasets/segmentation/cityscapes/'
+
+# Method 2: Use backend_args, file_client_args in versions before 3.0.0rc6
+# backend_args = dict(
+#     backend='petrel',
+#     path_mapping=dict({
+#         './data/': 's3://openmmlab/datasets/segmentation/',
+#          'data/': 's3://openmmlab/datasets/segmentation/'
+#      }))
+backend_args = None
+
 train_pipeline = [
-    dict(type='LoadImageFromFile'),
+    dict(type='LoadImageFromFile', backend_args=backend_args),
     dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
     dict(
         type='RandomResize',
@@ -14,7 +29,7 @@
 ]
 
 test_pipeline = [
-    dict(type='LoadImageFromFile'),
+    dict(type='LoadImageFromFile', backend_args=backend_args),
     dict(type='Resize', scale=(2048, 1024), keep_ratio=True),
     # If you don't have a gt annotation, delete the pipeline
     dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
@@ -39,7 +54,8 @@
             ann_file='annotations/instancesonly_filtered_gtFine_train.json',
             data_prefix=dict(img='leftImg8bit/train/'),
             filter_cfg=dict(filter_empty_gt=True, min_size=32),
-            pipeline=train_pipeline)))
+            pipeline=train_pipeline,
+            backend_args=backend_args)))
 
 val_dataloader = dict(
     batch_size=1,
@@ -54,7 +70,8 @@
         data_prefix=dict(img='leftImg8bit/val/'),
         test_mode=True,
         filter_cfg=dict(filter_empty_gt=True, min_size=32),
-        pipeline=test_pipeline))
+        pipeline=test_pipeline,
+        backend_args=backend_args))
 
 test_dataloader = val_dataloader
 
@@ -63,13 +80,13 @@
         type='CocoMetric',
         ann_file=data_root +
         'annotations/instancesonly_filtered_gtFine_val.json',
-        metric=['bbox', 'segm']),
+        metric=['bbox', 'segm'],
+        backend_args=backend_args),
     dict(
         type='CityScapesMetric',
-        ann_file=data_root +
-        'annotations/instancesonly_filtered_gtFine_val.json',
-        seg_prefix=data_root + '/gtFine/val',
-        outfile_prefix='./work_dirs/cityscapes_metric/instance')
+        seg_prefix=data_root + 'gtFine/val',
+        outfile_prefix='./work_dirs/cityscapes_metric/instance',
+        backend_args=backend_args)
 ]
 
 test_evaluator = val_evaluator