fix bug in benchmark_test (#364)

fix bug in configs

Co-authored-by: Your Name <you@example.com>

.dev_scripts/benchmark_test.py

@@ -87,39 +87,34 @@ def replace_to_ceph(cfg):
             's3://openmmlab/datasets/classification/imagenet',
         }))
 
-    def _process_pipeline(dataset, name):
+    def _process_dataset(dataset):
 
-        def replace_img(pipeline):
-            if pipeline['type'] == 'LoadImageFromFile':
-                pipeline['file_client_args'] = file_client_args
-
-        def replace_ann(pipeline):
-            if pipeline['type'] == 'LoadAnnotations' or pipeline[
-                    'type'] == 'LoadPanopticAnnotations':
-                pipeline['file_client_args'] = file_client_args
+        def replace_pipline(pipelines):
+            for pipeline in pipelines:
+                if pipeline['type'] in [
+                        'LoadImageFromFile',
+                        'LoadAnnotations',
+                        'LoadPanopticAnnotations',
+                ]:
+                    pipeline['file_client_args'] = file_client_args
 
+        if dataset['type'] in ['CityscapesDataset']:
+            dataset['file_client_args'] = file_client_args
         if 'pipeline' in dataset:
-            replace_img(dataset.pipeline[0])
-            replace_ann(dataset.pipeline[1])
-            if 'dataset' in dataset:
-                # dataset wrapper
-                replace_img(dataset.dataset.pipeline[0])
-                replace_ann(dataset.dataset.pipeline[1])
-        else:
-            # dataset wrapper
-            replace_img(dataset.dataset.pipeline[0])
-            replace_ann(dataset.dataset.pipeline[1])
+            replace_pipline(dataset['pipeline'])
+        if 'dataset' in dataset:
+            _process_dataset(dataset['dataset'])
 
-    def _process_evaluator(evaluator, name):
+    def _process_evaluator(evaluator):
         if evaluator['type'] == 'CocoPanopticMetric':
             evaluator['file_client_args'] = file_client_args
 
     # half ceph
-    _process_pipeline(cfg.train_dataloader.dataset, cfg.filename)
-    _process_pipeline(cfg.val_dataloader.dataset, cfg.filename)
-    _process_pipeline(cfg.test_dataloader.dataset, cfg.filename)
-    _process_evaluator(cfg.val_evaluator, cfg.filename)
-    _process_evaluator(cfg.test_evaluator, cfg.filename)
+    _process_dataset(cfg.train_dataloader.dataset, )
+    _process_dataset(cfg.val_dataloader.dataset)
+    _process_dataset(cfg.test_dataloader.dataset)
+    _process_evaluator(cfg.val_evaluator)
+    _process_evaluator(cfg.test_evaluator)
 
 
 def create_test_job_batch(commands, model_info, args, port):
@@ -169,7 +164,7 @@ def create_test_job_batch(commands, model_info, args, port):
 
     launcher = 'none' if args.local else 'slurm'
     runner = 'python' if args.local else 'srun python'
-    master_port = f'NASTER_PORT={port}'
+    master_port = f'MASTER_PORT={port}'
 
     script_name = osp.join('tools', 'test.py')
     job_script = (f'#!/bin/bash\n'

configs/distill/mmdet/pkd/pkd_fpn_retina_x101_retina_r50_2x_coco.py

@@ -1,4 +1,4 @@
-_base_ = ['./pkd_fpn_frcnn_r101_frcnn_r50_2x_coco.py']
+_base_ = ['./pkd_fpn_faster-rcnn_r101_faster-rcnn_r50_2x_coco.py']
 
 teacher_ckpt = 'https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_64x4d_fpn_1x_coco/retinanet_x101_64x4d_fpn_1x_coco_20200130-366f5af1.pth'  # noqa: E501
 

configs/nas/mmcls/autoslim/metafile.yml

@@ -26,7 +26,7 @@ Models:
         Metrics:
           Top 1 Accuracy: 74.23
           Top 5 Accuracy: 91.73
-    Config: configs/pruning/mmcls/autoslim/autoslim_mbv2_1.5x_subnet_8xb256_in1k_flops-530M.py
+    Config: configs/nas/mmcls/autoslim/autoslim_mbv2_1.5x_subnet_8xb256_in1k_flops-530M.py
     Weights: https://download.openmmlab.com/mmrazor/v1/autoslim/autoslim_mbv2_subnet_8xb256_in1k_flops-530M_acc-74.23_20220715-aa8754fe.pth
   - Name: autoslim_mbv2_1.5x_subnet_8xb256_in1k_flops-320M
     In Collection: AutoSlim
@@ -41,7 +41,7 @@ Models:
         Metrics:
           Top 1 Accuracy: 72.73
           Top 5 Accuracy: 90.84
-    Config: configs/pruning/mmcls/autoslim/autoslim_mbv2_1.5x_subnet_8xb256_in1k_flops-320M.py
+    Config: configs/nas/mmcls/autoslim/autoslim_mbv2_1.5x_subnet_8xb256_in1k_flops-320M.py
     Weights: https://download.openmmlab.com/mmrazor/v1/autoslim/autoslim_mbv2_subnet_8xb256_in1k_flops-320M_acc-72.73_20220715-9aa8f8ae.pth
   - Name: autoslim_mbv2_1.5x_subnet_8xb256_in1k_flops-220M
     In Collection: AutoSlim
@@ -56,5 +56,5 @@ Models:
         Metrics:
           Top 1 Accuracy: 71.4
           Top 5 Accuracy: 90.08
-    Config: configs/pruning/mmcls/autoslim/autoslim_mbv2_1.5x_subnet_8xb256_in1k_flops-220M.py
+    Config: configs/nas/mmcls/autoslim/autoslim_mbv2_1.5x_subnet_8xb256_in1k_flops-220M.py
     Weights: https://download.openmmlab.com/mmrazor/v1/autoslim/autoslim_mbv2_subnet_8xb256_in1k_flops-220M_acc-71.4_20220715-9c288f3b.pth

configs/nas/mmcls/darts/metafile.yml

@@ -24,5 +24,5 @@ Models:
         Metrics:
           Top 1 Accuracy: 97.32
           Top 5 Accuracy: 99.94
-    Config: configs/nas/darts/darts_subnet_1xb96_cifar10_2.0.py
+    Config: configs/nas/mmcls/darts/darts_subnet_1xb96_cifar10_2.0.py
     Weights: https://download.openmmlab.com/mmrazor/v0.1/nas/darts/darts_subnetnet_1xb96_cifar10/darts_subnetnet_1xb96_cifar10_acc-97.32_20211222-e5727921.pth