rearrange the filepath of examples

examples/pedestrian_tracking/README.md → examples/MOT17/multiedge_inference_bench/pedestrian_tracking/README.md

@@ -47,7 +47,7 @@ Next, download pretrained model via [[google]](https://drive.google.com/file/d/1
 We are now ready to run the ianvs for benchmarking pedestrian tracking on the MOT17 dataset.
 
 ```python
-ianvs -f ./examples/pedestrian_tracking/multiedge_inference_bench/tracking_job.yaml
+ianvs -f ./examples/MOT17/multiedge_inference_bench/pedestrian_tracking/tracking_job.yaml
 ```
 
 The benchmarking process takes a few minutes and varies depending on devices.
@@ -78,7 +78,7 @@ Next, download pretrained model via [[google]](https://drive.google.com/drive/fo
 We are now ready to run the ianvs for benchmarking pedestrian re-identification on the MOT17 dataset.
 
 ```python
-ianvs -f ./examples/pedestrian_tracking/multiedge_inference_bench/reid_job.yaml
+ianvs -f ./examples/MOT17/multiedge_inference_bench/pedestrian_tracking/reid_job.yaml
 ```
 
 The benchmarking process takes a few minutes and varies depending on devices.
@@ -93,9 +93,9 @@ The final output might look like this:
 ## Step 3. Generate test report
 
 ```shell
-python ./examples/pedestrian_tracking/multiedge_inference_bench/generate_reports.py \
--t ./examples/pedestrian_tracking/multiedge_inference_bench/tracking_job.yaml \
--r ./examples/pedestrian_tracking/multiedge_inference_bench/reid_job.yaml
+python ./examples/MOT17/multiedge_inference_bench/pedestrian_tracking/generate_reports.py \
+-t ./examples/MOT17/multiedge_inference_bench/pedestrian_tracking/tracking_job.yaml \
+-r ./examples/MOT17/multiedge_inference_bench/pedestrian_tracking/reid_job.yaml
 ```
 
 Finally, the report is generated under <Ianvs_HOME>/examples/pedestrian_tracking/multiedge_inference_bench/reports. You can also check the sample report under the current directory.

examples/bdd/lifelong_learning_bench/README.md → examples/bdd/lifelong_learning_bench/curb-detection/README.md

@@ -64,7 +64,7 @@ we have done that for you and the interested readers can refer to [testenv.yaml]
 
 Related algorithm is also ready in this quick start. 
 ``` shell
-export PYTHONPATH=$PYTHONPATH:/ianvs/project/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet
+export PYTHONPATH=$PYTHONPATH:/ianvs/project/examples/bdd/lifelong_learning_bench/curb-detection/testalgorithms/rfnet/RFNet
 ```
 
 The URL address of this algorithm then should be filled in the configuration file ``algorithm.yaml``. In this quick
@@ -115,7 +115,7 @@ We are now ready to run the ianvs for benchmarking.
 
 ``` shell
 cd /ianvs/project
-ianvs -f examples/bdd/lifelong_learning_bench/benchmarkingjob.yaml
+ianvs -f examples/bdd/lifelong_learning_bench/curb-detection/benchmarkingjob.yaml
 ```
 
 Finally, the user can check the result of benchmarking on the console and also in the output path(

examples/bdd/lifelong_learning_bench/testalgorithms/yolo/model_selector/choose_net_b64.py → examples/bdd/lifelong_learning_bench/curb-detection/testalgorithms/yolo/model_selector/choose_net_b64.py

@@ -1,106 +1,106 @@
-model = dict(
-    type='ImageClassifier',
-    backbone=dict(
-        type='ResNet',
-        depth=18,
-        num_stages=4,
-        out_indices=(3, ),
-        style='pytorch'),
-    neck=dict(type='GlobalAveragePooling'),
-    head=dict(
-        type='MultiLabelLinearClsHead',
-        num_classes=20,
-        in_channels=512,
-        loss=dict(type='CrossEntropyLoss', loss_weight=1.0, use_soft=True)))
-dataset_type = 'BDD_Performance'
-img_norm_cfg = dict(
-    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='Resize', size=(256, -1)),
-    dict(
-        type='Normalize',
-        mean=[123.675, 116.28, 103.53],
-        std=[58.395, 57.12, 57.375],
-        to_rgb=True),
-    dict(type='ImageToTensor', keys=['img']),
-    dict(type='ToTensor', keys=['gt_label']),
-    dict(type='Collect', keys=['img', 'gt_label'])
-]
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='Resize', size=(256, -1)),
-    dict(
-        type='Normalize',
-        mean=[123.675, 116.28, 103.53],
-        std=[58.395, 57.12, 57.375],
-        to_rgb=True),
-    dict(type='ImageToTensor', keys=['img']),
-    dict(type='Collect', keys=['img'])
-]
-data = dict(
-    samples_per_gpu=64,
-    workers_per_gpu=2,
-    train=dict(
-        type='BDD_Performance',
-        data_prefix='',
-        ann_file=
-        '/home/liyunzhe/Mobile-Inference/algorithm/labels/0129_real_world_multi_label_remo_xyxy_bdd_train.txt',
-        pipeline=[
-            dict(type='LoadImageFromFile'),
-            dict(type='Resize', size=(256, -1)),
-            dict(
-                type='Normalize',
-                mean=[123.675, 116.28, 103.53],
-                std=[58.395, 57.12, 57.375],
-                to_rgb=True),
-            dict(type='ImageToTensor', keys=['img']),
-            dict(type='ToTensor', keys=['gt_label']),
-            dict(type='Collect', keys=['img', 'gt_label'])
-        ]),
-    val=dict(
-        type='BDD_Performance',
-        data_prefix='',
-        ann_file=
-        '/home/liyunzhe/Mobile-Inference/algorithm/labels/0129_real_world_multi_label_remo_xyxy_bdd_val.txt',
-        pipeline=[
-            dict(type='LoadImageFromFile'),
-            dict(type='Resize', size=(256, -1)),
-            dict(
-                type='Normalize',
-                mean=[123.675, 116.28, 103.53],
-                std=[58.395, 57.12, 57.375],
-                to_rgb=True),
-            dict(type='ImageToTensor', keys=['img']),
-            dict(type='Collect', keys=['img'])
-        ]),
-    test=dict(
-        type='BDD_Performance',
-        data_prefix='',
-        ann_file=
-        '/home/liyunzhe/Mobile-Inference/algorithm/labels/0129_real_world_multi_label_remo_xyxy_bdd_val.txt',
-        pipeline=[
-            dict(type='LoadImageFromFile'),
-            dict(type='Resize', size=(256, -1)),
-            dict(
-                type='Normalize',
-                mean=[123.675, 116.28, 103.53],
-                std=[58.395, 57.12, 57.375],
-                to_rgb=True),
-            dict(type='ImageToTensor', keys=['img']),
-            dict(type='Collect', keys=['img'])
-        ]))
-evaluation = dict(interval=1, metric='mAP')
-optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001)
-optimizer_config = dict(grad_clip=None)
-lr_config = dict(policy='step', step=[30, 60, 90])
-runner = dict(type='EpochBasedRunner', max_epochs=100)
-checkpoint_config = dict(interval=1)
-log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')])
-dist_params = dict(backend='nccl')
-log_level = 'INFO'
-load_from = None
-resume_from = None
-workflow = [('train', 1)]
-work_dir = 'work_dirs/220208-bdd-best'
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='ResNet',
+        depth=18,
+        num_stages=4,
+        out_indices=(3, ),
+        style='pytorch'),
+    neck=dict(type='GlobalAveragePooling'),
+    head=dict(
+        type='MultiLabelLinearClsHead',
+        num_classes=20,
+        in_channels=512,
+        loss=dict(type='CrossEntropyLoss', loss_weight=1.0, use_soft=True)))
+dataset_type = 'BDD_Performance'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='Resize', size=(256, -1)),
+    dict(
+        type='Normalize',
+        mean=[123.675, 116.28, 103.53],
+        std=[58.395, 57.12, 57.375],
+        to_rgb=True),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='ToTensor', keys=['gt_label']),
+    dict(type='Collect', keys=['img', 'gt_label'])
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='Resize', size=(256, -1)),
+    dict(
+        type='Normalize',
+        mean=[123.675, 116.28, 103.53],
+        std=[58.395, 57.12, 57.375],
+        to_rgb=True),
+    dict(type='ImageToTensor', keys=['img']),
+    dict(type='Collect', keys=['img'])
+]
+data = dict(
+    samples_per_gpu=64,
+    workers_per_gpu=2,
+    train=dict(
+        type='BDD_Performance',
+        data_prefix='',
+        ann_file=
+        '/home/liyunzhe/Mobile-Inference/algorithm/labels/0129_real_world_multi_label_remo_xyxy_bdd_train.txt',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(type='Resize', size=(256, -1)),
+            dict(
+                type='Normalize',
+                mean=[123.675, 116.28, 103.53],
+                std=[58.395, 57.12, 57.375],
+                to_rgb=True),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='ToTensor', keys=['gt_label']),
+            dict(type='Collect', keys=['img', 'gt_label'])
+        ]),
+    val=dict(
+        type='BDD_Performance',
+        data_prefix='',
+        ann_file=
+        '/home/liyunzhe/Mobile-Inference/algorithm/labels/0129_real_world_multi_label_remo_xyxy_bdd_val.txt',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(type='Resize', size=(256, -1)),
+            dict(
+                type='Normalize',
+                mean=[123.675, 116.28, 103.53],
+                std=[58.395, 57.12, 57.375],
+                to_rgb=True),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img'])
+        ]),
+    test=dict(
+        type='BDD_Performance',
+        data_prefix='',
+        ann_file=
+        '/home/liyunzhe/Mobile-Inference/algorithm/labels/0129_real_world_multi_label_remo_xyxy_bdd_val.txt',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(type='Resize', size=(256, -1)),
+            dict(
+                type='Normalize',
+                mean=[123.675, 116.28, 103.53],
+                std=[58.395, 57.12, 57.375],
+                to_rgb=True),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img'])
+        ]))
+evaluation = dict(interval=1, metric='mAP')
+optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001)
+optimizer_config = dict(grad_clip=None)
+lr_config = dict(policy='step', step=[30, 60, 90])
+runner = dict(type='EpochBasedRunner', max_epochs=100)
+checkpoint_config = dict(interval=1)
+log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')])
+dist_params = dict(backend='nccl')
+log_level = 'INFO'
+load_from = None
+resume_from = None
+workflow = [('train', 1)]
+work_dir = 'work_dirs/220208-bdd-best'
 gpu_ids = range(0, 1)

examples/curb-detection/lifelong_learning_bench/README.md → examples/cityscapes-synthia/lifelong_learning_bench/curb-detection/README.md

@@ -68,7 +68,7 @@ we have done that for you and the interested readers can refer to [testenv.yaml]
 
 Related algorithm is also ready in this quick start. 
 ``` shell
-export PYTHONPATH=$PYTHONPATH:/ianvs/project/ianvs/examples/curb-detection/lifelong_learning_bench/testalgorithms/rfnet/RFNet
+export PYTHONPATH=$PYTHONPATH:/ianvs/project/ianvs/examples/cityscapes-synthia/lifelong_learning_bench/curb-detection/testalgorithms/rfnet/RFNet
 ```
 
 The URL address of this algorithm then should be filled in the configuration file ``algorithm.yaml``. In this quick
@@ -80,7 +80,7 @@ We are now ready to run the ianvs for benchmarking.
 
 ``` shell
 cd /ianvs/project/ianvs
-ianvs -f examples/curb-detection/lifelong_learning_bench/benchmarkingjob.yaml
+ianvs -f examples/cityscapes-synthia/lifelong_learning_bench/curb-detection/benchmarkingjob.yaml
 ```
 
 Finally, the user can check the result of benchmarking on the console and also in the output path(

examples/semantic_segmentation/lifelong_learning_bench/README.md → examples/cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/README.md

@@ -13,6 +13,7 @@ Before using Ianvs, you might want to have the device ready:
 - Internet connection for GitHub and pip, etc
 - Python 3.6+ installed
 
+
 In this example, we are using the Linux platform with Python 3.8.5. If you are using Windows, most steps should still apply but a few like commands and package requirements might be different. 
 
 ## Step 1. Ianvs Preparation
@@ -65,15 +66,15 @@ we have done that for you and the interested readers can refer to [testenv.yaml]
 Copy the index files of dataset.
 
 ``` shell
-cp /ianvs/project/ianvs/examples/semantic_segmentation/lifelong_learning_bench/indexes/* /root/data/semantic_segmentation_dataset/
+cp /ianvs/project/ianvs/examples/cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/indexes/* /root/data/semantic_segmentation_dataset/
 ```
 
 <!-- Please put the downloaded dataset on the above dataset path, e.g., `/ianvs/dataset`. One can transfer the dataset to the path, e.g., on a remote Linux system using [XFTP].  -->
 
 
 Related algorithm is also ready in this quick start. 
 ``` shell
-export PYTHONPATH=$PYTHONPATH:/ianvs/project/ianvs/examples/semantic_segmentation/lifelong_learning_bench/testalgorithms/rfnet/RFNet
+export PYTHONPATH=$PYTHONPATH:/ianvs/project/ianvs/examples/cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/testalgorithms/rfnet/RFNet
 ```
 
 The URL address of this algorithm then should be filled in the configuration file ``algorithm.yaml``. In this quick
@@ -85,7 +86,7 @@ We are now ready to run the ianvs for benchmarking.
 
 ``` shell
 cd /ianvs/project/ianvs
-ianvs -f examples/semantic_segmentation/lifelong_learning_bench/benchmarkingjob-smalltest.yaml
+ianvs -f examples/cityscapes-synthia/lifelong_learning_bench/semantic-segmentation/benchmarkingjob-smalltest.yaml
 ```
 
 Finally, the user can check the result of benchmarking on the console and also in the output path(