forked from bcaitech1/p3-ims-obd-hansarang
-
Notifications
You must be signed in to change notification settings - Fork 0
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
- Loading branch information
Showing
1,573 changed files
with
147,144 additions
and
0 deletions.
There are no files selected for viewing
Large diffs are not rendered by default.
Oops, something went wrong.
Large diffs are not rendered by default.
Oops, something went wrong.
Binary file not shown.
Large diffs are not rendered by default.
Oops, something went wrong.
16 changes: 16 additions & 0 deletions
16
chanyub_det/configs/_base_/.ipynb_checkpoints/default_runtime-checkpoint.py
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,16 @@ | ||
| checkpoint_config = dict(interval=1) | ||
| # yapf:disable | ||
| log_config = dict( | ||
| interval=50, | ||
| hooks=[ | ||
| dict(type='TextLoggerHook'), | ||
| # dict(type='TensorboardLoggerHook') | ||
| ]) | ||
| # yapf:enable | ||
| custom_hooks = [dict(type='NumClassCheckHook')] | ||
|
|
||
| dist_params = dict(backend='nccl') | ||
| log_level = 'INFO' | ||
| load_from = None | ||
| resume_from = None | ||
| workflow = [('train', 1)] |
55 changes: 55 additions & 0 deletions
55
chanyub_det/configs/_base_/datasets/.ipynb_checkpoints/cityscapes_instance-checkpoint.py
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,55 @@ | ||
| dataset_type = 'CityscapesDataset' | ||
| data_root = 'data/cityscapes/' | ||
| 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='LoadAnnotations', with_bbox=True, with_mask=True), | ||
| dict( | ||
| type='Resize', img_scale=[(2048, 800), (2048, 1024)], keep_ratio=True), | ||
| dict(type='RandomFlip', flip_ratio=0.5), | ||
| dict(type='Normalize', **img_norm_cfg), | ||
| dict(type='Pad', size_divisor=32), | ||
| dict(type='DefaultFormatBundle'), | ||
| dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), | ||
| ] | ||
| test_pipeline = [ | ||
| dict(type='LoadImageFromFile'), | ||
| dict( | ||
| type='MultiScaleFlipAug', | ||
| img_scale=(2048, 1024), | ||
| flip=False, | ||
| transforms=[ | ||
| dict(type='Resize', keep_ratio=True), | ||
| dict(type='RandomFlip'), | ||
| dict(type='Normalize', **img_norm_cfg), | ||
| dict(type='Pad', size_divisor=32), | ||
| dict(type='ImageToTensor', keys=['img']), | ||
| dict(type='Collect', keys=['img']), | ||
| ]) | ||
| ] | ||
| data = dict( | ||
| samples_per_gpu=1, | ||
| workers_per_gpu=2, | ||
| train=dict( | ||
| type='RepeatDataset', | ||
| times=8, | ||
| dataset=dict( | ||
| type=dataset_type, | ||
| ann_file=data_root + | ||
| 'annotations/instancesonly_filtered_gtFine_train.json', | ||
| img_prefix=data_root + 'leftImg8bit/train/', | ||
| pipeline=train_pipeline)), | ||
| val=dict( | ||
| type=dataset_type, | ||
| ann_file=data_root + | ||
| 'annotations/instancesonly_filtered_gtFine_val.json', | ||
| img_prefix=data_root + 'leftImg8bit/val/', | ||
| pipeline=test_pipeline), | ||
| test=dict( | ||
| type=dataset_type, | ||
| ann_file=data_root + | ||
| 'annotations/instancesonly_filtered_gtFine_test.json', | ||
| img_prefix=data_root + 'leftImg8bit/test/', | ||
| pipeline=test_pipeline)) | ||
| evaluation = dict(metric=['bbox', 'segm']) |
48 changes: 48 additions & 0 deletions
48
chanyub_det/configs/_base_/datasets/.ipynb_checkpoints/coco_detection-checkpoint.py
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,48 @@ | ||
| dataset_type = 'CocoDataset' | ||
| data_root = 'data/coco/' | ||
| 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='LoadAnnotations', with_bbox=True), | ||
| dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), | ||
| dict(type='RandomFlip', flip_ratio=0.5), | ||
| dict(type='Normalize', **img_norm_cfg), | ||
| dict(type='Pad', size_divisor=32), | ||
| dict(type='DefaultFormatBundle'), | ||
| dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), | ||
| ] | ||
| test_pipeline = [ | ||
| dict(type='LoadImageFromFile'), | ||
| dict( | ||
| type='MultiScaleFlipAug', | ||
| img_scale=(1333, 800), | ||
| flip=False, | ||
| transforms=[ | ||
| dict(type='Resize', keep_ratio=True), | ||
| dict(type='RandomFlip'), | ||
| dict(type='Normalize', **img_norm_cfg), | ||
| dict(type='Pad', size_divisor=32), | ||
| dict(type='ImageToTensor', keys=['img']), | ||
| dict(type='Collect', keys=['img']), | ||
| ]) | ||
| ] | ||
| data = dict( | ||
| samples_per_gpu=2, | ||
| workers_per_gpu=2, | ||
| train=dict( | ||
| type=dataset_type, | ||
| ann_file=data_root + 'annotations/instances_train2017.json', | ||
| img_prefix=data_root + 'train2017/', | ||
| pipeline=train_pipeline), | ||
| val=dict( | ||
| type=dataset_type, | ||
| ann_file=data_root + 'annotations/instances_val2017.json', | ||
| img_prefix=data_root + 'val2017/', | ||
| pipeline=test_pipeline), | ||
| test=dict( | ||
| type=dataset_type, | ||
| ann_file=data_root + 'annotations/instances_val2017.json', | ||
| img_prefix=data_root + 'val2017/', | ||
| pipeline=test_pipeline)) | ||
| evaluation = dict(interval=1, metric='bbox') |
48 changes: 48 additions & 0 deletions
48
chanyub_det/configs/_base_/datasets/.ipynb_checkpoints/coco_instance-checkpoint.py
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,48 @@ | ||
| dataset_type = 'CocoDataset' | ||
| data_root = 'data/coco/' | ||
| 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='LoadAnnotations', with_bbox=True, with_mask=True), | ||
| dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), | ||
| dict(type='RandomFlip', flip_ratio=0.5), | ||
| dict(type='Normalize', **img_norm_cfg), | ||
| dict(type='Pad', size_divisor=32), | ||
| dict(type='DefaultFormatBundle'), | ||
| dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), | ||
| ] | ||
| test_pipeline = [ | ||
| dict(type='LoadImageFromFile'), | ||
| dict( | ||
| type='MultiScaleFlipAug', | ||
| img_scale=(1333, 800), | ||
| flip=False, | ||
| transforms=[ | ||
| dict(type='Resize', keep_ratio=True), | ||
| dict(type='RandomFlip'), | ||
| dict(type='Normalize', **img_norm_cfg), | ||
| dict(type='Pad', size_divisor=32), | ||
| dict(type='ImageToTensor', keys=['img']), | ||
| dict(type='Collect', keys=['img']), | ||
| ]) | ||
| ] | ||
| data = dict( | ||
| samples_per_gpu=2, | ||
| workers_per_gpu=2, | ||
| train=dict( | ||
| type=dataset_type, | ||
| ann_file=data_root + 'annotations/instances_train2017.json', | ||
| img_prefix=data_root + 'train2017/', | ||
| pipeline=train_pipeline), | ||
| val=dict( | ||
| type=dataset_type, | ||
| ann_file=data_root + 'annotations/instances_val2017.json', | ||
| img_prefix=data_root + 'val2017/', | ||
| pipeline=test_pipeline), | ||
| test=dict( | ||
| type=dataset_type, | ||
| ann_file=data_root + 'annotations/instances_val2017.json', | ||
| img_prefix=data_root + 'val2017/', | ||
| pipeline=test_pipeline)) | ||
| evaluation = dict(metric=['bbox', 'segm']) |
53 changes: 53 additions & 0 deletions
53
chanyub_det/configs/_base_/datasets/.ipynb_checkpoints/coco_instance_semantic-checkpoint.py
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,53 @@ | ||
| dataset_type = 'CocoDataset' | ||
| data_root = 'data/coco/' | ||
| 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='LoadAnnotations', with_bbox=True, with_mask=True, with_seg=True), | ||
| dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), | ||
| dict(type='RandomFlip', flip_ratio=0.5), | ||
| dict(type='Normalize', **img_norm_cfg), | ||
| dict(type='Pad', size_divisor=32), | ||
| dict(type='SegRescale', scale_factor=1 / 8), | ||
| dict(type='DefaultFormatBundle'), | ||
| dict( | ||
| type='Collect', | ||
| keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks', 'gt_semantic_seg']), | ||
| ] | ||
| test_pipeline = [ | ||
| dict(type='LoadImageFromFile'), | ||
| dict( | ||
| type='MultiScaleFlipAug', | ||
| img_scale=(1333, 800), | ||
| flip=False, | ||
| transforms=[ | ||
| dict(type='Resize', keep_ratio=True), | ||
| dict(type='RandomFlip', flip_ratio=0.5), | ||
| dict(type='Normalize', **img_norm_cfg), | ||
| dict(type='Pad', size_divisor=32), | ||
| dict(type='ImageToTensor', keys=['img']), | ||
| dict(type='Collect', keys=['img']), | ||
| ]) | ||
| ] | ||
| data = dict( | ||
| samples_per_gpu=2, | ||
| workers_per_gpu=2, | ||
| train=dict( | ||
| type=dataset_type, | ||
| ann_file=data_root + 'annotations/instances_train2017.json', | ||
| img_prefix=data_root + 'train2017/', | ||
| seg_prefix=data_root + 'stuffthingmaps/train2017/', | ||
| pipeline=train_pipeline), | ||
| val=dict( | ||
| type=dataset_type, | ||
| ann_file=data_root + 'annotations/instances_val2017.json', | ||
| img_prefix=data_root + 'val2017/', | ||
| pipeline=test_pipeline), | ||
| test=dict( | ||
| type=dataset_type, | ||
| ann_file=data_root + 'annotations/instances_val2017.json', | ||
| img_prefix=data_root + 'val2017/', | ||
| pipeline=test_pipeline)) | ||
| evaluation = dict(metric=['bbox', 'segm']) |
55 changes: 55 additions & 0 deletions
55
chanyub_det/configs/_base_/datasets/cityscapes_detection.py
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,55 @@ | ||
| dataset_type = 'CityscapesDataset' | ||
| data_root = 'data/cityscapes/' | ||
| 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='LoadAnnotations', with_bbox=True), | ||
| dict( | ||
| type='Resize', img_scale=[(2048, 800), (2048, 1024)], keep_ratio=True), | ||
| dict(type='RandomFlip', flip_ratio=0.5), | ||
| dict(type='Normalize', **img_norm_cfg), | ||
| dict(type='Pad', size_divisor=32), | ||
| dict(type='DefaultFormatBundle'), | ||
| dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), | ||
| ] | ||
| test_pipeline = [ | ||
| dict(type='LoadImageFromFile'), | ||
| dict( | ||
| type='MultiScaleFlipAug', | ||
| img_scale=(2048, 1024), | ||
| flip=False, | ||
| transforms=[ | ||
| dict(type='Resize', keep_ratio=True), | ||
| dict(type='RandomFlip'), | ||
| dict(type='Normalize', **img_norm_cfg), | ||
| dict(type='Pad', size_divisor=32), | ||
| dict(type='ImageToTensor', keys=['img']), | ||
| dict(type='Collect', keys=['img']), | ||
| ]) | ||
| ] | ||
| data = dict( | ||
| samples_per_gpu=1, | ||
| workers_per_gpu=2, | ||
| train=dict( | ||
| type='RepeatDataset', | ||
| times=8, | ||
| dataset=dict( | ||
| type=dataset_type, | ||
| ann_file=data_root + | ||
| 'annotations/instancesonly_filtered_gtFine_train.json', | ||
| img_prefix=data_root + 'leftImg8bit/train/', | ||
| pipeline=train_pipeline)), | ||
| val=dict( | ||
| type=dataset_type, | ||
| ann_file=data_root + | ||
| 'annotations/instancesonly_filtered_gtFine_val.json', | ||
| img_prefix=data_root + 'leftImg8bit/val/', | ||
| pipeline=test_pipeline), | ||
| test=dict( | ||
| type=dataset_type, | ||
| ann_file=data_root + | ||
| 'annotations/instancesonly_filtered_gtFine_test.json', | ||
| img_prefix=data_root + 'leftImg8bit/test/', | ||
| pipeline=test_pipeline)) | ||
| evaluation = dict(interval=1, metric='bbox') |
55 changes: 55 additions & 0 deletions
55
chanyub_det/configs/_base_/datasets/cityscapes_instance.py
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,55 @@ | ||
| dataset_type = 'CityscapesDataset' | ||
| data_root = 'data/cityscapes/' | ||
| 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='LoadAnnotations', with_bbox=True, with_mask=True), | ||
| dict( | ||
| type='Resize', img_scale=[(2048, 800), (2048, 1024)], keep_ratio=True), | ||
| dict(type='RandomFlip', flip_ratio=0.5), | ||
| dict(type='Normalize', **img_norm_cfg), | ||
| dict(type='Pad', size_divisor=32), | ||
| dict(type='DefaultFormatBundle'), | ||
| dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), | ||
| ] | ||
| test_pipeline = [ | ||
| dict(type='LoadImageFromFile'), | ||
| dict( | ||
| type='MultiScaleFlipAug', | ||
| img_scale=(2048, 1024), | ||
| flip=False, | ||
| transforms=[ | ||
| dict(type='Resize', keep_ratio=True), | ||
| dict(type='RandomFlip'), | ||
| dict(type='Normalize', **img_norm_cfg), | ||
| dict(type='Pad', size_divisor=32), | ||
| dict(type='ImageToTensor', keys=['img']), | ||
| dict(type='Collect', keys=['img']), | ||
| ]) | ||
| ] | ||
| data = dict( | ||
| samples_per_gpu=1, | ||
| workers_per_gpu=2, | ||
| train=dict( | ||
| type='RepeatDataset', | ||
| times=8, | ||
| dataset=dict( | ||
| type=dataset_type, | ||
| ann_file=data_root + | ||
| 'annotations/instancesonly_filtered_gtFine_train.json', | ||
| img_prefix=data_root + 'leftImg8bit/train/', | ||
| pipeline=train_pipeline)), | ||
| val=dict( | ||
| type=dataset_type, | ||
| ann_file=data_root + | ||
| 'annotations/instancesonly_filtered_gtFine_val.json', | ||
| img_prefix=data_root + 'leftImg8bit/val/', | ||
| pipeline=test_pipeline), | ||
| test=dict( | ||
| type=dataset_type, | ||
| ann_file=data_root + | ||
| 'annotations/instancesonly_filtered_gtFine_test.json', | ||
| img_prefix=data_root + 'leftImg8bit/test/', | ||
| pipeline=test_pipeline)) | ||
| evaluation = dict(metric=['bbox', 'segm']) |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,48 @@ | ||
| dataset_type = 'CocoDataset' | ||
| data_root = 'data/coco/' | ||
| 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='LoadAnnotations', with_bbox=True), | ||
| dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), | ||
| dict(type='RandomFlip', flip_ratio=0.5), | ||
| dict(type='Normalize', **img_norm_cfg), | ||
| dict(type='Pad', size_divisor=32), | ||
| dict(type='DefaultFormatBundle'), | ||
| dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), | ||
| ] | ||
| test_pipeline = [ | ||
| dict(type='LoadImageFromFile'), | ||
| dict( | ||
| type='MultiScaleFlipAug', | ||
| img_scale=(1333, 800), | ||
| flip=False, | ||
| transforms=[ | ||
| dict(type='Resize', keep_ratio=True), | ||
| dict(type='RandomFlip'), | ||
| dict(type='Normalize', **img_norm_cfg), | ||
| dict(type='Pad', size_divisor=32), | ||
| dict(type='ImageToTensor', keys=['img']), | ||
| dict(type='Collect', keys=['img']), | ||
| ]) | ||
| ] | ||
| data = dict( | ||
| samples_per_gpu=2, | ||
| workers_per_gpu=2, | ||
| train=dict( | ||
| type=dataset_type, | ||
| ann_file=data_root + 'annotations/instances_train2017.json', | ||
| img_prefix=data_root + 'train2017/', | ||
| pipeline=train_pipeline), | ||
| val=dict( | ||
| type=dataset_type, | ||
| ann_file=data_root + 'annotations/instances_val2017.json', | ||
| img_prefix=data_root + 'val2017/', | ||
| pipeline=test_pipeline), | ||
| test=dict( | ||
| type=dataset_type, | ||
| ann_file=data_root + 'annotations/instances_val2017.json', | ||
| img_prefix=data_root + 'val2017/', | ||
| pipeline=test_pipeline)) | ||
| evaluation = dict(interval=1, metric='bbox') |
Oops, something went wrong.