forked from open-mmlab/mmsegmentation
-
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.
[Feature] Support FastFCN (open-mmlab#885)
* FastFCN first commit * FastFCN first commit * Fixing lint error * Fixing lint error * use for loop on JPU * Use For Loop * Refactor FastFCN * FastFCN * FastFCN * temp * Uploading models & logs (4x4) * Fixing typos * fix typos * rename config * change README.md * use _delete_=True * change configs * change start_level to 0 * change start_level to 0 * jpu * add unittest for start_level!=0
- Loading branch information
1 parent
7db1cbb
commit a9d1295
Showing
15 changed files
with
466 additions
and
1 deletion.
There are no files selected for viewing
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
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
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 @@ | ||
| # model settings | ||
| norm_cfg = dict(type='SyncBN', requires_grad=True) | ||
| model = dict( | ||
| type='EncoderDecoder', | ||
| pretrained='open-mmlab://resnet50_v1c', | ||
| backbone=dict( | ||
| type='ResNetV1c', | ||
| depth=50, | ||
| num_stages=4, | ||
| dilations=(1, 1, 2, 4), | ||
| strides=(1, 2, 2, 2), | ||
| out_indices=(1, 2, 3), | ||
| norm_cfg=norm_cfg, | ||
| norm_eval=False, | ||
| style='pytorch', | ||
| contract_dilation=True), | ||
| neck=dict( | ||
| type='JPU', | ||
| in_channels=(512, 1024, 2048), | ||
| mid_channels=512, | ||
| start_level=0, | ||
| end_level=-1, | ||
| dilations=(1, 2, 4, 8), | ||
| align_corners=False, | ||
| norm_cfg=norm_cfg), | ||
| decode_head=dict( | ||
| type='PSPHead', | ||
| in_channels=2048, | ||
| in_index=2, | ||
| channels=512, | ||
| pool_scales=(1, 2, 3, 6), | ||
| dropout_ratio=0.1, | ||
| num_classes=19, | ||
| norm_cfg=norm_cfg, | ||
| align_corners=False, | ||
| loss_decode=dict( | ||
| type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), | ||
| auxiliary_head=dict( | ||
| type='FCNHead', | ||
| in_channels=1024, | ||
| in_index=1, | ||
| channels=256, | ||
| num_convs=1, | ||
| concat_input=False, | ||
| dropout_ratio=0.1, | ||
| num_classes=19, | ||
| norm_cfg=norm_cfg, | ||
| align_corners=False, | ||
| loss_decode=dict( | ||
| type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), | ||
| # model training and testing settings | ||
| train_cfg=dict(), | ||
| test_cfg=dict(mode='whole')) |
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 +1,41 @@ | ||
| # FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation | ||
|
|
||
| ## Introduction | ||
|
|
||
| <!-- [ALGORITHM] --> | ||
|
|
||
| <a href="https://github.com/wuhuikai/FastFCN">Official Repo</a> | ||
|
|
||
| <a href="https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/jpu.py#L12">Code Snippet</a> | ||
|
|
||
| <details> | ||
| <summary align="right"><a href="https://arxiv.org/abs/1903.11816">FastFCN (ArXiv'2019) </a></summary> | ||
|
|
||
| ```latex | ||
| @article{wu2019fastfcn, | ||
| title={Fastfcn: Rethinking dilated convolution in the backbone for semantic segmentation}, | ||
| author={Wu, Huikai and Zhang, Junge and Huang, Kaiqi and Liang, Kongming and Yu, Yizhou}, | ||
| journal={arXiv preprint arXiv:1903.11816}, | ||
| year={2019} | ||
| } | ||
| ``` | ||
|
|
||
| </details> | ||
|
|
||
| ## Results and models | ||
|
|
||
| ### Cityscapes | ||
|
|
||
| | Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | | ||
| | --------- | --------- | --------- | ------: | -------- | -------------- | ----: | ------------- | --------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | ||
| | DeepLabV3 + JPU | R-50-D32 | 512x1024 | 80000 | 5.67 | 2.64 | 79.12 | 80.58 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fastfcn/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes_20210928_053722-5d1a2648.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes_20210928_053722.log.json) | | ||
| | DeepLabV3 + JPU (4x4) | R-50-D32 | 512x1024 | 80000 | 9.79 | - | 79.52 | 80.91 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fastfcn/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes_20210924_214357-72220849.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes_20210924_214357.log.json) | | ||
| | PSPNet + JPU | R-50-D32 | 512x1024 | 80000 | 5.67 | 4.40 | 79.26 | 80.86 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fastfcn/fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn/fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes_20210928_053722-57749bed.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes_20210928_053722.log.json) | | ||
| | PSPNet + JPU (4x4) | R-50-D32 | 512x1024 | 80000 | 9.94 | - | 78.76 | 80.03 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fastfcn/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes_20210925_061841-77e87b0a.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes_20210925_061841.log.json) | | ||
| | EncNet + JPU | R-50-D32 | 512x1024 | 80000 | 8.15 | 4.77 | 77.97 |79.92 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fastfcn/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes_20210928_030036-78da5046.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes_20210928_030036.log.json) | | ||
| | EncNet + JPU (4x4)| R-50-D32 | 512x1024 | 80000 | 15.45 | - | 78.6 | 80.25 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fastfcn/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes_20210926_093217-e1eb6dbb.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes_20210926_093217.log.json) | | ||
|
|
||
| Note: | ||
|
|
||
| - `4x4` means 4 GPUs with 4 samples per GPU in training, default setting is 4 GPUs with 2 samples per GPU in training. | ||
| - Results of [DeepLabV3 (mIoU: 79.32)](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3), [PSPNet (mIoU: 78.55)](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet) and [ENCNet (mIoU: 77.94)](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/encnet) can be found in each original repository. |
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 +1,126 @@ | ||
| Collections: | ||
| - Name: fastfcn | ||
| Metadata: | ||
| Training Data: | ||
| - Cityscapes | ||
| Paper: | ||
| URL: https://arxiv.org/abs/1903.11816 | ||
| Title: 'FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation' | ||
| README: configs/fastfcn/README.md | ||
| Code: | ||
| URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/jpu.py#L12 | ||
| Version: v0.18.0 | ||
| Converted From: | ||
| Code: https://github.com/wuhuikai/FastFCN | ||
| Models: | ||
| - Name: fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes | ||
| In Collection: fastfcn | ||
| Metadata: | ||
| backbone: R-50-D32 | ||
| crop size: (512,1024) | ||
| lr schd: 80000 | ||
| inference time (ms/im): | ||
| - value: 378.79 | ||
| hardware: V100 | ||
| backend: PyTorch | ||
| batch size: 1 | ||
| mode: FP32 | ||
| resolution: (512,1024) | ||
| memory (GB): 5.67 | ||
| Results: | ||
| - Task: Semantic Segmentation | ||
| Dataset: Cityscapes | ||
| Metrics: | ||
| mIoU: 79.12 | ||
| mIoU(ms+flip): 80.58 | ||
| Config: configs/fastfcn/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes.py | ||
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes_20210928_053722-5d1a2648.pth | ||
| - Name: fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes | ||
| In Collection: fastfcn | ||
| Metadata: | ||
| backbone: R-50-D32 | ||
| crop size: (512,1024) | ||
| lr schd: 80000 | ||
| memory (GB): 9.79 | ||
| Results: | ||
| - Task: Semantic Segmentation | ||
| Dataset: Cityscapes | ||
| Metrics: | ||
| mIoU: 79.52 | ||
| mIoU(ms+flip): 80.91 | ||
| Config: configs/fastfcn/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes.py | ||
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes_20210924_214357-72220849.pth | ||
| - Name: fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes | ||
| In Collection: fastfcn | ||
| Metadata: | ||
| backbone: R-50-D32 | ||
| crop size: (512,1024) | ||
| lr schd: 80000 | ||
| inference time (ms/im): | ||
| - value: 227.27 | ||
| hardware: V100 | ||
| backend: PyTorch | ||
| batch size: 1 | ||
| mode: FP32 | ||
| resolution: (512,1024) | ||
| memory (GB): 5.67 | ||
| Results: | ||
| - Task: Semantic Segmentation | ||
| Dataset: Cityscapes | ||
| Metrics: | ||
| mIoU: 79.26 | ||
| mIoU(ms+flip): 80.86 | ||
| Config: configs/fastfcn/fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes.py | ||
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn/fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes_20210928_053722-57749bed.pth | ||
| - Name: fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes | ||
| In Collection: fastfcn | ||
| Metadata: | ||
| backbone: R-50-D32 | ||
| crop size: (512,1024) | ||
| lr schd: 80000 | ||
| memory (GB): 9.94 | ||
| Results: | ||
| - Task: Semantic Segmentation | ||
| Dataset: Cityscapes | ||
| Metrics: | ||
| mIoU: 78.76 | ||
| mIoU(ms+flip): 80.03 | ||
| Config: configs/fastfcn/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes.py | ||
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes_20210925_061841-77e87b0a.pth | ||
| - Name: fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes | ||
| In Collection: fastfcn | ||
| Metadata: | ||
| backbone: R-50-D32 | ||
| crop size: (512,1024) | ||
| lr schd: 80000 | ||
| inference time (ms/im): | ||
| - value: 209.64 | ||
| hardware: V100 | ||
| backend: PyTorch | ||
| batch size: 1 | ||
| mode: FP32 | ||
| resolution: (512,1024) | ||
| memory (GB): 8.15 | ||
| Results: | ||
| - Task: Semantic Segmentation | ||
| Dataset: Cityscapes | ||
| Metrics: | ||
| mIoU: 77.97 | ||
| mIoU(ms+flip): 79.92 | ||
| Config: configs/fastfcn/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes.py | ||
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes_20210928_030036-78da5046.pth | ||
| - Name: fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes | ||
| In Collection: fastfcn | ||
| Metadata: | ||
| backbone: R-50-D32 | ||
| crop size: (512,1024) | ||
| lr schd: 80000 | ||
| memory (GB): 15.45 | ||
| Results: | ||
| - Task: Semantic Segmentation | ||
| Dataset: Cityscapes | ||
| Metrics: | ||
| mIoU: 78.6 | ||
| mIoU(ms+flip): 80.25 | ||
| Config: configs/fastfcn/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes.py | ||
| Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes_20210926_093217-e1eb6dbb.pth |
6 changes: 6 additions & 0 deletions
6
configs/fastfcn/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes.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 +1,6 @@ | ||
| # model settings | ||
| _base_ = './fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes.py' | ||
| data = dict( | ||
| samples_per_gpu=4, | ||
| workers_per_gpu=4, | ||
| ) |
20 changes: 20 additions & 0 deletions
20
configs/fastfcn/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes.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 +1,20 @@ | ||
| # model settings | ||
| _base_ = './fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes.py' | ||
| norm_cfg = dict(type='SyncBN', requires_grad=True) | ||
| model = dict( | ||
| decode_head=dict( | ||
| _delete_=True, | ||
| type='ASPPHead', | ||
| in_channels=2048, | ||
| in_index=2, | ||
| channels=512, | ||
| dilations=(1, 12, 24, 36), | ||
| dropout_ratio=0.1, | ||
| num_classes=19, | ||
| norm_cfg=norm_cfg, | ||
| align_corners=False, | ||
| loss_decode=dict( | ||
| type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), | ||
| # model training and testing settings | ||
| train_cfg=dict(), | ||
| test_cfg=dict(mode='whole')) |
6 changes: 6 additions & 0 deletions
6
configs/fastfcn/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes.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 +1,6 @@ | ||
| # model settings | ||
| _base_ = './fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes.py' | ||
| data = dict( | ||
| samples_per_gpu=4, | ||
| workers_per_gpu=4, | ||
| ) |
24 changes: 24 additions & 0 deletions
24
configs/fastfcn/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes.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 +1,24 @@ | ||
| # model settings | ||
| _base_ = './fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes.py' | ||
| norm_cfg = dict(type='SyncBN', requires_grad=True) | ||
| model = dict( | ||
| decode_head=dict( | ||
| _delete_=True, | ||
| type='EncHead', | ||
| in_channels=[512, 1024, 2048], | ||
| in_index=(0, 1, 2), | ||
| channels=512, | ||
| num_codes=32, | ||
| use_se_loss=True, | ||
| add_lateral=False, | ||
| dropout_ratio=0.1, | ||
| num_classes=19, | ||
| norm_cfg=norm_cfg, | ||
| align_corners=False, | ||
| loss_decode=dict( | ||
| type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), | ||
| loss_se_decode=dict( | ||
| type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.2)), | ||
| # model training and testing settings | ||
| train_cfg=dict(), | ||
| test_cfg=dict(mode='whole')) |
9 changes: 9 additions & 0 deletions
9
configs/fastfcn/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes.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 +1,9 @@ | ||
| _base_ = [ | ||
| '../_base_/models/fastfcn_r50-d32_jpu_psp.py', | ||
| '../_base_/datasets/cityscapes.py', '../_base_/default_runtime.py', | ||
| '../_base_/schedules/schedule_80k.py' | ||
| ] | ||
| data = dict( | ||
| samples_per_gpu=4, | ||
| workers_per_gpu=4, | ||
| ) |
5 changes: 5 additions & 0 deletions
5
configs/fastfcn/fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes.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 +1,5 @@ | ||
| _base_ = [ | ||
| '../_base_/models/fastfcn_r50-d32_jpu_psp.py', | ||
| '../_base_/datasets/cityscapes.py', '../_base_/default_runtime.py', | ||
| '../_base_/schedules/schedule_80k.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 |
|---|---|---|
| @@ -1,7 +1,8 @@ | ||
| # Copyright (c) OpenMMLab. All rights reserved. | ||
| from .fpn import FPN | ||
| from .ic_neck import ICNeck | ||
| from .jpu import JPU | ||
| from .mla_neck import MLANeck | ||
| from .multilevel_neck import MultiLevelNeck | ||
|
|
||
| __all__ = ['FPN', 'MultiLevelNeck', 'MLANeck', 'ICNeck'] | ||
| __all__ = ['FPN', 'MultiLevelNeck', 'MLANeck', 'ICNeck', 'JPU'] |
Oops, something went wrong.