diff --git a/configs/distill/cwd/README.md b/configs/distill/cwd/README.md
index 9033221ce..9328790cd 100644
--- a/configs/distill/cwd/README.md
+++ b/configs/distill/cwd/README.md
@@ -15,12 +15,12 @@ Knowledge distillation (KD) has been proven to be a simple and effective tool fo
### Segmentation
|Location|Dataset|Teacher|Student|mIoU|mIoU(T)|mIou(S)|Config | Download |
:--------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:------:|:---------|
-| logits |cityscapes|[pspnet_r101](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r101-d8_512x1024_80k_cityscapes.py)|[pspnet_r18](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r18-d8_512x1024_80k_cityscapes.py)| 75.54 | 79.76 | 74.87 |[config]()|[teacher](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_80k_cityscapes/pspnet_r101-d8_512x1024_80k_cityscapes_20200606_112211-e1e1100f.pth) |[model](https://openmmlab-share.oss-cn-hangzhou.aliyuncs.com/mmrazor/v0.1/distill/cwd/cwd_cls_head_pspnet_r101_d8_pspnet_r18_d8_512x1024_cityscapes_80k/cwd_cls_head_pspnet_r101_d8_pspnet_r18_d8_512x1024_cityscapes_80k_mIoU-75.54_20211222-3a26ee1c.pth?versionId=CAEQHxiBgMCPxIKJ7xciIGU1N2JhYzgzYWE0YTRhYmRiZjVmMTA3MTA3NDk1ZWNl) | [log](https://openmmlab-share.oss-cn-hangzhou.aliyuncs.com/mmrazor/v0.1/distill/cwd/cwd_cls_head_pspnet_r101_d8_pspnet_r18_d8_512x1024_cityscapes_80k/cwd_cls_head_pspnet_r101_d8_pspnet_r18_d8_512x1024_cityscapes_80k_20211212_205711.log.json?versionId=CAEQHxiBgMDZ_oOJ7xciIDJjYzIxYTYyODYzMzQzNDk5Mjg1NTIwMWFkODliMGFk)|
+| logits |cityscapes|[pspnet_r101](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r101-d8_512x1024_80k_cityscapes.py)|[pspnet_r18](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet/pspnet_r18-d8_512x1024_80k_cityscapes.py)| 75.54 | 79.76 | 74.87 |[config]()|[teacher](https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_80k_cityscapes/pspnet_r101-d8_512x1024_80k_cityscapes_20200606_112211-e1e1100f.pth) |[model](https://download.openmmlab.com/mmrazor/v0.1/distill/cwd/cwd_cls_head_pspnet_r101_d8_pspnet_r18_d8_512x1024_cityscapes_80k/cwd_cls_head_pspnet_r101_d8_pspnet_r18_d8_512x1024_cityscapes_80k_mIoU-75.54_20211222-3a26ee1c.pth) | [log](https://download.openmmlab.com/mmrazor/v0.1/distill/cwd/cwd_cls_head_pspnet_r101_d8_pspnet_r18_d8_512x1024_cityscapes_80k/cwd_cls_head_pspnet_r101_d8_pspnet_r18_d8_512x1024_cityscapes_80k_20211212_205711.log.json?)|
### Detection
|Location|Dataset|Teacher|Student|mAP|mAP(T)|mAP(S)|Config | Download |
:--------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:------:|:---------|
-| cls head |COCO|[gfl_r101_2x](https://github.com/open-mmlab/mmdetection/tree/master/configs/gfl/gfl_r101_fpn_mstrain_2x_coco.py)|[gfl_r50_1x](https://github.com/open-mmlab/mmdetection/tree/master/configs/gfl/gfl_r50_fpn_1x_coco.py)| 41.9 | 44.7 | 40.2 |[config]()|[teacher](https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_mstrain_2x_coco/gfl_r101_fpn_mstrain_2x_coco_20200629_200126-dd12f847.pth) |[model](https://openmmlab-share.oss-cn-hangzhou.aliyuncs.com/mmrazor/v0.1/distill/cwd/cwd_cls_head_gfl_r101_fpn_gfl_r50_fpn_1x_coco/cwd_cls_head_gfl_r101_fpn_gfl_r50_fpn_1x_coco_20211222-655dff39.pth?versionId=CAEQHxiBgMD7.uuI7xciIDY1MDRjYzlkN2ExOTRiY2NhNmU4NGJlMmExNjA2YzMy) | [log](https://openmmlab-share.oss-cn-hangzhou.aliyuncs.com/mmrazor/v0.1/distill/cwd/cwd_cls_head_gfl_r101_fpn_gfl_r50_fpn_1x_coco/cwd_cls_head_gfl_r101_fpn_gfl_r50_fpn_1x_coco_20211212_205444.log.json?versionId=CAEQHxiBgID.o_WI7xciIDgyZjRjYTU4Y2ZjNjRjOGU5MTBlMTQ3ZjEyMTE4OTJl)|
+| cls head |COCO|[gfl_r101_2x](https://github.com/open-mmlab/mmdetection/tree/master/configs/gfl/gfl_r101_fpn_mstrain_2x_coco.py)|[gfl_r50_1x](https://github.com/open-mmlab/mmdetection/tree/master/configs/gfl/gfl_r50_fpn_1x_coco.py)| 41.9 | 44.7 | 40.2 |[config]()|[teacher](https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_mstrain_2x_coco/gfl_r101_fpn_mstrain_2x_coco_20200629_200126-dd12f847.pth) |[model](https://download.openmmlab.com/mmrazor/v0.1/distill/cwd/cwd_cls_head_gfl_r101_fpn_gfl_r50_fpn_1x_coco/cwd_cls_head_gfl_r101_fpn_gfl_r50_fpn_1x_coco_20211222-655dff39.pth) | [log](https://download.openmmlab.com/mmrazor/v0.1/distill/cwd/cwd_cls_head_gfl_r101_fpn_gfl_r50_fpn_1x_coco/cwd_cls_head_gfl_r101_fpn_gfl_r50_fpn_1x_coco_20211212_205444.log.json)|
## Citation
diff --git a/configs/distill/cwd/cwd_cls_head_gfl_r101_fpn_gfl_r50_fpn_1x_coco.py b/configs/distill/cwd/cwd_cls_head_gfl_r101_fpn_gfl_r50_fpn_1x_coco.py
index 272fd3c91..e55b733cf 100644
--- a/configs/distill/cwd/cwd_cls_head_gfl_r101_fpn_gfl_r50_fpn_1x_coco.py
+++ b/configs/distill/cwd/cwd_cls_head_gfl_r101_fpn_gfl_r50_fpn_1x_coco.py
@@ -57,8 +57,11 @@
nms=dict(type='nms', iou_threshold=0.6),
max_per_img=100))
+checkpoint = 'https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_mstrain_2x_coco/gfl_r101_fpn_mstrain_2x_coco_20200629_200126-dd12f847.pth' # noqa: E501
+
teacher = dict(
type='mmdet.GFL',
+ init_cfg=dict(type='Pretrained', checkpoint=checkpoint),
backbone=dict(
type='ResNet',
depth=101,
diff --git a/configs/distill/cwd/cwd_cls_head_pspnet_r101_d8_pspnet_r18_d8_512x1024_cityscapes_80k.py b/configs/distill/cwd/cwd_cls_head_pspnet_r101_d8_pspnet_r18_d8_512x1024_cityscapes_80k.py
index 0ff436c83..c3fd00b12 100644
--- a/configs/distill/cwd/cwd_cls_head_pspnet_r101_d8_pspnet_r18_d8_512x1024_cityscapes_80k.py
+++ b/configs/distill/cwd/cwd_cls_head_pspnet_r101_d8_pspnet_r18_d8_512x1024_cityscapes_80k.py
@@ -50,9 +50,12 @@
train_cfg=dict(),
test_cfg=dict(mode='whole'))
+checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_80k_cityscapes/pspnet_r101-d8_512x1024_80k_cityscapes_20200606_112211-e1e1100f.pth' # noqa: E501
+
# pspnet r101
teacher = dict(
type='mmseg.EncoderDecoder',
+ init_cfg=dict(type='Pretrained', checkpoint=checkpoint),
backbone=dict(
type='ResNetV1c',
depth=101,
diff --git a/configs/distill/wsld/README.md b/configs/distill/wsld/README.md
index 10f259b3b..8c9e78100 100644
--- a/configs/distill/wsld/README.md
+++ b/configs/distill/wsld/README.md
@@ -27,7 +27,7 @@ effectiveness of our method.
### Classification
|Location|Dataset|Teacher|Student|Acc|Acc(T)|Acc(S)|Config | Download |
:--------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:------:|:---------|
-| cls head |ImageNet|[resnet34](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet34_8xb32_in1k.py)|[resnet18](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet18_8xb32_in1k.py)| 71.54 | 73.62 | 69.90 |[config](./wsld_cls_head_resnet34_resnet18_8xb32_in1k.py)|[teacher](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth) |[model](https://openmmlab-share.oss-cn-hangzhou.aliyuncs.com/mmrazor/v0.1/distill/wsld/wsld_cls_head_resnet34_resnet18_8xb32_in1k/wsld_cls_head_resnet34_resnet18_8xb32_in1k_acc-71.54_20211222-91f28cf6.pth?versionId=CAEQHxiBgMC6memK7xciIGMzMDFlYTA4YzhlYTRiMTNiZWU0YTVhY2I5NjVkMjY2) | [log](https://openmmlab-share.oss-cn-hangzhou.aliyuncs.com/mmrazor/v0.1/distill/wsld/wsld_cls_head_resnet34_resnet18_8xb32_in1k/wsld_cls_head_resnet34_resnet18_8xb32_in1k_20211221_181516.log.json?versionId=CAEQHxiBgIDLmemK7xciIGNkM2FiN2Y4N2E5YjRhNDE4NDVlNmExNDczZDIxN2E5)|
+| cls head |ImageNet|[resnet34](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet34_8xb32_in1k.py)|[resnet18](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet18_8xb32_in1k.py)| 71.54 | 73.62 | 69.90 |[config](./wsld_cls_head_resnet34_resnet18_8xb32_in1k.py)|[teacher](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth) |[model](https://download.openmmlab.com/mmrazor/v0.1/distill/wsld/wsld_cls_head_resnet34_resnet18_8xb32_in1k/wsld_cls_head_resnet34_resnet18_8xb32_in1k_acc-71.54_20211222-91f28cf6.pth) | [log](https://download.openmmlab.com/mmrazor/v0.1/distill/wsld/wsld_cls_head_resnet34_resnet18_8xb32_in1k/wsld_cls_head_resnet34_resnet18_8xb32_in1k_20211221_181516.log.json)|
diff --git a/configs/distill/wsld/wsld_cls_head_resnet34_resnet18_8xb32_in1k.py b/configs/distill/wsld/wsld_cls_head_resnet34_resnet18_8xb32_in1k.py
index be4ec7246..06ac8c328 100644
--- a/configs/distill/wsld/wsld_cls_head_resnet34_resnet18_8xb32_in1k.py
+++ b/configs/distill/wsld/wsld_cls_head_resnet34_resnet18_8xb32_in1k.py
@@ -22,9 +22,12 @@
topk=(1, 5),
))
+checkpoint = 'https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth' # noqa: E501
+
# teacher settings
teacher = dict(
type='mmcls.ImageClassifier',
+ init_cfg=dict(type='Pretrained', checkpoint=checkpoint),
backbone=dict(
type='ResNet',
depth=34,
diff --git a/configs/nas/darts/README.md b/configs/nas/darts/README.md
index 3eb4d38d3..bf28d670f 100644
--- a/configs/nas/darts/README.md
+++ b/configs/nas/darts/README.md
@@ -22,8 +22,8 @@ Dataset|Unroll|Config|Download|
Dataset|Params(M)|Flops(G)|Top-1 Acc|Top-5 Acc|Subnet|Config|Download|Remarks|
|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:------:|:------:|:------:|
-|Cifar10|3.42 | 0.48 | 97.32 |99.94|[mutable](https://openmmlab-share.oss-cn-hangzhou.aliyuncs.com/mmrazor/v0.1/nas/darts/darts_subnetnet_1xb96_cifar10/darts_subnetnet_1xb96_cifar10_acc-97.32_20211222-e5727921_mutable_cfg.yaml?versionId=CAEQHxiBgMDn0ICL7xciIDAwNzUzZTU3ZjE4OTQ0MDg5YmZiMmYzYzExZTQ3YTRm)|[config](./darts_subnetnet_1xb96_cifar10.py)| [model](https://openmmlab-share.oss-cn-hangzhou.aliyuncs.com/mmrazor/v0.1/nas/darts/darts_subnetnet_1xb96_cifar10/darts_subnetnet_1xb96_cifar10_acc-97.32_20211222-e5727921.pth?versionId=CAEQHxiBgID20ICL7xciIDllOWZmNTliMzkwNzQ5YzdhODk2MzY1MWEyOTQ1Yjlk) | [log](https://openmmlab-share.oss-cn-hangzhou.aliyuncs.com/mmrazor/v0.1/nas/darts/darts_subnetnet_1xb96_cifar10/darts_subnetnet_1xb96_cifar10_20211222-e5727921.log.json?versionId=CAEQHxiBgMDz0ICL7xciIGRhMjk0NDU0OTVhZjQwMDg4N2ZkMDAzZDM1ZWU4N2Ri)|MMRazor searched
-|Cifar10|3.83 | 0.55 | 97.27 |99.98|[mutable](https://openmmlab-share.oss-cn-hangzhou.aliyuncs.com/mmrazor/v0.1/nas/darts/darts_subnetnet_1xb96_cifar10/darts_subnetnet_1xb96_cifar10_acc-97.27_20211222-17e42600_mutable_cfg.yaml?versionId=CAEQHxiBgICrnpmL7xciIGFmYzUxYjdmYWM1YzQ3N2I5NGU1MDE2ZjIxYmJhY2E0)|[config](./darts_subnetnet_1xb96_cifar10.py)| [model](https://openmmlab-share.oss-cn-hangzhou.aliyuncs.com/mmrazor/v0.1/nas/darts/darts_subnetnet_1xb96_cifar10/darts_subnetnet_1xb96_cifar10_acc-97.27_20211222-17e42600.pth?versionId=CAEQHxiBgIDQnpmL7xciIGQzOTRkMTViMDgzNzQ2MWI5MmUyNzIxZDk4OTUzZDgz) | [log](https://openmmlab-share.oss-cn-hangzhou.aliyuncs.com/mmrazor/v0.1/nas/darts/darts_subnetnet_1xb96_cifar10/darts_subnetnet_1xb96_cifar10_20211222-17e42600.log.json?versionId=CAEQHxiBgMDPnpmL7xciIDViYTVlYTIyYmQ2OTQ1ZDZhNTNhMjVkODA2NDRlMTI1)|official
+|Cifar10|3.42 | 0.48 | 97.32 |99.94|[mutable](https://download.openmmlab.com/mmrazor/v0.1/nas/darts/darts_subnetnet_1xb96_cifar10/darts_subnetnet_1xb96_cifar10_acc-97.32_20211222-e5727921_mutable_cfg.yaml)|[config](./darts_subnetnet_1xb96_cifar10.py)| [model](https://download.openmmlab.com/mmrazor/v0.1/nas/darts/darts_subnetnet_1xb96_cifar10/darts_subnetnet_1xb96_cifar10_acc-97.32_20211222-e5727921.pth) | [log](https://download.openmmlab.com/mmrazor/v0.1/nas/darts/darts_subnetnet_1xb96_cifar10/darts_subnetnet_1xb96_cifar10_20211222-e5727921.log.json)|MMRazor searched
+|Cifar10|3.83 | 0.55 | 97.27 |99.98|[mutable](https://download.openmmlab.com/mmrazor/v0.1/nas/darts/darts_subnetnet_1xb96_cifar10/darts_subnetnet_1xb96_cifar10_acc-97.27_20211222-17e42600_mutable_cfg.yaml)|[config](./darts_subnetnet_1xb96_cifar10.py)| [model](https://download.openmmlab.com/mmrazor/v0.1/nas/darts/darts_subnetnet_1xb96_cifar10/darts_subnetnet_1xb96_cifar10_acc-97.27_20211222-17e42600.pth) | [log](https://download.openmmlab.com/mmrazor/v0.1/nas/darts/darts_subnetnet_1xb96_cifar10/darts_subnetnet_1xb96_cifar10_20211222-17e42600.log.json)|official
## Citation
diff --git a/configs/nas/darts/darts_subnetnet_1xb96_cifar10.py b/configs/nas/darts/darts_subnet_1xb96_cifar10.py
similarity index 90%
rename from configs/nas/darts/darts_subnetnet_1xb96_cifar10.py
rename to configs/nas/darts/darts_subnet_1xb96_cifar10.py
index 8134ad59f..1e979578f 100644
--- a/configs/nas/darts/darts_subnetnet_1xb96_cifar10.py
+++ b/configs/nas/darts/darts_subnet_1xb96_cifar10.py
@@ -28,6 +28,9 @@
cal_acc=True),
)
+# FIXME: you may replace this with the mutable_cfg searched by yourself
+mutable_cfg = 'https://download.openmmlab.com/mmrazor/v0.1/nas/darts/darts_subnetnet_1xb96_cifar10/darts_subnetnet_1xb96_cifar10_acc-97.32_20211222-e5727921_mutable_cfg.yaml' # noqa: E501
+
algorithm = dict(
type='Darts',
architecture=dict(type='MMClsArchitecture', model=model),
@@ -69,7 +72,8 @@
)),
),
retraining=True,
- unroll=False)
+ unroll=False,
+ mutable_cfg=mutable_cfg)
data = dict(workers_per_gpu=8)
diff --git a/configs/nas/detnas/README.md b/configs/nas/detnas/README.md
index 60fb57a91..4a66866c2 100644
--- a/configs/nas/detnas/README.md
+++ b/configs/nas/detnas/README.md
@@ -38,7 +38,7 @@ python ./tools/mmdet/search_mmdet.py \
python ./tools/mmcls/train_mmcls.py \
configs/nas/detnas/detnas_subnet_shufflenetv2_8xb128_in1k.py \
--work-dir $WORK_DIR \
- --cfg-options algorithm.mutable_cfg=$STEP3_SUBNET_YAML
+ --cfg-options algorithm.mutable_cfg=$STEP3_SUBNET_YAML # or modify the config directly
```
### Step 5: Subnet fine-tuning on COCO
@@ -46,13 +46,13 @@ python ./tools/mmcls/train_mmcls.py \
python ./tools/mmdet/train_mmdet.py \
configs/nas/detnas/detnas_subnet_frcnn_shufflenetv2_fpn_1x_coco.py \
--work-dir $WORK_DIR \
- --cfg-options algorithm.mutable_cfg=$STEP3_SUBNET_YAML load_from=$STEP4_CKPT
+ --cfg-options algorithm.mutable_cfg=$STEP3_SUBNET_YAML load_from=$STEP4_CKPT # or modify the config directly
```
## Results and models
|Dataset| Supernet | Subnet |Params(M)| Flops(G) | mAP | Config | Download | Remarks|
|:---------------:|:---------------:|:-----------:|:-----------:|:-----------:|:--------------:|:------:|:--------:|:--------:|
-|COCO| FRCNN-ShuffleNetV2| [mutable](https://openmmlab-share.oss-cn-hangzhou.aliyuncs.com/mmrazor/v0.1/nas/detnas/detnas_subnet_frcnn_shufflenetv2_fpn_1x_coco/detnas_subnet_frcnn_shufflenetv2_fpn_1x_coco_bbox_backbone_flops-0.34M_mAP-37.5_20211222-67fea61f_mutable_cfg.yaml?versionId=CAEQHxiBgMDU3taI7xciIDUzMmM4MTg4YTgwZDRhYjY4NjA3M2NkZDA0NWExNmY1) | 3.35(backbone)|0.34(backbone) | 37.5 |[config](./detnas_subnet_frcnn_shufflenetv2_fpn_1x_coco.py)|[pretrain](https://openmmlab-share.oss-cn-hangzhou.aliyuncs.com/mmrazor/v0.1/nas/detnas/detnas_subnet_frcnn_shufflenetv2_fpn_1x_coco/detnas_subnet_shufflenetv2_8xb128_in1k_acc-74.08_20211223-92e9b66a.pth?versionId=CAEQHxiBgICBxuuL7xciIGEyNzZkZmRmZmM5NzRjNDViOTNjOWZkNjk0OWYyYTdm) |[model](https://openmmlab-share.oss-cn-hangzhou.aliyuncs.com/mmrazor/v0.1/nas/detnas/detnas_subnet_frcnn_shufflenetv2_fpn_1x_coco/detnas_subnet_frcnn_shufflenetv2_fpn_1x_coco_bbox_backbone_flops-0.34M_mAP-37.5_20211222-67fea61f.pth?versionId=CAEQHxiBgIDd3taI7xciIDIxYmUzMDE4ZmZmMjQ4ZGNiNzI1YjcxOGM4OGM5NDZl) | [log](https://openmmlab-share.oss-cn-hangzhou.aliyuncs.com/mmrazor/v0.1/nas/detnas/detnas_subnet_frcnn_shufflenetv2_fpn_1x_coco/detnas_subnet_frcnn_shufflenetv2_fpn_1x_coco_bbox_backbone_flops-0.34M_mAP-37.5_20211222-67fea61f.log.json?versionId=CAEQHxiBgMCSq9mM7xciIDViODRmMDE1Yjk1MDQwMTViMDBmYzZlMjg0OTJjYTlh)|MMRazor searched
+|COCO| FRCNN-ShuffleNetV2| [mutable](https://download.openmmlab.com/mmrazor/v0.1/nas/detnas/detnas_subnet_frcnn_shufflenetv2_fpn_1x_coco/detnas_subnet_frcnn_shufflenetv2_fpn_1x_coco_bbox_backbone_flops-0.34M_mAP-37.5_20211222-67fea61f_mutable_cfg.yaml) | 3.35(backbone)|0.34(backbone) | 37.5 |[config](./detnas_subnet_frcnn_shufflenetv2_fpn_1x_coco.py)|[pretrain](https://download.openmmlab.com/mmrazor/v0.1/nas/detnas/detnas_subnet_frcnn_shufflenetv2_fpn_1x_coco/detnas_subnet_shufflenetv2_8xb128_in1k_acc-74.08_20211223-92e9b66a.pth) |[model](https://download.openmmlab.com/mmrazor/v0.1/nas/detnas/detnas_subnet_frcnn_shufflenetv2_fpn_1x_coco/detnas_subnet_frcnn_shufflenetv2_fpn_1x_coco_bbox_backbone_flops-0.34M_mAP-37.5_20211222-67fea61f.pth) | [log](https://download.openmmlab.com/mmrazor/v0.1/nas/detnas/detnas_subnet_frcnn_shufflenetv2_fpn_1x_coco/detnas_subnet_frcnn_shufflenetv2_fpn_1x_coco_bbox_backbone_flops-0.34M_mAP-37.5_20211222-67fea61f.log.json)|MMRazor searched
**Note**:
diff --git a/configs/nas/detnas/detnas_subnet_frcnn_shufflenetv2_fpn_1x_coco.py b/configs/nas/detnas/detnas_subnet_frcnn_shufflenetv2_fpn_1x_coco.py
index be40b5535..dc929cc88 100644
--- a/configs/nas/detnas/detnas_subnet_frcnn_shufflenetv2_fpn_1x_coco.py
+++ b/configs/nas/detnas/detnas_subnet_frcnn_shufflenetv2_fpn_1x_coco.py
@@ -1,3 +1,6 @@
_base_ = ['./detnas_supernet_frcnn_shufflenetv2_fpn_1x_coco.py']
-algorithm = dict(retraining=True)
+# FIXME: you may replace this with the mutable_cfg searched by yourself
+mutable_cfg = 'https://download.openmmlab.com/mmrazor/v0.1/nas/detnas/detnas_subnet_frcnn_shufflenetv2_fpn_1x_coco/detnas_subnet_frcnn_shufflenetv2_fpn_1x_coco_bbox_backbone_flops-0.34M_mAP-37.5_20211222-67fea61f_mutable_cfg.yaml' # noqa: E501
+
+algorithm = dict(retraining=True, mutable_cfg=mutable_cfg)
diff --git a/configs/nas/detnas/detnas_subnet_shufflenetv2_8xb128_in1k.py b/configs/nas/detnas/detnas_subnet_shufflenetv2_8xb128_in1k.py
index 3e373ec08..9486cba6f 100644
--- a/configs/nas/detnas/detnas_subnet_shufflenetv2_8xb128_in1k.py
+++ b/configs/nas/detnas/detnas_subnet_shufflenetv2_8xb128_in1k.py
@@ -1,3 +1,8 @@
_base_ = [
'../spos/spos_subnet_shufflenetv2_8xb128_in1k.py',
]
+
+# FIXME: you may replace this with the mutable_cfg searched by yourself
+mutable_cfg = 'https://download.openmmlab.com/mmrazor/v0.1/nas/detnas/detnas_subnet_frcnn_shufflenetv2_fpn_1x_coco/detnas_subnet_frcnn_shufflenetv2_fpn_1x_coco_bbox_backbone_flops-0.34M_mAP-37.5_20211222-67fea61f_mutable_cfg.yaml' # noqa: E501
+
+algorithm = dict(mutable_cfg=mutable_cfg)
diff --git a/configs/nas/spos/README.md b/configs/nas/spos/README.md
index 68075cf88..a21272fdb 100644
--- a/configs/nas/spos/README.md
+++ b/configs/nas/spos/README.md
@@ -36,14 +36,14 @@ python ./tools/mmcls/search_mmcls.py \
python ./tools/mmcls/train_mmcls.py \
configs/nas/spos/spos_subnet_shufflenetv2_8xb128_in1k.py \
--work-dir $WORK_DIR \
- --cfg-options algorithm.mutable_cfg=$STEP2_SUBNET_YAML
+ --cfg-options algorithm.mutable_cfg=$STEP2_SUBNET_YAML # or modify the config directly
```
## Results and models
| Dataset | Supernet | Subnet | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | Remarks |
| :------: |:----------------------:| :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-------: | :------: | :-------: | :-------: | :----------------------------------------------: |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------:|
-| ImageNet | ShuffleNetV2 | [mutable](https://openmmlab-share.oss-cn-hangzhou.aliyuncs.com/mmrazor/v0.1/nas/spos/spos_shufflenetv2_subnet_8xb128_in1k/spos_shufflenetv2_subnet_8xb128_in1k_flops_0.33M_acc_73.87_20211222-454627be_mutable_cfg.yaml?versionId=CAEQHxiBgICw5b6I7xciIGY5MjVmNWFhY2U5MjQzN2M4NDViYzI2YWRmYWE1YzQx) | 3.35 | 0.33 | 73.87 | 91.6 | [config](./spos_subnet_shufflenetv2_8xb128_in1k.py) | [model](https://openmmlab-share.oss-cn-hangzhou.aliyuncs.com/mmrazor/v0.1/nas/spos/spos_shufflenetv2_subnet_8xb128_in1k/spos_shufflenetv2_subnet_8xb128_in1k_flops_0.33M_acc_73.87_20211222-1f0a0b4d.pth?versionId=CAEQHxiBgIDK5b6I7xciIDM1YjIwZjQxN2UyMDRjYjA5YTM5NTBlMGNhMTdkNjI2) | [log](https://openmmlab-share.oss-cn-hangzhou.aliyuncs.com/mmrazor/v0.1/nas/spos/spos_shufflenetv2_subnet_8xb128_in1k/spos_shufflenetv2_subnet_8xb128_in1k_flops_0.33M_acc_73.87_20211222-1f0a0b4d.log.json?versionId=CAEQHxiBgIDr9cuL7xciIDBmOTZiZGUyYjRiMDQ5NzhhZjY0NWUxYmUzNDlmNTg5) | MMRazor searched |
+| ImageNet | ShuffleNetV2 | [mutable](https://download.openmmlab.com/mmrazor/v0.1/nas/spos/spos_shufflenetv2_subnet_8xb128_in1k/spos_shufflenetv2_subnet_8xb128_in1k_flops_0.33M_acc_73.87_20211222-454627be_mutable_cfg.yaml) | 3.35 | 0.33 | 73.87 | 91.6 | [config](./spos_subnet_shufflenetv2_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmrazor/v0.1/nas/spos/spos_shufflenetv2_subnet_8xb128_in1k/spos_shufflenetv2_subnet_8xb128_in1k_flops_0.33M_acc_73.87_20211222-1f0a0b4d.pth) | [log](https://download.openmmlab.com/mmrazor/v0.1/nas/spos/spos_shufflenetv2_subnet_8xb128_in1k/spos_shufflenetv2_subnet_8xb128_in1k_flops_0.33M_acc_73.87_20211222-1f0a0b4d.log.json) | MMRazor searched |
| ImageNet | MobileNet-ProxylessGPU | [mutable](https://download.openmmlab.com/mmrazor/v0.1/nas/spos/spos_mobilenet_subnet/spos_angelnas_flops_0.49G_acc_75.98_20220307-54f4698f_mutable_cfg.yaml) | 5.94 | 0.49* | 75.98 | 92.77 | [config](./spos_mobilenet_for_check_ckpt_from_anglenas.py) | | [AngleNAS](https://github.com/megvii-model/AngleNAS) searched |
**Note**:
diff --git a/configs/nas/spos/spos_subnet_mobilenet_proxyless_gpu_8xb128_in1k.py b/configs/nas/spos/spos_subnet_mobilenet_proxyless_gpu_8xb128_in1k.py
index 198a9c053..54d5ff5fb 100644
--- a/configs/nas/spos/spos_subnet_mobilenet_proxyless_gpu_8xb128_in1k.py
+++ b/configs/nas/spos/spos_subnet_mobilenet_proxyless_gpu_8xb128_in1k.py
@@ -2,7 +2,10 @@
'./spos_supernet_mobilenet_proxyless_gpu_8xb128_in1k.py',
]
-algorithm = dict(retraining=True)
+# FIXME: you may replace this with the mutable_cfg searched by yourself
+mutable_cfg = 'https://download.openmmlab.com/mmrazor/v0.1/nas/spos/spos_mobilenet_subnet/spos_angelnas_flops_0.49G_acc_75.98_20220307-54f4698f_mutable_cfg.yaml' # noqa: E501
+
+algorithm = dict(retraining=True, mutable_cfg=mutable_cfg)
evaluation = dict(interval=10000, metric='accuracy')
checkpoint_config = dict(interval=30000)
diff --git a/configs/nas/spos/spos_subnet_shufflenetv2_8xb128_in1k.py b/configs/nas/spos/spos_subnet_shufflenetv2_8xb128_in1k.py
index e849579fe..110ee047b 100644
--- a/configs/nas/spos/spos_subnet_shufflenetv2_8xb128_in1k.py
+++ b/configs/nas/spos/spos_subnet_shufflenetv2_8xb128_in1k.py
@@ -2,7 +2,10 @@
'./spos_supernet_shufflenetv2_8xb128_in1k.py',
]
-algorithm = dict(retraining=True)
+# FIXME: you may replace this with the mutable_cfg searched by yourself
+mutable_cfg = 'https://download.openmmlab.com/mmrazor/v0.1/nas/spos/spos_shufflenetv2_subnet_8xb128_in1k/spos_shufflenetv2_subnet_8xb128_in1k_flops_0.33M_acc_73.87_20211222-454627be_mutable_cfg.yaml' # noqa: E501
+
+algorithm = dict(retraining=True, mutable_cfg=mutable_cfg)
runner = dict(max_iters=300000)
find_unused_parameters = False
diff --git a/configs/pruning/autoslim/README.md b/configs/pruning/autoslim/README.md
index 84d3b7e5f..fdd15d073 100644
--- a/configs/pruning/autoslim/README.md
+++ b/configs/pruning/autoslim/README.md
@@ -48,15 +48,15 @@ python ./tools/model_converters/split_checkpoint.py \
python ./tools/mmcls/test_mmcls.py \
configs/pruning/autoslim/autoslim_mbv2_subnet_8xb256_in1k.py \
your_splitted_checkpoint_path --metrics accuracy \
- --cfg-options algorithm.channel_cfg=configs/pruning/autoslim/AUTOSLIM_MBV2_530M_OFFICIAL.yaml
+ --cfg-options algorithm.channel_cfg=configs/pruning/autoslim/AUTOSLIM_MBV2_530M_OFFICIAL.yaml # or modify the config directly
## Results and models
### Subnet retrain
| Supernet | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | Subnet | Remark |
| :----------------- | :-------: | -------: | :-------: | :-------: | :----: | :------: | :-------------: | :----: |
-| MobileNet v2(x1.5) | 6.5 | 0.53 | 74.23 | 91.74 | [config](./autoslim_mbv2_subnet_8xb256_in1k.py) | [model](https://openmmlab-share.oss-cn-hangzhou.aliyuncs.com/mmrazor/v0.1/pruning/autoslim/autoslim_mbv2_subnet_8xb256_in1k/autoslim_mbv2_subnet_8xb256_in1k_flops-0.53M_acc-74.23_20211222-e5208bbd.pth?versionId=CAEQHxiBgICYsIaI7xciIDE1MGIxM2Q5NDk1NjRlOTFiMjgwOTRmYzJlMDBmZDY0) | [log](https://openmmlab-share.oss-cn-hangzhou.aliyuncs.com/mmrazor/v0.1/pruning/autoslim/autoslim_mbv2_subnet_8xb256_in1k/autoslim_mbv2_subnet_8xb256_in1kautoslim_mbv2_subnet_8xb256_in1k_paper_channel_cfg.log.json?versionId=CAEQHxiBgMCjj9SL7xciIDFmYmM4NTExZmIzNjRmNmQ4MmMyZWI4YzJmMmM2MDdl) | [channel](https://openmmlab-share.oss-cn-hangzhou.aliyuncs.com/mmrazor/v0.1/pruning/autoslim/autoslim_mbv2_subnet_8xb256_in1k/autoslim_mbv2_subnet_8xb256_in1k_flops-0.53M_acc-74.23_20211222-e5208bbd_channel_cfg.yaml?versionId=CAEQHxiBgMDwr4aI7xciIDQ2MmRhMDFhNGMyODQyYmU5ZTIyOTcxMmRlN2RmYjg2) | official channel cfg |
-| MobileNet v2(x1.5) | 5.77 | 0.32 | 72.73 | 90.83 | [config](./autoslim_mbv2_subnet_8xb256_in1k.py) | [model](https://openmmlab-share.oss-cn-hangzhou.aliyuncs.com/mmrazor/v0.1/pruning/autoslim/autoslim_mbv2_subnet_8xb256_in1k/autoslim_mbv2_subnet_8xb256_in1k_flops-0.32M_acc-72.73_20211222-b5b0b33c.pth?versionId=CAEQHxiBgMCasIaI7xciIDEzN2FkZjZkNWMwYjRiOTg5NTY0MzY0ODk5ODE2N2Yz) | [log](https://openmmlab-share.oss-cn-hangzhou.aliyuncs.com/mmrazor/v0.1/pruning/autoslim/autoslim_mbv2_subnet_8xb256_in1k/autoslim_mbv2_subnet_8xb256_in1kautoslim_mbv2_subnet_8xb256_in1k_paper_channel_cfg.log.json?versionId=CAEQHxiBgMCjj9SL7xciIDFmYmM4NTExZmIzNjRmNmQ4MmMyZWI4YzJmMmM2MDdl) | [channel](https://openmmlab-share.oss-cn-hangzhou.aliyuncs.com/mmrazor/v0.1/pruning/autoslim/autoslim_mbv2_subnet_8xb256_in1k/autoslim_mbv2_subnet_8xb256_in1k_flops-0.32M_acc-72.73_20211222-b5b0b33c_channel_cfg.yaml?versionId=CAEQHxiCgMDwr4aI7xciIDhjMmUzZjlmZTJjODQzMDRhMmQxMzkyM2MwOTZhNjE3) | official channel cfg |
-| MobileNet v2(x1.5) | 4.13 |0.22 | 71.39 | 90.08 | [config](./autoslim_mbv2_subnet_8xb256_in1k.py) | [model](https://openmmlab-share.oss-cn-hangzhou.aliyuncs.com/mmrazor/v0.1/pruning/autoslim/autoslim_mbv2_subnet_8xb256_in1k/autoslim_mbv2_subnet_8xb256_in1k_flops-0.22M_acc-71.39_20211222-43117c7b.pth?versionId=CAEQHxiBgICRsIaI7xciIDVlY2MxMTkwZjg0ODQ3M2I5NTJmYjFiNDk1MDEwNjAy) | [log](https://openmmlab-share.oss-cn-hangzhou.aliyuncs.com/mmrazor/v0.1/pruning/autoslim/autoslim_mbv2_subnet_8xb256_in1k/autoslim_mbv2_subnet_8xb256_in1kautoslim_mbv2_subnet_8xb256_in1k_paper_channel_cfg.log.json?versionId=CAEQHxiBgMCjj9SL7xciIDFmYmM4NTExZmIzNjRmNmQ4MmMyZWI4YzJmMmM2MDdl) | [channel](https://openmmlab-share.oss-cn-hangzhou.aliyuncs.com/mmrazor/v0.1/pruning/autoslim/autoslim_mbv2_subnet_8xb256_in1k/autoslim_mbv2_subnet_8xb256_in1k_flops-0.22M_acc-71.39_20211222-43117c7b_channel_cfg.yaml.?versionId=CAEQHxiBgIDzr4aI7xciIDViNGY0ZDA1ODkxZTRkMGFhNTg2M2FlZmQyZTFiMDgx) | official channel cfg |
+| MobileNet v2(x1.5) | 6.5 | 0.53 | 74.23 | 91.74 | [config](./autoslim_mbv2_subnet_8xb256_in1k.py) | [model](https://download.openmmlab.com/mmrazor/v0.1/pruning/autoslim/autoslim_mbv2_subnet_8xb256_in1k/autoslim_mbv2_subnet_8xb256_in1k_flops-0.53M_acc-74.23_20211222-e5208bbd.pth) | [log](https://download.openmmlab.com/mmrazor/v0.1/pruning/autoslim/autoslim_mbv2_subnet_8xb256_in1k/autoslim_mbv2_subnet_8xb256_in1kautoslim_mbv2_subnet_8xb256_in1k_paper_channel_cfg.log.json) | [channel](https://download.openmmlab.com/mmrazor/v0.1/pruning/autoslim/autoslim_mbv2_subnet_8xb256_in1k/autoslim_mbv2_subnet_8xb256_in1k_flops-0.53M_acc-74.23_20211222-e5208bbd_channel_cfg.yaml) | official channel cfg |
+| MobileNet v2(x1.5) | 5.77 | 0.32 | 72.73 | 90.83 | [config](./autoslim_mbv2_subnet_8xb256_in1k.py) | [model](https://download.openmmlab.com/mmrazor/v0.1/pruning/autoslim/autoslim_mbv2_subnet_8xb256_in1k/autoslim_mbv2_subnet_8xb256_in1k_flops-0.32M_acc-72.73_20211222-b5b0b33c.pth) | [log](https://download.openmmlab.com/mmrazor/v0.1/pruning/autoslim/autoslim_mbv2_subnet_8xb256_in1k/autoslim_mbv2_subnet_8xb256_in1kautoslim_mbv2_subnet_8xb256_in1k_paper_channel_cfg.log.json) | [channel](https://download.openmmlab.com/mmrazor/v0.1/pruning/autoslim/autoslim_mbv2_subnet_8xb256_in1k/autoslim_mbv2_subnet_8xb256_in1k_flops-0.32M_acc-72.73_20211222-b5b0b33c_channel_cfg.yaml) | official channel cfg |
+| MobileNet v2(x1.5) | 4.13 |0.22 | 71.39 | 90.08 | [config](./autoslim_mbv2_subnet_8xb256_in1k.py) | [model](https://download.openmmlab.com/mmrazor/v0.1/pruning/autoslim/autoslim_mbv2_subnet_8xb256_in1k/autoslim_mbv2_subnet_8xb256_in1k_flops-0.22M_acc-71.39_20211222-43117c7b.pth) | [log](https://download.openmmlab.com/mmrazor/v0.1/pruning/autoslim/autoslim_mbv2_subnet_8xb256_in1k/autoslim_mbv2_subnet_8xb256_in1kautoslim_mbv2_subnet_8xb256_in1k_paper_channel_cfg.log.json) | [channel](https://download.openmmlab.com/mmrazor/v0.1/pruning/autoslim/autoslim_mbv2_subnet_8xb256_in1k/autoslim_mbv2_subnet_8xb256_in1k_flops-0.22M_acc-71.39_20211222-43117c7b_channel_cfg.yaml) | official channel cfg |
Note that we ran the official code and the Top-1 Acc of the models with official
channel cfg are 73.8%, 72.5% and 71.1%. And there are 3 differences between our
diff --git a/configs/pruning/autoslim/autoslim_mbv2_subnet_8xb256_in1k.py b/configs/pruning/autoslim/autoslim_mbv2_subnet_8xb256_in1k.py
index afc974eb3..00d2f841a 100644
--- a/configs/pruning/autoslim/autoslim_mbv2_subnet_8xb256_in1k.py
+++ b/configs/pruning/autoslim/autoslim_mbv2_subnet_8xb256_in1k.py
@@ -10,12 +10,19 @@
label_smooth_val=0.1,
loss_weight=1.0)))
+# FIXME: you may replace this with the channel_cfg searched by yourself
+channel_cfg = [
+ 'https://download.openmmlab.com/mmrazor/v0.1/pruning/autoslim/autoslim_mbv2_subnet_8xb256_in1k/autoslim_mbv2_subnet_8xb256_in1k_flops-0.53M_acc-74.23_20211222-e5208bbd_channel_cfg.yaml', # noqa: E501
+ 'https://download.openmmlab.com/mmrazor/v0.1/pruning/autoslim/autoslim_mbv2_subnet_8xb256_in1k/autoslim_mbv2_subnet_8xb256_in1k_flops-0.32M_acc-72.73_20211222-b5b0b33c_channel_cfg.yaml', # noqa: E501
+ 'https://download.openmmlab.com/mmrazor/v0.1/pruning/autoslim/autoslim_mbv2_subnet_8xb256_in1k/autoslim_mbv2_subnet_8xb256_in1k_flops-0.22M_acc-71.39_20211222-43117c7b_channel_cfg.yaml' # noqa: E501
+]
+
algorithm = dict(
architecture=dict(type='MMClsArchitecture', model=model),
distiller=None,
retraining=True,
bn_training_mode=False,
-)
+ channel_cfg=channel_cfg)
runner = dict(type='EpochBasedRunner', max_epochs=300)
diff --git a/docs/en/conf.py b/docs/en/conf.py
index 8b4db443a..8e69fcc18 100644
--- a/docs/en/conf.py
+++ b/docs/en/conf.py
@@ -41,12 +41,8 @@ def get_version():
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
# ones.
extensions = [
- 'sphinx.ext.autodoc',
- 'sphinx.ext.napoleon',
- 'sphinx.ext.viewcode',
- 'recommonmark',
- 'sphinx_markdown_tables',
- 'sphinx_copybutton',
+ 'sphinx.ext.autodoc', 'sphinx.ext.napoleon', 'sphinx.ext.viewcode',
+ 'sphinx_markdown_tables', 'sphinx_copybutton', 'myst_parser'
]
autodoc_mock_imports = [
diff --git a/docs/en/model_zoo.md b/docs/en/model_zoo.md
index 9c947ddca..e2ad66278 100644
--- a/docs/en/model_zoo.md
+++ b/docs/en/model_zoo.md
@@ -4,24 +4,24 @@
### CWD
-Please refer to [CWD](https://github.com/open-mmlab/mmrazor/blob/master/configs/distill/cwd/README.md) for details.
+Please refer to [CWD](https://github.com/open-mmlab/mmrazor/blob/master/configs/distill/cwd) for details.
### WSLD
-Please refer to [WSLD](https://github.com/open-mmlab/mmrazor/blob/master/configs/distill/wsld/README.md) for details.
+Please refer to [WSLD](https://github.com/open-mmlab/mmrazor/blob/master/configs/distill/wsld) for details.
### DARTS
-Please refer to [DARTS](https://github.com/open-mmlab/mmrazor/blob/master/configs/nas/darts/README.md) for details.
+Please refer to [DARTS](https://github.com/open-mmlab/mmrazor/blob/master/configs/nas/darts) for details.
### DETNAS
-Please refer to [DETNAS](https://github.com/open-mmlab/mmrazor/blob/master/configs/nas/detnas/README.md) for details.
+Please refer to [DETNAS](https://github.com/open-mmlab/mmrazor/blob/master/configs/nas/detnas) for details.
### SPOS
-Please refer to [SPOS](https://github.com/open-mmlab/mmrazor/blob/master/configs/nas/spos/README.md) for details.
+Please refer to [SPOS](https://github.com/open-mmlab/mmrazor/blob/master/configs/nas/spos) for details.
### AUTOSLIM
-Please refer to [AUTOSLIM](https://github.com/open-mmlab/mmrazor/blob/master/configs/pruning/autoslim/README.md) for details.
+Please refer to [AUTOSLIM](https://github.com/open-mmlab/mmrazor/blob/master/configs/pruning/autoslim) for details.
diff --git a/docs/en/train.md b/docs/en/train.md
index b186e916c..ad93b8892 100644
--- a/docs/en/train.md
+++ b/docs/en/train.md
@@ -59,6 +59,13 @@ python ./tools/mmcls/train_mmcls.py \
--cfg-options algorithm.mutable_cfg=configs/nas/spos/SPOS_SHUFFLENETV2_330M_IN1k_PAPER.yaml
+We note that instead of using ``--cfg-options``, you can also directly modify ``configs/nas/spos/spos_subnet_shufflenetv2_8xb128_in1k.py`` like this:
+
+
+mutable_cfg = 'configs/nas/spos/SPOS_SHUFFLENETV2_330M_IN1k_PAPER.yaml'
+algorithm = dict(..., mutable_cfg=mutable_cfg)
+
+
## Pruning
Pruning has three steps, including **supernet pre-training**, **search for subnet on the trained supernet** and **subnet retraining**. The commands of the first two steps are similar to NAS, except that we need to use `CONFIG_FILE` of Pruning here. The commands of the **subnet retraining** are as follows.
@@ -95,7 +102,7 @@ python tools/${task}/train_${task}.py ${CONFIG_FILE} --cfg-options algorithm.dis
For example,
-python ./tools/mmdet/train_mmdet.py \
+python ./tools/mmseg/train_mmseg.py \
configs/distill/cwd/cwd_cls_head_pspnet_r101_d8_pspnet_r18_d8_512x1024_cityscapes_80k.py \
--work-dir your_work_dir \
--cfg-options algorithm.distiller.teacher.init_cfg.type=Pretrained algorithm.distiller.teacher.init_cfg.checkpoint=https://download.openmmlab.com/mmsegmentation/v0.5/pspnet/pspnet_r101-d8_512x1024_80k_cityscapes/pspnet_r101-d8_512x1024_80k_cityscapes_20200606_112211-e1e1100f.pth
diff --git a/mmrazor/apis/mmcls/train.py b/mmrazor/apis/mmcls/train.py
index 91aafc4bf..89e2a5c50 100644
--- a/mmrazor/apis/mmcls/train.py
+++ b/mmrazor/apis/mmcls/train.py
@@ -65,7 +65,24 @@ def train_mmcls_model(model,
train_dataset = dataset[0]
dataset[0] = split_dataset(train_dataset)
- sampler_cfg = cfg.data.get('sampler', None)
+ loader_cfg = dict(
+ # cfg.gpus will be ignored if distributed
+ num_gpus=len(cfg.gpu_ids),
+ dist=distributed,
+ round_up=True,
+ seed=cfg.get('seed'),
+ sampler_cfg=cfg.get('sampler', None),
+ )
+ # The overall dataloader settings
+ loader_cfg.update({
+ k: v
+ for k, v in cfg.data.items() if k not in [
+ 'train', 'val', 'test', 'train_dataloader', 'val_dataloader',
+ 'test_dataloader'
+ ]
+ })
+ # The specific dataloader settings
+ train_loader_cfg = {**loader_cfg, **cfg.data.get('train_dataloader', {})}
# Difference from mmclassification.
# Build multi dataloaders according the splited datasets.
@@ -73,28 +90,11 @@ def train_mmcls_model(model,
for dset in dataset:
if isinstance(dset, list):
data_loader = [
- build_dataloader(
- item_ds,
- cfg.data.samples_per_gpu,
- cfg.data.workers_per_gpu,
- # cfg.gpus will be ignored if distributed
- num_gpus=len(cfg.gpu_ids),
- dist=distributed,
- round_up=True,
- seed=cfg.seed,
- sampler_cfg=sampler_cfg) for item_ds in dset
+ build_dataloader(item_ds, **train_loader_cfg)
+ for item_ds in dset
]
else:
- data_loader = build_dataloader(
- dset,
- cfg.data.samples_per_gpu,
- cfg.data.workers_per_gpu,
- # cfg.gpus will be ignored if distributed
- num_gpus=len(cfg.gpu_ids),
- dist=distributed,
- round_up=True,
- seed=cfg.seed,
- sampler_cfg=sampler_cfg)
+ data_loader = build_dataloader(dset, **train_loader_cfg)
data_loaders.append(data_loader)
@@ -188,13 +188,13 @@ def train_mmcls_model(model,
# register eval hooks
if validate:
val_dataset = build_dataset(cfg.data.val, dict(test_mode=True))
- val_dataloader = build_dataloader(
- val_dataset,
- samples_per_gpu=cfg.data.samples_per_gpu,
- workers_per_gpu=cfg.data.workers_per_gpu,
- dist=distributed,
- shuffle=False,
- round_up=True)
+ val_loader_cfg = {
+ **loader_cfg,
+ 'shuffle': False, # Not shuffle by default
+ 'sampler_cfg': None, # Not use sampler by default
+ **cfg.data.get('val_dataloader', {}),
+ }
+ val_dataloader = build_dataloader(val_dataset, **val_loader_cfg)
eval_cfg = cfg.get('evaluation', {})
eval_cfg['by_epoch'] = cfg.runner['type'] != 'IterBasedRunner'
diff --git a/mmrazor/apis/mmseg/train.py b/mmrazor/apis/mmseg/train.py
index 16de892f9..ed09df78e 100644
--- a/mmrazor/apis/mmseg/train.py
+++ b/mmrazor/apis/mmseg/train.py
@@ -57,17 +57,25 @@ def train_mmseg_model(model,
# prepare data loaders
dataset = dataset if isinstance(dataset, (list, tuple)) else [dataset]
- data_loaders = [
- build_dataloader(
- ds,
- cfg.data.samples_per_gpu,
- cfg.data.workers_per_gpu,
- # cfg.gpus will be ignored if distributed
- len(cfg.gpu_ids),
- dist=distributed,
- seed=cfg.seed,
- drop_last=True) for ds in dataset
- ]
+ # The default loader config
+ loader_cfg = dict(
+ # cfg.gpus will be ignored if distributed
+ num_gpus=len(cfg.gpu_ids),
+ dist=distributed,
+ seed=cfg.seed,
+ drop_last=True)
+ # The overall dataloader settings
+ loader_cfg.update({
+ k: v
+ for k, v in cfg.data.items() if k not in [
+ 'train', 'val', 'test', 'train_dataloader', 'val_dataloader',
+ 'test_dataloader'
+ ]
+ })
+
+ # The specific dataloader settings
+ train_loader_cfg = {**loader_cfg, **cfg.data.get('train_dataloader', {})}
+ data_loaders = [build_dataloader(ds, **train_loader_cfg) for ds in dataset]
# put model on gpus
if distributed:
@@ -130,12 +138,14 @@ def train_mmseg_model(model,
# register eval hooks
if validate:
val_dataset = build_dataset(cfg.data.val, dict(test_mode=True))
- val_dataloader = build_dataloader(
- val_dataset,
- samples_per_gpu=1,
- workers_per_gpu=cfg.data.workers_per_gpu,
- dist=distributed,
- shuffle=False)
+ # The specific dataloader settings
+ val_loader_cfg = {
+ **loader_cfg,
+ 'samples_per_gpu': 1,
+ 'shuffle': False, # Not shuffle by default
+ **cfg.data.get('val_dataloader', {}),
+ }
+ val_dataloader = build_dataloader(val_dataset, **val_loader_cfg)
eval_cfg = cfg.get('evaluation', {})
eval_cfg['by_epoch'] = cfg.runner['type'] != 'IterBasedRunner'
eval_hook = DistEvalHook if distributed else EvalHook
diff --git a/mmrazor/core/searcher/evolution_search.py b/mmrazor/core/searcher/evolution_search.py
index 43cfe558a..a4fdee41c 100644
--- a/mmrazor/core/searcher/evolution_search.py
+++ b/mmrazor/core/searcher/evolution_search.py
@@ -135,14 +135,11 @@ def search(self):
if self.check_constraints():
self.candidate_pool.append(candidate)
-
- broadcast_candidate_pool = self.candidate_pool
else:
- broadcast_candidate_pool = [None] * self.candidate_pool_size
- broadcast_candidate_pool = broadcast_object_list(
- broadcast_candidate_pool)
+ self.candidate_pool = [None] * self.candidate_pool_size
+ broadcast_object_list(self.candidate_pool)
- for i, candidate in enumerate(broadcast_candidate_pool):
+ for i, candidate in enumerate(self.candidate_pool):
self.algorithm.mutator.set_subnet(candidate)
outputs = self.test_fn(self.algorithm_for_test,
self.dataloader)
@@ -213,7 +210,7 @@ def search(self):
self.logger.info(
f'Epoch:[{epoch + 1}/{self.max_epoch}], top1_score: '
f'{list(self.top_k_candidates_with_score.keys())[0]}')
- self.candidate_pool = broadcast_object_list(self.candidate_pool)
+ broadcast_object_list(self.candidate_pool)
if rank == 0:
final_subnet_dict = list(
diff --git a/mmrazor/core/searcher/greedy_search.py b/mmrazor/core/searcher/greedy_search.py
index 2d1410389..215d0fffe 100644
--- a/mmrazor/core/searcher/greedy_search.py
+++ b/mmrazor/core/searcher/greedy_search.py
@@ -146,7 +146,7 @@ def search(self):
# Broadcasts scores in broadcast_scores to the whole
# group.
- broadcast_scores = broadcast_object_list(broadcast_scores)
+ broadcast_object_list(broadcast_scores)
score = broadcast_scores[0]
self.logger.info(
f'Slimming group {name}, {self.score_key}: {score}')
diff --git a/mmrazor/core/utils/__init__.py b/mmrazor/core/utils/__init__.py
index 415b5418b..9267327cc 100644
--- a/mmrazor/core/utils/__init__.py
+++ b/mmrazor/core/utils/__init__.py
@@ -1,5 +1,9 @@
# Copyright (c) OpenMMLab. All rights reserved.
from .broadcast import broadcast_object_list
from .lr import set_lr
+from .utils import get_backend, get_default_group, get_rank, get_world_size
-__all__ = ['broadcast_object_list', 'set_lr']
+__all__ = [
+ 'broadcast_object_list', 'set_lr', 'get_world_size', 'get_rank',
+ 'get_backend', 'get_default_group'
+]
diff --git a/mmrazor/core/utils/broadcast.py b/mmrazor/core/utils/broadcast.py
index 6326311c0..41c2998b4 100644
--- a/mmrazor/core/utils/broadcast.py
+++ b/mmrazor/core/utils/broadcast.py
@@ -1,49 +1,155 @@
# Copyright (c) OpenMMLab. All rights reserved.
-import os.path as osp
-import shutil
-import tempfile
+import pickle
+import warnings
+from typing import Any, List, Optional, Tuple
-import mmcv.fileio
import torch
-import torch.distributed as dist
-from mmcv.runner import get_dist_info
+from mmcv.utils import TORCH_VERSION, digit_version
+from torch import Tensor
+from torch import distributed as dist
+from .utils import get_backend, get_default_group, get_rank, get_world_size
-def broadcast_object_list(object_list, src=0):
- """Broadcasts picklable objects in ``object_list`` to the whole group.
- Note that all objects in ``object_list`` must be picklable in order to be
- broadcasted.
+def _object_to_tensor(obj: Any) -> Tuple[Tensor, Tensor]:
+ """Serialize picklable python object to tensor."""
+ byte_storage = torch.ByteStorage.from_buffer(pickle.dumps(obj))
+ # Do not replace `torch.ByteTensor` or `torch.LongTensor` with torch.tensor
+ # and specifying dtype. Otherwise, it will cause 100X slowdown.
+ # See: https://github.com/pytorch/pytorch/issues/65696
+ byte_tensor = torch.ByteTensor(byte_storage)
+ local_size = torch.LongTensor([byte_tensor.numel()])
+ return byte_tensor, local_size
- Args:
- object_list (List[Any]): List of input objects to broadcast.
- Each object must be picklable. Only objects on the src rank will be
- broadcast, but each rank must provide lists of equal sizes.
- src (int): Source rank from which to broadcast ``object_list``.
+
+def _tensor_to_object(tensor: Tensor, tensor_size: int) -> Any:
+ """Deserialize tensor to picklable python object."""
+ buf = tensor.cpu().numpy().tobytes()[:tensor_size]
+ return pickle.loads(buf)
+
+
+def _broadcast_object_list(object_list: List[Any],
+ src: int = 0,
+ group: Optional[dist.ProcessGroup] = None) -> None:
+ """Broadcast picklable objects in ``object_list`` to the whole group.
+
+ Similar to :func:`broadcast`, but Python objects can be passed in. Note
+ that all objects in ``object_list`` must be picklable in order to be
+ broadcasted.
"""
- my_rank, _ = get_dist_info()
+ if dist.distributed_c10d._rank_not_in_group(group):
+ return
- MAX_LEN = 512
- # 32 is whitespace
- dir_tensor = torch.full((MAX_LEN, ), 32, dtype=torch.uint8, device='cuda')
- object_list_return = list()
+ my_rank = get_rank()
+ # Serialize object_list elements to tensors on src rank.
if my_rank == src:
- mmcv.mkdir_or_exist('.dist_broadcast')
- tmpdir = tempfile.mkdtemp(dir='.dist_broadcast')
- mmcv.dump(object_list, osp.join(tmpdir, 'object_list.pkl'))
- tmpdir = torch.tensor(
- bytearray(tmpdir.encode()), dtype=torch.uint8, device='cuda')
- dir_tensor[:len(tmpdir)] = tmpdir
+ tensor_list, size_list = zip(
+ *[_object_to_tensor(obj) for obj in object_list])
+ object_sizes_tensor = torch.cat(size_list)
+ else:
+ object_sizes_tensor = torch.empty(len(object_list), dtype=torch.long)
- dist.broadcast(dir_tensor, src)
- tmpdir = dir_tensor.cpu().numpy().tobytes().decode().rstrip()
+ # Current device selection.
+ # To preserve backwards compatibility, ``device`` is ``None`` by default.
+ # in which case we run current logic of device selection, i.e.
+ # ``current_device`` is CUDA if backend is NCCL otherwise CPU device. In
+ # the case it is not ``None`` we move the size and object tensors to be
+ # broadcasted to this device.
+ group_backend = get_backend(group)
+ is_nccl_backend = group_backend == dist.Backend.NCCL
+ current_device = torch.device('cpu')
+ if is_nccl_backend:
+ # See note about using torch.cuda.current_device() here in
+ # docstring. We cannot simply use my_rank since rank == device is
+ # not necessarily true.
+ current_device = torch.device('cuda', torch.cuda.current_device())
+ object_sizes_tensor = object_sizes_tensor.to(current_device)
- if my_rank != src:
- object_list_return = mmcv.load(osp.join(tmpdir, 'object_list.pkl'))
+ # Broadcast object sizes
+ dist.broadcast(object_sizes_tensor, src=src, group=group)
- dist.barrier()
+ # Concatenate and broadcast serialized object tensors
if my_rank == src:
- shutil.rmtree(tmpdir)
- object_list_return = object_list
+ object_tensor = torch.cat(tensor_list)
+ else:
+ object_tensor = torch.empty(
+ torch.sum(object_sizes_tensor).int().item(),
+ dtype=torch.uint8,
+ )
+
+ if is_nccl_backend:
+ object_tensor = object_tensor.to(current_device)
+ dist.broadcast(object_tensor, src=src, group=group)
+ # Deserialize objects using their stored sizes.
+ offset = 0
+ if my_rank != src:
+ for i, obj_size in enumerate(object_sizes_tensor):
+ obj_view = object_tensor[offset:offset + obj_size]
+ obj_view = obj_view.type(torch.uint8)
+ if obj_view.device != torch.device('cpu'):
+ obj_view = obj_view.cpu()
+ offset += obj_size
+ object_list[i] = _tensor_to_object(obj_view, obj_size)
+
+
+def broadcast_object_list(data: List[Any],
+ src: int = 0,
+ group: Optional[dist.ProcessGroup] = None) -> None:
+ """Broadcasts picklable objects in ``object_list`` to the whole group.
+ Similar to :func:`broadcast`, but Python objects can be passed in. Note
+ that all objects in ``object_list`` must be picklable in order to be
+ broadcasted.
+ Note:
+ Calling ``broadcast_object_list`` in non-distributed environment does
+ nothing.
+ Args:
+ data (List[Any]): List of input objects to broadcast.
+ Each object must be picklable. Only objects on the ``src`` rank
+ will be broadcast, but each rank must provide lists of equal sizes.
+ src (int): Source rank from which to broadcast ``object_list``.
+ group: (ProcessGroup, optional): The process group to work on. If None,
+ the default process group will be used. Default is ``None``.
+ device (``torch.device``, optional): If not None, the objects are
+ serialized and converted to tensors which are moved to the
+ ``device`` before broadcasting. Default is ``None``.
+ Note:
+ For NCCL-based process groups, internal tensor representations of
+ objects must be moved to the GPU device before communication starts.
+ In this case, the used device is given by
+ ``torch.cuda.current_device()`` and it is the user's responsibility to
+ ensure that this is correctly set so that each rank has an individual
+ GPU, via ``torch.cuda.set_device()``.
+ Examples:
+ >>> import torch
+ >>> import mmrazor.core.utils as dist
+ >>> # non-distributed environment
+ >>> data = ['foo', 12, {1: 2}]
+ >>> dist.broadcast_object_list(data)
+ >>> data
+ ['foo', 12, {1: 2}]
+ >>> # distributed environment
+ >>> # We have 2 process groups, 2 ranks.
+ >>> if dist.get_rank() == 0:
+ >>> # Assumes world_size of 3.
+ >>> data = ["foo", 12, {1: 2}] # any picklable object
+ >>> else:
+ >>> data = [None, None, None]
+ >>> dist.broadcast_object_list(data)
+ >>> data
+ ["foo", 12, {1: 2}] # Rank 0
+ ["foo", 12, {1: 2}] # Rank 1
+ """
+ warnings.warn(
+ '`broadcast_object_list` is now without return value, '
+ 'and it\'s input parameters are: `data`,`src` and '
+ '`group`, but its function is similar to the old\'s', UserWarning)
+ assert isinstance(data, list)
+
+ if get_world_size(group) > 1:
+ if group is None:
+ group = get_default_group()
- return object_list_return
+ if digit_version(TORCH_VERSION) >= digit_version('1.8.0'):
+ dist.broadcast_object_list(data, src, group)
+ else:
+ _broadcast_object_list(data, src, group)
diff --git a/mmrazor/core/utils/utils.py b/mmrazor/core/utils/utils.py
new file mode 100644
index 000000000..58b87d2e5
--- /dev/null
+++ b/mmrazor/core/utils/utils.py
@@ -0,0 +1,89 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from typing import Optional
+
+from torch import distributed as dist
+
+
+def is_distributed() -> bool:
+ """Return True if distributed environment has been initialized."""
+ return dist.is_available() and dist.is_initialized()
+
+
+def get_default_group() -> Optional[dist.ProcessGroup]:
+ """Return default process group."""
+
+ return dist.distributed_c10d._get_default_group()
+
+
+def get_rank(group: Optional[dist.ProcessGroup] = None) -> int:
+ """Return the rank of the given process group.
+
+ Rank is a unique identifier assigned to each process within a distributed
+ process group. They are always consecutive integers ranging from 0 to
+ ``world_size``.
+ Note:
+ Calling ``get_rank`` in non-distributed environment will return 0.
+ Args:
+ group (ProcessGroup, optional): The process group to work on. If None,
+ the default process group will be used. Defaults to None.
+ Returns:
+ int: Return the rank of the process group if in distributed
+ environment, otherwise 0.
+ """
+
+ if is_distributed():
+ # handle low versions of torch like 1.5.0 which does not support
+ # passing in None for group argument
+ if group is None:
+ group = get_default_group()
+ return dist.get_rank(group)
+ else:
+ return 0
+
+
+def get_backend(group: Optional[dist.ProcessGroup] = None) -> Optional[str]:
+ """Return the backend of the given process group.
+
+ Note:
+ Calling ``get_backend`` in non-distributed environment will return
+ None.
+ Args:
+ group (ProcessGroup, optional): The process group to work on. The
+ default is the general main process group. If another specific
+ group is specified, the calling process must be part of
+ :attr:`group`. Defaults to None.
+ Returns:
+ str or None: Return the backend of the given process group as a lower
+ case string if in distributed environment, otherwise None.
+ """
+ if is_distributed():
+ # handle low versions of torch like 1.5.0 which does not support
+ # passing in None for group argument
+ if group is None:
+ group = get_default_group()
+ return dist.get_backend(group)
+ else:
+ return None
+
+
+def get_world_size(group: Optional[dist.ProcessGroup] = None) -> int:
+ """Return the number of the given process group.
+
+ Note:
+ Calling ``get_world_size`` in non-distributed environment will return
+ 1.
+ Args:
+ group (ProcessGroup, optional): The process group to work on. If None,
+ the default process group will be used. Defaults to None.
+ Returns:
+ int: Return the number of processes of the given process group if in
+ distributed environment, otherwise 1.
+ """
+ if is_distributed():
+ # handle low versions of torch like 1.5.0 which does not support
+ # passing in None for group argument
+ if group is None:
+ group = get_default_group()
+ return dist.get_world_size(group)
+ else:
+ return 1
diff --git a/mmrazor/models/pruners/ratio_pruning.py b/mmrazor/models/pruners/ratio_pruning.py
index 218b142d3..151677d99 100644
--- a/mmrazor/models/pruners/ratio_pruning.py
+++ b/mmrazor/models/pruners/ratio_pruning.py
@@ -2,6 +2,7 @@
import numpy as np
import torch
import torch.nn as nn
+from torch.nn.modules import GroupNorm
from mmrazor.models.builder import PRUNERS
from .structure_pruning import StructurePruner
@@ -31,6 +32,22 @@ def __init__(self, ratios, **kwargs):
self.ratios = ratios
self.min_ratio = ratios[0]
+ def _check_pruner(self, supernet):
+ for module in supernet.model.modules():
+ if isinstance(module, GroupNorm):
+ num_channels = module.num_channels
+ num_groups = module.num_groups
+ for ratio in self.ratios:
+ new_channels = int(round(num_channels * ratio))
+ assert (num_channels * ratio) % num_groups == 0, \
+ f'Expected number of channels in input of GroupNorm ' \
+ f'to be divisible by num_groups, but number of ' \
+ f'channels may be {new_channels} according to ' \
+ f'ratio {ratio} and num_groups={num_groups}'
+
+ def prepare_from_supernet(self, supernet):
+ super(RatioPruner, self).prepare_from_supernet(supernet)
+
def get_channel_mask(self, out_mask):
"""Randomly choose a width ratio of a layer from ``ratios``"""
out_channels = out_mask.size(1)
diff --git a/mmrazor/models/pruners/structure_pruning.py b/mmrazor/models/pruners/structure_pruning.py
index af1eae2b0..b755c2e5d 100644
--- a/mmrazor/models/pruners/structure_pruning.py
+++ b/mmrazor/models/pruners/structure_pruning.py
@@ -6,9 +6,12 @@
import torch
import torch.nn as nn
+from mmcv import digit_version
from mmcv.runner import BaseModule
from ordered_set import OrderedSet
+from torch.nn.modules import GroupNorm
from torch.nn.modules.batchnorm import _BatchNorm
+from torch.nn.modules.instancenorm import _InstanceNorm
from mmrazor.models.builder import PRUNERS
from .utils import SwitchableBatchNorm2d
@@ -19,14 +22,13 @@
FC = ('ThAddmmBackward', 'AddmmBackward', 'MmBackward')
BN = ('ThnnBatchNormBackward', 'CudnnBatchNormBackward',
'NativeBatchNormBackward')
+GN = ('NativeGroupNormBackward', )
CONCAT = ('CatBackward', )
# the modules which contains NON_PASS grad_fn need to change the parameter size
# according to channels after pruning
NON_PASS = CONV + FC
-NON_PASS_MODULE = (nn.Conv2d, nn.Linear)
-
-PASS = BN
-PASS_MODULE = (_BatchNorm)
+PASS = BN + GN
+NORM = BN + GN
BACKWARD_PARSER_DICT = dict()
MAKE_GROUP_PARSER_DICT = dict()
@@ -122,6 +124,12 @@ def prepare_from_supernet(self, supernet):
tmp_shared_module_hook_handles = list()
for name, module in supernet.model.named_modules():
+ if isinstance(module, nn.GroupNorm):
+ min_required_version = '1.6.0'
+ assert digit_version(torch.__version__) >= digit_version(
+ min_required_version
+ ), f'Requires pytorch>={min_required_version} to auto-trace' \
+ f'GroupNorm correctly.'
if hasattr(module, 'weight'):
# trace shared modules
module.cnt = 0
@@ -172,10 +180,10 @@ def prepare_from_supernet(self, supernet):
self.trace_non_pass_path(pseudo_loss.grad_fn, module2name, var2module,
cur_non_pass_path, non_pass_paths, visited)
- bn_conv_links = dict()
- self.trace_bn_conv_links(pseudo_loss.grad_fn, module2name, var2module,
- bn_conv_links, visited)
- self.bn_conv_links = bn_conv_links
+ norm_conv_links = dict()
+ self.trace_norm_conv_links(pseudo_loss.grad_fn, module2name,
+ var2module, norm_conv_links, visited)
+ self.norm_conv_links = norm_conv_links
# a node can be the name of a conv module or a str like 'concat_{id}'
node2parents = self.find_node_parents(non_pass_paths)
@@ -268,12 +276,12 @@ def set_subnet(self, subnet_dict):
module = self.name2module[module_name]
module.out_mask = subnet_dict[space_id].to(module.out_mask.device)
- for bn, conv in self.bn_conv_links.items():
- module = self.name2module[bn]
+ for norm, conv in self.norm_conv_links.items():
+ module = self.name2module[norm]
conv_space_id = self.get_space_id(conv)
# conv_space_id is None means the conv layer in front of
- # this bn module can not be pruned. So we should not set
- # the out_mask of this bn layer
+ # this normalization module can not be pruned. So we should not set
+ # the out_mask of this normalization layer
if conv_space_id is not None:
module.out_mask = subnet_dict[conv_space_id].to(
module.out_mask.device)
@@ -458,7 +466,9 @@ def add_pruning_attrs(self, module):
module.register_buffer(
'out_mask', module.weight.new_ones((1, module.out_features), ))
module.forward = self.modify_fc_forward(module)
- if isinstance(module, nn.modules.batchnorm._BatchNorm):
+ if (isinstance(module, _BatchNorm)
+ or isinstance(module, _InstanceNorm)
+ or isinstance(module, GroupNorm)):
module.register_buffer(
'out_mask',
module.weight.new_ones((1, len(module.weight), 1, 1), ))
@@ -625,15 +635,18 @@ def trace_non_pass_path(self, grad_fn, module2name, var2module, cur_path,
else:
result_paths.append(copy.deepcopy(cur_path))
- def trace_bn_conv_links(self, grad_fn, module2name, var2module,
- bn_conv_links, visited):
- """Get the convolutional layer placed before a bn layer in the model.
+ def trace_norm_conv_links(self, grad_fn, module2name, var2module,
+ norm_conv_links, visited):
+ """Get the convolutional layer placed before a normalization layer in
+ the model.
Example:
>>> conv = nn.Conv2d(3, 3, 3)
- >>> bn = nn.BatchNorm2d(3)
+ >>> norm = nn.BatchNorm2d(3)
>>> pseudo_img = torch.rand(1, 3, 224, 224)
- >>> out = bn(conv(pseudo_img))
+ >>> out = norm(conv(pseudo_img))
+ >>> print(out.grad_fn)
+
>>> print(out.grad_fn.next_functions)
((, 0),
(, 0),
@@ -641,23 +654,60 @@ def trace_bn_conv_links(self, grad_fn, module2name, var2module,
>>> # op.next_functions[0][0] is ThnnConv2DBackward means
>>> # the parent of this NativeBatchNormBackward op is
>>> # ThnnConv2DBackward
- >>> # op.next_functions[1][0].variable is the weight of this bn
- >>> # module
- >>> # op.next_functions[2][0].variable is the bias of this bn
- >>> # module
+ >>> # op.next_functions[1][0].variable is the weight of this
+ >>> # normalization module
+ >>> # op.next_functions[2][0].variable is the bias of this
+ >>> # normalization module
+
+ >>> # Things are different in InstanceNorm
+ >>> conv = nn.Conv2d(3, 3, 3)
+ >>> norm = nn.InstanceNorm2d(3, affine=True)
+ >>> out = norm(conv(pseudo_img))
+ >>> print(out.grad_fn)
+
+ >>> print(out.grad_fn.next_functions)
+ ((, 0),)
+ >>> print(out.grad_fn.next_functions[0][0].next_functions)
+ ((, 0),
+ (, 0),
+ (, 0))
+ >>> # Hence, a dfs is necessary.
"""
- grad_fn = grad_fn[0] if isinstance(grad_fn, (list, tuple)) else grad_fn
- if grad_fn is not None:
- is_bn_grad_fn = False
- for fn_name in BN:
+
+ def is_norm_grad_fn(grad_fn):
+ for fn_name in NORM:
if type(grad_fn).__name__.startswith(fn_name):
- is_bn_grad_fn = True
- break
+ return True
+ return False
+
+ def is_conv_grad_fn(grad_fn):
+ for fn_name in CONV:
+ if type(grad_fn).__name__.startswith(fn_name):
+ return True
+ return False
- if is_bn_grad_fn:
+ def is_leaf_grad_fn(grad_fn):
+ if type(grad_fn).__name__ == 'AccumulateGrad':
+ return True
+ return False
+
+ grad_fn = grad_fn[0] if isinstance(grad_fn, (list, tuple)) else grad_fn
+ if grad_fn is not None:
+ if is_norm_grad_fn(grad_fn):
conv_grad_fn = grad_fn.next_functions[0][0]
- conv_var = conv_grad_fn.next_functions[1][0].variable
- bn_var = grad_fn.next_functions[1][0].variable
+ while not is_conv_grad_fn(conv_grad_fn):
+ conv_grad_fn = conv_grad_fn.next_functions[0][0]
+
+ leaf_grad_fn = conv_grad_fn.next_functions[1][0]
+ while not is_leaf_grad_fn(leaf_grad_fn):
+ leaf_grad_fn = leaf_grad_fn.next_functions[0][0]
+ conv_var = leaf_grad_fn.variable
+
+ leaf_grad_fn = grad_fn.next_functions[1][0]
+ while not is_leaf_grad_fn(leaf_grad_fn):
+ leaf_grad_fn = leaf_grad_fn.next_functions[0][0]
+ bn_var = leaf_grad_fn.variable
+
conv_module = var2module[id(conv_var)]
bn_module = var2module[id(bn_var)]
conv_name = module2name[conv_module]
@@ -666,20 +716,20 @@ def trace_bn_conv_links(self, grad_fn, module2name, var2module,
pass
else:
visited[bn_name] = True
- bn_conv_links[bn_name] = conv_name
+ norm_conv_links[bn_name] = conv_name
- self.trace_bn_conv_links(conv_grad_fn, module2name,
- var2module, bn_conv_links,
- visited)
+ self.trace_norm_conv_links(conv_grad_fn, module2name,
+ var2module, norm_conv_links,
+ visited)
else:
# If the op is AccumulateGrad, parents is (),
parents = grad_fn.next_functions
if parents is not None:
for parent in parents:
- self.trace_bn_conv_links(parent, module2name,
- var2module, bn_conv_links,
- visited)
+ self.trace_norm_conv_links(parent, module2name,
+ var2module, norm_conv_links,
+ visited)
def find_backward_parser(self, grad_fn):
for name, parser in BACKWARD_PARSER_DICT.items():
diff --git a/mmrazor/version.py b/mmrazor/version.py
index 59c76465c..fd746a6c1 100644
--- a/mmrazor/version.py
+++ b/mmrazor/version.py
@@ -1,6 +1,6 @@
# Copyright (c) OpenMMLab. All rights reserved
-__version__ = '0.3.0'
+__version__ = '0.3.1'
def parse_version_info(version_str):
diff --git a/requirements/docs.txt b/requirements/docs.txt
index 55ae36bc5..6934d41bd 100644
--- a/requirements/docs.txt
+++ b/requirements/docs.txt
@@ -2,7 +2,6 @@ docutils==0.16.0
m2r
myst-parser
git+https://github.com/open-mmlab/pytorch_sphinx_theme.git#egg=pytorch_sphinx_theme
-recommonmark
sphinx==4.0.2
sphinx-copybutton
sphinx_markdown_tables
diff --git a/tests/test_models/test_pruner.py b/tests/test_models/test_pruner.py
index 835c4894f..12323022a 100644
--- a/tests/test_models/test_pruner.py
+++ b/tests/test_models/test_pruner.py
@@ -3,7 +3,7 @@
import pytest
import torch
-from mmcv import ConfigDict
+from mmcv import ConfigDict, digit_version
from mmrazor.models.builder import ARCHITECTURES, PRUNERS
@@ -86,7 +86,7 @@ def test_ratio_pruner():
losses = architecture(imgs, return_loss=True, gt_label=label)
assert losses['loss'].item() > 0
- # test making groups logic when there are shared modules in the model
+ # test models with shared module
model_cfg = ConfigDict(
type='mmdet.RetinaNet',
backbone=dict(
@@ -159,13 +159,127 @@ def test_ratio_pruner():
pruner = PRUNERS.build(pruner_cfg)
pruner.prepare_from_supernet(architecture)
subnet_dict = pruner.sample_subnet()
- assert isinstance(subnet_dict, dict)
pruner.set_subnet(subnet_dict)
subnet_dict = pruner.export_subnet()
- assert isinstance(subnet_dict, dict)
pruner.deploy_subnet(architecture, subnet_dict)
architecture.forward_dummy(imgs)
+ # test models with concat operations
+ model_cfg = ConfigDict(
+ type='mmdet.YOLOX',
+ input_size=(640, 640),
+ random_size_range=(15, 25),
+ random_size_interval=10,
+ backbone=dict(type='CSPDarknet', deepen_factor=0.33, widen_factor=0.5),
+ neck=dict(
+ type='YOLOXPAFPN',
+ in_channels=[128, 256, 512],
+ out_channels=128,
+ num_csp_blocks=1),
+ bbox_head=dict(
+ type='YOLOXHead',
+ num_classes=80,
+ in_channels=128,
+ feat_channels=128),
+ train_cfg=dict(
+ assigner=dict(type='SimOTAAssigner', center_radius=2.5)),
+ # In order to align the source code, the threshold of the val phase is
+ # 0.01, and the threshold of the test phase is 0.001.
+ test_cfg=dict(
+ score_thr=0.01, nms=dict(type='nms', iou_threshold=0.65)))
+
+ architecture_cfg = dict(
+ type='MMDetArchitecture',
+ model=model_cfg,
+ )
+
+ architecture = ARCHITECTURES.build(architecture_cfg)
+ pruner.prepare_from_supernet(architecture)
+ subnet_dict = pruner.sample_subnet()
+ pruner.set_subnet(subnet_dict)
+ subnet_dict = pruner.export_subnet()
+ pruner.deploy_subnet(architecture, subnet_dict)
+ architecture.forward_dummy(imgs)
+
+ # test models with groupnorm
+ model_cfg = ConfigDict(
+ type='mmdet.ATSS',
+ backbone=dict(
+ type='ResNet',
+ depth=50,
+ num_stages=4,
+ out_indices=(0, 1, 2, 3),
+ frozen_stages=1,
+ norm_cfg=dict(type='BN', requires_grad=True),
+ norm_eval=True,
+ style='pytorch',
+ init_cfg=dict(
+ type='Pretrained', checkpoint='torchvision://resnet50')),
+ neck=dict(
+ type='FPN',
+ in_channels=[256, 512, 1024, 2048],
+ out_channels=256,
+ start_level=1,
+ add_extra_convs='on_output',
+ num_outs=5),
+ bbox_head=dict(
+ type='ATSSHead',
+ num_classes=80,
+ in_channels=256,
+ stacked_convs=4,
+ feat_channels=256,
+ anchor_generator=dict(
+ type='AnchorGenerator',
+ ratios=[1.0],
+ octave_base_scale=8,
+ scales_per_octave=1,
+ strides=[8, 16, 32, 64, 128]),
+ bbox_coder=dict(
+ type='DeltaXYWHBBoxCoder',
+ target_means=[.0, .0, .0, .0],
+ target_stds=[0.1, 0.1, 0.2, 0.2]),
+ loss_cls=dict(
+ type='FocalLoss',
+ use_sigmoid=True,
+ gamma=2.0,
+ alpha=0.25,
+ loss_weight=1.0),
+ loss_bbox=dict(type='GIoULoss', loss_weight=2.0),
+ loss_centerness=dict(
+ type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)),
+ # training and testing settings
+ train_cfg=dict(
+ assigner=dict(type='ATSSAssigner', topk=9),
+ allowed_border=-1,
+ pos_weight=-1,
+ debug=False),
+ test_cfg=dict(
+ nms_pre=1000,
+ min_bbox_size=0,
+ score_thr=0.05,
+ nms=dict(type='nms', iou_threshold=0.6),
+ max_per_img=100))
+
+ architecture_cfg = dict(
+ type='MMDetArchitecture',
+ model=model_cfg,
+ )
+
+ architecture = ARCHITECTURES.build(architecture_cfg)
+ # ``StructurePruner`` requires pytorch>=1.6.0 to auto-trace GroupNorm
+ # correctly
+ min_required_version = '1.6.0'
+ if digit_version(torch.__version__) < digit_version(min_required_version):
+ with pytest.raises(AssertionError):
+ pruner.prepare_from_supernet(architecture)
+ else:
+ pruner.prepare_from_supernet(architecture)
+ subnet_dict = pruner.sample_subnet()
+ pruner.set_subnet(subnet_dict)
+ subnet_dict = pruner.export_subnet()
+ pruner.deploy_subnet(architecture, subnet_dict)
+ architecture.forward_dummy(imgs)
+
def _test_reset_bn_running_stats(architecture_cfg, pruner_cfg, should_fail):
import os
diff --git a/tools/mmcls/test_mmcls.py b/tools/mmcls/test_mmcls.py
index 8ee6351f4..26b55077f 100644
--- a/tools/mmcls/test_mmcls.py
+++ b/tools/mmcls/test_mmcls.py
@@ -120,15 +120,32 @@ def main():
init_dist(args.launcher, **cfg.dist_params)
# build the dataloader
- dataset = build_dataset(cfg.data.test)
- # the extra round_up data will be removed during gpu/cpu collect
- data_loader = build_dataloader(
- dataset,
- samples_per_gpu=cfg.data.samples_per_gpu,
- workers_per_gpu=cfg.data.workers_per_gpu,
+ dataset = build_dataset(cfg.data.test, default_args=dict(test_mode=True))
+
+ # build the dataloader
+ # The default loader config
+ loader_cfg = dict(
+ # cfg.gpus will be ignored if distributed
+ num_gpus=len(cfg.gpu_ids),
dist=distributed,
- shuffle=False,
- round_up=True)
+ round_up=True,
+ )
+ # The overall dataloader settings
+ loader_cfg.update({
+ k: v
+ for k, v in cfg.data.items() if k not in [
+ 'train', 'val', 'test', 'train_dataloader', 'val_dataloader',
+ 'test_dataloader'
+ ]
+ })
+ test_loader_cfg = {
+ **loader_cfg,
+ 'shuffle': False, # Not shuffle by default
+ 'sampler_cfg': None, # Not use sampler by default
+ **cfg.data.get('test_dataloader', {}),
+ }
+ # the extra round_up data will be removed during gpu/cpu collect
+ data_loader = build_dataloader(dataset, **test_loader_cfg)
# build the algorithm and load checkpoint
algorithm = build_algorithm(cfg.algorithm)
diff --git a/tools/mmseg/test_mmseg.py b/tools/mmseg/test_mmseg.py
index 3528ff8d1..50a06eb4e 100644
--- a/tools/mmseg/test_mmseg.py
+++ b/tools/mmseg/test_mmseg.py
@@ -154,12 +154,28 @@ def main():
# build the dataloader
# TODO: support multiple images per gpu (only minor changes are needed)
dataset = build_dataset(cfg.data.test)
- data_loader = build_dataloader(
- dataset,
- samples_per_gpu=1,
- workers_per_gpu=cfg.data.workers_per_gpu,
+ # The default loader config
+ loader_cfg = dict(
+ # cfg.gpus will be ignored if distributed
+ num_gpus=len(cfg.gpu_ids),
dist=distributed,
shuffle=False)
+ # The overall dataloader settings
+ loader_cfg.update({
+ k: v
+ for k, v in cfg.data.items() if k not in [
+ 'train', 'val', 'test', 'train_dataloader', 'val_dataloader',
+ 'test_dataloader'
+ ]
+ })
+ test_loader_cfg = {
+ **loader_cfg,
+ 'samples_per_gpu': 1,
+ 'shuffle': False, # Not shuffle by default
+ **cfg.data.get('test_dataloader', {})
+ }
+ # build the dataloader
+ data_loader = build_dataloader(dataset, **test_loader_cfg)
# build the algorithm and load checkpoint
# Difference from mmsegmentation
diff --git a/tools/mmseg/train_mmseg.py b/tools/mmseg/train_mmseg.py
index d80978d92..b7c6e9ffd 100644
--- a/tools/mmseg/train_mmseg.py
+++ b/tools/mmseg/train_mmseg.py
@@ -177,7 +177,7 @@ def main():
seed = seed + dist.get_rank() if args.diff_seed else seed
logger.info(f'Set random seed to {args.seed}, deterministic: '
f'{args.deterministic}')
- set_random_seed(args.seed, deterministic=args.deterministic)
+ set_random_seed(seed, deterministic=args.deterministic)
cfg.seed = seed
meta['seed'] = args.seed
meta['exp_name'] = osp.basename(args.config)