Merge branch 'add_Recommendation' into 'main'

添加openmmlab测试用例 See merge request dcutoolkit/deeplearing/dlexamples_new!32

Merge branch 'add_Recommendation' into 'main'
添加openmmlab测试用例 See merge request dcutoolkit/deeplearing/dlexamples_new!32
322546ff · sunxx1 · 1f4ba993 · 8c867a92 · 322546ff · 322546ff
Commit 322546ff authored Jul 30, 2022 by sunxx1
20 changed files
--- a/openmmlab_test/mmclassification-speed-benchmark/configs/resnet/resnetv1d101_b32x8_imagenet.py
+++ b/openmmlab_test/mmclassification-speed-benchmark/configs/resnet/resnetv1d101_b32x8_imagenet.py
+_base_ = [
+    '../_base_/models/resnetv1d101.py',
+    '../_base_/datasets/imagenet_bs32_pil_resize.py',
+    '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
+]
--- a/openmmlab_test/mmclassification-speed-benchmark/configs/resnet/resnetv1d152_b32x8_imagenet.py
+++ b/openmmlab_test/mmclassification-speed-benchmark/configs/resnet/resnetv1d152_b32x8_imagenet.py
+_base_ = [
+    '../_base_/models/resnetv1d152.py',
+    '../_base_/datasets/imagenet_bs32_pil_resize.py',
+    '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
+]
--- a/openmmlab_test/mmclassification-speed-benchmark/configs/resnet/resnetv1d50_b32x8_imagenet.py
+++ b/openmmlab_test/mmclassification-speed-benchmark/configs/resnet/resnetv1d50_b32x8_imagenet.py
+_base_ = [
+    '../_base_/models/resnetv1d50.py',
+    '../_base_/datasets/imagenet_bs32_pil_resize.py',
+    '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
+]
--- a/openmmlab_test/mmclassification-speed-benchmark/configs/resnext/README.md
+++ b/openmmlab_test/mmclassification-speed-benchmark/configs/resnext/README.md
+# Aggregated Residual Transformations for Deep Neural Networks
+## Introduction
+<!-- [ALGORITHM] -->
+```latex
+@inproceedings{xie2017aggregated,
+  title={Aggregated residual transformations for deep neural networks},
+  author={Xie, Saining and Girshick, Ross and Doll{\'a}r, Piotr and Tu, Zhuowen and He, Kaiming},
+  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
+  pages={1492--1500},
+  year={2017}
+}
+```
+## Results and models
+### ImageNet
+|         Model         | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download |
+|:---------------------:|:---------:|:--------:|:---------:|:---------:|:---------:|:--------:|
+| ResNeXt-32x4d-50      | 25.03     | 4.27     | 77.90 | 93.66 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnext/resnext50_32x4d_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnext/resnext50_32x4d_b32x8_imagenet_20210429-56066e27.pth) &#124; [log](https://download.openmmlab.com/mmclassification/v0/resnext/resnext50_32x4d_b32x8_imagenet_20210429-56066e27.log.json) |
+| ResNeXt-32x4d-101     | 44.18     | 8.03     | 78.71  | 94.12 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnext/resnext101_32x4d_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth) &#124; [log](https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.log.json) |
+| ResNeXt-32x8d-101     | 88.79     | 16.5     | 79.23 | 94.58 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnext/resnext101_32x8d_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x8d_b32x8_imagenet_20210506-23a247d5.pth) &#124; [log](https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x8d_b32x8_imagenet_20210506-23a247d5.log.json) |
+| ResNeXt-32x4d-152     | 59.95     | 11.8     | 78.93 | 94.41 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnext/resnext152_32x4d_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnext/resnext152_32x4d_b32x8_imagenet_20210524-927787be.pth) &#124; [log](https://download.openmmlab.com/mmclassification/v0/resnext/resnext152_32x4d_b32x8_imagenet_20210524-927787be.log.json) |
--- a/openmmlab_test/mmclassification-speed-benchmark/configs/resnext/metafile.yml
+++ b/openmmlab_test/mmclassification-speed-benchmark/configs/resnext/metafile.yml
+Collections:
+  - Name: ResNeXt
+    Metadata:
+      Training Data: ImageNet
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Epochs: 100
+      Batch Size: 256
+      Architecture:
+        - ResNeXt
+    Paper: https://openaccess.thecvf.com/content_cvpr_2017/html/Xie_Aggregated_Residual_Transformations_CVPR_2017_paper.html
+    README: configs/resnext/README.md
+Models:
+- Config: configs/resnext/resnext50_32x4d_b32x8_imagenet.py
+  In Collection: ResNeXt
+  Metadata:
+    FLOPs: 4270000000
+    Parameters: 25030000
+  Name: resnext50_32x4d_b32x8_imagenet
+  Results:
+  - Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 77.92
+      Top 5 Accuracy: 93.74
+    Task: Image Classification
+  Weights: https://download.openmmlab.com/mmclassification/v0/resnext/resnext50_32x4d_batch256_imagenet_20200708-c07adbb7.pth
+- Config: configs/resnext/resnext101_32x4d_b32x8_imagenet.py
+  In Collection: ResNeXt
+  Metadata:
+    FLOPs: 8030000000
+    Parameters: 44180000
+  Name: resnext101_32x4d_b32x8_imagenet
+  Results:
+  - Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 78.7
+      Top 5 Accuracy: 94.34
+    Task: Image Classification
+  Weights: https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_batch256_imagenet_20200708-87f2d1c9.pth
+- Config: configs/resnext/resnext101_32x8d_b32x8_imagenet.py
+  In Collection: ResNeXt
+  Metadata:
+    FLOPs: 16500000000
+    Parameters: 88790000
+  Name: resnext101_32x8d_b32x8_imagenet
+  Results:
+  - Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 79.22
+      Top 5 Accuracy: 94.52
+    Task: Image Classification
+  Weights: https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x8d_batch256_imagenet_20200708-1ec34aa7.pth
+- Config: configs/resnext/resnext152_32x4d_b32x8_imagenet.py
+  In Collection: ResNeXt
+  Metadata:
+    FLOPs: 11800000000
+    Parameters: 59950000
+  Name: resnext152_32x4d_b32x8_imagenet
+  Results:
+  - Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 79.06
+      Top 5 Accuracy: 94.47
+    Task: Image Classification
+  Weights: https://download.openmmlab.com/mmclassification/v0/resnext/resnext152_32x4d_batch256_imagenet_20200708-aab5034c.pth
--- a/openmmlab_test/mmclassification-speed-benchmark/configs/resnext/resnext101_32x4d_b32x8_imagenet.py
+++ b/openmmlab_test/mmclassification-speed-benchmark/configs/resnext/resnext101_32x4d_b32x8_imagenet.py
+_base_ = [
+    '../_base_/models/resnext101_32x4d.py',
+    '../_base_/datasets/imagenet_bs32_pil_resize.py',
+    '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
+]
--- a/openmmlab_test/mmclassification-speed-benchmark/configs/resnext/resnext101_32x8d_b32x8_imagenet.py
+++ b/openmmlab_test/mmclassification-speed-benchmark/configs/resnext/resnext101_32x8d_b32x8_imagenet.py
+_base_ = [
+    '../_base_/models/resnext101_32x8d.py',
+    '../_base_/datasets/imagenet_bs32_pil_resize.py',
+    '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
+]
--- a/openmmlab_test/mmclassification-speed-benchmark/configs/resnext/resnext152_32x4d_b32x8_imagenet.py
+++ b/openmmlab_test/mmclassification-speed-benchmark/configs/resnext/resnext152_32x4d_b32x8_imagenet.py
+_base_ = [
+    '../_base_/models/resnext152_32x4d.py',
+    '../_base_/datasets/imagenet_bs32_pil_resize.py',
+    '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
+]
--- a/openmmlab_test/mmclassification-speed-benchmark/configs/resnext/resnext50_32x4d_b32x8_imagenet.py
+++ b/openmmlab_test/mmclassification-speed-benchmark/configs/resnext/resnext50_32x4d_b32x8_imagenet.py
+_base_ = [
+    '../_base_/models/resnext50_32x4d.py',
+    '../_base_/datasets/imagenet_bs32_pil_resize.py',
+    '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
+]
--- a/openmmlab_test/mmclassification-speed-benchmark/configs/seresnet/README.md
+++ b/openmmlab_test/mmclassification-speed-benchmark/configs/seresnet/README.md
+# Squeeze-and-Excitation Networks
+## Introduction
+<!-- [ALGORITHM] -->
+```latex
+@inproceedings{hu2018squeeze,
+  title={Squeeze-and-excitation networks},
+  author={Hu, Jie and Shen, Li and Sun, Gang},
+  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
+  pages={7132--7141},
+  year={2018}
+}
+```
+## Results and models
+### ImageNet
+|         Model         | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download |
+|:---------------------:|:---------:|:--------:|:---------:|:---------:|:---------:|:--------:|
+| SE-ResNet-50          | 28.09     | 4.13     | 77.74 | 93.84 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/seresnet50/seresnet50_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet50_batch256_imagenet_20200804-ae206104.pth) &#124; [log](https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet50_batch256_imagenet_20200708-657b3c36.log.json) |
+| SE-ResNet-101         | 49.33     | 7.86     | 78.26 | 94.07 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/seresnet101/seresnet101_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet101_batch256_imagenet_20200804-ba5b51d4.pth) &#124; [log](https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet101_batch256_imagenet_20200708-038a4d04.log.json) |
--- a/openmmlab_test/mmclassification-speed-benchmark/configs/seresnet/metafile.yml
+++ b/openmmlab_test/mmclassification-speed-benchmark/configs/seresnet/metafile.yml
+Collections:
+  - Name: SEResNet
+    Metadata:
+      Training Data: ImageNet
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Epochs: 140
+      Batch Size: 256
+      Architecture:
+        - ResNet
+    Paper: https://openaccess.thecvf.com/content_cvpr_2018/html/Hu_Squeeze-and-Excitation_Networks_CVPR_2018_paper.html
+    README: configs/seresnet/README.md
+Models:
+- Config: configs/seresnet50/seresnet50_b32x8_imagenet.py
+  In Collection: SEResNet
+  Metadata:
+    FLOPs: 4130000000
+    Parameters: 28090000
+  Name: seresnet50_b32x8_imagenet
+  Results:
+  - Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 77.74
+      Top 5 Accuracy: 93.84
+    Task: Image Classification
+  Weights: https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet50_batch256_imagenet_20200804-ae206104.pth
+- Config: configs/seresnet101/seresnet101_b32x8_imagenet.py
+  In Collection: SEResNet
+  Metadata:
+    FLOPs: 7860000000
+    Parameters: 49330000
+  Name: seresnet101_b32x8_imagenet
+  Results:
+  - Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 78.26
+      Top 5 Accuracy: 94.07
+    Task: Image Classification
+  Weights: https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet101_batch256_imagenet_20200804-ba5b51d4.pth
--- a/openmmlab_test/mmclassification-speed-benchmark/configs/seresnet/seresnet101_b32x8_imagenet.py
+++ b/openmmlab_test/mmclassification-speed-benchmark/configs/seresnet/seresnet101_b32x8_imagenet.py
+_base_ = [
+    '../_base_/models/seresnet101.py',
+    '../_base_/datasets/imagenet_bs32_pil_resize.py',
+    '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
+]
--- a/openmmlab_test/mmclassification-speed-benchmark/configs/seresnet/seresnet50_b32x8_imagenet.py
+++ b/openmmlab_test/mmclassification-speed-benchmark/configs/seresnet/seresnet50_b32x8_imagenet.py
+_base_ = [
+    '../_base_/models/seresnet50.py',
+    '../_base_/datasets/imagenet_bs32_pil_resize.py',
+    '../_base_/schedules/imagenet_bs256_140e.py',
+    '../_base_/default_runtime.py'
+]
--- a/openmmlab_test/mmclassification-speed-benchmark/configs/seresnext/README.md
+++ b/openmmlab_test/mmclassification-speed-benchmark/configs/seresnext/README.md
+# Squeeze-and-Excitation Networks
+## Introduction
+<!-- [ALGORITHM] -->
+```latex
+@inproceedings{hu2018squeeze,
+  title={Squeeze-and-excitation networks},
+  author={Hu, Jie and Shen, Li and Sun, Gang},
+  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
+  pages={7132--7141},
+  year={2018}
+}
+```
--- a/openmmlab_test/mmclassification-speed-benchmark/configs/seresnext/seresnext101_32x4d_b32x8_imagenet.py
+++ b/openmmlab_test/mmclassification-speed-benchmark/configs/seresnext/seresnext101_32x4d_b32x8_imagenet.py
+_base_ = [
+    '../_base_/models/seresnext101_32x4d.py',
+    '../_base_/datasets/imagenet_bs32_pil_resize.py',
+    '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
+]
--- a/openmmlab_test/mmclassification-speed-benchmark/configs/seresnext/seresnext50_32x4d_b32x8_imagenet.py
+++ b/openmmlab_test/mmclassification-speed-benchmark/configs/seresnext/seresnext50_32x4d_b32x8_imagenet.py
+_base_ = [
+    '../_base_/models/seresnext50_32x4d.py',
+    '../_base_/datasets/imagenet_bs32_pil_resize.py',
+    '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
+]
--- a/openmmlab_test/mmclassification-speed-benchmark/configs/shufflenet_v1/README.md
+++ b/openmmlab_test/mmclassification-speed-benchmark/configs/shufflenet_v1/README.md
+# ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
+## Introduction
+<!-- [ALGORITHM] -->
+```latex
+@inproceedings{zhang2018shufflenet,
+  title={Shufflenet: An extremely efficient convolutional neural network for mobile devices},
+  author={Zhang, Xiangyu and Zhou, Xinyu and Lin, Mengxiao and Sun, Jian},
+  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
+  pages={6848--6856},
+  year={2018}
+}
+```
+## Results and models
+### ImageNet
+|         Model         | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download |
+|:---------------------:|:---------:|:--------:|:---------:|:---------:|:---------:|:--------:|
+| ShuffleNetV1 1.0x (group=3)   | 1.87      | 0.146    | 68.13 | 87.81 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/shufflenet_v1/shufflenet_v1_1x_b64x16_linearlr_bn_nowd_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/shufflenet_v1/shufflenet_v1_batch1024_imagenet_20200804-5d6cec73.pth) &#124; [log](https://download.openmmlab.com/mmclassification/v0/shufflenet_v1/shufflenet_v1_batch1024_imagenet_20200804-5d6cec73.log.json) |
--- a/openmmlab_test/mmclassification-speed-benchmark/configs/shufflenet_v1/metafile.yml
+++ b/openmmlab_test/mmclassification-speed-benchmark/configs/shufflenet_v1/metafile.yml
+Collections:
+  - Name: Shufflenet V1
+    Metadata:
+      Training Data: ImageNet
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+        - No BN decay
+      Training Resources: 8x 1080 GPUs
+      Epochs: 300
+      Batch Size: 1024
+      Architecture:
+        - Shufflenet V1
+    Paper: https://openaccess.thecvf.com/content_cvpr_2018/html/Zhang_ShuffleNet_An_Extremely_CVPR_2018_paper.html
+    README: configs/shufflenet_v1/README.md
+Models:
+- Config: configs/shufflenet_v1/shufflenet_v1_1x_b64x16_linearlr_bn_nowd_imagenet.py
+  In Collection: Shufflenet V1
+  Metadata:
+    FLOPs: 146000000
+    Parameters: 1870000
+  Name: shufflenet_v1_1x_b64x16_linearlr_bn_nowd_imagenet
+  Results:
+  - Dataset: ImageNet
+    Metrics:
+      Top 1 Accuracy: 68.13
+      Top 5 Accuracy: 87.81
+    Task: Image Classification
+  Weights: https://download.openmmlab.com/mmclassification/v0/shufflenet_v1/shufflenet_v1_batch1024_imagenet_20200804-5d6cec73.pth
--- a/openmmlab_test/mmclassification-speed-benchmark/configs/shufflenet_v1/shufflenet_v1_1x_b64x16_linearlr_bn_nowd_imagenet.py
+++ b/openmmlab_test/mmclassification-speed-benchmark/configs/shufflenet_v1/shufflenet_v1_1x_b64x16_linearlr_bn_nowd_imagenet.py
+_base_ = [
+    '../_base_/models/shufflenet_v1_1x.py',
+    '../_base_/datasets/imagenet_bs64_pil_resize.py',
+    '../_base_/schedules/imagenet_bs1024_linearlr_bn_nowd.py',
+    '../_base_/default_runtime.py'
+]
--- a/openmmlab_test/mmclassification-speed-benchmark/configs/shufflenet_v2/README.md
+++ b/openmmlab_test/mmclassification-speed-benchmark/configs/shufflenet_v2/README.md
+# Shufflenet v2: Practical guidelines for efficient cnn architecture design
+## Introduction
+<!-- [ALGORITHM] -->
+```latex
+@inproceedings{ma2018shufflenet,
+  title={Shufflenet v2: Practical guidelines for efficient cnn architecture design},
+  author={Ma, Ningning and Zhang, Xiangyu and Zheng, Hai-Tao and Sun, Jian},
+  booktitle={Proceedings of the European conference on computer vision (ECCV)},
+  pages={116--131},
+  year={2018}
+}
+```
+## Results and models
+### ImageNet
+|         Model         | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download |
+|:---------------------:|:---------:|:--------:|:---------:|:---------:|:---------:|:--------:|
+| ShuffleNetV2 1.0x     | 2.28      | 0.149    | 69.55 | 88.92 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/shufflenet_v2/shufflenet_v2_1x_b64x16_linearlr_bn_nowd_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/shufflenet_v2/shufflenet_v2_batch1024_imagenet_20200812-5bf4721e.pth) &#124; [log](https://download.openmmlab.com/mmclassification/v0/shufflenet_v2/shufflenet_v2_batch1024_imagenet_20200804-8860eec9.log.json) |