[Feature] Support EfficientFormer. (open-mmlab#954)

* add efficient backbone * Update Readme and metafile * Add unit tests * fix confict * fix lint * update efficientformer head unit tests * update README * fix unit test * fix Readme * fix example * fix typo * recover api modification * Update EfficiemtFormer Backbone * fix unit tests * add efficientformer to readme and model zoo
Ezra-Yu · Sep 6, 2022 · 2c6b375 · 2c6b375
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diff --git a/README.md b/README.md
@@ -143,6 +143,7 @@ Results and models are available in the [model zoo](https://mmclassification.rea
 - [x] [CSPNet](https://github.com/open-mmlab/mmclassification/tree/master/configs/cspnet)
 - [x] [PoolFormer](https://github.com/open-mmlab/mmclassification/tree/master/configs/poolformer)
 - [x] [MViT](https://github.com/open-mmlab/mmclassification/tree/master/configs/mvit)
+- [x] [EfficientFormer](https://github.com/open-mmlab/mmclassification/tree/master/configs/efficientformer)
 
 </details>
 

diff --git a/configs/efficientformer/README.md b/configs/efficientformer/README.md
@@ -0,0 +1,47 @@
+# EfficientFormer
+
+> [EfficientFormer: Vision Transformers at MobileNet Speed](https://arxiv.org/abs/2206.01191)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+Vision Transformers (ViT) have shown rapid progress in computer vision tasks, achieving promising results on various benchmarks. However, due to the massive number of parameters and model design, e.g., attention mechanism, ViT-based models are generally times slower than lightweight convolutional networks. Therefore, the deployment of ViT for real-time applications is particularly challenging, especially on resource-constrained hardware such as mobile devices. Recent efforts try to reduce the computation complexity of ViT through network architecture search or hybrid design with MobileNet block, yet the inference speed is still unsatisfactory. This leads to an important question: can transformers run as fast as MobileNet while obtaining high performance? To answer this, we first revisit the network architecture and operators used in ViT-based models and identify inefficient designs. Then we introduce a dimension-consistent pure transformer (without MobileNet blocks) as a design paradigm.  Finally, we perform latency-driven slimming to get a series of final models dubbed EfficientFormer. Extensive experiments show the superiority of EfficientFormer in performance and speed on mobile devices. Our fastest model, EfficientFormer-L1, achieves 79.2% top-1 accuracy on ImageNet-1K with only 1.6 ms inference latency on iPhone 12 (compiled with CoreML), which runs as fast as MobileNetV2×1.4 (1.6 ms, 74.7% top-1), and our largest model, EfficientFormer-L7, obtains 83.3% accuracy with only 7.0 ms latency. Our work proves that properly designed transformers can reach extremely low latency on mobile devices while maintaining high performance.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/18586273/180713426-9d3d77e3-3584-42d8-9098-625b4170d796.png" width="100%"/>
+</div>
+
+## Results and models
+
+### ImageNet-1k
+
+|        Model         | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) |                                 Config                                  |                                  Download                                  |
+| :------------------: | :-------: | :------: | :-------: | :-------: | :---------------------------------------------------------------------: | :------------------------------------------------------------------------: |
+| EfficientFormer-l1\* |   12.19   |   1.30   |   80.46   |   94.99   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientformer/efficientformer-l1_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientformer/efficientformer-l1_3rdparty_in1k_20220803-d66e61df.pth) |
+| EfficientFormer-l3\* |   31.41   |   3.93   |   82.45   |   96.18   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientformer/efficientformer-l3_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientformer/efficientformer-l3_3rdparty_in1k_20220803-dde1c8c5.pth) |
+| EfficientFormer-l7\* |   82.23   |  10.16   |   83.40   |   96.60   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientformer/efficientformer-l7_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientformer/efficientformer-l7_3rdparty_in1k_20220803-41a552bb.pth) |
+
+*Models with * are converted from the [official repo](https://github.com/snap-research/EfficientFormer). The config files of these models are only for inference. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.*
+
+## Citation
+
+```bibtex
+@misc{https://doi.org/10.48550/arxiv.2206.01191,
+  doi = {10.48550/ARXIV.2206.01191},
+
+  url = {https://arxiv.org/abs/2206.01191},
+
+  author = {Li, Yanyu and Yuan, Geng and Wen, Yang and Hu, Eric and Evangelidis, Georgios and Tulyakov, Sergey and Wang, Yanzhi and Ren, Jian},
+
+  keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},
+
+  title = {EfficientFormer: Vision Transformers at MobileNet Speed},
+
+  publisher = {arXiv},
+
+  year = {2022},
+
+  copyright = {Creative Commons Attribution 4.0 International}
+}
+```
diff --git a/configs/efficientformer/efficientformer-l1_8xb128_in1k.py b/configs/efficientformer/efficientformer-l1_8xb128_in1k.py
@@ -0,0 +1,24 @@
+_base_ = [
+    '../_base_/datasets/imagenet_bs128_poolformer_small_224.py',
+    '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
+    '../_base_/default_runtime.py',
+]
+
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='EfficientFormer',
+        arch='l1',
+        drop_path_rate=0,
+        init_cfg=[
+            dict(
+                type='TruncNormal',
+                layer=['Conv2d', 'Linear'],
+                std=.02,
+                bias=0.),
+            dict(type='Constant', layer=['GroupNorm'], val=1., bias=0.),
+            dict(type='Constant', layer=['LayerScale'], val=1e-5)
+        ]),
+    neck=dict(type='GlobalAveragePooling', dim=1),
+    head=dict(
+        type='EfficientFormerClsHead', in_channels=448, num_classes=1000))
diff --git a/configs/efficientformer/efficientformer-l3_8xb128_in1k.py b/configs/efficientformer/efficientformer-l3_8xb128_in1k.py
@@ -0,0 +1,24 @@
+_base_ = [
+    '../_base_/datasets/imagenet_bs128_poolformer_small_224.py',
+    '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
+    '../_base_/default_runtime.py',
+]
+
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='EfficientFormer',
+        arch='l3',
+        drop_path_rate=0,
+        init_cfg=[
+            dict(
+                type='TruncNormal',
+                layer=['Conv2d', 'Linear'],
+                std=.02,
+                bias=0.),
+            dict(type='Constant', layer=['GroupNorm'], val=1., bias=0.),
+            dict(type='Constant', layer=['LayerScale'], val=1e-5)
+        ]),
+    neck=dict(type='GlobalAveragePooling', dim=1),
+    head=dict(
+        type='EfficientFormerClsHead', in_channels=512, num_classes=1000))
diff --git a/configs/efficientformer/efficientformer-l7_8xb128_in1k.py b/configs/efficientformer/efficientformer-l7_8xb128_in1k.py
@@ -0,0 +1,24 @@
+_base_ = [
+    '../_base_/datasets/imagenet_bs128_poolformer_small_224.py',
+    '../_base_/schedules/imagenet_bs1024_adamw_swin.py',
+    '../_base_/default_runtime.py',
+]
+
+model = dict(
+    type='ImageClassifier',
+    backbone=dict(
+        type='EfficientFormer',
+        arch='l7',
+        drop_path_rate=0,
+        init_cfg=[
+            dict(
+                type='TruncNormal',
+                layer=['Conv2d', 'Linear'],
+                std=.02,
+                bias=0.),
+            dict(type='Constant', layer=['GroupNorm'], val=1., bias=0.),
+            dict(type='Constant', layer=['LayerScale'], val=1e-5)
+        ]),
+    neck=dict(type='GlobalAveragePooling', dim=1),
+    head=dict(
+        type='EfficientFormerClsHead', in_channels=768, num_classes=1000))
diff --git a/configs/efficientformer/metafile.yml b/configs/efficientformer/metafile.yml
@@ -0,0 +1,67 @@
+Collections:
+  - Name: EfficientFormer
+    Metadata:
+      Training Data: ImageNet-1k
+      Architecture:
+        - Pooling
+        - 1x1 Convolution
+        - LayerScale
+        - MetaFormer
+    Paper:
+      URL: https://arxiv.org/pdf/2206.01191.pdf
+      Title: "EfficientFormer: Vision Transformers at MobileNet Speed"
+    README: configs/efficientformer/README.md
+    Code:
+      Version: v0.24.0
+      URL: https://github.com/open-mmlab/mmclassification/blob/v0.24.0/mmcls/models/backbones/efficientformer.py
+
+Models:
+  - Name: efficientformer-l1_3rdparty_8xb128_in1k
+    Metadata:
+      FLOPs: 1304601088     # 1.3G
+      Parameters: 12278696   # 12M
+    In Collections: EfficientFormer
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 80.46
+          Top 5 Accuracy: 94.99
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/efficientformer/efficientformer-l1_3rdparty_in1k_20220803-d66e61df.pth
+    Config: configs/efficientformer/efficientformer-l1_8xb128_in1k.py
+    Converted From:
+      Weights: https://drive.google.com/file/d/11SbX-3cfqTOc247xKYubrAjBiUmr818y/view?usp=sharing
+      Code: https://github.com/snap-research/EfficientFormer
+  - Name: efficientformer-l3_3rdparty_8xb128_in1k
+    Metadata:
+      Training Data: ImageNet-1k
+      FLOPs: 3737045760        # 3.7G
+      Parameters: 31406000     # 31M
+    In Collections: EfficientFormer
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 82.45
+          Top 5 Accuracy: 96.18
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/efficientformer/efficientformer-l3_3rdparty_in1k_20220803-dde1c8c5.pth
+    Config: configs/efficientformer/efficientformer-l3_8xb128_in1k.py
+    Converted From:
+      Weights: https://drive.google.com/file/d/1OyyjKKxDyMj-BcfInp4GlDdwLu3hc30m/view?usp=sharing
+      Code: https://github.com/snap-research/EfficientFormer
+  - Name: efficientformer-l7_3rdparty_8xb128_in1k
+    Metadata:
+      FLOPs: 10163951616      # 10.2G
+      Parameters: 82229328    # 82M
+    In Collections: EfficientFormer
+    Results:
+      - Dataset: ImageNet-1k
+        Metrics:
+          Top 1 Accuracy: 83.40
+          Top 5 Accuracy: 96.60
+        Task: Image Classification
+    Weights: https://download.openmmlab.com/mmclassification/v0/efficientformer/efficientformer-l7_3rdparty_in1k_20220803-41a552bb.pth
+    Config: configs/efficientformer/efficientformer-l7_8xb128_in1k.py
+    Converted From:
+      Weights: https://drive.google.com/file/d/1cVw-pctJwgvGafeouynqWWCwgkcoFMM5/view?usp=sharing
+      Code: https://github.com/snap-research/EfficientFormer
diff --git a/docs/en/api/models.rst b/docs/en/api/models.rst
@@ -87,6 +87,7 @@ Backbones
    VAN
    VGG
    VisionTransformer
+   EfficientFormer
 
 .. _necks:
 

diff --git a/docs/en/model_zoo.md b/docs/en/model_zoo.md
@@ -145,6 +145,9 @@ The ResNet family models below are trained by standard data augmentations, i.e.,
 |           MViTv2-small\*           |               34.87               |              7.00               |   83.63   |   96.51   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mvit/mvitv2-small_8xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/mvit/mvitv2-small_3rdparty_in1k_20220722-986bd741.pth) |
 |           MViTv2-base\*            |               51.47               |              10.20              |   84.34   |   96.86   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mvit/mvitv2-base_8xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/mvit/mvitv2-base_3rdparty_in1k_20220722-9c4f0a17.pth) |
 |           MViTv2-large\*           |              217.99               |              42.10              |   85.25   |   97.14   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mvit/mvitv2-large_8xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/mvit/mvitv2-large_3rdparty_in1k_20220722-2b57b983.pth) |
+|        EfficientFormer-l1\*        |               12.19               |              1.30               |   80.46   |   94.99   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientformer/efficientformer-l1_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientformer/efficientformer-l1_3rdparty_in1k_20220803-d66e61df.pth) |
+|        EfficientFormer-l3\*        |               31.41               |              3.93               |   82.45   |   96.18   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientformer/efficientformer-l3_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientformer/efficientformer-l3_3rdparty_in1k_20220803-dde1c8c5.pth) |
+|        EfficientFormer-l7\*        |               82.23               |              10.16              |   83.40   |   96.60   | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientformer/efficientformer-l7_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientformer/efficientformer-l7_3rdparty_in1k_20220803-41a552bb.pth) |
 
 *Models with * are converted from other repos, others are trained by ourselves.*
 

diff --git a/mmcls/models/backbones/__init__.py b/mmcls/models/backbones/__init__.py
@@ -6,6 +6,7 @@
 from .cspnet import CSPDarkNet, CSPNet, CSPResNet, CSPResNeXt
 from .deit import DistilledVisionTransformer
 from .densenet import DenseNet
+from .efficientformer import EfficientFormer
 from .efficientnet import EfficientNet
 from .hrnet import HRNet
 from .lenet import LeNet5
@@ -44,5 +45,5 @@
     'Res2Net', 'RepVGG', 'Conformer', 'MlpMixer', 'DistilledVisionTransformer',
     'PCPVT', 'SVT', 'EfficientNet', 'ConvNeXt', 'HRNet', 'ResNetV1c',
     'ConvMixer', 'CSPDarkNet', 'CSPResNet', 'CSPResNeXt', 'CSPNet',
-    'RepMLPNet', 'PoolFormer', 'DenseNet', 'VAN', 'MViT'
+    'RepMLPNet', 'PoolFormer', 'DenseNet', 'VAN', 'MViT', 'EfficientFormer'
 ]
-Original file line number
+Diff line change
@@ Expand Up / @@ -87,6 +87,7 @@ Backbones @@
        VAN
        VGG
        VisionTransformer
+       EfficientFormer
     .. _necks:
@@ Expand Down @@