[fbsync] Add RegNet Architecture in TorchVision (#4403)

Summary:
* initial code

* add SqueezeExcitation

* initial code

* add SqueezeExcitation

* add SqueezeExcitation

* regnet blocks, stems and model definition

* nit

* add fc layer

* use Callable instead of Enum for block, stem and activation

* add regnet_x and regnet_y model build functions, add docs

* remove unused depth

* use BN/activation constructor and ConvBNActivation

* add expected test pkl files

* allow custom activation in SqueezeExcitation

* use ReLU as the default activation

* initial code

* add SqueezeExcitation

* initial code

* add SqueezeExcitation

* add SqueezeExcitation

* regnet blocks, stems and model definition

* nit

* add fc layer

* use Callable instead of Enum for block, stem and activation

* add regnet_x and regnet_y model build functions, add docs

* remove unused depth

* use BN/activation constructor and ConvBNActivation

* reuse SqueezeExcitation from efficientnet

* refactor RegNetParams into BlockParams

* use nn.init, replace np with torch

* update README

* construct model with stem, block, classifier instances

* Revert "construct model with stem, block, classifier instances"

This reverts commit 850f5f3.

* remove unused blocks

* support scaled model

* fuse into ConvBNActivation

* make reset_parameters private

* fix type errors

* fix for unit test

* add pretrained weights for 6 variant models, update docs

Reviewed By: prabhat00155, NicolasHug

Differential Revision: D31309546

fbshipit-source-id: 56e01f105279c3d3c5514607c23d2835896b3d03

Author: datumbox

docs/source/models.rst

@@ -37,6 +37,7 @@ architectures for image classification:
 -  `Wide ResNet`_
 -  `MNASNet`_
 -  `EfficientNet`_
+-  `RegNet`_
 
 You can construct a model with random weights by calling its constructor:
 
@@ -65,6 +66,20 @@ You can construct a model with random weights by calling its constructor:
     efficientnet_b5 = models.efficientnet_b5()
     efficientnet_b6 = models.efficientnet_b6()
     efficientnet_b7 = models.efficientnet_b7()
+    regnet_y_400mf = models.regnet_y_400mf()
+    regnet_y_800mf = models.regnet_y_800mf()
+    regnet_y_1_6gf = models.regnet_y_1_6gf()
+    regnet_y_3_2gf = models.regnet_y_3_2gf()
+    regnet_y_8gf = models.regnet_y_8gf()
+    regnet_y_16gf = models.regnet_y_16gf()
+    regnet_y_32gf = models.regnet_y_32gf()
+    regnet_x_400mf = models.regnet_x_400mf()
+    regnet_x_800mf = models.regnet_x_800mf()
+    regnet_x_1_6gf = models.regnet_x_1_6gf()
+    regnet_x_3_2gf = models.regnet_x_3_2gf()
+    regnet_x_8gf = models.regnet_x_8gf()
+    regnet_x_16gf = models.regnet_x_16gf()
+    regnet_x_32gf = models.regnet_x_32gf()
 
 We provide pre-trained models, using the PyTorch :mod:`torch.utils.model_zoo`.
 These can be constructed by passing ``pretrained=True``:
@@ -94,6 +109,12 @@ These can be constructed by passing ``pretrained=True``:
     efficientnet_b5 = models.efficientnet_b5(pretrained=True)
     efficientnet_b6 = models.efficientnet_b6(pretrained=True)
     efficientnet_b7 = models.efficientnet_b7(pretrained=True)
+    regnet_y_400mf = models.regnet_y_400mf(pretrained=True)
+    regnet_y_800mf = models.regnet_y_800mf(pretrained=True)
+    regnet_y_8gf = models.regnet_y_8gf(pretrained=True)
+    regnet_x_400mf = models.regnet_x_400mf(pretrained=True)
+    regnet_x_800mf = models.regnet_x_800mf(pretrained=True)
+    regnet_x_8gf = models.regnet_x_8gf(pretrained=True)
 
 Instancing a pre-trained model will download its weights to a cache directory.
 This directory can be set using the `TORCH_MODEL_ZOO` environment variable. See
@@ -188,6 +209,12 @@ EfficientNet-B4                   83.384          96.594
 EfficientNet-B5                   83.444          96.628
 EfficientNet-B6                   84.008          96.916
 EfficientNet-B7                   84.122          96.908
+regnet_x_400mf                    72.834          90.950     
+regnet_x_800mf                    75.190          92.418
+regnet_x_8gf                      79.324          94.694
+regnet_y_400mf                    74.024          91.680
+regnet_y_800mf                    76.420          93.136
+regnet_y_8gf                      79.966          95.100
 ================================  =============   =============
 
 
@@ -204,6 +231,7 @@ EfficientNet-B7                   84.122          96.908
 .. _ResNeXt: https://arxiv.org/abs/1611.05431
 .. _MNASNet: https://arxiv.org/abs/1807.11626
 .. _EfficientNet: https://arxiv.org/abs/1905.11946
+.. _RegNet: https://arxiv.org/abs/2003.13678
 
 .. currentmodule:: torchvision.models
 
@@ -317,6 +345,24 @@ EfficientNet
 .. autofunction:: efficientnet_b6
 .. autofunction:: efficientnet_b7
 
+RegNet
+------------
+
+.. autofunction:: regnet_y_400mf
+.. autofunction:: regnet_y_800mf
+.. autofunction:: regnet_y_1_6gf
+.. autofunction:: regnet_y_3_2gf
+.. autofunction:: regnet_y_8gf
+.. autofunction:: regnet_y_16gf
+.. autofunction:: regnet_y_32gf
+.. autofunction:: regnet_x_400mf
+.. autofunction:: regnet_x_800mf
+.. autofunction:: regnet_x_1_6gf
+.. autofunction:: regnet_x_3_2gf
+.. autofunction:: regnet_x_8gf
+.. autofunction:: regnet_x_16gf
+.. autofunction:: regnet_x_32gf
+
 Quantized Models
 ----------------
 

hubconf.py

@@ -17,6 +17,10 @@
     mnasnet1_3
 from torchvision.models.efficientnet import efficientnet_b0, efficientnet_b1, efficientnet_b2, \
     efficientnet_b3, efficientnet_b4, efficientnet_b5, efficientnet_b6, efficientnet_b7
+from torchvision.models.regnet import regnet_y_400mf, regnet_y_800mf, \
+    regnet_y_1_6gf, regnet_y_3_2gf, regnet_y_8gf, regnet_y_16gf, regnet_y_32gf, \
+    regnet_x_400mf, regnet_x_800mf, regnet_x_1_6gf, regnet_x_3_2gf, regnet_x_8gf, \
+    regnet_x_16gf, regnet_x_32gf
 
 # segmentation
 from torchvision.models.segmentation import fcn_resnet50, fcn_resnet101, \

references/classification/README.md

@@ -79,6 +79,36 @@ The weights of the B0-B4 variants are ported from Ross Wightman's [timm repo](ht
 
 The weights of the B5-B7 variants are ported from Luke Melas' [EfficientNet-PyTorch repo](https://github.com/lukemelas/EfficientNet-PyTorch/blob/1039e009545d9329ea026c9f7541341439712b96/efficientnet_pytorch/utils.py#L562-L564).
 
+
+### RegNet
+
+#### Small models
+```
+torchrun --nproc_per_node=8 train.py\
+     --model $MODEL --epochs 100 --batch-size 128 --wd 0.00005 --lr=0.8\
+     --lr-scheduler=cosineannealinglr --lr-warmup-method=linear\
+     --lr-warmup-epochs=5 --lr-warmup-decay=0.1
+```
+Here `$MODEL` is one of `regnet_x_400mf`, `regnet_x_800mf`, `regnet_x_1_6gf`, `regnet_y_400mf`, `regnet_y_800mf` and `regnet_y_1_6gf`. Please note we used learning rate 0.4 for `regent_y_400mf` to get the same Acc@1 as [the paper)(https://arxiv.org/abs/2003.13678).
+
+### Medium models
+```
+torchrun --nproc_per_node=8 train.py\
+     --model $MODEL --epochs 100 --batch-size 64 --wd 0.00005 --lr=0.4\
+     --lr-scheduler=cosineannealinglr --lr-warmup-method=linear\
+     --lr-warmup-epochs=5 --lr-warmup-decay=0.1
+```
+Here `$MODEL` is one of `regnet_x_3_2gf`, `regnet_x_8gf`, `regnet_x_16gf`, `regnet_y_3_2gf` and `regnet_y_8gf`.
+
+### Large models
+```
+torchrun --nproc_per_node=8 train.py\
+     --model $MODEL --epochs 100 --batch-size 32 --wd 0.00005 --lr=0.2\
+     --lr-scheduler=cosineannealinglr --lr-warmup-method=linear\
+     --lr-warmup-epochs=5 --lr-warmup-decay=0.1
+```
+Here `$MODEL` is one of `regnet_x_32gf`, `regnet_y_16gf` and `regnet_y_32gf`.
+
 ## Mixed precision training
 Automatic Mixed Precision (AMP) training on GPU for Pytorch can be enabled with the [NVIDIA Apex extension](https://github.com/NVIDIA/apex).
 

test/expect/ModelTester.test_regnet_x_16gf_expect.pkl

@@ -9,6 +9,7 @@
 from .mnasnet import *
 from .shufflenetv2 import *
 from .efficientnet import *
+from .regnet import *
 from . import segmentation
 from . import detection
 from . import video