[Feature] Support TIMMBackbone (#7020)

* add TIMMBackbone

based on
open-mmlab/mmpretrain#427
open-mmlab/mmsegmentation#998

* update and clean

* fix unit test

* Revert

* add example configs

configs/timm_example/README.md

@@ -0,0 +1,62 @@
+# Timm Example
+
+> [PyTorch Image Models](https://github.com/rwightman/pytorch-image-models)
+
+<!-- [OTHERS] -->
+
+## Abstract
+
+Py**T**orch **Im**age **M**odels (`timm`) is a collection of image models, layers, utilities, optimizers, schedulers, data-loaders / augmentations, and reference training / validation scripts that aim to pull together a wide variety of SOTA models with ability to reproduce ImageNet training results.
+
+<!--
+<div align=center>
+<img src="" height="400" />
+</div>
+-->
+
+## Results and Models
+
+### RetinaNet
+
+| Backbone        | Style   | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download |
+|:---------------:|:-------:|:-------:|:--------:|:--------------:|:------:|:------:|:--------:|
+| R-50            | pytorch | 1x      |          |                |        | [config](./retinanet_timm_tv_resnet50_fpn_1x_coco.py) |      |
+| EfficientNet-B1 | -       | 1x      |          |                |        | [config](./retinanet_timm_efficientnet_b1_fpn_1x_coco.py) |      |
+
+## Usage
+
+### Install additional requirements
+
+MMDetection supports timm backbones via `TIMMBackbone`, a wrapper class in MMClassification.
+Thus, you need to install `mmcls` in addition to timm.
+If you have already installed requirements for mmdet, run
+
+```shell
+pip install 'dataclasses; python_version<"3.7"'
+pip install timm
+pip install 'mmcls>=0.20.0'
+```
+
+See [this document](https://mmclassification.readthedocs.io/en/latest/install.html) for the details of MMClassification installation.
+
+### Edit config
+
+* See example configs for basic usage.
+* See the documents of [timm feature extraction](https://rwightman.github.io/pytorch-image-models/feature_extraction/#multi-scale-feature-maps-feature-pyramid) and [TIMMBackbone](https://mmclassification.readthedocs.io/en/latest/api.html#mmcls.models.backbones.TIMMBackbone) for details.
+* Which feature map is output depends on the backbone.
+  Please check `backbone out_channels` and `backbone out_strides` in your log, and modify `model.neck.in_channels` and `model.backbone.out_indices` if necessary.
+* If you use Vision Transformer models that do not support `features_only=True`, add `custom_hooks = []` to your config to disable `NumClassCheckHook`.
+
+## Citation
+
+```latex
+@misc{rw2019timm,
+  author = {Ross Wightman},
+  title = {PyTorch Image Models},
+  year = {2019},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  doi = {10.5281/zenodo.4414861},
+  howpublished = {\url{https://github.com/rwightman/pytorch-image-models}}
+}
+```

configs/timm_example/retinanet_timm_efficientnet_b1_fpn_1x_coco.py

@@ -0,0 +1,20 @@
+_base_ = [
+    '../_base_/models/retinanet_r50_fpn.py',
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+
+# please install mmcls>=0.20.0
+# import mmcls.models to trigger register_module in mmcls
+custom_imports = dict(imports=['mmcls.models'], allow_failed_imports=False)
+model = dict(
+    backbone=dict(
+        _delete_=True,
+        type='mmcls.TIMMBackbone',
+        model_name='efficientnet_b1',
+        features_only=True,
+        pretrained=True,
+        out_indices=(1, 2, 3, 4)),
+    neck=dict(in_channels=[24, 40, 112, 320]))
+
+optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)

configs/timm_example/retinanet_timm_tv_resnet50_fpn_1x_coco.py

@@ -0,0 +1,19 @@
+_base_ = [
+    '../_base_/models/retinanet_r50_fpn.py',
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+
+# please install mmcls>=0.20.0
+# import mmcls.models to trigger register_module in mmcls
+custom_imports = dict(imports=['mmcls.models'], allow_failed_imports=False)
+model = dict(
+    backbone=dict(
+        _delete_=True,
+        type='mmcls.TIMMBackbone',
+        model_name='tv_resnet50',  # ResNet-50 with torchvision weights
+        features_only=True,
+        pretrained=True,
+        out_indices=(1, 2, 3, 4)))
+
+optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)

requirements/optional.txt

@@ -2,3 +2,4 @@ cityscapesscripts
 imagecorruptions
 scipy
 sklearn
+timm