[Project] Add project instructions and example project.

.gitignore

@@ -125,6 +125,8 @@ venv.bak/
 *.pkl.json
 *.log.json
 /work_dirs
+/projects/*/work_dirs
+/projects/*/data
 /mmcls/.mim
 .DS_Store
 

projects/README.md

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+# Welcome to Projects of MMClassification
+
+In this folder, we welcome all contribution of vision deep-learning backbone from community.
+
+Here, these requirements, e.g. code standards, are not that strict as in core package. Thus, developers from the community can implement their algorithms much more easily and efficiently in MMClassification. We appreciate all contributions from community to make MMClassification greater.
+
+Here is an [example project](./example_project) about how to add your algorithms easily.
+
+We also provide some documentation listed below:
+
+- [New Model Guide](https://mmclassification.readthedocs.io/en/dev-1.x/advanced_guides/modules.html)
+
+  The documentation of adding new models.
+
+- [Contribution Guide](https://mmclassification.readthedocs.io/en/dev-1.x/notes/contribution_guide.html)
+
+  The guides for new contributors about how to add your projects to MMClassification.
+
+- [Discussions](https://github.com/open-mmlab/mmclassification/discussions)
+
+  Welcome to start discussion!

projects/example_project/README.md

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+# Example Project
+
+This is an example README for community `projects/`. You can write your README in your own project. Here are
+some recommended parts of a README for others to understand and use your project, you can copy or modify them
+according to your project.
+
+## Usage
+
+### Setup Environment
+
+Please refer to [Get Started](https://mmclassification.readthedocs.io/en/1.x/get_started.html) to install
+MMClassification.
+
+At first, add the current folder to `PYTHONPATH`, so that Python can find your code. Run command in the current directory to add it.
+
+> Please run it every time after you opened a new shell.
+
+```shell
+export PYTHONPATH=`pwd`:$PYTHONPATH
+```
+
+### Data Preparation
+
+Prepare the ImageNet-2012 dataset according to the [instruction](https://mmclassification.readthedocs.io/en/dev-1.x/user_guides/dataset_prepare.html#imagenet).
+
+### Training commands
+
+**To train with single GPU:**
+
+```bash
+mim train mmcls configs/examplenet_8xb32_in1k.py
+```
+
+**To train with multiple GPUs:**
+
+```bash
+mim train mmcls configs/examplenet_8xb32_in1k.py --launcher pytorch --gpus 8
+```
+
+**To train with multiple GPUs by slurm:**
+
+```bash
+mim train mmcls configs/examplenet_8xb32_in1k.py --launcher slurm \
+    --gpus 16 --gpus-per-node 8 --partition $PARTITION
+```
+
+### Testing commands
+
+**To test with single GPU:**
+
+```bash
+mim test mmcls configs/examplenet_8xb32_in1k.py $CHECKPOINT
+```
+
+**To test with multiple GPUs:**
+
+```bash
+mim test mmcls configs/examplenet_8xb32_in1k.py $CHECKPOINT --launcher pytorch --gpus 8
+```
+
+**To test with multiple GPUs by slurm:**
+
+```bash
+mim test mmcls configs/examplenet_8xb32_in1k.py $CHECKPOINT --launcher slurm \
+    --gpus 16 --gpus-per-node 8 --partition $PARTITION
+```
+
+## Results
+
+|       Model        |   Pretrain   | Top-1 (%) | Top-5 (%) |                 Config                  |                Download                |
+| :----------------: | :----------: | :-------: | :-------: | :-------------------------------------: | :------------------------------------: |
+|  ExampleNet-tiny   | From scratch |   82.33   |   96.15   | [config](./mvitv2-tiny_8xb256_in1k.py)  | [model](MODEL-LINK) \| [log](LOG-LINK) |
+| ExampleNet-small\* | From scratch |   83.63   |   96.51   | [config](./mvitv2-small_8xb256_in1k.py) |          [model](MODEL-LINK)           |
+| ExampleNet-base\*  | From scratch |   84.34   |   96.86   | [config](./mvitv2-base_8xb256_in1k.py)  |          [model](MODEL-LINK)           |
+
+*Models with * are converted from the [official repo](REPO-LINK). 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
+
+<!-- Replace to the citation of the paper your project refers to. -->
+
+```bibtex
+@misc{2020mmclassification,
+    title={OpenMMLab's Image Classification Toolbox and Benchmark},
+    author={MMClassification Contributors},
+    howpublished = {\url{https://github.com/open-mmlab/mmclassification}},
+    year={2020}
+}
+```
+
+## Checklist
+
+Here is a checklist of this project's progress. And you can ignore this part if you don't plan to contribute
+to MMClassification projects.
+
+- [ ] Milestone 1: PR-ready, and acceptable to be one of the `projects/`.
+
+  - [ ] Finish the code
+
+    <!-- The code's design shall follow existing interfaces and convention. For example, each model component should be registered into `mmcls.registry.MODELS` and configurable via a config file. -->
+
+  - [ ] Basic docstrings & proper citation
+
+    <!-- Each major class should contains a docstring, describing its functionality and arguments. If your code is copied or modified from other open-source projects, don't forget to cite the source project in docstring and make sure your behavior is not against its license. Typically, we do not accept any code snippet under GPL license. [A Short Guide to Open Source Licenses](https://medium.com/nationwide-technology/a-short-guide-to-open-source-licenses-cf5b1c329edd) -->
+
+  - [ ] Converted checkpoint and results (Only for reproduction)
+
+    <!-- If you are reproducing the result from a paper, make sure the model in the project can match that results. Also please provide checkpoint links or a checkpoint conversion script for others to get the pre-trained model. -->
+
+- [ ] Milestone 2: Indicates a successful model implementation.
+
+  - [ ] Training results
+
+    <!-- If you are reproducing the result from a paper, train your model from scratch and verified that the final result can match the original result. Usually, ±0.1% is acceptable for the image classification task on ImageNet-1k. -->
+
+- [ ] Milestone 3: Good to be a part of our core package!
+
+  - [ ] Unit tests
+
+    <!-- Unit tests for the major module are required. [Example](https://github.com/open-mmlab/mmclassification/blob/1.x/tests/test_models/test_backbones/test_vision_transformer.py) -->
+
+  - [ ] Code style
+
+    <!-- Refactor your code according to reviewer's comment. -->
+
+  - [ ] `metafile.yml` and `README.md`
+
+    <!-- It will used for MMClassification to acquire your models. [Example](https://github.com/open-mmlab/mmclassification/blob/1.x/configs/mvit/metafile.yml). In particular, you may have to refactor this README into a standard one. [Example](https://github.com/open-mmlab/mmclassification/blob/1.x/configs/swin_transformer/README.md) -->

projects/example_project/configs/examplenet_8xb32_in1k.py

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+# Directly inherit the entire recipe you want to use.
+_base_ = 'mmcls::resnet/resnet50_8xb32_in1k.py'
+
+# This line is to import your own modules.
+custom_imports = dict(imports='models')
+
+# Modify the backbone to use your own backbone.
+_base_['model']['backbone'] = dict(type='ExampleNet', depth=18)
+# Modify the in_channels of classifier head to fit your backbone.
+_base_['model']['head']['in_channels'] = 512

projects/example_project/models/__init__.py

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+from .example_net import ExampleNet
+
+__all__ = ['ExampleNet']

projects/example_project/models/example_net.py

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+from mmcls.models import ResNet
+from mmcls.registry import MODELS
+
+
+# Register your model to the `MODELS`.
+@MODELS.register_module()
+class ExampleNet(ResNet):
+    """Implements an example backbone.
+
+    Implement the backbone network just like a normal pytorch network.
+    """
+
+    def __init__(self, **kwargs) -> None:
+        print('#############################\n'
+              '#  Hello MMClassification!  #\n'
+              '#############################')
+        super().__init__(**kwargs)
+
+    def forward(self, x):
+        """The forward method of the network.
+
+        Args:
+            x (torch.Tensor): A tensor of image batch with shape
+                ``(batch_size, num_channels, height, width)``.
+
+        Returns:
+            Tuple[torch.Tensor]: Please return a tuple of tensors and every
+            tensor is a feature map of specified scale. If you only want the
+            final feature map, simply return a tuple with one item.
+        """
+        return super().forward(x)