[Feature] add model script, training recipe and trained weights of Mi…

…xNet

configs/mixnet/README.MD

@@ -0,0 +1,89 @@
+# MixNet
+> [MixConv: Mixed Depthwise Convolutional Kernels](https://arxiv.org/abs/1907.09595)
+
+## Introduction
+
+Depthwise convolution is becoming increasingly popular in modern efficient ConvNets, but its kernel size is often
+overlooked. In this paper, the authors systematically study the impact of different kernel sizes, and observe that
+combining the benefits of multiple kernel sizes can lead to better accuracy and efficiency. Based on this observation,
+the authors propose a new mixed depthwise convolution (MixConv), which naturally mixes up multiple kernel sizes in a
+single convolution. As a simple drop-in replacement of vanilla depthwise convolution, our MixConv improves the accuracy
+and efficiency for existing MobileNets on both ImageNet classification and COCO object detection.[[1](#references)]
+
+<p align="center">
+  <img src="https://user-images.githubusercontent.com/53842165/219263295-75de649e-d38b-4b05-bd26-1c96896f7e83.png" width=800 />
+</p>
+<p align="center">
+  <em>Figure 1. Architecture of MixNet [<a href="#references">1</a>] </em>
+</p>
+
+## Results
+
+Our reproduced model performance on ImageNet-1K is reported as follows.
+
+<div align="center">
+
+| Model    | Context  | Top-1 (%) | Top-5 (%) | Params (M) | Recipe                                                                                        | Download                                                                               |
+|----------|----------|-----------|-----------|------------|-----------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------|
+| MixNet_s | D910x8-G | 75.63     | 92.52     | 4.17       | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/mixnet/mixnet_s_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/mixnet/mixnet_s-2a5ef3a3.ckpt) |
+
+</div>
+
+#### Notes
+
+- Context: Training context denoted as {device}x{pieces}-{MS mode}, where mindspore mode can be G - graph mode or F - pynative mode with ms function. For example, D910x8-G is for training on 8 pieces of Ascend 910 NPU using graph mode.
+- Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K.
+
+## Quick Start
+
+### Preparation
+
+#### Installation
+Please refer to the [installation instruction](https://github.com/mindspore-ecosystem/mindcv#installation) in MindCV.
+
+#### Dataset Preparation
+Please download the [ImageNet-1K](https://www.image-net.org/challenges/LSVRC/2012/index.php) dataset for model training and validation.
+
+### Training
+
+* Distributed Training
+
+It is easy to reproduce the reported results with the pre-defined training recipe. For distributed training on multiple Ascend 910 devices, please run
+
+```shell
+# distrubted training on multiple GPU/Ascend devices
+mpirun -n 8 python train.py --config configs/mixnet/mixnet_s_ascend.yaml --data_dir /path/to/imagenet
+```
+
+> If the script is executed by the root user, the `--allow-run-as-root` parameter must be added to `mpirun`.
+
+Similarly, you can train the model on multiple GPU devices with the above `mpirun` command.
+
+For detailed illustration of all hyper-parameters, please refer to [config.py](https://github.com/mindspore-lab/mindcv/blob/main/config.py).
+
+**Note:**  As the global batch size  (batch_size x num_devices) is an important hyper-parameter, it is recommended to keep the global batch size unchanged for reproduction or adjust the learning rate linearly to a new global batch size.
+
+* Standalone Training
+
+If you want to train or finetune the model on a smaller dataset without distributed training, please run:
+
+```shell
+# standalone training on a CPU/GPU/Ascend device
+python train.py --config configs/mixnet/mixnet_s_ascend.yaml --data_dir /path/to/dataset --distribute False
+```
+
+### Validation
+
+To validate the accuracy of the trained model, you can use `validate.py` and parse the checkpoint path with `--ckpt_path`.
+
+```shell
+python validate.py -c configs/mixnet/mixnet_s_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt
+```
+
+### Deployment
+
+Please refer to the [deployment tutorial](https://github.com/mindspore-lab/mindcv/blob/main/tutorials/deployment.md) in MindCV.
+
+## References
+
+[1] Tan M, Le Q V. Mixconv: Mixed depthwise convolutional kernels[J]. arXiv preprint arXiv:1907.09595, 2019.

configs/mixnet/mixnet_s_ascend.yaml

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+# system
+mode: 0
+distribute: True
+num_parallel_workers: 8
+val_while_train: True
+
+# dataset
+dataset: "imagenet"
+data_dir: "path/to/imagenet"
+shuffle: True
+dataset_download: False
+batch_size: 128
+drop_remainder: True
+
+# augmentation
+image_resize: 224
+scale: [0.08, 1.0]
+ratio: [0.75, 1.333]
+hflip: 0.5
+interpolation: "bicubic"
+auto_augment: "randaug-m9-mstd0.5"
+re_prob: 0.25
+crop_pct: 0.875
+mixup: 0.2
+cutmix: 1.0
+
+# model
+model: "mixnet_s"
+num_classes: 1000
+pretrained: False
+ckpt_path: ''
+keep_checkpoint_max: 10
+ckpt_save_dir: "./ckpt"
+epoch_size: 600
+dataset_sink_mode: True
+amp_level: "O3"
+
+# loss
+loss: "CE"
+label_smoothing: 0.1
+
+# lr scheduler
+scheduler: "warmup_cosine_decay"
+lr: 0.2
+min_lr: 0.00001
+decay_epochs: 585
+warmup_epochs: 15
+
+# optimizer
+opt: "momentum"
+filter_bias_and_bn: True
+momentum: 0.9
+weight_decay: 0.00002
+loss_scale: 256
+use_nesterov: False

mindcv/models/__init__.py

@@ -13,6 +13,7 @@
     inception_v3,
     inception_v4,
     layers,
+    mixnet,
     mnasnet,
     mobilenet_v1,
     mobilenet_v2,
@@ -54,6 +55,7 @@
 from .inception_v3 import *
 from .inception_v4 import *
 from .layers import *
+from .mixnet import *
 from .mnasnet import *
 from .mobilenet_v1 import *
 from .mobilenet_v2 import *
@@ -99,6 +101,7 @@
 __all__.extend(["InceptionV3", "inception_v3"])
 __all__.extend(["InceptionV4", "inception_v4"])
 __all__.extend(layers.__all__)
+__all__.extend(mixnet.__all__)
 __all__.extend(mnasnet.__all__)
 __all__.extend(mobilenet_v1.__all__)
 __all__.extend(mobilenet_v2.__all__)