feat: add model script, training recipe and pretrained weight of cmt_s

configs/cmt/README.md

@@ -0,0 +1,85 @@
+# CMT: Convolutional Neural Networks Meet Vision Transformers
+
+> [CMT: Convolutional Neural Networks Meet Vision Transformers](https://arxiv.org/abs/2107.06263)
+
+## Introduction
+
+CMT is a method to make full use of the advantages of CNN and transformers so that the model could capture long-range
+dependencies and extract local information. In addition, to reduce computation cost, this method use lightweight MHSA(multi-head self-attention)
+and depthwise convolution and pointwise convolution like MobileNet. By combing these parts, CMT could get a SOTA performance
+on ImageNet-1K dataset.
+
+
+## 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                                                                             |
+|-----------| -------- |-----------|-----------|-----------|---------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------|
+| cmt_small | D910x8-G | 83.24     | 96.41     | 26.09     | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/cmt/cmt_small_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/cmt/cmt_small-6858ee22.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-lab/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
+# distributed training on multiple GPU/Ascend devices
+mpirun -n 8 python train.py --config configs/cmt/cmt_small_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/cmt/cmt_small_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`.
+
+```
+python validate.py -c configs/cmt/cmt_small_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt
+```
+
+### Deployment
+
+Please refer to the [deployment tutorial](https://mindspore-lab.github.io/mindcv/tutorials/deployment/).
+
+## References
+
+<!--- Guideline: Citation format should follow GB/T 7714. -->
+[1] Guo J, Han K, Wu H, et al. Cmt: Convolutional neural networks meet vision transformers[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022: 12175-12185.

configs/cmt/cmt_small_ascend.yaml

@@ -0,0 +1,61 @@
+# 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]
+re_value: "random"
+hflip: 0.5
+interpolation: "bicubic"
+auto_augment: "randaug-m9-mstd0.5"
+aug_repeats: 3
+re_prob: 0.25
+crop_pct: 0.9
+mixup: 0.8
+cutmix: 1.0
+
+# model
+model: "cmt_small"
+num_classes: 1000
+pretrained: False
+ckpt_path: ""
+keep_checkpoint_max: 10
+ckpt_save_dir: "./ckpt"
+epoch_size: 300
+drop_path_rate: 0.1
+dataset_sink_mode: True
+amp_level: "O2"
+
+# loss
+loss: "ce"
+label_smoothing: 0.1
+
+# lr scheduler
+scheduler: "cosine_decay"
+lr: 0.002
+min_lr: 0.00001
+lr_epoch_stair: True
+decay_epochs: 295
+warmup_epochs: 5
+
+# optimizer
+opt: "adamw"
+filter_bias_and_bn: True
+momentum: 0.9
+weight_decay: 0.05
+loss_scale_type: 'dynamic'
+loss_scale: 16777216.0
+drop_overflow_update: True
+use_nesterov: False

mindcv/models/__init__.py

@@ -2,6 +2,7 @@
 from . import (
     bit,
     cait,
+    cmt,
     coat,
     convit,
     convnext,
@@ -55,6 +56,7 @@
 )
 from .bit import *
 from .cait import *
+from .cmt import *
 from .coat import *
 from .convit import *
 from .convnext import *
@@ -112,6 +114,7 @@
 __all__ = []
 __all__.extend(bit.__all__)
 __all__.extend(cait.__all__)
+__all__.extend(cmt.__all__)
 __all__.extend(coat.__all__)
 __all__.extend(convit.__all__)
 __all__.extend(convnext.__all__)