mindspore-lab · geniuspatrick · Mar 24, 2023 · Mar 22, 2023
diff --git a/configs/convnext/README.md b/configs/convnext/README.md
@@ -0,0 +1,91 @@
+# ConvNeXt
+> [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545)
+
+## Introduction
+
+In this work, the authors reexamine the design spaces and test the limits of what a pure ConvNet can achieve.
+The authors gradually "modernize" a standard ResNet toward the design of a vision Transformer, and discover several key
+components that contribute to the performance difference along the way. The outcome of this exploration is a family of
+pure ConvNet models dubbed ConvNeXt. Constructed entirely from standard ConvNet modules, ConvNeXts compete favorably
+with Transformers in terms of accuracy and scalability, achieving 87.8% ImageNet top-1 accuracy, while maintaining the
+simplicity and efficiency of standard ConvNets.[[1](#references)]
+
+<p align="center">
+  <img src="https://user-images.githubusercontent.com/53842165/223907142-3bf6acfb-080a-49f5-b021-233e003318c3.png" width=250 />
+</p>
+<p align="center">
+  <em>Figure 1. Architecture of ConvNeXt [<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                                                                                       |
+|----------------|-----------|-----------|-----------|------------|-------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------|
+| ConvNeXt_tiny  | D910x64-G | 81.91     | 95.79     | 28.59      | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convnext/convnext_tiny_ascend.yaml)  | [weights](https://download.mindspore.cn/toolkits/mindcv/convnext/convnext_tiny-ae5ff8d7.ckpt)  |
+| ConvNeXt_small | D910x64-G | 83.40     | 96.36     | 50.22      | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convnext/convnext_small_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/convnext/convnext_small-e23008f3.ckpt) |
+| ConvNeXt_base  | D910x64-G | 83.32     | 96.24     | 88.59      | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convnext/convnext_base_ascend.yaml)  | [weights](https://download.mindspore.cn/toolkits/mindcv/convnext/convnext_base-ee3544b8.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
+# distributed training on multiple GPU/Ascend devices
+mpirun -n 8 python train.py --config configs/convnext/convnext_tiny_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/convnext/convnext_tiny_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/convnext/convnext_tiny_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] Liu Z, Mao H, Wu C Y, et al. A convnet for the 2020s[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022: 11976-11986.
diff --git a/configs/convnext/convnext_base_ascend.yaml b/configs/convnext/convnext_base_ascend.yaml
@@ -0,0 +1,58 @@
+# 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: 16
+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-inc1'
+re_prob: 0.25
+crop_pct: 0.95
+mixup: 0.8
+cutmix: 1.0
+
+# model
+model: 'convnext_base'
+num_classes: 1000
+pretrained: False
+ckpt_path: ''
+keep_checkpoint_max: 10
+ckpt_save_dir: './ckpt'
+epoch_size: 450
+drop_path_rate: 0.5
+dataset_sink_mode: True
+amp_level: 'O2'
+
+# loss
+loss: 'ce'
+label_smoothing: 0.1
+
+# lr scheduler
+scheduler: 'cosine_decay'
+lr: 0.002
+min_lr: 0.0000003
+decay_epochs: 430
+warmup_factor: 0.0000175
+warmup_epochs: 20
+
+# optimizer
+opt: 'adamw'
+filter_bias_and_bn: True
+momentum: 0.9
+weight_decay: 0.05
+loss_scale_type: 'auto'
+use_nesterov: False
diff --git a/configs/convnext/convnext_small_ascend.yaml b/configs/convnext/convnext_small_ascend.yaml
@@ -0,0 +1,58 @@
+# 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: 16
+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-inc1'
+re_prob: 0.25
+crop_pct: 0.95
+mixup: 0.8
+cutmix: 1.0
+
+# model
+model: 'convnext_small'
+num_classes: 1000
+pretrained: False
+ckpt_path: ''
+keep_checkpoint_max: 10
+ckpt_save_dir: './ckpt'
+epoch_size: 450
+drop_path_rate: 0.4
+dataset_sink_mode: True
+amp_level: 'O2'
+
+# loss
+loss: 'ce'
+label_smoothing: 0.1
+
+# lr scheduler
+scheduler: 'cosine_decay'
+lr: 0.002
+min_lr: 0.0000003
+decay_epochs: 430
+warmup_factor: 0.0000175
+warmup_epochs: 20
+
+# optimizer
+opt: 'adamw'
+filter_bias_and_bn: True
+momentum: 0.9
+weight_decay: 0.05
+loss_scale_type: 'auto'
+use_nesterov: False
diff --git a/configs/convnext/convnext_tiny_ascend.yaml b/configs/convnext/convnext_tiny_ascend.yaml
@@ -0,0 +1,59 @@
+# system
+mode: 0
+distribute: True
+num_parallel_workers: 16
+val_while_train: True
+
+# dataset
+dataset: 'imagenet'
+data_dir: '/path/to/imagenet'
+shuffle: True
+dataset_download: False
+batch_size: 16
+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-inc1'
+re_prob: 0.25
+crop_pct: 0.95
+mixup: 0.8
+cutmix: 1.0
+
+# model
+model: 'convnext_tiny'
+num_classes: 1000
+pretrained: False
+ckpt_path: ''
+keep_checkpoint_max: 10
+ckpt_save_dir: './ckpt'
+epoch_size: 450
+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: 0.0000003
+decay_epochs: 430
+warmup_factor: 0.0000175
+warmup_epochs: 20
+
+# optimizer
+opt: 'adamw'
+filter_bias_and_bn: True
+momentum: 0.9
+weight_decay: 0.05
+loss_scale_type: 'dynamic'
+drop_overflow_update: True
+use_nesterov: False
diff --git a/mindcv/models/convnext.py b/mindcv/models/convnext.py
@@ -37,9 +37,9 @@ def _cfg(url="", **kwargs):
 
 
 default_cfgs = {
-    "convnext_tiny": _cfg(url="https://download.mindspore.cn/toolkits/mindcv/convnext/convnext_tiny_224.ckpt"),
-    "convnext_small": _cfg(url=""),
-    "convnext_base": _cfg(url=""),
+    "convnext_tiny": _cfg(url="https://download.mindspore.cn/toolkits/mindcv/convnext/convnext_tiny-ae5ff8d7.ckpt"),
+    "convnext_small": _cfg(url="https://download.mindspore.cn/toolkits/mindcv/convnext/convnext_small-e23008f3.ckpt"),
+    "convnext_base": _cfg(url="https://download.mindspore.cn/toolkits/mindcv/convnext/convnext_base-ee3544b8.ckpt"),
     "convnext_large": _cfg(url=""),
     "convnext_xlarge": _cfg(url=""),
 }