refactor: uniform all model names

mindspore-lab · Jul 11, 2023 · 06ab4a6 · 06ab4a6
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diff --git a/README.md b/README.md
@@ -134,7 +134,7 @@ It is easy to train your model on a standard or customized dataset using `train.
     You can configure your model and other components either by specifying external parameters or by writing a yaml config file. Here is an example of training using a preset yaml file.
 
     ```shell
-    mpirun --allow-run-as-root -n 4 python train.py -c configs/squeezenet/squeezenet_1.0_gpu.yaml
+    mpirun --allow-run-as-root -n 4 python train.py -c configs/squeezenet/squeezenet1_0_gpu.yaml
     ```
 
     **Pre-defined Training Strategies:**
@@ -216,7 +216,7 @@ Currently, MindCV supports the model families listed below. More models with pre
 * EfficientNet (MBConvNet Family) https://arxiv.org/abs/1905.11946
 * EfficientNet V2 - https://arxiv.org/abs/2104.00298
 * GhostNet - https://arxiv.org/abs/1911.11907
-* GoogleNet - https://arxiv.org/abs/1409.4842
+* GoogLeNet - https://arxiv.org/abs/1409.4842
 * Inception-V3 - https://arxiv.org/abs/1512.00567
 * Inception-ResNet-V2 and Inception-V4 - https://arxiv.org/abs/1602.07261
 * MNASNet - https://arxiv.org/abs/1807.11626

diff --git a/README_CN.md b/README_CN.md
@@ -135,7 +135,7 @@ python infer.py --model=swin_tiny --image_path='./dog.jpg'
     您可以编写yaml文件或设置外部参数来指定配置数据、模型、优化器等组件及其超参。以下是使用预设的训练策略（yaml文件）进行模型训练的示例。
 
     ```shell
-    mpirun --allow-run-as-root -n 4 python train.py -c configs/squeezenet/squeezenet_1.0_gpu.yaml
+    mpirun --allow-run-as-root -n 4 python train.py -c configs/squeezenet/squeezenet1_0_gpu.yaml
     ```
 
     **预定义的训练策略**
@@ -217,7 +217,7 @@ python train.py --model=resnet50 --dataset=cifar10 \
 * EfficientNet (MBConvNet Family) https://arxiv.org/abs/1905.11946
 * EfficientNet V2 - https://arxiv.org/abs/2104.00298
 * GhostNet - https://arxiv.org/abs/1911.11907
-* GoogleNet - https://arxiv.org/abs/1409.4842
+* GoogLeNet - https://arxiv.org/abs/1409.4842
 * Inception-V3 - https://arxiv.org/abs/1512.00567
 * Inception-ResNet-V2 and Inception-V4 - https://arxiv.org/abs/1602.07261
 * MNASNet - https://arxiv.org/abs/1807.11626

diff --git a/RELEASE.md b/RELEASE.md
@@ -123,7 +123,7 @@
 `mindcv.models` now expose `num_classes` and `in_channels` as constructor arguments:
 
 - Add DenseNet models and pre-trained weights
-- Add GoogleNet models and pre-trained weights
+- Add GoogLeNet models and pre-trained weights
 - Add Inception V3 models and pre-trained weights
 - Add Inception V4 models and pre-trained weights
 - Add MnasNet models and pre-trained weights

diff --git a/benchmark_results.md b/benchmark_results.md
diff --git a/configs/README.md b/configs/README.md
@@ -56,10 +56,10 @@ For consistency, it is recommended to provide distributed training commands base
 
   ```shell
   # standalone training on a gpu or ascend device
-  python train.py --config configs/densenet/densenet_121_gpu.yaml --data_dir /path/to/dataset --distribute False
+  python train.py --config configs/densenet/densenet121_gpu.yaml --data_dir /path/to/dataset --distribute False
 
   # distributed training on gpu or ascend divices
-  mpirun -n 8 python train.py --config configs/densenet/densenet_121_ascend.yaml --data_dir /path/to/imagenet
+  mpirun -n 8 python train.py --config configs/densenet/densenet121_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`.

diff --git a/configs/bit/bit_resnet101_ascend.yaml b/configs/bit/bit_resnet101_ascend.yaml
@@ -18,7 +18,7 @@ hflip: 0.5
 crop_pct: 0.875
 
 # model
-model: 'BiTresnet101'
+model: 'BiT_resnet101'
 num_classes: 1000
 pretrained: False
 ckpt_path: ''

diff --git a/configs/bit/bit_resnet50_ascend.yaml b/configs/bit/bit_resnet50_ascend.yaml
@@ -18,7 +18,7 @@ hflip: 0.5
 crop_pct: 0.875
 
 # model
-model: 'BiTresnet50'
+model: 'BiT_resnet50'
 num_classes: 1000
 pretrained: False
 ckpt_path: ''

diff --git a/configs/bit/bit_resnet50x3_ascend.yaml b/configs/bit/bit_resnet50x3_ascend.yaml
@@ -20,7 +20,7 @@ crop_pct: 0.875
 auto_augment: "randaug-m7-mstd0.5"
 
 # model
-model: 'BiTresnet50x3'
+model: 'BiT_resnet50x3'
 num_classes: 1000
 pretrained: False
 ckpt_path: ''

diff --git a/configs/convnext/README.md b/configs/convnext/README.md
@@ -25,9 +25,9 @@ Our reproduced model performance on ImageNet-1K is reported as follows.
 
 | 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)  |
+| 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>
 

diff --git a/configs/convnextv2/README.md b/configs/convnextv2/README.md
@@ -22,9 +22,9 @@ Our reproduced model performance on ImageNet-1K is reported as follows.
 
 <div align="center">
 
-| Model           | Context  | Top-1 (%) | Top-5 (%) | Params (M) | Recipe                                                                                                   | Download                                                                                          |
-|-----------------|----------|-----------|-----------|------------|----------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------|
-| ConvNeXtV2_tiny | D910x8-G | 82.43     | 95.98     | 28.64      | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convnextv2/convnextv2_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/convnextv2/convnextv2_tiny-d441ba2c.ckpt) |
+| Model            | Context  | Top-1 (%) | Top-5 (%) | Params (M) | Recipe                                                                                                   | Download                                                                                          |
+|------------------|----------|-----------|-----------|------------|----------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------|
+| convnextv2_tiny | D910x8-G | 82.43     | 95.98     | 28.64      | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/convnextv2/convnextv2_tiny_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/convnextv2/convnextv2_tiny-d441ba2c.ckpt) |
 
 </div>
 

diff --git a/configs/crossvit/README.md b/configs/crossvit/README.md
@@ -1,4 +1,4 @@
-# Crossvit
+# CrossViT
 > [CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification](https://arxiv.org/abs/2103.14899)
 
 ## Introduction
@@ -77,7 +77,7 @@ python train.py --config configs/crossvit/crossvit_15_ascend.yaml --data_dir /pa
 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/crossvit/crossvit15_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt
+python validate.py -c configs/crossvit/crossvit_15_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt
 ```
 
 ### Deployment

diff --git a/configs/crossvit/crossvit_15_ascend.yaml b/configs/crossvit/crossvit_15_ascend.yaml
@@ -28,7 +28,7 @@ crop_pct: 0.935
 ema: True
 
 # model
-model: 'crossvit15'
+model: 'crossvit_15'
 num_classes: 1000
 pretrained: False
 ckpt_path: ''

diff --git a/configs/crossvit/crossvit_18_ascend.yaml b/configs/crossvit/crossvit_18_ascend.yaml
@@ -28,7 +28,7 @@ crop_pct: 0.935
 ema: True
 
 # model
-model: 'crossvit18'
+model: 'crossvit_18'
 num_classes: 1000
 pretrained: False
 ckpt_path: ''

diff --git a/configs/crossvit/crossvit_9_ascend.yaml b/configs/crossvit/crossvit_9_ascend.yaml
@@ -27,7 +27,7 @@ color_jitter: 0.4
 crop_pct: 0.935
 
 # model
-model: 'crossvit9'
+model: 'crossvit_9'
 num_classes: 1000
 pretrained: False
 ckpt_path: ''

diff --git a/configs/densenet/README.md b/configs/densenet/README.md
@@ -37,12 +37,12 @@ Our reproduced model performance on ImageNet-1K is reported as follows.
 
 <div align="center">
 
-| Model        | Context  | Top-1 (%) | Top-5 (%) | Params (M) | Recipe                                                                                              | Download                                                                                           |
-|--------------|----------|-----------|-----------|------------|-----------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------|
-| densenet_121 | D910x8-G | 75.64     | 92.84     | 8.06       | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/densenet/densenet_121_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/densenet/densenet121-120_5004_Ascend.ckpt) |
-| densenet_161 | D910x8-G | 79.09     | 94.66     | 28.90      | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/densenet/densenet_161_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/densenet/densenet161-120_5004_Ascend.ckpt) |
-| densenet_169 | D910x8-G | 77.26     | 93.71     | 14.31      | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/densenet/densenet_169_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/densenet/densenet169-120_5004_Ascend.ckpt) |
-| densenet_201 | D910x8-G | 78.14     | 94.08     | 20.24      | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/densenet/densenet_201_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/densenet/densenet201-120_5004_Ascend.ckpt) |
+| Model       | Context  | Top-1 (%) | Top-5 (%) | Params (M) | Recipe                                                                                             | Download                                                                                           |
+|-------------|----------|-----------|-----------|------------|----------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------|
+| densenet121 | D910x8-G | 75.64     | 92.84     | 8.06       | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/densenet/densenet121_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/densenet/densenet121-120_5004_Ascend.ckpt) |
+| densenet161 | D910x8-G | 79.09     | 94.66     | 28.90      | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/densenet/densenet161_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/densenet/densenet161-120_5004_Ascend.ckpt) |
+| densenet169 | D910x8-G | 77.26     | 93.71     | 14.31      | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/densenet/densenet169_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/densenet/densenet169-120_5004_Ascend.ckpt) |
+| densenet201 | D910x8-G | 78.14     | 94.08     | 20.24      | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/densenet/densenet201_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/densenet/densenet201-120_5004_Ascend.ckpt) |
 
 </div>
 
@@ -70,7 +70,7 @@ It is easy to reproduce the reported results with the pre-defined training recip
 
 ```shell
 # distributed training on multiple GPU/Ascend devices
-mpirun -n 8 python train.py --config configs/densenet/densenet_121_ascend.yaml --data_dir /path/to/imagenet
+mpirun -n 8 python train.py --config configs/densenet/densenet121_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`.
 
@@ -86,7 +86,7 @@ If you want to train or finetune the model on a smaller dataset without distribu
 
 ```shell
 # standalone training on a CPU/GPU/Ascend device
-python train.py --config configs/densenet/densenet_121_ascend.yaml --data_dir /path/to/dataset --distribute False
+python train.py --config configs/densenet/densenet121_ascend.yaml --data_dir /path/to/dataset --distribute False
 ```
 
 ### Validation

diff --git a/configs/densenet/densenet_121_ascend.yaml → configs/densenet/densenet121_ascend.yaml b/configs/densenet/densenet_121_ascend.yaml → configs/densenet/densenet121_ascend.yaml
diff --git a/configs/densenet/densenet_121_gpu.yaml → configs/densenet/densenet121_gpu.yaml b/configs/densenet/densenet_121_gpu.yaml → configs/densenet/densenet121_gpu.yaml
diff --git a/configs/densenet/densenet_161_ascend.yaml → configs/densenet/densenet161_ascend.yaml b/configs/densenet/densenet_161_ascend.yaml → configs/densenet/densenet161_ascend.yaml
diff --git a/configs/densenet/densenet_161_gpu.yaml → configs/densenet/densenet161_gpu.yaml b/configs/densenet/densenet_161_gpu.yaml → configs/densenet/densenet161_gpu.yaml
diff --git a/configs/densenet/densenet_169_ascend.yaml → configs/densenet/densenet169_ascend.yaml b/configs/densenet/densenet_169_ascend.yaml → configs/densenet/densenet169_ascend.yaml
diff --git a/configs/densenet/densenet_169_gpu.yaml → configs/densenet/densenet169_gpu.yaml b/configs/densenet/densenet_169_gpu.yaml → configs/densenet/densenet169_gpu.yaml
diff --git a/configs/densenet/densenet_201_ascend.yaml → configs/densenet/densenet201_ascend.yaml b/configs/densenet/densenet_201_ascend.yaml → configs/densenet/densenet201_ascend.yaml
diff --git a/configs/densenet/densenet_201_gpu.yaml → configs/densenet/densenet201_gpu.yaml b/configs/densenet/densenet_201_gpu.yaml → configs/densenet/densenet201_gpu.yaml
diff --git a/configs/dpn/README.md b/configs/dpn/README.md
@@ -32,12 +32,12 @@ Our reproduced model performance on ImageNet-1K is reported as follows.
 
 <div align="center">
 
-| Model | Context  | Top-1 (%) | Top-5 (%) | Params (M) | Recipe                                                                                   | Download                                                                          |
-|-------|----------|-----------|-----------|------------|------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------|
-| dpn92 | D910x8-G | 79.46     | 94.49     | 37.79      | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/dpn/dpn92_ascend.yaml)  | [weights](https://download.mindspore.cn/toolkits/mindcv/dpn/dpn92-e3e0fca.ckpt)   |
-| dpn98 | D910x8-G | 79.94     | 94.57     | 61.74      | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/dpn/dpn98_ascend.yaml)  | [weights](https://download.mindspore.cn/toolkits/mindcv/dpn/dpn98-119a8207.ckpt)  |
-| dpn107 | D910x8-G | 80.05     | 94.74     | 87.13      | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/dpn/dpn107_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/dpn/dpn107-7d7df07b.ckpt) |
-| dpn131 | D910x8-G | 80.07     | 94.72     | 79.48      | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/dpn/dpn131_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/dpn/dpn131-47f084b3.ckpt) |
+| Model   | Context  | Top-1 (%) | Top-5 (%) | Params (M) | Recipe                                                                                   | Download                                                                          |
+|---------|----------|-----------|-----------|------------|------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------|
+| dpn92   | D910x8-G | 79.46     | 94.49     | 37.79      | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/dpn/dpn92_ascend.yaml)  | [weights](https://download.mindspore.cn/toolkits/mindcv/dpn/dpn92-e3e0fca.ckpt)   |
+| dpn98   | D910x8-G | 79.94     | 94.57     | 61.74      | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/dpn/dpn98_ascend.yaml)  | [weights](https://download.mindspore.cn/toolkits/mindcv/dpn/dpn98-119a8207.ckpt)  |
+| dpn107  | D910x8-G | 80.05     | 94.74     | 87.13      | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/dpn/dpn107_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/dpn/dpn107-7d7df07b.ckpt) |
+| dpn131  | D910x8-G | 80.07     | 94.72     | 79.48      | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/dpn/dpn131_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/dpn/dpn131-47f084b3.ckpt) |
 
 </div>
 

diff --git a/configs/ghostnet/README.md b/configs/ghostnet/README.md
@@ -29,9 +29,9 @@ Our reproduced model performance on ImageNet-1K is reported as follows.
 
 | Model        | Context  | Top-1 (%) | Top-5 (%) | Params (M) | Recipe                                                                                              | Download                                                                                     |
 |--------------|----------|-----------|-----------|------------|-----------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------|
-| GhostNet_050 | D910x8-G | 66.03     | 86.64     | 2.60       | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/ghostnet/ghostnet_050_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/ghostnet/ghostnet_050-85b91860.ckpt) |
-| GhostNet_100 | D910x8-G | 73.78     | 91.66     | 5.20       | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/ghostnet/ghostnet_100_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/ghostnet/ghostnet_100-bef8025a.ckpt) |
-| GhostNet_130 | D910x8-G | 75.50     | 92.56     | 7.39       | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/ghostnet/ghostnet_130_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/ghostnet/ghostnet_130-cf4c235c.ckpt) |
+| ghostnet_050 | D910x8-G | 66.03     | 86.64     | 2.60       | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/ghostnet/ghostnet_050_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/ghostnet/ghostnet_050-85b91860.ckpt) |
+| ghostnet_100 | D910x8-G | 73.78     | 91.66     | 5.20       | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/ghostnet/ghostnet_100_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/ghostnet/ghostnet_100-bef8025a.ckpt) |
+| ghostnet_130 | D910x8-G | 75.50     | 92.56     | 7.39       | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/ghostnet/ghostnet_130_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/ghostnet/ghostnet_130-cf4c235c.ckpt) |
 
 </div>
 

diff --git a/configs/googlenet/README.md b/configs/googlenet/README.md
@@ -14,7 +14,7 @@ training results.[[1](#references)]
   <img src="https://user-images.githubusercontent.com/53842165/210749903-5ff23c0e-547f-487d-bb64-70b6e99031ea.jpg" width=180 />
 </p>
 <p align="center">
-  <em>Figure 1. Architecture of GoogLENet [<a href="#references">1</a>] </em>
+  <em>Figure 1. Architecture of GoogLeNet [<a href="#references">1</a>] </em>
 </p>
 
 ## Results
@@ -25,7 +25,7 @@ Our reproduced model performance on ImageNet-1K is reported as follows.
 
 | Model     | Context  | Top-1 (%) | Top-5 (%) | Params (M) | Recipe                                                                                            | Download                                                                                   |
 |-----------|----------|-----------|-----------|------------|---------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------|
-| GoogLeNet | D910x8-G | 72.68     | 90.89     | 6.99       | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/googlenet/googlenet_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/googlenet/googlenet-5552fcd3.ckpt) |
+| googlenet | D910x8-G | 72.68     | 90.89     | 6.99       | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/googlenet/googlenet_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/googlenet/googlenet-5552fcd3.ckpt) |
 
 </div>