Add new configuration files for StyleGAN2

.dev_scripts/test_benchmark.yml

@@ -49,7 +49,7 @@ cases:
   params:
     checkpoint: stylegan2_c2_ffhq_256_b4x8_20210407_160709-7890ae1f.pth
     checkpoint_url: https://download.openmmlab.com/mmediting/stylegan2/stylegan2_c2_ffhq_256_b4x8_20210407_160709-7890ae1f.pth
-    config: configs/styleganv2/stylegan2_c2-PL_8xb4-fp16-partial-GD-no-scaler-800kiters_ffhq-256x256.py
+    config: configs/styleganv2/stylegan2_c2_PL_8xb4_fp16_partial_GD_no_scaler_800kiters_ffhq_256x256.py
     cpus_per_node: 4
     gpus: 8
     gpus_per_node: 8

.dev_scripts/train_benchmark.yml

@@ -49,7 +49,7 @@ cases:
   params:
     checkpoint: stylegan2_c2_ffhq_256_b4x8_20210407_160709-7890ae1f.pth
     checkpoint_url: https://download.openmmlab.com/mmediting/stylegan2/stylegan2_c2_ffhq_256_b4x8_20210407_160709-7890ae1f.pth
-    config: configs/styleganv2/stylegan2_c2-PL_8xb4-fp16-partial-GD-no-scaler-800kiters_ffhq-256x256.py
+    config: configs/styleganv2/stylegan2_c2_PL_8xb4_fp16_partial_GD_no_scaler_800kiters_ffhq_256x256.py
     cpus_per_node: 4
     gpus: 8
     gpus_per_node: 8

configs/styleganv2/README.md

@@ -28,14 +28,14 @@ The style-based GAN architecture (StyleGAN) yields state-of-the-art results in d
 
 |                                  Model                                   |   Dataset   |     Comment     | FID50k | Precision50k | Recall50k |                                  Download                                   |
 | :----------------------------------------------------------------------: | :---------: | :-------------: | :----: | :----------: | :-------: | :-------------------------------------------------------------------------: |
-| [stylegan2_c2_8xb4_ffhq-1024x1024](./stylegan2_c2_8xb4_ffhq-1024x1024.py) |    FFHQ     | official weight | 2.8134 |    62.856    |  49.400   | [model](https://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-ffhq-config-f-official_20210327_171224-bce9310c.pth) |
-| [stylegan2_c2_8xb4_lsun-car-384x512](./stylegan2_c2_8xb4_lsun-car-384x512.py) |  LSUN_CAR   | official weight | 5.4316 |    65.986    |  48.190   | [model](https://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-car-config-f-official_20210327_172340-8cfe053c.pth) |
-| [stylegan2_c2_8xb4-800kiters_lsun-horse-256x256](./stylegan2_c2_8xb4-800kiters_lsun-horse-256x256.py) | LSUN_HORSE  | official weight |   -    |      -       |     -     | [model](https://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-horse-config-f-official_20210327_173203-ef3e69ca.pth) |
-| [stylegan2_c2_8xb4-800kiters_lsun-church-256x256](./stylegan2_c2_8xb4-800kiters_lsun-church-256x256.py) | LSUN_CHURCH | official weight |   -    |      -       |     -     | [model](https://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-church-config-f-official_20210327_172657-1d42b7d1.pth) |
-| [stylegan2_c2_8xb4-800kiters_lsun-cat-256x256](./stylegan2_c2_8xb4-800kiters_lsun-cat-256x256.py) |  LSUN_CAT   | official weight |   -    |      -       |     -     | [model](https://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-cat-config-f-official_20210327_172444-15bc485b.pth) |
-| [stylegan2_c2_8xb4-800kiters_ffhq-256x256](./stylegan2_c2_8xb4-800kiters_ffhq-256x256.py) |    FFHQ     |  our training   | 3.992  |    69.012    |  40.417   | [model](https://download.openmmlab.com/mmediting/stylegan2/stylegan2_c2_ffhq_256_b4x8_20210407_160709-7890ae1f.pth) |
-| [stylegan2_c2_8xb4_ffhq-1024x1024](./stylegan2_c2_8xb4_ffhq-1024x1024.py) |    FFHQ     |  our training   | 2.8185 |    68.236    |  49.583   | [model](https://download.openmmlab.com/mmediting/stylegan2/stylegan2_c2_ffhq_1024_b4x8_20210407_150045-618c9024.pth) |
-| [stylegan2_c2_8xb4_lsun-car-384x512](./stylegan2_c2_8xb4_lsun-car-384x512.py) |  LSUN_CAR   |  our training   | 2.4116 |    66.760    |  50.576   | [model](https://download.openmmlab.com/mmediting/stylegan2/stylegan2_c2_lsun-car_384x512_b4x8_1800k_20210424_160929-fc9072ca.pth) |
+| [stylegan2_c2_8xb4_ffhq-1024x1024](./stylegan2_c2_8xb4_ffhq_1024x1024.py) |    FFHQ     | official weight | 2.8134 |    62.856    |  49.400   | [model](https://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-ffhq-config-f-official_20210327_171224-bce9310c.pth) |
+| [stylegan2_c2_8xb4_lsun-car-384x512](./stylegan2_c2_8xb4_lsun_car_384x512.py) |  LSUN_CAR   | official weight | 5.4316 |    65.986    |  48.190   | [model](https://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-car-config-f-official_20210327_172340-8cfe053c.pth) |
+| [stylegan2_c2_8xb4-800kiters_lsun-horse-256x256](./stylegan2_c2_8xb4_800kiters_lsun_horse_256x256.py) | LSUN_HORSE  | official weight |   -    |      -       |     -     | [model](https://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-horse-config-f-official_20210327_173203-ef3e69ca.pth) |
+| [stylegan2_c2_8xb4-800kiters_lsun-church-256x256](./stylegan2_c2_8xb4_800kiters_lsun_church_256x256.py) | LSUN_CHURCH | official weight |   -    |      -       |     -     | [model](https://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-church-config-f-official_20210327_172657-1d42b7d1.pth) |
+| [stylegan2_c2_8xb4-800kiters_lsun-cat-256x256](./stylegan2_c2_8xb4_800kiters_lsun_cat_256x256.py) |  LSUN_CAT   | official weight |   -    |      -       |     -     | [model](https://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-cat-config-f-official_20210327_172444-15bc485b.pth) |
+| [stylegan2_c2_8xb4-800kiters_ffhq-256x256](./stylegan2_c2_8xb4_800kiters_ffhq_256x256.py) |    FFHQ     |  our training   | 3.992  |    69.012    |  40.417   | [model](https://download.openmmlab.com/mmediting/stylegan2/stylegan2_c2_ffhq_256_b4x8_20210407_160709-7890ae1f.pth) |
+| [stylegan2_c2_8xb4_ffhq-1024x1024](./stylegan2_c2_8xb4_ffhq_1024x1024.py) |    FFHQ     |  our training   | 2.8185 |    68.236    |  49.583   | [model](https://download.openmmlab.com/mmediting/stylegan2/stylegan2_c2_ffhq_1024_b4x8_20210407_150045-618c9024.pth) |
+| [stylegan2_c2_8xb4_lsun-car-384x512](./stylegan2_c2_8xb4_lsun_car_384x512.py) |  LSUN_CAR   |  our training   | 2.4116 |    66.760    |  50.576   | [model](https://download.openmmlab.com/mmediting/stylegan2/stylegan2_c2_lsun-car_384x512_b4x8_1800k_20210424_160929-fc9072ca.pth) |
 
 ## FP16 Support and Experiments
 
@@ -49,16 +49,16 @@ Currently, we have supported FP16 training for StyleGAN2, and here are the resul
 
 As shown in the figure, we provide **3** ways to do mixed-precision training for `StyleGAN2`:
 
-- [stylegan2_c2_fp16_PL-no-scaler](./stylegan2_c2-PL_8xb4-fp16-partial-GD-no-scaler-800kiters_ffhq-256x256.py): In this setting, we try our best to follow the official FP16 implementation in [StyleGAN2-ADA](https://github.com/NVlabs/stylegan2-ada). Similar to the official version, we only adopt FP16 training for the higher-resolution feature maps (the last 4 stages in G and the first 4 stages). Note that we do not adopt the `clamp` way to avoid gradient overflow used in the official implementation. We use the `autocast` function from `torch.cuda.amp` package.
-- [stylegan2_c2_fp16-globalG-partialD_PL-R1-no-scaler](./stylegan2_c2-PL-R1_8xb4-fp16-globalG-partialD-no-scaler-800kiters_ffhq-256x256.py): In this config, we try to adopt mixed-precision training for the whole generator, but in partial discriminator (the first 4 higher-resolution stages). Note that we do not apply the loss scaler in the path length loss and gradient penalty loss. Because we always meet divergence after adopting the loss scaler to scale the gradient in these two losses.
-- [stylegan2_c2_apex_fp16_PL-R1-no-scaler](./stylegan2_c2-PL-R1_8xb4-apex-fp16-no-scaler-800kiters_ffhq-256x256.py): In this setting, we adopt the [APEX](https://github.com/NVIDIA/apex) toolkit to implement mixed-precision training with multiple loss/gradient scalers. In APEX, you can assign different loss scalers for the generator and the discriminator respectively. Note that we still ignore the gradient scaler in the path length loss and gradient penalty loss.
+- [stylegan2_c2_fp16_PL-no-scaler](./stylegan2_c2_PL_8xb4_fp16_partial_GD_no_scaler_800kiters_ffhq_256x256.py): In this setting, we try our best to follow the official FP16 implementation in [StyleGAN2-ADA](https://github.com/NVlabs/stylegan2-ada). Similar to the official version, we only adopt FP16 training for the higher-resolution feature maps (the last 4 stages in G and the first 4 stages). Note that we do not adopt the `clamp` way to avoid gradient overflow used in the official implementation. We use the `autocast` function from `torch.cuda.amp` package.
+- [stylegan2_c2_fp16-globalG-partialD_PL-R1-no-scaler](./stylegan2_c2_PL_R1_8xb4_fp16_globalG_partialD_no_scaler_800kiters_ffhq_256x256.py): In this config, we try to adopt mixed-precision training for the whole generator, but in partial discriminator (the first 4 higher-resolution stages). Note that we do not apply the loss scaler in the path length loss and gradient penalty loss. Because we always meet divergence after adopting the loss scaler to scale the gradient in these two losses.
+- [stylegan2_c2_apex_fp16_PL-R1-no-scaler](./stylegan2_c2_PL_R1_8xb4_apex_fp16_no_scaler_800kiters_ffhq_256x256.py): In this setting, we adopt the [APEX](https://github.com/NVIDIA/apex) toolkit to implement mixed-precision training with multiple loss/gradient scalers. In APEX, you can assign different loss scalers for the generator and the discriminator respectively. Note that we still ignore the gradient scaler in the path length loss and gradient penalty loss.
 
 |                                  Model                                   |                 Comment                 | Dataset | FID50k |                                   Download                                   |
 | :----------------------------------------------------------------------: | :-------------------------------------: | :-----: | :----: | :--------------------------------------------------------------------------: |
-| [stylegan2_c2_8xb4-800kiters_ffhq-256x256](./stylegan2_c2_8xb4-800kiters_ffhq-256x256.py) |                baseline                 | FFHQ256 | 3.992  | [ckpt](https://download.openmmlab.com/mmediting/stylegan2/stylegan2_c2_ffhq_256_b4x8_20210407_160709-7890ae1f.pth) |
-| [stylegan2_c2-PL_8xb4-fp16-partial-GD-no-scaler-800kiters_ffhq-256x256](./stylegan2_c2-PL_8xb4-fp16-partial-GD-no-scaler-800kiters_ffhq-256x256.py) |         partial layers in fp16          | FFHQ256 | 4.331  | [ckpt](https://download.openmmlab.com/mmediting/stylegan2/stylegan2_c2_fp16_partial-GD_PL-no-scaler_ffhq_256_b4x8_800k_20210508_114854-dacbe4c9.pth) |
-| [stylegan2_c2-PL-R1_8xb4-fp16-globalG-partialD-no-scaler-800kiters_ffhq-256x256](./stylegan2_c2-PL-R1_8xb4-fp16-globalG-partialD-no-scaler-800kiters_ffhq-256x256.py) |           the whole G in fp16           | FFHQ256 | 4.362  | [ckpt](https://download.openmmlab.com/mmediting/stylegan2/stylegan2_c2_fp16-globalG-partialD_PL-R1-no-scaler_ffhq_256_b4x8_800k_20210508_114930-ef8270d4.pth) |
-| [stylegan2_c2-PL-R1_8xb4-apex-fp16-no-scaler-800kiters_ffhq-256x256](./stylegan2_c2-PL-R1_8xb4-apex-fp16-no-scaler-800kiters_ffhq-256x256.py) | the whole G&D in fp16 + two loss scaler | FFHQ256 | 4.614  | [ckpt](https://download.openmmlab.com/mmediting/stylegan2/stylegan2_c2_apex_fp16_PL-R1-no-scaler_ffhq_256_b4x8_800k_20210508_114701-c2bb8afd.pth) |
+| [stylegan2_c2_8xb4-800kiters_ffhq-256x256](./stylegan2_c2_8xb4_800kiters_ffhq_256x256.py) |                baseline                 | FFHQ256 | 3.992  | [ckpt](https://download.openmmlab.com/mmediting/stylegan2/stylegan2_c2_ffhq_256_b4x8_20210407_160709-7890ae1f.pth) |
+| [stylegan2_c2-PL_8xb4-fp16-partial-GD-no-scaler-800kiters_ffhq-256x256](./stylegan2_c2_PL_8xb4_fp16_partial_GD_no_scaler_800kiters_ffhq_256x256.py) |         partial layers in fp16          | FFHQ256 | 4.331  | [ckpt](https://download.openmmlab.com/mmediting/stylegan2/stylegan2_c2_fp16_partial-GD_PL-no-scaler_ffhq_256_b4x8_800k_20210508_114854-dacbe4c9.pth) |
+| [stylegan2_c2-PL-R1_8xb4-fp16-globalG-partialD-no-scaler-800kiters_ffhq-256x256](./stylegan2_c2_PL_R1_8xb4_fp16_globalG_partialD_no_scaler_800kiters_ffhq_256x256.py) |           the whole G in fp16           | FFHQ256 | 4.362  | [ckpt](https://download.openmmlab.com/mmediting/stylegan2/stylegan2_c2_fp16-globalG-partialD_PL-R1-no-scaler_ffhq_256_b4x8_800k_20210508_114930-ef8270d4.pth) |
+| [stylegan2_c2-PL-R1_8xb4-apex-fp16-no-scaler-800kiters_ffhq-256x256](./stylegan2_c2_PL_R1_8xb4_apex_fp16_no_scaler_800kiters_ffhq_256x256.py) | the whole G&D in fp16 + two loss scaler | FFHQ256 | 4.614  | [ckpt](https://download.openmmlab.com/mmediting/stylegan2/stylegan2_c2_apex_fp16_PL-R1-no-scaler_ffhq_256_b4x8_800k_20210508_114701-c2bb8afd.pth) |
 
 As shown in this table, `P&R50k_full` is the metric used in StyleGANv1 and StyleGANv2. `full` indicates that we use the whole dataset for extracting the real distribution, e.g., 70000 images in FFHQ dataset. However, adopting the VGG16 provided from [Tero](https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/vgg16.pt) requires that your PyTorch version must fulfill `>=1.6.0`. Be careful about using the PyTorch's VGG16 to extract features, which will cause higher precision and recall.
 

configs/styleganv2/metafile.yml

@@ -8,9 +8,9 @@ Collections:
   - unconditional gans
   Year: 2020
 Models:
-- Config: configs/styleganv2/stylegan2_c2_8xb4_ffhq-1024x1024.py
+- Config: configs/styleganv2/stylegan2_c2_8xb4_ffhq_1024x1024.py
   In Collection: StyleGANv2
-  Name: stylegan2_c2_8xb4_ffhq-1024x1024
+  Name: stylegan2_c2_8xb4_ffhq_1024x1024
   Results:
   - Dataset: FFHQ
     Metrics:
@@ -25,9 +25,9 @@ Models:
       Recall50k: 49.583
     Task: Unconditional GANs
   Weights: https://download.openmmlab.com/mmediting/stylegan2/stylegan2_c2_ffhq_1024_b4x8_20210407_150045-618c9024.pth
-- Config: configs/styleganv2/stylegan2_c2_8xb4_lsun-car-384x512.py
+- Config: configs/styleganv2/stylegan2_c2_8xb4_lsun_car_384x512.py
   In Collection: StyleGANv2
-  Name: stylegan2_c2_8xb4_lsun-car-384x512
+  Name: stylegan2_c2_8xb4_lsun_car_384x512
   Results:
   - Dataset: LSUN_CAR
     Metrics:
@@ -42,33 +42,33 @@ Models:
       Recall50k: 50.576
     Task: Unconditional GANs
   Weights: https://download.openmmlab.com/mmediting/stylegan2/stylegan2_c2_lsun-car_384x512_b4x8_1800k_20210424_160929-fc9072ca.pth
-- Config: configs/styleganv2/stylegan2_c2_8xb4-800kiters_lsun-horse-256x256.py
+- Config: configs/styleganv2/stylegan2_c2_8xb4_800kiters_lsun_horse_256x256.py
   In Collection: StyleGANv2
-  Name: stylegan2_c2_8xb4-800kiters_lsun-horse-256x256
+  Name: stylegan2_c2_8xb4_800kiters_lsun_horse_256x256
   Results:
   - Dataset: LSUN_HORSE
     Metrics: {}
     Task: Unconditional GANs
   Weights: https://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-horse-config-f-official_20210327_173203-ef3e69ca.pth
-- Config: configs/styleganv2/stylegan2_c2_8xb4-800kiters_lsun-church-256x256.py
+- Config: configs/styleganv2/stylegan2_c2_8xb4_800kiters_lsun_church_256x256.py
   In Collection: StyleGANv2
-  Name: stylegan2_c2_8xb4-800kiters_lsun-church-256x256
+  Name: stylegan2_c2_8xb4_800kiters_lsun_church_256x256
   Results:
   - Dataset: LSUN_CHURCH
     Metrics: {}
     Task: Unconditional GANs
   Weights: https://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-church-config-f-official_20210327_172657-1d42b7d1.pth
-- Config: configs/styleganv2/stylegan2_c2_8xb4-800kiters_lsun-cat-256x256.py
+- Config: configs/styleganv2/stylegan2_c2_8xb4_800kiters_lsun_cat_256x256.py
   In Collection: StyleGANv2
-  Name: stylegan2_c2_8xb4-800kiters_lsun-cat-256x256
+  Name: stylegan2_c2_8xb4_800kiters_lsun_cat_256x256
   Results:
   - Dataset: LSUN_CAT
     Metrics: {}
     Task: Unconditional GANs
   Weights: https://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-cat-config-f-official_20210327_172444-15bc485b.pth
-- Config: configs/styleganv2/stylegan2_c2_8xb4-800kiters_ffhq-256x256.py
+- Config: configs/styleganv2/stylegan2_c2_8xb4_800kiters_ffhq_256x256.py
   In Collection: StyleGANv2
-  Name: stylegan2_c2_8xb4-800kiters_ffhq-256x256
+  Name: stylegan2_c2_8xb4_800kiters_ffhq_256x256
   Results:
   - Dataset: FFHQ
     Metrics:
@@ -81,27 +81,27 @@ Models:
       FID50k: 3.992
     Task: Unconditional GANs
   Weights: https://download.openmmlab.com/mmediting/stylegan2/stylegan2_c2_ffhq_256_b4x8_20210407_160709-7890ae1f.pth
-- Config: configs/styleganv2/stylegan2_c2-PL_8xb4-fp16-partial-GD-no-scaler-800kiters_ffhq-256x256.py
+- Config: configs/styleganv2/stylegan2_c2_PL_8xb4_fp16_partial_GD_no_scaler_800kiters_ffhq_256x256.py
   In Collection: StyleGANv2
-  Name: stylegan2_c2-PL_8xb4-fp16-partial-GD-no-scaler-800kiters_ffhq-256x256
+  Name: stylegan2_c2_PL_8xb4_fp16_partial_GD_no_scaler_800kiters_ffhq_256x256
   Results:
   - Dataset: FFHQ256
     Metrics:
       FID50k: 4.331
     Task: Unconditional GANs
   Weights: https://download.openmmlab.com/mmediting/stylegan2/stylegan2_c2_fp16_partial-GD_PL-no-scaler_ffhq_256_b4x8_800k_20210508_114854-dacbe4c9.pth
-- Config: configs/styleganv2/stylegan2_c2-PL-R1_8xb4-fp16-globalG-partialD-no-scaler-800kiters_ffhq-256x256.py
+- Config: configs/styleganv2/stylegan2_c2_PL_R1_8xb4_fp16_globalG_partialD_no_scaler_800kiters_ffhq_256x256.py
   In Collection: StyleGANv2
-  Name: stylegan2_c2-PL-R1_8xb4-fp16-globalG-partialD-no-scaler-800kiters_ffhq-256x256
+  Name: stylegan2_c2_PL_R1_8xb4_fp16_globalG_partialD_no_scaler_800kiters_ffhq_256x256
   Results:
   - Dataset: FFHQ256
     Metrics:
       FID50k: 4.362
     Task: Unconditional GANs
   Weights: https://download.openmmlab.com/mmediting/stylegan2/stylegan2_c2_fp16-globalG-partialD_PL-R1-no-scaler_ffhq_256_b4x8_800k_20210508_114930-ef8270d4.pth
-- Config: configs/styleganv2/stylegan2_c2-PL-R1_8xb4-apex-fp16-no-scaler-800kiters_ffhq-256x256.py
+- Config: configs/styleganv2/stylegan2_c2_PL_R1_8xb4_apex_fp16_no_scaler_800kiters_ffhq_256x256.py
   In Collection: StyleGANv2
-  Name: stylegan2_c2-PL-R1_8xb4-apex-fp16-no-scaler-800kiters_ffhq-256x256
+  Name: stylegan2_c2_PL_R1_8xb4_apex_fp16_no_scaler_800kiters_ffhq_256x256
   Results:
   - Dataset: FFHQ256
     Metrics:

configs/styleganv2/stylegan2_c2-PL_8xb4-fp16-partial-GD-no-scaler-800kiters_ffhq-256x256.py → configs/styleganv2/stylegan2_c2_PL_8xb4_fp16_partial_GD_no_scaler_800kiters_ffhq_256x256.py

@@ -1,6 +1,6 @@
 """Config for the `config-f` setting in StyleGAN2."""
 
-_base_ = ['./stylegan2_c2_8xb4-800kiters_ffhq-256x256.py']
+_base_ = ['./stylegan2_c2_8xb4_800kiters_ffhq_256x256.py']
 
 model = dict(
     generator=dict(out_size=256, num_fp16_scales=4),

configs/styleganv2/stylegan2_c2-PL-R1_8xb4-apex-fp16-no-scaler-800kiters_ffhq-256x256.py → configs/styleganv2/stylegan2_c2_PL_R1_8xb4_apex_fp16_no_scaler_800kiters_ffhq_256x256.py

@@ -1,6 +1,6 @@
 """Config for the `config-f` setting in StyleGAN2."""
 
-_base_ = ['./stylegan2_c2_8xb4-800kiters_ffhq-256x256.py']
+_base_ = ['./stylegan2_c2_8xb4_800kiters_ffhq_256x256.py']
 
 model = dict(loss_config=dict(r1_use_apex_amp=False, g_reg_use_apex_amp=False))
 

configs/styleganv2/stylegan2_c2-PL-R1_8xb4-fp16-globalG-partialD-no-scaler-800kiters_ffhq-256x256.py → configs/styleganv2/stylegan2_c2_PL_R1_8xb4_fp16_globalG_partialD_no_scaler_800kiters_ffhq_256x256.py

@@ -1,6 +1,6 @@
 """Config for the `config-f` setting in StyleGAN2."""
 
-_base_ = ['./stylegan2_c2_8xb4-800kiters_ffhq-256x256.py']
+_base_ = ['./stylegan2_c2_8xb4_800kiters_ffhq_256x256.py']
 
 model = dict(
     generator=dict(out_size=256, fp16_enabled=True),

docs/en/user_guides/useful_tools.md

@@ -67,5 +67,5 @@ MMGeneration incorporates config mechanism to set parameters used for training a
 An Example:
 
 ```shell
-python tools/misc/print_config.py configs/styleganv2/stylegan2_c2-PL_8xb4-fp16-partial-GD-no-scaler-800kiters_ffhq-256x256.py
+python tools/misc/print_config.py configs/styleganv2/stylegan2_c2_PL_8xb4_fp16_partial_GD_no_scaler_800kiters_ffhq_256x256.py
 ```