stylegan2_mmcv

build/lib/mmgen/.mim/configs/sngan_proj/sngan_proj_128_wReLUinplace_Glr-2e-4_Dlr-5e-5_ndisc5_imagenet1k_b128x2.py

+_base_ = [
+    '../_base_/models/sngan_proj/sngan_proj_128x128.py',
+    '../_base_/datasets/imagenet_128.py', '../_base_/default_runtime.py'
+]
+
+num_classes = 1000
+init_cfg = dict(type='studio')
+model = dict(
+    num_classes=num_classes,
+    generator=dict(
+        num_classes=num_classes,
+        act_cfg=dict(type='ReLU', inplace=True),
+        init_cfg=init_cfg),
+    discriminator=dict(
+        num_classes=num_classes,
+        act_cfg=dict(type='ReLU', inplace=True),
+        init_cfg=init_cfg))
+
+n_disc = 5
+train_cfg = dict(disc_steps=n_disc)
+lr_config = None
+
+checkpoint_config = dict(interval=50000, by_epoch=False, max_keep_ckpts=20)
+custom_hooks = [
+    dict(
+        type='VisualizeUnconditionalSamples',
+        output_dir='training_samples',
+        interval=5000)
+]
+
+log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')])
+
+inception_pkl = './work_dirs/inception_pkl/imagenet.pkl'
+
+evaluation = dict(
+    type='GenerativeEvalHook',
+    interval=dict(milestones=[800000], interval=[10000, 4000]),
+    metrics=[
+        dict(
+            type='FID',
+            num_images=50000,
+            inception_pkl=inception_pkl,
+            bgr2rgb=True,
+            inception_args=dict(type='StyleGAN')),
+        dict(type='IS', num_images=50000)
+    ],
+    best_metric=['fid', 'is'],
+    sample_kwargs=dict(sample_model='orig'))
+
+total_iters = 500000 * n_disc
+# use ddp wrapper for faster training
+use_ddp_wrapper = True
+find_unused_parameters = False
+
+runner = dict(
+    type='DynamicIterBasedRunner',
+    is_dynamic_ddp=False,  # Note that this flag should be False.
+    pass_training_status=True)
+
+metrics = dict(
+    fid50k=dict(
+        type='FID',
+        num_images=50000,
+        inception_pkl=inception_pkl,
+        inception_args=dict(type='StyleGAN')),
+    IS50k=dict(type='IS', num_images=50000))
+
+optimizer = dict(
+    generator=dict(type='Adam', lr=0.0002, betas=(0.0, 0.999)),
+    discriminator=dict(type='Adam', lr=0.00005, betas=(0.0, 0.999)))
+
+# train on 2 gpus
+data = dict(samples_per_gpu=128)

build/lib/mmgen/.mim/configs/sngan_proj/sngan_proj_128_woReLUinplace_Glr-2e-4_Dlr-5e-5_ndisc5_imagenet1k_b128x2.py

+_base_ = [
+    '../_base_/models/sngan_proj/sngan_proj_128x128.py',
+    '../_base_/datasets/imagenet_128.py', '../_base_/default_runtime.py'
+]
+
+num_classes = 1000
+init_cfg = dict(type='studio')
+model = dict(
+    num_classes=num_classes,
+    generator=dict(num_classes=num_classes, init_cfg=init_cfg),
+    discriminator=dict(num_classes=num_classes, init_cfg=init_cfg))
+
+n_disc = 5
+train_cfg = dict(disc_steps=n_disc)
+lr_config = None
+
+checkpoint_config = dict(interval=50000, by_epoch=False, max_keep_ckpts=20)
+custom_hooks = [
+    dict(
+        type='VisualizeUnconditionalSamples',
+        output_dir='training_samples',
+        interval=5000)
+]
+
+log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')])
+
+inception_pkl = './work_dirs/inception_pkl/imagenet.pkl'
+
+evaluation = dict(
+    type='GenerativeEvalHook',
+    interval=dict(milestones=[800000], interval=[10000, 4000]),
+    metrics=[
+        dict(
+            type='FID',
+            num_images=50000,
+            inception_pkl=inception_pkl,
+            bgr2rgb=True,
+            inception_args=dict(type='StyleGAN')),
+        dict(type='IS', num_images=50000)
+    ],
+    best_metric=['fid', 'is'],
+    sample_kwargs=dict(sample_model='orig'))
+
+total_iters = 500000 * n_disc
+# use ddp wrapper for faster training
+use_ddp_wrapper = True
+find_unused_parameters = False
+
+runner = dict(
+    type='DynamicIterBasedRunner',
+    is_dynamic_ddp=False,  # Note that this flag should be False.
+    pass_training_status=True)
+
+metrics = dict(
+    fid50k=dict(
+        type='FID',
+        num_images=50000,
+        inception_pkl=inception_pkl,
+        inception_args=dict(type='StyleGAN')),
+    IS50k=dict(type='IS', num_images=50000))
+
+optimizer = dict(
+    generator=dict(type='Adam', lr=0.0002, betas=(0.0, 0.999)),
+    discriminator=dict(type='Adam', lr=0.00005, betas=(0.0, 0.999)))
+
+# train on 2 gpus
+data = dict(samples_per_gpu=128)

build/lib/mmgen/.mim/configs/sngan_proj/sngan_proj_32_cvt_studioGAN.py

+_base_ = ['../_base_/models/sngan_proj/sngan_proj_32x32.py']

build/lib/mmgen/.mim/configs/sngan_proj/sngan_proj_32_wReLUinplace_lr-2e-4_ndisc5_cifar10_b64x1.py

+# follow pytorch GAN-Studio, random flip is used in the dataset
+
+_base_ = [
+    '../_base_/models/sngan_proj/sngan_proj_32x32.py',
+    '../_base_/datasets/cifar10_nopad.py', '../_base_/default_runtime.py'
+]
+
+num_classes = 10
+init_cfg = dict(type='studio')
+model = dict(
+    num_classes=num_classes,
+    generator=dict(
+        act_cfg=dict(type='ReLU', inplace=True),
+        num_classes=num_classes,
+        init_cfg=init_cfg),
+    discriminator=dict(
+        act_cfg=dict(type='ReLU', inplace=True),
+        num_classes=num_classes,
+        init_cfg=init_cfg))
+
+n_disc = 5
+lr_config = None
+checkpoint_config = dict(interval=10000, by_epoch=False, max_keep_ckpts=20)
+custom_hooks = [
+    dict(
+        type='VisualizeUnconditionalSamples',
+        output_dir='training_samples',
+        interval=5000)
+]
+
+inception_pkl = './work_dirs/inception_pkl/cifar10.pkl'
+
+evaluation = dict(
+    type='GenerativeEvalHook',
+    interval=10000,
+    metrics=[
+        dict(
+            type='FID',
+            num_images=50000,
+            inception_pkl=inception_pkl,
+            bgr2rgb=True,
+            inception_args=dict(type='StyleGAN')),
+        dict(type='IS', num_images=50000)
+    ],
+    best_metric=['fid', 'is'],
+    sample_kwargs=dict(sample_model='orig'))
+
+total_iters = 100000 * n_disc
+# use ddp wrapper for faster training
+use_ddp_wrapper = True
+find_unused_parameters = False
+
+runner = dict(
+    type='DynamicIterBasedRunner',
+    is_dynamic_ddp=False,  # Note that this flag should be False.
+    pass_training_status=True)
+
+metrics = dict(
+    fid50k=dict(
+        type='FID',
+        num_images=50000,
+        inception_pkl=inception_pkl,
+        inception_args=dict(type='StyleGAN')),
+    IS50k=dict(type='IS', num_images=50000))
+
+optimizer = dict(
+    generator=dict(type='Adam', lr=0.0002, betas=(0.5, 0.999)),
+    discriminator=dict(type='Adam', lr=0.0002, betas=(0.5, 0.999)))
+
+data = dict(samples_per_gpu=64)

build/lib/mmgen/.mim/configs/sngan_proj/sngan_proj_32_woReLUinplace_lr-2e-4_ndisc5_cifar10_b64x1.py

+# follow pytorch GAN-Studio, random flip is used in the dataset
+
+_base_ = [
+    '../_base_/models/sngan_proj/sngan_proj_32x32.py',
+    '../_base_/datasets/cifar10_nopad.py', '../_base_/default_runtime.py'
+]
+
+num_classes = 10
+init_cfg = dict(type='studio')
+model = dict(
+    num_classes=num_classes,
+    generator=dict(num_classes=num_classes, init_cfg=init_cfg),
+    discriminator=dict(num_classes=num_classes, init_cfg=init_cfg))
+
+n_disc = 5
+lr_config = None
+checkpoint_config = dict(interval=10000, by_epoch=False, max_keep_ckpts=20)
+custom_hooks = [
+    dict(
+        type='VisualizeUnconditionalSamples',
+        output_dir='training_samples',
+        interval=5000)
+]
+
+inception_pkl = './work_dirs/inception_pkl/cifar10.pkl'
+
+evaluation = dict(
+    type='GenerativeEvalHook',
+    interval=10000,
+    metrics=[
+        dict(
+            type='FID',
+            num_images=50000,
+            inception_pkl=inception_pkl,
+            bgr2rgb=True,
+            inception_args=dict(type='StyleGAN')),
+        dict(type='IS', num_images=50000)
+    ],
+    best_metric=['fid', 'is'],
+    sample_kwargs=dict(sample_model='orig'))
+
+total_iters = 100000 * n_disc
+# use ddp wrapper for faster training
+use_ddp_wrapper = True
+find_unused_parameters = False
+
+runner = dict(
+    type='DynamicIterBasedRunner',
+    is_dynamic_ddp=False,  # Note that this flag should be False.
+    pass_training_status=True)
+
+metrics = dict(
+    fid50k=dict(
+        type='FID',
+        num_images=50000,
+        inception_pkl=inception_pkl,
+        inception_args=dict(type='StyleGAN')),
+    IS50k=dict(type='IS', num_images=50000))
+
+optimizer = dict(
+    generator=dict(type='Adam', lr=0.0002, betas=(0.5, 0.999)),
+    discriminator=dict(type='Adam', lr=0.0002, betas=(0.5, 0.999)))
+
+data = dict(samples_per_gpu=64)

build/lib/mmgen/.mim/configs/styleganv1/metafile.yml

+Collections:
+- Metadata:
+    Architecture:
+    - StyleGANv1
+  Name: StyleGANv1
+  Paper:
+  - https://openaccess.thecvf.com/content_CVPR_2019/html/Karras_A_Style-Based_Generator_Architecture_for_Generative_Adversarial_Networks_CVPR_2019_paper.html
+  README: configs/styleganv1/README.md
+Models:
+- Config: https://github.com/open-mmlab/mmgeneration/tree/master/configs/styleganv1/styleganv1_ffhq_256_g8_25Mimg.py
+  In Collection: StyleGANv1
+  Metadata:
+    Training Data: FFHQ
+  Name: styleganv1_ffhq_256_g8_25Mimg
+  Results:
+  - Dataset: FFHQ
+    Metrics:
+      FID50k: 6.09
+      P&R50k_full: 70.228/27.050
+    Task: Unconditional GANs
+  Weights: https://download.openmmlab.com/mmgen/styleganv1/styleganv1_ffhq_256_g8_25Mimg_20210407_161748-0094da86.pth
+- Config: https://github.com/open-mmlab/mmgeneration/tree/master/configs/styleganv1/styleganv1_ffhq_1024_g8_25Mimg.py
+  In Collection: StyleGANv1
+  Metadata:
+    Training Data: FFHQ
+  Name: styleganv1_ffhq_1024_g8_25Mimg
+  Results:
+  - Dataset: FFHQ
+    Metrics:
+      FID50k: 4.056
+      P&R50k_full: 70.302/36.869
+    Task: Unconditional GANs
+  Weights: https://download.openmmlab.com/mmgen/styleganv1/styleganv1_ffhq_1024_g8_25Mimg_20210407_161627-850a7234.pth

build/lib/mmgen/.mim/configs/styleganv1/styleganv1_ffhq_1024_g8_25Mimg.py

+_base_ = [
+    '../_base_/models/stylegan/styleganv1_base.py',
+    '../_base_/datasets/grow_scale_imgs_ffhq_styleganv1.py',
+    '../_base_/default_runtime.py',
+]
+
+model = dict(generator=dict(out_size=1024), discriminator=dict(in_size=1024))
+
+train_cfg = dict(
+    nkimgs_per_scale={
+        '8': 1200,
+        '16': 1200,
+        '32': 1200,
+        '64': 1200,
+        '128': 1200,
+        '256': 1200,
+        '512': 1200,
+        '1024': 166000
+    })
+
+checkpoint_config = dict(interval=5000, by_epoch=False, max_keep_ckpts=20)
+lr_config = None
+
+ema_half_life = 10.  # G_smoothing_kimg
+
+custom_hooks = [
+    dict(
+        type='VisualizeUnconditionalSamples',
+        output_dir='training_samples',
+        interval=5000),
+    dict(type='PGGANFetchDataHook', interval=1),
+    dict(
+        type='ExponentialMovingAverageHook',
+        module_keys=('generator_ema', ),
+        interval=1,
+        interp_cfg=dict(momentum=0.5**(32. / (ema_half_life * 1000.))),
+        priority='VERY_HIGH')
+]
+
+total_iters = 670000
+
+metrics = dict(
+    fid50k=dict(
+        type='FID',
+        num_images=50000,
+        inception_pkl='work_dirs/inception_pkl/ffhq-1024-50k-rgb.pkl',
+        bgr2rgb=True),
+    pr50k3=dict(type='PR', num_images=50000, k=3),
+    ppl_wend=dict(type='PPL', space='W', sampling='end', num_images=50000))

build/lib/mmgen/.mim/configs/styleganv1/styleganv1_ffhq_256_g8_25Mimg.py

+_base_ = [
+    '../_base_/models/stylegan/styleganv1_base.py',
+    '../_base_/datasets/grow_scale_imgs_ffhq_styleganv1.py',
+    '../_base_/default_runtime.py',
+]
+
+model = dict(generator=dict(out_size=256), discriminator=dict(in_size=256))
+
+train_cfg = dict(nkimgs_per_scale={
+    '8': 1200,
+    '16': 1200,
+    '32': 1200,
+    '64': 1200,
+    '128': 1200,
+    '256': 190000
+})
+
+checkpoint_config = dict(interval=5000, by_epoch=False, max_keep_ckpts=20)
+lr_config = None
+
+ema_half_life = 10.  # G_smoothing_kimg
+
+custom_hooks = [
+    dict(
+        type='VisualizeUnconditionalSamples',
+        output_dir='training_samples',
+        interval=5000),
+    dict(type='PGGANFetchDataHook', interval=1),
+    dict(
+        type='ExponentialMovingAverageHook',
+        module_keys=('generator_ema', ),
+        interval=1,
+        interp_cfg=dict(momentum=0.5**(32. / (ema_half_life * 1000.))),
+        priority='VERY_HIGH')
+]
+
+total_iters = 670000
+
+metrics = dict(
+    fid50k=dict(
+        type='FID',
+        num_images=50000,
+        inception_pkl='work_dirs/inception_pkl/ffhq-256-50k-rgb.pkl',
+        bgr2rgb=True),
+    pr50k3=dict(type='PR', num_images=50000, k=3),
+    ppl_wend=dict(type='PPL', space='W', sampling='end', num_images=50000))

build/lib/mmgen/.mim/configs/styleganv2/metafile.yml

+Collections:
+- Metadata:
+    Architecture:
+    - StyleGANv2
+  Name: StyleGANv2
+  Paper:
+  - https://openaccess.thecvf.com/content_CVPR_2020/html/Karras_Analyzing_and_Improving_the_Image_Quality_of_StyleGAN_CVPR_2020_paper.html
+  README: configs/styleganv2/README.md
+Models:
+- Config: https://github.com/open-mmlab/mmgeneration/tree/master/configs/styleganv2/stylegan2_c2_ffhq_1024_b4x8.py
+  In Collection: StyleGANv2
+  Metadata:
+    Training Data: FFHQ
+  Name: stylegan2_c2_ffhq_1024_b4x8
+  Results:
+  - Dataset: FFHQ
+    Metrics:
+      Comment: official weight
+      FID50k: 2.8134
+      P&R50k: 62.856/49.400
+    Task: Unconditional GANs
+  Weights: https://download.openmmlab.com/mmgen/stylegan2/official_weights/stylegan2-ffhq-config-f-official_20210327_171224-bce9310c.pth
+- Config: https://github.com/open-mmlab/mmgeneration/tree/master/configs/styleganv2/stylegan2_c2_lsun-car_384x512_b4x8.py
+  In Collection: StyleGANv2
+  Metadata:
+    Training Data: LSUN
+  Name: stylegan2_c2_lsun-car_384x512_b4x8
+  Results:
+  - Dataset: LSUN
+    Metrics:
+      Comment: official weight
+      FID50k: 5.4316
+      P&R50k: 65.986/48.190
+    Task: Unconditional GANs
+  Weights: https://download.openmmlab.com/mmgen/stylegan2/official_weights/stylegan2-car-config-f-official_20210327_172340-8cfe053c.pth
+- Config: https://github.com/open-mmlab/mmgeneration/tree/master/configs/styleganv2/stylegan2_c2_lsun-horse_256_b4x8_800k.py
+  In Collection: StyleGANv2
+  Metadata:
+    Training Data: LSUN
+  Name: stylegan2_c2_lsun-horse_256_b4x8_800k
+  Results:
+  - Dataset: LSUN
+    Metrics:
+      Comment: official weight
+      FID50k: '-'
+      P&R50k: '-'
+    Task: Unconditional GANs
+  Weights: https://download.openmmlab.com/mmgen/stylegan2/official_weights/stylegan2-horse-config-f-official_20210327_173203-ef3e69ca.pth
+- Config: https://github.com/open-mmlab/mmgeneration/tree/master/configs/styleganv2/stylegan2_c2_lsun-church_256_b4x8_800k.py
+  In Collection: StyleGANv2
+  Metadata:
+    Training Data: LSUN
+  Name: stylegan2_c2_lsun-church_256_b4x8_800k
+  Results:
+  - Dataset: LSUN
+    Metrics:
+      Comment: official weight
+      FID50k: '-'
+      P&R50k: '-'
+    Task: Unconditional GANs
+  Weights: https://download.openmmlab.com/mmgen/stylegan2/official_weights/stylegan2-church-config-f-official_20210327_172657-1d42b7d1.pth
+- Config: https://github.com/open-mmlab/mmgeneration/tree/master/configs/styleganv2/stylegan2_c2_lsun-cat_256_b4x8_800k.py
+  In Collection: StyleGANv2
+  Metadata:
+    Training Data: LSUN
+  Name: stylegan2_c2_lsun-cat_256_b4x8_800k
+  Results:
+  - Dataset: LSUN
+    Metrics:
+      Comment: official weight
+      FID50k: '-'
+      P&R50k: '-'
+    Task: Unconditional GANs
+  Weights: https://download.openmmlab.com/mmgen/stylegan2/official_weights/stylegan2-cat-config-f-official_20210327_172444-15bc485b.pth
+- Config: https://github.com/open-mmlab/mmgeneration/tree/master/configs/styleganv2/stylegan2_c2_ffhq_256_b4x8_800k.py
+  In Collection: StyleGANv2
+  Metadata:
+    Training Data: FFHQ
+  Name: stylegan2_c2_ffhq_256_b4x8_800k
+  Results:
+  - Dataset: FFHQ
+    Metrics:
+      Comment: our training
+      FID50k: 3.992
+      P&R50k: 69.012/40.417
+    Task: Unconditional GANs
+  Weights: https://download.openmmlab.com/mmgen/stylegan2/stylegan2_c2_ffhq_256_b4x8_20210407_160709-7890ae1f.pth
+- Config: https://github.com/open-mmlab/mmgeneration/tree/master/configs/styleganv2/stylegan2_c2_ffhq_1024_b4x8.py
+  In Collection: StyleGANv2
+  Metadata:
+    Training Data: FFHQ
+  Name: stylegan2_c2_ffhq_1024_b4x8
+  Results:
+  - Dataset: FFHQ
+    Metrics:
+      Comment: our training
+      FID50k: 2.8185
+      P&R50k: 68.236/49.583
+    Task: Unconditional GANs
+  Weights: https://download.openmmlab.com/mmgen/stylegan2/stylegan2_c2_ffhq_1024_b4x8_20210407_150045-618c9024.pth
+- Config: https://github.com/open-mmlab/mmgeneration/tree/master/configs/styleganv2/stylegan2_c2_lsun-car_384x512_b4x8.py
+  In Collection: StyleGANv2
+  Metadata:
+    Training Data: LSUN
+  Name: stylegan2_c2_lsun-car_384x512_b4x8
+  Results:
+  - Dataset: LSUN
+    Metrics:
+      Comment: our training
+      FID50k: 2.4116
+      P&R50k: 66.760/50.576
+    Task: Unconditional GANs
+  Weights: https://download.openmmlab.com/mmgen/stylegan2/stylegan2_c2_lsun-car_384x512_b4x8_1800k_20210424_160929-fc9072ca.pth
+- Config: https://github.com/open-mmlab/mmgeneration/tree/master/configs/styleganv2/stylegan2_c2_ffhq_256_b4x8_800k.py
+  In Collection: StyleGANv2
+  Metadata:
+    Training Data: FFHQ
+  Name: stylegan2_c2_ffhq_256_b4x8_800k
+  Results:
+  - Dataset: FFHQ
+    Metrics:
+      Comment: baseline
+      FID50k: 3.992
+    Task: Unconditional GANs
+  Weights: https://download.openmmlab.com/mmgen/stylegan2/stylegan2_c2_ffhq_256_b4x8_20210407_160709-7890ae1f.pth
+- Config: https://github.com/open-mmlab/mmgeneration/tree/master/configs/styleganv2/stylegan2_c2_fp16_partial-GD_PL-no-scaler_ffhq_256_b4x8_800k.py
+  In Collection: StyleGANv2
+  Metadata:
+    Training Data: FFHQ
+  Name: stylegan2_c2_fp16_partial-GD_PL-no-scaler_ffhq_256_b4x8_800k
+  Results:
+  - Dataset: FFHQ
+    Metrics:
+      Comment: partial layers in fp16
+      FID50k: 4.331
+    Task: Unconditional GANs
+  Weights: https://download.openmmlab.com/mmgen/stylegan2/stylegan2_c2_fp16_partial-GD_PL-no-scaler_ffhq_256_b4x8_800k_20210508_114854-dacbe4c9.pth
+- Config: https://github.com/open-mmlab/mmgeneration/tree/master/configs/styleganv2/stylegan2_c2_fp16-globalG-partialD_PL-R1-no-scaler_ffhq_256_b4x8_800k.py
+  In Collection: StyleGANv2
+  Metadata:
+    Training Data: FFHQ
+  Name: stylegan2_c2_fp16-globalG-partialD_PL-R1-no-scaler_ffhq_256_b4x8_800k
+  Results:
+  - Dataset: FFHQ
+    Metrics:
+      Comment: the whole G in fp16
+      FID50k: 4.362
+    Task: Unconditional GANs
+  Weights: https://download.openmmlab.com/mmgen/stylegan2/stylegan2_c2_fp16-globalG-partialD_PL-R1-no-scaler_ffhq_256_b4x8_800k_20210508_114930-ef8270d4.pth
+- Config: https://github.com/open-mmlab/mmgeneration/tree/master/configs/styleganv2/stylegan2_c2_apex_fp16_PL-R1-no-scaler_ffhq_256_b4x8_800k.py
+  In Collection: StyleGANv2
+  Metadata:
+    Training Data: FFHQ
+  Name: stylegan2_c2_apex_fp16_PL-R1-no-scaler_ffhq_256_b4x8_800k
+  Results:
+  - Dataset: FFHQ
+    Metrics:
+      Comment: the whole G&D in fp16 + two loss scaler
+      FID50k: 4.614
+    Task: Unconditional GANs
+  Weights: https://download.openmmlab.com/mmgen/stylegan2/stylegan2_c2_apex_fp16_PL-R1-no-scaler_ffhq_256_b4x8_800k_20210508_114701-c2bb8afd.pth
+- Config: https://github.com/open-mmlab/mmgeneration/tree/master/configs/styleganv2/stylegan2_c2_ffhq_1024_b4x8.py
+  In Collection: StyleGANv2
+  Metadata:
+    Training Data: FFHQ
+  Name: stylegan2_c2_ffhq_1024_b4x8
+  Results:
+  - Dataset: FFHQ
+    Metrics:
+      Comment: official weight
+      FID Version: Tero's StyleGAN
+      FID50k: 2.8732
+    Task: Unconditional GANs
+  Weights: https://download.openmmlab.com/mmgen/stylegan2/official_weights/stylegan2-ffhq-config-f-official_20210327_171224-bce9310c.pth
+- Config: https://github.com/open-mmlab/mmgeneration/tree/master/configs/styleganv2/stylegan2_c2_ffhq_1024_b4x8.py
+  In Collection: StyleGANv2
+  Metadata:
+    Training Data: FFHQ
+  Name: stylegan2_c2_ffhq_1024_b4x8
+  Results:
+  - Dataset: FFHQ
+    Metrics:
+      Comment: our training
+      FID Version: Tero's StyleGAN
+      FID50k: 2.9413
+    Task: Unconditional GANs
+  Weights: https://download.openmmlab.com/mmgen/stylegan2/stylegan2_c2_ffhq_1024_b4x8_20210407_150045-618c9024.pth
+- Config: https://github.com/open-mmlab/mmgeneration/tree/master/configs/styleganv2/stylegan2_c2_ffhq_1024_b4x8.py
+  In Collection: StyleGANv2
+  Metadata:
+    Training Data: FFHQ
+  Name: stylegan2_c2_ffhq_1024_b4x8
+  Results:
+  - Dataset: FFHQ
+    Metrics:
+      Comment: official weight
+      FID Version: Our PyTorch
+      FID50k: 2.8134
+    Task: Unconditional GANs
+  Weights: https://download.openmmlab.com/mmgen/stylegan2/official_weights/stylegan2-ffhq-config-f-official_20210327_171224-bce9310c.pth
+- Config: https://github.com/open-mmlab/mmgeneration/tree/master/configs/styleganv2/stylegan2_c2_ffhq_1024_b4x8.py
+  In Collection: StyleGANv2
+  Metadata:
+    Training Data: FFHQ
+  Name: stylegan2_c2_ffhq_1024_b4x8
+  Results:
+  - Dataset: FFHQ
+    Metrics:
+      Comment: our training
+      FID Version: Our PyTorch
+      FID50k: 2.8185
+    Task: Unconditional GANs
+  Weights: https://download.openmmlab.com/mmgen/stylegan2/stylegan2_c2_ffhq_1024_b4x8_20210407_150045-618c9024.pth

build/lib/mmgen/.mim/configs/styleganv2/stylegan2_c2_apex_fp16_PL-R1-no-scaler_ffhq_256_b4x8_800k.py

+"""Config for the `config-f` setting in StyleGAN2."""
+
+_base_ = ['./stylegan2_c2_ffhq_256_b4x8_800k.py']
+
+model = dict(
+    disc_auxiliary_loss=dict(use_apex_amp=False),
+    gen_auxiliary_loss=dict(use_apex_amp=False),
+)
+
+total_iters = 800002
+
+apex_amp = dict(mode='gan', init_args=dict(opt_level='O1', num_losses=2))
+resume_from = None

build/lib/mmgen/.mim/configs/styleganv2/stylegan2_c2_apex_fp16_quicktest_ffhq_256_b4x8_800k.py

+"""Config for the `config-f` setting in StyleGAN2."""
+
+_base_ = ['./stylegan2_c2_ffhq_256_b4x8_800k.py']
+
+model = dict(
+    generator=dict(out_size=256),
+    discriminator=dict(in_size=256, convert_input_fp32=False),
+    # disc_auxiliary_loss=dict(use_apex_amp=True),
+    # gen_auxiliary_loss=dict(use_apex_amp=True),
+)
+
+dataset_type = 'QuickTestImageDataset'
+data = dict(
+    samples_per_gpu=2,
+    train=dict(type=dataset_type, size=(256, 256)),
+    val=dict(type=dataset_type, size=(256, 256)))
+
+log_config = dict(interval=1)
+
+total_iters = 800002
+
+apex_amp = dict(
+    mode='gan', init_args=dict(opt_level='O1', num_losses=2, loss_scale=512.))
+
+evaluation = dict(
+    type='GenerativeEvalHook',
+    interval=10000,
+    metrics=dict(
+        type='FID', num_images=50000, inception_pkl=None, bgr2rgb=True),
+    sample_kwargs=dict(sample_model='ema'))

build/lib/mmgen/.mim/configs/styleganv2/stylegan2_c2_ffhq_1024_b4x8.py

+"""Config for the `config-f` setting in StyleGAN2."""
+
+_base_ = [
+    '../_base_/datasets/ffhq_flip.py',
+    '../_base_/models/stylegan/stylegan2_base.py',
+    '../_base_/default_runtime.py'
+]
+
+ema_half_life = 10.  # G_smoothing_kimg
+
+model = dict(generator=dict(out_size=1024), discriminator=dict(in_size=1024))
+
+data = dict(
+    samples_per_gpu=2,
+    train=dict(dataset=dict(imgs_root='./data/images')),
+    val=dict(imgs_root='./data/images'))
+
+custom_hooks = [
+    dict(
+        type='VisualizeUnconditionalSamples',
+        output_dir='training_samples',
+        interval=5000),
+    dict(
+        type='ExponentialMovingAverageHook',
+        module_keys=('generator_ema', ),
+        interval=1,
+        interp_cfg=dict(momentum=0.5**(32. / (ema_half_life * 1000.))),
+        priority='VERY_HIGH')
+]
+
+metrics = dict(
+    fid50k=dict(
+        type='FID',
+        num_images=50000,
+        inception_pkl='work_dirs/inception_pkl/ffhq-1024-50k-rgb.pkl',
+        bgr2rgb=True),
+    pr50k3=dict(type='PR', num_images=50000, k=3),
+    ppl_wend=dict(type='PPL', space='W', sampling='end', num_images=50000))
+
+evaluation = dict(
+    type='GenerativeEvalHook',
+    interval=10000,
+    metrics=dict(
+        type='FID',
+        num_images=50000,
+        inception_pkl='work_dirs/inception_pkl/ffhq-1024-50k-rgb.pkl',
+        bgr2rgb=True),
+    sample_kwargs=dict(sample_model='ema'))
+
+checkpoint_config = dict(interval=10000, by_epoch=False, max_keep_ckpts=30)
+lr_config = None
+
+total_iters = 800002

build/lib/mmgen/.mim/configs/styleganv2/stylegan2_c2_ffhq_256_b4x8_800k.py

+"""Config for the `config-f` setting in StyleGAN2."""
+
+_base_ = [
+    '../_base_/datasets/ffhq_flip.py',
+    '../_base_/models/stylegan/stylegan2_base.py',
+    '../_base_/default_runtime.py'
+]
+
+model = dict(generator=dict(out_size=256), discriminator=dict(in_size=256))
+
+data = dict(
+    samples_per_gpu=4,
+    train=dict(dataset=dict(imgs_root='./data/ffhq/ffhq_imgs/ffhq_256')),
+    val=dict(imgs_root='./data/ffhq/ffhq_imgs/ffhq_256'))
+
+ema_half_life = 10.  # G_smoothing_kimg
+
+custom_hooks = [
+    dict(
+        type='VisualizeUnconditionalSamples',
+        output_dir='training_samples',
+        interval=5000),
+    dict(
+        type='ExponentialMovingAverageHook',
+        module_keys=('generator_ema', ),
+        interval=1,
+        interp_cfg=dict(momentum=0.5**(32. / (ema_half_life * 1000.))),
+        priority='VERY_HIGH')
+]
+
+checkpoint_config = dict(interval=10000, by_epoch=False, max_keep_ckpts=30)
+lr_config = None
+
+log_config = dict(
+    interval=100,
+    hooks=[
+        dict(type='TextLoggerHook'),
+        # dict(type='TensorboardLoggerHook'),
+    ])
+
+total_iters = 800002
+
+metrics = dict(
+    fid50k=dict(
+        type='FID',
+        num_images=50000,
+        inception_pkl='work_dirs/inception_pkl/ffhq-256-50k-rgb.pkl',
+        bgr2rgb=True),
+    pr50k3=dict(type='PR', num_images=50000, k=3),
+    ppl_wend=dict(type='PPL', space='W', sampling='end', num_images=50000))
+
+evaluation = dict(
+    type='GenerativeEvalHook',
+    interval=10000,
+    metrics=dict(
+        type='FID',
+        num_images=50000,
+        inception_pkl='work_dirs/inception_pkl/ffhq-256-50k-rgb.pkl',
+        bgr2rgb=True),
+    sample_kwargs=dict(sample_model='ema'))

build/lib/mmgen/.mim/configs/styleganv2/stylegan2_c2_fp16-globalG-partialD_PL-R1-no-scaler_ffhq_256_b4x8_800k.py

+"""Config for the `config-f` setting in StyleGAN2."""
+
+_base_ = ['./stylegan2_c2_ffhq_256_b4x8_800k.py']
+
+model = dict(
+    generator=dict(out_size=256, fp16_enabled=True),
+    discriminator=dict(in_size=256, fp16_enabled=False, num_fp16_scales=4),
+)
+
+total_iters = 800000
+
+# use ddp wrapper for faster training
+use_ddp_wrapper = True
+find_unused_parameters = False
+
+runner = dict(
+    fp16_loss_scaler=dict(init_scale=512),
+    is_dynamic_ddp=  # noqa
+    False,  # Note that this flag should be False to use DDP wrapper.
+)

build/lib/mmgen/.mim/configs/styleganv2/stylegan2_c2_fp16-global_quicktest_ffhq_256_b4x8_800k.py

+"""Config for the `config-f` setting in StyleGAN2."""
+
+_base_ = ['./stylegan2_c2_ffhq_256_b4x8_800k.py']
+
+model = dict(
+    generator=dict(out_size=256, fp16_enabled=True),
+    discriminator=dict(in_size=256, fp16_enabled=True),
+    disc_auxiliary_loss=dict(data_info=dict(loss_scaler='loss_scaler')),
+    # gen_auxiliary_loss=dict(data_info=dict(loss_scaler='loss_scaler')),
+)
+
+dataset_type = 'QuickTestImageDataset'
+data = dict(
+    samples_per_gpu=2,
+    train=dict(type=dataset_type, size=(256, 256)),
+    val=dict(type=dataset_type, size=(256, 256)))
+
+log_config = dict(interval=1)
+
+total_iters = 800002
+
+runner = dict(fp16_loss_scaler=dict(init_scale=512))
+
+evaluation = dict(
+    type='GenerativeEvalHook',
+    interval=10000,
+    metrics=dict(
+        type='FID', num_images=50000, inception_pkl=None, bgr2rgb=True),
+    sample_kwargs=dict(sample_model='ema'))

build/lib/mmgen/.mim/configs/styleganv2/stylegan2_c2_fp16_partial-GD_PL-no-scaler_ffhq_256_b4x8_800k.py

+"""Config for the `config-f` setting in StyleGAN2."""
+
+_base_ = ['./stylegan2_c2_ffhq_256_b4x8_800k.py']
+
+model = dict(
+    generator=dict(out_size=256, num_fp16_scales=4),
+    discriminator=dict(in_size=256, num_fp16_scales=4),
+    disc_auxiliary_loss=dict(data_info=dict(loss_scaler='loss_scaler')),
+    # gen_auxiliary_loss=dict(data_info=dict(loss_scaler='loss_scaler')),
+)
+
+total_iters = 800002
+
+# use ddp wrapper for faster training
+use_ddp_wrapper = True
+find_unused_parameters = False
+
+runner = dict(
+    fp16_loss_scaler=dict(init_scale=512),
+    is_dynamic_ddp=  # noqa
+    False,  # Note that this flag should be False to use DDP wrapper.
+)

build/lib/mmgen/.mim/configs/styleganv2/stylegan2_c2_fp16_quicktest_ffhq_256_b4x8_800k.py

+"""Config for the `config-f` setting in StyleGAN2."""
+
+_base_ = ['./stylegan2_c2_ffhq_256_b4x8_800k.py']
+
+model = dict(
+    generator=dict(out_size=256, num_fp16_scales=4),
+    discriminator=dict(in_size=256, num_fp16_scales=4),
+    disc_auxiliary_loss=dict(data_info=dict(loss_scaler='loss_scaler')),
+    # gen_auxiliary_loss=dict(data_info=dict(loss_scaler='loss_scaler')),
+)
+
+dataset_type = 'QuickTestImageDataset'
+data = dict(
+    samples_per_gpu=2,
+    train=dict(type=dataset_type, size=(256, 256)),
+    val=dict(type=dataset_type, size=(256, 256)))
+
+log_config = dict(interval=1)
+
+total_iters = 800002
+
+runner = dict(fp16_loss_scaler=dict(init_scale=512))
+
+evaluation = dict(
+    type='GenerativeEvalHook',
+    interval=10000,
+    metrics=dict(
+        type='FID', num_images=50000, inception_pkl=None, bgr2rgb=True),
+    sample_kwargs=dict(sample_model='ema'))

build/lib/mmgen/.mim/configs/styleganv2/stylegan2_c2_lsun-car_384x512_b4x8.py

+_base_ = [
+    '../_base_/datasets/lsun-car_pad_512.py',
+    '../_base_/models/stylegan/stylegan2_base.py',
+    '../_base_/default_runtime.py'
+]
+
+model = dict(generator=dict(out_size=512), discriminator=dict(in_size=512))
+
+data = dict(
+    samples_per_gpu=4,
+    train=dict(dataset=dict(imgs_root='./data/lsun/images/car')),
+    val=dict(imgs_root='./data/lsun/images/car'))
+
+ema_half_life = 10.  # G_smoothing_kimg
+
+custom_hooks = [
+    dict(
+        type='VisualizeUnconditionalSamples',
+        output_dir='training_samples',
+        interval=5000),
+    dict(
+        type='ExponentialMovingAverageHook',
+        module_keys=('generator_ema', ),
+        interval=1,
+        interp_cfg=dict(momentum=0.5**(32. / (ema_half_life * 1000.))),
+        priority='VERY_HIGH')
+]
+
+checkpoint_config = dict(interval=10000, by_epoch=False, max_keep_ckpts=40)
+lr_config = None
+
+total_iters = 1800002
+
+metrics = dict(
+    fid50k=dict(
+        type='FID', num_images=50000, inception_pkl=None, bgr2rgb=True),
+    pr50k3=dict(type='PR', num_images=50000, k=3),
+    ppl_wend=dict(type='PPL', space='W', sampling='end', num_images=50000))
+
+evaluation = dict(
+    type='GenerativeEvalHook',
+    interval=10000,
+    metrics=dict(
+        type='FID',
+        num_images=50000,
+        inception_pkl='work_dirs/inception_pkl/lsun-car-512-50k-rgb.pkl',
+        bgr2rgb=True),
+    sample_kwargs=dict(sample_model='ema'))

build/lib/mmgen/.mim/configs/styleganv2/stylegan2_c2_lsun-cat_256_b4x8_800k.py

+"""Note that this config is just for testing."""
+
+_base_ = [
+    '../_base_/datasets/lsun_stylegan.py',
+    '../_base_/models/stylegan/stylegan2_base.py',
+    '../_base_/default_runtime.py'
+]
+
+model = dict(generator=dict(out_size=256), discriminator=dict(in_size=256))
+
+data = dict(
+    samples_per_gpu=4, train=dict(dataset=dict(imgs_root='./data/lsun-cat')))
+
+ema_half_life = 10.  # G_smoothing_kimg
+
+custom_hooks = [
+    dict(
+        type='VisualizeUnconditionalSamples',
+        output_dir='training_samples',
+        interval=5000),
+    dict(
+        type='ExponentialMovingAverageHook',
+        module_keys=('generator_ema', ),
+        interval=1,
+        interp_cfg=dict(momentum=0.5**(32. / (ema_half_life * 1000.))),
+        priority='VERY_HIGH')
+]
+
+checkpoint_config = dict(interval=10000, by_epoch=False, max_keep_ckpts=30)
+lr_config = None
+
+log_config = dict(
+    interval=100,
+    hooks=[
+        dict(type='TextLoggerHook'),
+        # dict(type='TensorboardLoggerHook'),
+    ])
+
+total_iters = 800002  # need to modify
+
+metrics = dict(
+    fid50k=dict(
+        type='FID', num_images=50000, inception_pkl=None, bgr2rgb=True),
+    pr50k3=dict(type='PR', num_images=50000, k=3),
+    ppl_wend=dict(type='PPL', space='W', sampling='end', num_images=50000))

build/lib/mmgen/.mim/configs/styleganv2/stylegan2_c2_lsun-church_256_b4x8_800k.py

+"""Note that this config is just for testing."""
+
+_base_ = [
+    '../_base_/datasets/lsun_stylegan.py',
+    '../_base_/models/stylegan/stylegan2_base.py',
+    '../_base_/default_runtime.py'
+]
+
+model = dict(generator=dict(out_size=256), discriminator=dict(in_size=256))
+
+data = dict(
+    samples_per_gpu=4,
+    train=dict(dataset=dict(imgs_root='./data/lsun-church')))
+
+ema_half_life = 10.  # G_smoothing_kimg
+
+custom_hooks = [
+    dict(
+        type='VisualizeUnconditionalSamples',
+        output_dir='training_samples',
+        interval=5000),
+    dict(
+        type='ExponentialMovingAverageHook',
+        module_keys=('generator_ema', ),
+        interval=1,
+        interp_cfg=dict(momentum=0.5**(32. / (ema_half_life * 1000.))),
+        priority='VERY_HIGH')
+]
+
+checkpoint_config = dict(interval=10000, by_epoch=False, max_keep_ckpts=30)
+lr_config = None
+
+log_config = dict(
+    interval=100,
+    hooks=[
+        dict(type='TextLoggerHook'),
+        # dict(type='TensorboardLoggerHook'),
+    ])
+
+total_iters = 800002  # need to modify
+
+metrics = dict(
+    fid50k=dict(
+        type='FID', num_images=50000, inception_pkl=None, bgr2rgb=True),
+    pr50k3=dict(type='PR', num_images=50000, k=3),
+    ppl_wend=dict(type='PPL', space='W', sampling='end', num_images=50000))