stylegan2_mmcv

build/lib/mmgen/.mim/configs/positional_encoding_in_gans/singan_interp-pad_disc-nobn_balloons.py

+_base_ = ['../singan/singan_balloons.py']
+
+model = dict(
+    type='PESinGAN',
+    generator=dict(
+        type='SinGANMSGeneratorPE', interp_pad=True, noise_with_pad=True),
+    discriminator=dict(norm_cfg=None))
+
+train_cfg = dict(fixed_noise_with_pad=True)
+
+dist_params = dict(backend='nccl')

build/lib/mmgen/.mim/configs/positional_encoding_in_gans/singan_interp-pad_disc-nobn_fish.py

+_base_ = ['../singan/singan_fish.py']
+
+model = dict(
+    type='PESinGAN',
+    generator=dict(
+        type='SinGANMSGeneratorPE', interp_pad=True, noise_with_pad=True),
+    discriminator=dict(norm_cfg=None))
+
+train_cfg = dict(fixed_noise_with_pad=True)
+
+data = dict(
+    train=dict(
+        img_path='./data/singan/fish-crop.jpg',
+        min_size=25,
+        max_size=300,
+    ))
+
+dist_params = dict(backend='nccl')

build/lib/mmgen/.mim/configs/positional_encoding_in_gans/singan_spe-dim4_bohemian.py

+_base_ = ['../singan/singan_bohemian.py']
+
+embedding_dim = 4
+num_scales = 10  # start from zero
+model = dict(
+    type='PESinGAN',
+    generator=dict(
+        type='SinGANMSGeneratorPE',
+        num_scales=num_scales,
+        padding=1,
+        pad_at_head=False,
+        first_stage_in_channels=embedding_dim * 2,
+        positional_encoding=dict(
+            type='SPE',
+            embedding_dim=embedding_dim,
+            padding_idx=0,
+            init_size=512,
+            div_half_dim=False,
+            center_shift=200)),
+    discriminator=dict(num_scales=num_scales))
+
+train_cfg = dict(first_fixed_noises_ch=embedding_dim * 2)
+
+data = dict(
+    train=dict(
+        img_path='./data/singan/bohemian.png',
+        min_size=25,
+        max_size=500,
+    ))
+
+dist_params = dict(backend='nccl')
+total_iters = 22000

build/lib/mmgen/.mim/configs/positional_encoding_in_gans/singan_spe-dim4_fish.py

+_base_ = ['../singan/singan_fish.py']
+
+embedding_dim = 4
+num_scales = 10  # start from zero
+model = dict(
+    type='PESinGAN',
+    generator=dict(
+        type='SinGANMSGeneratorPE',
+        num_scales=num_scales,
+        padding=1,
+        pad_at_head=False,
+        first_stage_in_channels=embedding_dim * 2,
+        positional_encoding=dict(
+            type='SPE',
+            embedding_dim=embedding_dim,
+            padding_idx=0,
+            init_size=512,
+            div_half_dim=False,
+            center_shift=200)),
+    discriminator=dict(num_scales=num_scales))
+
+data = dict(
+    train=dict(
+        img_path='./data/singan/fish-crop.jpg',
+        min_size=25,
+        max_size=300,
+    ))
+
+dist_params = dict(backend='nccl')
+total_iters = 22000

build/lib/mmgen/.mim/configs/positional_encoding_in_gans/singan_spe-dim8_bohemian.py

+_base_ = ['../singan/singan_bohemian.py']
+
+embedding_dim = 4
+num_scales = 10  # start from zero
+model = dict(
+    type='PESinGAN',
+    generator=dict(
+        type='SinGANMSGeneratorPE',
+        num_scales=num_scales,
+        padding=1,
+        pad_at_head=False,
+        first_stage_in_channels=embedding_dim * 2,
+        positional_encoding=dict(
+            type='SPE',
+            embedding_dim=embedding_dim,
+            padding_idx=0,
+            init_size=512,
+            div_half_dim=False,
+            center_shift=200)),
+    discriminator=dict(num_scales=num_scales))
+
+train_cfg = dict(first_fixed_noises_ch=embedding_dim * 2)
+
+data = dict(
+    train=dict(
+        img_path='./data/singan/bohemian.png',
+        min_size=25,
+        max_size=500,
+    ))
+
+dist_params = dict(backend='nccl')
+total_iters = 22000

build/lib/mmgen/.mim/configs/positional_encoding_in_gans/stylegan2_c2_ffhq_256_b3x8_1100k.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=3,
+    train=dict(dataset=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')
+]
+
+metrics = dict(
+    fid50k=dict(
+        type='FID',
+        num_images=50000,
+        inception_pkl='work_dirs/inception_pkl/ffhq-256-50k-rgb.pkl',
+        bgr2rgb=True),
+    pr10k3=dict(type='PR', num_images=10000, k=3))
+
+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 = 1100002

build/lib/mmgen/.mim/configs/positional_encoding_in_gans/stylegan2_c2_ffhq_512_b3x8_1100k.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=512), discriminator=dict(in_size=512))
+
+data = dict(
+    samples_per_gpu=3,
+    train=dict(dataset=dict(imgs_root='./data/ffhq/ffhq_imgs/ffhq_512')))
+
+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')
+]
+
+metrics = dict(
+    fid50k=dict(
+        type='FID',
+        num_images=50000,
+        inception_pkl='work_dirs/inception_pkl/ffhq-512-50k-rgb.pkl',
+        bgr2rgb=True),
+    pr10k3=dict(type='PR', num_images=10000, k=3))
+
+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 = 1100002

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

+Collections:
+- Metadata:
+    Architecture:
+    - SAGAN
+  Name: SAGAN
+  Paper:
+  - https://proceedings.mlr.press/v97/zhang19d.html
+  README: configs/sagan/README.md
+Models:
+- Config: https://github.com/open-mmlab/mmgeneration/tree/master/configs/sagan/sagan_32_woReLUinplace_lr-2e-4_ndisc5_cifar10_b64x1.py
+  In Collection: SAGAN
+  Metadata:
+    Training Data: CIFAR
+  Name: sagan_32_woReLUinplace_lr-2e-4_ndisc5_cifar10_b64x1
+  Results:
+  - Dataset: CIFAR
+    Metrics:
+      FID: 10.503
+      IS: 9.3217
+      Inplace ReLU: w/o
+      Iter: 400000.0
+      Log: '[Log]'
+      Total Batchsize (BZ_PER_GPU * NGPU): 64x1
+      Total Iters\*: 500000.0
+      dist_step: 5.0
+    Task: Conditional GANs
+  Weights: https://download.openmmlab.com/mmgen/sagan/sagan_cifar10_32_lr2e-4_ndisc5_b64x1_woReUinplace_is-iter400000_20210730_125743-4008a9ca.pth
+- Config: https://github.com/open-mmlab/mmgeneration/tree/master/configs/sagan/sagan_32_woReLUinplace_lr-2e-4_ndisc5_cifar10_b64x1.py
+  In Collection: SAGAN
+  Metadata:
+    Training Data: CIFAR
+  Name: sagan_32_woReLUinplace_lr-2e-4_ndisc5_cifar10_b64x1
+  Results:
+  - Dataset: CIFAR
+    Metrics:
+      FID: 9.4252
+      IS: 9.3174
+      Inplace ReLU: w/o
+      Iter: 480000.0
+      Log: '[Log]'
+      Total Batchsize (BZ_PER_GPU * NGPU): 64x1
+      Total Iters\*: 500000.0
+      dist_step: 5.0
+    Task: Conditional GANs
+  Weights: https://download.openmmlab.com/mmgen/sagan/sagan_cifar10_32_lr2e-4_ndisc5_b64x1_woReUinplace_fid-iter480000_20210730_125449-d50568a4.pth
+- Config: https://github.com/open-mmlab/mmgeneration/tree/master/configs/sagan/sagan_32_wReLUinplace_lr-2e-4_ndisc5_cifar10_b64x1.py
+  In Collection: SAGAN
+  Metadata:
+    Training Data: CIFAR
+  Name: sagan_32_wReLUinplace_lr-2e-4_ndisc5_cifar10_b64x1
+  Results:
+  - Dataset: CIFAR
+    Metrics:
+      FID: 11.776
+      IS: 9.2286
+      Inplace ReLU: w
+      Iter: 380000.0
+      Log: '[Log]'
+      Total Batchsize (BZ_PER_GPU * NGPU): 64x1
+      Total Iters\*: 500000.0
+      dist_step: 5.0
+    Task: Conditional GANs
+  Weights: https://download.openmmlab.com/mmgen/sagan/sagan_cifar10_32_lr2e-4_ndisc5_b64x1_wReLUinplace_is-iter380000_20210730_124937-c77b4d25.pth
+- Config: https://github.com/open-mmlab/mmgeneration/tree/master/configs/sagan/sagan_32_wReLUinplace_lr-2e-4_ndisc5_cifar10_b64x1.py
+  In Collection: SAGAN
+  Metadata:
+    Training Data: CIFAR
+  Name: sagan_32_wReLUinplace_lr-2e-4_ndisc5_cifar10_b64x1
+  Results:
+  - Dataset: CIFAR
+    Metrics:
+      FID: 10.7781
+      IS: 9.2061
+      Inplace ReLU: w
+      Iter: 460000.0
+      Log: '[Log]'
+      Total Batchsize (BZ_PER_GPU * NGPU): 64x1
+      Total Iters\*: 500000.0
+      dist_step: 5.0
+    Task: Conditional GANs
+  Weights: https://download.openmmlab.com/mmgen/sagan/sagan_cifar10_32_lr2e-4_ndisc5_b64x1_wReLUinplace_fid-iter460000_20210730_125155-cbefb354.pth
+- Config: https://github.com/open-mmlab/mmgeneration/tree/master/configs/sagan/sagan_128_woReLUinplace_Glr-1e-4_Dlr-4e-4_ndisc1_imagenet1k_b64x4.py
+  In Collection: SAGAN
+  Metadata:
+    Training Data: IMAGENET
+  Name: sagan_128_woReLUinplace_Glr-1e-4_Dlr-4e-4_ndisc1_imagenet1k_b64x4
+  Results:
+  - Dataset: IMAGENET
+    Metrics:
+      FID: 36.7712
+      IS: 31.5938
+      Inplace ReLU: w/o
+      Iter: 980000.0
+      Log: '[Log]'
+      Total Batchsize (BZ_PER_GPU * NGPU): 64x4
+      Total Iters\*: 1000000.0
+      dist_step: 1.0
+    Task: Conditional GANs
+  Weights: https://download.openmmlab.com/mmgen/sagan/sagan_imagenet1k_128_Glr1e-4_Dlr4e-4_ndisc1_b32x4_woReLUinplace_is-iter980000_20210730_163140-cfbebfc6.pth
+- Config: https://github.com/open-mmlab/mmgeneration/tree/master/configs/sagan/sagan_128_woReLUinplace_Glr-1e-4_Dlr-4e-4_ndisc1_imagenet1k_b64x4.py
+  In Collection: SAGAN
+  Metadata:
+    Training Data: IMAGENET
+  Name: sagan_128_woReLUinplace_Glr-1e-4_Dlr-4e-4_ndisc1_imagenet1k_b64x4
+  Results:
+  - Dataset: IMAGENET
+    Metrics:
+      FID: 34.7838
+      IS: 28.4936
+      Inplace ReLU: w/o
+      Iter: 950000.0
+      Log: '[Log]'
+      Total Batchsize (BZ_PER_GPU * NGPU): 64x4
+      Total Iters\*: 1000000.0
+      dist_step: 1.0
+    Task: Conditional GANs
+  Weights: https://download.openmmlab.com/mmgen/sagan/sagan_imagenet1k_128_Glr1e-4_Dlr4e-4_ndisc1_b32x4_woReLUinplace_fid-iter950000_20210730_163431-d7916963.pth
+- Config: https://github.com/open-mmlab/mmgeneration/tree/master/configs/sagan/sagan_128_woReLUinplace_noaug_bigGAN_Glr-1e-4_Dlr-4e-4_ndisc1_imagenet1k_b32x8.py
+  In Collection: SAGAN
+  Metadata:
+    Training Data: IMAGENET
+  Name: sagan_128_woReLUinplace_noaug_bigGAN_Glr-1e-4_Dlr-4e-4_ndisc1_imagenet1k_b32x8
+  Results:
+  - Dataset: IMAGENET
+    Metrics:
+      FID: 12.8295
+      IS: 69.535
+      Inplace ReLU: w/o
+      Iter: 826000.0
+      Log: '[Log]'
+      Total Batchsize (BZ_PER_GPU * NGPU): 32x8
+      Total Iters\*: 1000000.0
+      dist_step: 1.0
+    Task: Conditional GANs
+  Weights: https://download.openmmlab.com/mmgen/sagan/sagan_128_woReLUinplace_noaug_bigGAN_imagenet1k_b32x8_Glr1e-4_Dlr-4e-4_ndisc1_20210818_210232-3f5686af.pth
+- Config: https://github.com/open-mmlab/mmgeneration/tree/master/configs/sagan/sagan_128_woReLUinplace_noaug_bigGAN_Glr-1e-4_Dlr-4e-4_ndisc1_imagenet1k_b32x8.py
+  In Collection: SAGAN
+  Metadata:
+    Training Data: IMAGENET
+  Name: sagan_128_woReLUinplace_noaug_bigGAN_Glr-1e-4_Dlr-4e-4_ndisc1_imagenet1k_b32x8
+  Results:
+  - Dataset: IMAGENET
+    Metrics:
+      FID: 12.8295
+      IS: 69.535
+      Inplace ReLU: w/o
+      Iter: 826000.0
+      Log: '[Log]'
+      Total Batchsize (BZ_PER_GPU * NGPU): 32x8
+      Total Iters\*: 1000000.0
+      dist_step: 1.0
+    Task: Conditional GANs
+  Weights: https://download.openmmlab.com/mmgen/sagan/sagan_128_woReLUinplace_noaug_bigGAN_imagenet1k_b32x8_Glr1e-4_Dlr-4e-4_ndisc1_20210818_210232-3f5686af.pth
+- Config: https://github.com/open-mmlab/mmgeneration/tree/master/configs/sagan/sagan_32_cvt_studioGAN.py
+  In Collection: SAGAN
+  Metadata:
+    Training Data: Others
+  Name: sagan_32_cvt_studioGAN
+  Results:
+  - Dataset: Others
+    Metrics:
+      FID (Our Pipeline): 10.2011
+      FID (StudioGAN): 14.009
+      IS (Our Pipeline): 9.116
+      IS (StudioGAN): 8.68
+      Inplace ReLU: w
+      Total Iters: 100000.0
+      n_disc: 5.0
+    Task: Conditional GANs
+  Weights: https://download.openmmlab.com/mmgen/sagan/sagan_32_cifar10_convert-studio-rgb_20210730_153321-080da7e2.pth
+- Config: https://github.com/open-mmlab/mmgeneration/tree/master/configs/sagan/sagan_32_cvt_studioGAN.py
+  In Collection: SAGAN
+  Metadata:
+    Training Data: Others
+  Name: sagan_32_cvt_studioGAN
+  Results:
+  - Dataset: Others
+    Metrics:
+      FID (Our Pipeline): 40.1162
+      FID (StudioGAN): 34.726
+      IS (Our Pipeline): 27.367
+      IS (StudioGAN): 29.848
+      Inplace ReLU: w
+      Total Iters: 1000000.0
+      n_disc: 1.0
+    Task: Conditional GANs
+  Weights: https://download.openmmlab.com/mmgen/sagan/sagan_128_imagenet1k_convert-studio-rgb_20210730_153357-eddb0d1d.pth

build/lib/mmgen/.mim/configs/sagan/sagan_128_cvt_studioGAN.py

+_base_ = ['../_base_/models/sagan/sagan_128x128.py']

build/lib/mmgen/.mim/configs/sagan/sagan_128_woReLUinplace_Glr-1e-4_Dlr-4e-4_ndisc1_imagenet1k_b64x4.py

+_base_ = [
+    '../_base_/models/sagan/sagan_128x128.py',
+    '../_base_/datasets/imagenet_128.py', '../_base_/default_runtime.py'
+]
+
+init_cfg = dict(type='studio')
+model = dict(
+    generator=dict(num_classes=1000, init_cfg=init_cfg),
+    discriminator=dict(num_classes=1000, init_cfg=init_cfg),
+)
+
+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=1000)
+]
+
+inception_pkl = './work_dirs/inception_pkl/imagenet.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='ema'))
+
+n_disc = 1
+total_iters = 1000000 * 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=0.0001, betas=(0.0, 0.999)),
+    discriminator=dict(type='Adam', lr=0.0004, betas=(0.0, 0.999)))
+
+# train on 4 gpus
+data = dict(samples_per_gpu=64)

build/lib/mmgen/.mim/configs/sagan/sagan_128_woReLUinplace_noaug_bigGAN_Glr-1e-4_Dlr-4e-4_ndisc1_imagenet1k_b32x8.py

+# In this config, we follow the setting `launch_SAGAN_bz128x2_ema.sh` from
+# BigGAN's repo. Please refer to https://github.com/ajbrock/BigGAN-PyTorch/blob/master/scripts/launch_SAGAN_bs128x2_ema.sh  # noqa
+# In summary, in this config:
+# 1. use eps=1e-8 for Spectral Norm
+# 2. not use syncBN
+# 3. not use  Spectral Norm for embedding layers in cBN
+# 4. start EMA at iterations
+# 5. use xavier_uniform for weight initialization
+# 6. no data augmentation
+
+_base_ = [
+    '../_base_/models/sagan/sagan_128x128.py',
+    '../_base_/datasets/imagenet_noaug_128.py', '../_base_/default_runtime.py'
+]
+
+init_cfg = dict(type='BigGAN')
+model = dict(
+    num_classes=1000,
+    generator=dict(
+        num_classes=1000,
+        init_cfg=init_cfg,
+        norm_eps=1e-5,
+        sn_eps=1e-8,
+        auto_sync_bn=False,
+        with_embedding_spectral_norm=False),
+    discriminator=dict(num_classes=1000, init_cfg=init_cfg, sn_eps=1e-8),
+)
+
+n_disc = 1
+train_cfg = dict(disc_step=n_disc, use_ema=True)
+
+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=1000),
+    dict(
+        type='ExponentialMovingAverageHook',
+        module_keys=('generator_ema'),
+        interval=n_disc,
+        start_iter=2000 * n_disc,
+        interp_cfg=dict(momentum=0.999),
+        priority='VERY_HIGH')
+]
+
+inception_pkl = './work_dirs/inception_pkl/imagenet.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='ema'))
+
+total_iters = 1000000 * 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=0.0001, betas=(0.0, 0.999)),
+    discriminator=dict(type='Adam', lr=0.0004, betas=(0.0, 0.999)))
+
+# train on 8 gpus
+data = dict(samples_per_gpu=32)

build/lib/mmgen/.mim/configs/sagan/sagan_32_cvt_studioGAN.py

+_base_ = ['../_base_/models/sagan/sagan_32x32.py']

build/lib/mmgen/.mim/configs/sagan/sagan_32_wReLUinplace_lr-2e-4_ndisc5_cifar10_b64x1.py

+_base_ = [
+    '../_base_/models/sagan/sagan_32x32.py',
+    '../_base_/datasets/cifar10_nopad.py', '../_base_/default_runtime.py'
+]
+
+init_cfg = dict(type='studio')
+model = dict(
+    num_classes=10,
+    generator=dict(
+        num_classes=10,
+        act_cfg=dict(type='ReLU', inplace=True),
+        init_cfg=init_cfg),
+    discriminator=dict(
+        num_classes=10,
+        act_cfg=dict(type='ReLU', inplace=True),
+        init_cfg=init_cfg),
+)
+
+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=1000)
+]
+
+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'))
+
+n_disc = 5
+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/sagan/sagan_32_woReLUinplace_lr-2e-4_ndisc5_cifar10_b64x1.py

+_base_ = [
+    '../_base_/models/sagan/sagan_32x32.py',
+    '../_base_/datasets/cifar10_nopad.py', '../_base_/default_runtime.py'
+]
+
+init_cfg = dict(type='studio')
+model = dict(
+    num_classes=10,
+    generator=dict(num_classes=10, init_cfg=init_cfg),
+    discriminator=dict(num_classes=10, init_cfg=init_cfg))
+
+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=1000)
+]
+
+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'))
+
+n_disc = 5
+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/singan/metafile.yml

+Collections:
+- Metadata:
+    Architecture:
+    - SinGAN
+  Name: SinGAN
+  Paper:
+  - https://openaccess.thecvf.com/content_ICCV_2019/html/Shaham_SinGAN_Learning_a_Generative_Model_From_a_Single_Natural_Image_ICCV_2019_paper.html
+  README: configs/singan/README.md
+Models:
+- Config: https://github.com/open-mmlab/mmgeneration/tree/master/configs/singan/singan_balloons.py
+  In Collection: SinGAN
+  Metadata:
+    Training Data: Others
+  Name: singan_balloons
+  Results:
+  - Dataset: Others
+    Metrics:
+      Num Scales: 8.0
+    Task: Internal Learning
+  Weights: https://download.openmmlab.com/mmgen/singan/singan_balloons_20210406_191047-8fcd94cf.pth
+- Config: https://github.com/open-mmlab/mmgeneration/tree/master/configs/singan/singan_fish.py
+  In Collection: SinGAN
+  Metadata:
+    Training Data: Others
+  Name: singan_fish
+  Results:
+  - Dataset: Others
+    Metrics:
+      Num Scales: 10.0
+    Task: Internal Learning
+  Weights: https://download.openmmlab.com/mmgen/singan/singan_fis_20210406_201006-860d91b6.pth
+- Config: https://github.com/open-mmlab/mmgeneration/tree/master/configs/singan/singan_bohemian.py
+  In Collection: SinGAN
+  Metadata:
+    Training Data: Others
+  Name: singan_bohemian
+  Results:
+  - Dataset: Others
+    Metrics:
+      Num Scales: 10.0
+    Task: Internal Learning
+  Weights: https://download.openmmlab.com/mmgen/singan/singan_bohemian_20210406_175439-f964ee38.pth

build/lib/mmgen/.mim/configs/singan/singan_balloons.py

+_base_ = [
+    '../_base_/models/singan/singan.py', '../_base_/datasets/singan.py',
+    '../_base_/default_runtime.py'
+]
+
+num_scales = 8  # start from zero
+model = dict(
+    generator=dict(num_scales=num_scales),
+    discriminator=dict(num_scales=num_scales))
+
+train_cfg = dict(
+    noise_weight_init=0.1,
+    iters_per_scale=2000,
+)
+
+# test_cfg = dict(
+#     _delete_ = True
+#     pkl_data = 'path to pkl data'
+# )
+
+data = dict(train=dict(img_path='./data/singan/balloons.png'))
+
+optimizer = None
+lr_config = None
+checkpoint_config = dict(by_epoch=False, interval=2000, max_keep_ckpts=3)
+
+custom_hooks = [
+    dict(
+        type='MMGenVisualizationHook',
+        output_dir='visual',
+        interval=500,
+        bgr2rgb=True,
+        res_name_list=['fake_imgs', 'recon_imgs', 'real_imgs']),
+    dict(
+        type='PickleDataHook',
+        output_dir='pickle',
+        interval=-1,
+        after_run=True,
+        data_name_list=['noise_weights', 'fixed_noises', 'curr_stage'])
+]
+
+total_iters = 18000

build/lib/mmgen/.mim/configs/singan/singan_bohemian.py

+_base_ = [
+    '../_base_/models/singan/singan.py', '../_base_/datasets/singan.py',
+    '../_base_/default_runtime.py'
+]
+
+num_scales = 10  # start from zero
+model = dict(
+    generator=dict(num_scales=num_scales),
+    discriminator=dict(num_scales=num_scales))
+
+train_cfg = dict(
+    noise_weight_init=0.1,
+    iters_per_scale=2000,
+)
+
+# test_cfg = dict(
+#     _delete_ = True
+#     pkl_data = 'path to pkl data'
+# )
+
+data = dict(
+    train=dict(
+        img_path='./data/singan/bohemian.png', min_size=25, max_size=500))
+
+optimizer = None
+lr_config = None
+checkpoint_config = dict(by_epoch=False, interval=2000, max_keep_ckpts=3)
+
+custom_hooks = [
+    dict(
+        type='MMGenVisualizationHook',
+        output_dir='visual',
+        interval=500,
+        bgr2rgb=True,
+        res_name_list=['fake_imgs', 'recon_imgs', 'real_imgs']),
+    dict(
+        type='PickleDataHook',
+        output_dir='pickle',
+        interval=-1,
+        after_run=True,
+        data_name_list=['noise_weights', 'fixed_noises', 'curr_stage'])
+]
+
+total_iters = 22000

build/lib/mmgen/.mim/configs/singan/singan_fish.py

+_base_ = [
+    '../_base_/models/singan/singan.py', '../_base_/datasets/singan.py',
+    '../_base_/default_runtime.py'
+]
+
+num_scales = 10  # start from zero
+model = dict(
+    generator=dict(num_scales=num_scales),
+    discriminator=dict(num_scales=num_scales))
+
+train_cfg = dict(
+    noise_weight_init=0.1,
+    iters_per_scale=2000,
+)
+
+# test_cfg = dict(
+#     _delete_ = True
+#     pkl_data = 'path to pkl data'
+# )
+
+data = dict(
+    train=dict(
+        img_path='./data/singan/fish-crop.jpg', min_size=25, max_size=300))
+
+optimizer = None
+lr_config = None
+checkpoint_config = dict(by_epoch=False, interval=2000, max_keep_ckpts=3)
+
+custom_hooks = [
+    dict(
+        type='MMGenVisualizationHook',
+        output_dir='visual',
+        interval=500,
+        bgr2rgb=True,
+        res_name_list=['fake_imgs', 'recon_imgs', 'real_imgs']),
+    dict(
+        type='PickleDataHook',
+        output_dir='pickle',
+        interval=-1,
+        after_run=True,
+        data_name_list=['noise_weights', 'fixed_noises', 'curr_stage'])
+]
+
+total_iters = 22000

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

+Collections:
+- Metadata:
+    Architecture:
+    - SNGAN
+  Name: SNGAN
+  Paper:
+  - https://openreview.net/forum?id=B1QRgziT-
+  README: configs/sngan_proj/README.md
+Models:
+- Config: https://github.com/open-mmlab/mmgeneration/tree/master/configs/sngan_proj/sngan_proj_32_woReLUinplace_lr-2e-4_ndisc5_cifar10_b64x1.py
+  In Collection: SNGAN
+  Metadata:
+    Training Data: CIFAR
+  Name: sngan_proj_32_woReLUinplace_lr-2e-4_ndisc5_cifar10_b64x1
+  Results:
+  - Dataset: CIFAR
+    Metrics:
+      FID: 9.8203
+      IS: 9.6919
+      Inplace ReLU: w/o
+      Iter: 400000.0
+      Log: '[Log]'
+      Total Iters\*: 500000.0
+      disc_step: 5.0
+    Task: Conditional GANs
+  Weights: https://download.openmmlab.com/mmgen/sngan_proj/sngan_proj_cifar10_32_lr-2e-4_b64x1_woReLUinplace_is-iter400000_20210709_163823-902ce1ae.pth
+- Config: https://github.com/open-mmlab/mmgeneration/tree/master/configs/sngan_proj/sngan_proj_32_woReLUinplace_lr-2e-4_ndisc5_cifar10_b64x1.py
+  In Collection: SNGAN
+  Metadata:
+    Training Data: CIFAR
+  Name: sngan_proj_32_woReLUinplace_lr-2e-4_ndisc5_cifar10_b64x1
+  Results:
+  - Dataset: CIFAR
+    Metrics:
+      FID: 8.1158
+      IS: 9.5659
+      Inplace ReLU: w/o
+      Iter: 490000.0
+      Log: '[Log]'
+      Total Iters\*: 500000.0
+      disc_step: 5.0
+    Task: Conditional GANs
+  Weights: https://download.openmmlab.com/mmgen/sngan_proj/sngan_proj_cifar10_32_lr-2e-4_b64x1_woReLUinplace_fid-iter490000_20210709_163329-ba0862a0.pth
+- Config: https://github.com/open-mmlab/mmgeneration/tree/master/configs/sngan_proj/sngan_proj_32_wReLUinplace_lr-2e-4_ndisc5_cifar10_b64x1.py
+  In Collection: SNGAN
+  Metadata:
+    Training Data: CIFAR
+  Name: sngan_proj_32_wReLUinplace_lr-2e-4_ndisc5_cifar10_b64x1
+  Results:
+  - Dataset: CIFAR
+    Metrics:
+      FID: 8.3462
+      IS: 9.5564
+      Inplace ReLU: w
+      Iter: 490000.0
+      Log: '[Log]'
+      Total Iters\*: 500000.0
+      disc_step: 5.0
+    Task: Conditional GANs
+  Weights: https://download.openmmlab.com/mmgen/sngan_proj/sngan_proj_cifar10_32_lr-2e-4_b64x1_wReLUinplace_is-iter490000_20210709_202230-cd863c74.pth
+- Config: https://github.com/open-mmlab/mmgeneration/tree/master/configs/sngan_proj/sngan_proj_32_wReLUinplace_lr-2e-4_ndisc5_cifar10_b64x1.py
+  In Collection: SNGAN
+  Metadata:
+    Training Data: CIFAR
+  Name: sngan_proj_32_wReLUinplace_lr-2e-4_ndisc5_cifar10_b64x1
+  Results:
+  - Dataset: CIFAR
+    Metrics:
+      FID: 8.3462
+      IS: 9.5564
+      Inplace ReLU: w
+      Iter: 490000.0
+      Log: '[Log]'
+      Total Iters\*: 500000.0
+      disc_step: 5.0
+    Task: Conditional GANs
+  Weights: https://download.openmmlab.com/mmgen/sngan_proj/sngan_proj_cifar10_32_lr-2e-4-b64x1_wReLUinplace_fid-iter490000_20210709_203038-191b2648.pth
+- Config: https://github.com/open-mmlab/mmgeneration/tree/master/configs/sngan_proj/sngan_proj_128_woReLUinplace_Glr-2e-4_Dlr-5e-5_ndisc5_imagenet1k_b128x2.py
+  In Collection: SNGAN
+  Metadata:
+    Training Data: IMAGENET
+  Name: sngan_proj_128_woReLUinplace_Glr-2e-4_Dlr-5e-5_ndisc5_imagenet1k_b128x2
+  Results:
+  - Dataset: IMAGENET
+    Metrics:
+      FID: 33.4682
+      IS: 30.0651
+      Inplace ReLU: w/o
+      Iter: 952000.0
+      Log: '[Log]'
+      Total Iters\*: 1000000.0
+      disc_step: 5.0
+    Task: Conditional GANs
+  Weights: https://download.openmmlab.com/mmgen/sngan_proj/sngan_proj_imagenet1k_128_Glr2e-4_Dlr5e-5_ndisc5_b128x2_woReLUinplace_is-iter952000_20210730_132027-9c884a21.pth
+- Config: https://github.com/open-mmlab/mmgeneration/tree/master/configs/sngan_proj/sngan_proj_128_woReLUinplace_Glr-2e-4_Dlr-5e-5_ndisc5_imagenet1k_b128x2.py
+  In Collection: SNGAN
+  Metadata:
+    Training Data: IMAGENET
+  Name: sngan_proj_128_woReLUinplace_Glr-2e-4_Dlr-5e-5_ndisc5_imagenet1k_b128x2
+  Results:
+  - Dataset: IMAGENET
+    Metrics:
+      FID: 32.6193
+      IS: 29.5779
+      Inplace ReLU: w/o
+      Iter: 989000.0
+      Log: '[Log]'
+      Total Iters\*: 1000000.0
+      disc_step: 5.0
+    Task: Conditional GANs
+  Weights: https://download.openmmlab.com/mmgen/sngan_proj/sngan_proj_imagenet1k_128_Glr2e-4_Dlr5e-5_ndisc5_b128x2_woReLUinplace_fid-iter988000_20210730_131424-061bf803.pth
+- Config: https://github.com/open-mmlab/mmgeneration/tree/master/configs/sngan_proj/sngan_proj_128_wReLUinplace_Glr-2e-4_Dlr-5e-5_ndisc5_imagenet1k_b128x2.py
+  In Collection: SNGAN
+  Metadata:
+    Training Data: IMAGENET
+  Name: sngan_proj_128_wReLUinplace_Glr-2e-4_Dlr-5e-5_ndisc5_imagenet1k_b128x2
+  Results:
+  - Dataset: IMAGENET
+    Metrics:
+      FID: 34.3383
+      IS: 28.1799
+      Inplace ReLU: w
+      Iter: 944000.0
+      Log: '[Log]'
+      Total Iters\*: 1000000.0
+      disc_step: 5.0
+    Task: Conditional GANs
+  Weights: https://download.openmmlab.com/mmgen/sngan_proj/sngan_proj_imagenet1k_128_Glr2e-4_Dlr5e-5_ndisc5_b128x2_wReLUinplace_is-iter944000_20210730_132714-ca0ccd07.pth
+- Config: https://github.com/open-mmlab/mmgeneration/tree/master/configs/sngan_proj/sngan_proj_128_wReLUinplace_Glr-2e-4_Dlr-5e-5_ndisc5_imagenet1k_b128x2.py
+  In Collection: SNGAN
+  Metadata:
+    Training Data: IMAGENET
+  Name: sngan_proj_128_wReLUinplace_Glr-2e-4_Dlr-5e-5_ndisc5_imagenet1k_b128x2
+  Results:
+  - Dataset: IMAGENET
+    Metrics:
+      FID: 33.4821
+      IS: 27.7948
+      Inplace ReLU: w
+      Iter: 988000.0
+      Log: '[Log]'
+      Total Iters\*: 1000000.0
+      disc_step: 5.0
+    Task: Conditional GANs
+  Weights: https://download.openmmlab.com/mmgen/sngan_proj/sngan_proj_imagenet1k_128_Glr2e-4_Dlr5e-5_ndisc5_b128x2_wReLUinplace_fid-iter988000_20210730_132401-9a682411.pth
+- Config: https://github.com/open-mmlab/mmgeneration/tree/master/configs/sngan_proj/sngan_proj_32_cvt_studioGAN.py
+  In Collection: SNGAN
+  Metadata:
+    Training Data: Others
+  Name: sngan_proj_32_cvt_studioGAN
+  Results:
+  - Dataset: Others
+    Metrics:
+      FID (Our Pipeline): 10.2011
+      FID (StudioGAN): 13.248
+      IS (Our Pipeline): 9.372
+      IS (StudioGAN): 8.677
+      Inplace ReLU: w
+      Total Iters: 100000.0
+      disc_step: 5.0
+    Task: Conditional GANs
+  Weights: https://download.openmmlab.com/mmgen/sngan_proj/sngan_cifar10_convert-studio-rgb_20210709_111346-2979202d.pth
+- Config: https://github.com/open-mmlab/mmgeneration/tree/master/configs/sngan_proj/sngan_proj_32_cvt_studioGAN.py
+  In Collection: SNGAN
+  Metadata:
+    Training Data: Others
+  Name: sngan_proj_32_cvt_studioGAN
+  Results:
+  - Dataset: Others
+    Metrics:
+      FID (Our Pipeline): 29.8199
+      FID (StudioGAN): 26.792
+      IS (Our Pipeline): 30.218
+      IS (StudioGAN): 32.247
+      Inplace ReLU: w
+      Total Iters: 1000000.0
+      disc_step: 2.0
+    Task: Conditional GANs
+  Weights: https://download.openmmlab.com/mmgen/sngan_proj/sngan_imagenet1k_convert-studio-rgb_20210709_111406-877b1130.pth

build/lib/mmgen/.mim/configs/sngan_proj/sngan_proj_128_cvt_studioGAN.py

+_base_ = ['../_base_/models/sngan_proj/sngan_proj_128x128.py']