stylegan2_mmcv

1401de15 · dongchy920 · 1401de15 · 1401de15 · 1401de15 · 1401de15
Commit 1401de15 authored Jun 28, 2024 by dongchy920
20 changed files
--- a/build/lib/mmgen/.mim/configs/_base_/models/wgangp/wgangp_base.py
+++ b/build/lib/mmgen/.mim/configs/_base_/models/wgangp/wgangp_base.py
+model = dict(
+    type='StaticUnconditionalGAN',
+    generator=dict(type='WGANGPGenerator', noise_size=128, out_scale=128),
+    discriminator=dict(
+        type='WGANGPDiscriminator',
+        in_channel=3,
+        in_scale=128,
+        conv_module_cfg=dict(
+            conv_cfg=None,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            bias=True,
+            act_cfg=dict(type='LeakyReLU', negative_slope=0.2),
+            norm_cfg=dict(type='GN'),
+            order=('conv', 'norm', 'act'))),
+    gan_loss=dict(type='GANLoss', gan_type='wgan'),
+    disc_auxiliary_loss=[
+        dict(
+            type='GradientPenaltyLoss',
+            loss_weight=10,
+            norm_mode='HWC',
+            data_info=dict(
+                discriminator='disc',
+                real_data='real_imgs',
+                fake_data='fake_imgs'))
+    ])
+
+train_cfg = dict(disc_steps=5)
+
+test_cfg = None
+
+optimizer = dict(
+    generator=dict(type='Adam', lr=0.0001, betas=(0.5, 0.9)),
+    discriminator=dict(type='Adam', lr=0.0001, betas=(0.5, 0.9)))
--- a/build/lib/mmgen/.mim/configs/ada/metafile.yml
+++ b/build/lib/mmgen/.mim/configs/ada/metafile.yml
+Collections:
+- Metadata:
+    Architecture:
+    - ADA
+  Name: ADA
+  Paper:
+  - https://arxiv.org/pdf/2006.06676.pdf
+  README: configs/ada/README.md
+Models:
+- Config: https://github.com/open-mmlab/mmgeneration/tree/master/configs/styleganv3/stylegan3_t_ada_fp16_gamma6.6_metfaces_1024_b4x8.py
+  In Collection: ADA
+  Metadata:
+    Training Data: Others
+  Name: stylegan3_t_ada_fp16_gamma6.6_metfaces_1024_b4x8
+  Results:
+  - Dataset: Others
+    Metrics:
+      FID50k: 15.09
+      Iter: 130000.0
+      Log: '[log]'
+    Task: Tricks for GANs
+  Weights: https://download.openmmlab.com/mmgen/stylegan3/stylegan3_t_ada_fp16_gamma6.6_metfaces_1024_b4x8_best_fid_iter_130000_20220401_115101-f2ef498e.pth
--- a/build/lib/mmgen/.mim/configs/ada/stylegan3_r_ada_fp16_gamma3.3_metfaces_1024_b4x8.py
+++ b/build/lib/mmgen/.mim/configs/ada/stylegan3_r_ada_fp16_gamma3.3_metfaces_1024_b4x8.py
+_base_ = [
+    '../_base_/models/stylegan/stylegan3_base.py',
+    '../_base_/datasets/ffhq_flip.py', '../_base_/default_runtime.py'
+]
+
+synthesis_cfg = {
+    'type': 'SynthesisNetwork',
+    'channel_base': 65536,
+    'channel_max': 1024,
+    'magnitude_ema_beta': 0.999,
+    'conv_kernel': 1,
+    'use_radial_filters': True
+}
+r1_gamma = 3.3  # set by user
+d_reg_interval = 16
+
+load_from = 'https://download.openmmlab.com/mmgen/stylegan3/stylegan3_r_ffhq_1024_b4x8_cvt_official_rgb_20220329_234933-ac0500a1.pth'  # noqa
+
+# ada settings
+aug_kwargs = {
+    'xflip': 1,
+    'rotate90': 1,
+    'xint': 1,
+    'scale': 1,
+    'rotate': 1,
+    'aniso': 1,
+    'xfrac': 1,
+    'brightness': 1,
+    'contrast': 1,
+    'lumaflip': 1,
+    'hue': 1,
+    'saturation': 1
+}
+
+model = dict(
+    type='StaticUnconditionalGAN',
+    generator=dict(
+        out_size=1024,
+        img_channels=3,
+        rgb2bgr=True,
+        synthesis_cfg=synthesis_cfg),
+    discriminator=dict(
+        type='ADAStyleGAN2Discriminator',
+        in_size=1024,
+        input_bgr2rgb=True,
+        data_aug=dict(type='ADAAug', aug_pipeline=aug_kwargs, ada_kimg=100)),
+    gan_loss=dict(type='GANLoss', gan_type='wgan-logistic-ns'),
+    disc_auxiliary_loss=dict(loss_weight=r1_gamma / 2.0 * d_reg_interval))
+
+imgs_root = 'data/metfaces/images/'
+data = dict(
+    samples_per_gpu=4,
+    train=dict(dataset=dict(imgs_root=imgs_root)),
+    val=dict(imgs_root=imgs_root))
+
+ema_half_life = 10.  # G_smoothing_kimg
+
+ema_kimg = 10
+ema_nimg = ema_kimg * 1000
+ema_beta = 0.5**(32 / max(ema_nimg, 1e-8))
+
+custom_hooks = [
+    dict(
+        type='VisualizeUnconditionalSamples',
+        output_dir='training_samples',
+        interval=5000),
+    dict(
+        type='ExponentialMovingAverageHook',
+        module_keys=('generator_ema', ),
+        interp_mode='lerp',
+        interp_cfg=dict(momentum=ema_beta),
+        interval=1,
+        start_iter=0,
+        priority='VERY_HIGH')
+]
+
+inception_pkl = 'work_dirs/inception_pkl/metface_1024x1024_noflip.pkl'
+metrics = dict(
+    fid50k=dict(
+        type='FID',
+        num_images=50000,
+        inception_pkl=inception_pkl,
+        inception_args=dict(type='StyleGAN'),
+        bgr2rgb=True))
+
+evaluation = dict(
+    type='GenerativeEvalHook',
+    interval=dict(milestones=[100000], interval=[10000, 5000]),
+    metrics=dict(
+        type='FID',
+        num_images=50000,
+        inception_pkl=inception_pkl,
+        inception_args=dict(type='StyleGAN'),
+        bgr2rgb=True),
+    sample_kwargs=dict(sample_model='ema'))
+
+lr_config = None
+
+total_iters = 160000
--- a/build/lib/mmgen/.mim/configs/ada/stylegan3_t_ada_fp16_gamma6.6_metfaces_1024_b4x8.py
+++ b/build/lib/mmgen/.mim/configs/ada/stylegan3_t_ada_fp16_gamma6.6_metfaces_1024_b4x8.py
+_base_ = [
+    '../_base_/models/stylegan/stylegan3_base.py',
+    '../_base_/datasets/ffhq_flip.py', '../_base_/default_runtime.py'
+]
+
+synthesis_cfg = {
+    'type': 'SynthesisNetwork',
+    'channel_base': 32768,
+    'channel_max': 512,
+    'magnitude_ema_beta': 0.999
+}
+r1_gamma = 6.6  # set by user
+d_reg_interval = 16
+
+load_from = 'https://download.openmmlab.com/mmgen/stylegan3/stylegan3_t_ffhq_1024_b4x8_cvt_official_rgb_20220329_235113-db6c6580.pth'  # noqa
+# ada settings
+aug_kwargs = {
+    'xflip': 1,
+    'rotate90': 1,
+    'xint': 1,
+    'scale': 1,
+    'rotate': 1,
+    'aniso': 1,
+    'xfrac': 1,
+    'brightness': 1,
+    'contrast': 1,
+    'lumaflip': 1,
+    'hue': 1,
+    'saturation': 1
+}
+
+model = dict(
+    type='StaticUnconditionalGAN',
+    generator=dict(
+        out_size=1024,
+        img_channels=3,
+        rgb2bgr=True,
+        synthesis_cfg=synthesis_cfg),
+    discriminator=dict(
+        type='ADAStyleGAN2Discriminator',
+        in_size=1024,
+        input_bgr2rgb=True,
+        data_aug=dict(type='ADAAug', aug_pipeline=aug_kwargs, ada_kimg=100)),
+    gan_loss=dict(type='GANLoss', gan_type='wgan-logistic-ns'),
+    disc_auxiliary_loss=dict(loss_weight=r1_gamma / 2.0 * d_reg_interval))
+
+imgs_root = 'data/metfaces/images/'
+data = dict(
+    samples_per_gpu=4,
+    train=dict(dataset=dict(imgs_root=imgs_root)),
+    val=dict(imgs_root=imgs_root))
+
+ema_half_life = 10.  # G_smoothing_kimg
+
+ema_kimg = 10
+ema_nimg = ema_kimg * 1000
+ema_beta = 0.5**(32 / max(ema_nimg, 1e-8))
+
+custom_hooks = [
+    dict(
+        type='VisualizeUnconditionalSamples',
+        output_dir='training_samples',
+        interval=5000),
+    dict(
+        type='ExponentialMovingAverageHook',
+        module_keys=('generator_ema', ),
+        interp_mode='lerp',
+        interp_cfg=dict(momentum=ema_beta),
+        interval=1,
+        start_iter=0,
+        priority='VERY_HIGH')
+]
+
+inception_pkl = 'work_dirs/inception_pkl/metface_1024x1024_noflip.pkl'
+metrics = dict(
+    fid50k=dict(
+        type='FID',
+        num_images=50000,
+        inception_pkl=inception_pkl,
+        inception_args=dict(type='StyleGAN'),
+        bgr2rgb=True))
+
+evaluation = dict(
+    type='GenerativeEvalHook',
+    interval=dict(milestones=[80000], interval=[10000, 5000]),
+    metrics=dict(
+        type='FID',
+        num_images=50000,
+        inception_pkl=inception_pkl,
+        inception_args=dict(type='StyleGAN'),
+        bgr2rgb=True),
+    sample_kwargs=dict(sample_model='ema'))
+
+lr_config = None
+
+total_iters = 160000
--- a/build/lib/mmgen/.mim/configs/biggan/biggan-deep_128x128_cvt_hugging-face_rgb.py
+++ b/build/lib/mmgen/.mim/configs/biggan/biggan-deep_128x128_cvt_hugging-face_rgb.py
+model = dict(
+    type='BasiccGAN',
+    generator=dict(
+        type='BigGANDeepGenerator',
+        output_scale=128,
+        noise_size=128,
+        num_classes=1000,
+        base_channels=128,
+        shared_dim=128,
+        with_shared_embedding=True,
+        sn_eps=1e-6,
+        sn_style='torch',
+        init_type='ortho',
+        act_cfg=dict(type='ReLU', inplace=True),
+        concat_noise=True,
+        auto_sync_bn=False,
+        rgb2bgr=True),
+    discriminator=dict(
+        type='BigGANDeepDiscriminator',
+        input_scale=128,
+        num_classes=1000,
+        base_channels=128,
+        sn_eps=1e-6,
+        sn_style='torch',
+        init_type='ortho',
+        act_cfg=dict(type='ReLU', inplace=True),
+        with_spectral_norm=True),
+    gan_loss=dict(type='GANLoss', gan_type='hinge'))
+
+train_cfg = dict(
+    disc_steps=8, gen_steps=1, batch_accumulation_steps=8, use_ema=True)
+test_cfg = None
+optimizer = dict(
+    generator=dict(type='Adam', lr=0.0001, betas=(0.0, 0.999), eps=1e-6),
+    discriminator=dict(type='Adam', lr=0.0004, betas=(0.0, 0.999), eps=1e-6))
--- a/build/lib/mmgen/.mim/configs/biggan/biggan-deep_256x256_cvt_hugging-face_rgb.py
+++ b/build/lib/mmgen/.mim/configs/biggan/biggan-deep_256x256_cvt_hugging-face_rgb.py
+model = dict(
+    type='BasiccGAN',
+    generator=dict(
+        type='BigGANDeepGenerator',
+        output_scale=256,
+        noise_size=128,
+        num_classes=1000,
+        base_channels=128,
+        shared_dim=128,
+        with_shared_embedding=True,
+        sn_eps=1e-6,
+        sn_style='torch',
+        init_type='ortho',
+        act_cfg=dict(type='ReLU', inplace=True),
+        concat_noise=True,
+        auto_sync_bn=False,
+        rgb2bgr=True),
+    discriminator=dict(
+        type='BigGANDeepDiscriminator',
+        input_scale=256,
+        num_classes=1000,
+        base_channels=128,
+        sn_eps=1e-6,
+        sn_style='torch',
+        init_type='ortho',
+        act_cfg=dict(type='ReLU', inplace=True),
+        with_spectral_norm=True),
+    gan_loss=dict(type='GANLoss', gan_type='hinge'))
+
+train_cfg = dict(
+    disc_steps=8, gen_steps=1, batch_accumulation_steps=8, use_ema=True)
+test_cfg = None
+optimizer = dict(
+    generator=dict(type='Adam', lr=0.0001, betas=(0.0, 0.999), eps=1e-6),
+    discriminator=dict(type='Adam', lr=0.0004, betas=(0.0, 0.999), eps=1e-6))
--- a/build/lib/mmgen/.mim/configs/biggan/biggan-deep_512x512_cvt_hugging-face_rgb.py
+++ b/build/lib/mmgen/.mim/configs/biggan/biggan-deep_512x512_cvt_hugging-face_rgb.py
+model = dict(
+    type='BasiccGAN',
+    generator=dict(
+        type='BigGANDeepGenerator',
+        output_scale=512,
+        noise_size=128,
+        num_classes=1000,
+        base_channels=128,
+        shared_dim=128,
+        with_shared_embedding=True,
+        sn_eps=1e-6,
+        sn_style='torch',
+        init_type='ortho',
+        act_cfg=dict(type='ReLU', inplace=True),
+        concat_noise=True,
+        auto_sync_bn=False,
+        rgb2bgr=True),
+    discriminator=dict(
+        type='BigGANDeepDiscriminator',
+        input_scale=512,
+        num_classes=1000,
+        base_channels=128,
+        sn_eps=1e-6,
+        sn_style='torch',
+        init_type='ortho',
+        act_cfg=dict(type='ReLU', inplace=True),
+        with_spectral_norm=True),
+    gan_loss=dict(type='GANLoss', gan_type='hinge'))
+
+train_cfg = dict(
+    disc_steps=8, gen_steps=1, batch_accumulation_steps=8, use_ema=True)
+test_cfg = None
+optimizer = dict(
+    generator=dict(type='Adam', lr=0.0001, betas=(0.0, 0.999), eps=1e-6),
+    discriminator=dict(type='Adam', lr=0.0004, betas=(0.0, 0.999), eps=1e-6))
--- a/build/lib/mmgen/.mim/configs/biggan/biggan_128x128_cvt_BigGAN-PyTorch_rgb.py
+++ b/build/lib/mmgen/.mim/configs/biggan/biggan_128x128_cvt_BigGAN-PyTorch_rgb.py
+model = dict(
+    type='BasiccGAN',
+    generator=dict(
+        type='BigGANGenerator',
+        output_scale=128,
+        noise_size=120,
+        num_classes=1000,
+        base_channels=96,
+        shared_dim=128,
+        with_shared_embedding=True,
+        sn_eps=1e-6,
+        init_type='ortho',
+        act_cfg=dict(type='ReLU', inplace=True),
+        split_noise=True,
+        auto_sync_bn=False,
+        rgb2bgr=True),
+    discriminator=dict(
+        type='BigGANDiscriminator',
+        input_scale=128,
+        num_classes=1000,
+        base_channels=96,
+        sn_eps=1e-6,
+        init_type='ortho',
+        act_cfg=dict(type='ReLU', inplace=True),
+        with_spectral_norm=True),
+    gan_loss=dict(type='GANLoss', gan_type='hinge'))
+
+train_cfg = dict(
+    disc_steps=8, gen_steps=1, batch_accumulation_steps=8, use_ema=True)
+test_cfg = None
+optimizer = dict(
+    generator=dict(type='Adam', lr=0.0001, betas=(0.0, 0.999), eps=1e-6),
+    discriminator=dict(type='Adam', lr=0.0004, betas=(0.0, 0.999), eps=1e-6))
--- a/build/lib/mmgen/.mim/configs/biggan/biggan_ajbrock-sn_imagenet1k_128x128_b32x8_1500k.py
+++ b/build/lib/mmgen/.mim/configs/biggan/biggan_ajbrock-sn_imagenet1k_128x128_b32x8_1500k.py
+_base_ = [
+    '../_base_/models/biggan/biggan_128x128.py',
+    '../_base_/datasets/imagenet_noaug_128.py', '../_base_/default_runtime.py'
+]
+
+# define dataset
+# you must set `samples_per_gpu`
+data = dict(samples_per_gpu=32, workers_per_gpu=8)
+
+# adjust running config
+lr_config = None
+checkpoint_config = dict(interval=5000, by_epoch=False, max_keep_ckpts=10)
+custom_hooks = [
+    dict(
+        type='VisualizeUnconditionalSamples',
+        output_dir='training_samples',
+        interval=10000),
+    dict(
+        type='ExponentialMovingAverageHook',
+        module_keys=('generator_ema', ),
+        interval=8,
+        start_iter=160000,
+        interp_cfg=dict(momentum=0.9999, momentum_nontrainable=0.9999),
+        priority='VERY_HIGH')
+]
+
+# Traning sets' datasize 1,281,167
+total_iters = 1500000
+
+# 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)
+
+# Note set your inception_pkl's path
+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),
+        dict(type='IS', num_images=50000)
+    ],
+    sample_kwargs=dict(sample_model='ema'),
+    best_metric=['fid', 'is'])
+
+metrics = dict(
+    fid50k=dict(
+        type='FID',
+        num_images=50000,
+        inception_pkl=inception_pkl,
+        bgr2rgb=True,
+        inception_args=dict(type='StyleGAN')),
+    is50k=dict(type='IS', num_images=50000))
--- a/build/lib/mmgen/.mim/configs/biggan/biggan_cifar10_32x32_b25x2_500k.py
+++ b/build/lib/mmgen/.mim/configs/biggan/biggan_cifar10_32x32_b25x2_500k.py
+_base_ = [
+    '../_base_/models/biggan/biggan_32x32.py',
+    '../_base_/datasets/cifar10_noaug.py', '../_base_/default_runtime.py'
+]
+
+# define dataset
+# you must set `samples_per_gpu`
+data = dict(samples_per_gpu=25, workers_per_gpu=8)
+
+# adjust running config
+lr_config = None
+checkpoint_config = dict(interval=5000, by_epoch=False, max_keep_ckpts=20)
+custom_hooks = [
+    dict(
+        type='VisualizeUnconditionalSamples',
+        output_dir='training_samples',
+        interval=5000),
+    dict(
+        type='ExponentialMovingAverageHook',
+        module_keys=('generator_ema', ),
+        interval=4,
+        start_iter=4000,
+        interp_cfg=dict(momentum=0.9999),
+        priority='VERY_HIGH')
+]
+
+total_iters = 500000
+
+# 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)
+
+# Note set your inception_pkl's path
+inception_pkl = None
+evaluation = dict(
+    type='GenerativeEvalHook',
+    interval=10000,
+    metrics=[
+        dict(
+            type='FID',
+            num_images=50000,
+            inception_pkl=inception_pkl,
+            bgr2rgb=True),
+        dict(type='IS', num_images=50000)
+    ],
+    sample_kwargs=dict(sample_model='ema'),
+    best_metric=['fid', 'is'])
+
+metrics = dict(
+    fid50k=dict(
+        type='FID',
+        num_images=50000,
+        inception_pkl=inception_pkl,
+        bgr2rgb=True),
+    is50k=dict(type='IS', num_images=50000))
--- a/build/lib/mmgen/.mim/configs/biggan/biggan_torch-sn_imagenet1k_128x128_b32x8_1500k.py
+++ b/build/lib/mmgen/.mim/configs/biggan/biggan_torch-sn_imagenet1k_128x128_b32x8_1500k.py
+_base_ = [
+    '../_base_/models/biggan/biggan_128x128.py',
+    '../_base_/datasets/imagenet_noaug_128.py', '../_base_/default_runtime.py'
+]
+
+# define dataset
+# you must set `samples_per_gpu`
+data = dict(samples_per_gpu=32, workers_per_gpu=8)
+
+model = dict(
+    generator=dict(sn_style='torch'), discriminator=dict(sn_style='torch'))
+
+# adjust running config
+lr_config = None
+checkpoint_config = dict(interval=5000, by_epoch=False, max_keep_ckpts=10)
+custom_hooks = [
+    dict(
+        type='VisualizeUnconditionalSamples',
+        output_dir='training_samples',
+        interval=10000),
+    dict(
+        type='ExponentialMovingAverageHook',
+        module_keys=('generator_ema', ),
+        interval=8,
+        start_iter=160000,
+        interp_cfg=dict(momentum=0.9999, momentum_nontrainable=0.9999),
+        priority='VERY_HIGH')
+]
+
+# Traning sets' datasize 1,281,167
+total_iters = 1500000
+
+# 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)
+
+# Note set your inception_pkl's path
+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),
+        dict(type='IS', num_images=50000)
+    ],
+    sample_kwargs=dict(sample_model='ema'),
+    best_metric=['fid', 'is'])
+
+metrics = dict(
+    fid50k=dict(
+        type='FID',
+        num_images=50000,
+        inception_pkl=inception_pkl,
+        bgr2rgb=True,
+        inception_args=dict(type='StyleGAN')),
+    is50k=dict(type='IS', num_images=50000))
--- a/build/lib/mmgen/.mim/configs/biggan/metafile.yml
+++ b/build/lib/mmgen/.mim/configs/biggan/metafile.yml
+Collections:
+- Metadata:
+    Architecture:
+    - BigGAN
+  Name: BigGAN
+  Paper:
+  - https://openreview.net/forum?id=B1xsqj09Fm
+  README: configs/biggan/README.md
+Models:
+- Config: https://github.com/open-mmlab/mmgeneration/blob/master/configs/biggan/biggan_cifar10_32x32_b25x2_500k.py
+  In Collection: BigGAN
+  Metadata:
+    Training Data: CIFAR
+  Name: biggan_cifar10_32x32_b25x2_500k
+  Results:
+  - Dataset: CIFAR
+    Metrics:
+      FID: 9.78
+      IS: 8.7
+    Task: Conditional GANs
+  Weights: https://download.openmmlab.com/mmgen/biggan/biggan_cifar10_32x32_b25x2_500k_20210728_110906-08b61a44.pth
+- Config: https://github.com/open-mmlab/mmgeneration/blob/master/configs/biggan/biggan_ajbrock-sn_imagenet1k_128x128_b32x8_1500k.py
+  In Collection: BigGAN
+  Metadata:
+    Training Data: IMAGENET
+  Name: biggan_ajbrock-sn_imagenet1k_128x128_b32x8_1500k
+  Results:
+  - Dataset: IMAGENET
+    Metrics:
+      FID: 8.69
+      IS: 101.15
+    Task: Conditional GANs
+  Weights: https://download.openmmlab.com/mmgen/biggan/biggan_imagenet1k_128x128_b32x8_best_fid_iter_1232000_20211111_122548-5315b13d.pth
+- Config: https://github.com/open-mmlab/mmgeneration/blob/master/configs/biggan/biggan_ajbrock-sn_imagenet1k_128x128_b32x8_1500k.py
+  In Collection: BigGAN
+  Metadata:
+    Training Data: IMAGENET
+  Name: biggan_ajbrock-sn_imagenet1k_128x128_b32x8_1500k
+  Results:
+  - Dataset: IMAGENET
+    Metrics:
+      FID: 13.51
+      IS: 129.07
+    Task: Conditional GANs
+  Weights: https://download.openmmlab.com/mmgen/biggan/biggan_imagenet1k_128x128_b32x8_best_is_iter_1328000_20211111_122911-28c688bc.pth
+- Config: https://github.com/open-mmlab/mmgeneration/blob/master/configs/_base_/models/biggan/biggan_128x128_cvt_BigGAN-PyTorch_rgb.py
+  In Collection: BigGAN
+  Metadata:
+    Training Data: Others
+  Name: biggan_128x128_cvt_BigGAN-PyTorch_rgb
+  Results:
+  - Dataset: Others
+    Metrics:
+      FID: 10.1414
+      IS: 96.728
+    Task: Conditional GANs
+  Weights: https://download.openmmlab.com/mmgen/biggan/biggan_imagenet1k_128x128_cvt_BigGAN-PyTorch_rgb_20210730_125223-3e353fef.pth
+- Config: https://github.com/open-mmlab/mmgeneration/blob/master/configs/_base_/models/biggan/biggan-deep_128x128_cvt_hugging-face_rgb.py
+  In Collection: BigGAN
+  Metadata:
+    Training Data: Others
+  Name: biggan-deep_128x128_cvt_hugging-face_rgb
+  Results:
+  - Dataset: Others
+    Metrics:
+      FID: 5.9471
+      IS: 107.161
+    Task: Conditional GANs
+  Weights: https://download.openmmlab.com/mmgen/biggan/biggan-deep_imagenet1k_128x128_cvt_hugging-face_rgb_20210728_111659-099e96f9.pth
+- Config: https://github.com/open-mmlab/mmgeneration/blob/master/configs/_base_/models/biggan/biggan-deep_256x256_cvt_hugging-face_rgb.py
+  In Collection: BigGAN
+  Metadata:
+    Training Data: Others
+  Name: biggan-deep_256x256_cvt_hugging-face_rgb
+  Results:
+  - Dataset: Others
+    Metrics:
+      FID: 11.3151
+      IS: 135.107
+    Task: Conditional GANs
+  Weights: https://download.openmmlab.com/mmgen/biggan/biggan-deep_imagenet1k_256x256_cvt_hugging-face_rgb_20210728_111735-28651569.pth
+- Config: https://github.com/open-mmlab/mmgeneration/blob/master/configs/_base_/models/biggan/biggan-deep_512x512_cvt_hugging-face_rgb.py
+  In Collection: BigGAN
+  Metadata:
+    Training Data: Others
+  Name: biggan-deep_512x512_cvt_hugging-face_rgb
+  Results:
+  - Dataset: Others
+    Metrics:
+      FID: 16.8728
+      IS: 124.368
+    Task: Conditional GANs
+  Weights: https://download.openmmlab.com/mmgen/biggan/biggan-deep_imagenet1k_512x512_cvt_hugging-face_rgb_20210728_112346-a42585f2.pth
--- a/build/lib/mmgen/.mim/configs/cyclegan/cyclegan_lsgan_id0_resnet_in_facades_b1x1_80k.py
+++ b/build/lib/mmgen/.mim/configs/cyclegan/cyclegan_lsgan_id0_resnet_in_facades_b1x1_80k.py
+_base_ = [
+    '../_base_/models/cyclegan/cyclegan_lsgan_resnet.py',
+    '../_base_/datasets/unpaired_imgs_256x256.py',
+    '../_base_/default_runtime.py'
+]
+train_cfg = dict(buffer_size=50)
+test_cfg = None
+domain_a = 'mask'
+domain_b = 'photo'
+model = dict(
+    default_domain=domain_b,
+    reachable_domains=[domain_a, domain_b],
+    related_domains=[domain_a, domain_b],
+    gen_auxiliary_loss=[
+        dict(
+            type='L1Loss',
+            loss_weight=10.0,
+            loss_name='cycle_loss',
+            data_info=dict(
+                pred=f'cycle_{domain_a}', target=f'real_{domain_a}'),
+            reduction='mean'),
+        dict(
+            type='L1Loss',
+            loss_weight=10.0,
+            loss_name='cycle_loss',
+            data_info=dict(
+                pred=f'cycle_{domain_b}',
+                target=f'real_{domain_b}',
+            ),
+            reduction='mean')
+    ])
+train_pipeline = [
+    dict(
+        type='LoadImageFromFile',
+        io_backend='disk',
+        key=f'img_{domain_a}',
+        flag='color'),
+    dict(
+        type='LoadImageFromFile',
+        io_backend='disk',
+        key=f'img_{domain_b}',
+        flag='color'),
+    dict(
+        type='Resize',
+        keys=[f'img_{domain_a}', f'img_{domain_b}'],
+        scale=(286, 286),
+        interpolation='bicubic'),
+    dict(
+        type='Crop',
+        keys=[f'img_{domain_a}', f'img_{domain_b}'],
+        crop_size=(256, 256),
+        random_crop=True),
+    dict(type='Flip', keys=[f'img_{domain_a}'], direction='horizontal'),
+    dict(type='Flip', keys=[f'img_{domain_b}'], direction='horizontal'),
+    dict(type='RescaleToZeroOne', keys=[f'img_{domain_a}', f'img_{domain_b}']),
+    dict(
+        type='Normalize',
+        keys=[f'img_{domain_a}', f'img_{domain_b}'],
+        to_rgb=False,
+        mean=[0.5, 0.5, 0.5],
+        std=[0.5, 0.5, 0.5]),
+    dict(type='ImageToTensor', keys=[f'img_{domain_a}', f'img_{domain_b}']),
+    dict(
+        type='Collect',
+        keys=[f'img_{domain_a}', f'img_{domain_b}'],
+        meta_keys=[f'img_{domain_a}_path', f'img_{domain_b}_path'])
+]
+
+dataroot = './data/unpaired_facades'
+
+test_pipeline = [
+    dict(
+        type='LoadImageFromFile',
+        io_backend='disk',
+        key=f'img_{domain_a}',
+        flag='color'),
+    dict(
+        type='LoadImageFromFile',
+        io_backend='disk',
+        key=f'img_{domain_b}',
+        flag='color'),
+    dict(
+        type='Resize',
+        keys=[f'img_{domain_a}', f'img_{domain_b}'],
+        scale=(256, 256),
+        interpolation='bicubic'),
+    dict(type='RescaleToZeroOne', keys=[f'img_{domain_a}', f'img_{domain_b}']),
+    dict(
+        type='Normalize',
+        keys=[f'img_{domain_a}', f'img_{domain_b}'],
+        to_rgb=False,
+        mean=[0.5, 0.5, 0.5],
+        std=[0.5, 0.5, 0.5]),
+    dict(type='ImageToTensor', keys=[f'img_{domain_a}', f'img_{domain_b}']),
+    dict(
+        type='Collect',
+        keys=[f'img_{domain_a}', f'img_{domain_b}'],
+        meta_keys=[f'img_{domain_a}_path', f'img_{domain_b}_path'])
+]
+
+data = dict(
+    train=dict(
+        dataroot=dataroot,
+        pipeline=train_pipeline,
+        domain_a=domain_a,
+        domain_b=domain_b),
+    val=dict(
+        dataroot=dataroot,
+        domain_a=domain_a,
+        domain_b=domain_b,
+        pipeline=test_pipeline),
+    test=dict(
+        dataroot=dataroot,
+        domain_a=domain_a,
+        domain_b=domain_b,
+        pipeline=test_pipeline))
+
+optimizer = dict(
+    generators=dict(type='Adam', lr=0.0002, betas=(0.5, 0.999)),
+    discriminators=dict(type='Adam', lr=0.0002, betas=(0.5, 0.999)))
+
+# learning policy
+lr_config = dict(
+    policy='Linear', by_epoch=False, target_lr=0, start=40000, interval=400)
+
+checkpoint_config = dict(interval=10000, save_optimizer=True, by_epoch=False)
+custom_hooks = [
+    dict(
+        type='MMGenVisualizationHook',
+        output_dir='training_samples',
+        res_name_list=[f'fake_{domain_a}', f'fake_{domain_b}'],
+        interval=5000)
+]
+
+runner = None
+use_ddp_wrapper = True
+total_iters = 80000
+workflow = [('train', 1)]
+exp_name = 'cyclegan_facades_id0'
+work_dir = f'./work_dirs/experiments/{exp_name}'
+num_images = 106
+metrics = dict(
+    FID=dict(type='FID', num_images=num_images, image_shape=(3, 256, 256)),
+    IS=dict(
+        type='IS',
+        num_images=num_images,
+        image_shape=(3, 256, 256),
+        inception_args=dict(type='pytorch')))
+
+evaluation = dict(
+    type='TranslationEvalHook',
+    target_domain=domain_b,
+    interval=10000,
+    metrics=[
+        dict(type='FID', num_images=num_images, bgr2rgb=True),
+        dict(
+            type='IS',
+            num_images=num_images,
+            inception_args=dict(type='pytorch'))
+    ],
+    best_metric=['fid', 'is'])
--- a/build/lib/mmgen/.mim/configs/cyclegan/cyclegan_lsgan_id0_resnet_in_horse2zebra_b1x1_270k.py
+++ b/build/lib/mmgen/.mim/configs/cyclegan/cyclegan_lsgan_id0_resnet_in_horse2zebra_b1x1_270k.py
+_base_ = [
+    '../_base_/models/cyclegan/cyclegan_lsgan_resnet.py',
+    '../_base_/datasets/unpaired_imgs_256x256.py',
+    '../_base_/default_runtime.py'
+]
+domain_a = 'horse'
+domain_b = 'zebra'
+model = dict(
+    default_domain=domain_b,
+    reachable_domains=[domain_a, domain_b],
+    related_domains=[domain_a, domain_b],
+    gen_auxiliary_loss=[
+        dict(
+            type='L1Loss',
+            loss_weight=10.0,
+            loss_name='cycle_loss',
+            data_info=dict(
+                pred=f'cycle_{domain_a}', target=f'real_{domain_a}'),
+            reduction='mean'),
+        dict(
+            type='L1Loss',
+            loss_weight=10.0,
+            loss_name='cycle_loss',
+            data_info=dict(
+                pred=f'cycle_{domain_b}',
+                target=f'real_{domain_b}',
+            ),
+            reduction='mean')
+    ])
+dataroot = './data/horse2zebra'
+train_pipeline = [
+    dict(
+        type='LoadImageFromFile',
+        io_backend='disk',
+        key=f'img_{domain_a}',
+        flag='color'),
+    dict(
+        type='LoadImageFromFile',
+        io_backend='disk',
+        key=f'img_{domain_b}',
+        flag='color'),
+    dict(
+        type='Resize',
+        keys=[f'img_{domain_a}', f'img_{domain_b}'],
+        scale=(286, 286),
+        interpolation='bicubic'),
+    dict(
+        type='Crop',
+        keys=[f'img_{domain_a}', f'img_{domain_b}'],
+        crop_size=(256, 256),
+        random_crop=True),
+    dict(type='Flip', keys=[f'img_{domain_a}'], direction='horizontal'),
+    dict(type='Flip', keys=[f'img_{domain_b}'], direction='horizontal'),
+    dict(type='RescaleToZeroOne', keys=[f'img_{domain_a}', f'img_{domain_b}']),
+    dict(
+        type='Normalize',
+        keys=[f'img_{domain_a}', f'img_{domain_b}'],
+        to_rgb=False,
+        mean=[0.5, 0.5, 0.5],
+        std=[0.5, 0.5, 0.5]),
+    dict(type='ImageToTensor', keys=[f'img_{domain_a}', f'img_{domain_b}']),
+    dict(
+        type='Collect',
+        keys=[f'img_{domain_a}', f'img_{domain_b}'],
+        meta_keys=[f'img_{domain_a}_path', f'img_{domain_b}_path'])
+]
+
+test_pipeline = [
+    dict(
+        type='LoadImageFromFile',
+        io_backend='disk',
+        key=f'img_{domain_a}',
+        flag='color'),
+    dict(
+        type='LoadImageFromFile',
+        io_backend='disk',
+        key=f'img_{domain_b}',
+        flag='color'),
+    dict(
+        type='Resize',
+        keys=[f'img_{domain_a}', f'img_{domain_b}'],
+        scale=(256, 256),
+        interpolation='bicubic'),
+    dict(type='RescaleToZeroOne', keys=[f'img_{domain_a}', f'img_{domain_b}']),
+    dict(
+        type='Normalize',
+        keys=[f'img_{domain_a}', f'img_{domain_b}'],
+        to_rgb=False,
+        mean=[0.5, 0.5, 0.5],
+        std=[0.5, 0.5, 0.5]),
+    dict(type='ImageToTensor', keys=[f'img_{domain_a}', f'img_{domain_b}']),
+    dict(
+        type='Collect',
+        keys=[f'img_{domain_a}', f'img_{domain_b}'],
+        meta_keys=[f'img_{domain_a}_path', f'img_{domain_b}_path'])
+]
+
+data = dict(
+    train=dict(
+        dataroot=dataroot,
+        pipeline=train_pipeline,
+        domain_a=domain_a,
+        domain_b=domain_b),
+    val=dict(
+        dataroot=dataroot,
+        domain_a=domain_a,
+        domain_b=domain_b,
+        pipeline=test_pipeline),
+    test=dict(
+        dataroot=dataroot,
+        domain_a=domain_a,
+        domain_b=domain_b,
+        pipeline=test_pipeline))
+
+optimizer = dict(
+    generators=dict(type='Adam', lr=0.0002, betas=(0.5, 0.999)),
+    discriminators=dict(type='Adam', lr=0.0002, betas=(0.5, 0.999)))
+
+# learning policy
+lr_config = dict(
+    policy='Linear', by_epoch=False, target_lr=0, start=135000, interval=1350)
+
+checkpoint_config = dict(interval=10000, save_optimizer=True, by_epoch=False)
+custom_hooks = [
+    dict(
+        type='MMGenVisualizationHook',
+        output_dir='training_samples',
+        res_name_list=[f'fake_{domain_a}', f'fake_{domain_b}'],
+        interval=5000)
+]
+
+runner = None
+use_ddp_wrapper = True
+total_iters = 270000
+workflow = [('train', 1)]
+exp_name = 'cyclegan_horse2zebra_id0'
+work_dir = f'./work_dirs/experiments/{exp_name}'
+num_images = 140
+metrics = dict(
+    FID=dict(type='FID', num_images=num_images, image_shape=(3, 256, 256)),
+    IS=dict(
+        type='IS',
+        num_images=num_images,
+        image_shape=(3, 256, 256),
+        inception_args=dict(type='pytorch')))
+
+evaluation = dict(
+    type='TranslationEvalHook',
+    target_domain=domain_b,
+    interval=10000,
+    metrics=[
+        dict(type='FID', num_images=num_images, bgr2rgb=True),
+        dict(
+            type='IS',
+            num_images=num_images,
+            inception_args=dict(type='pytorch'))
+    ],
+    best_metric=['fid', 'is'])
--- a/build/lib/mmgen/.mim/configs/cyclegan/cyclegan_lsgan_id0_resnet_in_summer2winter_b1x1_250k.py
+++ b/build/lib/mmgen/.mim/configs/cyclegan/cyclegan_lsgan_id0_resnet_in_summer2winter_b1x1_250k.py
+_base_ = [
+    '../_base_/models/cyclegan/cyclegan_lsgan_resnet.py',
+    '../_base_/datasets/unpaired_imgs_256x256.py',
+    '../_base_/default_runtime.py'
+]
+domain_a = 'summer'
+domain_b = 'winter'
+model = dict(
+    default_domain=domain_b,
+    reachable_domains=[domain_a, domain_b],
+    related_domains=[domain_a, domain_b],
+    gen_auxiliary_loss=[
+        dict(
+            type='L1Loss',
+            loss_weight=10.0,
+            loss_name='cycle_loss',
+            data_info=dict(
+                pred=f'cycle_{domain_a}', target=f'real_{domain_a}'),
+            reduction='mean'),
+        dict(
+            type='L1Loss',
+            loss_weight=10.0,
+            loss_name='cycle_loss',
+            data_info=dict(
+                pred=f'cycle_{domain_b}',
+                target=f'real_{domain_b}',
+            ),
+            reduction='mean')
+    ])
+dataroot = './data/summer2winter_yosemite'
+train_pipeline = [
+    dict(
+        type='LoadImageFromFile',
+        io_backend='disk',
+        key=f'img_{domain_a}',
+        flag='color'),
+    dict(
+        type='LoadImageFromFile',
+        io_backend='disk',
+        key=f'img_{domain_b}',
+        flag='color'),
+    dict(
+        type='Resize',
+        keys=[f'img_{domain_a}', f'img_{domain_b}'],
+        scale=(286, 286),
+        interpolation='bicubic'),
+    dict(
+        type='Crop',
+        keys=[f'img_{domain_a}', f'img_{domain_b}'],
+        crop_size=(256, 256),
+        random_crop=True),
+    dict(type='Flip', keys=[f'img_{domain_a}'], direction='horizontal'),
+    dict(type='Flip', keys=[f'img_{domain_b}'], direction='horizontal'),
+    dict(type='RescaleToZeroOne', keys=[f'img_{domain_a}', f'img_{domain_b}']),
+    dict(
+        type='Normalize',
+        keys=[f'img_{domain_a}', f'img_{domain_b}'],
+        to_rgb=False,
+        mean=[0.5, 0.5, 0.5],
+        std=[0.5, 0.5, 0.5]),
+    dict(type='ImageToTensor', keys=[f'img_{domain_a}', f'img_{domain_b}']),
+    dict(
+        type='Collect',
+        keys=[f'img_{domain_a}', f'img_{domain_b}'],
+        meta_keys=[f'img_{domain_a}_path', f'img_{domain_b}_path'])
+]
+
+test_pipeline = [
+    dict(
+        type='LoadImageFromFile',
+        io_backend='disk',
+        key=f'img_{domain_a}',
+        flag='color'),
+    dict(
+        type='LoadImageFromFile',
+        io_backend='disk',
+        key=f'img_{domain_b}',
+        flag='color'),
+    dict(
+        type='Resize',
+        keys=[f'img_{domain_a}', f'img_{domain_b}'],
+        scale=(256, 256),
+        interpolation='bicubic'),
+    dict(type='RescaleToZeroOne', keys=[f'img_{domain_a}', f'img_{domain_b}']),
+    dict(
+        type='Normalize',
+        keys=[f'img_{domain_a}', f'img_{domain_b}'],
+        to_rgb=False,
+        mean=[0.5, 0.5, 0.5],
+        std=[0.5, 0.5, 0.5]),
+    dict(type='ImageToTensor', keys=[f'img_{domain_a}', f'img_{domain_b}']),
+    dict(
+        type='Collect',
+        keys=[f'img_{domain_a}', f'img_{domain_b}'],
+        meta_keys=[f'img_{domain_a}_path', f'img_{domain_b}_path'])
+]
+
+data = dict(
+    train=dict(
+        dataroot=dataroot,
+        pipeline=train_pipeline,
+        domain_a=domain_a,
+        domain_b=domain_b),
+    val=dict(
+        dataroot=dataroot,
+        domain_a=domain_a,
+        domain_b=domain_b,
+        pipeline=test_pipeline),
+    test=dict(
+        dataroot=dataroot,
+        domain_a=domain_a,
+        domain_b=domain_b,
+        pipeline=test_pipeline))
+
+optimizer = dict(
+    generators=dict(type='Adam', lr=0.0002, betas=(0.5, 0.999)),
+    discriminators=dict(type='Adam', lr=0.0002, betas=(0.5, 0.999)))
+
+# learning policy
+lr_config = dict(
+    policy='Linear', by_epoch=False, target_lr=0, start=125000, interval=1250)
+
+checkpoint_config = dict(interval=10000, save_optimizer=True, by_epoch=False)
+custom_hooks = [
+    dict(
+        type='MMGenVisualizationHook',
+        output_dir='training_samples',
+        res_name_list=[f'fake_{domain_a}', f'fake_{domain_b}'],
+        interval=5000)
+]
+
+runner = None
+use_ddp_wrapper = True
+total_iters = 250000
+workflow = [('train', 1)]
+exp_name = 'cyclegan_summer2winter_id0'
+work_dir = f'./work_dirs/experiments/{exp_name}'
+# testA: 309, testB:238
+num_images = 238
+metrics = dict(
+    FID=dict(type='FID', num_images=num_images, image_shape=(3, 256, 256)),
+    IS=dict(
+        type='IS',
+        num_images=num_images,
+        image_shape=(3, 256, 256),
+        inception_args=dict(type='pytorch')))
+
+evaluation = dict(
+    type='TranslationEvalHook',
+    target_domain=domain_b,
+    interval=10000,
+    metrics=[
+        dict(type='FID', num_images=num_images, bgr2rgb=True),
+        dict(
+            type='IS',
+            num_images=num_images,
+            inception_args=dict(type='pytorch'))
+    ],
+    best_metric=['fid', 'is'])
--- a/build/lib/mmgen/.mim/configs/cyclegan/cyclegan_lsgan_resnet_in_facades_b1x1_80k.py
+++ b/build/lib/mmgen/.mim/configs/cyclegan/cyclegan_lsgan_resnet_in_facades_b1x1_80k.py
--- a/build/lib/mmgen/.mim/configs/cyclegan/cyclegan_lsgan_resnet_in_horse2zebra_b1x1_270k.py
+++ b/build/lib/mmgen/.mim/configs/cyclegan/cyclegan_lsgan_resnet_in_horse2zebra_b1x1_270k.py
--- a/build/lib/mmgen/.mim/configs/cyclegan/cyclegan_lsgan_resnet_in_summer2winter_b1x1_250k.py
+++ b/build/lib/mmgen/.mim/configs/cyclegan/cyclegan_lsgan_resnet_in_summer2winter_b1x1_250k.py
--- a/build/lib/mmgen/.mim/configs/cyclegan/metafile.yml
+++ b/build/lib/mmgen/.mim/configs/cyclegan/metafile.yml
--- a/build/lib/mmgen/.mim/configs/dcgan/dcgan_celeba-cropped_64_b128x1_300k.py
+++ b/build/lib/mmgen/.mim/configs/dcgan/dcgan_celeba-cropped_64_b128x1_300k.py
+_base_ = [
+    '../_base_/models/dcgan/dcgan_64x64.py',
+    '../_base_/datasets/unconditional_imgs_64x64.py',
+    '../_base_/default_runtime.py'
+]
+
+# define dataset
+# you must set `samples_per_gpu` and `imgs_root`
+data = dict(
+    samples_per_gpu=128,
+    train=dict(imgs_root='data/celeba-cropped/cropped_images_aligned_png'))
+
+# adjust running config
+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=10000)
+]
+
+total_iters = 300002
+
+# 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(
+    ms_ssim10k=dict(type='MS_SSIM', num_images=10000),
+    swd16k=dict(type='SWD', num_images=16384, image_shape=(3, 64, 64)))