stylegan2_mmcv

build/lib/mmgen/.mim/configs/dcgan/dcgan_lsun-bedroom_64x64_b128x1_5e.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/lsun/bedroom_train'))
+
+# adjust running config
+lr_config = None
+checkpoint_config = dict(interval=100000, by_epoch=False)
+custom_hooks = [
+    dict(
+        type='VisualizeUnconditionalSamples',
+        output_dir='training_samples',
+        interval=10000)
+]
+
+total_iters = 1500002
+
+metrics = dict(
+    ms_ssim10k=dict(type='MS_SSIM', num_images=10000),
+    swd16k=dict(type='SWD', num_images=16384, image_shape=(3, 64, 64)))

build/lib/mmgen/.mim/configs/dcgan/dcgan_mnist-64_b128x1_Glr4e-4_Dlr1e-4_5k.py

+_base_ = [
+    '../_base_/models/dcgan/dcgan_64x64.py',
+    '../_base_/datasets/unconditional_imgs_64x64.py',
+    '../_base_/default_runtime.py'
+]
+
+# output single channel
+model = dict(generator=dict(out_channels=1), discriminator=dict(in_channels=1))
+
+# define dataset
+# modify train_pipeline to load gray scale images
+train_pipeline = [
+    dict(
+        type='LoadImageFromFile',
+        key='real_img',
+        flag='grayscale',
+        io_backend='disk'),
+    dict(type='Resize', keys=['real_img'], scale=(64, 64)),
+    dict(
+        type='Normalize',
+        keys=['real_img'],
+        mean=[127.5],
+        std=[127.5],
+        to_rgb=False),
+    dict(type='ImageToTensor', keys=['real_img']),
+    dict(type='Collect', keys=['real_img'], meta_keys=['real_img_path'])
+]
+
+# you must set `samples_per_gpu` and `imgs_root`
+data = dict(
+    samples_per_gpu=128,
+    train=dict(imgs_root='data/mnist_64/train', pipeline=train_pipeline),
+    val=None)
+
+# adjust running config
+lr_config = None
+checkpoint_config = dict(interval=500, by_epoch=False)
+custom_hooks = [
+    dict(
+        type='VisualizeUnconditionalSamples',
+        output_dir='training_samples',
+        interval=100)
+]
+
+log_config = dict(
+    interval=100, hooks=[
+        dict(type='TextLoggerHook'),
+    ])
+
+total_iters = 5000
+
+metrics = dict(
+    ms_ssim10k=dict(type='MS_SSIM', num_images=10000),
+    swd16k=dict(type='SWD', num_images=16384, image_shape=(3, 64, 64)))
+
+optimizer = dict(
+    generator=dict(type='Adam', lr=0.0004, betas=(0.5, 0.999)),
+    discriminator=dict(type='Adam', lr=0.0001, betas=(0.5, 0.999)))

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

+Collections:
+- Metadata:
+    Architecture:
+    - Unsupervised Representation Learning with Deep Convolutional Generative Adversarial
+      Networks
+  Name: Unsupervised Representation Learning with Deep Convolutional Generative Adversarial
+    Networks
+  Paper:
+  - https://arxiv.org/abs/1511.06434
+  README: configs/dcgan/README.md
+Models:
+- Config: https://github.com/open-mmlab/mmgeneration/tree/master/configs/dcgan/dcgan_mnist-64_b128x1_Glr4e-4_Dlr1e-4_5k.py
+  In Collection: Unsupervised Representation Learning with Deep Convolutional Generative
+    Adversarial Networks
+  Metadata:
+    Training Data: Others
+  Name: dcgan_mnist-64_b128x1_Glr4e-4_Dlr1e-4_5k
+  Results:
+  - Dataset: Others
+    Metrics:
+      MS-SSIM: 0.1395
+      SWD: 21.16, 4.4, 8.41/11.32
+    Task: Unconditional GANs
+  Weights: https://download.openmmlab.com//mmgen/dcgan/dcgan_mnist-64_b128x1_Glr4e-4_Dlr1e-4_5k_20210512_163926-207a1eaf.pth
+- Config: https://github.com/open-mmlab/mmgeneration/tree/master/configs/dcgan/dcgan_celeba-cropped_64_b128x1_300k.py
+  In Collection: Unsupervised Representation Learning with Deep Convolutional Generative
+    Adversarial Networks
+  Metadata:
+    Training Data: CELEBA
+  Name: dcgan_celeba-cropped_64_b128x1_300k
+  Results:
+  - Dataset: CELEBA
+    Metrics:
+      MS-SSIM: 0.2899
+      SWD: 8.93,10.53,50.32/23.26
+    Task: Unconditional GANs
+  Weights: https://download.openmmlab.com/mmgen/dcgan/dcgan_celeba-cropped_64_b128x1_300kiter_20210408_161607-1f8a2277.pth
+- Config: https://github.com/open-mmlab/mmgeneration/tree/master/configs/dcgan/dcgan_lsun-bedroom_64x64_b128x1_5e.py
+  In Collection: Unsupervised Representation Learning with Deep Convolutional Generative
+    Adversarial Networks
+  Metadata:
+    Training Data: LSUN
+  Name: dcgan_lsun-bedroom_64x64_b128x1_5e
+  Results:
+  - Dataset: LSUN
+    Metrics:
+      MS-SSIM: 0.2095
+      SWD: 42.79, 34.55, 98.46/58.6
+    Task: Unconditional GANs
+  Weights: https://download.openmmlab.com/mmgen/dcgan/dcgan_lsun-bedroom_64_b128x1_5e_20210408_161713-117c498b.pth

build/lib/mmgen/.mim/configs/ggan/ggan_celeba-cropped_dcgan-archi_lr-1e-3_64_b128x1_12m.py

+_base_ = [
+    '../_base_/models/dcgan/dcgan_64x64.py',
+    '../_base_/datasets/unconditional_imgs_64x64.py',
+    '../_base_/default_runtime.py'
+]
+model = dict(
+    discriminator=dict(output_scale=4, out_channels=1),
+    gan_loss=dict(type='GANLoss', gan_type='hinge'))
+# define dataset
+# you must set `samples_per_gpu` and `imgs_root`
+data = dict(
+    samples_per_gpu=128,
+    train=dict(imgs_root='./data/celeba/cropped_images_aligned_png/'),
+    val=dict(imgs_root='./data/celeba/cropped_images_aligned_png/'))
+
+optimizer = dict(
+    generator=dict(type='Adam', lr=0.001, betas=(0.5, 0.99)),
+    discriminator=dict(type='Adam', lr=0.001, betas=(0.5, 0.99)))
+
+# 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=5000)
+]
+
+evaluation = dict(
+    type='GenerativeEvalHook',
+    interval=10000,
+    metrics=dict(
+        type='FID', num_images=50000, inception_pkl=None, bgr2rgb=True),
+    sample_kwargs=dict(sample_model='orig'))
+
+total_iters = 100000
+# 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)),
+    fid50k=dict(type='FID', num_images=50000, inception_pkl=None))

build/lib/mmgen/.mim/configs/ggan/ggan_celeba-cropped_dcgan-archi_lr-1e-4_128_b64x1_10m.py

+_base_ = [
+    '../_base_/models/dcgan/dcgan_128x128.py',
+    '../_base_/datasets/unconditional_imgs_128x128.py',
+    '../_base_/default_runtime.py'
+]
+model = dict(
+    discriminator=dict(output_scale=4, out_channels=1),
+    gan_loss=dict(type='GANLoss', gan_type='hinge'))
+# define dataset
+# you must set `samples_per_gpu` and `imgs_root`
+data = dict(
+    samples_per_gpu=64,
+    train=dict(imgs_root='./data/celeba/cropped_images_aligned_png/'),
+    val=dict(imgs_root='./data/celeba/cropped_images_aligned_png/'))
+
+optimizer = dict(
+    generator=dict(type='Adam', lr=0.0001, betas=(0.5, 0.99)),
+    discriminator=dict(type='Adam', lr=0.0001, betas=(0.5, 0.99)))
+
+# 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=5000)
+]
+
+evaluation = dict(
+    type='GenerativeEvalHook',
+    interval=10000,
+    metrics=dict(
+        type='FID', num_images=50000, inception_pkl=None, bgr2rgb=True),
+    sample_kwargs=dict(sample_model='orig'))
+
+total_iters = 160000
+# 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, 128, 128)),
+    fid50k=dict(type='FID', num_images=50000, inception_pkl=None))

build/lib/mmgen/.mim/configs/ggan/ggan_lsun-bedroom_lsgan_archi_lr-1e-4_64_b128x1_20m.py

+_base_ = [
+    '../_base_/datasets/unconditional_imgs_64x64.py',
+    '../_base_/default_runtime.py'
+]
+
+model = dict(
+    type='StaticUnconditionalGAN',
+    generator=dict(type='LSGANGenerator', output_scale=64),
+    discriminator=dict(type='LSGANDiscriminator', input_scale=64),
+    gan_loss=dict(type='GANLoss', gan_type='hinge'))
+train_cfg = dict(disc_steps=1)
+test_cfg = None
+# define dataset
+# you must set `samples_per_gpu` and `imgs_root`
+data = dict(
+    samples_per_gpu=128,
+    train=dict(imgs_root='data/lsun/bedroom_train'),
+    val=dict(imgs_root='data/lsun/bedroom_train'))
+
+optimizer = dict(
+    generator=dict(type='Adam', lr=0.0001, betas=(0.5, 0.99)),
+    discriminator=dict(type='Adam', lr=0.0001, betas=(0.5, 0.99)))
+
+# 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=5000)
+]
+
+evaluation = dict(
+    type='GenerativeEvalHook',
+    interval=10000,
+    metrics=dict(
+        type='FID', num_images=50000, inception_pkl=None, bgr2rgb=True),
+    sample_kwargs=dict(sample_model='orig'))
+
+total_iters = 160000
+# 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)),
+    fid50k=dict(type='FID', num_images=50000, inception_pkl=None))

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

+Collections:
+- Metadata:
+    Architecture:
+    - GGAN
+  Name: GGAN
+  Paper:
+  - https://arxiv.org/abs/1705.02894
+  README: configs/ggan/README.md
+Models:
+- Config: https://github.com/open-mmlab/mmgeneration/tree/master/configs/ggan/ggan_celeba-cropped_dcgan-archi_lr-1e-3_64_b128x1_12m.py
+  In Collection: GGAN
+  Metadata:
+    Training Data: CELEBA
+  Name: ggan_celeba-cropped_dcgan-archi_lr-1e-3_64_b128x1_12m
+  Results:
+  - Dataset: CELEBA
+    Metrics:
+      FID: 20.1797
+      MS-SSIM: 0.3318
+      SWD: 11.18, 12.21, 39.16/20.85
+    Task: Unconditional GANs
+  Weights: https://download.openmmlab.com/mmgen/ggan/ggan_celeba-cropped_dcgan-archi_lr-1e-3_64_b128x1_12m.pth
+- Config: https://github.com/open-mmlab/mmgeneration/tree/master/configs/ggan/ggan_celeba-cropped_dcgan-archi_lr-1e-4_128_b64x1_10m.py
+  In Collection: GGAN
+  Metadata:
+    Training Data: CELEBA
+  Name: ggan_celeba-cropped_dcgan-archi_lr-1e-4_128_b64x1_10m
+  Results:
+  - Dataset: CELEBA
+    Metrics:
+      FID: 18.7647
+      MS-SSIM: 0.3149
+      SWD: 9.81, 11.29, 19.22, 47.79/22.03
+    Task: Unconditional GANs
+  Weights: https://download.openmmlab.com/mmgen/ggan/ggan_celeba-cropped_dcgan-archi_lr-1e-4_128_b64x1_10m_20210430_143027-516423dc.pth
+- Config: https://github.com/open-mmlab/mmgeneration/tree/master/configs/ggan/ggan_lsun-bedroom_lsgan_archi_lr-1e-4_64_b128x1_20m.py
+  In Collection: GGAN
+  Metadata:
+    Training Data: LSUN
+  Name: ggan_lsun-bedroom_lsgan_archi_lr-1e-4_64_b128x1_20m
+  Results:
+  - Dataset: LSUN
+    Metrics:
+      FID: 85.6629
+      MS-SSIM: 0.0649
+      SWD: 9.1, 6.2, 12.27/9.19
+    Task: Unconditional GANs
+  Weights: https://download.openmmlab.com/mmgen/ggan/ggan_lsun-bedroom_lsgan_archi_lr-1e-4_64_b128x1_20m_20210430_143114-5d99b76c.pth

build/lib/mmgen/.mim/configs/improved_ddpm/ddpm_cosine_hybird_timestep-4k_drop0.3_cifar10_32x32_b8x16_500k.py

+_base_ = [
+    '../_base_/models/improved_ddpm/ddpm_32x32.py',
+    '../_base_/datasets/cifar10_noaug.py', '../_base_/default_runtime.py'
+]
+
+lr_config = None
+checkpoint_config = dict(interval=10000, by_epoch=False, max_keep_ckpts=20)
+custom_hooks = [
+    dict(
+        type='MMGenVisualizationHook',
+        output_dir='training_samples',
+        res_name_list=['real_imgs', 'x_0_pred', 'x_t', 'x_t_1'],
+        padding=1,
+        interval=1000),
+    dict(
+        type='ExponentialMovingAverageHook',
+        module_keys=('denoising_ema'),
+        interval=1,
+        start_iter=0,
+        interp_cfg=dict(momentum=0.9999),
+        priority='VERY_HIGH')
+]
+
+# do not evaluation in training process because evaluation take too much time.
+evaluation = None
+
+total_iters = 500000  # 500k
+data = dict(samples_per_gpu=16)  # 8x16=128
+
+# 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)
+
+inception_pkl = './work_dirs/inception_pkl/cifar10.pkl'
+metrics = dict(
+    fid50k=dict(
+        type='FID',
+        num_images=50000,
+        bgr2rgb=True,
+        inception_pkl=inception_pkl,
+        inception_args=dict(type='StyleGAN')))

build/lib/mmgen/.mim/configs/improved_ddpm/ddpm_cosine_hybird_timestep-4k_drop0.3_imagenet1k_64x64_b8x16_1500k.py

+_base_ = [
+    '../_base_/models/improved_ddpm/ddpm_64x64.py',
+    '../_base_/datasets/imagenet_noaug_64.py', '../_base_/default_runtime.py'
+]
+
+# set dropout prob as 0.3
+model = dict(denoising=dict(dropout=0.3))
+
+lr_config = None
+checkpoint_config = dict(interval=10000, by_epoch=False, max_keep_ckpts=20)
+custom_hooks = [
+    dict(
+        type='MMGenVisualizationHook',
+        output_dir='training_samples',
+        res_name_list=['real_imgs', 'x_0_pred', 'x_t', 'x_t_1'],
+        padding=1,
+        interval=1000),
+    dict(
+        type='ExponentialMovingAverageHook',
+        module_keys=('denoising_ema'),
+        interval=1,
+        start_iter=0,
+        interp_cfg=dict(momentum=0.9999),
+        priority='VERY_HIGH')
+]
+
+# do not evaluation in training process because evaluation take too much time.
+evaluation = None
+
+total_iters = 1500000  # 1500k
+data = dict(samples_per_gpu=16)  # 8x16=128
+
+# 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)
+
+inception_pkl = './work_dirs/inception_pkl/imagenet_64x64.pkl'
+metrics = dict(
+    fid50k=dict(
+        type='FID',
+        num_images=50000,
+        bgr2rgb=True,
+        inception_pkl=inception_pkl,
+        inception_args=dict(type='StyleGAN')))

build/lib/mmgen/.mim/configs/improved_ddpm/ddpm_cosine_hybird_timestep-4k_imagenet1k_64x64_b8x16_1500k.py

+_base_ = [
+    '../_base_/models/improved_ddpm/ddpm_64x64.py',
+    '../_base_/datasets/imagenet_noaug_64.py', '../_base_/default_runtime.py'
+]
+
+lr_config = None
+checkpoint_config = dict(interval=10000, by_epoch=False, max_keep_ckpts=20)
+custom_hooks = [
+    dict(
+        type='MMGenVisualizationHook',
+        output_dir='training_samples',
+        res_name_list=['real_imgs', 'x_0_pred', 'x_t', 'x_t_1'],
+        padding=1,
+        interval=1000),
+    dict(
+        type='ExponentialMovingAverageHook',
+        module_keys=('denoising_ema'),
+        interval=1,
+        start_iter=0,
+        interp_cfg=dict(momentum=0.9999),
+        priority='VERY_HIGH')
+]
+
+# do not evaluation in training process because evaluation take too much time.
+evaluation = None
+
+total_iters = 1500000  # 1500k
+data = dict(samples_per_gpu=16)  # 8x16=128
+
+# 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)
+
+inception_pkl = './work_dirs/inception_pkl/imagenet_64x64.pkl'
+metrics = dict(
+    fid50k=dict(
+        type='FID',
+        num_images=50000,
+        bgr2rgb=True,
+        inception_pkl=inception_pkl,
+        inception_args=dict(type='StyleGAN')))

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

+Collections:
+- Metadata:
+    Architecture:
+    - Improved-DDPM
+  Name: Improved-DDPM
+  Paper:
+  - https://arxiv.org/abs/2102.09672
+  README: configs/improved_ddpm/README.md
+Models:
+- Config: https://github.com/open-mmlab/mmgeneration/blob/master/configs/improved_ddpm/ddpm_cosine_hybird_timestep-4k_drop0.3_cifar10_32x32_b8x16_500k.py
+  In Collection: Improved-DDPM
+  Metadata:
+    Training Data: CIFAR
+  Name: ddpm_cosine_hybird_timestep-4k_drop0.3_cifar10_32x32_b8x16_500k
+  Results:
+  - Dataset: CIFAR
+    Metrics:
+      FID: 3.8848
+    Task: Denoising Diffusion Probabilistic Models
+  Weights: https://download.openmmlab.com/mmgen/improved_ddpm/ddpm_cosine_hybird_timestep-4k_drop0.3_cifar10_32x32_b8x16_500k_20220103_222621-2f42f476.pth
+- Config: https://github.com/open-mmlab/mmgeneration/tree/master/configs/improve_ddpm/ddpm_cosine_hybird_timestep-4k_imagenet1k_64x64_b8x16_1500k.py
+  In Collection: Improved-DDPM
+  Metadata:
+    Training Data: IMAGENET
+  Name: ddpm_cosine_hybird_timestep-4k_imagenet1k_64x64_b8x16_1500k
+  Results:
+  - Dataset: IMAGENET
+    Metrics:
+      FID: 13.5181
+    Task: Denoising Diffusion Probabilistic Models
+  Weights: https://download.openmmlab.com/mmgen/improved_ddpm/ddpm_cosine_hybird_timestep-4k_imagenet1k_64x64_b8x16_1500k_20220103_223919-b8f1a310.pth
+- Config: https://github.com/open-mmlab/mmgeneration/blob/master/configs/improved_ddpm/ddpm_cosine_hybird_timestep-4k_drop0.3_imagenet1k_64x64_b8x16_1500k.py
+  In Collection: Improved-DDPM
+  Metadata:
+    Training Data: IMAGENET
+  Name: ddpm_cosine_hybird_timestep-4k_drop0.3_imagenet1k_64x64_b8x16_1500k
+  Results:
+  - Dataset: IMAGENET
+    Metrics:
+      FID: 13.4094
+    Task: Denoising Diffusion Probabilistic Models
+  Weights: https://download.openmmlab.com/mmgen/improved_ddpm/ddpm_cosine_hybird_timestep-4k_drop0.3_imagenet1k_64x64_b8x16_1500k_20220103_224427-7bb55975.pth

build/lib/mmgen/.mim/configs/lsgan/lsgan_dcgan-archi_lr-1e-3_celeba-cropped_64_b128x1_12m.py

+_base_ = [
+    '../_base_/models/dcgan/dcgan_64x64.py',
+    '../_base_/datasets/unconditional_imgs_64x64.py',
+    '../_base_/default_runtime.py'
+]
+model = dict(gan_loss=dict(type='GANLoss', gan_type='lsgan'))
+# 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/'))
+
+optimizer = dict(
+    generator=dict(type='Adam', lr=0.001, betas=(0.5, 0.99)),
+    discriminator=dict(type='Adam', lr=0.001, betas=(0.5, 0.99)))
+
+# 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)
+]
+
+evaluation = dict(
+    type='GenerativeEvalHook',
+    interval=10000,
+    metrics=dict(
+        type='FID', num_images=50000, inception_pkl=None, bgr2rgb=True),
+    sample_kwargs=dict(sample_model='orig'))
+
+total_iters = 100000
+# 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)),
+    fid50k=dict(type='FID', num_images=50000, inception_pkl=None))

build/lib/mmgen/.mim/configs/lsgan/lsgan_dcgan-archi_lr-1e-4_celeba-cropped_128_b64x1_10m.py

+_base_ = [
+    '../_base_/models/dcgan/dcgan_128x128.py',
+    '../_base_/datasets/unconditional_imgs_128x128.py',
+    '../_base_/default_runtime.py'
+]
+model = dict(
+    discriminator=dict(output_scale=4, out_channels=1),
+    gan_loss=dict(type='GANLoss', gan_type='lsgan'))
+# define dataset
+# you must set `samples_per_gpu` and `imgs_root`
+data = dict(
+    samples_per_gpu=64,
+    train=dict(imgs_root='./data/celeba-cropped/cropped_images_aligned_png/'))
+
+optimizer = dict(
+    generator=dict(type='Adam', lr=0.0001, betas=(0.5, 0.99)),
+    discriminator=dict(type='Adam', lr=0.0001, betas=(0.5, 0.99)))
+
+# 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)
+]
+
+evaluation = dict(
+    type='GenerativeEvalHook',
+    interval=10000,
+    metrics=dict(
+        type='FID', num_images=50000, inception_pkl=None, bgr2rgb=True),
+    sample_kwargs=dict(sample_model='orig'))
+
+total_iters = 160000
+# 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, 128, 128)),
+    fid50k=dict(type='FID', num_images=50000, inception_pkl=None))

build/lib/mmgen/.mim/configs/lsgan/lsgan_dcgan-archi_lr-1e-4_lsun-bedroom_64_b128x1_12m.py

+_base_ = [
+    '../_base_/models/dcgan/dcgan_64x64.py',
+    '../_base_/datasets/unconditional_imgs_64x64.py',
+    '../_base_/default_runtime.py'
+]
+model = dict(
+    discriminator=dict(output_scale=4, out_channels=1),
+    gan_loss=dict(type='GANLoss', gan_type='lsgan'))
+# define dataset
+# you must set `samples_per_gpu` and `imgs_root`
+data = dict(
+    samples_per_gpu=128, train=dict(imgs_root='./data/lsun/bedroom_train'))
+
+optimizer = dict(
+    generator=dict(type='Adam', lr=0.0001, betas=(0.5, 0.99)),
+    discriminator=dict(type='Adam', lr=0.0001, betas=(0.5, 0.99)))
+
+# 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)
+]
+
+evaluation = dict(
+    type='GenerativeEvalHook',
+    interval=10000,
+    metrics=dict(
+        type='FID', num_images=50000, inception_pkl=None, bgr2rgb=True),
+    sample_kwargs=dict(sample_model='orig'))
+
+total_iters = 100000
+# 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)),
+    fid50k=dict(type='FID', num_images=50000, inception_pkl=None))

build/lib/mmgen/.mim/configs/lsgan/lsgan_lsgan-archi_lr-1e-4_lsun-bedroom_128_b64x1_10m.py

+_base_ = [
+    '../_base_/models/lsgan/lsgan_128x128.py',
+    '../_base_/datasets/unconditional_imgs_128x128.py',
+    '../_base_/default_runtime.py'
+]
+# define dataset
+# you must set `samples_per_gpu` and `imgs_root`
+data = dict(
+    samples_per_gpu=64, train=dict(imgs_root='./data/lsun/bedroom_train'))
+
+optimizer = dict(
+    generator=dict(type='Adam', lr=0.0001, betas=(0.5, 0.99)),
+    discriminator=dict(type='Adam', lr=0.0001, betas=(0.5, 0.99)))
+
+# 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)
+]
+
+evaluation = dict(
+    type='GenerativeEvalHook',
+    interval=10000,
+    metrics=dict(
+        type='FID', num_images=50000, inception_pkl=None, bgr2rgb=True),
+    sample_kwargs=dict(sample_model='orig'))
+
+total_iters = 160000
+# 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, 128, 128)),
+    fid50k=dict(type='FID', num_images=50000, inception_pkl=None))

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

+Collections:
+- Metadata:
+    Architecture:
+    - LSGAN
+  Name: LSGAN
+  Paper:
+  - https://openaccess.thecvf.com/content_iccv_2017/html/Mao_Least_Squares_Generative_ICCV_2017_paper.html
+  README: configs/lsgan/README.md
+Models:
+- Config: https://github.com/open-mmlab/mmgeneration/tree/master/configs/lsgan/lsgan_dcgan-archi_lr-1e-3_celeba-cropped_64_b128x1_12m.py
+  In Collection: LSGAN
+  Metadata:
+    Training Data: CELEBA
+  Name: lsgan_dcgan-archi_lr-1e-3_celeba-cropped_64_b128x1_12m
+  Results:
+  - Dataset: CELEBA
+    Metrics:
+      FID: 11.9258
+      MS-SSIM: 0.3216
+      SWD: 6.16, 6.83, 37.64/16.87
+    Task: Unconditional GANs
+  Weights: https://download.openmmlab.com/mmgen/lsgan/lsgan_celeba-cropped_dcgan-archi_lr-1e-3_64_b128x1_12m_20210429_144001-92ca1d0d.pth
+- Config: https://github.com/open-mmlab/mmgeneration/tree/master/configs/lsgan/lsgan_dcgan-archi_lr-1e-4_lsun-bedroom_64_b128x1_12m.py
+  In Collection: LSGAN
+  Metadata:
+    Training Data: LSUN
+  Name: lsgan_dcgan-archi_lr-1e-4_lsun-bedroom_64_b128x1_12m
+  Results:
+  - Dataset: LSUN
+    Metrics:
+      FID: 30.739
+      MS-SSIM: 0.0671
+      SWD: 5.66, 9.0, 18.6/11.09
+    Task: Unconditional GANs
+  Weights: https://download.openmmlab.com/mmgen/lsgan/lsgan_lsun-bedroom_dcgan-archi_lr-1e-4_64_b128x1_12m_20210429_144602-ec4ec6bb.pth
+- Config: https://github.com/open-mmlab/mmgeneration/tree/master/configs/lsgan/lsgan_dcgan-archi_lr-1e-4_celeba-cropped_128_b64x1_10m.py
+  In Collection: LSGAN
+  Metadata:
+    Training Data: CELEBA
+  Name: lsgan_dcgan-archi_lr-1e-4_celeba-cropped_128_b64x1_10m
+  Results:
+  - Dataset: CELEBA
+    Metrics:
+      FID: 38.3752
+      MS-SSIM: 0.3691
+      SWD: 21.66, 9.83, 16.06, 70.76/29.58
+    Task: Unconditional GANs
+  Weights: https://download.openmmlab.com/mmgen/lsgan/lsgan_celeba-cropped_dcgan-archi_lr-1e-4_128_b64x1_10m_20210429_144229-01ba67dc.pth
+- Config: https://github.com/open-mmlab/mmgeneration/tree/master/configs/lsgan/lsgan_lsgan-archi_lr-1e-4_lsun-bedroom_128_b64x1_10m.py
+  In Collection: LSGAN
+  Metadata:
+    Training Data: LSUN
+  Name: lsgan_lsgan-archi_lr-1e-4_lsun-bedroom_128_b64x1_10m
+  Results:
+  - Dataset: LSUN
+    Metrics:
+      FID: 51.55
+      MS-SSIM: 0.0612
+      SWD: 19.52, 9.99, 7.48, 14.3/12.82
+    Task: Unconditional GANs
+  Weights: https://download.openmmlab.com/mmgen/lsgan/lsgan_lsun-bedroom_lsgan-archi_lr-1e-4_128_b64x1_10m_20210429_155605-cf78c0a8.pth

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

+Collections:
+- Metadata:
+    Architecture:
+    - PGGAN
+  Name: PGGAN
+  Paper:
+  - https://arxiv.org/abs/1710.10196
+  README: configs/pggan/README.md
+Models:
+- Config: https://github.com/open-mmlab/mmgeneration/tree/master/configs/pggan/pggan_celeba-cropped_128_g8_12Mimgs.py
+  In Collection: PGGAN
+  Metadata:
+    Training Data: CELEBA
+  Name: pggan_celeba-cropped_128_g8_12Mimgs
+  Results:
+  - Dataset: CELEBA
+    Metrics:
+      Details: celeba-cropped
+      MS-SSIM: 0.3023
+      SWD(xx,xx,xx,xx/avg): 3.42, 4.04, 4.78, 20.38/8.15
+    Task: Unconditional GANs
+  Weights: https://download.openmmlab.com/mmgen/pggan/pggan_celeba-cropped_128_g8_20210408_181931-85a2e72c.pth
+- Config: https://github.com/open-mmlab/mmgeneration/tree/master/configs/pggan/pggan_lsun-bedroom_128_g8_12Mimgs.py
+  In Collection: PGGAN
+  Metadata:
+    Training Data: LSUN
+  Name: pggan_lsun-bedroom_128_g8_12Mimgs
+  Results:
+  - Dataset: LSUN
+    Metrics:
+      Details: lsun-bedroom
+      MS-SSIM: 0.0602
+      SWD(xx,xx,xx,xx/avg): 3.5, 2.96, 2.76, 9.65/4.72
+    Task: Unconditional GANs
+  Weights: https://download.openmmlab.com/mmgen/pggan/pggan_lsun-bedroom_128x128_g8_20210408_182033-5e59f45d.pth
+- Config: https://github.com/open-mmlab/mmgeneration/tree/master/configs/pggan/pggan_celeba-hq_1024_g8_12Mimg.py
+  In Collection: PGGAN
+  Metadata:
+    Training Data: CELEBA
+  Name: pggan_celeba-hq_1024_g8_12Mimg
+  Results:
+  - Dataset: CELEBA
+    Metrics:
+      Details: celeba-hq
+      MS-SSIM: 0.3379
+      SWD(xx,xx,xx,xx/avg): 8.93, 3.98, 3.07, 2.64/4.655
+    Task: Unconditional GANs
+  Weights: https://download.openmmlab.com/mmgen/pggan/pggan_celeba-hq_1024_g8_20210408_181911-f1ef51c3.pth

build/lib/mmgen/.mim/configs/pggan/pggan_celeba-cropped_128_g8_12Mimgs.py

+_base_ = [
+    '../_base_/models/pggan/pggan_128x128.py',
+    '../_base_/datasets/grow_scale_imgs_128x128.py',
+    '../_base_/default_runtime.py'
+]
+
+optimizer = None
+checkpoint_config = dict(interval=10000, by_epoch=False, max_keep_ckpts=20)
+
+data = dict(
+    samples_per_gpu=64,
+    train=dict(
+        imgs_roots={'128': './data/celeba-cropped/cropped_images_aligned_png'},
+        gpu_samples_base=4,
+        # note that this should be changed with total gpu number
+        gpu_samples_per_scale={
+            '4': 64,
+            '8': 32,
+            '16': 16,
+            '32': 8,
+            '64': 4
+        }))
+
+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,
+        priority='VERY_HIGH')
+]
+
+lr_config = None
+
+total_iters = 280000
+
+metrics = dict(
+    ms_ssim10k=dict(type='MS_SSIM', num_images=10000),
+    swd16k=dict(type='SWD', num_images=16384, image_shape=(3, 128, 128)))

build/lib/mmgen/.mim/configs/pggan/pggan_celeba-hq_1024_g8_12Mimg.py

+_base_ = [
+    '../_base_/models/pggan/pggan_1024.py',
+    '../_base_/datasets/grow_scale_imgs_celeba-hq.py',
+    '../_base_/default_runtime.py'
+]
+
+optimizer = None
+checkpoint_config = dict(interval=5000, by_epoch=False, max_keep_ckpts=20)
+
+data = dict(
+    samples_per_gpu=64,
+    train=dict(
+        gpu_samples_base=4,
+        # note that this should be changed with total gpu number
+        gpu_samples_per_scale={
+            '4': 64,
+            '8': 32,
+            '16': 16,
+            '32': 8,
+            '64': 4
+        },
+    ))
+
+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,
+        priority='VERY_HIGH')
+]
+
+lr_config = None
+
+total_iters = 280000
+
+metrics = dict(
+    ms_ssim10k=dict(type='MS_SSIM', num_images=10000),
+    swd16k=dict(type='SWD', num_images=16384, image_shape=(3, 1024, 1024)))

build/lib/mmgen/.mim/configs/pggan/pggan_lsun-bedroom_128_g8_12Mimgs.py

+_base_ = [
+    '../_base_/models/pggan/pggan_128x128.py',
+    '../_base_/datasets/grow_scale_imgs_128x128.py',
+    '../_base_/default_runtime.py'
+]
+
+optimizer = None
+checkpoint_config = dict(interval=10000, by_epoch=False, max_keep_ckpts=20)
+
+data = dict(
+    samples_per_gpu=64,
+    train=dict(
+        imgs_roots={'128': './data/lsun/bedroom_train'},
+        gpu_samples_base=4,
+        # note that this should be changed with total gpu number
+        gpu_samples_per_scale={
+            '4': 64,
+            '8': 32,
+            '16': 16,
+            '32': 8,
+            '64': 4
+        },
+    ))
+
+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,
+        priority='VERY_HIGH')
+]
+
+lr_config = None
+
+total_iters = 280000
+
+metrics = dict(
+    ms_ssim10k=dict(type='MS_SSIM', num_images=10000),
+    swd16k=dict(type='SWD', num_images=16384, image_shape=(3, 128, 128)))