[CodeCamp2023-648]MMagic 新 config 体验与适配 GuidedDiffusion

mmagic/configs/_base_/datasets/imagenet_512.py

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+# Copyright (c) OpenMMLab. All rights reserved.
+from mmengine.dataset.sampler import DefaultSampler
+
+from mmagic.datasets.imagenet_dataset import ImageNet
+from mmagic.datasets.transforms.aug_shape import Flip, Resize
+from mmagic.datasets.transforms.crop import (CenterCropLongEdge,
+                                             RandomCropLongEdge)
+from mmagic.datasets.transforms.formatting import PackInputs
+from mmagic.datasets.transforms.loading import LoadImageFromFile
+
+# dataset settings
+dataset_type = ImageNet
+
+# different from mmcls, we adopt the setting used in BigGAN.
+# We use `RandomCropLongEdge` in training and `CenterCropLongEdge` in testing.
+train_pipeline = [
+    dict(type=LoadImageFromFile, key='gt'),
+    dict(type=RandomCropLongEdge, keys='gt'),
+    dict(type=Resize, scale=(512, 512), keys='gt', backend='pillow'),
+    dict(type=Flip, keys='gt', flip_ratio=0.5, direction='horizontal'),
+    dict(type=PackInputs)
+]
+
+test_pipeline = [
+    dict(type=LoadImageFromFile, key='gt'),
+    dict(type=CenterCropLongEdge, keys='gt'),
+    dict(type=Resize, scale=(512, 512), keys='gt', backend='pillow'),
+    dict(type=PackInputs)
+]
+
+train_dataloader = dict(
+    batch_size=None,
+    num_workers=5,
+    dataset=dict(
+        type=dataset_type,
+        data_root='./data/imagenet/',
+        ann_file='meta/train.txt',
+        data_prefix='train',
+        pipeline=train_pipeline),
+    sampler=dict(type=DefaultSampler, shuffle=True),
+    persistent_workers=True)
+
+val_dataloader = dict(
+    batch_size=None,
+    num_workers=5,
+    dataset=dict(
+        type=dataset_type,
+        data_root='./data/imagenet/',
+        ann_file='meta/train.txt',
+        data_prefix='train',
+        pipeline=test_pipeline),
+    sampler=dict(type=DefaultSampler, shuffle=False),
+    persistent_workers=True)
+
+test_dataloader = val_dataloader

mmagic/configs/_base_/datasets/imagenet_64.py

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+# Copyright (c) OpenMMLab. All rights reserved.
+from mmengine.dataset.sampler import DefaultSampler
+
+from mmagic.datasets.imagenet_dataset import ImageNet
+from mmagic.datasets.transforms.aug_shape import Flip, Resize
+from mmagic.datasets.transforms.crop import (CenterCropLongEdge,
+                                             RandomCropLongEdge)
+from mmagic.datasets.transforms.formatting import PackInputs
+from mmagic.datasets.transforms.loading import LoadImageFromFile
+
+# dataset settings
+dataset_type = ImageNet
+
+# different from mmcls, we adopt the setting used in BigGAN.
+# We use `RandomCropLongEdge` in training and `CenterCropLongEdge` in testing.
+train_pipeline = [
+    dict(type=LoadImageFromFile, key='gt'),
+    dict(type=RandomCropLongEdge, keys='gt'),
+    dict(type=Resize, scale=(64, 64), keys='gt', backend='pillow'),
+    dict(type=Flip, keys='gt', flip_ratio=0.5, direction='horizontal'),
+    dict(type=PackInputs)
+]
+
+test_pipeline = [
+    dict(type=LoadImageFromFile, key='gt'),
+    dict(type=CenterCropLongEdge, keys='gt'),
+    dict(type=Resize, scale=(64, 64), keys='gt', backend='pillow'),
+    dict(type=PackInputs)
+]
+
+train_dataloader = dict(
+    batch_size=None,
+    num_workers=5,
+    dataset=dict(
+        type=dataset_type,
+        data_root='./data/imagenet/',
+        ann_file='meta/train.txt',
+        data_prefix='train',
+        pipeline=train_pipeline),
+    sampler=dict(type=DefaultSampler, shuffle=True),
+    persistent_workers=True)
+
+val_dataloader = dict(
+    batch_size=64,
+    num_workers=5,
+    dataset=dict(
+        type=dataset_type,
+        data_root='./data/imagenet/',
+        ann_file='meta/train.txt',
+        data_prefix='train',
+        pipeline=test_pipeline),
+    sampler=dict(type=DefaultSampler, shuffle=False),
+    persistent_workers=True)
+
+test_dataloader = val_dataloader

mmagic/configs/guided_diffusion/adm-g_ddim25_8xb32_imagenet_256x256.py

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+# Copyright (c) OpenMMLab. All rights reserved.
+from mmengine.config import read_base
+
+with read_base():
+    from .adm_ddim250_8xb32_imagenet_256x256 import *  # noqa: F401,F403
+
+from mmagic.evaluation.metrics import FrechetInceptionDistance
+from mmagic.models.editors.guided_diffusion.classifier import EncoderUNetModel
+
+model.update(
+    dict(
+        classifier=dict(
+            type=EncoderUNetModel,
+            image_size=256,
+            in_channels=3,
+            model_channels=128,
+            out_channels=1000,
+            num_res_blocks=2,
+            attention_resolutions=(8, 16, 32),
+            channel_mult=(1, 1, 2, 2, 4, 4),
+            use_fp16=False,
+            num_head_channels=64,
+            use_scale_shift_norm=True,
+            resblock_updown=True,
+            pool='attention')))
+
+metrics = [
+    dict(
+        type=FrechetInceptionDistance,
+        prefix='FID-Full-50k',
+        fake_nums=50000,
+        inception_style='StyleGAN',
+        sample_model='orig',
+        sample_kwargs=dict(
+            num_inference_steps=250, show_progress=True, classifier_scale=1.))
+]
+
+val_evaluator = dict(metrics=metrics)
+test_evaluator = dict(metrics=metrics)

mmagic/configs/guided_diffusion/adm-g_ddim25_8xb32_imagenet_512x512.py

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+# Copyright (c) OpenMMLab. All rights reserved.
+from mmengine.config import read_base
+
+with read_base():
+    from .adm_ddim250_8xb32_imagenet_512x512 import *  # noqa: F401,F403
+
+from mmagic.evaluation.metrics import FrechetInceptionDistance
+from mmagic.models.editors.guided_diffusion.classifier import EncoderUNetModel
+
+model.update(
+    dict(
+        classifier=dict(
+            type=EncoderUNetModel,
+            image_size=512,
+            in_channels=3,
+            model_channels=128,
+            out_channels=1000,
+            num_res_blocks=2,
+            attention_resolutions=(16, 32, 64),
+            channel_mult=(0.5, 1, 1, 2, 2, 4, 4),
+            use_fp16=False,
+            num_head_channels=64,
+            use_scale_shift_norm=True,
+            resblock_updown=True,
+            pool='attention')))
+
+metrics = [
+    dict(
+        type=FrechetInceptionDistance,
+        prefix='FID-Full-50k',
+        fake_nums=50000,
+        inception_style='StyleGAN',
+        sample_model='orig',
+        sample_kwargs=dict(
+            num_inference_steps=250, show_progress=True, classifier_scale=1.))
+]
+
+val_evaluator = dict(metrics=metrics)
+test_evaluator = dict(metrics=metrics)

mmagic/configs/guided_diffusion/adm-g_ddim25_8xb32_imagenet_64x64.py

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+# Copyright (c) OpenMMLab. All rights reserved.
+from mmengine.config import read_base
+
+with read_base():
+    from .adm_ddim250_8xb32_imagenet_64x64 import *  # noqa: F401,F403
+
+from mmagic.evaluation.metrics import FrechetInceptionDistance
+from mmagic.models.editors.guided_diffusion.classifier import EncoderUNetModel
+
+model.update(
+    dict(
+        classifier=dict(
+            type=EncoderUNetModel,
+            image_size=64,
+            in_channels=3,
+            model_channels=128,
+            out_channels=1000,
+            num_res_blocks=4,
+            attention_resolutions=(2, 4, 8),
+            channel_mult=(1, 2, 3, 4),
+            use_fp16=False,
+            num_head_channels=64,
+            use_scale_shift_norm=True,
+            resblock_updown=True,
+            pool='attention')))
+
+metrics = [
+    dict(
+        type=FrechetInceptionDistance,
+        prefix='FID-Full-50k',
+        fake_nums=50000,
+        inception_style='StyleGAN',
+        sample_model='orig',
+        sample_kwargs=dict(
+            num_inference_steps=250, show_progress=True, classifier_scale=1.))
+]
+
+val_evaluator = dict(metrics=metrics)
+test_evaluator = dict(metrics=metrics)

mmagic/configs/guided_diffusion/adm_ddim250_8xb32_imagenet_256x256.py

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+# Copyright (c) OpenMMLab. All rights reserved.
+from mmengine.config import read_base
+
+with read_base():
+    from .._base_.datasets.imagenet_64 import *  # noqa: F401,F403
+    from .._base_.gen_default_runtime import *  # noqa: F401,F403
+
+from mmagic.engine.hooks.visualization_hook import VisualizationHook
+from mmagic.evaluation.metrics import FrechetInceptionDistance
+from mmagic.models.data_preprocessors.data_preprocessor import DataPreprocessor
+from mmagic.models.diffusion_schedulers.ddim_scheduler import EditDDIMScheduler
+from mmagic.models.editors.ddpm.denoising_unet import (DenoisingUnet,
+                                                       MultiHeadAttentionBlock)
+from mmagic.models.editors.guided_diffusion.adm import AblatedDiffusionModel
+
+model = dict(
+    type=AblatedDiffusionModel,
+    data_preprocessor=dict(type=DataPreprocessor),
+    unet=dict(
+        type=DenoisingUnet,
+        image_size=256,
+        in_channels=3,
+        base_channels=256,
+        resblocks_per_downsample=2,
+        attention_res=(32, 16, 8),
+        norm_cfg=dict(type='GN32', num_groups=32),
+        dropout=0.1,
+        num_classes=1000,
+        use_fp16=False,
+        resblock_updown=True,
+        attention_cfg=dict(
+            type=MultiHeadAttentionBlock,
+            num_heads=4,
+            num_head_channels=64,
+            use_new_attention_order=False),
+        use_scale_shift_norm=True),
+    diffusion_scheduler=dict(
+        type=EditDDIMScheduler,
+        variance_type='learned_range',
+        beta_schedule='linear'),
+    rgb2bgr=True,
+    use_fp16=False)
+
+test_dataloader.update(dict(batch_size=32, num_workers=8))
+train_cfg = dict(max_iters=100000)
+metrics = [
+    dict(
+        type=FrechetInceptionDistance,
+        prefix='FID-Full-50k',
+        fake_nums=50000,
+        inception_style='StyleGAN',
+        sample_model='orig',
+        sample_kwargs=dict(
+            num_inference_steps=250, show_progress=True, classifier_scale=1.))
+]
+
+val_evaluator = dict(metrics=metrics)
+test_evaluator = dict(metrics=metrics)
+
+# VIS_HOOK
+custom_hooks = [dict(type=VisualizationHook, interval=5000, fixed_input=True)]

mmagic/configs/guided_diffusion/adm_ddim250_8xb32_imagenet_512x512.py

@@ -0,0 +1,57 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from mmengine.config import read_base
+
+with read_base():
+    from .._base_.datasets.imagenet_512 import *  # noqa: F401,F403
+    from .._base_.gen_default_runtime import *  # noqa: F401,F403
+
+from mmagic.evaluation.metrics import FrechetInceptionDistance
+from mmagic.models.data_preprocessors.data_preprocessor import DataPreprocessor
+from mmagic.models.diffusion_schedulers.ddim_scheduler import EditDDIMScheduler
+from mmagic.models.editors.ddpm.denoising_unet import (DenoisingUnet,
+                                                       MultiHeadAttentionBlock)
+from mmagic.models.editors.guided_diffusion import AblatedDiffusionModel
+
+model = dict(
+    type=AblatedDiffusionModel,
+    data_preprocessor=dict(type=DataPreprocessor),
+    unet=dict(
+        type=DenoisingUnet,
+        image_size=512,
+        in_channels=3,
+        base_channels=256,
+        resblocks_per_downsample=2,
+        attention_res=(32, 16, 8),
+        norm_cfg=dict(type='GN32', num_groups=32),
+        dropout=0.1,
+        num_classes=1000,
+        use_fp16=False,
+        resblock_updown=True,
+        attention_cfg=dict(
+            type=MultiHeadAttentionBlock,
+            num_heads=4,
+            num_head_channels=64,
+            use_new_attention_order=False),
+        use_scale_shift_norm=True),
+    diffusion_scheduler=dict(
+        type=EditDDIMScheduler,
+        variance_type='learned_range',
+        beta_schedule='linear'),
+    rgb2bgr=True,
+    use_fp16=False)
+
+test_dataloader.update(dict(batch_size=32, num_workers=8))
+train_cfg = dict(max_iters=100000)
+metrics = [
+    dict(
+        type=FrechetInceptionDistance,
+        prefix='FID-Full-50k',
+        fake_nums=50000,
+        inception_style='StyleGAN',
+        sample_model='orig',
+        sample_kwargs=dict(
+            num_inference_steps=250, show_progress=True, classifier_scale=1.))
+]
+
+val_evaluator = dict(metrics=metrics)
+test_evaluator = dict(metrics=metrics)