add new config

mmagic/configs/_base_/datasets/basicvsr_test_config.py

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+# Copyright (c) OpenMMLab. All rights reserved.
+from mmengine.dataset import DefaultSampler
+
+from mmagic.datasets import BasicFramesDataset
+from mmagic.datasets.transforms import (GenerateSegmentIndices,
+                                        LoadImageFromFile, MirrorSequence,
+                                        PackInputs)
+from mmagic.engine.runner import MultiTestLoop
+from mmagic.evaluation import PSNR, SSIM
+
+# configs for REDS4
+reds_data_root = 'data/REDS'
+
+reds_pipeline = [
+    dict(type=GenerateSegmentIndices, interval_list=[1]),
+    dict(type=LoadImageFromFile, key='img', channel_order='rgb'),
+    dict(type=LoadImageFromFile, key='gt', channel_order='rgb'),
+    dict(type=PackInputs)
+]
+
+reds_dataloader = dict(
+    num_workers=1,
+    batch_size=1,
+    persistent_workers=False,
+    sampler=dict(type=DefaultSampler, shuffle=False),
+    dataset=dict(
+        type=BasicFramesDataset,
+        metainfo=dict(dataset_type='reds_reds4', task_name='vsr'),
+        data_root=reds_data_root,
+        data_prefix=dict(img='train_sharp_bicubic/X4', gt='train_sharp'),
+        ann_file='meta_info_reds4_val.txt',
+        depth=1,
+        num_input_frames=100,
+        fixed_seq_len=100,
+        pipeline=reds_pipeline))
+
+reds_evaluator = [
+    dict(type=PSNR, prefix='REDS4-BIx4-RGB'),
+    dict(type=SSIM, prefix='REDS4-BIx4-RGB')
+]
+
+# configs for vimeo90k-bd and vimeo90k-bi
+vimeo_90k_data_root = 'data/vimeo90k'
+vimeo_90k_file_list = [
+    'im1.png', 'im2.png', 'im3.png', 'im4.png', 'im5.png', 'im6.png', 'im7.png'
+]
+
+vimeo_90k_pipeline = [
+    dict(type=LoadImageFromFile, key='img', channel_order='rgb'),
+    dict(type=LoadImageFromFile, key='gt', channel_order='rgb'),
+    dict(type=MirrorSequence, keys=['img']),
+    dict(type=PackInputs)
+]
+
+vimeo_90k_bd_dataloader = dict(
+    num_workers=1,
+    batch_size=1,
+    persistent_workers=False,
+    sampler=dict(type=DefaultSampler, shuffle=False),
+    dataset=dict(
+        type=BasicFramesDataset,
+        metainfo=dict(dataset_type='vimeo90k_seq', task_name='vsr'),
+        data_root=vimeo_90k_data_root,
+        data_prefix=dict(img='BDx4', gt='GT'),
+        ann_file='meta_info_Vimeo90K_test_GT.txt',
+        depth=2,
+        num_input_frames=7,
+        fixed_seq_len=7,
+        load_frames_list=dict(img=vimeo_90k_file_list, gt=['im4.png']),
+        pipeline=vimeo_90k_pipeline))
+
+vimeo_90k_bi_dataloader = dict(
+    num_workers=1,
+    batch_size=1,
+    persistent_workers=False,
+    sampler=dict(type=DefaultSampler, shuffle=False),
+    dataset=dict(
+        type=BasicFramesDataset,
+        metainfo=dict(dataset_type='vimeo90k_seq', task_name='vsr'),
+        data_root=vimeo_90k_data_root,
+        data_prefix=dict(img='BIx4', gt='GT'),
+        ann_file='meta_info_Vimeo90K_test_GT.txt',
+        depth=2,
+        num_input_frames=7,
+        fixed_seq_len=7,
+        load_frames_list=dict(img=vimeo_90k_file_list, gt=['im4.png']),
+        pipeline=vimeo_90k_pipeline))
+
+vimeo_90k_bd_evaluator = [
+    dict(type=PSNR, convert_to='Y', prefix='Vimeo-90K-T-BDx4-Y'),
+    dict(type=SSIM, convert_to='Y', prefix='Vimeo-90K-T-BDx4-Y'),
+]
+
+vimeo_90k_bi_evaluator = [
+    dict(type=PSNR, convert_to='Y', prefix='Vimeo-90K-T-BIx4-Y'),
+    dict(type=SSIM, convert_to='Y', prefix='Vimeo-90K-T-BIx4-Y'),
+]
+
+# config for UDM10 (BDx4)
+udm10_data_root = 'data/UDM10'
+
+udm10_pipeline = [
+    dict(
+        type=GenerateSegmentIndices,
+        interval_list=[1],
+        filename_tmpl='{:04d}.png'),
+    dict(type=LoadImageFromFile, key='img', channel_order='rgb'),
+    dict(type=LoadImageFromFile, key='gt', channel_order='rgb'),
+    dict(type=PackInputs)
+]
+
+udm10_dataloader = dict(
+    num_workers=1,
+    batch_size=1,
+    persistent_workers=False,
+    sampler=dict(type=DefaultSampler, shuffle=False),
+    dataset=dict(
+        type=BasicFramesDataset,
+        metainfo=dict(dataset_type='udm10', task_name='vsr'),
+        data_root=udm10_data_root,
+        data_prefix=dict(img='BDx4', gt='GT'),
+        pipeline=udm10_pipeline))
+
+udm10_evaluator = [
+    dict(type=PSNR, convert_to='Y', prefix='UDM10-BDx4-Y'),
+    dict(type=SSIM, convert_to='Y', prefix='UDM10-BDx4-Y')
+]
+
+# config for vid4
+vid4_data_root = 'data/Vid4'
+
+vid4_pipeline = [
+    dict(type=GenerateSegmentIndices, interval_list=[1]),
+    dict(type=LoadImageFromFile, key='img', channel_order='rgb'),
+    dict(type=LoadImageFromFile, key='gt', channel_order='rgb'),
+    dict(type=PackInputs)
+]
+vid4_bd_dataloader = dict(
+    num_workers=1,
+    batch_size=1,
+    persistent_workers=False,
+    sampler=dict(type=DefaultSampler, shuffle=False),
+    dataset=dict(
+        type=BasicFramesDataset,
+        metainfo=dict(dataset_type='vid4', task_name='vsr'),
+        data_root=vid4_data_root,
+        data_prefix=dict(img='BDx4', gt='GT'),
+        ann_file='meta_info_Vid4_GT.txt',
+        depth=1,
+        pipeline=vid4_pipeline))
+
+vid4_bi_dataloader = dict(
+    num_workers=1,
+    batch_size=1,
+    persistent_workers=False,
+    sampler=dict(type=DefaultSampler, shuffle=False),
+    dataset=dict(
+        type=BasicFramesDataset,
+        metainfo=dict(dataset_type='vid4', task_name='vsr'),
+        data_root=vid4_data_root,
+        data_prefix=dict(img='BIx4', gt='GT'),
+        ann_file='meta_info_Vid4_GT.txt',
+        depth=1,
+        pipeline=vid4_pipeline))
+
+vid4_bd_evaluator = [
+    dict(type=PSNR, convert_to='Y', prefix='VID4-BDx4-Y'),
+    dict(type=SSIM, convert_to='Y', prefix='VID4-BDx4-Y'),
+]
+vid4_bi_evaluator = [
+    dict(type=PSNR, convert_to='Y', prefix='VID4-BIx4-Y'),
+    dict(type=SSIM, convert_to='Y', prefix='VID4-BIx4-Y'),
+]
+
+# config for test
+test_cfg = dict(type=MultiTestLoop)
+test_dataloader = [
+    reds_dataloader,
+    vimeo_90k_bd_dataloader,
+    vimeo_90k_bi_dataloader,
+    udm10_dataloader,
+    vid4_bd_dataloader,
+    vid4_bi_dataloader,
+]
+test_evaluator = [
+    reds_evaluator,
+    vimeo_90k_bd_evaluator,
+    vimeo_90k_bi_evaluator,
+    udm10_evaluator,
+    vid4_bd_evaluator,
+    vid4_bi_evaluator,
+]

mmagic/configs/_base_/datasets/celeba.py

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+# Copyright (c) OpenMMLab. All rights reserved.
+from mmengine.dataset import DefaultSampler, InfiniteSampler
+
+from mmagic.evaluation import MAE, PSNR, SSIM
+
+# Base config for CelebA-HQ dataset
+
+# dataset settings
+dataset_type = 'BasicImageDataset'
+data_root = 'data/CelebA-HQ'
+
+train_dataloader = dict(
+    num_workers=4,
+    persistent_workers=False,
+    sampler=dict(type=InfiniteSampler, shuffle=True),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        data_prefix=dict(gt=''),
+        ann_file='train_celeba_img_list.txt',
+        test_mode=False,
+    ))
+
+val_dataloader = dict(
+    num_workers=4,
+    persistent_workers=False,
+    drop_last=False,
+    sampler=dict(type=DefaultSampler, shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        data_prefix=dict(gt=''),
+        ann_file='val_celeba_img_list.txt',
+        test_mode=True,
+    ))
+
+test_dataloader = val_dataloader
+
+val_evaluator = [
+    dict(type=MAE, mask_key='mask', scaling=100),
+    # By default, compute with pixel value from 0-1
+    # scale=2 to align with 1.0
+    # scale=100 seems to align with readme
+    dict(type=PSNR),
+    dict(type=SSIM),
+]
+
+test_evaluator = val_evaluator

mmagic/configs/_base_/datasets/cifar10_nopad.py

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+# Copyright (c) OpenMMLab. All rights reserved.
+from mmengine.dataset import DefaultSampler, InfiniteSampler
+
+from mmagic.datasets import CIFAR10
+from mmagic.datasets.transforms import Flip, PackInputs
+
+cifar_pipeline = [
+    dict(type=Flip, keys=['gt'], flip_ratio=0.5, direction='horizontal'),
+    dict(type=PackInputs)
+]
+cifar_dataset = dict(
+    type=CIFAR10,
+    data_root='./data',
+    data_prefix='cifar10',
+    test_mode=False,
+    pipeline=cifar_pipeline)
+
+# test dataset do not use flip
+cifar_pipeline_test = [dict(type=PackInputs)]
+cifar_dataset_test = dict(
+    type=CIFAR10,
+    data_root='./data',
+    data_prefix='cifar10',
+    test_mode=False,
+    pipeline=cifar_pipeline_test)
+
+train_dataloader = dict(
+    num_workers=2,
+    dataset=cifar_dataset,
+    sampler=dict(type=InfiniteSampler, shuffle=True),
+    persistent_workers=True)
+
+val_dataloader = dict(
+    batch_size=32,
+    num_workers=2,
+    dataset=cifar_dataset_test,
+    sampler=dict(type=DefaultSampler, shuffle=False),
+    persistent_workers=True)
+
+test_dataloader = dict(
+    batch_size=32,
+    num_workers=2,
+    dataset=cifar_dataset_test,
+    sampler=dict(type=DefaultSampler, shuffle=False),
+    persistent_workers=True)

mmagic/configs/_base_/datasets/comp1k.py

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+# Copyright (c) OpenMMLab. All rights reserved.
+from mmengine.dataset import DefaultSampler, InfiniteSampler
+
+from mmagic.evaluation import SAD, ConnectivityError, GradientError, MattingMSE
+
+# Base config for Composition-1K dataset
+
+# dataset settings
+dataset_type = 'AdobeComp1kDataset'
+data_root = 'data/adobe_composition-1k'
+
+train_dataloader = dict(
+    num_workers=4,
+    persistent_workers=False,
+    sampler=dict(type=InfiniteSampler, shuffle=True),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file='training_list.json',
+        test_mode=False,
+    ))
+
+val_dataloader = dict(
+    num_workers=4,
+    persistent_workers=False,
+    drop_last=False,
+    sampler=dict(type=DefaultSampler, shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file='test_list.json',
+        test_mode=True,
+    ))
+
+test_dataloader = val_dataloader
+
+# TODO: matting
+val_evaluator = [
+    dict(type=SAD),
+    dict(type=MattingMSE),
+    dict(type=GradientError),
+    dict(type=ConnectivityError),
+]
+
+test_evaluator = val_evaluator