[Feature] Add configs for AOT-GAN

configs/inpainting/AOT-GAN/AOT-GAN_512x512_places.py

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+model = dict(
+    type='AOTInpaintor',
+    encdec=dict(
+        type='AOTEncoderDecoder',
+        encoder=dict(type='AOTEncoder'),
+        decoder=dict(type='AOTDecoder'),
+        dilation_neck=dict(
+            type='AOTBlockNeck', dilation_rates='1+2+4+8', num_aotblock=8)),
+    disc=dict(
+        type='SoftMaskPatchDiscriminator',
+        in_channels=3,
+        base_channels=64,
+        num_conv=3,
+        with_spectral_norm=True,
+    ),
+    loss_gan=dict(
+        type='GANLoss',
+        gan_type='smgan',
+        loss_weight=0.01,
+    ),
+    loss_composed_percep=dict(
+        type='PerceptualLoss',
+        vgg_type='vgg19',
+        layer_weights_perceptual={
+            '1': 1.,
+            '6': 1.,
+            '11': 1.,
+            '20': 1.,
+            '29': 1.,
+        },
+        layer_weights_style={
+            '8': 1.,
+            '17': 1.,
+            '26': 1.,
+            '31': 1.,
+        },
+        perceptual_weight=0.1,
+        style_weight=250),
+    loss_out_percep=True,
+    loss_l1_valid=dict(
+        type='L1Loss',
+        loss_weight=1.,
+    ),
+    pretrained=None)
+
+train_cfg = dict(disc_step=1)
+test_cfg = dict(metrics=['l1', 'psnr', 'ssim'])
+
+dataset_type = 'ImgInpaintingDataset'
+input_shape = (512, 512)
+
+mask_root = 'data/masks'
+
+train_pipeline = [
+    dict(type='LoadImageFromFile', key='gt_img', channel_order='rgb'),
+    dict(
+        type='LoadMask',
+        mask_mode='set',
+        mask_config=dict(
+            mask_list_file=f'{mask_root}/train_places_mask_list.txt',
+            prefix=mask_root,
+            io_backend='disk',
+            flag='unchanged',
+            file_client_kwargs=dict())),
+    dict(
+        type='RandomResizedCrop',
+        keys=['gt_img'],
+        crop_size=input_shape,
+    ),
+    dict(type='Flip', keys=['gt_img', 'mask'], direction='horizontal'),
+    dict(
+        type='Resize',
+        keys=['mask'],
+        scale=input_shape,
+        keep_ratio=False,
+        interpolation='nearest'),
+    dict(type='RandomRotation', keys=['mask'], degrees=(0.0, 45.0)),
+    dict(
+        type='ColorJitter',
+        keys=['gt_img'],
+        brightness=0.5,
+        contrast=0.5,
+        saturation=0.5,
+        hue=0.5),
+    dict(
+        type='Normalize',
+        keys=['gt_img'],
+        mean=[127.5] * 3,
+        std=[127.5] * 3,
+        to_rgb=False),
+    dict(type='GetMaskedImage'),
+    dict(
+        type='Collect',
+        keys=['gt_img', 'masked_img', 'mask'],
+        meta_keys=['gt_img_path']),
+    dict(type='ImageToTensor', keys=['gt_img', 'masked_img', 'mask'])
+]
+
+test_pipeline = [
+    dict(type='LoadImageFromFile', key='gt_img', channel_order='rgb'),
+    dict(
+        type='LoadMask',
+        mask_mode='set',
+        mask_config=dict(
+            mask_list_file=f'{mask_root}/val_places_mask_list.txt',
+            prefix=mask_root,
+            io_backend='disk',
+            flag='unchanged',
+            file_client_kwargs=dict())),
+    dict(
+        type='Crop',
+        keys=['gt_img'],
+        crop_size=(512, 512),
+        random_crop=False,
+    ),
+    dict(
+        type='Normalize',
+        keys=['gt_img'],
+        mean=[127.5] * 3,
+        std=[127.5] * 3,
+        to_rgb=True),
+    dict(type='GetMaskedImage'),
+    dict(
+        type='Collect',
+        keys=['gt_img', 'masked_img', 'mask'],
+        meta_keys=['gt_img_path']),
+    dict(type='ImageToTensor', keys=['gt_img', 'masked_img', 'mask'])
+]
+
+data_root = 'data/places365'
+
+data = dict(
+    workers_per_gpu=4,
+    train_dataloader=dict(samples_per_gpu=12, drop_last=True),
+    val_dataloader=dict(samples_per_gpu=1),
+    test_dataloader=dict(samples_per_gpu=1),
+    train=dict(
+        type=dataset_type,
+        ann_file=f'{data_root}/train_places_img_list.txt',
+        data_prefix=data_root,
+        pipeline=train_pipeline,
+        test_mode=False),
+    val=dict(
+        type=dataset_type,
+        ann_file=f'{data_root}/val_places_img_list.txt',
+        data_prefix=data_root,
+        pipeline=test_pipeline,
+        test_mode=True),
+    test=dict(
+        type=dataset_type,
+        ann_file=(f'{data_root}/val_places_img_list.txt'),
+        data_prefix=data_root,
+        pipeline=test_pipeline,
+        test_mode=True))
+
+optimizers = dict(
+    generator=dict(type='Adam', lr=0.0001, betas=(0.0, 0.9)),
+    disc=dict(type='Adam', lr=0.0001, betas=(0.0, 0.9)))
+
+lr_config = dict(policy='Fixed', by_epoch=False)
+
+checkpoint_config = dict(by_epoch=False, interval=10000)
+log_config = dict(
+    interval=100,
+    hooks=[
+        dict(type='TextLoggerHook', by_epoch=False),
+        dict(type='TensorboardLoggerHook'),
+        dict(type='PaviLoggerHook', init_kwargs=dict(project='mmedit'))
+    ])
+
+visual_config = dict(
+    type='VisualizationHook',
+    output_dir='visual',
+    interval=1000,
+    res_name_list=['gt_img', 'masked_img', 'fake_res', 'fake_img'],
+)
+
+evaluation = dict(interval=50000)
+
+total_iters = 500002
+dist_params = dict(backend='nccl')
+log_level = 'INFO'
+work_dir = './workdirs/aotgan_places'
+load_from = None
+resume_from = None
+workflow = [('train', 10000)]
+exp_name = 'AOT-GAN_512x512_places'
+find_unused_parameters = False