Source code for base_deepfillv2

# Copyright (c) OpenMMLab. All rights reserved.
from mmengine.model import MMSeparateDistributedDataParallel
from mmengine.optim import OptimWrapper

from mmagic.models import DataPreprocessor
from mmagic.models.archs import MultiLayerDiscriminator
from mmagic.models.base_models import TwoStageInpaintor
from mmagic.models.editors import (ContextualAttentionNeck, DeepFillDecoder,
                                   DeepFillEncoder, DeepFillEncoderDecoder,
                                   DeepFillRefiner, GLDilationNeck,
                                   GLEncoderDecoder)
from mmagic.models.losses import GANLoss, L1Loss

# DistributedDataParallel
model_wrapper_cfg = dict(type=MMSeparateDistributedDataParallel)

model = dict(
    type=TwoStageInpaintor,
    disc_input_with_mask=True,
    data_preprocessor=dict(
        type=DataPreprocessor,
        mean=[127.5],
        std=[127.5],
    ),
    encdec=dict(
        type=DeepFillEncoderDecoder,
        stage1=dict(
            type=GLEncoderDecoder,
            encoder=dict(
                type=DeepFillEncoder,
                conv_type='gated_conv',
                channel_factor=0.75,
                padding_mode='reflect'),
            decoder=dict(
                type=DeepFillDecoder,
                conv_type='gated_conv',
                in_channels=96,
                channel_factor=0.75,
                out_act_cfg=dict(type='Tanh'),
                padding_mode='reflect'),
            dilation_neck=dict(
                type=GLDilationNeck,
                in_channels=96,
                conv_type='gated_conv',
                act_cfg=dict(type='ELU'),
                padding_mode='reflect')),
        stage2=dict(
            type=DeepFillRefiner,
            encoder_attention=dict(
                type=DeepFillEncoder,
                encoder_type='stage2_attention',
                conv_type='gated_conv',
                channel_factor=0.75,
                padding_mode='reflect'),
            encoder_conv=dict(
                type=DeepFillEncoder,
                encoder_type='stage2_conv',
                conv_type='gated_conv',
                channel_factor=0.75,
                padding_mode='reflect'),
            dilation_neck=dict(
                type=GLDilationNeck,
                in_channels=96,
                conv_type='gated_conv',
                act_cfg=dict(type='ELU'),
                padding_mode='reflect'),
            contextual_attention=dict(
                type=ContextualAttentionNeck,
                in_channels=96,
                conv_type='gated_conv',
                padding_mode='reflect'),
            decoder=dict(
                type=DeepFillDecoder,
                in_channels=192,
                conv_type='gated_conv',
                out_act_cfg=dict(type='Tanh'),
                padding_mode='reflect'))),
    disc=dict(
        type=MultiLayerDiscriminator,
        in_channels=4,
        max_channels=256,
        fc_in_channels=None,
        num_convs=6,
        norm_cfg=None,
        act_cfg=dict(type='LeakyReLU', negative_slope=0.2),
        out_act_cfg=dict(type='LeakyReLU', negative_slope=0.2),
        with_spectral_norm=True,
    ),
    stage1_loss_type=('loss_l1_hole', 'loss_l1_valid'),
    stage2_loss_type=('loss_l1_hole', 'loss_l1_valid', 'loss_gan'),
    loss_gan=dict(
        type=GANLoss,
        gan_type='hinge',
        loss_weight=0.1,
    ),
    loss_l1_hole=dict(
        type=L1Loss,
        loss_weight=1.0,
    ),
    loss_l1_valid=dict(
        type=L1Loss,
        loss_weight=1.0,
    ),
)

# optimizer
optim_wrapper = dict(
    constructor='MultiOptimWrapperConstructor',
    generator=dict(type=OptimWrapper, optimizer=dict(type='Adam', lr=0.0001)),
    disc=dict(type=OptimWrapper, optimizer=dict(type='Adam', lr=0.0001)))

# learning policy
# Fixed