Merge e6411f6 into e1bd41a

mmagic/configs/real_basicvsr/realbasicvsr_c64b20_1x30x8_8xb1_lr5e_5_150k_reds.py

@@ -0,0 +1,94 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+
+# Please refer to https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta for more details. # noqa
+# mmcv >= 2.0.1
+# mmengine >= 0.8.0
+
+from mmengine.config import read_base
+from mmengine.optim.optimizer import OptimWrapper
+from mmengine.runner.loops import IterBasedTrainLoop
+from torch.optim.adam import Adam
+
+from mmagic.engine import MultiOptimWrapperConstructor
+from mmagic.models.data_preprocessors import DataPreprocessor
+from mmagic.models.editors import (RealBasicVSR, RealBasicVSRNet,
+                                   UNetDiscriminatorWithSpectralNorm)
+from mmagic.models.losses import GANLoss, L1Loss, PerceptualLoss
+
+with read_base():
+    from .realbasicvsr_wogan_c64b20_2x30x8_8xb2_lr1e_4_300k_reds import *
+
+experiment_name = 'realbasicvsr_c64b20-1x30x8_8xb1-lr5e-5-150k_reds'
+work_dir = f'./work_dirs/{experiment_name}'
+save_dir = './work_dirs/'
+
+# load_from = 'https://download.openmmlab.com/mmediting/restorers/real_basicvsr/realbasicvsr_wogan_c64b20_2x30x8_lr1e-4_300k_reds_20211027-0e2ff207.pth'  # noqa
+
+scale = 4
+
+# model settings
+model.update(
+    dict(
+        type=RealBasicVSR,
+        generator=dict(
+            type=RealBasicVSRNet,
+            mid_channels=64,
+            num_propagation_blocks=20,
+            num_cleaning_blocks=20,
+            dynamic_refine_thres=255,  # change to 5 for test
+            spynet_pretrained=
+            'https://download.openmmlab.com/mmediting/restorers/'
+            'basicvsr/spynet_20210409-c6c1bd09.pth',
+            is_fix_cleaning=False,
+            is_sequential_cleaning=False),
+        discriminator=dict(
+            type=UNetDiscriminatorWithSpectralNorm,
+            in_channels=3,
+            mid_channels=64,
+            skip_connection=True),
+        pixel_loss=dict(type=L1Loss, loss_weight=1.0, reduction='mean'),
+        cleaning_loss=dict(type=L1Loss, loss_weight=1.0, reduction='mean'),
+        perceptual_loss=dict(
+            type=PerceptualLoss,
+            layer_weights={
+                '2': 0.1,
+                '7': 0.1,
+                '16': 1.0,
+                '25': 1.0,
+                '34': 1.0,
+            },
+            vgg_type='vgg19',
+            perceptual_weight=1.0,
+            style_weight=0,
+            norm_img=False),
+        gan_loss=dict(
+            type=GANLoss,
+            gan_type='vanilla',
+            loss_weight=5e-2,
+            real_label_val=1.0,
+            fake_label_val=0),
+        is_use_sharpened_gt_in_pixel=True,
+        is_use_sharpened_gt_in_percep=True,
+        is_use_sharpened_gt_in_gan=False,
+        is_use_ema=True,
+        data_preprocessor=dict(
+            type=DataPreprocessor,
+            mean=[0., 0., 0.],
+            std=[255., 255., 255.],
+        )))
+
+# optimizer
+optim_wrapper.update(
+    dict(
+        _delete_=True,
+        constructor=MultiOptimWrapperConstructor,
+        generator=dict(
+            type=OptimWrapper,
+            optimizer=dict(type=Adam, lr=5e-5, betas=(0.9, 0.99))),
+        discriminator=dict(
+            type=OptimWrapper,
+            optimizer=dict(type=Adam, lr=1e-4, betas=(0.9, 0.99))),
+    ))
+
+train_cfg.update(
+    dict(type=IterBasedTrainLoop, max_iters=150_000, val_interval=5000))