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tof_x4_official_vimeo90k.py
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tof_x4_official_vimeo90k.py
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# only testing the official model is supported
_base_ = '../_base_/default_runtime.py'
experiment_name = 'tof_x4_official_vimeo90k'
work_dir = f'./work_dirs/{experiment_name}'
save_dir = './work_dirs'
# model settings
model = dict(
type='EDVR', # use the shared model with EDVR
generator=dict(type='TOFlowVSRNet', adapt_official_weights=True),
pixel_loss=dict(type='CharbonnierLoss', loss_weight=1.0, reduction='sum'),
data_preprocessor=dict(
type='DataPreprocessor',
mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],
std=[0.229 * 255, 0.224 * 255, 0.225 * 255],
))
val_pipeline = [
dict(type='GenerateFrameIndiceswithPadding', padding='reflection_circle'),
dict(
type='LoadImageFromFile',
key='img',
color_type='color',
channel_order='rgb'),
dict(
type='LoadImageFromFile',
key='gt',
color_type='color',
channel_order='rgb'),
dict(type='PackInputs')
]
demo_pipeline = [
dict(type='GenerateSegmentIndices', interval_list=[1]),
dict(
type='LoadImageFromFile',
key='img',
color_type='color',
channel_order='rgb'),
dict(type='PackInputs')
]
data_root = 'data/Vid4'
val_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=data_root,
data_prefix=dict(img='BIx4up_direct', gt='GT'),
ann_file='meta_info_Vid4_GT.txt',
depth=2,
num_input_frames=7,
pipeline=val_pipeline))
# TODO: data is not uploaded yet
# test_dataloader = val_dataloader
val_evaluator = dict(
type='Evaluator',
metrics=[
dict(type='MAE'),
dict(type='PSNR'),
dict(type='SSIM'),
])
# test_evaluator = val_evaluator
val_cfg = dict(type='MultiValLoop')
# test_cfg = dict(type='MultiTestLoop')