Source code for tdan_test_config
# Copyright (c) OpenMMLab. All rights reserved.
from mmengine.dataset import DefaultSampler
from mmagic.datasets import BasicFramesDataset
from mmagic.datasets.transforms import (GenerateFrameIndiceswithPadding,
GenerateSegmentIndices,
LoadImageFromFile, PackInputs)
from mmagic.engine.runner import MultiTestLoop
from mmagic.evaluation import PSNR, SSIM, Evaluator
# configs for SPMCS-30
[docs]SPMC_pipeline = [
dict(type=GenerateFrameIndiceswithPadding, padding='reflection'),
dict(type=LoadImageFromFile, key='img', channel_order='rgb'),
dict(type=LoadImageFromFile, key='gt', channel_order='rgb'),
dict(type=PackInputs)
]
[docs]SPMC_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='spmcs', task_name='vsr'),
data_root=SPMC_data_root,
data_prefix=dict(img='BDx4', gt='GT'),
ann_file='meta_info_SPMCS_GT.txt',
depth=2,
num_input_frames=5,
pipeline=SPMC_pipeline))
[docs]SPMC_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='spmcs', task_name='vsr'),
data_root=SPMC_data_root,
data_prefix=dict(img='BIx4', gt='GT'),
ann_file='meta_info_SPMCS_GT.txt',
depth=2,
num_input_frames=5,
pipeline=SPMC_pipeline))
[docs]SPMC_bd_evaluator = dict(
type=Evaluator,
metrics=[
dict(type=PSNR, crop_border=8, convert_to='Y', prefix='SPMCS-BDx4-Y'),
dict(type=SSIM, crop_border=8, convert_to='Y', prefix='SPMCS-BDx4-Y'),
])
[docs]SPMC_bi_evaluator = dict(
type=Evaluator,
metrics=[
dict(type=PSNR, crop_border=8, convert_to='Y', prefix='SPMCS-BIx4-Y'),
dict(type=SSIM, crop_border=8, convert_to='Y', prefix='SPMCS-BIx4-Y'),
])
# config for vid4
[docs]vid4_pipeline = [
# dict(type=GenerateSegmentIndices, interval_list=[1]),
dict(type=GenerateFrameIndiceswithPadding, padding='reflection'),
dict(type=LoadImageFromFile, key='img', channel_order='rgb'),
dict(type=LoadImageFromFile, key='gt', channel_order='rgb'),
dict(type=PackInputs)
]
[docs]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=2,
num_input_frames=5,
pipeline=vid4_pipeline))
[docs]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=2,
num_input_frames=5,
pipeline=vid4_pipeline))
[docs]vid4_bd_evaluator = dict(
type=Evaluator,
metrics=[
dict(type=PSNR, convert_to='Y', prefix='VID4-BDx4-Y'),
dict(type=SSIM, convert_to='Y', prefix='VID4-BDx4-Y'),
])
[docs]vid4_bi_evaluator = dict(
type=Evaluator,
metrics=[
dict(type=PSNR, convert_to='Y', prefix='VID4-BIx4-Y'),
dict(type=SSIM, convert_to='Y', prefix='VID4-BIx4-Y'),
])
# config for test
[docs]test_dataloader = [
SPMC_bd_dataloader,
SPMC_bi_dataloader,
vid4_bd_dataloader,
vid4_bi_dataloader,
]
[docs]test_evaluator = [
SPMC_bd_evaluator,
SPMC_bi_evaluator,
vid4_bd_evaluator,
vid4_bi_evaluator,
]