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[Fix] delete __init__ in TestVFIDataset (open-mmlab#731)
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* [Doc] Add docs of Ref-SR demo and video frame interpolation demo

* [Fix] delete '__init__'

* back

* Fix
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@Yshuo-Li
Yshuo-Li authored Feb 11, 2022
1 parent 29946e1 commit 4c96a54
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Showing 3 changed files with 26 additions and 28 deletions.
18 changes: 8 additions & 10 deletions tests/test_data/test_datasets/test_vfi_dataset.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,16 +6,14 @@

class TestVFIDataset:

def __init__(self):
self.pipeline = [
dict(
type='LoadImageFromFileList', io_backend='disk', key='inputs'),
dict(type='LoadImageFromFile', io_backend='disk', key='target'),
dict(type='FramesToTensor', keys=['inputs']),
dict(type='ImageToTensor', keys=['target']),
]
self.folder = 'tests/data/vimeo90k'
self.ann_file = 'tests/data/vimeo90k/vfi_ann.txt'
pipeline = [
dict(type='LoadImageFromFileList', io_backend='disk', key='inputs'),
dict(type='LoadImageFromFile', io_backend='disk', key='target'),
dict(type='FramesToTensor', keys=['inputs']),
dict(type='ImageToTensor', keys=['target']),
]
folder = 'tests/data/vimeo90k'
ann_file = 'tests/data/vimeo90k/vfi_ann.txt'

def test_base_vfi_dataset(self):

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18 changes: 9 additions & 9 deletions tests/test_models/test_restorers/test_basic_restorer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -35,8 +35,8 @@ def test_basic_restorer():
assert isinstance(restorer.pixel_loss, L1Loss)

# prepare data
inputs = torch.rand(1, 3, 2, 2)
targets = torch.rand(1, 3, 8, 8)
inputs = torch.rand(1, 3, 20, 20)
targets = torch.rand(1, 3, 80, 80)
data_batch = {'lq': inputs, 'gt': targets}

# prepare optimizer
Expand All @@ -56,20 +56,20 @@ def test_basic_restorer():
assert torch.equal(outputs['results']['lq'], data_batch['lq'])
assert torch.equal(outputs['results']['gt'], data_batch['gt'])
assert torch.is_tensor(outputs['results']['output'])
assert outputs['results']['output'].size() == (1, 3, 8, 8)
assert outputs['results']['output'].size() == (1, 3, 80, 80)

# test forward_test
with torch.no_grad():
outputs = restorer(**data_batch, test_mode=True)
assert torch.equal(outputs['lq'], data_batch['lq'])
assert torch.is_tensor(outputs['output'])
assert outputs['output'].size() == (1, 3, 8, 8)
assert outputs['output'].size() == (1, 3, 80, 80)

# test forward_dummy
with torch.no_grad():
output = restorer.forward_dummy(data_batch['lq'])
assert torch.is_tensor(output)
assert output.size() == (1, 3, 8, 8)
assert output.size() == (1, 3, 80, 80)

# test train_step
outputs = restorer.train_step(data_batch, optimizer)
Expand All @@ -80,7 +80,7 @@ def test_basic_restorer():
assert torch.equal(outputs['results']['lq'], data_batch['lq'])
assert torch.equal(outputs['results']['gt'], data_batch['gt'])
assert torch.is_tensor(outputs['results']['output'])
assert outputs['results']['output'].size() == (1, 3, 8, 8)
assert outputs['results']['output'].size() == (1, 3, 80, 80)

# test train_step and forward_test (gpu)
if torch.cuda.is_available():
Expand All @@ -99,14 +99,14 @@ def test_basic_restorer():
assert torch.equal(outputs['results']['lq'], data_batch['lq'].cpu())
assert torch.equal(outputs['results']['gt'], data_batch['gt'].cpu())
assert torch.is_tensor(outputs['results']['output'])
assert outputs['results']['output'].size() == (1, 3, 8, 8)
assert outputs['results']['output'].size() == (1, 3, 80, 80)

# forward_test
with torch.no_grad():
outputs = restorer(**data_batch, test_mode=True)
assert torch.equal(outputs['lq'], data_batch['lq'].cpu())
assert torch.is_tensor(outputs['output'])
assert outputs['output'].size() == (1, 3, 8, 8)
assert outputs['output'].size() == (1, 3, 80, 80)

# train_step
outputs = restorer.train_step(data_batch, optimizer)
Expand All @@ -117,7 +117,7 @@ def test_basic_restorer():
assert torch.equal(outputs['results']['lq'], data_batch['lq'].cpu())
assert torch.equal(outputs['results']['gt'], data_batch['gt'].cpu())
assert torch.is_tensor(outputs['results']['output'])
assert outputs['results']['output'].size() == (1, 3, 8, 8)
assert outputs['results']['output'].size() == (1, 3, 80, 80)

# test with metric and save image
test_cfg = dict(metrics=('PSNR', 'SSIM'), crop_border=0)
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18 changes: 9 additions & 9 deletions tests/test_models/test_video_interpolator/test_basic_interpolator.py
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Original file line number Diff line number Diff line change
Expand Up @@ -62,8 +62,8 @@ def test_basic_interpolator():
assert isinstance(restorer.pixel_loss, L1Loss)

# prepare data
inputs = torch.rand(1, 2, 3, 8, 8)
target = torch.rand(1, 3, 8, 8)
inputs = torch.rand(1, 2, 3, 20, 20)
target = torch.rand(1, 3, 20, 20)
data_batch = {'inputs': inputs, 'target': target}

# prepare optimizer
Expand All @@ -83,21 +83,21 @@ def test_basic_interpolator():
assert torch.equal(outputs['results']['inputs'], data_batch['inputs'])
assert torch.equal(outputs['results']['target'], data_batch['target'])
assert torch.is_tensor(outputs['results']['output'])
assert outputs['results']['output'].size() == (1, 3, 8, 8)
assert outputs['results']['output'].size() == (1, 3, 20, 20)

# test forward_test
with torch.no_grad():
restorer.val_step(data_batch)
outputs = restorer(**data_batch, test_mode=True)
assert torch.equal(outputs['inputs'], data_batch['inputs'])
assert torch.is_tensor(outputs['output'])
assert outputs['output'].size() == (1, 3, 8, 8)
assert outputs['output'].size() == (1, 3, 20, 20)

# test forward_dummy
with torch.no_grad():
output = restorer.forward_dummy(data_batch['inputs'])
assert torch.is_tensor(output)
assert output.size() == (1, 3, 8, 8)
assert output.size() == (1, 3, 20, 20)

# test train_step
outputs = restorer.train_step(data_batch, optimizer)
Expand All @@ -108,7 +108,7 @@ def test_basic_interpolator():
assert torch.equal(outputs['results']['inputs'], data_batch['inputs'])
assert torch.equal(outputs['results']['target'], data_batch['target'])
assert torch.is_tensor(outputs['results']['output'])
assert outputs['results']['output'].size() == (1, 3, 8, 8)
assert outputs['results']['output'].size() == (1, 3, 20, 20)

# test train_step and forward_test (gpu)
if torch.cuda.is_available():
Expand All @@ -129,15 +129,15 @@ def test_basic_interpolator():
assert torch.equal(outputs['results']['target'],
data_batch['target'].cpu())
assert torch.is_tensor(outputs['results']['output'])
assert outputs['results']['output'].size() == (1, 3, 8, 8)
assert outputs['results']['output'].size() == (1, 3, 20, 20)

# forward_test
with torch.no_grad():
restorer.val_step(data_batch)
outputs = restorer(**data_batch, test_mode=True)
assert torch.equal(outputs['inputs'], data_batch['inputs'].cpu())
assert torch.is_tensor(outputs['output'])
assert outputs['output'].size() == (1, 3, 8, 8)
assert outputs['output'].size() == (1, 3, 20, 20)

# train_step
outputs = restorer.train_step(data_batch, optimizer)
Expand All @@ -150,7 +150,7 @@ def test_basic_interpolator():
assert torch.equal(outputs['results']['target'],
data_batch['target'].cpu())
assert torch.is_tensor(outputs['results']['output'])
assert outputs['results']['output'].size() == (1, 3, 8, 8)
assert outputs['results']['output'].size() == (1, 3, 20, 20)

# test with metric and save image
test_cfg = dict(metrics=('PSNR', 'SSIM'), crop_border=0)
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