def test_vsnet(shape, cfg, center_fractions, accelerations):
"""
Test VSNet with different parameters
Args:
shape: shape of the input
cfg: configuration of the model
center_fractions: center fractions
accelerations: accelerations
Returns:
None
"""
mask_func = RandomMaskFunc(center_fractions, accelerations)
x = create_input(shape)
outputs, masks = [], []
for i in range(x.shape[0]):
output, mask, _ = transforms.apply_mask(x[i : i + 1], mask_func, seed=123)
outputs.append(output)
masks.append(mask)
output = torch.cat(outputs)
mask = torch.cat(masks)
cfg = OmegaConf.create(cfg)
cfg = OmegaConf.create(OmegaConf.to_container(cfg, resolve=True))
vsnet = VSNet(cfg)
with torch.no_grad():
y = vsnet.forward(output, output, mask, output, target=torch.abs(torch.view_as_complex(output)))
if y.shape[1:] != x.shape[2:4]:
raise AssertionError
def test_xpdnet(shape, cfg, center_fractions, accelerations):
"""
Test the XPDNet model.
Args:
shape (): The shape of the input data.
cfg (): The configuration of the model.
center_fractions (): The center fractions of the subsampling.
accelerations (): The accelerations of the subsampling.
Returns:
None.
"""
mask_func = RandomMaskFunc(center_fractions, accelerations)
x = create_input(shape)
outputs, masks = [], []
for i in range(x.shape[0]):
output, mask, _ = transforms.apply_mask(x[i:i + 1],
mask_func,
seed=123)
outputs.append(output)
masks.append(mask)
output = torch.cat(outputs)
mask = torch.cat(masks)
cfg = OmegaConf.create(cfg)
cfg = OmegaConf.create(OmegaConf.to_container(cfg, resolve=True))
xpdnet = XPDNet(cfg)
with torch.no_grad():
y = xpdnet.forward(output,
output,
mask,
output,
target=torch.abs(torch.view_as_complex(output)))
if y.shape[1:] != x.shape[2:4]:
raise AssertionError