def test_script(self):
net = VNet(spatial_dims=3,
in_channels=1,
out_channels=3,
dropout_dim=3)
test_data = torch.randn(1, 1, 32, 32, 32)
test_script_save(net, test_data)
def test_script(self):
net = VNet(spatial_dims=3,
in_channels=1,
out_channels=3,
dropout_dim=3)
test_data = torch.randn(1, 1, 32, 32, 32)
out_orig, out_reloaded = test_script_save(net, test_data)
assert torch.allclose(out_orig, out_reloaded)
def test_vnet_shape(self, input_param, input_data, expected_shape):
net = VNet(**input_param)
net.eval()
with torch.no_grad():
result = net.forward(input_data)
self.assertEqual(result.shape, expected_shape)
def test_vnet_shape(self, input_param, input_shape, expected_shape):
net = VNet(**input_param).to(device)
with eval_mode(net):
result = net.forward(torch.randn(input_shape).to(device))
self.assertEqual(result.shape, expected_shape)