def test_vae_shape(self, input_param, input_shape, expected_shape):
net = SegResNetVAE(**input_param)
with torch.no_grad():
result, _ = net(torch.randn(input_shape))
self.assertEqual(result.shape, expected_shape)
def test_script(self):
input_param, input_shape, expected_shape = TEST_CASE_SEGRESNET_VAE[0]
net = SegResNetVAE(**input_param)
test_data = torch.randn(input_shape)
test_script_save(net, test_data)
def test_script(self):
input_param, input_shape, expected_shape = TEST_CASE_SEGRESNET_VAE[0]
net = SegResNetVAE(**input_param)
test_data = torch.randn(input_shape)
out_orig, out_reloaded = test_script_save(net, test_data)
assert torch.allclose(out_orig[0], out_reloaded[0])
def test_vae_shape(self, input_param, input_shape, expected_shape):
net = SegResNetVAE(**input_param).to(device)
with eval_mode(net):
result, _ = net(torch.randn(input_shape).to(device))
self.assertEqual(result.shape, expected_shape)