def test_ahnet_shape(self, input_param, input_shape, expected_shape, fcn_input_param):
net = AHNet(**input_param).to(device)
net2d = FCN(**fcn_input_param).to(device)
net.copy_from(net2d)
with eval_mode(net):
result = net.forward(torch.randn(input_shape).to(device))
self.assertEqual(result.shape, expected_shape)
def test_ahnet_shape(self, input_param, input_data, expected_shape,
fcn_input_param):
net = AHNet(**input_param)
net2d = FCN(**fcn_input_param)
net.copy_from(net2d)
net.eval()
with torch.no_grad():
result = net.forward(input_data)
self.assertEqual(result.shape, expected_shape)
def test_initialize_pretrained(self):
net = AHNet(
spatial_dims=3,
upsample_mode="transpose",
in_channels=2,
out_channels=3,
pretrained=True,
progress=True,
)
input_data = torch.randn(2, 2, 128, 128, 64)
with torch.no_grad():
result = net.forward(input_data)
self.assertEqual(result.shape, (2, 3, 128, 128, 64))
def test_initialize_pretrained(self):
net = AHNet(
spatial_dims=3,
upsample_mode="transpose",
in_channels=2,
out_channels=3,
psp_block_num=2,
pretrained=True,
progress=True,
).to(device)
input_data = torch.randn(2, 2, 32, 32, 64).to(device)
with eval_mode(net):
result = net.forward(input_data)
self.assertEqual(result.shape, (2, 3, 32, 32, 64))
def test_fcn_shape(self, input_param, input_data, expected_shape):
net = AHNet(**input_param)
net.eval()
with torch.no_grad():
result = net.forward(input_data)
self.assertEqual(result.shape, expected_shape)
def test_ahnet_shape(self, input_param, input_shape, expected_shape):
net = AHNet(**input_param)
net.eval()
with torch.no_grad():
result = net.forward(torch.randn(input_shape))
self.assertEqual(result.shape, expected_shape)
def test_ahnet_shape_3d(self, input_param, input_shape, expected_shape):
net = AHNet(**input_param).to(device)
with eval_mode(net):
result = net.forward(torch.randn(input_shape).to(device))
self.assertEqual(result.shape, expected_shape)
