def test_rsconv(self):
from torch_points3d.applications.rsconv import RSConv
input_nc = 2
num_layers = 4
output_nc = 5
model = RSConv(
architecture="encoder",
input_nc=input_nc,
output_nc=output_nc,
num_layers=num_layers,
multiscale=True,
config=None,
)
dataset = MockDataset(input_nc, num_points=1024)
self.assertEqual(len(model._modules["down_modules"]), num_layers)
self.assertEqual(len(model._modules["inner_modules"]), 1)
try:
data_out = model.forward(dataset[0])
self.assertEqual(data_out.x.shape[1], output_nc)
except Exception as e:
print("Model failing:")
print(model)
raise e
def __init__(self, USE_NORMAL):
super().__init__()
self.encoder = RSConv("encoder",
input_nc=3 * USE_NORMAL,
output_nc=8,
num_layers=4)
self.log_softmax = torch.nn.LogSoftmax(dim=-1)
self.loss_function = torch.nn.MSELoss()
def test_rsconv(self):
from torch_points3d.applications.rsconv import RSConv
input_nc = 2
num_layers = 4
model = RSConv(
architecture="unet",
input_nc=input_nc,
output_nc=5,
num_layers=num_layers,
multiscale=True,
config=None,
)
dataset = MockDataset(input_nc, num_points=1024)
model.set_input(dataset[0], device)
self.assertEqual(len(model._modules["down_modules"]), num_layers)
self.assertEqual(len(model._modules["inner_modules"]), 2)
self.assertEqual(len(model._modules["up_modules"]), num_layers + 1)
try:
model.forward()
except Exception as e:
print("Model failing:")
print(model)
raise e