def test_device(self): in_channels, D = 2, 2 device = torch.device("cuda") coords, feats, labels = data_loader(in_channels, batch_size=1) feats = feats.double() feats.requires_grad_() use_feat = (torch.rand(feats.size(0)) < 0.5).to(device) pruning = MinkowskiPruning() input = SparseTensor(feats, coords, device=device) output = pruning(input, use_feat) print(input) print(output) fn = MinkowskiPruningFunction() self.assertTrue( gradcheck( fn, ( input.F, use_feat, input.coordinate_map_key, output.coordinate_map_key, input.coordinate_manager, ), ))
def test_empty(self): in_channels = 2 coords, feats, labels = data_loader(in_channels, batch_size=1) feats = feats.double() feats.requires_grad_() input = SparseTensor(feats, coords) use_feat = torch.BoolTensor(len(input)) use_feat.zero_() pruning = MinkowskiPruning() output = pruning(input, use_feat) print(input) print(use_feat) print(output) # Check backward fn = MinkowskiPruningFunction() self.assertTrue( gradcheck( fn, ( input.F, use_feat, input.coordinate_map_key, output.coordinate_map_key, input.coordinate_manager, ), ))
def test_pruning(self): in_channels, D = 2, 2 coords, feats, labels = data_loader(in_channels) feats = feats.double() feats.requires_grad_() input = SparseTensor(feats, coords=coords) use_feat = torch.rand(feats.size(0)) < 0.5 pruning = MinkowskiPruning(D) output = pruning(input, use_feat) print(use_feat, output) # Check backward fn = MinkowskiPruningFunction() self.assertTrue( gradcheck(fn, (input.F, use_feat, input.coords_key, output.coords_key, input.coords_man))) device = torch.device('cuda') with torch.cuda.device(0): input = input.to(device) output = pruning(input, use_feat) print(output) self.assertTrue( gradcheck(fn, (input.F, use_feat, input.coords_key, output.coords_key, input.coords_man)))