def test_unpool(self): in_channels, out_channels, D = 2, 3, 2 coords, feats, labels = data_loader(in_channels) feats = feats.double() input = SparseTensor(feats, coords) conv = MinkowskiConvolution( in_channels, out_channels, kernel_size=3, stride=2, dimension=D ) conv = conv.double() unpool = MinkowskiPoolingTranspose(kernel_size=3, stride=2, dimension=D) input = conv(input) output = unpool(input) print(output) # Check backward fn = MinkowskiLocalPoolingTransposeFunction() self.assertTrue( gradcheck( fn, ( input.F, unpool.pooling_mode, unpool.kernel_generator, input.coordinate_map_key, None, input.coordinate_manager, ), ) )
def test_unpool(self): in_channels, out_channels, D = 2, 3, 2 coords, feats, labels = data_loader(in_channels) feats = feats.double() input = SparseTensor(feats, coords=coords) conv = MinkowskiConvolution(in_channels, out_channels, kernel_size=3, stride=2, dimension=D) conv = conv.double() unpool = MinkowskiPoolingTranspose(kernel_size=3, stride=2, dimension=D) input = conv(input) output = unpool(input) print(output) # Check backward fn = MinkowskiPoolingTransposeFunction() self.assertTrue( gradcheck(fn, (input.F, input.tensor_stride, unpool.stride, unpool.kernel_size, unpool.dilation, unpool.region_type_, unpool.region_offset_, False, input.coords_key, None, input.coords_man)))
def test_unpool_gpu(self): if not torch.cuda.is_available(): return in_channels, out_channels, D = 2, 3, 2 coords, feats, labels = data_loader(in_channels) feats = feats.double() input = SparseTensor(feats, coords) conv = MinkowskiConvolution(in_channels, out_channels, kernel_size=3, stride=2, dimension=D) conv = conv.double() unpool = MinkowskiPoolingTranspose(kernel_size=3, stride=2, dimension=D) input = conv(input) output = unpool(input) print(output) # Check backward fn = MinkowskiLocalPoolingTransposeFunction() self.assertTrue( gradcheck( fn, ( input.F, unpool.pooling_mode, unpool.kernel_generator, input.coordinate_map_key, None, input.coordinate_manager, ), )) with torch.cuda.device(0): conv = conv.to("cuda") input = SparseTensor(feats, coords, device="cuda") input = conv(input) input.requires_grad_() output = unpool(input) print(output) # Check backward self.assertTrue( gradcheck( fn, ( input.F, unpool.pooling_mode, unpool.kernel_generator, input.coordinate_map_key, None, input.coordinate_manager, ), ))
def test_unpooling_gpu(self): if not torch.cuda.is_available(): return in_channels, out_channels, D = 2, 3, 2 coords, feats, labels = data_loader(in_channels) feats = feats.double() input = SparseTensor(feats, coords=coords) conv = MinkowskiConvolution( in_channels, out_channels, kernel_size=3, stride=2, dimension=D ) conv = conv.double() unpool = MinkowskiPoolingTranspose(kernel_size=3, stride=2, dimension=D) input = conv(input) output = unpool(input) print(output) # Check backward fn = MinkowskiPoolingTransposeFunction() self.assertTrue( gradcheck( fn, ( input.F, input.tensor_stride, unpool.stride, unpool.kernel_size, unpool.dilation, unpool.region_type_, unpool.region_offset_, False, input.coords_key, None, input.coords_man, ), ) ) device = torch.device("cuda") with torch.cuda.device(0): input = input.to(device) output = unpool(input) print(output) # Check backward self.assertTrue( gradcheck( fn, ( input.F, input.tensor_stride, unpool.stride, unpool.kernel_size, unpool.dilation, unpool.region_type_, unpool.region_offset_, True, input.coords_key, None, input.coords_man, ), ) )