CHANGELOG.md

Change Log

[0.5.5]

• MKL compilation fix (#358)
• Example updates for has_bias (PR #356)
• Add thrust exception handling (issue #357)
• min_coordinate to device in dense()
• cpu_kernel_region to_gpu now uses shared_ptr allocator for gpu memory
• Docker installation instruction added
• Fix for GPU coo_spmm when nnz == 0
  • Fix MinkowskiInterpolationGPU for invalid samples (issue #383)
• gradcheck wrap func 1.9
• Handles dense for an empty tensor (issue #384)
• Handles an emtpy tensor for convolution (issue #384)
• When coordinates is provided in MinkowskiToSparseTensor, remove_zeros will be ignored (issue #387)
• Pybind equality error from the package (issue #414)
• Fix undefined coordinate merge for multiple coordinate unions
• Add cross-shaped kernel support (issue #436)

[0.5.4]

• Fix TensorField.sparse() for no duplicate coordinates
• Skip unnecessary spmm if SparseTensor.initialize_coordinates() has no duplicate coordinates
• Model summary utility function added
• TensorField.splat function for splat features to a sparse tensor
• SparseTensor.interpolate function for extracting interpolated features
• coordinate_key property function for SparseTensor and TensorField
• Fix .dense() for GPU tensors. (PR #319)

[0.5.3]

• Updated README for pytorch 1.8.1 support
• Use custom gpu_storage instead of thrust vector for faster constructors
• pytorch installation instruction updates
• fix transpose kernel map with kernel_size == stride_size
• Update reconstruction and vae examples for v0.5 API
• stack_unet.py example, API updates
• MinkowskiToFeature layer

[0.5.2]

• spmm average cuda function
• SparseTensor list operators (cat, mean, sum, var)
• MinkowskiStack containers
• Replace all at::cuda::getCurrentCUDASparseHandle with custom getCurrentCUDASparseHandle (issue #308)
• fix coordinate manager kernel map python function
• direct max pool
  • SparseTensorQuantizationMode.MAX_POOL
• TensorField global max pool
  • origin field
  • origin field map
  • MinkowskiGlobalMaxPool CPU/GPU updates for a field input
• SparseTensor.dense() raises a value error when a coordinate is negative rather than subtracting the minimum coordinate from a sparse tensor. (issue #316)
• Added to_sparse() that removes zeros. (issue #317)
  • Previous to_sparse() was renamed to to_sparse_all()
  • MinkowskiToSparseTensor takes an optional remove_zeros boolean argument.
• Fix global max pool with batch size 1
• Use separate memory chunks for in, out map, and kernel indices for gpu_kernel_map for gpu memory misaligned error

[0.5.1]

• v0.5 documentation updates
• Nonlinear functionals and modules
• Warning when using cuda without ME cuda support
• diagnostics test
• TensorField slice
  • Cache the unique map and inverse map pair in the coordinate manager
  • generate inverse_mapping on the fly
• CoordinateManager
  • field_to_sparse_insert_and_map
  • exists_field_to_sparse
  • get_field_to_sparse_map
  • fix kernel_map with empty coordinate maps
• CoordiateFieldMap
  • quantize_coordinates
• TensorField binary ops fix
• MinkowskiSyncBatchNorm
  • Support tfield
  • conver sync batchnorm updates
• TensorField to sparse with coordinate map key
• Sparse matrix multiplication
  • force contiguous matrix
• Fix AveragePooling cudaErrorMisalignedAddress error for CUDA 10 (#246)

[0.5.0] - 2020-12-24

[0.5.0a] - 2020-08-05

Changed

• Remove Makefile for installation as pytorch supports multithreaded compilation
• GPU coordinate map support
• Coordinate union
• Sparse tensor binary operators
• CUDA 11.1 support
• quantization function updates
• Multi GPU examples
• Pytorch-lightning multi-gpu example
• Transpose pooling layers
• TensorField updates
• Batch-wise decomposition
• inverse_map when sparse() called (slice)
• ChannelwiseConvolution
• TensorField support for non-linearities

[0.4.3] - 2020-05-29

Changed

• Use CPU_ONLY compile when torch fails to detect a GPU (Issue #105)
• Fix get_kernel_map for CPU_ONLY (Issue #107)
• Update get_union_map doc (Issue #108)
• Abstract getattr minkowski backend functions
• Add coordinates_and_features_at(batch_index) function in the SparseTensor class.
• Add MinkowskiChannelwiseConvolution (Issue #92)
• Update MinkowskiPruning to generate an empty sparse tensor as output (Issue #102)
• Add return_index for sparse_quantize
• Templated CoordsManager for coords to int and coords to vector classes
• Sparse tensor quantization mode
  • Features at duplicated coordinates will be averaged automatically with quantization_mode=ME.SparseTensorQuantizationMode.UNWEIGHTED_AVERAGE
• SparseTensor.slice() slicing features on discrete coordinates to continuous coordinates
• CoordsManager.getKernelMapGPU returns long type tensors (Issue #125)
• SyncBatchNorm error fix (Issue #129)
• Sparse Tensor dense() doc update (Issue #126)
• Installation arguments --cuda_home=<value>, --force_cuda, --blas_include_dirs=<comma_separated_values>, and '--blas_library_dirs=<comma_separated_values>`. (Issue #135)
• SparseTensor query by coordinates features_at_coords (Issue #137)
• Memory manager control. CUDA | Pytorch memory manager for cuda malloc

[0.4.2] - 2020-03-13

Added

• Completion and VAE examples
• GPU version of getKernelMap: getKernelMapGPU

Changed

• Fix dtype double to float on the multi-gpu example
• Remove the dimension input argument on GlobalPooling, Broadcast functions
• Kernel map generation has tensor stride > 0 check
• Fix SparseTensor.set_tensor_stride
• Track whether the batch indices are set first when initializing coords, The initial batch indices will be used throughout the lifetime of a sparse tensor
• Add a memory warning on ModelNet40 training example (Issue #86)
• Update the readme, definition
• Fix an error in examples.convolution
• Changed features_at, coordinates_at to take a batch index not the index of the unique batch indices. (Issue #100)
• Fix an error torch.range --> torch.arange in sparse_quantize (Issue #101)
• Fix BLAS installation link error (Issue #94)
• Fix MinkowskiBroadcast and MinkowskiBroadcastConcatenation to use arbitrary channel sizes
• Fix pointnet.py example (Issue #103)

[0.4.1] - 2020-01-28

Changed

• Kernel maps with region size 1 do not require Region class initialization.
• Faster union map with out map initialization
• Batch index order hot fix on dense(), sparse()

[0.4.0] - 2020-01-26

Added

• Add MinkowskiGlobalSumPooling, MinkowskiGlobalAvgPooling
• Add examples/convolution.py to showcase various usages
• Add examples/sparse_tensor_basic.py and a SparseTensor tutorial page
• Add convolution, kernel map gifs
• Add batch decomposition functions
  • Add SparseTensor.decomposed_coordinates
  • Add SparseTensor.decomposed_features
  • Add SparseTensor.coordinates_at(batch_index)
  • Add SparseTensor.features_at(batch_index)
  • Add CoordsManager.get_row_indices_at(coords_key, batch_index)

Changed

• SparseTensor additional coords.device guard
• MinkowskiConvolution, Minkowski*Pooling output coordinates will be equal to the input coordinates if stride == 1. Before this change, they generated output coordinates previously defined for a specific tensor stride.
• MinkowskiUnion and Ops.cat will take a variable number of sparse tensors not a list of sparse tensors
• Namespace cleanup
• Fix global in out map with uninitialized global map
• getKernelMap now can generate new kernel map if it doesn't exist
• MinkowskiPruning initialization takes no argument
• Batched coordinates with batch indices prepended before coordinates

[0.3.3] - 2020-01-07

Added

• Add get_coords_map on CoordsManager.
• Add get_coords_map on MinkowskiEngine.utils.
• Sparse Tensor Sparse Tensor binary operations (+,-,*,/)
  • Binary operations between sparse tensors or sparse tensor + pytorch tensor
  • Inplace operations for the same coords key
• Sparse Tensor operation mode
  • Add set_sparse_tensor_operation_mode sharing the global coords manager by default

Changed

• Minor changes on setup.py for torch installation check and system assertions.
• Update BLAS installation configuration.
• Update union kernel map and union coords to use reference wrappers.
• namespace minkowski for all cpp, cu files
• MinkowskiConvolution and MinkowskiConvolutionTranspose now support output coordinate specification on the function call.
• Minkowski[Avg|Max|Sum]Pooling and Minkowski[Avg|Max|Sum]PoolingTranspose now support output coordinate specification on the function call.

[0.3.2] - 2019-12-25

Added

• Synchronized Batch Norm: ME.MinkowskiSyncBatchNorm
  • ME.MinkowskiSyncBatchNorm.convert_sync_batchnorm converts a MinkowskiNetwork automatically to use synched batch norm.
• examples/multigpu.py update for ME.MinkowskiSynchBatchNorm.
• Add MinkowskiUnion
• Add CoordsManager functions
  • get_batch_size
  • get_batch_indices
  • set_origin_coords_key
• Add quantize_th, quantize_label_th
• Add MANIFEST

Changed

• Update MinkowskiUnion, MinkowskiPruning docs
• Update multigpu documentation
• Update GIL release
• Use cudaMalloc instead of at::Tensor for GPU memory management for illegal memory access, invalid arg.
• Minor error fixes on examples/modelnet40.py
• CoordsMap size initialization updates
• Region hypercube iterator with even numbered kernel
• Fix global reduction in-out map with non contiguous batch indices
• GlobalPooling with torch reduction
• Update CoordsManager function get_row_indices_per_batch to return a list of torch.LongTensor for mapping indices. The corresponding batch indices is accessible by get_batch_indices.
• Update MinkowskiBroadcast, MinkowskiBroadcastConcatenation to use row indices per batch (getRowIndicesPerBatch)
• Update SparseTensor
  • allow_duplicate_coords argument support
  • update documentation, add unittest
• Update the training demo and documentation.
• Update MinkowskiInstanceNorm: no dimension argument.
• Fix CPU only build

[0.3.1] - 2019-12-15

• Cache in-out mapping on device
• Robinhood unordered map for coordinate management
• hash based quantization to C++ CoordsManager based quantization with label collision
• CUDA compilation to support older devices (compute_30, 35)
• OMP_NUM_THREADS to initialize the number of threads

[0.3.0] - 2019-12-08

• Change the hash map from google-sparsehash to Threading Building Blocks (TBB) concurrent_unordered_map.
  • Optional duplicate coords (CoordsMap.initialize, TODO: make mapping more user-friendly)
  • Explicit coords generation (CoordsMap.stride, CoordsMap.reduce, CoordsMap.transposed_stride)
  • Speed up for pooling with kernel_size == stride_size.
• Faster SparseTensor.dense function.
• Force scratch memory space to be contiguous.
• CUDA error checks
• Update Makefile
  • Architecture and sm updates for CUDA > 10.0
  • Optional cblas

[0.2.9] - 2019-11-17

• Pytorch 1.3 support
  • Update torch cublas, cusparse handles.
• Global max pooling layers.
• Minor error fix in the coordinate manager
  • Fix cases to return in_coords_key when stride is identity.

[0.2.8] - 2019-10-18

• ModelNet40 training.
• open3d v0.8 update.
• Dynamic coordinate generation.

[0.2.7] - 2019-09-04

Use std::vector for all internal coordinates to support arbitrary spatial dimensions.

• Vectorized coordinates to support arbitrary spatial dimensions.
• Removed all dimension template instantiation.
• Use assertion macro for cleaner exception handling.

[0.2.6] - 2019-08-28

Use OpenMP for multi-threaded kernel map generation and minor renaming and explicit coordinate management for future upgrades.

• Major speed up
  • Suboptimal kernels were introduced, and optimal kernels removed for faulty cleanup in v0.2.5. CUDA kernels were re-introduced and major speed up was restored.
• Minor name changes in CoordsManager.
• CoordsManager saves all coordinates for future updates.
• CoordsManager functions createInOutPerKernel and createInOutPerKernelTranspose now support multi-threaded kernel map generation by default using OpenMP.
  • Thus, all manual thread functions such as createInOutPerKernelInThreads, initialize_nthreads removed.
    • Use export OMP_NUM_THREADS to control the number of threads.

[0.2.5a0] - 2019-07-12

• Added the MinkowskiBroadcast and MinkowskiBroadcastConcatenation module.

[0.2.5] - 2019-07-02

• Better GPU memory management:
  • GPU Memory management is now delegated to pytorch. Before the change, we need to cleanup the GPU cache that pytorch created to call cudaMalloc, which not only is slow but also hampers the long-running training that dies due to Out Of Memory (OOM).