Added #

• Arithmetic Operations (arithmetic_op.dart):
  • AddOp - Element-wise addition (scalar or tensor)
  • SubOp - Element-wise subtraction (scalar or tensor)
  • MulOp - Element-wise multiplication (scalar or tensor)
  • DivOp - Element-wise division (scalar or tensor)
  • PowOp - Element-wise power operation
• Math Operations (math_op.dart):
  • AbsOp - Element-wise absolute value
  • NegOp - Element-wise negation
  • SqrtOp - Element-wise square root
  • ExpOp - Element-wise exponential (e^x)
  • LogOp - Element-wise natural logarithm
• Activation Functions (activation_op.dart):
  • ReLUOp - Rectified Linear Unit
  • LeakyReLUOp - Leaky ReLU with configurable negative slope
  • SigmoidOp - Sigmoid activation
  • TanhOp - Hyperbolic tangent activation
  • SoftmaxOp - Softmax along specified axis
• TensorBuffer Factory Methods:
  • TensorBuffer.full() - Create tensor filled with specified value
  • TensorBuffer.random() - Create tensor with uniform random values [0, 1)
  • TensorBuffer.randn() - Create tensor with standard normal distribution
  • TensorBuffer.eye() - Create identity matrix (supports rectangular)
  • TensorBuffer.linspace() - Create tensor with evenly spaced values
  • TensorBuffer.arange() - Create tensor with sequence values
• Utility Libraries (lib/src/utils/):
  • index_utils.dart - Index manipulation utilities (reflectIndex, replicateIndex, circularIndex)
  • validation_utils.dart - Common tensor validation patterns

Changed #

• Exception Consistency: TensorStorage._checkBounds() now throws IndexOutOfBoundsException instead of RangeError for consistent exception handling across the library

Internal #

• Extracted duplicate _reflectIndex code from pad_op.dart and augmentation_op.dart into shared utility
• Added TensorValidation extension with requireRank3Or4(), requireExactRank(), requireMinRank() methods

Added #

• Performance benchmark suite (benchmark/ directory):
  • tensor_creation_benchmark.dart - Tensor creation performance
  • tensor_ops_benchmark.dart - Zero-copy and copy operations
  • pipeline_benchmark.dart - Pipeline sync/async comparison
  • memory_benchmark.dart - Memory usage measurement
  • run_all.dart - Unified benchmark runner
  • utils/benchmark_utils.dart - Benchmark utilities

Fixed #

• Removed unused variables in benchmark files
• Fixed lint issues in benchmark files

Added #

• ClipOp - Element-wise value clamping with factory presets (unit, symmetric, uint8)
• PadOp - Padding with multiple modes (constant, reflect, replicate, circular)
• SliceOp - Python-like tensor slicing with support for negative indices and steps
• RandomCropOp - Random cropping for data augmentation with deterministic seed support
• GaussianBlurOp - Gaussian blur using separable convolution with factory presets
• concat() - Utility function for tensor concatenation along specified axis

Fixed #

• concat() axis-based copy logic now correctly handles multi-axis concatenation

Changed #

• BREAKING: Unified exception handling across the library
  • All exceptions now extend TensorException sealed class
  • ArgumentError → ShapeMismatchException, InvalidParameterException
  • RangeError → IndexOutOfBoundsException

Added #

• IndexOutOfBoundsException - Thrown when an index or axis is out of valid range
• DTypeMismatchException - Thrown when tensor data types do not match

Changed #

• BREAKING: Unified exception handling across the library
  • All exceptions now extend TensorException sealed class
  • ArgumentError → ShapeMismatchException, InvalidParameterException
  • RangeError → IndexOutOfBoundsException
  • StateError → NonContiguousException, DTypeMismatchException
• Shape validation now happens before buffer creation in zeros() and ones()

Migration Guide #

If you were catching standard Dart exceptions, update your code:

Added #

• Reduction operations for TensorBuffer:
  • sum() - Returns the sum of all elements
  • mean() - Returns the arithmetic mean of all elements
  • min() - Returns the minimum value
  • max() - Returns the maximum value
• Axis-wise reduction operations:
  • sumAxis(int axis, {bool keepDims}) - Sum along a specific axis
  • meanAxis(int axis, {bool keepDims}) - Mean along a specific axis
  • minAxis(int axis, {bool keepDims}) - Min along a specific axis
  • maxAxis(int axis, {bool keepDims}) - Max along a specific axis
• Support for negative axis indexing in axis-wise operations
• Comprehensive test coverage for all reduction operations (49 tests)

Added #

• TensorBuffer.toList() method for extracting tensor data as List<double>

Fixed #

• Unused import in test file (dart:math)
• Unused variable in test file
• prefer_final_locals lint warnings in test files
• dangling_library_doc_comments lint warnings in test files

Added #

• .gitignore file for Git
• .pubignore file for pub.dev publishing

Added #

• Comprehensive dartdoc comments for all public API elements
• Library-level documentation with usage examples

Added #

• Core tensor operations

  • TensorBuffer with shape, strides, and view/storage separation
  • TensorStorage for immutable typed data wrapper
  • DType enum with ONNX-compatible data types

• Transform operations

  • ResizeOp with nearest, bilinear, bicubic interpolation
  • ResizeShortestOp for aspect-ratio preserving resize
  • CenterCropOp for center cropping
  • NormalizeOp with ImageNet, CIFAR-10, symmetric presets
  • ScaleOp for value scaling
  • PermuteOp for axis reordering
  • ToTensorOp for HWC uint8 to CHW float32 conversion
  • ToImageOp for CHW float32 to HWC uint8 conversion
  • UnsqueezeOp, SqueezeOp, ReshapeOp, FlattenOp for shape manipulation
  • TypeCastOp for dtype conversion

• Pipeline system

  • TensorPipeline for chaining operations
  • PipelinePresets with ImageNet, ResNet, YOLO, CLIP, ViT, MobileNet presets
  • Async execution via Isolate.run

• Zero-copy operations

  • transpose() via stride manipulation
  • squeeze(), unsqueeze() as shape-only changes