dart_tensor_preprocessing 0.1.2

High-performance tensor preprocessing library for Flutter/Dart. NumPy-like transforms pipeline for ONNX Runtime inference.

dart_tensor_preprocessing

Tensor preprocessing library for Flutter/Dart. NumPy-like transforms pipeline for ONNX Runtime, TFLite, and other AI inference engines.

Features

• PyTorch Compatible: Matches PyTorch/torchvision tensor operations
• Non-blocking: Isolate-based async execution prevents UI jank
• Type-safe: ONNX-compatible tensor types (Float32, Int64, Uint8, etc.)
• Zero-copy: View/stride manipulation for reshape/transpose operations
• Declarative: Chain operations into reusable pipelines

Installation

dependencies:
  dart_tensor_preprocessing: ^0.1.2

Quick Start

import 'package:dart_tensor_preprocessing/dart_tensor_preprocessing.dart';

// Create a tensor from image data (HWC format, Uint8)
final imageData = Uint8List.fromList([/* RGBA pixel data */]);
final tensor = TensorBuffer.fromUint8List(imageData, [height, width, channels]);

// Use a preset pipeline for ImageNet models
final pipeline = PipelinePresets.imagenetClassification();
final result = await pipeline.runAsync(tensor);

// result.shape: [1, 3, 224, 224] (NCHW, Float32, normalized)

Pipeline Presets

Preset	Output Shape	Use Case
imagenetClassification()	[1, 3, 224, 224]	ResNet, VGG, etc.
objectDetection()	[1, 3, 640, 640]	YOLO, SSD
faceRecognition()	[1, 3, 112, 112]	ArcFace, FaceNet
clip()	[1, 3, 224, 224]	CLIP models
mobileNet()	[1, 3, 224, 224]	MobileNet family

Custom Pipeline

final pipeline = TensorPipeline([
  ResizeOp(height: 224, width: 224),
  ToTensorOp(normalize: true),  // HWC -> CHW, scale to [0,1]
  NormalizeOp.imagenet(),       // ImageNet mean/std
  UnsqueezeOp.batch(),          // Add batch dimension
]);

// Sync execution
final result = pipeline.run(input);

// Async execution (runs in isolate)
final result = await pipeline.runAsync(input);

Available Operations

Resize & Crop

• ResizeOp - Resize to fixed dimensions (nearest, bilinear, bicubic)
• ResizeShortestOp - Resize preserving aspect ratio
• CenterCropOp - Center crop to fixed dimensions

Normalization

• NormalizeOp - Channel-wise normalization (presets: ImageNet, CIFAR-10, symmetric)
• ScaleOp - Scale values (e.g., [0-255] to [0-1])

Layout

• PermuteOp - Axis reordering (e.g., HWC to CHW)
• ToTensorOp - HWC uint8 to CHW float32 with optional scaling
• ToImageOp - CHW float32 to HWC uint8

Shape

• UnsqueezeOp - Add dimension
• SqueezeOp - Remove size-1 dimensions
• ReshapeOp - Reshape tensor (supports -1 for inference)
• FlattenOp - Flatten dimensions

Type

• TypeCastOp - Convert between data types

Core Classes

TensorBuffer

Tensor with shape and stride metadata over physical storage.

// Create tensors
final zeros = TensorBuffer.zeros([3, 224, 224]);
final ones = TensorBuffer.ones([3, 224, 224], dtype: DType.float32);
final fromData = TensorBuffer.fromFloat32List(data, [3, 224, 224]);

// Access elements
final value = tensor[[0, 100, 100]];

// Zero-copy operations
final transposed = tensor.transpose([2, 0, 1]);  // Changes strides only
final squeezed = tensor.squeeze();

// Copy operations
final contiguous = tensor.contiguous();  // Force contiguous memory
final cloned = tensor.clone();

DType

ONNX-compatible data types with onnxId for runtime integration.

DType.float32  // ONNX ID: 1
DType.int64    // ONNX ID: 7
DType.uint8    // ONNX ID: 2

Memory Formats

Format	Layout	Strides (for [1,3,224,224])
contiguous	NCHW	[150528, 50176, 224, 1]
channelsLast	NHWC	[150528, 1, 672, 3]

PyTorch Compatibility

This library is designed to produce identical results to PyTorch/torchvision operations:

Operation	PyTorch Equivalent
TensorBuffer.zeros()	torch.zeros()
TensorBuffer.ones()	torch.ones()
tensor.transpose()	tensor.permute()
tensor.reshape()	tensor.reshape()
tensor.squeeze()	tensor.squeeze()
tensor.unsqueeze()	tensor.unsqueeze()
NormalizeOp.imagenet()	transforms.Normalize(mean, std)
ResizeOp(mode: bilinear)	F.interpolate(mode='bilinear')
ToTensorOp()	transforms.ToTensor()

Requirements

• Dart SDK ^3.0.0

License

MIT