v0.5.0

• Added tensor shape tracking to support dynamic shapes for flatten, squeeze, unsqueeze, view, reshape, interpolate, and getitem methods
• Added EasyOCR example
• Added the DatasetRecorder context manager, allowing to easily capture of module inputs in large pipeline for calibration and shape inference
• Added support for legacy max_batch_size using optimization profiles
• Added support for nested tuple, dict and list module inputs and outputs via. the Flattener class
• Added ability to accept dataset as inputs argument, and infer optimization profiles from the data
• Added Dataset, TensorBatchDataset, ListDataset, and FolderDatset classes
• Added support for dynamic shapes
  • Known limitation: Currently some converters (ie: View) may have unexpected behavior if their arguments are defined with dynamic Tensor shapes.

v0.4.0 - TensorRT 8, DLA, Native plugins library, explicit batch

• Added converter for torch.nn.functional.group_norm using native TensorRT layers
• Added converter for torch.nn.ReflectionPad2d using plugin layer
• Added torch2trt_plugins library
• Added support for Deep Learning Accelerator (DLA)
• Added support for explicit batch
• Added support for TensorRT 8

v0.3.0 - contrib QAT example, additional converters

This version includes the introduction of the Quantization Aware Training workflow in torch2trt.contrib (thanks to @SrivastavaKshitij).
It also contains various converters added since the previous release. Please see the notes below.

Added

• Added converter for torch.nn.functional.adaptive_avg_pool3d
• Added converter for torch.nn.functional.adaptive_max_pool3d
• Added converter for torch.maxpool3d and torch.nn.functional.max_pool3d
• Added Quantization Aware Training (QAT) workflow to contrib
• Added converter for torch.roll
• Added converter for torch.nn.functional.layer_norm
• Added converter for torch.nn.functional.gelu
• Added converter for torch.nn.functional.linear
• Added converter for torch.nn.functional.silu

v0.2.0

Added

• Added converter for torch.Tensor.expand
• Added support for custom converters for methods defined outside of torch module
• Added names for TensorRT layers
• Added GroupNorm plugin which internally uses PyTorch aten::group_norm
• Replaced Tensor.ndim references with len(tensor.shape) to support older pytorch versions
• Added reduced precision documentation page
• Added converters for floordiv, mod, ne, and torch.tensor operations
• Extended relu converter to support Tensor.relu operation
• Extended sigmoid converter to support Tensor.sigmoid operation

Initial release

• torch2trt method
  • conversion hooks
  • conversion context
• TRTModule class
• converters to support most torchvision image classification models
• image classification example notebook