v1.7.0

New features:

• Adjust Padding feature to support accurate execution of U4 on VPU - when setting "target_device" to "VPU", the training-time padding values for quantized convolutions will be adjusted to better reflect VPU inference process.
• Weighted layers that are "frozen" (i.e. have requires_grad set to False at compressed model creation time) are no longer considered for compression, to better handle transfer learning cases.
• Quantization algorithm now sets up quantizers without giving an option for requantization, which guarantees best performance, although at some cost to quantizer configuration flexibility.
• Pruning models with FCOS detection heads and instance normalization operations now supported
• Added a mean percentile initializer for the quantization algorithm
• Now possible to additionally quantize model outputs (separate control for each output quantization is supported)
• Models quantized for CPU now use effective 7-bit quantization for weights - the ONNX-exported model is still configured to use 8 bits for quantization, but only the middle 128 quanta of the total possible 256 are actually used, which allows for better OpenVINO inference accuracy alignment with PyTorch on non-VNNI CPUs
• Bumped target PyTorch version to 1.8.1 and relaxed package requirements constraints to allow installation into environments with PyTorch >=1.5.0

Notable bugfixes:

• Fixed bias pruning in depthwise convolution
• Made per-tensor quantization available for all operations that support per-channel quantization
• Fixed progressive training performance degradation when an output tensor of an NNCF-compressed model is reused as its input.
• pip install . path of installing NNCF from a checked-out repository is now supported.
• Nested with no_nncf_trace() blocks now function as expected.
• NNCF compression API now formally abstract to guard against virtual function calls
• Now possible to load AutoQ and HAWQ-produced checkpoints to evaluate them or export to ONNX

Removed features:

• Pattern-based quantizer setup mode for quantization algorithm - due to its logic, it did not guarantee that all required operation inputs are ultimately quantized.