Nncf

- Fixed a bug with where compressed models that were supposed to return named tuples actually returned regular tuples
- Fixed an issue with batch norm adaptation-enabled compression runs hanging in the DDP scenario

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.

- Added AutoQ - an AutoML-based mixed-precision initialization mode for quantization, which utilizes the power of reinforcement learning to select the best quantizer configuration for any model in terms of quality metric for a given HW architecture type.
- NNCF now supports inserting compression operations as pre-hooks to PyTorch operations, instead of abusing the post-hooking; the flexibility of quantization setups has been improved as a result of this change.
- Improved the pruning algorithm to group together dependent filters from different layers in the network and prune these together
- Extended the ONNX compressed model exporting interface with an option to explicitly name input and output tensors
- Changed the compression scheduler so that the correspondingepoch_step and step methods should now be called in the beginning of the epoch and before the optimizer step (previously these were called in the end of the epoch and after the optimizer step respectively)
- Data-dependent compression algorithm initialization is now specified in terms of dataset samples instead of training batches, e.g. `"num_init_samples"` should be used in place of "num_init_steps" in NNCF config files.
- Custom user modules to be registered for compression can now be specified to be ignored for certain compression algorithms
- Batch norm adaptation now being applied by default for all compression algorithms
- Bumped target PyTorch version to 1.7.0
- Custom OpenVINO operations such as "FakeQuantize" that appear in NNCF-exported ONNX models now have their ONNX `domain` set to org.openvinotoolkit
- The quantization algorithm will now quantize nn.Embedding and nn.EmbeddingBag weights when targeting CPU
- Added an option to optimize logarithms of quantizer scales instead of scales themselves directly, a technique which improves convergence in certain cases
- Added reference checkpoints for filter-pruned models: UNetMapillary (25% of filters pruned), SSD300VOC (40% of filters pruned)

Fixed packaging