Added citations

change distiller citation as suggested in issue #492

Remove warning regarding Distiller release 0.3 (breaking backward compat)

The gitdb versioning issue is resolved internally in gitpython
3.1.0, so moving to that and removing specific gitb requirements

* BUGFIX: Fixed wrong attribute name for zero-point in conversion
  of eltwise add/mult and concat
* Add PyTorch PTQ convert for embedding (converted to FP32
  embedding + quant op)
* Fix conversion function to work with tuple/list model inputs

* Move image classification specific setup code to separate script at
  examples/classifier_compression/ptq_lapq.py
* Make ptq_coordinate_search function completely independent of
  command line arguments
* Change LAPQ command line args function to update existing
  pre-existing parser (changed CLAs perfix to 'lapq' for more clarity)
* Enable LAPQ from compress_classifier.py (trigger with --qe-lapq)
* Add pointers in documentation

Convert Distiller PTQ models to "native" PyTorch PTQ (#458)

* New API: distiller.quantization.convert_distiller_ptq_model_to_pytorch()
* Can also be called from PostTrainLinearQuantizer instance:
    quantizer.convert_to_pytorch()
* Can also trigger from command line in image classification sample
* Can save/load converted modules via apputils.load/save_checkpoint
* Added Jupyter notebook tutorial

* Converted modules have only the absolutely necessary quant-dequant
  operations. For a fully quantized model, this means just quantization
  of model input and de-quantization of model output. If a user keeps
  specific internal layers in FP32, quant-dequant operations are added
  as needed
* Can configure either 'fbgemm' or 'qnnpack' backend. For 'fbgemm' we
  take care of preventing overflows (aka "reduce_range" in the PyTorch
  API)

"Loss Aware Post-Training Quantization" (Nahshan et al., 2019)

Paper: https://arxiv.org/abs/1911.07190 
Reference implementation:
  https://github.com/ynahshan/nn-quantization-pytorch/tree/master/lapq

Proper documentation is still TODO, for now see the example YAML file
at 'examples/quantization/post_train_quant/resnet18_imagenet_post_train_lapq.yaml'

* Implemented in distiller/quantization/ptq_coordinate_search.py
* At the moment that file both the model-independent algorithm
  implementation and image-classification specific sample script.
  Still TODO: Refactor that

* Post train quantization changes (range_linear):
  * Added getters/setters for quantization parameters (scale/zero_point)
    and clipping values
  * Add option to save backup of FP32 weights to allow re-quantization
    after quantizer was created.
  * Add option to clip weights in addition to activations
  * Fix fusions to not occur only when activations aren't quantized
  * RangeLinearFakeQuantWrapper:
    * Make inputs quantization optional
    * In case of ReLU + ACIQ, clip according to input stats

* Data loaders:
  * Add option to not load train set at all from disk (to speed up
    loading time in post-training runs)
  * Modified "image_classifier.py" accordingly

Temp patch until moving to torchvision 0.5.
See https://github.com/pytorch/vision/issues/1712#issuecomment-575036523

Fix the formatting of a ValueError raised when a module is missing
an attribute, when collecting activation statistics.

(we use 8-bit values below, but this applies to any bit-width)
* We use the notion of "full" and "restricted" quantized range for
  symmetric quantization (see section 2.2 in https://arxiv.org/abs/1806.08342)
* "Full" quantized range ==> [-128, 127], "restircted" ==> [-127, 127]
* Until now, when doing symmetric quantization we assumed a "full"
  range when saturating after quantization, but calculated the scale
  factor as if the range was restricted. This means we weren't making
  full utilization of the quantized range.
* On the other hand, in some other implementations of quantization (e.g.
  TensorFlow), the "restricted" range is used.
* So, we make it an option to use either the proper "full" range
  (q_min = -128) or "restricted" range (q_min = -127).
* LinearQuantMode.SYMMETRIC now means the "full" range is used, and
  added LinearQuantMode.SYMMETRIC_RESTRICTED for using the "restricted"
  range.
* Updated tests and documentation.

* Fake quant wrapper now also works on (fake) quantized inputs
* Remove 'requires_quantized_inputs' flag
* Unrelated: Moved LinearQuantMode enum to q_utils

In PostTrainLinearQuantizer and QuantAwareTrainRangeLinearQuantizer

Add directionality to SummaryActivationStatsCollector to allow collection of statistics on incoming and outgoing activations/feature-maps; instead of just outgoing activations.

Also includes some code refactoring.

* Limit batch size to 128 when initiating from image classification app
* Don't raise inplace error in case of Dropout module

* Make it easier to find sample apps for different workload types
* Add READMEs for sample apps the didn't have any
* Update readmes with experiment results where applicable

Added tables of results for 85% sparsity

* Weights-only PTQ:
  * Allow RangeLinearQuantWrapper to accept num_bits_acts = None, in
    which case it'll act as a simple pass-through during forward
  * In RangeLinearQuantParamLayerWrapper, if bits_activations is None
    and num_bits_params > 0, Perform quant and de-quant of the
    parameters instead of just quant.
* Activations-only PTQ:
  * Enable activations only quantization for conv/linear modules. When
    PostTrainLinearQuantizer detects # bits != None for activations 
    and # bits == None for weights, a fake-quantization wrapper will
    be used.
* Allow passing 0 in the `--qe-bits-acts` and `--qe-bits-wts` command
  line arguments to invoke weights/activations-only quantization,
  respectively.
* Minor refactoring for clarity in PostTrainLinearQuantizer's replace_*
  functions

Results changed following commit 9e7ef987 (#402)

compute-summary and png-summary currently work with image classifiers
only.

When multi-processing, we want only one process to generate the
summary, while the other processes do nothing (lazy bums!)

Add an example of compressing OD pytorch models.

In this example we compress torchvision's object detection models - FasterRCNN / MaskRCNN / KeypointRCNN.
We've modified the reference code for object detection to allow easy compression scheduling with YAML configuration.

of a model by name relative to the root of the model.