Use tabulate instead of texttable

[AlexanderDokuchaev]

Changes

Using tabulate instead of texttable package to create tables.

Reason for changes

Avoid installation two packages to same task. Tabulate is optional dependencies of pandas package, that used in NNCF. https://github.com/openvinotoolkit/nncf/pull/2542

Related tickets

134603

[codecov[bot]]

Codecov Report

All modified and coverable lines are covered by tests :white_check_mark:

Project coverage is 84.54%. Comparing base (1349247) to head (891fd55). Report is 1 commits behind head on develop.

Additional details and impacted files

@@             Coverage Diff             @@
##           develop    #2574      +/-   ##
===========================================
- Coverage    90.89%   84.54%   -6.35%     
===========================================
  Files          492      492              
  Lines        45101    45096       -5     
===========================================
- Hits         40995    38128    -2867     
- Misses        4106     6968    +2862     

Files	Coverage Δ
nncf/common/utils/helpers.py	100.00% <100.00%> (ø)
nncf/torch/pruning/base_algo.py	92.15% <100.00%> (-0.44%)	:arrow_down:
nncf/torch/pruning/filter_pruning/algo.py	96.50% <100.00%> (ø)
setup.py	0.00% <ø> (ø)

... and 56 files with indirect coverage changes

Flag	Coverage Δ
COMMON	42.77% <50.00%> (+<0.01%)	:arrow_up:
ONNX	34.66% <36.36%> (+<0.01%)	:arrow_up:
OPENVINO	∅ <ø> (∅)
TENSORFLOW	29.87% <45.45%> (+<0.01%)	:arrow_up:
TORCH	65.76% <100.00%> (-0.01%)	:arrow_down:

Flags with carried forward coverage won't be shown. Click here to find out more.

Components	Coverage Δ
common	93.11% <100.00%> (-0.65%)	:arrow_down:
torch	93.59% <100.00%> (-0.01%)	:arrow_down:
tensorflow	93.74% <ø> (ø)
onnx	93.05% <ø> (ø)
openvino	25.58% <ø> (-68.49%)	:arrow_down:
ptq	67.31% <ø> (-20.43%)	:arrow_down:

[AlexanderDokuchaev]

Example of tables:

https://github.com/openvinotoolkit/nncf/tree/develop/examples/quantization_aware_training/torch/resnet18

+------------------+----------+-----------+-------------------------------+
|                  |   FP32   |   INT8    |            Summary            |
+==================+==========+===========+===============================+
| Accuracy@1       | 55.520   | 56.715    | 55.360 (init) + 1.355 (tuned) |
+------------------+----------+-----------+-------------------------------+
| Model Size, Mb   | 43.067   | 10.939    | Compression rate is 3.937     |
+------------------+----------+-----------+-------------------------------+
| Performance, FPS | 3743.990 | 12734.460 | Speedup x3.401                |
+------------------+----------+-----------+-------------------------------+

┍━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━┯━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┑
│                  │     FP32 │      INT8 │ Summary                       │
┝━━━━━━━━━━━━━━━━━━┿━━━━━━━━━━┿━━━━━━━━━━━┿━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┥
│ Accuracy@1       │   55.520 │    56.715 │ 55.360 (init) + 1.355 (tuned) │
├──────────────────┼──────────┼───────────┼───────────────────────────────┤
│ Model Size, Mb   │   43.067 │    10.939 │ Compression rate is 3.937     │
├──────────────────┼──────────┼───────────┼───────────────────────────────┤
│ Performance, FPS │ 3743.990 │ 12734.460 │ Speedup x3.401                │
┕━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━┷━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┙

https://github.com/openvinotoolkit/nncf/tree/develop/examples/llm_compression/openvino/tiny_llama

+--------------+---------------------------+-----------------------------------+
| Num bits (N) | % all parameters (layers) |    % ratio-defining parameters    |
|              |                           |             (layers)              |
+==============+===========================+===================================+
| 8            | 30% (20 / 156)            | 21% (18 / 154)                    |
+--------------+---------------------------+-----------------------------------+
| 4            | 70% (136 / 156)           | 79% (136 / 154)                   |
+--------------+---------------------------+-----------------------------------+

┍━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┑
│   Num bits (N) │ % all parameters (layers)   │ % ratio-defining parameters (layers)   │
┝━━━━━━━━━━━━━━━━┿━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┿━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┥
│              8 │ 30% (20 / 156)              │ 21% (18 / 154)                         │
├────────────────┼─────────────────────────────┼────────────────────────────────────────┤
│              4 │ 70% (136 / 156)             │ 79% (136 / 154)                        │
┕━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┙