tensorflow / compression
Conditional Complexity

The distribution of complexity of units (measured with McCabe index).

Intro
  • Conditional complexity (also called cyclomatic complexity) is a term used to measure the complexity of software. The term refers to the number of possible paths through a program function. A higher value ofter means higher maintenance and testing costs (infosecinstitute.com).
  • Conditional complexity is calculated by counting all conditions in the program that can affect the execution path (e.g. if statement, loops, switches, and/or operators, try and catch blocks...).
  • Conditional complexity is measured at the unit level (methods, functions...).
  • Units are classified in four categories based on the measured McCabe index: 1-5 (simple units), 6-10 (medium complex units), 11-25 (complex units), 26+ (very complex units).
Learn more...
Conditional Complexity Overall
  • There are 613 units with 6,968 lines of code in units (80.6% of code).
    • 0 very complex units (0 lines of code)
    • 2 complex units (156 lines of code)
    • 14 medium complex units (681 lines of code)
    • 49 simple units (1,460 lines of code)
    • 548 very simple units (4,671 lines of code)
0% | 2% | 9% | 20% | 67%
Legend:
51+
26-50
11-25
6-10
1-5
Alternative Visuals
Conditional Complexity per Extension
51+
26-50
11-25
6-10
1-5
py0% | 2% | 10% | 16% | 70%
cc0% | 0% | 8% | 36% | 55%
h0% | 0% | 0% | 0% | 100%
Conditional Complexity per Logical Component
primary logical decomposition
51+
26-50
11-25
6-10
1-5
tensorflow_compression/python/entropy_models0% | 10% | 13% | 14% | 62%
tensorflow_compression/python/layers0% | 8% | 19% | 17% | 54%
models/hific0% | 0% | 12% | 19% | 68%
tensorflow_compression/cc/kernels0% | 0% | 6% | 33% | 60%
models0% | 0% | 5% | 10% | 84%
models/toy_sources0% | 0% | 9% | 42% | 47%
tensorflow_compression/cc/lib0% | 0% | 18% | 45% | 36%
tensorflow_compression/python/ops0% | 0% | 0% | 34% | 65%
tensorflow_compression/python/util0% | 0% | 0% | 37% | 62%
tensorflow_compression/python/distributions0% | 0% | 0% | 0% | 100%
ROOT0% | 0% | 0% | 0% | 100%
tensorflow_compression/python/datasets0% | 0% | 0% | 0% | 100%
Most Complex Units
Top 20 most complex units
Unit# linesMcCabe index# params
def call()
in tensorflow_compression/python/layers/signal_conv.py
 76 48 2
def __init__()
in tensorflow_compression/python/entropy_models/continuous_batched.py
 80 29 16
Status ParseHeader()
in tensorflow_compression/cc/kernels/y4m_dataset_kernels.cc
 93 25 5
def call()
in tensorflow_compression/python/layers/gdn.py
 42 23 2
def build_model()
in models/hific/model.py
 52 21 3
def _up_convolve_transpose_explicit()
in tensorflow_compression/python/layers/signal_conv.py
 53 17 4
def __init__()
in models/hific/model.py
 63 15 6
def _up_convolve_transpose_valid()
in tensorflow_compression/python/layers/signal_conv.py
 61 14 4
def _correlate_down_valid()
in tensorflow_compression/python/layers/signal_conv.py
 29 13 3
def __init__()
in tensorflow_compression/python/entropy_models/continuous_indexed.py
 50 13 13
def call()
in models/ms2020.py
 43 13 3
def compress()
in models/tfci.py
 36 12 5
def build_input()
in models/hific/model.py
 23 12 5
def __init__()
in tensorflow_compression/python/entropy_models/universal.py
 50 11 13
absl::Status RangeDecoder::CheckForErrorInternal()
in tensorflow_compression/cc/lib/range_coder.cc
 37 11 3
def plot_transfer()
in models/toy_sources/ntc.py
 49 11 5
def build()
in tensorflow_compression/python/layers/signal_conv.py
 32 10 2
Status GetNextInternal()
in tensorflow_compression/cc/kernels/y4m_dataset_kernels.cc
 71 10 3
def encode_decode()
in models/toy_sources/ntc.py
 32 9 8
def plot_quantization()
in models/toy_sources/compression_model.py
 55 9 4