tensorflow / tensorrt
Unit Size

The distribution of size of units (measured in lines of code).

Intro
  • Unit size measurements show the distribution of size of units of code (methods, functions...).
  • Units are classified in four categories based on their size (lines of code): 1-20 (small units), 20-50 (medium size units), 51-100 (long units), 101+ (very long units).
  • You should aim at keeping units small (< 20 lines). Long units may become "bloaters", code that have increased to such gargantuan proportions that they are hard to work with.
Learn more...
Unit Size Overall
  • There are 3 units with 40 lines of code in units (32.5% of code).
    • 0 very long units (0 lines of code)
    • 0 long units (0 lines of code)
    • 1 medium size units (24 lines of code)
    • 1 small units (13 lines of code)
    • 1 very small units (3 lines of code)
0% | 0% | 60% | 32% | 7%
Legend:
101+
51-100
21-50
11-20
1-10
Unit Size per Extension
101+
51-100
21-50
11-20
1-10
py0% | 0% | 60% | 32% | 7%
Unit Size per Logical Component
primary logical decomposition
101+
51-100
21-50
11-20
1-10
tftrt/blog_posts/Leveraging TensorFlow-TensorRT integration for Low latency Inference0% | 0% | 60% | 32% | 7%
Alternative Visuals
Longest Units
Top 3 longest units
Unit# linesMcCabe index# params
def load_with_converter()
in tftrt/blog_posts/Leveraging TensorFlow-TensorRT integration for Low latency Inference/tf2_inference.py
 24 3 3
def dataloader_fn()
in tftrt/blog_posts/Leveraging TensorFlow-TensorRT integration for Low latency Inference/tf2_inference.py
 13 1 2
def calibration_input_fn()
in tftrt/blog_posts/Leveraging TensorFlow-TensorRT integration for Low latency Inference/tf2_inference.py
 3 2 0