pyctb 0.0.4

Python Customized Tracebacks

Python Customized Tracebacks

Installation

pip install pyctb 

Synopsis

import pyctb
# add custom rendering functions
pyctb.add_group('torch')
# activate the custom tracebacks
pyctb.on()

# call your program code, get customized tracebacks if exceptions happen
...

Introduction

pyctb is tool for customizing Python traceback information. For each traceback frame, pyctb adds extra lines of the form var = render(value) for each variable in the currently executing function of that frame. By default, render simply calls str(value). However, the user may register sub-functions for rendering specific types of their choice.

You may render a new function to render instances of a class with:

pyctb.add(cls, func)

Objects are rendered using the most specific (in method-resolution order) rendering function registered. For example:

pyctb.add(Tabby, render_tabby)
pyctb.add(Cat, render_cat)
pyctb.add(Animal, render_animal)

Then, a Tabby will be rendered with render_tabby. A Persian will use render_cat, and Dog will use render_animal, and so forth. By default, object is registered with str.

Example

Here you can see an example

import torch
import pyctb

# show custom render functions for types
pyctb.inventory()
# Returns
# tf:   Render a tf.Tensor, tf.Variable, or EagerTensor
# torch:   Render a torch.Tensor or torch.nn.parameter.Parameter

# generate an exception with default traceback
conv(inp)

# add the torch render group
pyctb.add_group('torch')

# Turn on the custom traceback
pyctb.on()

# generate except with custom traceback showing argument values
conv(inp)

pyctb.off()

The tracebacks are as follows:

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/henry/miniconda3/envs/pytorch181/lib/python3.9/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/home/henry/miniconda3/envs/pytorch181/lib/python3.9/site-packages/torch/nn/modules/conv.py", line 263, in forward
    return self._conv_forward(input, self.weight, self.bias)
  File "/home/henry/miniconda3/envs/pytorch181/lib/python3.9/site-packages/torch/nn/modules/conv.py", line 259, in _conv_forward
    return F.conv1d(input, weight, bias, self.stride,
RuntimeError: Expected 3-dimensional input for 3-dimensional weight [5, 5, 10], but got 2-dimensional input of size [5, 100] instead

and the customized traceback, using the registered torch group:

Custom Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/henry/miniconda3/envs/pytorch181/lib/python3.9/site-packages/torch/nn/modules/module.py", line 889, in Module._call_impl
     888 else:
-->  889     result = self.forward(*input, **kwargs)
     890 for hook in itertools.chain(
     891         _global_forward_hooks.values(),
     892         self._forward_hooks.values()):
File "/home/henry/miniconda3/envs/pytorch181/lib/python3.9/site-packages/torch/nn/modules/conv.py", line 263, in Conv1d.forward
     262 def forward(self, input: Tensor) -> Tensor:
-->  263     return self._conv_forward(input, self.weight, self.bias)
-->      input = [5,100]:float32:cpu
File "/home/henry/miniconda3/envs/pytorch181/lib/python3.9/site-packages/torch/nn/modules/conv.py", line 259, in Conv1d._conv_forward
     256     return F.conv1d(F.pad(input, self._reversed_padding_repeated_twice, mode=self.padding_mode),
     257                     weight, bias, self.stride,
     258                     _single(0), self.dilation, self.groups)
-->  259 return F.conv1d(input, weight, bias, self.stride,
     260                 self.padding, self.dilation, self.groups)
-->      input = [5,100]:float32:cpu
-->      weight = [5,5,10]:float32:cpu
-->      bias = [5]:float32:cpu
-->      self.stride = ('1',)
-->      self.padding = ('0',)
-->      self.dilation = ('1',)
-->      self.groups = 1
RuntimeError: Expected 3-dimensional input for 3-dimensional weight [5, 5, 10], but got 2-dimensional input of size [5, 100] instead