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Framework-agnostic library for checking array shapes at runtime.

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Framework-agnostic library for checking array/tensor shapes at runtime.

Finding the root of shape mismatches can be troublesome, especially with broadcasting rules and mutable arrays. Documenting shapes with comments can become stale as code evolves. This library aims to solve both of those problems by ensuring function pre and post shape expectations are met. The concise syntax for expressing shapes serves to document code as well, so new users can quickly understand what's going on.

With frameworks like JAX or TensorFlow, "runtime" is actually "compile" or "trace" time, so you don't pay any cost during execution. For frameworks like PyTorch, asynchronous execution will hide the cost of shape checking. You only pay a small overhead with synchronous, eager frameworks like numpy.

Install Library

From PyPI:

pip install shapecheck

To install the latest version:

pip install --upgrade git+


import numpy as np
from shapecheck import check_shapes

@check_shapes({'imgs': 'N,W,W,-1', 'labels': 'N,1'}, aux_info=None, out_='N')
def per_eg_loss(batch, aux_info):
    # do something with aux_info
    return (batch['imgs'].mean((1, 2, 3)) - batch['labels'].squeeze())**2

per_eg_loss({'imgs': np.ones((3, 2, 2, 1)), 'labels': np.ones((3,1))}, np.ones(1))
per_eg_loss({'imgs': np.ones((5, 3, 3, 4)), 'labels': np.ones((5,1))}, 'any')
# Below line fails:
per_eg_loss({'imgs': np.ones((3, 5, 2, 1)), 'labels': np.ones((3,1))}, 'any')

Error message:

shapecheck.exception.ShapeError: in function per_eg_loss.
Named Dimensions: {'N': 3, 'W': 5}.
    Argument: batch  Type: <class 'dict'>
        MisMatch: Key: imgs Expected Shape: ('N', 'W', 'W', -1) Actual Shape: (3, 5, 2, 1).
        Match:    Key: labels Expected Shape: ('N', 1) Actual Shape: (3, 1).
    Skipped:  Argument: aux_info.

In the above example, we compute per example loss with a batch of data, which is a dictionary with images and labels. We specify that we want N square images, where we can have any number of channels (indicated by the -1). Inputs to check_shape can be arbitrarily nested dicts/lists/tuples, as long as it matches the structure of the inputs to the decorated function.

We specify that aux_info shouldn't be checked. Equivalently, we could've excluded it from the definition:

@check_shapes({'imgs': 'N,W,W,-1', 'labels': 'N,1'}, out_='N'): ...

or passed it as a positional argument.

@check_shapes({'imgs': 'N,W,W,-1', 'labels': 'N,1'}, None, out_='N'): ...

Finally, we specify the output shape should be ('N',). All non-input shape arguments to check_shape have an underscore after them so they don't conflict with the decorated function's arguments.

If you have a function with shapechecking that calls many other functions with shapechecking, you can optionally enforce that dimensions with the same letter name in the parent correspond to the same sized dimension in the children. That is, you can check that a function's input named dimensions match the same named dimensions of all functions higher in the call stack. For example:

@check_shapes('N', 'M', 'O', out_='N')
def child_fn(x, y, z):
    return x

@check_shapes('M', 'N', 'R')
def parent_fn_1(x, y, z):
    return child_fn(x, y, z)

@check_shapes('M', 'N', 'R', match_callees_=True)
def parent_fn_2(x, y, z):
    return child_fn(x, y, z)

parent_fn_1(np.ones(5), np.ones(6), np.ones(7))  # succeeds
parent_fn_2(np.ones(5), np.ones(6), np.ones(7))  # fails

Here, we swapped 'N' and 'M'. So, parent_fn_1 succeeds because the inputs are compatible for each individual function. But parent_fn_2 fails because the named dimensions are inconsistent between the parent and child functions. The following error would be produced:

shapecheck.exception.ShapeError: in function child_fn.
Named Dimensions: {'M': 5, 'N': 6, 'R': 7, 'O': 7}.
    MisMatch: Argument: x Expected Shape: ('N',) Actual Shape: (5,).
    MisMatch: Argument: y Expected Shape: ('M',) Actual Shape: (6,).
    Match:    Argument: z Expected Shape: ('O',) Actual Shape: (7,).

This library also supports variadic dimensions. You can use '...' to indicate 0 or more dimensions:

@check_shapes('1,...,1', '...,1,1')
def g(a, b):

g(np.ones((1, 3, 4, 1)), np.ones((2, 1, 1)))  # succeeds
g(np.ones((1, 1)), np.ones((1, 1)))  # succeeds
g(np.ones((2, 3, 4, 1)), np.ones((1, 1)))  # fails

The last statement fails with the following error:

shapecheck.exception.ShapeError: in function g.
Named Dimensions: {}.
    MisMatch: Argument: a Expected Shape: (1, '...', 1) Actual Shape: (2, 3, 4, 1).
    Match:    Argument: b Expected Shape: ('...', 1, 1) Actual Shape: (1, 1).

You can also name variadic dimensions, to ensure that a contiguous sequence of dimensions match between arguments. For example:

@check_shapes('batch,variadic...', 'variadic...')
def h(a, b):

h(np.ones((7, 1, 2)), np.ones((1, 2)))  # succeeds
h(np.ones((6, 2)), np.ones((1, 1)))  # fails
h(np.ones((6, 2)), np.ones((1)))  # fails

You can enable/disable shapechecking globally as shown below:

from shapecheck import is_checking_enabled, set_checking_enabled

assert is_checking_enabled()
assert not is_checking_enabled()
assert is_checking_enabled()

Or via a context manager:

assert is_checking_enabled()
with set_checking_enabled(False):
    assert not is_checking_enabled()
assert is_checking_enabled()

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