ShapeCheck

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. Comments documenting shapes can easily become out of date as code evolves. This library aims to solve both of those problems by ensuring function input/output 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.

This library has minimal overhead, and can be easily disabled globally after prototyping if desired. 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.

This library was inspired by many other tools, including nptyping, einops, shapecheck, torch named tensors, tensorcheck.

Install Library

From PyPI:

pip install --upgrade shapecheck

To install the latest version:

pip install --upgrade git+https://github.com/n2cholas/shapecheck.git

Usage

import numpy as np
from shapecheck import check_shapes

@check_shapes({'imgs': 'N,W,W,-1', 'labels': 'N,1'}, 'N', out_='')
def loss_fn(batch, weights):
    diff = (batch['imgs'].mean((1, 2, 3)) - batch['labels'].squeeze())
    return np.mean(weights * diff**2)

loss_fn({'imgs': np.ones((3, 2, 2, 1)), 'labels': np.ones((3, 1))},
        weights=np.ones(3))  # succeeds
loss_fn({'imgs': np.ones((5, 3, 3, 4)), 'labels': np.ones((5, 1))},
        weights=np.ones(5))  # succeeds
loss_fn({'imgs': np.ones((3, 5, 2, 1)), 'labels': np.ones((3, 1))},
        weights=np.ones(3))  # fails

Error message:

ShapeError: in function loss_fn.
Named Dimensions: {'N': 3, 'W': 5}.
Input:
    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).
    Match:    Argument: weights Expected Shape: ('N',) Actual Shape: (3,).

In the above example, we compute the "loss" with a batch of data, which is a dictionary with 'imgs' and 'labels'. We specify that 'imgs' should be of shape N,W,W,-1, i.e., N square images which can have any number of channels (indicated by the -1). We want the 'labels' to have shape N,1, and weights to be a vector of size N.

Finally, we specify the output shape should be a scalar via out_=''. All non-input shape arguments to check_shape have an underscore after them so they don't conflict with the decorated function's arguments (for now, just out_ and match_callees_).

Inputs to check_shape can be arbitrarily nested dicts/lists/tuples, as long as the structure of the shape specification matches the structure of the inputs to the decorated function.

The shapes can be specified as positional arguments (like in the example), or as key word arguments. For example, the following two are equivalent to the example above:

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

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

Skipping Arguments

If you want to not check an argument (e.g. if it's not an array), you can exclude that argument, or specify that the shape is None. For example, the following two are equivalent:

@check_shapes(None, 'N,2')
def fn(arg1_that_isnt_checked, arg2):
    ...

@check_shapes(arg2='N,2')
def fn(arg1_that_isnt_checked, arg2):
    ...

Consistent Shapes between Functionc Calls

If you have a function with shape-checking that calls other functions with shape-checking, you can optionally enforce that dimensions with the same letter name in the caller correspond to the same sized dimension in the callees via match_callees_=True. That is, you can check that named dimensions are consistent between functions on the call stack.

@check_shapes('M')
def fn2(x):
    return x

@check_shapes('M', 'N', match_callees_=True)
def fn1(x, y):
    return fn2(y)

fn1(np.ones(5), np.ones(6))  # fails

Here, we (mistakenly) used y instead of x when calling fn2. When match_callees_=False, this function would work just fine. When match_callees_=True, we get the following error:

ShapeError: in function fn2.
Named Dimensions: {'M': 5, 'N': 6}.
Input:
    MisMatch: Argument: a Expected Shape: ('M',) Actual Shape: (6,).

Variadic Dimensions

This library also supports variadic dimensions. You can use '...' to indicate 0 or more dimensions (the spaces in the example strings are optional):

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

g(np.ones((2, 3, 4, 1)), np.ones((5, 2, 1)))  # succeeds
g(np.ones((3, 1)), np.ones((3, 1)))  # succeeds
g(np.ones((2, 3, 4, 1)), np.ones((1, 1)))  # fails

The last statement fails with the following error, since dim doesn't match:

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

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

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

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

The error message for the first fail would be:

ShapeError: in function h.
Named Dimensions: {'batch': 6, 'variadic...': (2, 1)}.
Input:
    Match:    Argument: a Expected Shape: ('batch', 'variadic...') Actual Shape: (6, 2, 1).
    MisMatch: Argument: b Expected Shape: ('variadic...',) Actual Shape: (1, 1).

Toggling Checking Globally

You can enable/disable shapechecking globally as shown below:

from shapecheck import is_checking_enabled, set_checking_enabled

assert is_checking_enabled()
set_checking_enabled(False)
assert not is_checking_enabled()
set_checking_enabled(True)
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()

If you have any questions or issues with the library, please raise an issue on GitHub. Hope you enjoy using the library!