Caffe2 - Python API
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caffe2.python.hypothesis_test_util.HypothesisTestCase Class Reference
Inheritance diagram for caffe2.python.hypothesis_test_util.HypothesisTestCase:
caffe2.python.test_util.TestCase

Public Member Functions

def assertDeviceChecks (self, device_options, op, inputs, outputs_to_check, input_device_options=None, threshold=0.01)
 
def assertGradientChecks (self, device_option, op, inputs, outputs_to_check, outputs_with_grads, grad_ops=None, threshold=0.005, stepsize=0.05, input_device_options=None)
 
def assertReferenceChecks (self, device_option, op, inputs, reference, input_device_options=None, threshold=1e-4, output_to_grad=None, grad_reference=None, atol=None, outputs_to_check=None)
 
def assertValidationChecks (self, device_option, op, inputs, validator, input_device_options=None, as_kwargs=True, init_net=None)
 
def assertRunOpRaises (self, device_option, op, inputs, input_device_options=None, exception=(Exception,), regexp=None)
 
- Public Member Functions inherited from caffe2.python.test_util.TestCase
def setUpClass (cls)
 
def setUp (self)
 
def tearDown (self)
 

Additional Inherited Members

- Public Attributes inherited from caffe2.python.test_util.TestCase
 ws
 

Detailed Description

A unittest.TestCase subclass with some helper functions for
utilizing the `hypothesis` (hypothesis.readthedocs.io) library.

Definition at line 330 of file hypothesis_test_util.py.

Member Function Documentation

def caffe2.python.hypothesis_test_util.HypothesisTestCase.assertDeviceChecks (   self,
  device_options,
  op,
  inputs,
  outputs_to_check,
  input_device_options = None,
  threshold = 0.01 
)
Asserts that the operator computes the same outputs, regardless of
which device it is executed on.

Useful for checking the consistency of GPU and CPU
implementations of operators.

Usage example:

    @given(inputs=hu.tensors(n=2), in_place=st.booleans(), **hu.gcs)
    def test_sum(self, inputs, in_place, gc, dc):
op = core.CreateOperator("Sum", ["X1", "X2"],
                                ["Y" if not in_place else "X1"])
X1, X2 = inputs
self.assertDeviceChecks(dc, op, [X1, X2], [0])

Definition at line 343 of file hypothesis_test_util.py.

def caffe2.python.hypothesis_test_util.HypothesisTestCase.assertGradientChecks (   self,
  device_option,
  op,
  inputs,
  outputs_to_check,
  outputs_with_grads,
  grad_ops = None,
  threshold = 0.005,
  stepsize = 0.05,
  input_device_options = None 
)
Implements a standard numerical gradient checker for the operator
in question.

Useful for checking the consistency of the forward and
backward implementations of operators.

Usage example:

    @given(inputs=hu.tensors(n=2), in_place=st.booleans(), **hu.gcs)
    def test_sum(self, inputs, in_place, gc, dc):
op = core.CreateOperator("Sum", ["X1", "X2"],
                                ["Y" if not in_place else "X1"])
X1, X2 = inputs
self.assertGradientChecks(gc, op, [X1, X2], 0, [0])

Definition at line 379 of file hypothesis_test_util.py.

def caffe2.python.hypothesis_test_util.HypothesisTestCase.assertReferenceChecks (   self,
  device_option,
  op,
  inputs,
  reference,
  input_device_options = None,
  threshold = 1e-4,
  output_to_grad = None,
  grad_reference = None,
  atol = None,
  outputs_to_check = None 
)
This runs the reference Python function implementation
(effectively calling `reference(*inputs)`, and compares that
to the output of output, with an absolute/relative tolerance
given by the `threshold` parameter.

Useful for checking the implementation matches the Python
(typically NumPy) implementation of the same functionality.

Usage example:

    @given(X=hu.tensor(), inplace=st.booleans(), **hu.gcs)
    def test_softsign(self, X, inplace, gc, dc):
op = core.CreateOperator(
    "Softsign", ["X"], ["X" if inplace else "Y"])

def softsign(X):
    return (X / (1 + np.abs(X)),)

self.assertReferenceChecks(gc, op, [X], softsign)

Definition at line 515 of file hypothesis_test_util.py.


The documentation for this class was generated from the following file: