def test_feed(self, x):
def f(inputs, _):
self.assertEqual(x.shape, inputs[0].shape)
self.assertEqual(type(inputs[0].shape), tuple)
self.assertEqual(type(inputs[0].data), np.ndarray)
np.testing.assert_almost_equal(x, inputs[0].data)
op = CreatePythonOperator(f, ["x"], [])
workspace.FeedBlob("x", x)
workspace.RunOperatorOnce(op)
def test_caught_exception_doesnt_terminate(self, x):
def f(inputs, outputs):
try:
raise Exception("Exception in handler")
except:
pass
op = CreatePythonOperator(f, ["x"], ["y"])
workspace.FeedBlob("x", x)
workspace.RunOperatorOnce(op)
def test_feed_with_gc(self, x):
def f(inputs, _):
self.assertEqual(x.shape, inputs[0].shape)
np.testing.assert_almost_equal(x, inputs[0].data)
op = CreatePythonOperator(f, ["x"], [])
workspace.FeedBlob("x", x)
workspace.RunOperatorOnce(op)
del f
workspace.FeedBlob("x", x)
workspace.RunOperatorOnce(op)
def test_reshape(self, x):
def f(inputs, outputs):
outputs[0].reshape(inputs[0].shape)
self.assertEqual(x.shape, inputs[0].shape)
self.assertEqual(x.shape, outputs[0].shape)
outputs[0].data[...] = inputs[0].data
op = CreatePythonOperator(f, ["x"], ["y"])
workspace.FeedBlob("x", x)
workspace.RunOperatorOnce(op)
y = workspace.FetchBlob("y")
np.testing.assert_almost_equal(x, y)
def test_gradient(self, x, in_place):
def f(inputs, outputs):
outputs[0].reshape(inputs[0].shape)
outputs[0].data[...] = inputs[0].data * 2
def grad_f(inputs, outputs):
# Ordering is [inputs, outputs, grad_outputs]
grad_output = inputs[2]
grad_input = outputs[0]
grad_input.reshape(grad_output.shape)
grad_input.data[...] = grad_output.data * 2
op = CreatePythonOperator(f, ["x"], ["x" if in_place else "y"],
grad_f=grad_f)
self.assertGradientChecks(hu.cpu_do, op, [x], 0, [0])
def test_multithreaded_evaluation(self, x, n, w):
def f(inputs, outputs):
outputs[0].reshape(inputs[0].shape)
outputs[0].data[...] = inputs[0].data
ops = [CreatePythonOperator(f, ["x"], [str(i)]) for i in range(n)]
net = core.Net("net")
net.Proto().op.extend(ops)
net.Proto().type = "dag"
net.Proto().num_workers = w
iters = 100
plan = core.Plan("plan")
plan.AddStep(core.ExecutionStep("test-step", net, iters))
workspace.FeedBlob("x", x)
workspace.RunPlan(plan.Proto().SerializeToString())
for i in range(n):
y = workspace.FetchBlob(str(i))
np.testing.assert_almost_equal(x, y)
def test_gradient_multiple(self, inputs):
(x1, x2) = inputs
def f(inputs, outputs):
for idx in [0, 1]:
outputs[idx].reshape(inputs[idx].shape)
outputs[idx].data[...] = inputs[idx].data * 2
def grad_f(inputs, outputs):
# Ordering is [inputs, outputs, grad_outputs]
self.assertEqual(len(inputs), 6)
self.assertEqual(len(outputs), 2)
for (grad_output_idx, grad_input_idx) in [(4, 0), (5, 1)]:
grad_output = inputs[grad_output_idx]
grad_input = outputs[grad_input_idx]
grad_input.reshape(grad_output.shape)
grad_input.data[...] = grad_output.data * 2
op = CreatePythonOperator(f, ["x1", "x2"], ["y1", "y2"], grad_f=grad_f)
for idx in [0, 1]:
self.assertGradientChecks(hu.cpu_do, op, [x1, x2], idx, [0, 1])
def test_exception(self):
op = CreatePythonOperator(MainOpFunctionThatThrowsRuntimeError, [], [])
with self.assertRaises(RuntimeError):
workspace.RunOperatorOnce(op)
