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from caffe2.python import workspace, core
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
from hypothesis import given, settings
import hypothesis.strategies as st
import numpy as np
class TestNegateGradient(serial.SerializedTestCase):
@given(X=hu.tensor(), inplace=st.booleans(), **hu.gcs)
@settings(deadline=10000)
def test_forward(self, X, inplace, gc, dc):
def neg_grad_ref(X):
return (X,)
op = core.CreateOperator("NegateGradient", ["X"], ["Y" if not inplace else "X"])
self.assertReferenceChecks(gc, op, [X], neg_grad_ref)
self.assertDeviceChecks(dc, op, [X], [0])
@given(size=st.lists(st.integers(min_value=1, max_value=20),
min_size=1, max_size=5))
def test_grad(self, size):
X = np.random.random_sample(size)
workspace.ResetWorkspace()
workspace.FeedBlob("X", X.astype(np.float32))
net = core.Net("negate_grad_test")
Y = net.NegateGradient(["X"], ["Y"])
grad_map = net.AddGradientOperators([Y])
workspace.RunNetOnce(net)
# check X_grad == negate of Y_grad
x_val, y_val = workspace.FetchBlobs(['X', 'Y'])
x_grad_val, y_grad_val = workspace.FetchBlobs([grad_map['X'],
grad_map['Y']])
np.testing.assert_array_equal(x_val, y_val)
np.testing.assert_array_equal(x_grad_val, y_grad_val * (-1))
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