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from caffe2.python import 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 TestMarginRankingCriterion(serial.SerializedTestCase):
@given(N=st.integers(min_value=10, max_value=20),
seed=st.integers(min_value=0, max_value=65535),
margin=st.floats(min_value=-0.5, max_value=0.5),
**hu.gcs)
@settings(deadline=10000)
def test_margin_ranking_criterion(self, N, seed, margin, gc, dc):
np.random.seed(seed)
X1 = np.random.randn(N).astype(np.float32)
X2 = np.random.randn(N).astype(np.float32)
Y = np.random.choice([-1, 1], size=N).astype(np.int32)
op = core.CreateOperator(
"MarginRankingCriterion", ["X1", "X2", "Y"], ["loss"],
margin=margin)
def ref_cec(X1, X2, Y):
result = np.maximum(-Y * (X1 - X2) + margin, 0)
return (result, )
inputs = [X1, X2, Y]
# This checks the op implementation against a reference function in
# python.
self.assertReferenceChecks(gc, op, inputs, ref_cec)
# This checks the op implementation over multiple device options (e.g.
# CPU and CUDA). [0] means that the 0-th output is checked.
self.assertDeviceChecks(dc, op, inputs, [0])
# Make singular points less sensitive
X1[np.abs(margin - Y * (X1 - X2)) < 0.1] += 0.1
X2[np.abs(margin - Y * (X1 - X2)) < 0.1] -= 0.1
# Check dX1
self.assertGradientChecks(gc, op, inputs, 0, [0])
# Check dX2
self.assertGradientChecks(gc, op, inputs, 1, [0])
if __name__ == "__main__":
import unittest
unittest.main()
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