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import numpy as np
from hypothesis import given
import hypothesis.strategies as st
from caffe2.python import core
from caffe2.python import workspace
import caffe2.python.hypothesis_test_util as hu
class TestWeightedMultiSample(hu.HypothesisTestCase):
@given(
num_samples=st.integers(min_value=0, max_value=128),
data_len=st.integers(min_value=0, max_value=10000),
**hu.gcs_cpu_only
)
def test_weighted_multi_sample(self, num_samples, data_len, gc, dc):
weights = np.zeros((data_len))
expected_indices = []
if data_len > 0:
weights[-1] = 1.5
expected_indices = np.repeat(data_len - 1, num_samples)
workspace.FeedBlob("weights", weights.astype(np.float32))
op = core.CreateOperator(
"WeightedMultiSampling",
["weights"],
["sample_indices"],
num_samples=num_samples,
)
workspace.RunOperatorOnce(op)
result_indices = workspace.FetchBlob("sample_indices")
np.testing.assert_allclose(expected_indices, result_indices)
self.assertDeviceChecks(
dc,
op,
[weights.astype(np.float32)],
[0]
)
# test shape input
shape = np.zeros((num_samples))
workspace.FeedBlob("shape", shape)
op2 = core.CreateOperator(
"WeightedMultiSampling",
["weights", "shape"],
["sample_indices_2"]
)
workspace.RunOperatorOnce(op2)
result_indices_2 = workspace.FetchBlob("sample_indices_2")
if data_len > 0:
assert len(result_indices_2) == num_samples
for i in range(num_samples):
assert 0 <= result_indices_2[i] < data_len
else:
assert len(result_indices_2) == 0
self.assertDeviceChecks(dc, op2, [weights.astype(np.float32), shape], [0])
if __name__ == "__main__":
import unittest
unittest.main()
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