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from caffe2.python import core
from hypothesis import given, settings
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.serialized_test.serialized_test_util as serial
import hypothesis.strategies as st
import numpy as np
class TestExpandOp(serial.SerializedTestCase):
def _rand_shape(self, X_shape, max_length):
length = np.random.randint(max_length)
shape = np.ones(length, dtype=np.int64)
i = len(X_shape) - 1
for j in reversed(range(length)):
if i >= 0:
k = np.random.choice([1, X_shape[i]])
i -= 1
else:
k = np.random.randint(3) + 1
shape[j] = k
return shape
def _run_expand_op_test(self, X, shape, gc, dc):
shape = np.array(shape)
op = core.CreateOperator(
'Expand',
["X", "shape"],
["Y"],
)
def ref(X, shape):
return (X * np.ones(abs(shape)),)
self.assertReferenceChecks(gc, op, [X, shape], ref)
self.assertDeviceChecks(dc, op, [X, shape], [0])
self.assertGradientChecks(gc, op, [X, shape], 0, [0])
@serial.given(X=hu.tensor(max_dim=5, dtype=np.float32),
**hu.gcs)
def test_expand_rand_shape(self, X, gc, dc):
shape = self._rand_shape(X.shape, 5)
self._run_expand_op_test(X, shape, gc, dc)
@given(X=st.sampled_from([np.ones([1, 3, 1]),
np.ones([3, 1, 3]),
np.ones([1, 3])]),
**hu.gcs)
def test_expand_nonrand_shape1(self, X, gc, dc):
self._run_expand_op_test(X, [3, 1, 3], gc, dc)
self._run_expand_op_test(X, [3, -1, 3], gc, dc)
@given(X=st.sampled_from([np.ones([4, 4, 2, 1]),
np.ones([1, 4, 1, 2]),
np.ones([4, 1, 2])]),
**hu.gcs)
@settings(deadline=10000)
def test_expand_nonrand_shape2(self, X, gc, dc):
self._run_expand_op_test(X, [4, 1, 2, 2], gc, dc)
self._run_expand_op_test(X, [4, -1, 2, 2], gc, dc)
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