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from caffe2.python import core
from hypothesis import given
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np
class TestATen(hu.HypothesisTestCase):
@given(inputs=hu.tensors(n=2), **hu.gcs)
def test_add(self, inputs, gc, dc):
op = core.CreateOperator(
"ATen",
["X", "Y"],
["Z"],
operator="add")
def ref(X, Y):
return [X + Y]
self.assertReferenceChecks(gc, op, inputs, ref)
@given(inputs=hu.tensors(n=2, dtype=np.float16), **hu.gcs_gpu_only)
def test_add_half(self, inputs, gc, dc):
op = core.CreateOperator(
"ATen",
["X", "Y"],
["Z"],
operator="add")
def ref(X, Y):
return [X + Y]
self.assertReferenceChecks(gc, op, inputs, ref)
@given(inputs=hu.tensors(n=1), **hu.gcs)
def test_pow(self, inputs, gc, dc):
op = core.CreateOperator(
"ATen",
["S"],
["Z"],
operator="pow", exponent=2.0)
def ref(X):
return [np.square(X)]
self.assertReferenceChecks(gc, op, inputs, ref)
@given(x=st.integers(min_value=2, max_value=8), **hu.gcs)
def test_sort(self, x, gc, dc):
inputs = [np.random.permutation(x)]
op = core.CreateOperator(
"ATen",
["S"],
["Z", "I"],
operator="sort")
def ref(X):
return [np.sort(X), np.argsort(X)]
self.assertReferenceChecks(gc, op, inputs, ref)
@given(inputs=hu.tensors(n=1), **hu.gcs)
def test_sum(self, inputs, gc, dc):
op = core.CreateOperator(
"ATen",
["S"],
["Z"],
operator="sum")
def ref(X):
return [np.sum(X)]
self.assertReferenceChecks(gc, op, inputs, ref)
@given(**hu.gcs)
def test_index_uint8(self, gc, dc):
# Indexing with uint8 is deprecated, but we need to provide backward compatibility for some old models exported through ONNX
op = core.CreateOperator(
"ATen",
['self', 'mask'],
["Z"],
operator="index")
def ref(self, mask):
return (self[mask.astype(np.bool_)],)
tensor = np.random.randn(2, 3, 4).astype(np.float32)
mask = np.array([[1, 0, 0], [1, 1, 0]]).astype(np.uint8)
self.assertReferenceChecks(gc, op, [tensor, mask], ref)
@given(**hu.gcs)
def test_index_put(self, gc, dc):
op = core.CreateOperator(
"ATen",
['self', 'indices', 'values'],
["Z"],
operator="index_put")
def ref(self, indices, values):
self[indices] = values
return (self,)
tensor = np.random.randn(3, 3).astype(np.float32)
mask = np.array([[True, True, True], [True, False, False], [True, True, False]])
values = np.random.randn(6).astype(np.float32)
self.assertReferenceChecks(gc, op, [tensor, mask, values], ref)
@given(**hu.gcs)
def test_unique(self, gc, dc):
op = core.CreateOperator(
"ATen",
['self'],
["output"],
sorted=True,
return_inverse=True,
# return_counts=False,
operator="_unique")
def ref(self):
index, _ = np.unique(self, return_index=False, return_inverse=True, return_counts=False)
return (index,)
tensor = np.array([1, 2, 6, 4, 2, 3, 2])
print(ref(tensor))
self.assertReferenceChecks(gc, op, [tensor], ref)
if __name__ == "__main__":
import unittest
unittest.main()
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