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import hypothesis.strategies as st
import numpy as np
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
class TestArgOps(serial.SerializedTestCase):
@given(
X=hu.tensor(dtype=np.float32), axis=st.integers(-1, 5),
keepdims=st.booleans(), **hu.gcs)
@settings(deadline=None)
def test_argmax(self, X, axis, keepdims, gc, dc):
if axis >= len(X.shape):
axis %= len(X.shape)
op = core.CreateOperator(
"ArgMax", ["X"], ["Indices"], axis=axis, keepdims=keepdims,
device_option=gc)
def argmax_ref(X):
indices = np.argmax(X, axis=axis)
if keepdims:
out_dims = list(X.shape)
out_dims[axis] = 1
indices = indices.reshape(tuple(out_dims))
return [indices]
self.assertReferenceChecks(gc, op, [X], argmax_ref)
self.assertDeviceChecks(dc, op, [X], [0])
@given(
X=hu.tensor(dtype=np.float32), axis=st.integers(-1, 5),
keepdims=st.booleans(), **hu.gcs)
@settings(deadline=None)
def test_argmin(self, X, axis, keepdims, gc, dc):
if axis >= len(X.shape):
axis %= len(X.shape)
op = core.CreateOperator(
"ArgMin", ["X"], ["Indices"], axis=axis, keepdims=keepdims,
device_option=gc)
def argmin_ref(X):
indices = np.argmin(X, axis=axis)
if keepdims:
out_dims = list(X.shape)
out_dims[axis] = 1
indices = indices.reshape(tuple(out_dims))
return [indices]
self.assertReferenceChecks(gc, op, [X], argmin_ref)
self.assertDeviceChecks(dc, op, [X], [0])
if __name__ == "__main__":
import unittest
unittest.main()
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