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import unittest
import hypothesis.strategies as st
from hypothesis import given, settings
import numpy as np
from caffe2.python import core, workspace
import caffe2.python.hypothesis_test_util as hu
import caffe2.python.ideep_test_util as mu
@unittest.skipIf(not workspace.C.use_mkldnn, "No MKLDNN support.")
class ShapeTest(hu.HypothesisTestCase):
@given(n=st.integers(1, 128),
c=st.integers(1, 128),
h=st.integers(1, 128),
w=st.integers(1, 128),
**mu.gcs)
@settings(max_examples=10, deadline=None)
def test_shape(self, n, c, h, w, gc, dc):
op0 = core.CreateOperator(
"Shape",
["X0"],
["Y0"],
device_option=dc[0]
)
op1 = core.CreateOperator(
"Shape",
["X1"],
["Y1"],
device_option=dc[1]
)
X = np.random.rand(n, c, h, w).astype(np.float32) - 0.5
workspace.FeedBlob('X0', X, dc[0])
workspace.FeedBlob('X1', X, dc[1])
workspace.RunOperatorOnce(op0)
workspace.RunOperatorOnce(op1)
Y0 = workspace.FetchBlob('Y0')
Y1 = workspace.FetchBlob('Y1')
if not np.allclose(Y0, Y1, atol=0, rtol=0):
print(Y1.flatten())
print(Y0.flatten())
print(np.max(np.abs(Y1 - Y0)))
self.assertTrue(False)
@given(n=st.integers(1, 128),
c=st.integers(1, 128),
h=st.integers(1, 128),
w=st.integers(1, 128),
axes=st.lists(st.integers(0, 3), min_size=1, max_size=3),
**mu.gcs)
@settings(max_examples=10, deadline=None)
def test_shape_with_axes(self, n, c, h, w, axes, gc, dc):
axes = list(set(axes)).sort()
op0 = core.CreateOperator(
"Shape",
["X0"],
["Y0"],
axes = axes,
device_option=dc[0]
)
op1 = core.CreateOperator(
"Shape",
["X1"],
["Y1"],
axes = axes,
device_option=dc[1]
)
X = np.random.rand(n, c, h, w).astype(np.float32) - 0.5
workspace.FeedBlob('X0', X, dc[0])
workspace.FeedBlob('X1', X, dc[1])
workspace.RunOperatorOnce(op0)
workspace.RunOperatorOnce(op1)
Y0 = workspace.FetchBlob('Y0')
Y1 = workspace.FetchBlob('Y1')
if not np.allclose(Y0, Y1, atol=0, rtol=0):
print(Y1.flatten())
print(Y0.flatten())
print(np.max(np.abs(Y1 - Y0)))
self.assertTrue(False)
if __name__ == "__main__":
unittest.main()
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