mirror of
https://github.com/saymrwulf/pytorch.git
synced 2026-05-15 21:00:47 +00:00
4f1db36cff
Summary: DivOp missed a gradient for CUDA, so implemented it. Also added operator test. Differential Revision: D4396638 fbshipit-source-id: 9949e47aa3735bb418a0db003e2b2f4896056a71
38 lines
1 KiB
Python
38 lines
1 KiB
Python
from __future__ import absolute_import
|
|
from __future__ import division
|
|
from __future__ import print_function
|
|
from __future__ import unicode_literals
|
|
|
|
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 TestElementwiseOps(hu.HypothesisTestCase):
|
|
|
|
@given(n=st.integers(2, 10), m=st.integers(4, 6),
|
|
d=st.integers(2, 3), **hu.gcs)
|
|
def test_div(self, n, m, d, gc, dc):
|
|
X = np.random.rand(n, m, d).astype(np.float32)
|
|
Y = np.random.rand(n, m, d).astype(np.float32) + 5.0
|
|
|
|
def div_op(X, Y):
|
|
return [np.divide(X, Y)]
|
|
|
|
op = core.CreateOperator(
|
|
"Div",
|
|
["X", "Y"],
|
|
["Z"]
|
|
)
|
|
|
|
self.assertReferenceChecks(
|
|
device_option=gc,
|
|
op=op,
|
|
inputs=[X, Y],
|
|
reference=div_op,
|
|
)
|
|
|
|
self.assertGradientChecks(
|
|
gc, op, [X, Y], 0, [0], stepsize=1e-4, threshold=1e-2)
|