pytorch/caffe2/python/operator_test/weighted_sum_test.py
Pieter Noordhuis 348e29c49b Don't run CUDA tests for ops without CUDA implementation
Summary: Closes https://github.com/caffe2/caffe2/pull/1434

Reviewed By: houseroad, ilia-cher

Differential Revision: D6272614

Pulled By: pietern

fbshipit-source-id: 7b998b08ec02b03f88a6fd24a949b0d199b2aa37
2017-11-08 10:28:02 -08:00

61 lines
1.8 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 TestWeightedSumOp(hu.HypothesisTestCase):
@given(n=st.integers(5, 8), m=st.integers(1, 1),
d=st.integers(2, 4), grad_on_w=st.booleans(),
**hu.gcs_cpu_only)
def test_weighted_sum(self, n, m, d, grad_on_w, gc, dc):
input_names = []
input_vars = []
for i in range(m):
X_name = 'X' + str(i)
w_name = 'w' + str(i)
input_names.extend([X_name, w_name])
var = np.random.rand(n, d).astype(np.float32)
vars()[X_name] = var
input_vars.append(var)
var = np.random.rand(1).astype(np.float32)
vars()[w_name] = var
input_vars.append(var)
def weighted_sum_op_ref(*args):
res = np.zeros((n, d))
for i in range(m):
res = res + args[2 * i + 1] * args[2 * i]
return (res, )
op = core.CreateOperator(
"WeightedSum",
input_names,
['Y'],
grad_on_w=grad_on_w,
)
self.assertReferenceChecks(
device_option=gc,
op=op,
inputs=input_vars,
reference=weighted_sum_op_ref,
)
output_to_check_grad = range(2 * m) if grad_on_w else range(0, 2 * m, 2)
for i in output_to_check_grad:
self.assertGradientChecks(
device_option=gc,
op=op,
inputs=input_vars,
outputs_to_check=i,
outputs_with_grads=[0],
)