mirror of
https://github.com/saymrwulf/pytorch.git
synced 2026-05-14 20:57:59 +00:00
27c7158166
Summary: There is a module called `2to3` which you can target for future specifically to remove these, the directory of `caffe2` has the most redundant imports: ```2to3 -f future -w caffe2``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/45033 Reviewed By: seemethere Differential Revision: D23808648 Pulled By: bugra fbshipit-source-id: 38971900f0fe43ab44a9168e57f2307580d36a38
57 lines
1.5 KiB
Python
57 lines
1.5 KiB
Python
|
|
|
|
|
|
|
|
|
|
import numpy as np
|
|
import hypothesis.strategies as st
|
|
import unittest
|
|
import caffe2.python.hypothesis_test_util as hu
|
|
from caffe2.python import core, workspace
|
|
from hypothesis import given
|
|
import caffe2.python.ideep_test_util as mu
|
|
|
|
|
|
@unittest.skipIf(not workspace.C.use_mkldnn, "No MKLDNN support.")
|
|
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(),
|
|
**mu.gcs_ideep_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,
|
|
)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|