FM / ideep

/usr/local/lib64/python3.6/site-packages/caffe2/python/ideep/

/usr/local/lib64/python3.6/site-packages/caffe2/python/ideep

Name	Size	Mode	Actions
__pycache__/	-	0755	rm
adam_op_test.py	3212	0644	edit dl rm
blobs_queue_db_test.py	4045	0644	edit dl rm
channel_shuffle_op_test.py	1286	0644	edit dl rm
concat_split_op_test.py	5532	0644	edit dl rm
convfusion_op_test.py	31919	0644	edit dl rm
conv_op_test.py	5666	0644	edit dl rm
conv_transpose_test.py	2595	0644	edit dl rm
copy_op_test.py	3074	0644	edit dl rm
dropout_op_test.py	2075	0644	edit dl rm
elementwise_sum_op_test.py	6317	0644	edit dl rm
expanddims_squeeze_op_test.py	4285	0644	edit dl rm
fc_op_test.py	11745	0644	edit dl rm
leaky_relu_op_test.py	2854	0644	edit dl rm
LRN_op_test.py	1195	0644	edit dl rm
moment_sgd_op_test.py	1777	0644	edit dl rm
operator_fallback_op_test.py	3450	0644	edit dl rm
order_switch_op_test.py	2299	0644	edit dl rm
pool_op_test.py	4296	0644	edit dl rm
pre_convert_test.py	4115	0644	edit dl rm
relu_op_test.py	3705	0644	edit dl rm
reshape_op_test.py	5919	0644	edit dl rm
shape_op_test.py	2631	0644	edit dl rm
sigmoid_op_test.py	775	0644	edit dl rm
softmax_op_test.py	932	0644	edit dl rm
spatial_bn_op_test.py	5237	0644	edit dl rm
test_ideep_net.py	4094	0644	edit dl rm
transform_ideep_net.py	11683	0644	edit dl rm
transpose_op_test.py	1284	0644	edit dl rm
weightedsum_op_test.py	1560	0644	edit dl rm
__init__.py	0	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/caffe2/python/ideep/weightedsum_op_test.py (1560B)

@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()

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()