FM / mkl

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

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

Name	Size	Mode	Actions
__pycache__/	-	0755	rm
mkl_concat_op_test.py	1173	0644	edit dl rm
mkl_conv_op_test.py	1684	0644	edit dl rm
mkl_copy_op_test.py	2149	0644	edit dl rm
mkl_elementwise_add_op_test.py	1201	0644	edit dl rm
mkl_elementwise_sum_op_test.py	1334	0644	edit dl rm
mkl_fc_op_test.py	927	0644	edit dl rm
mkl_fc_speed_test.py	3849	0644	edit dl rm
mkl_fill_op_test.py	1019	0644	edit dl rm
mkl_LRN_op_test.py	1160	0644	edit dl rm
mkl_LRN_speed_test.py	3182	0644	edit dl rm
mkl_pool_op_test.py	1332	0644	edit dl rm
mkl_pool_speed_test.py	4210	0644	edit dl rm
mkl_relu_op_test.py	988	0644	edit dl rm
mkl_sbn_op_test.py	3110	0644	edit dl rm
mkl_sbn_speed_test.py	4600	0644	edit dl rm
mkl_sigmoid_op_test.py	833	0644	edit dl rm
mkl_speed_test.py	3079	0644	edit dl rm
mkl_squeeze_op_test.py	968	0644	edit dl rm
rewrite_graph.py	8433	0644	edit dl rm
rewrite_graph_test.py	7889	0644	edit dl rm
__init__.py	0	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/caffe2/python/mkl/mkl_conv_op_test.py (1684B)

@unittest.skipIf(not workspace.C.has_mkldnn,
                 "Skipping as we do not have mkldnn.")
class MKLConvTest(hu.HypothesisTestCase):
    @given(stride=st.integers(1, 3),
           pad=st.integers(0, 3),
           kernel=st.integers(3, 5),
           size=st.integers(8, 20),
           input_channels=st.integers(1, 16),
           output_channels=st.integers(1, 16),
           batch_size=st.integers(1, 3),
           use_bias=st.booleans(),
           group=st.integers(1, 8),
           **mu.gcs)
    def test_mkl_convolution(self, stride, pad, kernel, size,
                             input_channels, output_channels,
                             batch_size, use_bias, group, gc, dc):
        op = core.CreateOperator(
            "Conv",
            ["X", "w", "b"] if use_bias else ["X", "w"],
            ["Y"],
            stride=stride,
            pad=pad,
            kernel=kernel,
            group=group
        )
        X = np.random.rand(
            batch_size, input_channels * group, size, size).astype(np.float32) - 0.5
        w = np.random.rand(
                output_channels * group, input_channels, kernel, kernel) \
            .astype(np.float32) - 0.5
        b = np.random.rand(output_channels * group).astype(np.float32) - 0.5

inputs = [X, w, b] if use_bias else [X, w]
        self.assertDeviceChecks(dc, op, inputs, [0])

if __name__ == "__main__":
    import unittest
    unittest.main()

import unittest import hypothesis.strategies as st from hypothesis import given import numpy as np from caffe2.python import core, workspace import caffe2.python.hypothesis_test_util as hu import caffe2.python.mkl_test_util as mu @unittest.skipIf(not workspace.C.has_mkldnn, "Skipping as we do not have mkldnn.") class MKLConvTest(hu.HypothesisTestCase): @given(stride=st.integers(1, 3), pad=st.integers(0, 3), kernel=st.integers(3, 5), size=st.integers(8, 20), input_channels=st.integers(1, 16), output_channels=st.integers(1, 16), batch_size=st.integers(1, 3), use_bias=st.booleans(), group=st.integers(1, 8), **mu.gcs) def test_mkl_convolution(self, stride, pad, kernel, size, input_channels, output_channels, batch_size, use_bias, group, gc, dc): op = core.CreateOperator( "Conv", ["X", "w", "b"] if use_bias else ["X", "w"], ["Y"], stride=stride, pad=pad, kernel=kernel, group=group ) X = np.random.rand( batch_size, input_channels * group, size, size).astype(np.float32) - 0.5 w = np.random.rand( output_channels * group, input_channels, kernel, kernel) \ .astype(np.float32) - 0.5 b = np.random.rand(output_channels * group).astype(np.float32) - 0.5 inputs = [X, w, b] if use_bias else [X, w] self.assertDeviceChecks(dc, op, inputs, [0]) if __name__ == "__main__": import unittest unittest.main()