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_sbn_op_test.py (3110B)

@unittest.skipIf(not workspace.C.has_mkldnn,
                 "Skipping as we do not have mkldnn.")
class MKLSpatialBNTest(hu.HypothesisTestCase):
    @given(size=st.integers(7, 10),
           input_channels=st.integers(1, 10),
           batch_size=st.integers(1, 3),
           seed=st.integers(0, 65535),
           #order=st.sampled_from(["NCHW", "NHWC"]),
           order=st.sampled_from(["NCHW"]),
           epsilon=st.floats(1e-5, 1e-2),
           **mu.gcs)
    def test_spatialbn_test_mode(self, size, input_channels,
                                 batch_size, seed, order, epsilon, gc, dc):
        np.random.seed(seed)
        scale = np.random.rand(input_channels).astype(np.float32) + 0.5
        bias = np.random.rand(input_channels).astype(np.float32) - 0.5
        mean = np.random.randn(input_channels).astype(np.float32)
        var = np.random.rand(input_channels).astype(np.float32) + 0.5
        X = np.random.rand(
            batch_size, input_channels, size, size).astype(np.float32) - 0.5

op = core.CreateOperator(
            "SpatialBN",
            ["X", "scale", "bias", "mean", "var"],
            ["Y"],
            order=order,
            is_test=True,
            epsilon=epsilon,
        )

self.assertDeviceChecks(dc, op, [X, scale, bias, mean, var], [0])

@given(size=st.integers(7, 10),
           input_channels=st.integers(1, 10),
           batch_size=st.integers(1, 3),
           seed=st.integers(0, 65535),
           #order=st.sampled_from(["NCHW", "NHWC"]),
           order=st.sampled_from(["NCHW"]),
           epsilon=st.floats(1e-5, 1e-2),
           **mu.gcs)
    def test_spatialbn_train_mode(
            self, size, input_channels, batch_size, seed, order, epsilon,
            gc, dc):
        op = core.CreateOperator(
            "SpatialBN",
            ["X", "scale", "bias", "running_mean", "running_var"],
            ["Y", "running_mean", "running_var", "saved_mean", "saved_var"],
            order=order,
            is_test=False,
            epsilon=epsilon,
        )
        np.random.seed(seed)
        scale = np.random.rand(input_channels).astype(np.float32) + 0.5
        bias = np.random.rand(input_channels).astype(np.float32) - 0.5
        mean = np.random.randn(input_channels).astype(np.float32)
        var = np.random.rand(input_channels).astype(np.float32) + 0.5
        X = np.random.rand(
            batch_size, input_channels, size, size).astype(np.float32) - 0.5
        # Note: it seems that the running mean and var do not pass the device
        # test, suggesting that the semantics are a bit different. Only
        # checking the output and saved mean and var at this stage.
        self.assertDeviceChecks(dc, op, [X, scale, bias, mean, var],
                                [0, 3, 4])

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 MKLSpatialBNTest(hu.HypothesisTestCase): @given(size=st.integers(7, 10), input_channels=st.integers(1, 10), batch_size=st.integers(1, 3), seed=st.integers(0, 65535), #order=st.sampled_from(["NCHW", "NHWC"]), order=st.sampled_from(["NCHW"]), epsilon=st.floats(1e-5, 1e-2), **mu.gcs) def test_spatialbn_test_mode(self, size, input_channels, batch_size, seed, order, epsilon, gc, dc): np.random.seed(seed) scale = np.random.rand(input_channels).astype(np.float32) + 0.5 bias = np.random.rand(input_channels).astype(np.float32) - 0.5 mean = np.random.randn(input_channels).astype(np.float32) var = np.random.rand(input_channels).astype(np.float32) + 0.5 X = np.random.rand( batch_size, input_channels, size, size).astype(np.float32) - 0.5 op = core.CreateOperator( "SpatialBN", ["X", "scale", "bias", "mean", "var"], ["Y"], order=order, is_test=True, epsilon=epsilon, ) self.assertDeviceChecks(dc, op, [X, scale, bias, mean, var], [0]) @given(size=st.integers(7, 10), input_channels=st.integers(1, 10), batch_size=st.integers(1, 3), seed=st.integers(0, 65535), #order=st.sampled_from(["NCHW", "NHWC"]), order=st.sampled_from(["NCHW"]), epsilon=st.floats(1e-5, 1e-2), **mu.gcs) def test_spatialbn_train_mode( self, size, input_channels, batch_size, seed, order, epsilon, gc, dc): op = core.CreateOperator( "SpatialBN", ["X", "scale", "bias", "running_mean", "running_var"], ["Y", "running_mean", "running_var", "saved_mean", "saved_var"], order=order, is_test=False, epsilon=epsilon, ) np.random.seed(seed) scale = np.random.rand(input_channels).astype(np.float32) + 0.5 bias = np.random.rand(input_channels).astype(np.float32) - 0.5 mean = np.random.randn(input_channels).astype(np.float32) var = np.random.rand(input_channels).astype(np.float32) + 0.5 X = np.random.rand( batch_size, input_channels, size, size).astype(np.float32) - 0.5 # Note: it seems that the running mean and var do not pass the device # test, suggesting that the semantics are a bit different. Only # checking the output and saved mean and var at this stage. self.assertDeviceChecks(dc, op, [X, scale, bias, mean, var], [0, 3, 4]) if __name__ == "__main__": import unittest unittest.main()