Method testArcCosineFeatureMap

caffe2/python/layers_test.py:1755–1862

(self, batch_size, input_dims, output_dims, s, scale,
                                set_weight_as_global_constant)

        set_weight_as_global_constant=st.booleans()
    )
    def testArcCosineFeatureMap(self, batch_size, input_dims, output_dims, s, scale,
                                set_weight_as_global_constant):

        def _arc_cosine_hypothesis_test(ac_output, X, W, b, s):
            '''
            Runs hypothesis test for Arc Cosine layer.

            Inputs:
                ac_output -- output of net after running arc cosine layer
                X -- input data
                W -- weight parameter from train_init_net
                b -- bias parameter from train_init_net
                s -- degree parameter
            '''
            # Get output from net
            net_output = workspace.FetchBlob(ac_output)

            # Computing output directly
            x_rand = np.matmul(X, np.transpose(W)) + b
            x_pow = np.power(x_rand, s)
            if s > 0:
                h_rand_features = np.piecewise(x_rand,
                                               [x_rand <= 0, x_rand > 0],
                                               [0, 1])
            else:
                h_rand_features = np.piecewise(x_rand,
                                               [x_rand <= 0, x_rand > 0],
                                               [0, lambda x: x / (1 + x)])
            output_ref = np.multiply(x_pow, h_rand_features)

            # Comparing net output and computed output
            npt.assert_allclose(net_output, output_ref, rtol=1e-3, atol=1e-3)

        X = np.random.normal(size=(batch_size, input_dims)).astype(np.float32)
        input_record = self.new_record(schema.Scalar((np.float32, (input_dims,))))
        schema.FeedRecord(input_record, [X])
        input_blob = input_record.field_blobs()[0]

        ac_output = self.model.ArcCosineFeatureMap(
            input_record,
            output_dims,
            s=s,
            scale=scale,
            set_weight_as_global_constant=set_weight_as_global_constant
        )
        self.model.output_schema = schema.Struct()
        self.assertEqual(
            schema.Scalar((np.float32, (output_dims, ))),
            ac_output
        )

        train_init_net, train_net = self.get_training_nets()

        # Run create_init_net to initialize the global constants, and W and b
        workspace.RunNetOnce(train_init_net)
        workspace.RunNetOnce(self.model.create_init_net(name='init_net'))

        if set_weight_as_global_constant: