Method testAdaptiveWeight

caffe2/python/layers_test.py:2125–2163

(
        self, num, feed_weight, use_inv_var_parameterization, use_log_barrier,
        enable_diagnose, gc, dc
    )

    )
    @settings(deadline=1000)
    def testAdaptiveWeight(
        self, num, feed_weight, use_inv_var_parameterization, use_log_barrier,
        enable_diagnose, gc, dc
    ):
        input_record = self.new_record(schema.RawTuple(num))
        data = np.random.random(num)
        schema.FeedRecord(
            input_record, [np.array(x).astype(np.float32) for x in data]
        )
        weights = np.random.random(num) if feed_weight else None
        result = self.model.AdaptiveWeight(
            input_record,
            weights=weights,
            estimation_method=(
                'inv_var' if use_inv_var_parameterization else 'log_std'
            ),
            pos_optim_method=(
                'log_barrier' if use_log_barrier else 'pos_grad_proj'
            ),
            enable_diagnose=enable_diagnose
        )
        train_init_net, train_net = self.get_training_nets(True)
        workspace.RunNetOnce(train_init_net)
        workspace.RunNetOnce(train_net)
        result = workspace.FetchBlob(result())
        if not feed_weight:
            weights = np.array([1. / num for _ in range(num)])
        expected = np.sum(weights * data + 0.5 * np.log(1. / 2. / weights))
        npt.assert_allclose(expected, result, atol=1e-4, rtol=1e-4)
        if enable_diagnose:
            assert len(self.model.ad_hoc_plot_blobs) == num
            reconst_weights_from_ad_hoc = np.array(
                [workspace.FetchBlob(b) for b in self.model.ad_hoc_plot_blobs]
            ).flatten()
            npt.assert_allclose(
                reconst_weights_from_ad_hoc, weights, atol=1e-4, rtol=1e-4
            )
        else:
            assert len(self.model.ad_hoc_plot_blobs) == 0

    @given(num=st.integers(min_value=10, max_value=100), **hu.gcs)
    def testConstantWeight(self, num, gc, dc):