| from __future__ import absolute_import |
| from __future__ import division |
| from __future__ import print_function |
| from __future__ import unicode_literals |
| |
| import numpy as np |
| from scipy.sparse import coo_matrix |
| |
| from hypothesis import given |
| import hypothesis.strategies as st |
| |
| from caffe2.python import core |
| import caffe2.python.hypothesis_test_util as hu |
| |
| |
| class TestFunHash(hu.HypothesisTestCase): |
| @given(n_out=st.integers(min_value=5, max_value=20), |
| n_in=st.integers(min_value=10, max_value=20), |
| n_data=st.integers(min_value=2, max_value=8), |
| n_weight=st.integers(min_value=8, max_value=15), |
| n_alpha=st.integers(min_value=3, max_value=8), |
| sparsity=st.floats(min_value=0.1, max_value=1.0), |
| **hu.gcs) |
| def test_funhash(self, n_out, n_in, n_data, n_weight, n_alpha, sparsity, |
| gc, dc): |
| A = np.random.rand(n_data, n_in) |
| A[A > sparsity] = 0 |
| A_coo = coo_matrix(A) |
| val, key, seg = A_coo.data, A_coo.col, A_coo.row |
| |
| weight = np.random.rand(n_weight).astype(np.float32) |
| alpha = np.random.rand(n_alpha).astype(np.float32) |
| val = val.astype(np.float32) |
| key = key.astype(np.int64) |
| seg = seg.astype(np.int32) |
| |
| op = core.CreateOperator( |
| 'FunHash', |
| ['val', 'key', 'seg', 'weight', 'alpha'], |
| ['out'], |
| num_outputs=n_out) |
| |
| # Check over multiple devices |
| self.assertDeviceChecks( |
| dc, op, [val, key, seg, weight, alpha], [0]) |
| # Gradient check wrt weight |
| self.assertGradientChecks( |
| gc, op, [val, key, seg, weight, alpha], 3, [0]) |
| # Gradient check wrt alpha |
| self.assertGradientChecks( |
| gc, op, [val, key, seg, weight, alpha], 4, [0]) |
| |
| op2 = core.CreateOperator( |
| 'FunHash', |
| ['val', 'key', 'seg', 'weight'], |
| ['out'], |
| num_outputs=n_out) |
| |
| # Check over multiple devices |
| self.assertDeviceChecks( |
| dc, op2, [val, key, seg, weight], [0]) |
| # Gradient check wrt weight |
| self.assertGradientChecks( |
| gc, op2, [val, key, seg, weight], 3, [0]) |