| from __future__ import absolute_import |
| from __future__ import division |
| from __future__ import print_function |
| from __future__ import unicode_literals |
| |
| import hypothesis.strategies as st |
| import numpy as np |
| import numpy.testing as npt |
| from hypothesis import given |
| |
| import caffe2.python.hypothesis_test_util as hu |
| |
| from caffe2.python import ( |
| layer_model_instantiator, |
| schema, |
| workspace, |
| ) |
| from caffe2.python.layers.layers import ( |
| InstantiationContext, |
| ) |
| from caffe2.python.layers.tags import Tags |
| from caffe2.python.layer_test_util import ( |
| LayersTestCase, |
| OpSpec, |
| ) |
| from caffe2.python.layers.layers import ( |
| set_request_only, |
| is_request_only_scalar, |
| ) |
| |
| |
| class TestLayers(LayersTestCase): |
| |
| def testFCWithoutBias(self): |
| output_dims = 2 |
| fc_without_bias = self.model.FCWithoutBias( |
| self.model.input_feature_schema.float_features, output_dims) |
| self.model.output_schema = fc_without_bias |
| |
| self.assertEqual( |
| schema.Scalar((np.float32, (output_dims, ))), |
| fc_without_bias |
| ) |
| |
| train_init_net, train_net = self.get_training_nets() |
| |
| init_ops = self.assertNetContainOps( |
| train_init_net, |
| [ |
| OpSpec("UniformFill", None, None), |
| ] |
| ) |
| |
| mat_mul_spec = OpSpec( |
| "MatMul", |
| [ |
| self.model.input_feature_schema.float_features(), |
| init_ops[0].output[0], |
| ], |
| fc_without_bias.field_blobs() |
| ) |
| |
| self.assertNetContainOps(train_net, [mat_mul_spec]) |
| |
| predict_net = self.get_predict_net() |
| self.assertNetContainOps(predict_net, [mat_mul_spec]) |
| |
| def testSamplingTrain(self): |
| output_dims = 1000 |
| |
| indices = self.new_record(schema.Scalar((np.int32, (10,)))) |
| sampling_prob = self.new_record(schema.Scalar((np.float32, (10, )))) |
| |
| sampled_fc = self.model.SamplingTrain( |
| schema.Struct( |
| ('input', self.model.input_feature_schema.float_features), |
| ('indices', indices), |
| ('sampling_prob', sampling_prob), |
| ), |
| "FC", |
| output_dims, |
| ) |
| self.model.output_schema = sampled_fc |
| |
| # Check that we don't add prediction layer into the model |
| self.assertEqual(1, len(self.model.layers)) |
| |
| self.assertEqual( |
| schema.Scalar((np.float32, (output_dims, ))), |
| sampled_fc |
| ) |
| |
| train_init_net, train_net = self.get_training_nets() |
| |
| init_ops = self.assertNetContainOps( |
| train_init_net, |
| [ |
| OpSpec("UniformFill", None, None), |
| OpSpec("UniformFill", None, None), |
| ] |
| ) |
| |
| sampled_fc_layer = self.model.layers[0] |
| |
| gather_w_spec = OpSpec( |
| "Gather", |
| [ |
| init_ops[0].output[0], |
| indices(), |
| ], |
| [ |
| sampled_fc_layer._prediction_layer.train_param_blobs[0] |
| ] |
| ) |
| gather_b_spec = OpSpec( |
| "Gather", |
| [ |
| init_ops[1].output[0], |
| indices(), |
| ], |
| [ |
| sampled_fc_layer._prediction_layer.train_param_blobs[1] |
| ] |
| ) |
| train_fc_spec = OpSpec( |
| "FC", |
| [ |
| self.model.input_feature_schema.float_features(), |
| ] + sampled_fc_layer._prediction_layer.train_param_blobs, |
| sampled_fc.field_blobs() |
| ) |
| log_spec = OpSpec("Log", [sampling_prob()], [None]) |
| sub_spec = OpSpec( |
| "Sub", |
| [sampled_fc.field_blobs()[0], None], |
| sampled_fc.field_blobs() |
| ) |
| |
| train_ops = self.assertNetContainOps( |
| train_net, |
| [gather_w_spec, gather_b_spec, train_fc_spec, log_spec, sub_spec]) |
| |
| self.assertEqual(train_ops[3].output[0], train_ops[4].input[1]) |
| |
| predict_net = self.get_predict_net() |
| self.assertNetContainOps( |
| predict_net, |
| [ |
| OpSpec( |
| "FC", |
| [ |
| self.model.input_feature_schema.float_features(), |
| init_ops[0].output[0], |
| init_ops[1].output[0], |
| ], |
| sampled_fc.field_blobs() |
| ) |
| ] |
| ) |
| |
| def testBatchLRLoss(self): |
| input_record = self.new_record(schema.Struct( |
| ('label', schema.Scalar((np.float64, (1,)))), |
| ('prediction', schema.Scalar((np.float32, (2,)))), |
| ('weight', schema.Scalar((np.float64, (1,)))) |
| )) |
| loss = self.model.BatchLRLoss(input_record) |
| self.assertEqual(schema.Scalar((np.float32, tuple())), loss) |
| |
| def testBatchMSELoss(self): |
| input_record = self.new_record(schema.Struct( |
| ('label', schema.Scalar((np.float64, (1,)))), |
| ('prediction', schema.Scalar((np.float32, (2,)))), |
| )) |
| loss = self.model.BatchMSELoss(input_record) |
| self.assertEqual(schema.Scalar((np.float32, tuple())), loss) |
| |
| def testBatchSigmoidCrossEntropyLoss(self): |
| input_record = self.new_record(schema.Struct( |
| ('label', schema.Scalar((np.float32, (32,)))), |
| ('prediction', schema.Scalar((np.float32, (32,)))) |
| )) |
| loss = self.model.BatchSigmoidCrossEntropyLoss(input_record) |
| self.assertEqual(schema.Scalar((np.float32, tuple())), loss) |
| |
| def testBatchSoftmaxLoss(self): |
| input_record = self.new_record(schema.Struct( |
| ('label', schema.Scalar((np.float32, tuple()))), |
| ('prediction', schema.Scalar((np.float32, (32,)))) |
| )) |
| loss = self.model.BatchSoftmaxLoss(input_record) |
| self.assertEqual(schema.Struct( |
| ('softmax', schema.Scalar((np.float32, (32,)))), |
| ('loss', schema.Scalar(np.float32)), |
| ), loss) |
| |
| def testBatchSoftmaxLossWeight(self): |
| input_record = self.new_record(schema.Struct( |
| ('label', schema.Scalar((np.float32, tuple()))), |
| ('prediction', schema.Scalar((np.float32, (32,)))), |
| ('weight', schema.Scalar((np.float64, (1,)))) |
| )) |
| loss = self.model.BatchSoftmaxLoss(input_record) |
| self.assertEqual(schema.Struct( |
| ('softmax', schema.Scalar((np.float32, (32,)))), |
| ('loss', schema.Scalar(np.float32)), |
| ), loss) |
| |
| @given( |
| X=hu.arrays(dims=[2, 5]), |
| ) |
| def testBatchNormalization(self, X): |
| input_record = self.new_record(schema.Scalar((np.float32, (5,)))) |
| schema.FeedRecord(input_record, [X]) |
| bn_output = self.model.BatchNormalization(input_record) |
| self.assertEqual(schema.Scalar((np.float32, (5,))), bn_output) |
| self.model.output_schema = schema.Struct() |
| |
| train_init_net, train_net = self.get_training_nets() |
| |
| init_ops = self.assertNetContainOps( |
| train_init_net, |
| [ |
| OpSpec("ConstantFill", None, None), |
| OpSpec("ConstantFill", None, None), |
| OpSpec("ConstantFill", None, None), |
| OpSpec("ConstantFill", None, None), |
| ] |
| ) |
| |
| input_blob = input_record.field_blobs()[0] |
| output_blob = bn_output.field_blobs()[0] |
| |
| expand_dims_spec = OpSpec( |
| "ExpandDims", |
| [input_blob], |
| [input_blob], |
| ) |
| |
| train_bn_spec = OpSpec( |
| "SpatialBN", |
| [input_blob, init_ops[0].output[0], init_ops[1].output[0], |
| init_ops[2].output[0], init_ops[3].output[0]], |
| [output_blob, init_ops[2].output[0], init_ops[3].output[0], None, None], |
| {'is_test': 0, 'order': 'NCHW', 'momentum': 0.9}, |
| ) |
| |
| test_bn_spec = OpSpec( |
| "SpatialBN", |
| [input_blob, init_ops[0].output[0], init_ops[1].output[0], |
| init_ops[2].output[0], init_ops[3].output[0]], |
| [output_blob], |
| {'is_test': 1, 'order': 'NCHW', 'momentum': 0.9}, |
| ) |
| |
| squeeze_spec = OpSpec( |
| "Squeeze", |
| [output_blob], |
| [output_blob], |
| ) |
| |
| self.assertNetContainOps( |
| train_net, |
| [expand_dims_spec, train_bn_spec, squeeze_spec] |
| ) |
| |
| eval_net = self.get_eval_net() |
| |
| self.assertNetContainOps( |
| eval_net, |
| [expand_dims_spec, test_bn_spec, squeeze_spec] |
| ) |
| |
| predict_net = self.get_predict_net() |
| |
| self.assertNetContainOps( |
| predict_net, |
| [expand_dims_spec, test_bn_spec, squeeze_spec] |
| ) |
| |
| workspace.RunNetOnce(train_init_net) |
| workspace.RunNetOnce(train_net) |
| |
| schema.FeedRecord(input_record, [X]) |
| workspace.RunNetOnce(eval_net) |
| |
| schema.FeedRecord(input_record, [X]) |
| workspace.RunNetOnce(predict_net) |
| |
| @given( |
| X=hu.arrays(dims=[5, 2]), |
| num_to_collect=st.integers(min_value=1, max_value=10), |
| ) |
| def testLastNWindowCollector(self, X, num_to_collect): |
| input_record = self.new_record(schema.Scalar(np.float32)) |
| schema.FeedRecord(input_record, [X]) |
| last_n = self.model.LastNWindowCollector(input_record, num_to_collect) |
| self.run_train_net_forward_only() |
| output_record = schema.FetchRecord(last_n) |
| start = max(0, 5 - num_to_collect) |
| npt.assert_array_equal(X[start:], output_record()) |
| |
| def testUniformSampling(self): |
| input_record = self.new_record(schema.Scalar(np.int32)) |
| input_array = np.array([3, 10, 11, 15, 20, 99], dtype=np.int32) |
| schema.FeedRecord(input_record, [input_array]) |
| num_samples = 20 |
| num_elements = 100 |
| uniform_sampling_output = self.model.UniformSampling( |
| input_record, num_samples, num_elements) |
| self.model.loss = uniform_sampling_output |
| self.run_train_net() |
| samples = workspace.FetchBlob(uniform_sampling_output.samples()) |
| sampling_prob = workspace.FetchBlob( |
| uniform_sampling_output.sampling_prob()) |
| self.assertEqual(num_samples, len(samples)) |
| np.testing.assert_array_equal(input_array, samples[:len(input_array)]) |
| np.testing.assert_almost_equal( |
| np.array([float(num_samples) / num_elements] * num_samples, |
| dtype=np.float32), |
| sampling_prob |
| ) |
| |
| def testGatherRecord(self): |
| indices = np.array([1, 3, 4], dtype=np.int32) |
| dense = np.array(list(range(20)), dtype=np.float32).reshape(10, 2) |
| lengths = np.array(list(range(10)), dtype=np.int32) |
| items = np.array(list(range(lengths.sum())), dtype=np.int64) |
| items_lengths = np.array(list(range(lengths.sum())), dtype=np.int32) |
| items_items = np.array(list(range(items_lengths.sum())), dtype=np.int64) |
| record = self.new_record(schema.Struct( |
| ('dense', schema.Scalar(np.float32)), |
| ('sparse', schema.Struct( |
| ('list', schema.List(np.int64)), |
| ('list_of_list', schema.List(schema.List(np.int64))), |
| )), |
| ('empty_struct', schema.Struct()) |
| )) |
| indices_record = self.new_record(schema.Scalar(np.int32)) |
| input_record = schema.Struct( |
| ('indices', indices_record), |
| ('record', record), |
| ) |
| schema.FeedRecord( |
| input_record, |
| [indices, dense, lengths, items, lengths, items_lengths, |
| items_items]) |
| gathered_record = self.model.GatherRecord(input_record) |
| self.assertTrue(schema.equal_schemas(gathered_record, record)) |
| |
| self.run_train_net_forward_only() |
| gathered_dense = workspace.FetchBlob(gathered_record.dense()) |
| np.testing.assert_array_equal( |
| np.concatenate([dense[i:i + 1] for i in indices]), gathered_dense) |
| gathered_lengths = workspace.FetchBlob( |
| gathered_record.sparse.list.lengths()) |
| np.testing.assert_array_equal( |
| np.concatenate([lengths[i:i + 1] for i in indices]), |
| gathered_lengths) |
| gathered_items = workspace.FetchBlob( |
| gathered_record.sparse.list.items()) |
| offsets = lengths.cumsum() - lengths |
| np.testing.assert_array_equal( |
| np.concatenate([ |
| items[offsets[i]: offsets[i] + lengths[i]] |
| for i in indices |
| ]), gathered_items) |
| |
| gathered_items_lengths = workspace.FetchBlob( |
| gathered_record.sparse.list_of_list.items.lengths()) |
| np.testing.assert_array_equal( |
| np.concatenate([ |
| items_lengths[offsets[i]: offsets[i] + lengths[i]] |
| for i in indices |
| ]), |
| gathered_items_lengths |
| ) |
| |
| nested_offsets = [] |
| nested_lengths = [] |
| nested_offset = 0 |
| j = 0 |
| for l in lengths: |
| nested_offsets.append(nested_offset) |
| nested_length = 0 |
| for _i in range(l): |
| nested_offset += items_lengths[j] |
| nested_length += items_lengths[j] |
| j += 1 |
| nested_lengths.append(nested_length) |
| |
| gathered_items_items = workspace.FetchBlob( |
| gathered_record.sparse.list_of_list.items.items()) |
| np.testing.assert_array_equal( |
| np.concatenate([ |
| items_items[nested_offsets[i]: |
| nested_offsets[i] + nested_lengths[i]] |
| for i in indices |
| ]), |
| gathered_items_items |
| ) |
| |
| def testMapToRange(self): |
| input_record = self.new_record(schema.Scalar(np.int32)) |
| map_to_range_output = self.model.MapToRange(input_record, |
| max_index=100) |
| self.model.output_schema = schema.Struct() |
| |
| train_init_net, train_net = self.get_training_nets() |
| |
| schema.FeedRecord( |
| input_record, |
| [np.array([10, 3, 20, 99, 15, 11, 3, 11], dtype=np.int32)] |
| ) |
| workspace.RunNetOnce(train_init_net) |
| workspace.RunNetOnce(train_net) |
| indices = workspace.FetchBlob(map_to_range_output()) |
| np.testing.assert_array_equal( |
| np.array([1, 2, 3, 4, 5, 6, 2, 6], dtype=np.int32), |
| indices |
| ) |
| |
| schema.FeedRecord( |
| input_record, |
| [np.array([10, 3, 23, 35, 60, 15, 10, 15], dtype=np.int32)] |
| ) |
| workspace.RunNetOnce(train_net) |
| indices = workspace.FetchBlob(map_to_range_output()) |
| np.testing.assert_array_equal( |
| np.array([1, 2, 7, 8, 9, 5, 1, 5], dtype=np.int32), |
| indices |
| ) |
| |
| eval_net = self.get_eval_net() |
| |
| schema.FeedRecord( |
| input_record, |
| [np.array([10, 3, 23, 35, 60, 15, 200], dtype=np.int32)] |
| ) |
| workspace.RunNetOnce(eval_net) |
| indices = workspace.FetchBlob(map_to_range_output()) |
| np.testing.assert_array_equal( |
| np.array([1, 2, 7, 8, 9, 5, 0], dtype=np.int32), |
| indices |
| ) |
| |
| schema.FeedRecord( |
| input_record, |
| [np.array([10, 3, 23, 15, 101, 115], dtype=np.int32)] |
| ) |
| workspace.RunNetOnce(eval_net) |
| indices = workspace.FetchBlob(map_to_range_output()) |
| np.testing.assert_array_equal( |
| np.array([1, 2, 7, 5, 0, 0], dtype=np.int32), |
| indices |
| ) |
| |
| predict_net = self.get_predict_net() |
| |
| schema.FeedRecord( |
| input_record, |
| [np.array([3, 3, 20, 23, 151, 35, 60, 15, 200], dtype=np.int32)] |
| ) |
| workspace.RunNetOnce(predict_net) |
| indices = workspace.FetchBlob(map_to_range_output()) |
| np.testing.assert_array_equal( |
| np.array([2, 2, 3, 7, 0, 8, 9, 5, 0], dtype=np.int32), |
| indices |
| ) |
| |
| def testSelectRecordByContext(self): |
| float_features = self.model.input_feature_schema.float_features |
| |
| float_array = np.array([1.0, 2.0], dtype=np.float32) |
| |
| schema.FeedRecord(float_features, [float_array]) |
| |
| with Tags(Tags.EXCLUDE_FROM_PREDICTION): |
| log_float_features, = self.model.Log(float_features, 1) |
| joined = self.model.SelectRecordByContext( |
| schema.Struct( |
| (InstantiationContext.PREDICTION, float_features), |
| (InstantiationContext.TRAINING, log_float_features), |
| # TODO: TRAIN_ONLY layers are also generated in eval |
| (InstantiationContext.EVAL, log_float_features), |
| ) |
| ) |
| |
| # model.output_schema has to a struct |
| self.model.output_schema = schema.Struct(( |
| 'joined', joined |
| )) |
| predict_net = layer_model_instantiator.generate_predict_net(self.model) |
| workspace.RunNetOnce(predict_net) |
| predict_output = schema.FetchRecord(predict_net.output_record()) |
| npt.assert_array_equal(float_array, |
| predict_output['joined']()) |
| eval_net = layer_model_instantiator.generate_eval_net(self.model) |
| workspace.RunNetOnce(eval_net) |
| eval_output = schema.FetchRecord(eval_net.output_record()) |
| npt.assert_array_equal(np.log(float_array), |
| eval_output['joined']()) |
| _, train_net = ( |
| layer_model_instantiator.generate_training_nets_forward_only( |
| self.model |
| ) |
| ) |
| workspace.RunNetOnce(train_net) |
| train_output = schema.FetchRecord(train_net.output_record()) |
| npt.assert_array_equal(np.log(float_array), |
| train_output['joined']()) |
| |
| def testFunctionalLayer(self): |
| def normalize(net, in_record, out_record): |
| mean = net.ReduceFrontMean(in_record(), 1) |
| net.Sub( |
| [in_record(), mean], |
| out_record[0](), |
| broadcast=1) |
| normalized = self.model.Functional( |
| self.model.input_feature_schema.float_features, 1, |
| normalize, name="normalizer") |
| |
| # Attach metadata to one of the outputs and use it in FC |
| normalized[0].set_type((np.float32, 32)) |
| self.model.output_schema = self.model.FC(normalized[0], 2) |
| |
| predict_net = layer_model_instantiator.generate_predict_net( |
| self.model) |
| ops = predict_net.Proto().op |
| assert len(ops) == 3 |
| assert ops[0].type == "ReduceFrontMean" |
| assert ops[1].type == "Sub" |
| assert ops[2].type == "FC" |
| assert len(ops[0].input) == 1 |
| assert ops[0].input[0] ==\ |
| self.model.input_feature_schema.float_features() |
| assert len(ops[1].output) == 1 |
| assert ops[1].output[0] in ops[2].input |
| |
| def testFunctionalLayerHelper(self): |
| mean = self.model.ReduceFrontMean( |
| self.model.input_feature_schema.float_features, 1) |
| normalized = self.model.Sub( |
| schema.Tuple( |
| self.model.input_feature_schema.float_features, mean[0]), |
| 1, broadcast=1) |
| # Attach metadata to one of the outputs and use it in FC |
| normalized[0].set_type((np.float32, (32,))) |
| self.model.output_schema = self.model.FC(normalized[0], 2) |
| |
| predict_net = layer_model_instantiator.generate_predict_net( |
| self.model) |
| ops = predict_net.Proto().op |
| assert len(ops) == 3 |
| assert ops[0].type == "ReduceFrontMean" |
| assert ops[1].type == "Sub" |
| assert ops[2].type == "FC" |
| assert len(ops[0].input) == 1 |
| assert ops[0].input[0] ==\ |
| self.model.input_feature_schema.float_features() |
| assert len(ops[1].output) == 1 |
| assert ops[1].output[0] in ops[2].input |
| |
| def testFunctionalLayerHelperAutoInference(self): |
| softsign = self.model.Softsign( |
| schema.Tuple(self.model.input_feature_schema.float_features), |
| 1) |
| assert len(softsign.field_types()) == 1 |
| assert softsign.field_types()[0].base == np.float32 |
| assert softsign.field_types()[0].shape == (32,) |
| self.model.output_schema = self.model.FC(softsign[0], 2) |
| |
| predict_net = layer_model_instantiator.generate_predict_net( |
| self.model) |
| ops = predict_net.Proto().op |
| assert len(ops) == 2 |
| assert ops[0].type == "Softsign" |
| assert ops[1].type == "FC" |
| assert len(ops[0].input) == 1 |
| assert ops[0].input[0] ==\ |
| self.model.input_feature_schema.float_features() |
| assert len(ops[0].output) == 1 |
| assert ops[0].output[0] in ops[1].input |
| |
| def testFunctionalLayerHelperAutoInferenceScalar(self): |
| loss = self.model.AveragedLoss(self.model.input_feature_schema, 1) |
| self.assertEqual(1, len(loss.field_types())) |
| self.assertEqual(np.float32, loss.field_types()[0].base) |
| self.assertEqual(tuple(), loss.field_types()[0].shape) |
| |
| def testFunctionalLayerInputCoercion(self): |
| one = self.model.global_constants['ONE'] |
| two = self.model.Add([one, one], 1) |
| self.model.loss = two |
| self.run_train_net() |
| data = workspace.FetchBlob(two.field_blobs()[0]) |
| np.testing.assert_array_equal([2.0], data) |
| |
| def testFunctionalLayerWithOutputNames(self): |
| k = 3 |
| topk = self.model.TopK( |
| self.model.input_feature_schema, |
| output_names_or_num=['values', 'indices'], |
| k=k, |
| ) |
| self.assertEqual(2, len(topk.field_types())) |
| self.assertEqual(np.float32, topk.field_types()[0].base) |
| self.assertEqual((k,), topk.field_types()[0].shape) |
| self.assertEqual(np.int32, topk.field_types()[1].base) |
| self.assertEqual((k,), topk.field_types()[1].shape) |
| self.assertEqual(['TopK/values', 'TopK/indices'], topk.field_blobs()) |
| |
| def testFunctionalLayerWithOutputDtypes(self): |
| loss = self.model.AveragedLoss( |
| self.model.input_feature_schema, |
| 1, |
| output_dtypes=(np.float32, (1,)), |
| ) |
| self.assertEqual(1, len(loss.field_types())) |
| self.assertEqual(np.float32, loss.field_types()[0].base) |
| self.assertEqual((1,), loss.field_types()[0].shape) |
| |
| def testPropagateRequestOnly(self): |
| # test case when output is request only |
| input_record = self.new_record(schema.Struct( |
| ('input1', schema.Scalar((np.float32, (32, )))), |
| ('input2', schema.Scalar((np.float32, (64, )))), |
| ('input3', schema.Scalar((np.float32, (16, )))), |
| )) |
| |
| set_request_only(input_record) |
| concat_output = self.model.Concat(input_record) |
| self.assertEqual(is_request_only_scalar(concat_output), True) |
| |
| # test case when output is not request only |
| input_record2 = self.new_record(schema.Struct( |
| ('input4', schema.Scalar((np.float32, (100, )))) |
| )) + input_record |
| |
| concat_output2 = self.model.Concat(input_record2) |
| self.assertEqual(is_request_only_scalar(concat_output2), False) |
| |
| def testSetRequestOnly(self): |
| input_record = schema.Scalar(np.int64) |
| schema.attach_metadata_to_scalars( |
| input_record, |
| schema.Metadata( |
| categorical_limit=100000000, |
| expected_value=99, |
| feature_specs=schema.FeatureSpec( |
| feature_ids=[1, 100, 1001] |
| ) |
| ) |
| ) |
| |
| set_request_only(input_record) |
| self.assertEqual(input_record.metadata.categorical_limit, 100000000) |
| self.assertEqual(input_record.metadata.expected_value, 99) |
| self.assertEqual( |
| input_record.metadata.feature_specs.feature_ids, |
| [1, 100, 1001] |
| ) |
| |
| @given( |
| X=hu.arrays(dims=[5, 5]), # Shape of X is irrelevant |
| ) |
| def testDropout(self, X): |
| input_record = self.new_record(schema.Scalar((np.float32, (1,)))) |
| schema.FeedRecord(input_record, [X]) |
| d_output = self.model.Dropout(input_record) |
| self.assertEqual(schema.Scalar((np.float32, (1,))), d_output) |
| self.model.output_schema = schema.Struct() |
| |
| train_init_net, train_net = self.get_training_nets() |
| |
| input_blob = input_record.field_blobs()[0] |
| output_blob = d_output.field_blobs()[0] |
| |
| train_d_spec = OpSpec( |
| "Dropout", |
| [input_blob], |
| [output_blob, None], |
| {'is_test': 0, 'ratio': 0.5} |
| ) |
| |
| test_d_spec = OpSpec( |
| "Dropout", |
| [input_blob], |
| [output_blob, None], |
| {'is_test': 1, 'ratio': 0.5} |
| ) |
| |
| self.assertNetContainOps( |
| train_net, |
| [train_d_spec] |
| ) |
| |
| eval_net = self.get_eval_net() |
| |
| self.assertNetContainOps( |
| eval_net, |
| [test_d_spec] |
| ) |
| |
| predict_net = self.get_predict_net() |
| |
| self.assertNetContainOps( |
| predict_net, |
| [test_d_spec] |
| ) |
| |
| workspace.RunNetOnce(train_init_net) |
| workspace.RunNetOnce(train_net) |
| |
| schema.FeedRecord(input_record, [X]) |
| workspace.RunNetOnce(eval_net) |
| |
| schema.FeedRecord(input_record, [X]) |
| workspace.RunNetOnce(predict_net) |
| |
| @given( |
| batch_size=st.integers(min_value=2, max_value=10), |
| input_dims=st.integers(min_value=5, max_value=10), |
| output_dims=st.integers(min_value=5, max_value=10), |
| bandwidth=st.floats(min_value=0.1, max_value=5), |
| ) |
| def testRandomFourierFeatures(self, batch_size, input_dims, output_dims, bandwidth): |
| X = np.random.random((batch_size, input_dims)).astype(np.float32) |
| scale = np.sqrt(2.0 / output_dims) |
| input_record = self.new_record(schema.Scalar((np.float32, (input_dims,)))) |
| schema.FeedRecord(input_record, [X]) |
| input_blob = input_record.field_blobs()[0] |
| rff_output = self.model.RandomFourierFeatures(input_record, |
| output_dims, |
| bandwidth) |
| self.model.output_schema = schema.Struct() |
| |
| self.assertEqual( |
| schema.Scalar((np.float32, (output_dims, ))), |
| rff_output |
| ) |
| |
| train_init_net, train_net = self.get_training_nets() |
| |
| # Init net assertions |
| init_ops = self.assertNetContainOps( |
| train_init_net, |
| [ |
| OpSpec("GaussianFill", None, None), |
| OpSpec("UniformFill", None, None), |
| ] |
| ) |
| |
| # Operation specifications |
| mat_mul_spec = OpSpec("MatMul", [input_blob, init_ops[0].output[0]], |
| None) |
| add_spec = OpSpec("Add", [None, init_ops[1].output[0]], None, |
| {'broadcast': 1, 'axis': 1}) |
| cosine_spec = OpSpec("Cos", None, None) |
| scale_spec = OpSpec("Scale", None, rff_output.field_blobs(), |
| {'scale': scale}) |
| |
| # Train net assertions |
| self.assertNetContainOps( |
| train_net, |
| [ |
| mat_mul_spec, |
| add_spec, |
| cosine_spec, |
| scale_spec |
| ] |
| ) |
| |
| workspace.RunNetOnce(train_init_net) |
| W = workspace.FetchBlob(self.model.layers[0].w) |
| b = workspace.FetchBlob(self.model.layers[0].b) |
| |
| workspace.RunNetOnce(train_net) |
| train_output = workspace.FetchBlob(rff_output()) |
| train_ref = scale * np.cos(np.dot(X, W) + b) |
| npt.assert_almost_equal(train_output, train_ref) |
| |
| # Eval net assertions |
| eval_net = self.get_eval_net() |
| self.assertNetContainOps( |
| eval_net, |
| [ |
| mat_mul_spec, |
| add_spec, |
| cosine_spec, |
| scale_spec |
| ] |
| ) |
| schema.FeedRecord(input_record, [X]) |
| workspace.RunNetOnce(eval_net) |
| |
| eval_output = workspace.FetchBlob(rff_output()) |
| eval_ref = scale * np.cos(np.dot(X, W) + b) |
| npt.assert_almost_equal(eval_output, eval_ref) |
| |
| # Predict net assertions |
| predict_net = self.get_predict_net() |
| self.assertNetContainOps( |
| predict_net, |
| [ |
| mat_mul_spec, |
| add_spec, |
| cosine_spec, |
| scale_spec |
| ] |
| ) |
| |
| schema.FeedRecord(input_record, [X]) |
| workspace.RunNetOnce(predict_net) |
| |
| predict_output = workspace.FetchBlob(rff_output()) |
| predict_ref = scale * np.cos(np.dot(X, W) + b) |
| npt.assert_almost_equal(predict_output, predict_ref) |
