| |
| |
| |
| |
| |
| import hypothesis.strategies as st |
| import numpy as np |
| import numpy.testing as npt |
| from hypothesis import given, settings |
| |
| import caffe2.python.hypothesis_test_util as hu |
| |
| from caffe2.python import ( |
| layer_model_instantiator, |
| core, |
| schema, |
| workspace, |
| ) |
| from caffe2.python.layers.layers import ( |
| AccessedFeatures, |
| almost_equal_schemas, |
| get_key, |
| IdList, |
| IdScoreList, |
| InstantiationContext, |
| is_request_only_scalar, |
| set_request_only, |
| ) |
| from caffe2.python.layers.tags import Tags |
| from caffe2.python.layer_test_util import ( |
| LayersTestCase, |
| OpSpec, |
| ) |
| import logging |
| logger = logging.getLogger(__name__) |
| |
| |
| class TestLayers(LayersTestCase): |
| def testSparseDropoutWithReplacement(self): |
| input_record = schema.NewRecord(self.model.net, IdList) |
| self.model.output_schema = schema.Struct() |
| |
| lengths_blob = input_record.field_blobs()[0] |
| values_blob = input_record.field_blobs()[1] |
| lengths = np.array([1] * 10).astype(np.int32) |
| values = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]).astype(np.int64) |
| workspace.FeedBlob(lengths_blob, lengths) |
| workspace.FeedBlob(values_blob, values) |
| |
| out = self.model.SparseDropoutWithReplacement( |
| input_record, 0.0, 0.5, 1.0, -1, output_names_or_num=1) |
| self.assertEqual(schema.List(schema.Scalar(np.int64,)), out) |
| |
| train_init_net, train_net = self.get_training_nets() |
| eval_net = self.get_eval_net() |
| predict_net = self.get_predict_net() |
| |
| workspace.RunNetOnce(train_init_net) |
| workspace.RunNetOnce(train_net) |
| out_values = workspace.FetchBlob(out.items()) |
| out_lengths = workspace.FetchBlob(out.lengths()) |
| self.assertBlobsEqual(out_values, values) |
| self.assertBlobsEqual(out_lengths, lengths) |
| |
| workspace.RunNetOnce(eval_net) |
| |
| workspace.RunNetOnce(predict_net) |
| predict_values = workspace.FetchBlob("values_auto_0") |
| predict_lengths = workspace.FetchBlob("lengths_auto_0") |
| self.assertBlobsEqual(predict_values, np.array([-1] * 10).astype(np.int64)) |
| self.assertBlobsEqual(predict_lengths, lengths) |
| |
| def testAddLoss(self): |
| input_record_LR = self.new_record( |
| schema.Struct( |
| ('label', schema.Scalar((np.float64, (1, )))), |
| ('logit', schema.Scalar((np.float32, (2, )))), |
| ('weight', schema.Scalar((np.float64, (1, )))) |
| ) |
| ) |
| loss_LR = self.model.BatchLRLoss(input_record_LR) |
| |
| self.model.add_loss(loss_LR) |
| assert 'unnamed' in self.model.loss |
| self.assertEqual( |
| schema.Scalar((np.float32, tuple())), self.model.loss.unnamed |
| ) |
| self.assertEqual(loss_LR, self.model.loss.unnamed) |
| |
| self.model.add_loss(loss_LR, 'addLoss') |
| assert 'addLoss' in self.model.loss |
| self.assertEqual( |
| schema.Scalar((np.float32, tuple())), self.model.loss.addLoss |
| ) |
| self.assertEqual(loss_LR, self.model.loss.addLoss) |
| |
| self.model.add_loss( |
| schema.Scalar( |
| dtype=np.float32, blob=core.BlobReference('loss_blob_1') |
| ), 'addLoss' |
| ) |
| assert 'addLoss_auto_0' in self.model.loss |
| self.assertEqual( |
| schema.Scalar((np.float32, tuple())), self.model.loss.addLoss_auto_0 |
| ) |
| assert core.BlobReference('loss_blob_1') in self.model.loss.field_blobs() |
| |
| self.model.add_loss( |
| schema.Struct( |
| ( |
| 'structName', schema.Scalar( |
| dtype=np.float32, |
| blob=core.BlobReference('loss_blob_2') |
| ) |
| ) |
| ), 'addLoss' |
| ) |
| assert 'addLoss_auto_1' in self.model.loss |
| self.assertEqual( |
| schema.Struct(('structName', schema.Scalar((np.float32, tuple())))), |
| self.model.loss.addLoss_auto_1 |
| ) |
| assert core.BlobReference('loss_blob_2') in self.model.loss.field_blobs() |
| |
| loss_in_tuple_0 = schema.Scalar( |
| dtype=np.float32, blob=core.BlobReference('loss_blob_in_tuple_0') |
| ) |
| |
| loss_in_tuple_1 = schema.Scalar( |
| dtype=np.float32, blob=core.BlobReference('loss_blob_in_tuple_1') |
| ) |
| |
| loss_tuple = schema.NamedTuple( |
| 'loss_in_tuple', * [loss_in_tuple_0, loss_in_tuple_1] |
| ) |
| self.model.add_loss(loss_tuple, 'addLoss') |
| assert 'addLoss_auto_2' in self.model.loss |
| self.assertEqual( |
| schema.Struct( |
| ('loss_in_tuple_0', schema.Scalar((np.float32, tuple()))), |
| ('loss_in_tuple_1', schema.Scalar((np.float32, tuple()))) |
| ), self.model.loss.addLoss_auto_2 |
| ) |
| assert core.BlobReference('loss_blob_in_tuple_0')\ |
| in self.model.loss.field_blobs() |
| assert core.BlobReference('loss_blob_in_tuple_1')\ |
| in self.model.loss.field_blobs() |
| |
| def testFilterMetricSchema(self): |
| self.model.add_metric_field("a:b", schema.Scalar()) |
| self.model.add_metric_field("a:c", schema.Scalar()) |
| self.model.add_metric_field("d", schema.Scalar()) |
| |
| self.assertEqual( |
| self.model.metrics_schema, |
| schema.Struct( |
| ("a", schema.Struct( |
| ("b", schema.Scalar()), |
| ("c", schema.Scalar()), |
| )), |
| ("d", schema.Scalar()), |
| )) |
| |
| self.model.filter_metrics_schema({"a:b", "d"}) |
| self.assertEqual( |
| self.model.metrics_schema, |
| schema.Struct( |
| ("a", schema.Struct( |
| ("b", schema.Scalar()), |
| )), |
| ("d", schema.Scalar()), |
| )) |
| |
| def testAddOutputSchema(self): |
| # add the first field |
| self.model.add_output_schema('struct', schema.Struct()) |
| expected_output_schema = schema.Struct(('struct', schema.Struct())) |
| self.assertEqual( |
| self.model.output_schema, |
| expected_output_schema, |
| ) |
| |
| # add the second field |
| self.model.add_output_schema('scalar', schema.Scalar(np.float64)) |
| expected_output_schema = schema.Struct( |
| ('struct', schema.Struct()), |
| ('scalar', schema.Scalar(np.float64)), |
| ) |
| self.assertEqual( |
| self.model.output_schema, |
| expected_output_schema, |
| ) |
| |
| # overwrite a field should raise |
| with self.assertRaises(AssertionError): |
| self.model.add_output_schema('scalar', schema.Struct()) |
| |
| def _test_net(self, net, ops_list): |
| ''' |
| Helper function to assert the net contains some set of operations and |
| then to run the net. |
| |
| Inputs: |
| net -- the network to test and run |
| ops_list -- the list of operation specifications to check for |
| in the net |
| ''' |
| ops_output = self.assertNetContainOps(net, ops_list) |
| workspace.RunNetOnce(net) |
| return ops_output |
| |
| 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 testFCWithBootstrap(self): |
| output_dims = 1 |
| fc_with_bootstrap = self.model.FCWithBootstrap( |
| self.model.input_feature_schema.float_features, |
| output_dims=output_dims, |
| num_bootstrap=2, |
| max_fc_size=-1 |
| ) |
| self.model.output_schema = fc_with_bootstrap |
| |
| |
| self.assertEqual(len(fc_with_bootstrap), 4) |
| |
| # must be in this order |
| assert ( |
| core.BlobReference("fc_with_bootstrap/bootstrap_iteration_0/indices") == fc_with_bootstrap[0].field_blobs()[0] |
| ) |
| assert ( |
| core.BlobReference("fc_with_bootstrap/bootstrap_iteration_0/preds") == fc_with_bootstrap[1].field_blobs()[0] |
| ) |
| assert ( |
| core.BlobReference("fc_with_bootstrap/bootstrap_iteration_1/indices") == fc_with_bootstrap[2].field_blobs()[0] |
| ) |
| assert ( |
| core.BlobReference("fc_with_bootstrap/bootstrap_iteration_1/preds") == fc_with_bootstrap[3].field_blobs()[0] |
| ) |
| |
| train_init_net, train_net = self.get_training_nets() |
| predict_net = layer_model_instantiator.generate_predict_net(self.model) |
| |
| train_proto = train_net.Proto() |
| eval_proto = predict_net.Proto() |
| |
| train_ops = train_proto.op |
| eval_ops = eval_proto.op |
| |
| master_train_ops = [ |
| "Shape", |
| "GivenTensorInt64Fill", |
| "Gather", |
| "GivenTensorIntFill", |
| "GivenTensorIntFill", |
| "Cast", |
| "Sub", |
| "UniformIntFill", |
| "Gather", |
| "FC", |
| "UniformIntFill", |
| "Gather", |
| "FC", |
| ] |
| |
| master_eval_ops = [ |
| "Shape", |
| "GivenTensorInt64Fill", |
| "Gather", |
| "GivenTensorIntFill", |
| "GivenTensorIntFill", |
| "Cast", |
| "Sub", |
| "UniformIntFill", |
| "FC", |
| "UniformIntFill", |
| "FC", |
| ] |
| |
| assert len(train_ops) == len(master_train_ops) |
| assert len(eval_ops) == len(master_eval_ops) |
| |
| assert train_proto.external_input == eval_proto.external_input |
| assert train_proto.external_output == list() |
| |
| # make sure all the ops are present and unchanged for train_net and eval_net |
| for idx, op in enumerate(master_train_ops): |
| assert train_ops[idx].type == op |
| |
| for idx, op in enumerate(master_eval_ops): |
| assert eval_ops[idx].type == op |
| |
| |
| def testFCwithAxis2(self): |
| input_dim = 10 |
| output_dim = 30 |
| max_length = 20 |
| input_record = self.new_record( |
| schema.Struct( |
| ('history_sequence', schema.Scalar((np.float32, (max_length, |
| input_dim)))), |
| ) |
| ) |
| fc_out = self.model.FC( |
| input_record.history_sequence, output_dim, |
| axis=2) |
| self.model.output_schema = fc_out |
| self.assertEqual( |
| schema.Scalar((np.float32, (max_length, output_dim))), |
| fc_out |
| ) |
| |
| train_init_net, train_net = self.get_training_nets() |
| |
| def testFCTransposed(self): |
| input_dim = 10 |
| output_dim = 30 |
| max_length = 20 |
| input_record = self.new_record( |
| schema.Struct( |
| ('history_sequence', schema.Scalar((np.float32, (max_length, |
| input_dim)))), |
| ) |
| ) |
| fc_transposed_out = self.model.FC( |
| input_record.history_sequence, output_dim, |
| axis=2, transposed=True) |
| self.model.output_schema = fc_transposed_out |
| self.assertEqual( |
| schema.Scalar((np.float32, (max_length, output_dim))), |
| fc_transposed_out |
| ) |
| |
| train_init_net, train_net = self.get_training_nets() |
| |
| def testFCTransposedWithMaxFCSize(self): |
| input_dim = 10 |
| output_dim = 30 |
| max_length = 20 |
| input_record = self.new_record( |
| schema.Struct( |
| ('history_sequence', schema.Scalar((np.float32, (max_length, |
| input_dim)))), |
| ) |
| ) |
| fc_transposed_out = self.model.FC( |
| input_record.history_sequence, output_dim, |
| max_fc_size=input_dim * output_dim // 2, |
| axis=2, transposed=True) |
| self.model.output_schema = fc_transposed_out |
| self.assertEqual( |
| schema.Scalar((np.float32, (max_length, output_dim))), |
| fc_transposed_out |
| ) |
| |
| train_init_net, train_net = self.get_training_nets() |
| |
| def testSparseLookupSumPoolingWithEviction(self): |
| # Create test embedding table of 1 row |
| record = schema.NewRecord(self.model.net, schema.Struct( |
| ('sparse', schema.Struct( |
| ('sparse_feature_0', schema.ListWithEvicted( |
| schema.Scalar(np.int64, |
| metadata=schema.Metadata(categorical_limit=1)),)),)), |
| )) |
| embedding_dim = 8 |
| lengths_blob = record.sparse.sparse_feature_0.lengths.get() |
| values_blob = record.sparse.sparse_feature_0.items.get() |
| evicted_values_blob = record.sparse.sparse_feature_0._evicted_values.get() |
| lengths = np.array([1]).astype(np.int32) |
| values = np.array([0]).astype(np.int64) |
| # Need to reset row 0 |
| evicted_values = np.array([0]).astype(np.int64) |
| workspace.FeedBlob(lengths_blob, lengths) |
| workspace.FeedBlob(values_blob, values) |
| workspace.FeedBlob(evicted_values_blob, evicted_values) |
| |
| embedding_after_pooling = self.model.SparseLookup( |
| record.sparse.sparse_feature_0, [embedding_dim], 'Sum', weight_init=("ConstantFill", {"value": 1.0})) |
| |
| self.model.output_schema = schema.Struct() |
| self.assertEqual( |
| schema.Scalar((np.float32, (embedding_dim, ))), |
| embedding_after_pooling |
| ) |
| train_init_net, train_net = self.get_training_nets() |
| workspace.RunNetOnce(train_init_net) |
| embedding_after_init = workspace.FetchBlob("sparse_lookup/w") |
| # Change row 0's value before reset |
| new_values = np.array([[2, 2, 2, 2, 2, 2, 2, 2]]).astype(np.float32) |
| workspace.FeedBlob("sparse_lookup/w", new_values) |
| workspace.RunNetOnce(train_net.Proto()) |
| embedding_after_training = workspace.FetchBlob("sparse_lookup/w") |
| # Verify row 0's value does not change after reset |
| self.assertEqual(embedding_after_training.all(), embedding_after_init.all()) |
| |
| |
| def testSparseLookupSumPooling(self): |
| record = schema.NewRecord(self.model.net, schema.Struct( |
| ('sparse', schema.Struct( |
| ('sparse_feature_0', schema.List( |
| schema.Scalar(np.int64, |
| metadata=schema.Metadata(categorical_limit=1000)))), |
| )), |
| )) |
| embedding_dim = 64 |
| embedding_after_pooling = self.model.SparseLookup( |
| record.sparse.sparse_feature_0, [embedding_dim], 'Sum') |
| self.model.output_schema = schema.Struct() |
| self.assertEqual( |
| schema.Scalar((np.float32, (embedding_dim, ))), |
| embedding_after_pooling |
| ) |
| |
| train_init_net, train_net = self.get_training_nets() |
| |
| init_ops = self.assertNetContainOps( |
| train_init_net, |
| [ |
| OpSpec("UniformFill", None, None), |
| OpSpec("ConstantFill", None, None), |
| ] |
| ) |
| sparse_lookup_op_spec = OpSpec( |
| 'SparseLengthsSum', |
| [ |
| init_ops[0].output[0], |
| record.sparse.sparse_feature_0.items(), |
| record.sparse.sparse_feature_0.lengths(), |
| ], |
| [embedding_after_pooling()] |
| ) |
| self.assertNetContainOps(train_net, [sparse_lookup_op_spec]) |
| |
| predict_net = self.get_predict_net() |
| self.assertNetContainOps(predict_net, [sparse_lookup_op_spec]) |
| |
| @given( |
| use_hashing=st.booleans(), |
| modulo=st.integers(min_value=100, max_value=200), |
| use_divide_mod=st.booleans(), |
| divisor=st.integers(min_value=10, max_value=20), |
| ) |
| def testSparseFeatureHashIdList(self, use_hashing, modulo, use_divide_mod, divisor): |
| record = schema.NewRecord( |
| self.model.net, |
| schema.List(schema.Scalar( |
| np.int64, |
| metadata=schema.Metadata(categorical_limit=60000) |
| )) |
| ) |
| use_divide_mod = use_divide_mod if use_hashing is False else False |
| output_schema = self.model.SparseFeatureHash( |
| record, |
| modulo=modulo, |
| use_hashing=use_hashing, |
| use_divide_mod=use_divide_mod, |
| divisor=divisor, |
| ) |
| |
| self.model.output_schema = output_schema |
| |
| self.assertEqual(len(self.model.layers), 1) |
| self.assertEqual(output_schema._items.metadata.categorical_limit, |
| modulo) |
| train_init_net, train_net = self.get_training_nets() |
| if use_divide_mod: |
| self.assertEqual(len(train_net.Proto().op), 3) |
| else: |
| self.assertEqual(len(train_net.Proto().op), 2) |
| |
| @given( |
| use_hashing=st.booleans(), |
| modulo=st.integers(min_value=100, max_value=200), |
| ) |
| def testSparseFeatureHashIdScoreList(self, use_hashing, modulo): |
| record = schema.NewRecord(self.model.net, |
| schema.Map(schema.Scalar(np.int64, |
| metadata=schema.Metadata( |
| categorical_limit=60000)), |
| np.float32)) |
| |
| output_schema = self.model.SparseFeatureHash( |
| record, |
| modulo=modulo, |
| use_hashing=use_hashing) |
| |
| self.model.output_schema = output_schema |
| |
| self.assertEqual(len(self.model.layers), 1) |
| self.assertEqual(output_schema._items.keys.metadata.categorical_limit, |
| modulo) |
| train_init_net, train_net = self.get_training_nets() |
| |
| def testSparseLookupIncorrectPositionWeightedOnIdList(self): |
| ''' |
| Currently the implementation of SparseLookup assumed input is id_score_list |
| when use PositionWeighted. |
| ''' |
| record = schema.NewRecord(self.model.net, schema.Struct( |
| ('sparse', schema.Struct( |
| ('sparse_feature_0', schema.List( |
| schema.Scalar(np.int64, |
| metadata=schema.Metadata(categorical_limit=1000)))), |
| )), |
| )) |
| |
| embedding_dim = 64 |
| with self.assertRaises(AssertionError): |
| self.model.SparseLookup( |
| record.sparse.sparse_feature_0, [embedding_dim], 'PositionWeighted') |
| |
| def testSparseLookupPositionWeightedOnIdList(self): |
| record = schema.NewRecord(self.model.net, schema.Struct( |
| ('sparse', schema.Struct( |
| ('sparse_feature_0', schema.List( |
| schema.Scalar(np.int64, |
| metadata=schema.Metadata(categorical_limit=1000)))), |
| )), |
| )) |
| |
| # convert id_list to id_score_list with PositionWeighted layer |
| sparse_segment = record.sparse.sparse_feature_0 |
| pos_w_layer = self.model.PositionWeighted(sparse_segment) |
| |
| sparse_segment = schema.Map( |
| keys=get_key(sparse_segment), |
| values=pos_w_layer.position_weights, |
| lengths_blob=sparse_segment.lengths |
| ) |
| |
| embedding_dim = 64 |
| embedding_after_pooling = self.model.SparseLookup( |
| sparse_segment, [embedding_dim], 'PositionWeighted') |
| self.model.output_schema = schema.Struct() |
| self.assertEqual( |
| schema.Scalar((np.float32, (embedding_dim, ))), |
| embedding_after_pooling |
| ) |
| |
| train_init_net, train_net = self.get_training_nets() |
| |
| self.assertNetContainOps( |
| train_init_net, |
| [ |
| OpSpec("ConstantFill", None, None), # position_weights/pos_w |
| OpSpec("UniformFill", None, None), |
| OpSpec("ConstantFill", None, None), |
| ] |
| ) |
| self.assertNetContainOps(train_net, [ |
| OpSpec("LengthsRangeFill", None, None), |
| OpSpec("Gather", None, None), |
| OpSpec("SparseLengthsWeightedSum", None, None), |
| ]) |
| |
| predict_net = self.get_predict_net() |
| self.assertNetContainOps(predict_net, [ |
| OpSpec("LengthsRangeFill", None, None), |
| OpSpec("Gather", None, None), |
| OpSpec("SparseLengthsWeightedSum", None, None), |
| ]) |
| |
| def testSparseLookupPositionWeightedOnIdScoreList(self): |
| record = schema.NewRecord(self.model.net, schema.Struct( |
| ('sparse', schema.Struct( |
| ('id_score_list_0', schema.Map( |
| schema.Scalar( |
| np.int64, |
| metadata=schema.Metadata( |
| categorical_limit=1000 |
| ), |
| ), |
| np.float32 |
| )), |
| )), |
| )) |
| |
| embedding_dim = 64 |
| embedding_after_pooling = self.model.SparseLookup( |
| record.sparse.id_score_list_0, [embedding_dim], 'PositionWeighted') |
| self.model.output_schema = schema.Struct() |
| self.assertEqual( |
| schema.Scalar((np.float32, (embedding_dim, ))), |
| embedding_after_pooling |
| ) |
| |
| train_init_net, train_net = self.get_training_nets() |
| |
| init_ops = self.assertNetContainOps( |
| train_init_net, |
| [ |
| OpSpec("UniformFill", None, None), |
| OpSpec("ConstantFill", None, None), |
| ] |
| ) |
| sparse_lookup_op_spec = OpSpec( |
| 'SparseLengthsWeightedSum', |
| [ |
| init_ops[0].output[0], |
| record.sparse.id_score_list_0.values(), |
| record.sparse.id_score_list_0.keys(), |
| record.sparse.id_score_list_0.lengths(), |
| ], |
| [embedding_after_pooling()] |
| ) |
| self.assertNetContainOps(train_net, [sparse_lookup_op_spec]) |
| |
| predict_net = self.get_predict_net() |
| self.assertNetContainOps(predict_net, [sparse_lookup_op_spec]) |
| |
| def testSparseLookupIncorrectRecencyWeightedOnIdList(self): |
| ''' |
| Currently the implementation of SparseLookup assumed input is id_score_list |
| when use RecencyWeighted. |
| ''' |
| record = schema.NewRecord(self.model.net, schema.Struct( |
| ('sparse', schema.Struct( |
| ('sparse_feature_0', schema.List( |
| schema.Scalar(np.int64, |
| metadata=schema.Metadata(categorical_limit=1000)))), |
| )), |
| )) |
| |
| embedding_dim = 64 |
| with self.assertRaises(AssertionError): |
| self.model.SparseLookup( |
| record.sparse.sparse_feature_0, [embedding_dim], 'RecencyWeighted') |
| |
| def testSparseLookupRecencyWeightedOnIdScoreList(self): |
| record = schema.NewRecord(self.model.net, schema.Struct( |
| ('sparse', schema.Struct( |
| ('id_score_list_0', schema.Map( |
| schema.Scalar( |
| np.int64, |
| metadata=schema.Metadata( |
| categorical_limit=1000 |
| ), |
| ), |
| np.float32 |
| )), |
| )), |
| )) |
| |
| embedding_dim = 64 |
| embedding_after_pooling = self.model.SparseLookup( |
| record.sparse.id_score_list_0, [embedding_dim], 'RecencyWeighted') |
| self.model.output_schema = schema.Struct() |
| self.assertEqual( |
| schema.Scalar((np.float32, (embedding_dim, ))), |
| embedding_after_pooling |
| ) |
| |
| train_init_net, train_net = self.get_training_nets() |
| |
| init_ops = self.assertNetContainOps( |
| train_init_net, |
| [ |
| OpSpec("UniformFill", None, None), |
| OpSpec("ConstantFill", None, None), |
| ] |
| ) |
| sparse_lookup_op_spec = OpSpec( |
| 'SparseLengthsWeightedSum', |
| [ |
| init_ops[0].output[0], |
| record.sparse.id_score_list_0.values(), |
| record.sparse.id_score_list_0.keys(), |
| record.sparse.id_score_list_0.lengths(), |
| ], |
| [embedding_after_pooling()] |
| ) |
| self.assertNetContainOps(train_net, [sparse_lookup_op_spec]) |
| |
| predict_net = self.get_predict_net() |
| self.assertNetContainOps(predict_net, [sparse_lookup_op_spec]) |
| |
| def testPairwiseSimilarityWithAllEmbeddings(self): |
| embedding_dim = 64 |
| N = 5 |
| record = schema.NewRecord(self.model.net, schema.Struct( |
| ('all_embeddings', schema.Scalar( |
| ((np.float32, (N, embedding_dim))) |
| )), |
| )) |
| current = self.model.PairwiseSimilarity( |
| record, N * N) |
| |
| self.assertEqual( |
| schema.Scalar((np.float32, (N * N, ))), |
| current |
| ) |
| |
| train_init_net, train_net = self.get_training_nets() |
| self.assertNetContainOps(train_init_net, []) |
| self.assertNetContainOps(train_net, [ |
| OpSpec("BatchMatMul", None, None), |
| OpSpec("Flatten", None, None), |
| ]) |
| |
| def testPairwiseSimilarityWithXandYEmbeddings(self): |
| embedding_dim = 64 |
| record = schema.NewRecord(self.model.net, schema.Struct( |
| ('x_embeddings', schema.Scalar( |
| ((np.float32, (5, embedding_dim))) |
| )), |
| ('y_embeddings', schema.Scalar( |
| ((np.float32, (6, embedding_dim))) |
| )), |
| )) |
| current = self.model.PairwiseSimilarity( |
| record, 5 * 6) |
| |
| self.assertEqual( |
| schema.Scalar((np.float32, (5 * 6, ))), |
| current |
| ) |
| |
| train_init_net, train_net = self.get_training_nets() |
| self.assertNetContainOps(train_init_net, []) |
| self.assertNetContainOps(train_net, [ |
| OpSpec("BatchMatMul", None, None), |
| OpSpec("Flatten", None, None), |
| ]) |
| |
| def testPairwiseSimilarityWithXandYEmbeddingsAndGather(self): |
| embedding_dim = 64 |
| |
| output_idx = [1, 3, 5] |
| output_idx_blob = self.model.add_global_constant( |
| str(self.model.net.NextScopedBlob('pairwise_dot_product_gather')), |
| output_idx, |
| dtype=np.int32, |
| ) |
| indices_to_gather = schema.Scalar( |
| (np.int32, len(output_idx)), |
| output_idx_blob, |
| ) |
| |
| record = schema.NewRecord(self.model.net, schema.Struct( |
| ('x_embeddings', schema.Scalar( |
| ((np.float32, (5, embedding_dim))) |
| )), |
| ('y_embeddings', schema.Scalar( |
| ((np.float32, (6, embedding_dim))) |
| )), |
| ('indices_to_gather', indices_to_gather), |
| )) |
| current = self.model.PairwiseSimilarity( |
| record, len(output_idx)) |
| |
| # This assert is not necessary, |
| # output size is passed into PairwiseSimilarity |
| self.assertEqual( |
| schema.Scalar((np.float32, (len(output_idx), ))), |
| current |
| ) |
| |
| train_init_net, train_net = self.get_training_nets() |
| self.assertNetContainOps(train_init_net, []) |
| self.assertNetContainOps(train_net, [ |
| OpSpec("BatchMatMul", None, None), |
| OpSpec("Flatten", None, None), |
| OpSpec("BatchGather", None, None), |
| ]) |
| |
| def testPairwiseSimilarityIncorrectInput(self): |
| embedding_dim = 64 |
| record = schema.NewRecord(self.model.net, schema.Struct( |
| ('x_embeddings', schema.Scalar( |
| ((np.float32, (5, embedding_dim))) |
| )), |
| )) |
| with self.assertRaises(AssertionError): |
| self.model.PairwiseSimilarity( |
| record, 25) |
| |
| record = schema.NewRecord(self.model.net, schema.Struct( |
| ('all_embeddings', schema.List(np.float32)) |
| )) |
| with self.assertRaises(AssertionError): |
| self.model.PairwiseSimilarity( |
| record, 25) |
| |
| def testConcat(self): |
| embedding_dim = 64 |
| input_record = self.new_record(schema.Struct( |
| ('input1', schema.Scalar((np.float32, (embedding_dim, )))), |
| ('input2', schema.Scalar((np.float32, (embedding_dim, )))), |
| ('input3', schema.Scalar((np.float32, (embedding_dim, )))), |
| )) |
| |
| output = self.model.Concat(input_record) |
| self.assertEqual( |
| schema.Scalar((np.float32, ((len(input_record.fields) * embedding_dim, )))), |
| output |
| ) |
| |
| # Note that in Concat layer we assume first dimension is batch. |
| # so input is B * embedding_dim |
| # add_axis=1 make it B * 1 * embedding_dim |
| # concat on axis=1 make it B * N * embedding_dim |
| output = self.model.Concat(input_record, axis=1, add_axis=1) |
| self.assertEqual( |
| schema.Scalar((np.float32, ((len(input_record.fields), embedding_dim)))), |
| output |
| ) |
| |
| 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,)))), |
| ('logit', 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 testBatchLRLossWithUncertainty(self): |
| input_record = self.new_record(schema.Struct( |
| ('label', schema.Scalar((np.float64, (1,)))), |
| ('logit', schema.Scalar((np.float32, (2,)))), |
| ('weight', schema.Scalar((np.float64, (1,)))), |
| ('log_variance', schema.Scalar((np.float64, (1,)))), |
| )) |
| loss = self.model.BatchLRLoss(input_record) |
| self.assertEqual(schema.Scalar((np.float32, tuple())), loss) |
| |
| def testMarginRankLoss(self): |
| input_record = self.new_record(schema.Struct( |
| ('pos_prediction', schema.Scalar((np.float32, (1,)))), |
| ('neg_prediction', schema.List(np.float32)), |
| )) |
| pos_items = np.array([0.1, 0.2, 0.3], dtype=np.float32) |
| neg_lengths = np.array([1, 2, 3], dtype=np.int32) |
| neg_items = np.array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6], dtype=np.float32) |
| schema.FeedRecord( |
| input_record, |
| [pos_items, neg_lengths, neg_items] |
| ) |
| loss = self.model.MarginRankLoss(input_record) |
| self.run_train_net_forward_only() |
| self.assertEqual(schema.Scalar((np.float32, tuple())), loss) |
| |
| def testBPRLoss(self): |
| input_record = self.new_record(schema.Struct( |
| ('pos_prediction', schema.Scalar((np.float32, (1,)))), |
| ('neg_prediction', schema.List(np.float32)), |
| )) |
| pos_items = np.array([0.8, 0.9], dtype=np.float32) |
| neg_lengths = np.array([1, 2], dtype=np.int32) |
| neg_items = np.array([0.1, 0.2, 0.3], dtype=np.float32) |
| schema.FeedRecord( |
| input_record, |
| [pos_items, neg_lengths, neg_items] |
| ) |
| loss = self.model.BPRLoss(input_record) |
| self.run_train_net_forward_only() |
| self.assertEqual(schema.Scalar((np.float32, tuple())), loss) |
| result = workspace.FetchBlob('bpr_loss/output') |
| np.testing.assert_array_almost_equal(np.array(1.24386, dtype=np.float32), result) |
| |
| 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 testBatchHuberLoss(self): |
| input_record = self.new_record(schema.Struct( |
| ('label', schema.Scalar((np.float32, (1,)))), |
| ('prediction', schema.Scalar((np.float32, (2,)))), |
| )) |
| loss = self.model.BatchHuberLoss(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], |
| None, |
| ) |
| |
| train_bn_spec = OpSpec( |
| "SpatialBN", |
| [None, 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", |
| [None, 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=[2, 5, 6]), |
| use_layer_norm_op=st.booleans(), |
| ) |
| def testLayerNormalization(self, X, use_layer_norm_op): |
| expect = (5, 6,) |
| if not use_layer_norm_op: |
| X = X.reshape(10, 6) |
| expect = (6,) |
| input_record = self.new_record(schema.Scalar((np.float32, expect))) |
| schema.FeedRecord(input_record, [X]) |
| ln_output = self.model.LayerNormalization( |
| input_record, use_layer_norm_op=use_layer_norm_op |
| ) |
| self.assertEqual(schema.Scalar((np.float32, expect)), ln_output) |
| self.model.output_schema = schema.Struct() |
| |
| train_init_net, train_net = self.get_training_nets(add_constants=True) |
| workspace.RunNetOnce(train_init_net) |
| workspace.RunNetOnce(train_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.last_n) |
| start = max(0, 5 - num_to_collect) |
| npt.assert_array_equal(X[start:], output_record()) |
| num_visited = schema.FetchRecord(last_n.num_visited) |
| npt.assert_array_equal([5], num_visited()) |
| |
| @given( |
| X=hu.arrays(dims=[5, 2]), |
| num_to_collect=st.integers(min_value=3, max_value=3), |
| ) |
| @settings(deadline=1000) |
| def testReservoirSamplingWithID(self, X, num_to_collect): |
| ID = np.array([1, 2, 3, 1, 2], dtype=np.int64) |
| input_record = self.new_record( |
| schema.Struct( |
| ('record', schema.Struct( |
| ('dense', schema.Scalar()), |
| )), |
| ('object_id', schema.Scalar(np.int64)), |
| ) |
| ) |
| schema.FeedRecord(input_record, [X, ID]) |
| packed_record = self.model.PackRecords( |
| input_record.record, 1, fields=input_record.record.field_names()) |
| reservoir_input = schema.Struct( |
| ('data', packed_record), |
| ('object_id', input_record.object_id), |
| ) |
| reservoir = self.model.ReservoirSampling( |
| reservoir_input, num_to_collect) |
| self.model.output_schema = schema.Struct() |
| train_init_net, train_net = \ |
| layer_model_instantiator.generate_training_nets_forward_only( |
| self.model) |
| workspace.RunNetOnce(train_init_net) |
| workspace.CreateNet(train_net) |
| workspace.RunNet(train_net.Proto().name, num_iter=2) |
| num_visited = schema.FetchRecord(reservoir.num_visited) |
| npt.assert_array_equal([3], num_visited()) |
| for param in self.model.params: |
| serialized = workspace.SerializeBlob(str(param)) |
| workspace.DeserializeBlob(str(param), serialized) |
| ID = np.array([3, 5, 3, 3, 5], dtype=np.int64) |
| schema.FeedRecord(input_record.object_id, [ID]) |
| workspace.RunNet(train_net.Proto().name, num_iter=2) |
| num_visited = schema.FetchRecord(reservoir.num_visited) |
| npt.assert_array_equal([2], num_visited()) |
| |
| 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 testUniformSamplingWithIncorrectSampleSize(self): |
| input_record = self.new_record(schema.Scalar(np.int32)) |
| num_samples = 200 |
| num_elements = 100 |
| with self.assertRaises(AssertionError): |
| self.model.UniformSampling(input_record, num_samples, num_elements) |
| |
| 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)) |
| indices_blob = self.model.MapToRange(input_record, |
| max_index=100).indices |
| 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(indices_blob()) |
| 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(indices_blob()) |
| 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(indices_blob()) |
| 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(indices_blob()) |
| 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(indices_blob()) |
| 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(), |
| 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.set_type((np.float32, 32)) |
| self.model.output_schema = self.model.FC(normalized, 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), |
| 1, broadcast=1) |
| # Attach metadata to one of the outputs and use it in FC |
| normalized.set_type((np.float32, (32,))) |
| self.model.output_schema = self.model.FC(normalized, 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 softsign.field_type().base == np.float32 |
| assert softsign.field_type().shape == (32,) |
| self.model.output_schema = self.model.FC(softsign, 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 testHalfToFloatTypeInference(self): |
| input = self.new_record(schema.Scalar((np.float32, (32,)))) |
| |
| output = self.model.FloatToHalf(input, 1) |
| assert output.field_type().base == np.float16 |
| assert output.field_type().shape == (32, ) |
| |
| output = self.model.HalfToFloat(output, 1) |
| assert output.field_type().base == np.float32 |
| assert output.field_type().shape == (32, ) |
| |
| 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 testFunctionalLayerSameOperatorOutputNames(self): |
| Con1 = self.model.ConstantFill([], 1, value=1) |
| Con2 = self.model.ConstantFill([], 1, value=2) |
| self.assertNotEqual(str(Con1), str(Con2)) |
| |
| 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 |
| dropout_for_eval=st.booleans(), |
| ) |
| def testDropout(self, X, dropout_for_eval): |
| input_record = self.new_record(schema.Scalar((np.float32, (1,)))) |
| schema.FeedRecord(input_record, [X]) |
| d_output = self.model.Dropout( |
| input_record, |
| dropout_for_eval=dropout_for_eval |
| ) |
| 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] |
| |
| with_d_spec = OpSpec( |
| "Dropout", |
| [input_blob], |
| [output_blob, None], |
| {'is_test': 0, 'ratio': 0.5} |
| ) |
| |
| without_d_spec = OpSpec( |
| "Dropout", |
| [input_blob], |
| [output_blob, None], |
| {'is_test': 1, 'ratio': 0.5} |
| ) |
| |
| self.assertNetContainOps( |
| train_net, |
| [with_d_spec] |
| ) |
| |
| eval_net = self.get_eval_net() |
| predict_net = self.get_predict_net() |
| |
| if dropout_for_eval: |
| self.assertNetContainOps( |
| eval_net, |
| [with_d_spec] |
| ) |
| self.assertNetContainOps( |
| predict_net, |
| [with_d_spec] |
| ) |
| else: |
| self.assertNetContainOps( |
| eval_net, |
| [without_d_spec] |
| ) |
| self.assertNetContainOps( |
| predict_net, |
| [without_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( |
| num_inputs=st.integers(1, 3), |
| batch_size=st.integers(5, 10) |
| ) |
| def testMergeIdListsLayer(self, num_inputs, batch_size): |
| inputs = [] |
| for _ in range(num_inputs): |
| lengths = np.random.randint(5, size=batch_size).astype(np.int32) |
| size = lengths.sum() |
| values = np.random.randint(1, 10, size=size).astype(np.int64) |
| inputs.append(lengths) |
| inputs.append(values) |
| input_schema = schema.Tuple( |
| *[schema.List( |
| schema.Scalar(dtype=np.int64, metadata=schema.Metadata( |
| categorical_limit=20 |
| ))) for _ in range(num_inputs)] |
| ) |
| |
| input_record = schema.NewRecord(self.model.net, input_schema) |
| schema.FeedRecord(input_record, inputs) |
| output_schema = self.model.MergeIdLists(input_record) |
| assert schema.equal_schemas( |
| output_schema, IdList, |
| check_field_names=False) |
| |
| @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): |
| |
| def _rff_hypothesis_test(rff_output, X, W, b, scale): |
| ''' |
| Runs hypothesis test for Semi Random Features layer. |
| |
| Inputs: |
| rff_output -- output of net after running random fourier features layer |
| X -- input data |
| W -- weight parameter from train_init_net |
| b -- bias parameter from train_init_net |
| scale -- value by which to scale the output vector |
| ''' |
| output = workspace.FetchBlob(rff_output) |
| output_ref = scale * np.cos(np.dot(X, np.transpose(W)) + b) |
| npt.assert_allclose(output, output_ref, rtol=1e-3, atol=1e-3) |
| |
| 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_list = [ |
| OpSpec("GaussianFill", None, None), |
| OpSpec("UniformFill", None, None), |
| ] |
| init_ops = self._test_net(train_init_net, init_ops_list) |
| W = workspace.FetchBlob(self.model.layers[0].w) |
| b = workspace.FetchBlob(self.model.layers[0].b) |
| |
| # Operation specifications |
| fc_spec = OpSpec("FC", [input_blob, init_ops[0].output[0], |
| init_ops[1].output[0]], None) |
| cosine_spec = OpSpec("Cos", None, None) |
| scale_spec = OpSpec("Scale", None, rff_output.field_blobs(), |
| {'scale': scale}) |
| ops_list = [ |
| fc_spec, |
| cosine_spec, |
| scale_spec |
| ] |
| |
| # Train net assertions |
| self._test_net(train_net, ops_list) |
| _rff_hypothesis_test(rff_output(), X, W, b, scale) |
| |
| # Eval net assertions |
| eval_net = self.get_eval_net() |
| self._test_net(eval_net, ops_list) |
| _rff_hypothesis_test(rff_output(), X, W, b, scale) |
| |
| # Predict net assertions |
| predict_net = self.get_predict_net() |
| self._test_net(predict_net, ops_list) |
| _rff_hypothesis_test(rff_output(), X, W, b, scale) |
| |
| @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), |
| s=st.integers(min_value=0, max_value=3), |
| scale=st.floats(min_value=0.1, max_value=5), |
| 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: |
| W = workspace.FetchBlob( |
| self.model.global_constants['arc_cosine_feature_map_fixed_rand_W'] |
| ) |
| b = workspace.FetchBlob( |
| self.model.global_constants['arc_cosine_feature_map_fixed_rand_b'] |
| ) |
| else: |
| W = workspace.FetchBlob(self.model.layers[0].random_w) |
| b = workspace.FetchBlob(self.model.layers[0].random_b) |
| |
| # Operation specifications |
| fc_spec = OpSpec("FC", [input_blob, None, None], None) |
| softsign_spec = OpSpec("Softsign", None, None) |
| relu_spec = OpSpec("Relu", None, None) |
| relu_spec_output = OpSpec("Relu", None, ac_output.field_blobs()) |
| pow_spec = OpSpec("Pow", None, None, {'exponent': float(s - 1)}) |
| mul_spec = OpSpec("Mul", None, ac_output.field_blobs()) |
| |
| if s == 0: |
| ops_list = [ |
| fc_spec, |
| softsign_spec, |
| relu_spec_output, |
| ] |
| elif s == 1: |
| ops_list = [ |
| fc_spec, |
| relu_spec_output, |
| ] |
| else: |
| ops_list = [ |
| fc_spec, |
| relu_spec, |
| pow_spec, |
| mul_spec, |
| ] |
| |
| # Train net assertions |
| self._test_net(train_net, ops_list) |
| _arc_cosine_hypothesis_test(ac_output(), X, W, b, s) |
| |
| # Eval net assertions |
| eval_net = self.get_eval_net() |
| self._test_net(eval_net, ops_list) |
| _arc_cosine_hypothesis_test(ac_output(), X, W, b, s) |
| |
| # Predict net assertions |
| predict_net = self.get_predict_net() |
| self._test_net(predict_net, ops_list) |
| _arc_cosine_hypothesis_test(ac_output(), X, W, b, s) |
| |
| @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), |
| s=st.integers(min_value=0, max_value=3), |
| scale=st.floats(min_value=0.1, max_value=5), |
| set_weight_as_global_constant=st.booleans(), |
| use_struct_input=st.booleans(), |
| ) |
| def testSemiRandomFeatures(self, batch_size, input_dims, output_dims, s, scale, |
| set_weight_as_global_constant, use_struct_input): |
| |
| def _semi_random_hypothesis_test(srf_output, X_full, X_random, rand_w, |
| rand_b, s): |
| ''' |
| Runs hypothesis test for Semi Random Features layer. |
| |
| Inputs: |
| srf_output -- output of net after running semi random features layer |
| X_full -- full input data |
| X_random -- random-output input data |
| rand_w -- random-initialized weight parameter from train_init_net |
| rand_b -- random-initialized bias parameter from train_init_net |
| s -- degree parameter |
| |
| ''' |
| # Get output from net |
| net_output = workspace.FetchBlob(srf_output) |
| |
| # Fetch learned parameter blobs |
| learned_w = workspace.FetchBlob(self.model.layers[0].learned_w) |
| learned_b = workspace.FetchBlob(self.model.layers[0].learned_b) |
| |
| # Computing output directly |
| x_rand = np.matmul(X_random, np.transpose(rand_w)) + rand_b |
| x_learn = np.matmul(X_full, np.transpose(learned_w)) + learned_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(np.multiply(x_pow, h_rand_features), x_learn) |
| |
| # Comparing net output and computed output |
| npt.assert_allclose(net_output, output_ref, rtol=1e-3, atol=1e-3) |
| |
| X_full = np.random.normal(size=(batch_size, input_dims)).astype(np.float32) |
| if use_struct_input: |
| X_random = np.random.normal(size=(batch_size, input_dims)).\ |
| astype(np.float32) |
| input_data = [X_full, X_random] |
| input_record = self.new_record(schema.Struct( |
| ('full', schema.Scalar( |
| (np.float32, (input_dims,)) |
| )), |
| ('random', schema.Scalar( |
| (np.float32, (input_dims,)) |
| )) |
| )) |
| else: |
| X_random = X_full |
| input_data = [X_full] |
| input_record = self.new_record(schema.Scalar( |
| (np.float32, (input_dims,)) |
| )) |
| |
| schema.FeedRecord(input_record, input_data) |
| srf_output = self.model.SemiRandomFeatures( |
| input_record, |
| output_dims, |
| s=s, |
| scale_random=scale, |
| scale_learned=scale, |
| set_weight_as_global_constant=set_weight_as_global_constant |
| ) |
| |
| self.model.output_schema = schema.Struct() |
| |
| self.assertEqual( |
| schema.Struct( |
| ('full', schema.Scalar( |
| (np.float32, (output_dims,)) |
| )), |
| ('random', schema.Scalar( |
| (np.float32, (output_dims,)) |
| )) |
| ), |
| srf_output |
| ) |
| |
| init_ops_list = [ |
| OpSpec("GaussianFill", None, None), |
| OpSpec("UniformFill", None, None), |
| OpSpec("GaussianFill", None, None), |
| OpSpec("UniformFill", None, None), |
| ] |
| train_init_net, train_net = self.get_training_nets() |
| |
| # Need to run to initialize the global constants for layer |
| workspace.RunNetOnce(self.model.create_init_net(name='init_net')) |
| |
| if set_weight_as_global_constant: |
| # If weight params are global constants, they won't be in train_init_net |
| init_ops = self._test_net(train_init_net, init_ops_list[:2]) |
| rand_w = workspace.FetchBlob( |
| self.model.global_constants['semi_random_features_fixed_rand_W'] |
| ) |
| rand_b = workspace.FetchBlob( |
| self.model.global_constants['semi_random_features_fixed_rand_b'] |
| ) |
| |
| # Operation specifications |
| fc_random_spec = OpSpec("FC", [None, None, None], None) |
| fc_learned_spec = OpSpec("FC", [None, init_ops[0].output[0], |
| init_ops[1].output[0]], None) |
| else: |
| init_ops = self._test_net(train_init_net, init_ops_list) |
| rand_w = workspace.FetchBlob(self.model.layers[0].random_w) |
| rand_b = workspace.FetchBlob(self.model.layers[0].random_b) |
| |
| # Operation specifications |
| fc_random_spec = OpSpec("FC", [None, init_ops[0].output[0], |
| init_ops[1].output[0]], None) |
| fc_learned_spec = OpSpec("FC", [None, init_ops[2].output[0], |
| init_ops[3].output[0]], None) |
| |
| softsign_spec = OpSpec("Softsign", None, None) |
| relu_spec = OpSpec("Relu", None, None) |
| relu_output_spec = OpSpec("Relu", None, srf_output.random.field_blobs()) |
| pow_spec = OpSpec("Pow", None, None, {'exponent': float(s - 1)}) |
| mul_interim_spec = OpSpec("Mul", None, srf_output.random.field_blobs()) |
| mul_spec = OpSpec("Mul", None, srf_output.full.field_blobs()) |
| |
| if s == 0: |
| ops_list = [ |
| fc_learned_spec, |
| fc_random_spec, |
| softsign_spec, |
| relu_output_spec, |
| mul_spec, |
| ] |
| elif s == 1: |
| ops_list = [ |
| fc_learned_spec, |
| fc_random_spec, |
| relu_output_spec, |
| mul_spec, |
| ] |
| else: |
| ops_list = [ |
| fc_learned_spec, |
| fc_random_spec, |
| relu_spec, |
| pow_spec, |
| mul_interim_spec, |
| mul_spec, |
| ] |
| |
| # Train net assertions |
| self._test_net(train_net, ops_list) |
| _semi_random_hypothesis_test(srf_output.full(), X_full, X_random, |
| rand_w, rand_b, s) |
| |
| # Eval net assertions |
| eval_net = self.get_eval_net() |
| self._test_net(eval_net, ops_list) |
| _semi_random_hypothesis_test(srf_output.full(), X_full, X_random, |
| rand_w, rand_b, s) |
| |
| # Predict net assertions |
| predict_net = self.get_predict_net() |
| self._test_net(predict_net, ops_list) |
| _semi_random_hypothesis_test(srf_output.full(), X_full, X_random, |
| rand_w, rand_b, s) |
| |
| def testConv(self): |
| batch_size = 50 |
| H = 1 |
| W = 10 |
| C = 50 |
| output_dims = 32 |
| kernel_h = 1 |
| kernel_w = 3 |
| stride_h = 1 |
| stride_w = 1 |
| pad_t = 0 |
| pad_b = 0 |
| pad_r = None |
| pad_l = None |
| |
| input_record = self.new_record(schema.Scalar((np.float32, (H, W, C)))) |
| X = np.random.random((batch_size, H, W, C)).astype(np.float32) |
| schema.FeedRecord(input_record, [X]) |
| conv = self.model.Conv( |
| input_record, |
| output_dims, |
| kernel_h=kernel_h, |
| kernel_w=kernel_w, |
| stride_h=stride_h, |
| stride_w=stride_w, |
| pad_t=pad_t, |
| pad_b=pad_b, |
| pad_r=pad_r, |
| pad_l=pad_l, |
| order='NHWC' |
| ) |
| |
| self.assertEqual( |
| schema.Scalar((np.float32, (output_dims,))), |
| conv |
| ) |
| |
| self.run_train_net_forward_only() |
| output_record = schema.FetchRecord(conv) |
| # check the number of output channels is the same as input in this example |
| assert output_record.field_types()[0].shape == (H, W, output_dims) |
| assert output_record().shape == (batch_size, H, W, output_dims) |
| |
| train_init_net, train_net = self.get_training_nets() |
| # Init net assertions |
| init_ops = self.assertNetContainOps( |
| train_init_net, |
| [ |
| OpSpec("XavierFill", None, None), |
| OpSpec("ConstantFill", None, None), |
| ] |
| ) |
| conv_spec = OpSpec( |
| "Conv", |
| [ |
| input_record.field_blobs()[0], |
| init_ops[0].output[0], |
| init_ops[1].output[0], |
| ], |
| conv.field_blobs() |
| ) |
| |
| # Train net assertions |
| self.assertNetContainOps(train_net, [conv_spec]) |
| |
| # Predict net assertions |
| predict_net = self.get_predict_net() |
| self.assertNetContainOps(predict_net, [conv_spec]) |
| |
| # Eval net assertions |
| eval_net = self.get_eval_net() |
| self.assertNetContainOps(eval_net, [conv_spec]) |
| |
| @given( |
| num=st.integers(min_value=10, max_value=100), |
| feed_weight=st.booleans(), |
| use_inv_var_parameterization=st.booleans(), |
| use_log_barrier=st.booleans(), |
| enable_diagnose=st.booleans(), |
| **hu.gcs |
| ) |
| @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): |
| 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) |
| result = self.model.ConstantWeight(input_record, weights=weights) |
| train_init_net, train_net = self.get_training_nets(True) |
| workspace.RunNetOnce(train_init_net) |
| workspace.RunNetOnce(train_net) |
| result = workspace.FetchBlob(result()) |
| expected = np.sum(weights * data) |
| npt.assert_allclose(expected, result, atol=1e-4, rtol=1e-4) |
| |
| @given(**hu.gcs) |
| @settings(deadline=10000) |
| def testHomotopyWeight(self, gc, dc): |
| input_record = self.new_record(schema.RawTuple(2)) |
| data = np.random.random(2) |
| schema.FeedRecord( |
| input_record, [np.array(x).astype(np.float32) for x in data] |
| ) |
| # ensure: quad_life > 2 * half_life |
| half_life = int(np.random.random() * 1e2 + 1) |
| quad_life = int(np.random.random() * 1e3 + 2 * half_life + 1) |
| min_weight = np.random.random() |
| max_weight = np.random.random() + min_weight + 1e-5 |
| result = self.model.HomotopyWeight( |
| input_record, |
| min_weight=min_weight, |
| max_weight=max_weight, |
| half_life=half_life, |
| quad_life=quad_life, |
| ) |
| train_init_net, train_net = self.get_training_nets(True) |
| workspace.RunNetOnce(train_init_net) |
| workspace.CreateNet(train_net) |
| workspace.RunNet(train_net.Name(), num_iter=half_life) |
| half_life_result = workspace.FetchBlob(result()) |
| workspace.RunNet(train_net.Name(), num_iter=quad_life - half_life) |
| quad_life_result = workspace.FetchBlob(result()) |
| |
| alpha = (min_weight + max_weight) / 2. |
| beta = (min_weight + max_weight) / 2. |
| expected_half_life_result = alpha * data[0] + beta * data[1] |
| alpha = (3 * min_weight + max_weight) / 4. |
| beta = (min_weight + 3 * max_weight) / 4. |
| expected_quad_life_result = alpha * data[0] + beta * data[1] |
| npt.assert_allclose( |
| expected_half_life_result, half_life_result, atol=1e-2, rtol=1e-2 |
| ) |
| npt.assert_allclose( |
| expected_quad_life_result, quad_life_result, atol=1e-2, rtol=1e-2 |
| ) |
| |
| def _testLabelSmooth(self, categories, binary_prob_label, bsz): |
| label = self.new_record(schema.Scalar((np.float32, (1, )))) |
| label_np = np.random.randint(categories, size=bsz).astype(np.float32) |
| schema.FeedRecord(label, [label_np]) |
| smooth_matrix_shape = ( |
| 2 if binary_prob_label else (categories, categories) |
| ) |
| smooth_matrix = np.random.random(smooth_matrix_shape) |
| smoothed_label = self.model.LabelSmooth(label, smooth_matrix) |
| train_init_net, train_net = self.get_training_nets(True) |
| workspace.RunNetOnce(train_init_net) |
| workspace.RunNetOnce(train_net) |
| smoothed_label_np = workspace.FetchBlob(smoothed_label()) |
| if binary_prob_label: |
| expected = np.array( |
| [ |
| smooth_matrix[0] if x == 0.0 else smooth_matrix[1] |
| for x in label_np |
| ] |
| ) |
| else: |
| expected = np.array([smooth_matrix[int(x)] for x in label_np]) |
| npt.assert_allclose(expected, smoothed_label_np, atol=1e-4, rtol=1e-4) |
| |
| @given( |
| categories=st.integers(min_value=2, max_value=10), |
| bsz=st.integers(min_value=10, max_value=100), |
| **hu.gcs |
| ) |
| def testLabelSmoothForCategoricalLabel(self, categories, bsz, gc, dc): |
| self._testLabelSmooth(categories, False, bsz) |
| |
| @given( |
| bsz=st.integers(min_value=10, max_value=100), |
| **hu.gcs |
| ) |
| def testLabelSmoothForBinaryProbLabel(self, bsz, gc, dc): |
| self._testLabelSmooth(2, True, bsz) |
| |
| @given( |
| num_inputs=st.integers(min_value=2, max_value=10), |
| batch_size=st.integers(min_value=2, max_value=10), |
| input_dim=st.integers(min_value=5, max_value=10), |
| seed=st.integers(1, 10), |
| ) |
| def testBlobWeightedSum(self, num_inputs, batch_size, input_dim, seed): |
| |
| def get_blob_weighted_sum(): |
| weights = [] |
| for i in range(num_inputs): |
| w_blob_name = 'blob_weighted_sum/w_{0}'.format(i) |
| assert workspace.HasBlob(w_blob_name), ( |
| "cannot fine blob {}".format(w_blob_name) |
| ) |
| w = workspace.FetchBlob(w_blob_name) |
| weights.append(w) |
| |
| result = np.sum([ |
| input_data[idx] * weights[idx] for idx in range(num_inputs) |
| ], axis=0) |
| return result |
| |
| np.random.seed(seed) |
| expected_output_schema = schema.Scalar((np.float32, (input_dim,))) |
| input_schema = schema.Tuple( |
| *[expected_output_schema for _ in range(num_inputs)] |
| ) |
| input_data = [ |
| np.random.random((batch_size, input_dim)).astype(np.float32) |
| for _ in range(num_inputs) |
| ] |
| input_record = self.new_record(input_schema) |
| schema.FeedRecord(input_record, input_data) |
| |
| # test output schema |
| ws_output = self.model.BlobWeightedSum(input_record) |
| self.assertEqual(len(self.model.layers), 1) |
| assert schema.equal_schemas(ws_output, expected_output_schema) |
| |
| # test train net |
| train_init_net, train_net = self.get_training_nets() |
| workspace.RunNetOnce(train_init_net) |
| workspace.RunNetOnce(train_net) |
| output = workspace.FetchBlob(ws_output()) |
| npt.assert_almost_equal(get_blob_weighted_sum(), output, decimal=5) |
| |
| self.run_train_net_forward_only() |
| output = workspace.FetchBlob(ws_output()) |
| npt.assert_almost_equal(get_blob_weighted_sum(), output, decimal=5) |
| |
| # test eval net |
| eval_net = self.get_eval_net() |
| workspace.RunNetOnce(eval_net) |
| output = workspace.FetchBlob(ws_output()) |
| npt.assert_almost_equal(get_blob_weighted_sum(), output, decimal=5) |
| |
| # test pred net |
| pred_net = self.get_predict_net() |
| workspace.RunNetOnce(pred_net) |
| output = workspace.FetchBlob(ws_output()) |
| npt.assert_almost_equal(get_blob_weighted_sum(), output, decimal=5) |
| |
| def testFeatureSparseToDenseGetAccessedFeatures(self): |
| float_features_column = "float_features" |
| float_features_type = "FLOAT" |
| float_features_ids = [1, 2, 3] |
| |
| id_list_features_column = "id_list_features" |
| id_list_features_type = "ID_LIST" |
| id_list_features_ids = [4, 5, 6] |
| |
| id_score_list_features_column = "id_score_list_features" |
| id_score_list_features_type = "ID_SCORE_LIST" |
| id_score_list_features_ids = [7, 8 , 9] |
| |
| feature_names = ["a", "b", "c"] |
| |
| input_record = self.new_record(schema.Struct( |
| (float_features_column, schema.Map(np.int32, np.float32)), |
| (id_list_features_column, |
| schema.Map(np.int32, schema.List(np.int64))), |
| (id_score_list_features_column, |
| schema.Map(np.int32, schema.Map(np.int64, np.float32))), |
| )) |
| |
| input_specs = [ |
| ( |
| float_features_column, |
| schema.FeatureSpec( |
| feature_type=float_features_type, |
| feature_ids=float_features_ids, |
| feature_names=feature_names, |
| ), |
| ), |
| ( |
| id_list_features_column, |
| schema.FeatureSpec( |
| feature_type=id_list_features_type, |
| feature_ids=id_list_features_ids, |
| feature_names=feature_names, |
| ), |
| ), |
| ( |
| id_score_list_features_column, |
| schema.FeatureSpec( |
| feature_type=id_score_list_features_type, |
| feature_ids=id_score_list_features_ids, |
| feature_names=feature_names, |
| ), |
| ), |
| ] |
| |
| self.model.FeatureSparseToDense(input_record, input_specs) |
| |
| expected_accessed_features = { |
| float_features_column: [ |
| AccessedFeatures(float_features_type, set(float_features_ids))], |
| id_list_features_column: [ |
| AccessedFeatures(id_list_features_type, set(id_list_features_ids))], |
| id_score_list_features_column: [ |
| AccessedFeatures(id_score_list_features_type, set(id_score_list_features_ids))], |
| } |
| |
| self.assertEqual(len(self.model.layers), 1) |
| self.assertEqual( |
| self.model.layers[0].get_accessed_features(), |
| expected_accessed_features |
| ) |
| |
| def test_get_key(self): |
| def _is_id_list(input_record): |
| return almost_equal_schemas(input_record, IdList) |
| |
| |
| def _is_id_score_list(input_record): |
| return almost_equal_schemas(input_record, |
| IdScoreList, |
| check_field_types=False) |
| |
| def old_get_sparse_key_logic(input_record): |
| if _is_id_list(input_record): |
| sparse_key = input_record.items() |
| elif _is_id_score_list(input_record): |
| sparse_key = input_record.keys() |
| else: |
| raise NotImplementedError() |
| return sparse_key |
| |
| id_score_list_record = schema.NewRecord( |
| self.model.net, |
| schema.Map( |
| schema.Scalar( |
| np.int64, |
| metadata=schema.Metadata( |
| categorical_limit=1000 |
| ), |
| ), |
| np.float32 |
| ) |
| ) |
| |
| self.assertEqual( |
| get_key(id_score_list_record)(), |
| old_get_sparse_key_logic(id_score_list_record) |
| ) |
| |
| id_list_record = schema.NewRecord( |
| self.model.net, |
| schema.List( |
| schema.Scalar( |
| np.int64, |
| metadata=schema.Metadata(categorical_limit=1000) |
| ) |
| ) |
| ) |
| |
| self.assertEqual( |
| get_key(id_list_record)(), |
| old_get_sparse_key_logic(id_list_record) |
| ) |
| |
| def testSparseLookupWithAttentionWeightOnIdScoreList(self): |
| record = schema.NewRecord( |
| self.model.net, |
| schema.Map( |
| schema.Scalar( |
| np.int64, |
| metadata=schema.Metadata(categorical_limit=1000), |
| ), |
| np.float32, |
| ), |
| ) |
| embedding_dim = 64 |
| embedding_after_pooling = self.model.SparseLookup( |
| record, [embedding_dim], "Sum", use_external_weights=True |
| ) |
| self.model.output_schema = schema.Struct() |
| self.assertEqual( |
| schema.Scalar((np.float32, (embedding_dim,))), embedding_after_pooling |
| ) |
| |
| train_init_net, train_net = self.get_training_nets() |
| |
| init_ops = self.assertNetContainOps( |
| train_init_net, |
| [OpSpec("UniformFill", None, None), OpSpec("ConstantFill", None, None)], |
| ) |
| sparse_lookup_op_spec = OpSpec( |
| "SparseLengthsWeightedSum", |
| [ |
| init_ops[0].output[0], |
| record.values(), |
| record.keys(), |
| record.lengths(), |
| ], |
| [embedding_after_pooling()], |
| ) |
| self.assertNetContainOps(train_net, [sparse_lookup_op_spec]) |
| |
| predict_net = self.get_predict_net() |
| self.assertNetContainOps(predict_net, [sparse_lookup_op_spec]) |
| |
| def testSparseItemwiseDropoutWithReplacement(self): |
| input_record = schema.NewRecord(self.model.net, IdList) |
| self.model.output_schema = schema.Struct() |
| |
| lengths_blob = input_record.field_blobs()[0] |
| values_blob = input_record.field_blobs()[1] |
| lengths = np.array([1] * 10).astype(np.int32) |
| values = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]).astype(np.int64) |
| workspace.FeedBlob(lengths_blob, lengths) |
| workspace.FeedBlob(values_blob, values) |
| |
| out = self.model.SparseItemwiseDropoutWithReplacement( |
| input_record, 0.0, 0.5, 1.0, -1, output_names_or_num=1) |
| self.assertEqual(schema.List(schema.Scalar(np.int64,)), out) |
| |
| train_init_net, train_net = self.get_training_nets() |
| eval_net = self.get_eval_net() |
| predict_net = self.get_predict_net() |
| |
| workspace.RunNetOnce(train_init_net) |
| workspace.RunNetOnce(train_net) |
| out_values = workspace.FetchBlob(out.items()) |
| out_lengths = workspace.FetchBlob(out.lengths()) |
| self.assertBlobsEqual(out_values, values) |
| self.assertBlobsEqual(out_lengths, lengths) |
| |
| workspace.RunNetOnce(eval_net) |
| |
| workspace.RunNetOnce(predict_net) |
| predict_values = workspace.FetchBlob("values_auto_0") |
| predict_lengths = workspace.FetchBlob("lengths_auto_0") |
| self.assertBlobsEqual(predict_values, np.array([-1] * 10).astype(np.int64)) |
| self.assertBlobsEqual(predict_lengths, lengths) |
