from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals import numpy as np from caffe2.python import ( layer_model_instantiator, schema, workspace, ) from caffe2.python.layer_test_util import ( LayersTestCase, OpSpec, ) 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.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, ) # 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 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 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(range(20), dtype=np.float32).reshape(10, 2) lengths = np.array(range(10), dtype=np.int32) items = np.array(range(lengths.sum()), dtype=np.int64) items_lengths = np.array(range(lengths.sum()), dtype=np.int32) items_items = np.array(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))), )), )) 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)) # just to make run_train_net works self.model.loss = self.model.StopGradient(gathered_record.dense, 1) self.run_train_net() 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 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.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.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.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())