From 26318544d27bba2c6f88d15005e61119db7f8a7b Mon Sep 17 00:00:00 2001 From: zhoukunsheng Date: Wed, 3 Jul 2019 11:31:27 +0800 Subject: [PATCH] support Tensor input for chunk_eval op (#18226) * test=develop support Tensor input for chunk_eval op * test=develop fix testcase for chunk_eval op * test=develop fix typos in nn.py --- paddle/fluid/API.spec | 2 +- paddle/fluid/operators/chunk_eval_op.cc | 13 ++++++ paddle/fluid/operators/chunk_eval_op.h | 45 ++++++++++++++----- python/paddle/fluid/layers/nn.py | 12 +++-- .../tests/unittests/test_chunk_eval_op.py | 33 +++++++++++++- 5 files changed, 89 insertions(+), 16 deletions(-) diff --git a/paddle/fluid/API.spec b/paddle/fluid/API.spec index 3085c54bc3..4cf1850a33 100644 --- a/paddle/fluid/API.spec +++ b/paddle/fluid/API.spec @@ -105,7 +105,7 @@ paddle.fluid.layers.cos_sim (ArgSpec(args=['X', 'Y'], varargs=None, keywords=Non paddle.fluid.layers.cross_entropy (ArgSpec(args=['input', 'label', 'soft_label', 'ignore_index'], varargs=None, keywords=None, defaults=(False, -100)), ('document', 'f43c659ca1749a3f0ff2231e6dfda07d')) paddle.fluid.layers.bpr_loss (ArgSpec(args=['input', 'label', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '6263dfdeb6c670fa0922c9cbc8fb1bf4')) paddle.fluid.layers.square_error_cost (ArgSpec(args=['input', 'label'], varargs=None, keywords=None, defaults=None), ('document', 'bbb9e708bab250359864fefbdf48e9d9')) -paddle.fluid.layers.chunk_eval (ArgSpec(args=['input', 'label', 'chunk_scheme', 'num_chunk_types', 'excluded_chunk_types'], varargs=None, keywords=None, defaults=(None,)), ('document', '5aa25d023acea1fb49a0de56be86990b')) +paddle.fluid.layers.chunk_eval (ArgSpec(args=['input', 'label', 'chunk_scheme', 'num_chunk_types', 'excluded_chunk_types', 'seq_length'], varargs=None, keywords=None, defaults=(None, None)), ('document', 'b02844e0ad4bd713c5fe6802aa13219c')) paddle.fluid.layers.sequence_conv (ArgSpec(args=['input', 'num_filters', 'filter_size', 'filter_stride', 'padding', 'bias_attr', 'param_attr', 'act', 'name'], varargs=None, keywords=None, defaults=(3, 1, None, None, None, None, None)), ('document', '3d8e8f3e0e1cf520156be37605e83ccd')) paddle.fluid.layers.conv2d (ArgSpec(args=['input', 'num_filters', 'filter_size', 'stride', 'padding', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(1, 0, 1, None, None, None, True, None, None)), ('document', '114c7fe6b0adfc6d6371122f9b9f506e')) paddle.fluid.layers.conv3d (ArgSpec(args=['input', 'num_filters', 'filter_size', 'stride', 'padding', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(1, 0, 1, None, None, None, True, None, None)), ('document', '367293b5bada54136a91621078d38334')) diff --git a/paddle/fluid/operators/chunk_eval_op.cc b/paddle/fluid/operators/chunk_eval_op.cc index dc43c69be0..21dfaf912a 100644 --- a/paddle/fluid/operators/chunk_eval_op.cc +++ b/paddle/fluid/operators/chunk_eval_op.cc @@ -48,6 +48,15 @@ class ChunkEvalOp : public framework::OperatorWithKernel { PADDLE_ENFORCE(inference_dim == label_dim, "Inference's shape must be the same as Label's shape."); + bool use_padding = ctx->HasInput("SeqLength"); + if (use_padding) { + PADDLE_ENFORCE(inference_dim.size() == 3, + "when SeqLength is provided, Inference should be of dim 3 " + "(batch, bucket, 1)"); + auto seq_length_dim = ctx->GetInputDim("SeqLength"); + PADDLE_ENFORCE(seq_length_dim.size() == 1, "seq_length should be rank 1"); + } + ctx->SetOutputDim("Precision", {1}); ctx->SetOutputDim("Recall", {1}); ctx->SetOutputDim("F1-Score", {1}); @@ -72,6 +81,10 @@ class ChunkEvalOpMaker : public framework::OpProtoAndCheckerMaker { "Predictions from the network."); AddInput("Label", "(Tensor, default: Tensor). The true tag sequences."); + AddInput("SeqLength", + "(Tensor, default: Tensor). The length of each sequence, " + "used when Inference and Label are Tensor type .") + .AsDispensable(); AddOutput("Precision", "(float). The evaluated precision (called positive predictive " "value) of chunks on the given mini-batch."); diff --git a/paddle/fluid/operators/chunk_eval_op.h b/paddle/fluid/operators/chunk_eval_op.h index 8631415062..63c77e52fb 100644 --- a/paddle/fluid/operators/chunk_eval_op.h +++ b/paddle/fluid/operators/chunk_eval_op.h @@ -173,18 +173,41 @@ class ChunkEvalKernel : public framework::OpKernel { *num_correct_chunks_data = 0; auto lod = label->lod(); - PADDLE_ENFORCE_EQ(lod.size(), 1UL, "Only support one level sequence now."); - PADDLE_ENFORCE(lod == inference->lod(), - "LoD must be same between Inference and Label."); - int num_sequences = lod[0].size() - 1; - for (int i = 0; i < num_sequences; ++i) { - int seq_length = lod[0][i + 1] - lod[0][i]; - EvalOneSeq(inference_data + lod[0][i], label_data + lod[0][i], seq_length, - &output_segments, &label_segments, num_infer_chunks_data, - num_label_chunks_data, num_correct_chunks_data, - num_chunk_types, num_tag_types, other_chunk_type, tag_begin, - tag_inside, tag_end, tag_single, excluded_chunk_types); + bool use_padding = lod.empty(); + int num_sequences = 0; + + if (use_padding) { + auto dim1 = inference->dims()[1]; + auto* seq_length_t = context.Input("SeqLength"); + auto* seq_length_data = seq_length_t->data(); + num_sequences = seq_length_t->dims()[0]; + + for (int i = 0; i < num_sequences; ++i) { + int seq_length = seq_length_data[i]; + EvalOneSeq(inference_data + i * dim1, label_data + i * dim1, seq_length, + &output_segments, &label_segments, num_infer_chunks_data, + num_label_chunks_data, num_correct_chunks_data, + num_chunk_types, num_tag_types, other_chunk_type, tag_begin, + tag_inside, tag_end, tag_single, excluded_chunk_types); + } + } else { + PADDLE_ENFORCE_EQ(lod.size(), 1UL, + "Only support one level sequence now."); + PADDLE_ENFORCE(lod == inference->lod(), + "LoD must be same between Inference and Label."); + num_sequences = lod[0].size() - 1; + + for (int i = 0; i < num_sequences; ++i) { + int seq_length = lod[0][i + 1] - lod[0][i]; + EvalOneSeq(inference_data + lod[0][i], label_data + lod[0][i], + seq_length, &output_segments, &label_segments, + num_infer_chunks_data, num_label_chunks_data, + num_correct_chunks_data, num_chunk_types, num_tag_types, + other_chunk_type, tag_begin, tag_inside, tag_end, tag_single, + excluded_chunk_types); + } } + *precision_data = !(*num_infer_chunks_data) ? 0 : static_cast(*num_correct_chunks_data) / diff --git a/python/paddle/fluid/layers/nn.py b/python/paddle/fluid/layers/nn.py index ae441cde4f..029adda678 100644 --- a/python/paddle/fluid/layers/nn.py +++ b/python/paddle/fluid/layers/nn.py @@ -1679,7 +1679,8 @@ def chunk_eval(input, label, chunk_scheme, num_chunk_types, - excluded_chunk_types=None): + excluded_chunk_types=None, + seq_length=None): """ **Chunk Evaluator** @@ -1751,6 +1752,7 @@ def chunk_eval(input, chunk_scheme (str): ${chunk_scheme_comment} num_chunk_types (int): ${num_chunk_types_comment} excluded_chunk_types (list): ${excluded_chunk_types_comment} + seq_length(Variable): 1-D Tensor specifying sequence length when input and label are Tensor type. Returns: tuple: tuple containing: precision, recall, f1_score, @@ -1792,10 +1794,14 @@ def chunk_eval(input, num_correct_chunks = helper.create_variable_for_type_inference( dtype="int64") + this_input = {"Inference": [input], "Label": [label]} + + if seq_length: + this_input["SeqLength"] = [seq_length] + helper.append_op( type="chunk_eval", - inputs={"Inference": [input], - "Label": [label]}, + inputs=this_input, outputs={ "Precision": [precision], "Recall": [recall], diff --git a/python/paddle/fluid/tests/unittests/test_chunk_eval_op.py b/python/paddle/fluid/tests/unittests/test_chunk_eval_op.py index 48eb8e9f75..2b7f92656d 100644 --- a/python/paddle/fluid/tests/unittests/test_chunk_eval_op.py +++ b/python/paddle/fluid/tests/unittests/test_chunk_eval_op.py @@ -150,7 +150,7 @@ class TestChunkEvalOp(OpTest): lod = [] for i in range(len(starts) - 1): lod.append(starts[i + 1] - starts[i]) - self.inputs = {'Inference': (infer, [lod]), 'Label': (label, [lod])} + self.set_input(infer, label, lod) precision = float( self.num_correct_chunks ) / self.num_infer_chunks if self.num_infer_chunks else 0 @@ -173,6 +173,9 @@ class TestChunkEvalOp(OpTest): [self.num_correct_chunks], dtype='int64') } + def set_input(self, infer, label, lod): + self.inputs = {'Inference': (infer, [lod]), 'Label': (label, [lod])} + def setUp(self): self.op_type = 'chunk_eval' self.set_confs() @@ -198,5 +201,33 @@ class TestChunkEvalOpWithExclude(TestChunkEvalOp): self.num_correct_chunks, self.num_infer_chunks, self.num_label_chunks = 15, 18, 20 +class TestChunkEvalOpWithTensorInput(TestChunkEvalOp): + def set_input(self, infer, label, lod): + max_len = np.max(lod) + pad_infer = [] + pad_label = [] + start = 0 + for i in range(len(lod)): + end = lod[i] + start + pad_infer.append( + np.pad(infer[start:end], (0, max_len - lod[i]), + 'constant', + constant_values=(-1, ))) + pad_label.append( + np.pad(label[start:end], (0, max_len - lod[i]), + 'constant', + constant_values=(-1, ))) + start = end + + pad_infer = np.expand_dims(np.array(pad_infer, dtype='int64'), 2) + pad_label = np.expand_dims(np.array(pad_label, dtype='int64'), 2) + lod = np.array(lod, dtype='int64') + self.inputs = { + 'Inference': pad_infer, + 'Label': pad_label, + 'SeqLength': lod + } + + if __name__ == '__main__': unittest.main() -- GitLab