Add ChunkEvaluator for multi-batches

paddle/operators/chunk_eval_op.cc

@@ -32,6 +32,13 @@ class ChunkEvalOp : public framework::OperatorWithKernel {
                    "Output(Recall) of ChunkEvalOp should not be null.");
     PADDLE_ENFORCE(ctx->HasOutput("F1-Score"),
                    "Output(F1-Score) of ChunkEvalOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("NumInferChunks"),
+                   "Output(NumInferChunks) of ChunkEvalOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("NumLabelChunks"),
+                   "Output(NumLabelChunks) of ChunkEvalOp should not be null.");
+    PADDLE_ENFORCE(
+        ctx->HasOutput("NumCorrectChunks"),
+        "Output(NumCorrectChunks) of ChunkEvalOp should not be null.");
 
     auto inference_dim = ctx->GetInputDim("Inference");
     auto label_dim = ctx->GetInputDim("Label");
@@ -42,6 +49,9 @@ class ChunkEvalOp : public framework::OperatorWithKernel {
     ctx->SetOutputDim("Precision", {1});
     ctx->SetOutputDim("Recall", {1});
     ctx->SetOutputDim("F1-Score", {1});
+    ctx->SetOutputDim("NumInferChunks", {1});
+    ctx->SetOutputDim("NumLabelChunks", {1});
+    ctx->SetOutputDim("NumCorrectChunks", {1});
   }
 
  protected:
@@ -70,6 +80,14 @@ class ChunkEvalOpMaker : public framework::OpProtoAndCheckerMaker {
               "sensitivity) of chunks on the given mini-batch.");
     AddOutput("F1-Score",
               "(float). The evaluated F1-Score on the given mini-batch.");
+    AddOutput(
+        "NumInferChunks",
+        "(int). The number of chunks in Inference on the given mini-batch.");
+    AddOutput("NumLabelChunks",
+              "(int). The number of chunks in Label on the given mini-batch.");
+    AddOutput("NumCorrectChunks",
+              "(int). The number of chunks both in Inference and Label on the "
+              "given mini-batch.");
     AddAttr<int>("num_chunk_types",
                  "(int). The number of chunk type. See below for details.");
     AddAttr<std::string>(

paddle/operators/chunk_eval_op.h

@@ -111,9 +111,7 @@ class ChunkEvalKernel : public framework::OpKernel<T> {
     std::vector<Segment> label_segments;
     std::vector<Segment> output_segments;
     std::set<int> excluded_chunk_types;
-    int64_t num_output_segments = 0;
-    int64_t num_label_segments = 0;
-    int64_t num_correct = 0;
+
     if (context.Attr<std::string>("chunk_scheme") == "IOB") {
       num_tag_types = 2;
       tag_begin = 0;
@@ -151,12 +149,24 @@ class ChunkEvalKernel : public framework::OpKernel<T> {
     auto* precision = context.Output<Tensor>("Precision");
     auto* recall = context.Output<Tensor>("Recall");
     auto* f1 = context.Output<Tensor>("F1-Score");
+    auto* num_infer_chunks = context.Output<Tensor>("NumInferChunks");
+    auto* num_label_chunks = context.Output<Tensor>("NumLabelChunks");
+    auto* num_correct_chunks = context.Output<Tensor>("NumCorrectChunks");
 
     const int64_t* inference_data = inference->data<int64_t>();
     const int64_t* label_data = label->data<int64_t>();
     T* precision_data = precision->mutable_data<T>(context.GetPlace());
     T* racall_data = recall->mutable_data<T>(context.GetPlace());
     T* f1_data = f1->mutable_data<T>(context.GetPlace());
+    int64_t* num_infer_chunks_data =
+        num_infer_chunks->mutable_data<int64_t>(context.GetPlace());
+    int64_t* num_label_chunks_data =
+        num_label_chunks->mutable_data<int64_t>(context.GetPlace());
+    int64_t* num_correct_chunks_data =
+        num_correct_chunks->mutable_data<int64_t>(context.GetPlace());
+    *num_infer_chunks_data = 0;
+    *num_label_chunks_data = 0;
+    *num_correct_chunks_data = 0;
 
     auto lod = label->lod();
     PADDLE_ENFORCE_EQ(lod.size(), 1UL, "Only support one level sequence now.");
@@ -166,16 +176,22 @@ class ChunkEvalKernel : public framework::OpKernel<T> {
     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_output_segments,
-                 num_label_segments, num_correct, num_chunk_types,
-                 num_tag_types, other_chunk_type, tag_begin, tag_inside,
-                 tag_end, tag_single, excluded_chunk_types);
-    }
-    *precision_data = !num_output_segments ? 0 : static_cast<T>(num_correct) /
-                                                     num_output_segments;
-    *racall_data = !num_label_segments ? 0 : static_cast<T>(num_correct) /
-                                                 num_label_segments;
-    *f1_data = !num_correct ? 0 : 2 * (*precision_data) * (*racall_data) /
+                 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<T>(*num_correct_chunks_data) /
+                                (*num_infer_chunks_data);
+    *racall_data = !(*num_label_chunks_data)
+                       ? 0
+                       : static_cast<T>(*num_correct_chunks_data) /
+                             (*num_label_chunks_data);
+    *f1_data = !(*num_correct_chunks_data)
+                   ? 0
+                   : 2 * (*precision_data) * (*racall_data) /
                          ((*precision_data) + (*racall_data));
   }
 

python/paddle/v2/fluid/evaluator.py

@@ -4,7 +4,7 @@ import layers
 from framework import Program, unique_name, Variable
 from layer_helper import LayerHelper
 
-__all__ = ['Accuracy']
+__all__ = ['Accuracy', 'ChunkEvaluator']
 
 
 def _clone_var_(block, var):
@@ -132,3 +132,74 @@ class Accuracy(Evaluator):
         correct = layers.cast(correct, dtype='float32', **kwargs)
         out = layers.elementwise_div(x=correct, y=total, **kwargs)
         return np.array(executor.run(eval_program, fetch_list=[out])[0])
+
+
+class ChunkEvaluator(Evaluator):
+    """
+    Accumulate counter numbers output by chunk_eval from mini-batches and 
+    compute the precision recall and F1-score using the accumulated counter 
+    numbers.
+    """
+
+    def __init__(self,
+                 input,
+                 label,
+                 chunk_scheme,
+                 num_chunk_types,
+                 excluded_chunk_types=None,
+                 **kwargs):
+        super(ChunkEvaluator, self).__init__("chunk_eval", **kwargs)
+        main_program = self.helper.main_program
+        if main_program.current_block().idx != 0:
+            raise ValueError("You can only invoke Evaluator in root block")
+
+        self.num_infer_chunks = self.create_state(
+            dtype='int64', shape=[1], suffix='num_infer_chunks')
+        self.num_label_chunks = self.create_state(
+            dtype='int64', shape=[1], suffix='num_label_chunks')
+        self.num_correct_chunks = self.create_state(
+            dtype='int64', shape=[1], suffix='num_correct_chunks')
+        kwargs = {'main_program': main_program}
+        precision, recall, f1_score, num_infer_chunks, num_label_chunks, num_correct_chunks = layers.chunk_eval(
+            input=input,
+            label=label,
+            chunk_scheme=chunk_scheme,
+            num_chunk_types=num_chunk_types,
+            excluded_chunk_types=excluded_chunk_types,
+            **kwargs)
+        layers.sums(
+            input=[self.num_infer_chunks, num_infer_chunks],
+            out=self.num_infer_chunks,
+            **kwargs)
+        layers.sums(
+            input=[self.num_label_chunks, num_label_chunks],
+            out=self.num_label_chunks,
+            **kwargs)
+        layers.sums(
+            input=[self.num_correct_chunks, num_correct_chunks],
+            out=self.num_correct_chunks,
+            **kwargs)
+
+        self.metrics.extend([precision, recall, f1_score])
+
+    def eval(self, executor, eval_program=None):
+        if eval_program is None:
+            eval_program = Program()
+        block = eval_program.current_block()
+        kwargs = {'main_program': eval_program}
+        num_infer_chunks, num_label_chunks, num_correct_chunks = executor.run(
+            eval_program,
+            fetch_list=[_clone_var_(block, state) for state in self.states])
+        num_infer_chunks = num_infer_chunks[0]
+        num_label_chunks = num_label_chunks[0]
+        num_correct_chunks = num_correct_chunks[0]
+        precision = float(
+            num_correct_chunks) / num_infer_chunks if num_infer_chunks else 0
+        recall = float(
+            num_correct_chunks) / num_label_chunks if num_label_chunks else 0
+        f1_score = float(2 * precision * recall) / (
+            precision + recall) if num_correct_chunks else 0
+        return np.array(
+            [precision], dtype='float32'), np.array(
+                [recall], dtype='float32'), np.array(
+                    [f1_score], dtype='float32')

python/paddle/v2/fluid/layers.py

@@ -640,8 +640,8 @@ def chunk_eval(input,
                excluded_chunk_types=None,
                **kwargs):
     """
-    This function computes the accuracy using the input and label.
-    The output is the top_k inputs and their indices.
+    This function computes and outputs the precision, recall and 
+    F1-score of chunk detection.
     """
     helper = LayerHelper("chunk_eval", **kwargs)
 
@@ -649,6 +649,9 @@ def chunk_eval(input,
     precision = helper.create_tmp_variable(dtype="float32")
     recall = helper.create_tmp_variable(dtype="float32")
     f1_score = helper.create_tmp_variable(dtype="float32")
+    num_infer_chunks = helper.create_tmp_variable(dtype="int64")
+    num_label_chunks = helper.create_tmp_variable(dtype="int64")
+    num_correct_chunks = helper.create_tmp_variable(dtype="int64")
 
     helper.append_op(
         type="chunk_eval",
@@ -657,14 +660,17 @@ def chunk_eval(input,
         outputs={
             "Precision": [precision],
             "Recall": [recall],
-            "F1-Score": [f1_score]
+            "F1-Score": [f1_score],
+            "NumInferChunks": [num_infer_chunks],
+            "NumLabelChunks": [num_label_chunks],
+            "NumCorrectChunks": [num_correct_chunks]
         },
         attrs={
             "num_chunk_types": num_chunk_types,
             'chunk_scheme': chunk_scheme,
             'excluded_chunk_types': excluded_chunk_types or []
         })
-    return precision, recall, f1_score
+    return precision, recall, f1_score, num_infer_chunks, num_label_chunks, num_correct_chunks
 
 
 def sequence_conv(input,

python/paddle/v2/fluid/tests/book/test_label_semantic_roles.py

@@ -150,7 +150,7 @@ def main():
     crf_decode = fluid.layers.crf_decoding(
         input=feature_out, param_attr=fluid.ParamAttr(name='crfw'))
 
-    precision, recall, f1_score = fluid.layers.chunk_eval(
+    chunk_evaluator = fluid.evaluator.ChunkEvaluator(
         input=crf_decode,
         label=target,
         chunk_scheme="IOB",
@@ -176,14 +176,16 @@ def main():
 
     batch_id = 0
     for pass_id in xrange(PASS_NUM):
+        chunk_evaluator.reset(exe)
         for data in train_data():
             outs = exe.run(fluid.default_main_program(),
                            feed=feeder.feed(data),
-                           fetch_list=[avg_cost, precision, recall, f1_score])
+                           fetch_list=[avg_cost] + chunk_evaluator.metrics)
+            precision, recall, f1_score = chunk_evaluator.eval(exe)
             avg_cost_val = np.array(outs[0])
-            precision_val = np.array(outs[1])
-            recall_val = np.array(outs[2])
-            f1_score_val = np.array(outs[3])
+            precision_val = np.array(precision)
+            recall_val = np.array(recall)
+            f1_score_val = np.array(f1_score)
 
             if batch_id % 10 == 0:
                 print("avg_cost=" + str(avg_cost_val))

python/paddle/v2/fluid/tests/test_chunk_eval_op.py

@@ -147,7 +147,13 @@ class TestChunkEvalOp(OpTest):
             'Recall': np.asarray(
                 [recall], dtype='float32'),
             'F1-Score': np.asarray(
-                [f1], dtype='float32')
+                [f1], dtype='float32'),
+            'NumInferChunks': np.asarray(
+                [self.num_infer_chunks], dtype='int64'),
+            'NumLabelChunks': np.asarray(
+                [self.num_label_chunks], dtype='int64'),
+            'NumCorrectChunks': np.asarray(
+                [self.num_correct_chunks], dtype='int64')
         }
 
     def setUp(self):