import numpy as np import layers from framework import Program, unique_name, Variable, program_guard from layer_helper import LayerHelper __all__ = [ 'Accuracy', 'ChunkEvaluator', ] def _clone_var_(block, var): assert isinstance(var, Variable) return block.create_var( name=var.name, shape=var.shape, dtype=var.dtype, type=var.type, lod_level=var.lod_level, persistable=True) class Evaluator(object): """ Base Class for all evaluators Args: name(str): The name of evaluator. such as, "accuracy". Used for generate temporary variable name. main_program(Program, optional): The evaluator should be added to this main_program. Default default_main_program() startup_program(Program, optional):The parameter should be added to this startup_program. Default default_startup_program() Attributes: states(list): The list of state variables. states will be reset to zero when `reset` is invoked. metrics(list): The list of metrics variables. They will be calculate every mini-batch """ def __init__(self, name, **kwargs): self.states = [] self.metrics = [] self.helper = LayerHelper(name, **kwargs) def reset(self, executor, reset_program=None): """ reset metric states at the begin of each pass/user specified batch """ if reset_program is None: reset_program = Program() with program_guard(main_program=reset_program): for var in self.states: assert isinstance(var, Variable) g_var = _clone_var_(reset_program.current_block(), var) layers.fill_constant( shape=g_var.shape, value=0.0, dtype=g_var.dtype, out=g_var) executor.run(reset_program) def eval(self, executor, eval_program=None): """ Evaluate the statistics merged by multiple mini-batches. """ raise NotImplementedError() def create_state(self, suffix, dtype, shape): """ Create state variable. NOTE: It is not a public API. Args: suffix(str): the state suffix. dtype(str|core.DataType): the state data type shape(tuple|list): the shape of state Returns: State variable """ state = self.helper.create_variable( name="_".join([unique_name(self.helper.name), suffix]), persistable=True, dtype=dtype, shape=shape) self.states.append(state) return state class Accuracy(Evaluator): """ Average Accuracy for multiple mini-batches. """ def __init__(self, input, label, k=1, **kwargs): super(Accuracy, self).__init__("accuracy", **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.total = self.create_state(dtype='int64', shape=[1], suffix='total') self.correct = self.create_state( dtype='int64', shape=[1], suffix='correct') total = self.helper.create_tmp_variable(dtype='int') correct = self.helper.create_tmp_variable(dtype='int') acc = layers.accuracy( input=input, label=label, k=k, total=total, correct=correct) total = layers.cast(x=total, dtype='int64') correct = layers.cast(x=correct, dtype='int64') layers.sums(input=[self.total, total], out=self.total) layers.sums(input=[self.correct, correct], out=self.correct) self.metrics.append(acc) def eval(self, executor, eval_program=None): if eval_program is None: eval_program = Program() block = eval_program.current_block() with program_guard(main_program=eval_program): total = _clone_var_(block, self.total) correct = _clone_var_(block, self.correct) total = layers.cast(total, dtype='float32') correct = layers.cast(correct, dtype='float32') out = layers.elementwise_div(x=correct, y=total) 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, ): super(ChunkEvaluator, self).__init__("chunk_eval") 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') 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, ) layers.sums( input=[self.num_infer_chunks, num_infer_chunks], out=self.num_infer_chunks) layers.sums( input=[self.num_label_chunks, num_label_chunks], out=self.num_label_chunks) layers.sums( input=[self.num_correct_chunks, num_correct_chunks], out=self.num_correct_chunks) 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() 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')