# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License" # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import absolute_import from __future__ import division from __future__ import print_function import time import paddle.fluid as fluid import paddlehub as hub import numpy as np from paddlehub.common.logger import logger def evaluate_cls_task(task, data_reader, feed_list, phase="test", config=None): logger.info("Evaluation on {} dataset start".format(phase)) inference_program = task.inference_program() main_program = task.main_program() loss = task.variable("loss") accuracy = task.variable("accuracy") batch_size = config.batch_size place, dev_count = hub.common.get_running_device_info(config) exe = fluid.Executor(place=place) with fluid.program_guard(inference_program): data_feeder = fluid.DataFeeder(feed_list=feed_list, place=place) num_eval_examples = acc_sum = loss_sum = 0 test_reader = data_reader.data_generator( batch_size=batch_size, phase=phase) eval_time_begin = time.time() eval_step = 0 for batch in test_reader(): num_batch_examples = len(batch) eval_step += 1 loss_v, accuracy_v = exe.run( feed=data_feeder.feed(batch), fetch_list=[loss.name, accuracy.name]) num_eval_examples += num_batch_examples if num_eval_examples % 10000 == 0: logger.info("{} examples evaluated.".format(num_eval_examples)) acc_sum += accuracy_v * num_batch_examples loss_sum += loss_v * num_batch_examples eval_time_used = time.time() - eval_time_begin avg_loss = loss_sum / num_eval_examples avg_acc = acc_sum / num_eval_examples eval_speed = eval_step / eval_time_used logger.info( "[%s dataset evaluation result] loss=%.5f acc=%.5f [step/sec: %.2f]" % (phase, avg_loss, avg_acc, eval_speed)) return avg_loss, avg_acc, eval_speed def evaluate_seq_labeling_task(task, data_reader, feed_list, phase="test", config=None): fetch_list = [ task.variable("labels").name, task.variable("infers").name, task.variable("seq_len").name, task.variable("loss").name ] logger.info("Evaluation on {} dataset start".format(phase)) inference_program = task.inference_program() batch_size = config.batch_size place, dev_count = hub.common.get_running_device_info(config) exe = fluid.Executor(place=place) num_labels = len(data_reader.get_labels()) with fluid.program_guard(inference_program): data_feeder = fluid.DataFeeder(feed_list=feed_list, place=place) num_eval_examples = acc_sum = loss_sum = 0 test_reader = data_reader.data_generator( batch_size=batch_size, phase=phase) eval_time_begin = time.time() eval_step = 0 total_label, total_infer, total_correct = 0.0, 0.0, 0.0 for batch in test_reader(): num_batch_examples = len(batch) eval_step += 1 np_labels, np_infers, np_lens, _ = exe.run( feed=data_feeder.feed(batch), fetch_list=fetch_list) label_num, infer_num, correct_num = chunk_eval( np_labels, np_infers, np_lens, num_labels, dev_count) total_infer += infer_num total_label += label_num total_correct += correct_num precision, recall, f1 = calculate_f1(total_label, total_infer, total_correct) eval_time_used = time.time() - eval_time_begin eval_speed = eval_step / eval_time_used logger.info( "[%s evaluation] F1-Score=%f, precision=%f, recall=%f [step/sec: %.2f]" % (phase, f1, precision, recall, eval_speed)) # Sequence label evaluation functions def chunk_eval(np_labels, np_infers, np_lens, tag_num, dev_count=1): def extract_bio_chunk(seq): chunks = [] cur_chunk = None null_index = tag_num - 1 for index in range(len(seq)): tag = seq[index] tag_type = tag // 2 tag_pos = tag % 2 if tag == null_index: if cur_chunk is not None: chunks.append(cur_chunk) cur_chunk = None continue if tag_pos == 0: if cur_chunk is not None: chunks.append(cur_chunk) cur_chunk = {} cur_chunk = {"st": index, "en": index + 1, "type": tag_type} else: if cur_chunk is None: cur_chunk = {"st": index, "en": index + 1, "type": tag_type} continue if cur_chunk["type"] == tag_type: cur_chunk["en"] = index + 1 else: chunks.append(cur_chunk) cur_chunk = {"st": index, "en": index + 1, "type": tag_type} if cur_chunk is not None: chunks.append(cur_chunk) return chunks null_index = tag_num - 1 num_label = 0 num_infer = 0 num_correct = 0 labels = np_labels.reshape([-1]).astype(np.int32).tolist() infers = np_infers.reshape([-1]).astype(np.int32).tolist() all_lens = np_lens.reshape([dev_count, -1]).astype(np.int32).tolist() base_index = 0 for dev_index in range(dev_count): lens = all_lens[dev_index] max_len = 0 for l in lens: max_len = max(max_len, l) for i in range(len(lens)): seq_st = base_index + i * max_len + 1 seq_en = seq_st + (lens[i] - 2) infer_chunks = extract_bio_chunk(infers[seq_st:seq_en]) label_chunks = extract_bio_chunk(labels[seq_st:seq_en]) num_infer += len(infer_chunks) num_label += len(label_chunks) infer_index = 0 label_index = 0 while label_index < len(label_chunks) \ and infer_index < len(infer_chunks): if infer_chunks[infer_index]["st"] \ < label_chunks[label_index]["st"]: infer_index += 1 elif infer_chunks[infer_index]["st"] \ > label_chunks[label_index]["st"]: label_index += 1 else: if infer_chunks[infer_index]["en"] \ == label_chunks[label_index]["en"] \ and infer_chunks[infer_index]["type"] \ == label_chunks[label_index]["type"]: num_correct += 1 infer_index += 1 label_index += 1 base_index += max_len * len(lens) return num_label, num_infer, num_correct def calculate_f1(num_label, num_infer, num_correct): if num_infer == 0: precision = 0.0 else: precision = num_correct * 1.0 / num_infer if num_label == 0: recall = 0.0 else: recall = num_correct * 1.0 / num_label if num_correct == 0: f1 = 0.0 else: f1 = 2 * precision * recall / (precision + recall) return precision, recall, f1