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										 |  |  | # copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve. | 
					
						
							|  |  |  | # | 
					
						
							|  |  |  | # 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. | 
					
						
							|  |  |  | import numpy as np | 
					
						
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										 |  |  | class TableMetric(object): | 
					
						
							|  |  |  |     def __init__(self, main_indicator='acc', **kwargs): | 
					
						
							|  |  |  |         self.main_indicator = main_indicator | 
					
						
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										 |  |  |         self.eps = 1e-5 | 
					
						
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										 |  |  |         self.reset() | 
					
						
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							|  |  |  |     def __call__(self, pred, batch, *args, **kwargs): | 
					
						
							|  |  |  |         structure_probs = pred['structure_probs'].numpy() | 
					
						
							|  |  |  |         structure_labels = batch[1] | 
					
						
							|  |  |  |         correct_num = 0 | 
					
						
							|  |  |  |         all_num = 0 | 
					
						
							|  |  |  |         structure_probs = np.argmax(structure_probs, axis=2) | 
					
						
							|  |  |  |         structure_labels = structure_labels[:, 1:] | 
					
						
							|  |  |  |         batch_size = structure_probs.shape[0] | 
					
						
							|  |  |  |         for bno in range(batch_size): | 
					
						
							|  |  |  |             all_num += 1 | 
					
						
							|  |  |  |             if (structure_probs[bno] == structure_labels[bno]).all(): | 
					
						
							|  |  |  |                 correct_num += 1 | 
					
						
							|  |  |  |         self.correct_num += correct_num | 
					
						
							|  |  |  |         self.all_num += all_num | 
					
						
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										 |  |  |         return {'acc': correct_num * 1.0 / (all_num + self.eps), } | 
					
						
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							|  |  |  |     def get_metric(self): | 
					
						
							|  |  |  |         """
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							|  |  |  |         return metrics { | 
					
						
							|  |  |  |                  'acc': 0, | 
					
						
							|  |  |  |             } | 
					
						
							|  |  |  |         """
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										 |  |  |         acc = 1.0 * self.correct_num / (self.all_num + self.eps) | 
					
						
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										 |  |  |         self.reset() | 
					
						
							|  |  |  |         return {'acc': acc} | 
					
						
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							|  |  |  |     def reset(self): | 
					
						
							|  |  |  |         self.correct_num = 0 | 
					
						
							|  |  |  |         self.all_num = 0 |