def get_results(self): """ Reruns the evaluation using the accumulated detections, returns VOC results with IOU and Accuracy metrics :return: dict with PASCAL VOC metrics """ if self.cached_results: return self.results self.voc_evaluator = ConfusionMatrix(21) self.voc_evaluator.update(self.targets.astype(np.int64), self.outputs.astype(np.int64)) acc_global, acc, iu = self.voc_evaluator.compute() self.results = { "Accuracy": acc_global.item(), "Mean IOU": iu.mean().item(), } self.speed_mem_metrics[ 'Max Memory Allocated (Total)'] = get_max_memory_allocated() return self.results
def get_results(self): if self.cached_results: return self.results self.results = self.metrics.get_results() self.speed_mem_metrics['Max Memory Allocated (Total)'] = get_max_memory_allocated() return self.results
def get_results(self): """ Gets the results for the evaluator. :return: dict with `EM` (exact match score) and `F1`. """ if self.cached_results: return self.results self.results = self.metrics.get_results() self.speed_mem_metrics[ 'Max Memory Allocated (Total)'] = get_max_memory_allocated() return self.results
def get_results(self): """ Calculates the perplexity and measure the performance of the model :return: dict with `Test perplexity` """ if self.cached_results: return self.results perplexity = np.exp(self._neglogloss / self.dataset.testset_size) self.results = {'Test perplexity': perplexity} self.speed_mem_metrics[ 'Max Memory Allocated (Total)'] = get_max_memory_allocated() exec_speed = (time.time() - self.init_time) count = self.dataset.testset_size self.speed_mem_metrics['Tasks / Evaluation Time'] = count / exec_speed self.speed_mem_metrics['Tasks'] = count self.speed_mem_metrics['Evaluation Time'] = exec_speed return self.results
def get_results(self): """ Reruns the evaluation using the accumulated detections, returns COCO results with AP metrics :return: dict with COCO AP metrics """ if self.cached_results: return self.results self.coco_evaluator = CocoEvaluator(self.coco, self.iou_types) self.coco_evaluator.update(self.detections) self.coco_evaluator.evaluate() self.coco_evaluator.accumulate() self.coco_evaluator.summarize() self.results = get_coco_metrics(self.coco_evaluator) self.speed_mem_metrics[ 'Max Memory Allocated (Total)'] = get_max_memory_allocated() return self.results