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