| """ Real labels evaluator for ImageNet |
| Paper: `Are we done with ImageNet?` - https://arxiv.org/abs/2006.07159 |
| Based on Numpy example at https://github.com/google-research/reassessed-imagenet |
| |
| Hacked together by / Copyright 2020 Ross Wightman |
| """ |
| import os |
| import json |
| import numpy as np |
| import pkgutil |
|
|
|
|
| class RealLabelsImagenet: |
|
|
| def __init__(self, filenames, real_json=None, topk=(1, 5)): |
| if real_json is not None: |
| with open(real_json) as real_labels: |
| real_labels = json.load(real_labels) |
| else: |
| real_labels = json.loads( |
| pkgutil.get_data(__name__, os.path.join('_info', 'imagenet_real_labels.json')).decode('utf-8')) |
| real_labels = {f'ILSVRC2012_val_{i + 1:08d}.JPEG': labels for i, labels in enumerate(real_labels)} |
| self.real_labels = real_labels |
| self.filenames = filenames |
| assert len(self.filenames) == len(self.real_labels) |
| self.topk = topk |
| self.is_correct = {k: [] for k in topk} |
| self.sample_idx = 0 |
|
|
| def add_result(self, output): |
| maxk = max(self.topk) |
| _, pred_batch = output.topk(maxk, 1, True, True) |
| pred_batch = pred_batch.cpu().numpy() |
| for pred in pred_batch: |
| filename = self.filenames[self.sample_idx] |
| filename = os.path.basename(filename) |
| if self.real_labels[filename]: |
| for k in self.topk: |
| self.is_correct[k].append( |
| any([p in self.real_labels[filename] for p in pred[:k]])) |
| self.sample_idx += 1 |
|
|
| def get_accuracy(self, k=None): |
| if k is None: |
| return {k: float(np.mean(self.is_correct[k])) * 100 for k in self.topk} |
| else: |
| return float(np.mean(self.is_correct[k])) * 100 |
|
|
