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