in easycv/core/evaluation/retrival_topk_eval.py [0:0]
def _evaluate(self, results, gt_label, step=100, feature_keyword='neck'):
"""Retrival evaluate do the topK retrival, by measuring the distance of every 1 vs other.
get the topK nearest, and count the match of ID. if Retrival = 1, Miss = 0. Finally average all
RetrivalRate.
"""
# first print() is to show shape clearly in multi-process situation. don't comment it
print()
print('retrieval available keys : ', results.keys())
results = results[feature_keyword]
gt_label = gt_label
if len(results.shape) > 2:
results = results.view(results.shape[0], -1)
print("retrieval results' shape:", results.shape)
print('ground truth label shape:', gt_label.shape)
if self.pca > 0:
print(self.pca)
pca_model = PCA(n_components=self.pca)
c_results = results.cpu().detach().numpy()
pca_model.fit(c_results)
results_numpy = pca_model.transform(c_results)
results = torch.from_numpy(results_numpy).to(gt_label.device)
print('After pca')
print(results.shape)
assert results.size(0) == gt_label.size(0)
# add GPU resouce to speed up
try:
results = results.cuda()
gt_label = gt_label.cuda()
except:
pass
if self.norm > 0:
results = torch.nn.functional.normalize(
results, p=self.norm, dim=1)
gt_label = torch.nn.functional.normalize(
gt_label, p=self.norm, dim=1)
distance_dict = {
'cos': CosineSimilarity,
'dot': DotproductSimilarity,
'lp': LpDistance,
}
distance_mult = {
'cos': -1,
'dot': -1,
'lp': 1,
}
distance = distance_dict[self.metric]
topk_list = self._topk
topk_res = {}
if type(topk_list) == int:
topk_list = [topk_list]
for k in topk_list:
topk_res[k] = 0
if self.save_results:
retrival_index_results = torch.zeros([1, max(topk_list)],
dtype=torch.int64).to(
results.device)
retrival_distance_results = torch.zeros([1, max(topk_list)],
dtype=torch.float32).to(
results.device)
retrival_topk_results = torch.zeros(
[results.size(0), len(topk_list)], dtype=torch.int8)
for start_idx in tqdm(range(0, results.size(0), step)):
dis_matrix = distance_mult[self.metric] * distance(
results[start_idx:start_idx + step, ...], results)
# this diag to eliminate distance with its self, which should be smallest, so we add max to kill it
diag = torch.ones(dis_matrix.size(0)).to(
dis_matrix.device) * torch.max(dis_matrix)
diag = diag.to(dis_matrix.device)
dis_matrix[range(dis_matrix.size(0)),
range(start_idx, start_idx + dis_matrix.size(0))] = diag
query_distance, query_index = torch.topk(
dis_matrix, k=max(topk_list), dim=1, largest=False)
if self.save_results:
retrival_index_results = torch.cat(
(retrival_index_results, query_index), 0)
retrival_distance_results = torch.cat(
(retrival_distance_results, query_distance), 0)
for idx in range(dis_matrix.size(0)):
gt_query = torch.index_select(
gt_label, dim=0, index=query_index[idx]).cpu().numpy()
for topk in topk_list:
gt_query_k = gt_query[:topk]
catch = False
for qi in range(topk):
if gt_query_k[qi] == gt_label[start_idx + idx]:
catch = True
if self.save_results:
retrival_topk_results[
start_idx + idx,
topk_list.index(topk)] = 1
break
if catch:
topk_res[topk] += 1
if self.save_results:
retrival_index_results = retrival_index_results[1:, ].cpu().numpy()
retrival_distance_results = retrival_distance_results[
1:, ].cpu().numpy()
retrival_topk_results = retrival_topk_results.cpu().numpy()
save_results_dict = {}
save_results_dict[
'retrival_index_results'] = retrival_index_results
save_results_dict[
'retrival_distance_results'] = retrival_distance_results
save_results_dict['retrival_topk_results'] = retrival_topk_results
save_results_dict['gt_label'] = gt_label.cpu().numpy()
if self.dataset_name is not None:
file_name = self.save_results_dir / '{}_retrival_results.npy'.format(
self.dataset_name)
else:
file_name = self.save_results_dir / 'retrival_results.npy'
np.save(file_name, save_results_dict)
eval_res = {}
for k in topk_list:
key_name = 'R@K={}'.format(k)
eval_res[key_name] = (float(topk_res[k]) / results.size(0)) * 100
print("Retrieval Eval of %s 's %s feature Result :" %
(self.dataset_name, feature_keyword))
print(eval_res)
return eval_res