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- """use re-ranking technology"""
- # Copyright 2021 Huawei Technologies Co., Ltd
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
- # ============================================================================
- #!/usr/bin/env python3
- # -*- coding: utf-8 -*-
-
- import numpy as np
-
- import mindspore.ops as ops
-
- def re_ranking(probFea, galFea, k1, k2, lambda_value, local_distmat=None, only_local=False):
- """compute reranking when test"""
- query_num = probFea.shape[0]
- all_num = query_num + galFea.shape[0]
- op_concat = ops.Concat()
- op_pow = ops.Pow()
- op_sum = ops.ReduceSum(keep_dims=True)
- op_broad = ops.BroadcastTo((all_num, all_num))
- op_transpose = ops.Transpose()
-
- if only_local:
- original_dist = local_distmat
- else:
- feat = op_concat([probFea, galFea])
- feat2 = feat
-
- feat = op_pow(feat, 2)
- feat = op_sum(feat, 1)
- feat = op_broad(feat)
- feat_transpose = op_transpose(feat, (1, 0))
- distmat = feat+feat_transpose
- distmat = distmat.asnumpy()
-
- feat2_transpose = op_transpose(feat2, (1, 0))
- feat2 = feat2.asnumpy()
- feat2_transpose = feat2_transpose.asnumpy()
- opt1 = np.dot(feat2, feat2_transpose)
- distmat = distmat + opt1*(-2)
-
- original_dist = distmat
- del feat
- if not local_distmat is None:
- original_dist = original_dist + local_distmat
- gallery_num = original_dist.shape[0]
- original_dist = np.transpose(original_dist / np.max(original_dist, axis=0))
- V = np.zeros_like(original_dist).astype(np.float16)
- initial_rank = np.argsort(original_dist).astype(np.int32)
-
- print('starting re_ranking')
- for i in range(all_num):
- # k-reciprocal neighbors
- forward_k_neigh_index = initial_rank[i, :k1 + 1]
- backward_k_neigh_index = initial_rank[forward_k_neigh_index, :k1 + 1]
- fi = np.where(backward_k_neigh_index == i)[0]
- k_reciprocal_index = forward_k_neigh_index[fi]
- k_reciprocal_expansion_index = k_reciprocal_index
- for j in range(len(k_reciprocal_index)):
- candidate = k_reciprocal_index[j]
- candidate_forward_k_neigh_index = initial_rank[candidate, :int(np.around(k1 / 2)) + 1]
- candidate_backward_k_neigh_index = initial_rank[candidate_forward_k_neigh_index, :int(np.around(k1/2))+1]
- fi_candidate = np.where(candidate_backward_k_neigh_index == candidate)[0]
- candidate_k_reciprocal_index = candidate_forward_k_neigh_index[fi_candidate]
- leng1 = len(np.intersect1d(candidate_k_reciprocal_index, k_reciprocal_index))
- if leng1 > 2/3*len(candidate_k_reciprocal_index):
- k_reciprocal_expansion_index = np.append(k_reciprocal_expansion_index, candidate_k_reciprocal_index)
-
- k_reciprocal_expansion_index = np.unique(k_reciprocal_expansion_index)
- weight = np.exp(-original_dist[i, k_reciprocal_expansion_index])
- V[i, k_reciprocal_expansion_index] = weight / np.sum(weight)
- original_dist = original_dist[:query_num,]
- if k2 != 1:
- V_qe = np.zeros_like(V, dtype=np.float16)
- for i in range(all_num):
- V_qe[i, :] = np.mean(V[initial_rank[i, :k2], :], axis=0)
- V = V_qe
- del V_qe
- del initial_rank
- invIndex = []
- for i in range(gallery_num):
- invIndex.append(np.where(V[:, i] != 0)[0])
-
- jaccard_dist = np.zeros_like(original_dist, dtype=np.float16)
-
- for i in range(query_num):
- temp_min = np.zeros(shape=[1, gallery_num], dtype=np.float16)
- indNonZero = np.where(V[i, :] != 0)[0]
- indImages = [invIndex[ind] for ind in indNonZero]
- for j in range(len(indNonZero)):
- temp_min[0, indImages[j]] = temp_min[0, indImages[j]] + np.minimum(V[i, indNonZero[j]],
- V[indImages[j], indNonZero[j]])
- jaccard_dist[i] = 1 - temp_min / (2 - temp_min)
-
- final_dist = jaccard_dist * (1 - lambda_value) + original_dist * lambda_value
- del original_dist
- del V
- del jaccard_dist
- final_dist = final_dist[:query_num, query_num:]
- return final_dist
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