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- # 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.
- # ============================================================================
- """reranking"""
- import numpy as np
-
- def k_reciprocal_neigh(initial_rank, i, k1):
- """k_reciprocal_neigh"""
- 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]
- return forward_k_neigh_index[fi]
-
- def re_ranking(q_g_dist, q_q_dist, g_g_dist, k1=20, k2=6, lambda_value=0.3):
- """The following naming, e.g. gallery_num, is different from outer scope.
- """
- # Don't care about it.
- original_dist = np.concatenate([np.concatenate([q_q_dist, q_g_dist], axis=1),
- np.concatenate([q_g_dist.T, g_g_dist], axis=1)], axis=0)
- original_dist = 2. - 2 * original_dist
- original_dist = np.power(original_dist, 2).astype(np.float32)
- original_dist = np.transpose(1. * original_dist/np.max(original_dist, axis=0))
- V = np.zeros_like(original_dist).astype(np.float32)
- # initial_rank = np.argsort(original_dist).astype(np.int32)
- # top K1+1
- initial_rank = np.argpartition(original_dist, range(1, k1+1))
-
- query_num = q_g_dist.shape[0]
- all_num = original_dist.shape[0]
-
- for i in range(all_num):
- # k-reciprocal neighbors
- k_reciprocal_index = k_reciprocal_neigh(initial_rank, i, k1)
- k_reciprocal_expansion_index = k_reciprocal_index
- for j in range(len(k_reciprocal_index)):
- candidate = k_reciprocal_index[j]
- candidate_k_reciprocal_index = k_reciprocal_neigh(initial_rank, candidate, int(np.around(k1/2)))
- if len(np.intersect1d(candidate_k_reciprocal_index, k_reciprocal_index)) > \
- 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] = 1.*weight/np.sum(weight)
-
- original_dist = original_dist[:query_num,]
- if k2 != 1:
- V_qe = np.zeros_like(V, dtype=np.float32)
- 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(all_num):
- invIndex.append(np.where(V[:, i] != 0)[0])
-
- jaccard_dist = np.zeros_like(original_dist, dtype=np.float32)
-
- for i in range(query_num):
- temp_min = np.zeros(shape=[1, all_num], dtype=np.float32)
- indNonZero = np.where(V[i, :] != 0)[0]
- indImages = []
- 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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