mirror of https://github.com/JDAI-CV/fast-reid.git
44 lines
1.4 KiB
Python
44 lines
1.4 KiB
Python
"""Numpy version of euclidean distance, etc.
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Notice the input/output shape of methods, so that you can better understand
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the meaning of these methods."""
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import torch
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import numpy as np
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def normalize(nparray, order=2, axis=0):
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"""Normalize a N-D numpy array along the specified axis."""
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norm = np.linalg.norm(nparray, ord=order, axis=axis, keepdims=True)
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return nparray / (norm + np.finfo(np.float32).eps)
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def compute_dsr_dist(array1, array2, distmat, scores, topk=30):
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""" Compute the sptial feature reconstruction of all pairs
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array: [M, N, C] M: the number of query, N: the number of spatial feature, C: the dimension of each spatial feature
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array2: [M, N, C] M: the number of gallery
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:return:
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numpy array with shape [m1, m2]
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"""
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dist = 100 * torch.ones(len(array1), len(array2))
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dist = dist.cuda()
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index = np.argsort(distmat, axis=1)
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for i in range(0, len(array1)):
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q = torch.FloatTensor(array1[i])
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q = q.view(q.size(0), q.size(1))
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q = q.cuda()
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score = scores[i]
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for j in range(topk):
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g = array2[index[i, j]]
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g = torch.FloatTensor(g)
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g = g.view(g.size(0), g.size(1))
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g = g.cuda()
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sim = torch.matmul(q.t(), g)
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min_value, min_index = (1 - sim).min(1)
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dist[i, index[i, j]] = (min_value * score).sum()
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dist = dist.cpu()
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dist = dist.numpy()
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return dist
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