darkliang
89 lines
3.0 KiB
Python
89 lines
3.0 KiB
Python
import torch
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import numpy as np
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import torch.nn.functional as F
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from utils import is_tensor
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def cuda_dist(x, y, metric='euc'):
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x = torch.from_numpy(x).cuda()
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y = torch.from_numpy(y).cuda()
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if metric == 'cos':
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x = F.normalize(x, p=2, dim=1) # n c p
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y = F.normalize(y, p=2, dim=1) # n c p
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num_bin = x.size(2)
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n_x = x.size(0)
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n_y = y.size(0)
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dist = torch.zeros(n_x, n_y).cuda()
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for i in range(num_bin):
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_x = x[:, :, i]
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_y = y[:, :, i]
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if metric == 'cos':
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dist += torch.matmul(_x, _y.transpose(0, 1))
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else:
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_dist = torch.sum(_x ** 2, 1).unsqueeze(1) + torch.sum(_y ** 2, 1).unsqueeze(
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0) - 2 * torch.matmul(_x, _y.transpose(0, 1))
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dist += torch.sqrt(F.relu(_dist))
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return 1 - dist/num_bin if metric == 'cos' else dist / num_bin
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def mean_iou(msk1, msk2, eps=1.0e-9):
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if not is_tensor(msk1):
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msk1 = torch.from_numpy(msk1).cuda()
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if not is_tensor(msk2):
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msk2 = torch.from_numpy(msk2).cuda()
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n = msk1.size(0)
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inter = msk1 * msk2
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union = ((msk1 + msk2) > 0.).float()
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miou = inter.view(n, -1).sum(-1) / (union.view(n, -1).sum(-1) + eps)
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return miou
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def compute_ACC_mAP(distmat, q_pids, g_pids, q_views=None, g_views=None, rank=1):
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num_q, _ = distmat.shape
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# indices = np.argsort(distmat, axis=1)
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# matches = (g_pids[indices] == q_pids[:, np.newaxis]).astype(np.int32)
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all_ACC = []
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all_AP = []
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num_valid_q = 0. # number of valid query
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for q_idx in range(num_q):
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q_idx_dist = distmat[q_idx]
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q_idx_glabels = g_pids
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if q_views is not None and g_views is not None:
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q_idx_mask = np.isin(g_views, q_views[q_idx], invert=True) | np.isin(
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g_pids, q_pids[q_idx], invert=True)
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q_idx_dist = q_idx_dist[q_idx_mask]
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q_idx_glabels = q_idx_glabels[q_idx_mask]
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assert(len(q_idx_glabels) >
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0), "No gallery after excluding identical-view cases!"
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q_idx_indices = np.argsort(q_idx_dist)
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q_idx_matches = (q_idx_glabels[q_idx_indices]
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== q_pids[q_idx]).astype(np.int32)
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# binary vector, positions with value 1 are correct matches
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# orig_cmc = matches[q_idx]
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orig_cmc = q_idx_matches
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cmc = orig_cmc.cumsum()
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cmc[cmc > 1] = 1
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all_ACC.append(cmc[rank-1])
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# compute average precision
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# reference: https://en.wikipedia.org/wiki/Evaluation_measures_(information_retrieval)#Average_precision
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num_rel = orig_cmc.sum()
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if num_rel > 0:
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num_valid_q += 1.
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tmp_cmc = orig_cmc.cumsum()
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tmp_cmc = [x / (i + 1.) for i, x in enumerate(tmp_cmc)]
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tmp_cmc = np.asarray(tmp_cmc) * orig_cmc
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AP = tmp_cmc.sum() / num_rel
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all_AP.append(AP)
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# all_ACC = np.asarray(all_ACC).astype(np.float32)
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ACC = np.mean(all_ACC)
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mAP = np.mean(all_AP)
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return ACC, mAP
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