refactor evaluation and add cross view gallery

2022-11-24 22:07:50 +08:00
parent c5b4fb0806
commit 793405ad7c
18 changed files with 388 additions and 309 deletions
@@ -0,0 +1,88 @@
+import torch
+import numpy as np
+import torch.nn.functional as F
+
+from utils import is_tensor
+
+
+def cuda_dist(x, y, metric='euc'):
+    x = torch.from_numpy(x).cuda()
+    y = torch.from_numpy(y).cuda()
+    if metric == 'cos':
+        x = F.normalize(x, p=2, dim=1)  # n c p
+        y = F.normalize(y, p=2, dim=1)  # n c p
+    num_bin = x.size(2)
+    n_x = x.size(0)
+    n_y = y.size(0)
+    dist = torch.zeros(n_x, n_y).cuda()
+    for i in range(num_bin):
+        _x = x[:, :, i]
+        _y = y[:, :, i]
+        if metric == 'cos':
+            dist += torch.matmul(_x, _y.transpose(0, 1))
+        else:
+            _dist = torch.sum(_x ** 2, 1).unsqueeze(1) + torch.sum(_y ** 2, 1).unsqueeze(
+                0) - 2 * torch.matmul(_x, _y.transpose(0, 1))
+            dist += torch.sqrt(F.relu(_dist))
+    return 1 - dist/num_bin if metric == 'cos' else dist / num_bin
+
+
+def mean_iou(msk1, msk2, eps=1.0e-9):
+    if not is_tensor(msk1):
+        msk1 = torch.from_numpy(msk1).cuda()
+    if not is_tensor(msk2):
+        msk2 = torch.from_numpy(msk2).cuda()
+    n = msk1.size(0)
+    inter = msk1 * msk2
+    union = ((msk1 + msk2) > 0.).float()
+    miou = inter.view(n, -1).sum(-1) / (union.view(n, -1).sum(-1) + eps)
+    return miou
+
+
+def compute_ACC_mAP(distmat, q_pids, g_pids, q_views=None, g_views=None, rank=1):
+    num_q, _ = distmat.shape
+    # indices = np.argsort(distmat, axis=1)
+    # matches = (g_pids[indices] == q_pids[:, np.newaxis]).astype(np.int32)
+
+    all_ACC = []
+    all_AP = []
+    num_valid_q = 0.  # number of valid query
+    for q_idx in range(num_q):
+        q_idx_dist = distmat[q_idx]
+        q_idx_glabels = g_pids
+        if q_views is not None and g_views is not None:
+            q_idx_mask = np.isin(g_views, [q_views[q_idx]], invert=True) | np.isin(
+                g_pids, [q_pids[q_idx]], invert=True)
+            q_idx_dist = q_idx_dist[q_idx_mask]
+            q_idx_glabels = q_idx_glabels[q_idx_mask]
+
+        assert(len(q_idx_glabels) >
+               0), "No gallery after excluding identical-view cases!"
+        q_idx_indices = np.argsort(q_idx_dist)
+        q_idx_matches = (q_idx_glabels[q_idx_indices]
+                         == q_pids[q_idx]).astype(np.int32)
+
+        # binary vector, positions with value 1 are correct matches
+        # orig_cmc = matches[q_idx]
+        orig_cmc = q_idx_matches
+        cmc = orig_cmc.cumsum()
+        cmc[cmc > 1] = 1
+        all_ACC.append(cmc[rank-1])
+
+        # compute average precision
+        # reference: https://en.wikipedia.org/wiki/Evaluation_measures_(information_retrieval)#Average_precision
+        num_rel = orig_cmc.sum()
+
+        if num_rel > 0:
+            num_valid_q += 1.
+            tmp_cmc = orig_cmc.cumsum()
+            tmp_cmc = [x / (i + 1.) for i, x in enumerate(tmp_cmc)]
+            tmp_cmc = np.asarray(tmp_cmc) * orig_cmc
+            AP = tmp_cmc.sum() / num_rel
+            all_AP.append(AP)
+
+    # all_ACC = np.asarray(all_ACC).astype(np.float32)
+    ACC = np.mean(all_ACC)
+    mAP = np.mean(all_AP)
+
+    return ACC, mAP