150 lines
5.4 KiB
Python
150 lines
5.4 KiB
Python
from __future__ import absolute_import
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from __future__ import print_function
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from __future__ import division
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import numpy as np
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import copy
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from collections import defaultdict
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import sys
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try:
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from torchreid.eval_lib.cython_eval import eval_market1501_wrap
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CYTHON_EVAL_AVAI = True
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print("Cython evaluation is AVAILABLE")
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except ImportError:
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CYTHON_EVAL_AVAI = False
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print("Warning: Cython evaluation is UNAVAILABLE")
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def eval_cuhk03(distmat, q_pids, g_pids, q_camids, g_camids, max_rank, N=100):
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"""Evaluation with cuhk03 metric
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Key: one image for each gallery identity is randomly sampled for each query identity.
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Random sampling is performed N times (default: N=100).
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"""
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num_q, num_g = distmat.shape
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if num_g < max_rank:
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max_rank = num_g
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print("Note: number of gallery samples is quite small, got {}".format(num_g))
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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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# compute cmc curve for each query
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all_cmc = []
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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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# get query pid and camid
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q_pid = q_pids[q_idx]
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q_camid = q_camids[q_idx]
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# remove gallery samples that have the same pid and camid with query
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order = indices[q_idx]
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remove = (g_pids[order] == q_pid) & (g_camids[order] == q_camid)
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keep = np.invert(remove)
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# compute cmc curve
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orig_cmc = matches[q_idx][keep] # binary vector, positions with value 1 are correct matches
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if not np.any(orig_cmc):
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# this condition is true when query identity does not appear in gallery
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continue
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kept_g_pids = g_pids[order][keep]
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g_pids_dict = defaultdict(list)
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for idx, pid in enumerate(kept_g_pids):
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g_pids_dict[pid].append(idx)
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cmc, AP = 0., 0.
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for repeat_idx in range(N):
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mask = np.zeros(len(orig_cmc), dtype=np.bool)
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for _, idxs in g_pids_dict.items():
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# randomly sample one image for each gallery person
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rnd_idx = np.random.choice(idxs)
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mask[rnd_idx] = True
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masked_orig_cmc = orig_cmc[mask]
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_cmc = masked_orig_cmc.cumsum()
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_cmc[_cmc > 1] = 1
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cmc += _cmc[:max_rank].astype(np.float32)
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# compute AP
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num_rel = masked_orig_cmc.sum()
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tmp_cmc = masked_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) * masked_orig_cmc
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AP += tmp_cmc.sum() / num_rel
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cmc /= N
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AP /= N
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all_cmc.append(cmc)
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all_AP.append(AP)
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num_valid_q += 1.
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assert num_valid_q > 0, "Error: all query identities do not appear in gallery"
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all_cmc = np.asarray(all_cmc).astype(np.float32)
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all_cmc = all_cmc.sum(0) / num_valid_q
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mAP = np.mean(all_AP)
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return all_cmc, mAP
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def eval_market1501(distmat, q_pids, g_pids, q_camids, g_camids, max_rank):
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"""Evaluation with market1501 metric
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Key: for each query identity, its gallery images from the same camera view are discarded.
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"""
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num_q, num_g = distmat.shape
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if num_g < max_rank:
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max_rank = num_g
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print("Note: number of gallery samples is quite small, got {}".format(num_g))
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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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# compute cmc curve for each query
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all_cmc = []
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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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# get query pid and camid
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q_pid = q_pids[q_idx]
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q_camid = q_camids[q_idx]
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# remove gallery samples that have the same pid and camid with query
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order = indices[q_idx]
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remove = (g_pids[order] == q_pid) & (g_camids[order] == q_camid)
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keep = np.invert(remove)
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# compute cmc curve
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orig_cmc = matches[q_idx][keep] # binary vector, positions with value 1 are correct matches
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if not np.any(orig_cmc):
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# this condition is true when query identity does not appear in gallery
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continue
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cmc = orig_cmc.cumsum()
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cmc[cmc > 1] = 1
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all_cmc.append(cmc[:max_rank])
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num_valid_q += 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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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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assert num_valid_q > 0, "Error: all query identities do not appear in gallery"
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all_cmc = np.asarray(all_cmc).astype(np.float32)
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all_cmc = all_cmc.sum(0) / num_valid_q
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mAP = np.mean(all_AP)
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return all_cmc, mAP
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def evaluate(distmat, q_pids, g_pids, q_camids, g_camids, max_rank=50, use_metric_cuhk03=False, use_cython=True):
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if use_metric_cuhk03:
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return eval_cuhk03(distmat, q_pids, g_pids, q_camids, g_camids, max_rank)
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else:
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if use_cython and CYTHON_EVAL_AVAI:
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return eval_market1501_wrap(distmat, q_pids, g_pids, q_camids, g_camids, max_rank)
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else:
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return eval_market1501(distmat, q_pids, g_pids, q_camids, g_camids, max_rank) |