fast-reid/engine/inference.py

# encoding: utf-8
"""
@author:  liaoxingyu
@contact: sherlockliao01@gmail.com
"""
import logging

import torch
from data.datasets.eval_reid import evaluate
from fastai.vision import *



def inference(
        cfg,
        model,
        data_bunch,
        test_labels,
        num_query
):
    logger = logging.getLogger("reid_baseline.inference")
    logger.info("Start inferencing")

    pids = []
    camids = []
    for p, c in test_labels:
        pids.append(p)
        camids.append(c)
    q_pids = np.asarray(pids[:num_query])
    g_pids = np.asarray(pids[num_query:])
    q_camids = np.asarray(camids[:num_query])
    g_camids = np.asarray(camids[num_query:])
    feats = []
    model.eval()
    for imgs, _ in data_bunch.test_dl:
        with torch.no_grad():
            feat = model(imgs)
        feats.append(feat)
    feats = torch.cat(feats, dim=0)

    qf = feats[:num_query]
    gf = feats[num_query:]
    m, n = qf.shape[0], gf.shape[0]
    distmat = torch.pow(qf,2).sum(dim=1,keepdim=True).expand(m,n) + \
        torch.pow(gf,2).sum(dim=1,keepdim=True).expand(n,m).t()
    distmat.addmm_(1, -2, qf, gf.t())
    distmat = to_np(distmat)
    cmc, mAP = evaluate(distmat, q_pids, g_pids, q_camids, g_camids)
    logger.info('Test Results')
    logger.info("mAP: {:.1%}".format(mAP))
    for r in [1, 5, 10]:
        logger.info("CMC curve, Rank-{:<3}:{:.1%}".format(r, cmc[r - 1]))