test_sysu.py

# -*- coding: utf-8 -*-
from __future__ import print_function, absolute_import
import argparse
import os.path as osp
import random
import numpy as np
import sys
import collections
import time
from datetime import timedelta

from sklearn.cluster import DBSCAN
from PIL import Image
import torch
from torch import nn
from torch.backends import cudnn
from torch.utils.data import DataLoader
import torch.nn.functional as F

from clustercontrast import datasets
from clustercontrast import models
from clustercontrast.models.cm import ClusterMemory
# from clustercontrast.trainers import ClusterContrastTrainer_camTokens
from clustercontrast.evaluators import Evaluator, extract_features
from clustercontrast.utils.data import IterLoader
from clustercontrast.utils.data import transforms as T
from clustercontrast.utils.data.preprocessor import Preprocessor,Preprocessor_color
from clustercontrast.utils.logging import Logger
from clustercontrast.utils.serialization import load_checkpoint, save_checkpoint
from clustercontrast.utils.faiss_rerank import compute_jaccard_distance,compute_ranked_list
from clustercontrast.utils.data.sampler import RandomMultipleGallerySampler, RandomMultipleGallerySamplerNoCam
import os
import torch.utils.data as data
from torch.autograd import Variable
from scipy.optimize import linear_sum_assignment

import math
from ChannelAug import ChannelAdap, ChannelAdapGray, ChannelRandomErasing,ChannelExchange,Gray
from collections import Counter

from sklearn.metrics.cluster import adjusted_rand_score
from sklearn.metrics.cluster import normalized_mutual_info_score
from sklearn.metrics.cluster import adjusted_mutual_info_score
from sklearn.metrics.cluster import fowlkes_mallows_score
from torchvision.transforms import InterpolationMode
import faiss


def get_data(name, data_dir):
    root = osp.join(data_dir, name)
    dataset = datasets.create(name, root)
    return dataset

def get_train_loader_ir(args, dataset, height, width, batch_size, workers,
                     num_instances, iters, trainset=None, no_cam=False,train_transformer=None):
    
    train_set = sorted(dataset.train) if trainset is None else sorted(trainset)
    rmgs_flag = num_instances > 0
    if rmgs_flag:
        if no_cam:
            sampler = RandomMultipleGallerySamplerNoCam(train_set, num_instances)
        else:
            sampler = RandomMultipleGallerySampler(train_set, num_instances)
    else:
        sampler = None
    train_loader = IterLoader(
        DataLoader(Preprocessor(train_set, root=dataset.images_dir, transform=train_transformer),
                   batch_size=batch_size, num_workers=workers, sampler=sampler,
                   shuffle=not rmgs_flag, pin_memory=True, drop_last=True), length=iters)

    return train_loader

def get_train_loader_color(args, dataset, height, width, batch_size, workers,
                     num_instances, iters, trainset=None, no_cam=False,train_transformer=None,train_transformer1=None):



    train_set = sorted(dataset.train) if trainset is None else sorted(trainset)
    rmgs_flag = num_instances > 0
    if rmgs_flag:
        if no_cam:
            sampler = RandomMultipleGallerySamplerNoCam(train_set, num_instances)
        else:
            sampler = RandomMultipleGallerySampler(train_set, num_instances)
    else:
        sampler = None
    if train_transformer1 is None:
        train_loader = IterLoader(
            DataLoader(Preprocessor(train_set, root=dataset.images_dir, transform=train_transformer),
                       batch_size=batch_size, num_workers=workers, sampler=sampler,
                       shuffle=not rmgs_flag, pin_memory=True, drop_last=True), length=iters)
    else:
        train_loader = IterLoader(
            DataLoader(Preprocessor_color(train_set, root=dataset.images_dir, transform=train_transformer,transform1=train_transformer1),
                       batch_size=batch_size, num_workers=workers, sampler=sampler,
                       shuffle=not rmgs_flag, pin_memory=True, drop_last=True), length=iters)

    return train_loader


def get_test_loader(dataset, height, width, batch_size, workers, testset=None,test_transformer=None):
    normalizer = T.Normalize(mean=[0.485, 0.456, 0.406],
                             std=[0.229, 0.224, 0.225])
    if test_transformer is None:
        test_transformer = T.Compose([
            T.Resize((height, width), interpolation=3),
            T.ToTensor(),
            normalizer
        ])

    if testset is None:
        testset = list(set(dataset.query) | set(dataset.gallery))

    test_loader = DataLoader(
        Preprocessor(testset, root=dataset.images_dir, transform=test_transformer),
        batch_size=batch_size, num_workers=workers,
        shuffle=False, pin_memory=True)

    return test_loader


def create_model(args):
    if args.arch == 'vit_base':
        print('vit_base')
        model = models.create(args.arch,img_size=(args.height,args.width),drop_path_rate=args.drop_path_rate
                , pretrained_path = args.pretrained_path,hw_ratio=args.hw_ratio, conv_stem=args.conv_stem)
    else:
        model = models.create(args.arch, num_features=args.features, norm=True, dropout=args.dropout,
                          num_classes=0, pooling_type=args.pooling_type)
        
    # use CUDA
    model.cuda()
    model = nn.DataParallel(model)#,output_device=1)
    return model



class TestData(data.Dataset):
    def __init__(self, test_img_file, test_label, transform=None, img_size = (144,288)):

        test_image = []
        for i in range(len(test_img_file)):
            img = Image.open(test_img_file[i])
            img = img.resize((img_size[0], img_size[1]), Image.LANCZOS) # Image.ANTIALIAS
            pix_array = np.array(img)
            test_image.append(pix_array)
        test_image = np.array(test_image)
        self.test_image = test_image
        self.test_label = test_label
        self.transform = transform

    def __getitem__(self, index):
        img1,  target1 = self.test_image[index],  self.test_label[index]
        img1 = self.transform(img1)
        return img1, target1

    def __len__(self):
        return len(self.test_image)

def process_query_sysu(data_path, mode = 'all', relabel=False):
    if mode== 'all':
        ir_cameras = ['cam3','cam6']
    elif mode =='indoor':
        ir_cameras = ['cam3','cam6']
    
    file_path = os.path.join(data_path,'exp/test_id.txt')
    files_rgb = []
    files_ir = []

    with open(file_path, 'r') as file:
        ids = file.read().splitlines()
        ids = [int(y) for y in ids[0].split(',')]
        ids = ["%04d" % x for x in ids]

    for id in sorted(ids):
        for cam in ir_cameras:
            img_dir = os.path.join(data_path,cam,id)
            if os.path.isdir(img_dir):
                new_files = sorted([img_dir+'/'+i for i in os.listdir(img_dir)])
                files_ir.extend(new_files)
    query_img = []
    query_id = []
    query_cam = []
    for img_path in files_ir:
        camid, pid = int(img_path[-15]), int(img_path[-13:-9])
        query_img.append(img_path)
        query_id.append(pid)
        query_cam.append(camid)
    return query_img, np.array(query_id), np.array(query_cam)

def process_gallery_sysu(data_path, mode = 'all', trial = 0, relabel=False):
    
    random.seed(trial)
    
    if mode== 'all':
        rgb_cameras = ['cam1','cam2','cam4','cam5']
    elif mode =='indoor':
        rgb_cameras = ['cam1','cam2']
        
    file_path = os.path.join(data_path,'exp/test_id.txt')
    files_rgb = []
    with open(file_path, 'r') as file:
        ids = file.read().splitlines()
        ids = [int(y) for y in ids[0].split(',')]
        ids = ["%04d" % x for x in ids]

    for id in sorted(ids):
        for cam in rgb_cameras:
            img_dir = os.path.join(data_path,cam,id)
            if os.path.isdir(img_dir):
                new_files = sorted([img_dir+'/'+i for i in os.listdir(img_dir)])
                files_rgb.append(random.choice(new_files))
    gall_img = []
    gall_id = []
    gall_cam = []
    for img_path in files_rgb:
        camid, pid = int(img_path[-15]), int(img_path[-13:-9])
        gall_img.append(img_path)
        gall_id.append(pid)
        gall_cam.append(camid)
    return gall_img, np.array(gall_id), np.array(gall_cam)
    

def fliplr(img):
    '''flip horizontal'''
    inv_idx = torch.arange(img.size(3)-1,-1,-1).long()  # N x C x H x W
    img_flip = img.index_select(3,inv_idx)
    return img_flip
def extract_gall_feat(model,gall_loader,ngall):
    pool_dim=768*cls_token_num    ########################768 2048
    net = model
    net.eval()
    print ('Extracting Gallery Feature...')
    start = time.time()
    ptr = 0
    gall_feat_pool = np.zeros((ngall, pool_dim))
    gall_feat_fc = np.zeros((ngall, pool_dim))
    with torch.no_grad():
        for batch_idx, (input, label ) in enumerate(gall_loader):
            batch_num = input.size(0)
            flip_input = fliplr(input)
            input = Variable(input.cuda())
            feat_fc = net( input,input, 1)
            flip_input = Variable(flip_input.cuda())
            feat_fc_1 = net( flip_input,flip_input, 1)
            feature_fc = (feat_fc.detach() + feat_fc_1.detach())/2
            fnorm_fc = torch.norm(feature_fc, p=2, dim=1, keepdim=True)
            feature_fc = feature_fc.div(fnorm_fc.expand_as(feature_fc))
            gall_feat_fc[ptr:ptr+batch_num,: ]   = feature_fc.cpu().numpy()
            ptr = ptr + batch_num
    print('Extracting Time:\t {:.3f}'.format(time.time()-start))
    return gall_feat_fc
    
def extract_query_feat(model,query_loader,nquery):
    pool_dim=768*cls_token_num     ############################# 768 2048
    net = model
    net.eval()
    print ('Extracting Query Feature...')
    start = time.time()
    ptr = 0
    query_feat_pool = np.zeros((nquery, pool_dim))
    query_feat_fc = np.zeros((nquery, pool_dim))
    with torch.no_grad():
        for batch_idx, (input, label ) in enumerate(query_loader):
            batch_num = input.size(0)
            flip_input = fliplr(input)
            input = Variable(input.cuda())
            feat_fc = net( input, input,2)
            flip_input = Variable(flip_input.cuda())
            feat_fc_1 = net( flip_input,flip_input, 2)
            feature_fc = (feat_fc.detach() + feat_fc_1.detach())/2
            fnorm_fc = torch.norm(feature_fc, p=2, dim=1, keepdim=True)
            feature_fc = feature_fc.div(fnorm_fc.expand_as(feature_fc))
            query_feat_fc[ptr:ptr+batch_num,: ]   = feature_fc.cpu().numpy()
            
            ptr = ptr + batch_num         
    print('Extracting Time:\t {:.3f}'.format(time.time()-start))
    return query_feat_fc



def eval_sysu(distmat, q_pids, g_pids, q_camids, g_camids, max_rank = 20):
    """Evaluation with sysu metric
    Key: for each query identity, its gallery images from the same camera view are discarded. "Following the original setting in ite dataset"
    """
    num_q, num_g = distmat.shape
    if num_g < max_rank:
        max_rank = num_g
        print("Note: number of gallery samples is quite small, got {}".format(num_g))
    indices = np.argsort(distmat, axis=1)
    pred_label = g_pids[indices]
    matches = (g_pids[indices] == q_pids[:, np.newaxis]).astype(np.int32)
    
    # compute cmc curve for each query
    new_all_cmc = []
    all_cmc = []
    all_AP = []
    all_INP = []
    num_valid_q = 0. # number of valid query
    for q_idx in range(num_q):
        # get query pid and camid
        q_pid = q_pids[q_idx]
        q_camid = q_camids[q_idx]

        # remove gallery samples that have the same pid and camid with query
        order = indices[q_idx]
        remove = (q_camid == 3) & (g_camids[order] == 2)
        keep = np.invert(remove)
        
        # compute cmc curve
        # the cmc calculation is different from standard protocol
        # we follow the protocol of the author's released code
        new_cmc = pred_label[q_idx][keep]
        new_index = np.unique(new_cmc, return_index=True)[1]
        new_cmc = [new_cmc[index] for index in sorted(new_index)]
        
        new_match = (new_cmc == q_pid).astype(np.int32)
        new_cmc = new_match.cumsum()
        new_all_cmc.append(new_cmc[:max_rank])
        
        orig_cmc = matches[q_idx][keep] # binary vector, positions with value 1 are correct matches
        if not np.any(orig_cmc):
            # this condition is true when query identity does not appear in gallery
            continue

        cmc = orig_cmc.cumsum()

        # compute mINP
        # refernece Deep Learning for Person Re-identification: A Survey and Outlook
        pos_idx = np.where(orig_cmc == 1)
        pos_max_idx = np.max(pos_idx)
        inp = cmc[pos_max_idx]/ (pos_max_idx + 1.0)
        all_INP.append(inp)

        cmc[cmc > 1] = 1

        all_cmc.append(cmc[:max_rank])
        num_valid_q += 1.

        # compute average precision
        # reference: https://en.wikipedia.org/wiki/Evaluation_measures_(information_retrieval)#Average_precision
        num_rel = orig_cmc.sum()
        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)

    assert num_valid_q > 0, "Error: all query identities do not appear in gallery"
    
    all_cmc = np.asarray(all_cmc).astype(np.float32)
    all_cmc = all_cmc.sum(0) / num_valid_q   # standard CMC
    
    new_all_cmc = np.asarray(new_all_cmc).astype(np.float32)
    new_all_cmc = new_all_cmc.sum(0) / num_valid_q
    mAP = np.mean(all_AP)
    mINP = np.mean(all_INP)
    return new_all_cmc, mAP, mINP

def pairwise_distance(features_q, features_g):
    x = torch.from_numpy(features_q)
    y = torch.from_numpy(features_g)
    m, n = x.size(0), y.size(0)
    x = x.view(m, -1)
    y = y.view(n, -1)
    dist_m = torch.pow(x, 2).sum(dim=1, keepdim=True).expand(m, n) + \
           torch.pow(y, 2).sum(dim=1, keepdim=True).expand(n, m).t()
    dist_m.addmm_(1, -2, x, y.t())
    return dist_m.numpy()



def main(args):
    global start_epoch, best_mAP
    global cls_token_num
    cls_token_num=args.cls_token_num 
    args.img_w=args.width
    args.img_h=args.height
    
    log_name='sysu_s2'   #model path########################################
    checkpoint = load_checkpoint(osp.join(args.logs_dir + '/' + log_name, 'model_best.pth.tar'))
    
    #args.logs_dir = osp.join(args.logs_dir+'/'+log_name)
    start_time = time.monotonic()
    cudnn.benchmark = True

    #sys.stdout = Logger(osp.join(args.logs_dir, 'test_log.txt'))
    #print("==========\nArgs:{}\n==========".format(args))

    # Create model
    #model = create_model(args)
    model = models.create(args.arch,img_size=(args.height,args.width),drop_path_rate=args.drop_path_rate1
            , pretrained_path = args.pretrained_path,hw_ratio=args.hw_ratio, conv_stem=args.conv_stem,cls_token_num=cls_token_num)
    model.cuda()
    model = nn.DataParallel(model)#,output_device=1)
    model.load_state_dict(checkpoint['state_dict'])


    normalize = T.Normalize(mean=[0.485, 0.456, 0.406],
                             std=[0.229, 0.224, 0.225])
    transform_test = T.Compose([
        T.ToPILImage(),
        T.Resize((args.img_h,args.img_w)),
        T.ToTensor(),
        normalize,
    ])


#################################
    mode='all'
    print('==> Test with the best model: ',mode)
    
    data_path=args.data_dir
    query_img, query_label, query_cam = process_query_sysu(data_path, mode=mode)
    nquery = len(query_label)
    queryset = TestData(query_img, query_label, transform=transform_test, img_size=(args.img_w, args.img_h))
    query_loader = data.DataLoader(queryset, batch_size=args.test_batch, shuffle=False, num_workers=4)
    query_feat_fc = extract_query_feat(model,query_loader,nquery)
    for trial in range(10):
        gall_img, gall_label, gall_cam = process_gallery_sysu(data_path, mode=mode, trial=trial)
        ngall = len(gall_label)
        trial_gallset = TestData(gall_img, gall_label, transform=transform_test, img_size=(args.img_w, args.img_h))
        trial_gall_loader = data.DataLoader(trial_gallset, batch_size=args.test_batch, shuffle=False, num_workers=4)

        gall_feat_fc = extract_gall_feat(model,trial_gall_loader,ngall)
        # fc feature
        distmat = np.matmul(query_feat_fc, np.transpose(gall_feat_fc))

        cmc, mAP, mINP = eval_sysu(-distmat, query_label, gall_label, query_cam, gall_cam)
        if trial == 0:
            all_cmc = cmc
            all_mAP = mAP
            all_mINP = mINP

        else:
            all_cmc = all_cmc + cmc
            all_mAP = all_mAP + mAP
            all_mINP = all_mINP + mINP


        print('Test Trial: {}'.format(trial))
        print(
            'FC:   Rank-1: {:.2%} | Rank-5: {:.2%} | Rank-10: {:.2%}| Rank-20: {:.2%}| mAP: {:.2%}| mINP: {:.2%}'.format(
                cmc[0], cmc[4], cmc[9], cmc[19], mAP, mINP))
    cmc = all_cmc / 10
    mAP = all_mAP / 10
    mINP = all_mINP / 10
    print('All Average:')
    print('FC:     Rank-1: {:.2%} | Rank-5: {:.2%} | Rank-10: {:.2%}| Rank-20: {:.2%}| mAP: {:.2%}| mINP: {:.2%}'.format(
            cmc[0], cmc[4], cmc[9], cmc[19], mAP, mINP))
    end_time = time.monotonic()

############################
    mode='indoor'
    print('==> Test with the best model: ',mode)

    
    query_img, query_label, query_cam = process_query_sysu(data_path, mode=mode)
    nquery = len(query_label)
    queryset = TestData(query_img, query_label, transform=transform_test, img_size=(args.img_w, args.img_h))
    query_loader = data.DataLoader(queryset, batch_size=args.test_batch, shuffle=False, num_workers=4)
    query_feat_fc = extract_query_feat(model,query_loader,nquery)
    for trial in range(10):
        gall_img, gall_label, gall_cam = process_gallery_sysu(data_path, mode=mode, trial=trial)
        ngall = len(gall_label)
        trial_gallset = TestData(gall_img, gall_label, transform=transform_test, img_size=(args.img_w, args.img_h))
        trial_gall_loader = data.DataLoader(trial_gallset, batch_size=args.test_batch, shuffle=False, num_workers=4)

        gall_feat_fc = extract_gall_feat(model,trial_gall_loader,ngall)
        # fc feature
        distmat = np.matmul(query_feat_fc, np.transpose(gall_feat_fc))

        cmc, mAP, mINP = eval_sysu(-distmat, query_label, gall_label, query_cam, gall_cam)
        if trial == 0:
            all_cmc = cmc
            all_mAP = mAP
            all_mINP = mINP

        else:
            all_cmc = all_cmc + cmc
            all_mAP = all_mAP + mAP
            all_mINP = all_mINP + mINP


        print('Test Trial: {}'.format(trial))
        print(
            'FC:   Rank-1: {:.2%} | Rank-5: {:.2%} | Rank-10: {:.2%}| Rank-20: {:.2%}| mAP: {:.2%}| mINP: {:.2%}'.format(
                cmc[0], cmc[4], cmc[9], cmc[19], mAP, mINP))
    cmc = all_cmc / 10
    mAP = all_mAP / 10
    mINP = all_mINP / 10
    print('All Average:')
    print('FC:     Rank-1: {:.2%} | Rank-5: {:.2%} | Rank-10: {:.2%}| Rank-20: {:.2%}| mAP: {:.2%}| mINP: {:.2%}'.format(
            cmc[0], cmc[4], cmc[9], cmc[19], mAP, mINP))
    end_time = time.monotonic()
    print('Total running time: ', timedelta(seconds=end_time - start_time))



if __name__ == '__main__':
    parser = argparse.ArgumentParser(description="Augmented Dual-Contrastive Aggregation Learning for USL-VI-ReID")
    # data
    parser.add_argument('-d', '--dataset', type=str, default='sysu')
    parser.add_argument('-b', '--batch-size', type=int, default=2)
    parser.add_argument('--test-batch', type=int, default=64)
    parser.add_argument('-j', '--workers', type=int, default=8)
    parser.add_argument('--height', type=int, default=384, help="input height")  # 288   384
    parser.add_argument('--width', type=int, default=128, help="input width")    # 144   128
    parser.add_argument('--num-instances', type=int, default=4,
                        help="each minibatch consist of "
                             "(batch_size // num_instances) identities, and "
                             "each identity has num_instances instances, "
                             "default: 0 (NOT USE)")
    # cluster
    parser.add_argument('--eps', type=float, default=0.6,
                        help="max neighbor distance for DBSCAN")
    parser.add_argument('--eps-gap', type=float, default=0.02,
                        help="multi-scale criterion for measuring cluster reliability")
    parser.add_argument('--k1', type=int, default=30,
                        help="hyperparameter for jaccard distance")
    parser.add_argument('--k2', type=int, default=6,
                        help="hyperparameter for jaccard distance")

    # model
    parser.add_argument('-a', '--arch', type=str, default='vit_base',
                        choices=models.names())
    parser.add_argument('-pp', '--pretrained-path', type=str, default='')
    parser.add_argument('--features', type=int, default=0)
    parser.add_argument('--dropout', type=float, default=0)
    parser.add_argument('--momentum0', type=float, default=0.2,
                        help="update momentum0 for the hybrid memory")
    parser.add_argument('--momentum1', type=float, default=0.8,
                        help="update momentum1 for the hybrid memory")
    #vit
    parser.add_argument('--drop-path-rate0', type=float, default=0.3)
    parser.add_argument('--drop-path-rate1', type=float, default=0.3)
    parser.add_argument('--hw-ratio', type=int, default=2)
    parser.add_argument('--self-norm', action="store_true")
    parser.add_argument('--conv-stem', action="store_true")
    # optimizer
    parser.add_argument('--lr0', type=float, default=0.00035,
                        help="learning rate0")
    parser.add_argument('--lr1', type=float, default=0.000035,
                        help="learning rate1")
    parser.add_argument('--weight-decay0', type=float, default=5e-4)  #5e-4
    parser.add_argument('--weight-decay1', type=float, default=5e-4)
    parser.add_argument('--epochs', type=int, default=50)
    parser.add_argument('--iters', type=int, default=200)
    parser.add_argument('--step-size', type=int, default=20)
    # training configs
    parser.add_argument('--seed', type=int, default=1)
    parser.add_argument('--print-freq', type=int, default=20)
    parser.add_argument('--eval-step', type=int, default=1)
    parser.add_argument('--temp', type=float, default=0.05,
                        help="temperature for scaling contrastive loss")
    # path
    working_dir = osp.dirname(osp.abspath(__file__))
    parser.add_argument('--data-dir', type=str, metavar='PATH',
                        default="D:/DeepLearning/code/U_RI_Reid/ADCA-master/data/")
    parser.add_argument('--logs-dir', type=str, metavar='PATH',
                        default=osp.join(working_dir, 'logs'))
    parser.add_argument('--pooling-type', type=str, default='gem')
    parser.add_argument('--use-hard', action="store_true")  # �������м���--use-hard��Ϊtrue��������Ϊfalse������Ϊfalse
    parser.add_argument('--no-cam',  action="store_true")

    parser.add_argument('--lamba-mate', type=float, default=0.1)  #########################
    parser.add_argument('--lamba-cross', type=float, default=0.4)
    parser.add_argument('--lamba-neighbor', type=float, default=0.25)
    parser.add_argument('--cls-token-num', type=int, default=4)
    parser.add_argument('--k', type=float, default=0.9)
    parser.add_argument('--x', type=int, default=4)
    parser.add_argument('--lamba-k', type=int, default=2)
    args = parser.parse_args()
    main(args)