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Shifited_test_and_evaluate.py
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# -*- coding: utf-8 -*-
import glob
import os
import time
import model_
import torch
import scipy.io
import shutil
import argparse
import numpy as np
import pandas as pd
from torch import nn
from utils import fliplr, load_network, which_view, get_id, get_yaml_value
from Create_MultiModal_Dataset import Multimodel_Dateset_flip
from U1652_test_and_evaluate import evaluate
from torchvision import datasets, models, transforms
from Multi_HBP import Hybird_ViT
if torch.cuda.is_available():
device = torch.device("cuda:0")
def extract_feature(model, dataloaders, view_index=1):
features = torch.FloatTensor()
count = 0
for data in dataloaders:
img, text, label = data
n, c, h, w = img.size()
count += n
text = text.to(device)
ff = torch.FloatTensor(n, 512, 4).zero_().cuda()
# why for in range(2)：
# 1. for flip img
# 2. for normal img
for i in range(2):
if i == 1:
img = fliplr(img)
input_img = img.to(device)
outputs = None
if view_index == 1:
outputs, _ = model(input_img, None, text, None)
elif view_index == 2:
_, outputs = model(None, input_img, None, text)
# print(outputs.shape)
# print(ff.shape)
ff += outputs
fnorm = torch.norm(ff, p=2, dim=1, keepdim=True) * np.sqrt(4)
# print("fnorm", fnorm.shape)
ff = ff.div(fnorm.expand_as(ff))
# print("ff", ff.shape)
ff = ff.view(ff.size(0), -1)
features = torch.cat((features, ff.data.cpu()), 0) # 在维度0上拼接
return features
############################### main function #######################################
def eval_and_test(query_name, config_file, net_path, save_path, gap):
param = get_yaml_value(config_file)
data_path = param["dataset_path"]
data_transforms = transforms.Compose([
transforms.Resize((384, 384), interpolation=3),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
table_path = os.path.join(save_path, param["model"] + "_" + str(1652) + "_" + "shifted_query_" +
".csv")
evaluate_csv = pd.DataFrame(index=["recall@1", "recall@5", "recall@10", "recall@1p", "AP", "time"])
image_datasets = {x: Multimodel_Dateset_flip(os.path.join(data_path, 'test', x), data_transforms, gap) for x in
['gallery_satellite', 'gallery_drone', 'query_satellite', 'query_drone']}
data_loader = {x: torch.utils.data.DataLoader(image_datasets[x],
batch_size=param["batch_size"],
# batch_size=16,
shuffle=False) for x in
['gallery_satellite', 'gallery_drone', 'query_satellite', 'query_drone']}
model = Hybird_ViT(701, 0.1)
model.load_state_dict(torch.load(net_path))
for i in range(2):
cls_name = 'classifier' + str(i)
c = getattr(model, cls_name)
c.classifier = nn.Sequential()
model = model.eval()
model = model.cuda()
if "drone" in query_name:
gallery_name = "gallery_satellite"
query_name = "query_drone"
else:
gallery_name = "gallery_drone"
query_name = "query_satellite"
which_query = which_view(query_name)
which_gallery = which_view(gallery_name)
gallery_path = image_datasets[gallery_name].imgs
query_path = image_datasets[query_name].imgs
gallery_label, gallery_path = get_id(gallery_path)
query_label, query_path = get_id(query_path)
with torch.no_grad():
query_feature = extract_feature(model, data_loader[query_name], which_query)
gallery_feature = extract_feature(model, data_loader[gallery_name], which_gallery)
# fed tensor to GPU
query_feature = query_feature.cuda()
gallery_feature = gallery_feature.cuda()
# CMC = recall
CMC = torch.IntTensor(len(gallery_label)).zero_()
# ap = average precision
ap = 0.0
for i in range(len(query_label)):
ap_tmp, CMC_tmp = evaluate(query_feature[i], query_label[i], gallery_feature, gallery_label)
if CMC_tmp[0] == -1:
continue
CMC += CMC_tmp
ap += ap_tmp
CMC = CMC.float()
CMC = CMC / len(query_label)
# print(len(query_label))
recall_1 = CMC[0] * 100
recall_5 = CMC[4] * 100
recall_10 = CMC[9] * 100
recall_1p = CMC[round(len(gallery_label) * 0.01)] * 100
AP = ap / len(query_label) * 100
evaluate_result = 'Recall@1:%.4f Recall@5:%.4f Recall@10:%.4f Recall@top1:%.4f AP:%.4f' % (
recall_1, recall_5, recall_10, recall_1p, AP)
evaluate_csv["shifted_query" + "_" + str(gap) +
"_" + str(1652)] = \
[float(recall_1), float(recall_5),
float(recall_10), float(recall_1p),
float(AP), float(0)]
print(evaluate_csv)
evaluate_csv.columns.name = ""
evaluate_csv.index.name = "index"
evaluate_csv = evaluate_csv.T
evaluate_csv.to_csv(table_path)
print(evaluate_result)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--query', type=str, default="drone", help='query set: drone or satellite')
parser.add_argument('--cfg', type=str, default='settings.yaml', help='config file XXX.yaml path')
parser.add_argument('--weight', type=str, default=None, help='evaluate which weight, path')
parser.add_argument('--csv_save_path', type=str, default="./result", help="evaluation result table store path")
parser.add_argument('--gap', type=int, default=10, help='shifted gap')
opt = parser.parse_known_args()[0]
eval_and_test(opt.query, opt.cfg, opt.weight, opt.csv_save_path, opt.gap)