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test_i2t.py
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import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader
from config import opt
from dataset import MyDataset, collate_fn
from eval import i2t, t2i, encode_data
from model import MyModelAll
from utils import Utils, logger
'''
可视化实验
'''
def main():
str_time = Utils.get_time()
print(str_time)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
logger.info(
"##########################################" + str_time + "###################################################")
str_model_path = "C:/AMyFile/实验数据/f30k model/model_best_f30k.pth"
assert os.path.exists(str_model_path), "{} path does not exist.".format(str_model_path)
img_path = ""
txt = ""
###########################################################################
# 定义网络，迁移到GPU
net = MyModelAll()
net.to(device)
logger.info("《——模型结构——》")
logger.info(net.show_model())
# if os.path.isfile(str_model_path):
# print("=> loading checkpoint '{}'".format(str_model_path))
# checkpoint = torch.load(str_model_path)
# # logger.info(checkpoint)
# start_epoch = checkpoint['epoch'] + 1
# best_rsum = checkpoint['best_rsum']
# net.load_state_dict(checkpoint['model'])
# net.Eiters = checkpoint['Eiters']
# print("=> loaded checkpoint '{}' (epoch {}, best_rsum {})"
# .format(str_model_path, start_epoch, best_rsum))
# else:
# print("=> no checkpoint found at '{}'".format(str_model_path))
loc = 'D:/xunlei_download/archive/data/data/f30k_precomp/'
data_split = 'test'
# images_matrix = np.load(loc + '{}_ims.npy'.format(data_split))
data_path = "D:/experiment/project_experiment/data" # 数据集路径
data_name = "f30k_precomp" # 数据集名称
batch_size = 64
validate_dataset = MyDataset(data_path, data_name, "test", train=False)
# 加载测试数据
logger.info("开始加载测试数据")
test_loader = DataLoader(validate_dataset, batch_size, shuffle=False, pin_memory=True,
collate_fn=collate_fn, drop_last=False)
logger.info("测试数据加载完毕")
sims = evalrank(net, test_loader, opt, logger)
sims = sims.cpu()
sims = sims.numpy()
np.save('./sims.npy', sims)
##################################################################
sims = np.load('./sims.npy')
# sims = np.load("")
# exit(0)
randint = random.randint(0, 999)
index = randint
# index = 660
print(randint)
inds = np.argsort(sims[index])[::-1] # 倒序
my_data_test = inds[:5]
resu = sims[index, my_data_test]
my_data_should = [5 * index + i for i in range(5)]
print("前五测试排名：", my_data_test)
print("前五测试相似度结果：", resu)
# print("前五应有数据：", my_data_should)
# Score
sims1 = np.load('./sims_scan.npy') # 基线模型
inds1 = np.argsort(sims1[index])[::-1] # 倒序
my_data_test1 = inds1[:5]
resu1 = sims1[index, my_data_test1]
my_data_should1 = [5 * index + i for i in range(5)]
print()
print("前五测试排名：", my_data_test1)
print("前五测试相似度结果：", resu1)
print()
print("前五应有数据：", my_data_should1)
# rank = 1e20
# # x = np.zeros(5)
# for i in range(5 * index, 5 * index + 5, 1):
# tmp = np.where(inds == i)[0][0]
# if tmp < rank:
# rank = tmp
def evalrank(model, data_loader, opt_, mylog, fold5=False, max_violation=True):
mylog.info("-------- evaluation --------")
model.eval()
with torch.no_grad():
img_embs, cap_embs, pool_imgs, cap_pool, cap_lens = encode_data(model, data_loader, max_violation=max_violation)
if not fold5:
# no cross-validation, full evaluation
# img_embs = np.array([img_embs[i] for i in range(0, len(img_embs), 5)])
# 图像去除冗余
selected_indices = torch.arange(0, len(img_embs), 5)
img_embs = img_embs[selected_indices]
pool_imgs = pool_imgs[selected_indices]
from eval import shard_xattn
sims = shard_xattn(model, pool_imgs, img_embs, cap_pool, cap_embs, cap_lens, opt_, shard_size=64)
return sims
if __name__ == '__main__':
main()