目录
- 说明
- 说明
- 往期文章和专栏
- 代码及数据集下载
- 什么是跨模态检索
- 代码
- 导入需要的库
- 构建图像、文本子网络
- 加载数据集
- 计算mAP
- 损失函数构建
- 总损失函数
- 训练部分
- 主函数+参数设置
- 查看结果
说明
说明
还是读研时候剩下的代码，是我从几篇论文里面摘出来的代码做了简单的拼装，整体框架相对于论文更简单，也更便于理解一些，初学者可以用于学习。
稍作修改之后，其实效果还不错
往期文章和专栏
👉往期文章可以关注我的专栏
下巴同学的数据加油小站
会不定期分享数据挖掘、机器学习、风控模型、深度学习、NLP等方向的学习项目，关注不一定能学到你想学的东西，但是可以学到我想学和正在学的东西😀
代码及数据集下载
和鲸社区上传了一份可以在线运行的，fork后可以下载代码和数据集：简单实现跨模态检索
什么是跨模态检索
跨模态检索指的是：根据一个模态的查询样本，在另一个模态上搜索相关的样本。
例如，给出一张图像，去检索包含相同对象或主题的文本描述；或是给出一段文本，去检索带有其描述对象的图片。
但由于各模态之间具有不同的数据表现形式，所以不同模态的样本间并不能直接进行相似性比较。
代码
导入需要的库
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data.dataset import Dataset
from scipy.io import loadmat, savemat
from torch.utils.data import DataLoader
import numpy as np
import scipy.spatial
import torchvision
import time
import copy
构建图像、文本子网络
class ImageNN(nn.Module):
def __init__(self, input_dim=4096, output_dim=1024):
super(ImageNN, self).__init__()
self.denseL1 = nn.Linear(input_dim, output_dim)
def forward(self, x):
out = F.relu(self.denseL1(x))
return out
class TextNN(nn.Module):
def __init__(self, input_dim=300, output_dim=1024):
super(TextNN, self).__init__()
self.denseL1 = nn.Linear(input_dim, output_dim)
def forward(self, x):
out = F.relu(self.denseL1(x))
return out
class IDCM_NN(nn.Module):
def __init__(self, img_input_dim=4096, img_output_dim=2048,
text_input_dim=1024, text_output_dim=2048, minus_one_dim=1024, output_dim=20):
super(IDCM_NN, self).__init__()
self.img_net = ImageNN(img_input_dim, img_output_dim)
self.text_net = TextNN(text_input_dim, text_output_dim)
self.linearLayer = nn.Linear(img_output_dim, minus_one_dim)
self.linearLayer1 = nn.Linear(text_output_dim, minus_one_dim)
self.linearLayer2 = nn.Linear(minus_one_dim, output_dim)
def forward(self, img, text):
view1_feature = self.img_net(img)
view2_feature = self.text_net(text)
view1_feature = self.linearLayer(view1_feature)
view2_feature = self.linearLayer1(view2_feature)
view1_predict = self.linearLayer2(view1_feature)
view2_predict = self.linearLayer2(view2_feature)
return view1_feature, view2_feature, view1_predict, view2_predict
加载数据集
class CustomDataSet(Dataset):
def __init__(
self,
images,
texts,
labels):
self.images = images
self.texts = texts
self.labels = labels
def __getitem__(self, index):
img = self.images[index]
text = self.texts[index]
label = self.labels[index]
return img, text, label
def __len__(self):
count = len(self.images)
assert len(
self.images) == len(self.labels)
return count
def ind2vec(ind, N=None):
ind = np.asarray(ind)
if N is None:
N = ind.max() + 1
return np.arange(N) == np.repeat(ind, N, axis=1)
def get_loader(path, batch_size):
img_train = loadmat(path+"train_img.mat")['train_img']
img_test = loadmat(path + "test_img.mat")['test_img']
text_train = loadmat(path+"train_txt.mat")['train_txt']
text_test = loadmat(path + "test_txt.mat")['test_txt']
label_train = loadmat(path+"train_img_lab.mat")['train_img_lab']
label_test = loadmat(path + "test_img_lab.mat")['test_img_lab']
label_train = ind2vec(label_train).astype(int)
label_test = ind2vec(label_test).astype(int)
imgs = {'train': img_train, 'test': img_test}
texts = {'train': text_train, 'test': text_test}
labels = {'train': label_train, 'test': label_test}
dataset = {x: CustomDataSet(images=imgs[x], texts=texts[x], labels=labels[x])
for x in ['train', 'test']}
shuffle = {'train': False, 'test': False}
dataloader = {x: DataLoader(dataset[x], batch_size=batch_size,
shuffle=shuffle[x], num_workers=0) for x in ['train', 'test']}
img_dim = img_train.shape[1]
text_dim = text_train.shape[1]
num_class = label_train.shape[1]
input_data_par = {}
input_data_par['img_test'] = img_test
input_data_par['text_test'] = text_test
input_data_par['label_test'] = label_test
input_data_par['img_train'] = img_train
input_data_par['text_train'] = text_train
input_data_par['label_train'] = label_train
input_data_par['img_dim'] = img_dim
input_data_par['text_dim'] = text_dim
input_data_par['num_class'] = num_class
return dataloader, input_data_par
计算mAP
平均查准率均值（mean Average Precision，mAP）：mAP是对查询样本和所有返回的检索样本之间进行余弦相似度的计算，综合考虑了排序信息和精度。
def fx_calc_map_label(image, text, label, k=0, dist_method='COS'):
if dist_method == 'L2':
dist = scipy.spatial.distance.cdist(image, text, 'euclidean')
elif dist_method == 'COS':
dist = scipy.spatial.distance.cdist(image, text, 'cosine')
ord = dist.argsort()
numcases = dist.shape[0]
if k == 0:
k = numcases
res = []
for i in range(numcases):
order = ord[i]
p = 0.0
r = 0.0
for j in range(k):
if label[i] == label[order[j]]:
r += 1
p += (r / (j + 1))
if r > 0:
res += [p / r]
else:
res += [0]
return np.mean(res)
损失函数构建
构建三元组损失
#余弦距离
def cos_distance(source, target):
cos_sim = F.cosine_similarity(source.unsqueeze(1), target, dim=-1)
distances = torch.clamp(1 - cos_sim, 0)
return distances
class TripletLoss(nn.Module):
def __init__(self, reduction='mean'):
super(TripletLoss, self).__init__()
self.reduction = reduction
def forward(self, source, s_labels, target=None, t_labels=None, margin=0):
if target is None:
target = source
if t_labels is None:
t_labels = s_labels
pairwise_dist = cos_distance(source, target)
#锚点与正样本的距离a
anchor_positive_dist = pairwise_dist.unsqueeze(2)
#锚点与负样本的距离b
anchor_negative_dist = pairwise_dist.unsqueeze(1)
#a-b+边距
triplet_loss = anchor_positive_dist - anchor_negative_dist + margin
#将小于0的值修改为0
triplet_loss = triplet_loss.clamp(0)
valid_triplets = triplet_loss.gt(1e-16).float()
num_positive_triplets = valid_triplets.sum()
if self.reduction == 'mean':
triplet_loss = triplet_loss.sum() / (num_positive_triplets + 1e-16)
elif self.reduction == 'sum':
triplet_loss = triplet_loss.sum()
return triplet_loss
总损失函数
构建损失函数从三个方向出发：
1.模态间的异质性差异，即尽可能消除不同模态数据差异，比如floss；
2.模态间的判别性，即放缩模态间具有不同或相同标签的数据的距离，比如tri_loss ；
3.模态内的判别性，即模态不同标签数据保持距离，不会混淆，比如lloss
def calc_loss(view1_feature, view2_feature, view1_predict, view2_predict, labels_1, labels_2, alpha, beta,gamma):
#三元组损失
tri_loss = TripletLoss(reduction='mean')
tri_i2t = tri_loss(view1_feature, labels_1.float(), target=view2_feature, margin=0.1)
tri_t2i = tri_loss(view2_feature, labels_2.float(), target=view1_feature, margin=0.1)
cos_tri = tri_i2t + tri_t2i
#计算图文特征矩阵的距离损失，即差的F范数
floss = ((view1_feature - view2_feature)**2).sum(1).sqrt().mean()
#计算标签矩阵的损失
lloss = ((view1_predict-labels_1.float())**2).sum(1).sqrt().mean() + ((view2_predict-labels_2.float())**2).sum(1).sqrt().mean()
#损失求和，并给出超参数
im_loss = alpha * cos_tri + beta*floss+ gamma*lloss
return im_loss
训练部分
def train_model(model, data_loaders, optimizer, alpha, beta,gamma, num_epochs):
since = time.time()
test_img_acc_history = []
test_txt_acc_history = []
epoch_loss_history =[]
best_model_wts = copy.deepcopy(model.state_dict())
best_acc = 0.0
for epoch in range(num_epochs):
print('Epoch {}/{}'.format(epoch+1, num_epochs))
print('-' * 20)
# Each epoch has a training and validation phase
for phase in ['train', 'test']:
if phase == 'train':
model.train()
else:
model.eval()
running_loss = 0.0
running_corrects_img = 0
running_corrects_txt = 0
for imgs, txts, labels in data_loaders[phase]:
if torch.sum(imgs != imgs)>1 or torch.sum(txts != txts)>1:
print("Data contains Nan.")
optimizer.zero_grad()
with torch.set_grad_enabled(phase == 'train'):
if torch.cuda.is_available():
imgs = imgs.cuda()
txts = txts.cuda()
labels = labels.cuda()
optimizer.zero_grad()
view1_feature, view2_feature, view1_predict, view2_predict = model(imgs, txts)
loss = calc_loss(view1_feature, view2_feature, view1_predict,
view2_predict, labels, labels, alpha, beta,gamma)
img_preds = view1_predict
txt_preds = view2_predict
if phase == 'train':
loss.backward()
optimizer.step()
running_loss += loss.item()
running_corrects_img += torch.sum(torch.argmax(img_preds, dim=1) == torch.argmax(labels, dim=1))
running_corrects_txt += torch.sum(torch.argmax(txt_preds, dim=1) == torch.argmax(labels, dim=1))
epoch_loss = running_loss / len(data_loaders[phase].dataset)
t_imgs, t_txts, t_labels = [], [], []
with torch.no_grad():
for imgs, txts, labels in data_loaders['test']:
if torch.cuda.is_available():
imgs = imgs.cuda()
txts = txts.cuda()
labels = labels.cuda()
t_view1_feature, t_view2_feature, _, _ = model(imgs, txts)
t_imgs.append(t_view1_feature.cpu().numpy())
t_txts.append(t_view2_feature.cpu().numpy())
t_labels.append(labels.cpu().numpy())
t_imgs = np.concatenate(t_imgs)
t_txts = np.concatenate(t_txts)
t_labels = np.concatenate(t_labels).argmax(1)
img2text = fx_calc_map_label(t_imgs, t_txts, t_labels)
txt2img = fx_calc_map_label(t_txts, t_imgs, t_labels)
print('{} Loss: {:.4f} Img2Txt: {:.4f} Txt2Img: {:.4f}'.format(phase, epoch_loss, img2text, txt2img))
if phase == 'test' and (img2text + txt2img) / 2. > best_acc:
best_acc = (img2text + txt2img) / 2.
best_model_wts = copy.deepcopy(model.state_dict())
if phase == 'test':
test_img_acc_history.append(img2text)
test_txt_acc_history.append(txt2img)
epoch_loss_history.append(epoch_loss)
time_elapsed = time.time() - since
print('Training complete in {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))
print('Best average ACC: {:4f}'.format(best_acc))
model.load_state_dict(best_model_wts)
return model, test_img_acc_history, test_txt_acc_history, epoch_loss_history
主函数+参数设置
if __name__ == '__main__':
dataset = 'pascal'
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
DATA_DIR = 'data/' + dataset + '/'
MAX_EPOCH = 130
alpha = 1e-3
beta = 1
gamma=10
weight_decay = 0
batch_size = 100
lr = 1e-4
betas = (0.5, 0.999)
print('加载数据')
data_loader, input_data_par = get_loader(DATA_DIR, batch_size)
model_ft = IDCM_NN(img_input_dim=input_data_par['img_dim'], text_input_dim=input_data_par['text_dim'], output_dim=input_data_par['num_class']).to(device)
params_to_update = list(model_ft.parameters())
optimizer = optim.Adam(params_to_update, lr=lr, betas=betas,weight_decay=weight_decay)
print('开始训练')
model_ft, img_acc_hist, txt_acc_hist, loss_hist = train_model(model_ft, data_loader, optimizer, alpha, beta,gamma,MAX_EPOCH)
print('训练完成')
print('在测试集评估')
view1_feature, view2_feature, view1_predict, view2_predict = model_ft(torch.tensor(input_data_par['img_test']).to(device), torch.tensor(input_data_par['text_test']).to(device))
label = torch.argmax(torch.tensor(input_data_par['label_test']), dim=1)
view1_feature = view1_feature.detach().cpu().numpy()
view2_feature = view2_feature.detach().cpu().numpy()
view1_predict = view1_predict.detach().cpu().numpy()
view2_predict = view2_predict.detach().cpu().numpy()
img_to_txt = fx_calc_map_label(view1_feature, view2_feature, label)
print('...Image to Text MAP = {}'.format(img_to_txt))
txt_to_img = fx_calc_map_label(view2_feature, view1_feature, label)
print('...Text to Image MAP = {}'.format(txt_to_img))
print('...Average MAP = {}'.format(((img_to_txt + txt_to_img) / 2.)))
查看结果
加载数据
开始训练
Epoch 1/130
--------------------
train Loss: 0.2704 Img2Txt: 0.2171 Txt2Img: 0.2132
test Loss: 0.2217 Img2Txt: 0.2171 Txt2Img: 0.2132
Epoch 2/130
--------------------
train Loss: 0.2229 Img2Txt: 0.4099 Txt2Img: 0.4798
test Loss: 0.1951 Img2Txt: 0.4099 Txt2Img: 0.4798
Epoch 3/130
--------------------
train Loss: 0.1990 Img2Txt: 0.5426 Txt2Img: 0.5804
test Loss: 0.1780 Img2Txt: 0.5426 Txt2Img: 0.5804
Epoch 4/130
--------------------
train Loss: 0.1784 Img2Txt: 0.5932 Txt2Img: 0.6240
test Loss: 0.1654 Img2Txt: 0.5932 Txt2Img: 0.6240
Epoch 5/130
--------------------
train Loss: 0.1610 Img2Txt: 0.6113 Txt2Img: 0.6585
test Loss: 0.1577 Img2Txt: 0.6113 Txt2Img: 0.6585
Epoch 6/130
--------------------
train Loss: 0.1489 Img2Txt: 0.6362 Txt2Img: 0.6808
test Loss: 0.1550 Img2Txt: 0.6362 Txt2Img: 0.6808
Epoch 7/130
--------------------
train Loss: 0.1426 Img2Txt: 0.6357 Txt2Img: 0.6748
test Loss: 0.1537 Img2Txt: 0.6357 Txt2Img: 0.6748
Epoch 8/130
--------------------
train Loss: 0.1369 Img2Txt: 0.6615 Txt2Img: 0.7022
test Loss: 0.1532 Img2Txt: 0.6615 Txt2Img: 0.7022
Epoch 9/130
--------------------
train Loss: 0.1342 Img2Txt: 0.6626 Txt2Img: 0.7007
test Loss: 0.1536 Img2Txt: 0.6626 Txt2Img: 0.7007
Epoch 10/130
--------------------
train Loss: 0.1314 Img2Txt: 0.6698 Txt2Img: 0.6956
test Loss: 0.1516 Img2Txt: 0.6698 Txt2Img: 0.6956
Epoch 11/130
--------------------
train Loss: 0.1272 Img2Txt: 0.6803 Txt2Img: 0.7025
test Loss: 0.1508 Img2Txt: 0.6803 Txt2Img: 0.7025
Epoch 12/130
--------------------
train Loss: 0.1222 Img2Txt: 0.6670 Txt2Img: 0.7078
test Loss: 0.1518 Img2Txt: 0.6670 Txt2Img: 0.7078
Epoch 13/130
--------------------
train Loss: 0.1232 Img2Txt: 0.6635 Txt2Img: 0.7079
test Loss: 0.1471 Img2Txt: 0.6635 Txt2Img: 0.7079
Epoch 14/130
--------------------
train Loss: 0.1175 Img2Txt: 0.6911 Txt2Img: 0.7148
test Loss: 0.1468 Img2Txt: 0.6911 Txt2Img: 0.7148
Epoch 15/130
--------------------
train Loss: 0.1161 Img2Txt: 0.6827 Txt2Img: 0.7136
test Loss: 0.1455 Img2Txt: 0.6827 Txt2Img: 0.7136
Epoch 16/130
--------------------
省略………………………………………………
--------------------
train Loss: 0.0548 Img2Txt: 0.6535 Txt2Img: 0.6922
test Loss: 0.1257 Img2Txt: 0.6535 Txt2Img: 0.6922
Epoch 125/130
--------------------
train Loss: 0.0554 Img2Txt: 0.6636 Txt2Img: 0.6888
test Loss: 0.1250 Img2Txt: 0.6636 Txt2Img: 0.6888
Epoch 126/130
--------------------
train Loss: 0.0531 Img2Txt: 0.6429 Txt2Img: 0.6794
test Loss: 0.1263 Img2Txt: 0.6429 Txt2Img: 0.6794
Epoch 127/130
--------------------
train Loss: 0.0565 Img2Txt: 0.6624 Txt2Img: 0.6901
test Loss: 0.1250 Img2Txt: 0.6624 Txt2Img: 0.6901
Epoch 128/130
--------------------
train Loss: 0.0502 Img2Txt: 0.6387 Txt2Img: 0.6858
test Loss: 0.1259 Img2Txt: 0.6387 Txt2Img: 0.6858
Epoch 129/130
--------------------
train Loss: 0.0574 Img2Txt: 0.6618 Txt2Img: 0.6885
test Loss: 0.1253 Img2Txt: 0.6618 Txt2Img: 0.6885
Epoch 130/130
--------------------
train Loss: 0.0500 Img2Txt: 0.6357 Txt2Img: 0.6810
test Loss: 0.1261 Img2Txt: 0.6357 Txt2Img: 0.6810
Training complete in 0m 53s
Best average ACC: 0.711302
训练完成
在测试集评估
...Image to Text MAP = 0.690430024096876
...Text to Image MAP = 0.7321743013743085
...Average MAP = 0.7113021627355922
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