latex paper update

LearningToCompare_ZSL/classification_MLP/Classification_40.py

@@ -7,6 +7,37 @@ import torch.optim as optim
 import torch.nn.functional as F
 from torch.optim.lr_scheduler import StepLR
 
+"""
+batch_size = 5
+seq_len = 10
+embed_dim = 2
+value_dim = 12
+num_heads = 1
+
+query = torch.randn(seq_len, batch_size,embed_dim)
+key = torch.randn(seq_len, batch_size,embed_dim)
+value = torch.randn(seq_len, batch_size,embed_dim)
+
+mha = nn.MultiheadAttention(embed_dim, num_heads)
+attn_out,_ = mha(query, key, value)
+attn_out = attn_out.permute(1,0,2)
+
+m = nn.BatchNorm1d(3, affine=False)
+input = torch.tensor([[1., 2., 3.], [1., 2., 3.], [1., 2., 3.], [1., 2., 3.]]) #batchsize,channels 4,3
+print(input.shape)
+print(input)
+output = m(input)
+print(output)
+
+m = nn.BatchNorm1d(3, affine=False)
+input = torch.tensor([[1., 1., 1.], [2., 2., 2.], [3., 3., 3.], [4., 4., 4.]]) #batchsize,channels 4,3
+print(input.shape)
+print(input)
+output = m(input)
+print(output)
+
+exit()
+"""
 
 
 class Net(nn.Module):
@@ -14,8 +45,10 @@ class Net(nn.Module):
         super().__init__()
         self.fc1 = nn.Linear(256, 256)
         self.fc2 = nn.Linear(256, 40)
+        self.bn = nn.BatchNorm1d(256,affine=True)
        
     def forward(self, x):
+        x = self.bn(x)
         x = F.relu(self.fc1(x))
         x = self.fc2(x)
         return x
@@ -23,15 +56,71 @@ class Net(nn.Module):
 class Net2(nn.Module):
     def __init__(self):
         super().__init__()
+        self.bn = nn.BatchNorm1d(256,affine=False)
         self.fc1 = nn.Linear(256, 40)
        
     def forward(self, x):
+        x = self.bn(x)
         x = self.fc1(x)
         return x
 
+class Net_attention(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.self_attn = nn.MultiheadAttention(1, 1)
+        self.fc1 = nn.Linear(256, 40)
+       
+    def forward(self, x):
+        x = x.view(-1, 256, 1)          #batchsize, features, embedding_size
+        x = x.permute(1,0,2)            #features, batchsize, embedding_size
+        x,_ = self.self_attn(x, x, x)   #features, batchsize, embedding_size
+        x = x.permute(1,0,2)            #batchsize, features, embedding_size
+        x = x.view(-1,256)              #batchsize, features
+        x = self.fc1(x)
+        return x
+
+class Net_attention2(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.self_attn = nn.MultiheadAttention(256, 1, dropout=0.5)
+        self.fc1 = nn.Linear(256, 40)
+       
+    def forward(self, x):
+        x = x.view(-1, 1, 256)          #batchsize, features, embedding_size
+        x = x.permute(1,0,2)            #features, batchsize, embedding_size
+        x,_ = self.self_attn(x, x, x)   #features, batchsize, embedding_size
+        x = x.permute(1,0,2)            #batchsize, features, embedding_size
+        x = x.view(-1,256)              #batchsize, features
+        x = self.fc1(x)
+        return x
+
+class Net_attention3(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.self_attn = nn.MultiheadAttention(64, 1)
+        self.fc1 = nn.Linear(256, 40)
+        
+    def forward(self, x):
+        x = x.view(-1, 4, 64)          #batchsize, features, embedding_size
+        x = x.permute(1,0,2)            #features, batchsize, embedding_size
+        x,_ = self.self_attn(x, x, x)   #features, batchsize, embedding_size
+        x = x.permute(1,0,2)          #batchsize, features, embedding_size
+        x = torch.flatten(x,start_dim=1)              #batchsize, features
+        #print(x.shape)
+        x = self.fc1(x)
+        return x
 
 def main():
 
+    # init weights with seed
+    print("init seed")
+    seed = 23
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+    torch.backends.cudnn.deterministic = True
+    torch.backends.cudnn.benchmark = False
+    np.random.seed(seed)
+
     #init CUDA
     device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
     print(device)
@@ -71,7 +160,7 @@ def main():
     train_data = TensorDataset(train_features,train_label)
     val_data = TensorDataset(val_features,val_label)
 
-    trainloader = DataLoader(train_data, batch_size=256,shuffle=True)
+    trainloader = DataLoader(train_data, batch_size=128,shuffle=True)
     testloader = DataLoader(val_data, batch_size=32,shuffle=False)
 
     #load net for valdiation
@@ -83,22 +172,26 @@ def main():
     net = Net().to(device)
 
     criterion = nn.CrossEntropyLoss().to(device)
-    optimizer = optim.Adam(net.parameters(), lr=0.01)
+    #optimizer = optim.Adam(net.parameters(), lr=0.0001)
     #scheduler = StepLR(optimizer,step_size=500,gamma=0.5)
-    #optimizer = torch.optim.Adagrad(net.parameters(), lr=0.01, lr_decay=0.0, weight_decay=0, initial_accumulator_value=0, eps=1e-10)
+    optimizer = torch.optim.Adagrad(net.parameters(), lr=0.01, lr_decay=0.0, weight_decay=0, initial_accumulator_value=0, eps=1e-10)
     #optimizer = torch.optim.Adadelta(net.parameters(), lr=1.0, rho=0.9, eps=1e-06, weight_decay=0)
 
     class_accuracy = 0
 
+    print("Dimension of trainable parameters: ")
+    for parameter in net.parameters():
+        print(parameter.shape)
+        #print(parameter)
+
     print("start training...")
 
     for epoch in range(2000):  # loop over the dataset multiple times
-
+        net.train()
         running_loss = 0.0
         for i, data in enumerate(trainloader):
             # get the inputs; data is a list of [inputs, labels]
             inputs, labels = data
-           
             # zero the parameter gradients
             optimizer.zero_grad()
 
@@ -112,9 +205,12 @@ def main():
         # print statistics
         if epoch % 10 == 9:    # print every 10 epochs
             print('[%d] loss: %.3f' % (epoch + 1, loss.item()))
+            #for parameter in net.parameters():
+            #    print(parameter)
             
         
-        if epoch % 100 == 99:
+        if epoch % 10 == 9:
+            net.eval()
             correct = 0
             total = 0
             # since we're not training, we don't need to calculate the gradients for our outputs
@@ -130,7 +226,7 @@ def main():
 
                 print('Accuracy: %.2f %%' % (100 * correct / total))
 
-        if epoch % 500 == 499:
+        if epoch % 100 == 99:
             # prepare to count predictions for each class
             correct_pred = np.zeros(40)
             total_pred = np.zeros(40)

Template/latex/egbib.bib

@@ -44,11 +44,34 @@
 	title={Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks}, 
 	author={Nils Reimers and Iryna Gurevych},
 	year={2019},
-	jornal={arXiv:1908.10084},
+	journal={arXiv:1908.10084},
 	archivePrefix={arXiv},
 	primaryClass={cs.CL}
 }
 
+@article{duan2021revisiting,
+	title={Revisiting Skeleton-based Action Recognition}, 
+	author={Haodong Duan and Yue Zhao and Kai Chen and Dian Shao and Dahua Lin and Bo Dai},
+	year={2021},
+	journal={arXiv:2104.13586},
+	archivePrefix={arXiv},
+	primaryClass={cs.CV}
+}
+
+@article{Liu_2020,
+	title={NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding},
+	volume={42},
+	ISSN={1939-3539},
+	url={http://dx.doi.org/10.1109/TPAMI.2019.2916873},
+	DOI={10.1109/tpami.2019.2916873},
+	number={10},
+	journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
+	publisher={Institute of Electrical and Electronics Engineers (IEEE)},
+	author={Liu, Jun and Shahroudy, Amir and Perez, Mauricio and Wang, Gang and Duan, Ling-Yu and Kot, Alex C.},
+	year={2020},
+	month={Oct},
+	pages={2684–2701}
+}
 
 @misc{Authors14,
  author = {Authors},

Template/latex/paper_working_design.aux

@@ -16,43 +16,54 @@
 \gdef\HyperFirstAtBeginDocument#1{#1}
 \providecommand\HyField@AuxAddToFields[1]{}
 \providecommand\HyField@AuxAddToCoFields[2]{}
+\citation{duan2021revisiting}
+\citation{Liu_2020}
+\citation{jasani2019skeleton}
+\citation{reimers2019sentencebert}
 \@writefile{toc}{\contentsline {section}{\numberline {1}\hskip -1em.\nobreakspace  {}Introduction}{1}{section.1}}
 \@writefile{toc}{\contentsline {subsection}{\numberline {1.1}\hskip -1em.\nobreakspace  {}Zero-shot learning}{1}{subsection.1.1}}
 \@writefile{toc}{\contentsline {subsection}{\numberline {1.2}\hskip -1em.\nobreakspace  {}Skeleton-based visual recognition}{1}{subsection.1.2}}
-\@writefile{toc}{\contentsline {subsection}{\numberline {1.3}\hskip -1em.\nobreakspace  {}Data augmentation}{1}{subsection.1.3}}
+\@writefile{toc}{\contentsline {subsection}{\numberline {1.3}\hskip -1em.\nobreakspace  {}Related work}{1}{subsection.1.3}}
+\@writefile{lof}{\contentsline {figure}{\numberline {1}{\ignorespaces Architecture of the network.}}{2}{figure.1}}
+\newlabel{fig:long}{{1}{2}{Architecture of the network}{figure.1}{}}
+\newlabel{fig:onecol}{{1}{2}{Architecture of the network}{figure.1}{}}
+\@writefile{toc}{\contentsline {subsection}{\numberline {1.4}\hskip -1em.\nobreakspace  {}Data augmentation}{2}{subsection.1.4}}
+\@writefile{toc}{\contentsline {section}{\numberline {2}\hskip -1em.\nobreakspace  {}Method}{2}{section.2}}
+\@writefile{toc}{\contentsline {subsection}{\numberline {2.1}\hskip -1em.\nobreakspace  {}Architecture}{2}{subsection.2.1}}
+\@writefile{toc}{\contentsline {subsubsection}{\numberline {2.1.1}Visual path}{2}{subsubsection.2.1.1}}
+\@writefile{toc}{\contentsline {subsubsection}{\numberline {2.1.2}Semantic Path}{2}{subsubsection.2.1.2}}
+\@writefile{toc}{\contentsline {subsubsection}{\numberline {2.1.3}Similarity-Learning-Part}{2}{subsubsection.2.1.3}}
 \citation{liu2019roberta}
 \citation{ma2019nlpaug}
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-\@writefile{toc}{\contentsline {subsubsection}{\numberline {2.1.1}Automatic Augmentation}{2}{subsubsection.2.1.1}}
 \citation{jasani2019skeleton}
 \citation{sung2018learning}
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-\@writefile{toc}{\contentsline {subsection}{\numberline {2.2}\hskip -1em.\nobreakspace  {}Experiments}{3}{subsection.2.2}}
+\@writefile{lot}{\contentsline {table}{\numberline {1}{\ignorespaces Three descriptive labels for class "Squat down".}}{3}{table.1}}
+\newlabel{tab:multi_label}{{1}{3}{Three descriptive labels for class "Squat down"}{table.1}{}}
+\@writefile{toc}{\contentsline {subsection}{\numberline {2.2}\hskip -1em.\nobreakspace  {}Augmentation}{3}{subsection.2.2}}
+\@writefile{toc}{\contentsline {subsubsection}{\numberline {2.2.1}Descriptive labels}{3}{subsubsection.2.2.1}}
+\@writefile{toc}{\contentsline {subsubsection}{\numberline {2.2.2}Multiple labels per class}{3}{subsubsection.2.2.2}}
+\@writefile{toc}{\contentsline {subsubsection}{\numberline {2.2.3}Automatic augmentation}{3}{subsubsection.2.2.3}}
+\@writefile{lot}{\contentsline {table}{\numberline {2}{\ignorespaces Descriptive label and two automatic augmentations for "Squat down".}}{3}{table.2}}
+\newlabel{tab:auto_aug}{{2}{3}{Descriptive label and two automatic augmentations for "Squat down"}{table.2}{}}
+\@writefile{lot}{\contentsline {table}{\numberline {3}{\ignorespaces ZSL and GZSL results for different approaches.}}{3}{table.3}}
+\newlabel{tab:ZSL_GZSL}{{3}{3}{ZSL and GZSL results for different approaches}{table.3}{}}
+\@writefile{toc}{\contentsline {subsection}{\numberline {2.3}\hskip -1em.\nobreakspace  {}Experiments}{3}{subsection.2.3}}
 \citation{jasani2019skeleton}
 \citation{ma2019nlpaug}
-\bibstyle{ieee_fullname}
-\bibdata{egbib}
-\bibcite{jasani2019skeleton}{1}
+\@writefile{lot}{\contentsline {table}{\numberline {4}{\ignorespaces Unseen top-1 and top-5 accuracies in detail.}}{4}{table.4}}
+\newlabel{tab:top1_top5}{{4}{4}{Unseen top-1 and top-5 accuracies in detail}{table.4}{}}
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-\bibcite{liu2019roberta}{2}
-\bibcite{ma2019nlpaug}{3}
-\bibcite{sung2018learning}{4}
+\bibstyle{ieee_fullname}
+\bibdata{egbib}
+\bibcite{duan2021revisiting}{1}
+\bibcite{jasani2019skeleton}{2}
+\bibcite{Liu_2020}{3}
+\bibcite{liu2019roberta}{4}
+\bibcite{ma2019nlpaug}{5}
+\bibcite{reimers2019sentencebert}{6}
+\bibcite{sung2018learning}{7}

Template/latex/paper_working_design.bbl

 \begin{thebibliography}{1}\itemsep=-1pt
 
+\bibitem{duan2021revisiting}
+Haodong Duan, Yue Zhao, Kai Chen, Dian Shao, Dahua Lin, and Bo Dai.
+\newblock Revisiting skeleton-based action recognition.
+\newblock {\em arXiv:2104.13586}, 2021.
+
 \bibitem{jasani2019skeleton}
 Bhavan Jasani and Afshaan Mazagonwalla.
 \newblock Skeleton based zero shot action recognition in joint pose-language
   semantic space.
 \newblock {\em arXiv:1911.11344}, 2019.
 
+\bibitem{Liu_2020}
+Jun Liu, Amir Shahroudy, Mauricio Perez, Gang Wang, Ling-Yu Duan, and Alex~C.
+  Kot.
+\newblock Ntu rgb+d 120: A large-scale benchmark for 3d human activity
+  understanding.
+\newblock {\em IEEE Transactions on Pattern Analysis and Machine Intelligence},
+  42(10):2684–2701, Oct 2020.
+
 \bibitem{liu2019roberta}
 Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer
   Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov.
@@ -17,6 +30,11 @@ Edward Ma.
 \newblock Nlp augmentation.
 \newblock https://github.com/makcedward/nlpaug, 2019.
 
+\bibitem{reimers2019sentencebert}
+Nils Reimers and Iryna Gurevych.
+\newblock Sentence-bert: Sentence embeddings using siamese bert-networks.
+\newblock {\em arXiv:1908.10084}, 2019.
+
 \bibitem{sung2018learning}
 Flood Sung, Yongxin Yang, Li Zhang, Tao Xiang, Philip H.~S. Torr, and
   Timothy~M. Hospedales.

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 Here is how much of TeX's memory you used:
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+Output written on paper_working_design.pdf (5 pages, 243662 bytes).
 PDF statistics:
- 102 PDF objects out of 1000 (max. 8388607)
- 31 named destinations out of 1000 (max. 500000)
- 16 words of extra memory for PDF output out of 10000 (max. 10000000)
+ 116 PDF objects out of 1000 (max. 8388607)
+ 40 named destinations out of 1000 (max. 500000)
+ 6 words of extra memory for PDF output out of 10000 (max. 10000000)