early_concat_self_atten.py

import sys
import glob
import scipy.io as sio
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
import torchvision.models as models
from matplotlib import pyplot as plt
import numpy as np
import h5py
from PIL import Image
from sklearn.externals import joblib
import shutil
import os
import random
import pickle
import time
import gc
import re
from tensorboardX import SummaryWriter
import time
import math
from torchvision import datasets, models, transforms
import matplotlib.cm as cm
import cv2
import pandas as pd
from sklearn.metrics import precision_score, recall_score, confusion_matrix, classification_report, accuracy_score, f1_score
from torch.utils.data import Dataset, DataLoader
from mosei_dataloader import mosei
from torch.nn.parameter import Parameter
from models.highway import GatedMemUpdate
from models.text_encoders import SelfAttention_B as SelfAtten

torch.manual_seed(777)
torch.cuda.manual_seed(777)
np.random.seed(777)

preprocess = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
'---------------------------------------------------LSTM VocalNet-------------------------------------------------------'

class VocalNet(nn.Module):
    def __init__(self,input_size,hidden_size,num_layers):
        super(VocalNet, self).__init__()
        self.lstm = nn.LSTM(input_size,hidden_size,num_layers,bidirectional=True)
        self.linear = nn.Linear(hidden_size, 6)


    def forward(self,x):
        x = torch.transpose(x,0,1)
        hiddens,_ = self.lstm(x)
        hiddens = hiddens.squeeze(1)
        return hiddens
'---------------------------------------------------LSTM TextNet-------------------------------------------------------'

class WordvecNet(nn.Module):
    # def __init__(self,input_size,hidden_size,num_layers,out_size,dropout=0.2,bidirectional=False):
    def __init__(self,input_size,hidden_size,num_layers,dropout=0.2,bidirectional=True):
        super(WordvecNet, self).__init__()
        # self.rnn = nn.LSTM(input_size,hidden_size,num_layers,dropout,bidirectional,batch_first=True)
        self.rnn = nn.LSTM(input_size,hidden_size,num_layers,dropout,bidirectional=bidirectional)

    def forward(self,x):
        """
        param x: tensor of shape (batch_size, seq_len, in_size)
        """
        # output,final_hiddens = self.rnn(x)
        # return output,final_hiddens
        x = torch.transpose(x,0,1)
        hiddens,_ = self.rnn(x)
        hiddens = hiddens.squeeze(1)
        return hiddens




'---------------------------------------------------LSTM VisualNet-------------------------------------------------------'

class VisionNet(nn.Module):
    def __init__(self,input_size,hidden_size,num_layers):
        super(VisionNet, self).__init__()
        self.lstm = nn.LSTM(input_size,hidden_size,num_layers,bidirectional=True)


    def forward(self,x):
        x = torch.transpose(x,0,1)
        hiddens,_ = self.lstm(x)
        hiddens = hiddens.squeeze(1)
        return hiddens


'---------------------------------------------------Gated Attention----------------------------------------------------'

class GatedAttention(nn.Module):
    def __init__(self,att_input_size,att_hidden_size,att_num_layers,no_of_emotions):
        super(GatedAttention, self).__init__()
        self.lstm = nn.LSTM(att_input_size,att_hidden_size,att_num_layers)
        self.linear = nn.Linear(att_hidden_size, no_of_emotions)


    def forward(self,vocal,vision):

        vocal = vocal.repeat(45,1)
        vision = vision.squeeze(2)
        vision = vision.squeeze(2)
        fusion = vocal*vision
        fusion = fusion.unsqueeze(0)
        fusion = fusion.transpose(0,1)
        hiddens,_ = self.lstm(fusion)
        outputs = self.linear(hiddens[-1])
        return outputs

'---------------------------------------------------Early concat with self-attention----------------------------------------------------'

class EarlyCatSelfAtten(nn.Module):
    def __init__(self, no_of_emotions, enc_hidden_size, self_atten_hid_size):
        super(EarlyCatSelfAtten, self).__init__()
        self.self_atten = SelfAtten(enc_hidden_size*3, self_atten_hid_size) # three stacked hiddens
        self.fc = nn.Linear(self_atten_hid_size, no_of_emotions)


    def forward(self, vocal, vision, emb):
        enc_concat = torch.cat((vision, vocal, emb), 1) # concatenate along time dimension
        print('enc_concat', enc_concat.size())
        context = self.self_atten(enc_concat) # attention context - the convex combination (along time) of concat'd stacked encodings



        '-------------------------------------------------Prediction--------------------------------------------------'
        # return m_two
        # outputs = self.fc(m_two)
        # # print(outputs)
        # return outputs
'---------------------------------------------------Memory to Emotion Decoder------------------------------------------'
class predictor(nn.Module):
    def __init__(self,no_of_emotions,hidden_size,output_scale_factor = 1, output_shift = 0):
        super(predictor, self).__init__()
        self.fc = nn.Linear(hidden_size, no_of_emotions)
        # self.output_scale_factor = Parameter(torch.FloatTensor([output_scale_factor]), requires_grad=False)
        # self.output_shift = Parameter(torch.FloatTensor([output_shift]), requires_grad=False)

    def forward(self,x):
        x = self.fc(x)
        # x = F.sigmoid(x)
        # x = x*self.output_scale_factor + self.output_shift

        return x
'------------------------------------------------------Hyperparameters-------------------------------------------------'
batch_size = 1
mega_batch_size = 1
no_of_emotions = 6
use_CUDA = True
use_pretrained = False
num_workers = 20

test_mode = False
val_mode = False
train_mode = True

no_of_epochs = 1000
vocal_input_size = 74 # Dont Change
vision_input_size = 35 # Dont Change
wordvec_input_size = 300
vocal_num_layers = 2
vision_num_layers = 2
wordvec_num_layers = 2
vocal_hidden_size = 512
vision_hidden_size = 512
wordvec_hidden_size = 512
dan_hidden_size = 1024
self_atten_hidden_size = 128
gated_mem = True
'----------------------------------------------------------------------------------------------------------------------'
Vocal_encoder = VocalNet(vocal_input_size, vocal_hidden_size, vocal_num_layers)
Vision_encoder = VisionNet(vision_input_size, vision_hidden_size, vision_num_layers)
Wordvec_encoder = WordvecNet(wordvec_input_size, wordvec_hidden_size, wordvec_num_layers)
# Attention = TripleAttention(no_of_emotions,dan_hidden_size,attention_hidden_size,gated_mem)
Attention = EarlyCatSelfAtten(no_of_emotions, vision_hidden_size, self_atten_hidden_size)
Predictor = predictor(no_of_emotions,self_atten_hidden_size)
if train_mode:
    train_dataset = mosei(mode= "train")
    data_loader = torch.utils.data.DataLoader(dataset=train_dataset,
                                        batch_size=batch_size,
                                        shuffle=True,num_workers = num_workers)
elif val_mode:
    val_dataset = mosei(mode = "val")
    data_loader = torch.utils.data.DataLoader(dataset=val_dataset,
                                        batch_size=1,
                                        shuffle=False,num_workers = num_workers)
    no_of_epochs = 1
else:
    test_dataset = mosei(mode = "test")
    data_loader = torch.utils.data.DataLoader(dataset=test_dataset,
                                        batch_size=1,
                                        shuffle=False,num_workers = num_workers)
    no_of_epochs = 1
curr_epoch = 0
total = 0
'----------------------------------------------------------------------------------------------------------------------'
Vocal_encoder = Vocal_encoder.cuda()
Attention = Attention.cuda()
Vision_encoder = Vision_encoder.cuda()
Wordvec_encoder = Wordvec_encoder.cuda()
Predictor = Predictor.cuda()
'----------------------------------------------------------------------------------------------------------------------'
criterion = nn.MSELoss(size_average = False)
# params =  list(Vocal_encoder.parameters())+ list(Attention.parameters()) + list(Wordvec_encoder.parameters()) + list(Vision_encoder.parameters()) + list(Predictor.parameters())[2:]
params =  list(Vocal_encoder.parameters())+ list(Attention.parameters()) + list(Wordvec_encoder.parameters()) + list(Vision_encoder.parameters()) + list(Predictor.parameters())
print('Parameters in the model = ' + str(len(params)))
optimizer = torch.optim.Adam(params, lr = 0.0001)
# optimizer = torch.optim.SGD(params, lr =0.001,momentum = 0.9 )

'------------------------------------------Saving Intermediate Models--------------------------------------------------'


def save_checkpoint(state, is_final, filename='attention_net'):
    filename = filename +'_'+str(state['epoch'])+'.pth.tar'
    os.system("mkdir -p EarlyCat_self_attention")
    torch.save(state, './EarlyCat_self_attention/'+filename)
    if is_final:
        shutil.copyfile(filename, 'model_final.pth.tar')


'-------------------------------------------Setting into train mode----------------------------------------------------'

if not train_mode:
    Vision_encoder.train(False)
    Vocal_encoder.train(False)
    Wordvec_encoder.train(False)
    Attention.train(False)
    Predictor.train(False)
else:
    Vision_encoder.train(True)
    Vocal_encoder.train(True)
    Wordvec_encoder.train(True)
    Attention.train(True)
    Predictor.train(True)

'----------------------------------------------------------------------------------------------------------------------'
epoch = 0
y_true = []
y_pred = []
while epoch<no_of_epochs:
    j_start = 0
    running_loss = 0
    running_corrects = 0
    if use_pretrained:
        pretrained_file = './EarlyCat_self_attention/triple_attention_net__4.pth.tar'
        # pretrained_file = './TAN/triple_attention_net__8.pth.tar'

        checkpoint = torch.load(pretrained_file)
        Vocal_encoder.load_state_dict(checkpoint['Vocal_encoder'])
        Vision_encoder.load_state_dict(checkpoint['Vision_encoder'])
        Wordvec_encoder.load_state_dict(checkpoint['Wordvec_encoder'])
        Attention.load_state_dict(checkpoint['Attention'])
        Predictor.load_state_dict(checkpoint['Predictor'])
        use_pretrained = False
        if train_mode:
            epoch = checkpoint['epoch']+1
            optimizer.load_state_dict(checkpoint['optimizer'])

    K = 0
    for i,(vision,vocal,emb,gt) in enumerate(data_loader):
        if use_CUDA:
            # if i==0 or i==1:
            #   print('To load data into CUDA')
            #   print(vision.size())
            #   print(vocal.size())
            #   print(emb.size())
            vision = Variable(vision.float()).cuda()
            vocal = Variable(vocal.float()).cuda()
            emb = Variable(emb.float()).cuda()
            gt = Variable(gt.float()).cuda()

        vision_output = Vision_encoder(vision)
        vocal_output = Vocal_encoder(vocal)
        emb_output = Wordvec_encoder(emb)
        # output = Attention(vocal_output,vision_output)
        output = Attention(vocal_output,vision_output,emb_output)
        outputs = Predictor(output)
        outputs = torch.clamp(outputs,0,3)
        loss = criterion(outputs, gt)
        if train_mode and K%mega_batch_size==0:
            loss.backward()
            optimizer.step()
            optimizer.zero_grad()
            Vocal_encoder.zero_grad()
            Vision_encoder.zero_grad()
            Wordvec_encoder.zero_grad()
            Attention.zero_grad()
            Predictor.zero_grad()

        # outputs_ = Variable(torch.FloatTensor([ 0.1565 ,0.1233,  0.0401,  0.4836 , 0.1596,  0.04842])).cuda()
        # loss = criterion(outputs_, gt)

        running_loss += loss.data[0]
        K+=1
        average_loss = running_loss/K
        if train_mode and K%mega_batch_size==0:
            print('Training -- Epoch [%d], Sample [%d], Average Loss: %.4f'
            % (epoch+1, K, average_loss))
        elif val_mode:
            print('Validating -- Epoch [%d], Sample [%d], Average Loss: %.4f'
            % (epoch+1, K, average_loss))
        elif test_mode:
            print('Testing -- Epoch [%d], Sample [%d], Average Loss: %.4f'
             % (epoch+1, K, average_loss))

        if train_mode:
            if K%4000==0:
                save_checkpoint({
                    'epoch': epoch,
                    'loss' : running_loss,
                    'j_start' : 0,
                    'Vocal_encoder': Vocal_encoder.state_dict(),
                    'Vision_encoder' :  Vision_encoder.state_dict(),
                    'Wordvec_encoder' : Wordvec_encoder.state_dict(),
                    'Attention' : Attention.state_dict(),
                    'Predictor' : Predictor.state_dict(),
                    'optimizer': optimizer.state_dict(),
                }, False,'triple_attention_net_iter_'+str(K))
    '-------------------------------------------------Saving model after every epoch-----------------------------------'
    if train_mode:
        save_checkpoint({
            'epoch': epoch,
            'loss' : running_loss,
            'correct' : running_corrects,
            'j_start' : 0,
            'Vocal_encoder': Vocal_encoder.state_dict(),
            'Vision_encoder' :  Vision_encoder.state_dict(),
            'Wordvec_encoder' : Wordvec_encoder.state_dict(),
            'Attention' : Attention.state_dict(),
            'Predictor' : Predictor.state_dict(),
            'optimizer': optimizer.state_dict(),
        }, False,'triple_attention_net_')
    epoch+= 1
'------------------------------------------------------Saving model after training completion--------------------------'
if train_mode:
    save_checkpoint({
        'epoch': epoch,
        'loss' : running_loss,
        'j_start' : 0,
        'Vocal_encoder': Vocal_encoder.state_dict(),
        'Vision_encoder' :  Vision_encoder.state_dict(),
        'Wordvec_encoder' : Wordvec_encoder.state_dict(),
        'Attention' : Attention.state_dict(),
        'Predictor' : Predictor.state_dict(),
        'optimizer': optimizer.state_dict(),
    }, False)

# print('Accuracy:', accuracy_score(y_true, y_pred))
# print('F1 score:', f1_score(y_true, y_pred,average = 'weighted'))
# print('Recall:', recall_score(y_true, y_pred,average ='weighted'))
# print('Precision:', precision_score(y_true, y_pred,average = 'weighted'))