app.py

import gradio as gr

import argparse
import cv2
import imageio
import math
from math import ceil
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import numpy as np
from PIL import Image
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Function


class RelationModuleMultiScale(torch.nn.Module):

    def __init__(self, img_feature_dim, num_bottleneck, num_frames):
        super(RelationModuleMultiScale, self).__init__()
        self.subsample_num = 3
        self.img_feature_dim = img_feature_dim
        self.scales = [i for i in range(num_frames, 1, -1)]
        self.relations_scales = []
        self.subsample_scales = []
        for scale in self.scales:
            relations_scale = self.return_relationset(num_frames, scale)
            self.relations_scales.append(relations_scale)
            self.subsample_scales.append(min(self.subsample_num, len(relations_scale)))
        self.num_frames = num_frames
        self.fc_fusion_scales = nn.ModuleList() # high-tech modulelist
        for i in range(len(self.scales)):
            scale = self.scales[i]
            fc_fusion = nn.Sequential(nn.ReLU(), nn.Linear(scale * self.img_feature_dim, num_bottleneck), nn.ReLU())
            self.fc_fusion_scales += [fc_fusion]

    def forward(self, input):
        act_scale_1 = input[:, self.relations_scales[0][0] , :]
        act_scale_1 = act_scale_1.view(act_scale_1.size(0), self.scales[0] * self.img_feature_dim)
        act_scale_1 = self.fc_fusion_scales[0](act_scale_1)
        act_scale_1 = act_scale_1.unsqueeze(1)
        act_all = act_scale_1.clone()
        for scaleID in range(1, len(self.scales)):
            act_relation_all = torch.zeros_like(act_scale_1)
            num_total_relations = len(self.relations_scales[scaleID])
            num_select_relations = self.subsample_scales[scaleID]
            idx_relations_evensample = [int(ceil(i * num_total_relations / num_select_relations)) for i in range(num_select_relations)]
            for idx in idx_relations_evensample:
                act_relation = input[:, self.relations_scales[scaleID][idx], :]
                act_relation = act_relation.view(act_relation.size(0), self.scales[scaleID] * self.img_feature_dim)
                act_relation = self.fc_fusion_scales[scaleID](act_relation)
                act_relation = act_relation.unsqueeze(1)
                act_relation_all += act_relation
            act_all = torch.cat((act_all, act_relation_all), 1)
        return act_all

    def return_relationset(self, num_frames, num_frames_relation):
        import itertools
        return list(itertools.combinations([i for i in range(num_frames)], num_frames_relation))


parser = argparse.ArgumentParser()
parser.add_argument('--dataset',  default='Sprite', help='datasets')
parser.add_argument('--data_root', default='dataset', help='root directory for data')
parser.add_argument('--num_class',  type=int, default=15, help='the number of class for jester dataset')
parser.add_argument('--input_type',  default='image', choices=['feature', 'image'], help='the type of input')
parser.add_argument('--src',  default='domain_1', help='source domain')
parser.add_argument('--tar',  default='domain_2', help='target domain')
parser.add_argument('--num_segments', type=int, default=8, help='the number of frame segment')
parser.add_argument('--backbone', type=str, default="dcgan", choices=['dcgan', 'resnet101', 'I3Dpretrain','I3Dfinetune'], help='backbone')
parser.add_argument('--channels', default=3, type=int, help='input channels for image inputs')
parser.add_argument('--add_fc', default=1, type=int, metavar='M', help='number of additional fc layers (excluding the last fc layer) (e.g. 0, 1, 2)')
parser.add_argument('--fc_dim', type=int, default=1024, help='dimension of added fc')
parser.add_argument('--frame_aggregation', type=str, default='trn', choices=[ 'rnn', 'trn'], help='aggregation of frame features (none if baseline_type is not video)')
parser.add_argument('--dropout_rate', default=0.5, type=float, help='dropout ratio for frame-level feature (default: 0.5)')
parser.add_argument('--f_dim', type=int, default=512, help='dim of f')
parser.add_argument('--z_dim', type=int, default=512, help='dimensionality of z_t')
parser.add_argument('--f_rnn_layers', type=int, default=1, help='number of layers (content lstm)')
parser.add_argument('--use_bn', type=str, default='none', choices=['none', 'AdaBN', 'AutoDIAL'], help='normalization-based methods')
parser.add_argument('--prior_sample', type=str, default='random', choices=['random', 'post'], help='how to sample prior')
parser.add_argument('--batch_size', default=128, type=int, help='-batch size')
parser.add_argument('--use_attn', type=str, default='TransAttn', choices=['none', 'TransAttn', 'general'], help='attention-mechanism')
parser.add_argument('--data_threads', type=int, default=5, help='number of data loading threads')
opt = parser.parse_args(args=[])


class GradReverse(Function):
    @staticmethod
    def forward(ctx, x, beta):
        ctx.beta = beta
        return x.view_as(x)

    @staticmethod
    def backward(ctx, grad_output):
        grad_input = grad_output.neg() * ctx.beta
        return grad_input, None   


class TransferVAE_Video(nn.Module):

    def __init__(self, opt):
        super(TransferVAE_Video, self).__init__()
        self.f_dim = opt.f_dim
        self.z_dim = opt.z_dim
        self.fc_dim = opt.fc_dim
        self.channels = opt.channels
        self.input_type = opt.input_type
        self.frames = opt.num_segments
        self.use_bn = opt.use_bn
        self.frame_aggregation = opt.frame_aggregation
        self.batch_size = opt.batch_size
        self.use_attn = opt.use_attn
        self.dropout_rate = opt.dropout_rate
        self.num_class = opt.num_class
        self.prior_sample = opt.prior_sample
        
        if self.input_type == 'image':
            import dcgan_64
            self.encoder = dcgan_64.encoder(self.fc_dim, self.channels)
            self.decoder = dcgan_64.decoder_woSkip(self.z_dim + self.f_dim, self.channels)
            self.fc_output_dim = self.fc_dim
        elif self.input_type == 'feature':
            if opt.backbone == 'resnet101':
                model_backnone = getattr(torchvision.models, opt.backbone)(True) # model_test is only used for getting the dim #
                self.input_dim = model_backnone.fc.in_features
            elif opt.backbone == 'I3Dpretrain':
                self.input_dim = 2048
            elif opt.backbone == 'I3Dfinetune':
                self.input_dim = 2048
            self.add_fc = opt.add_fc
            self.enc_fc_layer1 = nn.Linear(self.input_dim, self.fc_dim)
            self.dec_fc_layer1 = nn.Linear(self.fc_dim, self.input_dim)
            self.fc_output_dim = self.fc_dim    

            if self.use_bn == 'shared':
                self.bn_enc_layer1 = nn.BatchNorm1d(self.fc_output_dim)
                self.bn_dec_layer1 = nn.BatchNorm1d(self.input_dim)
            elif self.use_bn == 'separated':
                self.bn_S_enc_layer1 = nn.BatchNorm1d(self.fc_output_dim) 
                self.bn_T_enc_layer1 = nn.BatchNorm1d(self.fc_output_dim) 
                self.bn_S_dec_layer1 = nn.BatchNorm1d(self.input_dim) 
                self.bn_T_dec_layer1 = nn.BatchNorm1d(self.input_dim)
            
            if self.add_fc > 1:
                self.enc_fc_layer2 = nn.Linear(self.fc_dim, self.fc_dim)
                self.dec_fc_layer2 = nn.Linear(self.fc_dim, self.fc_dim)
                self.fc_output_dim = self.fc_dim
                ## use batchnormalization or not (if yes whether the source and target share the same batchnormalization)
                if self.use_bn == 'shared':
                    self.bn_enc_layer2 = nn.BatchNorm1d(self.fc_output_dim)
                    self.bn_dec_layer2 = nn.BatchNorm1d(self.fc_dim)
                elif self.use_bn == 'separated':
                    self.bn_S_enc_layer2 = nn.BatchNorm1d(self.fc_output_dim) 
                    self.bn_T_enc_layer2 = nn.BatchNorm1d(self.fc_output_dim)
                    self.bn_S_dec_layer2 = nn.BatchNorm1d(self.fc_dim) 
                    self.bn_T_dec_layer2 = nn.BatchNorm1d(self.fc_dim)
            
            if self.add_fc > 2:
                self.enc_fc_layer3 = nn.Linear(self.fc_dim, self.fc_dim)
                self.dec_fc_layer3 = nn.Linear(self.fc_dim, self.fc_dim)
                self.fc_output_dim = self.fc_dim
                ## use batchnormalization or not (if yes whether the source and target share the same batchnormalization)
                if self.use_bn == 'shared':
                    self.bn_enc_layer3 = nn.BatchNorm1d(self.fc_output_dim)
                    self.bn_dec_layer3 = nn.BatchNorm1d(self.fc_dim)
                elif self.use_bn == 'separated':
                    self.bn_S_enc_layer3 = nn.BatchNorm1d(self.fc_output_dim) 
                    self.bn_T_enc_layer3 = nn.BatchNorm1d(self.fc_output_dim)
                    self.bn_S_dec_layer3 = nn.BatchNorm1d(self.fc_dim) 
                    self.bn_T_dec_layer3 = nn.BatchNorm1d(self.fc_dim)
            
            self.z_2_out = nn.Linear(self.z_dim + self.f_dim, self.fc_output_dim)

            
        ## nonlinearity and dropout 
        self.relu = nn.LeakyReLU(0.1)
        self.dropout_f = nn.Dropout(p=self.dropout_rate)
        self.dropout_v = nn.Dropout(p=self.dropout_rate)
        # -------------------------------
     
        ## Disentangle strcuture
        # -------------------------------
        #self.hidden_dim = opt.rnn_size
        self.hidden_dim = opt.z_dim
        self.f_rnn_layers = opt.f_rnn_layers

        # Prior of content is a uniform Gaussian and prior of the dynamics is an LSTM
        self.z_prior_lstm_ly1 = nn.LSTMCell(self.z_dim, self.hidden_dim)
        self.z_prior_lstm_ly2 = nn.LSTMCell(self.hidden_dim, self.hidden_dim)

        self.z_prior_mean = nn.Linear(self.hidden_dim, self.z_dim)
        self.z_prior_logvar = nn.Linear(self.hidden_dim, self.z_dim)

        # POSTERIOR DISTRIBUTION NETWORKS
        # content and motion features share one lstm
        self.z_lstm = nn.LSTM(self.fc_output_dim, self.hidden_dim, self.f_rnn_layers, bidirectional=True, batch_first=True)
        self.f_mean = nn.Linear(self.hidden_dim * 2, self.f_dim)
        self.f_logvar = nn.Linear(self.hidden_dim * 2, self.f_dim)

        self.z_rnn = nn.RNN(self.hidden_dim * 2, self.hidden_dim, batch_first=True)
        # Each timestep is for each z so no reshaping and feature mixing
        self.z_mean = nn.Linear(self.hidden_dim, self.z_dim)
        self.z_logvar = nn.Linear(self.hidden_dim, self.z_dim)
         # -------------------------------
        
        ## z_t constraints
        # -------------------------------
        ## adversarial loss for frame features z_t 
        self.fc_feature_domain_frame = nn.Linear(self.z_dim, self.z_dim)
        self.fc_classifier_domain_frame = nn.Linear(self.z_dim, 2)
        
        ## #------ aggregate frame-based features (frame feature --> video feature) ------#
        if self.frame_aggregation == 'rnn': 
            self.bilstm = nn.LSTM(self.z_dim, self.z_dim * 2, self.f_rnn_layers, bidirectional=True, batch_first=True)
            self.feat_aggregated_dim = self.z_dim * 2
        elif self.frame_aggregation == 'trn': # 4. TRN (ECCV 2018) ==> fix segment # for both train/val
            self.num_bottleneck = 256 # 256
            self.TRN = RelationModuleMultiScale(self.z_dim, self.num_bottleneck, self.frames)
            self.bn_trn_S = nn.BatchNorm1d(self.num_bottleneck)
            self.bn_trn_T = nn.BatchNorm1d(self.num_bottleneck)
            self.feat_aggregated_dim = self.num_bottleneck
            
        ## adversarial loss for video features  
        self.fc_feature_domain_video = nn.Linear(self.feat_aggregated_dim, self.feat_aggregated_dim)
        self.fc_classifier_domain_video = nn.Linear(self.feat_aggregated_dim, 2)
        
        ## adversarial loss for each relation of features 
        if self.frame_aggregation == 'trn':
            self.relation_domain_classifier_all = nn.ModuleList()
            for i in range(self.frames-1):
                relation_domain_classifier = nn.Sequential(
                    nn.Linear(self.feat_aggregated_dim, self.feat_aggregated_dim),
                    nn.ReLU(),
                    nn.Linear(self.feat_aggregated_dim, 2)
                )
                self.relation_domain_classifier_all += [relation_domain_classifier]
        
        ## classifier for action prediction task 
        self.pred_classifier_video = nn.Linear(self.feat_aggregated_dim, self.num_class)
        
        ## classifier for prediction domains
        self.fc_feature_domain_latent = nn.Linear(self.f_dim, self.f_dim)
        self.fc_classifier_doamin_latent = nn.Linear(self.f_dim, 2)
        
        ## attention option
        if self.use_attn == 'general':
            self.attn_layer = nn.Sequential(
                nn.Linear(self.feat_aggregated_dim, self.feat_aggregated_dim),
                nn.Tanh(),
                nn.Linear(self.feat_aggregated_dim, 1)
                )
    
    def domain_classifier_frame(self, feat, beta):
        feat_fc_domain_frame = GradReverse.apply(feat, beta)
        feat_fc_domain_frame = self.fc_feature_domain_frame(feat_fc_domain_frame)
        feat_fc_domain_frame = self.relu(feat_fc_domain_frame)
        pred_fc_domain_frame = self.fc_classifier_domain_frame(feat_fc_domain_frame)
        return pred_fc_domain_frame
    
    def domain_classifier_video(self, feat_video, beta):
        feat_fc_domain_video = GradReverse.apply(feat_video, beta)
        feat_fc_domain_video = self.fc_feature_domain_video(feat_fc_domain_video)
        feat_fc_domain_video = self.relu(feat_fc_domain_video)
        pred_fc_domain_video = self.fc_classifier_domain_video(feat_fc_domain_video)
        return pred_fc_domain_video
    
    def domain_classifier_latent(self, f):
        feat_fc_domain_latent = self.fc_feature_domain_latent(f)
        feat_fc_domain_latent = self.relu(feat_fc_domain_latent)
        pred_fc_domain_latent = self.fc_classifier_doamin_latent(feat_fc_domain_latent)
        return pred_fc_domain_latent
    
    def domain_classifier_relation(self, feat_relation, beta):
        pred_fc_domain_relation_video = None
        for i in range(len(self.relation_domain_classifier_all)):
            feat_relation_single = feat_relation[:,i,:].squeeze(1) # 128x1x256 --> 128x256
            feat_fc_domain_relation_single = GradReverse.apply(feat_relation_single, beta) # the same beta for all relations (for now)

            pred_fc_domain_relation_single = self.relation_domain_classifier_all[i](feat_fc_domain_relation_single)

            if pred_fc_domain_relation_video is None:
                pred_fc_domain_relation_video = pred_fc_domain_relation_single.view(-1,1,2)
            else:
                pred_fc_domain_relation_video = torch.cat((pred_fc_domain_relation_video, pred_fc_domain_relation_single.view(-1,1,2)), 1)

        pred_fc_domain_relation_video = pred_fc_domain_relation_video.view(-1,2)

        return pred_fc_domain_relation_video
    
    def get_trans_attn(self, pred_domain):
        softmax = nn.Softmax(dim=1)
        logsoftmax = nn.LogSoftmax(dim=1)
        entropy = torch.sum(-softmax(pred_domain) * logsoftmax(pred_domain), 1)
        weights = 1 - entropy
        return weights

    def get_general_attn(self, feat):
        num_segments = feat.size()[1]
        feat = feat.view(-1, feat.size()[-1]) # reshape features: 128x4x256 --> (128x4)x256
        weights = self.attn_layer(feat) # e.g. (128x4)x1
        weights = weights.view(-1, num_segments, weights.size()[-1]) # reshape attention weights: (128x4)x1 --> 128x4x1
        weights = F.softmax(weights, dim=1)  # softmax over segments ==> 128x4x1
        return weights
    
    def get_attn_feat_relation(self, feat_fc, pred_domain, num_segments):
        if self.use_attn == 'TransAttn':
            weights_attn = self.get_trans_attn(pred_domain)
        elif self.use_attn == 'general':
            weights_attn = self.get_general_attn(feat_fc)

        weights_attn = weights_attn.view(-1, num_segments-1, 1).repeat(1,1,feat_fc.size()[-1]) # reshape & repeat weights (e.g. 16 x 4 x 256)
        feat_fc_attn = (weights_attn+1) * feat_fc

        return feat_fc_attn, weights_attn[:,:,0]
    
          
    def encode_and_sample_post(self, x):
        if isinstance(x, list):
            conv_x = self.encoder_frame(x[0])
        else:
            conv_x = self.encoder_frame(x)
        
        # pass the bidirectional lstm
        lstm_out, _ = self.z_lstm(conv_x)
        
        # get f:
        backward = lstm_out[:, 0, self.hidden_dim:2 * self.hidden_dim]
        frontal = lstm_out[:, self.frames - 1, 0:self.hidden_dim]
        lstm_out_f = torch.cat((frontal, backward), dim=1)
        f_mean = self.f_mean(lstm_out_f)
        f_logvar = self.f_logvar(lstm_out_f)
        f_post = self.reparameterize(f_mean, f_logvar, random_sampling=False)

        # pass to one direction rnn
        features, _ = self.z_rnn(lstm_out)
        z_mean = self.z_mean(features)
        z_logvar = self.z_logvar(features)
        z_post = self.reparameterize(z_mean, z_logvar, random_sampling=False)

        if isinstance(x, list):
            f_mean_list = [f_mean]
            f_post_list = [f_post]
            for t in range(1,3,1):
                conv_x = self.encoder_frame(x[t])
                lstm_out, _ = self.z_lstm(conv_x)
                # get f:
                backward = lstm_out[:, 0, self.hidden_dim:2 * self.hidden_dim]
                frontal = lstm_out[:, self.frames - 1, 0:self.hidden_dim]
                lstm_out_f = torch.cat((frontal, backward), dim=1)
                f_mean = self.f_mean(lstm_out_f)
                f_logvar = self.f_logvar(lstm_out_f)
                f_post = self.reparameterize(f_mean, f_logvar, random_sampling=False)
                f_mean_list.append(f_mean)
                f_post_list.append(f_post)
            f_mean = f_mean_list
            f_post = f_post_list
        # f_mean and f_post are list if triple else not
        return f_mean, f_logvar, f_post, z_mean, z_logvar, z_post
    
    def decoder_frame(self,zf):
        if self.input_type == 'image':
            recon_x = self.decoder(zf)
            return recon_x
        
        if self.input_type == 'feature':
            zf = self.z_2_out(zf) # batch,frames,(z_dim+f_dim) -> batch,frames,fc_output_dim
            zf = self.relu(zf)
            
            if self.add_fc > 2:
                zf = self.dec_fc_layer3(zf)
                if self.use_bn == 'shared':
                    zf = self.bn_dec_layer3(zf)
                elif self.use_bn == 'separated':
                    zf_src = self.bn_S_dec_layer3(zf[:self.batchsize,:,:])
                    zf_tar = self.bn_T_dec_layer3(zf[self.batchsize:,:,:])
                    zf = torch.cat([zf_src,zf_tar],axis=0)
                zf = self.relu(zf)
            
            if self.add_fc > 1:
                zf = self.dec_fc_layer2(zf)
                if self.use_bn == 'shared':
                    zf = self.bn_dec_layer2(zf)
                elif self.use_bn == 'separated':
                    zf_src = self.bn_S_dec_layer2(zf[:self.batchsize,:,:])
                    zf_tar = self.bn_T_dec_layer2(zf[self.batchsize:,:,:])
                    zf = torch.cat([zf_src,zf_tar],axis=0)
                zf = self.relu(zf)
            
            
            zf = self.dec_fc_layer1(zf) 
            if self.use_bn == 'shared':
                zf = self.bn_dec_layer2(zf)
            elif self.use_bn == 'separated':
                zf_src = self.bn_S_dec_layer2(zf[:self.batchsize,:,:])
                zf_tar = self.bn_T_dec_layer2(zf[self.batchsize:,:,:])
                zf = torch.cat([zf_src,zf_tar],axis=0)
            recon_x = self.relu(zf)
            return recon_x

    def encoder_frame(self, x):
        if self.input_type == 'image':
            # input x is list of length Frames [batchsize, channels, size, size]
            # convert it to [batchsize, frames, channels, size, size]
            # [batch_size, frames, channels, size, size] to [batch_size * frames, channels, size, size]
            x_shape = x.shape
            x = x.view(-1, x_shape[-3], x_shape[-2], x_shape[-1])
            x_embed = self.encoder(x)[0]
            # to [batch_size,frames,embed_dim]
            
            return x_embed.view(x_shape[0], x_shape[1], -1)
        
        
        if self.input_type == 'feature':
            # input is [batchsize, framew, input_dim]
            x_embed = self.enc_fc_layer1(x)
            ## use batchnormalization or not (if yes whether the source and target share the same batchnormalization)
            if self.use_bn == 'shared':
                x_embed = self.bn_enc_layer1(x_embed)
            elif self.use_bn == 'separated':
                x_embed_src = self.bn_S_enc_layer1(x_embed[:self.batchsize,:,:])
                x_embed_tar = self.bn_T_enc_layer1(x_embed[self.batchsize:,:,:])
                x_embed = torch.cat([x_embed_src,x_embed_tar],axis=0)
            x_embed = self.relu(x_embed)
 
            if self.add_fc > 1:
                x_embed = self.enc_fc_layer2(x_embed)
                if self.use_bn == 'shared':
                    x_embed = self.bn_enc_layer2(x_embed)
                elif self.use_bn == 'separated':
                    x_embed_src = self.bn_S_enc_layer2(x_embed[:self.batchsize,:,:])
                    x_embed_tar = self.bn_T_enc_layer2(x_embed[self.batchsize:,:,:])
                    x_embed = torch.cat([x_embed_src,x_embed_tar],axis=0)
                x_embed = self.relu(x_embed)
                
            if self.add_fc > 2:
                x_embed = self.enc_fc_layer3(x_embed)
                if self.use_bn == 'shared':
                    x_embed = self.bn_enc_layer3(x_embed)
                elif self.use_bn == 'separated':
                    x_embed_src = self.bn_S_enc_layer3(x_embed[:self.batchsize,:,:])
                    x_embed_tar = self.bn_T_enc_layer3(x_embed[self.batchsize:,:,:])
                    x_embed = torch.cat([x_embed_src,x_embed_tar],axis=0)
                x_embed = self.relu(x_embed)
                
            ## [batchsize, frame, output_dim]
            return x_embed 
    

    def reparameterize(self, mean, logvar, random_sampling=True):
        # Reparametrization occurs only if random sampling is set to true, otherwise mean is returned
        if random_sampling is True:
            eps = torch.randn_like(logvar)
            std = torch.exp(0.5 * logvar)
            z = mean + eps * std
            return z
        else:
            return mean

    def sample_z_prior_train(self, z_post, random_sampling=True):
        z_out = None  # This will ultimately store all z_s in the format [batch_size, frames, z_dim]
        z_means = None
        z_logvars = None
        batch_size = z_post.shape[0]

        z_t = torch.zeros(batch_size, self.z_dim).cpu()
        h_t_ly1 = torch.zeros(batch_size, self.hidden_dim).cpu()
        c_t_ly1 = torch.zeros(batch_size, self.hidden_dim).cpu()
        h_t_ly2 = torch.zeros(batch_size, self.hidden_dim).cpu()
        c_t_ly2 = torch.zeros(batch_size, self.hidden_dim).cpu()

        for i in range(self.frames):
            # two layer LSTM and two one-layer FC
            h_t_ly1, c_t_ly1 = self.z_prior_lstm_ly1(z_t, (h_t_ly1, c_t_ly1))
            h_t_ly2, c_t_ly2 = self.z_prior_lstm_ly2(h_t_ly1, (h_t_ly2, c_t_ly2))

            z_mean_t = self.z_prior_mean(h_t_ly2)
            z_logvar_t = self.z_prior_logvar(h_t_ly2)
            z_prior = self.reparameterize(z_mean_t, z_logvar_t, random_sampling)
            if z_out is None:
                # If z_out is none it means z_t is z_1, hence store it in the format [batch_size, 1, z_dim]
                z_out = z_prior.unsqueeze(1)
                z_means = z_mean_t.unsqueeze(1)
                z_logvars = z_logvar_t.unsqueeze(1)
            else:
                # If z_out is not none, z_t is not the initial z and hence append it to the previous z_ts collected in z_out
                z_out = torch.cat((z_out, z_prior.unsqueeze(1)), dim=1)
                z_means = torch.cat((z_means, z_mean_t.unsqueeze(1)), dim=1)
                z_logvars = torch.cat((z_logvars, z_logvar_t.unsqueeze(1)), dim=1)
            z_t = z_post[:,i,:]
        return z_means, z_logvars, z_out

    # If random sampling is true, reparametrization occurs else z_t is just set to the mean
    def sample_z(self, batch_size, random_sampling=True):
        z_out = None  # This will ultimately store all z_s in the format [batch_size, frames, z_dim]
        z_means = None
        z_logvars = None

        # All states are initially set to 0, especially z_0 = 0
        z_t = torch.zeros(batch_size, self.z_dim).cpu()
        # z_mean_t = torch.zeros(batch_size, self.z_dim)
        # z_logvar_t = torch.zeros(batch_size, self.z_dim)
        h_t_ly1 = torch.zeros(batch_size, self.hidden_dim).cpu()
        c_t_ly1 = torch.zeros(batch_size, self.hidden_dim).cpu()
        h_t_ly2 = torch.zeros(batch_size, self.hidden_dim).cpu()
        c_t_ly2 = torch.zeros(batch_size, self.hidden_dim).cpu()
        for _ in range(self.frames):
            # h_t, c_t = self.z_prior_lstm(z_t, (h_t, c_t))
            # two layer LSTM and two one-layer FC
            h_t_ly1, c_t_ly1 = self.z_prior_lstm_ly1(z_t, (h_t_ly1, c_t_ly1))
            h_t_ly2, c_t_ly2 = self.z_prior_lstm_ly2(h_t_ly1, (h_t_ly2, c_t_ly2))

            z_mean_t = self.z_prior_mean(h_t_ly2)
            z_logvar_t = self.z_prior_logvar(h_t_ly2)
            z_t = self.reparameterize(z_mean_t, z_logvar_t, random_sampling)
            if z_out is None:
                # If z_out is none it means z_t is z_1, hence store it in the format [batch_size, 1, z_dim]
                z_out = z_t.unsqueeze(1)
                z_means = z_mean_t.unsqueeze(1)
                z_logvars = z_logvar_t.unsqueeze(1)
            else:
                # If z_out is not none, z_t is not the initial z and hence append it to the previous z_ts collected in z_out
                z_out = torch.cat((z_out, z_t.unsqueeze(1)), dim=1)
                z_means = torch.cat((z_means, z_mean_t.unsqueeze(1)), dim=1)
                z_logvars = torch.cat((z_logvars, z_logvar_t.unsqueeze(1)), dim=1)
        return z_means, z_logvars, z_out

    def forward(self, x, beta):
        # beta [beta_relation, beta_video, beta_frame]
        f_mean, f_logvar, f_post, z_mean_post, z_logvar_post, z_post = self.encode_and_sample_post(x)
        if self.prior_sample == 'random':
            z_mean_prior, z_logvar_prior, z_prior = self.sample_z(z_post.size(0),random_sampling=False)
        elif self.prior_sample == 'post':
            z_mean_prior, z_logvar_prior, z_prior = self.sample_z_prior_train(z_post, random_sampling=False)
        
        
        if isinstance(f_post, list):
            f_expand = f_post[0].unsqueeze(1).expand(-1, self.frames, self.f_dim)
        else:
            f_expand = f_post.unsqueeze(1).expand(-1, self.frames, self.f_dim)
        zf = torch.cat((z_post, f_expand), dim=2) # batch,frames,(z_dim+f_dim)
        
        ## reconcstruct x
        recon_x = self.decoder_frame(zf)
        
        ## For constraints on z_post [batch,frame,z_dim] and f_post [batch,f_dim]
        pred_domain_all = [] # list save domain predictions (1) z_post (frame level) (2) each z_post_relation (if trn) (3) z_post (video level) (4)f_post
        
        #1. adversarial on z_post (frame level)
        z_post_feat = z_post.view(-1, z_post.size()[-1]) # e.g. 32 x 5 x 2048 --> 160 x 2048
        z_post_feat = self.dropout_f(z_post_feat)
        pred_fc_domain_frame = self.domain_classifier_frame(z_post_feat, beta[2])
        pred_fc_domain_frame = pred_fc_domain_frame.view((z_post.size(0), self.frames) + pred_fc_domain_frame.size()[-1:])
        pred_domain_all.append(pred_fc_domain_frame)
        
        #2 adversarial on z_post (video level, relation level if trn is used) 
    
        if self.frame_aggregation == 'rnn': 
            self.bilstm.flatten_parameters()
            z_post_video_feat, _ = self.bilstm(z_post)
            backward = z_post_video_feat[:, 0, self.z_dim:2 * self.z_dim]
            frontal = z_post_video_feat[:, self.frames - 1, 0:self.z_dim]
            z_post_video_feat = torch.cat((frontal, backward), dim=1)
            pred_fc_domain_relation = []
            pred_domain_all.append(pred_fc_domain_relation)

        elif self.frame_aggregation == 'trn':  
            z_post_video_relation = self.TRN(z_post) ## [batch, frame-1, self.feat_aggregated_dim]

            # adversarial branch for each relation 
            pred_fc_domain_relation = self.domain_classifier_relation(z_post_video_relation, beta[0])
            pred_domain_all.append(pred_fc_domain_relation.view((z_post.size(0), z_post_video_relation.size()[1]) + pred_fc_domain_relation.size()[-1:]))

            # transferable attention
            if self.use_attn != 'none': # get the attention weighting
                z_post_video_relation_attn, _ = self.get_attn_feat_relation(z_post_video_relation, pred_fc_domain_relation, self.frames)

            # sum up relation features (ignore 1-relation)
            z_post_video_feat = torch.sum(z_post_video_relation_attn, 1)


        z_post_video_feat = self.dropout_v(z_post_video_feat)

        pred_fc_domain_video = self.domain_classifier_video(z_post_video_feat, beta[1])
        pred_fc_domain_video = pred_fc_domain_video.view((z_post.size(0),) + pred_fc_domain_video.size()[-1:])
        pred_domain_all.append(pred_fc_domain_video)
        
        
        #3. video prediction 
        pred_video_class = self.pred_classifier_video(z_post_video_feat)
        
        #4. domain prediction on f
        if isinstance(f_post, list):
            pred_fc_domain_latent = self.domain_classifier_latent(f_post[0]) 
        else:
            pred_fc_domain_latent = self.domain_classifier_latent(f_post) 
        pred_domain_all.append(pred_fc_domain_latent)
        
        return f_mean, f_logvar, f_post, z_mean_post, z_logvar_post, z_post, z_mean_prior, z_logvar_prior, z_prior, recon_x, pred_domain_all, pred_video_class
    

def name2seq(file_name):
    images = []

    for frame in range(8):
        frame_name = '%d' % (frame)
        image_filename = file_name + frame_name + '.png'
        image = imageio.imread(image_filename)
        images.append(image[:, :, :3])

    images = np.asarray(images, dtype='f') / 256.0
    images = images.transpose((0, 3, 1, 2))
    print(images.shape)
    images = torch.Tensor(images).unsqueeze(dim=0)
    return images
    
    
def display_gif(file_name, save_name):
    images = []

    for frame in range(8):
        frame_name = '%d' % (frame)
        image_filename = file_name + frame_name + '.png'
        images.append(imageio.imread(image_filename))

    gif_filename = 'avatar_source.gif'
    return imageio.mimsave(gif_filename, images)


def display_gif_pad(file_name, save_name):
    images = []

    for frame in range(8):
        frame_name = '%d' % (frame)
        image_filename = file_name + frame_name + '.png'
        image = imageio.imread(image_filename)
        image = image[:, :, :3]
        image_pad = cv2.copyMakeBorder(image, 0, 0, 125, 125, cv2.BORDER_CONSTANT, value=0)
        images.append(image_pad)

    return imageio.mimsave(save_name, images)
    

def display_image(file_name):

    image_filename = file_name + '0' + '.png'
    print(image_filename)
    image = imageio.imread(image_filename)
    imageio.imwrite('image.png', image)
    

def run(domain_source, action_source, hair_source, top_source, bottom_source, domain_target, action_target, hair_target, top_target, bottom_target):

    # == Source Avatar ==
    # body
    body_source = '0'
    
    # hair
    if hair_source == "green": hair_source = '0'
    elif hair_source == "yellow": hair_source = '2'
    elif hair_source == "rose": hair_source = '4'
    elif hair_source == "red": hair_source = '7'
    elif hair_source == "wine": hair_source = '8'
    
    # top
    if top_source == "brown": top_source = '0'
    elif top_source == "blue": top_source = '1'
    elif top_source == "white": top_source = '2'
    
    # bottom
    if bottom_source == "white": bottom_source = '0'
    elif bottom_source == "golden": bottom_source = '1'
    elif bottom_source == "red": bottom_source = '2'
    elif bottom_source == "silver": bottom_source = '3'
    
    file_name_source = './Sprite/frames/domain_1/' + action_source + '/'
    file_name_source = file_name_source + 'front' + '_' + str(body_source) + str(bottom_source) + str(top_source) + str(hair_source) + '_'
    
    gif = display_gif_pad(file_name_source, 'avatar_source.gif')
    
    # == Target Avatar ==
    # body
    body_target = '1'
    
    # hair
    if hair_target == "violet": hair_target = '1'
    elif hair_target == "silver": hair_target = '3'
    elif hair_target == "purple": hair_target = '5'
    elif hair_target == "grey": hair_target = '6'
    elif hair_target == "golden": hair_target = '9'
    
    # top
    if top_target == "grey": top_target = '3'
    elif top_target == "khaki": top_target = '4'
    elif top_target == "linen": top_target = '5'
    elif top_target == "ocre": top_target = '6'
    
    # bottom
    if bottom_target == "denim": bottom_target = '4'
    elif bottom_target == "olive": bottom_target = '5'
    elif bottom_target == "brown": bottom_target = '6'
    
    file_name_target = './Sprite/frames/domain_2/' + action_target + '/'
    file_name_target = file_name_target + 'front' + '_' + str(body_target) + str(bottom_target) + str(top_target) + str(hair_target) + '_'
    
    gif_target = display_gif_pad(file_name_target, 'avatar_target.gif')
    
    
    # == Load Model ==
    model = TransferVAE_Video(opt)
    model.load_state_dict(torch.load('TransferVAE.pth.tar', map_location=torch.device('cpu'))['state_dict'])
    model.eval()
    
    return 'demo.gif'


gr.Interface(
    run,
    inputs=[
        gr.Textbox(value="Source Avatar - Human", interactive=False),
        gr.Radio(choices=["slash", "spellcard", "walk"], value="slash"),
        gr.Radio(choices=["green", "yellow", "rose", "red", "wine"], value="green"),
        gr.Radio(choices=["brown", "blue", "white"], value="brown"),
        gr.Radio(choices=["white", "golden", "red", "silver"], value="white"),
        gr.Textbox(value="Target Avatar - Alien", interactive=False),
        gr.Radio(choices=["slash", "spellcard", "walk"], value="walk"),
        gr.Radio(choices=["violet", "silver", "purple", "grey", "golden"], value="golden"),
        gr.Radio(choices=["grey", "khaki", "linen", "ocre"], value="ocre"),
        gr.Radio(choices=["denim", "olive", "brown"], value="brown"),
    ],
    outputs=[
        gr.components.Image(type="file", label="Domain Disentanglement"),
    ],
    live=True,
    title="TransferVAE for Unsupervised Video Domain Adaptation",
).launch()