app.py

import gradio as gr

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


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()
        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))


class TransferVAE_Video(nn.Module):

    def __init__(self):
        super(TransferVAE_Video, self).__init__()
        self.f_dim = 512
        self.z_dim = 512
        self.fc_dim = 1024
        self.channels = 3
        self.frames = 8
        self.batch_size = 128
        self.dropout_rate = 0.5
        self.num_class = 15
        self.prior_sample = 'random'
        
        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

        self.relu = nn.LeakyReLU(0.1)
        self.dropout_f = nn.Dropout(p=self.dropout_rate)
        self.dropout_v = nn.Dropout(p=self.dropout_rate)
     
        self.hidden_dim = 512
        self.f_rnn_layers = 1

        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)

        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)
        self.z_mean = nn.Linear(self.hidden_dim, self.z_dim)
        self.z_logvar = nn.Linear(self.hidden_dim, self.z_dim)
        
        self.fc_feature_domain_frame = nn.Linear(self.z_dim, self.z_dim)
        self.fc_classifier_domain_frame = nn.Linear(self.z_dim, 2)
        
        self.num_bottleneck = 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
            
        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)
        
        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]
        
        self.pred_classifier_video = nn.Linear(self.feat_aggregated_dim, self.num_class)
        self.fc_feature_domain_latent = nn.Linear(self.f_dim, self.f_dim)
        self.fc_classifier_doamin_latent = nn.Linear(self.f_dim, 2)
    
          
    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)

        lstm_out, _ = self.z_lstm(conv_x)

        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)

        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)
                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
        return f_mean, f_logvar, f_post, z_mean, z_logvar, z_post

    
    def decoder_frame(self,zf):
        recon_x = self.decoder(zf)
        return recon_x


    def encoder_frame(self, x):
        x_shape = x.shape
        x = x.view(-1, x_shape[-3], x_shape[-2], x_shape[-1])
        x_embed = self.encoder(x)[0]
        return x_embed.view(x_shape[0], x_shape[1], -1)
    

    def reparameterize(self, mean, logvar, random_sampling=True):
        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 forward(self, x, beta):
        _, _, f_post, _, _, z_post = self.encode_and_sample_post(x)
        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)
        recon_x = self.decoder_frame(zf)
        return f_post, z_post, recon_x
    

    

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))
    images = torch.Tensor(images).unsqueeze(dim=0)
    return images
    

def concat(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)

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

def MyPlot(frame_id, src_orig, tar_orig, src_recon, tar_recon, src_Zt, tar_Zt, src_Zf_tar_Zt, tar_Zf_src_Zt):
    
    fig, axs = plt.subplots(2, 4, sharex=True, sharey=True, figsize=(10, 5))
    
    axs[0, 0].imshow(src_orig)
    axs[0, 0].set_title("\n\n\nOriginal\nInput")
    axs[0, 0].axis('off')

    axs[1, 0].imshow(tar_orig)
    axs[1, 0].axis('off')

    axs[0, 1].imshow(src_recon)
    axs[0, 1].set_title("\n\n\nReconstructed\nOutput")
    axs[0, 1].axis('off')

    axs[1, 1].imshow(tar_recon)
    axs[1, 1].axis('off')

    axs[0, 2].imshow(src_Zt)
    axs[0, 2].set_title("\n\n\nOutput\nw/ Zt")
    axs[0, 2].axis('off')

    axs[1, 2].imshow(tar_Zt)
    axs[1, 2].axis('off')
    
    axs[0, 3].imshow(tar_Zf_src_Zt)
    axs[0, 3].set_title("\n\n\nExchange\nZt and Zf")
    axs[0, 3].axis('off')

    axs[1, 3].imshow(src_Zf_tar_Zt)
    axs[1, 3].axis('off')
    
    plt.subplots_adjust(hspace=0.06, wspace=0.05)
    
    save_name = 'MyPlot_{}.png'.format(frame_id)
    
    plt.savefig(save_name, dpi=200, format='png', bbox_inches='tight', pad_inches=0.0)
    

# == Load Model ==
model = TransferVAE_Video()
model.load_state_dict(torch.load('TransferVAE.pth.tar', map_location=torch.device('cpu'))['state_dict'])
model.eval()
    
  
def run(action_source, hair_source, top_source, bottom_source, 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) + '_'
    
    
    # == 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) + '_'
    
    
    # == Load Input ==
    images_source = name2seq(file_name_source)
    images_target = name2seq(file_name_target)
    x = torch.cat((images_source, images_target), dim=0)
    
    
    # == Forward ==
    with torch.no_grad():
        f_post, z_post, recon_x = model(x, [0]*3)
     
    src_orig_sample = x[0, :, :, :, :]
    src_recon_sample = recon_x[0, :, :, :, :]
    src_f_post = f_post[0, :].unsqueeze(0)
    src_z_post = z_post[0, :, :].unsqueeze(0)

    tar_orig_sample = x[1, :, :, :, :]
    tar_recon_sample = recon_x[1, :, :, :, :]
    tar_f_post = f_post[1, :].unsqueeze(0)
    tar_z_post = z_post[1, :, :].unsqueeze(0)   
    
    
    # == Visualize ==
    for frame in range(8):
    
        # original frame
        src_orig = src_orig_sample[frame, :, :, :].detach().numpy().transpose((1, 2, 0))
        tar_orig = tar_orig_sample[frame, :, :, :].detach().numpy().transpose((1, 2, 0))
    
        # reconstructed frame
        src_recon = src_recon_sample[frame, :, :, :].detach().numpy().transpose((1, 2, 0))
        tar_recon = tar_recon_sample[frame, :, :, :].detach().numpy().transpose((1, 2, 0))
    
        # Zt
        f_expand_src = 0 * src_f_post.unsqueeze(1).expand(-1, 8, 512)
        zf_src = torch.cat((src_z_post, f_expand_src), dim=2)
        recon_x_src = model.decoder_frame(zf_src)
        src_Zt = recon_x_src.squeeze()[frame, :, :, :].detach().numpy().transpose((1, 2, 0))
    
        f_expand_tar = 0 * tar_f_post.unsqueeze(1).expand(-1, 8, 512)
        zf_tar = torch.cat((tar_z_post, f_expand_tar), dim=2)
        recon_x_tar = model.decoder_frame(zf_tar)
        tar_Zt = recon_x_tar.squeeze()[frame, :, :, :].detach().numpy().transpose((1, 2, 0))
    
        # Zf_Zt
        f_expand_src = src_f_post.unsqueeze(1).expand(-1, 8, 512)
        zf_srcZf_tarZt = torch.cat((tar_z_post, f_expand_src), dim=2)
        recon_x_srcZf_tarZt = model.decoder_frame(zf_srcZf_tarZt)
        src_Zf_tar_Zt = recon_x_srcZf_tarZt.squeeze()[frame, :, :, :].detach().numpy().transpose((1, 2, 0))
    
        f_expand_tar = tar_f_post.unsqueeze(1).expand(-1, 8, 512)
        zf_tarZf_srcZt = torch.cat((src_z_post, f_expand_tar), dim=2)
        recon_x_tarZf_srcZt = model.decoder_frame(zf_tarZf_srcZt)
        tar_Zf_src_Zt = recon_x_tarZf_srcZt.squeeze()[frame, :, :, :].detach().numpy().transpose((1, 2, 0))

        MyPlot(frame, src_orig, tar_orig, src_recon, tar_recon, src_Zt, tar_Zt, src_Zf_tar_Zt, tar_Zf_src_Zt)
        
    a = concat('MyPlot_')
    
    return 'demo.gif'


gr.Interface(
    fn=run,
    inputs=[
        gr.Markdown(
            """
            Source Avatar - Human 👦🏻
            """
        ),
        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.Markdown(
            """
            Target Avatar - Alien 👽
            """
        ),
        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,
    cache_examples=True,
    title="TransferVAE for Unsupervised Video Domain Adaptation",
).launch()