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 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 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(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) + '_' # == 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( run, inputs=[ gr.Textbox(value="Source Avatar - Human", show_label=False, 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", show_label=False, 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()