From 0cc93e3a59ea59cd63e3610bb6a579f07c2a6c87 Mon Sep 17 00:00:00 2001 From: hack Date: Tue, 22 Dec 2020 13:17:19 +0530 Subject: [PATCH 01/17] update requirements.txt --- requirements.txt | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/requirements.txt b/requirements.txt index 64f3408..1a42320 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,11 +1,11 @@ -ffmpeg-python -opencv-python -face_alignment -scikit-learn -pydub -pynormalize -soundfile -librosa -pysptk -pyworld -resemblyzer \ No newline at end of file +ffmpeg-python==0.2.0 +opencv-python==4.4.0.46 +face_alignment==1.1.1 +scikit-learn==0.23.2 +pydub==0.24.1 +pynormalize==0.1.4 +soundfile==0.10.3.post1 +librosa==0.8.0 +pysptk==0.1.18 +pyworld==0.2.12 +resemblyzer==0.1.1.dev0 From c292066e16b24ac2c32f7d3d320566d00b5d6f09 Mon Sep 17 00:00:00 2001 From: hack Date: Tue, 22 Dec 2020 19:18:16 +0530 Subject: [PATCH 02/17] add test.py --- src/approaches/train_audio2landmark.py | 151 +++++++++++++--------- src/approaches/train_image_translation.py | 150 ++++++++++++--------- src/models/model_image_translation.py | 134 ++++++++++++------- testg.py | 56 ++++++++ 4 files changed, 327 insertions(+), 164 deletions(-) create mode 100644 testg.py diff --git a/src/approaches/train_audio2landmark.py b/src/approaches/train_audio2landmark.py index 57fc602..dd2cecf 100644 --- a/src/approaches/train_audio2landmark.py +++ b/src/approaches/train_audio2landmark.py @@ -30,17 +30,19 @@ def __init__(self, opt_parser, jpg_shape=None): # Step 1 : load opt_parser self.opt_parser = opt_parser - self.std_face_id = np.loadtxt('src/dataset/utils/STD_FACE_LANDMARKS.txt') + self.std_face_id = np.loadtxt( + 'src/dataset/utils/STD_FACE_LANDMARKS.txt') if(jpg_shape is not None): self.std_face_id = jpg_shape self.std_face_id = self.std_face_id.reshape(1, 204) - self.std_face_id = torch.tensor(self.std_face_id, requires_grad=False, dtype=torch.float).to(device) + self.std_face_id = torch.tensor( + self.std_face_id, requires_grad=False, dtype=torch.float).to(device) self.eval_data = Audio2landmark_Dataset(dump_dir='examples/dump', dump_name='random', status='val', - num_window_frames=18, - num_window_step=1) + num_window_frames=18, + num_window_step=1) self.eval_dataloader = torch.utils.data.DataLoader(self.eval_data, batch_size=1, shuffle=False, num_workers=0, collate_fn=self.eval_data.my_collate_in_segments) @@ -48,34 +50,39 @@ def __init__(self, opt_parser, jpg_shape=None): # Step 3: Load model self.G = Audio2landmark_pos(drop_out=0.5, - spk_emb_enc_size=128, - c_enc_hidden_size=256, - transformer_d_model=32, N=2, heads=2, - z_size=128, audio_dim=256) - print('G: Running on {}, total num params = {:.2f}M'.format(device, get_n_params(self.G)/1.0e6)) + spk_emb_enc_size=128, + c_enc_hidden_size=256, + transformer_d_model=32, N=2, heads=2, + z_size=128, audio_dim=256) + print('G: Running on {}, total num params = {:.2f}M'.format( + device, get_n_params(self.G)/1.0e6)) model_dict = self.G.state_dict() ckpt = torch.load(opt_parser.load_a2l_G_name) - pretrained_dict = {k: v for k, v in ckpt['G'].items() if k.split('.')[0] not in ['comb_mlp']} + pretrained_dict = {k: v for k, v in ckpt['G'].items() if k.split('.')[ + 0] not in ['comb_mlp']} model_dict.update(pretrained_dict) self.G.load_state_dict(model_dict) - print('======== LOAD PRETRAINED FACE ID MODEL {} ========='.format(opt_parser.load_a2l_G_name)) + print('======== LOAD PRETRAINED FACE ID MODEL {} ========='.format( + opt_parser.load_a2l_G_name)) self.G.to(device) ''' baseline model ''' self.C = Audio2landmark_content(num_window_frames=18, - in_size=80, use_prior_net=True, - bidirectional=False, drop_out=0.5) + in_size=80, use_prior_net=True, + bidirectional=False, drop_out=0.5) ckpt = torch.load(opt_parser.load_a2l_C_name) self.C.load_state_dict(ckpt['model_g_face_id']) # self.C.load_state_dict(ckpt['C']) - print('======== LOAD PRETRAINED FACE ID MODEL {} ========='.format(opt_parser.load_a2l_C_name)) + print('======== LOAD PRETRAINED FACE ID MODEL {} ========='.format( + opt_parser.load_a2l_C_name)) self.C.to(device) self.t_shape_idx = (27, 28, 29, 30, 33, 36, 39, 42, 45) - self.anchor_t_shape = np.loadtxt('src/dataset/utils/STD_FACE_LANDMARKS.txt') + self.anchor_t_shape = np.loadtxt( + 'src/dataset/utils/STD_FACE_LANDMARKS.txt') self.anchor_t_shape = self.anchor_t_shape[self.t_shape_idx, :] with open(os.path.join('examples', 'dump', 'emb.pickle'), 'rb') as fp: @@ -95,28 +102,37 @@ def __train_face_and_pos__(self, fls, aus, embs, face_id, smooth_win=31, close_m face_id = face_id.requires_grad_(False) baseline_face_id = face_id.detach() - z = torch.tensor(torch.zeros(aus.shape[0], 128), requires_grad=False, dtype=torch.float).to(device) - fl_dis_pred, _, spk_encode = self.G(aus, embs * 3.0, face_id, fls_without_traj, z, add_z_spk=False) + z = torch.tensor(torch.zeros( + aus.shape[0], 128), requires_grad=False, dtype=torch.float).to(device) + fl_dis_pred, _, spk_encode = self.G( + aus, embs * 3.0, face_id, fls_without_traj, z, add_z_spk=False) # ADD CONTENT from scipy.signal import savgol_filter - smooth_length = int(min(fl_dis_pred.shape[0]-1, smooth_win) // 2 * 2 + 1) - fl_dis_pred = savgol_filter(fl_dis_pred.cpu().numpy(), smooth_length, 3, axis=0) + smooth_length = int( + min(fl_dis_pred.shape[0]-1, smooth_win) // 2 * 2 + 1) + fl_dis_pred = savgol_filter( + fl_dis_pred.cpu().numpy(), smooth_length, 3, axis=0) # ''' ================ close pose-branch mouth ================== ''' fl_dis_pred = fl_dis_pred.reshape((-1, 68, 3)) index1 = list(range(60-1, 55-1, -1)) index2 = list(range(68-1, 65-1, -1)) mean_out = 0.5 * fl_dis_pred[:, 49:54] + 0.5 * fl_dis_pred[:, index1] - fl_dis_pred[:, 49:54] = mean_out * close_mouth_ratio + fl_dis_pred[:, 49:54] * (1 - close_mouth_ratio) - fl_dis_pred[:, index1] = mean_out * close_mouth_ratio + fl_dis_pred[:, index1] * (1 - close_mouth_ratio) + fl_dis_pred[:, 49:54] = mean_out * close_mouth_ratio + \ + fl_dis_pred[:, 49:54] * (1 - close_mouth_ratio) + fl_dis_pred[:, index1] = mean_out * close_mouth_ratio + \ + fl_dis_pred[:, index1] * (1 - close_mouth_ratio) mean_in = 0.5 * (fl_dis_pred[:, 61:64] + fl_dis_pred[:, index2]) - fl_dis_pred[:, 61:64] = mean_in * close_mouth_ratio + fl_dis_pred[:, 61:64] * (1 - close_mouth_ratio) - fl_dis_pred[:, index2] = mean_in * close_mouth_ratio + fl_dis_pred[:, index2] * (1 - close_mouth_ratio) + fl_dis_pred[:, 61:64] = mean_in * close_mouth_ratio + \ + fl_dis_pred[:, 61:64] * (1 - close_mouth_ratio) + fl_dis_pred[:, index2] = mean_in * close_mouth_ratio + \ + fl_dis_pred[:, index2] * (1 - close_mouth_ratio) fl_dis_pred = fl_dis_pred.reshape(-1, 204) ''' ============================================================= ''' - fl_dis_pred = torch.tensor(fl_dis_pred).to(device) * self.opt_parser.amp_pos + fl_dis_pred = torch.tensor(fl_dis_pred).to( + device) * self.opt_parser.amp_pos residual_face_id = baseline_face_id @@ -128,11 +144,13 @@ def __train_face_and_pos__(self, fls, aus, embs, face_id, smooth_win=31, close_m return fl_dis_pred, face_id[0:1, :] def __calib_baseline_pred_fls_old_(self, baseline_pred_fls, residual_face_id, aus): - mean_face_id = torch.mean(baseline_pred_fls.detach(), dim=0, keepdim=True) + mean_face_id = torch.mean( + baseline_pred_fls.detach(), dim=0, keepdim=True) residual_face_id -= mean_face_id.view(1, 204) * 1. baseline_pred_fls, _ = self.C(aus, residual_face_id) baseline_pred_fls[:, 48 * 3::3] *= self.opt_parser.amp_lip_x # mouth x - baseline_pred_fls[:, 48 * 3 + 1::3] *= self.opt_parser.amp_lip_y # mouth y + baseline_pred_fls[:, 48 * 3 + + 1::3] *= self.opt_parser.amp_lip_y # mouth y return baseline_pred_fls def __calib_baseline_pred_fls__(self, baseline_pred_fls, ratio=0.5): @@ -142,9 +160,11 @@ def __calib_baseline_pred_fls__(self, baseline_pred_fls, ratio=0.5): min_k_idx = np.argpartition(np_fl_dis_pred[:, calib_i], K) m = np.mean(np_fl_dis_pred[min_k_idx[:K], calib_i]) np_fl_dis_pred[:, calib_i] = np_fl_dis_pred[:, calib_i] - m - baseline_pred_fls = torch.tensor(np_fl_dis_pred, requires_grad=False).to(device) + baseline_pred_fls = torch.tensor( + np_fl_dis_pred, requires_grad=False).to(device) baseline_pred_fls[:, 48 * 3::3] *= self.opt_parser.amp_lip_x # mouth x - baseline_pred_fls[:, 48 * 3 + 1::3] *= self.opt_parser.amp_lip_y # mouth y + baseline_pred_fls[:, 48 * 3 + + 1::3] *= self.opt_parser.amp_lip_y # mouth y return baseline_pred_fls def __train_pass__(self, au_emb=None, centerize_face=False, no_y_rotation=False, vis_fls=False): @@ -166,7 +186,7 @@ def __train_pass__(self, au_emb=None, centerize_face=False, no_y_rotation=False, keys = self.opt_parser.reuse_train_emb_list if(len(keys) == 0): keys = ['audio_embed'] - for key in keys: # ['45hn7-LXDX8']: #['sxCbrYjBsGA']:# + for key in keys: # ['45hn7-LXDX8']: #['sxCbrYjBsGA']:# # load saved emb if(au_emb is None): emb_val = self.test_embs[key] @@ -174,8 +194,10 @@ def __train_pass__(self, au_emb=None, centerize_face=False, no_y_rotation=False, emb_val = au_emb[i] inputs_emb = np.tile(emb_val, (inputs_emb.shape[0], 1)) - inputs_emb = torch.tensor(inputs_emb, dtype=torch.float, requires_grad=False) - inputs_fl, inputs_au, inputs_emb = inputs_fl.to(device), inputs_au.to(device), inputs_emb.to(device) + inputs_emb = torch.tensor( + inputs_emb, dtype=torch.float, requires_grad=False) + inputs_fl, inputs_au, inputs_emb = inputs_fl.to( + device), inputs_au.to(device), inputs_emb.to(device) std_fls_list, fls_pred_face_id_list, fls_pred_pos_list = [], [], [] seg_bs = 512 @@ -194,17 +216,20 @@ def __train_pass__(self, au_emb=None, centerize_face=False, no_y_rotation=False, fl_dis_pred_pos, input_face_id = \ self.__train_face_and_pos__(inputs_fl_segments, inputs_au_segments, inputs_emb_segments, - input_face_id) + input_face_id) - fl_dis_pred_pos = (fl_dis_pred_pos + input_face_id).data.cpu().numpy() + fl_dis_pred_pos = (fl_dis_pred_pos + + input_face_id).data.cpu().numpy() ''' solve inverse lip ''' - fl_dis_pred_pos = self.__solve_inverse_lip2__(fl_dis_pred_pos) + fl_dis_pred_pos = self.__solve_inverse_lip2__( + fl_dis_pred_pos) fls_pred_pos_list += [fl_dis_pred_pos] fake_fls_np = np.concatenate(fls_pred_pos_list) # revise nose top point - fake_fls_np[:, 27 * 3:28 * 3] = fake_fls_np[:, 28 * 3:29 * 3] * 2 - fake_fls_np[:, 29 * 3:30 * 3] + fake_fls_np[:, 27 * 3:28 * 3] = fake_fls_np[:, + 28 * 3:29 * 3] * 2 - fake_fls_np[:, 29 * 3:30 * 3] # fake_fls_np[:, 48*3+1::3] += 0.1 @@ -216,32 +241,38 @@ def __train_pass__(self, au_emb=None, centerize_face=False, no_y_rotation=False, std_m = np.mean(self.std_face_id.detach().cpu().numpy().reshape((1, 68, 3)), axis=1, keepdims=True) fake_fls_np = fake_fls_np.reshape((-1, 68, 3)) - fake_fls_np = fake_fls_np - np.mean(fake_fls_np, axis=1, keepdims=True) + std_m + fake_fls_np = fake_fls_np - \ + np.mean(fake_fls_np, axis=1, keepdims=True) + std_m fake_fls_np = fake_fls_np.reshape((-1, 68 * 3)) if(no_y_rotation): std = self.std_face_id.detach().cpu().numpy().reshape(68, 3) std_t_shape = std[self.t_shape_idx, :] - fake_fls_np = fake_fls_np.reshape((fake_fls_np.shape[0], 68, 3)) + fake_fls_np = fake_fls_np.reshape( + (fake_fls_np.shape[0], 68, 3)) frame_t_shape = fake_fls_np[:, self.t_shape_idx, :] from util.icp import icp from scipy.spatial.transform import Rotation as R for i in range(frame_t_shape.shape[0]): T, distance, itr = icp(frame_t_shape[i], std_t_shape) - landmarks = np.hstack((frame_t_shape[i], np.ones((9, 1)))) + landmarks = np.hstack( + (frame_t_shape[i], np.ones((9, 1)))) rot_mat = T[:3, :3] r = R.from_dcm(rot_mat).as_euler('xyz') r = [0., r[1], r[2]] r = R.from_euler('xyz', r).as_dcm() # print(frame_t_shape[i, 0], r) - landmarks = np.hstack((fake_fls_np[i] - T[:3, 3:4].T, np.ones((68, 1)))) + landmarks = np.hstack( + (fake_fls_np[i] - T[:3, 3:4].T, np.ones((68, 1)))) T2 = np.hstack((r, T[:3, 3:4])) fake_fls_np[i] = np.dot(T2, landmarks.T).T # print(frame_t_shape[i, 0]) fake_fls_np = fake_fls_np.reshape((-1, 68 * 3)) - filename = 'pred_fls_{}_{}.txt'.format(video_name.split('\\')[-1].split('/')[-1], key) - np.savetxt(os.path.join(self.opt_parser.output_folder, filename), fake_fls_np, fmt='%.6f') + filename = 'pred_fls_{}_{}.txt'.format( + video_name.split('\\')[-1].split('/')[-1], key) + np.savetxt(os.path.join(self.opt_parser.output_folder, + filename), fake_fls_np, fmt='%.6f') # ''' Visualize result in landmarks ''' if(vis_fls): @@ -249,7 +280,6 @@ def __train_pass__(self, au_emb=None, centerize_face=False, no_y_rotation=False, Vis(fls=fake_fls_np, filename=video_name.split('\\')[-1].split('/')[-1], fps=62.5, audio_filenam=os.path.join('examples', video_name.split('\\')[-1].split('/')[-1]+'.wav')) - def __close_face_lip__(self, fl): facelandmark = fl.reshape(-1, 68, 3) from util.geo_math import area_of_polygon @@ -270,26 +300,29 @@ def __solve_inverse_lip2__(self, fl_dis_pred_pos_numpy): init_face = self.std_face_id.detach().cpu().numpy() from util.geo_math import area_of_signed_polygon fls = fl_dis_pred_pos_numpy[j].reshape(68, 3) - area_of_mouth = area_of_signed_polygon(fls[list(range(60, 68)), 0:2]) + area_of_mouth = area_of_signed_polygon( + fls[list(range(60, 68)), 0:2]) if (area_of_mouth < 0): - fl_dis_pred_pos_numpy[j, 65 * 3:66 * 3] = 0.5 *(fl_dis_pred_pos_numpy[j, 63 * 3:64 * 3] + fl_dis_pred_pos_numpy[j, 65 * 3:66 * 3]) - fl_dis_pred_pos_numpy[j, 63 * 3:64 * 3] = fl_dis_pred_pos_numpy[j, 65 * 3:66 * 3] - fl_dis_pred_pos_numpy[j, 66 * 3:67 * 3] = 0.5 *(fl_dis_pred_pos_numpy[j, 62 * 3:63 * 3] + fl_dis_pred_pos_numpy[j, 66 * 3:67 * 3]) - fl_dis_pred_pos_numpy[j, 62 * 3:63 * 3] = fl_dis_pred_pos_numpy[j, 66 * 3:67 * 3] - fl_dis_pred_pos_numpy[j, 67 * 3:68 * 3] = 0.5 *(fl_dis_pred_pos_numpy[j, 61 * 3:62 * 3] + fl_dis_pred_pos_numpy[j, 67 * 3:68 * 3]) - fl_dis_pred_pos_numpy[j, 61 * 3:62 * 3] = fl_dis_pred_pos_numpy[j, 67 * 3:68 * 3] + fl_dis_pred_pos_numpy[j, 65 * 3:66 * 3] = 0.5 * ( + fl_dis_pred_pos_numpy[j, 63 * 3:64 * 3] + fl_dis_pred_pos_numpy[j, 65 * 3:66 * 3]) + fl_dis_pred_pos_numpy[j, 63 * 3:64 * + 3] = fl_dis_pred_pos_numpy[j, 65 * 3:66 * 3] + fl_dis_pred_pos_numpy[j, 66 * 3:67 * 3] = 0.5 * ( + fl_dis_pred_pos_numpy[j, 62 * 3:63 * 3] + fl_dis_pred_pos_numpy[j, 66 * 3:67 * 3]) + fl_dis_pred_pos_numpy[j, 62 * 3:63 * + 3] = fl_dis_pred_pos_numpy[j, 66 * 3:67 * 3] + fl_dis_pred_pos_numpy[j, 67 * 3:68 * 3] = 0.5 * ( + fl_dis_pred_pos_numpy[j, 61 * 3:62 * 3] + fl_dis_pred_pos_numpy[j, 67 * 3:68 * 3]) + fl_dis_pred_pos_numpy[j, 61 * 3:62 * + 3] = fl_dis_pred_pos_numpy[j, 67 * 3:68 * 3] p = max([j-1, 0]) fl_dis_pred_pos_numpy[j, 55 * 3+1:59 * 3+1:3] = fl_dis_pred_pos_numpy[j, 64 * 3+1:68 * 3+1:3] \ - + fl_dis_pred_pos_numpy[p, 55 * 3+1:59 * 3+1:3] \ - - fl_dis_pred_pos_numpy[p, 64 * 3+1:68 * 3+1:3] + + fl_dis_pred_pos_numpy[p, 55 * 3+1:59 * 3+1:3] \ + - fl_dis_pred_pos_numpy[p, 64 * 3+1:68 * 3+1:3] fl_dis_pred_pos_numpy[j, 59 * 3+1:60 * 3+1:3] = fl_dis_pred_pos_numpy[j, 60 * 3+1:61 * 3+1:3] \ - + fl_dis_pred_pos_numpy[p, 59 * 3+1:60 * 3+1:3] \ - - fl_dis_pred_pos_numpy[p, 60 * 3+1:61 * 3+1:3] + + fl_dis_pred_pos_numpy[p, 59 * 3+1:60 * 3+1:3] \ + - fl_dis_pred_pos_numpy[p, 60 * 3+1:61 * 3+1:3] fl_dis_pred_pos_numpy[j, 49 * 3+1:54 * 3+1:3] = fl_dis_pred_pos_numpy[j, 60 * 3+1:65 * 3+1:3] \ - + fl_dis_pred_pos_numpy[p, 49 * 3+1:54 * 3+1:3] \ - - fl_dis_pred_pos_numpy[p, 60 * 3+1:65 * 3+1:3] + + fl_dis_pred_pos_numpy[p, 49 * 3+1:54 * 3+1:3] \ + - fl_dis_pred_pos_numpy[p, 60 * 3+1:65 * 3+1:3] return fl_dis_pred_pos_numpy - - - - diff --git a/src/approaches/train_image_translation.py b/src/approaches/train_image_translation.py index dc79d19..461676c 100644 --- a/src/approaches/train_image_translation.py +++ b/src/approaches/train_image_translation.py @@ -15,7 +15,8 @@ import time import numpy as np import cv2 -import os, glob +import os +import glob from src.dataset.image_translation.image_translation_dataset import vis_landmark_on_img, vis_landmark_on_img98, vis_landmark_on_img74 @@ -26,6 +27,7 @@ device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + class Image_translation_block(): def __init__(self, opt_parser, single_test=False): @@ -37,9 +39,11 @@ def __init__(self, opt_parser, single_test=False): # model if(opt_parser.add_audio_in): - self.G = ResUnetGenerator(input_nc=7, output_nc=3, num_downs=6, use_dropout=False) + self.G = ResUnetGenerator( + input_nc=7, output_nc=3, num_downs=6, use_dropout=False) else: - self.G = ResUnetGenerator(input_nc=6, output_nc=3, num_downs=6, use_dropout=False) + self.G = ResUnetGenerator( + input_nc=6, output_nc=3, num_downs=6, use_dropout=False) if (opt_parser.load_G_name != ''): ckpt = torch.load(opt_parser.load_G_name) @@ -68,12 +72,13 @@ def __init__(self, opt_parser, single_test=False): image_translation_dataset else: from src.dataset.image_translation.image_translation_dataset import image_translation_raw98_dataset as \ - image_translation_dataset + image_translation_dataset else: from src.dataset.image_translation.image_translation_dataset import image_translation_preprocessed98_dataset as \ image_translation_dataset - self.dataset = image_translation_dataset(num_frames=opt_parser.num_frames) + self.dataset = image_translation_dataset( + num_frames=opt_parser.num_frames) self.dataloader = torch.utils.data.DataLoader(self.dataset, batch_size=opt_parser.batch_size, shuffle=True, @@ -83,16 +88,19 @@ def __init__(self, opt_parser, single_test=False): self.criterionL1 = nn.L1Loss() self.criterionVGG = VGGLoss() if torch.cuda.device_count() > 1: - print("Let's use", torch.cuda.device_count(), "GPUs in VGG model!") + print("Let's use", torch.cuda.device_count(), + "GPUs in VGG model!") self.criterionVGG = nn.DataParallel(self.criterionVGG) self.criterionVGG.to(device) # optimizer - self.optimizer = torch.optim.Adam(self.G.parameters(), lr=opt_parser.lr, betas=(0.5, 0.999)) + self.optimizer = torch.optim.Adam( + self.G.parameters(), lr=opt_parser.lr, betas=(0.5, 0.999)) # writer if(opt_parser.write): - self.writer = SummaryWriter(log_dir=os.path.join(opt_parser.log_dir, opt_parser.name)) + self.writer = SummaryWriter(log_dir=os.path.join( + opt_parser.log_dir, opt_parser.name)) self.count = 0 # =========================================================== @@ -104,8 +112,10 @@ def __init__(self, opt_parser, single_test=False): END_RELU = False NUM_LANDMARKS = 98 - self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") - model_ft = models.FAN(HG_BLOCKS, END_RELU, GRAY_SCALE, NUM_LANDMARKS) + self.device = torch.device( + "cuda:0" if torch.cuda.is_available() else "cpu") + model_ft = models.FAN(HG_BLOCKS, END_RELU, + GRAY_SCALE, NUM_LANDMARKS) checkpoint = torch.load(PRETRAINED_WEIGHTS) if 'state_dict' not in checkpoint: @@ -113,14 +123,15 @@ def __init__(self, opt_parser, single_test=False): else: pretrained_weights = checkpoint['state_dict'] model_weights = model_ft.state_dict() - pretrained_weights = {k: v for k, v in pretrained_weights.items() \ + pretrained_weights = {k: v for k, v in pretrained_weights.items() if k in model_weights} model_weights.update(pretrained_weights) model_ft.load_state_dict(model_weights) print('Load AWing model sucessfully') if torch.cuda.device_count() > 1: print("Let's use", torch.cuda.device_count(), "GPUs for AWing!") - self.fa_model = nn.DataParallel(model_ft).to(self.device).eval() + self.fa_model = nn.DataParallel( + model_ft).to(self.device).eval() else: self.fa_model = model_ft.to(self.device).eval() @@ -154,7 +165,8 @@ def __train_pass__(self, epoch, is_training=True): if(self.opt_parser.comb_fan_awing): image_in, image_out, fan_pred_landmarks = batch - fan_pred_landmarks = fan_pred_landmarks.reshape(-1, 68, 3).detach().cpu().numpy() + fan_pred_landmarks = fan_pred_landmarks.reshape( + -1, 68, 3).detach().cpu().numpy() elif(self.opt_parser.add_audio_in): image_in, image_out, audio_in = batch audio_in = audio_in.reshape(-1, 1, 256, 256).to(device) @@ -164,7 +176,8 @@ def __train_pass__(self, epoch, is_training=True): with torch.no_grad(): # # online landmark (AwingNet) image_in, image_out = \ - image_in.reshape(-1, 3, 256, 256).to(device), image_out.reshape(-1, 3, 256, 256).to(device) + image_in.reshape(-1, 3, 256, 256).to( + device), image_out.reshape(-1, 3, 256, 256).to(device) inputs = image_out outputs, boundary_channels = self.fa_model(inputs) pred_heatmap = outputs[-1][:, :-1, :, :].detach().cpu() @@ -175,7 +188,8 @@ def __train_pass__(self, epoch, is_training=True): if(self.opt_parser.comb_fan_awing): fl_jaw_eyebrow = fan_pred_landmarks[:, 0:27, 0:2] fl_rest = pred_landmarks[:, 51:, :] - pred_landmarks = np.concatenate([fl_jaw_eyebrow, fl_rest], axis=1).astype(np.int) + pred_landmarks = np.concatenate( + [fl_jaw_eyebrow, fl_rest], axis=1).astype(np.int) # draw landmark on while bg img_fls = [] @@ -187,7 +201,8 @@ def __train_pass__(self, epoch, is_training=True): img_fl = vis_landmark_on_img98(img_fl, pred_fl) # 98x2 img_fls.append(img_fl.transpose((2, 0, 1))) img_fls = np.stack(img_fls, axis=0).astype(np.float32) / 255.0 - image_fls_in = torch.tensor(img_fls, requires_grad=False).to(device) + image_fls_in = torch.tensor( + img_fls, requires_grad=False).to(device) if(self.opt_parser.add_audio_in): # print(image_fls_in.shape, image_in.shape, audio_in.shape) @@ -203,11 +218,12 @@ def __train_pass__(self, epoch, is_training=True): g_out = torch.tanh(g_out) loss_l1 = self.criterionL1(g_out, image_out) - loss_vgg, loss_style = self.criterionVGG(g_out, image_out, style=True) + loss_vgg, loss_style = self.criterionVGG( + g_out, image_out, style=True) loss_vgg, loss_style = torch.mean(loss_vgg), torch.mean(loss_style) - loss = loss_l1 + loss_vgg + loss_style + loss = loss_l1 + loss_vgg + loss_style if(is_training): self.optimizer.zero_grad() loss.backward() @@ -215,9 +231,12 @@ def __train_pass__(self, epoch, is_training=True): # log if(self.opt_parser.write): - self.writer.add_scalar('loss', loss.cpu().detach().numpy(), self.count) - self.writer.add_scalar('loss_l1', loss_l1.cpu().detach().numpy(), self.count) - self.writer.add_scalar('loss_vgg', loss_vgg.cpu().detach().numpy(), self.count) + self.writer.add_scalar( + 'loss', loss.cpu().detach().numpy(), self.count) + self.writer.add_scalar( + 'loss_l1', loss_l1.cpu().detach().numpy(), self.count) + self.writer.add_scalar( + 'loss_vgg', loss_vgg.cpu().detach().numpy(), self.count) self.count += 1 # save image to track training process @@ -228,10 +247,12 @@ def __train_pass__(self, epoch, is_training=True): g_out[0].cpu().detach().numpy().transpose((1, 2, 0))], axis=1) vis = np.concatenate([vis_in, vis_out], axis=0) try: - os.makedirs(os.path.join(self.opt_parser.jpg_dir, self.opt_parser.name)) + os.makedirs(os.path.join( + self.opt_parser.jpg_dir, self.opt_parser.name)) except: pass - cv2.imwrite(os.path.join(self.opt_parser.jpg_dir, self.opt_parser.name, 'e{:03d}_b{:04d}.jpg'.format(epoch, i)), vis * 255.0) + cv2.imwrite(os.path.join(self.opt_parser.jpg_dir, self.opt_parser.name, + 'e{:03d}_b{:04d}.jpg'.format(epoch, i)), vis * 255.0) # save ckpt if (i % self.opt_parser.ckpt_last_freq == 0): self.__save_model__('last', epoch) @@ -242,33 +263,33 @@ def __train_pass__(self, epoch, is_training=True): loss_l1.cpu().detach().numpy(), loss_vgg.cpu().detach().numpy(), loss_style.cpu().detach().numpy(), - time.time() - st_batch)) + time.time() - st_batch)) g_time += time.time() - st_batch - if(self.opt_parser.test_speed): if(i >= 100): break - print('Epoch time usage:', time.time() - st_epoch, 'I/O time usage:', time.time() - st_epoch - g_time, '\n=========================') + print('Epoch time usage:', time.time() - st_epoch, 'I/O time usage:', + time.time() - st_epoch - g_time, '\n=========================') if(self.opt_parser.test_speed): exit(0) if(epoch % self.opt_parser.ckpt_epoch_freq == 0): self.__save_model__('{:02d}'.format(epoch), epoch) - def __save_model__(self, save_type, epoch): try: - os.makedirs(os.path.join(self.opt_parser.ckpt_dir, self.opt_parser.name)) + os.makedirs(os.path.join( + self.opt_parser.ckpt_dir, self.opt_parser.name)) except: pass if (self.opt_parser.write): torch.save({ - 'G': self.G.state_dict(), - 'opt': self.optimizer, - 'epoch': epoch - }, os.path.join(self.opt_parser.ckpt_dir, self.opt_parser.name, 'ckpt_{}.pth'.format(save_type))) + 'G': self.G.state_dict(), + 'opt': self.optimizer, + 'epoch': epoch + }, os.path.join(self.opt_parser.ckpt_dir, self.opt_parser.name, 'ckpt_{}.pth'.format(save_type))) def train(self): for epoch in range(self.opt_parser.nepoch): @@ -283,7 +304,8 @@ def test(self): from src.dataset.image_translation.image_translation_dataset import image_translation_raw98_test_dataset as image_translation_test_dataset else: from src.dataset.image_translation.image_translation_dataset import image_translation_preprocessed98_test_dataset as image_translation_test_dataset - self.dataset = image_translation_test_dataset(num_frames=self.opt_parser.num_frames) + self.dataset = image_translation_test_dataset( + num_frames=self.opt_parser.num_frames) self.dataloader = torch.utils.data.DataLoader(self.dataset, batch_size=1, shuffle=True, @@ -304,7 +326,8 @@ def test(self): # # online landmark (AwingNet) with torch.no_grad(): image_in, image_out = \ - image_in.reshape(-1, 3, 256, 256).to(device), image_out.reshape(-1, 3, 256, 256).to(device) + image_in.reshape(-1, 3, 256, 256).to( + device), image_out.reshape(-1, 3, 256, 256).to(device) pred_landmarks = [] for j in range(image_in.shape[0] // 16): @@ -322,7 +345,8 @@ def test(self): img_fl = vis_landmark_on_img98(img_fl, pred_fl) # 98x2 img_fls.append(img_fl.transpose((2, 0, 1))) img_fls = np.stack(img_fls, axis=0).astype(np.float32) / 255.0 - image_fls_in = torch.tensor(img_fls, requires_grad=False).to(device) + image_fls_in = torch.tensor( + img_fls, requires_grad=False).to(device) if (self.opt_parser.add_audio_in): # print(image_fls_in.shape, image_in.shape, audio_in.shape) @@ -330,7 +354,8 @@ def test(self): image_in[0:image_fls_in.shape[0]], audio_in[0:image_fls_in.shape[0]]], dim=1) else: - image_in = torch.cat([image_fls_in, image_in[0:image_fls_in.shape[0]]], dim=1) + image_in = torch.cat( + [image_fls_in, image_in[0:image_fls_in.shape[0]]], dim=1) # normal 68 test dataset # image_in, image_out = image_in.reshape(-1, 6, 256, 256), image_out.reshape(-1, 3, 256, 256) @@ -340,7 +365,8 @@ def test(self): image_in, image_out = image_in.to(device), image_out.to(device) - writer = cv2.VideoWriter('tmp_{:04d}.mp4'.format(i), cv2.VideoWriter_fourcc(*'mjpg'), 25, (256*4, 256)) + writer = cv2.VideoWriter('tmp_{:04d}.mp4'.format( + i), cv2.VideoWriter_fourcc(*'mjpg'), 25, (256*4, 256)) for j in range(image_in.shape[0] // 16): g_out = self.G(image_in[j*16:j*16+16]) @@ -353,20 +379,24 @@ def test(self): # fls_in = np.swapaxes(image_in[j * 16:j * 16 + 16, 0:3, :, :].cpu().detach().numpy(), 1, 3) g_out = g_out.cpu().detach().numpy().transpose((0, 2, 3, 1)) g_out[g_out < 0] = 0 - ref_out = image_out[j * 16:j * 16 + 16].cpu().detach().numpy().transpose((0, 2, 3, 1)) - ref_in = image_in[j * 16:j * 16 + 16, 3:6, :, :].cpu().detach().numpy().transpose((0, 2, 3, 1)) - fls_in = image_in[j * 16:j * 16 + 16, 0:3, :, :].cpu().detach().numpy().transpose((0, 2, 3, 1)) + ref_out = image_out[j * 16:j * 16 + + 16].cpu().detach().numpy().transpose((0, 2, 3, 1)) + ref_in = image_in[j * 16:j * 16 + 16, 3:6, :, + :].cpu().detach().numpy().transpose((0, 2, 3, 1)) + fls_in = image_in[j * 16:j * 16 + 16, 0:3, :, + :].cpu().detach().numpy().transpose((0, 2, 3, 1)) for k in range(g_out.shape[0]): - frame = np.concatenate((ref_in[k], g_out[k], fls_in[k], ref_out[k]), axis=1) * 255.0 + frame = np.concatenate( + (ref_in[k], g_out[k], fls_in[k], ref_out[k]), axis=1) * 255.0 writer.write(frame.astype(np.uint8)) writer.release() - os.system('ffmpeg -y -i tmp_{:04d}.mp4 -pix_fmt yuv420p random_{:04d}.mp4'.format(i, i)) + os.system( + 'ffmpeg -y -i tmp_{:04d}.mp4 -pix_fmt yuv420p random_{:04d}.mp4'.format(i, i)) os.system('rm tmp_{:04d}.mp4'.format(i)) - def single_test(self, jpg=None, fls=None, filename=None, prefix='', grey_only=False): import time st = time.time() @@ -383,21 +413,25 @@ def single_test(self, jpg=None, fls=None, filename=None, prefix='', grey_only=Fa fls[:, 0::3] += 130 fls[:, 1::3] += 80 - writer = cv2.VideoWriter('out.mp4', cv2.VideoWriter_fourcc(*'mjpg'), 62.5, (256 * 3, 256)) + writer = cv2.VideoWriter( + 'out.mp4', cv2.VideoWriter_fourcc(*'mjpg'), 62.5, (256 * 3, 256)) for i, frame in enumerate(fls): img_fl = np.ones(shape=(256, 256, 3)) * 255 fl = frame.astype(int) img_fl = vis_landmark_on_img(img_fl, np.reshape(fl, (68, 3))) - frame = np.concatenate((img_fl, jpg), axis=2).astype(np.float32)/255.0 + frame = np.concatenate( + (img_fl, jpg), axis=2).astype(np.float32)/255.0 - image_in, image_out = frame.transpose((2, 0, 1)), np.zeros(shape=(3, 256, 256)) + image_in, image_out = frame.transpose( + (2, 0, 1)), np.zeros(shape=(3, 256, 256)) # image_in, image_out = frame.transpose((2, 1, 0)), np.zeros(shape=(3, 256, 256)) image_in, image_out = torch.tensor(image_in, requires_grad=False), \ - torch.tensor(image_out, requires_grad=False) + torch.tensor(image_out, requires_grad=False) - image_in, image_out = image_in.reshape(-1, 6, 256, 256), image_out.reshape(-1, 3, 256, 256) + image_in, image_out = image_in.reshape( + -1, 6, 256, 256), image_out.reshape(-1, 3, 256, 256) image_in, image_out = image_in.to(device), image_out.to(device) g_out = self.G(image_in) @@ -405,20 +439,23 @@ def single_test(self, jpg=None, fls=None, filename=None, prefix='', grey_only=Fa g_out = g_out.cpu().detach().numpy().transpose((0, 2, 3, 1)) g_out[g_out < 0] = 0 - ref_in = image_in[:, 3:6, :, :].cpu().detach().numpy().transpose((0, 2, 3, 1)) - fls_in = image_in[:, 0:3, :, :].cpu().detach().numpy().transpose((0, 2, 3, 1)) + ref_in = image_in[:, 3:6, :, :].cpu( + ).detach().numpy().transpose((0, 2, 3, 1)) + fls_in = image_in[:, 0:3, :, :].cpu( + ).detach().numpy().transpose((0, 2, 3, 1)) # g_out = g_out.cpu().detach().numpy().transpose((0, 3, 2, 1)) # g_out[g_out < 0] = 0 # ref_in = image_in[:, 3:6, :, :].cpu().detach().numpy().transpose((0, 3, 2, 1)) # fls_in = image_in[:, 0:3, :, :].cpu().detach().numpy().transpose((0, 3, 2, 1)) if(grey_only): - g_out_grey =np.mean(g_out, axis=3, keepdims=True) - g_out[:, :, :, 0:1] = g_out[:, :, :, 1:2] = g_out[:, :, :, 2:3] = g_out_grey - + g_out_grey = np.mean(g_out, axis=3, keepdims=True) + g_out[:, :, :, 0:1] = g_out[:, :, :, + 1:2] = g_out[:, :, :, 2:3] = g_out_grey for i in range(g_out.shape[0]): - frame = np.concatenate((ref_in[i], g_out[i], fls_in[i]), axis=1) * 255.0 + frame = np.concatenate( + (ref_in[i], g_out[i], fls_in[i]), axis=1) * 255.0 writer.write(frame.astype(np.uint8)) writer.release() @@ -432,8 +469,3 @@ def single_test(self, jpg=None, fls=None, filename=None, prefix='', grey_only=Fa # os.system('rm out.mp4') print('Time - ffmpeg add audio:', time.time() - st) - - - - - diff --git a/src/models/model_image_translation.py b/src/models/model_image_translation.py index 8a1dae8..ea7f28b 100755 --- a/src/models/model_image_translation.py +++ b/src/models/model_image_translation.py @@ -54,7 +54,8 @@ def init_weights(net, init_type='normal'): elif init_type == 'kaiming': net.apply(weights_init_kaiming) else: - raise NotImplementedError('initialization method [%s] is not implemented' % init_type) + raise NotImplementedError( + 'initialization method [%s] is not implemented' % init_type) class FeatureExtraction(nn.Module): @@ -65,12 +66,15 @@ def __init__(self, input_nc, ngf=64, n_layers=3, norm_layer=nn.BatchNorm2d, use_ for i in range(n_layers): in_ngf = 2 ** i * ngf if 2 ** i * ngf < 512 else 512 out_ngf = 2 ** (i + 1) * ngf if 2 ** i * ngf < 512 else 512 - downconv = nn.Conv2d(in_ngf, out_ngf, kernel_size=4, stride=2, padding=1) + downconv = nn.Conv2d( + in_ngf, out_ngf, kernel_size=4, stride=2, padding=1) model += [downconv, nn.ReLU(True)] model += [norm_layer(out_ngf)] - model += [nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1), nn.ReLU(True)] + model += [nn.Conv2d(512, 512, kernel_size=3, + stride=1, padding=1), nn.ReLU(True)] model += [norm_layer(512)] - model += [nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1), nn.ReLU(True)] + model += [nn.Conv2d(512, 512, kernel_size=3, + stride=1, padding=1), nn.ReLU(True)] self.model = nn.Sequential(*model) init_weights(self.model, init_type='normal') @@ -85,7 +89,8 @@ def __init__(self): def forward(self, feature): epsilon = 1e-6 - norm = torch.pow(torch.sum(torch.pow(feature, 2), 1) + epsilon, 0.5).unsqueeze(1).expand_as(feature) + norm = torch.pow(torch.sum(torch.pow(feature, 2), 1) + + epsilon, 0.5).unsqueeze(1).expand_as(feature) return torch.div(feature, norm) @@ -100,7 +105,8 @@ def forward(self, feature_A, feature_B): feature_B = feature_B.view(b, c, h * w).transpose(1, 2) # perform matrix mult. feature_mul = torch.bmm(feature_B, feature_A) - correlation_tensor = feature_mul.view(b, h, w, h * w).transpose(2, 3).transpose(1, 2) + correlation_tensor = feature_mul.view( + b, h, w, h * w).transpose(2, 3).transpose(1, 2) return correlation_tensor @@ -146,7 +152,8 @@ def __init__(self, out_h=256, out_w=192, out_ch=3): def forward(self, theta): theta = theta.contiguous() batch_size = theta.size()[0] - out_size = torch.Size((batch_size, self.out_ch, self.out_h, self.out_w)) + out_size = torch.Size( + (batch_size, self.out_ch, self.out_h, self.out_w)) return F.affine_grid(theta, out_size) @@ -160,7 +167,8 @@ def __init__(self, out_h=256, out_w=192, use_regular_grid=True, grid_size=3, reg # create grid in numpy self.grid = np.zeros([self.out_h, self.out_w, 3], dtype=np.float32) # sampling grid with dim-0 coords (Y) - self.grid_X, self.grid_Y = np.meshgrid(np.linspace(-1, 1, out_w), np.linspace(-1, 1, out_h)) + self.grid_X, self.grid_Y = np.meshgrid( + np.linspace(-1, 1, out_w), np.linspace(-1, 1, out_h)) # grid_X,grid_Y: size [1,H,W,1,1] self.grid_X = torch.FloatTensor(self.grid_X).unsqueeze(0).unsqueeze(3) self.grid_Y = torch.FloatTensor(self.grid_Y).unsqueeze(0).unsqueeze(3) @@ -180,8 +188,10 @@ def __init__(self, out_h=256, out_w=192, use_regular_grid=True, grid_size=3, reg self.P_X_base = P_X.clone() self.P_Y_base = P_Y.clone() self.Li = self.compute_L_inverse(P_X, P_Y).unsqueeze(0) - self.P_X = P_X.unsqueeze(2).unsqueeze(3).unsqueeze(4).transpose(0, 4) - self.P_Y = P_Y.unsqueeze(2).unsqueeze(3).unsqueeze(4).transpose(0, 4) + self.P_X = P_X.unsqueeze(2).unsqueeze( + 3).unsqueeze(4).transpose(0, 4) + self.P_Y = P_Y.unsqueeze(2).unsqueeze( + 3).unsqueeze(4).transpose(0, 4) if use_cuda: self.P_X = self.P_X.cuda() self.P_Y = self.P_Y.cuda() @@ -189,7 +199,8 @@ def __init__(self, out_h=256, out_w=192, use_regular_grid=True, grid_size=3, reg self.P_Y_base = self.P_Y_base.cuda() def forward(self, theta): - warped_grid = self.apply_transformation(theta, torch.cat((self.grid_X, self.grid_Y), 3)) + warped_grid = self.apply_transformation( + theta, torch.cat((self.grid_X, self.grid_Y), 3)) return warped_grid @@ -198,14 +209,17 @@ def compute_L_inverse(self, X, Y): # construct matrix K Xmat = X.expand(N, N) Ymat = Y.expand(N, N) - P_dist_squared = torch.pow(Xmat - Xmat.transpose(0, 1), 2) + torch.pow(Ymat - Ymat.transpose(0, 1), 2) - P_dist_squared[P_dist_squared == 0] = 1 # make diagonal 1 to avoid NaN in log computation + P_dist_squared = torch.pow( + Xmat - Xmat.transpose(0, 1), 2) + torch.pow(Ymat - Ymat.transpose(0, 1), 2) + # make diagonal 1 to avoid NaN in log computation + P_dist_squared[P_dist_squared == 0] = 1 K = torch.mul(P_dist_squared, torch.log(P_dist_squared)) # construct matrix L O = torch.FloatTensor(N, 1).fill_(1) Z = torch.FloatTensor(3, 3).fill_(0) P = torch.cat((O, X, Y), 1) - L = torch.cat((torch.cat((K, P), 1), torch.cat((P.transpose(0, 1), Z), 1)), 0) + L = torch.cat((torch.cat((K, P), 1), torch.cat( + (P.transpose(0, 1), Z), 1)), 0) Li = torch.inverse(L) if self.use_cuda: Li = Li.cuda() @@ -236,19 +250,27 @@ def apply_transformation(self, theta, points): P_Y = self.P_Y.expand((1, points_h, points_w, 1, self.N)) # compute weigths for non-linear part - W_X = torch.bmm(self.Li[:, :self.N, :self.N].expand((batch_size, self.N, self.N)), Q_X) - W_Y = torch.bmm(self.Li[:, :self.N, :self.N].expand((batch_size, self.N, self.N)), Q_Y) + W_X = torch.bmm(self.Li[:, :self.N, :self.N].expand( + (batch_size, self.N, self.N)), Q_X) + W_Y = torch.bmm(self.Li[:, :self.N, :self.N].expand( + (batch_size, self.N, self.N)), Q_Y) # reshape # W_X,W,Y: size [B,H,W,1,N] - W_X = W_X.unsqueeze(3).unsqueeze(4).transpose(1, 4).repeat(1, points_h, points_w, 1, 1) - W_Y = W_Y.unsqueeze(3).unsqueeze(4).transpose(1, 4).repeat(1, points_h, points_w, 1, 1) + W_X = W_X.unsqueeze(3).unsqueeze(4).transpose( + 1, 4).repeat(1, points_h, points_w, 1, 1) + W_Y = W_Y.unsqueeze(3).unsqueeze(4).transpose( + 1, 4).repeat(1, points_h, points_w, 1, 1) # compute weights for affine part - A_X = torch.bmm(self.Li[:, self.N:, :self.N].expand((batch_size, 3, self.N)), Q_X) - A_Y = torch.bmm(self.Li[:, self.N:, :self.N].expand((batch_size, 3, self.N)), Q_Y) + A_X = torch.bmm(self.Li[:, self.N:, :self.N].expand( + (batch_size, 3, self.N)), Q_X) + A_Y = torch.bmm(self.Li[:, self.N:, :self.N].expand( + (batch_size, 3, self.N)), Q_Y) # reshape # A_X,A,Y: size [B,H,W,1,3] - A_X = A_X.unsqueeze(3).unsqueeze(4).transpose(1, 4).repeat(1, points_h, points_w, 1, 1) - A_Y = A_Y.unsqueeze(3).unsqueeze(4).transpose(1, 4).repeat(1, points_h, points_w, 1, 1) + A_X = A_X.unsqueeze(3).unsqueeze(4).transpose( + 1, 4).repeat(1, points_h, points_w, 1, 1) + A_Y = A_Y.unsqueeze(3).unsqueeze(4).transpose( + 1, 4).repeat(1, points_h, points_w, 1, 1) # compute distance P_i - (grid_X,grid_Y) # grid is expanded in point dim 4, but not in batch dim 0, as points P_X,P_Y are fixed for all batch @@ -262,8 +284,10 @@ def apply_transformation(self, theta, points): delta_Y = points_Y_for_summation - P_Y else: # use expanded P_X,P_Y in batch dimension - delta_X = points_X_for_summation - P_X.expand_as(points_X_for_summation) - delta_Y = points_Y_for_summation - P_Y.expand_as(points_Y_for_summation) + delta_X = points_X_for_summation - \ + P_X.expand_as(points_X_for_summation) + delta_Y = points_Y_for_summation - \ + P_Y.expand_as(points_Y_for_summation) dist_squared = torch.pow(delta_X, 2) + torch.pow(delta_Y, 2) # U: size [1,H,W,1,N] @@ -274,18 +298,20 @@ def apply_transformation(self, theta, points): points_X_batch = points[:, :, :, 0].unsqueeze(3) points_Y_batch = points[:, :, :, 1].unsqueeze(3) if points_b == 1: - points_X_batch = points_X_batch.expand((batch_size,) + points_X_batch.size()[1:]) - points_Y_batch = points_Y_batch.expand((batch_size,) + points_Y_batch.size()[1:]) + points_X_batch = points_X_batch.expand( + (batch_size,) + points_X_batch.size()[1:]) + points_Y_batch = points_Y_batch.expand( + (batch_size,) + points_Y_batch.size()[1:]) points_X_prime = A_X[:, :, :, :, 0] + \ - torch.mul(A_X[:, :, :, :, 1], points_X_batch) + \ - torch.mul(A_X[:, :, :, :, 2], points_Y_batch) + \ - torch.sum(torch.mul(W_X, U.expand_as(W_X)), 4) + torch.mul(A_X[:, :, :, :, 1], points_X_batch) + \ + torch.mul(A_X[:, :, :, :, 2], points_Y_batch) + \ + torch.sum(torch.mul(W_X, U.expand_as(W_X)), 4) points_Y_prime = A_Y[:, :, :, :, 0] + \ - torch.mul(A_Y[:, :, :, :, 1], points_X_batch) + \ - torch.mul(A_Y[:, :, :, :, 2], points_Y_batch) + \ - torch.sum(torch.mul(W_Y, U.expand_as(W_Y)), 4) + torch.mul(A_Y[:, :, :, :, 1], points_X_batch) + \ + torch.mul(A_Y[:, :, :, :, 2], points_Y_batch) + \ + torch.sum(torch.mul(W_Y, U.expand_as(W_Y)), 4) return torch.cat((points_X_prime, points_Y_prime), 3) @@ -309,7 +335,8 @@ def __init__(self, input_nc, output_nc, num_downs, ngf=64, norm_layer=norm_layer) unet_block = UnetSkipConnectionBlock(ngf * 2, ngf * 4, input_nc=None, submodule=unet_block, norm_layer=norm_layer) - unet_block = UnetSkipConnectionBlock(ngf, ngf * 2, input_nc=None, submodule=unet_block, norm_layer=norm_layer) + unet_block = UnetSkipConnectionBlock( + ngf, ngf * 2, input_nc=None, submodule=unet_block, norm_layer=norm_layer) unet_block = UnetSkipConnectionBlock(output_nc, ngf, input_nc=input_nc, submodule=unet_block, outermost=True, norm_layer=norm_layer) @@ -341,14 +368,16 @@ def __init__(self, outer_nc, inner_nc, input_nc=None, if outermost: upsample = nn.Upsample(scale_factor=2, mode='bilinear') - upconv = nn.Conv2d(inner_nc * 2, outer_nc, kernel_size=3, stride=1, padding=1, bias=use_bias) + upconv = nn.Conv2d(inner_nc * 2, outer_nc, + kernel_size=3, stride=1, padding=1, bias=use_bias) down = [downconv] # up = [uprelu, upsample, upconv, upnorm] up = [uprelu, upsample, upconv] model = down + [submodule] + up elif innermost: upsample = nn.Upsample(scale_factor=2, mode='bilinear') - upconv = nn.Conv2d(inner_nc, outer_nc, kernel_size=3, stride=1, padding=1, bias=use_bias) + upconv = nn.Conv2d(inner_nc, outer_nc, kernel_size=3, + stride=1, padding=1, bias=use_bias) down = [downrelu, downconv] if norm_layer == None: up = [uprelu, upsample, upconv] @@ -357,7 +386,8 @@ def __init__(self, outer_nc, inner_nc, input_nc=None, model = down + up else: upsample = nn.Upsample(scale_factor=2, mode='bilinear') - upconv = nn.Conv2d(inner_nc * 2, outer_nc, kernel_size=3, stride=1, padding=1, bias=use_bias) + upconv = nn.Conv2d(inner_nc * 2, outer_nc, + kernel_size=3, stride=1, padding=1, bias=use_bias) if norm_layer == None: down = [downrelu, downconv] up = [uprelu, upsample, upconv] @@ -455,8 +485,10 @@ def __init__(self, outer_nc, inner_nc, input_nc=None, downconv = nn.Conv2d(input_nc, inner_nc, kernel_size=3, stride=2, padding=1, bias=use_bias) # add two resblock - res_downconv = [ResidualBlock(inner_nc, norm_layer), ResidualBlock(inner_nc, norm_layer)] - res_upconv = [ResidualBlock(outer_nc, norm_layer), ResidualBlock(outer_nc, norm_layer)] + res_downconv = [ResidualBlock( + inner_nc, norm_layer), ResidualBlock(inner_nc, norm_layer)] + res_upconv = [ResidualBlock( + outer_nc, norm_layer), ResidualBlock(outer_nc, norm_layer)] # res_downconv = [ResidualBlock(inner_nc)] # res_upconv = [ResidualBlock(outer_nc)] @@ -469,14 +501,16 @@ def __init__(self, outer_nc, inner_nc, input_nc=None, if outermost: upsample = nn.Upsample(scale_factor=2, mode='nearest') - upconv = nn.Conv2d(inner_nc * 2, outer_nc, kernel_size=3, stride=1, padding=1, bias=use_bias) + upconv = nn.Conv2d(inner_nc * 2, outer_nc, + kernel_size=3, stride=1, padding=1, bias=use_bias) down = [downconv, downrelu] + res_downconv # up = [uprelu, upsample, upconv, upnorm] up = [upsample, upconv] model = down + [submodule] + up elif innermost: upsample = nn.Upsample(scale_factor=2, mode='nearest') - upconv = nn.Conv2d(inner_nc, outer_nc, kernel_size=3, stride=1, padding=1, bias=use_bias) + upconv = nn.Conv2d(inner_nc, outer_nc, kernel_size=3, + stride=1, padding=1, bias=use_bias) down = [downconv, downrelu] + res_downconv if norm_layer == None: up = [upsample, upconv, uprelu] + res_upconv @@ -485,7 +519,8 @@ def __init__(self, outer_nc, inner_nc, input_nc=None, model = down + up else: upsample = nn.Upsample(scale_factor=2, mode='nearest') - upconv = nn.Conv2d(inner_nc * 2, outer_nc, kernel_size=3, stride=1, padding=1, bias=use_bias) + upconv = nn.Conv2d(inner_nc * 2, outer_nc, + kernel_size=3, stride=1, padding=1, bias=use_bias) if norm_layer == None: down = [downconv, downrelu] + res_downconv up = [upsample, upconv, uprelu] + res_upconv @@ -539,6 +574,7 @@ def forward(self, X): out = [h_relu1, h_relu2, h_relu3, h_relu4, h_relu5] return out + def gram_matrix(input): a, b, c, d = input.size() # a=batch size(=1) # b=number of feature maps @@ -559,6 +595,7 @@ def forward(self, x, y): Gy = gram_matrix(y) return F.mse_loss(Gx, Gy) * 30000000 + class VGGLoss(nn.Module): def __init__(self, model=None): super(VGGLoss, self).__init__() @@ -592,7 +629,8 @@ def forward(self, x, y, style=False): return loss, style_loss for i in range(len(x_vgg)): - this_loss = (self.weights[i] * self.criterion(x_vgg[i], y_vgg[i].detach())) + this_loss = ( + self.weights[i] * self.criterion(x_vgg[i], y_vgg[i].detach())) loss += this_loss return loss @@ -603,12 +641,16 @@ class GMM(nn.Module): def __init__(self, opt, input_nc): super(GMM, self).__init__() - self.extractionA = FeatureExtraction(input_nc, ngf=64, n_layers=3, norm_layer=nn.BatchNorm2d) - self.extractionB = FeatureExtraction(3, ngf=64, n_layers=3, norm_layer=nn.BatchNorm2d) + self.extractionA = FeatureExtraction( + input_nc, ngf=64, n_layers=3, norm_layer=nn.BatchNorm2d) + self.extractionB = FeatureExtraction( + 3, ngf=64, n_layers=3, norm_layer=nn.BatchNorm2d) self.l2norm = FeatureL2Norm() self.correlation = FeatureCorrelation() - self.regression = FeatureRegression(input_nc=192, output_dim=2 * opt.grid_size ** 2, use_cuda=True) - self.gridGen = TpsGridGen(opt.fine_height, opt.fine_width, use_cuda=True, grid_size=opt.grid_size) + self.regression = FeatureRegression( + input_nc=192, output_dim=2 * opt.grid_size ** 2, use_cuda=True) + self.gridGen = TpsGridGen( + opt.fine_height, opt.fine_width, use_cuda=True, grid_size=opt.grid_size) def forward(self, inputA, inputB): featureA = self.extractionA(inputA) diff --git a/testg.py b/testg.py new file mode 100644 index 0000000..b3a4152 --- /dev/null +++ b/testg.py @@ -0,0 +1,56 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from torchsummary import summary +import numpy as np +from src.models.model_image_translation import ResUnetGenerator, VGGLoss +import torch_pruning as tp + +model = ResUnetGenerator(input_nc=6, output_nc=3, + num_downs=6, use_dropout=False) +# params = sum([np.prod(p.size()) for p in model.parameters()]) +# print("Number of Parameters: %.1fM" % (params/1e6)) + +# i = 0 +# for m in model.modules(): +# # print(m) +# if isinstance(m, nn.Conv2d): +# print(m) +# i += 1 +# print(i) + + +def prune_model(model): + model.cpu() + DG = tp.DependencyGraph().build_dependency(model, torch.randn(10, 6, 256, 256)) + + def prune_conv(conv, pruned_prob): + weight = conv.weight.detach().cpu().numpy() + out_channels = weight.shape[0] + L1_norm = np.sum(np.abs(weight), axis=(1, 2, 3)) + num_pruned = int(out_channels * pruned_prob) + # remove filters with small L1-Norm + prune_index = np.argsort(L1_norm)[:num_pruned].tolist() + plan = DG.get_pruning_plan(conv, tp.prune_conv, prune_index) + plan.exec() + + block_prune_probs = [] + for i in range(90): + if i < 40: + block_prune_probs.append(0.1) + if i > 40 and i < 80: + block_prune_probs.append(0.2) + if i > 80: + block_prune_probs.append(0.3) + blk_id = 0 + for m in model.modules(): + if isinstance(m, nn.Conv2d): + prune_conv(m, block_prune_probs[blk_id]) + blk_id += 1 + return model + + +model = prune_model(model).to('cuda') + +params = sum([np.prod(p.size()) for p in model.parameters()]) +print("Number of Parameters: %.1fM" % (params/1e6)) From c64e0661a7d850a6af75e6a75ca97dca6c4ab899 Mon Sep 17 00:00:00 2001 From: hack Date: Wed, 23 Dec 2020 10:11:41 +0530 Subject: [PATCH 03/17] modify preprocessing --- main_train_image_translation.py | 27 ++++++++------- .../data_preparation_with_preprocessing.py | 20 +++++++---- testg.py | 33 ++++++++++++------- 3 files changed, 50 insertions(+), 30 deletions(-) diff --git a/main_train_image_translation.py b/main_train_image_translation.py index af807c5..92b64b4 100644 --- a/main_train_image_translation.py +++ b/main_train_image_translation.py @@ -8,16 +8,17 @@ """ +import glob +import os +import torch +import platform +from src.approaches.train_image_translation import Image_translation_block +from src.dataset.image_translation import landmark_extraction, landmark_image_to_data +import argparse +import cv2 +import numpy as np import sys sys.path.append('thirdparty/AdaptiveWingLoss') -import os, glob -import numpy as np -import cv2 -import argparse -from src.dataset.image_translation import landmark_extraction, landmark_image_to_data -from approaches.train_image_translation import Image_translation_block -import platform -import torch if platform.release() == '4.4.0-83-generic': @@ -26,7 +27,7 @@ jpg_dir = r'img_output' ckpt_dir = r'img_output' log_dir = r'img_output' -else: # 3.10.0-957.21.2.el7.x86_64 +else: # 3.10.0-957.21.2.el7.x86_64 # root = r'/mnt/nfs/scratch1/yangzhou/VoxCeleb2_imagetranslation' root = r'/mnt/nfs/scratch1/yangzhou/PreprocessedVox_imagetranslation' src_dir = os.path.join(root, 'raw_fl3d') @@ -45,10 +46,12 @@ ''' Step 2. Train the network ''' parser = argparse.ArgumentParser() -parser.add_argument('--nepoch', type=int, default=150, help='number of epochs to train for') +parser.add_argument('--nepoch', type=int, default=150, + help='number of epochs to train for') parser.add_argument('--batch_size', type=int, default=8, help='batch size') parser.add_argument('--num_frames', type=int, default=1, help='') -parser.add_argument('--num_workers', type=int, default=4, help='number of frames extracted from each video') +parser.add_argument('--num_workers', type=int, default=4, + help='number of frames extracted from each video') parser.add_argument('--lr', type=float, default=0.0001, help='') parser.add_argument('--write', default=False, action='store_true') @@ -87,4 +90,4 @@ model.train() else: with torch.no_grad(): - model.test() \ No newline at end of file + model.test() diff --git a/src/dataset/image_translation/data_preparation_with_preprocessing.py b/src/dataset/image_translation/data_preparation_with_preprocessing.py index ee8828f..1cc4332 100644 --- a/src/dataset/image_translation/data_preparation_with_preprocessing.py +++ b/src/dataset/image_translation/data_preparation_with_preprocessing.py @@ -8,16 +8,23 @@ """ -import os, glob, time, sys +import os +import glob +import time +import sys from src.dataset.utils.Av2Flau_Convertor import Av2Flau_Convertor -out_dir = r'/mnt/nfs/scratch1/yangzhou/PreprocessedVox_imagetranslation' -src_dir = r'/mnt/nfs/scratch1/yangzhou/vox_p3/train' +# out_dir = r'/mnt/nfs/scratch1/yangzhou/PreprocessedVox_imagetranslation' +# src_dir = r'/mnt/nfs/scratch1/yangzhou/vox_p3/train' + +out_dir = r'/content/MakeItTalk/PreprocessedVox_imagetranslation' +src_dir = r'/content/MakeItTalk/mp4' ''' Step 1. Data preparation ''' # landmark extraction # landmark_extraction(int(sys.argv[1]), int(sys.argv[2])) + def landmark_extraction(si, ei): ''' @@ -32,7 +39,6 @@ def landmark_extraction(si, ei): except: pass - if(not os.path.isfile(os.path.join(out_dir, 'filename_index.txt'))): # generate all file list files = glob.glob1(src_dir, '*.mp4') @@ -50,5 +56,7 @@ def landmark_extraction(si, ei): c = Av2Flau_Convertor(video_dir=os.path.join(src_dir, file), out_dir=out_dir, idx=idx) - c.convert(show=False) # (save_audio=False, register=False, show=False) - print('Idx: {}, Processed time (min): {}'.format(idx, (time.time() - st) / 60.0)) + # (save_audio=False, register=False, show=False) + c.convert(show=False) + print('Idx: {}, Processed time (min): {}'.format( + idx, (time.time() - st) / 60.0)) diff --git a/testg.py b/testg.py index b3a4152..890830f 100644 --- a/testg.py +++ b/testg.py @@ -3,7 +3,7 @@ import torch.nn.functional as F from torchsummary import summary import numpy as np -from src.models.model_image_translation import ResUnetGenerator, VGGLoss +from src.models.model_image_translation import ResUnetGenerator, ResidualBlock, ResidualBlock import torch_pruning as tp model = ResUnetGenerator(input_nc=6, output_nc=3, @@ -13,10 +13,13 @@ # i = 0 # for m in model.modules(): -# # print(m) -# if isinstance(m, nn.Conv2d): -# print(m) -# i += 1 + +# if isinstance(m, ResidualBlock): +# # print((m.block.modules())) +# for j in m.block.modules(): +# if isinstance(j, nn.Conv2d): +# i += 1 +# pass # print(i) @@ -35,22 +38,28 @@ def prune_conv(conv, pruned_prob): plan.exec() block_prune_probs = [] - for i in range(90): - if i < 40: + for i in range(100): + if i < 30: block_prune_probs.append(0.1) - if i > 40 and i < 80: + if i > 30 and i < 50: block_prune_probs.append(0.2) - if i > 80: + if i > 50: block_prune_probs.append(0.3) blk_id = 0 for m in model.modules(): if isinstance(m, nn.Conv2d): prune_conv(m, block_prune_probs[blk_id]) blk_id += 1 + # if isinstance(m, ResidualBlock): + # for j in m.block.modules(): + # if isinstance(j, nn.Conv2d): + # prune_conv(j, block_prune_probs[blk_id]) + # blk_id += 1 + return model model = prune_model(model).to('cuda') - -params = sum([np.prod(p.size()) for p in model.parameters()]) -print("Number of Parameters: %.1fM" % (params/1e6)) +# params = sum([np.prod(p.size()) for p in model.parameters()]) +# print("Number of Parameters: %.1fM" % (params/1e6)) +torch.save(model, 'model.h5') From 8319cd238cee0f0e8b88e0e8df7c987bfedc0fc4 Mon Sep 17 00:00:00 2001 From: hack Date: Wed, 23 Dec 2020 11:37:45 +0530 Subject: [PATCH 04/17] i2i load --- src/approaches/train_image_translation.py | 25 ++++++++++--------- .../data_preparation_with_preprocessing.py | 10 +++++++- testg.py | 4 +-- 3 files changed, 24 insertions(+), 15 deletions(-) diff --git a/src/approaches/train_image_translation.py b/src/approaches/train_image_translation.py index 461676c..70f9a18 100644 --- a/src/approaches/train_image_translation.py +++ b/src/approaches/train_image_translation.py @@ -42,18 +42,19 @@ def __init__(self, opt_parser, single_test=False): self.G = ResUnetGenerator( input_nc=7, output_nc=3, num_downs=6, use_dropout=False) else: - self.G = ResUnetGenerator( - input_nc=6, output_nc=3, num_downs=6, use_dropout=False) - - if (opt_parser.load_G_name != ''): - ckpt = torch.load(opt_parser.load_G_name) - try: - self.G.load_state_dict(ckpt['G']) - except: - tmp = nn.DataParallel(self.G) - tmp.load_state_dict(ckpt['G']) - self.G.load_state_dict(tmp.module.state_dict()) - del tmp + self.G = torch.load(opt_parser.load_G_name) + # self.G = ResUnetGenerator( + # input_nc=6, output_nc=3, num_downs=6, use_dropout=False) + + # if (opt_parser.load_G_name != ''): + # ckpt = torch.load(opt_parser.load_G_name) + # try: + # self.G.load_state_dict(ckpt['G']) + # except: + # tmp = nn.DataParallel(self.G) + # tmp.load_state_dict(ckpt['G']) + # self.G.load_state_dict(tmp.module.state_dict()) + # del tmp if torch.cuda.device_count() > 1: print("Let's use", torch.cuda.device_count(), "GPUs in G mode!") diff --git a/src/dataset/image_translation/data_preparation_with_preprocessing.py b/src/dataset/image_translation/data_preparation_with_preprocessing.py index 1cc4332..591c593 100644 --- a/src/dataset/image_translation/data_preparation_with_preprocessing.py +++ b/src/dataset/image_translation/data_preparation_with_preprocessing.py @@ -41,7 +41,15 @@ def landmark_extraction(si, ei): if(not os.path.isfile(os.path.join(out_dir, 'filename_index.txt'))): # generate all file list - files = glob.glob1(src_dir, '*.mp4') + # files = glob.glob1(src_dir, '*.mp4') + files = [] + folders = os.listdir(src_dir) + for folder in folders: + for i in os.listdir(os.path.join(src_dir, folder)): + file = glob.glob(os.path.join(src_dir, folder, i, '*.mp4')) + for j in file: + files.append(j) + with open(os.path.join(out_dir, 'filename_index.txt'), 'w') as f: for i, file in enumerate(files): f.write('{} {}\n'.format(i, file)) diff --git a/testg.py b/testg.py index 890830f..d4ed9df 100644 --- a/testg.py +++ b/testg.py @@ -60,6 +60,6 @@ def prune_conv(conv, pruned_prob): model = prune_model(model).to('cuda') -# params = sum([np.prod(p.size()) for p in model.parameters()]) -# print("Number of Parameters: %.1fM" % (params/1e6)) +params = sum([np.prod(p.size()) for p in model.parameters()]) +print("Number of Parameters: %.1fM" % (params/1e6)) torch.save(model, 'model.h5') From 1e7befe22aaf686e1aebef975ff1052b17146048 Mon Sep 17 00:00:00 2001 From: hack Date: Wed, 23 Dec 2020 12:18:49 +0530 Subject: [PATCH 05/17] change --- src/approaches/train_image_translation.py | 26 +++++++++++------------ testg.py | 12 +++++++++-- 2 files changed, 23 insertions(+), 15 deletions(-) diff --git a/src/approaches/train_image_translation.py b/src/approaches/train_image_translation.py index 70f9a18..5745ec3 100644 --- a/src/approaches/train_image_translation.py +++ b/src/approaches/train_image_translation.py @@ -42,19 +42,19 @@ def __init__(self, opt_parser, single_test=False): self.G = ResUnetGenerator( input_nc=7, output_nc=3, num_downs=6, use_dropout=False) else: - self.G = torch.load(opt_parser.load_G_name) - # self.G = ResUnetGenerator( - # input_nc=6, output_nc=3, num_downs=6, use_dropout=False) - - # if (opt_parser.load_G_name != ''): - # ckpt = torch.load(opt_parser.load_G_name) - # try: - # self.G.load_state_dict(ckpt['G']) - # except: - # tmp = nn.DataParallel(self.G) - # tmp.load_state_dict(ckpt['G']) - # self.G.load_state_dict(tmp.module.state_dict()) - # del tmp + # self.G = torch.load(opt_parser.load_G_name) + self.G = ResUnetGenerator( + input_nc=6, output_nc=3, num_downs=6, use_dropout=False) + + if (opt_parser.load_G_name != ''): + ckpt = torch.load(opt_parser.load_G_name) + try: + self.G.load_state_dict(ckpt['G']) + except: + tmp = nn.DataParallel(self.G) + tmp.load_state_dict(ckpt['G']) + self.G.load_state_dict(tmp.module.state_dict()) + del tmp if torch.cuda.device_count() > 1: print("Let's use", torch.cuda.device_count(), "GPUs in G mode!") diff --git a/testg.py b/testg.py index d4ed9df..8c96386 100644 --- a/testg.py +++ b/testg.py @@ -1,3 +1,4 @@ +from operator import xor import torch import torch.nn as nn import torch.nn.functional as F @@ -8,8 +9,8 @@ model = ResUnetGenerator(input_nc=6, output_nc=3, num_downs=6, use_dropout=False) -# params = sum([np.prod(p.size()) for p in model.parameters()]) -# print("Number of Parameters: %.1fM" % (params/1e6)) +params = sum([np.prod(p.size()) for p in model.parameters()]) +print("Number of Parameters: %.1fM" % (params/1e6)) # i = 0 # for m in model.modules(): @@ -63,3 +64,10 @@ def prune_conv(conv, pruned_prob): params = sum([np.prod(p.size()) for p in model.parameters()]) print("Number of Parameters: %.1fM" % (params/1e6)) torch.save(model, 'model.h5') + + +x = torch.load('model.h5') + +img = torch.zeros((3, 6, 256, 256)).to('cuda') +print(x(img)) +# summary(x, input_size=(3, 6, 256, 256)) From 1a6f1838f3ec02306de4b4c7fa91b2b22edd6b75 Mon Sep 17 00:00:00 2001 From: hack Date: Wed, 23 Dec 2020 16:17:02 +0530 Subject: [PATCH 06/17] update paths --- main_train_image_translation.py | 26 +- .../image_translation/data_preparation.py | 49 +-- .../image_translation_dataset.py | 282 +++++++++--------- testg.py | 2 +- thirdparty/AdaptiveWingLoss/core/models.py | 10 +- 5 files changed, 191 insertions(+), 178 deletions(-) diff --git a/main_train_image_translation.py b/main_train_image_translation.py index 92b64b4..fc297a1 100644 --- a/main_train_image_translation.py +++ b/main_train_image_translation.py @@ -13,29 +13,21 @@ import torch import platform from src.approaches.train_image_translation import Image_translation_block -from src.dataset.image_translation import landmark_extraction, landmark_image_to_data +from src.dataset.image_translation.data_preparation import landmark_extraction, landmark_image_to_data import argparse import cv2 import numpy as np import sys sys.path.append('thirdparty/AdaptiveWingLoss') - -if platform.release() == '4.4.0-83-generic': - src_dir = r'/mnt/ntfs/Dataset/TalkingToon/VoxCeleb2_imagetranslation/raw_fl3d' - mp4_dir = r'/mnt/ntfs/Dataset/VoxCeleb2/train_set/dev/mp4' - jpg_dir = r'img_output' - ckpt_dir = r'img_output' - log_dir = r'img_output' -else: # 3.10.0-957.21.2.el7.x86_64 - # root = r'/mnt/nfs/scratch1/yangzhou/VoxCeleb2_imagetranslation' - root = r'/mnt/nfs/scratch1/yangzhou/PreprocessedVox_imagetranslation' - src_dir = os.path.join(root, 'raw_fl3d') - # mp4_dir = r'/mnt/nfs/work1/kalo/yangzhou/VoxCeleb2/train_set/dev/mp4' - mp4_dir = r'/mnt/nfs/scratch1/yangzhou/PreprocessedVox_mp4' - jpg_dir = os.path.join(root, 'tmp_v') - ckpt_dir = os.path.join(root, 'ckpt') - log_dir = os.path.join(root, 'log') +# root = r'/mnt/nfs/scratch1/yangzhou/PreprocessedVox_imagetranslation' +root = r'/content/MakeItTalk/PreprocessedVox_imagetranslation' +src_dir = os.path.join(root, 'raw_fl3d') +# mp4_dir = r'/mnt/nfs/scratch1/yangzhou/PreprocessedVox_mp4' +mp4_dir = r'/content/MakeItTalk/mp4' +# jpg_dir = os.path.join(root, 'tmp_v') +ckpt_dir = os.path.join(root, 'ckpt') +log_dir = os.path.join(root, 'log') ''' Step 1. Data preparation ''' # landmark extraction diff --git a/src/dataset/image_translation/data_preparation.py b/src/dataset/image_translation/data_preparation.py index 8cc4d59..3167e69 100644 --- a/src/dataset/image_translation/data_preparation.py +++ b/src/dataset/image_translation/data_preparation.py @@ -8,18 +8,18 @@ """ -import os, glob, time, sys +import os +import glob +import time +import sys import numpy as np import cv2 from src.dataset.utils.Av2Flau_Convertor import Av2Flau_Convertor import platform -if platform.release() == '4.4.0-83-generic': - src_dir = r'/mnt/ntfs/Dataset/TalkingToon/VoxCeleb2_imagetranslation/raw_fl3d' - mp4_dir = r'/mnt/ntfs/Dataset/VoxCeleb2/train_set/dev/mp4' -else: - src_dir = r'/mnt/nfs/work1/kalo/yangzhou/VoxCeleb2/train_set/dev/mp4' - out_dir = r'/mnt/nfs/scratch1/yangzhou/VoxCeleb2_compressed_imagetranslation' +src_dir = r'/content/MakeItTalk/mp4' +out_dir = r'/content/MakeItTalk/PreprocessedVox_imagetranslation' + def landmark_extraction(si, ei): ''' @@ -35,7 +35,6 @@ def landmark_extraction(si, ei): except: pass - if(not os.path.isfile(os.path.join(out_dir, 'filename_index_new.txt'))): # generate all file list @@ -50,7 +49,7 @@ def landmark_extraction(si, ei): clips.sort() for clip in clips: videos = glob.glob1(os.path.join(src_dir, id, clip), '*.mp4') - clip_len_count[len(videos)] +=1 + clip_len_count[len(videos)] += 1 # if(len(videos) > 10 and len(videos) < 30): # id_clip_list.append((id, clip)) id_clip_list.append((id, clip)) @@ -98,8 +97,10 @@ def landmark_extraction(si, ei): c = Av2Flau_Convertor(video_dir=os.path.join(src_dir, file), out_dir=out_dir, idx=idx) - c.convert() # (save_audio=False, register=False, show=False) - print('Idx: {}, Processed time (min): {}'.format(idx, (time.time() - st) / 60.0)) + c.convert() # (save_audio=False, register=False, show=False) + print('Idx: {}, Processed time (min): {}'.format( + idx, (time.time() - st) / 60.0)) + def landmark_image_to_data(si, ei, show=False): ''' @@ -132,13 +133,15 @@ def landmark_image_to_data(si, ei, show=False): print('Unable to open video file') exit(0) - if(show==True): + if(show == True): length = int(video.get(cv2.CAP_PROP_FRAME_COUNT)) fps = video.get(cv2.CAP_PROP_FPS) w = int(video.get(cv2.CAP_PROP_FRAME_WIDTH)) h = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT)) - print('Process Video {}, len: {}, FPS: {:.2f}, W X H: {} x {}'.format(video_dir, length, fps, w, h)) - writer = cv2.VideoWriter('a.mp4', cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'), fps, (512, 256)) + print('Process Video {}, len: {}, FPS: {:.2f}, W X H: {} x {}'.format( + video_dir, length, fps, w, h)) + writer = cv2.VideoWriter('a.mp4', cv2.VideoWriter_fourcc( + 'M', 'J', 'P', 'G'), fps, (512, 256)) # skip first several frames due to landmark extraction start_idx = fls[0, 0].astype(int) @@ -184,9 +187,9 @@ def landmark_image_to_data(si, ei, show=False): ret, img_video = video.read() frame = np.concatenate((img_fl, img_video), axis=2) - frame = cv2.resize(frame, (256, 256)) # 256 x 256 6 + frame = cv2.resize(frame, (256, 256)) # 256 x 256 6 frames.append(frame) - frames = np.stack(frames, axis=0).astype(int) # N x 256 x 256 x 6 + frames = np.stack(frames, axis=0).astype(int) # N x 256 x 256 x 6 pf[fls_filename] = frames # save to pickle file @@ -201,7 +204,8 @@ def vis_landmark_on_img(img, shape, linewidth=2): def draw_curve(idx_list, color=(0, 255, 0), loop=False, lineWidth=linewidth): for i in idx_list: - cv2.line(img, (shape[i, 0], shape[i, 1]), (shape[i + 1, 0], shape[i + 1, 1]), color, lineWidth) + cv2.line(img, (shape[i, 0], shape[i, 1]), + (shape[i + 1, 0], shape[i + 1, 1]), color, lineWidth) if (loop): cv2.line(img, (shape[idx_list[0], 0], shape[idx_list[0], 1]), (shape[idx_list[-1] + 1, 0], shape[idx_list[-1] + 1, 1]), color, lineWidth) @@ -225,7 +229,8 @@ def vis_landmark_on_img98(img, shape, linewidth=2): def draw_curve(idx_list, color=(0, 255, 0), loop=False, lineWidth=linewidth): for i in idx_list: - cv2.line(img, (shape[i, 0], shape[i, 1]), (shape[i + 1, 0], shape[i + 1, 1]), color, lineWidth) + cv2.line(img, (shape[i, 0], shape[i, 1]), + (shape[i + 1, 0], shape[i + 1, 1]), color, lineWidth) if (loop): cv2.line(img, (shape[idx_list[0], 0], shape[idx_list[0], 1]), (shape[idx_list[-1] + 1, 0], shape[idx_list[-1] + 1, 1]), color, lineWidth) @@ -249,13 +254,15 @@ def vis_landmark_on_img74(img, shape, linewidth=2): def draw_curve(idx_list, color=(0, 255, 0), loop=False, lineWidth=linewidth): for i in idx_list: - cv2.line(img, (shape[i, 0], shape[i, 1]), (shape[i + 1, 0], shape[i + 1, 1]), color, lineWidth) + cv2.line(img, (shape[i, 0], shape[i, 1]), + (shape[i + 1, 0], shape[i + 1, 1]), color, lineWidth) if (loop): cv2.line(img, (shape[idx_list[0], 0], shape[idx_list[0], 1]), (shape[idx_list[-1] + 1, 0], shape[idx_list[-1] + 1, 1]), color, lineWidth) draw_curve(list(range(0, 16)), color=(255, 144, 25)) # jaw - draw_curve(list(range(17, 21)), color=(50, 205, 50), loop=False) # eye brow + draw_curve(list(range(17, 21)), color=( + 50, 205, 50), loop=False) # eye brow draw_curve(list(range(22, 26)), color=(50, 205, 50), loop=False) draw_curve(list(range(27, 35)), color=(208, 224, 63)) # nose draw_curve(list(range(36, 43)), loop=True, color=(71, 99, 255)) # eyes @@ -263,4 +270,4 @@ def draw_curve(idx_list, color=(0, 255, 0), loop=False, lineWidth=linewidth): draw_curve(list(range(52, 63)), loop=True, color=(238, 130, 238)) # mouth draw_curve(list(range(64, 71)), loop=True, color=(238, 130, 238)) - return img \ No newline at end of file + return img diff --git a/src/dataset/image_translation/image_translation_dataset.py b/src/dataset/image_translation/image_translation_dataset.py index caf9886..0ba8381 100644 --- a/src/dataset/image_translation/image_translation_dataset.py +++ b/src/dataset/image_translation/image_translation_dataset.py @@ -1,15 +1,17 @@ """ # Copyright 2020 Adobe # All Rights Reserved. - + # NOTICE: Adobe permits you to use, modify, and distribute this file in # accordance with the terms of the Adobe license agreement accompanying # it. - + """ import torch.utils.data as data -import os, glob, platform +import os +import glob +import platform import numpy as np import cv2 import torch @@ -18,7 +20,7 @@ from thirdparty.AdaptiveWingLoss.utils.utils import get_preds_fromhm -from scipy.io import wavfile as wav +from scipy.io import wavfile as wav from scipy.signal import stft @@ -26,12 +28,8 @@ class image_translation_raw_dataset(data.Dataset): def __init__(self, num_frames=16): - if platform.release() == '4.4.0-83-generic': # stargazer - self.src_dir = r'/mnt/ntfs/Dataset/TalkingToon/VoxCeleb2_imagetranslation/raw_fl3d' - self.mp4_dir = r'/mnt/ntfs/Dataset/VoxCeleb2/train_set/dev/mp4' - else: # gypsum - self.src_dir = r'/mnt/nfs/scratch1/yangzhou/VoxCeleb2_compressed_imagetranslation/raw_fl3d' # raw vox with 1 per vid - self.mp4_dir = r'/mnt/nfs/work1/kalo/yangzhou/VoxCeleb2/train_set/dev/mp4' + self.src_dir = r'/content/MakeItTalk/PreprocessedVox_imagetranslation/raw_fl3d' + self.mp4_dir = r'/content/MakeItTalk/mp4' self.fls_filenames = glob.glob1(self.src_dir, '*') self.num_random_frames = num_frames + 1 @@ -54,7 +52,8 @@ def __getitem__(self, item): mp4_id = mp4_filename[0].split('_')[-1] mp4_vname = mp4_filename[1] mp4_vid = mp4_filename[2][:-3] - video_dir = os.path.join(self.mp4_dir, mp4_id, mp4_vname, mp4_vid + '.mp4') + video_dir = os.path.join(self.mp4_dir, mp4_id, + mp4_vname, mp4_vid + '.mp4') # print('============================\nvideo_dir : ' + video_dir, item) # ====================================================================== @@ -70,7 +69,8 @@ def __getitem__(self, item): # save video and landmark in parallel frames = [] - random_frame_indices = np.random.permutation(fls.shape[0]-2)[0:self.num_random_frames] + random_frame_indices = np.random.permutation( + fls.shape[0]-2)[0:self.num_random_frames] for j in range(int(fls.shape[0])): ret, img_video = video.read() @@ -86,29 +86,29 @@ def __getitem__(self, item): frame = cv2.resize(frame, (256, 256)) # 256 x 256 6 frames.append(frame) - frames = np.stack(frames, axis=0).astype(np.float32)/255.0 # N x 256 x 256 x 6 + frames = np.stack(frames, axis=0).astype( + np.float32)/255.0 # N x 256 x 256 x 6 - image_in = np.concatenate([frames[0:-1, :, :, 0:3], frames[1:, :, :, 3:6]], axis=3) + image_in = np.concatenate( + [frames[0:-1, :, :, 0:3], frames[1:, :, :, 3:6]], axis=3) image_out = frames[0:-1, :, :, 3:6] - image_in, image_out = np.swapaxes(image_in, 1, 3), np.swapaxes(image_out, 1, 3) + image_in, image_out = np.swapaxes( + image_in, 1, 3), np.swapaxes(image_out, 1, 3) return image_in, image_out def my_collate(self, batch): - batch = filter(lambda x:x is not None, batch) + batch = filter(lambda x: x is not None, batch) return default_collate(batch) + class image_translation_raw74_dataset(data.Dataset): def __init__(self, num_frames=16): - if platform.release() == '4.4.0-83-generic': # stargazer - self.src_dir = r'/mnt/ntfs/Dataset/TalkingToon/VoxCeleb2_imagetranslation/raw_fl3d' - self.mp4_dir = r'/mnt/ntfs/Dataset/VoxCeleb2/train_set/dev/mp4' - else: # gypsum - self.src_dir = r'/mnt/nfs/scratch1/yangzhou/VoxCeleb2_compressed_imagetranslation/raw_fl3d' # raw vox with 1 per vid - self.mp4_dir = r'/mnt/nfs/work1/kalo/yangzhou/VoxCeleb2/train_set/dev/mp4' + self.src_dir = r'/content/MakeItTalk/PreprocessedVox_imagetranslation/raw_fl3d' + self.mp4_dir = r'/content/MakeItTalk/mp4' self.fls_filenames = glob.glob1(self.src_dir, '*') self.num_random_frames = num_frames + 1 @@ -131,7 +131,8 @@ def __getitem__(self, item): mp4_id = mp4_filename[0].split('_')[-1] mp4_vname = mp4_filename[1] mp4_vid = mp4_filename[2][:-3] - video_dir = os.path.join(self.mp4_dir, mp4_id, mp4_vname, mp4_vid + '.mp4') + video_dir = os.path.join(self.mp4_dir, mp4_id, + mp4_vname, mp4_vid + '.mp4') # print('============================\nvideo_dir : ' + video_dir, item) # ====================================================================== @@ -148,7 +149,8 @@ def __getitem__(self, item): # save video and landmark in parallel frames = [] fan_predict_landmarks = [] - random_frame_indices = np.random.permutation(fls.shape[0]-2)[0:self.num_random_frames] + random_frame_indices = np.random.permutation( + fls.shape[0]-2)[0:self.num_random_frames] for j in range(int(fls.shape[0])): ret, img_video = video.read() @@ -169,19 +171,16 @@ def __getitem__(self, item): return image_in, image_out, fan_predict_landmarks[0:-1] def my_collate(self, batch): - batch = filter(lambda x:x is not None, batch) + batch = filter(lambda x: x is not None, batch) return default_collate(batch) + class image_translation_raw_test_dataset(data.Dataset): def __init__(self, num_frames=16): - if platform.release() == '4.4.0-83-generic': - self.src_dir = r'/mnt/ntfs/Dataset/TalkingToon/VoxCeleb2_imagetranslation/raw_fl3d' - self.mp4_dir = r'/mnt/ntfs/Dataset/VoxCeleb2/train_set/dev/mp4' - else: - self.src_dir = r'/mnt/nfs/scratch1/yangzhou/VoxCeleb2_compressed_imagetranslation/raw_fl3d' - self.mp4_dir = r'/mnt/nfs/work1/kalo/yangzhou/VoxCeleb2/train_set/dev/mp4' + self.src_dir = r'/content/MakeItTalk/PreprocessedVox_imagetranslation/raw_fl3d' + self.mp4_dir = r'/content/MakeItTalk/mp4' self.fls_filenames = glob.glob1(self.src_dir, '*') self.num_random_frames = num_frames + 1 @@ -205,7 +204,8 @@ def __getitem__(self, item): mp4_id = mp4_filename[0].split('_')[-1] mp4_vname = mp4_filename[1] mp4_vid = mp4_filename[2][:-3] - random_video_dir = os.path.join(self.mp4_dir, mp4_id, mp4_vname, mp4_vid + '.mp4') + random_video_dir = os.path.join( + self.mp4_dir, mp4_id, mp4_vname, mp4_vid + '.mp4') print('============================\nvideo_dir : ' + random_video_dir, item) random_video = cv2.VideoCapture(random_video_dir) if (random_video.isOpened() == False): @@ -219,7 +219,8 @@ def __getitem__(self, item): mp4_id = mp4_filename[0].split('_')[-1] mp4_vname = mp4_filename[1] mp4_vid = mp4_filename[2][:-3] - video_dir = os.path.join(self.mp4_dir, mp4_id, mp4_vname, mp4_vid + '.mp4') + video_dir = os.path.join(self.mp4_dir, mp4_id, + mp4_vname, mp4_vid + '.mp4') # print('============================\nvideo_dir : ' + video_dir, item) # ====================================================================== @@ -249,27 +250,26 @@ def __getitem__(self, item): frame = cv2.resize(frame, (256, 256)) # 256 x 256 6 frames.append(frame) - frames = np.stack(frames, axis=0).astype(np.float32)/255.0 # N x 256 x 256 x 6 + frames = np.stack(frames, axis=0).astype( + np.float32)/255.0 # N x 256 x 256 x 6 image_in = frames[:, :, :, 0:6] image_out = frames[:, :, :, 6:9] - image_in, image_out = np.swapaxes(image_in, 1, 3), np.swapaxes(image_out, 1, 3) + image_in, image_out = np.swapaxes( + image_in, 1, 3), np.swapaxes(image_out, 1, 3) return image_in, image_out def my_collate(self, batch): - batch = filter(lambda x:x is not None, batch) + batch = filter(lambda x: x is not None, batch) return default_collate(batch) + class image_translation_preprocessed_dataset(data.Dataset): def __init__(self, num_frames=16): - if platform.release() == '4.4.0-83-generic': - self.src_dir = r'/mnt/ntfs/Dataset/TalkingToon/VoxCeleb2_imagetranslation/raw_fl3d' - self.mp4_dir = r'/mnt/ntfs/Dataset/VoxCeleb2/train_set/dev/mp4' - else: - self.src_dir = r'/mnt/nfs/scratch1/yangzhou/PreprocessedVox_imagetranslation/raw_fl3d' # first order - self.mp4_dir = r'/mnt/nfs/scratch1/yangzhou/PreprocessedVox_mp4' + self.src_dir = r'/content/MakeItTalk/PreprocessedVox_imagetranslation/raw_fl3d' + self.mp4_dir = r'/content/MakeItTalk/mp4' self.fls_filenames = glob.glob1(self.src_dir, '*') self.num_random_frames = num_frames + 1 @@ -303,7 +303,8 @@ def __getitem__(self, item): # save video and landmark in parallel frames = [] - random_frame_indices = np.random.permutation(int(fls.shape[0]//self.fps_scale)-2)[0:self.num_random_frames] + random_frame_indices = np.random.permutation( + int(fls.shape[0]//self.fps_scale)-2)[0:self.num_random_frames] for j in range(int(fls.shape[0]//self.fps_scale)): ret, img_video = video.read() @@ -317,30 +318,28 @@ def __getitem__(self, item): frame = np.concatenate((img_fl, img_video), axis=2) frames.append(frame) - frames = np.stack(frames, axis=0).astype(np.float32)/255.0 # N x 256 x 256 x 6 + frames = np.stack(frames, axis=0).astype( + np.float32)/255.0 # N x 256 x 256 x 6 - image_in = np.concatenate([frames[0:-1, :, :, 0:3], frames[1:, :, :, 3:6]], axis=3) + image_in = np.concatenate( + [frames[0:-1, :, :, 0:3], frames[1:, :, :, 3:6]], axis=3) image_out = frames[0:-1, :, :, 3:6] - image_in, image_out = np.swapaxes(image_in, 1, 3), np.swapaxes(image_out, 1, 3) + image_in, image_out = np.swapaxes( + image_in, 1, 3), np.swapaxes(image_out, 1, 3) return image_in, image_out def my_collate(self, batch): - batch = filter(lambda x:x is not None, batch) + batch = filter(lambda x: x is not None, batch) return default_collate(batch) + class image_translation_preprocessed_test_dataset(data.Dataset): def __init__(self, num_frames=16): - if platform.release() == '4.4.0-83-generic': - self.src_dir = r'/mnt/ntfs/Dataset/TalkingToon/VoxCeleb2_imagetranslation/raw_fl3d' - self.mp4_dir = r'/mnt/ntfs/Dataset/VoxCeleb2/train_set/dev/mp4' - else: - # self.src_dir = r'/mnt/nfs/scratch1/yangzhou/VoxCeleb2_imagetranslation/raw_fl3d' - # self.mp4_dir = r'/mnt/nfs/work1/kalo/yangzhou/VoxCeleb2/train_set/dev/mp4' - self.src_dir = r'/mnt/nfs/scratch1/yangzhou/PreprocessedVox_imagetranslation/raw_fl3d' - self.mp4_dir = r'/mnt/nfs/scratch1/yangzhou/PreprocessedVox_mp4' + self.src_dir = r'/content/MakeItTalk/PreprocessedVox_imagetranslation/raw_fl3d' + self.mp4_dir = r'/content/MakeItTalk/mp4' self.fls_filenames = glob.glob1(self.src_dir, '*') self.num_random_frames = num_frames + 1 @@ -363,7 +362,8 @@ def __getitem__(self, item): # load random face random_fls_filename = self.fls_filenames[max(item-1, 0)] # random_fls_filename = self.fls_filenames[max(item-1, 0)] - random_video_dir = os.path.join(self.mp4_dir, random_fls_filename[10:-7] + '.mp4') + random_video_dir = os.path.join( + self.mp4_dir, random_fls_filename[10:-7] + '.mp4') random_video = cv2.VideoCapture(random_video_dir) if (random_video.isOpened() == False): print('Unable to open video file') @@ -399,18 +399,21 @@ def __getitem__(self, item): # frame = cv2.resize(frame, (256, 256)) # 256 x 256 6 frames.append(frame) - frames = np.stack(frames, axis=0).astype(np.float32)/255.0 # N x 256 x 256 x 9 + frames = np.stack(frames, axis=0).astype( + np.float32)/255.0 # N x 256 x 256 x 9 image_in = frames[:, :, :, 0:6] image_out = frames[:, :, :, 6:9] - image_in, image_out = np.swapaxes(image_in, 1, 3), np.swapaxes(image_out, 1, 3) + image_in, image_out = np.swapaxes( + image_in, 1, 3), np.swapaxes(image_out, 1, 3) return image_in, image_out def my_collate(self, batch): - batch = filter(lambda x:x is not None, batch) + batch = filter(lambda x: x is not None, batch) return default_collate(batch) + class image_translation_raw98_dataset(data.Dataset): """ Online landmark extraction with AWings @@ -419,15 +422,12 @@ class image_translation_raw98_dataset(data.Dataset): def __init__(self, num_frames=1): - if platform.release() == '4.4.0-83-generic': # stargazer - self.src_dir = r'/mnt/ntfs/Dataset/TalkingToon/VoxCeleb2_imagetranslation' - self.mp4_dir = r'/mnt/ntfs/Dataset/VoxCeleb2/train_set/dev/mp4' - else: - self.src_dir = r'/mnt/nfs/scratch1/yangzhou/VoxCeleb2_compressed_imagetranslation' - self.mp4_dir = r'/mnt/nfs/work1/kalo/yangzhou/VoxCeleb2/train_set/dev/mp4' + self.src_dir = r'/content/MakeItTalk/PreprocessedVox_imagetranslation/raw_fl3d' + self.mp4_dir = r'/content/MakeItTalk/mp4' # self.fls_filenames = glob.glob1(self.src_dir, '*') - self.fls_filenames = np.loadtxt(os.path.join(self.src_dir, 'filename_index.txt'), dtype=str)[:, 1] + self.fls_filenames = np.loadtxt(os.path.join( + self.src_dir, 'filename_index.txt'), dtype=str)[:, 1] self.num_random_frames = num_frames + 1 print(os.name, self.fls_filenames.shape) @@ -441,7 +441,8 @@ def __getitem__(self, item): """ for i in range(5): - fls_filename = self.fls_filenames[(item+i)%self.fls_filenames.shape[0]] + fls_filename = self.fls_filenames[( + item+i) % self.fls_filenames.shape[0]] # load mp4 file # ================= raw VOX version ================================ @@ -449,7 +450,8 @@ def __getitem__(self, item): mp4_id = mp4_filename[0].split('_')[-1] mp4_vname = mp4_filename[1] mp4_vid = mp4_filename[2] - video_dir = os.path.join(self.mp4_dir, mp4_id, mp4_vname, mp4_vid + '.mp4') + video_dir = os.path.join( + self.mp4_dir, mp4_id, mp4_vname, mp4_vid + '.mp4') # print('============================\nvideo_dir : ' + video_dir, item) # ====================================================================== @@ -463,7 +465,8 @@ def __getitem__(self, item): # save video and landmark in parallel frames = [] - random_frame_indices = np.random.permutation(length-2)[0:self.num_random_frames] + random_frame_indices = np.random.permutation( + length-2)[0:self.num_random_frames] for j in range(length): ret, img = video.read() @@ -475,7 +478,7 @@ def __getitem__(self, item): frames = np.stack(frames, axis=0).astype(np.float32)/255.0 image_in = frames[1:, :, :] - image_out = frames[0:-1, :, :] # N x 3 x 256 x 256 + image_out = frames[0:-1, :, :] # N x 3 x 256 x 256 return image_in, image_out @@ -492,7 +495,8 @@ def __getitem_along_with_fa__(self, item): mp4_id = mp4_filename[0].split('_')[-1] mp4_vname = mp4_filename[1] mp4_vid = mp4_filename[2] - video_dir = os.path.join(self.mp4_dir, mp4_id, mp4_vname, mp4_vid + '.mp4') + video_dir = os.path.join(self.mp4_dir, mp4_id, + mp4_vname, mp4_vid + '.mp4') # print('============================\nvideo_dir : ' + video_dir, item) # ====================================================================== @@ -504,7 +508,8 @@ def __getitem_along_with_fa__(self, item): # save video and landmark in parallel frames = [] - random_frame_indices = np.random.permutation(length-2)[0:self.num_random_frames] + random_frame_indices = np.random.permutation( + length-2)[0:self.num_random_frames] for j in range(length): ret, img = video.read() @@ -513,7 +518,8 @@ def __getitem_along_with_fa__(self, item): # online landmark img_video = cv2.resize(img, (256, 256)) img = img_video.transpose((2, 0, 1)) / 255.0 - inputs = torch.tensor(img, dtype=torch.float32, requires_grad=False).unsqueeze(0).to(self.device) + inputs = torch.tensor( + img, dtype=torch.float32, requires_grad=False).unsqueeze(0).to(self.device) with torch.no_grad(): outputs, boundary_channels = self.model(inputs) pred_heatmap = outputs[-1][:, :-1, :, :][0].detach().cpu() @@ -521,33 +527,34 @@ def __getitem_along_with_fa__(self, item): pred_landmarks = pred_landmarks.squeeze().numpy() * 4 img_fl = np.ones(shape=(256, 256, 3)) * 255 - img_fl = vis_landmark_on_img98(img_fl * 255.0, pred_landmarks) # 98x2 + img_fl = vis_landmark_on_img98( + img_fl * 255.0, pred_landmarks) # 98x2 frame = np.concatenate((img_fl, img_video), axis=2) frames.append(frame) - frames = np.stack(frames, axis=0).astype(np.float32)/255.0 # N x 256 x 256 x 6 + frames = np.stack(frames, axis=0).astype( + np.float32)/255.0 # N x 256 x 256 x 6 - image_in = np.concatenate([frames[0:-1, :, :, 0:3], frames[1:, :, :, 3:6]], axis=3) + image_in = np.concatenate( + [frames[0:-1, :, :, 0:3], frames[1:, :, :, 3:6]], axis=3) image_out = frames[0:-1, :, :, 3:6] - image_in, image_out = np.swapaxes(image_in, 1, 3), np.swapaxes(image_out, 1, 3) + image_in, image_out = np.swapaxes( + image_in, 1, 3), np.swapaxes(image_out, 1, 3) return image_in, image_out def my_collate(self, batch): - batch = filter(lambda x:x is not None, batch) + batch = filter(lambda x: x is not None, batch) return default_collate(batch) + class image_translation_preprocessed98_dataset(data.Dataset): def __init__(self, num_frames=16): - if platform.release() == '4.4.0-83-generic': - self.src_dir = r'/mnt/ntfs/Dataset/TalkingToon/VoxCeleb2_imagetranslation' - self.mp4_dir = r'/mnt/ntfs/Dataset/VoxCeleb2/train_set/dev/mp4' - else: - self.src_dir = r'/mnt/nfs/scratch1/yangzhou/PreprocessedVox_imagetranslation/raw_fl3d' # first order - self.mp4_dir = r'/mnt/nfs/scratch1/yangzhou/PreprocessedVox_mp4' + self.src_dir = r'/content/MakeItTalk/PreprocessedVox_imagetranslation/raw_fl3d' + self.mp4_dir = r'/content/MakeItTalk/mp4' self.fls_filenames = glob.glob1(self.src_dir, '*') self.num_random_frames = num_frames + 1 @@ -573,7 +580,8 @@ def __getitem__(self, item): # save video and landmark in parallel frames = [] - random_frame_indices = np.random.permutation(length-2)[0:self.num_random_frames] + random_frame_indices = np.random.permutation( + length-2)[0:self.num_random_frames] for j in range(length): ret, img_video = video.read() @@ -582,7 +590,8 @@ def __getitem__(self, item): img_video = cv2.resize(img_video, (256, 256)) frames.append(img_video.transpose((2, 0, 1))) - frames = np.stack(frames, axis=0).astype(np.float32)/255.0 # N x 256 x 256 x 6 + frames = np.stack(frames, axis=0).astype( + np.float32)/255.0 # N x 256 x 256 x 6 image_in = frames[1:, :, :] image_out = frames[0:-1, :, :] # N x 3 x 256 x 256 @@ -590,22 +599,20 @@ def __getitem__(self, item): return image_in, image_out def my_collate(self, batch): - batch = filter(lambda x:x is not None, batch) + batch = filter(lambda x: x is not None, batch) return default_collate(batch) + class image_translation_raw98_test_dataset(data.Dataset): def __init__(self, num_frames=16): - if platform.release() == '4.4.0-83-generic': - self.src_dir = r'/mnt/ntfs/Dataset/TalkingToon/VoxCeleb2_imagetranslation' - self.mp4_dir = r'/mnt/ntfs/Dataset/VoxCeleb2/train_set/dev/mp4' - else: - self.src_dir = r'/mnt/nfs/scratch1/yangzhou/VoxCeleb2_compressed_imagetranslation' - self.mp4_dir = r'/mnt/nfs/work1/kalo/yangzhou/VoxCeleb2/train_set/dev/mp4' + self.src_dir = r'/content/MakeItTalk/PreprocessedVox_imagetranslation/raw_fl3d' + self.mp4_dir = r'/content/MakeItTalk/mp4' # self.fls_filenames = glob.glob1(self.src_dir, '*') - self.fls_filenames = np.loadtxt(os.path.join(self.src_dir, 'filename_index.txt'), dtype=str)[:, 1] + self.fls_filenames = np.loadtxt(os.path.join( + self.src_dir, 'filename_index.txt'), dtype=str)[:, 1] self.num_random_frames = num_frames + 1 @@ -623,7 +630,8 @@ def __getitem__(self, item): mp4_id = mp4_filename[0].split('_')[-1] mp4_vname = mp4_filename[1] mp4_vid = mp4_filename[2] - random_video_dir = os.path.join(self.mp4_dir, mp4_id, mp4_vname, mp4_vid + '.mp4') + random_video_dir = os.path.join( + self.mp4_dir, mp4_id, mp4_vname, mp4_vid + '.mp4') print('============================\nvideo_dir : ' + random_video_dir, item) random_video = cv2.VideoCapture(random_video_dir) if (random_video.isOpened() == False): @@ -638,7 +646,8 @@ def __getitem__(self, item): mp4_id = mp4_filename[0].split('_')[-1] mp4_vname = mp4_filename[1] mp4_vid = mp4_filename[2] - video_dir = os.path.join(self.mp4_dir, mp4_id, mp4_vname, mp4_vid + '.mp4') + video_dir = os.path.join(self.mp4_dir, mp4_id, + mp4_vname, mp4_vid + '.mp4') # print('============================\nvideo_dir : ' + video_dir, item) # ====================================================================== @@ -658,28 +667,24 @@ def __getitem__(self, item): frame = np.concatenate((random_face, img_video), axis=2) frames.append(frame.transpose((2, 0, 1))) - frames = np.stack(frames, axis=0).astype(np.float32) / 255.0 # N x 256 x 256 x 9 + frames = np.stack(frames, axis=0).astype( + np.float32) / 255.0 # N x 256 x 256 x 9 image_in = frames[:, 0:3] image_out = frames[:, 3:6] return image_in, image_out def my_collate(self, batch): - batch = filter(lambda x:x is not None, batch) + batch = filter(lambda x: x is not None, batch) return default_collate(batch) + class image_translation_preprocessed98_test_dataset(data.Dataset): def __init__(self, num_frames=16): - if platform.release() == '4.4.0-83-generic': - self.src_dir = r'/mnt/ntfs/Dataset/TalkingToon/VoxCeleb2_imagetranslation/raw_fl3d' - self.mp4_dir = r'/mnt/ntfs/Dataset/VoxCeleb2/train_set/dev/mp4' - else: - # self.src_dir = r'/mnt/nfs/scratch1/yangzhou/VoxCeleb2_imagetranslation/raw_fl3d' - # self.mp4_dir = r'/mnt/nfs/work1/kalo/yangzhou/VoxCeleb2/train_set/dev/mp4' - self.src_dir = r'/mnt/nfs/scratch1/yangzhou/PreprocessedVox_imagetranslation/raw_fl3d' - self.mp4_dir = r'/mnt/nfs/scratch1/yangzhou/PreprocessedVox_mp4' + self.src_dir = r'/content/MakeItTalk/PreprocessedVox_imagetranslation/raw_fl3d' + self.mp4_dir = r'/content/MakeItTalk/mp4' self.fls_filenames = glob.glob1(self.src_dir, '*') self.num_random_frames = num_frames + 1 @@ -695,7 +700,8 @@ def __getitem__(self, item): # load random face random_fls_filename = self.fls_filenames[max(item-10, 0)] # random_fls_filename = self.fls_filenames[max(item-1, 0)] - random_video_dir = os.path.join(self.mp4_dir, random_fls_filename[10:-7] + '.mp4') + random_video_dir = os.path.join( + self.mp4_dir, random_fls_filename[10:-7] + '.mp4') random_video = cv2.VideoCapture(random_video_dir) if (random_video.isOpened() == False): print('Unable to open video file') @@ -722,7 +728,8 @@ def __getitem__(self, item): frame = np.concatenate((random_face, img_video), axis=2) frames.append(frame.transpose((2, 0, 1))) - frames = np.stack(frames, axis=0).astype(np.float32)/255.0 # N x 256 x 256 x 9 + frames = np.stack(frames, axis=0).astype( + np.float32)/255.0 # N x 256 x 256 x 9 image_in = frames[:, 0:3] image_out = frames[:, 3:6] @@ -730,9 +737,10 @@ def __getitem__(self, item): return image_in, image_out def my_collate(self, batch): - batch = filter(lambda x:x is not None, batch) + batch = filter(lambda x: x is not None, batch) return default_collate(batch) + class image_translation_raw98_with_audio_dataset(data.Dataset): """ Online landmark extraction with AWings @@ -741,15 +749,12 @@ class image_translation_raw98_with_audio_dataset(data.Dataset): def __init__(self, num_frames=1): - if platform.release() == '4.4.0-83-generic': # stargazer - self.src_dir = r'/mnt/ntfs/Dataset/TalkingToon/VoxCeleb2_imagetranslation' - self.mp4_dir = r'/mnt/ntfs/Dataset/VoxCeleb2/train_set/dev/mp4' - else: - self.src_dir = r'/mnt/nfs/scratch1/yangzhou/VoxCeleb2_compressed_imagetranslation' - self.mp4_dir = r'/mnt/nfs/work1/kalo/yangzhou/VoxCeleb2/train_set/dev/mp4' + self.src_dir = r'/content/MakeItTalk/PreprocessedVox_imagetranslation' + self.mp4_dir = r'/content/MakeItTalk/mp4' # self.fls_filenames = glob.glob1(self.src_dir, '*') - self.fls_filenames = np.loadtxt(os.path.join(self.src_dir, 'filename_index.txt'), dtype=str)[:, 1] + self.fls_filenames = np.loadtxt(os.path.join( + self.src_dir, 'filename_index.txt'), dtype=str)[:, 1] self.num_random_frames = num_frames + 1 print(os.name, self.fls_filenames.shape) @@ -763,7 +768,8 @@ def __getitem__(self, item): """ for i in range(5): - fls_filename = self.fls_filenames[(item+i)%self.fls_filenames.shape[0]] + fls_filename = self.fls_filenames[( + item+i) % self.fls_filenames.shape[0]] # load mp4 file # ================= raw VOX version ================================ @@ -771,7 +777,8 @@ def __getitem__(self, item): mp4_id = mp4_filename[0].split('_')[-1] mp4_vname = mp4_filename[1] mp4_vid = mp4_filename[2] - video_dir = os.path.join(self.mp4_dir, mp4_id, mp4_vname, mp4_vid + '.mp4') + video_dir = os.path.join( + self.mp4_dir, mp4_id, mp4_vname, mp4_vid + '.mp4') # print('============================\nvideo_dir : ' + video_dir, item) # ====================================================================== @@ -785,7 +792,8 @@ def __getitem__(self, item): # save video and landmark in parallel frames = [] - random_frame_indices = np.random.permutation(max(1, length-12))[0:self.num_random_frames] + random_frame_indices = np.random.permutation( + max(1, length-12))[0:self.num_random_frames] random_frame_indices = [item + 5 for item in random_frame_indices] for j in range(length): @@ -798,7 +806,7 @@ def __getitem__(self, item): frames = np.stack(frames, axis=0).astype(np.float32)/255.0 image_in = frames[1:, :, :] - image_out = frames[0:-1, :, :] # N x 3 x 256 x 256 + image_out = frames[0:-1, :, :] # N x 3 x 256 x 256 # audio os.system('ffmpeg -y -loglevel error -i {} -vn -ar 16000 -ac 1 {}'.format( @@ -822,7 +830,8 @@ def __getitem__(self, item): for item in random_frame_indices: sel_audio_clip = stft_abs[:, (item-5)*8:(item+5)*8] assert sel_audio_clip.shape[1] == 80 - audio_in.append(np.expand_dims(cv2.resize(sel_audio_clip, (256, 256)), axis=0)) + audio_in.append(np.expand_dims( + cv2.resize(sel_audio_clip, (256, 256)), axis=0)) audio_in = np.stack(audio_in[0:-1], axis=0).astype(np.float32) # image_in = np.concatenate([image_in, audio_in], axis=1) @@ -830,9 +839,10 @@ def __getitem__(self, item): return image_in, image_out, audio_in def my_collate(self, batch): - batch = filter(lambda x:x is not None, batch) + batch = filter(lambda x: x is not None, batch) return default_collate(batch) + class image_translation_raw98_with_audio_test_dataset(data.Dataset): """ Online landmark extraction with AWings @@ -841,15 +851,12 @@ class image_translation_raw98_with_audio_test_dataset(data.Dataset): def __init__(self, num_frames=1): - if platform.release() == '4.4.0-83-generic': # stargazer - self.src_dir = r'/mnt/ntfs/Dataset/TalkingToon/VoxCeleb2_imagetranslation' - self.mp4_dir = r'/mnt/ntfs/Dataset/VoxCeleb2/train_set/dev/mp4' - else: - self.src_dir = r'/mnt/nfs/scratch1/yangzhou/VoxCeleb2_compressed_imagetranslation' - self.mp4_dir = r'/mnt/nfs/work1/kalo/yangzhou/VoxCeleb2/train_set/dev/mp4' + self.src_dir = r'/content/MakeItTalk/PreprocessedVox_imagetranslation' + self.mp4_dir = r'/content/MakeItTalk/mp4' # self.fls_filenames = glob.glob1(self.src_dir, '*') - self.fls_filenames = np.loadtxt(os.path.join(self.src_dir, 'filename_index.txt'), dtype=str)[:, 1] + self.fls_filenames = np.loadtxt(os.path.join( + self.src_dir, 'filename_index.txt'), dtype=str)[:, 1] self.num_random_frames = num_frames + 1 print(os.name, self.fls_filenames.shape) @@ -867,7 +874,8 @@ def __getitem__(self, item): mp4_id = mp4_filename[0].split('_')[-1] mp4_vname = mp4_filename[1] mp4_vid = mp4_filename[2] - random_video_dir = os.path.join(self.mp4_dir, mp4_id, mp4_vname, mp4_vid + '.mp4') + random_video_dir = os.path.join( + self.mp4_dir, mp4_id, mp4_vname, mp4_vid + '.mp4') print('============================\nvideo_dir : ' + random_video_dir, item) random_video = cv2.VideoCapture(random_video_dir) if (random_video.isOpened() == False): @@ -876,7 +884,6 @@ def __getitem__(self, item): _, random_face = random_video.read() random_face = cv2.resize(random_face, (256, 256)) - fls_filename = self.fls_filenames[item] # load mp4 file # ================= raw VOX version ================================ @@ -884,7 +891,8 @@ def __getitem__(self, item): mp4_id = mp4_filename[0].split('_')[-1] mp4_vname = mp4_filename[1] mp4_vid = mp4_filename[2] - video_dir = os.path.join(self.mp4_dir, mp4_id, mp4_vname, mp4_vid + '.mp4') + video_dir = os.path.join(self.mp4_dir, mp4_id, + mp4_vname, mp4_vid + '.mp4') # print('============================\nvideo_dir : ' + video_dir, item) # ====================================================================== @@ -903,7 +911,8 @@ def __getitem__(self, item): frame = np.concatenate((random_face, img_video), axis=2) frames.append(frame.transpose((2, 0, 1))) - frames = np.stack(frames, axis=0).astype(np.float32) / 255.0 # N x 256 x 256 x 9 + frames = np.stack(frames, axis=0).astype( + np.float32) / 255.0 # N x 256 x 256 x 9 image_in = frames[:, 0:3] image_out = frames[:, 3:6] @@ -930,7 +939,8 @@ def __getitem__(self, item): for item in range(5, length-5): sel_audio_clip = stft_abs[:, (item-5)*8:(item+5)*8] assert sel_audio_clip.shape[1] == 80 - audio_in.append(np.expand_dims(cv2.resize(sel_audio_clip, (256, 256)), axis=0)) + audio_in.append(np.expand_dims( + cv2.resize(sel_audio_clip, (256, 256)), axis=0)) audio_in = np.stack(audio_in, axis=0).astype(np.float32) # image_in = np.concatenate([image_in, audio_in], axis=1) @@ -938,7 +948,7 @@ def __getitem__(self, item): return image_in, image_out, audio_in def my_collate(self, batch): - batch = filter(lambda x:x is not None, batch) + batch = filter(lambda x: x is not None, batch) return default_collate(batch) @@ -947,4 +957,4 @@ def my_collate(self, batch): d_loader = torch.utils.data.DataLoader(d, batch_size=4, shuffle=True) print(len(d)) for i, batch in enumerate(d_loader): - print(i, batch[0].shape, batch[1].shape) \ No newline at end of file + print(i, batch[0].shape, batch[1].shape) diff --git a/testg.py b/testg.py index 8c96386..e554773 100644 --- a/testg.py +++ b/testg.py @@ -26,7 +26,7 @@ def prune_model(model): model.cpu() - DG = tp.DependencyGraph().build_dependency(model, torch.randn(10, 6, 256, 256)) + DG = tp.DependencyGraph().build_dependency(model, torch.randn(5, 6, 256, 256)) def prune_conv(conv, pruned_prob): weight = conv.weight.detach().cpu().numpy() diff --git a/thirdparty/AdaptiveWingLoss/core/models.py b/thirdparty/AdaptiveWingLoss/core/models.py index c3d77c1..33e3978 100644 --- a/thirdparty/AdaptiveWingLoss/core/models.py +++ b/thirdparty/AdaptiveWingLoss/core/models.py @@ -2,16 +2,17 @@ import torch.nn as nn import torch.nn.functional as F import math -from core.coord_conv import CoordConvTh +from thirdparty.AdaptiveWingLoss.core.coord_conv import CoordConvTh def conv3x3(in_planes, out_planes, strd=1, padding=1, - bias=False,dilation=1): + bias=False, dilation=1): "3x3 convolution with padding" return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=strd, padding=padding, bias=bias, dilation=dilation) + class BasicBlock(nn.Module): expansion = 1 @@ -43,6 +44,7 @@ def forward(self, x): return out + class ConvBlock(nn.Module): def __init__(self, in_planes, out_planes): super(ConvBlock, self).__init__() @@ -89,6 +91,7 @@ def forward(self, x): return out3 + class HourGlass(nn.Module): def __init__(self, num_modules, depth, num_features, first_one=False): super(HourGlass, self).__init__() @@ -141,6 +144,7 @@ def forward(self, x, heatmap): x, last_channel = self.coordconv(x, heatmap) return self._forward(self.depth, x), last_channel + class FAN(nn.Module): def __init__(self, num_modules=1, end_relu=False, gray_scale=False, @@ -216,7 +220,7 @@ def forward(self, x): # Predict heatmaps tmp_out = self._modules['l' + str(i)](ll) if self.end_relu: - tmp_out = F.relu(tmp_out) # HACK: Added relu + tmp_out = F.relu(tmp_out) # HACK: Added relu outputs.append(tmp_out) boundary_channels.append(boundary_channel) From e0f36c729b56086d01c185bc95725ddad6915131 Mon Sep 17 00:00:00 2001 From: hack Date: Wed, 23 Dec 2020 16:19:15 +0530 Subject: [PATCH 07/17] fix path --- main_train_image_translation.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/main_train_image_translation.py b/main_train_image_translation.py index fc297a1..d86bf5c 100644 --- a/main_train_image_translation.py +++ b/main_train_image_translation.py @@ -25,7 +25,7 @@ src_dir = os.path.join(root, 'raw_fl3d') # mp4_dir = r'/mnt/nfs/scratch1/yangzhou/PreprocessedVox_mp4' mp4_dir = r'/content/MakeItTalk/mp4' -# jpg_dir = os.path.join(root, 'tmp_v') +jpg_dir = os.path.join(root, 'tmp_v') ckpt_dir = os.path.join(root, 'ckpt') log_dir = os.path.join(root, 'log') From ae6773c8378653ae65024a12b36f2954010e4d3c Mon Sep 17 00:00:00 2001 From: hack Date: Fri, 25 Dec 2020 17:03:54 +0530 Subject: [PATCH 08/17] minor fix in dataset --- main_train_image_translation.py | 2 +- src/approaches/train_image_translation.py | 5 +- .../image_translation_dataset.py | 8 +- src/dataset/utils/Av2Flau_Convertor.py | 99 +++++++++++-------- testg.py | 85 ++++++++-------- 5 files changed, 113 insertions(+), 86 deletions(-) diff --git a/main_train_image_translation.py b/main_train_image_translation.py index d86bf5c..4acf287 100644 --- a/main_train_image_translation.py +++ b/main_train_image_translation.py @@ -67,7 +67,7 @@ parser.add_argument('--comb_fan_awing', default=False, action='store_true') parser.add_argument('--fan_2or3D', type=str, default='3D') -parser.add_argument('--single_test', type=str, default='') +parser.add_argument('--single_test', type=str, default='False') opt_parser = parser.parse_args() diff --git a/src/approaches/train_image_translation.py b/src/approaches/train_image_translation.py index 5745ec3..bc94e4d 100644 --- a/src/approaches/train_image_translation.py +++ b/src/approaches/train_image_translation.py @@ -204,7 +204,10 @@ def __train_pass__(self, epoch, is_training=True): img_fls = np.stack(img_fls, axis=0).astype(np.float32) / 255.0 image_fls_in = torch.tensor( img_fls, requires_grad=False).to(device) - + print("#######################################") + print(image_fls_in.shape) + print(image_in.shape) + print("#######################################") if(self.opt_parser.add_audio_in): # print(image_fls_in.shape, image_in.shape, audio_in.shape) image_in = torch.cat([image_fls_in, image_in, audio_in], dim=1) diff --git a/src/dataset/image_translation/image_translation_dataset.py b/src/dataset/image_translation/image_translation_dataset.py index 0ba8381..8ee9d08 100644 --- a/src/dataset/image_translation/image_translation_dataset.py +++ b/src/dataset/image_translation/image_translation_dataset.py @@ -446,10 +446,10 @@ def __getitem__(self, item): # load mp4 file # ================= raw VOX version ================================ - mp4_filename = fls_filename[:-4].split('_x_') - mp4_id = mp4_filename[0].split('_')[-1] - mp4_vname = mp4_filename[1] - mp4_vid = mp4_filename[2] + mp4_filename = fls_filename[:-4].split('/') + mp4_id = mp4_filename[1] + mp4_vname = mp4_filename[2] + mp4_vid = mp4_filename[3] video_dir = os.path.join( self.mp4_dir, mp4_id, mp4_vname, mp4_vid + '.mp4') # print('============================\nvideo_dir : ' + video_dir, item) diff --git a/src/dataset/utils/Av2Flau_Convertor.py b/src/dataset/utils/Av2Flau_Convertor.py index 91303de..6471c16 100644 --- a/src/dataset/utils/Av2Flau_Convertor.py +++ b/src/dataset/utils/Av2Flau_Convertor.py @@ -49,7 +49,8 @@ def __init__(self, video_dir, out_dir, idx=0): self.input_format = self.video_dir[-4:] # landmark predictor = FANet - self.predictor = face_alignment.FaceAlignment(face_alignment.LandmarksType._2D, device='cuda', flip_input=True) + self.predictor = face_alignment.FaceAlignment( + face_alignment.LandmarksType._2D, device='cuda', flip_input=True) # landmark register self.t_shape_idx = (27, 28, 29, 30, 33, 36, 39, 42, 45) @@ -63,7 +64,8 @@ def convert(self, max_num_frames=250, save_audio=False, show=False, register=Fal # Step 2: detect facial landmark wfn = self.video_dir.replace(self.input_format, '_preclean.mp4') - ret, fl2d, fl3d = self.__video_facial_landmark_detection__(video_dir=wfn, display=False, max_num_frames=max_num_frames) + ret, fl2d, fl3d = self.__video_facial_landmark_detection__( + video_dir=wfn, display=False, max_num_frames=max_num_frames) if (not ret): return if (len(fl3d) < 9): @@ -75,7 +77,8 @@ def convert(self, max_num_frames=250, save_audio=False, show=False, register=Fal np.savetxt(os.path.join(self.out_dir, 'raw_fl3d/fan_{:05d}_{}_3d.txt'.format(self.idx, self.video_name[:-4])), fl3d, fmt='%.2f') if (save_audio): - self.__save_audio__(video_dir=self.video_dir.replace(self.input_format, '_preclean.mp4'), fl3d=fl3d) + self.__save_audio__(video_dir=self.video_dir.replace( + self.input_format, '_preclean.mp4'), fl3d=fl3d) # Step 3.5: merge a/v together (optional) if (show): @@ -86,8 +89,10 @@ def convert(self, max_num_frames=250, save_audio=False, show=False, register=Fal self.idx, self.video_name[:-4])) ) self.__ffmpeg_merge_av__( - video_dir=self.video_dir.replace(self.input_format, '_fl_detect.mp4'), - audio_dir=self.video_dir.replace(self.input_format, '_preclean.mp4'), + video_dir=self.video_dir.replace( + self.input_format, '_fl_detect.mp4'), + audio_dir=self.video_dir.replace( + self.input_format, '_preclean.mp4'), WriteFileName=os.path.join(self.out_dir, 'tmp_v', '{:05d}_{}_fl_av.mp4'.format( self.idx, self.video_name[:-4])), start_end_frame=(int(sf), int(ef))) @@ -95,7 +100,8 @@ def convert(self, max_num_frames=250, save_audio=False, show=False, register=Fal # Step 4: remove tmp files os.remove(self.video_dir.replace(self.input_format, '_preclean.mp4')) if(os.path.isfile(self.video_dir.replace(self.input_format, '_fl_detect.mp4'))): - os.remove(self.video_dir.replace(self.input_format, '_fl_detect.mp4')) + os.remove(self.video_dir.replace( + self.input_format, '_fl_detect.mp4')) # Step 5: register fl3d if (register): @@ -113,8 +119,8 @@ def __preclean_video__(self, WriteFileName='_preclean.mp4', fps=25, sample_rate= Pre-clean downloaded videos. Return false if more than 2 streams found. Then convert it to fps=25, sample_rate=16kHz ''' - input_video_dir = self.video_dir if '_x_' not in self.video_dir else self.video_dir.replace('_x_', '/') - + input_video_dir = self.video_dir if '_x_' not in self.video_dir else self.video_dir.replace( + '_x_', '/') probe = ffmpeg.probe(input_video_dir) # print(probe['streams']) # print(len(probe['streams'])) @@ -131,13 +137,13 @@ def __preclean_video__(self, WriteFileName='_preclean.mp4', fps=25, sample_rate= # create preclean video ( ffmpeg - .input(input_video_dir) - .output(self.video_dir.replace(self.input_format, WriteFileName), - # vcodec=codec['video'], - # acodec=codec['audio'], - r=fps, ar=sample_rate) - .overwrite_output().global_args('-loglevel', 'quiet') - .run() + .input(input_video_dir) + .output(self.video_dir.replace(self.input_format, WriteFileName), + # vcodec=codec['video'], + # acodec=codec['audio'], + r=fps, ar=sample_rate) + .overwrite_output().global_args('-loglevel', 'quiet') + .run() ) return True, self.video_dir.replace(self.input_format, WriteFileName) @@ -168,14 +174,16 @@ def __video_facial_landmark_detection__(self, video_dir=None, display=False, Wri fps = video.get(cv2.CAP_PROP_FPS) w = int(video.get(cv2.CAP_PROP_FRAME_WIDTH)) h = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT)) - print('Process Video {}, len: {}, FPS: {:.2f}, W X H: {} x {}'.format(video_dir, length, fps, w, h)) + print('Process Video {}, len: {}, FPS: {:.2f}, W X H: {} x {}'.format( + video_dir, length, fps, w, h)) if(write): writer = cv2.VideoWriter(self.video_dir.replace(self.input_format, WriteFileName), - cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'), fps, (w, h)) + cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'), fps, (w, h)) video_facial_landmark = [] # face-landmark np array per frame =: idx + [x,y] * 68 - video_facial_landmark_3d = [] # face-landmark np array per frame =: idx + [x,y,z] * 68 + # face-landmark np array per frame =: idx + [x,y,z] * 68 + video_facial_landmark_3d = [] frame_id = 0 not_detected_frames = 0 @@ -195,7 +203,8 @@ def __video_facial_landmark_detection__(self, video_dir=None, display=False, Wri break # dlib facial landmark detect - img_ret, shape, shape_3d = self.__image_facial_landmark_detection__(img=frame) + img_ret, shape, shape_3d = self.__image_facial_landmark_detection__( + img=frame) # successfully detected if (img_ret): @@ -213,12 +222,14 @@ def __video_facial_landmark_detection__(self, video_dir=None, display=False, Wri def interp(last, cur, num, dims=68 * 2 + 1): interp_xys_np = np.zeros((num, dims)) for dim in range(dims): - interp_xys_np[:, dim] = np.interp(np.arange(0, num), [-1, num], [last[dim], cur[dim]]) + interp_xys_np[:, dim] = np.interp( + np.arange(0, num), [-1, num], [last[dim], cur[dim]]) interp_xys_np = np.round(interp_xys_np).astype('int') interp_xys = [list(xy) for xy in interp_xys_np] return interp_xys - interp_xys = interp(video_facial_landmark[-1], [frame_id] + xys, not_detected_frames) + interp_xys = interp( + video_facial_landmark[-1], [frame_id] + xys, not_detected_frames) video_facial_landmark += interp_xys not_detected_frames = 0 @@ -226,7 +237,8 @@ def interp(last, cur, num, dims=68 * 2 + 1): # save landmark/frame_index video_facial_landmark.append([frame_id] + xys) if (shape_3d.any()): - video_facial_landmark_3d.append([frame_id] + list(np.reshape(shape_3d, -1))) + video_facial_landmark_3d.append( + [frame_id] + list(np.reshape(shape_3d, -1))) if(write): frame = self.__vis_landmark_on_img__(frame, shape) @@ -299,7 +311,8 @@ def draw_curve(idx_list, color=(0, 255, 0), loop=False, lineWidth=linewidth): else: def draw_curve(idx_list, color=(0, 255, 0), loop=False, lineWidth=linewidth): for i in idx_list: - cv2.line(img, (shape[i, 0], shape[i, 1]), (shape[i + 1, 0], shape[i + 1, 1]), color, lineWidth) + cv2.line(img, (shape[i, 0], shape[i, 1]), + (shape[i + 1, 0], shape[i + 1, 1]), color, lineWidth) if (loop): cv2.line(img, (shape[idx_list[0], 0], shape[idx_list[0], 1]), (shape[idx_list[-1] + 1, 0], shape[idx_list[-1] + 1, 1]), color, lineWidth) @@ -325,7 +338,8 @@ def __ffmpeg_merge_av__(self, video_dir, audio_dir, WriteFileName, start_end_fra vin = ffmpeg.input(video_dir).video # ain = ffmpeg.input(audio_dir).audio # out = ffmpeg.output(vin, ain, WriteFileName, codec='copy', ss=st, t=tt, shortest=None) - out = ffmpeg.output(vin, WriteFileName, codec='copy', ss=st, t=tt, shortest=None) + out = ffmpeg.output(vin, WriteFileName, codec='copy', + ss=st, t=tt, shortest=None) out = out.overwrite_output().global_args('-loglevel', 'quiet') out.run() @@ -343,13 +357,14 @@ def __save_audio__(self, video_dir, fl3d): spf = float(fps.split('/')[1]) / float(fps.split('/')[0]) st, tt = sf * spf, ef * spf - sf * spf - audio_dir = os.path.join(self.out_dir, 'raw_wav', '{:05d}_{}_audio.wav'.format(self.idx, self.video_name[:-4])) + audio_dir = os.path.join(self.out_dir, 'raw_wav', '{:05d}_{}_audio.wav'.format( + self.idx, self.video_name[:-4])) ( ffmpeg - .input(video_dir) - .output(audio_dir, ss=st, t=tt) - .overwrite_output().global_args('-loglevel', 'quiet') - .run() + .input(video_dir) + .output(audio_dir, ss=st, t=tt) + .overwrite_output().global_args('-loglevel', 'quiet') + .run() ) ''' ======================================================================== @@ -368,11 +383,14 @@ def __single_landmark_3d_register__(self, fl3d, display=False): lines = savgol_filter(fl3d, 7, 3, axis=0) all_landmarks = lines[:, 1:].reshape((-1, 68, 3)) # remove frame idx - w, h = int(np.max(all_landmarks[:, :, 0])) + 20, int(np.max(all_landmarks[:, :, 1])) + 20 + w, h = int(np.max(all_landmarks[:, :, 0])) + \ + 20, int(np.max(all_landmarks[:, :, 1])) + 20 # Step 2 : setup anchor face - print('Using exisiting ' + 'dataset/utils/ANCHOR_T_SHAPE_{}.txt'.format(len(self.t_shape_idx))) - anchor_t_shape = np.loadtxt('dataset/utils/ANCHOR_T_SHAPE_{}.txt'.format(len(self.t_shape_idx))) + print('Using exisiting ' + + 'dataset/utils/ANCHOR_T_SHAPE_{}.txt'.format(len(self.t_shape_idx))) + anchor_t_shape = np.loadtxt( + 'dataset/utils/ANCHOR_T_SHAPE_{}.txt'.format(len(self.t_shape_idx))) registered_landmarks_to_save = [] registered_affine_mat_to_save = [] @@ -389,19 +407,23 @@ def __single_landmark_3d_register__(self, fl3d, display=False): # Step 4 : Affine transform landmarks = np.hstack((landmarks, np.ones((68, 1)))) registered_landmarks = np.dot(T, landmarks.T).T - err = np.mean(np.sqrt(np.sum((registered_landmarks[self.t_shape_idx, 0:3] - anchor_t_shape) ** 2, axis=1))) + err = np.mean(np.sqrt(np.sum( + (registered_landmarks[self.t_shape_idx, 0:3] - anchor_t_shape) ** 2, axis=1))) # print(err, distance, itr) # Step 5 : Save is requested - registered_landmarks_to_save.append([frame_id] + list(registered_landmarks[:, 0:3].reshape(-1))) - registered_affine_mat_to_save.append([frame_id] + list(T.reshape(-1))) + registered_landmarks_to_save.append( + [frame_id] + list(registered_landmarks[:, 0:3].reshape(-1))) + registered_affine_mat_to_save.append( + [frame_id] + list(T.reshape(-1))) # Step 5.5 (optional) : visualize ori / registered faces (Isolated in Black BG) if (display): img = np.zeros((h, w * 2, 3), np.uint8) self.__vis_landmark_on_img__(img, landmarks.astype(np.int)) registered_landmarks[:, 0] += w - self.__vis_landmark_on_img__(img, registered_landmarks.astype(np.int)) + self.__vis_landmark_on_img__( + img, registered_landmarks.astype(np.int)) cv2.imshow('img', img) if (cv2.waitKey(30) == ord('q')): break @@ -418,8 +440,7 @@ def __single_landmark_3d_register__(self, fl3d, display=False): if __name__ == '__main__': - video_dir = r'C:\Users\yangzhou\Videos\004_1.mp4' - out_dir = r'C:\Users\yangzhou\Videos' + video_dir = r'mp4/id00154/6eX78POXvNg/00004.mp4' + out_dir = r'mp4/id00154/' c = Av2Flau_Convertor(video_dir, out_dir, idx=0) c.convert() - diff --git a/testg.py b/testg.py index e554773..1e92355 100644 --- a/testg.py +++ b/testg.py @@ -8,9 +8,12 @@ import torch_pruning as tp model = ResUnetGenerator(input_nc=6, output_nc=3, - num_downs=6, use_dropout=False) -params = sum([np.prod(p.size()) for p in model.parameters()]) -print("Number of Parameters: %.1fM" % (params/1e6)) + num_downs=6, use_dropout=False).to("cuda") +# params = sum([np.prod(p.size()) for p in model.parameters()]) +# print("Number of Parameters: %.1fM" % (params/1e6)) + +print(model) +# summary(model, input_size=(6, 256, 256)) # i = 0 # for m in model.modules(): @@ -24,50 +27,50 @@ # print(i) -def prune_model(model): - model.cpu() - DG = tp.DependencyGraph().build_dependency(model, torch.randn(5, 6, 256, 256)) +# def prune_model(model): +# model.cpu() +# DG = tp.DependencyGraph().build_dependency(model, torch.randn(5, 6, 256, 256)) - def prune_conv(conv, pruned_prob): - weight = conv.weight.detach().cpu().numpy() - out_channels = weight.shape[0] - L1_norm = np.sum(np.abs(weight), axis=(1, 2, 3)) - num_pruned = int(out_channels * pruned_prob) - # remove filters with small L1-Norm - prune_index = np.argsort(L1_norm)[:num_pruned].tolist() - plan = DG.get_pruning_plan(conv, tp.prune_conv, prune_index) - plan.exec() +# def prune_conv(conv, pruned_prob): +# weight = conv.weight.detach().cpu().numpy() +# out_channels = weight.shape[0] +# L1_norm = np.sum(np.abs(weight), axis=(1, 2, 3)) +# num_pruned = int(out_channels * pruned_prob) +# # remove filters with small L1-Norm +# prune_index = np.argsort(L1_norm)[:num_pruned].tolist() +# plan = DG.get_pruning_plan(conv, tp.prune_conv, prune_index) +# plan.exec() - block_prune_probs = [] - for i in range(100): - if i < 30: - block_prune_probs.append(0.1) - if i > 30 and i < 50: - block_prune_probs.append(0.2) - if i > 50: - block_prune_probs.append(0.3) - blk_id = 0 - for m in model.modules(): - if isinstance(m, nn.Conv2d): - prune_conv(m, block_prune_probs[blk_id]) - blk_id += 1 - # if isinstance(m, ResidualBlock): - # for j in m.block.modules(): - # if isinstance(j, nn.Conv2d): - # prune_conv(j, block_prune_probs[blk_id]) - # blk_id += 1 +# block_prune_probs = [] +# for i in range(100): +# if i < 30: +# block_prune_probs.append(0.1) +# if i > 30 and i < 50: +# block_prune_probs.append(0.2) +# if i > 50: +# block_prune_probs.append(0.3) +# blk_id = 0 +# for m in model.modules(): +# if isinstance(m, nn.Conv2d): +# prune_conv(m, block_prune_probs[blk_id]) +# blk_id += 1 +# # if isinstance(m, ResidualBlock): +# # for j in m.block.modules(): +# # if isinstance(j, nn.Conv2d): +# # prune_conv(j, block_prune_probs[blk_id]) +# # blk_id += 1 - return model +# return model -model = prune_model(model).to('cuda') -params = sum([np.prod(p.size()) for p in model.parameters()]) -print("Number of Parameters: %.1fM" % (params/1e6)) -torch.save(model, 'model.h5') +# model = prune_model(model).to('cuda') +# params = sum([np.prod(p.size()) for p in model.parameters()]) +# print("Number of Parameters: %.1fM" % (params/1e6)) +# torch.save(model, 'model.h5') -x = torch.load('model.h5') +# x = torch.load('model.h5') -img = torch.zeros((3, 6, 256, 256)).to('cuda') -print(x(img)) +# img = torch.zeros((3, 6, 256, 256)).to('cuda') +# print(x(img)) # summary(x, input_size=(3, 6, 256, 256)) From 6dd9ee8ffef2a09a85ab2a3f919f0e06da2cd4e0 Mon Sep 17 00:00:00 2001 From: hack Date: Fri, 25 Dec 2020 17:13:23 +0530 Subject: [PATCH 09/17] modify path --- .../image_translation_dataset.py | 49 ++++++++++--------- 1 file changed, 27 insertions(+), 22 deletions(-) diff --git a/src/dataset/image_translation/image_translation_dataset.py b/src/dataset/image_translation/image_translation_dataset.py index 8ee9d08..81f49f9 100644 --- a/src/dataset/image_translation/image_translation_dataset.py +++ b/src/dataset/image_translation/image_translation_dataset.py @@ -24,12 +24,17 @@ from scipy.signal import stft +src_dir = r'/content/MakeItTalk/PreprocessedVox_imagetranslation/raw_fl3d' +mp4_dir = r'/content/MakeItTalk/mp4' +src_outer_dir = r'/content/MakeItTalk/PreprocessedVox_imagetranslation' + + class image_translation_raw_dataset(data.Dataset): def __init__(self, num_frames=16): - self.src_dir = r'/content/MakeItTalk/PreprocessedVox_imagetranslation/raw_fl3d' - self.mp4_dir = r'/content/MakeItTalk/mp4' + self.src_dir = src_dir + self.mp4_dir = mp4_dir self.fls_filenames = glob.glob1(self.src_dir, '*') self.num_random_frames = num_frames + 1 @@ -107,8 +112,8 @@ class image_translation_raw74_dataset(data.Dataset): def __init__(self, num_frames=16): - self.src_dir = r'/content/MakeItTalk/PreprocessedVox_imagetranslation/raw_fl3d' - self.mp4_dir = r'/content/MakeItTalk/mp4' + self.src_dir = src_dir + self.mp4_dir = mp4_dir self.fls_filenames = glob.glob1(self.src_dir, '*') self.num_random_frames = num_frames + 1 @@ -179,8 +184,8 @@ class image_translation_raw_test_dataset(data.Dataset): def __init__(self, num_frames=16): - self.src_dir = r'/content/MakeItTalk/PreprocessedVox_imagetranslation/raw_fl3d' - self.mp4_dir = r'/content/MakeItTalk/mp4' + self.src_dir = src_dir + self.mp4_dir = mp4_dir self.fls_filenames = glob.glob1(self.src_dir, '*') self.num_random_frames = num_frames + 1 @@ -268,8 +273,8 @@ class image_translation_preprocessed_dataset(data.Dataset): def __init__(self, num_frames=16): - self.src_dir = r'/content/MakeItTalk/PreprocessedVox_imagetranslation/raw_fl3d' - self.mp4_dir = r'/content/MakeItTalk/mp4' + self.src_dir = src_dir + self.mp4_dir = mp4_dir self.fls_filenames = glob.glob1(self.src_dir, '*') self.num_random_frames = num_frames + 1 @@ -338,8 +343,8 @@ class image_translation_preprocessed_test_dataset(data.Dataset): def __init__(self, num_frames=16): - self.src_dir = r'/content/MakeItTalk/PreprocessedVox_imagetranslation/raw_fl3d' - self.mp4_dir = r'/content/MakeItTalk/mp4' + self.src_dir = src_dir + self.mp4_dir = mp4_dir self.fls_filenames = glob.glob1(self.src_dir, '*') self.num_random_frames = num_frames + 1 @@ -422,8 +427,8 @@ class image_translation_raw98_dataset(data.Dataset): def __init__(self, num_frames=1): - self.src_dir = r'/content/MakeItTalk/PreprocessedVox_imagetranslation/raw_fl3d' - self.mp4_dir = r'/content/MakeItTalk/mp4' + self.src_dir = src_dir + self.mp4_dir = mp4_dir # self.fls_filenames = glob.glob1(self.src_dir, '*') self.fls_filenames = np.loadtxt(os.path.join( @@ -553,8 +558,8 @@ class image_translation_preprocessed98_dataset(data.Dataset): def __init__(self, num_frames=16): - self.src_dir = r'/content/MakeItTalk/PreprocessedVox_imagetranslation/raw_fl3d' - self.mp4_dir = r'/content/MakeItTalk/mp4' + self.src_dir = src_dir + self.mp4_dir = mp4_dir self.fls_filenames = glob.glob1(self.src_dir, '*') self.num_random_frames = num_frames + 1 @@ -607,8 +612,8 @@ class image_translation_raw98_test_dataset(data.Dataset): def __init__(self, num_frames=16): - self.src_dir = r'/content/MakeItTalk/PreprocessedVox_imagetranslation/raw_fl3d' - self.mp4_dir = r'/content/MakeItTalk/mp4' + self.src_dir = src_dir + self.mp4_dir = mp4_dir # self.fls_filenames = glob.glob1(self.src_dir, '*') self.fls_filenames = np.loadtxt(os.path.join( @@ -683,8 +688,8 @@ class image_translation_preprocessed98_test_dataset(data.Dataset): def __init__(self, num_frames=16): - self.src_dir = r'/content/MakeItTalk/PreprocessedVox_imagetranslation/raw_fl3d' - self.mp4_dir = r'/content/MakeItTalk/mp4' + self.src_dir = src_dir + self.mp4_dir = mp4_dir self.fls_filenames = glob.glob1(self.src_dir, '*') self.num_random_frames = num_frames + 1 @@ -749,8 +754,8 @@ class image_translation_raw98_with_audio_dataset(data.Dataset): def __init__(self, num_frames=1): - self.src_dir = r'/content/MakeItTalk/PreprocessedVox_imagetranslation' - self.mp4_dir = r'/content/MakeItTalk/mp4' + self.src_dir = src_outer_dir + self.mp4_dir = mp4_dir # self.fls_filenames = glob.glob1(self.src_dir, '*') self.fls_filenames = np.loadtxt(os.path.join( @@ -851,8 +856,8 @@ class image_translation_raw98_with_audio_test_dataset(data.Dataset): def __init__(self, num_frames=1): - self.src_dir = r'/content/MakeItTalk/PreprocessedVox_imagetranslation' - self.mp4_dir = r'/content/MakeItTalk/mp4' + self.src_dir = src_outer_dir + self.mp4_dir = mp4_dir # self.fls_filenames = glob.glob1(self.src_dir, '*') self.fls_filenames = np.loadtxt(os.path.join( From 9b027d27de2651a95c880e75cf0fe96d8c560efc Mon Sep 17 00:00:00 2001 From: hack Date: Fri, 25 Dec 2020 17:58:08 +0530 Subject: [PATCH 10/17] train --- main_train_image_translation.py | 4 ++-- src/approaches/train_image_translation.py | 5 +---- .../image_translation/image_translation_dataset.py | 8 ++++---- 3 files changed, 7 insertions(+), 10 deletions(-) diff --git a/main_train_image_translation.py b/main_train_image_translation.py index 4acf287..25f32bd 100644 --- a/main_train_image_translation.py +++ b/main_train_image_translation.py @@ -10,6 +10,7 @@ import glob import os +from pickle import FALSE import torch import platform from src.approaches.train_image_translation import Image_translation_block @@ -66,8 +67,7 @@ parser.add_argument('--add_audio_in', default=False, action='store_true') parser.add_argument('--comb_fan_awing', default=False, action='store_true') parser.add_argument('--fan_2or3D', type=str, default='3D') - -parser.add_argument('--single_test', type=str, default='False') +parser.add_argument('--single_test', type=str, default='') opt_parser = parser.parse_args() diff --git a/src/approaches/train_image_translation.py b/src/approaches/train_image_translation.py index bc94e4d..5745ec3 100644 --- a/src/approaches/train_image_translation.py +++ b/src/approaches/train_image_translation.py @@ -204,10 +204,7 @@ def __train_pass__(self, epoch, is_training=True): img_fls = np.stack(img_fls, axis=0).astype(np.float32) / 255.0 image_fls_in = torch.tensor( img_fls, requires_grad=False).to(device) - print("#######################################") - print(image_fls_in.shape) - print(image_in.shape) - print("#######################################") + if(self.opt_parser.add_audio_in): # print(image_fls_in.shape, image_in.shape, audio_in.shape) image_in = torch.cat([image_fls_in, image_in, audio_in], dim=1) diff --git a/src/dataset/image_translation/image_translation_dataset.py b/src/dataset/image_translation/image_translation_dataset.py index 81f49f9..d4e5c76 100644 --- a/src/dataset/image_translation/image_translation_dataset.py +++ b/src/dataset/image_translation/image_translation_dataset.py @@ -24,9 +24,9 @@ from scipy.signal import stft -src_dir = r'/content/MakeItTalk/PreprocessedVox_imagetranslation/raw_fl3d' -mp4_dir = r'/content/MakeItTalk/mp4' -src_outer_dir = r'/content/MakeItTalk/PreprocessedVox_imagetranslation' +src_dir = r'content/MakeItNow/PreprocessedVox_imagetranslation/raw_fl3d' +mp4_dir = r'content/MakeItNow/mp4' +src_outer_dir = r'content/MakeItNow/PreprocessedVox_imagetranslation' class image_translation_raw_dataset(data.Dataset): @@ -427,7 +427,7 @@ class image_translation_raw98_dataset(data.Dataset): def __init__(self, num_frames=1): - self.src_dir = src_dir + self.src_dir = src_outer_dir self.mp4_dir = mp4_dir # self.fls_filenames = glob.glob1(self.src_dir, '*') From 27b5a0cce6a71106f32c050a88af9f3ea646e032 Mon Sep 17 00:00:00 2001 From: hack Date: Wed, 30 Dec 2020 19:31:02 +0530 Subject: [PATCH 11/17] path change --- main_train_image_translation.py | 13 ++++---- .../image_translation_dataset.py | 31 ++++++++++--------- 2 files changed, 24 insertions(+), 20 deletions(-) diff --git a/main_train_image_translation.py b/main_train_image_translation.py index 25f32bd..b6c1718 100644 --- a/main_train_image_translation.py +++ b/main_train_image_translation.py @@ -39,9 +39,9 @@ ''' Step 2. Train the network ''' parser = argparse.ArgumentParser() -parser.add_argument('--nepoch', type=int, default=150, +parser.add_argument('--nepoch', type=int, default=20, help='number of epochs to train for') -parser.add_argument('--batch_size', type=int, default=8, help='batch size') +parser.add_argument('--batch_size', type=int, default=2, help='batch size') parser.add_argument('--num_frames', type=int, default=1, help='') parser.add_argument('--num_workers', type=int, default=4, help='number of frames extracted from each video') @@ -56,11 +56,12 @@ parser.add_argument('--ckpt_dir', type=str, default=ckpt_dir) parser.add_argument('--log_dir', type=str, default=log_dir) -parser.add_argument('--jpg_freq', type=int, default=50, help='') -parser.add_argument('--ckpt_last_freq', type=int, default=1000, help='') -parser.add_argument('--ckpt_epoch_freq', type=int, default=1, help='') +parser.add_argument('--jpg_freq', type=int, default=2, help='') +parser.add_argument('--ckpt_last_freq', type=int, default=2, help='') +parser.add_argument('--ckpt_epoch_freq', type=int, default=2, help='') -parser.add_argument('--load_G_name', type=str, default='') +parser.add_argument('--load_G_name', type=str, + default='examples/ckpt/ckpt_116_i2i_comb.pth') parser.add_argument('--use_vox_dataset', type=str, default='raw') diff --git a/src/dataset/image_translation/image_translation_dataset.py b/src/dataset/image_translation/image_translation_dataset.py index d4e5c76..77d2976 100644 --- a/src/dataset/image_translation/image_translation_dataset.py +++ b/src/dataset/image_translation/image_translation_dataset.py @@ -24,9 +24,12 @@ from scipy.signal import stft -src_dir = r'content/MakeItNow/PreprocessedVox_imagetranslation/raw_fl3d' -mp4_dir = r'content/MakeItNow/mp4' -src_outer_dir = r'content/MakeItNow/PreprocessedVox_imagetranslation' +src_dir = r'/content/MakeItTalk/PreprocessedVox_imagetranslation/raw_fl3d' +mp4_dir = r'/content/MakeItTalk/mp4' +src_outer_dir = r'/content/MakeItTalk/PreprocessedVox_imagetranslation' +# src_dir = r'PreprocessedVox_imagetranslation/raw_fl3d' +# mp4_dir = r'mp4' +# src_outer_dir = r'PreprocessedVox_imagetranslation' class image_translation_raw_dataset(data.Dataset): @@ -452,9 +455,9 @@ def __getitem__(self, item): # load mp4 file # ================= raw VOX version ================================ mp4_filename = fls_filename[:-4].split('/') - mp4_id = mp4_filename[1] - mp4_vname = mp4_filename[2] - mp4_vid = mp4_filename[3] + mp4_id = mp4_filename[-3] # 1 + mp4_vname = mp4_filename[-2] # 2 + mp4_vid = mp4_filename[-1] # 3 video_dir = os.path.join( self.mp4_dir, mp4_id, mp4_vname, mp4_vid + '.mp4') # print('============================\nvideo_dir : ' + video_dir, item) @@ -631,10 +634,10 @@ def __getitem__(self, item): # load random face random_fls_filename = self.fls_filenames[max(item - 10, 0)] - mp4_filename = random_fls_filename[:-4].split('_x_') - mp4_id = mp4_filename[0].split('_')[-1] - mp4_vname = mp4_filename[1] - mp4_vid = mp4_filename[2] + mp4_filename = fls_filename[:-4].split('/') + mp4_id = mp4_filename[1] # -3 + mp4_vname = mp4_filename[2] # -2 + mp4_vid = mp4_filename[3] # -1 random_video_dir = os.path.join( self.mp4_dir, mp4_id, mp4_vname, mp4_vid + '.mp4') print('============================\nvideo_dir : ' + random_video_dir, item) @@ -647,10 +650,10 @@ def __getitem__(self, item): # load mp4 file # ================= raw VOX version ================================ - mp4_filename = fls_filename[:-4].split('_x_') - mp4_id = mp4_filename[0].split('_')[-1] - mp4_vname = mp4_filename[1] - mp4_vid = mp4_filename[2] + mp4_filename = fls_filename[:-4].split('/') + mp4_id = mp4_filename[1] # -3 + mp4_vname = mp4_filename[2] # -2 + mp4_vid = mp4_filename[3] # -1 video_dir = os.path.join(self.mp4_dir, mp4_id, mp4_vname, mp4_vid + '.mp4') # print('============================\nvideo_dir : ' + video_dir, item) From 7d08f8ca18224df69e0c58bd9ec9b7737ba0dc17 Mon Sep 17 00:00:00 2001 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+ "## TDLR version\n", + "\n", + "Remember to change to GPU runtime first!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7PZQxNvvuuNZ" + }, + "source": [ + "## Preparation (run only once)" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "56UHwyJKuaWw", + "outputId": "8a3fef39-8790-497b-a939-94c451d99317" + }, + "source": [ + "print('Git clone project and install requirements...')\n", + "!git clone https://github.com/Garvit-32/MakeItTalk.git &> /dev/null\n", + "%cd MakeItTalk/\n", + "!export PYTHONPATH=/content/MakeItTalk:$PYTHONPATH\n", + "!pip install -r requirements.txt &> /dev/null\n", + "!pip install tensorboardX &> /dev/null\n", + "!mkdir examples/dump\n", + "!mkdir examples/ckpt\n", + "!pip install gdown &> /dev/null\n", + "print('Done!')\n", + "print('Download pre-trained models...')\n", + "!gdown -O examples/ckpt/ckpt_116_i2i_comb.pth https://drive.google.com/uc?id=1i2LJXKp-yWKIEEgJ7C6cE3_2NirfY_0a\n", + "!gdown -O examples/ckpt/ckpt_autovc.pth https://drive.google.com/uc?id=1ZiwPp_h62LtjU0DwpelLUoodKPR85K7x\n", + "!gdown -O examples/ckpt/ckpt_content_branch.pth https://drive.google.com/uc?id=1r3bfEvTVl6pCNw5xwUhEglwDHjWtAqQp\n", + "!gdown -O examples/ckpt/ckpt_speaker_branch.pth https://drive.google.com/uc?id=1rV0jkyDqPW-aDJcj7xSO6Zt1zSXqn1mu\n", + "!gdown -O examples/dump/emb.pickle https://drive.google.com/uc?id=18-0CYl5E6ungS3H4rRSHjfYvvm-WwjTI\n", + "!gdown -O thirdparty/AdaptiveWingLoss/ckpt/WFLW_4HG.pth https://drive.google.com/uc?id=1HZaSjLoorQ4QCEx7PRTxOmg0bBPYSqhH\n", + "print('Done!')" + ], + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Git clone project and install requirements...\n", + "/content/MakeItTalk\n", + "Done!\n", + "Download pre-trained models...\n", + "Downloading...\n", + "From: https://drive.google.com/uc?id=1i2LJXKp-yWKIEEgJ7C6cE3_2NirfY_0a\n", + "To: /content/MakeItTalk/examples/ckpt/ckpt_116_i2i_comb.pth\n", + "839MB [00:07, 115MB/s]\n", + "Downloading...\n", + "From: https://drive.google.com/uc?id=1ZiwPp_h62LtjU0DwpelLUoodKPR85K7x\n", + "To: /content/MakeItTalk/examples/ckpt/ckpt_autovc.pth\n", + "172MB [00:03, 55.1MB/s]\n", + "Downloading...\n", + "From: https://drive.google.com/uc?id=1r3bfEvTVl6pCNw5xwUhEglwDHjWtAqQp\n", + "To: /content/MakeItTalk/examples/ckpt/ckpt_content_branch.pth\n", + "7.88MB [00:00, 69.1MB/s]\n", + "Downloading...\n", + "From: https://drive.google.com/uc?id=1rV0jkyDqPW-aDJcj7xSO6Zt1zSXqn1mu\n", + "To: /content/MakeItTalk/examples/ckpt/ckpt_speaker_branch.pth\n", + "15.4MB [00:00, 72.2MB/s]\n", + "Downloading...\n", + "From: https://drive.google.com/uc?id=18-0CYl5E6ungS3H4rRSHjfYvvm-WwjTI\n", + "To: /content/MakeItTalk/examples/dump/emb.pickle\n", + "30.9MB [00:00, 85.0MB/s]\n", + "Downloading...\n", + "From: https://drive.google.com/uc?id=1HZaSjLoorQ4QCEx7PRTxOmg0bBPYSqhH\n", + "To: /content/MakeItTalk/thirdparty/AdaptiveWingLoss/ckpt/WFLW_4HG.pth\n", + "194MB [00:02, 69.1MB/s]\n", + "Done!\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "dujNy0RNU8Vk" + }, + "source": [ + "# from google.colab import drive\n", + "# drive.mount('drive')\n", + "# !unzip /content/MakeItTalk/drive/MyDrive/MakeItTalk/pruned_i2i.zip" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jlEgsX1Ivzbm" + }, + "source": [ + "## Animate your photos here!\n", + "\n", + "- Upload your images to `examples` with size `256x256`. Or use existing ones.\n", + "\n", + "- Upload your speech audio files (could be many) to `examples`. Since it will process all `.wav` files under `examples`, remember to delete non-necessary `.wav` files. Or use an existing one `M6_04_16k.wav`." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "F1IVS6EpHhK6" + }, + "source": [ + "## Step 1/3: Choose your image (in below Dropdown). " + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 565, + "referenced_widgets": [ + "d0b45a014ccf4483b65f8cf2b621ff07", + "8d9f1f171de94bf5bc44c34afce77361", + "f8883303fe484e4483feb56998d9413f" + ] + }, + "id": "xe8DGOmwuqlx", + "outputId": "d8fbe5af-fef6-4ec8-9d48-4f4fd30c3ebb" + }, + "source": [ + "import ipywidgets as widgets\n", + "import glob\n", + "import matplotlib.pyplot as plt\n", + "print(\"Choose the image name to animate: (saved in folder 'examples/')\")\n", + "img_list = glob.glob1('examples', '*.jpg')\n", + "img_list.sort()\n", + "img_list = [item.split('.')[0] for item in img_list]\n", + "print(img_list)\n", + "default_head_name = widgets.Dropdown(options=img_list, value='paint_boy')\n", + "def on_change(change):\n", + " if change['type'] == 'change' and change['name'] == 'value':\n", + " plt.imshow(plt.imread('examples/{}.jpg'.format(default_head_name.value)))\n", + " plt.axis('off')\n", + " plt.show()\n", + "default_head_name.observe(on_change)\n", + "display(default_head_name)\n", + "plt.imshow(plt.imread('examples/{}.jpg'.format(default_head_name.value)))\n", + "plt.axis('off')\n", + "plt.show()" + ], + "execution_count": 17, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Choose the image name to animate: (saved in folder 'examples/')\n", + "['angelina', 'anne', 'anne2', 'audrey', 'aya', 'captain', 'captain2', 'cesi', 'chris', 'chris2', 'dali', 'donald', 'dragonmom', 'dwayne', 'dwayne2', 'dwayne3', 'harry', 'hermione', 'hermione2', 'hound', 'jali', 'john', 'johncartoon', 'johnny', 'kalo', 'lab1', 'lab2', 'lab3', 'lab4', 'leo', 'leo2', 'monalisa2', 'monalisa3', 'morgan', 'mulan', 'natalie', 'natalie2', 'neo', 'obama', 'paint1', 'paint3', 'paint_boy', 'paint_boy2', 'rihanna', 'ron', 'scarlett', 'statue1', 'statue2', 'stephen', 'taylor', 'trump', 'trump2']\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "d0b45a014ccf4483b65f8cf2b621ff07", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "Dropdown(index=41, options=('angelina', 'anne', 'anne2', 'audrey', 'aya', 'captain', 'captain2', 'cesi', 'chri…" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "display_data", + "data": { + "image/png": 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h75OvTsZXlwtj5ruaXvh869yuYGKsKzxZoc9J6ErRF95dLAmkcKoZHk5Q0KZE93zdGXXAUVmBGIM+J1IK4R1HCA1UCjrnEaQTEUEj8N6ZUhAsq6kGGsHu4PvgEvDufOJFjd/sn5jDExZH5b47v/r1zl//9ScU53YPXl6c2/POdgtiKKelsCwVW4TLQ+Orr523r5UqgkRBTVjWJN2Wmn3F+fQDCaE5MovNEbRiOMmItvXEvg98BhFOsUrfd8a9Z8URJei0pjxcztzvH7i/CLfaaSWDfF0rY3cGlr3FUvKQebBfN1QMWsLgYhXvWzJuZkgo3gcigsdERdAD16dck6zbl7YcUWIf7NtGWRoSgc9On4HERFB0psQyPVIWwfBioJYscjjnxbh/DupaeFyEW3EqQQ3nQYVaoEigAYsPZCiB0KqwVGjFsQExSEa5FsQUU5AQjKCWJMjUMrgt8pkkeZXvI5lPsl/3hL8SQkT2nFOMINGgqhzHOrIP/b4yGSFJuIUqdqCP8JmBOR1m9nkBmNkhf0GxQES5D6e1yiUywE3hZ68X9jEYAU2F2y1YRdhp3D/smBUIeP8y6Qhvl0dexs513Fi8EDtgxu2QIEQE1YLgR2wGolDM8HnIJjpRSWLNVZCq+D7B812ER0rqUvM5EfiYYFClYAQih4wXYDp4aDXTm8KcG+dXlUtrfPtb581j5e2DcJJBnCuG0qrxcDHOq7IUqE04X4ylOXNMPJxWG1Yn4WACbk5b/vhU2J8Mzs/fDS6vCrfnHS4L58fG3DtzGj4m2+5MFxrJAJaixHCWVbi/BBJGrSno9vvktnWQSndBrDDHQEvS7KUpcwZ+H6gUrAA+aWujLIX9DmwBeye0HofOkv6eyf5GdyIclYrPjlXDA2IfRHW2/c4Sge8DK1BqViF10FB0TsZ9R2phXRrUgokwIxCMVp2LKvs+eL1WiipthUWU12Xw9FgwGfgd+hVwpZSk9W1AeB4UCWHsUBdF1GmL4kOwPhkj5YjwrAIOfJnqEyJhuBpWjt7cM3CE/I7FQNQYHilXEYcclfqaKQiCWErDAdnzRyCRQVeLsW/79y0CIlhJNr24IzV7dRU4Abo5ElnBxVKnDIQThtXBDPiLtRIIvwR+87Hz8RMUnKKKNGOijK2jBcLg7vl3bt0RaZlpIhBVpieJh2omHA/cChFHQvGeEkaAaEU0Mon7lzQ1AcFcMXGaOzuBLIaVRpnON8uZQLha5zbgft95uXcef3HmP/zliV/+XLntGx/fT3wrMJWn10prjkiknHVK5ltcwQOTnu3adIqBy6TYHw/BPx2c7zsPrwqXx0rfnfvnO21V1Dyliq2zrg9s1xtLWYka7GPHSrKr231nn07IoYvtAdJ53gb9VohpYE5IOj80CioNOwExiDkIn8yeGXNsmQyEfsCtZGaLpePHfX5PxYuDWUHmpMvg4fKK/TYYY0ciiCmUarAq+21PpwqBhuBhFJTTqpxL9qO33WlWefdQsSp4ga/W7PV8CIsrpzEpxfClYCdhexkpT5ilwQJFZRJh+FBYJL+vBu1kbNMxyYTjBFKE0QOLghkp2nuSNGrpvIqpSOYBrB4VxbMiLq3Se2bqL66rDBvPKgGI5mchqRWqptySEaagqfmJpC4NZAuiJavSl4rdg6kCR/Bk1U2Sr5qiPWgDzudKfxD+gyJs9853L4PvZuNsyrdx5+Ybn+4DlYZqEjxqKYdghe6pncrRVyKJ3hRJ6SIG4fqHhBaKWcpNgfwhyB3cMnEZ0MTp48rb9hX7MD70Z/pB3Ky10PeNf/cPV/7pzyt4o61Ce2qczh3dG9uLcDqBtEm9ZDUNc8wU/fKMQrJlIFGeuGZC+SHBORh89/7Owxvj9Zugb7DdhfWh0c4rD31hu+3YsuCeMG1szhgDM8HM6HtHXBEVtnBwxe/G/X5HxSjRIARD2bcbp7KwrA0JYW4TKU73jdiTwNACFvnix9zQoog2RIyijpoypyMhuHdiQFnO+Bj4HIQ4TfPljK3SmrHUQvSdUylgnVvfKY9nHs9wXgo/e1P53fsdonCpk+sM5oRtKl9bRWWwnDX11psREugirEseeLNgzoS0iZMcXZQxJzUEFWME9FAUpbjj4dTFMhi7M6QwLbBmiME9YDFjzpHabTFsKcQMRjgC9DEOCCtH9egpepsgmiRQ0eMIRPbsMSfdk/AjUixXSf0wVEjqVzDLns/nIdWooQH3nqSNWUXnZBGYZugI/Oz8dt9p4vzTp0q8rfyrX98ot4D1xB5B63Dfg6r3hPBqLIswpxMGcp9IzKPsJ5EoaszhlFqRUGLuiBoxnZidyUSoGSCkcOQmaAh7TLoJjy6IO7DxUBY2f+A+PnO7bbhNrAbv39/Yrk+Hw2fw6lzQtIqxnAu2GnpO2TFiT4IqJjEDbSdgoj7SCyCOT5A/NNR/XnBqJAV9fRYuF2GpSSRcXwYSHbOEkVUUt2DbU9TGk0E1mxRxgk5II4YzLCuTuiElRdpaGhLO5WGlFQUNKkY9NQSnhNB14NdOuOCuyBY0M5BgINmDzsNtdGinsTtzguJsgyR+POAEpUEXo5ZC359ppaAGaxNCja7C41q5LIUxgndPlffvJw3BRNhmBhUO5Ysn1BQ9KTIm9+fg1JKJnTOILQOzYlCz+rmki2a/eXpLQ5DZmeUIBCsojvtEJcmm2hrWNHsmD+rasg8rioiwRzDpKSmQlTlCGBF4KGYJq2UEdhAjJunCoYD3PStOLXiAWEE0e1CdiVYY0GcyviZKbQpT6X1SLbg0hZGGDkN4WAujOJel8s7Tf/urm/NvPw32Lnz9yvj1Z2clkwZ0lvWROQLfr9TMB8whnJeVO7fDyFOZPtAjAfV9EBZYXRLWElAF8QrRCVFCEoEIAqYIwVBAlMXh73//G/7qq19QZeJjS5kkHEK57sHvrjtqSlXF2zjk46CuiiwCZRIWIGlLpXfEICz1dalHm6KKDDi+/J8fnJ9fBmLBq9fO2J3w1HIWC6Zv+EipYttu1JbECQKjD3xkdSvaKHXD1gfmthPXSaC4QhHD0hlNqFOXlSKSUoMa5SA+hjpz3hENmlRGH5TiqS1pozVj3pKtc1dcnNYa4o7Hhvd+sLmKSpInrSqBI3PjoVXMCjUmJ1u4rCuvGpyLcrGUeJaT8fOfGtInT7WyASeCc8ueIqYxXJgWNNE0BAynb4VQxyWIUPbhMKFqJjAqbLuz7zteNf9/BFYqooaq0bSwjT39vJbPxnWipWKmadog8AkeaVbwESiKSRIowyciC6ITnSmYS3GiJzurejCdwJwTL0eFlWR/Z5/0kYc0Zat0i8XRx4YEZhy2uQk4osZiBY1gStDVORV4r4V+n6hPfvpg7Gp8/vSRVY1nT+dWPWC615RIknx1sImIMobjctw3gjKSL/JAXL6HvVYbzGyNsheOZMAjEBlp/VRhC6OE4Hvwm4+/p7TCIsbTeuFzv3OnMz1wWZlksy4oZVVYKlIkDS6SyV0kFYJIej2RyiyE3IABskJpyKHp/9nB+fKyUUvju283RAdP7wo+grJkNr3fR2ZSnOvLnSB7hCBp9SDFfL0VbtcXYgZMzT5RksQIF+bs1CXJJKn5Ml1Jh8xMunwiWFspriwjeHh8zS7BPTrjvtMPvRLygDFz+kS0oKpYVXzuqCgmAtOROVAJzmtaY1ok5Hl7Uv7ZN4WHFjQNiqV7qYvi5uwxOFVj0WBpE1uE+y1RBa64KHm2BZd0I7kJkP7Vokf/OHOCAYNaC1vMzKqRPZMgWBGaJWEWKvTIvslUQSYzHImEkkJWTxHhmDVARKhNGSO+J6Tm0VOKCmOkyczDGDNoSn4vLckUS/53ePZGIhmYX0KCIyAgg2ceLVSxkn2/BjGSRR4R9Cl0d97VgFPhd1vnHz5shBq7OPfpVFVGvxEoZYGYgQ8htHO/b9lTYodZJa2egoJAMU0zuzvr6Qym3PsdiuX5I62FHPZAF6VPuB2MrxDctzsPdsZEEg5zx7nhs/G3v3rh1s9obUgpSIkMQFGCJVu4pH6P5Bak+TpJuIiGyEJIAZfjPf+A4NzmzufbZ8pHZXZYzxdOF+Hzpzu1XRKuxcBE2CNdKGNO3IO+5c1bVYoVZk9/qwrHQ53pq8UpNUmHHoM+HWFShhB7pjvXhTkCdSFiYrUiq6HTmc8bfYxDsFZC4jisg6KCH9myLZW+aWL8SBh41sIawVqS/a0B6wier5PtHvyyTXoINxSdg5eb8uqkqAa1GK0FVp1TM+Z0NAxB2bdkCkONSeQYWVUYTikZoB5C706d6U8OlKkTU5jhNJW0B0ZCZjVBSjnkKw6yJllIUzkOXhyH4VA4RXDPfldMqQpzz6rhOqjaaK2w75PyRX4qwslOjMNiJjV54C+oI+aXiQs55IlkmnPUT7P/NcNEkcP0vXuweY5JTQ9OxTg/NCJ2/vWnzpiBqfPdfVCt8eqUpvo9JLVt16OSGmNUttjTmoemyUQAzZYm9dIkh7Z+p7IcLVZhHBMrX1xpfjyqGUKIcMVpNugUttEZAnO8MBWUBRfj22fnwzV7ejdHygGRKTAtA1+F6HuOkEkaONKd5QhrElORhhPVH2hCEBG224DzJIby8dsrpb6i3zsff/8JkUrvjtagqXHrTn8ZbNdkWsdUdCYLGgK4MBjITLo8m0OQWBhDqCfDJHA3RBWZxhqFGoUizjT40De8VfqA+3an33ei1xwzMiFGwlvRtKPJCKwGc9uoJAWvKOEjCSRX3iyJCFyUxwfltk0+PU/6Q5oRTIMRUC14KMJJlbVNXp2/HFyhnYT7PQNfqxJjEpGByExJJA0Ah9QTwTG0gBXluu2ICcvSvp+uaWujb7d084SCBKXkoZKjz28oOgIp4NO/h3Vq2RvK8R7lcCYd7ThLWZLmHwcba45GxV2ScJt+6KmZ8bMxS5dNaoEF16MaownPBJCcOJlC2hpHkjdLLXR16JEQH+fVyfjL1wUT+HV3FhwLZ9t3assWRxyYwVoFa8ImO9TGuCXcVVPcBy6BWs4VM4NSKsvlxB5OKQWtitw7474x3TNgJTkP/0ITmbA5TAQfkab7mQgu79kYXthnwDIJlUM7VQjLoCP94X6QVXCYOA7/RI60ZXVV+MMM858bnLVWbs9pzi21sT8b73975eGtsW2Bjw4EJyvsc0dGmrn73alSGWNjOMdQ6TFRMIVpIBJ5YyKZubszKNTVeKwrOLwrKw/WmPsOzfh4feFBC88yeLne8DuIN6Z0/ICqOTJUMvBn9gHhFbOgBlyWwtw6ZRivlsaUFy7rK6oo+wxeneGntvBpFH67G1+XydkVqoFuvBGjrQXRmT2XO+FBq0F3Z4yE7kSg5mmHs5xbtIBajVom7gWdKQDMDgVhP/yHZvkZRJrYa6lMU1AFBZ3JopYAJxhEyi/HoIAQWbmM7/tdpBIlsqftI0fvKLgPmgmlWE7geDB9IFrSITSTYZeYqKQnuY+O1qyetz6oZP+pCCFGlyDcGWhq2BrUppQulHC6wHXvFBF+9lAZVrhO5z7zfVY1hhoffGOJE1MOptZz1nL0nP8NTbbZJf2vEUGklSmhJg69c3p8xRiDGTtWDxPKvoH4YU2EfQ7wQxryYMaAmOk2ErJhFaeo8/rrSm05xojMfLYzJaov8lSpRsQgnGR01XNGdIIWx8kzwh9XUv50cJ40uLmzj2D3wf1loqcz+0uO6AR+6G1BscIug9aCtir7bYLCfe84E7FJCaOjyAwCYc6BSMmSX5Ul4NyDMjfe6YmftZU+jU8hXLvzPCef5s5tm1gEtgq+TXQjXSQKYmlzK95ZqtI3RT2QFcBpraQzB+GrN6/wfmLbOufHC60I6wK/fKt8uilaBCs5mmQMlnWhtkLjkHWmECHcP2/QambNg3wr1VgWI2TiYmgEKjtuk2KVgrOeD3g4ySpvLSueBqLBmP0gjQzznNKJmVMf4zCky5R0JWmgmuaFFBjJyZKZUgMSWJE8SzPtfWM61gpMpw/PliEk5Slger4nORw0zgBp2NKoluxoCaOktStbCpPUanNMKDdPRPqct91xL1mFzFjJYfHHVXlzUe53RcKwZvz2euW2f8epPrGa49GZMojpSE/0k/Ktp4VQUwpSz7GzmMNpAboAACAASURBVMH9+TNWK/f7PVHFSD1X1MAMPeyTGtl3ohMTZ5bIym+F2hb6uB66u2M2OZ2BplAMN1KGAoiswkrkxJXrsWggAxtN62uoInN+X0l/UHBqKWhzbtvGcqvURWn3zrOnrnR+KJyacLsNzo8VK7DfByKOSlCKwpwIKcaCpDvGkzCIGURMaltZm/CmC4/lxDfLib9aVj4M5ePufAznWSfvY6QGGelfnSMn0iVyG0JY4jb3wE7C0oTrPUfMJCSdN7NzWs+ZeUN4falpySs506fkPOn5BItMWquo5/2spiwKRSPtVyOrUrNK9wMaSWbOL7ax0KxuMbOamTbmFEod1NYIhOgBklKME1RVRC1Z5zUtfIODaRShRyTJZUJYrhmRY5uDlpS/iGTXZXpqpzW3BhBgVZHDEmjV6O7EDIoYrRaCSZ9ZBfJKuEZYanPibN3Z9/m9BjpdjjUxgy6DqkbxrOSI0LsyCTAHl/zZDMoI3lllvjpRHP7+Y6fvO9vcOUmjArMVdA5G34muadULkMgzJId9kKmZnMQRVfBCuDF6TzfaTDgrOBqe/uNDCkGz9XIOWKuFEaAzOY1lSWmkFOHy1JDziisw7tmueEGio/WwVkYmyZjpGxcZ6Sf1wMdANYf/5Y+C2v+P4Ax1So30xd46jYZ97rR1MFHa+mVOTdjvGYSjD0b3pLonFFHUJmVJTK4BhYIWY86dtiycJfg6Ft5Y4S8vT7w7L3zcbvzN5498uw8++cY0Y46R8oiUzOSHt1RqQjj6Dp6bAqqWzPiWwrpNOK9GQQ5mM/j4cuft5cLb18rjQ6FEDjB3cU5FaeGYwKmlabkp6XaKtLT5mEnKlOQM0XzUQloC/YBYJsZMawtWQCKZZY+DsTZhaZX9mFG0lqNURex78kUjPzt7LMc0HUQRiiVhnslvpgFfJCNazaB/CcIkRBJqJVHlkSSVR4B/2UyRsBRN+UnIkbuYuZ+ne/adIunddc81HSKRdy8psRXy5xEHzNbCjHnwVQEjKB40db45FZ6flW0qPpWLVZZwnn2mq0aVcUyeiOdQxCArn8RENe2fIgZfwMOxTwiUapXp9zRizMPP/T3S8e//jGfJY5RERTGdUKhSUSlEREpK7QS6Z5KbHaIkVzLJxBgDpGROI62ICVcM9XnQLfEF5Pz5wen+xebloI3SKtstaEtWpenO3o3WQMLzJiOrzByDfQczpYTk5MMEJJ0x0ndKQBvOa1He1Sfenh/56tz4fb/xv333W371+coIzSVOp0Yfe84gGtDTeUExRLKKWinghs2d8+mRqoNy35izUCQDT0ZapiLS5/hydy6vhFcn4V0rLCUo09nm4LwunOrhHdagWsI3s9xLhDtWhdBkRUtJb+ruyZyuWtJoEWT/hzIjyGUcehzuXGylp8Kqyom8j5AM9qL6vbQkhwkdzcUOBsTRK4nl55SWe3RKST/s7dbBjrnV4+8LSYbTtDB7mv1Lyc+J7oderfnu6hczg9A7OBNVmDOP1BeoPadTzA6jujFH55B0YUaiqfzXw2KZZ+LUlCmkQ2sp1AoPUTir8u1tJ/qgiaG1pP7qOVFaCGpVpijjSALD852q5Hyq+04Mp5UVa+Adguzl8w3llWc8IXxRI3wkEipKcCz80twF9Rd/eeEnP7vg40ZsV4pv2SaoMlwzoHpBbQHbk/MYcWjqheiAJSEZGgei/Pdff/wnpF9TD/O1mvH0VBmzgyva8gFvL87ox2Hv6Rs0PSQTcdwyWJsqVnIJUol0jlym8npXLjNF26jG7/3Ov3n5wO9f0uQQCLPvhAuvXr/OiZe+p6heU/SeMyl7bQtuO6dTY4tBHzsR+/cvwfugCIdJWynqRCjbrlSCr8+Tn6/Bz1bn3WK8qkozzf42kkxpSgr7Mx0w8xhLWhY7fJLBdBgOlHI4WQZaJqrCfU//r5SkD8KDakYfCXu0GFaSGY1jv86ML6tVsp0Ye8K3GHnak6U9YOzhdpmeEFlLIOYM0lKZXqSJFQf2NKpbJuE5kvqv1ai1YiUD2Wdn94n7H4T+eeieRS03SkQcu3Gy/5UjcAlFpBxTL4JgOQigytmMiwftvmViMuHnb4xfvjOkZK/epPBQGuVwRy1n4aJGkcFJPFeJkNWUkY6fOLTYQ+th+mS/zRwt1JkkDl+8xoJoodWFGMIcASi9D3rvHNQNEp379Zn//L984quvlrRlTmV4Wjk1nMrMTYtCjiZ+vjI+3YltY27O9fnGmD2rqu2pVf/QHUL3fWcOQVS5Pe9c7yunZeG2ObIb6ykoOqke6NRkcMVRg/GlGWYeH6IYyl0HmxY0hEsJXrfGTx/f8qFv/NtPL9yfB88evMTIauG5AqI/X1FP4tuFxOtiTJzSSo5+eXBeL9z7ztg7OhymUWr2PTo7dTlBDArB68czb14VXq3BmzMsTB4wWqmsJ+EelvuMyhd/6WBpOZGTHaAiRyWdEQwPCKeWrDq56mPkJoUvBoQxcQt0SRM6ZoQapSp7BD0UGc6UDIKGfr/lLTQJHbwjWo8eMntliTjmHjUXALh/P/dpixAjqzYztx4OhMUsPbK7JXRVaGbJMgMjgrEH4Yb3nogHyemWmmsp9zEwQJpimmNW93mYUTw7ZSF5Bj8M4CLBIlkxEXhshW975+tlZY/Bb58Hp+VEud55WlfUlM9zUosiU9jLxipLGkcExiBJt+b02ZOB7ZLapwrTd0QLIiV3TX2ZB/0Ck+dEWiBlEDQ8oNoBa11YH1bmCP7H/+k/5r//b79ikdTwN4I5jf2TUC1RIxOkbOxb574FZZ2USz3smsptTBYmZRU+XJ1f/U755z/7AcG5X0EGlBBu18H77+48vWrEtjO7MuqhhU5YxYkI2lqOlRhJnOSe2Pm9wVoVmgaPI9F9XSrrWvh0/5Zv71euYexD0gcJRHyxa3X6h1vuMrJKDMmZPRraMvNLBN4HbDvbdGpdMI3cuOeTTsJSnx0tjXNbePOgXGxjqYXTRWgIRZ3aKmeP3NcTSafH3pnz2BxwDDggCQ2HHnOjM4kafHLfss9U4XsiQiSF8hg9+24fUJwyCqhkZlVHjoVcw4zFcitEHAK/H12KH+Nieoj9GgEl00aI0rThc+aES8s+y9TQ/bBQyrGi0iY+c0611PR8+pyIHZa3zdmHIKHf2/lKSbni9kX3hGQiPb7/VY+50kkONRdL+K+iWKSInzuLoIpTq9J2pa3KW5SPz4XPGzz3G4sqSOVFNiqVshRGTKpC08lUA6tYBx9fJnHKkRzscLINBDt2FB3fTLOnZBbMCt4j2xQL1gWqBtUab7+58J/9pxdk7uwfnbnfeLnv3O/OvE3gzqnWrOJLJoBSFSlK3zdEKz6TI3iZlfe/mfzrv5/8u9++8M//qx8QnETFRzpWBOXTh45ZoDJZR2XMhG1ShdVzveI2nPvm+ExTfPZKSrWsHBvBay08rI3rdkNL5f3tmY9zZyuV/Z4EiXD8eXKBk4hgVoE/MIDZu6TZAZlYgEXelkpQl8beg0ULiw60tGzQPSjVKCJclsI7S9j97usTb8vk+jxwUtp0T8ucT2dooYSlFS6O7QHjyM6Si4RTfC6IGDImepj781Bm95/upXbIHUIxSQhlaS1U+TI4cBA1knLG8VIg/h/O3qXJtuO68/utlZl7n3PqdS8AAnzIlCjalqIdjnY7GO3oiT3zwN/E38gDTzxwODxpyxG21bYsyRGi9YhuhlpUdEsyKT5BEI+Le6tu1Tln752Za3mw8hQ4ESMEcEAABFFVp3buXOv/NGrjWYdrLcba8JXFSVHiew8jtZBFniV4mnSYE4Z5Oyum4QCWFAR8s4g4SaL0FKh667HnaYo8n/hxWgB1HhNFd6FP8f26j9HRO90MH/Y5USHnjHmn60ZOynv7HZYSZ2uUKfNR7by7u+K0PjFruJZcE8vYVde1YUlozUkUag8fsPRxk2ele4tRekglxQcvOVBv90CYhXAyJUmkOUzcORcO08TVpHz7t6757W/O/Oxv3tA+LnzjRlBfeTx3xBLJK2HgVlpX0i6zuzHeeW/H7jqTC6TJsbZRvfA3Pzryf//5Ez/7eGW+hv/2Hzh+vx4Qajz70HJSWms8LcZ+Fk7nDS0RldGbBaJswro52xIuCilC2+JNm1IE9Nbjyq2XkK9J4vPzkYZRxy8890YL8We87fQC6Q//XpxLXJRUIu/GCQuY/0pQ9TwL0MEbSTPX+z3dhN46c9rx9ff2fPMd5SvXytevJ3bFyWNXmjS4wTxZSBAdeksYndydtsUtKUqk9nlMEEa8hdfWmbIyERm9KKAyKBKPAGMNa1kmEgq2zUL4PucADC7E9+WAXj4FH95Gjdu5NUGHDFJV4kXa5Zl20V8Jy1KIv5dCxZJ0gH3xcUe6nwjJ0qAXBjKN0a0HIJzygE6crXZooZ7x7nQ3Oom+hYNGPWSBlmPXrDWuSWXkIJkiHQ4TrGbMWfjmyz1yv/BQEjd3E/MipK48LCtVKy79WXB/0SJXizR8az08vZ6wNGJfknDJFBK9oL06nCahQe49kGu6M09C2u+e0dXdvOOdq8x7xZlOG07lvIBqOGqmOVaKZem8PRp1U5xGc+fuZTAeN3fK7/ynt3z4UPm9P/qY733/iYc3Tpngnfe/pHzPL/yojzdxinG1W+N4bkyi6KEwZaWr0yByON2QJGxrp2+h63Q6y9LRJeb57gGcvGkr7DKWlHdurnhKJ841EvPaxbbnNnaVkaZtFg+wx4EMcDGok2SRitC2zrL50B8HElnbiubEzXxgL4l3d8oHB+e6OLMYaavIlMKdnofBmHBpuAgpx0OoEjdQ7V/g4DL4zFCZ+LjVdbgf4pYxj/1oLgHmdA1VSdyMSi4RgCwXWkaeN9ugbtRDsKCR78OFuJEW2UyXv3dRn4uhJW4qhlY2aUjJJMfD6v3idAlKJklCJh0vgvHrr840CW1I0wSoa6Va3D6MvCHGuJgl3P4XhNgk6KBpShdAP9Q24hSETCJPwu6QeLs0rsQjYf8gXE2Ft8c6gCfIJNJc0BQWtr45ZwLYa2ZARjRwkqgHacMIoM/SQ5ywwXGR14GrIzS0dna7W0iJ2s6ca+K8zFid+eB94YPrwj5tzJOyimAVbNGIO8nO8VhRUWpX2rlyOne8Cj/68Vv+u9/7mH/z12de3CRKgVyE5fQFm/yPOpw6BXydNYFX3MC8U1tUKmwXxK9MNIOUOuZK74I1o/XI4lKz6B9ZOlnDazdP8UBuOcdfF9gsfuirqxvuHzdkc9rG0JKOm1A1dolhJ/P4PQfoAQMBCzBkmhW3DDmz9ZVslUmFWc8cinKzn7iZhL0YSTLJI8CKHAfBDJC48dbeAuRJ8bX6EjuzD+RVPA6VIRGfgUKyoRUOPusSFdK6kizyajfiDb7bFzTL847N5bB0p440PbfgIoc2K25sixehu7HVgP1FdfBnGqofK7h/ES0iQzCgHoIC1eGk6IZJDQQxB9iFgZSETOEtVUmBzLawW7Wk+NqoDSSHPvVCbYnEDcf4nEhhfRMTJnPMapgexMk7JU+dXXfudsrXrgrbU+d1Euq846k9kEXpZeKmlEDzNZE0EHLxYbAf/6FHtkUQmCF4uLwEDSN7otNI80yr8ZApsK+gy8o5Q60rRZyffQST3PKt9/dMKtzsMi9eQJuNdRHOD4l1EabeOMzKVocOeprQlHlTO//j//FLvv/9t7x8EWDjbp4CIf81+r1fH/AVau2IsmhR1NJbaDBbMo7TgtmM2cJ+H9UNQkDL5gMA0XGANkGPGt7HBGWekRbSvrkUfBi535kPPKwnOivWcyhSNILDoOAWhUnuAY37SBW3buQyqAt35qkwzcp2qnjd8LyhJG5L5v2DcKudHZkkRsqJnGOPXFeNAiEz+lDTzCVQwk7YwJat06rTW9x6tjT6FDeUAi5Gt5CIZYHVGuoR3eJt9Hg0oIwbWUNlY+KUFBEkJuFhFAlNMKOOws3H1wnaoA9RgRDvrdAwhvwu1lOl0lARSikRQYpFrJrA2m0ETGfU9XlsduLrY6EZ1uxgOTYFD4+oitK2Gp+5GWZCZsJd4mbQL6aP51DoXZjayVDKFMou9xhzU0RG7jfnGyIc1OjnxL9/cN4vL7h/PPO4bMy7mRd31zytC8flieKDS6bHHqnBQ7sHj/isER5CgzQAxqSR9HeheVBnkkSvjbZFFUSrjc/fPCAi/PzriW9/kNkfgrdNWZl2xrYYbMY0Ik8Oh4zuO2kPP3t74l997y3f/+FK2Wd0zlzdTOz3ibZ23jx8ydza6SD4Nj2Tv7lssUA7VIPj5tS0sTPYmjEvCU0etSHD8+c95v7ePTJuevgJH1uj6hgRc8RUvri65tQrSzd2uxnplVob6jNoeuanYphTXAJguIA8kTwXD457o3aC2xuWsrkkXl7tmEqLdICton2ml6ghuPy7L+KLaUrPkrqcQ3RxsWw5I5FBItz4wq5rip83qAwwVSRPISDokQanCS6qVR235GWEDKrCweMQIhb1C+N7ujxgl5s4ALIeAomBjj7vUw6oRyyHXRLmnKTOVBK1NqaphO/URmyJxOoS3lSLG9Dje6+tosHTDDeKcDYiHI2x+8oQKsCwSfErHKwwZ0HneHnmFM4iITGPSgQR5+pKOeygmTOVQNBLEhbNJE5gyouXO55en0gqzKrUahiJNBS3oda6JN/LWAUCDNOs9HNwtr5uJA0qSTyyj2dPTL1RpdMUTAvHU+WnnzWm/cTVTQPpzIf4nBKQcJ6OUXy1v2qcU+Yvf3Lkf//Te/7y3z1h5sxz4fZq4uZOubktHN86n3z6JW/Ob/zGHW0lWqSeTrSaIvlsEqxqACMFqndK60wtRXZQ+MMi04WOkkfAs1G00Mjcn87060JKia0b+xLRmH2xURegUbGWUhDuvaPFaFi8JYNMhTnsQL52aBUpkPTiMHdyybS10V0jDkVBNQK8EkJWD6FBGlJDIUQNOROtGD6MbfHwQOxtpcShiISB0fmhfLHHuEMXPA0hx5hIw0oV6K6jZI3KBE1wCciOe2+klT9rWcbullJoha3ThvXqmbBwAy1xu0qAH96hZ0Fl8KBONMdlQepFXhe7+yUx3m2MopfXhYSuNtEjo4l42dYWu+7zbZ5BRvmUFhlp8mGAKEK8tNxCrHI7x/dbG1mdak7CSEkorjSvWOukkvj6i8zpvLFMmdfnjFnl/nVjWxpTiT32VCOnSrogJpE9iTwHogkN1YlcdtR6RHrclM+YhkZbWMozB4np56TO0jutgafGL99syM0N5ZYYkW8qxToiE2JQ9ondO3tOm/N//clr/uDPHvjZh+fQ/hrs50xS5/Zu4v0PJv72s1MAhl/mcH71q3sAti2znne8frVxejxxOtdwhPRM2xxVoyls3cm5E+8hRamoe3gyC5y0s5ijCVZ15lKodQt/XVGO23nwaMJWO9sao7WEihJ6eX5kcEN9ZOkIkXA2EgAi6jHHIStCnhI5KbtpQjziR+YSt9IlkDrnEIOHPSgqCCNuM7YCnXIEYCVHUsezxM7VoG0+dmB51qiObzJ2Chu0BnGbiYQrJoCKACtSykFlXI7FmEtFgps0q5EMqHELaRp3xAWNJRIBXCP/VofR283RnLh0comH17NJjwMscXD7sEsJPO9nocD1MCP7UNw8vzBie8uJ4JwqA5LyCGFLglkbxnSwHPm8a+2QlEYLgYaGv1MkojVBkB4ZtC+vd3zt3FEWzjnxVJW78xRbY4MdAeTEQRzo7JA59hovLnAiJgSQhtNACnmXaG15jgOV8eKsg7Lbj8qH6Lga8kUx7PoaeccRPWFlga2Tr0APe2ab0Osd/893H/kf/re3aGvsdxO1RyDBPEd64FrhFx+eef1gnNs/3GT0aw/npx8+cXunHK4Tt9eZq0PieJw4PjTevF05nlZO58aUFM2Op0Q9B9md5xbBTGaobOyngs4TdUts2wkZNWn1HChsL51tgBi1dtal09vYIRKI+YC8h7LGjO6Chk0dJITXSsSSeDeSR1rC5CslX7OTxO0Edzthyp08ZXYlsxMhE3ycuaOFkH3Rgk9SHda20ddRYk/bmjKnSM3rI7ArSlsTbemsPagZT2NchiiCVWfzNjiyxNYc1VCdhHN+cHM63CPROwHK8MWOYI5MyONcGJkk8VHor9RTZIk4UBXSeEnhkaifU9QCWh+Akwek3LeQR+rIDurN6JuhhOCiWuQVdXdKCUXVegrif54LbYqMopjORzK/BvU0aWHal+CZs9BzoowdNqccSRiqXO+vUF35yvGRu2nml0/Gz9+eOUwJJXGN0k4xXuslJMviRecDyPIBFrlEvlNKOvhOSDlRV4kpSy6vlRpe4ykxSYA6mymdipFZ6sabhwq/dY0lw2ZD766QU0JOE9qFn3688C//8CPqqbGfO9O0g2ZoUbZWqWfnFx/CenRevz2y1vOXO5wfv174+PXGi93MOy/3HG4KtzfCB1+94s39gdefP7I8dT75ZfjldMRUCKHctwt/QCzf4PS+0swiZOq8YBZtWFtVUgna4ny0L0AlyaF3tKi5i3ayAcbQ6ZfoExSVTG/gvjGlkKu5R/8JdqbMMyULJWXurnbsh9hburMNInlXwn3AyJPpz6qeINjFbeyCA4EdFMu2+bPgwhoD6Y1DQjdUS6C4I0U97qVANp9h/+54HmPkoCZUCEfIRZw9gsHGWSWoFJ7VODoMvw7jZoQsQhdHPaoLco//n6pC8nEoid+hhYLJqtElUE6v8SJBIktI3bAUN5ZK1Dwcrke1nUJtQE90q2gqMR3QqF1IsyA5dvOo7IuERsYtXkbiIEQp8Pvv7Hh6qrw6w+2ceJqEKWXME4+10DxCzEpZqM2JxLIvej7HrgHSx94dM0RdKiJppEyEEKOkmbfrkRci3OoOkcaNOudJ0DRxtTvEqK55WPSu8fSCLs7Wnnj9+cZ//z99xM9+3iil00e+sFgKUUd1+hPcvzqRi3M9FV7efkme8+nzBZ2Utm48neHmunKzB/2a8uJl5nB1S1uNm+vMJ59uPD2dxqXmJDI+QJytGkevlH3mIIXl9SlkdpbAI+ajdWNZPaRwG2BfePOSgKeRahYnkVwUa1+AHDIeRsfJmkPXKTyX3RQ37q6Eq1mZcubdw8SLg4QwXMIorWrYQDoFozcwS3jql4mOaSr0UbVepoya0xHEFmQglZFqmciutLrRHVRt5KJqFPPkFNyoj+BkTaM2DgatOdREIX/DQ28bxGRQIQPEfY4wwcOqlZJGX6QBF7HfQH81RTSXaIjpe1iFwojcgke1FiVGIhFO3Q1URkiVGpiRVcKTG8RujNTEw997I7lQSrSidRrmjTxPzLsJTX3wXyF6EElYbdTWIr1eItby+lCoFc5b5esvM3fzzE+L89nZ+HSNtWhvMerfXe1Y3p6irzQry6BW3BmSwhjZU7gO6BIGBNeY7pJHJ8vKSvFbXpQ9b9qZhNGt0ttGVmjNh4lgvFw0DA9r2/jjv3jN//rdB65SAKhzytRqtBalSPOcub2LlIRtc8oUZVpf6nDKFkkDS3bq3lh75u1JeThmbm4btzcFt8bdi8zusGNZdqznxuvXC8djx9fwHCpw7kZPHVqkeORhnEJGLMQYva1+0WCdROlBYIxAptgJdVbqUsdT3HGJcRPpkTavQfJLB4phFA7TAWmZ3kKJksRpPWgdVxvgUNyM7kFTmFkkAubYscIqBiKJchB6jQQ/b508ReW8+NjrNPS8ovJ8q0VURad5wcVQUqCeF7LejciiAXzkKElDLWgcxktKxUeWrQb3PNRBl33cXJ5T2q3yPFbGgshQVYXtjE7UWDRINpwpPm4ZQswRIFGMseKBel4KIII7GokLPhwqRDynllg5XCLhQIYJQ/IIApMAgFTCzoUM+aP7+PkDRHpxveOdW6XXxO2c+cufHVmlc95yBHwxkdaFfVDmNLfAILg0rQ2BBDn4YeJnDONKg5GSn4YhIOfEi9016xIv6152PPRXPB5rrA2REY9Ihhbxq599Wvnu905cFae2jUQawXPOXJSb9w7xzK2wrhtPT52tEw3rX+Zw6vVMO53Dj/bUsLVR9yEyOK47Xt+vzJMzZ+Vwo7z3/kxrheu7Pa8+Xnjz5i1r75RUohahd6wx+jriQIoOaqKn4WYnxkGVGInaGohnUbQZva0gJQ4r4J5wDyWOPu9QDfyilxFq30j7fcgDFW7nzP4wHjIJ87JZGMhdNfgy+1UaIMh+J/yKOcVoZiKRZ5MjehJPz0ipMyR4gza5pO5LCoFDGghuxIo43hUvMh5OIWePPZq4FRMJksTLyoZQYUQrqkacio0T7Hbh7uJrJh02rxGNIU6otjxuAm+ABf/aegvPrEQPqPWQ9ylCjbn9GV1u8IVCSByTRrUYl6cSBnXo2Hg5ZI0XpyZ9poFCfueDJ70ATRoSPeJ2n6YACc+S2O+N/+Are/Sxcd42qvfgZ4dtTYdfk/H7EwvMwofyKiyQjZQKeVJaC4rsIq7KOvGmN16vowg6J6wfudrfUrswXweg5mTIhrczj29P/MW/feDvf/xEr1HWlDx4Vk0x5S1vO+dz47wMHa7rCLn+kl0pttVnPk4Yzv9T57gcyfPKMsfDutfEeYVuM7u5MJfG174xcf3ymvv7xvlYqecwJUtxWGNcAhmEdaE36C0S3eOHyoPMS8hUmA4T28OC9z4Su41UNAzXViPFPNbbeBgI4CQORgAmKkTQVIa9wrJuLE/KflZcEuOc4INeYPw7WgPNcVsZI5NWg/tUzzSpyDCV1214HQegIgIlJZpHFEhHn5Pd0gi6FmWMj8Ikob+9CM87ykTstjbG9gso5Ni4XQPjlYu4vbeQTao+0xfWh4VsRDfmVJ51ykb0W1oLoCdcG1+kOkTh2AiRFkFyIFOqEYrGAL5aH1NMiiR5G4KGpJG/KwnMG9ThFgnKlMAibMRvl6CHhQAAIABJREFUSjzcKbpUVKIg64JlHHbCV2+ErSsfJudmP3M6b2z1NEbrwANE4/eeZPSniDyvn72HCSHvMrZEMqMPLjhpYu2dxZ3k0SRg2tiXid2slH0Yvw1BvFFX5ZNPNv7ke488PPXBXcuIZXVKAffO8dFYN+NwpeRdYTmFeaH+mq6UX2u27msbfBDxA6Mhi6qZdmxs92dkA5kmjqfGJx+feP3JiW2N4K8PPrjm2//RHb/5W7e88+6BJCnQu3FoZPjtbKCtbsHhld2ew4s7lIb2CA/rpygFirWrBhqZBgyaNGIap0z3hNXBKw5OcjfNsWeYczVlJhNsSahkUhPaEr+wPqIl3ZTe48HrHjEcvUbleZJMl5DppXSJFIlspEugc7zQgtt65sQ16AVFRrNx2L8iBuQLUb+rDH4ytKkJw9IlRJS4LXuEQcPFvuY8B3iJx61oOnZmBk8Z7WrBZ4aQJHatQMKt2dBFx+FwLgDTQIBFQkNtjRaXd8TQWIru1gaTJnY5R/s2bRySAEXKlJmmREKhReq8tPGzjv02VE4C2mKXlQCcdNREJoGcjP1VobXI3zmvzkf3T4gbJRWaDXopy+CoHUspUPzBBQuJ7Xzi6eGE1ShBIj5CaM5mK82jCjFr5iCJ5bxwe3dgty8DlDO8VepS+dGHCz//6Yq5ceobm1l4ZwdttVXjVDt5B1c3hXlSykig5MvK96Kv0Ef48yADZAKJ0UcscXpcqUvDM1G84/C0GLsJXrwsvPtBYT/PTEnZzYn7hyWSDFpwkVi8ucQTJEV3B7wktu1MF6NPmZvrPeeHx1E7EGocTRPe4wbOmpBRdc+wJbXWmOeZnJ1DmZhKjuXbO6KFRn++bcU9RjjJUeTLRXEUY5sZuKToSckBmDjg0qlrH8BDp/e4IZQSaPQSfaI+in+0eyCRPqIrcsfHPuqEz9FbpNi5BtrYLEh71ZD5CWDjz3WgwTmnGDIsXhJoOPrTMBy0JlQDNNNN2ZYO2Umu9K0HJ9jjxrdqlBRRk3g86KoR6iwC8zyNkbphtKBTcjDbOZcx9naUErrdkuIzGYL7bj1s9+OFEtzvs2R/PG9RpqseL23RFLuzCakkCoqR+ODFHinGL94eaHamWYuyWzO8ha46lQs95VifxsSjTIcrPDlbjZ5ZTdEqkKVw7pXNnauUmBFIO45tJU3Ofp9GxGXnuK788Kdv+P0/fMVn9xXTSDHMxaiemBPs98JyDuBvmnbkEqmFIsbWOqt9ycN5OdUX+1FEJA6COhphcGBbjdSF87bF6LRTWk/UTXl8u/DuV/Ycrgt5GnXr1Xl8OI+Hv0f2jwzebJdDw3ta8NZIOXH8/GFcAcPKdPFy+hBtA9KddtpiR/CLYqahnsfNpRymFPKxCxSKstWKmlCro60yD4dFTzxTIQH2xa4SPSDhAWybs61hJC8lDVdE7LAqgkyZzXxwiPGCcwtvZTOL4p+BnLpItD13HxLANA7ksKwNhZVotGRbulAygXhGvGUfD/eFTAl5YTfDUxqmAYsX4tKwfinS/YLjhMuIHXpUG7tcaHYDNPGLuILIo6U6uQR3Gd2ZkcNTVIc5Xsb4HGCPpB5CDwfUyFMkpT+HWGdlWxutdXKOR7T2OERmILXym+9krtfM/ektSGefJjbvaE7YYpgpzGPExrDeBsgXrWxznpnnTKsNH7+XSwp8U4kIVwkjROvCvBf+y//qA25uMtTGm9dP/N0v3vL7f/CGf/d3K1c3M2/eHjG+SHdHEilHJE7sC0atwtunyrbBUp1tqLH+0YdTNOxKxpBjiUaQdCcAF7EhZknD+wnnvlIsR+/lduSpVtaqzLvG9UG5vd2xnDaOT0ukqRG3X0qCutGfTvg0qtxcMAujbhoP96WgxnwLRFYTaMatj4Nr5Nypa7hgtJRARYHrWbmahTKWD/EIvB4kAQ6sSycV8BJeUumRqyMqwVMO7ECTgCmt+3hjB6m+VR8PkQcoMuxbkbZnOELXkQ4+xETlclv7yAFWGb0v8VEXlaBT4h+iDRWVZoUKvUbItPllEJVntDm6YabgazW8kVkdsdG4LDo8u2EeiA9j0D42hISZZ9xBJPZNMx2mhECJzUN7W63RUsSQqsiIRYlEeZEQuPulUmJwRt07SfOY1Iy2VrYtemBoEkL5oVAShmjdoZ431tpZeiXRyCKc63DslDScRduvOIaiQFckx7g7sqcEjZeNdFR6NMG1Myc6Picejo9857/5D/nOPz0w2cbnn2/88Z99yv/53Tf89d+dcVOub3YhPyVylaYBAtbNgg7MidY6bx+E+6OzWed8ButfEhCScYukKVT4lz4OZ1AX8Wt5JskvdWb13PBjoxdlOkw4C3I0luOOFy+F/X7m6nrm4WFhWICxpPEwmOPreGOTUckRMTmM0365tYmeThMlT5m+WSh8aqR6dyXUMxIBzyll2qUQJ9vQvRqaQnVkHiNV7xZQ5KiHQyJNPWvc2mb+DNd3c6YphA+1jjCuHj0p1qHnoV0dB0uGRECJw9UJBDXnGMM9D7pjfC9mkTnbbRQijXS72DEvHKFhXOr2Ync2vxwlYm/T2H+9BSjUPf7agG2rIW63ePGI23NHLh6jtuBED0rQST78qSUnmrVIEEjxRawFX7if5hivfbSN90t2sY+DbWPnZlAecTsHqBMvLCHM01lLcMHA43mhr0bfwuheUtBWmh2VeUxPgZNsLZ6j4GOjAfsSNWodqvQwyA/Zp4jTB2Xc28qTbNzfbxy+fs2/+OfXXGf40U9O/Nn33vC//MErfv7zM6s0siRaLyOLNtIJ1ZWtG9saU1yXTrWISK0dWlfaqqEZ/DKH031I4xhp2RezKpD3E45HPbnGUu1d6SLQeizEFNra8ebsdhNP25n11cbVtOPmZk81D/3s1kiMWAmPNG+GwdnoTLuZdloiWCpd6G6NmMHW8dTJJWr4+mjTOkxQtxi+uneyVrLuhg+qISluVDSqEmB0g/jl1qkhAdREY0Q7emE9bTGG5tiBenWsB5TvPaD73jsNpTRDktNLZNgkNXIft9YIy/Lq9NRJI5e1bi06MWW8LGiRvyRQLUqfFGgtQrg8DfuYhl9TpI+fgxhnJfY786BMeg8QqNVxUKvHSK/jICOjoiBuNBnOH9t82K2CE+5jgkkaO3uZEkhi80iLgEC24+UUDp6QFI7MYel4b2RR0qT0Hr+apIwDEyNp7426rqQUvStPD0uoz3yiIJxPK/SNlCae1oXuo03MwnkkbtS2jvFfLzZgEGddbWi3fXhBFe2JavBEZ6fO+qryH/+La652ne/+2Sf86b95zff/7onXb47UvtGYoES/qWqYA3qDliPc7bwJuzmjT42tK85G605tY83gS2proSIy4ZZDbyotRsk5Iyng+SKKWaafttjxZAr+xg2s4sdME+d87uyvM3ro3J8fcIR0VZhm4fi6sS3r2B9DVSQDVrcO7XgCYCpTAFEq0CLyETe8b3iKMl4Z7dGlxK4VmT4W2a+toSUgc8buiuSQ5EW+FjpcBjHyKDqFUsm2xuZQTNh6j92kR2ZPXVfQ2LfcPSoPaDSJ1PCUAAk0tPZKQajdyEOEQMto7tgKVQ0ZGTw5XSSP8bKzIceTi71K0+BULV4QHgIJdx+J6BppEmajvTKECe4yMn3iz1VjZ8V61B2IDrAsEOTWnJLj9saIWNS2xb5dZhxjPVZUYNuMbdhh0i6+z9piNSmphFJKw1xNC9klY69167TmLEtFFeYpXizn80qZC0WICJY1cXObeHU6UzfjKu95XWsEq0mntzxseCs3L+7QJNx//oBTIiamO1qDqvKR4ii/kuiwKizeQTL53ZndjfA//96P+Lf//g1vj1FStS4Cc6Y2x7xyFKeQmbN+EWhNVGHuyrDymbHLhSffqEsfaO2XHGt3N5EmtjZDSwE6aT+HGOG0goQmETMkDeKbFkoQiQfGiL6P1leOx5D0aR7VdTmjU+buxTXrk7EtC2ItQpCHt44hQBYnkumMiEEcbLokJ01lPDh9DI5Rw36QCbyFWRjBa0f3Go7/RuxB4wGvLSE5kcRg66wYc0q0reNF2arAQJmbt3AwdOHcO943zJQ6lkjxxC6nkVAQN7g6Ee2IsFVIc4zS1o3zeqZ7oatBBaxS9oXNnfTshQ3fqHmATqKXt/VAPYfQPfa2AfSIREdLUta6YRhF4maPKr9wxagb7ZK5ioDEZw0JN2VrHdkVvAskZ3vqtJ5Gg1hk+mwGtRunrVPNQw9d+0CnAzFdU0dFmKcQGZQcYnHrDFVQOIGmPOFsnJfGumzU1Wjd0HnH1fXMbqo8HDeenipzydztZo4SIpHH9ZEyx87+cI7kQesWo75GQgMOvSjJdOy6A/Qc5oFbCiUJzSFfw7/+68/44SeP3Ewz81ypDjY7mpXUA09YGqTiXB320WogIPOOWDqceVd4OjXcYTfBco7P+TkK6B97ONOc8Q5FGqoNyLSlAht5LoH69UbKOqL//DkaUYgs2UDghjl3c46vK1Kc3dVEmWe6Bf9jCD2upth1bCST+wVQ8cgE9aizD0V4wkMMSs6ZLoZqjpboeUcRD1zVjENOHCbFtQ3zsWFJyN6GnchRcyBRJYK9qoVpyizaokRhMfjsbLxdjI/uOz+8P+It9i/LMRYfds5XrhovD5nUHZohnni5yxzUeHjs7GeNnJwMN/sY32zqlK5sZ9iZkA8lYlGyIiVGcM2CD2eJuIQHMZWBBhJ7tTfUcoRsMSRxSUNl5bH5ije6w7E2UvWoq/PQgsq4ZRkIqyeh1s5cEr07py2UHibCZlG8i1j0q3j4X5/WjtT4/rrC/pDZJ0HLDtPGVgO5zZNQcqatkY5eitDEqD3RfKX1yrYKqcLkneWsnE/Kq4eN3hK3szA14/39gdUb031nSpmnXilMLMcnlq2S04Fn9mHs3D5oRh95MykVXCN/uHVHLfF666yvViYS69bGftrZpQD1thR021adpJ19is6aaZeZdwOpHYq343GjWgYSOTcsOddz+nKHs9UhYRo6zXC/99h7LjvOBSav8UZS0QHDXzJkHPcUt+ugZPpWWOlYP5JmkPkayRHjgV5EAMO9LsQLQJQBEwNBlLcWWagpNTRndvMt29N5yHwGUqeJ/bzjMElUK6TENGdsNBJ707h9R+9lXDiZJD08juYsCr/8/MzbLfP5An9/37hfnFdvT7w5b6HsSZFTqpIRYFeEq0m5nSX2Knde7hIvdsLShfPJ+fptcGE3ubPPif2svLtnRGQYkiPusQ8kVvexe5oZNqRw7rCuw0Mrg0fUSKSQocISEaaS6FmiH9PHYSRSFV0vwoKhnPHIw41nVy7DC/XCyYnw2BorPUZzd7bqVINz7bypEYTy1UPm9WPjsXe+8ZWZ928ymZWrXQaEczVup/is5aLOl7B2JTNk2tG7cDpuWIWjd46nxrI05j3MIszqyNl5ejxj28Zunqitsq2BAWgKW5hjIQ81onsllxCXjLLdmDiIJEQSiwMNjkOU0qwz70ZAaWdUTSpZY0U47BO3h8xhNzylpTPtErYJ6zZa1Q7Kcg7QZneAaU68ePElD+clvh8u+TRGEY/gKhUk57AFeSdpp3XirYDQVmfUd8bbSWRIMBwVxy2zHDvF4madUobDxLZUwlLfUM10N3bXhyg9bVFpYChzuoQEb4jkOFgKaQptaikzqRtOZZqjZu7FXrkeGT09D7G4h8xLU9jHNgt5YHKD3Hnozvd+VvnXH64ca9jOjptwXDdWi1R6czjWiq0Vt9g9p2nCuzGViXmOdAK1xj4nXtzMeIe/f7NydzXRqnEQ473Dnm99JSJJ3rfKb2Tn2pwF8J2y219+XRddbqDH29pIGmR/0hKSu+FNFQnHiZYY06Lq/kJhObusrClxPG54c653BVvbQC9DCbW48fbYaebMqfBpbfzg7cbrt51jddalctyIxPotDtjLu4mdL7x+akjOfPDGeP9uIktnlzOHXeLuduYbHT6YLKYM0rNxXTVFcZKHBW9dGuzBJGxuETQWFFsGliUSGEpKrK0DMXm1FuM5l8QHH8W5OUXWDsFKiITgo0hIKM+1h5smQ7f4mqlo9LxKDu0zsJtCXLOb4usdz8bx3FibUHsFOtPk3N3suL4+4HWhaqDjV7vCzX76coez5JEmN9wgSKS0VB88IUYa9Qs46JSC/DWnVQ+en/gv0bAGMSxIKZ1J+wzTno7w+HiCZngTJAd/6a0PCmcLH6HG2zyXuPmqGTsSeQ6O7Xw6ssth6K19oUgZo51xPcGudGaClC5jmddBwLsZWHBqfTZ++nHjpPCT+8qf/mSN/UUFX5ZICBeludCshfZ0TArNBJGJqkpfGktbkHVFiLqIIsonp07JoOKkY2WXE3V74nba+LvXwt2c+fqLwkenid9+V/jqlNjVguYVbgpZArmMG5JBBcXOH/RphFzpuI1yjgQCL0FVmNXgg8OBgCRjVzJMztut8qbHGHtejarOj984P329stvteHg88tHDytk6p2OjT/GJtm0JflSEqcx8snbOy0rJmTl1PnowyieF3Vx4PK8cdsL7twsHadzOzj//zRu+9c7MLlV2uXCVEuu6sbWN3W6KegVTDlfBIhmQNiWvcBiOJXMnp4ge3TyEJb1FYLY2RXOm+RaRMNWCH3bGnKvgNaYPAtS6BJwxCYeriVyMXVb2OXGJND3czog3Xt+vvH7rHJfEVAKFd5xcBpfeF77+/jUv3515/XpB1DDr3L/5kmZrUaOMXhPDB2neSDrjXoERKYlQpWLu9NZjpLBBBK9fcHyXEK6M03VHag6poUzYFlpMzUq1NsKQ43Pr2zaiaEapa9vIKUzDpmA9hSi9VZDC2jauJwVNJIS9ROpdWxubhjggCbTq5C6UyVgdmjjL0virj1b++CcrD2fY1pXNV6oVpmmm90ZfKp6Cf+3mSC84URLr7ug8IylTvT5L4LLOQA2wZjvTeiKXhLDx9rRhTXmb3vDLVCjqfLre8NEZfvCp8bvvFb75jvMb+8qeifmQKGmm9UoeHGPSGJ+N+B0F3jvkfsPP2HpY3CRBlkJP0cTtc6F65cefNX7weoXDjr/9yZHHJXFqG5/cNzRlzvUVbd3Yto3zFuj67DtaXek9XCfmwvF8Zp4mSoHt4YR4QxPM08w8zWSH5ZS4v1dW66x14V/9+wfevRL+s2+95Dvfuubbdxt3l2LmKQCVZXWSVdp5Y0sT7kais9NEGdm8JStbV4xOGtlOMp5f7c5Eom0N202QCn46jfE/bm7oNIkYGgD3ieu9I7lTknM9Z5IGE3B1lSFVfvzhxudvhCLObnZ6rxEJq2B0rClbynz8+T0fvHPLy7sdbx5q0HCXFId/7OHMA1oWVwpKLjGCmQRBHpVv0Vkh7LC+oJkwGa9Oj7pgQEbz01CToEgNQEaLsfWGd0E1xowiCc8jgHgzaEEcIx2dJmhOXQ3VMEG3RRCmEDBY7MI1z9GxmSKRzduGWhDF1mE9RiBwT8JuEubUeGqJP/r/jvz+D8/kvkYqoFrU5NWVpR1JukPItG2Nn6U7XeIF49IhZXo9YT0zXe/Zzmd6a5BlePxW1DPr1tm2GmjhSMBvHoLujc6PPr/nvjb26nx0PPD+m41/cla+9WLldp95/73M1agOcAnlDPigQuLBMok8JbPQ/boNgYIZWGM5Nn746pFPFufTR+UHHxqfr8ab5Q2vH04htKiREZVYsW6sNWreq09IV+pbw20iZWEbSiFMWLeFdYlJyyU0WutTNGxlFeY5Re+pdJJs9BVe1cyf/fDMjz43vnoH/+w3dvzOTeJmdfqxhZWsTEg6ME/OwZTXj51rgdsJniwyeiwRhnQf4y0h8pdZAqhsEhrmNgK8RUYiR9QiTnPGltGiTWNtnat9WOXacE2VFLLLX3y28vg28IVpZ0yTIZTAZtTBC81DjLFtiU8/X/jKezvuXhSeHja+dMtYJlwiSLyDiwg6zdS+oZKY8+GZgwMfkZDD8eBG2RXaqXPJr4NxVi0cBWJOySWg/Qv53aMfJQ3pXsQVxi0QWkWle4cecY5dRxUgBMVjIc9TDx1ocZiScDcnSlGcyB5qa8XTRJmFM5Wfv+38+Q8f+atfLBRWGg1houQbdqXQrdHbRncnT4XeM9tm3FxPWHfqesJ1xobw3HHqdmaSoFREoy1MRKm9UaYDTmPro4nLgBTBX1immfGLN0/sEjwtlbf1jvua+eGrxrfeF/7JVPmNF3BFJAfaBXAbnaU6XqzWA2E8b8bTUmlmzFQejp0//NsnXj05n54692fl4VhZ2jk0t+P3WpsxT4ky77m/X4NSKqMLR0brGESDs8YhiD8EJI082Yg+UR2umS5sa8V6I48bSlsnJWdbV370iyN/+6Hw/Z8mfuudW77z9QP/ySzceiDfSRqnJ+Hp6HRLHGa4mQtzE05bFNdaH4bxlCNWdCiQ+pBIBngWv4/LHzIEKee6RGXG4JJV2nDmZHKWSPdT4WkxHh4SOTnX1xvTXNhNieu5sLUIYqsdlq2RJHNsK4t1Pnt95isvdhyuM+v5SwrflRACqJbhlEsR6uydnCeSjhBiF+gxwvVqWFVSUfKkUbLTguj2HiBGRBKGhOv8uF4EgYzJGZVhHHbCTC3EA+xGPS2gOnJmZbQtE/UCWSPVHEdSmKInEXY5UtvVnTQNvWjKVBVSdj56K3z3x40ffNppJqhekUrivduJm6vMO1eZzaAvTsnG4aqAdx7e1tDJurJse2rrPG3KujmYkZLT3Lg/bqwtbEjRdmX0HqOeag6qRoL7vUR+hHa3UJvx5twwPYEKx8X58KHjqtzsrimemfOI2tRIaZ8k4kdai27UrXf++mcLD2ujdeMqJ/70b+/5y0+MbTnGGOhByKcUih7NE8u2sTUn03lqzlIlYjlrGBaCtB8I98AdZDzUyRkV80GniYeyazfPg98L5ZMND4JbCNDXbcPpFMlsJ+GvHh94XB355h2/Oym7tbLVzGkzzkNnmFyZFabs6BYqqEhk0Isk9/l5Qwe+kIRUorY+zPbGYVcoc2bdFgQbJU/OfDgE8FOU3c7ZFsM98XisLEvl5V3mvReHaNCbE9Os9MdnNTRnOtU6hzIjGi/Jj18tvPtix+07XxIQmnKOvcg1BCQjvFjzFLtg38KZYvGNdBS73GQZ3M6RherC7mofO9+5jaS5eBDdocwTglDPNrSVQZILHSwPnyShlSVMt5e0dxGFLRSrOuDwVErYqHon7SbKlCkpJgFpQQqfrDMX5dVT5y9+euQnr4y7w54XV8Eb7K8T/8W3D1xP8G6Kt2QxmFV4cJgLFL3i9bny6tR5agewCLJeq/Dq6Jy3Duq8uJn45JVz1orXNkT6G26ZS5CNew0NrYUpfJ6mKAYysC68GUaBfSHiOibhN9/bIXvICru5sJ9hKonWG+vWqEtQSX/z6cIPfmncr42//7Sy1MZHn73lcW206ohmUg6Dc1s65TDTLexPhvO0eFjsJA2TMaHAseCdS3ZKySH6zhPt6S3dlbN19imxv7rmbi5MxXj35RXv7grZhfu1cVyN47LiHm1x5/OJ1pSGsm0LYp2ff9r4fwXk63f853slrYHTRGs6rBa0Sm8tXh6Epc5aH/E1A3keIKZLtIeLG5ttjLAJSpnGyO8kDY6/bY0k1yRttL5wPgvqIao/naI0SZIx7TNZjDLFuHsRhDQPUUx3I1u0B7SUOZ4br94sbPUfPn+/9nC++7Vrzkej1U4yJ0tGVajrShjLheqOyoTIQjZjFzn8mIR0qdaOaKbWAJByIVwkrSLELJ7G0t6GtjU5EQa2f8HpeGSp25D1xfdldlEPXSTl4f/DOxlBp6gtyDpzmBMvD5mchO6NdRse1dl4bMYf/fAt37/3uImL8k9/84pv3cE7d4Vv7I3cE9YaWwmKQ6WzPcvyjA/mxPk6cxblZCvaY1R/XIXPnyrn2nl4avyiXPHRm4554/FceVwbp63RWuhxJY90CIs1ZDnHTospkpQNqE0xqWQxfvy68gd/88R3vrELdPdOab1yezUhArU7W4OP7yt/8oPGm2PjtDgf3i/cn84sRx/9NvHZNYnkC9yZrHLeVrbug2uO2MsI3PPhUslDPrlRcmKaIbWog1/3MzeT8v5h5uXtxAcvr3hxyLxzSNzuhL07j015sg5deWoHSinUpfGT+4XPz/DLN2dOZ4VeWdeNv/r5L6hifPN33uNrB1geFyqXIOweumxipK09mtymYSTYzKibkaahBttGbWEiPMLOiGVZRzJ9CfHAGm3gdXtDL3OsVylQk96NNjjyWjNpSBOVaHrHnNacp6VxPkNKM2cJV9LVFMF2W3Pu77/kztn6ivVLbOBEqy0qDBKA0F3IZLx3chNS2ZMlAJ/T2qnHhZRmSA2RjksOfWiuIe4eXFNro68SRvSkcp13XF9ds6wrWgN9NPdL/kjslJpHst2IuUDwIZaOl7szUbndZ2YJZQYqaO2YC79cGx9tO779rZn3rjO/+5XEP/tK4i4rzWA5N9oSgdazTrTh7UwpuDKzQs7CXVautdN3B7YehPW7t8a3Xky8eWp8Mhl7LdzmQjXlzbHz6fHEw9I4bZXj6UwfhzCqs8KW5RZ0S8eY88SpbeTm7KfMae28XRI/+mzjnV3n/duCOXz2sJGzYLVyWuEHn2786NMjP3/YqL1Tt8a2dM59o5T9iEMRtnXFW3C/2xrjX/aosgh/gIOv7KbCYTYmVa4OV3hvFFH2FHa3hRdz4ipXvna34yuTcKtwNTllblynhC6GzGDaKFmQneJSqN1od/Bbdzt+eTZef/3AJ0+NTz4/8er+iaUan7yq/Pl7J/7rb+zIa8GWiolRicqiWY2lO7XWEFlMCTejb+NGbYKMNIrWNsQVHYCRSPxvU0ksa6WukHNidxNFxqaFpVe0jxXNDc2Gr5lusFWnpE7D6FtkZT0unYcHQ3NmOTXwcNkcAAAgAElEQVSWJry4rexzJqtxbJ1av+xYW2a4CtQ1lucYn9YteDAhk3WU6+QSvkODddtYTxuuE3n2IHHd6N4ia6JHhKO1cA4kUqiNYhXgazlxmyc+efuGrXbcM5eeFDd9dtHXwb0aF9pF0C40zyznit4UDqrsupAnkGbU5mQJbeV9y3Q9853fnfjt68I39omXLqzrkd7T8Dc66kKfIsc1T8AIfqpEUZNgSBbmkikZNhGybWQaV7Pw/3P2Jr26neeZ3vX2a33t7s4+HXnYSKKlSLIdV2BXuYwYSYCgkIzzJzNJgEwyyKSSFFBpqhDbiixTtESRh4en2f3XrLXeNoNnHSoIYCXhgCMS3N23mvd57vu61r3hIjWCKhz2jVAqoXnOveXu2LgyPeOU2MWCagGtpcYkLKOGqUVWOKXRTCPoRraG3W7kZVbkZUCXKpE2NOe95WqvuR0Lv3iXeHtoNDIBxSEred3GMk1RBmnynipnsFxmto/8rRa+o5bM0lg6Y+iDoXcyKT7xnqXVbF1mFSxr0zgNhnUI+NkMvV2GWfCjBK+ZoSlpoajWqMOENgZjGgTFqXF8toBBaV49GF4tFF9f9Hz+bmKMmt89wG/PDJ+uCqss/tdWjfhfmhTgvQ8CBItz60T9/q3rfR/uPQ8pz7lr6wxVZ3TfCFpxzIaqCzED2ZPVkZUDpT1Fzdlt39gfCynBm3f3nK0D1oLOAi0bpkwcNVo3crV4W2S4Zq08QXtL9N9zIFSVtD6UlrherYUpznQ2I3QmobCD15ZUK0NM7I+RUvWsQVDQ8hw8QH5J5b2sR5oPJUo8zWhNLYm17zg3noHMQRVGWyTjWmaCWoNKY73ZkOKROKZZjSEdfas0wXoUDe+hX8B7Gq10/wz3Bl7vE3/1sy1/fGY58xCKxL7ysZGSMDx103Knd+CVoV94bPDk0phKFTJgq1RThcamJDanonwI+q5xXhsqJkJV9LniisZrOCRDUIpFB3f7AW8HWmuMpZLRVCzvtX+tzIGJWjHW4rXlZh+ZooWS+OW7kXf3Ez/9sOf1fuD1g+ZmaNweC6lIgDQ3mOIgkTUlyE7mQZQx8zmtZXrvOFss8VrhrSHHPVvfs3IOqxTr4DhZKE57xUlQbLvASms2XrPqLbozLIJi0Tlh8bYm+3Ct2d/v5D6rDK3UWZcHTsuHcSyZrBSdanQh8QR43lk+XDve7Qu/fjfxv327Y3thWRoxQVhdscjxaLn0lCHivZXE2jwcM05aPjFOEpixzANKcJ1luQxU51h2hn3Z4/tC6CBFQ8oVh3CZ2pytUYD3MjluxTEWOMTCqgklY4iJIc2ExgLWFk7WklSzVo4dWgVOt9/ztZYmbXujFGkS2JWyFqfmUm9BWK/akVJiSIljkh6f68BZRDePoPYrYkhW79P4BsGPAK2o2XPi2OXMk15zqhyv2p2cUVFSfZuDxxXoF9JlnI5QtSAltFY4pzBOmiAr5yRaVfiu2KqsNBY+edrz1/9BYKMLtRl0hjhF4mS/6zt2vUF7Tehlauw6Ny+XZ0+IhpIyNWW5gLzGaU0zjUwjN82uFLTTbDpQkyL7zPR+69Fb1hF8cCz7Dm1gHAcecuWYIeWCNo6YxhlSJR3T1mBMmYXz3A6Jf/e1tL4+fFz53buB/dFgmmacS+BaGQ55lCex0hQFSsnZyKqC04rOBbYLxaZb4TRQMh5QxnMSApcLI/ngleN8rVkHmZKuvGHdGZxVgpv0ioVVdFqLoa40khZr2Xc7RJXRVmGdFbxKEohaiaJmwlRsVayMhFYuguLOGz5eLvj1IXI1Nba9IlRLZ6FThd4H+pR4GJPA23TlMCT5zGkoNaLKDB2rer5JCemhqYzT82fQavpVQbXGmKGVxjQ2tFdYl/Bz7thZy+lJ4/ahkaJmNJlu7uUOKVOSDEOtzZycaE47LxXDKNfOkI4s7Pr7XZzaKJzTMtFrTWhzRtJBBUFLWK2YYmE3JA7TiFKSNWwq451Ub4S9qshFkj+51pm0Phe6q1DImR0gTYMuhWVt9CZQtSZPCZSg9/vekqbK7dUNWnVoJd7OahTWObQVuJUzskapqVFam2nfkqE8XTs+fQ4rLTCq1gplqsSpkkZRL6A11Wi0k31vq0Kj198hRyQ2WEuWiZ+VWhrIeF+ZxjQ/gVWrWFVZeMtUYDFTtMtchjc68G4oDDGzVo5F54ggr1DOMMxDsZQmppnW4BV0Xv7Yb+4a3sGv3maubhUPQ2HhYMqyp4s5UmvEG8kRe62Z8oT3C9ZW45w8D0xJmJqxReHRnHjHycLx7Czw/NRx2kOwlt7pGS0jT4N+ZSV5ZDXJNPlg5YwqQqwwSeYAViuK1eSSBTytFCQrOdR5wFeVIFQcv6cc2qbobOPDoAhBM8SC6wLsJwzSPirzbtMa2TfnWigtSWsHNdMX3NxFlvSZMVqK4joTo0yvi1IEb+m8IU2Jw75RJqkpalegaYIW295m5SlZINGjhujFH1uKBq3pFpngJFaYaCzt/LaiYRM05Th+v4tToAaVMUpDw8zOi6pm9IfSpJQ4jIlDjCgXWDhDI1PrfNRu75NGgDIz6Emmk2n2Whjr0CD9ziJn1ps8SrVJK5RTMOXvzg4NIe6lUtCuEXpRALSUZUdVF0SdqL2VfGmZFzBzUVt7xYePHJtlRUcllbVaicdEnQAltLlcGqREGwvVKmEe1YxC1AixFkqOAg0Tz7WQF+bd2ZQaqlQuAhyaIjXNRMU5z7oDHSuHqRA6g7MaYx13x4mr/f18IXW4YFn3gcXjFVY3piHxxc1ALqJJDEHqYrmBjoo3734vENoXJT/DvOrypqMLle0isA49MU4YZVk7y27a8zDeoZTFK8O5D5z2lRfnnucbx+U6EBRIrD7jnMYE+afzCquZSfSJUioJsE3OsLpCPGSakiGLMuCDl4l+lTcyG4TWZ0HqadrijCLmBNpgGwyxUHPj3Fl2pZAH0X94owleo8eKEbCLYFTno46yMudQur2XwcmASMn2vs3rQOdlABZqYGECnVfs7UG6N60Sk6ENYBfQG9AYcktsFp5aIkM2xAS9f28wUHSdofcaNYPP4T3bqbGcuc/f6+IUupwscL1V0Or81FOoplEVhjGxi0dWyxWLEIjTkVzAKItqEat7QliQhkw0hqJGemcYRpkM1lZRTvZkccoYpxnGyKEVFtqicySP0jyBBAam6jBV06oh1jTDpAx11sdbKkaDtk7AzRZ0Uwhi3uGdpncZS4duhWlMxDFDarQsT/A6g6dyQfaSCSbqd6GIVjKpSvpmPFamUpmKZkpV4GSloHIlN4XtDDY4ggXbZ8qQmErDYnC5zWQFw9pXll1P7w2v7vdi155GOB5Z+S1n28BmZXl6tuSb0aOLhLifnQnucr+fkSUeoQwUuTEuu441jZYbJwvLdmE5X3eooeeb64nbw4FDigS94MwuWDvL8zPNJ+c9TzZyLAi64ZUlU+Tp4yUXmlPGJohkWUO0SkyN4DXGtTkzLSGHmiVbbZysaHKr8lZDYyoFQxAsqGrE2mhOU5qkxVRt2NYwveepaZhjmT9nDa8rmw5Wk8YNlt43hipne1U0ypTZbTzXs4qm1YSIkzSaI52DVb8gRk1psnKpsbFdL4DMNFWqKgST0VhqNcQUJUOu4Hzrub7P7A6VVC2pFIxtbHuHQzGVQtBSp4zz7CTXirXf88zZaIROo63kXuPUMJOiDI2iCvvjxDRlertg1XWUGLFtri0pReg8YRmYdlUCyFVeLUudCQZoOfeUIpoBlVBmCX0HtbJ1jr1esEOhdKC0EWfb7LqUA77WjZwKaKGeWdfoO8sq9KycxqiKDwaFI40zzjEolLLEmFi6QCqK2DKliTq9zoiJ0kRfMBwQapx3coZrcBjg+r7y7iFzt2s8TI3YAGPpvEbFTCqSoqmmEFRj6eGkk4RQxFBKxWkZjFjXmIrsGR8tDVPqeH04klOiGMv1dcLkxukTzw9XmmenUFvg+lDojCfYyEtVWHWGpIzs/ZRm5eycGsosOsvTE0vXW1puvNoN3B937NJAryyndsFH247LReXxmeZ8ZdGtCqfJeQYqE1BiJe4zh93IIVZUgY6KV1USP8bQeUNvBQoOUjHUSuRHy16xXkglUe6iAkxLJYMyaG3JLTMOGaPktRjFLDiWi/1saRiHzCHKov84VKYYSWmUJ2atDGmaoWgyoZcYqEREnXVoBYtNoV/ISkWXTO8VVWm8NkxxQjW4WCmGvjGmysJLQGPIswpRJfo5neZ95f46E5PCGM+mg95KQV0w43aGlxeyklWf4vsOhJgTFRmalajcOI7EoTKWylQbq0XHsvfko/BAjXXYIIVZbeVpKP28Jk+uhuQrKUK+Q8LltYFRBqXkQksUYsskLbIhY5KApGsR25ORdYrSdVYiSY2tpoLpOmmVlsx3igKacFQbYtPKVcbnNL4jtev5jl5gKoVYG5SK8g5nDSkV8hC53hW+vE682zewmsUycPHUsz3z+ABNVfIRbq5GjlmkwuNQeDhUvr5rMyR6YBkUSycRSOvkZqZotFz5YBsoNN7uZDF+n/bUW0vn1pyGwE/ONbb3XO0St/eRXYPlQrFVFbs0eGsIRlOnRknQN8PpqWW7EoXF33594Fevb9hHWOJ57D1POsvHq8LlaYf3VibpWjHkysNxopbCLkGcCvshszsWhlqwWrPuA362iKdSMKaiVaZl+Vz0WrF0hm2fuFhqzgfDqjfYYFFOyH45C25FWlwCBdO6UbRERHUzWKXkSY7hNmaO8xCwd0qAX1mikCXJCtAambiX0mQ/Pa+9rHdkMk/OO4pqDKO8CakC3jnQBaPkd1cMxATjJG6VOvOnxqN8XpJrKFXICVQRIojtBc5dEUVFbWKjk8OP5ji3sHL7nmfOWiVeV6siHwrH/cAwJcZUwATOzpacnDhqhJaa5BWNISwM1iuGQ0UrS9NVODpK45TUk+ysTpMQphICvFbUltBOkUzg9WHPUAvBiKfFafGZaG+wBaY4gQtApmXBVCoaKe8ZtMH23VwlEnRHMFWkUhnCzPmprXHYj7IEZ37tSDIpHXMDU2S4EzNvx8q768TNEbS3fPDY8eHzwJPLjm7tpCNpBc1x2Cf088B+jLSimPaJ/W3l7jbx7j7zdlf43e3Axms23mNiwXSNs1Ug+EKM8DgFprgll8yQB44l8+ZGsQ3w6RPHi0eGpyeWdwv4/F3GBcXSNPZTZrMwPOkt7VAZp8z5SrPqDQ9D5cuvD/zq5S13U2OhNBfW8NHS8WRruDzr8M7hLUytcj00Xu0z41gZxsz9IWO94XztePHxgpOV4SRYtksvrY3UOMREnCIpNh72mZtd5u1D5tV9xjw0tkHzYm14vLJ0XcZ3ntNebv6ST3+vE4SqK1ZpQGNsI/hGs4l8lCoWCMRtu9JsJk25LtiZo2RwSKUpYEzBzxA37wxGKS77jj9+fsY+Fb66GjmS2A9HShL51sKLJv7l7cBiscLpwv29JNuMhRI1U2lMRpSP61VgbDvAEiPcPmTxx2RJKdU8QUECLlFu4vafBiH8vzw5K6AUaYRhiOyHiWMuGG3YLjrWq0AIULSkILQyM3qwSTHbQMuGnNOMhrRMdRL8opKRM0qqTBo1uzcL3gbWJ0uSOmL2I32pPGgBhVltcEa6kDklmQjOZi9tZpq7KtRqcFpSI6lIoDt0Clc0piF81SgF3mms5Fw5HOTsVlLjWCqlibXs9iFyvcvcRAmVX24126VlGwxbo1gHjTeNmAukirEa2yq2KTYGDlMkjRmfMqeqsF5UTpvjt0bz8vDAfamch45g4e4QuTzx2K4SYuHRyjFFy5v7gUxmP0389o3lxZnng1PLYu14/ijQrOGb+0LLirtUeLrpeN43zEIM4ouF5vYu8erNyC/f7HmICY/nxGg+OnE823acrLzc4FTjrjle3U+8fZjYj5UxSbb2cuN5su347KOeD5529J0iqEowijxlhkEzVMv+eiD0gSnAvmuM54bf3g389sZxNUSuj4mPcs/KRs5XipVfoFoV4kYTFpFUL5gvVNmngzwIjFGsl57mEhwUWRu0ed8DFtOcmTutrRSsUVjT6K3l6bLnRad5stBsj4WhFM7XjjssXxvF3VhlPaYq3ihUVJilxrtKiUJhaKl8R9mjiKoyDgIwK61BlbkKTSDaY2pMLqOaJlWF0hYXAi1/zyencFkr42HkYRoYY8QZz+l2yWLR4TuNXxhaSigkHE/T5FQYDwJu1nNlyFhIKc4Fo5muN3NNtbYonSXaly1OWdYKTtcd5yc9l71jqHA1Vr45TFyXyv004KzcCGqt4K0snqckPkojsGatwC1kIb7qpEeqRmi5Uob5/JqhHBXjLsviWCvGBMeYiYfGfipUb3lyYtk6icbFMXJfM8erzO00gnFc30fGAi0Y2lDJh0y/qJAU076gU0Xlgm8KVQsfrhzNrvjNux3fpANnLPEYrg+ZzmucNVycQYmKGHvuhiO5ZB6GwtfvCu8eKp9uFCrAcmHox8bX+4EuGM6DYruA1cISrOfhGNm9nPj124GXhyMKx4nzvDhxfHgZWC0cnTV0RvNuyvzi/sD9feH+OKKy4mLh+XhrWHVwum6cmUq82nM1FQ5JBmVxaNylyrFWjvtESokSMxsLy2AZBljaBp3lZjfx5e3Ei43D28Sbw8iJD6yMHG2KuI9pVc8ou0Y1kKum75zkp1slH4VftJsyN/cTAWkQNTQ+yPeVpkJQloW3PF/2/NF2wc+eBi5PO8ZYeDhMjHHiSYNV6PjmWPjibeQ40w21Mbx+vcOZhm6dtKhMo3eWKSIajtLY70fs7IER2HYjRcku19yIzcoRpoH3AiKv7XvG9+IxsjuMDCWSSmOz3HJ2tiZ0Gt8bTi7lnDfsNMF7pikxTWmG/laZ0FUBFuf0HkJlGJsIQ5Vq6CaTYI2TIYwpPF6t+MmZ4icXK56sLEsryZTYKjdD5d/944H/423jld0TI/P0q8jiHI0VKjI5F2Kycu5SleKEJq+LQkX5b4U7KmuZ1hpOC6ZimDL3g6xyLrce2zVSKhxy5WFs7EolHuB+3PHuWLibKofUeBhGEmK19sbRmcjlqkcrQ98KK1VZGsvGGRYGNgperDq+3e94d9/weslWNabBol3Da83jRz0hNH71mySpl5J4szvy1U3g8UWl7+HUa+JK8XZSnHrNyVKx2cirpi7is3lzNXC1m6hNs3COE2v44HzFdi2DtYjm9V3ib77dcXOQ/LFTlUfBce4bLo8E1ZOT4m++3HE1JH57feTdoBmStHDGZUcBYoqUmFhaR2cMWhsWqrI2E0sN687RUOxLZVkNd4cCWdi3fRBBsEZwAlU1QlB4K+u8FsTDWc1EmgqtKPYxc8wNbT0lTrPfRmYm3hp8r3m0WvLzJz1/9mHPZx+fsl54co7sDxNXN0eu70fqQyRaxZfGMI6JYWqUND+NG1Lwb4rSNL1F+L3VYWYYupo5WbTfn3uNllVbLXKEK8A4TgwVsv+n1dZ/8OK8vt8TS0Rrw/nZkovLU5wp2E5TQOozNJxzxFhIUSJ5Rmuyeq/rm9UKSlrmuSVsM5TZ/iF3x/fNN4M3jZ9/1PEvf7zhg0crdKwcdwNFQVCaF9qy3jQWf9/4H7923KmJhzKgmhR1VUtY5fFq7owqxTQIyLm4IMjNmdQegZQade77Oa8gFQEXG03fOzqjwGp2h4nbQXE1Nt4dR66Oibs4cYyZlBXGazaPzvmLf/Ezbt6+5e9+8YpS7rk/Zl7vj1K/a+CV5jQ4Hi0sT9Y9i6Tpm+aD1YYDmprnwQhV+LZj4cmJ4tlF4Hi/5naXyEVCH99cR354cCyWC7pV45FKPDs6+qA4W8+S27kOvd9HXt8lYoW11pzrxtOlYrtQoOEuNV7dT/zjt/fEqbA2DuMaJ8bQaaHy3zbNm/vEm2ngq33m1e7IEU1Y9vzpv/hjPny25L/9r/9npnFgHBLKKB4YpR+qCp1x9MFy1neclcrGwnQ0eF3pbSO4yjFGGpYOITua4IT35kTC23KjHsTN2pvGo4uAfmgMweOOieGwp9QydzEFXWKNYuUdPz51/NWnPT/9+Ay36hjHieA04SzQB8Vq4fA68Wa4YWstb0ZIR2lOVW2gNaySumJtmvup0qqb562zz6bN5oCZAFLJv7eotfdqjopF47Qhte9JfB9SxBrP2ekpmzOLD5rQS8OdCGmErpOC83CQmBZKpnlxKJTp99gMpa1AmGhYLYtfEc3InUZrxThFXjxb88//ZMOnj9ZobYjxSCuZIYOiEaxiu/X8yz9q3BfL371TxDQR57ZMbZLHCsbgjUUrqa85pZlKxmaDLTMyUgsOsilJrvRBPsqDUqgWcU4z5MLuLnITK692idf7yN1xz1gaVSu8D3gKZ88/4C/+1U/wGs4vC8+eLvi7f/+Kb15dyUCgJMrcj31bCnf7wqsh8azznCrD2cJzsTHYzmKCIU+RaSgUbdG6stDw4YUDZaTtnzPxULjfFZ4+kseEXTguFnVefclZR1spI7y9mXizbyhlWDnL6dLy5GKJ9o2hNN7cJb65ijxeB7YXlt2DhC1UgYHG2zHxZl94OyVuhkSqlZYLm80Z6/UGUxPPzk74T/+TH/DFF9d8/uuXeK1ISaFKw/qA1pZdbOzLwDul2HaGra5433E+79BjVnhdMMGwWGncWhMpxFFCG7YqOm/IOaLROBBcS26MBVJr5LmKaEvDK6H/f7Lt+cuPV/zg+YqqGjdXD8QEyjT6IEm47cZSo+JH44qXdzesveEwFJoWakJpioLERC1iH6vzE7O1OVFntOSy51K9bhrqfOlq8dwYpTBoCdGU74nG7Polq75ns5FVQre0KFV5uJEunLOaNMB4yKQoo+paIQ2VGrVcrHamwxXIKaKUVL1SbVBkghtsD7WwWhh+/PGSFx+d4qvi+PbI7nbPr3438PVb6VJulp6njxd8cKL5i08sU7Lc7Twp5xnylVFhITvQNlup3yeLvnvVUTN9QaJ4BRkwuFlCeX83EGNjVyuvh8KrfeI6Jh7GI2OMLP2C9apncbrm/HLFN19fs9/t+ft/87+ze8i4EJj2Bx52TZwsugEe6w0vPniMrY3bh4mcCq+nidd54knr+Pl5z88+DCzWjlQDJjVux4YzmkUu6KVjN0ScliaPn61gtpN3EKM6zh+JrMkKOhsFHPaFu4eKapaNbyyc4XS7YLE0ErXMEMfMxsMHZ57dLtOsYiqG1ynyOiYe4hyNs46fvFizWlh+8etvycMD99/s+TffvOLzz3/HD394xs3DSL9YsNIHclbsj/CjT5/x5KLn4Xpgdxh4cztwN03c1yMlgD0saLrSac1KN/qNpgsNWv4u7eM7xdorcoKaLEpDzrIPncbKLiYMQv93KmFVw2phFT0/CTw7s5iWeNgnfvnVnl9+HRlq5Wyl+eSy47NnPRenmh/Ent+8tbweHNd3EdN3BNvY3R5BB9osI27vn5JNOrhWycAwK4OuDeWFKJ/H9B4mLxxmJWkqVef46ve5OJ8+PZ1JBYp+qVA1M41iyApBysHjWJgGuSgVmhQzVYROaAfWW8Eatox3RlTbUQZDSimCMThvmaLhJ5/2/PmfPGV7cUZ7t2d6OPLrryb+7S8HFiFwsgy8vcq8/PaO1887fvbpgp9/vOI3+8rhKmN0Q/QpAjVuKHJV5KaZhkpoc81LKSyCTkw1gxZBkkLhKAQ0WmechmoUD0UUC956fvp4xZBXHHB8+NGGs6eWb79txNs9r79oDLlxHx8E2TIllNY4azHzRPqzH5/zbBX49uUNr1/uMWNgyEcmXfnieuDZo46fnVlpLyjDU2AaFOPDSEuay4VmCopUFT4VShT+knMKEvTeyB06NYw3tAj310fi0Fh6xUkX8FrR9xrrNNYqAo1Nr6AZru4St0fFVSy8zkcOqdGrjqebFcugefZ8xT//D8952Gd+89VbBhquQcqVL798zf/5j68INuBMxQVFsBVn4PnjE376yZZ0ceTX3zwQo6e5zHgceHesHCe4mQpnnzjONBhdCH1gbBmtK9ullZSQ1uSahVmUFRbNOlTqXtwmWiW0qljd6H0g18jz8zV/9GTJ0ns0jS+vj/zrX9zycIRdmWgK/u2vCn/56Qn/5c8fcbYwvFg7/v5qYNnBQ2oYb+nXPeM+C3XDisaiVYls0iTckJowIisZrR3Ka2k4CCoKhUQHp/ZdRev7XZzey4K+6z20JiXc1vBBkaZCTnLOFKuTRJTEcavlQrEKbazgMhRo7TDa4KwgMDRyLhynkTRpLk7WPDntUUPm7s2OL76J/M0Xka32vNganl505NZ4dRV583XCLUY+feH5k+sFD7sj9zGitZVSqFKiX1eGVGBokuzRDQiiewcpdhsrKyCtFabCIjQut2KB+vRS82SZeXNUvHzT+Ozxkv0If3sN27XjycWK0+2St292YJB2SmrYztGmhDMGRca1yPr8GT/60SM+W2rOTGRVGvc3E4/ykrWr/DoZ/vUXA+8a/PXHPVsj+2BnDd2qo6WJ2BeGLGawzjipXY1NJte1SK2pSEHbKEuJif2hUpXi2WVAyRGai9NANwfeG4Wu97zdTdyOhW+HhiLzkQ4slx5vFWYT+OjpgmcfbXlxseSV2XO57fnd24jREEJkPBoWeHKahKeUK61r9Ms1m5Ml65OO2+MDV7vI0+c9F77wzZXnstfYKkGQ27Hy6VaqZ/1K07JiehBuctfJa72KUgtENfrOsAyK/qYRjwlF+c67ElviZLnlrz8958+fa7ZB8fnbyH/3v7zjYSeuHZTGtkLnPL98ORHUHX/xgy3ni8DJwrKcAu/eDqTc47wQE3Ir1CxKEKVkYSMEP4gzn6ihKTnPhQ3ksYm84rY2284VMq37PhdnjrA9dUzjjPDTwkgpGcZRdmo5Nsqk5qSNYC+UrbNIR5b63dJRctEyVuEAACAASURBVCUnOd/pJnrv95j/cZpwpmfRBboQKLcDh5s9n7+eGEd41kuC/2IFymm8duiW+fZ3E4ZKZ+HxxrN/c6CU2aClZ97NHL6v2jCVhioN5yRzWr/bScrx3XkRBJ+dBBYzpl+7QrBLPn+1Z/HI8epm4JPLnp+6xkZrPvvoAv2vGm/e3DPcv8PZwALPMOyxRnKej84umaYjy+2GD7eWDQfWS4FJNXXEWsXCWn64gWAXLIDhWFhYoUyolrDW4F1j1VvakOmMw5aKdxprZtiak6hdTVBio+mM0ppiHE1V+tBwTbFeKh6fyCK8mkrQFsVIsJpVZziriVW34LKDddFUaxl6z6OtwVXF1XXCdYHnH13wxTdfsFgshSVkNK2NApK2mj/+yccYY3jYHdmsPV+9vOb1yx0dmr944fnmmwOPyJy2ypLK5cKy1YaV9bJ6GxOWxrp3GOtQAQwVryyHmImTQKMpBZMyFnH2GBNxruG04YdP13x8ojnzlq9uJ/77f3+Fmgwnm8AuFuw0EnCslKO3lt+9Trw4L6y8YWUKvmU23nDMGZUSwVqZ3mKFDmnlwdSk70itsyRKNUwT3hWIjMo5R22yMtRzxPC7Evj/34vz7jYxDJlgBcrcVGNz5hmHgZwkyJxjJUcpAFctFwJ5JrKpinVWvqkaaTnCTEWzRpHmBoM2Rj7IBjgM3L/c8+7lntvric5oegcnK0O/sGgvO9IpOvJt5KvfRdTGcLbp+Pau53CM5Fw5xpH7ZMnZUypkpckpERCPaC1QasPMBAW5r1Wslcia045cC00HFjkTnMHoTBsNr28LHz3z5Hbk7vWOTz8440/+7Bn/0/9wjbONVV9YuJ7jceDFDz7i6eMzXr6859NPzni80rTBs9vdc7HO/OjRlmmYCFlz5hUb3eidZnc38qYUFr6y6GcNvVW4YOgb1KgojALzGhKLRY9SmVxmxk8WqLaIeQrBVKySdcTGa0wtbDrNWCU88XSruXCO6xE+e2x5vA10vcZ7xTjAtzvF4SHzMD7gtx2PH6/5z/7ZB3z57R1f/eOexRI2CwvV8XB84D/+yz/l/FHP3/3iW05WHbv7I+V+4rRUfvrcc/fNjutvJ85CZZkUj5aOs96yXVoWvbREajKEhcfpJnVCLZP9PFbyJDRIDRyHxn6ArBsqDRjkbePydM2fveh51BnujvC3Xw+cLBwXveOhQNsD0bGymqXWuFbBeK4fJh49C6x8R2WUt59SWfUeXQTEfaiw2Xa0KokoM8ukZ04pIAk7JXUsebLmJO0sXQWv2hQ1fU8Sws3tjn7hRe1XKqtlx+5OSrG1ZkqpTGOSBspcZP6/E860tdRSGWOkFoFQNzKpilE4l0Kshd5YqJU6VR6uBo6vj3zzZiIn6BWCuFxq1pfCHTJOUyfL/pjnypFhs1D0neVwnFA0Si3cj5F98iyz7FjNbDb+Tr2gRbykFPPZsBKsNCGK0pgs2ckpa85OHYeh4Iviaqq8uZs4S4nbzzM1nvPDT894/fPn/PYfXnOMR+GHa8OUEveHkUfP1/yzHy9Jb+/ZXR1Yt4HSGe7ujuxuElZKrGyMordz6cAUlp1jufKI/7NQXJWJbY7zz2HIMZGbl/dVI7zUqqA0KQWkIeOV4Di8Fp2A1eBNFfQpoDtH87ApDefhEBPXR4WdDJ1XXK4L7WgYlCKlxHHMnJ4u+S/+6qf8N4fPuX+4ZziOAvXG8Hd//zXdP2hC13N63uEPRy67zMYrru53vH0TWaG47ODEa15sJW3mrVjVUJUSZfWDUTgvDs0pZuKYUMWIMwUppO9i45gE7mWUwaJ5tOr5ZOXZBsP/+ttbXNX8Rx9siK3w8i5yGCqjUkKjQN7iUkxMB6i1A+dIzVBSRlXYD5m11fS2cByFsmeModVJoOmA1pqqZO2iqVgNsc09ZTVfqE0GlAZJxH2vi9NaTYmZ+0OUkTyVcRBOq7YQ40Crst5otRGQwHApBWXEIpzfs2JKnY3JYjDOCskd6vcYwsb11Y7dxnPcZW6H+XnfGodUcUtNd66pRVEmxXqpWXeGXUrcHUemeT6pjFyYWnWUIvnYVgWH8v752JQSnqmeO6dV/p3SzOZs+dpaI77I0Hh62lOy5vXNCNkyHBpvhonV0Gi7gX4d+PNPzzmzgbevrtgfGm3jOFeWJwY+Pms8urniatcYponbIXN7k4n7Eds0i97h+oZKEvHyTrPuDeuto/MSAi9aytnHWshVuK9OGcpUSIdIt7AiINbCOjIYjpPA0YyR1YoJCmENaKytOOXwzbBLkVhh02tCgM1KE46FtzeZl69l2rgMmtO1Y9EqXN/x7u6ey3XPf/Wf/4C//fyWr19fkWICo/Gu46ILfPJ4wUfLxpLC/Tjx5asjb+5GHJZHW8uTteGy1yx6pBfr3r8KNvJUqUOS1orXdEvPOGXR982OmtYMucggThmFxVNVwWnNySZgWuGr28L+YeDnz064PF0yaTkLfq0VOy0dzjw3UQwFEx1xKoAEWYJzFF0Yc+GYNLHJJmI8gHVVGktZyvdKK7n+qkT/trZwHRupqTmgAKhKjtJ40n/gvfYPJ4TGSklIEj82WhmZrNSrUMIM9sFj34tJa6WV36vWUi1oozHMLpPWaErC8bs8onSj1w5vvVDpHjLjbpJkRqw4owlasZ9k6VyNo+aGD5raVxadZlEs7/aZSGXV9dwfIrEcaa2ytILErFrhnJ0zSII1TFXLB1TNjk8aLclNzWpNUXVu02gCMo18ctaLpv1uQmlFRnO3i+yPiXA3se0Nf3qiaP2aMmmS0vQL6GymHDN3EY4Pmf3VxGEElGG9DDzaaHplyGWm8FURC52e9qwXBqsgq0prhZolUlmRdr33QgBgLCjjMCtBaJCFADdFMDNBIiPM2RwrLejvVlqNIkSAnJiGSskO1xlCLZz2hpwrv3s7MY5aWkZ+pI+erlesXOKzpeMHf2TYPT9nTJVjgZY1XZOjAlPkd3cjX11F7NhYOMuTE8OTXrPpLMsgnhVjNMYmVIuosZEH+SAbJbBypRVjqQSl0U1YQ5lKrI2xJRoZjZQ1jHU8XsoN9h+uDrzYLHnyqKM5hYmyQlqFxoOa2Un1PfBb4bXQO0xp5FLxQVEGzURiqBntDJeXPZkCRhPjwPbilONuT61S+kjDNOcQmuw8VRMdpgKlLa3IpPcPuHP/8MW5exjQLOSVGU1uBbsRCU4sihwzyjasiRhliPMPKMkckbc6A2kaybEI2t5odlOkVljbDm86AVYFw7vbyG9eD1waIbSjoLeVuxGmZrCuZ4oJ00G/bixPLW2YQCuhcFNRKkqfVM9PSSooAVPTQYsyFSxFrF8CoRZOTE2g/ZwsaZWmQSlpyC/nHMjFqadQuNs3pgTbANFZbofCm32kvRbD8bbXbKwmHRpv8sQwZWyzdE0zHSp9ZzndWE4Xho5MyhGyo7ON4C2PLgOrLWQyvfGUDHZ+DbJKMCNWV3ItpGTJSTGlRNctqKqgiyPtxdexXHSk1hijmMWMkZKBqgpdmwwrNHTBEqfCOCaOQ2YqDd9rfvzBktNg+cdvRoapoaNiGiJTUNSbyjqM+ElJv1FDUpVxKuxvM7dHoaIvm+akdyyX8Hzj+cmLFSujyUNkuXa0qsgtsVxYAWBNciNVKZEqGG/Jk3yGVJbAS9OaYVLEYjlWzT5nLAZwaDwlGe4fJl6/nvijz3qCV4xVkVOh0x6rJnqTITcCiq5VnPcsvSVXSMrijSW0wlgLxnfEsdB5z+l5x+u3txizQmEY4ziLrTK6iFwql0pWAcwMQq8z5LqBmYEFJcXvd3HqpsCk+XUwo53saGq21KIwTtNUJtfEiMfpefiDbDNEVSdDH+PEtXkcjsSacEomo62JxFb3ikN1vDzAk1PDwhv2k/QwW1EMU6aUhLVCGrcLw9lZoL+d6HtFSBWmhsJLGKLJIKQWS8lFnkilyVMeRc6ZUjOxefo2r4EUkAxhmajOgBZ0itXitzRbCMbQ2Z5137i6myQ2WCJ+GVh2hsOhECvc3U7QdRiryLlSoyUCzjYuTgIXa1h7acekzFwSqASnODsJrFYO5+UpKsbwPPs4K4gKSoIdUZMmUdgrrUnHKHCxVHg/amhUjFX0xsz0QaBVYgQxZGt0ka+z6C2lZBH4tobKoGLkxRY23YbP3x24HzIbFwRREzP3N3IOzjUx5UpqCp0zbaqc60rYeLw2nHWGj58EHm8Ml5tA8JZSBKY2HSXsb1WFpKUbWeVvZZokcEwDYuYhg1WWmiVYMpTK2ylirdjWjbJYY7naNfL9QG6aY4KaHcvQMSDcp0DFa0uwjTMUzli6hWO1dOzHyGHMlCqmbWOknG29IcbM628fGGMhH+7QTXN8iNJTVo0UC03JqvA491OhyAOiQWtZrHDGMuR/+hL8w62UuT2BcbSmSFHRtIdUKDWhPJhq0aZSa0JZkcQ670Rq1AqlVpx30BTjMTKkgWA8vbbCTG3iqWitYoPhOmWO2rFaaXFhaPnjpFKJZe72KUS9TuGk0+y7zBgrKTiuJ0UshaIMQ0lU1YmAtUgp2On3vyBBXwxDpsUmN5454lcnIa01owgr0L3cWJRubJdCytucNFZbxfV95vo2MU0ZisK1hmmFdWdZBM2UMtZo7FKzCvCkV6y0YaIy5YqeAVhKG/wic3EaWG8cvhOUpKGKbqIKzsM48GhqrDhjMAqRFeGFmn8oNCu08VoVKWWmmIhFxMMNcYi+n6ajtZzhaHIsoXFy4jHHwn6UvfX+WFh6xcJm/uRJz24ovNkXxmJxtdAruTlQPYcaaQpO14v3uGZ6q3i8cVxuLRfbji4UtGpYDbZYdM0SWzQyZCzp962UnAXg7bRsB0JVxNYYm1T6ppT5ah+5K7L8T21Ca+hco+WJu0NmacXWXQ3gJXK67CWu6VvmkXNsjWGqjeAahcz9lLmPmWGCY4NMkypiFsphGovcsJUSVIlG+slNeFVYqa0NtSCZtFlDWIU9RKnf+Wa+18XJ7B6pOb/f15KnKCQBpbF6/qMWoZXlXLBe7sy1yDnOOkstheMQSVmWvbYZ+WFLwzqH9XJXUr5xlxU3qXG5tezHjKkK5xqpge4sVnliTKg29+lUxSPn30Unr8OHKJzbITcOtZCr7FuxclAvVcBOgnMV8LCePSwlzm7RAqUmWgavZ6iqOCjoHLhO4ZeGkzPN86eOd7vEcZJXxJaa7GJngzda/t+90SyK1NVSNrQ8E+msZrnQbDY9641huTCyn1SCtNBNvieQD7VuUsWTSbPCaCNRsCKv6VgBWdVWSTnLK/xMe6it0ao0JEww8iGqgipVuRKnROc8F6eWbsrc3Efi1Dg0hW9gTWPTw1kv8uChanJR5HmoVnWgUNh6zcI7CpJa2gTFImiUlnM8NcmUearUVNC6yffRFCC/R8WccmqGGBsqS5QxGLDa8jAUvtpHvhoyk5IbvK4V5zI/ebbkA1/4cocE75PoIbWzeOUgCtX+ImhOMDgZmuC9JdbGfUq8GzOxQGxJbn5axMuNKlJmpzDKMB0lq/37y+Z96UKctEIVFPcnWix30PBG49r/86L7/3hxqhn6XJGv3YrsMaUAW8l0lDSyXvZkJnQTul4Z5e7pvKXkyn4YiSUTcBgssR6JtWKMwTcviJDWsFbSRd8OmfMu0HWGdhBQ0sOuULGiFTwYVGssN57VXeTeFHStDA+FzjqC9dSSUFpxnDLHBZzj5UmlpD+qW0LXJvUxiUrC/AJScyNlYc4oCoSM6jRFN5y2suyfLcrLHjahsdpYCoYpFdIx0yZFHSErIyq90gjGYGKVbGipGK3FlBzgZPt/cfbmP7ZcR57fJ86SeZeqehtJUZSo1qhXYMYNePnRf71/tjHj8Yw90z2SWhuXR76llntvZp4TEf4hzi22AbcaQwIEBOKpXlXlzXMivmvh9mUh15Fn4+AS9RHiLRL1Rv9o36IHs3Uic6dkVIS1GZ46u1KhZLpY9HGGczbAMdVwvHRYN6cQBudUoGhCOzxeGq924dQ47gNPXM9RS5G6UbtTj5nb2biZhEPKlJQoVYJ6IjKL957H75UIzXIfHaqJpOBNsbWHp7/bcwK7QCA5MiYad5LGYaQy0hsXZ23KN+fOx9Z4bOdRlVH47O6Wv/n8wJt+4bcK52a8X4z7c+PwypEcYvNjzVxyIyEkSdwcEjILZ1UeW+P9acNSRMCeLxs5V6apxLOIqDO2LbpXwzKTnoMDAomNEl8jcKFEjg5XB/Uoa6p/5g388zenhkSJEd+nFrEjljOpG3qBUgqXi7J25e4gdI1QpVoLrXceTifWMdtLThHVyERJGiW7Fq5zqYXD6xnr8Kf3F3YfVl5LUC9FEstJaV7wtGOaL+RJUG2YE0Fapoz4oeCUckJx2mY8rolzVSSP0lMPCxmWkTpQNI+SGWEI5In+FSejj53ZMladloJH6+LIqB+PD39HPVIiPMXkYEmoxZ8T6oXrLWhM1Znnyn7v3N5m7m535OQDyRtunRwvYbWEtg4joXxpOnZOQWrc8MupMeUj4vFBIhXcE3XakWQLoTY5EF8NAFHNkB65uKUKYqG26Stoj0NxvxNydnal8vAYvGPr4bJQ3aFd2R2MeYqwbQohzE8g6iEMN8E3oytMLmx94XLaggHwsOttHoFqIhFMLSKQOinlUD+lhPbodUELF9v49hKG5vebAfP4s5kvbmf+4kZIT8JN2XinR/50MW4/Xtjd7dhbTFC7UvlgC8diaI4w6UWNd73zvjUeV0PI5KnApQdo1Tp1nvAs9EvHxyuCB84hkiL/qAEpsne7xG2aJT7/4iEVDbncj3w581TAGCDE9QuOcOWxnOOJrccvEY8ApVJ2LE15f3qkpMKOQjHoNir7xJkkclxSji7PaVLq5FAS51r4sBk/vS2sxaNpSyOE6cWxowRxfnpqPD4qyxpQ+yyZV8eJ75cF64oSpPDHdWOZ97yYQHwkzDPeQrueZuFOKVkiesLGjaqgF43qhSnsQdYdL5l0cLI427qR5zJGRkNyRndQu7ASX3vS6Cr1gbbW2dkdlNu7ymEvIdPP4aBxd1wbtHjIza4P3dmWLWrqUopw6Bq3UFvjezVxUp7ol8j6qTU0xmwd8XCWbN64lSmMCj3c/tnDyGxF2GWhACll9jvY7ZR0lzjsJz5+bGybRJ+KGKaZ0+KIbEyTUKUghUD4U0whySEr+Kpc2gpd6GdlGZ0iJYescFMn5QEmzoanRLcwXWeJ3FlvTuQGTyy+obmweuZ2X9HRSjBj7K3x8lD460/3fPy6cd92rFtGm2IpkebCx+8uPEji0xcT1ZQOPKiwqnF/gfPmrNrRpw7YKOTNNN0imNrC1USyaBQYEtYUfZWYx/Nwd/CIbo0ettjvrSk/Wvhu10XT7UrLAxZtVHVi2lXMGrROLgFtr01purBqJ3tmX2YyQh+AREJoaniqSCojoX1EXK4buWRsMr4/KScVXt8mvvreOJ2c+4/KZy/CA6enNfYmESJbLHNTjbKfKDnF3yc7Os4nZeK0KRyCb1UP32bxkGJNKaE9kvxM8uj3AEwwiUq30hNyAZVGPhuyqxTNUCWKmorTmtFHxkzNia0b7eJ4ClF0jD0w78KneHsXfY01pUCuS6YMcKaR0WZxi3aHMSXoyGlSAxMF6YgV0kC0j7tC2cO2KFuLEdBzxnTFexDfklOEl0nEbbTuSE0kUeZqTLVEKkUPKx0pGsVf3kHJxmVx+uq4KLspagn6aIKTBGKxP/ahyLLVubxrERinYfPqZmSD1WNUlxSicjNYN6Uo4c/FYx8tkX0rPigWj7Xo+8tK9zi8M50pJcyM+1X57Djx6cuJ+vaJ09OF+4dM9TvmXeUime8fh+xOG5vDkoX73tmk8dVT49wN86vfchcvV4pAaHfjIIJJZrUYc6O5O2GkoE0kujtrihvXLMcPJRr9Ou4/XoTgeIh7iayvcHLIeAhR+95XJ9VA69YutGSU1DnOlalOlKmQLGRREDsPXkmUZ0DjmlCulgJM2RXWg/PN1vjktpBq4rJ03r195OevK/uto5cVmmGmZALVfXMopJvEP3xfeFgrqQin1Sgvwy1w0Uz2HPXiOaNdmUWwbkj2oRoSSD5G+FE1yA9AhYiwqpNcaRIUi2QikDoLI6eTrhr5RB5USkpCqfDikLndJ/IuemhQIxnkGsFPZgoaTh/XmFISQaH0LrQtOk9NYEqRkZTokTCuo1tliexdHaf5fp94enTOl04tBXB060idkCuolBPelZwLU61sqnSP/ajWEkBUhptjYbdXtsWfgQ3VlbmG51ccrMX3fy1c1sfG6ePGIMyjYUx4toAld7amGEIzkA77ucbLTKwPXf0HZHOGWY0V509N2MyCpyRxLJlP9zVCyPd7qCumxtod7515J2wYf/j2xLIIh7kz5QntwrlBkZVvtPPHc49xVeIgjCjXeNYpJ1qPfNt9DmHIotEMHkDlONwJpDmnRB//W5KRMuh6RW7+ZUToz5utpz2XvkZMhFi4OzwhJSFZ6K3FOJuizk9XyHu4mXbc3e6ZambtLR7EnEEEbQk2H6dMABGSDLcoS0pJSalSdpVvHzp/YfDqaHz7nfHh/YXT45nUO7pu9ObQhCKZXJxf/PKGw7Hyf3yz4/Gy4aZsq3C/KJ8cCpetc5zzgLtjbHMfu5c7Uwn9ZkoG5pESnkITGS3Txnw9HdVhjZvSh8ZIdrGXMoVpW1VJuVATpJq4PSpvdpl9zmzqdA/t7lWZ4ma4CqoxMrmFQ1zNUIfLorQWipaUnF0eZ4HHyJdlFAHl8Dl6OMvZH2a6PMWNJCncQn3k25QSuyLhwunNoIYZ2HuEIScLygCLcuKSJ6a9omPs940wBojQeqO1QM+bhYVufdii9tEZ0s4wwgfRck28j++1daWUFFEt7qQS4JINjtfHC55LYlPj3SUorqwR2/zpXLnJEgqcIgH+NCVr5iCQutEN1oty2owvXlYOU+Zex++5KL/+/sz7UxvJ7tcYm3iJYtKL8qqzC1WgJme1q+hw3Ow5h864Dzmrh2wyfJ+xbuIJ/7Ev57qsyJVETaAoaXcYaocBnZtClFbThmt/92bmeDezLBslxcusXcfO6ojPGISYucaIICn2z1j6M3VOrCXzh1Pjr+5myv3G5alzWVeOKSBvPXe27mxboKif/ezA3d3Em//8wB8/JFQbuVY+nhp7geVQov6BkMT56DzJY1k3M2zsQdbjJQxBs48c1AQqYfa1FOOfC8uTYxO4KmlXEOAwF3a3hXNX0hR1iIc5sy9CEUFbCJ9LKoHiWYx1SWIctB6joakgHj2RWxsiEDeONbMrEjuyRNlP20L7rsVJIgEwjQ9W94gAadUpA3hp2tnF8kjvTh4g2NIaU4kYUhuTBQPASObjBiks2kK33aN6gjzAH1cydRijY5TefDj/CZSvDyeQGWiYfYM+kRTUSVNyCsrEbZwd5qgrSQsqsEuAB0h1VyEtwic3E8dd5fZQOdwduD00dsDk8KJk2mllMeir0Vqj2jVEPL72H56U//LdGr5N64MCMYzgMVsbAXEulCqUeWZqznnbMBnSSRx3vcKLoQkltLqhdAEkgrKfKyL+f/75M1gRkf9D/FKs5ehVXJ+CJG6O9QjpquYki4SxZVHevz/xh6/eczmHRWZZNkgjGkSg3GT2hwQpRjcAd+dy2vAeC7MJyG7mT6vxsW/c3u1oD53t0kOAkFMUHOUg82/3hV3JTLvEZ68KLhlNic07k0WY8qUFXF+SIAa0KGpKknGJWrbWnPM5HoA1pS0aDdfNsI2Ra5vQBqf7xvbYByWTx7ie6c3ZlngBdnNhqpDc0EWozORJIEW+aR9hVN2G0EAdUw13RGtIsngRzsq2dVQ7pko3wvmTYL+fSS6jUzQ6U/uqiBlzcnzrTJLZ1Hj/pDytTrPEso7R0+MmxSqSKmgJjs4HmEHQSrOMXGBCO53JTFJJlliWlb52kheqhSrLN2N5bFwWRXumm2AeDWNmShuVBpADUHGIJTWM8VH6MmJAhlM+1wSesJZ4WoOzfV1n+urczcKEkkR5dXdkvjlAScyS2E+V3WGizpFKf9qipyRLUJ4fW+eSO799ajycM8lmNI3PaIr9Xoc/uW+Rqzuk7jxalDSJSXSzmI/JwID0LE6AmKicoJQQDSHCv/DPv1rH4DbamojsEe9gZaNOEzpOwW0gjcVDPP54/0A5Qb95yVxX9rsJz46JUfaFUpy2ynOztSTHtGDWcTplqbS1k1NGc+HbRfjbGzg9Rl9LORbK5iSJbz8n4eYYiprpcMMnLx94ubtwaZ2lNXDlJhXeN+EvPUAQdyVnR7rQPNqcEeidUDMRoV80aC2AIBXhmEvQEh6Kj7N1ppJRD9olyUTdBT9qq6MJaqokCeH9qTdua8asY5pQj7bq6iETK+LkFGlv4gnSyuXUWDbHRs5vHgdnp0JJ1Bwjck2RLFiomClba5gF/ymaSD5xXhUUpluB1FhW45iAkQRXzIPQbxIHvht5qI8u28aulOfIUxEH6dH8vRrbtkFSbMrQQNeVbQsBiXunSsFF6V6Q4TO2cciAROaTDXNEqYg0bBPaRDiHHCiVTsak8XHLSCp8si+slqiSuJzDBYVubKeN1eJWPhShd0W5sG4r6+aQhWkqnFW4JHhvxn9+18kOXTx8yZ7xrNH3qZGdK4yayu68W1dySeQSE4arYGTENYK/5Jq4oIiX0TZWQgfu/uNvzlwnPGWcFH0PkpEaiJm3HuRtKc9KotY7vTva6wguUqSUqBk3mOZMrUpbjb4qtgGroVuiLYb1jK85bsch5k914v3mWK3c7ITlFDdN7GVXcbvQUDwnbl7f8eUvXvJmT8RgIjxuG4c5Oj6ftjHlp8D6DUOvXlTjeW9wU7pGuRESWk6uRTgeNXXaQ4+qZjH69UCyrXlUVHRCGtcjhdA9gpAvS/Cd8xRaWyFc4WNPwwAAIABJREFUJongR/vacTasrJwX5f290rqCd3IKjrQUoVaoObpDS3JSCbDBNPot26bo1tgXqCWTs1Gz4G6cTo3TAhfrrN5j/JTgE71Bb5BJI2Yldu6cM8rYG0cWk/a4vSP1JcbjvkYFxPnS2LaoABTz589HjEU5ALBmIazonVU3nFALZQmgpfVQcKnz7LNdPYqE7reNphvLumHd2EvgGJ/ezcy1BoUE1MMUfTzbRr90ns5GM0M8GtEvpixJ+X/enXk6NSQX+lCgBXzu0CPvKlmKG9NjaixJwDvd4oCMvK4+PkVRAhpG+QknHEJJJP5bzvBjb87eNnCQnMPl0Tuok1K8+ZgGB0nskngBMcw21gW0r5xPK6/e3PLyZk+ugm5OO2t4QiW4MprH6Y7gNZPmkdbXYxe9b86H7vyiJraLsnUju2PNQqfojbUHImsYP/+LO375+Z6vP244UcnWFaZauGhQA7hDcSZKvOhDa6sm0f40VEswkMUcoWBxfI+xxhNqKZL+xEfFnZF8EH0Kda7Pt4OUaPFMJgHWmJKAnCMkeWmNtkRDdp6ije3+0VjX2CGvchtJRqmJ3X5CcnyAXZT9tEfqWB9yYnLw5KBRj15yCAoqEgHM7siHRt/g1e0Ujc0ekZ9ojNilxs6Vx0EmA5BBUjhbklCrkSZHNSEuNCQCxFM8w9Y8qCpGKPTg/sQg0N7QYItwRbjIOWKy3KK4OS6BRPMAllY11t4pkiKszRzMqUW4ncMV0rbG+/szl3NnKomTCssmnFdn7cpnx5kiwmLKu6XxD+82piS46FUJSy6C95DtUR3ZBhIro75SAGSIKaJvUwb1+AwQWYgx0vAaYyl2a67tYz/i5XSHVAoyZaKttw/upmC00D7mYXtJoS+UZORcQCUiMrvwQZ5Y1oW72wNzrYPz9Ot3Dx4f0k3jxJlLxlqoYeoMHeH7k/I3nwq6Nk5PyvFYqbJFP4obj4+N+/uF7enE4WbmL//iBb/+euXtx85mcFqVUjPNhW4j4b+E2dolinGx648R5vGx+KBD35dlCJiD7g1lkPzwwTOM7LGfpWD1Q0zuUdgrs1DxyNAdQgJxIvdJlba2aKgqEoT4k3H/GOL4Mgqf3GPvm6ZMkshqcnH2u4kyp+fd3lKol/oWyO9UM/M0pOhd6B6WsHcfjba2AKYkMYUKnnkKMKd6FAlzfZnSVTQW+1Ot4CSUHk4fl6BA1rgVbaidzBxJiSkXFEV7VMEnG6HNPYCrJIF8b+sWxvdgHCIdISV01eBixvM4DKPzXCIzqmShJKV35duvH/jHX79jW4zdsUARtq5cOhScL24rp+6821b+y7snHhbHJeggnLB0bXHQpbmMJoPxyvlAcD12cyGAuZgiM3ZFsYZm7Xpgqjkpxde5Hv4/6uWUNNQqA6ww17DlDJgdge5bfFI9k2rsYtcTNGUHN9azoj16M3Ku6DBMuzVyKuznmYSTPFrAdI1bqdaMZsi18PakXH6640UWliej3UA5xgk05xLpAm8vPH14YMov+MUXt/xPvzrzv/+D8t3DmcetsSOzdBsfjFHfRtA6AUcrTTutpVEVMTjjK3w/btNAQoV2lacRKiqZ4qGoKUnHg1KPblK5EvSQcgBjuQrb2llXwXsn0vIzfevcXxpff994OgvHArss5JLYTYXjwak14bqRSmF3uyeZITmSapMHR3fVCycR5l1mbxVLhm/KrRSaO4+r8/5i9A8XynRgfzPhvT0fBNumlDmT81WP4pg4ORN0U1ekKXQowyWydUcvhHtJ48/YQILjXfPIOhJhSc7kwZObxv49DSFG4OQJa06zlVIzyVOsCa7clMJdVtZlJdHIPvHm5Y5aEvcfz/xfvz3x/ts1EO2xg3//5Lx7UF5MlRcH5x/fN/7b/cJvPkTaQzMQEu4F9zAmSE6kKaM9RDKuPW7EkeB+nbJ88MIJQb0AGmO6DF1tE0S2UK5J1Ez8aCqliEMyLMUii2+orvB8usQy61JDiR+DQEy4GpKnjlM9ow0W6UxzPCQ2pxtM1clWOaSoHVdT+tqYdEI3ECp9izq53390/ufXiSmFv7FOmYwzV8Ga8Ps/LHz2k3t+UhMvbzN/89c3fPX+wvunC+emwV+KsGof37sFvH/NEfKAt7MEeOUSgnHXQUwli7HNGUgc8WckI5LpvTOJUKaBLrojVZA5RhprobpaLg2ZBOuCtcTVWWBpBRLL1vn2o/H9CV7sKi8P0T1pEvEjpU6UOVq09sfKfEi0S+xGzRr7fUG6wzq6TAvkAnOtnE7n4XAxahGqOqsKD2d4d3bevBSOteIWsxHkWGtK6JV9KIG8K0qAIHbRuFUl3D1ZjdPWSBYvrarRPA65SHSIm8dzNL5taiSP9jgxgxrTibjhfSTaZX+umDQxmgr0UG813zAVUuq8eDGTc+X33zzxmz+dqKUCHuqcLnw8O2/fLfzykwmVxIdt44+Pna5hz+tDrC5Xg7RHZlJbt5h00jrya2J/tOv8elW3iwwU97oCDHR5mP7xSIQMfS0/XoSQdxP72x3333+kLT2kTmOcYQRDRdRlBHaZxkOXHuohkUSRgrJFHGaeKLWQckPbwlQm9lOMemaNzRqIxyjkMHmU0EYGkXJSxbyQtlDvdHpk5WRnnysP985/+fWFtN9xu0/sj/GSTkTuDuvGZassu5lcIw3ce8jrckrUKeG+xa0zkGgQGI3XChQR8FjqpcSILCQ8xV7jBmvr7OcwSzfpSBGkEKJ3D90qAyDxFE6/OEUTy2q8f3C+/aDMh4mff1G58yjf3XykBebwP96+nrh5vWfeTawPynK6sJMJxfEM3gxTIxWYJ+HgQjvMPGk48lUbswRY9NiN33z7yM2x8Fc/mZhzIq3Ry6LqUKL7VLIQc/2wYHkmF+h9i1IhRgWeB7KPxwubCKURichyBZBEFSEVYVstxm4BS04pidLjBU5CCG5zogNrSixdWJvyeFnAnELm5c2Om8l5e7/wn/54Rrvzk7tCLtH6Ne13fPtoQOGnr/f85v7E16vysTVWd0xKyBk1PSsFRCD7OCwkU2qltY6k8NeKBYouMpRX2jjezWgz1kGn+fN2OcqcPTTqxYUuP3LnbNpp90+4jtp1T4iFXjD0on6lvEgeQc3RTBxSpxiNOgzb7eXUuVza2CWm8LYtgvRLENzFqLPTtdGlIPmGtHamWrCurJeG5xnbQvpuJZMnYT8Vjhreu6//0FH9yJc/m3n56Z7PXu051szHTdlc2drMwxbPulal5NDSSgneNieJcW0s8c5A1gAIuWBI+SB4K7juIGoeN8qiTKkEaDdavkBIGZIGeb2q04kUCElC1shsenzofP1+o5nzszu422fmtcEU8SLTlJj3if2LyvHTifkuOkSn6pSXO8ycfoaJTC6Z7elCkVDFXNS4vZlItrGcF/alsA2YJyF8t2Z++3Xj5U3l85tCLop5o9vIXR3crFnYqBIByvWloS2mJmuN1pWaY7/3HmOwOON2FXKqdO2RHpjjNil5eCRzRgeVqhITmORrUmLcmFsrnDYLeaLEoTelmKBSKXxcGtuifDolvvzZzC8+uyMn+Oq7Cx/fn/jimHm/rPy3x8433bhfw5PswujugUwJbTmEB5VwCJkDQyxRMqyugxK5qpdyMA9uw2ubxm45RBc+uPaaqJJh/ZFFRrqMo48B0CG4XzDPlP0RWh8vCuD2w4NIV1lSmKli5B1ASYkqADDWzelbgEP7OcTVMeEF/7dsK2Ir3QpvXs08LsbHx5WfHPcR8jXB7ljYHxVPGXVFL8K3Xy/4KV6iw7HwySdHzm8jiOm0Kac1MSfYpeCj6hwBTN0shppmQR0xnBp5/Pwuz0idph/QXKcPPWVYvvJUSBW8NfoaBTbTFBLnZAR9gMffWwqnpeHq9M25PzfuL8br1xNfvNlx3GXmAvONh5siC2WG3U2Y3a070JDRruwK1TXqCm4ndoeErJ1Fz9Q1AKu6h4tGukUdMkUzOADff3fmN7fwyb+5Y8qFWngupMUc1wxEPo6pBaruQpkroom8+UAqA2W1IVBhGMdDUJBJqaCukUInEuP3MFgjMe7V6+oxwHU32BS0NR6XmDqOpdCasZOCVGHaT8w9ekD/zWcH/se/+xSZJx7en3n3cWW2MPL/+sPGh+bcr5FkKCmsXogMcf143vhoGTNwxZdAYuciUCP3VxgWP4nPSx87uwx3ivuIimX8HQibgyd9PgD+u19O/lkKTQT3CLvjkfVpxbYN1ysQfJW6QcplPJzROnZtEdvFiGBdOD/ZeEhCmYXDIbOvOTyGbmx2Vfc7rz+befE6s98l2gfh/hH6mwAZ9sfC3avCw9cLjrOriTt1vtsS7+6V+ruF21/sefPFgT+9vUQvYlfWXtk0s3k4BEqCSiCC4k41GYncKeRlFlrTOrytEsLXOClTjPc5XUcfIY/8IYj6OVpDL06bCxsp2pJzQkoeKQnQ6Cyt83COPf3Tl4XjIZOrBNJaYCPc/jln5pqgK+2cmA9BbeW5QItDz6wxz5lpnmiXzE478zm42zQl5ltn3YyTO4XEZhIrxqr8/qsLv3w98fltopTMD5OBBC1XRtKihUpGcqLOFVnh7EPsb0Z/bqgO12vEdMiIpRHIYy2S4F676lgbAqzrEmkBpYYJehPh0qMOYdNOllD/5KmQc+L2duKwg91USX/7ii8/uSHPhXffveftdxfef9j4fFdYcd4uTs+Z9xdFrYR+2mLKS8jz9+0EJuFmpFTGjml0DcGBjLF0vHNB+6Qw2ENMHNcXXeRKQwWl4qHy+JEvp8euhMepSc7IdCCJDUOyRbbOEAerjJoDD0uPj2865dCShq0n9iHJibqDOjm7eYrSWne6rqEnRbi5m/jiFzvmG8Wbc3p07lvn/qHx8sHY3ym3Lyd2h8rTyUmi3M6JLcH94nz77crmnU++OIT17Fth7Z1FPeiVpvgUwMKhZnalUhByUtpIuPdx8weDmgapPHbScThdofQk6Tldja6YXMO04lY1h55ypAFI8KBZIkPpdDZOZ+FpDf7w7lhJFfY3O2ZztraRPOR22qMKY+sW+55F8W97ajiZyznE4+VmFOrcTPiHTCIj2kOtU4U6V6apYgo5RSLi9+48XpyzdlKaQ7ztRmXoRsftICWH8B0jacYUttai5dlDH9xbvIQyyn4kSfwONGiEmmSsPz4QTYn7YIAmnh0tid2u4lVoawgSmik2wJgsiZLmAMGKUHzjiy9f8pdf3vHxq0d++/v33H/3xFffdUpzXt5OfN86H5rz0ZzHfhnjrLDbH1CHZVmGlStuzbA61njOEtz7BiQ00GMbr7Ff0dcwAZgrKRuJEOEM8HysSIPrTD8WEKoVSQlrI+ZsvXD67kyWaYAmTpknpFZ026hpcJ5+TU0DJqeWTG8apy9Ad1LtzHPiOB2oWejaWHsLKaA7u5vCm88zqa6hhpkrp2p88wDvPzjH3534/M0tdV/44lcHpmNheWqIFOTbKDl9fNy4fDDmG+GLn77gw/voiWhdWQ1yizrCnENi1gZ31sdoFiulUkioSQRAxXBPSqHriYqA8ZnKCbKQukbpU3LSXPDD6GGJ2uxQxGCwKuvibJvy8MG5/7hyaTFWuQlTSbEXTxN6NrQ50xR5QetjWJOmnWJL9HOYC83jcGTOtMcG0kgl3CoQ7piphrY4S0aXSIJ7eVNQUw6HmQ8P68BfEt3D6pTMA6VP9ryiaO9si1F6uDTa0tGmbK2TRKiWWbsOGi52a5HhcMmCj6kDUTYNkUcaU4lMmVRTtNRJYt2i26XR2MxYFbYtUgZnKbzMM+nU4b6hN0+cFuXrPz7y7b3x9VujL85dgosZ79bG02Xj9+vGZQuQz11JtZKTsy4dZEK9sb85gAmn0+W5iS5Q2hATuGk85zAyRfuYBELNYC+uTregSMeBxUCm/2U86F+JxiwbuuWh0rABjqTB7wR0LxK8Xd7N5H2hXxLb+TKohES2zLZuA7XKiBvHl4W7FzM1VdQaXaOioVkLlcdUON7M44d0+prpWZEifFTn3mD//YX7/2j8/FdHPv3swM2ne9oamaEv3iS++t2Fr3GeNuV8aUxz4uZuRk+ddYN1hboX1ubMNcjh5k7VGMemuUQUSFcMZfyukRruFCVGr5wTLo6WiObPOZPS9YETYu0SCqveLWSAYzxVNbZmPDzA6cnZNkE9msIuXUlTIWWhrxvnp4YalBqNYpEgM/y0EpSQ5BpOf4WmDWnGPINMEQXT6XETFZi7szx0LmdnsUDitw6nZRl5OOGASRJAhqZoxdLriS+RGw+Guo+o0xAUKEaz+PO1xIibdOhMY0MLLMLjd1ZyGW3Uw0qYUtAsJZOmRHPl0pxmUVh72WBrcdKLGik39qnzShLf/v7E8r7zuDR+837h7clILWxkJcOpdd49NeadwNLQniilIKlwfrqEqIWJUP6VoIaqk0sHn8BCnGFDbJM8WgvcYmSN92JUYkqJFnXTaLeToFZsAI3+rPb/ES+nax4SK/1B8kbEjPiwu+im6BILddskJGHjes8eVqUQHAZn6hZG1XVTejYGW0ZOzosXO2rJgcpucPqguArbDFRnNyW24nxYGl/sKx//tPH9240vvmz89Ms9b24ncobXP5m5ez3zyduVX//hCdsJW83MU+VyMZoazSrqic2drRtzTlhxNAtSIzSrJKETMrYkRh4SPhcfpbuBXgbDlKFCqTEOlTSQS4PjXEKj7GHTqikhos/k9bJ1zktj2YbhuCvvvr/w+U+POIXv/+mR379d2dy5O1ZuD8LrlzsON4XLuuEe8aPSO12hb51eMtpiAuiurM0wMmUX4vilN949NlatqEbFwFmN1hMvbwr7Kcd+JaNRkeDlfCighsECtdhZLYON/dk8Rt9UPL4Hl+GLLfH/8VgZck6DEQxVlPWQzKUa4E7KIf7fOiwjrmXTkThhIdlLw8w8TYELvHtKPKnycGo8fXR2JArKvgi5xDjqOJfkXCxGaccGoCPopkOaCIXM6XSiTImcpqhXlChQCtlg4APuacyqPvbtkYH0z1jMYD0HDD0wHEF+sJD997+cVy/nDylq8kzA+w9b8Oi69A00JyQWodiriAU5CF0Fcdql86H1sOxMzu2t8G/+es9PfxZpCN/+rvDxrdLPwsWAl4m5CPsbJa0T7981Vp+42U9882Hl//6PD/z6dyf+9q9mfvXZAS+J11/suHu5J98W1ix896S8eNlYTyuos6mxqpNT5OjoFPIvdaLKLjl1mmN/wp71xKEuihuzZHnOes1XEMGVVAZa7WDjJU4pj7rxAD9MFCPjKexv15Y2SdFy9sf3ys2vH5jE+e3XnX//9sRXpyfujjte7/b8/ReNf/fLA/s50RSmFmqWvoVuNKc4jKw7nhqLQqmJtjr/9O2Z33y98s2D8mJq/PVNJUvn3WJogl9+XrjZZ9TC6yoDgYd/5pTw4O0QxwgHRimVJMPcK7GLx88uQbcJRNpZ3JBqDslHeDSUIkiOFMaUU6DI2tk000msqmzNaSYsqrSRBjnlypwTivHq0z2HHbg3+iIsS9Q55JwHYhzP6u3jmQ+nRsnpKicZYvYweKsm3HvYCXuM4yISOMK4TISMeI4ggvHSM/TCMsAku7IWIvEe2kCgn3XbP7J2PrvSRBApMfur0nsjMcQIAfkgHnSKAmWaIh9Flwibsh/Go+suigPNAtFrwm4STg/G/bFw9zrx2c+dvsDjB6NvsJ6NeRet2Okmc/rYOKtzvDVe3mWWE5xOxn/9hw3ZnJ//6gXNEmad169nTh5p4W9vL3xHOBxa73Sr4c8DNlVaCsI55UDXPAXxXaWSUliOcgHMgoAWhms/fHmu4cqoYzdJc4bbjNSCE9K+pmF2tpRoBpvFruhZ2B0rTEbtcLo4//iHC7tsfDzFQ1V2/Ob9wj/aI7+9v+OPJ+V//bev2WF8+LDS1Vm3sL/NZKbJOM2ENepY+eZp4T/81zP/6fdPfHM58aCdv3v5gp/sK9aFb+43Pv/ywL/9uxvmK7LKQKUJ/a8ROl9JQDGmUQO/9UYlI2XwegMUuqizK1cE//qBlKtsGYhIF/HMvMvkyahVSLmyCaxEgrxJIOPKwAc0xH1TKcxTYSfCbjdzeCHsCuyeJjx31INiqlO4d5p2uguPF401Jl8PyxAOmwiiUHKcJt46arHIkKHWHX0bIJw1cppjUvTIjgrYMJBpnhFceDZauyBX2Z788Dv57345p1wRnOYdKSWEBmNOlvTPGNChhRRxdDmH79AJ9Go0FkckQ/SPRBz99cTY2LY9f/qd8nTfePVJ5fgiUw6Cvo+HvK2ZyxPkycA3NnM+LCuv2sxNLRxu4GBwVsfyhJdE36J01SucPi68KJWXN0FNaAtjtfXodNRe6EWwZLSrEXuokq78oUiICaa50vqGuyI1g18T20HL0CMrqESVXtllJMcDsZEqYYQlyyUOgZoTJSm7OfEiZ8pUeXt2vvv6zOOpk8z48uWev/z8wNvLxm/enflwWfjf/vGRMld+eVs5P66sy8quFPa7zIN2St7YT5Vpnzh/XPgPv184W+LFqx33wyz9s9dHFkt8PG188umB/+XvX/PzFxPt44VNO2sb0Y4o0xghgxPuI1okUjF6A9XOoj0cTC0+rPspAhLwQD7zMM6aRUVEdmcjkYpjbpQhhVRiDM2a0PFSNYvEgtWckjMlNn+OYtzNlcMxs0shmNkdMq+OM1NS6hxi+MsFLl24F+HURlSl2bBEp/AnS9zsh/3EZe3hnnLHpZAZ+u+rGTznWMvselNeOdqE2DVcQJ8nqAAtOlFsNF7aH8tzWs5ki+q+7XwhiJAQFoTfcgRjpciKiX1XoAewISNvJ4KRBy0BgXKlQUNoNEzbBA+nuAHwzDw39nPGdcdOoG3O+SFUPdoz7y/G5ydlXzKHmikTvLxN/OynR26OR9plIRLAnaJC6o3bOXS5l0EMN3WKwpYj2t+H1tEIJ4pi7PfXqA5Ha0i26m6OEiHVcZsm0pSYdhkvEuS8dTxnasqQc9RBDLlQjHFX5QikZHzyeoJ94lBDsPBTTzx9lvjq7cI3XyvbphxL4u9fz/zqxY7f3G/8+t2Z//OfHvAvX/BSCmtXlq2zdOd8Vg7TxKev4atvzvz2Ubm73fGrF4kiwt+83mE9oao89savvtzzP/zdSz7/pJIvYbVjLly20K2uOUCmxHDuAIs6ruHMSUTCgQz1lJqGrlV82Koy2SJYWi1uveuNoskpkklD3N9RuieSZS5bo3sJbEBD6bQpEYCWMlMSPr2p3B0rh31mmoM7vz0K203i5kUhl8T7DxcuYjy48YDzUTey7+hjNjALOkc9Ru1TbyFZdMG3kCJpN1T7oM4S01zZtg3HQ58dgyRX+457WBFBrsXww0ii12sN+TOW6j/7cl5aR5IyT4WsJdQcI/rfrryIhQhYbIpTdGivUorRxJqO7BiuCyueC/iIUvTKeuksW9AGc4UXr4Xbl3tevixsF+H82Jglsy0Zs4wU52nbUJ/ZulFNObyqfPbFgeOx4NopCVoL8+6uGuuToU+dWQurWIyz3eg1/TAqDX6tuVHnHELvFCOKSgRo6xaOllxij84SHk3fCfmQIoFPQbSGuD2FYyUBkkZe7wCG+hZxl7tdopZEORSqRN9KLYnPbmY+f1W4/2nj27eNd/dh6n5REn/5sjL5jq9PnT+9W5nezMy7hLXM0oW6T9S98Jv3J/7p7cbnX7zgs7vEnTg3KfHZq0RKHZknXr15yeef7DjsohhZMfLtjPYesjkEF2NTDb7z6hqxq+UrpI/JE1nCjSGjJzSXQJHMiOatMLdy2EewmHZlJ07NhTpFImFXQ1JmmF1opqjC1o1NE806lx7Kpt2U+eTFxO1tIc8OuZGSsNs7t3cwz8JqifWd8n5pPCW470of7iiTuNUk55j+xuFiXkFHOJc48Z3ID+CmEFMhU4jaXYfvN2I6TZSrbPYHhxDPqiFJROzPn/nnXy0ywuOXASkUEmbPaFb8bTGm5WnYZsJgGGnog3QuSZ5HYMMQaUOPmEbAV2aaw3ExT8KbNxMv3hSmI9TZWM9Ob87u6Eh28jFxahGv/7qCl8yUM1IMsY4uG9mCb7MUKXhNFdkiScBHooG2hs0TRqiAuguV2EVyjg9KU6O5ARFDWcZLKUiooWqADFwBaYkwbt06o90mZG8DMBrT/vizAXlOJRINSsnhahlEvKmxz4mXr3Z8dlN4WJ3vv1OeHp3LZeMnL2de3E58c9/5/fcrx1m4nYSSDEvCN48r70/w8s2el8fMy0Pmk6Pw+jhx2BfqlDkcK1MVPIXip2aQaUbT6F+tnW34GEsWio7sHA3EuvcOg0MlgXZiJL1OTYP/MwvAvwQ6Qyrx8/du7IowzQXL4eVsItHx4gR3qx6gnTtLc7rG13SPkK/bu8TxVWKzjXk/Q1Ja39gfE5nGeXE2nIvDOSe+Om24hQLpuvhKSYgHxuFAvzSumAplxKluAzsh0NnehhzxGgI3fl4kkZ6NuqEqw+PryUjuiIrJ2Hd/1MuZsSFCiP2o1Do8fFenXfyGcg6Xhl5/VhnBUT2c4sG9jtOC2L9EIE1QSvByOTuHfeLuOPHiVWLaxVw/H2B/Izy+V9gHAJOzsB7g3ceV/R5u7wJBq8lp22Vk3WQKQnfi+zU4zmEH86utR6IQyPFnEAOBmgvXigZzD/DLBfXGtItWqY7RhsXMJCik3pSc0gCKIgtWhljch6C6u0WODBHG7BZg91RKkNjx64vDK8X3757Y1UyqFhlOzeh5oizOS3PuKvzuvfJP7zasPTHljGrmcDPxq5/t2E+JT28rv/h55c1d5W5XhyHeKfgY1WL1KEPh5Dk6JMmZSLmPWJYiKTJ1Es9B4cSvB1MDiQ5QkRFnY0NTnQxGIJmasyxBomSJlz6lCPveekwwXeKjvHZlVWHtzqUpWx9mbhdB6Bp9AAAgAElEQVRyLhx2ldubDFOk3clO6Jo55YwWuCNjapzd6FPmm8vG908biuBEIgcj6tTNxisRbIRLGOhrmohUKH+miVQ0fjaPsmW3+Pf5P46X1v8/CRaBR8jVFIL9eLTWk2AavYeOo30NNf6UEPIQMkcvBkminOaK7HkkqIuPcIvoPgNGwJEZpSXUopLg8y8nvvgF0ILScJy2RjP065+FVeryFNky+QBbFZ7c0RYV3lvvdFUezi3iRhSmZFhNiFi0n+U8dJPxcpYcxT3dCGrDE54KpSREQt0SN1qiDTGCpF20qF3tWyLsk7ClUNCYj+jE8WKaXUn3iLAIPWrwmSRhKjl8mXXirMMzSDzUzey5BlA9coj6anhxZhwP+ye7OVNeJ+ou8/5j1P9lyfzqFwde32WeTo3dLlIZ1s2xXSTEFRnunhFAnXC0t0hfT4LX+Np9UTZPtOS0YtRd2MfsYQWX8YJWLIXetVjcenEpxIpzTXWPiSW0upgj1ZnmCSM48e7xOxUinaIhdDeWHo4Uc6N77KX7JNwewwjdrTHtCrsiaCk8duFijcfFuVjj7M5vL8o/vjvRdCRfDGVQ/G2VZjZ6T8YLJUpmNIcPCSbjs5koEVqnEXwXpUUDoR236BWJ9XFDiVuwj0MGGvLQH3lz1hL9E3nv9EVw1XCFk8ZSP+HeUBW6bVQPgXPyAIbcgo6QsSmHyyODNaYpxqP9bebnf7Vjvy/gynRInB4G6V+d3kdtwc/h3VewdkNWaE34k3beINytwrplluVM2e+QQ3qOpWySWT4+whS7CylCtpzgyhA4eHC1PhK9ER/lu/ESN1OSGHWa0SvMLuFtzCniFUtKo3hopJUmWHsLdDIN9YgkJIUCqI68npSCVxXdYkpxjxM2xcGjrVF8prlz7s55CZXNzWGmP51GtEfmxZz4y+OOn+wzl9MF88Lf/mTPzU3mbY2oysfHhK8LU1dujzOenBxQQYAdWKCsmtAZpGRqifbsq8naLEzyqCEyDm31sAbKNdIy1oJIBojawfGZxT0MBml8jqddIMCLXl+6HxIQuhsdQT2hbpHna7C1lVWFWYxDLsx0jh78+oagq0a8SKr87nHl4X7j/ZL4rw8PfFxaCPgZTps8IkU06EB3e147gvC0kUBppJTGWAp0o+xrOFaaj599OLI00F+SEjUNhWSJaBKIxIUIkeHHo7WtdciF7SnS3XOq0XUxpHsQGTYixpRr3CY5U447lscHoq05kYvhLcUgrEbZCXXvbEtwl20zXn3S2S4pXoI9uBVSscgoMqMcjNufFPp3idSVVBP3x8xvz8Zt7+x73IK7pDxszuGwR6aC9pBypSXxdGqRq8oWe+QoVQqHfYlfbIrITZH4WZoaOQ/BmQWa6xAmgBxSLlUnzUEVpVHJ4GnwWkOm1t1il5SM+GiGlRRNZinq4EKwMQzrLtSaeDiv0GGj8uFiLCtITSE0EOfuWEgpc1qEtp45ZkMrnC/Ktir5BZSkrDpz2hrZ4JId6crt7Y6WohaxpKjvk9YCwGrEkkh4EKcqbJvQt8jhESxuwipkvdIjQxsrmd5DDaYjlzjlOMSa9pA4jhs310xPgm5BZXhiOJLCQXS6KIvGwdYtyql8jKqrGEuHx2aUGdI5AqrVE+tivLsoW0t8t1X+/cd3fPe40DXsWgxuMoLm0zMNAjoqAgs5F0xX5v00kPk+OP2IJmlri8PYAc/PtrqUxjjv/vzu9eF8SmO0vcbd/Blp7b8y1pojORLu8rPmIcjoyCyNHTKoT48ZXp3ttCCMdD0LrvP57kyQJyhzdC+Wajx87OynicMhbtnEUFN4GjB3UDC7gzPNxL6SDJkT71T4Y1JuXdnrzLIkdA93U2U5dx6eLhGR2EM4nd0pEkl9LiWAmj52vaFkuTaDXduXRULN0jJUM1QTuThCAASeYr8oZWhEJcbKNG7LNgCSdPUsSmZ8ymJHRYZX1Ma+3sPnlxKFTN8ST0vjYYOPF2VnMM+Zw+2O/W0OQGttCLHn5pTR/5ezN+mRZEny/H4iqmpm7rHk9l7Vq+pleqZnmiAGIPj9j7wR4IEASQxAzPRSU8vbcokIX8xMVUV4EPXI4qFr0K8ulRWVkRHubqoq+l974+Vc+dbTa3XG+dRJiyBvlhBEbJ1Jg/ZiTDqSM9qjjcyuPWSMU8atM5Voktt7R0o8PVki9wkjok7zmJS6v2YmjacJl6EiGiFiadzHzzW4QRJkdUyhjsjROuiTPpIo9m5Ui+9de+O//XwlHeBvv8vcpcgQvlTj5dp5PsHvr8b/+1L5by8buwndQnDuGkSDG3zlQL6eYklH+qQEduJOxNHw55WOSkJe+zldBvLF4E1exTqgNhxcwzYWE39crX7R4hTAWo3dLMuItmivi/SmbxCGUTkHQtf3HUlxKZbR+nQDWxCj7lCWRJ4V6dBW4+fvjV//trDc+Wt0orogbSzGoUEUhes1iNzDQwixf78nls1Jl8Zfv1HePBzQLLAZuRnt4lzOjWvtiCbSg9NeQv41j7H7pnxEoLkzIaMFKgzNDmytx12/ByfmOjJJRelqYa8bxnbDKCm/ZuHouINi4wOyuD9NKQ+hRqxXqZ1MnObr6tTdKQKXtfJyNU678eSQm/Gb+8KHu8J6Np7WK2owp+jvsGbsW4uvaeJlVAn4Utg2JxXh6XnFWchJhlImFqMnhdYCADElpUyTW9yjj9zVQFrdEzozJGxhTO+7k/aBzI9OHLOgTXKOpDD30MoiI1colApIGjnEowjphqa6ByW1t8AGNCf2tvP9aePwp4ivXA6JMgn7DtsKXzblf/904r/+6YWXc4vMXQ/OcuhMXoVuYYEMTzL9dgDF6923GoeGjiNKBtpKuFBuQV+3Z9zlRrV8vaeq6tdrq47CJxkdMb9kccYjmQdfoxFf6GHlMWsjmmEs0PGG++BwfJx6cuu9H0++EEHEde8cl8w8JzQp3itfPmbKAd5+iLzUtgt7zDRsFxtVBco8J84vjh07yx1sufBjz7z9snKcJZQ6ScjXRrtU1pfOx6fO9eqkg4af0425N3qexocyUvUkxhGX0VVyA2VUwmHiYZWqu5KWrwHBnuJD6RjeY2qQHLtxaDHHGDce7kboSwMXiSpyH9GSEG1i2x7xGbm10JNaaIEPEq3Vy52QSjjxt2tjLnFap/GgrMO1f5gSU3WMibkop9NKT8oscM1tINBphD/X4PySQHOs9pHuHsCW3nhBV7QJ0pSS474VBmKLu3gJMYrfABQN8XdrkTOkmkbintFLjJUyFPZ7s+gpGVlV7kMc4PLabAZBcb00I5+UjYk395mqzse1czb4/rTx//zwzPnqtNc7XvyMOP7G6cZIKRgzaNBeX08ovakuboj+SNZHX5N8x6F7UwHBq7B9rP6bUIFXY8hYYX9hrv3LZmuN8cQsxhpVATF620EmcENtoIsK3jtd4oPU286SZlrfEYngZQZgkrVQZuH+MVOmjK3O5bnz/Ml580GZ5+gwaeZ4hb5HUY95EOzz7mwX43ivSN74/tR4+Rfj43nnP2/PkJ1DV66r83SB9UV4acLPi/NDbbh3JjG0Jvae6BZ9nVMqGC0Qy6SAkOTWq6FIa0wpv5YTRe5NiBS8BS9Su0N15knGnfwm7RKS5BAueNBUDHqp7Y2+GyKZtTqbM/g8oW+Ny05kqqoxz5mHCT4cDngznl6uJIu2K0mFPHdKFi4Xo9XKm4eZl5YjpFtgngvbaUNTjgKf1bj2ziRCX5yUIU/joRricuu3rFWgR/q6EgCWd3BVTI1sCUlOUwMd8R0e01VSZe/x99NNjSVjHMxBSXW/ZfXIjTKMq2+PE3hvHr5fhd/tlc9b5/fbzvt2x7tdeapXPq9hCzvVAJH60En7rZQL4NU58nVBxsK1MRmlcVWzQGalxgJzCZO4jvhLG7OjdEILkLDWgkp6PeRufKJiVl9b3XTQjr9ocYqUVxQK6QPhEpIH70WKX95IkRNzc/kDsX93zAMAeBUnCJg08lSou/Hli/H2aMwFHt9lvny58sMfDvhfKYej8fgm8+VTRbUwTcb1GtKucqdsz8p13XnzKNjB+dSc0w87P586vzlkfnts+Gp8fnFe1sTv1LgI7AiiKbyi1rhsE3VzbDHQThqBXGkOz2RtIb5whXkq5ALMkBLj9KuxyLqAjtgTn+IDyGmkl/qgUQIZRYOOsTVyTdfdQyZnwr5tSM5cr429KW80kNNszsMiPDyElGwq8HKqnL406PFgZFWWXFiWTt0c753jAe43o2unmUelRklcayzMMrJ8SkpYEw7HQTkNjtLGKG5+ixINeV7XkTzXBnhXhFY3ujqSg/8smfA09jg9Yi6A7nEypikjc4p0wwHUaLp5P2/IbmRT7RYCdU0pym8vF55W4QXnh3WjXRrb9RIntoVB3EfCfIyu8mcH3G3GHBzk0P4GUKUhHLldmMURK4h2uhpthF/7HswEqQbC7D5Q/DzE8GPNQExKFqopFYtNycOR84sWp3cbi81e7xTxAnVYfcA0oWrxQjxHiro4rp2cCvW6B+H96kSQ0bnhTCmR3dDkzA+JMneQI+cfjU9qfPvXwvEODsfMFcOrk4uwboY6LIeEt0RrCTMLsGiv/JefhP8qG4+zBEdpAbK0XDlICZpHC247hxSlOq2F8kQ6kIJC2V9r8aK6IGVB5wwprF06Jbi1pNVOtUDjclGyeWiKJWDz5rBVY+nOnBL7amz71ztZd+e0KdYq4krtjfOmPB4Dvj+kxEzn8RCqJD1msEZJynRI6NXYa6QjWDMWSWiqOHHSfvct6HmlXQxJnZyF56uxrc6jREUBg+tVq8gxgllQHV2qcR3pEob5NNDZmN9j/HWrgQC5RY7OeChzymTJ7LWGb5OwW5lGsn0fPKEMfrPuDTcnq4zcIotaB3PowRV/8c6phWi9BQzMvseEhguiYBJJgUhMIC7ymk38qtYaa0PUUNevE+9IDHQJJVhn9PeIY22PEVUKnjw2Am5Xu2Efs1AAMRR1CqFFv1ExQ0jxi09O5DYGBKIkDNlUQJh4rWNf0DhNJHSFx4cj3Z193cNH9+pdG742jFwm7u9mSgn7Dy6USXl463iD85dQBwVsHXcWH27fBLQagvxpShEJoTH64oleL8E/2RSxISnMvFMWcmlkmbicNtbVoqVL497XLMbYgkdGTg8XhUgkF2RVujdKKqRJg/NVaLVF5+ZA4UKf2RDKTWsSd04It0I3am2YRf5M0AeJSsgFJw3rXXHjsDgzhcMOy53ycJ/YvUEyJs3kSfnmG2PdA31d94bXsBC/vU8cDzNTCeHHN1PGlsaksJedvW7g0U953uLkXM14dqVrD+P4SKYrHqdf742UU9TRE7ykdGAkH4iO5uoBsHSLjSzhY9IIiZ95gEndA4DDb5V68Y6pDtBMAsm1VqnNaa6cVfnTl6fI/SVR+40KGeh+nBrjuY3eHR8KJW5n9xAbvIKb4gO6jTF0Pizs+xZRp1Zfn//YahngTxtA5S0Q7MaRCsGnxUkajWI3Ta5+vZO+YjG/ZHG6cFOtvzIy3tAUzWFxHwlOx/rQH5pwWSuY0baGePCHKj76I0KRkTx0stOSmE24no1theOD8ebXhfNn5+WnENKXeZxaOrSr2Wkb7LuhV2AT8AiUanVYjzSRS/xOaCz8svj4Bzo6dTrKOmiD5hK+QUJ+Fs+Gjn0lRnYfmaruFvXtoyTqFoKVcpwyPvheBfYWnGYaMjjMaaMGfa2jQElip90l38QjHBcNX+YMqXe+WTL5TpiXwiKJnjozGevC+/cT16vx6ecz3TI9O2/fJn71IfH2fsJb3LmmnNA7KCTuipIIG9W+OtOesAaXFl7TSwtB/pSFWnfeLjl0xYMXljRsdAQaH/fzxHBIYx52riTg3sf7E691ShPbSIGPcLrbIg1qKqWQXm5m0eRGRJT0DlIyp2487xXxjBEeygAov2bNjnPxq+14LK+b+AcZJq8b2DW+Fv8xWt95TeALiDWQWvdXSSqvQNkt6C1+RrAnN5RaXu+urw6UV7TJ/5y9+bctzthcQhCMyEibV3xvaFJMQ1Tp7rTWefPuwHZp1Gsdu1IYXG+DjDII7Tnuelt3Jps5P1dQ5c1jQbUAwvGt8PFPjZdPsTjL0UfhbowOmhVpRq0g2tnWcEhsazQg5Wki32XMexjdSqTSuyVUHV0KltttjsWsY+SBpsUHVHJ5rQU0Hz0a1umS4n25kZUitG7kKcT+tw/JneHlDM60tRYO+w2+nI1LjRC0eSzqtAx/6JDkHQ7BpOXWOT4WKB5Fb9Vpa+d8unD51MlMPKhz9+FAK8rz88b79zNv70BX5/mlUh5ADqDHqI/IYpQpk4vg98JhgvUCX75sNFM+Xxvtaef9Q8Zb3MPv7hTRHO9fClQyecSAtr1CHS6PXdHm5MmRPu6bFrnC8xQ61aSKdodM3IF73NndY1StZuMgU/Ze42tN6dK59P4azmw3RU/MJei4I3+Vxemwgo1FoWOy8dDWvho4xkknEqBXH4F09ufPvdiNc4Fh+RMdyZS349QjUDrPS+AyY4HLOJlfF/BYYH/h4PwfhEp7R0nYmJkZwLGSx9gydg2JH76uccfJE+Rc2LYrfYdpmlkm4XquQEdLptbOj983Tkfn4b7w3XcLJTuVIcJOyv0b5eOPF+7TkX6B3hxvxnaB6VDQbYtymVF3XvfoH0lJme8n0B4eAFVatddRrBOh0U2M3RTPEROSuuE2uKc+hN8yTMHiXC8DNJEoeZ2yBhHfEio5qvMk7lvi8f2JFC4Ya6/31x8/Vj5dwi84J+GYNb4X5362oD6SUw6JJIl7mdjbznZa+fx95dMfG+tPBieh5IW7Y2IqxvRd5v2/d777ZkIanP6w88M/v/DyDOlemL5JPPztgV//JiHLRLkLasclws4er86xGF9Wo1R4vnY4de7SxHWNTcjFybOQ+04uc1BJOdG1UHsfiyAh0sf7baSc6bWTmNCkbLWPZAOC+9VI3UiuUYJkPgTlmb5f6S2Kj5p3zlvjx+uF2jXUSLdHXWSc1un17hq+6UjWV3LcB42ggXKKXtWxgJPdMCIHSRGhInGF8li6dLnVEhJG+xy625RCPYaNWBYLuk1akN4iGRvdFBGyPlRg/KUchP/hWBscpd5OYxQ8UtwCOY4RJFCnxHbppJzIM9Q26JOxU7lYAEc+07dOKs7ju4kPbw+8+xaW+41tF2TNeFZUneOjULd7zi8rD29yqGU0+Mf1tI8PII0iUmPtlzgV5gUj0S+Opk4qxnIw3r+b6E2om7DS6DIM1gKKYT3RN2e3RjkWdOy/tUV0xpSDkF+mWFTGEF9MyjFN1CHTi9EnFjAokp3eC6fnyunHK3/4eWe+y7yZlDwr07EEGp6NdwdlSQmbEpYF7cb1vPHxn8789E8rn/7UuF46czlEO9tkbJPgljn/zrhcG/q2Uj/D8z81+tk47yvPf9pJ/6hM/6Xw07+75+/+5zvefjdHC7V1pnkCqXw3H5l/PJFNUS+c941OY7OZeu2c98renW/vJt69c1wai4c+tivROyOdVG8PNHhv9C4xvfSwhYlGi5cJWOthiNfQXicLLKDt7TVKdN131uZ8rhtfztG908cpqZ6+JqpbcM04MXq/TpABatpAzq03lEwKLR09OXclc/SJL3UdI25MWhDqnpBVxrLxXmOsH3et+JcDO1BLeOu4ZrARBM6tzUBG9Jberri/bHGmcamKkOcAZKJEltFUpdHkTNibsipmnbrGCwvZn9PWHduFsowX2sGXzNu3Rx7fJ8qhMy+FpMLZ2pDsGb0qeU7Yc+LpS+Xxnb4mwfVu5JKDwN8idDrrRJ7iDlT3jeN94nhQZIH7KdFsRyWTx0NgRdhWYx+NWns3miiWgmbptaMpuLnkTskRKF1HCY3t9dUuFQFsHkXIFklyaDQqe4eff1r5+ePG5cmoMvG4TJRJOMyJ5aiRJl8knPwlUVtHXoyf/3hi+5ed3//3ldOTsZDJmrFaWbtz2RVdK2+PB1w7X740aC3yjHrnsl9Z28bFd/DM4VPj+6eN9acLv/lPj3z490eWRyVNCc8zLTmHd3c86s7xbuJyzXgXttq4VqWq8uVlZ9tWenbe30806Yh2cgGdHFTZnwwxJYlFz8sUTXW7RXZwZ+AmI9lQJZDZQFUDjKs9+GMTZbdEo/LUBto6rG2aEmbtFTy6UZeOROXgQMNv46q6DPTUXxHY23hpYlxpgTYzGt2HKSL+Yh7qIEUGFSR8FcfrCN7u2qLlrfsrGntbhzcl2nR3ZDtfhk3tFyzOG7oVr/d2v9TgtoQQ+A5h91e+SAafmULKR3sdfaYlkQps+0oqgXzmSUdLmjMdCBK/hRC67UIuzv2D8PwkI6aE8Jc2w0ZtuJlQWywc1YD8S9l5fHvgcC9oaWjL+KakKar0psW5SOeKca6Z+ybsWanjbtK70ZpQ4FXRIZrordN2paUWNXEDEGijM+N2h0gpgQZU8PLS+MPPO+dLcF0fHpRfvUvMKXNYYJmhkEcAt1LXzk///MLP/7jx9McL/WOi3rg1iQfx0g2TRqvCbju93nOcCplAQ/e+UyVx6cKpXXmhsonwyMTRCvt/N84fN37854W7bwoffjvz7lcHDo8T82GCbrTm3B8n9mqsq/CyCS+rIXnix8uV/SfneExh0/KQo7mGuioVp24W6XQ4Ig0zGS3fccLpQFf7kL/F3T6etlAcOdcWmbXNG9de+bQZLeXo5emMv/vV+dTHnU4lxkfrLU4pjNeL3o139tDLBsoqbGtY1LJmEp0pT/Esjnz3kPsNXbfOeK+jqzMSPQxDvL9uEio38e6fDa8jVHq/rv//r/+bF+eNkxmwtsuoWRhpV64BGavJa+OvqIZ+dLzJMnaOXBRNhXW/MC2A75y+CC/HxL0q0yECk5NGSZCpM02EeOGNUvvE+iK0NWoCk2rcQYkyHesOnrGUuHsL3/6mcLwLYXmaJ3oPiVzWCI0WVTw71Yy1Vi418TAZe4fWE1luWS9KzgM5J3yQ3iPiRBlG4RR+z5QgK3QiSrMMXfHp3Hi6NnJRfnU38XffFt68meiuZHEmgid8/rhyUefzHze+/y8XLp8q0p3iFTejiXABzr7RPWrn2oDlf953vpOMW+O8raSy4JoiN8c2zi5cRWhSOdN40MzllPn8jzvT7xOf/vnIr76r/PofDnz46wNpTqOUCo4lMc/GsQn6qfJla2gpiMPL88rbD3eEe0BfQZY8aXhPCUWQQVRImNMDu+GGq2YkkiiM19qCrTa23di6sfe4XlxMuLwaBBgLLuiI4Bf19YDQPMVG3Vt4iS0NRNUCwR8gpY9/iyEPTEO1E/mzfTzXscCSJhAhJyWnRNX4em+jLtB9dKp8xWO0jNrA6/rKrYoQGUh8zZH6Ny/OWzkcfgtpYqQYhMQqTkgbaBavbw4aKFoi6hzcnLpVurSgYmbh4XDPMinb1bl/nyIoSxmL2chFSOr0pjQXlodGXQcYVKIL0X3Ei7S4pKc8k3Lh8cPO/Vvn4T7FSeTO+dkRV1oN94Uq5FnpCdbWuO6d9aBcm3PIPew9SSPgLEkkGvTGlJSURgIJBlFrG0XD3EzLcRcyjVzbl3Nn78q37wr//n3h/VFZ607dFcvCtjnbc+P888b1U+Onf75iW5Dhm3eu1tlpXNx4cmf1xqQJt51JDiRRliQcDoWXpysrxpspc9o2PtYLL+qsPVFNQTubh2GgyI5YY9oS68+dly+Vj9/v/NX/0rj/1UjAE2c5KEuZKKnx7ePEaYfT6RLWtSpc9sqUbPCZEl0oI5pUNUQBZmH9C1AlADAJdQC9dWrrdA/rmRP39vNmnLf47yvCx1pHQdB4qMciF3dc43vVbs/FHqAOYZqQNLKT+1cUV8bmAKH1jXXYRnqixIaPo5qZk8d1QmBZJpZpwWXGzLlc1zDqN4tIHBd2W1EzVA/hz+WrSMHHvVNu5u5fsjg1zwFV94qShrY2BYzeIjZT07h8a2gO40hXeofegiKJHNgUp6k09mq0JRA7s7gbWIdtbRzmzGFRtuojDa/T1wh8Or4p4FHiE+qKHnO/ZHRKqDaOj43Hd8r7D5lMZqud03NnXWODXM8RdanFgZ29FK61cWk7l005p0wR41iEkiM5YEqZZc5BkapFsY7G76Qa00JpMQb39DX2cNsq1xaFuO/eLPzVr2aO7mxX43ztXC6dvcPz1sitUb50Pv9jZdtv7v3OxTZO0rmYsPcWhLvHeK/uVF85KKSWeak7176yIfxuO/H9fuVLu4bqBljpI34yjORiHSEjqfPZTyy28c2PE9v/tvPX/+uRegRdJt6/LTTdKUXIWvmrN8J5FV62FUnHcONkR1uMss0d7RJZsUO6Vz38n3o76W7mbpzWG9ZDNeSMFrG9UxuYZap3vtSdT1tDmIK6si0cLN5AhccPH7i+XLieT2SNEADEEC2BHnuMsui4Gw4/J3jY3bS/RsMo4a4yi2rJKYe/t1pw4EWUh2Vm3Ve0GJMubC1CB0o+4K68vExstUbq/1a/8q3jUiyvOt9feHIyUK/0Z473cFcYWhzVzrwo2y4knXjzeMQsNIo3V3ztjb0F99m3DJqZDjEy1tWZS6Fk4/6g1D1Hw/GiockcERo+KuNSgukAl5eK9szhLrFvwYmVOfHwDbz/VvjNbyaOR+fH73fOl8bpxfCacResRuuTAmlR0mbsu7HuzrU6myV2g3k3mhoywZFEKkryyjKNEtwcyQmeNFqpZeQotSDpcUfM2daITfn1B+FYnP1p4/yl8/3urGfjUht6FT6I8PKj8bzuPPWNj3ZlR3jqja1nNMER4UOZaGZxmopwYudsjp8bL9fPLCqcFV62zskCGc1NsNzpKqxdRmJFpMkpneI3d82VUzKum3D+vxvLb2au886Hp8LdbCzzPQ9zZDb99puJHz5ljlmZljhRenNqSxJRntIAACAASURBVBGU5sbWOq1CtYSQIlvIjCrDoeOwWwsDvmSaK1uNlrlLyzid1RvVhc/bzmpx3WmthhPIOkh65SVLKWwjV9d9LDI6SAPL46QNZBVPCJFLHLKuMY1Zw4i0C0mJknJ4fy3um0kEq43Pz184lpmcMpIqNNCSMevMc6JMj1yr8+XpE+KN3kZerf3Zae3yZ3zsv3VxavBNpqGxfY2uT3AYSQZI4s0xo3nmWGYsKa1Xck6RYtCd8/mFp9rxFGFeVGF+O3E3Z3IStnPipI15CW6wNUeLsq+R3Jcy7NswuwoDbY02rb3FCKTaKeODuWxhD7u+KPseXr/awm3gnigW5uK7KZMexli5h/TtUhvHnDkm5dqDXrn2zlHSaxlNSYwuzuFRnDImwlYrLlEua9aZCanew93Mu4eCutF354cvFz5uGc3K04tTTk69wH4RPgM/SmcdM9tjyvw6KQ9SWNIcgnScKU2Yw8Wdj175rDuXbvxkO5tFl8djLixSWB4PLNNEmWYM5VqvvDyfuPYrzY2Kh261FZo1ntMLP56OvP+d0+9nfjyt/PrNhPDCb76ZKcfC/VHpVWEyehLsbNQXo1ewFOR8GpI519DYbr0GgMjQI7uNpIwS2cVE9tCphQvnpUPtwjPGdXkkrSdcQsLntX+VSopzen5G8ziV5XbnK7EgXDCLQtsA6UAGm2B9+GsHr6EjKqZ79KCKEOi3d7AccTG6k3vEm94zMefMMsXP7j0okswOnlju7zCrXJ+ecdeB4niILUSJYNVfsDjzyBkNntKjtEcMa5HVOR+VKRce5yNlmXj/zR1lmthrD72rK+u6g1X2trLZSka4G3GMIrGznj6HjEmImrykUaNmNdwLOi68dstzGRf6WqPAtEwTuQSpXHfh6XPcT/ua2RsgyjKHqiUnUDKXU8PVIqyrGOvaedo3potwDGUeh1XR5KzXyunFWRah7IIfwKVHT4qHt9E9/Ig6QrSTJdw7yyTcfVcw6bRLQ3untsSyJP7D3x348XPj9993vnyuXLWynhpHF76VzDsyd5JZCLl0kSXS37Iw5xwSxZxoJXKKnq4bn7aV3YyjCvcap9XDmzcsb+Yg3rvh6z2X4wPegrf8+bLyU9v5zJW1r6zAivHkK/MuTOcCyTlOcLw0vqmJhzu4eyP4nKJA6dzxlginMoF4D9pDNQ3Hi+IkNkLs0UdPpwz3S2uRGbQ22FuY268On2vjeWthHujhGwUfz8UYTx1st0HeBdxz8+B6N0RHuxsDQXV4jZy5JRQMWgUZ3z+cIzaECbyqjMLIsCSNNjevqAhTPlDKQm+htT5OTr3EhGgusXZ88DYjhNr518Nr/we5tXHsy1jhIhE6hXRUM493d4ByvDvw13//lne/WlgvnW3tnE871pxpLmxbZp4StSpzSZQSlQE5Cftm1KuzTcp85DXKwYb3s+7QTUk32kYiccF6p28ShUPHHGN2is7E81Pofq11NMPdY2hrczJKFrazsF6J8OKkTMfEfqlca+PlWjmWiTxOrmly5iRsZ+d8bdgBmBt5Bhn37V73uD9J7Ivp9tCIcJgzXYxWDVudQxLePig6K7+6F37z9sh//Btn35xP31/5/f8lbD9vaDeSGdkDHRaJ5u8pJeakJEksS2E6TDBllvuZ99edd5+e6d0pNgKSk6B1J++JY8nxMFrnLmWaQ+4VcWM245ty4JRmmkJ6M3P/RllmZZ4SdyUzpc7bu1sYdEMt0Vdj3z28qE7kCjtgkR9Vx8KJFvSBfgx24QYydoetw7orp924VGc159Lgkwk/ro3zeR3eTAJvGOuEcRYLKSIzJQT0rwup3n6OvWIB46Ee0SxRO3kjDF8zMRxaayjxOl4tIBqUXR8xKkmV89aZkzKVELNoKbhZVCJOShHncT7y+fTEtReKyyiGiqjNX7Q4JQWt2lsUzBwOiS9fOlNKHJYjc3lABO7eZT78dubuTeHdrxfq1fjxj2fOLx2Tzv0+U/uMXzsqzmHAnTpDtoDRL2ejHIXDXaC01iROSHd6DQCo9wCAUs5Ir+zWyHNiPkZDNmiEhlnjeBCOh8ThXknL+BBN2a6dbQ17WduDt6wzWAlqplnj6bKy6JG0dLamNDJdEq13ttq5p7zurCNYb0wZjAUq3Or9EKU1x6rhLQCF376bQo62GjIbh5x490Z5Uwz7Gf74sVLp7MM8OWlilhI626QsUwm7nsX4//Bmoo2Kwl/9+i07jdOPJ7LLa34QF9j3ThJo68a2wrruXLYd1cpRYHbnwzKjDwfe/8PMN28DsTZvzGNzqBYZPnpSpBt7DaokiyAaFYRhGIj3J2ksvm5piFUiIEsJy+DeOxdvXC2xI6xNuDblvEah7k+XlY+Xa/CjHtymym19DxT95jG2itxy7YaYPaR8t1RAGPMZ3jshftfAR8a/KXIjWb4u19txGjnGeaT3gXchTxZmcBW2tjElY8pz5FMJ3M+ZxymBHlBxfvqywohiec2N+iWLU5uwe6SJ7VtnvTamslCOlVRmpjRR5s7f/odH/u7v35EnON4LL1820nTk558bz5fKu1nZr1vAyN5Yjpk8geYQA+wItgnXz+EOz5Oz77HzeZeopG+Otzg5zXrkCkkn54ksjZRgX5XpIaxVk8SCLTMcj4lWjZdnY7vKCMAypglajje+HwrbtkcUYx/19BUOySPQqziLJrx2NAs5j4YwjEXCxZ+SgijWPAKWmyO5UyTRmlA3ONUrD2kKnvbcWC+O2cR6b5THwnf/oHCp/PF3J/YNMI9yV5lGuWwiTTH6yxQmArPOdtnY1srD3R1qG/NB8D2jg87YLjuWoJlxWTekwt522hhDs2Tul5l33yUe/t2B9387k5cAuKTGQrQO6wrb6mHzArC43cXfhbZGia5Jx7RgFqOqJ8hO5Bh7omQhi3BaawgRurPV6EGpLWiorTee1yu1DxNCbSGzfM3sCfMD4gOUC5ovTzOtbmOEHYdmIryZmsNPXAm969DbhsY+5HZ6Eyt4GKc1JVpdh3soAr6aZxwn9binbhhUSDTWXslpQdNE0cxhmrjs8OYOZhp/eNrZmjNL4s8S0P5ti7ON7+tVwAsqTsoXVOfQNvqVD98u/OY/PDIvTts2nn92Pr/snK6BYD6+WTifIl4yHO1g1ulVmIqQlxAZuzvXq5PP4cLfW2Oa4oHe2wo2hdHbjO2yotwzHQ7kuVIOcDgIx3thPko4N7pRZJS4SmHdOpdTQOXeO1jCk5HV8cWY7oR1L5wvnbl3LjskyRyzc9oay658c19ISbB+c9TH+ONhtxmWuXAltNagGt6CVqqjIj014WWrISPcQulpUjm9JB7rgal0fvU/zXFP+eMllD6e0RxAmKiztsbdEpEnak5fO4ZGqPbzKRZbEa51Rx1KDl9l6zsv+8pl66g1Uo4xvOOUWTh+M/PubyaOHwSdOnk64m1nPVdonSQJr47vsTg9J9Sc+SDMybkOB0qzxmEpEVPSg1fOImSBbdX4ie6c9sa2GSuJ1pTaohx3M2cV4Ye18/G64Z4DXwjteACVjBT54VyxEUAGTqs71iIh8la6hQ2PLsaUM4f5yMvTF/prcBcD/Q0MG2LBI04pidrjTEZDUNP2NkIGZpAOlZGBC8hKbx21zjyV0NhKVCYdSuZv3ix8cuF8Pkdy4S9ZnEJHU+SjuMWIVnfIKUeQclKSzvzwhwv/8k8/8+nTxvmls2+Ql8z7b+44PEwhQPCGi1FKIpXoRSmHIGXLAUSVbROeP/doNNYUyK4Y9Bmz8IS24T5JeSdNieND4vBws/V0WhWmRSiamUpoXH/6sbKed7ark6c0+kPBaiQ6lCIsD0qtUba6Wqe0QNaW5JTiXC7GvhgPh8S2NTyBlLH4R49loOQ2RligR/L8XiMyxJrSumMVqjcOKFlHllBrfPn5zJKcw/3Eb//zHXfvEj/9/oJehNpWrAdC7dVQmZibs68N0+iVvG5buDlyoa0Va0MMcY3Gt2vtXGtl9x2VsNglzeQ58fDtzLu/UqbH8LbmfmS9durauVwbR800d6z619iPHs+HdWf3HptRj4p6SXIT8iAS97f4Hx01Z6/RKH7ZG2dLNBPW5mwdzt350175l/OF3UKe1S1kcojGovORpCcxpsoIAEcddQ35ngcdEkFzjd4bpEBZ67bH1cqMiBO5XUPTK/OoQ4VkFuXA3QaV0qB2eDdFn6qOaNXqjSSFLDPeRwdsb2x1R1Kh9oa78eHxwJwSv6tX1r9w6fzLi3NKJBgNwiEgznmizIp6RWTi9OL8n//H93z//ReeTxtCEEcpCYfHmfe/KtzlmbZV8hRqoebOrHnUrgllCjHCdOicP1denoyHt0vQH2jEcvaQBdLDjzgf4fDoTIdw2OeiKIVSBMkddSOJcrka+wVaU/at0WvnsMjgYJ3kCZmMXJx5gZdJeDptLDpz7c7TJQKV7+dIs7srAUFoDuBHskbH57g79Oasp6hkSDoS4TWS4rpH14hp6FYvW2fKiSUHwOCtsyfYtg1PyvJt4R9+/Z72VPnywx4b39Wx3TmfK3sKF0y3BllYawWrLMvC8LUPqWHnKp3VQqOa6LgW5uXA+/cL9x8yj98WZHIurZMlw8uK98p2HR2VSaleYXgvgyVxkhsjiAI83EFWiFOzBn+aNDZDDIrCtTX2HvXszeDajbU2rrtwap2X2vj9y5nP1x3xKby24xQL0fkNWE0xTTFCrlN6vWu+Ko1eESEFC0DHav1KoSB/tjBlTLnhirJeWcrC8eFI/fIRN6GZcF4bZcqUElEx29hU+h51FlMxFtV4/3GuvVOyhyQUYd2uVAWdMlJ/IVqbU2JvhmQhi7IshdZX5lRIkrhcLqx14/uPn7i8tDjtpIE7VoXteuXpyxO//ebIm7uCSkSS9E3pkweIUW6NZaFcOSyZfRfqFhmmpp003rww1yrHx8zyGM1mqhEBIdLRXElppg0/5l6N8ynRLNHbjnsoVloNsOmWJ1O3GO+mRchH5bpmnt1ILrw0KFc4TJ1lMpZp4pBjfEs3UJBAma05fXfa5kw6RSVDN7THg8ywNdXur0n2a3NqbVRz0sAdS1H21nhaG/sh8/g+8XZZWK5G36BfnOtL53puWI9d3FrogD0Vrj1CxHxUD1qK07DnQsoTd0vi4WHmm1/NvPsmB5W07bRNcE3MtXO57OSBzpcsbNbQkka852hvHq8pavJCCTYfMilCeKKuEYuHuofoQQZCWXtQJucKm2curXPanecGf7isfL9e6T0HlZYCZe7DXIAHEs54LuJP+qpdDT7aueVd+aDpwplyiwy5ab8jRdCHHBTiXnxLbHd3sI5YBNZ5ClfVgrPvbaC2IymxxYZw87KGxtbxFn/uGKhEaHgHrYb/0sVp1mhdeP/hHQ93R+idul/BG5X4UPbryuUSBTxmdRhJB+xMQnt4OnMZwVhDs1j3TppC0pdcKCWa5NKU0dZZT5U8J/Kh4DR63bGe0OzcPYYHs1fY3ZkT0aWZMi9PHTQj5uzXyuUa2ltNERFiozOyLJE8by1GbEXpFXhbOFnm+lR5oCBinHbn86WjWTnOO6kE1+geQnjEmFTYO5greShN3DK0yNsVSWiCtsWI6eJcN8CdwxzRIbdYjjxrCOpJXM6NdtrRVGipM73LHD5k8kXRZ+d6hfNFWasxT4UlRQNzX/e4CjVnKonHAjUb88PM3QM83HXmQ+XzXjmda0wmnrFWuZtjlJsnjdqDDfTmPumBbIraSNWLnpg8KtdFYcqKVWWzxtesHIl+TYSLZS7NuLZIe1/xKPztnU+18/21svaRxk+YqG+2aGeksd8WqoawRAaEKzdKcuyaEeDtSA7KRdMUY3Lb4yF/TSoYf5aRyG9hyN/3DTkRGuHeo90AHWHpznVrwaGKxbTgQrc8qkkaW+8kj3a+s1mEd4/AMlrjkP91ce1fTkJohrpzOE78+m/e0NfK9enA588/sW0jgSAph2VmvTTc89ilfAjFncNsHJaEZuNwl9k3Yb30+Mk3S43AvAh9E06XkOJhgrUwxLqCpI7VTikaO7xDXoycU4jNTVifjXU1ulfa1aHlEGE7fPNrJRdjKZnjXSQj1CG67iaIGSnHA9munU+fV65EYluyxvPVKPPMaXOOM8wlTpUst9Q4hzqA98lpm6GboiY0Ge4HF/YmLOqsDVpXSpGoXK9G7VF6c9mNedbI3hWhoKM/NPHxpVFrJYmw4nxO8KSNZzHaViklbGMqnSkF8a1eKSUzH4THudFNuXxp8KkjKVNSwd2o+x6ZtFtjmuLEaTZS4DWqEESjmqKIwsgyLiUUUd2jyKr3YZbQwWwOfrKLcF6Nc3WqO7vB7spucf988cYP+8rTZiNK83a68dV8IaM+IuapgCQIMEi8RPq+h08zDX1sVBAGDZVIrHXDGHy03RwtX3lX59YGFlhG77eayzQ03ZAmZZqVl/Meif5DElhbjPllTrTmrE2ZpLG3HR8ofWuh411G+dUvWpyCICacny5cr/d8+PbIfr3wUB5w2chFeb6sPNwf2NZTtCspYTbuQUhP9xFP6UTwlllDc0gB3S0yUXui78a0OPOq7MFIRIiWRMjxzfKDB6pXJpiWFA9CyniH66VGmgGxsx/uhbt7ePfdzMMjbNdKWzu9NeZZmUTYqtD3xHoNW1xdnUoilwO1K5mwOV0q5JPzpcBd6fGaZgGNtLytht0sTTAdBa0pxlyGh9HDd6oSXGF3R5MM9DVoiJQTh2kYyJugSagWTsfWKzuKJ4+uylU4deNqcBKhDUPwue20V9VNWNrmUnj7kJlnwQv0RFQdemIpkC1a23BlrZWrC3epkASWKXOcNZIlmjNNIUaJnJ5h3etDCDDuVM06yeJ0SUPeXofNzvoYa5tz2YyNuINeuvLUO9+fN9aWYt/usRA0ZRQLgbyDesFSIL5uiTRNiO00NzQnIJFyhtaG28ToreE9RVIFvAJGo/hkPO+DO70dvTeRhHVSyhG3YpFFe75EemIXYbLGkhKr9NcQukvt4AkX49SETOI+dVpzqgn3RUkKW//X4dq/TKXU22Iwzk+Vuzu4+3CE1jmWhO2ddW10Kt++P/Lp5RxWoRa75vEw8fbNRJriN153e33NvYZ/M6bgTm0CPcYf87jDWHWmO6XMidNL3A9j0YdVrbbINLquDbZIS18myPfCt+8nvv1mDl9hqiRAW2btOyXFR7FVQJz54DSr1ItQK3gWpuNMuUT79Z4gubDunadzYsnhsM+zo9oomugbr04ViBNGPSrtBw2OWZTTJofkAUKFaDvuUFnjvY5TIhLtUgr9qDW47hbyNXeq9GG7CtrE3NkFxKP2vbXgVwXnUBJvlsybIxwn5ZAFGQokGffTlJwpKRuKzpkpR5zoXKKD5JCEyOcJJVS3yKONzpQ9umBEuSVQuhvaI59o6yOUa+0je1GpXTg357p19t1oCk8NzntsRt4G0W+GUUf7moTsjxCQi0TWZhJBdMHyjiRlzgu9bYEBeUddmJdDVPUNYcha9xFedzuGblI6eT1JXyvmzSnHiZKdy6Wi3nHpXMdUliUyhHrvzLkgKQwWniLDyM1ZDYo4Kp1KYlN4yELr0y9bnJEq1tnWlY8/feSwFB7f3bPcF94/FC5PK/LsnC4bWpTj3RQSqh5FOYc5cTxMpNkRLKrHc8zvZjfLjqA53p59Ha3ZEsCIVeg18k33zUg54ThFo9zWquASXR2P7wtv3i4ss/Dtr+HDm8L94cBla/z4qbNvPpLbA9lroyp+yol9jw6WUJRYIHXawRqKs/cImzIVnrZOuggkJ83GYwrtZm8Kc9ynbeTdejK83uRgcVJmBbXIyqVbbIAEnZJSmK/do1ohC2gp0Dt9ioY0q330oYbGc5kSrhHp0brBoLmE6P28nzPLQUE6qRSmHAtNVUhd6Op0Ubo4ew30+PGgw08rHCeNBPvhOHcZdqukoIwc3xxluwRqqxI6aMFZu3DdCRCOxLlWqjvXJmwNrnunuvKlNT6uPQpyh8snKvq+yv/8NfN1gCjGmCqcaSpgnV73cJb0OlIOolbj/jAz3xdqbXx5OY3/z7GxAP31tw8uM8zXPn5MnIjm4Uiijvtujc/Qc2G1qDzEhDQp2i0+Dx/9pInRKQo5GdcmzCmR9RcCQrfLfK+Vy8n46Yef2c87f/Of3vP+vXM/K+eXjS/Pz7yslamE79HF2fZrVKnbBAhpPJi9jzuadrpHMZD3kGGZxclacoABVivXU4uC2WnCtQ8FSJxQ5kJZ4NtvMn/39488PoRL5M2HmYdj5lCE5U44r0rfK5KMXjutBfDk1kiSaHunrdD2IK2TOltvWOsUorphNx+i7M6XNdw5KUX2T58k5JrNRqCzvqZT3ATVqvF3tEMSZ85R4JQ1NgZDmVIAU+6dkqPKYM5K3eJefJwUNKHV2NZKSkKaIrHgso/UN1FmBZIGBXRXeFwSh3kgkkTYVEmJkjP76I3pHfIs3B8KxwTNiazfsXHakOO5MDag4BhTCbRTbkkAQ796K2u67J3L3tmb0Ztz7rDuxnmPImQT4cUqn+vOqe6RlCej7WtwgM5NYDAmTQe4Zehm7h4eoAj12bgrkSy/9eiO9VEduF5PWJlZ1xolym5AgJWhyWWI9G8UzAjsQlFVTucnynSIREELqaeZA5nraBNzYLOOVkc9DCM6hPJpUDzNA5jUEVhwnH6xZWxE2Bu0vfFyOiMO8/KWN29mrq1xd594czxy/ukLl7WzXsd45p15MqzNtD2Kf8JJEgtonkbmaYuE7oYMJVJcmPMkTIdErfEwSPlaPecOda/cvZl4fK/89q9mjgcjK7x7mzkcE4cpcZyVTsbbmemgeBf2NdMt/HXCRLXgPs1i46g1hNXJO5MkJgT6zg5sDVSMtYahOr9Eu/KbB2U5hvnHLNIjehW0J7pE3yRdIl+mG1mC/4z+U7Dh4jeB4sacM2kWJDlK4zCQbgUOKHNLlCmkZZ4SixJ9mJK4tLgLLkWYZ2VZEqkIWWN68R4/J6ebe4N44LKQcuI+hzpLkRHKraQk0cQdJg+ad1qr5DJhRHaQpoS1mxCDsAK2GOlqJwqM3Gk7vGyN6z7umq3z3Dd2M/ouRH5smC46xBQ1BO1uo8NlYBv/H2lv0mtJcuT7/czcPSLOuUNm1sAqks1+/Z4mCJK+/1YbCdpoKUALQY0m2SSrcrp57xkiwt3NtDA/N6sXzcYrFUAUmUTeIU642/CfukCeJ+bjzOnzF7KDa8JGqFDgLHHYqXDZN5r5IOQbSLB8cspYC2wTbtTEG7sWXMI1viB4NYQc5BzfaT0qsNDH8iuxt1Aq4cP8bnB4b7HzbkLJwpTia/6qw+kEXS58aaHtG21a8MtO38Myc0rCw93M8pJ5uXR6TXTCIrNDuJqfGr1kypJIAtu2Y9fM4ZHXbMphORTEhASlBDHBXah1eBeNWLWpZPJj2F0+PjrHu8ayKD9+e8c8K+d1Y79sLOnAX3868/yy0vdYtCyH8Ny9ro1ma3wP/EalxJqHa/xQKswpYZ5wjFOPGPnDrFRT3l8NMNba+c5mpjluSevOMnxoPUHkAcT3mOccIluJtjUxKG8SQcKHpBxmpZtASbg20MRBI9xhb46rc7/chZjZIutUeid752mNanOYEvf3ynFxshklJY6iPOSYY00ctWDEaFFuRj9ZnWnK8XJqHOJpzrEYa2146kApJV64seQSCZaXJEAabRMuq/Ny7WwdrCvrvvFlF85bZ2+N1Z3PbXs1fO72lS+gpHCmG4bmN5fXWPbFBjwgjMTn93+FrZPmhdNJMG/orZ0c0yTjYlEVthZGZJGZ6a+XFHDbCsV/GV3PzVR6W6+REDdOjakw5Y42wclMEgSNLhEz+YqD54RkoVnwyLInZhfMogv7VYdTdTwpbtstoyj41ml7BP90i1v1uMxctutXh3SMbUuse6PvwY+VksY8lDkejlxOOxAk7TIV3BVvFZ1SkJNbEBpyCeqeJGE5wP27xjILmjvffnPg7ZvEw30h5848Q3fl+ePG+583fv648fNHo8yjbW6dVsNkOCdjD9JJuDskJ00xw2ChzcxJmD1hvVPpXEV5JChklx4BreJOKpV3Hu7iaLjl+VArBMspGCIzg4XiIDmsRs0CJ03LzFGFuYSEqqsgWvBmzEdh1kTajMnCtvMwC82F88WYPXHIyv3cuLsr3C2JZVaSCX2LBLD5oExZKe6v2SfBf4WpRELZhEUVzhrQiIcvTh8zoPWGCExFaTBaO/8FiSKE+XuHy2rUGqlwl2q8VOfT1qh7o1rjuTc2LN4tC45v3JK3f902wA4iaB8azGE8lkrCe0N7kCeaV7754becPn9ku64Dc4+l0s5IAMeH2qkPu5IggwRaExePjN8VgZRz7BR6RXVEOg6/q9ulPiVh7bD64HMLiLa4YAcfXPfoHOP7CLXD0+ac+6+cOcvEMKxS5qKkaeYwTXz5vPL4qNS1cTpHAvD9csfpapwvAbGYeHBVa6dpOKi1tTEVuL/L1H6mNSFLvBx2jI0kQ9zaxiFqu1MWgRJCbJKREuCd4yKINGpTtmvl/LyHePuyczp3zi+d04vx5SRobpTkr1HyuYC3UKBEUpnRaxC1oyVpqFjc6iJkh7k7e+p82De+nTMTwnULU+N8dmYahyWH44HE0kDJ+DAIIxltN6abb6pDUYkMEo1UaEYFyjk0qX0TrHYmnOWYmJaMJGE7t2gzVfj2PsdMlx2dhXnOEZXeQhnTN8UmBn4MunvE5HWHtVJ7JHRPChlFktNv4P3AGLc2XA/zTU7ltHHJdBEyAyZqoU09r53qIb5qIyr+dHW2rbH1yos1XtqOSgixxUN8756+5mgKw1nC4yWPzRCiEehcUlQzH6OEe+Hlywd6raNZjBnya0pajbaSkUw+5tL4H4GqioygXjxiSKwFf3e0d4PbAD5CLmvCNfJoK053heECv/stRyWsAJ1y3wAAIABJREFUUxmO8apQNWGubLb9usM5zYJVZZ6X2CzNmXePB9rqfPzpipiCR8JYykYu45frgDq9OhdzpIBbWD5IhmTG9XrlssaN/TAV9nam5Jm5ZFSiGektKlfvQZjHGykHZ7ZMynwIFUDdlU/nxtNT5XqBp+dGb5E6tu/CtQolOW1wHvC4gbP6iFs3wmMy/mMyNpEWkXqxCBnsj+GJdMqJt7NSd+H5At0T1hvveudxMRZNEcwzZp/Iu5QxR4VszbBXqZkmAbHx2kuo7auxbyFFkh6YaD4Kc05MUxDwMWe5H85zs5LmyIdpa1hTygzpaOwaogGrBMOpJLwFfpo0RoikAhhLViwJ160RPabhrQ3rTx3OiyFuP0wlZFY2dgEtKIlrg73CS+usVThvUIn9wtmMl9YQg7lkCvEMUlakC7lMtBbZrjcTLpPgzuY0BTXRnX29jqonmIaf3n69DngkYKRpKnHJ7sF6ipLn4TivgrRB8xuHVsZFkHMIppF4LrUaSROC0ojv+bgsnFbj1EOni1t480qEbcGtK495228/l8u4GOL9+lWH8y5P5KJMxwPffvfI93+YmS2zvqzUvXPZQnfXGqgLh+XI5dKpdSgISPTa0J6xHj/ISsb8Goe4Jq6t4d4oaWLdLtjdgVkzrTVaTTArZSqkHL1+Lp35IEzZ6N04X5TtunJdjf2ivDx39j3w0PUSxHlToClrbbQa1iiRqSIcj4rmMQtIAXfaHrRF15FcjeOakX1DxOj7ysmdxMJ9LmzW+LwFE6USkrjvD052oZohBHMla1ikbB5YrmoKVcQtIKdoyJIMvMXyyJNDG9ifBdRkopSSER1b8KwhnxKJNlOVqSipR2alEZrM3gKGCfAgKHGlKE0F9iASZBV8MIsOczCpcEGnQtYw0sZCjRLxB7FMMzP2Dc7XxrXDZsKld/bm7A4XC3+g5+58qWtQNlM83+RK1j4KmFBKXDDUwITEQ5ytqXB3t/Dy/BLeVsbwEBLoDUk6eNbhnhddbWSzhq1bjqQ14tlWoA3LG0xHLEdwj4omtu5AmGG//e4N2+XKdm2kFJGKJWeOU+PUIkE7qQa5xJ2keZgGBK0P70OAHpeDAd7GZfBrDufDYUEdvvnhjv/mf3lLovK3P37m7ttv+c0x8cf/5wOn0yAWpIU8K2W50HofW3DHvQwvmbiBr2u0FvOx0PuOo+y7kg5CqztcjOP9W6wLrXY0gU2J1namQ5DEnz4ryyTwbLh0DtPEtgkvXxrbFtU0MjyFbhEncW5BAGitoyZMUydP8wC6Yx7o9JG3SGBlUoblfmzXzAqtb+xtZa07W93Jb94FVtp3TnsQ890S4p23x6jEqQaIXlWxsckMs+NwwhdrqM70Gi1iN8dLbBVdQ653vXSaJPJizG8UU381O1OElDKicN13EiMzxFtAHj38c9s25i3RwUiKQ+wtbnzNsQ25aSEtzmGoQnrwn6d5WFR54NXXGkSGbTfO18p1cy7duVbhsjubCdcKV1Oe28bT9QuGsqgyaYoMEYXMYAGZcz6dQY2UZrpECy4o3neenjYi7l7CGrNbVO7bjkKIJYI4Kpm6R1pY1pj7zYM0EjhmXDDdO/ROzjlYRg5b3endx8Y6bHHi3ot4R4BP5z3GvTR2fj3afHfldAnXiaGhC4nhWFLZLxzob3Pxf/XhxCspLRwPQuqN//f/fuJ//b/+zPf3P/M//Xe/RUxYtw23RJWdT88n1u0W6DJuvR5Kdx3+OkrELLhvuIJXYzdGLMGBbjtfPq9gnblMpBwOfaTA+0QKdXdqHnOCRjTBtsO+B7VPvEf7sQnNg397OMDj2xwMHY0PU3MnabSdrWYuL8a+hbjWM6QmSE7ghlroV6cRbIOEUuPn0zPvliNlSqytDbumwBJTmphSYyrhJpE0to1JnClH+hgCqoWU+4iC6GHpUcdKvyfu7jLzEinQjrM1o2RFid8bLETPU2bOGWthpyJNQ/4FtB4qHLoOiVdDLIcsz4Ifq0LMWZ2RzBb8JtUcS6EurNeIX8waNL3aQBpcr2HMvVU47Y1TVbYmYQzd4KU679cIt3rUzFELuIYLRkp069TaUTKSS/hVmZHIWFZohtEpMg8E0l/zYVUEH6yyiMewYObUjpaIBWm1Ao6kxHVYhpr4yOcM0YQR7wXoYKkFzVREef/hzGGeUMnsvYFnhEZbLdhnxHsSqW1K9uD4ikT+S7ClBDyYXQPI5TWz87/2cFZJ3E1Cve48fVipe+G///53fHj6xP/2f/wLv/vhIR5evfLlfOJyiRbOb2CXhzZOdOQjjjakd0fyFO2iVNyjHXo8xFJhr9fh4J7ZtkgJmxJxg2ZF1GmVQVyAdnH6ruzrHsTjLsG9zcpxyRy/E959W/jmm8Lp80ZdDS2AQm1heTg4Z7QK29bQCktKrwddVZhKiRd9d7J3Os66Vz7aync6M6Ug/l93+GQxk303GWLBAzaLKIg0mCg5x/zR3dCWSEFRYpLYgoNSJuFYYF6CV+sj7UwgFhU3eVaD3XtQRS1YSIgjObyM2jaiDloPPWYWLE5hwAp4KE2aDwH18NohrD9U42fzAdq3ZlxbkN2lCtturNU47855F172Tmuxpf1UO5+3HerKXVKKhG2qm5GZEI0QqWqCSh/b2jTUKHtQIuPdx7y9StXgxukRBmA8Qo6CBDDN4SusneBfE2qQG/nHA1aPS34Q64sGiyj0yxIjWpcwR7PIXblhmBCG4n2QCqacqL0HPCfhEBgfU4xNUbBCDDH2jL++rQ02hnB67ux2Ack8HgvS3zLrC+veOEwTkjI5F0pqobkTXl0MXlOD3blNOyIdKFgDqzd1gfGhwffvJmZVLuvGtl2hG8djBtdBHg9Qt2gQsS+XTj1D3TqGsyyJx3eZ5c7DtqQ4h3sD2fnycUNdORyVZkFCthax8d4HrjqkSJPqUClEm3uLMjSNl8uDmU23TmsrH18qd4eZu2mh9o648nRxpDXeHeU1ph5CZhW4asTSaYrv8Qr0ByCBMrBQ9dvaPFrdNmrHyP1II3+0Ya94anLFGwiGtSFR0jDmDhQz2F+RbSKo9uG6HjS1q/XXhOk+ksHAxz0fVS3IKca1Ns575/nqvKydZwvLkq0ZH/fKaV9JfeeQlCJxAeegEYGHr9G6B3Ed0egEGfgihH2Ixztg3sazD3sRuFlXAlnRMjNPCbsEQ81LJ7mzr/ZKGdXbwbMhD3MDNURv2Sg6nk14REWkRLCCb5RT8KAyCpTlJtKOYiHigwLI69+J9jVc/ERD7YL//8hKue6XsPxDqE+VG6F7ngo/Ht4Nr5xIcpKDcF0/E/q7oY/TXziY+c1Otwdlr8ogOcSbKkniYXqmtrDB3804HpTjQ9zgvUJr0K5wbcb1Umk1XuD5mHh8W5gPcHwTTKK7u84yeRALXFmmRNYQIfez0Pd4A0pJbDVuQdWAo1Vg0tBGZlWKOt6DBZI0XAnFgxGzS4QHXdqOPQgPRXGUtcHP1blU4zei6JKYxkuUhq2GSixTqoQN6SS3aHsna4qK1u0VHA8tYA83ieGT60P2EhBBtEt73VGL9OjhgQUtgmxvsXhhiBXE7VbDd3YaX889fk8lrFbUZSgunNrDhLtW5+Wy87Ib5104bcbLbmyEi97TVnneryQzFpxkxGcMw+08aH7pBlWI4q6E6ZWPoKyIaTD/incm56sljN4ujdh2d29I3Xi4m2hduFwuLCl+53iMwcfuFhfNjVThrlEs1HEaksqr4wOjTZahKbv93O7BuS1lom6dtvcwo845ukSiMDlRokUl9L/0r9DQr2UIFRLX9YIelcNyQFwxdpIW5kNGdeJ6qfRdkJ64W5R1H8FCEj9c79FquQ4oRSL7khpkec1xWFiEb789IM34+HnF3Hl8PHI4Fo73zl6Fp0+V60Xoe8VMeXyceXjMHA9w9wbu3zaSNpblwDTH6t9dWaUiHaYpsKmw3QyGhuY8PIuChTRNib0GHyVYbQO4V0WyQU9sN0UH0Q4XEUoytt75dL7Qjnf8sESi2b7Dp1VYPzv9vpMOEhu74aLfREjmaHF2j0xL1ZsCp8VhARhREpH5IZgn0Ftka1wychslQjwJHsZil7XTh9cOKeh4yGBjeXhNTnPCJCphEeF+nhB3avUYMSQYQ73G11u3gEw+XZ3T6rzsxm5OlUQz+LxtPNeVYo0FZdaRrSOxEp1SiNNlXOSlTCRdqV1u/V50R8Md71a1uzt95JkQ52YcAEAN7WsoZbRyqT3SsS3Rex1LSgmFyw2mkT5+hljWqenwTI621H1ckANvfY2apwdvtgnry06ZMtM8DQFCf9WRImGBedPz4jK4Tja2w7/ycN7fHVnX08Bhncd3Tm8Te4uWytxZFjBRJhJrn8A6F3eSOZXKpDlYhxobQWk5Mg4HE6RbKMy3k/NePnN3KDw83LHvjSkr12fjQ5l5Pl05fbhySIXj/cT9m8TxQSkH5+7OOBzhOCs5TczHTsrhYYMYe5si0nytsYnblG3XiAhvznqOfA4pOaAfG2G2xCgjaswpSPppmWgunPY2rBUL5o1FMyXH+v3TywtJ7/nhcESLUGvl5er8uTX2mvjtsTNnJSco0pkTFMvU3clThxSsKHPBxHD1yIlUQZNjaw+sre2ReSoz3iObJEYRRy0OSduM9drIx4IlJ6tgGi+Le8dG+y7doqV3jZsfYe+da6/gnQmlXQK/fLl09s340jrvzxEAFU70mb0bf9lWni9nFoNDUo4aM/stxGjOMbv33lkDJKJocI/dBCmKEsu84JpFN4Dc0EvBuKHSYwyQHnO2RuDt87WGMFpB5QyeUKZXJ4zuY4QZGDbebrResg/XA2J2NInlIwRTDlosdiShBJe3GxyXjM6d/bRxS0oLekZoRH3MqcF8ioUevxbnXOZMljgoZo3aCx2njlmjVWealXoNk6PjdI/4xsv6zLpC0hK0LwsqSRKF1DGvARzb6ziPtEQ7J04Gx7vKw10M8J9fKu+fdpI33tzf8/A22tecYLnbWY5wWJzHR+G7HwpfPjWShjOCaiRLtTUsVHBnX6ONbCMqoFbwnqONFGN3Zdsrby2T1Zg1UyRa3pKVuQidgglcW1S31JVOxXvAHt4bf/vUOB0rv7m/42EupN5ZW+Xnl7CwPObGkoQlC8ciPEzG7ASskJw8aVSFPl7qbJAibTm8NhvbFEuxdV+5boHtlnQbv/rrC344TjBpAPDJx8LDyaVAD0zXWoJuJIx12yIefvCq3ZTTFhrRdeucq3HdjM8rnK8xH2rOnMz56bzyeX1hEeUoIdpWCUXSVJTWjUPObNYivqIL1kPCl3NBWiSq99SQauBKcgnbSTG862gXd0xkEFaC1TXOIs2CHzxpYl9XjvffkqfGtlWKzgBs2ynm2NF6KnDLsmxiSBtvpgrcdJ7Y6x7CxzVhFnTGRERFeo2Nt0gOtOLW2o6ZPf6+jj/49ze1/+HhbBU0T+Txw11OgWVhyrQUtq1x3cMy42axUaaJec5stSIWM2Z03YNEr4IwM1a5AdACkpTulWyKygRSqN3Y+wtg3B0Sd4/K4RHKsZOlc3wI/FHFWA4R9WZdKHPDG5xfejgHSiNPcdvDHDevBMGe/Sb98eFUEJBCIUKUsHBiKFP4rmaFxwXQjK0ETU6EqUrMQEPMW71zOr1A68jbOx6nzEEmpFZO10zNiWtR5h4znDvM5kxu3JNxCQpg0nBcUIfuldpC8RJx587L2alr8JxTimxMJQ6pipFLCcGzG1nAqyNDbLBu68gsBSyBOc0MamNvYaiVktKase9BKDitjZfNea5wMcVKInniadv4eb/ysq3MLtynxJtSWHLENxSF1jspF1SdVsNoa9wXJHHKlMgt+LuRYznohoMxpklpEm7ty+GeZnss88aCR3TgjATjaCqQy8TdMYdLvVZKibFIbY7t86hcN28gH+FdoySHDG6EDYUuV4a5mL9CeQAV4bLVESMfRcdiv8XXSZWxAOqvYpJ/v27+B4czIdQa2JBq4GNukQLm60738OZMGuqB3gWXzDyVWImPAf51KJAByqvi1LHxS6MXd1QS3eF6XrmeN6pHLsi7x0LJX+VYZXbujso0xQxQpgjB+fShcT2HDWVdjetpkKkl5GiJEvELe9yIzaC3QWi9tRhm9NqYNW58J/SlJQlzFhaFKQskp411/OYgvQW9q/dBZ3N2jPP1zE/q+OMD3yThmIMwfjGjVqEN+8q9NZYckEl1Y6nBY0UabRcOJQVBQUL/Gbijjw4g3oexTwyGWjeSEiQEVXYdW83meHOSWPx/Hhvg2nqQz3v47m5bbG9RYavBc35eO08brBZ0tyTK1Y0PlwsfrhfcOvcpcZcm3k6Zu5RDFJDicLXWuV/CHYINHCOlEsbYe2evNSxWRPCR52p0InMx/IBuhAMjeLFKATxgKcI+RTWq8TqWWqfLiiShdmHfVlqrYTb3FYgZc+7QpBJdSkoJay3ambG8ieXmqKAy2msJcfk2TMNU03Dnl/Fz2y/QCkOHoD4WXL+yrdWcyWMNXUWR0mltx5nYq1GtsbOjTchuUAKrm3NB00a/YW1DmiOjWnqOEByTcDxXHwO2RhLV9bpH3MDS2XvgZbnEQJ40lAoiFnPZAMfPL51WE/vW6efEVBLbXtmbBu4nGsbONRYCt3aDEXwjQ5kQvqY2JoWIGs8iFA8+76JGzoJn2MzZPdH2Fi2nC4cRJX9ljw2fK9t+5W9foM+ZH5eZOSdMherD1cCDJL155FqurTOnxDzDnGC/Ql2i1e0yRMQ9/Hi6ywDBx59p4JmGUIxQlQhYcr6sO4XYyNYaRP+kNiiEcUlcLit1D6FwN+G0V66103vhWuMiaipUh3Nr/OnlhZf1yqKFx7TwUDLLlLjLAs0pKZGH4/ySlcclcd4r01Ro+/DcUcU3CasaIw6MAwTU5CJhvN3jz90iUDm2zXGxWo2ZVPME45Bad5r0oH6O4lK3PmiZYRtabzKq0R5HTF+Isa3eDs5AFG6j563eyBBnmw1oKhwT/XUuDfxUJRzpb3NrQCy3Lda//8/fPZxbT6h20qQ0rxRgmgr7FsyJlBxqBAeRM0lTpD9LHg9jJE8TD9wsfhmrlcsOh8MC7PFsyohbaBtY3DzFHEtBUChzbAqvF/AkLJOGtUiLrde6hsHy+kXYgbtDpu2xrXx5cZbFEMucz8GzKZOiyZiyxk0aFHWgMC8CK6hGovGAkJEU+sZgexj3i7J2Yd/2se6PJcMkAmSSR0RtmKFf+Wl3rvvCbw53fPswMzHiB1C2auSu5B0yTkmxNHpzlzmWECkXnOphs5JvRNDxPmuP9fxtZkVi4bXXMUN1D1eNHkylANIDZnKLiIcvWyNdLITPhNveaRNW08AjRako12b8vK28P79wbZWHtPBNmribEndLjiS51gMeK5lChyTkSbiblKspvgWOWj26n1rr4JqmAdUOfFBlpKu32JjKzd7GX5dD6XjE25W6N+YJbO8DrQjrltoaqWWETkqMIKJ4eD6IGGGtMtrN8b399fSMGfJ2sl7PVbzT4tERCiksWq2OZiwsbNAb2WB8bY2OS/+O2/t/eDibNeh1eMRkFOWwOMe7kFpN+ch+7exu7N2YSmIuwnmNmHFPEfUaKoYwHfbuLG/e8jAJ6/klSADdsb3FQ7BEOsaaf19BNHNyo1wqTBl3ZT6GC3jfOybRFp2fZehHE7U1zp8qKQUUsO3C9Uuh9y3MwYC61XjUFjfzDc9692bm27cH9j9/Qe5BBjEh5wDQRcKzVrszTc6hwjYXeomk5RXhsl85euKQZ64YGWf1HXfl02njw3blH+o9v39zx0PJtL2zS2LdGAR8EInouMtWeTgIdwdnGq50SYK+l3sc/uAoxEIIYJqMJQV1L1wdoOYg8ls3rlZHKpdDrexr5/nifLpUjlpoLXF1oxFbWJcC0lnd+bJvvL+ufD6HK8Y3ZeExTdyXwpvjwpJifpRZ8NZJNKYSQoV5moazYLTlZj08fFxZZCbrFhYiAuSCDLtNhqWHYqRS0A7uUZncG+ITUEB2qKBppveG9xqVlyCgZ4klIWq4FTpBr5MYVEdHJsMnybgZffnt4r7xYIe8LNrb9HrsGGyjV6w2xVKPwT6K/cvA+weF0P99JOU/2tZC0gXNmTJHxkhKxg/fH8MKQjKXS+O6VqYp+vHtKlzWC0ka4gXTEEpjoXE7tc4394VyyHz48JlFC5L64LoKdW8UlmgH0srhMeFMfHmuTN8qh+HUbl04nQnVw9nZd2fdOtY82EIWkQh7NfLyiNoO5MAscxg7iQe9DQm4JCmctzPrh8o/SuB8iXC7n0toL0nhO3TIhWqNQ2msJZTv4jm2oPkubCC3yiEXVCrqid5g1Yatib9tZ76cd757vOPtnCk63PZCG0K3ztphM7g2mK5GKcZhzpRklNTJeWwUzF/jF1o3pkW4m8Kw6MbGaXu4mEff67ysK21tFFG2HsFL5sJTq1HpJaL1TGIe/Vgbny4rL61htsamWRce55lJM4dJmTMsKXSWvUXo8VwiRHYpBVT4tBHC7JSopNDwkqhc6QT/1D0FhZCE9rCgxJymhrRORMonIhi3sV++RB3VmW3bR4BxqEGEiJPHlUa8O0USlTbE3eFoEL6zgIShXKwwb1GGv3BKcB846XAa0pDLQbCLwjisfT11Ot6z4Bp9/Rp+IzL8ysP5/W/u2GtHJTHdTyCd46Qc3xZeXjqfPp54etp4ebliRTkcJlp13j9dsJ4GfiQDkIqBfsmFT399Au0cpGB+226F610Woe0VwclTocxKM6f0NDoF4XRqiGRaCzuPvim1dvat0rqxbU4iXPqmpbDcrzzcRbiNdRkODx461BQto3u4ym3nuPTcxr9fh35nKkqWmElMlKXAoSh1MuolYspTzux01KCl6AqypNhQJ2MWYcdYLTI0/vz5Cz/lzP088TjPvDksHIpRiLaymdKB0+bI7jxf9/jZNQ6dprj5syaWEr/XfoVr65FKbsNOcqg3WkB0tOrUapQ8xOtxQ2GESdgOXByerlee1o1TM+iVWYVjmiiiPEwTb44LE3C3wF0W5gxdOweN9n8pzjwF8aExqIiMpUmLKHgzZ+0tlkEe682kBW8r3fWWuIm40nssY+JADQhfM/GbBzzjo2llyMnCcVFHdVSaDAhE5bW9j7Hzxo4aqIIEjDJWE6Mq8gtmzxCCp2AA6W0YHV/nhjn7+FFe22T/enb/TuH8+4fz88eKWay203OlLMKzKH/964okpfXE3gufTyd+fv+ZxzePTIfE6bLHZlUStfXhE6txI7rjFhSuIL8F79BMqGtsxlQSklrkZK7C8S6RpWHAundky+MOEmoz2mbUPaplmYRvvilhGj1FtEEqleMhYJLenHoNcnSZQtngHtSu3qBZwtsVJ7M253EathOEcdmiShsSLMGZi/JmKWy7ha1H+ppunVVZhyC6qOJqWINpcrJbqDosIvM+n6MyHa4r3y4L94fEISeyxtbvBl93j42qa8ixeh9G3UmY8hS8ZXfcQ0QtDLkTMVsmTWSPhZHkRJrANWCTzeC0dU7dOO0bp7qHU7kbd3lm0sIhR+TDVJSlZLLCXUk8zsKSnZIcJPI3tXmYWE1pvJhxieSSRqxDzMjmxpwymcZO4A+t19j+e2SfMsy3BoIRUq8+iAbEZ8FwUJ8PB+hCW3dav4ImZHCm/YbgJR3vpAwahA9tqI5ljQ8QYSAODKcEABNEjAjBSXGIg0c43ntGC+xDLSNjyBxsIQKf9rEw+lWHM3iz4fFSdwPJPLw9opMxzZEg9fT5yp/+8jNv3jzyD//4Ayrw1w18MtZ143CnrGsDD42hWyhRGGEyooH7BAk4No+Sw+OW3lm/dFrveMv0kljuoTRYzThdVmqrHPPMPCeWu8Tdg3J8DDVJSs5ydEoqLIuxbcbpeeScIGxXgnmzCyoR4Fq3hkoJLHS44y1TRkXHgUu08UxyajFTJeF+yVxqWExqymEfqcO7tMfBymTyVNhbI7lRtYfOUnxgunVoInfSWZlSZkkTSy7ksfzJaWRdBgH3lTidHKyGXtPccBeKBtOpGhGFJ/HCxbInMOC6GxvO81a57jHz9ZSQaiSEQ0pMOjGnxOTCsUwcphwzeBEe5sxjUZYUfqwlB8TSW4+sG4lnCAK/dIYfPkDWerTbA1fEAOv4HtabKnkIBAJHvtUa8z5wyGHS5U6ZJrr3SGRfElqEUgk/ZGlxoMbyplsk4amkEcR0wyADOQC4keudr0uoKNFjkURDLNCAG2oYP5u/LkHldrA9YEjGPgECSvp7//zdw/lyPVMdSslkU67nxnU7c//mGJKh65VPnz5wOW+8+80b6h5tzB/+8R1394WnT5/ZLsbbb+94Pl14/7czz89nIIDp207avKFpHjNbJy+Z/WJ4CtyonUCkcV4bl2vi/U8bD4/CcUo8zDMPb2aO98LDO0HyxjSHpywm3N8p82KcvwiXF7h5yp7P0Hq46rmFXUlwP+GwLJQ1KlbJkWqdcrw8JuNre9gazjn8WZcWicurxXYXDaPmwzxTDdZWkRbRcPMyMXWLZGkJnmwtlS1ndut477TWuLTKcz+TiWVOvI0xmyqhzMkDS8xaKCrDFjKWLSDMWuLAqWIaXOfedrba2cMPNBz6IeiEGabeyCkzpcKsiSIhSrhLmWUqaBpGzbNyP8MkQQtcSmLKofZIGjK/rLFhNQs8tVlU+qKwj+1lLGacJBnkdjBirLh9Jv22uGPUSbOgVhLUOBGl2Y53ZT9dSYsyTROaCvu2j4lvQC/OgPdCgibcIJLBBhKGy0NYiQTPYLSwEPCgKGIFE0UlRP83mdkwdwpGxOvfIWbk2Abxb/iHv+Zwar/irfKyOZMeKGlmPwl1X9nazum0cz494x3W88bj8ZEf/2Hm7ZsI8rm/f8Mf//mZ3/5h5gcKb9/NvP/pjg8fL1wuZ7ChY38EAAAgAElEQVQUXrQyhU0EFZHEdqqYxMIj54SkStHM+exBpZNgxCyHhcPbxOHOuXvjPHyjpLQw6R7sDCu0zThfldOL4XuiLFCrDYW8Bmapw+e0K7ZV2qXCoHmZhXeNKKx9Z9HMlOIh96QULWx7DwMnIEuoabY6qlURsgm5dkRj44hm7suIom9hCtVJbNbZLNGGdGnFqINh0jvjJU5YNZrBuTfcdyQZrkGo77ZDz0HkV0NSGWZWDVRePZSSA11I2ZkH+6nkxERilhJwyJjp5pJZwnIh2mdNTBMc50RJwQws6mQM9dFh5InFifRnhdU6dHvFnYvqeDcjwBZpoUXtQ8mkjBe94ZJecWkf+Zx6cyfga2gRm2LJkd7QvXDdrtSxmMrqry4XcaBnetsCqrnxqL0HHKKG9k4uGSsT13UlMy6ZHqhCyMAc8UgZC/qgvVZYt69+tgPe/4pzjgMOGeTfP4J/93AeloK2RKmV7leabRjKdlGq71wuO60FvrO+vHC5W0g4+8n49LRzvXaOh4VPPzVMnGme+If/tPDb3x/4y78k1lW4XFf2jaG1u+nlgk9preIy0fZGxzgcQomyrhsuRk7CVODw2Hl4TMwpbBz3JqgvUX2ujdMKl09DXTF5JGl1GzPqFK2Lha7wZuh1mAtzTnSExrC3RBHJdNsDX0sZ1Wh5FiustQbU0SN3JN/EtwP3LEk4SgkvI3cOpTAni8rWIo17YSRFW+dOlG6N1ju7ONVD0lSxX9iWZqBhLRZqYhnriZxsdA+xHilpGjxQDapcDgpgtiCdL6VQJFNKIg+n8pwzc9HRUg84IYGIcTcpSyYSthwyXz2BPBvJQiKmKXSRSUJKdjscA8wMNpPYcEhPo0oNVc6YmRkzf1S+jg8hdjB64l11i3lTPZQv3Ty4wh7YtFm0l7gMCmkbl1RAZZ5iGSXD68c0UYc5dfjP2lDmBKOte4uaOPi9NpZFCK+sNxHFJfNalF/b8vi3i+PtVwYZPT4ULpeO5URtlT5YI+W4sNfMy6c2PnxwbWzXxqePnSk3/vrXMx/eX5iPE2XK1F2YZ+H3/3jPd9/PLMs3tJrZ6s7f/vTC58/rmE0TfnvwprStoRRkqUyzsCwTD/eJve5klPXkXIvSL1BXXjNRppKou7BtMV+23SnZEQ2aGRKXj+1GOUjEyA2Sd2Z8jre12mhT4oOBnPLAwKIlzqqIxowrzemjo9GUyB4VM0nMRwo8lDJiEEBIzIQAu1lE43mD5kI1wXLMrxWovVOtsaY9QoBzBBCZRQaUCsNkKgzWsgZXdNbYD4jMiMMEgV0TC6ecE1MqlJt+dJiRlRSuCy6OmQ7UppE00tesNbLG3qBIaFVziap4ywcRCb5u7SPA1/xVJiXidI9FXnLI2aDdKuogexALF3t1tY1q5O6kPLJv3CGCHF7tcHqtrxvY3m7rxzG39rGIVMUJphoWtiSMrS4elp7eBzSDEVKCmNsjDqRxGOZz6xpjzSD/BdNskBVEhs/WjZOLvK5r7e9w3/9+PqdBzs5u4Wv6+LjQe6Za59OXjdoCMDcJjuhhSXz6tHG97GxrCIKvX74M7Ed5eLzjz//6zOfPE2/fHXnzTnhbJrK/YT7MvP/pEy8vOzlFdoggSG6oZPLsoJ3LeolMS0+c1hW/Fj5/7kw583j/lrZ10pSRHEbTD487OYWXat2M62nnem3YazaLkKYcUAPByUyWQiWvvNpQuAwqXA8jLPH4+91tVFZjKuHJk81fMcecU1QWDXF1bkIBlkPGiECijof636NdVYdmQdZuzeiDx2s50T2x9wgBahYuBiZjFlZlyYVb0I+SyBptqJSAino3sgcNMkuilMwUgSjhkSRQBHIOsvduHdewrNxqGGjNA65o5pEBOhQncxFSFro7kkJE3Xobs52wG7jHhSIW/3+UG+M+Fw7a+WyBedOHwFkTpkOc4EMkMiCQ3kec3yC43gTk3mM7c+PthB9Q9Bl9zJ23IKPIYYnDYox9ArxSL1VGVGV2UpdROILiM6XCuzdvaa1yve6xKPII3Q0Drz66LR182ijzXyfRqMa/6nC+/2gs0y2rsGCWqdb4059/4uM5YFq7rbeBeZk4LDM//PYRJMr+x/cX/vqnL2xb43KurPvG+Wnn0/uVN+8K9/PEVDLffXdgX48s84TjfP6y4h4UwTTkUt0yT08VHKZitLbx8Djz7ZsH3nx7x29+fMvThwtt23nzEKlnzaHXhkhnO9V4gB3Oa6VfNtZ9onaLVC23kccS8epZh4O7hAOAY6RcfrFxi/Dd6x7zkthYOhCzquY8NnjxIUyjukh3kvYxf8YiSnKim6Ajwj0bFDMsx8KoWczZ7o75hHnQ84xIwxaPyIVMVM6S87CUlCBPjHXvLf1rUkgpMS+FqYQOMpr32DC7MSxbYgSoTegezyTc5wIiyxot/FKEKckrfqjDSuQGQ3Qz9ls6l4QUzFrgwUWULgwn9vCXdR9UT9/HEYv21hEY9iJOfF63QuQCbv0VZovYwLBTga8RG4JiHsL1WJzqoNjF32mjI0o6PJ4kRCBx4J2Quyfu7ybIxuWyc0sNvLmvJE3Mh/Kq6Lkx9W6HXV778V95OKe7I3lS3MLcau07T88XPn8JuAEdWycHTfFTvf32wB/+2xQubp75zW8Wlpy5rjt//PNH9kv41bCeeNkS98cHHpbCYYkH8Y//8I7LeUPE+fy0UlfDso7lygikrY267rx7c88//efv+fF373CHUhrfvik8fbqwrs6+VT4/nblW5+XzyrpeYguZD9wf4oM7n8+s+zNzyRymicf5Du0S0fHjQStKorCo0a2iTAykkCTKIUfOB6a01uLF7rHEiC4pUQSKO3MWfHfq0CiqxVbTETYN/aUKkSvahX3v0IM03pujMoPYaLUC6go6WbSb8YErc45rontUunCai/3ADXvMBM7Y802fEWFPalDFaKJsPcyow6DAKTkw5iULSWGaMveTMqfYqooqzT022A7dozvYh5t/bEeCdeXiaHLUM97rWMzJcCb4RVXrMlpb/cX8Fi1uKoVWK9J9YIujRPqofuPd9+6UaQ5CvBtpmsiirJedpEaSTPfO2ncmG25XOeZiUxtVM3jZ5rE8y1nD2HrfRyUUyjQxlZm3hwUtwtP5OUzCRtWM2VReZ9W/98/fPZz/5X/4lsPbzOm5sp8r73++cDrthIV75yZ5cZxluqO1Tsqdt28LSwqt4yE564+F5srvf/89//zPKx8/v3A9Z6Qrp37i5dm4PzxwPB64VKPMmW/ePpKz8NP7E9saDn6zV7CYke4fDvzT77/jh+/uyRjXlzPzvfPp/c6nj9doRUj86afPvKwrSwkA/aEcefPmbYDlbNwtmZ8+f+FycVicBw1YIKhVt+YjIuEjVEVuLhpDnqQcSmZdd/bkzIeC5CAWhNFTyJuSRGDupJl0gEu1G+QGA5Khd9Jy61QUzY7rAdv2AV9EG6hJOOSYX5MwDKj09TJZkjOnyIzZWxz+TRLrHlVlnlIoWlpYLasFH1Rk5INqSATb3mgNjGiDk2YisxTmpMwlcz8njnlwUm/7V/F4VH6r9AymVRD3p5TAYtMrrY8Wsw6y+ah7qmiJxO/exiLF43lifbzkYRczaAPgIScLlk9Q5twd1Uy+v0ME+mWlb7DcF1JyLmuDG5fWwmSt3DbBvb8aTd+yU/CIsBfi4nQCC80KLkopiTcPUxDpaWEng/7buVNia+63yvZrDufDOyXfZV5eNp5fKu8/PHM+X8G/5iXGkqCTinO5XvnyOfH0t4ZKY55mpnzA287Tx53Hd5nf/W7h+JDZ18pf/vxM8pmtb5zXlTwVnl527g+ZJMJxOvL9O1grvJw2rIXXzPE+87sfHpnnxNP7M2lyvBpfvjj/+pdnTqc6bAzjRfjDN7+haMALd4cDj28mRBreFJWMUqisaMl0i01xTunWTA35VGS6lCk2tLfcjD7i13NJzOKwh3Rprk6rsXkNJszIuJbYbh6mTNfwM2qtxeImKZNGPmg1ZWuxXEu5xDMWfW2Ds+ogU4SH7KKxYXU3sihJYBZ4KxlNsJqztQDcYaSaDbsQccayJlrqDmy1srWB53lsXufRyi4jvm5KULyHa7v8W7ZNvF2KbT3m3qxcNTjEAuwWbaWqosO1wcb3c4moerGg3iGxqTV36GH8JWO0qHvEFd6Yot5vh1W5JVVHSnql1g0sxpbTy0tUzJRwi4tl1F2aOF2D7n7L2PRR8V1GFbfG5dqZlmUEGcececwL3x0fMZyPL08RiTEOfzj6hbeia1xlv5pbq0XZTp1+FerFWNdwu3MGsRjAYnt5vWyoJGpdeP+3naePV775zvin/7xQ5kSeJ07PgQ39+JuJu3vhMGUue+fLk3A6bTx/+UJ1OE6Zu4NySHOkPds+BvUgTzshfXp6uoY95r6BOV9OjcvaYv2fhwohx2C+9TpcxWP8WveN62Xl5cvK+bLGC5/CzCtvPji/JVg2wlAROL1HNgy3qif+uhzPKT5YzJhHZbtskVpm6pTBzU3j5VLpWIoqYV3IJW7YkoSpBMbbLZEslBJxG3ukVokyTYEpuhnq4UWUh+1iLCLCSMuBgiFF2MfvUqb0SmnpCL7HiGIeHQHcFGk9aItJWNSYExyTsmhiyUK+Vc0WSzN0uEx4QFOCkMVQ6+EkMSvnLdrk1w60dw4lcTfP7Htj0YAsfMAroWy6DXWM+IqxVWU4E0jkuoR8K954VRn0U8Na+3r8BDIRvYHFPBmf4YghjJ55eAfDzf8kuuUhuJahL7XEVMLlAU88HGeOU3iH9B4mAK47D48Hkh7pDq3vrFtFrdJ+LZSStHD/7sCnnzdeTlfWffAUb5HdN4BegD7RrPOXD58ReeDL04k//+ULf/zjR5opZhPLUvDaOV+Eb76feffNwjcC3393x8cPVz58OJG6U2vnc618YYsIhV7ZayJpC9v7Cv+6xQau7ZGSZd2YcuFYEtPiPBwXxDqnbcMtLFNady5pZfvpyunlzGWrtK4h5k4W7UbvZE90d6o3RBOlRExc7TEFtR6bzZzj8WWJgCK8k6eEVB+K+sZUJryONnWQtROhgOnEKr1IpkoQ0NMIqzUJC4/mFvmP+ot5RYNTG3xfxuxbIvA3xWWhSpAM/OvSDrNoKbVHFUhRMW9sNSRevOASe8Q9WlxmM41ZnWMO9UnJ/roQ0pE7iShoCoGVOnXvQ8ER7faskRdlMpK8Xs9RtM06QqXEolq7GZYmFEPG7+s3Cp4Pto/Fs76xhYybXjuollgdLS5DtuW80nNuLarHkkyUXySWxch2A1KdwKvdBfGM45RbYl5ryNaZy8zDMnPdG0/bhW3fadZ4fDjwX/7xR86XjmjieF8QNf7lX37iy9Pl1x3OrXbcN16e43Due5RoI27fPM2kVGjbDhrsnro1fn7/QvLCujf+9Ocr+16pxEbubsrcLQe25vz4Q0Yl8e7tzNu3me++T/ztJ+Pl85Xn05W9MTxWY/NoY/Zo7qzbNrA7ONxnRDq/ffdIEeHn04U5z7GmThPbXtGknK87Xy4r1rYIf5XCup9foZ66dZrviMzMGuwhG5dFEmFvN4tDHXhn+Kx6czTHn82TU2u0L3dWYl4TDz9TiQVOF0gZZs2EGkexrPQc7ZdHl0QXmFzRSQZX18dLVGi9URIkjwbuNWTWI4wo6SBkD6PoUhI61Ct0Zes9/FuDoho2ohagfB+SpkT49RaJFyWwyEwpcEgS+taxbSzDq2g0krQeboWqcaFFilhI+CQLdEVa4IEy4hi2Vske9Ec3XqtgCKJt4Cix2FK4+XFhDlOZMXHatqJeYucwQnTDbeWX8EoaaQgt2mVCQhjzaRkugBDbBid5uBv4IMYzZuk8H3GzgLDuH1ALq5JK48vpyrY32tb4T3/4Df/z//iG//3//Mx6OfHNj9+Qc+Lu4cjd8c2vO5w//njPy9OFjx+/cL6eSCzh6dn6mFeEuu30WlkWofx/nL1Lj2RJlt/3O/a497p7hEdkZGZVV3dX90zPUJwhwSE5XFAraSEB+gDa6lvpGwgQtNBKG0LQRhCghSAtKGJEEiBnuqdquqqr8hUZEf6495rZOVoc88heSE0pC0igkMgIj3C/Znbs/xy8V3KTd+SwJY8L8/FMsJXUVj91FuPj+cCbxwcsvmS3G3g6rdxcZV6+cOnd49UNHz5kfvjxoxcPxR4aYj4Cmicx84yAoFztRoYhY2vlJ3c3WDPOy8LxvPB4nGltxHRlivByf8c4DhgQX73kxx/e0rQnMJQGo+tEx+yj45gSOSeaLKxFyUlAzFPje3krzyebMg3+oC+rN4ilZEiVZ4WNm9iFIQZiiMRuLYs5EpNPJaVTM6rNax5SoknrxHggSnJ1Dp4LnLq1DYNqcCn+JbojJnQ6w0di8U6Vyx2x+sZhYi4CR5/jQMYgbJKPc0GMQTw9b0ypiwhcnvfcsCW+qARc5oijtaUqTZ0W8qnCAbcYhVwTa62dEuKTyDxAlNJ5wUsCu/Ovz+Q+fgcsi4+Hkd5DaUYKE4YxZmVeDW1GFBeStOiVfq11WkUuVwGBblJz0YljCp7B73ytd4Y2llIIKNvtNTdXe5b5zKkW2qVOsTuUxiHwyz+649//ZuG771b+7jf3/PbdW15cbfn5F1983uL8/m8/8HffvOft23vm1TNxYhyZ4kjRlVpWxFyCVUqgPa7sdplxuyNL5no/Er+49o7M08zpdKIU5bye2HPFd98+EkfvJAkx8cXLK17ejWyngN5eUYvycDhS5j7O0T84rah44HOxxqowRS/bGTcDavD24yOneeU8O7KXZOH1iz3T4KkOeRyYpsEbvK4n5sXIQ+6WHs/pETG2Y2AaL7lFGVMXH6yl34HsEvrUwfQLTyqeP2skNPjxVGpDYmRMl2uB83m1ec+G4hrR2H2tjYi1rsbBMEm0oO6oCG7ZkmejtEvXOkqPidcz5uCi8NIUjYEh9Qr5ppTqiyNcTsoovSHOR84hwxA8dkXETdSbQfCUnkoFJMVPjg5xofuiXfAYPW6mlcaqjUK3CqrSmvXqwB5NKc5rxuAhW9pPSAueHyTw3EDdKWaeOy/lQuzLc5eq85N+iW6SiDlhoaFqjDmhdfbXTaFr7T036NLJKin4AYC/oHUhgSPAq4NMurIZrxnTxq9bITHPXoZbYjd7C3z7uzP364a//EevuBqNN++PHJ8m7q42vL7+TJ7zX/4f3/D2bW8OazAMnopQjiutdrI5Xqxg3uGYxw1KIG4jm6vA9joREJZD4OnBFS+nsiGQqMuGp9OBea4s5cT5DN+/PbAdBJHM2hoxRXRwEreWgNbKZjfy5e01789veDns2E8DpuJj63HmMK8cn2YPDgvCtIn85OW2j8YNkcQ0eGfH/cOBqm7dauaLQzs5PSU/faI0H8skE7qQWdRrChQjJ3+d1S4dk4GYYUPgcbUuGPfaCNFGIPcPzuvj0OaRnBFGHMU0wXmS5Gbjde02q+jcZI7i9EzKXWmj5Jz6Fa4x5NA7UHyaoUHFOUwreIKi9dFRBWuVKG4zW4MyhU+bTjVjGjw5cJsjKXb3hfDM1Wnwe6OaS/0u8rXVjBK8wkNXzyGqQG3VwRqlLxohS0atEOUSwObTkeFxmH33878zcKEvfYICk0YIGRqoqIvuxbOBgrhSS+lh0ObKL3Kfi7uxXvqhfaEjPLYkIFq9uk8vNkcwza4Ljn5XneeFp+OZlAaWVtDSyFPgfD7zN3/7nn/+j+/4yasNf/Vv32La2E6Rucfl/P9enD/8+EArSqtd+UCkrD5mpDz4iIu5PApvHzZRmlbWKrx73xgPwusvJr766YZXd8LxYITJOD0p794q+7svUWt8vP/Imx+eOM/KKRiQMHVQ3EMkPBYxJmMYhasJJG8Y88g8G8d54fB0crI7GEPM7DcjISubzUAy4TyvbKcdQw5oMw6nA+f1wPF0JorircMQJPdeC8ceUnIUd+6aTN+tjab9Lgl0ZMaldOqLVJKRFaQFhMo2Rhe2V30uvh2io7TWyfoQ3LBsAVoI3d/o4gWC27GCuvrG22vUDcz9dBMzN3yLL8xmhlXrjie/z4kaQVw547Ed3eNqeB1cvz+rGbX6ZLSLwnYQUrRniVsQB5Vc3HAhIugjovOopTnPKFFZ6kJTj5ZRcwBI+GR4d8DmEqYlwEVUUTt1gndy1sanNdQ3kc4a0MPJDNgMA00r86mC6HPKwrKuLsqXyLQV5iftxVv+vUR+r2BIeGYKqha/I4fwPDHNpTFkb0KrbQVTjsdzxyDczlbOhf/5X/wVv/r6P+U//ie/QocrajPe//jgKO/nLM7lVGhEJ4QlegxIrWz3LjhopZJjpuFcVFsr61KY5cz94wePjBTh8eM19etrXt1lpl3m6mZgGk+kOFEM0ph4dfea+aCsrXJ8OvHcYyECrT6joZtNIg2VeZ0pLfG7D4+EllmXhSElXu4npjGSkzHG5MZbhNZWdpuB43Li4WRIcCtQOc+kPJFzQ1RJsY+mAkOKRHrQdIzkoLCYVwymSM5Ql9Wxv6o0lJAiechYUNpSSSm4G7479gOJlKFZIydPFIgYpTZCj7pMXQQoPRXOvYs+rg3JmMRrH3L2pHqt7Zkvs85L1tZPbPHbkkjovjN33XjsjYsCsgSKiAdKmyI43+n5tzANgdspEZORunFa+71QJBBzeAaemvnfYb1AqVNHtTpUNJeOenegxrGsAFRydH7W+knoCLefdpeFYlp9SV7QXnGwy9RPyIq4qECMeS5dtWSIqauk/J3twdDK8eCG6SEnp8pq6Qu+p0aaorWRR49tpUIxWNrK1hKtDpgqxVy0gCohZ9cGa/NNlsCyHvlf/7d/y9V24nY/8as/+ZLXL+787vA5ixONxAASI6zuMB+vtm5BW338KuVE0Mu2Bst5oawzpg7z1wq/++ED3727xxRev9zz6naDWePF3Zbd9cCLfUTXyi//5AvKuvLj9weejgvLslDr2t3wPuqsa0EtcdADWhwoIK1c7yde7Cf2V6O7NGolmfTQrjNmgfunIx8PZ7f+IARNBPMOTBe4uyj78qlbVMwunSXO0TrrFqnmU8IwZEpZHdoPbihOATQKGiIZoFe0r6WQWmW/zR5wvBZf+EMkZj8RArE7izrtkmCQQDWoAV/gAcYkDKa0IJTLnbGbqZ3mjs7TBD/FtKmPhj4vE0ypZpTmapimjkpjoRvhfdGPKbEZ3ScZpXet4qdnDBBCIPaSntaFAmpu6JYQSeJ67CEEkkCzFcPBrAiULrG7mkZuQ0Ie5z6ZBIgdNe0nntMrl5FS6NjQ82mXYvTUDJrfNdVdOdrsE1obgre2+b5AKA4ztctB4DucYykxkofEvMysWtlOifNhYZM3vHix5fBx4bAsiC2MKaAasRifbWCOrvvGNObMv/mrv+X9h5n/4j//R/zDP/uK9PcmQvzMxXmJX8hDwrKQskddlLn6DK8dgu6Rl4P4mxiCt3YNU0JKZT01rsNAJfD05olvfvuBzahMY+Lu7pZf/nzLyxcDL79IfPX6ih++GHjzvrIsjYePR37727fkMKEIrfoOZlpZzbiajF/99AW764kYE7VUavNQZDPlw9OJt08H6ppp2kghc7PZs92OgNDKTB4D53lB6HeOWlhK5rTANpvzW/iJKgmaziyLayvj88PfICTUnILKITEmR3TbLEgMbNOAHhbWJbgTxpykDtb66Oz8mT/0UMUteUEyOXTvI4HSHBy5nIhZhJD8a1IIrKvfg1yD6gvKE9JdCKBasdLQ2tDioFQM4vpdE1b175FFeLH7xKWGTsRL9OQIS4FS9ZmrvBAVtSq0iMTASqV0SuJCJwXRC+nYKRsX9WtXnl3um64x6FYCUyc9RPG2NAV1KqtaQ2guL5ROB+Egnve5+nPpDWvR9dHigeTWqxFac69rCEIWes+sMU4DVVfKslJypGrkJ6+u+PO/+Ip/8d//n1y92iLNo2xm84xcN1r3aSn4aV2rkRj58W9/5L/97+75i3/yK17f3RBz4L/6Lz9jccZpJG1Hj81YC3peL+KLPpLo831cOqIl5nB8SsZ6PmMI+/3ANGTvBNGBPRtCgHk58+a7d/zwZuBqu+HqOmH6glevvVp8PmWSKPdvB+bqxldXbxTu9pmffblntxvIJJZmrLOXGJ3nlfNcOM+Np+NCa5kYAjebLTc3O17ebYkSUW2cDpHD+cC6LNy9vmGXAuHcKNV7YaYMuTsupjERM9jqjWnt0hQQvZQpiPyeDlOdVsJ3+9r8nnm12Xh5UC3klIgSn08EjcqQYvdIfjqJltqIWXoolRfgSuujnan/veD1CUAa8vP0EHMkxUw0X8ytNMLqPHWUSycqHsBsUEqjrMomB262I2NwIf44OL3gd9kI0SeKpr5prdVY1LwcwYTtJoNE2rmgxaM5a22UFlgbIL4J5SQ8FU+9CKJUqx7LYv31uhrqImbvxXz+Pgw4n6muznEaSLrgvQsN+iZgmEv/uuFZWQkhdYGEj/4XxNmCCyCqwsPjk2uj8WtbGBJv3j2y/KuVV1/veXw4QBqo50YWYz9mDmvzU7TTLy7a6Y4diazHwv/yP/4rF1DcZP6b//ozFud4s2U5zz4G9GhFLveX/tJyuRPhGltpME3+IAeuiD2JbZDEJhpN1DNMMc6DMG4Tx3PjeDzx9KHy7vszP/vFhq9/ecvuZWQ+CTf7CT0sveNEef1yz8++3PPiKjOXxtOxMC+Fx+OZnIWPD2eOJ+nDXWLYRvY313zxxRW3NyM3+0TuRPdv/rrw8ORJDYiyLJW1BkZzJHFeGvMAN1eZGKxL3waWpbG0SivGEBIpX9La6Gij71oShWkbKSvo2oh4Zms191Z22wuxGSqBhGLpshCdd5s2gTgIRHnO0Wmus/O7Vre66YXRMRcINPX6eOz3WkEs4HffrqQJPU3Vei9lU65iYLdNbLL6WIz/RwkAACAASURBVJ0dcFqrI5qGUKojotrDkQke5GbBGK8yKQlldtpnWRpL8XTCog6oRfw0d8uXp/gFoU8vziNHjIqDW584k8Alwyek7K6cFvtz+elZfMaTmoFERN1n6S749HzI2LPPsgNa1q15BEJINOthbtn7RGMILE8r7XrLtIu8fVdIRJoEiDANmZ0Ezov6knE1heMSBkEd+X/56sZlhX8g9f0PN1t/fEBiZLe94lgf/EJtPsOLXD5s6w+G9k4SQciEkNkOG26urgni/SMxC2nICHBezlyNETucOMuRL/dXnM8L53Phm28W1OCf/rMvePUq8+NvB8YR1rKwmUZur66oRXjzYebD4QgG59PC4bR67V+Dq6sNd7c7csrkzYaf/uKWIYOYMp9mbr8YWI6eB6OtYivoYenm19QdEp1Ak0vZUZd7BcFKpcwe39moDFMvPuqWJCsGFtysjHTDtYvFS+m5qNHd9q01cgoEk04ZOX1RxRjHQByMPCRat5ZJdb7NH0DxIOqeWhAkUItHcIQmVAFwKqxVexbaeOqDI+FB6B5OZTtlrkYP6lIaMScHo1R64oOPmaa+8YiIt1RXZRdguBrIA8xnpa6eZriWymmtLNUrKy69lK1TcCn0kGhtHvKF9J/P5XKY9FHXF2jop2NZyoXg5BJhKV0F9CnW0lGClAJxdAP+YV5RzazHhWAu2te+jsETL1xo77hKDhcXqG+I4yZzc7vl8emJS6uZYozZpx6tcHM1cZrPnGaPzwwheg0lgArLUpFI34Q/Y3HWtRCHzlldiN5eCR66vrA/wgTp1eAS2GyuaSzstiNRKo+nhe1mIF0FDoczrTTymBiHDZviDWB5HPnJl9c83D/w8Wnl++8eiYPx0+sJDYVlrdDcGXKeV+YzHNaVw/mImJDiBCxEybx8vefv/9lX/OzrK3784cTHh8LHj0dPOv945OHjE7+72/Dz1y8oWjmVwkhiBwwYZq0DKh4raYgnEeDC75CEaYjMp+DQXb5kyfrC1aaMfnS6YN5az9gJjAQCXrFnuANjLdbr5A0ZDE2RHN0kXZeCMbJog55OX5fqIEdwbjT0ILQogaUW6BJDNR91A8LQhJMpwawnOhi1CmtZMXOudpMjU3bJZEMJ4vXpKl4pYdGrB6xnwDYTD9FeCqbGfp9JQ+BhLhyOlbB2HrXb2bxtKxHx06/hR+ggyU+RLvww9d+thi7ulYsw3Udma61XfXSQyC58q+tttY+0QTz9QXJk2I5INGZ1JVY9rYgIWWK/3wdqaV1wbyit04eGWUJDRKShtaIi/PrffU8YPOrGrFEFJPh9Vi1AUoYh+XXFhNIaU3S31dIcXbdqhDF+3uIUiVCVx4cnrKv6g14iAv04Dl3PCa6C2e2vICm7YcPNzZbvf3zPh/dPNPHTdl0rT3PhH/zip+xeC1f7ibu7iePDzO1d5vVXr/nht2d+9/aBv/53j/ywOaHtTG3CEL2m4XG+x+rEeW3sNjuutxtAmcYtu+2Gl1/uuX0xcv9h5v79kXcfZj58fGKdZ98YYiQSGeSJ87l5G1aojAwM+C5easFyphaY10qxiLbsipLiY9mYYZ4rtYjvzH4muNIEI2ojx8AgGWvaNdTu8ghDYFVFV89KOtZKJbGelTEXhjEyTAPR4KCFsUZIPbiq879NoBanf+riiKzH4AiXWoHSUxKsKVbNx9EOBgVTomRv2c6946SJJ9alRA2Q1IuPgiQPwK5+oRlyhBg5zyulFO6uJ6YxcDqvnA9eIpyTywbl7N2nnvSKyy+bK6RydkHGjHfNXPhFjyDpdrHm8ZXhQqVKwmPXpMcf+8mmzTCr3Vrgk06TSiTy+HSGaOxvdtS5ojPuUUqBGgWpa88+cuCJPq5fnEyrrcS+eQiu4JJ68Z8qRmWuke0wsrvKHM8r52rsNyObINQi5DGwLkqdF4YI2yFy/gOesT9sGTP3xbXaCHiwcqP105NOavt4q2K8fv0aIpyOR17cvea0uAd0PhWaNoIkoglXZP7dt99R5DU3+ys2Q+Tlq8xyajTg6mrg5XLHbpz57vuPvLi55fq68fZpZhUhxS0pCvsrlxJutyPjNnNzO9KWCqr85q/f8Xff3nMqM+fTSquJLEIUr0w4nY68qzMPJ2UbhO1GqEEJaUNqDa0e9oX6ffF0amwsQXGx9DgI19cjwxQ5PC4c18IUnwsCXAFDL92N1umOQCDT1uJ5uEkYEqxZCHNglkYsnh5wWivn1V0ky3ElJJc4ao+4EHGpWuyxjq16zq4FoXYxfdTVZ7UWmFmoCFqMZJGcPMEhhNZ3d99vJfgm0wzX9opSmxBoLFWfO20Q4XxeKCgvb/dkM86Hynn2k3c7QG7G46qstVLUUHGiPsb43NKBNXIcKVr8nodHtlhrmEXvKRHtgek+9hMu3kifEByU/H1pjxI6ABCDt4+H5qPf6eOR1tQ1uKZYaYTaKRZxCaNq7v2dnsa4Vke6mxpJPN/KTLvf0/W9BFiX3hgefOrch8wY/DJ9vR9BlXk+sBsGYhaIis2fqRBSmtuHgt+lWqsdT+hvgvgdp2nh6vqWPGXmeWW73aMzvD8+UIvzWuH3UDMDkkb+7pt3vN3es522vNhf8/LlhsPbhSYD43bDOIzcX596eHHk7WEhMKAaePHijt0oPHycETHS4EDQ/f2Rdx8PLHOhlEbrMrUYHalr4ha0IRmrClVXXt1mAkatlUbrPtCGBvfplSrMS+McqwMowUgxkrIwDJHr/cjDqVGKR06OOSAWO3/Ic6RiM69TIAmFhqrn3E7RCAPupJnM01+iu0NoRqjmzdKXRmRcURO63vOCP0K3SZkR8Z/fqS2PFxlSIG8DSZXQs35K8c9RgIqS+0kQunRO+r1v6RlFxEBpzTWk0RHd1jzedF0bDWHIkSEYbfYO0KraR0/1qaGHaUn0n1Xa6oqamPxOK34Xjta/jobigWiGINbcS9n/rakfJNIpL182DsCkXndY+xiuBbegxYJq8l5WM/duRkjNfMwN/ZbZxQuhUzLa1WHB0atnLbbrB9WD5MbGy+trxjRgVqlavRi4VoZhZFZXgD0+HLm+2n/e4jTtYcUxEabIegpeKBL6B2oGWohDZtwMSBSur3dYXTkcH5lPK+UckXjZ2fyOAkaMiZi86+TxaWU+PzAvhRQSIVdu73aEKKQ8sawrY/RkgJiEq82WaTOylpVqyuPhxLuHI0/nhXmpHOcTg/YAZbrUSgSRxnYz9NDjwJvjib/8cuSr68g3b2rn8zxs+rkugl4fTuiglyNsNQZi6juoeGTnWj+5LzZRyDl3OZ9zoTSX0jXn+n30EogSSaERx+RJCAKtUxu03sUJFHF+k+ZKHKOX/JgnjgeBIAk1e+ZFJUJO2cl4g2CeamDVnSvB5Fl83+h6W4JzhNY9k7jlyxDWpdAsME2JnAPLXKnF9cZRhGEAImj1K0xT93TGHgQeupPouf9SvDl8FNyUbY7wSo9NMTOwwZ9HOkVieN6Q9zx0Ybz1qo8uIAFidL3xunhJr29AgZwyVKPWhuXY0yN+Ty8cvEPW8O8Xu8Xs96NKvOvkubmVS/qDSGCtxv7FjmiJpSyoKcd5ZrWGBm+RG6yx32252m4+b3GGJEz7jLZATJE0NtoiWDJEnXiX6MRt05Uxb6irsi4zYP3e4DaiEJQQB3abiev9xDgEaNkzYuxSmN64uomss7DO1cObrbKbAk/nM1W9x3NtRr4eCKty/OHEuZw5F6MWH2eSXdDV/kZiTuYPnn5+e33NeVkoGP/gq4m2FtaqTNEzTBvWIzLcydEU6mrUDC05dVbViO2CukJEmXLENDCfV+bS3LERhdKLhrK4CkkNqnaLmbiQR6KQEM9xbRCGQIguRUtmxNx7T5q6GVx9zKIrmFxR5BsJ0b2mwiV0y9FEFwd4jZ40V0/1+ivUBGl+wkQn/xxcuTwMCq1VQoDdlIDG4Wnl3IyJwJADOQUseALCfK59HPR0gti5Q7OAoj7mKqQsjBGGGHiZIz+ZAveLcdZObwQfbz1J0A3Pl7yiS7eRRJCU0HXtcSWXZD0h7yZMFqyoSxqtEceJ8SpzfDr6INz1yyaVOI6UVjCrbhwP4fe0v/Y8Pl8khlY7B9TvjmrKOq88ns5sp4kYI1dxy7wUsniTthvKfbN9On+m2VrEaK1QamRKiaZLN50Gj6U3iDHR2sqQ9izz+qw4GfPE43pijsIUYL/dc7Ub2I4bNpvJM4cOjTwaL6+z24Ay3ks5Fx4eTqz1wOtUyQI/zKu7j1PjeD5yOp7IKVCAeVZq6aebmH9A2DPK54NaQWJGBKYp83h65OspsJfAr58a59K4jsmjQNSIPSxbpHd1FON0LIQt7Kah2548ryaGQM7mCGJ/kLQm1sXjU2JwWVkVQYqPxHRuzTXDLoPTrsE1MzJCisF9js0oCk0ESUKO/TQTH8FUHQ21Ttdc6us+1djh8r0+vVQTqA6ZVG39bunuogFBuhCiWO87bQWxyNWUGEdjrSvHoyte4+BUyDQkFLfFrc1PabXA2npVhbgI3Hq8jQs38LG2Jyy8GhN/drvj2OBvHs68PxYsBSwWQnVLXejAUsRlVO6hdTxDJbi+X8SN31U4fjwjvR1AWiC0xnI4Ee5uSJuR5empMw5ukNeiz1peM+94berDMoCQnLe01uMyL8IY95uK+b/84eGB/bry05sXTOKpGUPyfGQVwdYVUFL4XPleE5aDJ6G3sBBjoFZzpYT4BVoRQsycSmW0hura1TcbkgT+8Z9f8+rViNbEy7sBrYHjGcapISFzOlUeH5QYlIf7B978sGASuRkDv9xFWhXePC4sq49faUggyuHjglIxrQ5JW8RSl9jV2eNUur3HEDY5EsbE9WYkRmWzjfxqGEkZPpxqX9Sux8w4bdJKARkQSwguSbRirKF50W9RgoIMrsJR8VNhuBpJGG3J3D+dXKImiVg8uKpVLxZaWyEgjENAVLySIfqJZj37Zhp6baLB0vxUXevFq+kAVMxAyKj6Qg7S82kt9FOmC9QFVq0+gfQRtzWX3RRVpqGPv2rM5iATCikkbnaZJMLjqbKsFYmJbY7YKOy6e0aLq4AWNVq3mrklUrDqd3IRl4CqCmqVHFyhFENlysI+CT+73fB6F/n2UPnXbw7Ull37SkCJBC8KBPMiYwKUZXEFUd/3cs9SSs1oIr5JtEa+Gshj4vjxEUWIOTHGkTovfqIFxZrLH4QeE0OEtKItdbrHd1TDm8p8Qfp7JSKYBcoKS2q8Pz1xO27ZTpP/u1xdfGIDErzn9bMWp3QNpGpgOXn3hnXPok/1AW2FqIl6fOT21ZeYjqxloawHxu3EV1/ccn0rjDGjVjmbF5zqBPPTzA8/HvjhxyNJJuanA0tVfn438Od3Ww5z5f268MOx0MqASaUsPk6/a/cMQ+U4r15NHn0/FTWauGnaqwXg1e2GzSYiceTl3TXv3n/kfKr86o8GboNwmo2lBZo1gnrs/iAbF9y7Z4sUXCyuBK9lHIyQDen5tKq4VjsKJOvBwkJavJ1tiEoCRAMWoycAWKNpc44PD8taxEOWYwANgSpu10rTyCZ1rq/BurhfcK6F2Bck6lCIWSTF3oGphmmlSWRtjXUu3lXpTUFurlYn6UX8YZeohBpQEdIobEd/Hx5PlbU4brAZEmkIlCyc50r0VGgIHlIWmxc0VfNRVIN/32qCtU9aVu1P+5gyMSoflsrNtfL3v9pzfX/k/WHmwxkOBoGAiiKaCNocIIouUkiYGwMuGbdqhKDUEFBVUsxMQ+obOASFGBLTNnmeUxGC+skp5oCcJ24EqjRSHZDgVw8EVHpKgnmGbam9A9WMMQ00LTzOhWaVMU9cpZFaV6wXdhGV49qYl/nzFmeMuTcJX/Jv6vNs3ZemjwOqlALjNDLPZ0LwsbZq4/vvnpi/XdjuMuu50dZAHDLTxketYJmfXF2z1sp6KryaEv/spxsGMb77OPPhsVHnRAjV3e1LYz4XXr2c2O+vODy8cQRPwWgEJ33ISdluMtvdxDAIKMxNuf/4wOP9Uz9ZRp7WxqkZo8A+Ju9R0UoJK6Ml1BK1NNoYECKoupBchdqEZA4gmQYwL81p1S1eQYz9NmGrsS4u1N+FwBCe50/fU/DxuaoSu7mYFjFpSPI7b1vcbJAyNFEXKKjfYyNCq4qGQGsNs8bQxyXzMFbGMRILBIu0s1MRXvzlOcBjSiTx8dpUWNRIKSM0lnn130mCm9SzMY1u+TqdCoO6KENVyV24cSrKuToKWtVF7GbipysXUYsSWxebB08/+LEW8vHI1RZ+sQ/8k59kfv2+8c1ROagLKkxcine5A3q0pbtmtCOpAR9HPTdXuLqK0GB+WlE1N5OLMQRhPp67Bzf1+Bt3zbj8p3mukVxGX8AiKhe03MG0C4ljeKeNmWcEz/PCu4cH5s3AEAc2OSA68HY+cK4z2zh83uLUi10H310ldI1hcQDnAoGZKjluWJbCUpYu/q7kIaOSMVbe/Hji1d2WX/zsms0m84tf7Hh9a3z76yPffjvz/ZsDt7fX/MUXW7RU/uYw8/6oHOeEBe80AacFTHycPZ9m2gWxEFedAAxR2V8nXtzeEmLgcD7wNDcXfQ/0nTpSTvDeKk3Uy2E7pxZDcMCrNiz5fUFbpZQAyW1WHo3XpW+lOUjWgEIn0z2BQDaRlBKHx5X5ceG0+nuZkW6/chQwdh5PQugLRLEKRHHfZpfTgecMiUAO0T8XL7PuGUBKq16F0WrP2DFYZq+eV/PXrdWLfmNO5OQneRJ/3WYuQSyr+xFTMGJIXtybHAZeSuNcCjFkNlNyN0xpYC5wKNVjUC7Zr0qPJ8HppN/nx1HPos0mNI38+/dn5mPjL36645++3jJK4SSwHhVdjQr9s3Y03eNJe3LCRa6II6eoEA3WJ8dIBglo9oxeVWV+KtTeJmQiDgJdvo3Js0/20//351A8AMD8ct1H3Y7ue2J0J3SUp/ORpRZ2w4ptt57kWJTdMLL7bMtY+yR09x/JR5VwseB0tMzM2O1GTNtzzZwWePFqy5/+2R03u5d8eLOScmS3Hfnhtws//m7m/ATf/u3K9+9O/Pjhnp+9vGGcIt9+nPnuYeXjyT9Ir7xwctnMRfSH48zx5Oqu0CMLUmhMY2a/2/DlT68Y85Z3H448HgrHY2MIcG4wr8p26yjl3DxEegh+4oYQidb9VuYPcdVIVSE2JeIxHJc81BgDoSktdi74khhYK8TMMERCVm5Dokrg9P7M47mQuhOj7ziexyNCiMZaK6mfvGaB3OMfMdeUxiDEBClHj4zsQdP1AtKZb6DaxeaicK5Kwxudk7qtjx4wTU9NEAkd5IKEnxYOZHhpj13SH1b1trCQ2Q2pp537fbKpbwKjZDQ2zh35rmo+ppfigdLiUjY61RRjIo0FVeNphW90pn4Hf/HFxH6M3AzCx1mZu1DhuRSov4WCb6w5D7TSMO1WNumfa/WpIOXI2iq19524pNA9sFOOLsBYPZOpmX+Li/3MAR/xcZZetNXfv98LNurG+i4nNLcvaoPjslJ6wFprBSuZPH3myZlC8HS1C7oohlnx2d8+FYUGolMAYgxxIPbRckjGT78IZGm8GEfefYB1rjwcFu4fVr7++orD3Hg8Lry62XOzSXz/ceX7c+PDSVEN/ZKNjxH2SYcozV8TE2JoDGPgZr/l5d2Vlxt9seHxYeXpeGT29Ai0rT2rRsAah3WirO6kuZkSQ4pYvbg8AiounV2bMsZMphCDI3KoE+AkD8lqAOrJ7yGALtU7OcQXvIzCtIdNjTw9GSUABFpVlmXtQVeuwPEN3IeonCtzan5qRqe0huTWtRQDKXounTYHh0qtSHPtrAvd1asDDRaMc6loEV5NA6iDHrFzhE268ABPgZAUiNmBJ1XxyBpthCgemt3Rb8HpIescsKl/XdLgG4DgFfcXQ7bzMo7gpi7OCEbMqWcvRT7Ohp0a4/sDf7zf8TIZ75LwIJXS6wifJS0BtN/9exWTCyRur1keDp6qL7DZRAjGeliZ8ugVi7WQ+sLa70ZyMI73jWONqLSLtPeZl+0pwM86HOuAkXXTeI8t7FyudCWUe1kVYzNENmmgjYFM4Fg/UyFk0clqRzsiZoVhyqyLI6RRAiEpyupZpBZIYYfgYuoX+x1jhPvfzeQ0cHoM6ABf/dGIrpmlFJo0ii1sdjeYwrdv77k/Nq+IA8+M6TIpBaQT8ubXSDabxM12w/XVDbd3wpdfXaMN3rw5cJwX5nVGrRFSYNpt2baVZa1EizydvAtkMWHEU8G9Pk67EMEtZ6VBwZhEiNaoxcEeicljOcikAEttPTbUP9C2GEUK025AshcRTVcDm5z5OFfOS7+bBl/QKWcWLdjac2tiQki0WlmaQfDOFrXZN05t5OCid7qMLETfOJu66N0RW+ceiUIw3zRLqyRgmzNDTIQglFIAIWQHaExcvreUAtUQktf9JbcBWuyuHLt4If0eKt10XtFneaEpaKn9YfcFZESQRA70072SEryaIuu5cb803k7Cy7VymxLXsXmtQ4Vskcbq9IyGbqjWT4mQCGWtGEZFmYaRYZM5Hp54sb+mNWU9OrMwiPlk1iDmgZqNtXr+bR4ia138EOj8qlPD1hVIgSbmFjhTxyzwh9MnXVcQmc6oBU4HQ7OSxNCk3B+ePm9x6trHFf+RAFhLo/u8UWAYJ+KYaHWlDQNDjF3n2dhuAzkEru8y54OQdsJvvztz+3pLW5R//68/ci5npDX08cyDGeuitDWSJDKOhd31hITAx/sT2vsRnVs08iYzjYmrzY799cjNi4mPh4VvfvuWd28aWo9oM1LeenGNnbiTwL3knlQOaytod8Bfdkcf8Zq7C/CNorVGDDhfhiepo1DUpW3RYMgZ7chtFs+8Ler3vSl6eRAhstsaGoSUK2WpzHMv9w3CwEAcjGkcIPX7aBh5kQLFjPOqLnJXb/+y5mJ0CbE7UEAs0MrK2nwsTeJqpRiVISbGTqvk0L2RNP99UnStqyRK81wcUadcUnCnTEiZT9aHy70mdjG+IrG7XtaIVsWaUEvvUYmCVelpAR6xsq1KCl6V+OE8Myb46W7kabNQTwNvl4VXufDzIfEqGu+C/97ngDuHLIA5eBbUPD8XX2zr0xlDebG/ZTdEylz46volKQvfvv2RIEIObm9MoXGcV949nV0yibqYY5Npp4SXJ7l0MEukafVsJPlU5yAKgeSumH4/9R5owzQSE8QcmdeZ3TTycD6j7XPvnFH7DtAlUSE4naDSHQLOe1ZbkBwYW2HVyiaN5GSUsvLhfuTqemDYJex4xpZKzpHjh0fO68LSCndD4nYc+ObhgOuAhXFUbq8HUopU4OXtjqVUBCXFgRTzs2tj2k3kbeTtuzO//eEN908HYk2Eljw+UgXKym0StlPiviyIGjkLWRON0n2o+AXXXHx+ifmPKEn9BEoXyZj5huV3TyOmgYuf8FL8WotCMFqNnENhipcW6+p3vxiREXL2bNfDWmkNRxBZiBYZJBLVLUw5R9KYmEVZ10JUQWLuVR6CTQ66iDbGYeyfmo+qhk8PLtt2wXhrnkbgLWK+5aq5NLC21pkRIZM8QS98UhxJACTSxOVrrceKxJAIQZlxALFU5USjYL0omC5/S25Kz+5xPZvy5riSo3C1SUxToh5n1jVxX5Q/vxt4YcY4r4wZTrX1IGwXb4TmwNCnDk6XDrk5IIAZu+3ENCYeHx8ZWmN39RqZRo7HR6q2bgRvSFCmcUetK2KVYRN5eqg9tzb2dfcpXLqaEdRFFtrfb+nPieItCfGC9qpLEh/npZcMr5+3OF3F5RqbfuXstIE9J7tZLWiBLEJtCyEkSoH9NnK3c5dIfLmlLSunQ6Gp8eHNzNO7lRQnqhV2Gc7LwuPZq+1jbOx3G66nCUJy8l2EJJkQA+OQGKaRzWZgXVyY/O7+yPt3Hzk+HRHLmFZyCogkNqGwsZUvxk2H7P2mPCRhVIfcT83YxtDrCHwmMfF7WMXFBWbC0tPrsuDpaiJoD0sOfdwhuKOlaCWFRIqBUivHtbC5hH6FTpanhFgjjYlRGlpw9NWDEEhiBK00c5O2mQMiuacyIJ44p80jJqsZwVr/DX2kTCF41pe4JXQQRzAl9vQE7XtSA1V/CKcYvKOhAxtDjgg+dqp1pBW/d1ftWt/LQ8JFKSScNVC72sqTGlxAb33BiAQkBt4dVt6fVr7YJ352E/nmMcN9ZSIx1wo58eIapqfKWLzqQUR6M4Y8q8NijFB94pAQiMmzhsdpIoXo2UAYN7sdw/WO0zJDaQSNPUFfqETWWggqzKeGZnzhh08SPjMcOAz+FQDaIzy5jL/PcZ9+vzaFZfb2u2dCUj7z5EzdXWHWq9j6nC3BxwizfkeRRqAR2kgIsN0F/vTvfcntbSRNfhGfz57qliJIXTgdF0iwC5FpSvx4/4iFgWaro5ExkVJ0UIXIdpeYNm7qHkbfANZWeFor59PKx6dHzocZ0e58DzCOkU0QrsZIssLVKBx7INXSAYqNBaI1noryKkV6JJxbsoIjpgGw5oL1GoyQ/D1x8MSF9QjPKeFCD65KiZBjl5UFxBprbSyrL+CGP8wRz3glCdOUiDTIru4RoRPfEX+X/Wu0qJcIL526MKP01zZxRDCGQB4SOSbSCDUKoSrZPIbTXOeIxEStPqLG4Kl/dNNyCLGXK7ksr6n4Q2MuzpeQaNUXqIGfZPhmstiFu+1f0O1uQeKzRjb0e+o3TycOrfCX+x27aLw9VKQZGuFU4Nv7E19db3k5BA5zI3JZ9NoXqUJQ4jDSzFMDHbNxFc9cC9SVcRgYNhs3jLdCLAvbIKxWuR4GTOBpXimtTxcK2rzGkEBPF+z8PgULkRgGZymsZxSJ01eX9AYL0icY6ZvXRSzy/64O+g8uznGTmA/VOTQu4l4fGS6yMNPKbrtju/F09FqV2/2Gn3y1I2cljo2UMy9fYHNwmgAADFtJREFUDty/OXP/Rvn4bvUUPY4kXSjLyIovNpoT1Kt6dcGUArEGdrtMnhqqibU2nk6Vp+PMu/sHWnXwQwEJxpACmxy5G4XBGqcy82oaCESW6vrVRRuntfLlELkZlKfWWDWyDZekPT8zpPloQkwugFfP+2na3Qutu1hC6CYAc/sS2j+o/Kz1zClga0WrgyX0O2wQLxTSfpKl4Cgu0asUhhghBSQ5Mh1DIike+7E2dPWIEUVYS+kfqyt5+nRFnARdG1QHm0Lw9P3Y79p0i1gOkRQ9jDpF8RQ/Pv3x05Ln8dck0cz7QQx/EBsQUoS1dpTcdcj0DS9e3tuuhGpq/O6wMsTMz3eZD2vh7dE3pUbjpPDYjD+7zrw6R74/LmxzYjahSFfmNBAidZ1BlenqmhwyHz985GFeuN3deNBZjNRavEmgVL9vp8AmjcQYmZdTv7cLZk5paacLL+wh5syJRSFK6qOwK5+euZ3naOvfA4Z6g5l/C1eewWfK906HmYBbvJ5jI8UfQLPGtB252my53mSsGuf1A7vthhf7HbWsXL+6JuSCk0yJNCaWapyPZzRuCbNwvYGrIVEfC84vuA/y/mlmmjLXOweETkulnpTzOrMW5eHg9rB67uO1OQi12Qzsr665agde2swjkU1UfnE7cr80jrVw2bA+LI0/vRv55bLhX3+s/K4s/DyPPY3N/6vlkjDoJ5eJo5Nzc6Q0J7+DKNqdNx5Eratn0Namz5RMLEZq9ow8B+0JATGy1IrG2JPnXXni3GFFcvcp9ugRLpxn7fKi4PK4UhpxGPr2GbvXdiVHQFJPSegnXIi06HlFIr6hSRBij1UheIykxAu5H8mdulDh2Qmj1txKB/31tI/Hnksb0A6E+vIWiYjfj9CgZDLfHhe+PZ34y1dX/GoP/8PfLshaaQFWi0RrHKpxWldPJ5TALvYIkNq4iM/9NpKw1jicV8bkoo1dSkwpAYFai4tEgFIWHz2DkHrKRBh3tHJiSMJm3LDWwnltrrVWRS16/Ek/CYNd9ACum0Zy//1aJ13cZE93tXwSCHQa9g+svz+srbXoJyYg1rzklQWxyLDdsN2NXO82tGVhO4384u/9ET//45dIa+StX3AOHwt1JwxZqRrYv9rzeN9IeDzEdhhpppQimEZMKpi3kf3uzRPvx4UhJ8yUtVTW1T2fZanU2ivbFCQJ++3INjRurfAiRMQCVhqvtxtebDO/PTeaGdejsCrcL4qkwOttYPqo3GtgH+EK9VhUE1YrkEbmZiRVlouONbgjZEBozXduCK62SQEL2vNYjaKNwQJRjWiB2n2AhMCsSixOwq9ro5rfVeOQQdwv2qp609oFGTQX3WsNlNKw2tMD+pidYqBWpZSKibKdRpbuk8yxx3ciaHUq7FI9r+qlxDF6FlLFPNIlJi4Unpr2KgPX/l7SalPsfsrmJ6Jp89JgEZrPeD542bPzkUECH2rlXz498avtxD98veU3h8pvPlRy9xDXo1IrvD8qh6q8mBJTCsy6sMmJGCJaXbNdNbksMOLThBlTithq6MY3mtN8ZswZK919FKLL/fLIZtpzOD+RTk+evNeUulaC9RA/iWSCS+LVnGpqHlh+SZKnb66+YfgCvrxvTqd+qp/3Sij9f1h5/x8Wp69rdwNIR/NiEmIKXF0lhhFIhd124PZ64stfXLPZR7IkthvharvjcN94++OZl68mwJiLkaaJ4+mMtIo2B1tCFzoYl5EC1lmY5wKydCrHd+sWFNHY0xX8Ej5EYR8Td8n4+a6hTfjdnJFB+YdfZabcWJZGksyf3F3x4XTi1x9XHufCH7/e8PNH+M3jSksB2UXm2Xg6L6TSmLInJSwVhk48T90IHLMxBR/banO+KwTDkjCEjC4FSsOaizSkpxKc1+I8oPiYWLrwwdQguizPS3cTVQ2JlUuFWGuNWrrv0txrqD38TMXvxfPqAoOUA7VWT+GT4BEjuHE9WGMK3WnUEebcZ3nB5XrgNHcI9qy7teD6VbUuKBCXCq/NIz1K88+q0ZjVF/AQ/edu6tm3CBSt/JunE4d14T/5oxc0E/733xXePlb+4usX/OOf7/mf/uoH/vpD4VSFd3Pjj/eZ/ZA4WuP6bscgyv27E1MxltU4KrRoBPNnpKnn6Q7WaLPnCcUA9Dp7CxDzyIuXX3CeT+xvrkkR3r99T4y+ESzVqbNtChyb+2LzZmAYM8enc1fcXgApVwNZR/c9Tb7riYxeY0G38hX0D5yd/4HF2X8BLgolIU+JKY9c3078/T97wXaM5Dwg1hgmYxwCmzEzZiFl5es/uuH/+ldn3rxfseC7oGe2FqCCTr448eQzaZeLcnPwwFwfifjJgbnqhR7rKBjbBDej8fWQ+NnNhnFS/ub+yMeq/Ow28/VPBn79RrlfF+42W/6j28S7Ycv3h4Xf3jd+dpv4+i7z9rzy7rgQ0kQ0l5KFProEMU86CBHaJSdWGZunvIHHRT5TKxcrUAyE2KvPTR0U6XdJVVfPqDg1E4L1q1/n6zoi7IlwQltdHB7EfyZrrWfY4CHG5vI610ELl0DHeV3RHgTdDFrxLszBnIO9JN+puOnZ5ZnSrws9FS+4fC9Ge3bgSO2FxiI9zkSpasxdw9ssMVcf+jEjirJ0gYeZ8mOZman8859csZ3grx/O/PrNzJ+82POf/fKGLzaR85++pPwGfjysPM7Gi5+OvLz+v9s7cyY3qigKf2/rbu3SmPHGGC8sZqpMYApTRcIf4B/wYwlJSEgIKFMOWIxdomaV1ep+G8F9miKxHTJBn1iBVHq33+1zzz1nx6uuxYSOZ8cH5LsNPz0/Y30esEmc9Lqc2YXywHEVZ5c7lLHMnKHvtozNCNfMCbmjaWp23Zbed9TK0rUbauckWrG4WiSliGWB3ZT/OHnh/TMicc2kkhyh5XwWOxLkOMsnlZBCGZFm6vz24nw3XcQ+Yl6V2VQmxsB8WvHRoxVPnh7xyeMVy6Xi/oMFTkP3ZotzilEt/ik+dcwXFTHCzaMFHz6akjBkownZYwxUlb4iJvRe+lPoaMoRI8fSEkh+YlIJrTzLOnNvDJ9PDccfOGoDv64v+XuTMWbKwxsTUsz8eZbwSnN/NWFUG26PLffmDa/fdLw6jRzMFJ8eWugD65OWuoscZCemXUqILhE1y/Jw52V3MvgoQu7iP5uzFIfEGkShbbU4zcU+llzKVHTIIjLXKKxSmJSwiqLRFUsUH5OsdUUho0KEPoh+1VhzRdlDxljRe8YgY5WcJDA3Rrm9UhJrDh8SOmpiJyMPlBZngowsXUNxUeeqBQs5EbLcAqnID1B7v1uxDo1ZicND0uxCou0Tfcx4ZN6rrEgBY4ys+56XXeCz5Ygnd2peXgZ+frlhZS1f355T68jWJ45XFd/eXzK2cgP2MTCxmQpD9J6tjzz7eMT33xzy+O6YkYmMiIyTxmWF0ZYYEu3W03ct3W6L1pqqHlG5Ec5V5Azeeyrr2GwuZARVNLLagBsbRrOGHkuwClUpqphFNaVyGa/IrDmEQIq+ZLPwH66GYqFS2ntE7jeb1W+tvvc6IQj9bVFGVoims4obhxMePFxSNYrNWtK7XKNZHUxotwlnNL3viG0gxshk4dDOMF+MmV1uCUGsD61WTGuHNqFoWcWzSCmRgsF+bFYYYp3KLp6m1olbM8thrfjyaMrCatY7xYvTM/5pFUlPmI0rDkeJ003mdLNhXk+4MzUilDeW4+WSTbfmr/Mti8mUR7fGXHQ9L042bO2YpnBqATA5UakK2dDZkzaGvvdst4qxdtBGrPMYLZR8yDJX1HnvuL5P79KiXU0lkk8bchYG1CHvfUmV98woB96V2VjMIku1StjUkMV5P8vaBxkpGJ0ltiGGhFGG3ot+1aYsXG6OdNHLXLAcoqb4QimlMJVCWbkFtAJcxigrJm+II19M0j76KCt0MZdsmAA+yMih1hQjabFkabLmUvW89jtujh13G8svrzw//nFGzjVffbTg1hh6n1Eu0FSOo8aileHEZ853mVpbVqOak2z47fcLFn3N04czvvtixQ9a8XzdctHBXGXOU6BHRmTS9htq05BVovMttrLFJkcT+oBvdyxmMyqXaPsOV8tv6XeeFGEXEprMyFlZfcuqCFjkSWYQT6K97RpQXiuFVGPfmSQv3+Ed2lp1lUM4YMCAa4X3tLUDBgz4vzAU54AB1xRDcQ4YcE0xFOeAAdcUQ3EOGHBNMRTngAHXFP8CID49fYBf9kUAAAAASUVORK5CYII=\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3bF2RPbCEM2I" + }, + "source": [ + "## Step 2/3: Setup your animation controllers (on right Sliders)." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "hzeK4TPzy_82" + }, + "source": [ + "#@markdown # Animation Controllers\n", + "#@markdown Amplify the lip motion in horizontal direction\n", + "AMP_LIP_SHAPE_X = 2 #@param {type:\"slider\", min:0.5, max:5.0, step:0.1}\n", + "\n", + "#@markdown Amplify the lip motion in vertical direction\n", + "AMP_LIP_SHAPE_Y = 2 #@param {type:\"slider\", min:0.5, max:5.0, step:0.1}\n", + "\n", + "#@markdown Amplify the head pose motion (usually smaller than 1.0, put it to 0. for a static head pose)\n", + "AMP_HEAD_POSE_MOTION = 0.35 #@param {type:\"slider\", min:0.0, max:1.0, step:0.05}\n", + "\n", + "#@markdown Add naive eye blink\n", + "ADD_NAIVE_EYE = True #@param [\"False\", \"True\"] {type:\"raw\"}\n", + "\n", + "#@markdown If your image has an opened mouth, put this as True, else False\n", + "CLOSE_INPUT_FACE_MOUTH = False #@param [\"False\", \"True\"] {type:\"raw\"} \n", + "\n", + "\n", + "#@markdown # Landmark Adjustment\n", + "\n", + "#@markdown Adjust upper lip thickness (postive value means thicker)\n", + "UPPER_LIP_ADJUST = -1 #@param {type:\"slider\", min:-3.0, max:3.0, step:1.0}\n", + "\n", + "#@markdown Adjust lower lip thickness (postive value means thicker)\n", + "LOWER_LIP_ADJUST = -1 #@param {type:\"slider\", min:-3.0, max:3.0, step:1.0}\n", + "\n", + "#@markdown Adjust static lip width (in multipication)\n", + "LIP_WIDTH_ADJUST = 1.0 #@param {type:\"slider\", min:0.8, max:1.2, step:0.01}" + ], + "execution_count": 18, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "A0KE1rLxB_Ce" + }, + "source": [ + "## Step 3/3: One-click to Run (just wait in seconds)." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 303, + "referenced_widgets": [ + "2f3af6facaaf41dd8ca7a92b083e211f", + "b1de2dbcf01640fdb611ced29d4161c5", + "5f9c7cca5ebf4c3ca9512f6c1b437d0b", + "07b20e609e414c8a91752d9dd9ce2dbd", + "2ae89becba5845328dfc440f01eb8c59", + "818559ff8d564f289062020831316b46", + "109a70b351854e14b652a578644d2266", + "4aaa0e825e9744d9a850c7775f0f013b", + "f85d79bed2db477699e3eb4d6075680a", + "2fea5812f17c413f927e76f7f5917a6d", + "473e5d8f1d814c8ea1fdfce5df4b52be", + "9a110d54bb074079b08beb817013ba50", + "e0fbada5b6c24345a4b612dde37211b8", + "56bde523e4b640a5b0afb5627d162bcb", + "53408a94438847cda9fe88d76dc9a09a", + "5db33822832c4e7ea52ee0780a837bd1", + "8d780a17c4c24640824eea53f5732ce0", + "b8ed0535c0ac4a8c875b93711ec65f31", + "1784bb704af440aea98d64f3e24a481c", + "88ec5202b1e844e59e73a151ddd49c60", + "af94bc9178dc46f68cf80cc6cfc145d1", + "ac50640604534f8992cf227232698c57", + "4b06097cba20453e9563f39bc664ce81", + "955c9fe938674bba847a682aa32bdab1" + ] + }, + "id": "XFQh-tlKzQCm", + "outputId": "28cf418c-f2df-41d1-e54a-375f44093e4d" + }, + "source": [ + "import sys\n", + "sys.path.append(\"thirdparty/AdaptiveWingLoss\")\n", + "import os, glob\n", + "import numpy as np\n", + "import cv2\n", + "import argparse\n", + "from src.approaches.train_image_translation import Image_translation_block\n", + "import torch\n", + "import pickle\n", + "import face_alignment\n", + "from src.autovc.AutoVC_mel_Convertor_retrain_version import AutoVC_mel_Convertor\n", + "import shutil\n", + "import time\n", + "import util.utils as util\n", + "from scipy.signal import savgol_filter\n", + "from src.approaches.train_audio2landmark import Audio2landmark_model\n", + "\n", + "sys.stdout = open(os.devnull, 'a')\n", + "\n", + "parser = argparse.ArgumentParser()\n", + "parser.add_argument('--jpg', type=str, default='{}.jpg'.format(default_head_name.value))\n", + "parser.add_argument('--close_input_face_mouth', default=CLOSE_INPUT_FACE_MOUTH, action='store_true')\n", + "parser.add_argument('--load_AUTOVC_name', type=str, default='examples/ckpt/ckpt_autovc.pth')\n", + "parser.add_argument('--load_a2l_G_name', type=str, default='examples/ckpt/ckpt_speaker_branch.pth')\n", + "parser.add_argument('--load_a2l_C_name', type=str, default='examples/ckpt/ckpt_content_branch.pth') #ckpt_audio2landmark_c.pth')\n", + "parser.add_argument('--load_G_name', type=str, default='/content/MakeItTalk/pruned_model1.h5') #ckpt_image2image.pth') #ckpt_i2i_finetune_150.pth') #c\n", + "parser.add_argument('--amp_lip_x', type=float, default=AMP_LIP_SHAPE_X)\n", + "parser.add_argument('--amp_lip_y', type=float, default=AMP_LIP_SHAPE_Y)\n", + "parser.add_argument('--amp_pos', type=float, default=AMP_HEAD_POSE_MOTION)\n", + "parser.add_argument('--reuse_train_emb_list', type=str, nargs='+', default=[]) # ['iWeklsXc0H8']) #['45hn7-LXDX8']) #['E_kmpT-EfOg']) #'iWeklsXc0H8', '29k8RtSUjE0', '45hn7-LXDX8',\n", + "parser.add_argument('--add_audio_in', default=False, action='store_true')\n", + "parser.add_argument('--comb_fan_awing', default=False, action='store_true')\n", + "parser.add_argument('--output_folder', type=str, default='examples')\n", + "parser.add_argument('--test_end2end', default=True, action='store_true')\n", + "parser.add_argument('--dump_dir', type=str, default='', help='')\n", + "parser.add_argument('--pos_dim', default=7, type=int)\n", + "parser.add_argument('--use_prior_net', default=True, action='store_true')\n", + "parser.add_argument('--transformer_d_model', default=32, type=int)\n", + "parser.add_argument('--transformer_N', default=2, type=int)\n", + "parser.add_argument('--transformer_heads', default=2, type=int)\n", + "parser.add_argument('--spk_emb_enc_size', default=16, type=int)\n", + "parser.add_argument('--init_content_encoder', type=str, default='')\n", + "parser.add_argument('--lr', type=float, default=1e-3, help='learning rate')\n", + "parser.add_argument('--reg_lr', type=float, default=1e-6, help='weight decay')\n", + "parser.add_argument('--write', default=False, action='store_true')\n", + "parser.add_argument('--segment_batch_size', type=int, default=1, help='batch size')\n", + "parser.add_argument('--emb_coef', default=3.0, type=float)\n", + "parser.add_argument('--lambda_laplacian_smooth_loss', default=1.0, type=float)\n", + "parser.add_argument('--use_11spk_only', default=False, action='store_true')\n", + "parser.add_argument('-f')\n", + "opt_parser = parser.parse_args()\n", + "\n", + "img = cv2.imread('examples/' + opt_parser.jpg)\n", + "predictor = face_alignment.FaceAlignment(face_alignment.LandmarksType._3D, device='cpu', flip_input=True)\n", + "shapes = predictor.get_landmarks(img)\n", + "if (not shapes or len(shapes) != 1):\n", + " print('Cannot detect face landmarks. Exit.')\n", + " exit(-1)\n", + "shape_3d = shapes[0]\n", + "if(opt_parser.close_input_face_mouth):\n", + " util.close_input_face_mouth(shape_3d)\n", + "shape_3d[48:, 0] = (shape_3d[48:, 0] - np.mean(shape_3d[48:, 0])) * LIP_WIDTH_ADJUST + np.mean(shape_3d[48:, 0]) # wider lips\n", + "shape_3d[49:54, 1] -= UPPER_LIP_ADJUST # thinner upper lip\n", + "shape_3d[55:60, 1] += LOWER_LIP_ADJUST # thinner lower lip\n", + "shape_3d[[37,38,43,44], 1] -=2. # larger eyes\n", + "shape_3d[[40,41,46,47], 1] +=2. # larger eyes\n", + "shape_3d, scale, shift = util.norm_input_face(shape_3d)\n", + "\n", + "print(\"Loaded Image...\", file=sys.stderr)\n", + "\n", + "au_data = []\n", + "au_emb = []\n", + "ains = glob.glob1('examples', '*.wav')\n", + "ains = [item for item in ains if item is not 'tmp.wav']\n", + "ains.sort()\n", + "for ain in ains:\n", + " os.system('ffmpeg -y -loglevel error -i examples/{} -ar 16000 examples/tmp.wav'.format(ain))\n", + " shutil.copyfile('examples/tmp.wav', 'examples/{}'.format(ain))\n", + "\n", + " # au embedding\n", + " from thirdparty.resemblyer_util.speaker_emb import get_spk_emb\n", + " me, ae = get_spk_emb('examples/{}'.format(ain))\n", + " au_emb.append(me.reshape(-1))\n", + "\n", + " print('Processing audio file', ain)\n", + " c = AutoVC_mel_Convertor('examples')\n", + "\n", + " au_data_i = c.convert_single_wav_to_autovc_input(audio_filename=os.path.join('examples', ain),\n", + " autovc_model_path=opt_parser.load_AUTOVC_name)\n", + " au_data += au_data_i\n", + "if(os.path.isfile('examples/tmp.wav')):\n", + " os.remove('examples/tmp.wav')\n", + "\n", + "print(\"Loaded audio...\", file=sys.stderr)\n", + "\n", + "# landmark fake placeholder\n", + "fl_data = []\n", + "rot_tran, rot_quat, anchor_t_shape = [], [], []\n", + "for au, info in au_data:\n", + " au_length = au.shape[0]\n", + " fl = np.zeros(shape=(au_length, 68 * 3))\n", + " fl_data.append((fl, info))\n", + " rot_tran.append(np.zeros(shape=(au_length, 3, 4)))\n", + " rot_quat.append(np.zeros(shape=(au_length, 4)))\n", + " anchor_t_shape.append(np.zeros(shape=(au_length, 68 * 3)))\n", + "\n", + "if(os.path.exists(os.path.join('examples', 'dump', 'random_val_fl.pickle'))):\n", + " os.remove(os.path.join('examples', 'dump', 'random_val_fl.pickle'))\n", + "if(os.path.exists(os.path.join('examples', 'dump', 'random_val_fl_interp.pickle'))):\n", + " os.remove(os.path.join('examples', 'dump', 'random_val_fl_interp.pickle'))\n", + "if(os.path.exists(os.path.join('examples', 'dump', 'random_val_au.pickle'))):\n", + " os.remove(os.path.join('examples', 'dump', 'random_val_au.pickle'))\n", + "if (os.path.exists(os.path.join('examples', 'dump', 'random_val_gaze.pickle'))):\n", + " os.remove(os.path.join('examples', 'dump', 'random_val_gaze.pickle'))\n", + "\n", + "with open(os.path.join('examples', 'dump', 'random_val_fl.pickle'), 'wb') as fp:\n", + " pickle.dump(fl_data, fp)\n", + "with open(os.path.join('examples', 'dump', 'random_val_au.pickle'), 'wb') as fp:\n", + " pickle.dump(au_data, fp)\n", + "with open(os.path.join('examples', 'dump', 'random_val_gaze.pickle'), 'wb') as fp:\n", + " gaze = {'rot_trans':rot_tran, 'rot_quat':rot_quat, 'anchor_t_shape':anchor_t_shape}\n", + " pickle.dump(gaze, fp)\n", + "\n", + "model = Audio2landmark_model(opt_parser, jpg_shape=shape_3d)\n", + "if(len(opt_parser.reuse_train_emb_list) == 0):\n", + " model.test(au_emb=au_emb)\n", + "else:\n", + " model.test(au_emb=None)\n", + "\n", + "print(\"Audio->Landmark...\", file=sys.stderr)\n", + "\n", + "fls = glob.glob1('examples', 'pred_fls_*.txt')\n", + "fls.sort()\n", + "\n", + "# print(len(fls))\n", + "for i in range(0,len(fls)):\n", + " fl = np.loadtxt(os.path.join('examples', fls[i])).reshape((-1, 68,3))\n", + " fl[:, :, 0:2] = -fl[:, :, 0:2]\n", + " fl[:, :, 0:2] = fl[:, :, 0:2] / scale - shift\n", + "\n", + " if (ADD_NAIVE_EYE):\n", + " fl = util.add_naive_eye(fl)\n", + "\n", + " # additional smooth\n", + " fl = fl.reshape((-1, 204))\n", + " fl[:, :48 * 3] = savgol_filter(fl[:, :48 * 3], 15, 3, axis=0)\n", + " fl[:, 48*3:] = savgol_filter(fl[:, 48*3:], 5, 3, axis=0)\n", + " fl = fl.reshape((-1, 68, 3))\n", + "\n", + " print(True)\n", + " ''' STEP 6: Imag2image translation '''\n", + " model = Image_translation_block(opt_parser, single_test=True)\n", + " print(model)\n", + " with torch.no_grad():\n", + " model.single_test(jpg=img, fls=fl, filename=fls[i], prefix=opt_parser.jpg.split('.')[0])\n", + " print('finish image2image gen')\n", + " os.remove(os.path.join('examples', fls[i]))\n", + "\n", + " print(\"{} / {}: Landmark->Face...\".format(i+1, len(fls)), file=sys.stderr)\n", + "print(\"Done!\", file=sys.stderr)" + ], + "execution_count": 19, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Downloading: \"https://www.adrianbulat.com/downloads/python-fan/s3fd-619a316812.pth\" to /root/.cache/torch/hub/checkpoints/s3fd-619a316812.pth\n" + ], + "name": "stderr" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "2f3af6facaaf41dd8ca7a92b083e211f", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=89843225.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "Downloading: \"https://www.adrianbulat.com/downloads/python-fan/3DFAN4-7835d9f11d.pth.tar\" to /root/.cache/torch/hub/checkpoints/3DFAN4-7835d9f11d.pth.tar\n" + ], + "name": "stderr" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "f85d79bed2db477699e3eb4d6075680a", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=95641761.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "Downloading: \"https://www.adrianbulat.com/downloads/python-fan/depth-2a464da4ea.pth.tar\" to /root/.cache/torch/hub/checkpoints/depth-2a464da4ea.pth.tar\n" + ], + "name": "stderr" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "8d780a17c4c24640824eea53f5732ce0", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=234708217.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "Loaded Image...\n", + "Loaded audio...\n", + "/content/MakeItTalk/src/approaches/train_audio2landmark.py:106: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " aus.shape[0], 128), requires_grad=False, dtype=torch.float).to(device)\n", + "Audio->Landmark...\n", + "1 / 1: Landmark->Face...\n", + "Done!\n" + ], + "name": "stderr" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ApCZszX-CvuP" + }, + "source": [ + "## Visualize your animation!" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 238 + }, + "id": "qFBozkB1Cf8g", + "outputId": "2d040fcd-ff32-48c9-f776-562c3a1ab26a" + }, + "source": [ + "from IPython.display import HTML\n", + "from base64 import b64encode\n", + "\n", + "for ain in ains:\n", + " OUTPUT_MP4_NAME = '{}_pred_fls_{}_audio_embed.mp4'.format(\n", + " opt_parser.jpg.split('.')[0],\n", + " ain.split('.')[0]\n", + " )\n", + " mp4 = open('examples/{}'.format(OUTPUT_MP4_NAME),'rb').read()\n", + " data_url = \"data:video/mp4;base64,\" + b64encode(mp4).decode()\n", + "\n", + " print('Display animation: examples/{}'.format(OUTPUT_MP4_NAME), file=sys.stderr)\n", + " display(HTML(\"\"\"\n", + " \n", + " \"\"\" % data_url))" + ], + "execution_count": 20, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Display animation: examples/obama_pred_fls_M6_04_16k_audio_embed.mp4\n" + ], + "name": "stderr" + }, + { + "output_type": "display_data", + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YjtFjUS2QwRK" + }, + "source": [ + "# Prune\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "4CtQrOMnlp-E", + "outputId": "49caa5e5-ede3-4e5a-f3c9-70c0934f7ebd" + }, + "source": [ + "!pip install torchsummary\n", + "!pip install torch_pruning\n", + "\n", + "# !gdown -O examples/ckpt/ckpt_116_i2i_comb.pth https://drive.google.com/uc?id=1i2LJXKp-yWKIEEgJ7C6cE3_2NirfY_0a" + ], + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Requirement already satisfied: torchsummary in /usr/local/lib/python3.6/dist-packages (1.5.1)\n", + "Collecting torch_pruning\n", + " Downloading https://files.pythonhosted.org/packages/62/87/8d1d64d10b6106f90e0b3cc92aa165c783f0e64111ad161f9d77831acfd5/torch_pruning-0.2.1-py3-none-any.whl\n", + "Requirement already satisfied: torch in /usr/local/lib/python3.6/dist-packages (from torch_pruning) (1.7.0+cu101)\n", + "Requirement already satisfied: dataclasses in /usr/local/lib/python3.6/dist-packages (from torch->torch_pruning) (0.8)\n", + "Requirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from torch->torch_pruning) (0.16.0)\n", + "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.6/dist-packages (from torch->torch_pruning) (3.7.4.3)\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from torch->torch_pruning) (1.19.4)\n", + "Installing collected packages: torch-pruning\n", + "Successfully installed torch-pruning-0.2.1\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "5WV3CgeqclnH" + }, + "source": [ + "# %cd MakeItTalk" + ], + "execution_count": 5, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "zMbA4xqsrtlS" + }, + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "import torch.nn.functional as F\n", + "import numpy as np\n", + "from src.models.model_image_translation import ResUnetGenerator, VGGLoss" + ], + "execution_count": 6, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "0dRHtbx2Iroe" + }, + "source": [ + "# x = torch.load('examples/ckpt/ckpt_116_i2i_comb.pth')\n", + "# print(x['opt'])" + ], + "execution_count": 7, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "j7neyubslODi" + }, + "source": [ + "ckpt = torch.load('/content/MakeItTalk/examples/ckpt/ckpt_116_i2i_comb.pth')\n", + "model = ResUnetGenerator(input_nc=6, output_nc=3, num_downs=6, use_dropout=False).to('cuda')\n", + "tmp = nn.DataParallel(model)\n", + "tmp.load_state_dict(ckpt['G'])\n", + "model.load_state_dict(tmp.module.state_dict())\n", + "del tmp" + ], + "execution_count": 8, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "uNftU9mWoO4u" + }, + "source": [ + "torch.save(model,'model.h5')" + ], + "execution_count": 10, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "fNokmxfwMF5N", + "outputId": "b6a6a883-828d-4477-87f2-36fe81bb4251" + }, + "source": [ + "params = sum([np.prod(p.size()) for p in model.parameters()])\n", + "print(\"Number of Parameters: %fM\" % (params/1e6))" + ], + "execution_count": 11, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Number of Parameters: 69.860736M\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Uy4chUQDMKDd" + }, + "source": [ + "import torch_pruning as tp\n", + "def prune_model(model):\n", + " # model.cpu()\n", + " DG = tp.DependencyGraph().build_dependency(model, torch.randn(8, 6, 256, 256))\n", + "\n", + " def prune_conv(conv, pruned_prob):\n", + " weight = conv.weight.detach().cpu().numpy()\n", + " out_channels = weight.shape[0]\n", + " print(out_channels)\n", + " L1_norm = np.sum(np.abs(weight), axis=(1, 2, 3))\n", + " num_pruned = int(out_channels * pruned_prob)\n", + " # remove filters with small L1-Norm\n", + " prune_index = np.argsort(L1_norm)[:num_pruned].tolist()\n", + " plan = DG.get_pruning_plan(conv, tp.prune_conv, prune_index)\n", + " plan.exec()\n", + "\n", + " block_prune_probs = []\n", + " for i in range(100):\n", + " if i < 30:\n", + " block_prune_probs.append(0.000000001)\n", + " if i > 30 and i < 50:\n", + " block_prune_probs.append(0.000000001)\n", + " if i > 50:\n", + " block_prune_probs.append(0.000000001)\n", + " blk_id = 0\n", + " for m in model.modules():\n", + " if isinstance(m, nn.Conv2d):\n", + " prune_conv(m, block_prune_probs[blk_id])\n", + " blk_id += 1\n", + " # if isinstance(m, ResidualBlock):\n", + " # for j in m.block.modules():\n", + " # if isinstance(j, nn.Conv2d):\n", + " # prune_conv(j, block_prune_probs[blk_id])\n", + " # blk_id += 1\n", + "\n", + " return model" + ], + "execution_count": 9, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "Via9rfNwMTut" + }, + "source": [ + "model = prune_model(model).to('cuda')" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "-S7IgttolJmm", + "outputId": "3603aea0-38c4-4c57-e5f0-c43891afa8df" + }, + "source": [ + "params = sum([np.prod(p.size()) for p in model.parameters()])\n", + "print(\"Number of Parameters: %fM\" % (params/1e6))" + ], + "execution_count": 14, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Number of Parameters: 69.860736M\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "JMyKE9ktPPk3" + }, + "source": [ + "model.load_state_dict(torch.load('/content/MakeItTalk/PreprocessedVox_imagetranslation/ckpt/tmp/ckpt_last.pth')['G'])" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "inmtXSVUMiW9" + }, + "source": [ + "torch.save(model, 'pruned_model1.h5')" + ], + "execution_count": 11, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "3Zu_tkexMIzu", + "outputId": "4bc638ee-5e11-4966-8119-3afbb8933b6c" + }, + "source": [ + "!zip -r \"/content/MakeItTalk/drive/MyDrive/MakeItTalk/pruned_i2i_3.zip\" \"pruned_model1.h5\"" + ], + "execution_count": 16, + "outputs": [ + { + "output_type": "stream", + "text": [ + " adding: pruned_model1.h5 (deflated 7%)\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "YiqXrUbn6LqD" + }, + "source": [ + "!unzip /content/MakeItTalk/drive/MyDrive/MakeItTalk/pruned_i2i.zip" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "TSE29N19OM5F" + }, + "source": [ + "model = torch.load('/content/MakeItTalk/pruned_model1.h5')" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "JrXsNjDBOVDE" + }, + "source": [ + "img = torch.zeros((3,6,256,256)).to('cuda')\n", + "print(model(img))" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "rQPSXTj69rcu" + }, + "source": [ + "import os\n", + "os.mkdir('/content/MakeItTalk/PreprocessedVox_imagetranslation')" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6M2EMqV0QcHJ" + }, + "source": [ + "# Test\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "fasYOxbd6HwH", + "outputId": "40648287-bace-4710-e804-45f9b9070d4e" + }, + "source": [ + "from google.colab import drive\n", + "drive.mount('drive')" + ], + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Mounted at drive\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "NM4rS5nfvxqn", + "outputId": "60562143-3fc1-4211-cecc-22c053d88ac6" + }, + "source": [ + "!unzip /content/MakeItTalk/drive/MyDrive/MakeItTalk/pruned_i2i_2.zip" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Archive: /content/MakeItTalk/drive/MyDrive/MakeItTalk/pruned_i2i_2.zip\n", + " inflating: pruned_model1.h5 \n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "0ySdck08E52C" + }, + "source": [ + "!unzip /content/MakeItTalk/drive/MyDrive/MakeItTalk/vox2_test_mp4.zip" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "vSAxzLLoZQD4" + }, + "source": [ + "!gdown -O /content/MakeItTalk/thirdparty/AdaptiveWingLoss/ckpt/WFLW_4HG.pth https://drive.google.com/uc?id=1HZaSjLoorQ4QCEx7PRTxOmg0bBPYSqhH" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "AUHjiOHu9S6W" + }, + "source": [ + "import os \n", + "out_dir = r'/content/MakeItTalk/PreprocessedVox_imagetranslation'\n", + "src_dir = r'/content/MakeItTalk/mp4'\n", + "os.makedirs(out_dir,exist_ok=True)\n", + "for folder_name in ['raw_wav', 'raw_fl3d', 'register_fl3d', 'dump', 'tmp_v', 'nn_result', 'ckpt', 'log']:\n", + " os.mkdir(os.path.join(out_dir, folder_name))" + ], + "execution_count": 5, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "wGcS_Q1L8-T3" + }, + "source": [ + "import os\n", + "import glob\n", + "import time\n", + "import sys\n", + "from src.dataset.utils.Av2Flau_Convertor import Av2Flau_Convertor\n", + "from src.dataset.image_translation.data_preparation_with_preprocessing import landmark_extraction " + ], + "execution_count": 6, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "zuR9Udr6-zH0" + }, + "source": [ + "out_dir = r'/content/MakeItTalk/PreprocessedVox_imagetranslation'\n", + "src_dir = r'/content/MakeItTalk/mp4'\n", + "\n", + "from tqdm.autonotebook import tqdm\n", + "''' Step 1. Data preparation '''\n", + "# landmark extraction\n", + "# landmark_extraction(int(sys.argv[1]), int(sys.argv[2]))\n", + "\n", + "\n", + "def landmark_extraction(si, ei):\n", + " '''\n", + "\n", + " :param si: start index\n", + " :param ei: end index\n", + " :return: save extracted landmarks to out_dir\n", + " '''\n", + "\n", + " for folder_name in ['raw_wav', 'raw_fl3d', 'register_fl3d', 'dump', 'tmp_v', 'nn_result', 'ckpt', 'log']:\n", + " try:\n", + " os.mkdir(os.path.join(out_dir, folder_name))\n", + " except:\n", + " pass\n", + " files = []\n", + " if(not os.path.isfile(os.path.join(out_dir, 'filename_index.txt'))):\n", + " # generate all file list]\n", + " folders = os.listdir(src_dir)\n", + " for folder in folders:\n", + " for i in os.listdir(os.path.join(src_dir,folder)):\n", + " file = glob.glob(os.path.join(src_dir,folder,i,'*.mp4'))\n", + " for j in file:\n", + " files.append(j)\n", + " print(len(files))\n", + " with open(os.path.join(out_dir, 'filename_index.txt'), 'w') as f:\n", + " for i, file in enumerate(files):\n", + " f.write('{} {}\\n'.format(i, file))\n", + " else:\n", + " with open(os.path.join(out_dir, 'filename_index.txt'), 'r') as f:\n", + " lines = f.readlines()\n", + "\n", + " print(sys.argv)\n", + " for line in tqdm(lines[si:ei]):\n", + " st = time.time()\n", + " idx, file = int(line.split(' ')[0]), line.split(' ')[1][:-1]\n", + " print(os.path.join(src_dir, file))\n", + " c = Av2Flau_Convertor(video_dir=os.path.join(src_dir, file),\n", + " out_dir=out_dir, idx=idx)\n", + " # (save_audio=False, register=False, show=False)\n", + " c.convert(show=False)\n", + " print('Idx: {}, Processed time (min): {}'.format(\n", + " idx, (time.time() - st) / 60.0))" + ], + "execution_count": 7, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "VvCz1n0s_MKe", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 287, + "referenced_widgets": [ + "b5d0875c12f04ee29935e6bcf8e5edfa", + "5206085eb0ba47abb9d3090fdce2fe05", + "accdea9952f84631906d445ca8cd3c11", + "4f529338c2504471bab90b2560121b56", + "fe43e16f4bda44d1937b93d8c62ad993", + "8c370c0cfdaf4794b5a556e0a96bdaca", + "e74207823db348a98427aa0b5a82ae0e", + "59b2a47d00d74a8a88f5c091669080cc" + ] + }, + "outputId": "9dd1edab-f315-4feb-ca0d-f833c3d41168" + }, + "source": [ + "landmark_extraction(10,200)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "['/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py', '-f', '/root/.local/share/jupyter/runtime/kernel-97428c04-7048-4f0b-8db8-4356b504ca1a.json']\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "b5d0875c12f04ee29935e6bcf8e5edfa", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=190.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "/content/MakeItTalk/mp4/id08701/yty4X4F1mQY/00453.mp4\n", + "video_dir : /content/MakeItTalk/mp4/id08701/yty4X4F1mQY/00453_preclean.mp4\n", + "Process Video /content/MakeItTalk/mp4/id08701/yty4X4F1mQY/00453_preclean.mp4, len: 186, FPS: 25.00, W X H: 224 x 224\n", + "\t ==> Final processed frames 186/186\n", + "Idx: 10, Processed time (min): 0.29549086491266885\n", + "/content/MakeItTalk/mp4/id08701/yty4X4F1mQY/00457.mp4\n", + "video_dir : /content/MakeItTalk/mp4/id08701/yty4X4F1mQY/00457_preclean.mp4\n", + "Process Video /content/MakeItTalk/mp4/id08701/yty4X4F1mQY/00457_preclean.mp4, len: 106, FPS: 25.00, W X H: 224 x 224\n", + "\t ==> Final processed frames 106/106\n", + "Idx: 11, Processed time (min): 0.17002590894699096\n", + "/content/MakeItTalk/mp4/id08701/yty4X4F1mQY/00462.mp4\n", + "video_dir : /content/MakeItTalk/mp4/id08701/yty4X4F1mQY/00462_preclean.mp4\n", + "Process Video /content/MakeItTalk/mp4/id08701/yty4X4F1mQY/00462_preclean.mp4, len: 149, FPS: 25.00, W X H: 224 x 224\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "XpfvwHWx_xhG", + "outputId": "e7e33fe5-8600-4fa3-e90c-43dcd453270b" + }, + "source": [ + "!zip -r \"/content/MakeItTalk/drive/MyDrive/MakeItTalk/preprocess.zip\" \"PreprocessedVox_imagetranslation/raw_fl3d\"" + ], + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "text": [ + "updating: PreprocessedVox_imagetranslation/raw_fl3d/ (stored 0%)\n", + "updating: PreprocessedVox_imagetranslation/raw_fl3d/fan_00000_00166_3d.txt (deflated 87%)\n", + "updating: PreprocessedVox_imagetranslation/raw_fl3d/fan_00004_00183_3d.txt (deflated 85%)\n", + "updating: PreprocessedVox_imagetranslation/raw_fl3d/fan_00003_00338_3d.txt (deflated 85%)\n", + "updating: PreprocessedVox_imagetranslation/raw_fl3d/fan_00002_00168_3d.txt (deflated 86%)\n", + "updating: PreprocessedVox_imagetranslation/raw_fl3d/fan_00001_00167_3d.txt (deflated 85%)\n", + " adding: PreprocessedVox_imagetranslation/raw_fl3d/fan_00007_00182_3d.txt (deflated 85%)\n", + " adding: PreprocessedVox_imagetranslation/raw_fl3d/fan_00005_00178_3d.txt (deflated 85%)\n", + " adding: PreprocessedVox_imagetranslation/raw_fl3d/fan_00009_00180_3d.txt (deflated 84%)\n", + " adding: PreprocessedVox_imagetranslation/raw_fl3d/fan_00008_00181_3d.txt (deflated 84%)\n", + " adding: PreprocessedVox_imagetranslation/raw_fl3d/fan_00006_00179_3d.txt (deflated 85%)\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "V7-5SCRoW9jv" + }, + "source": [ + "# import shutil\n", + "# shutil.rmtree(\"/content/MakeItTalk/PreprocessedVox_imagetranslation\")" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "a_mo8Be7JPBw" + }, + "source": [ + "!python main_train_image_translation.py" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "tJIu4XOSGKU-", + "outputId": "56949c36-94b2-4ff5-f3c4-0d82959a4b95" + }, + "source": [ + "import os \n", + "print(os.path.getsize(\"/content/MakeItTalk/model.h5\")/1e6)" + ], + "execution_count": 14, + "outputs": [ + { + "output_type": "stream", + "text": [ + "279.716596\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_2uVK_dXWs4V", + "outputId": "62679dab-6eb9-4d9c-ba9d-4c151d51f807" + }, + "source": [ + "import torch\n", + "x = torch.load('/content/MakeItTalk/PreprocessedVox_imagetranslation/ckpt/tmp/ckpt_09.pth')\n", + "print(x.keys())" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "dict_keys(['G', 'opt', 'epoch'])\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "3JYPC3vIIzXo" + }, + "source": [ + "print(model)" + ], + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/testg.py b/testg.py index 1e92355..a1d7a8f 100644 --- a/testg.py +++ b/testg.py @@ -1,3 +1,4 @@ +import cv2 from operator import xor import torch import torch.nn as nn @@ -6,13 +7,17 @@ import numpy as np from src.models.model_image_translation import ResUnetGenerator, ResidualBlock, ResidualBlock import torch_pruning as tp - +import matplotlib.pyplot as plt model = ResUnetGenerator(input_nc=6, output_nc=3, num_downs=6, use_dropout=False).to("cuda") # params = sum([np.prod(p.size()) for p in model.parameters()]) # print("Number of Parameters: %.1fM" % (params/1e6)) -print(model) +# print(model) +img = cv2.imread( + r'/home/hack/Downloads/Annotation (1)/Annotation/ROW_1278_11.jpg') +plt.imshow(img) +plt.show() # summary(model, input_size=(6, 256, 256)) # i = 0 From eb2a6fe1fc4e24f42a1e455037ee09d132e75303 Mon Sep 17 00:00:00 2001 From: hack Date: Wed, 6 Jan 2021 19:35:30 +0530 Subject: [PATCH 13/17] train --- main_train_image_translation.py | 12 +++--- src/approaches/train_image_translation.py | 40 ++++++++++--------- .../image_translation/data_preparation.py | 5 ++- src/dataset/utils/Av2Flau_Convertor.py | 3 +- 4 files changed, 32 insertions(+), 28 deletions(-) diff --git a/main_train_image_translation.py b/main_train_image_translation.py index b6c1718..d894ba4 100644 --- a/main_train_image_translation.py +++ b/main_train_image_translation.py @@ -41,14 +41,14 @@ parser = argparse.ArgumentParser() parser.add_argument('--nepoch', type=int, default=20, help='number of epochs to train for') -parser.add_argument('--batch_size', type=int, default=2, help='batch size') +parser.add_argument('--batch_size', type=int, default=8, help='batch size') parser.add_argument('--num_frames', type=int, default=1, help='') parser.add_argument('--num_workers', type=int, default=4, help='number of frames extracted from each video') parser.add_argument('--lr', type=float, default=0.0001, help='') -parser.add_argument('--write', default=False, action='store_true') -parser.add_argument('--train', default=False, action='store_true') +parser.add_argument('--write', default=True, action='store_true') +parser.add_argument('--train', default=True, action='store_true') parser.add_argument('--name', type=str, default='tmp') parser.add_argument('--test_speed', default=False, action='store_true') @@ -56,9 +56,9 @@ parser.add_argument('--ckpt_dir', type=str, default=ckpt_dir) parser.add_argument('--log_dir', type=str, default=log_dir) -parser.add_argument('--jpg_freq', type=int, default=2, help='') -parser.add_argument('--ckpt_last_freq', type=int, default=2, help='') -parser.add_argument('--ckpt_epoch_freq', type=int, default=2, help='') +parser.add_argument('--jpg_freq', type=int, default=50, help='') +parser.add_argument('--ckpt_last_freq', type=int, default=500, help='') +parser.add_argument('--ckpt_epoch_freq', type=int, default=1, help='') parser.add_argument('--load_G_name', type=str, default='examples/ckpt/ckpt_116_i2i_comb.pth') diff --git a/src/approaches/train_image_translation.py b/src/approaches/train_image_translation.py index 5745ec3..55561f5 100644 --- a/src/approaches/train_image_translation.py +++ b/src/approaches/train_image_translation.py @@ -16,6 +16,7 @@ import numpy as np import cv2 import os +import matplotlib.pyplot as plt import glob from src.dataset.image_translation.image_translation_dataset import vis_landmark_on_img, vis_landmark_on_img98, vis_landmark_on_img74 @@ -42,19 +43,19 @@ def __init__(self, opt_parser, single_test=False): self.G = ResUnetGenerator( input_nc=7, output_nc=3, num_downs=6, use_dropout=False) else: - # self.G = torch.load(opt_parser.load_G_name) - self.G = ResUnetGenerator( - input_nc=6, output_nc=3, num_downs=6, use_dropout=False) - - if (opt_parser.load_G_name != ''): - ckpt = torch.load(opt_parser.load_G_name) - try: - self.G.load_state_dict(ckpt['G']) - except: - tmp = nn.DataParallel(self.G) - tmp.load_state_dict(ckpt['G']) - self.G.load_state_dict(tmp.module.state_dict()) - del tmp + self.G = torch.load(opt_parser.load_G_name) + # self.G = ResUnetGenerator( + # input_nc=6, output_nc=3, num_downs=6, use_dropout=False) + + # if (opt_parser.load_G_name != ''): + # ckpt = torch.load(opt_parser.load_G_name) + # try: + # self.G.load_state_dict(ckpt['G']) + # except: + # tmp = nn.DataParallel(self.G) + # tmp.load_state_dict(ckpt['G']) + # self.G.load_state_dict(tmp.module.state_dict()) + # del tmp if torch.cuda.device_count() > 1: print("Let's use", torch.cuda.device_count(), "GPUs in G mode!") @@ -204,7 +205,6 @@ def __train_pass__(self, epoch, is_training=True): img_fls = np.stack(img_fls, axis=0).astype(np.float32) / 255.0 image_fls_in = torch.tensor( img_fls, requires_grad=False).to(device) - if(self.opt_parser.add_audio_in): # print(image_fls_in.shape, image_in.shape, audio_in.shape) image_in = torch.cat([image_fls_in, image_in, audio_in], dim=1) @@ -286,11 +286,13 @@ def __save_model__(self, save_type, epoch): except: pass if (self.opt_parser.write): - torch.save({ - 'G': self.G.state_dict(), - 'opt': self.optimizer, - 'epoch': epoch - }, os.path.join(self.opt_parser.ckpt_dir, self.opt_parser.name, 'ckpt_{}.pth'.format(save_type))) + torch.save(self.G, os.path.join(self.opt_parser.ckpt_dir, + self.opt_parser.name, 'ckpt_{}.pth'.format(save_type))) + # torch.save({ + # 'G': self.G.state_dict(), + # 'opt': self.optimizer, + # 'epoch': epoch + # }, os.path.join(self.opt_parser.ckpt_dir, self.opt_parser.name, 'ckpt_{}.pth'.format(save_type))) def train(self): for epoch in range(self.opt_parser.nepoch): diff --git a/src/dataset/image_translation/data_preparation.py b/src/dataset/image_translation/data_preparation.py index 3167e69..dd34472 100644 --- a/src/dataset/image_translation/data_preparation.py +++ b/src/dataset/image_translation/data_preparation.py @@ -1,11 +1,11 @@ """ # Copyright 2020 Adobe # All Rights Reserved. - + # NOTICE: Adobe permits you to use, modify, and distribute this file in # accordance with the terms of the Adobe license agreement accompanying # it. - + """ import os @@ -14,6 +14,7 @@ import sys import numpy as np import cv2 +import matplotlib.pyplot as plt from src.dataset.utils.Av2Flau_Convertor import Av2Flau_Convertor import platform diff --git a/src/dataset/utils/Av2Flau_Convertor.py b/src/dataset/utils/Av2Flau_Convertor.py index 6471c16..b1b8504 100644 --- a/src/dataset/utils/Av2Flau_Convertor.py +++ b/src/dataset/utils/Av2Flau_Convertor.py @@ -13,6 +13,7 @@ import ffmpeg import cv2 import face_alignment +from numpy.core import shape_base from src.dataset.utils import icp @@ -123,7 +124,7 @@ def __preclean_video__(self, WriteFileName='_preclean.mp4', fps=25, sample_rate= '_x_', '/') probe = ffmpeg.probe(input_video_dir) # print(probe['streams']) - # print(len(probe['streams'])) + # if(len(probe['streams']) != 2): # print('Error: not valid for # of a/v channel == 2.') # return False, None From 8550e803b47426e9526bb2add8389c56754ffd09 Mon Sep 17 00:00:00 2001 From: hack Date: Thu, 7 Jan 2021 10:35:11 +0530 Subject: [PATCH 14/17] epoch save --- main_train_image_translation.py | 5 +++-- src/approaches/train_image_translation.py | 13 ++++++++++--- testg.py | 13 +++++++------ 3 files changed, 20 insertions(+), 11 deletions(-) diff --git a/main_train_image_translation.py b/main_train_image_translation.py index d894ba4..d0675b8 100644 --- a/main_train_image_translation.py +++ b/main_train_image_translation.py @@ -53,7 +53,8 @@ parser.add_argument('--test_speed', default=False, action='store_true') parser.add_argument('--jpg_dir', type=str, default=jpg_dir) -parser.add_argument('--ckpt_dir', type=str, default=ckpt_dir) +parser.add_argument('--ckpt_dir', type=str, + default='/content/MakeItTalk/drive/MyDrive/MakeItTalk') parser.add_argument('--log_dir', type=str, default=log_dir) parser.add_argument('--jpg_freq', type=int, default=50, help='') @@ -61,7 +62,7 @@ parser.add_argument('--ckpt_epoch_freq', type=int, default=1, help='') parser.add_argument('--load_G_name', type=str, - default='examples/ckpt/ckpt_116_i2i_comb.pth') + default='/content/MakeItTalk/drive/MyDrive/MakeItTalk/tmp/ckpt_last.pth') parser.add_argument('--use_vox_dataset', type=str, default='raw') diff --git a/src/approaches/train_image_translation.py b/src/approaches/train_image_translation.py index 55561f5..1578afe 100644 --- a/src/approaches/train_image_translation.py +++ b/src/approaches/train_image_translation.py @@ -43,7 +43,8 @@ def __init__(self, opt_parser, single_test=False): self.G = ResUnetGenerator( input_nc=7, output_nc=3, num_downs=6, use_dropout=False) else: - self.G = torch.load(opt_parser.load_G_name) + self.ckpt = torch.load(opt_parser.load_G_name) + self.G = self.ckpt['G'] # self.G = ResUnetGenerator( # input_nc=6, output_nc=3, num_downs=6, use_dropout=False) @@ -151,6 +152,7 @@ def __init__(self, opt_parser, single_test=False): flip_input=True) def __train_pass__(self, epoch, is_training=True): + epoch += self.ckpt['epoch'] st_epoch = time.time() if(is_training): self.G.train() @@ -257,6 +259,7 @@ def __train_pass__(self, epoch, is_training=True): # save ckpt if (i % self.opt_parser.ckpt_last_freq == 0): self.__save_model__('last', epoch) + # os.system('!zip -r "/content/MakeItTalk/drive/MyDrive/MakeItTalk/last.zip" "PreprocessedVox_imagetranslation/ckpt/tmp/ckpt_last.pth"') print("Epoch {}, Batch {}/{}, loss {:.4f}, l1 {:.4f}, vggloss {:.4f}, styleloss {:.4f} time {:.4f}".format( epoch, i, len(self.dataset) // self.opt_parser.batch_size, @@ -286,8 +289,12 @@ def __save_model__(self, save_type, epoch): except: pass if (self.opt_parser.write): - torch.save(self.G, os.path.join(self.opt_parser.ckpt_dir, - self.opt_parser.name, 'ckpt_{}.pth'.format(save_type))) + # torch.save(self.G, os.path.join(self.opt_parser.ckpt_dir, + # self.opt_parser.name, 'ckpt_{}.pth'.format(save_type))) + torch.save({ + 'G': self.G, + 'epoch': epoch + }, os.path.join(self.opt_parser.ckpt_dir, self.opt_parser.name, 'ckpt_{}.pth'.format(save_type))) # torch.save({ # 'G': self.G.state_dict(), # 'opt': self.optimizer, diff --git a/testg.py b/testg.py index a1d7a8f..9313beb 100644 --- a/testg.py +++ b/testg.py @@ -8,16 +8,17 @@ from src.models.model_image_translation import ResUnetGenerator, ResidualBlock, ResidualBlock import torch_pruning as tp import matplotlib.pyplot as plt -model = ResUnetGenerator(input_nc=6, output_nc=3, - num_downs=6, use_dropout=False).to("cuda") +# model = ResUnetGenerator(input_nc=6, output_nc=3, +# num_downs=6, use_dropout=False).to("cuda") # params = sum([np.prod(p.size()) for p in model.parameters()]) # print("Number of Parameters: %.1fM" % (params/1e6)) + # print(model) -img = cv2.imread( - r'/home/hack/Downloads/Annotation (1)/Annotation/ROW_1278_11.jpg') -plt.imshow(img) -plt.show() +# img = cv2.imread( +# r'/home/hack/Downloads/Annotation (1)/Annotation/ROW_1278_11.jpg') +# plt.imshow(img) +# plt.show() # 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zhYWK8o_^AYV>0Rt%lkHx+cDHRm_I(=pMCe}h{FCl6YG)5%rkQDsp$wa1KO{WS;(J?21@`ewHB|ws@325BTEGjN#8~_N!lp{OzSHf5DghcLW|ldH#WAFU@$ErFMg|6s z0}5~DtG0b$+9t*D;CdJ{!=GDO3=Q$oybK?*S2Hry<(e`aSihEy;m6ji3=I266^#bL tXet=Z2%}}eXmL1NBaBvxBdA@#m~iQNl<(1L-N1$vgQu&X%Q~loCIC-U#n1o% literal 7656 zcmeHMO^n+_6n0fW&_7WwNWj5zITVWH@o#LaY1vJ(-AJTeZ5MS{LWs%CIBPZW7~8wa z_JTlMIQ4`$api<6I3qz195_^fghUUV5$XZ;#)$)O?8M2UO}i}!3CWS-cxJwNfAik= zM#@KPmoJ?>_QEkql1_G3TkDc^^b&kOdHN}sx&NX6Is6<+*Dozgx4wVr9t@u7FLjqB z>C2nP|HMzi_?dWhE0v^E--_>%kI$WYQId}1aAPyu?7ra>KCEHNd#pAd#(dOw;syEq*arbamSK53 z97L%mREq@={fJINqd zPKm|_Y{;T41*~3TAxhnD2|rNma9E<#ti1~sO?*2Loo?*KOj~Cu-$@8-?}ElNQ^V41 zoy|gXi0uF`H^?1Qp#xBx0vN@(mi6NWRMY;Zo9=FZyG zHo4gpxl)(Ub}Gr`1BRB5Pyy{ieo~y_66~qS zRgova$nkYuMfDk&(=gb3D3lUE;K>lf>4ihwXIdQfb8X$O--*%;M}&1+4FycCg(3B+ z=Tb{IE!AdBS9?$nDh^zx8gOOtwR)B?%?%)sgc$_a zDe+82p6PA!0Z+P|Ld^Hgcbd($ga_dO76e(zop#gFJqM1YrYo|@4Ik_PpRlOLhBBSp z6p209DJJU%f{&()=Va5?L)K@@Z$=@Ug`vnIyZqeHt3Y&M5a`P1*1|CxY_>uOZJHb5 zn%?}<*4&b(dxAysYW7#d6!LR#!b?}#yOXy;DCY(2V^TOkL?$z;Os2yF^5Ae0FT{?v z8HDNq$z8xw9%Lh&u%$l4cTok|`zlCVv^XEz{z-tzHjerXT9>9gzDH)=6h-K&X5e{Ho|dcPo@=Xw=rBtx zTAxLv^k@--Jc37_eD+!xQ9eq6(TaVC@rUqemnCyq7UKj%2WCmyP=bUHWgN$YkmQA? z?M8Ic*vcZ7GcM04Yhm0dOTxKanAcL8ApeW{9-euJV$dRx|Lr+x;&M^Yg7$aDD8{WK zm@DFV@}}({#0`hdMCjr^bcC`;JQ|x9`uRWY3Lue{-MO4Xwf0~ebL8AT5me_> zaQPIk<*gS6<-M6jz&HuqickNd{47b&J=landmark network''' -from src.approaches.train_audio2landmark import Audio2landmark_model model = Audio2landmark_model(opt_parser, jpg_shape=shape_3d) if(len(opt_parser.reuse_train_emb_list) == 0): model.test(au_emb=au_emb) @@ -132,11 +147,12 @@ fls_names = glob.glob1('examples_cartoon', 'pred_fls_*.txt') fls_names.sort() -for i in range(0,len(fls_names)): +for i in range(0, len(fls_names)): ains = glob.glob1('examples', '*.wav') ains.sort() ain = ains[i] - fl = np.loadtxt(os.path.join('examples_cartoon', fls_names[i])).reshape((-1, 68,3)) + fl = np.loadtxt(os.path.join('examples_cartoon', + fls_names[i])).reshape((-1, 68, 3)) output_dir = os.path.join('examples_cartoon', fls_names[i][:-4]) try: os.makedirs(output_dir) @@ -178,10 +194,12 @@ np.savetxt(os.path.join(output_dir, 'warped_points.txt'), fls, fmt='%.2f') # static_points.txt - static_frame = np.loadtxt(os.path.join('examples_cartoon', '{}_face_open_mouth.txt'.format(DEMO_CH))) + static_frame = np.loadtxt(os.path.join( + 'examples_cartoon', '{}_face_open_mouth.txt'.format(DEMO_CH))) static_frame = static_frame[r, 0:2] static_frame = np.concatenate((static_frame, bound.reshape(-1, 2)), axis=0) - np.savetxt(os.path.join(output_dir, 'reference_points.txt'), static_frame, fmt='%.2f') + np.savetxt(os.path.join(output_dir, 'reference_points.txt'), + static_frame, fmt='%.2f') # triangle_vtx_index.txt shutil.copy(os.path.join('examples_cartoon', DEMO_CH + '_delauney_tri.txt'), @@ -194,15 +212,15 @@ # ============================================== warp_exe = os.path.join(os.getcwd(), 'facewarp', 'facewarp.exe') import os - + if (os.path.exists(os.path.join(output_dir, 'output'))): shutil.rmtree(os.path.join(output_dir, 'output')) os.mkdir(os.path.join(output_dir, 'output')) os.chdir('{}'.format(os.path.join(output_dir, 'output'))) cur_dir = os.getcwd() print(cur_dir) - - os.system('{} {} {} {} {} {}'.format( + + os.system('wine {} {} {} {} {} {}'.format( warp_exe, os.path.join(cur_dir, '..', '..', opt_parser.jpg), os.path.join(cur_dir, '..', 'triangulation.txt'), diff --git a/testg.py b/testg.py index 9313beb..d5e2887 100644 --- a/testg.py +++ b/testg.py @@ -13,6 +13,13 @@ # params = sum([np.prod(p.size()) for p in model.parameters()]) # print("Number of Parameters: %.1fM" % (params/1e6)) +# img = cv2.imread('examples_cartoon/wilk.png') +# print(img.shape) + +bg = cv2.imread('examples_cartoon/wilk_bg.jpg') +bg = cv2.resize(bg, (839, 919)) +cv2.imwrite('examples_cartoon/wilk_bg.jpg', bg) + # print(model) # img = cv2.imread( From d62414040a58f797d17ce9a22b8eeb3d1a4536a4 Mon Sep 17 00:00:00 2001 From: hack Date: Thu, 14 Jan 2021 12:19:56 +0530 Subject: [PATCH 16/17] able to run facewrap.exe in ubuntu using wine --- README.md | 88 +++++++++++++++++++++++------------------ main_end2end_cartoon.py | 72 ++++++++++++++++++++------------- 2 files changed, 95 insertions(+), 65 deletions(-) diff --git a/README.md b/README.md index ab8940e..4dc935c 100644 --- a/README.md +++ b/README.md @@ -4,28 +4,28 @@ This is the code repository implementing the paper: > **MakeItTalk: Speaker-Aware Talking-Head Animation** > -> [Yang Zhou](https://people.umass.edu/~yangzhou), -> [Xintong Han](http://users.umiacs.umd.edu/~xintong/), -> [Eli Shechtman](https://research.adobe.com/person/eli-shechtman), -> [Jose Echevarria](http://www.jiechevarria.com) , -> [Evangelos Kalogerakis](https://people.cs.umass.edu/~kalo/), +> [Yang Zhou](https://people.umass.edu/~yangzhou), +> [Xintong Han](http://users.umiacs.umd.edu/~xintong/), +> [Eli Shechtman](https://research.adobe.com/person/eli-shechtman), +> [Jose Echevarria](http://www.jiechevarria.com) , +> [Evangelos Kalogerakis](https://people.cs.umass.edu/~kalo/), > [Dingzeyu Li](https://dingzeyu.li) > > SIGGRAPH Asia 2020 > -> **Abstract** We present a method that generates expressive talking-head videos from a single facial image with audio as the only input. In contrast to previous attempts to learn direct mappings from audio to raw pixels for creating talking faces, our method first disentangles the content and speaker information in the input audio signal. The audio content robustly controls the motion of lips and nearby facial regions, while the speaker information determines the specifics of facial expressions and the rest of the talking-head dynamics. Another key component of our method is the prediction of facial landmarks reflecting the speaker-aware dynamics. Based on this intermediate representation, our method works with many portrait images in a single unified framework, including artistic paintings, sketches, 2D cartoon characters, Japanese mangas, and stylized caricatures. -In addition, our method generalizes well for faces and characters that were not observed during training. We present extensive quantitative and qualitative evaluation of our method, in addition to user studies, demonstrating generated talking-heads of significantly higher quality compared to prior state-of-the-art methods. +> **Abstract** We present a method that generates expressive talking-head videos from a single facial image with audio as the only input. In contrast to previous attempts to learn direct mappings from audio to raw pixels for creating talking faces, our method first disentangles the content and speaker information in the input audio signal. The audio content robustly controls the motion of lips and nearby facial regions, while the speaker information determines the specifics of facial expressions and the rest of the talking-head dynamics. Another key component of our method is the prediction of facial landmarks reflecting the speaker-aware dynamics. Based on this intermediate representation, our method works with many portrait images in a single unified framework, including artistic paintings, sketches, 2D cartoon characters, Japanese mangas, and stylized caricatures. +> In addition, our method generalizes well for faces and characters that were not observed during training. We present extensive quantitative and qualitative evaluation of our method, in addition to user studies, demonstrating generated talking-heads of significantly higher quality compared to prior state-of-the-art methods. > -> [[Project page]](https://people.umass.edu/~yangzhou/MakeItTalk/) -> [[Paper]](https://people.umass.edu/~yangzhou/MakeItTalk/MakeItTalk_SIGGRAPH_Asia_Final_round-5.pdf) -> [[Video]](https://www.youtube.com/watch?v=OU6Ctzhpc6s) +> [[Project page]](https://people.umass.edu/~yangzhou/MakeItTalk/) +> [[Paper]](https://people.umass.edu/~yangzhou/MakeItTalk/MakeItTalk_SIGGRAPH_Asia_Final_round-5.pdf) +> [[Video]](https://www.youtube.com/watch?v=OU6Ctzhpc6s) > [[Arxiv]](https://arxiv.org/abs/2004.12992) > [[Colab Demo]](quick_demo.ipynb) > [[Colab Demo TDLR]](quick_demo_tdlr.ipynb) ![img](doc/teaser.png) -Figure. Given an audio speech signal and a single portrait image as input (left), our model generates speaker-aware talking-head animations (right). +Figure. Given an audio speech signal and a single portrait image as input (left), our model generates speaker-aware talking-head animations (right). Both the speech signal and the input face image are not observed during the model training process. Our method creates both non-photorealistic cartoon animations (top) and natural human face videos (bottom). @@ -37,16 +37,28 @@ Our method creates both non-photorealistic cartoon animations (top) and natural - [ ] Customized puppet creating tool ## Requirements + - Python environment 3.6 + ``` conda create -n makeittalk_env python=3.6 conda activate makeittalk_env ``` + - ffmpeg (https://ffmpeg.org/download.html) + ``` sudo apt-get install ffmpeg ``` + +- For ubuntu user + +``` +sudo apt-get install wine-stable +``` + - python packages + ``` pip install -r requirements.txt ``` @@ -55,13 +67,13 @@ pip install -r requirements.txt Download the following pre-trained models to `examples/ckpt` folder for testing your own animation. -| Model | Link to the model | -| :-------------: | :---------------: | -| Voice Conversion | [Link](https://drive.google.com/file/d/1ZiwPp_h62LtjU0DwpelLUoodKPR85K7x/view?usp=sharing) | -| Speech Content Module | [Link](https://drive.google.com/file/d/1r3bfEvTVl6pCNw5xwUhEglwDHjWtAqQp/view?usp=sharing) | -| Speaker-aware Module | [Link](https://drive.google.com/file/d/1rV0jkyDqPW-aDJcj7xSO6Zt1zSXqn1mu/view?usp=sharing) | -| Image2Image Translation Module | [Link](https://drive.google.com/file/d/1i2LJXKp-yWKIEEgJ7C6cE3_2NirfY_0a/view?usp=sharing) | -| Non-photorealistic Warping (.exe) | [Link](https://drive.google.com/file/d/1rlj0PAUMdX8TLuywsn6ds_G6L63nAu0P/view?usp=sharing) | +| Model | Link to the model | +| :-------------------------------: | :----------------------------------------------------------------------------------------: | +| Voice Conversion | [Link](https://drive.google.com/file/d/1ZiwPp_h62LtjU0DwpelLUoodKPR85K7x/view?usp=sharing) | +| Speech Content Module | [Link](https://drive.google.com/file/d/1r3bfEvTVl6pCNw5xwUhEglwDHjWtAqQp/view?usp=sharing) | +| Speaker-aware Module | [Link](https://drive.google.com/file/d/1rV0jkyDqPW-aDJcj7xSO6Zt1zSXqn1mu/view?usp=sharing) | +| Image2Image Translation Module | [Link](https://drive.google.com/file/d/1i2LJXKp-yWKIEEgJ7C6cE3_2NirfY_0a/view?usp=sharing) | +| Non-photorealistic Warping (.exe) | [Link](https://drive.google.com/file/d/1rlj0PAUMdX8TLuywsn6ds_G6L63nAu0P/view?usp=sharing) | ## Animate You Portraits! @@ -69,31 +81,29 @@ Download the following pre-trained models to `examples/ckpt` folder for testing ### _Nature Human Faces / Paintings_ -- crop your portrait image into size `256x256` and put it under `examples` folder with `.jpg` format. -Make sure the head is almost in the middle (check existing examples for a reference). +- crop your portrait image into size `256x256` and put it under `examples` folder with `.jpg` format. + Make sure the head is almost in the middle (check existing examples for a reference). - put test audio files under `examples` folder as well with `.wav` format. - animate! ``` -python main_end2end.py --jpg +python main_end2end.py --jpg ``` -- use addition args `--amp_lip_x --amp_lip_y --amp_pos ` -to amply lip motion (in x/y-axis direction) and head motion displacements, default values are `=2., =2., =.5` - +- use addition args `--amp_lip_x --amp_lip_y --amp_pos ` + to amply lip motion (in x/y-axis direction) and head motion displacements, default values are `=2., =2., =.5` - -### _Cartoon Faces_ +### _Cartoon Faces_ - put test audio files under `examples` folder as well with `.wav` format. - animate one of the existing puppets -| Puppet Name | wilk | roy | sketch | color | cartoonM | danbooru1 | -| :---: | :---: | :---: | :---: | :---: | :---: | :---: | -| Image | ![img](examples_cartoon/wilk_fullbody.jpg) | ![img](examples_cartoon/roy_full.png) | ![img](examples_cartoon/sketch.png) | ![img](examples_cartoon/color.jpg) | ![img](examples_cartoon/cartoonM.png) | ![img](examples_cartoon/danbooru1.jpg) | +| Puppet Name | wilk | roy | sketch | color | cartoonM | danbooru1 | +| :---------: | :----------------------------------------: | :-----------------------------------: | :---------------------------------: | :--------------------------------: | :-----------------------------------: | :------------------------------------: | +| Image | ![img](examples_cartoon/wilk_fullbody.jpg) | ![img](examples_cartoon/roy_full.png) | ![img](examples_cartoon/sketch.png) | ![img](examples_cartoon/color.jpg) | ![img](examples_cartoon/cartoonM.png) | ![img](examples_cartoon/danbooru1.jpg) | ``` python main_end2end_cartoon.py --jpg --jpg_bg @@ -101,28 +111,31 @@ python main_end2end_cartoon.py --jpg --jpg_ - `--jpg_bg` takes a same-size image as the background image to create the animation, such as the puppet's body, the overall fixed background image. If you want to use the background, make sure the puppet face image (i.e. `--jpg` image) is in `png` format and is transparent on the non-face area. If you don't need any background, please also create a same-size image (e.g. a pure white image) to hold the argument place. -- use addition args `--amp_lip_x --amp_lip_y --amp_pos ` -to amply lip motion (in x/y-axis direction) and head motion displacements, default values are `=2., =2., =.5` +- use addition args `--amp_lip_x --amp_lip_y --amp_pos ` + to amply lip motion (in x/y-axis direction) and head motion displacements, default values are `=2., =2., =.5` - create your own puppets (ToDo...) ## Train ### Train Voice Conversion Module + Todo... ### Train Content Branch + - Create dataset root directory `` - Dataset: Download preprocessed dataset [[here]](https://drive.google.com/drive/folders/1EwuAy3j1b9Zc1MsidUfxG_pJGc_cV60O?usp=sharing), and put it under `/dump`. - Train script: Run script below. Models will be saved in `/ckpt/`. - ```shell script - python main_train_content.py --train --write --root_dir --name - ``` - + ```shell script + python main_train_content.py --train --write --root_dir --name + ``` + ### Train Speaker-Aware Branch + Todo... ### Train Image-to-Image Translation @@ -134,13 +147,12 @@ Todo... ## Acknowledgement We would like to thank Timothy Langlois for the narration, and -[Kaizhi Qian](https://scholar.google.com/citations?user=uEpr4C4AAAAJ&hl=en) +[Kaizhi Qian](https://scholar.google.com/citations?user=uEpr4C4AAAAJ&hl=en) for the help with the [voice conversion module](https://auspicious3000.github.io/icassp-2020-demo/). We thank Daichi Ito for sharing the caricature image and Dave Werner -for Wilk, the gruff but ultimately lovable puppet. +for Wilk, the gruff but ultimately lovable puppet. This research is partially funded by NSF (EAGER-1942069) and a gift from Adobe. Our experiments were performed in the UMass GPU cluster obtained under the Collaborative Fund managed by the MassTech Collaborative. - diff --git a/main_end2end_cartoon.py b/main_end2end_cartoon.py index 0992f34..e37fc73 100644 --- a/main_end2end_cartoon.py +++ b/main_end2end_cartoon.py @@ -8,14 +8,16 @@ """ +import glob +import os +from src.approaches.train_audio2landmark import Audio2landmark_model +import shutil +from src.autovc.AutoVC_mel_Convertor_retrain_version import AutoVC_mel_Convertor +import pickle +import argparse +import numpy as np import sys sys.path.append('thirdparty/AdaptiveWingLoss') -import os, glob -import numpy as np -import argparse -import pickle -from src.autovc.AutoVC_mel_Convertor_retrain_version import AutoVC_mel_Convertor -import shutil ADD_NAIVE_EYE = False GEN_AUDIO = True @@ -24,26 +26,36 @@ DEMO_CH = 'wilk.png' parser = argparse.ArgumentParser() -parser.add_argument('--jpg', type=str, required=True, help='Puppet image name to animate (with filename extension), e.g. wilk.png') -parser.add_argument('--jpg_bg', type=str, required=True, help='Puppet image background (with filename extension), e.g. wilk_bg.jpg') +parser.add_argument('--jpg', type=str, required=True, + help='Puppet image name to animate (with filename extension), e.g. wilk.png') +parser.add_argument('--jpg_bg', type=str, required=True, + help='Puppet image background (with filename extension), e.g. wilk_bg.jpg') parser.add_argument('--out', type=str, default='out.mp4') -parser.add_argument('--load_AUTOVC_name', type=str, default='examples/ckpt/ckpt_autovc.pth') -parser.add_argument('--load_a2l_G_name', type=str, default='examples/ckpt/ckpt_speaker_branch.pth') #ckpt_audio2landmark_g.pth') # -parser.add_argument('--load_a2l_C_name', type=str, default='examples/ckpt/ckpt_content_branch.pth') #ckpt_audio2landmark_c.pth') -parser.add_argument('--load_G_name', type=str, default='examples/ckpt/ckpt_116_i2i_comb.pth') #ckpt_i2i_finetune_150.pth') #ckpt_image2image.pth') # +parser.add_argument('--load_AUTOVC_name', type=str, + default='examples/ckpt/ckpt_autovc.pth') +# ckpt_audio2landmark_g.pth') # +parser.add_argument('--load_a2l_G_name', type=str, + default='examples/ckpt/ckpt_speaker_branch.pth') +# ckpt_audio2landmark_c.pth') +parser.add_argument('--load_a2l_C_name', type=str, + default='examples/ckpt/ckpt_content_branch.pth') +# ckpt_i2i_finetune_150.pth') #ckpt_image2image.pth') # +parser.add_argument('--load_G_name', type=str, + default='examples/ckpt/ckpt_116_i2i_comb.pth') parser.add_argument('--amp_lip_x', type=float, default=2.0) parser.add_argument('--amp_lip_y', type=float, default=2.0) parser.add_argument('--amp_pos', type=float, default=0.5) -parser.add_argument('--reuse_train_emb_list', type=str, nargs='+', default=[]) # ['E_kmpT-EfOg']) # ['E_kmpT-EfOg']) # ['45hn7-LXDX8']) +# ['E_kmpT-EfOg']) # ['E_kmpT-EfOg']) # ['45hn7-LXDX8']) +parser.add_argument('--reuse_train_emb_list', type=str, nargs='+', default=[]) parser.add_argument('--add_audio_in', default=False, action='store_true') parser.add_argument('--comb_fan_awing', default=False, action='store_true') parser.add_argument('--output_folder', type=str, default='examples_cartoon') -#### NEW POSE MODEL +# NEW POSE MODEL parser.add_argument('--test_end2end', default=True, action='store_true') parser.add_argument('--dump_dir', type=str, default='', help='') parser.add_argument('--pos_dim', default=7, type=int) @@ -56,7 +68,8 @@ parser.add_argument('--lr', type=float, default=1e-3, help='learning rate') parser.add_argument('--reg_lr', type=float, default=1e-6, help='weight decay') parser.add_argument('--write', default=False, action='store_true') -parser.add_argument('--segment_batch_size', type=int, default=512, help='batch size') +parser.add_argument('--segment_batch_size', type=int, + default=512, help='batch size') parser.add_argument('--emb_coef', default=3.0, type=float) parser.add_argument('--lambda_laplacian_smooth_loss', default=1.0, type=float) parser.add_argument('--use_11spk_only', default=False, action='store_true') @@ -65,7 +78,8 @@ DEMO_CH = opt_parser.jpg.split('.')[0] -shape_3d = np.loadtxt('examples_cartoon/{}_face_close_mouth.txt'.format(DEMO_CH)) +shape_3d = np.loadtxt( + 'examples_cartoon/{}_face_close_mouth.txt'.format(DEMO_CH)) ''' STEP 3: Generate audio data as input to audio branch ''' au_data = [] @@ -74,7 +88,8 @@ ains = [item for item in ains if item is not 'tmp.wav'] ains.sort() for ain in ains: - os.system('ffmpeg -y -loglevel error -i examples/{} -ar 16000 examples/tmp.wav'.format(ain)) + os.system( + 'ffmpeg -y -loglevel error -i examples/{} -ar 16000 examples/tmp.wav'.format(ain)) shutil.copyfile('examples/tmp.wav', 'examples/{}'.format(ain)) # au embedding @@ -85,7 +100,7 @@ print('Processing audio file', ain) c = AutoVC_mel_Convertor('examples') au_data_i = c.convert_single_wav_to_autovc_input(audio_filename=os.path.join('examples', ain), - autovc_model_path=opt_parser.load_AUTOVC_name) + autovc_model_path=opt_parser.load_AUTOVC_name) au_data += au_data_i # os.remove(os.path.join('examples', 'tmp.wav')) if(os.path.isfile('examples/tmp.wav')): @@ -115,12 +130,12 @@ with open(os.path.join('examples', 'dump', 'random_val_au.pickle'), 'wb') as fp: pickle.dump(au_data, fp) with open(os.path.join('examples', 'dump', 'random_val_gaze.pickle'), 'wb') as fp: - gaze = {'rot_trans':rot_tran, 'rot_quat':rot_quat, 'anchor_t_shape':anchor_t_shape} + gaze = {'rot_trans': rot_tran, 'rot_quat': rot_quat, + 'anchor_t_shape': anchor_t_shape} pickle.dump(gaze, fp) ''' STEP 4: RUN audio->landmark network''' -from src.approaches.train_audio2landmark import Audio2landmark_model model = Audio2landmark_model(opt_parser, jpg_shape=shape_3d) if(len(opt_parser.reuse_train_emb_list) == 0): model.test(au_emb=au_emb) @@ -132,11 +147,12 @@ fls_names = glob.glob1('examples_cartoon', 'pred_fls_*.txt') fls_names.sort() -for i in range(0,len(fls_names)): +for i in range(0, len(fls_names)): ains = glob.glob1('examples', '*.wav') ains.sort() ain = ains[i] - fl = np.loadtxt(os.path.join('examples_cartoon', fls_names[i])).reshape((-1, 68,3)) + fl = np.loadtxt(os.path.join('examples_cartoon', + fls_names[i])).reshape((-1, 68, 3)) output_dir = os.path.join('examples_cartoon', fls_names[i][:-4]) try: os.makedirs(output_dir) @@ -178,10 +194,12 @@ np.savetxt(os.path.join(output_dir, 'warped_points.txt'), fls, fmt='%.2f') # static_points.txt - static_frame = np.loadtxt(os.path.join('examples_cartoon', '{}_face_open_mouth.txt'.format(DEMO_CH))) + static_frame = np.loadtxt(os.path.join( + 'examples_cartoon', '{}_face_open_mouth.txt'.format(DEMO_CH))) static_frame = static_frame[r, 0:2] static_frame = np.concatenate((static_frame, bound.reshape(-1, 2)), axis=0) - np.savetxt(os.path.join(output_dir, 'reference_points.txt'), static_frame, fmt='%.2f') + np.savetxt(os.path.join(output_dir, 'reference_points.txt'), + static_frame, fmt='%.2f') # triangle_vtx_index.txt shutil.copy(os.path.join('examples_cartoon', DEMO_CH + '_delauney_tri.txt'), @@ -194,15 +212,15 @@ # ============================================== warp_exe = os.path.join(os.getcwd(), 'facewarp', 'facewarp.exe') import os - + if (os.path.exists(os.path.join(output_dir, 'output'))): shutil.rmtree(os.path.join(output_dir, 'output')) os.mkdir(os.path.join(output_dir, 'output')) os.chdir('{}'.format(os.path.join(output_dir, 'output'))) cur_dir = os.getcwd() print(cur_dir) - - os.system('{} {} {} {} {} {}'.format( + + os.system('wine {} {} {} {} {} {}'.format( warp_exe, os.path.join(cur_dir, '..', '..', opt_parser.jpg), os.path.join(cur_dir, '..', 'triangulation.txt'), From 79df25bf722f74d03adb0698c20a31513b5cd0b0 Mon Sep 17 00:00:00 2001 From: hack Date: Tue, 19 Jan 2021 01:02:11 +0530 Subject: [PATCH 17/17] fixed --- examples/M6_04_16k.wav | Bin 156104 -> 156104 bytes examples/M6_04_16k_av.mp4 | Bin 199014 -> 242874 bytes .../dali_pred_fls_M6_04_16k_audio_embed.mp4 | Bin 0 -> 235779 bytes examples/tmp.wav | Bin 0 -> 156138 bytes .../pred_fls_M6_04_16k_audio_embed.txt | 287 ++++++++++ examples_cartoon/{wilk2.jpg => wilk2.png} | Bin .../{wilk_bg.png => wilk2_bg.jpg} | Bin main_end2end.py | 81 +-- main_end2end_cartoon.py | 196 +++---- out.mp4 | Bin 0 -> 6829175 bytes .../AutoVC_mel_Convertor_retrain_version.py | 94 ++-- .../vocoder_spec/extract_f0_func.py | 13 +- src/models/__init__.pyc | Bin 0 -> 385 bytes src/models/model_image_translation.pyc | Bin 0 -> 26332 bytes testg.py | 13 +- thirdparty/resemblyer_util/speaker_emb.py | 12 +- train_image_translation.py | 528 ++++++++++++++++++ 17 files changed, 1041 insertions(+), 183 deletions(-) create mode 100644 examples/dali_pred_fls_M6_04_16k_audio_embed.mp4 create mode 100644 examples/tmp.wav create mode 100644 examples_cartoon/pred_fls_M6_04_16k_audio_embed.txt rename examples_cartoon/{wilk2.jpg => wilk2.png} (100%) rename examples_cartoon/{wilk_bg.png => wilk2_bg.jpg} (100%) create mode 100644 out.mp4 create mode 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a/examples_cartoon/wilk_bg.png b/examples_cartoon/wilk2_bg.jpg similarity index 100% rename from examples_cartoon/wilk_bg.png rename to examples_cartoon/wilk2_bg.jpg diff --git a/main_end2end.py b/main_end2end.py index 64d09ef..16c4967 100644 --- a/main_end2end.py +++ b/main_end2end.py @@ -8,22 +8,23 @@ """ +import glob +import os +from src.approaches.train_audio2landmark import Audio2landmark_model +from scipy.signal import savgol_filter +import util.utils as util +import shutil +from src.autovc.AutoVC_mel_Convertor_retrain_version import AutoVC_mel_Convertor +import face_alignment +import pickle +import torch +from src.approaches.train_image_translation import Image_translation_block +import argparse +import cv2 +import numpy as np import sys sys.path.append('thirdparty/AdaptiveWingLoss') -import os, glob -import numpy as np -import cv2 -import argparse -from src.approaches.train_image_translation import Image_translation_block -import torch -import pickle -import face_alignment -from src.autovc.AutoVC_mel_Convertor_retrain_version import AutoVC_mel_Convertor -import shutil -import util.utils as util -from scipy.signal import savgol_filter -from src.approaches.train_audio2landmark import Audio2landmark_model default_head_name = 'dali' ADD_NAIVE_EYE = True @@ -31,18 +32,27 @@ parser = argparse.ArgumentParser() -parser.add_argument('--jpg', type=str, default='{}.jpg'.format(default_head_name)) -parser.add_argument('--close_input_face_mouth', default=CLOSE_INPUT_FACE_MOUTH, action='store_true') - -parser.add_argument('--load_AUTOVC_name', type=str, default='examples/ckpt/ckpt_autovc.pth') -parser.add_argument('--load_a2l_G_name', type=str, default='examples/ckpt/ckpt_speaker_branch.pth') -parser.add_argument('--load_a2l_C_name', type=str, default='examples/ckpt/ckpt_content_branch.pth') #ckpt_audio2landmark_c.pth') -parser.add_argument('--load_G_name', type=str, default='examples/ckpt/ckpt_116_i2i_comb.pth') #ckpt_image2image.pth') #ckpt_i2i_finetune_150.pth') #c +parser.add_argument('--jpg', type=str, + default='{}.jpg'.format(default_head_name)) +parser.add_argument('--close_input_face_mouth', + default=CLOSE_INPUT_FACE_MOUTH, action='store_true') + +parser.add_argument('--load_AUTOVC_name', type=str, + default='examples/ckpt/ckpt_autovc.pth') +parser.add_argument('--load_a2l_G_name', type=str, + default='examples/ckpt/ckpt_speaker_branch.pth') +# ckpt_audio2landmark_c.pth') +parser.add_argument('--load_a2l_C_name', type=str, + default='examples/ckpt/ckpt_content_branch.pth') +# ckpt_image2image.pth') #ckpt_i2i_finetune_150.pth') #c +parser.add_argument('--load_G_name', type=str, + default='examples/ckpt/epoch7size139.pth') parser.add_argument('--amp_lip_x', type=float, default=2.) parser.add_argument('--amp_lip_y', type=float, default=2.) parser.add_argument('--amp_pos', type=float, default=.5) -parser.add_argument('--reuse_train_emb_list', type=str, nargs='+', default=[]) # ['iWeklsXc0H8']) #['45hn7-LXDX8']) #['E_kmpT-EfOg']) #'iWeklsXc0H8', '29k8RtSUjE0', '45hn7-LXDX8', +# ['iWeklsXc0H8']) #['45hn7-LXDX8']) #['E_kmpT-EfOg']) #'iWeklsXc0H8', '29k8RtSUjE0', '45hn7-LXDX8', +parser.add_argument('--reuse_train_emb_list', type=str, nargs='+', default=[]) parser.add_argument('--add_audio_in', default=False, action='store_true') parser.add_argument('--comb_fan_awing', default=False, action='store_true') parser.add_argument('--output_folder', type=str, default='examples') @@ -59,7 +69,8 @@ parser.add_argument('--lr', type=float, default=1e-3, help='learning rate') parser.add_argument('--reg_lr', type=float, default=1e-6, help='weight decay') parser.add_argument('--write', default=False, action='store_true') -parser.add_argument('--segment_batch_size', type=int, default=1, help='batch size') +parser.add_argument('--segment_batch_size', type=int, + default=1, help='batch size') parser.add_argument('--emb_coef', default=3.0, type=float) parser.add_argument('--lambda_laplacian_smooth_loss', default=1.0, type=float) parser.add_argument('--use_11spk_only', default=False, action='store_true') @@ -67,8 +78,9 @@ opt_parser = parser.parse_args() ''' STEP 1: preprocess input single image ''' -img =cv2.imread('examples/' + opt_parser.jpg) -predictor = face_alignment.FaceAlignment(face_alignment.LandmarksType._3D, device='cuda', flip_input=True) +img = cv2.imread('examples/' + opt_parser.jpg) +predictor = face_alignment.FaceAlignment( + face_alignment.LandmarksType._3D, device='cuda', flip_input=True) shapes = predictor.get_landmarks(img) if (not shapes or len(shapes) != 1): print('Cannot detect face landmarks. Exit.') @@ -83,8 +95,8 @@ # shape_3d[48:, 0] = (shape_3d[48:, 0] - np.mean(shape_3d[48:, 0])) * 0.95 + np.mean(shape_3d[48:, 0]) shape_3d[49:54, 1] += 1. shape_3d[55:60, 1] -= 1. -shape_3d[[37,38,43,44], 1] -=2 -shape_3d[[40,41,46,47], 1] +=2 +shape_3d[[37, 38, 43, 44], 1] -= 2 +shape_3d[[40, 41, 46, 47], 1] += 2 ''' STEP 2: normalize face as input to audio branch ''' @@ -96,10 +108,11 @@ au_data = [] au_emb = [] ains = glob.glob1('examples', '*.wav') -ains = [item for item in ains if item is not 'tmp.wav'] +ains = [item for item in ains if item != 'tmp.wav'] ains.sort() for ain in ains: - os.system('ffmpeg -y -loglevel error -i examples/{} -ar 16000 examples/tmp.wav'.format(ain)) + os.system( + 'ffmpeg -y -loglevel error -i examples/{} -ar 16000 examples/tmp.wav'.format(ain)) shutil.copyfile('examples/tmp.wav', 'examples/{}'.format(ain)) # au embedding @@ -111,7 +124,7 @@ c = AutoVC_mel_Convertor('examples') au_data_i = c.convert_single_wav_to_autovc_input(audio_filename=os.path.join('examples', ain), - autovc_model_path=opt_parser.load_AUTOVC_name) + autovc_model_path=opt_parser.load_AUTOVC_name) au_data += au_data_i if(os.path.isfile('examples/tmp.wav')): os.remove('examples/tmp.wav') @@ -141,7 +154,8 @@ with open(os.path.join('examples', 'dump', 'random_val_au.pickle'), 'wb') as fp: pickle.dump(au_data, fp) with open(os.path.join('examples', 'dump', 'random_val_gaze.pickle'), 'wb') as fp: - gaze = {'rot_trans':rot_tran, 'rot_quat':rot_quat, 'anchor_t_shape':anchor_t_shape} + gaze = {'rot_trans': rot_tran, 'rot_quat': rot_quat, + 'anchor_t_shape': anchor_t_shape} pickle.dump(gaze, fp) @@ -157,8 +171,8 @@ fls = glob.glob1('examples', 'pred_fls_*.txt') fls.sort() -for i in range(0,len(fls)): - fl = np.loadtxt(os.path.join('examples', fls[i])).reshape((-1, 68,3)) +for i in range(0, len(fls)): + fl = np.loadtxt(os.path.join('examples', fls[i])).reshape((-1, 68, 3)) fl[:, :, 0:2] = -fl[:, :, 0:2] fl[:, :, 0:2] = fl[:, :, 0:2] / scale - shift @@ -174,6 +188,7 @@ ''' STEP 6: Imag2image translation ''' model = Image_translation_block(opt_parser, single_test=True) with torch.no_grad(): - model.single_test(jpg=img, fls=fl, filename=fls[i], prefix=opt_parser.jpg.split('.')[0]) + model.single_test( + jpg=img, fls=fl, filename=fls[i], prefix=opt_parser.jpg.split('.')[0]) print('finish image2image gen') os.remove(os.path.join('examples', fls[i])) diff --git a/main_end2end_cartoon.py b/main_end2end_cartoon.py index b39b344..1f4b128 100644 --- a/main_end2end_cartoon.py +++ b/main_end2end_cartoon.py @@ -26,9 +26,9 @@ DEMO_CH = 'wilk.png' parser = argparse.ArgumentParser() -parser.add_argument('--jpg', type=str, required=True, +parser.add_argument('--jpg', type=str, default='wilk.png', help='Puppet image name to animate (with filename extension), e.g. wilk.png') -parser.add_argument('--jpg_bg', type=str, required=True, +parser.add_argument('--jpg_bg', type=str, default='wilk_bg.jpg', help='Puppet image background (with filename extension), e.g. wilk_bg.jpg') parser.add_argument('--out', type=str, default='out.mp4') @@ -90,7 +90,7 @@ for ain in ains: os.system( 'ffmpeg -y -loglevel error -i examples/{} -ar 16000 examples/tmp.wav'.format(ain)) - # shutil.copyfile('examples/tmp.wav', 'examples/{}'.format(ain)) + shutil.copyfile('examples/tmp.wav', 'examples/{}'.format(ain)) # au embedding from thirdparty.resemblyer_util.speaker_emb import get_spk_emb @@ -98,13 +98,13 @@ au_emb.append(me.reshape(-1)) print('Processing audio file', ain) - c = AutoVC_mel_Convertor('examples') + c = AutoVC_mel_Convertor('example') au_data_i = c.convert_single_wav_to_autovc_input(audio_filename=os.path.join('examples', ain), autovc_model_path=opt_parser.load_AUTOVC_name) - au_data += au_data_i - # os.remove(os.path.join('examples', 'tmp.wav')) -if(os.path.isfile('examples/tmp.wav')): - os.remove('examples/tmp.wav') +# au_data += au_data_i +# # os.remove(os.path.join('examples', 'tmp.wav')) +# if(os.path.isfile('examples/tmp.wav')): +# os.remove('examples/tmp.wav') fl_data = [] rot_tran, rot_quat, anchor_t_shape = [], [], [] @@ -135,7 +135,7 @@ pickle.dump(gaze, fp) -''' STEP 4: RUN audio->landmark network''' +# ''' STEP 4: RUN audio->landmark network''' model = Audio2landmark_model(opt_parser, jpg_shape=shape_3d) if(len(opt_parser.reuse_train_emb_list) == 0): model.test(au_emb=au_emb) @@ -143,92 +143,92 @@ model.test(au_emb=None) print('finish gen fls') -''' STEP 5: de-normalize the output to the original image scale ''' -fls_names = glob.glob1('examples_cartoon', 'pred_fls_*.txt') -fls_names.sort() - -for i in range(0, len(fls_names)): - ains = glob.glob1('examples', '*.wav') - ains.sort() - ain = ains[i] - fl = np.loadtxt(os.path.join('examples_cartoon', - fls_names[i])).reshape((-1, 68, 3)) - output_dir = os.path.join('examples_cartoon', fls_names[i][:-4]) - try: - os.makedirs(output_dir) - except: - pass - - from util.utils import get_puppet_info - - bound, scale, shift = get_puppet_info(DEMO_CH, ROOT_DIR='examples_cartoon') - - fls = fl.reshape((-1, 68, 3)) - - fls[:, :, 0:2] = -fls[:, :, 0:2] - fls[:, :, 0:2] = (fls[:, :, 0:2] / scale) - fls[:, :, 0:2] -= shift.reshape(1, 2) - - fls = fls.reshape(-1, 204) - - # additional smooth - from scipy.signal import savgol_filter - fls[:, 0:48*3] = savgol_filter(fls[:, 0:48*3], 17, 3, axis=0) - fls[:, 48*3:] = savgol_filter(fls[:, 48*3:], 11, 3, axis=0) - fls = fls.reshape((-1, 68, 3)) - - if (DEMO_CH in ['paint', 'mulaney', 'cartoonM', 'beer', 'color', 'JohnMulaney', 'vangogh', 'jm', 'roy', 'lineface']): - r = list(range(0, 68)) - fls = fls[:, r, :] - fls = fls[:, :, 0:2].reshape(-1, 68 * 2) - fls = np.concatenate((fls, np.tile(bound, (fls.shape[0], 1))), axis=1) - fls = fls.reshape(-1, 160) - - else: - r = list(range(0, 48)) + list(range(60, 68)) - fls = fls[:, r, :] - fls = fls[:, :, 0:2].reshape(-1, 56 * 2) - fls = np.concatenate((fls, np.tile(bound, (fls.shape[0], 1))), axis=1) - fls = fls.reshape(-1, 112 + bound.shape[1]) - - np.savetxt(os.path.join(output_dir, 'warped_points.txt'), fls, fmt='%.2f') - - # static_points.txt - static_frame = np.loadtxt(os.path.join( - 'examples_cartoon', '{}_face_open_mouth.txt'.format(DEMO_CH))) - static_frame = static_frame[r, 0:2] - static_frame = np.concatenate((static_frame, bound.reshape(-1, 2)), axis=0) - np.savetxt(os.path.join(output_dir, 'reference_points.txt'), - static_frame, fmt='%.2f') - - # triangle_vtx_index.txt - shutil.copy(os.path.join('examples_cartoon', DEMO_CH + '_delauney_tri.txt'), - os.path.join(output_dir, 'triangulation.txt')) - - os.remove(os.path.join('examples_cartoon', fls_names[i])) - - # ============================================== - # Step 4 : Vector art morphing (only work in WINDOWS) - # ============================================== - warp_exe = os.path.join(os.getcwd(), 'facewarp', 'facewarp.exe') - import os - - if (os.path.exists(os.path.join(output_dir, 'output'))): - shutil.rmtree(os.path.join(output_dir, 'output')) - os.mkdir(os.path.join(output_dir, 'output')) - os.chdir('{}'.format(os.path.join(output_dir, 'output'))) - cur_dir = os.getcwd() - print(cur_dir) - - os.system('wine {} {} {} {} {} {}'.format( - warp_exe, - os.path.join(cur_dir, '..', '..', opt_parser.jpg), - os.path.join(cur_dir, '..', 'triangulation.txt'), - os.path.join(cur_dir, '..', 'reference_points.txt'), - os.path.join(cur_dir, '..', 'warped_points.txt'), - os.path.join(cur_dir, '..', '..', opt_parser.jpg_bg), - '-novsync -dump')) - os.system('ffmpeg -y -r 62.5 -f image2 -i "%06d.tga" -i {} -pix_fmt yuv420p -vf "pad=ceil(iw/2)*2:ceil(ih/2)*2" -shortest {}'.format( - os.path.join(cur_dir, '..', '..', '..', 'examples', ain), - os.path.join(cur_dir, '..', 'out.mp4') - )) +# ''' STEP 5: de-normalize the output to the original image scale ''' +# fls_names = glob.glob1('examples_cartoon', 'pred_fls_*.txt') +# fls_names.sort() + +# for i in range(0, len(fls_names)): +# ains = glob.glob1('examples', '*.wav') +# ains.sort() +# ain = ains[i] +# fl = np.loadtxt(os.path.join('examples_cartoon', +# fls_names[i])).reshape((-1, 68, 3)) +# output_dir = os.path.join('examples_cartoon', fls_names[i][:-4]) +# try: +# os.makedirs(output_dir) +# except: +# pass + +# from util.utils import get_puppet_info + +# bound, scale, shift = get_puppet_info(DEMO_CH, ROOT_DIR='examples_cartoon') + +# fls = fl.reshape((-1, 68, 3)) + +# fls[:, :, 0:2] = -fls[:, :, 0:2] +# fls[:, :, 0:2] = (fls[:, :, 0:2] / scale) +# fls[:, :, 0:2] -= shift.reshape(1, 2) + +# fls = fls.reshape(-1, 204) + +# # additional smooth +# from scipy.signal import savgol_filter +# fls[:, 0:48*3] = savgol_filter(fls[:, 0:48*3], 17, 3, axis=0) +# fls[:, 48*3:] = savgol_filter(fls[:, 48*3:], 11, 3, axis=0) +# fls = fls.reshape((-1, 68, 3)) + +# if (DEMO_CH in ['paint', 'mulaney', 'cartoonM', 'beer', 'color', 'JohnMulaney', 'vangogh', 'jm', 'roy', 'lineface']): +# r = list(range(0, 68)) +# fls = fls[:, r, :] +# fls = fls[:, :, 0:2].reshape(-1, 68 * 2) +# fls = np.concatenate((fls, np.tile(bound, (fls.shape[0], 1))), axis=1) +# fls = fls.reshape(-1, 160) + +# else: +# r = list(range(0, 48)) + list(range(60, 68)) +# fls = fls[:, r, :] +# fls = fls[:, :, 0:2].reshape(-1, 56 * 2) +# fls = np.concatenate((fls, np.tile(bound, (fls.shape[0], 1))), axis=1) +# fls = fls.reshape(-1, 112 + bound.shape[1]) + +# np.savetxt(os.path.join(output_dir, 'warped_points.txt'), fls, fmt='%.2f') + +# # static_points.txt +# static_frame = np.loadtxt(os.path.join( +# 'examples_cartoon', '{}_face_open_mouth.txt'.format(DEMO_CH))) +# static_frame = static_frame[r, 0:2] +# static_frame = np.concatenate((static_frame, bound.reshape(-1, 2)), axis=0) +# np.savetxt(os.path.join(output_dir, 'reference_points.txt'), +# static_frame, fmt='%.2f') + +# # triangle_vtx_index.txt +# shutil.copy(os.path.join('examples_cartoon', DEMO_CH + '_delauney_tri.txt'), +# os.path.join(output_dir, 'triangulation.txt')) + +# os.remove(os.path.join('examples_cartoon', fls_names[i])) + +# # ============================================== +# # Step 4 : Vector art morphing (only work in WINDOWS) +# # ============================================== +# warp_exe = os.path.join(os.getcwd(), 'facewarp', 'facewarp.exe') +# import os + +# if (os.path.exists(os.path.join(output_dir, 'output'))): +# shutil.rmtree(os.path.join(output_dir, 'output')) +# os.mkdir(os.path.join(output_dir, 'output')) +# os.chdir('{}'.format(os.path.join(output_dir, 'output'))) +# cur_dir = os.getcwd() +# print(cur_dir) + +# os.system('wine {} {} {} {} {} {}'.format( +# warp_exe, +# os.path.join(cur_dir, '..', '..', opt_parser.jpg), +# os.path.join(cur_dir, '..', 'triangulation.txt'), +# os.path.join(cur_dir, '..', 'reference_points.txt'), +# os.path.join(cur_dir, '..', 'warped_points.txt'), +# os.path.join(cur_dir, '..', '..', opt_parser.jpg_bg), +# '-novsync -dump')) +# os.system('ffmpeg -y -r 62.5 -f image2 -i "%06d.tga" -i {} -pix_fmt yuv420p -vf "pad=ceil(iw/2)*2:ceil(ih/2)*2" -shortest {}'.format( +# 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