import train import os import time import csv import sys import warnings import random import torch import torch.nn as nn import torch.nn.functional as F from torch.utils.tensorboard import SummaryWriter from torch.nn.parallel import DistributedDataParallel as DDP import numpy as np import time import pprint from loguru import logger from utils import rotation_conversions as rc import smplx from utils import config, logger_tools, other_tools, metric, data_transfer from dataloaders import data_tools from optimizers.optim_factory import create_optimizer from optimizers.scheduler_factory import create_scheduler from optimizers.loss_factory import get_loss_func from dataloaders.data_tools import joints_list import librosa class CustomTrainer(train.BaseTrainer): def __init__(self, args): super().__init__(args) self.args = args self.joints = self.train_data.joints self.ori_joint_list = joints_list[self.args.ori_joints] self.tar_joint_list_face = joints_list["beat_smplx_face"] self.tar_joint_list_upper = joints_list["beat_smplx_upper"] self.tar_joint_list_hands = joints_list["beat_smplx_hands"] self.tar_joint_list_lower = joints_list["beat_smplx_lower"] self.joint_mask_face = np.zeros(len(list(self.ori_joint_list.keys()))*3) self.joints = 55 for joint_name in self.tar_joint_list_face: self.joint_mask_face[self.ori_joint_list[joint_name][1] - self.ori_joint_list[joint_name][0]:self.ori_joint_list[joint_name][1]] = 1 self.joint_mask_upper = np.zeros(len(list(self.ori_joint_list.keys()))*3) for joint_name in self.tar_joint_list_upper: self.joint_mask_upper[self.ori_joint_list[joint_name][1] - self.ori_joint_list[joint_name][0]:self.ori_joint_list[joint_name][1]] = 1 self.joint_mask_hands = np.zeros(len(list(self.ori_joint_list.keys()))*3) for joint_name in self.tar_joint_list_hands: self.joint_mask_hands[self.ori_joint_list[joint_name][1] - self.ori_joint_list[joint_name][0]:self.ori_joint_list[joint_name][1]] = 1 self.joint_mask_lower = np.zeros(len(list(self.ori_joint_list.keys()))*3) for joint_name in self.tar_joint_list_lower: self.joint_mask_lower[self.ori_joint_list[joint_name][1] - self.ori_joint_list[joint_name][0]:self.ori_joint_list[joint_name][1]] = 1 self.tracker = other_tools.EpochTracker(["fid", "l1div", "bc", "rec", "trans", "vel", "transv", 'dis', 'gen', 'acc', 'transa', 'exp', 'lvd', 'mse', "cls", "rec_face", "latent", "cls_full", "cls_self", "cls_word", "latent_word","latent_self"], [False,True,True, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False,False,False,False]) vq_model_module = __import__(f"models.motion_representation", fromlist=["something"]) self.args.vae_layer = 2 self.args.vae_length = 256 self.args.vae_test_dim = 106 self.vq_model_face = getattr(vq_model_module, "VQVAEConvZero")(self.args).to(self.rank) # print(self.vq_model_face) other_tools.load_checkpoints(self.vq_model_face, self.args.data_path_1 + "pretrained_vq/last_790_face_v2.bin", args.e_name) self.args.vae_test_dim = 78 self.vq_model_upper = getattr(vq_model_module, "VQVAEConvZero")(self.args).to(self.rank) other_tools.load_checkpoints(self.vq_model_upper, self.args.data_path_1 + "pretrained_vq/upper_vertex_1layer_710.bin", args.e_name) self.args.vae_test_dim = 180 self.vq_model_hands = getattr(vq_model_module, "VQVAEConvZero")(self.args).to(self.rank) other_tools.load_checkpoints(self.vq_model_hands, self.args.data_path_1 + "pretrained_vq/hands_vertex_1layer_710.bin", args.e_name) self.args.vae_test_dim = 61 self.args.vae_layer = 4 self.vq_model_lower = getattr(vq_model_module, "VQVAEConvZero")(self.args).to(self.rank) other_tools.load_checkpoints(self.vq_model_lower, self.args.data_path_1 + "pretrained_vq/lower_foot_600.bin", args.e_name) self.args.vae_test_dim = 61 self.args.vae_layer = 4 self.global_motion = getattr(vq_model_module, "VAEConvZero")(self.args).to(self.rank) other_tools.load_checkpoints(self.global_motion, self.args.data_path_1 + "pretrained_vq/last_1700_foot.bin", args.e_name) self.args.vae_test_dim = 330 self.args.vae_layer = 4 self.args.vae_length = 240 self.vq_model_face.eval() self.vq_model_upper.eval() self.vq_model_hands.eval() self.vq_model_lower.eval() self.global_motion.eval() self.cls_loss = nn.NLLLoss().to(self.rank) self.reclatent_loss = nn.MSELoss().to(self.rank) self.vel_loss = torch.nn.L1Loss(reduction='mean').to(self.rank) self.rec_loss = get_loss_func("GeodesicLoss").to(self.rank) self.log_softmax = nn.LogSoftmax(dim=2).to(self.rank) def inverse_selection(self, filtered_t, selection_array, n): original_shape_t = np.zeros((n, selection_array.size)) selected_indices = np.where(selection_array == 1)[0] for i in range(n): original_shape_t[i, selected_indices] = filtered_t[i] return original_shape_t def inverse_selection_tensor(self, filtered_t, selection_array, n): selection_array = torch.from_numpy(selection_array).cuda() original_shape_t = torch.zeros((n, 165)).cuda() selected_indices = torch.where(selection_array == 1)[0] for i in range(n): original_shape_t[i, selected_indices] = filtered_t[i] return original_shape_t def _load_data(self, dict_data): tar_pose_raw = dict_data["pose"] tar_pose = tar_pose_raw[:, :, :165].to(self.rank) tar_contact = tar_pose_raw[:, :, 165:169].to(self.rank) tar_trans = dict_data["trans"].to(self.rank) tar_exps = dict_data["facial"].to(self.rank) in_audio = dict_data["audio"].to(self.rank) in_word = dict_data["word"].to(self.rank) tar_beta = dict_data["beta"].to(self.rank) tar_id = dict_data["id"].to(self.rank).long() bs, n, j = tar_pose.shape[0], tar_pose.shape[1], self.joints tar_pose_jaw = tar_pose[:, :, 66:69] tar_pose_jaw = rc.axis_angle_to_matrix(tar_pose_jaw.reshape(bs, n, 1, 3)) tar_pose_jaw = rc.matrix_to_rotation_6d(tar_pose_jaw).reshape(bs, n, 1*6) tar_pose_face = torch.cat([tar_pose_jaw, tar_exps], dim=2) tar_pose_hands = tar_pose[:, :, 25*3:55*3] tar_pose_hands = rc.axis_angle_to_matrix(tar_pose_hands.reshape(bs, n, 30, 3)) tar_pose_hands = rc.matrix_to_rotation_6d(tar_pose_hands).reshape(bs, n, 30*6) tar_pose_upper = tar_pose[:, :, self.joint_mask_upper.astype(bool)] tar_pose_upper = rc.axis_angle_to_matrix(tar_pose_upper.reshape(bs, n, 13, 3)) tar_pose_upper = rc.matrix_to_rotation_6d(tar_pose_upper).reshape(bs, n, 13*6) tar_pose_leg = tar_pose[:, :, self.joint_mask_lower.astype(bool)] tar_pose_leg = rc.axis_angle_to_matrix(tar_pose_leg.reshape(bs, n, 9, 3)) tar_pose_leg = rc.matrix_to_rotation_6d(tar_pose_leg).reshape(bs, n, 9*6) tar_pose_lower = torch.cat([tar_pose_leg, tar_trans, tar_contact], dim=2) # tar_pose = rc.axis_angle_to_matrix(tar_pose.reshape(bs, n, j, 3)) # tar_pose = rc.matrix_to_rotation_6d(tar_pose).reshape(bs, n, j*6) tar4dis = torch.cat([tar_pose_jaw, tar_pose_upper, tar_pose_hands, tar_pose_leg], dim=2) tar_index_value_face_top = self.vq_model_face.map2index(tar_pose_face) # bs*n/4 tar_index_value_upper_top = self.vq_model_upper.map2index(tar_pose_upper) # bs*n/4 tar_index_value_hands_top = self.vq_model_hands.map2index(tar_pose_hands) # bs*n/4 tar_index_value_lower_top = self.vq_model_lower.map2index(tar_pose_lower) # bs*n/4 latent_face_top = self.vq_model_face.map2latent(tar_pose_face) # bs*n/4 latent_upper_top = self.vq_model_upper.map2latent(tar_pose_upper) # bs*n/4 latent_hands_top = self.vq_model_hands.map2latent(tar_pose_hands) # bs*n/4 latent_lower_top = self.vq_model_lower.map2latent(tar_pose_lower) # bs*n/4 latent_in = torch.cat([latent_upper_top, latent_hands_top, latent_lower_top], dim=2) index_in = torch.stack([tar_index_value_upper_top, tar_index_value_hands_top, tar_index_value_lower_top], dim=-1).long() tar_pose_6d = rc.axis_angle_to_matrix(tar_pose.reshape(bs, n, 55, 3)) tar_pose_6d = rc.matrix_to_rotation_6d(tar_pose_6d).reshape(bs, n, 55*6) latent_all = torch.cat([tar_pose_6d, tar_trans, tar_contact], dim=-1) # print(tar_index_value_upper_top.shape, index_in.shape) return { "tar_pose_jaw": tar_pose_jaw, "tar_pose_face": tar_pose_face, "tar_pose_upper": tar_pose_upper, "tar_pose_lower": tar_pose_lower, "tar_pose_hands": tar_pose_hands, 'tar_pose_leg': tar_pose_leg, "in_audio": in_audio, "in_word": in_word, "tar_trans": tar_trans, "tar_exps": tar_exps, "tar_beta": tar_beta, "tar_pose": tar_pose, "tar4dis": tar4dis, "tar_index_value_face_top": tar_index_value_face_top, "tar_index_value_upper_top": tar_index_value_upper_top, "tar_index_value_hands_top": tar_index_value_hands_top, "tar_index_value_lower_top": tar_index_value_lower_top, "latent_face_top": latent_face_top, "latent_upper_top": latent_upper_top, "latent_hands_top": latent_hands_top, "latent_lower_top": latent_lower_top, "latent_in": latent_in, "index_in": index_in, "tar_id": tar_id, "latent_all": latent_all, "tar_pose_6d": tar_pose_6d, "tar_contact": tar_contact, } def _g_training(self, loaded_data, use_adv, mode="train", epoch=0): bs, n, j = loaded_data["tar_pose"].shape[0], loaded_data["tar_pose"].shape[1], self.joints # ------ full generatation task ------ # mask_val = torch.ones(bs, n, self.args.pose_dims+3+4).float().cuda() mask_val[:, :self.args.pre_frames, :] = 0.0 net_out_val = self.model( loaded_data['in_audio'], loaded_data['in_word'], mask=mask_val, in_id = loaded_data['tar_id'], in_motion = loaded_data['latent_all'], use_attentions = True) g_loss_final = 0 loss_latent_face = self.reclatent_loss(net_out_val["rec_face"], loaded_data["latent_face_top"]) loss_latent_lower = self.reclatent_loss(net_out_val["rec_lower"], loaded_data["latent_lower_top"]) loss_latent_hands = self.reclatent_loss(net_out_val["rec_hands"], loaded_data["latent_hands_top"]) loss_latent_upper = self.reclatent_loss(net_out_val["rec_upper"], loaded_data["latent_upper_top"]) loss_latent = self.args.lf*loss_latent_face + self.args.ll*loss_latent_lower + self.args.lh*loss_latent_hands + self.args.lu*loss_latent_upper self.tracker.update_meter("latent", "train", loss_latent.item()) g_loss_final += loss_latent rec_index_face_val = self.log_softmax(net_out_val["cls_face"]).reshape(-1, self.args.vae_codebook_size) rec_index_upper_val = self.log_softmax(net_out_val["cls_upper"]).reshape(-1, self.args.vae_codebook_size) rec_index_lower_val = self.log_softmax(net_out_val["cls_lower"]).reshape(-1, self.args.vae_codebook_size) rec_index_hands_val = self.log_softmax(net_out_val["cls_hands"]).reshape(-1, self.args.vae_codebook_size) tar_index_value_face_top = loaded_data["tar_index_value_face_top"].reshape(-1) tar_index_value_upper_top = loaded_data["tar_index_value_upper_top"].reshape(-1) tar_index_value_lower_top = loaded_data["tar_index_value_lower_top"].reshape(-1) tar_index_value_hands_top = loaded_data["tar_index_value_hands_top"].reshape(-1) loss_cls = self.args.cf*self.cls_loss(rec_index_face_val, tar_index_value_face_top)\ + self.args.cu*self.cls_loss(rec_index_upper_val, tar_index_value_upper_top)\ + self.args.cl*self.cls_loss(rec_index_lower_val, tar_index_value_lower_top)\ + self.args.ch*self.cls_loss(rec_index_hands_val, tar_index_value_hands_top) self.tracker.update_meter("cls_full", "train", loss_cls.item()) g_loss_final += loss_cls if mode == 'train': # # ------ masked gesture moderling------ # mask_ratio = (epoch / self.args.epochs) * 0.95 + 0.05 mask = torch.rand(bs, n, self.args.pose_dims+3+4) < mask_ratio mask = mask.float().cuda() net_out_self = self.model( loaded_data['in_audio'], loaded_data['in_word'], mask=mask, in_id = loaded_data['tar_id'], in_motion = loaded_data['latent_all'], use_attentions = True, use_word=False) loss_latent_face_self = self.reclatent_loss(net_out_self["rec_face"], loaded_data["latent_face_top"]) loss_latent_lower_self = self.reclatent_loss(net_out_self["rec_lower"], loaded_data["latent_lower_top"]) loss_latent_hands_self = self.reclatent_loss(net_out_self["rec_hands"], loaded_data["latent_hands_top"]) loss_latent_upper_self = self.reclatent_loss(net_out_self["rec_upper"], loaded_data["latent_upper_top"]) loss_latent_self = self.args.lf*loss_latent_face_self + self.args.ll*loss_latent_lower_self + self.args.lh*loss_latent_hands_self + self.args.lu*loss_latent_upper_self self.tracker.update_meter("latent_self", "train", loss_latent_self.item()) g_loss_final += loss_latent_self rec_index_face_self = self.log_softmax(net_out_self["cls_face"]).reshape(-1, self.args.vae_codebook_size) rec_index_upper_self = self.log_softmax(net_out_self["cls_upper"]).reshape(-1, self.args.vae_codebook_size) rec_index_lower_self = self.log_softmax(net_out_self["cls_lower"]).reshape(-1, self.args.vae_codebook_size) rec_index_hands_self = self.log_softmax(net_out_self["cls_hands"]).reshape(-1, self.args.vae_codebook_size) index_loss_top_self = self.cls_loss(rec_index_face_self, tar_index_value_face_top) + self.cls_loss(rec_index_upper_self, tar_index_value_upper_top) + self.cls_loss(rec_index_lower_self, tar_index_value_lower_top) + self.cls_loss(rec_index_hands_self, tar_index_value_hands_top) self.tracker.update_meter("cls_self", "train", index_loss_top_self.item()) g_loss_final += index_loss_top_self # ------ masked audio gesture moderling ------ # net_out_word = self.model( loaded_data['in_audio'], loaded_data['in_word'], mask=mask, in_id = loaded_data['tar_id'], in_motion = loaded_data['latent_all'], use_attentions = True, use_word=True) loss_latent_face_word = self.reclatent_loss(net_out_word["rec_face"], loaded_data["latent_face_top"]) loss_latent_lower_word = self.reclatent_loss(net_out_word["rec_lower"], loaded_data["latent_lower_top"]) loss_latent_hands_word = self.reclatent_loss(net_out_word["rec_hands"], loaded_data["latent_hands_top"]) loss_latent_upper_word = self.reclatent_loss(net_out_word["rec_upper"], loaded_data["latent_upper_top"]) loss_latent_word = self.args.lf*loss_latent_face_word + self.args.ll*loss_latent_lower_word + self.args.lh*loss_latent_hands_word + self.args.lu*loss_latent_upper_word self.tracker.update_meter("latent_word", "train", loss_latent_word.item()) g_loss_final += loss_latent_word rec_index_face_word = self.log_softmax(net_out_word["cls_face"]).reshape(-1, self.args.vae_codebook_size) rec_index_upper_word = self.log_softmax(net_out_word["cls_upper"]).reshape(-1, self.args.vae_codebook_size) rec_index_lower_word = self.log_softmax(net_out_word["cls_lower"]).reshape(-1, self.args.vae_codebook_size) rec_index_hands_word = self.log_softmax(net_out_word["cls_hands"]).reshape(-1, self.args.vae_codebook_size) index_loss_top_word = self.cls_loss(rec_index_face_word, tar_index_value_face_top) + self.cls_loss(rec_index_upper_word, tar_index_value_upper_top) + self.cls_loss(rec_index_lower_word, tar_index_value_lower_top) + self.cls_loss(rec_index_hands_word, tar_index_value_hands_top) self.tracker.update_meter("cls_word", "train", index_loss_top_word.item()) g_loss_final += index_loss_top_word if mode != 'train': if self.args.cu != 0: _, rec_index_upper = torch.max(rec_index_upper_val.reshape(-1, self.args.pose_length, self.args.vae_codebook_size), dim=2) rec_upper = self.vq_model_upper.decode(rec_index_upper) else: _, rec_index_upper, _, _ = self.vq_model_upper.quantizer(net_out_val["rec_upper"]) rec_upper = self.vq_model_upper.decoder(rec_index_upper) if self.args.cl != 0: _, rec_index_lower = torch.max(rec_index_lower_val.reshape(-1, self.args.pose_length, self.args.vae_codebook_size), dim=2) rec_lower = self.vq_model_lower.decode(rec_index_lower) else: _, rec_index_lower, _, _ = self.vq_model_lower.quantizer(net_out_val["rec_lower"]) rec_lower = self.vq_model_lower.decoder(rec_index_lower) if self.args.ch != 0: _, rec_index_hands = torch.max(rec_index_hands_val.reshape(-1, self.args.pose_length, self.args.vae_codebook_size), dim=2) rec_hands = self.vq_model_hands.decode(rec_index_hands) else: _, rec_index_hands, _, _ = self.vq_model_hands.quantizer(net_out_val["rec_hands"]) rec_hands = self.vq_model_hands.decoder(rec_index_hands) if self.args.cf != 0: _, rec_index_face = torch.max(rec_index_face_val.reshape(-1, self.args.pose_length, self.args.vae_codebook_size), dim=2) rec_face = self.vq_model_face.decode(rec_index_face) else: _, rec_index_face, _, _ = self.vq_model_face.quantizer(net_out_val["rec_face"]) rec_face = self.vq_model_face.decoder(rec_index_face) rec_pose_jaw = rec_face[:, :, :6] rec_pose_legs = rec_lower[:, :, :54] rec_pose_upper = rec_upper.reshape(bs, n, 13, 6) rec_pose_upper = rc.rotation_6d_to_matrix(rec_pose_upper)# rec_pose_upper = rc.matrix_to_axis_angle(rec_pose_upper).reshape(bs*n, 13*3) rec_pose_upper_recover = self.inverse_selection_tensor(rec_pose_upper, self.joint_mask_upper, bs*n) rec_pose_lower = rec_pose_legs.reshape(bs, n, 9, 6) rec_pose_lower = rc.rotation_6d_to_matrix(rec_pose_lower) rec_pose_lower = rc.matrix_to_axis_angle(rec_pose_lower).reshape(bs*n, 9*3) rec_pose_lower_recover = self.inverse_selection_tensor(rec_pose_lower, self.joint_mask_lower, bs*n) rec_pose_hands = rec_hands.reshape(bs, n, 30, 6) rec_pose_hands = rc.rotation_6d_to_matrix(rec_pose_hands) rec_pose_hands = rc.matrix_to_axis_angle(rec_pose_hands).reshape(bs*n, 30*3) rec_pose_hands_recover = self.inverse_selection_tensor(rec_pose_hands, self.joint_mask_hands, bs*n) rec_pose_jaw = rec_pose_jaw.reshape(bs*n, 6) rec_pose_jaw = rc.rotation_6d_to_matrix(rec_pose_jaw) rec_pose_jaw = rc.matrix_to_axis_angle(rec_pose_jaw).reshape(bs*n, 1*3) rec_pose = rec_pose_upper_recover + rec_pose_lower_recover + rec_pose_hands_recover rec_pose[:, 66:69] = rec_pose_jaw # print(rec_pose.shape, tar_pose.shape) # tar_trans = loaded_data["tar_trans"] # rec_trans_v_s = rec_lower[:, :, 54:57] # rec_x_trans = other_tools.velocity2position(rec_trans_v_s[:, :, 0:1], 1/self.args.pose_fps, tar_trans[:, 0, 0:1]) # rec_z_trans = other_tools.velocity2position(rec_trans_v_s[:, :, 2:3], 1/self.args.pose_fps, tar_trans[:, 0, 2:3]) # rec_y_trans = rec_trans_v_s[:,:,1:2] # rec_trans = torch.cat([rec_x_trans, rec_y_trans, rec_z_trans], dim=-1) # tar_pose = loaded_data["tar_pose"] # tar_pose = rc.axis_angle_to_matrix(tar_pose.reshape(bs, n, j, 3)) # tar_pose = rc.matrix_to_rotation_6d(tar_pose).reshape(bs, n, j*6) rec_pose = rc.axis_angle_to_matrix(rec_pose.reshape(bs, n, j, 3)) rec_pose = rc.matrix_to_rotation_6d(rec_pose).reshape(bs, n, j*6) if mode == 'train': return g_loss_final elif mode == 'val': return { 'rec_pose': rec_pose, # rec_trans': rec_pose_trans, 'tar_pose': loaded_data["tar_pose_6d"], } else: return { 'rec_pose': rec_pose, # 'rec_trans': rec_trans, 'tar_pose': loaded_data["tar_pose"], 'tar_exps': loaded_data["tar_exps"], 'tar_beta': loaded_data["tar_beta"], 'tar_trans': loaded_data["tar_trans"], # 'rec_exps': rec_exps, } def _g_test(self, loaded_data): mode = 'test' bs, n, j = loaded_data["tar_pose"].shape[0], loaded_data["tar_pose"].shape[1], self.joints tar_pose = loaded_data["tar_pose"] tar_beta = loaded_data["tar_beta"] in_word = loaded_data["in_word"] tar_exps = loaded_data["tar_exps"] tar_contact = loaded_data["tar_contact"] in_audio = loaded_data["in_audio"] tar_trans = loaded_data["tar_trans"] remain = n%8 if remain != 0: tar_pose = tar_pose[:, :-remain, :] tar_beta = tar_beta[:, :-remain, :] tar_trans = tar_trans[:, :-remain, :] in_word = in_word[:, :-remain] tar_exps = tar_exps[:, :-remain, :] tar_contact = tar_contact[:, :-remain, :] n = n - remain tar_pose_jaw = tar_pose[:, :, 66:69] tar_pose_jaw = rc.axis_angle_to_matrix(tar_pose_jaw.reshape(bs, n, 1, 3)) tar_pose_jaw = rc.matrix_to_rotation_6d(tar_pose_jaw).reshape(bs, n, 1*6) tar_pose_face = torch.cat([tar_pose_jaw, tar_exps], dim=2) tar_pose_hands = tar_pose[:, :, 25*3:55*3] tar_pose_hands = rc.axis_angle_to_matrix(tar_pose_hands.reshape(bs, n, 30, 3)) tar_pose_hands = rc.matrix_to_rotation_6d(tar_pose_hands).reshape(bs, n, 30*6) tar_pose_upper = tar_pose[:, :, self.joint_mask_upper.astype(bool)] tar_pose_upper = rc.axis_angle_to_matrix(tar_pose_upper.reshape(bs, n, 13, 3)) tar_pose_upper = rc.matrix_to_rotation_6d(tar_pose_upper).reshape(bs, n, 13*6) tar_pose_leg = tar_pose[:, :, self.joint_mask_lower.astype(bool)] tar_pose_leg = rc.axis_angle_to_matrix(tar_pose_leg.reshape(bs, n, 9, 3)) tar_pose_leg = rc.matrix_to_rotation_6d(tar_pose_leg).reshape(bs, n, 9*6) tar_pose_lower = torch.cat([tar_pose_leg, tar_trans, tar_contact], dim=2) tar_pose_6d = rc.axis_angle_to_matrix(tar_pose.reshape(bs, n, 55, 3)) tar_pose_6d = rc.matrix_to_rotation_6d(tar_pose_6d).reshape(bs, n, 55*6) latent_all = torch.cat([tar_pose_6d, tar_trans, tar_contact], dim=-1) rec_index_all_face = [] rec_index_all_upper = [] rec_index_all_lower = [] rec_index_all_hands = [] # rec_index_all_face_bot = [] # rec_index_all_upper_bot = [] # rec_index_all_lower_bot = [] # rec_index_all_hands_bot = [] roundt = (n - self.args.pre_frames) // (self.args.pose_length - self.args.pre_frames) remain = (n - self.args.pre_frames) % (self.args.pose_length - self.args.pre_frames) round_l = self.args.pose_length - self.args.pre_frames # pad latent_all_9 to the same length with latent_all # if n - latent_all_9.shape[1] >= 0: # latent_all = torch.cat([latent_all_9, torch.zeros(bs, n - latent_all_9.shape[1], latent_all_9.shape[2]).cuda()], dim=1) # else: # latent_all = latent_all_9[:, :n, :] for i in range(0, roundt): in_word_tmp = in_word[:, i*(round_l):(i+1)*(round_l)+self.args.pre_frames] # audio fps is 16000 and pose fps is 30 in_audio_tmp = in_audio[:, i*(16000//30*round_l):(i+1)*(16000//30*round_l)+16000//30*self.args.pre_frames] in_id_tmp = loaded_data['tar_id'][:, i*(round_l):(i+1)*(round_l)+self.args.pre_frames] mask_val = torch.ones(bs, self.args.pose_length, self.args.pose_dims+3+4).float().cuda() mask_val[:, :self.args.pre_frames, :] = 0.0 if i == 0: latent_all_tmp = latent_all[:, i*(round_l):(i+1)*(round_l)+self.args.pre_frames, :] else: latent_all_tmp = latent_all[:, i*(round_l):(i+1)*(round_l)+self.args.pre_frames, :] # print(latent_all_tmp.shape, latent_last.shape) latent_all_tmp[:, :self.args.pre_frames, :] = latent_last[:, -self.args.pre_frames:, :] net_out_val = self.model( in_audio = in_audio_tmp, in_word=in_word_tmp, mask=mask_val, in_motion = latent_all_tmp, in_id = in_id_tmp, use_attentions=True,) if self.args.cu != 0: rec_index_upper = self.log_softmax(net_out_val["cls_upper"]).reshape(-1, self.args.vae_codebook_size) _, rec_index_upper = torch.max(rec_index_upper.reshape(-1, self.args.pose_length, self.args.vae_codebook_size), dim=2) #rec_upper = self.vq_model_upper.decode(rec_index_upper) else: _, rec_index_upper, _, _ = self.vq_model_upper.quantizer(net_out_val["rec_upper"]) #rec_upper = self.vq_model_upper.decoder(rec_index_upper) if self.args.cl != 0: rec_index_lower = self.log_softmax(net_out_val["cls_lower"]).reshape(-1, self.args.vae_codebook_size) _, rec_index_lower = torch.max(rec_index_lower.reshape(-1, self.args.pose_length, self.args.vae_codebook_size), dim=2) #rec_lower = self.vq_model_lower.decode(rec_index_lower) else: _, rec_index_lower, _, _ = self.vq_model_lower.quantizer(net_out_val["rec_lower"]) #rec_lower = self.vq_model_lower.decoder(rec_index_lower) if self.args.ch != 0: rec_index_hands = self.log_softmax(net_out_val["cls_hands"]).reshape(-1, self.args.vae_codebook_size) _, rec_index_hands = torch.max(rec_index_hands.reshape(-1, self.args.pose_length, self.args.vae_codebook_size), dim=2) #rec_hands = self.vq_model_hands.decode(rec_index_hands) else: _, rec_index_hands, _, _ = self.vq_model_hands.quantizer(net_out_val["rec_hands"]) #rec_hands = self.vq_model_hands.decoder(rec_index_hands) if self.args.cf != 0: rec_index_face = self.log_softmax(net_out_val["cls_face"]).reshape(-1, self.args.vae_codebook_size) _, rec_index_face = torch.max(rec_index_face.reshape(-1, self.args.pose_length, self.args.vae_codebook_size), dim=2) #rec_face = self.vq_model_face.decoder(rec_index_face) else: _, rec_index_face, _, _ = self.vq_model_face.quantizer(net_out_val["rec_face"]) #rec_face = self.vq_model_face.decoder(rec_index_face) if i == 0: rec_index_all_face.append(rec_index_face) rec_index_all_upper.append(rec_index_upper) rec_index_all_lower.append(rec_index_lower) rec_index_all_hands.append(rec_index_hands) else: rec_index_all_face.append(rec_index_face[:, self.args.pre_frames:]) rec_index_all_upper.append(rec_index_upper[:, self.args.pre_frames:]) rec_index_all_lower.append(rec_index_lower[:, self.args.pre_frames:]) rec_index_all_hands.append(rec_index_hands[:, self.args.pre_frames:]) if self.args.cu != 0: rec_upper_last = self.vq_model_upper.decode(rec_index_upper) else: rec_upper_last = self.vq_model_upper.decoder(rec_index_upper) if self.args.cl != 0: rec_lower_last = self.vq_model_lower.decode(rec_index_lower) else: rec_lower_last = self.vq_model_lower.decoder(rec_index_lower) if self.args.ch != 0: rec_hands_last = self.vq_model_hands.decode(rec_index_hands) else: rec_hands_last = self.vq_model_hands.decoder(rec_index_hands) # if self.args.cf != 0: # rec_face_last = self.vq_model_face.decode(rec_index_face) # else: # rec_face_last = self.vq_model_face.decoder(rec_index_face) rec_pose_legs = rec_lower_last[:, :, :54] bs, n = rec_pose_legs.shape[0], rec_pose_legs.shape[1] rec_pose_upper = rec_upper_last.reshape(bs, n, 13, 6) rec_pose_upper = rc.rotation_6d_to_matrix(rec_pose_upper)# rec_pose_upper = rc.matrix_to_axis_angle(rec_pose_upper).reshape(bs*n, 13*3) rec_pose_upper_recover = self.inverse_selection_tensor(rec_pose_upper, self.joint_mask_upper, bs*n) rec_pose_lower = rec_pose_legs.reshape(bs, n, 9, 6) rec_pose_lower = rc.rotation_6d_to_matrix(rec_pose_lower) rec_pose_lower = rc.matrix_to_axis_angle(rec_pose_lower).reshape(bs*n, 9*3) rec_pose_lower_recover = self.inverse_selection_tensor(rec_pose_lower, self.joint_mask_lower, bs*n) rec_pose_hands = rec_hands_last.reshape(bs, n, 30, 6) rec_pose_hands = rc.rotation_6d_to_matrix(rec_pose_hands) rec_pose_hands = rc.matrix_to_axis_angle(rec_pose_hands).reshape(bs*n, 30*3) rec_pose_hands_recover = self.inverse_selection_tensor(rec_pose_hands, self.joint_mask_hands, bs*n) rec_pose = rec_pose_upper_recover + rec_pose_lower_recover + rec_pose_hands_recover rec_pose = rc.axis_angle_to_matrix(rec_pose.reshape(bs, n, j, 3)) rec_pose = rc.matrix_to_rotation_6d(rec_pose).reshape(bs, n, j*6) rec_trans_v_s = rec_lower_last[:, :, 54:57] rec_x_trans = other_tools.velocity2position(rec_trans_v_s[:, :, 0:1], 1/self.args.pose_fps, tar_trans[:, 0, 0:1]) rec_z_trans = other_tools.velocity2position(rec_trans_v_s[:, :, 2:3], 1/self.args.pose_fps, tar_trans[:, 0, 2:3]) rec_y_trans = rec_trans_v_s[:,:,1:2] rec_trans = torch.cat([rec_x_trans, rec_y_trans, rec_z_trans], dim=-1) latent_last = torch.cat([rec_pose, rec_trans, rec_lower_last[:, :, 57:61]], dim=-1) rec_index_face = torch.cat(rec_index_all_face, dim=1) rec_index_upper = torch.cat(rec_index_all_upper, dim=1) rec_index_lower = torch.cat(rec_index_all_lower, dim=1) rec_index_hands = torch.cat(rec_index_all_hands, dim=1) if self.args.cu != 0: rec_upper = self.vq_model_upper.decode(rec_index_upper) else: rec_upper = self.vq_model_upper.decoder(rec_index_upper) if self.args.cl != 0: rec_lower = self.vq_model_lower.decode(rec_index_lower) else: rec_lower = self.vq_model_lower.decoder(rec_index_lower) if self.args.ch != 0: rec_hands = self.vq_model_hands.decode(rec_index_hands) else: rec_hands = self.vq_model_hands.decoder(rec_index_hands) if self.args.cf != 0: rec_face = self.vq_model_face.decode(rec_index_face) else: rec_face = self.vq_model_face.decoder(rec_index_face) rec_exps = rec_face[:, :, 6:] rec_pose_jaw = rec_face[:, :, :6] rec_pose_legs = rec_lower[:, :, :54] bs, n = rec_pose_jaw.shape[0], rec_pose_jaw.shape[1] rec_pose_upper = rec_upper.reshape(bs, n, 13, 6) rec_pose_upper = rc.rotation_6d_to_matrix(rec_pose_upper)# rec_pose_upper = rc.matrix_to_axis_angle(rec_pose_upper).reshape(bs*n, 13*3) rec_pose_upper_recover = self.inverse_selection_tensor(rec_pose_upper, self.joint_mask_upper, bs*n) rec_pose_lower = rec_pose_legs.reshape(bs, n, 9, 6) rec_pose_lower = rc.rotation_6d_to_matrix(rec_pose_lower) rec_lower2global = rc.matrix_to_rotation_6d(rec_pose_lower.clone()).reshape(bs, n, 9*6) rec_pose_lower = rc.matrix_to_axis_angle(rec_pose_lower).reshape(bs*n, 9*3) rec_pose_lower_recover = self.inverse_selection_tensor(rec_pose_lower, self.joint_mask_lower, bs*n) rec_pose_hands = rec_hands.reshape(bs, n, 30, 6) rec_pose_hands = rc.rotation_6d_to_matrix(rec_pose_hands) rec_pose_hands = rc.matrix_to_axis_angle(rec_pose_hands).reshape(bs*n, 30*3) rec_pose_hands_recover = self.inverse_selection_tensor(rec_pose_hands, self.joint_mask_hands, bs*n) rec_pose_jaw = rec_pose_jaw.reshape(bs*n, 6) rec_pose_jaw = rc.rotation_6d_to_matrix(rec_pose_jaw) rec_pose_jaw = rc.matrix_to_axis_angle(rec_pose_jaw).reshape(bs*n, 1*3) rec_pose = rec_pose_upper_recover + rec_pose_lower_recover + rec_pose_hands_recover rec_pose[:, 66:69] = rec_pose_jaw to_global = rec_lower to_global[:, :, 54:57] = 0.0 to_global[:, :, :54] = rec_lower2global rec_global = self.global_motion(to_global) rec_trans_v_s = rec_global["rec_pose"][:, :, 54:57] rec_x_trans = other_tools.velocity2position(rec_trans_v_s[:, :, 0:1], 1/self.args.pose_fps, tar_trans[:, 0, 0:1]) rec_z_trans = other_tools.velocity2position(rec_trans_v_s[:, :, 2:3], 1/self.args.pose_fps, tar_trans[:, 0, 2:3]) rec_y_trans = rec_trans_v_s[:,:,1:2] rec_trans = torch.cat([rec_x_trans, rec_y_trans, rec_z_trans], dim=-1) tar_pose = tar_pose[:, :n, :] tar_exps = tar_exps[:, :n, :] tar_trans = tar_trans[:, :n, :] tar_beta = tar_beta[:, :n, :] rec_pose = rc.axis_angle_to_matrix(rec_pose.reshape(bs*n, j, 3)) rec_pose = rc.matrix_to_rotation_6d(rec_pose).reshape(bs, n, j*6) tar_pose = rc.axis_angle_to_matrix(tar_pose.reshape(bs*n, j, 3)) tar_pose = rc.matrix_to_rotation_6d(tar_pose).reshape(bs, n, j*6) return { 'rec_pose': rec_pose, 'rec_trans': rec_trans, 'tar_pose': tar_pose, 'tar_exps': tar_exps, 'tar_beta': tar_beta, 'tar_trans': tar_trans, 'rec_exps': rec_exps, } def train(self, epoch): #torch.autograd.set_detect_anomaly(True) use_adv = bool(epoch>=self.args.no_adv_epoch) self.model.train() # self.d_model.train() t_start = time.time() self.tracker.reset() for its, batch_data in enumerate(self.train_loader): loaded_data = self._load_data(batch_data) t_data = time.time() - t_start self.opt.zero_grad() g_loss_final = 0 g_loss_final += self._g_training(loaded_data, use_adv, 'train', epoch) #with torch.autograd.detect_anomaly(): g_loss_final.backward() if self.args.grad_norm != 0: torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.args.grad_norm) self.opt.step() mem_cost = torch.cuda.memory_cached() / 1E9 lr_g = self.opt.param_groups[0]['lr'] # lr_d = self.opt_d.param_groups[0]['lr'] t_train = time.time() - t_start - t_data t_start = time.time() if its % self.args.log_period == 0: self.train_recording(epoch, its, t_data, t_train, mem_cost, lr_g) if self.args.debug: if its == 1: break self.opt_s.step(epoch) # self.opt_d_s.step(epoch) def val(self, epoch): self.model.eval() # self.d_model.eval() with torch.no_grad(): for its, batch_data in enumerate(self.train_loader): loaded_data = self._load_data(batch_data) net_out = self._g_training(loaded_data, False, 'val', epoch) tar_pose = net_out['tar_pose'] rec_pose = net_out['rec_pose'] if (30/self.args.pose_fps) != 1: assert 30%self.args.pose_fps == 0 n *= int(30/self.args.pose_fps) tar_pose = torch.nn.functional.interpolate(tar_pose.permute(0, 2, 1), scale_factor=30/self.args.pose_fps, mode='linear').permute(0,2,1) rec_pose = torch.nn.functional.interpolate(rec_pose.permute(0, 2, 1), scale_factor=30/self.args.pose_fps, mode='linear').permute(0,2,1) n = tar_pose.shape[1] remain = n%self.args.vae_test_len tar_pose = tar_pose[:, :n-remain, :] rec_pose = rec_pose[:, :n-remain, :] latent_out = self.eval_copy.map2latent(rec_pose).reshape(-1, self.args.vae_length).cpu().numpy() latent_ori = self.eval_copy.map2latent(tar_pose).reshape(-1, self.args.vae_length).cpu().numpy() if its == 0: latent_out_motion_all = latent_out latent_ori_all = latent_ori else: latent_out_motion_all = np.concatenate([latent_out_motion_all, latent_out], axis=0) latent_ori_all = np.concatenate([latent_ori_all, latent_ori], axis=0) if self.args.debug: if its == 1: break fid_motion = data_tools.FIDCalculator.frechet_distance(latent_out_motion_all, latent_ori_all) self.tracker.update_meter("fid", "val", fid_motion) self.val_recording(epoch) def test(self, epoch): results_save_path = self.checkpoint_path + f"/{epoch}/" if os.path.exists(results_save_path): return 0 os.makedirs(results_save_path) start_time = time.time() total_length = 0 test_seq_list = self.test_data.selected_file align = 0 latent_out = [] latent_ori = [] l2_all = 0 lvel = 0 self.model.eval() self.smplx.eval() self.eval_copy.eval() with torch.no_grad(): for its, batch_data in enumerate(self.test_loader): loaded_data = self._load_data(batch_data) net_out = self._g_test(loaded_data) tar_pose = net_out['tar_pose'] rec_pose = net_out['rec_pose'] tar_exps = net_out['tar_exps'] tar_beta = net_out['tar_beta'] rec_trans = net_out['rec_trans'] tar_trans = net_out['tar_trans'] rec_exps = net_out['rec_exps'] # print(rec_pose.shape, tar_pose.shape) bs, n, j = tar_pose.shape[0], tar_pose.shape[1], self.joints if (30/self.args.pose_fps) != 1: assert 30%self.args.pose_fps == 0 n *= int(30/self.args.pose_fps) tar_pose = torch.nn.functional.interpolate(tar_pose.permute(0, 2, 1), scale_factor=30/self.args.pose_fps, mode='linear').permute(0,2,1) rec_pose = torch.nn.functional.interpolate(rec_pose.permute(0, 2, 1), scale_factor=30/self.args.pose_fps, mode='linear').permute(0,2,1) # print(rec_pose.shape, tar_pose.shape) rec_pose = rc.rotation_6d_to_matrix(rec_pose.reshape(bs*n, j, 6)) rec_pose = rc.matrix_to_rotation_6d(rec_pose).reshape(bs, n, j*6) tar_pose = rc.rotation_6d_to_matrix(tar_pose.reshape(bs*n, j, 6)) tar_pose = rc.matrix_to_rotation_6d(tar_pose).reshape(bs, n, j*6) remain = n%self.args.vae_test_len latent_out.append(self.eval_copy.map2latent(rec_pose[:, :n-remain]).reshape(-1, self.args.vae_length).detach().cpu().numpy()) # bs * n/8 * 240 latent_ori.append(self.eval_copy.map2latent(tar_pose[:, :n-remain]).reshape(-1, self.args.vae_length).detach().cpu().numpy()) rec_pose = rc.rotation_6d_to_matrix(rec_pose.reshape(bs*n, j, 6)) rec_pose = rc.matrix_to_axis_angle(rec_pose).reshape(bs*n, j*3) tar_pose = rc.rotation_6d_to_matrix(tar_pose.reshape(bs*n, j, 6)) tar_pose = rc.matrix_to_axis_angle(tar_pose).reshape(bs*n, j*3) vertices_rec = self.smplx( betas=tar_beta.reshape(bs*n, 300), transl=rec_trans.reshape(bs*n, 3)-rec_trans.reshape(bs*n, 3), expression=tar_exps.reshape(bs*n, 100)-tar_exps.reshape(bs*n, 100), jaw_pose=rec_pose[:, 66:69], global_orient=rec_pose[:,:3], body_pose=rec_pose[:,3:21*3+3], left_hand_pose=rec_pose[:,25*3:40*3], right_hand_pose=rec_pose[:,40*3:55*3], return_joints=True, leye_pose=rec_pose[:, 69:72], reye_pose=rec_pose[:, 72:75], ) # vertices_tar = self.smplx( # betas=tar_beta.reshape(bs*n, 300), # transl=rec_trans.reshape(bs*n, 3)-rec_trans.reshape(bs*n, 3), # expression=tar_exps.reshape(bs*n, 100)-tar_exps.reshape(bs*n, 100), # jaw_pose=tar_pose[:, 66:69], # global_orient=tar_pose[:,:3], # body_pose=tar_pose[:,3:21*3+3], # left_hand_pose=tar_pose[:,25*3:40*3], # right_hand_pose=tar_pose[:,40*3:55*3], # return_joints=True, # leye_pose=tar_pose[:, 69:72], # reye_pose=tar_pose[:, 72:75], # ) vertices_rec_face = self.smplx( betas=tar_beta.reshape(bs*n, 300), transl=rec_trans.reshape(bs*n, 3)-rec_trans.reshape(bs*n, 3), expression=rec_exps.reshape(bs*n, 100), jaw_pose=rec_pose[:, 66:69], global_orient=rec_pose[:,:3]-rec_pose[:,:3], body_pose=rec_pose[:,3:21*3+3]-rec_pose[:,3:21*3+3], left_hand_pose=rec_pose[:,25*3:40*3]-rec_pose[:,25*3:40*3], right_hand_pose=rec_pose[:,40*3:55*3]-rec_pose[:,40*3:55*3], return_verts=True, return_joints=True, leye_pose=rec_pose[:, 69:72]-rec_pose[:, 69:72], reye_pose=rec_pose[:, 72:75]-rec_pose[:, 72:75], ) vertices_tar_face = self.smplx( betas=tar_beta.reshape(bs*n, 300), transl=tar_trans.reshape(bs*n, 3)-tar_trans.reshape(bs*n, 3), expression=tar_exps.reshape(bs*n, 100), jaw_pose=tar_pose[:, 66:69], global_orient=tar_pose[:,:3]-tar_pose[:,:3], body_pose=tar_pose[:,3:21*3+3]-tar_pose[:,3:21*3+3], left_hand_pose=tar_pose[:,25*3:40*3]-tar_pose[:,25*3:40*3], right_hand_pose=tar_pose[:,40*3:55*3]-tar_pose[:,40*3:55*3], return_verts=True, return_joints=True, leye_pose=tar_pose[:, 69:72]-tar_pose[:, 69:72], reye_pose=tar_pose[:, 72:75]-tar_pose[:, 72:75], ) joints_rec = vertices_rec["joints"].detach().cpu().numpy().reshape(1, n, 127*3)[0, :n, :55*3] # joints_tar = vertices_tar["joints"].detach().cpu().numpy().reshape(1, n, 127*3)[0, :n, :55*3] facial_rec = vertices_rec_face['vertices'].reshape(1, n, -1)[0, :n] facial_tar = vertices_tar_face['vertices'].reshape(1, n, -1)[0, :n] face_vel_loss = self.vel_loss(facial_rec[1:, :] - facial_tar[:-1, :], facial_tar[1:, :] - facial_tar[:-1, :]) l2 = self.reclatent_loss(facial_rec, facial_tar) l2_all += l2.item() * n lvel += face_vel_loss.item() * n _ = self.l1_calculator.run(joints_rec) if self.alignmenter is not None: in_audio_eval, sr = librosa.load(self.args.data_path+"wave16k/"+test_seq_list.iloc[its]['id']+".wav") in_audio_eval = librosa.resample(in_audio_eval, orig_sr=sr, target_sr=self.args.audio_sr) a_offset = int(self.align_mask * (self.args.audio_sr / self.args.pose_fps)) onset_bt = self.alignmenter.load_audio(in_audio_eval[:int(self.args.audio_sr / self.args.pose_fps*n)], a_offset, len(in_audio_eval)-a_offset, True) beat_vel = self.alignmenter.load_pose(joints_rec, self.align_mask, n-self.align_mask, 30, True) # print(beat_vel) align += (self.alignmenter.calculate_align(onset_bt, beat_vel, 30) * (n-2*self.align_mask)) tar_pose_np = tar_pose.detach().cpu().numpy() rec_pose_np = rec_pose.detach().cpu().numpy() rec_trans_np = rec_trans.detach().cpu().numpy().reshape(bs*n, 3) rec_exp_np = rec_exps.detach().cpu().numpy().reshape(bs*n, 100) tar_exp_np = tar_exps.detach().cpu().numpy().reshape(bs*n, 100) tar_trans_np = tar_trans.detach().cpu().numpy().reshape(bs*n, 3) gt_npz = np.load(self.args.data_path+self.args.pose_rep +"/"+test_seq_list.iloc[its]['id']+".npz", allow_pickle=True) np.savez(results_save_path+"gt_"+test_seq_list.iloc[its]['id']+'.npz', betas=gt_npz["betas"], poses=tar_pose_np, expressions=tar_exp_np, trans=tar_trans_np, model='smplx2020', gender='neutral', mocap_frame_rate = 30 , ) np.savez(results_save_path+"res_"+test_seq_list.iloc[its]['id']+'.npz', betas=gt_npz["betas"], poses=rec_pose_np, expressions=rec_exp_np, trans=rec_trans_np, model='smplx2020', gender='neutral', mocap_frame_rate = 30, ) total_length += n logger.info(f"l2 loss: {l2_all/total_length}") logger.info(f"lvel loss: {lvel/total_length}") latent_out_all = np.concatenate(latent_out, axis=0) latent_ori_all = np.concatenate(latent_ori, axis=0) fid = data_tools.FIDCalculator.frechet_distance(latent_out_all, latent_ori_all) logger.info(f"fid score: {fid}") self.test_recording("fid", fid, epoch) align_avg = align/(total_length-2*len(self.test_loader)*self.align_mask) logger.info(f"align score: {align_avg}") self.test_recording("bc", align_avg, epoch) l1div = self.l1_calculator.avg() logger.info(f"l1div score: {l1div}") self.test_recording("l1div", l1div, epoch) # data_tools.result2target_vis(self.args.pose_version, results_save_path, results_save_path, self.test_demo, False) end_time = time.time() - start_time logger.info(f"total inference time: {int(end_time)} s for {int(total_length/self.args.pose_fps)} s motion") def test_demo(self, epoch): ''' input audio and text, output motion do not calculate loss and metric save video ''' results_save_path = self.checkpoint_path + f"/{epoch}/" if os.path.exists(results_save_path): return 0 os.makedirs(results_save_path) start_time = time.time() total_length = 0 test_seq_list = self.test_data.selected_file align = 0 latent_out = [] latent_ori = [] l2_all = 0 lvel = 0 self.model.eval() self.smplx.eval() # self.eval_copy.eval() with torch.no_grad(): for its, batch_data in enumerate(self.test_loader): loaded_data = self._load_data(batch_data) net_out = self._g_test(loaded_data) tar_pose = net_out['tar_pose'] rec_pose = net_out['rec_pose'] tar_exps = net_out['tar_exps'] tar_beta = net_out['tar_beta'] rec_trans = net_out['rec_trans'] tar_trans = net_out['tar_trans'] rec_exps = net_out['rec_exps'] # print(rec_pose.shape, tar_pose.shape) bs, n, j = tar_pose.shape[0], tar_pose.shape[1], self.joints # interpolate to 30fps if (30/self.args.pose_fps) != 1: assert 30%self.args.pose_fps == 0 n *= int(30/self.args.pose_fps) tar_pose = torch.nn.functional.interpolate(tar_pose.permute(0, 2, 1), scale_factor=30/self.args.pose_fps, mode='linear').permute(0,2,1) rec_pose = torch.nn.functional.interpolate(rec_pose.permute(0, 2, 1), scale_factor=30/self.args.pose_fps, mode='linear').permute(0,2,1) # print(rec_pose.shape, tar_pose.shape) rec_pose = rc.rotation_6d_to_matrix(rec_pose.reshape(bs*n, j, 6)) rec_pose = rc.matrix_to_axis_angle(rec_pose).reshape(bs*n, j*3) tar_pose = rc.rotation_6d_to_matrix(tar_pose.reshape(bs*n, j, 6)) tar_pose = rc.matrix_to_axis_angle(tar_pose).reshape(bs*n, j*3) tar_pose_np = tar_pose.detach().cpu().numpy() rec_pose_np = rec_pose.detach().cpu().numpy() rec_trans_np = rec_trans.detach().cpu().numpy().reshape(bs*n, 3) rec_exp_np = rec_exps.detach().cpu().numpy().reshape(bs*n, 100) tar_exp_np = tar_exps.detach().cpu().numpy().reshape(bs*n, 100) tar_trans_np = tar_trans.detach().cpu().numpy().reshape(bs*n, 3) gt_npz = np.load(self.args.data_path+self.args.pose_rep +"/"+test_seq_list.iloc[its]['id']+".npz", allow_pickle=True) np.savez(results_save_path+"gt_"+test_seq_list.iloc[its]['id']+'.npz', betas=gt_npz["betas"], poses=tar_pose_np, expressions=tar_exp_np, trans=tar_trans_np, model='smplx2020', gender='neutral', mocap_frame_rate = 30 , ) np.savez(results_save_path+"res_"+test_seq_list.iloc[its]['id']+'.npz', betas=gt_npz["betas"], poses=rec_pose_np, expressions=rec_exp_np, trans=rec_trans_np, model='smplx2020', gender='neutral', mocap_frame_rate = 30, ) total_length += n data_tools.result2target_vis(self.args.pose_version, results_save_path, results_save_path, self.test_demo, False) end_time = time.time() - start_time logger.info(f"total inference time: {int(end_time)} s for {int(total_length/self.args.pose_fps)} s motion")