import train import os import time import csv import sys import warnings import random import numpy as np import time import pprint import pickle 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 from loguru import logger import smplx from utils import config, logger_tools, other_tools, metric from utils import rotation_conversions as rc 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 scipy.spatial.transform import Rotation class CustomTrainer(train.BaseTrainer): """ motion representation learning """ def __init__(self, args): super().__init__(args) self.joints = self.train_data.joints self.smplx = smplx.create( self.args.data_path_1+"smplx_models/", model_type='smplx', gender='NEUTRAL_2020', use_face_contour=False, num_betas=300, num_expression_coeffs=100, ext='npz', use_pca=False, ).cuda().eval() self.tracker = other_tools.EpochTracker(["rec", "contact", "vel", "foot", "ver", "com", "kl", "acc", "trans", "transv"], [False,False, False, False, False, False, False, False, False, False]) if not self.args.rot6d: #"rot6d" not in args.pose_rep: logger.error(f"this script is for rot6d, your pose rep. is {args.pose_rep}") self.rec_loss = get_loss_func("GeodesicLoss") self.vel_loss = torch.nn.L1Loss(reduction='mean') self.vectices_loss = torch.nn.MSELoss(reduction='mean') def inverse_selection(self, filtered_t, selection_array, n): # 创建一个全为零的数组，形状为 n*165 original_shape_t = np.zeros((n, selection_array.size)) # 找到选择数组中为1的索引位置 selected_indices = np.where(selection_array == 1)[0] # 将 filtered_t 的值填充到 original_shape_t 中相应的位置 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): # 创建一个全为零的数组，形状为 n*165 selection_array = torch.from_numpy(selection_array).cuda() original_shape_t = torch.zeros((n, 165)).cuda() # 找到选择数组中为1的索引位置 selected_indices = torch.where(selection_array == 1)[0] # 将 filtered_t 的值填充到 original_shape_t 中相应的位置 for i in range(n): original_shape_t[i, selected_indices] = filtered_t[i] return original_shape_t def train(self, epoch): self.model.train() t_start = time.time() self.tracker.reset() for its, dict_data in enumerate(self.train_loader): tar_pose_raw = dict_data["pose"] tar_beta = dict_data["beta"].cuda() tar_trans = dict_data["trans"].cuda() # tar_trans_vel_x = other_tools.estimate_linear_velocity(tar_trans[:, :, 0:1], dt=1/self.args.pose_fps) # tar_trans_vel_z = other_tools.estimate_linear_velocity(tar_trans[:, :, 2:3], dt=1/self.args.pose_fps) tar_pose = tar_pose_raw[:, :, :27].cuda() tar_contact = tar_pose_raw[:, :, 27:31].cuda() bs, n, j = tar_pose.shape[0], tar_pose.shape[1], self.joints tar_exps = torch.zeros((bs, n, 100)).cuda() 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) tar_trans_copy = tar_trans tar_contact_copy = tar_contact in_tar_pose = torch.cat((tar_pose, tar_trans_copy, tar_contact_copy), dim=-1) t_data = time.time() - t_start self.opt.zero_grad() g_loss_final = 0 net_out = self.model(in_tar_pose) rec_pose = net_out["rec_pose"][:, :, :j*6] rec_pose = rec_pose.reshape(bs, n, j, 6) rec_pose = rc.rotation_6d_to_matrix(rec_pose)# tar_pose = rc.rotation_6d_to_matrix(tar_pose.reshape(bs, n, j, 6)) loss_rec = self.rec_loss(rec_pose, tar_pose) * self.args.rec_weight * self.args.rec_pos_weight self.tracker.update_meter("rec", "train", loss_rec.item()) g_loss_final += loss_rec rec_contact = net_out["rec_pose"][:, :, j*6+3:j*6+7] loss_contact = self.vectices_loss(rec_contact, tar_contact) * self.args.rec_weight * self.args.rec_pos_weight self.tracker.update_meter("contact", "train", loss_contact.item()) g_loss_final += loss_contact velocity_loss = self.vel_loss(rec_pose[:, 1:] - rec_pose[:, :-1], tar_pose[:, 1:] - tar_pose[:, :-1]) * self.args.rec_weight acceleration_loss = self.vel_loss(rec_pose[:, 2:] + rec_pose[:, :-2] - 2 * rec_pose[:, 1:-1], tar_pose[:, 2:] + tar_pose[:, :-2] - 2 * tar_pose[:, 1:-1]) * self.args.rec_weight self.tracker.update_meter("vel", "train", velocity_loss.item()) self.tracker.update_meter("acc", "train", acceleration_loss.item()) g_loss_final += velocity_loss g_loss_final += acceleration_loss # rec_trans = net_out["rec_pose"][:, :, j*6:j*6+3] # rec_x_trans = other_tools.velocity2position(rec_trans[:, :, 0:1], 1/self.args.pose_fps, tar_trans[:, 0, 0:1]) # rec_z_trans = other_tools.velocity2position(rec_trans[:, :, 2:3], 1/self.args.pose_fps, tar_trans[:, 0, 2:3]) # rec_y_trans = rec_trans[:,:,1:2] # rec_xyz_trans = torch.cat([rec_x_trans, rec_y_trans, rec_z_trans], dim=-1) # loss_trans_vel = self.vel_loss(rec_trans[:, :, 0:1], tar_trans_vel_x) * self.args.rec_weight \ # + self.vel_loss(rec_trans[:, :, 2:3], tar_trans_vel_z) * self.args.rec_weight # v3 = self.vel_loss(rec_trans[:, :, 0:1][:, 1:] - rec_trans[:, :, 0:1][:, :-1], tar_trans_vel_x[:, 1:] - tar_trans_vel_x[:, :-1]) * self.args.rec_weight \ # + self.vel_loss(rec_trans[:, :, 2:3][:, 1:] - rec_trans[:, :, 2:3][:, :-1], tar_trans_vel_z[:, 1:] - tar_trans_vel_z[:, :-1]) * self.args.rec_weight # a3 = self.vel_loss(rec_trans[:, :, 0:1][:, 2:] + rec_trans[:, :, 0:1][:, :-2] - 2 * rec_trans[:, :, 0:1][:, 1:-1], tar_trans_vel_x[:, 2:] + tar_trans_vel_x[:, :-2] - 2 * tar_trans_vel_x[:, 1:-1]) * self.args.rec_weight \ # + self.vel_loss(rec_trans[:, :, 2:3][:, 2:] + rec_trans[:, :, 2:3][:, :-2] - 2 * rec_trans[:, :, 2:3][:, 1:-1], tar_trans_vel_z[:, 2:] + tar_trans_vel_z[:, :-2] - 2 * tar_trans_vel_z[:, 1:-1]) * self.args.rec_weight # g_loss_final += 5*v3 # g_loss_final += 5*a3 # v2 = self.vel_loss(rec_xyz_trans[:, 1:] - rec_xyz_trans[:, :-1], tar_trans[:, 1:] - tar_trans[:, :-1]) * self.args.rec_weight # a2 = self.vel_loss(rec_xyz_trans[:, 2:] + rec_xyz_trans[:, :-2] - 2 * rec_xyz_trans[:, 1:-1], tar_trans[:, 2:] + tar_trans[:, :-2] - 2 * tar_trans[:, 1:-1]) * self.args.rec_weight # g_loss_final += 5*v2 # g_loss_final += 5*a2 # self.tracker.update_meter("transv", "train", loss_trans_vel.item()) # g_loss_final += loss_trans_vel # loss_trans = self.vel_loss(rec_xyz_trans, tar_trans) * self.args.rec_weight # self.tracker.update_meter("trans", "train", loss_trans.item()) # g_loss_final += loss_trans # vertices loss if self.args.rec_ver_weight > 0: # print(tar_pose.shape, bs, n, j) tar_pose = rc.matrix_to_axis_angle(tar_pose).reshape(bs*n, j*3) rec_pose = rc.matrix_to_axis_angle(rec_pose).reshape(bs*n, j*3) rec_pose = self.inverse_selection_tensor(rec_pose, self.train_data.joint_mask, rec_pose.shape[0]) tar_pose = self.inverse_selection_tensor(tar_pose, self.train_data.joint_mask, tar_pose.shape[0]) vertices_rec = 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=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_verts=False, return_joints=True, leye_pose=tar_pose[:, 69:72], reye_pose=tar_pose[:, 72:75], ) vertices_tar = 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], 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_verts=False, return_joints=True, leye_pose=tar_pose[:, 69:72], reye_pose=tar_pose[:, 72:75], ) joints_rec = vertices_rec['joints'] # print(joints_rec.shape) joints_rec = joints_rec.reshape(bs, n, -1, 3) vectices_loss = self.vectices_loss(vertices_rec['joints'], vertices_tar['joints']) foot_idx = [7, 8, 10, 11] model_contact = net_out["rec_pose"][:, :, j*6+3:j*6+7] # find static indices consistent with model's own predictions static_idx = model_contact > 0.95 # N x S x 4 # print(model_contact,static_idx) model_feet = joints_rec[:, :, foot_idx] # foot positions (N, S, 4, 3) model_foot_v = torch.zeros_like(model_feet) model_foot_v[:, :-1] = ( model_feet[:, 1:, :, :] - model_feet[:, :-1, :, :] ) # (N, S-1, 4, 3) model_foot_v[~static_idx] = 0 foot_loss = self.vel_loss( model_foot_v, torch.zeros_like(model_foot_v) ) self.tracker.update_meter("foot", "train", foot_loss.item()*self.args.rec_weight * self.args.rec_ver_weight*20) self.tracker.update_meter("ver", "train", vectices_loss.item()*self.args.rec_weight * self.args.rec_ver_weight) g_loss_final += (vectices_loss)*self.args.rec_weight*self.args.rec_ver_weight g_loss_final += foot_loss*self.args.rec_weight*self.args.rec_ver_weight*20 # ---------------------- vae -------------------------- # if "VQVAE" in self.args.g_name: loss_embedding = net_out["embedding_loss"] g_loss_final += loss_embedding self.tracker.update_meter("com", "train", loss_embedding.item()) # elif "VAE" in self.args.g_name: # pose_mu, pose_logvar = net_out["pose_mu"], net_out["pose_logvar"] # KLD = -0.5 * torch.sum(1 + pose_logvar - pose_mu.pow(2) - pose_logvar.exp()) # if epoch < 0: # KLD_weight = 0 # else: # KLD_weight = min(1.0, (epoch - 0) * 0.05) * 0.01 # loss += KLD_weight * KLD # self.tracker.update_meter("kl", "train", KLD_weight * KLD.item()) 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() t_train = time.time() - t_start - t_data t_start = time.time() mem_cost = torch.cuda.memory_cached() / 1E9 lr_g = self.opt.param_groups[0]['lr'] 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) def val(self, epoch): self.model.eval() t_start = time.time() with torch.no_grad(): for its, dict_data in enumerate(self.val_loader): tar_pose_raw = dict_data["pose"] tar_beta = dict_data["beta"].cuda() tar_trans = dict_data["trans"].cuda() tar_trans_vel_x = other_tools.estimate_linear_velocity(tar_trans[:, :, 0:1], dt=1/self.args.pose_fps) tar_trans_vel_z = other_tools.estimate_linear_velocity(tar_trans[:, :, 2:3], dt=1/self.args.pose_fps) #print(tar_pose.shape) tar_pose = tar_pose_raw[:, :, :27].cuda() tar_contact = tar_pose_raw[:, :, 27:31].cuda() bs, n, j = tar_pose.shape[0], tar_pose.shape[1], self.joints tar_exps = torch.zeros((bs, n, 100)).cuda() 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) tar_trans_copy = tar_trans tar_contact_copy = tar_contact in_tar_pose = torch.cat((tar_pose, tar_trans_copy, tar_contact_copy), dim=-1) t_data = time.time() - t_start #self.opt.zero_grad() #g_loss_final = 0 net_out = self.model(in_tar_pose) rec_pose = net_out["rec_pose"][:, :, :j*6] rec_pose = rec_pose.reshape(bs, n, j, 6) rec_pose = rc.rotation_6d_to_matrix(rec_pose)# tar_pose = rc.rotation_6d_to_matrix(tar_pose.reshape(bs, n, j, 6)) loss_rec = self.rec_loss(rec_pose, tar_pose) * self.args.rec_weight * self.args.rec_pos_weight self.tracker.update_meter("rec", "val", loss_rec.item()) rec_contact = net_out["rec_pose"][:, :, j*6+3:j*6+7] # print(rec_contact.shape, tar_contact.shape) loss_contact = self.vel_loss(rec_contact, tar_contact) * self.args.rec_weight * self.args.rec_pos_weight self.tracker.update_meter("contact", "val", loss_contact.item()) #g_loss_final += loss_rec rec_trans = net_out["rec_pose"][:, :, j*6:j*6+3] rec_x_trans = other_tools.velocity2position(rec_trans[:, :, 0:1], 1/self.args.pose_fps, tar_trans[:, 0, 0:1]) rec_z_trans = other_tools.velocity2position(rec_trans[:, :, 2:3], 1/self.args.pose_fps, tar_trans[:, 0, 2:3]) rec_y_trans = rec_trans[:,:,1:2] rec_xyz_trans = torch.cat([rec_x_trans, rec_y_trans, rec_z_trans], dim=-1) # rec_trans = net_out["rec_pose"][:, :, j*6:j*6+3] # rec_x_trans = other_tools.velocity2position(rec_trans[:, :, 0:1], 1/self.args.pose_fps, tar_trans[:, 0, 0:1]) # rec_z_trans = other_tools.velocity2position(rec_trans[:, :, 2:3], 1/self.args.pose_fps, tar_trans[:, 0, 2:3]) # rec_y_trans = rec_trans[:,:,1:2] # rec_xyz_trans = torch.cat([rec_x_trans, rec_y_trans, rec_z_trans], dim=-1) # loss_trans_vel = self.vel_loss(rec_trans[:, :, 0:1], tar_trans_vel_x) * self.args.rec_weight \ # + self.vel_loss(rec_trans[:, :, 2:3], tar_trans_vel_z) * self.args.rec_weight # v3 = self.vel_loss(rec_trans[:, :, 0:1][:, 1:] - rec_trans[:, :, 0:1][:, :-1], tar_trans_vel_x[:, 1:] - tar_trans_vel_x[:, :-1]) * self.args.rec_weight \ # + self.vel_loss(rec_trans[:, :, 2:3][:, 1:] - rec_trans[:, :, 2:3][:, :-1], tar_trans_vel_z[:, 1:] - tar_trans_vel_z[:, :-1]) * self.args.rec_weight # a3 = self.vel_loss(rec_trans[:, :, 0:1][:, 2:] + rec_trans[:, :, 0:1][:, :-2] - 2 * rec_trans[:, :, 0:1][:, 1:-1], tar_trans_vel_x[:, 2:] + tar_trans_vel_x[:, :-2] - 2 * tar_trans_vel_x[:, 1:-1]) * self.args.rec_weight \ # + self.vel_loss(rec_trans[:, :, 2:3][:, 2:] + rec_trans[:, :, 2:3][:, :-2] - 2 * rec_trans[:, :, 2:3][:, 1:-1], tar_trans_vel_z[:, 2:] + tar_trans_vel_z[:, :-2] - 2 * tar_trans_vel_z[:, 1:-1]) * self.args.rec_weight # #g_loss_final += 5*v3 # #g_loss_final += 5*a3 # v2 = self.vel_loss(rec_xyz_trans[:, 1:] - rec_xyz_trans[:, :-1], tar_trans[:, 1:] - tar_trans[:, :-1]) * self.args.rec_weight # a2 = self.vel_loss(rec_xyz_trans[:, 2:] + rec_xyz_trans[:, :-2] - 2 * rec_xyz_trans[:, 1:-1], tar_trans[:, 2:] + tar_trans[:, :-2] - 2 * tar_trans[:, 1:-1]) * self.args.rec_weight #g_loss_final += 5*v2 #g_loss_final += 5*a2 # self.tracker.update_meter("transv", "val", loss_trans_vel.item()) # #g_loss_final += loss_trans_vel # loss_trans = self.vel_loss(rec_xyz_trans, tar_trans) * self.args.rec_weight # self.tracker.update_meter("trans", "val", loss_trans.item()) #g_loss_final += loss_trans # vertices loss if self.args.rec_ver_weight > 0: # print(tar_pose.shape, bs, n, j) tar_pose = rc.matrix_to_axis_angle(tar_pose).reshape(bs*n, j*3) rec_pose = rc.matrix_to_axis_angle(rec_pose).reshape(bs*n, j*3) rec_pose = self.inverse_selection_tensor(rec_pose, self.train_data.joint_mask, rec_pose.shape[0]) tar_pose = self.inverse_selection_tensor(tar_pose, self.train_data.joint_mask, tar_pose.shape[0]) vertices_rec = 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=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_verts=False, return_joints=True, leye_pose=tar_pose[:, 69:72], reye_pose=tar_pose[:, 72:75], ) vertices_tar = 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], 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_verts=False, return_joints=True, leye_pose=tar_pose[:, 69:72], reye_pose=tar_pose[:, 72:75], ) joints_rec = vertices_rec['joints'] joints_rec = joints_rec.reshape(bs, n, -1, 3) vectices_loss = self.vectices_loss(vertices_rec['joints'], vertices_tar['joints']) foot_idx = [7, 8, 10, 11] model_contact = net_out["rec_pose"][:, :, j*6+3:j*6+7] # find static indices consistent with model's own predictions static_idx = model_contact > 0.95 # N x S x 4 # print(model_contact) model_feet = joints_rec[:, :, foot_idx] # foot positions (N, S, 4, 3) model_foot_v = torch.zeros_like(model_feet) model_foot_v[:, :-1] = ( model_feet[:, 1:, :, :] - model_feet[:, :-1, :, :] ) # (N, S-1, 4, 3) model_foot_v[~static_idx] = 0 foot_loss = self.vectices_loss( model_foot_v, torch.zeros_like(model_foot_v) ) self.tracker.update_meter("foot", "val", foot_loss.item()*self.args.rec_weight * self.args.rec_ver_weight) self.tracker.update_meter("ver", "val", vectices_loss.item()*self.args.rec_weight * self.args.rec_ver_weight) if "VQVAE" in self.args.g_name: loss_embedding = net_out["embedding_loss"] self.tracker.update_meter("com", "val", loss_embedding.item()) #g_loss_final += vectices_loss*self.args.rec_weight*self.args.rec_ver_weight if self.args.debug: if its == 1: break 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 self.model.eval() with torch.no_grad(): for its, dict_data in enumerate(self.test_loader): tar_pose_raw = dict_data["pose"] tar_trans = dict_data["trans"].to(self.rank) tar_pose = tar_pose_raw[:, :, :27].cuda() tar_contact = tar_pose_raw[:, :, 27:31].cuda() # tar_pose = tar_pose.cuda() bs, n, j = tar_pose.shape[0], tar_pose.shape[1], self.joints 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) remain = n%self.args.pose_length tar_pose = tar_pose[:, :n-remain, :] tar_contact = tar_contact[:, :n-remain, :] tar_trans_copy = tar_trans[:, :n-remain, :] tar_contact_copy = tar_contact in_tar_pose = torch.cat([tar_pose, tar_trans_copy, tar_contact_copy], dim=-1) #print(tar_pose.shape) if True: net_out = self.model(in_tar_pose) rec_pose = net_out["rec_pose"][:, :, :j*6] rec_trans = net_out["rec_pose"][:, :, j*6:j*6+3] - net_out["rec_pose"][:, :, j*6:j*6+3] # print(rec_trans.shape) rec_x_trans = other_tools.velocity2position(rec_trans[:, :, 0:1], 1/self.args.pose_fps, tar_trans[:, 0, 0:1]) rec_z_trans = other_tools.velocity2position(rec_trans[:, :, 2:3], 1/self.args.pose_fps, tar_trans[:, 0, 2:3]) rec_y_trans = rec_trans[:,:,1:2] rec_trans = torch.cat([rec_x_trans, rec_y_trans, rec_z_trans], dim=-1) n = rec_pose.shape[1] rec_trans = rec_trans.cpu().numpy().reshape(bs*n, 3) tar_pose = tar_pose[:, :n, :] rec_pose = rec_pose.reshape(bs, n, j, 6) rec_pose = rc.rotation_6d_to_matrix(rec_pose)# rec_pose = rc.matrix_to_axis_angle(rec_pose).reshape(bs*n, j*3) rec_pose = rec_pose.cpu().numpy() else: pass # for i in range(tar_pose.shape[1]//(self.args.vae_test_len)): # tar_pose_new = tar_pose[:,i*(self.args.vae_test_len):i*(self.args.vae_test_len)+self.args.vae_test_len,:] # net_out = self.model(**dict(inputs=tar_pose_new)) # rec_pose = net_out["rec_pose"] # rec_pose = (rec_pose.reshape(rec_pose.shape[0], rec_pose.shape[1], -1, 6) * self.joint_level_mask_cuda).reshape(rec_pose.shape[0], rec_pose.shape[1], -1) # if "rot6d" in self.args.pose_rep: # rec_pose = data_transfer.rotation_6d_to_matrix(rec_pose.reshape(tar_pose.shape[0], self.args.vae_test_len, -1, 6)) # rec_pose = data_transfer.matrix_to_euler_angles(rec_pose, "XYZ").reshape(rec_pose.shape[0], rec_pose.shape[1], -1) # if "smplx" not in self.args.pose_rep: # rec_pose = torch.rad2deg(rec_pose) # rec_pose = rec_pose * self.joint_mask_cuda # out_sub = rec_pose.cpu().numpy().reshape(-1, rec_pose.shape[2]) # if i != 0: # out_final = np.concatenate((out_final,out_sub), 0) # else: # out_final = out_sub 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 = tar_pose.cpu().numpy() total_length += n # --- save --- # if 'smplx' in self.args.pose_rep: gt_npz = np.load(self.args.data_path+self.args.pose_rep+"/"+test_seq_list.iloc[its]['id']+'.npz', allow_pickle=True) stride = int(30 / self.args.pose_fps) tar_pose = self.inverse_selection(tar_pose, self.test_data.joint_mask, tar_pose.shape[0]) np.savez(results_save_path+"gt_"+test_seq_list.iloc[its]['id']+'.npz', betas=gt_npz["betas"], poses=tar_pose[:n], expressions=gt_npz["expressions"]-gt_npz["expressions"], trans=rec_trans-rec_trans, model='smplx2020', gender='neutral', mocap_frame_rate = 30 , ) rec_pose = self.inverse_selection(rec_pose, self.test_data.joint_mask, rec_pose.shape[0]) np.savez(results_save_path+"res_"+test_seq_list.iloc[its]['id']+'.npz', betas=gt_npz["betas"], poses=rec_pose, expressions=gt_npz["expressions"]-gt_npz["expressions"], trans=rec_trans-rec_trans, model='smplx2020', gender='neutral', mocap_frame_rate = 30 , ) else: rec_pose = rc.axis_angle_to_matrix(torch.from_numpy(rec_pose.reshape(bs*n, j, 3))) rec_pose = np.rad2deg(rc.matrix_to_euler_angles(rec_pose, "XYZ")).reshape(bs*n, j*3).numpy() tar_pose = rc.axis_angle_to_matrix(torch.from_numpy(tar_pose.reshape(bs*n, j, 3))) tar_pose = np.rad2deg(rc.matrix_to_euler_angles(tar_pose, "XYZ")).reshape(bs*n, j*3).numpy() #trans="0.000000 0.000000 0.000000" with open(f"{self.args.data_path}{self.args.pose_rep}/{test_seq_list.iloc[its]['id']}.bvh", "r") as f_demo: with open(results_save_path+"gt_"+test_seq_list.iloc[its]['id']+'.bvh', 'w+') as f_gt: with open(results_save_path+"res_"+test_seq_list.iloc[its]['id']+'.bvh', 'w+') as f_real: for i, line_data in enumerate(f_demo.readlines()): if i < 431: f_real.write(line_data) f_gt.write(line_data) else: break for line_id in range(n): #,args.pre_frames, args.pose_length line_data = np.array2string(rec_pose[line_id], max_line_width=np.inf, precision=6, suppress_small=False, separator=' ') f_real.write(line_data[1:-2]+'\n') for line_id in range(n): #,args.pre_frames, args.pose_length line_data = np.array2string(tar_pose[line_id], max_line_width=np.inf, precision=6, suppress_small=False, separator=' ') f_gt.write(line_data[1:-2]+'\n') # with open(results_save_path+"gt_"+test_seq_list[its]+'.pkl', 'wb') as fw: # pickle.dump(new_dict, fw) # #new_dict2["fullpose"] = out_final # with open(results_save_path+"res_"+test_seq_list[its]+'.pkl', 'wb') as fw1: # pickle.dump(new_dict2, fw1) # other_tools.render_one_sequence( # results_save_path+"res_"+test_seq_list[its]+'.pkl', # results_save_path+"gt_"+test_seq_list[its]+'.pkl', # results_save_path, # self.args.data_path + self.args.test_data_path + 'wave16k/' + test_seq_list[its]+'.npy', # ) #if its == 1:break 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")