newapp

.gitattributes

@@ -33,7 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
-EMAGE/test_sequences/wave16k/2_scott_0_1_1.wav filter=lfs diff=lfs merge=lfs -text
-EMAGE/test_sequences/wave16k/2_scott_0_2_2.wav filter=lfs diff=lfs merge=lfs -text
-EMAGE/test_sequences/wave16k/2_scott_0_3_3.wav filter=lfs diff=lfs merge=lfs -text
-EMAGE/test_sequences/wave16k/2_scott_0_4_4.wav filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text

.ipynb_checkpoints/README-checkpoint.md

@@ -1,13 +0,0 @@
----
-title: EMAGE
-emoji: ⚡
-colorFrom: yellow
-colorTo: green
-sdk: gradio
-sdk_version: 4.24.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

.ipynb_checkpoints/app-checkpoint.py

@@ -1,664 +0,0 @@
-import spaces
-import os
-# os.system("Xvfb :99 -ac &")
-# os.environ["DISPLAY"] = ":99"
-import OpenGL.GL as gl
-os.environ["PYOPENGL_PLATFORM"] = "egl"
-os.environ["MESA_GL_VERSION_OVERRIDE"] = "4.1"
-import signal
-import time
-import csv
-import sys
-import warnings
-import random
-import gradio as gr
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.distributed as dist
-from torch.nn.parallel import DistributedDataParallel as DDP
-import torch.multiprocessing as mp
-import numpy as np
-import time
-import pprint
-from loguru import logger
-import smplx
-from torch.utils.tensorboard import SummaryWriter
-import wandb
-import matplotlib.pyplot as plt
-from utils import config, logger_tools, other_tools_hf, metric, data_transfer
-from dataloaders import data_tools
-from dataloaders.build_vocab import Vocab
-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
-from utils import rotation_conversions as rc
-import soundfile as sf
-import librosa 
-
-def inverse_selection_tensor(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
-
-@spaces.GPU(duration=120) 
-def test_demo_gpu(
-    model, vq_model_face, vq_model_upper, vq_model_hands, vq_model_lower, global_motion, smplx_model,
-    dict_data,
-    args,
-    joints, joint_mask_upper, joint_mask_lower, joint_mask_hands,
-    log_softmax,
-):
-    rank = 0
-    other_tools_hf.load_checkpoints(vq_model_face, args.data_path_1 + "pretrained_vq/last_790_face_v2.bin", args.e_name)
-    other_tools_hf.load_checkpoints(vq_model_upper, args.data_path_1 + "pretrained_vq/upper_vertex_1layer_710.bin", args.e_name)
-    other_tools_hf.load_checkpoints(vq_model_hands, args.data_path_1 + "pretrained_vq/hands_vertex_1layer_710.bin", args.e_name)
-    other_tools_hf.load_checkpoints(vq_model_lower, args.data_path_1 + "pretrained_vq/lower_foot_600.bin", args.e_name)
-    other_tools_hf.load_checkpoints(global_motion, args.data_path_1 + "pretrained_vq/last_1700_foot.bin", args.e_name)
-    other_tools_hf.load_checkpoints(model, args.test_ckpt, args.g_name)
-    model.to(rank).eval()
-    smplx_model.to(rank).eval()
-    vq_model_face.to(rank).eval()
-    vq_model_upper.to(rank).eval()
-    vq_model_hands.to(rank).eval()
-    vq_model_lower.to(rank).eval()
-    global_motion.to(rank).eval()
-        
-    with torch.no_grad():
-        tar_pose_raw = dict_data["pose"]
-        tar_pose = tar_pose_raw[:, :, :165].to(rank)
-        tar_contact = tar_pose_raw[:, :, 165:169].to(rank)
-        tar_trans = dict_data["trans"].to(rank)
-        tar_exps = dict_data["facial"].to(rank)
-        in_audio = dict_data["audio"].to(rank) 
-        in_word = None# dict_data["word"].to(rank)
-        tar_beta = dict_data["beta"].to(rank)
-        tar_id = dict_data["id"].to(rank).long()
-        bs, n, j = tar_pose.shape[0], tar_pose.shape[1], 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[:, :, 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[:, :, 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 = vq_model_face.map2index(tar_pose_face) # bs*n/4
-        tar_index_value_upper_top = vq_model_upper.map2index(tar_pose_upper) # bs*n/4
-        tar_index_value_hands_top = vq_model_hands.map2index(tar_pose_hands) # bs*n/4
-        tar_index_value_lower_top = vq_model_lower.map2index(tar_pose_lower) # bs*n/4
-      
-        latent_face_top = vq_model_face.map2latent(tar_pose_face) # bs*n/4
-        latent_upper_top = vq_model_upper.map2latent(tar_pose_upper) # bs*n/4
-        latent_hands_top = vq_model_hands.map2latent(tar_pose_hands) # bs*n/4
-        latent_lower_top = 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)
-
-        loaded_data = {
-            "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,
-        }
-
-        mode = 'test'
-        bs, n, j = loaded_data["tar_pose"].shape[0], loaded_data["tar_pose"].shape[1], joints 
-        tar_pose = loaded_data["tar_pose"]
-        tar_beta = loaded_data["tar_beta"]
-        in_word =None# 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[:, :, 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[:, :, 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 = []
-        
-        roundt = (n - args.pre_frames) // (args.pose_length - args.pre_frames)
-        remain = (n - args.pre_frames) % (args.pose_length - args.pre_frames)
-        round_l = args.pose_length - args.pre_frames
-
-        for i in range(0, roundt):
-            # in_word_tmp = in_word[:, i*(round_l):(i+1)*(round_l)+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*args.pre_frames]
-            in_id_tmp = loaded_data['tar_id'][:, i*(round_l):(i+1)*(round_l)+args.pre_frames]
-            mask_val = torch.ones(bs, args.pose_length, args.pose_dims+3+4).float().cuda()
-            mask_val[:, :args.pre_frames, :] = 0.0
-            if i == 0:
-                latent_all_tmp = latent_all[:, i*(round_l):(i+1)*(round_l)+args.pre_frames, :]
-            else:
-                latent_all_tmp = latent_all[:, i*(round_l):(i+1)*(round_l)+args.pre_frames, :]
-                # print(latent_all_tmp.shape, latent_last.shape)
-                latent_all_tmp[:, :args.pre_frames, :] = latent_last[:, -args.pre_frames:, :]
-            
-            net_out_val = model(
-                in_audio = in_audio_tmp,
-                in_word=None, #in_word_tmp,
-                mask=mask_val,
-                in_motion = latent_all_tmp,
-                in_id = in_id_tmp,
-                use_attentions=True,)
-            
-            if args.cu != 0:
-                rec_index_upper = log_softmax(net_out_val["cls_upper"]).reshape(-1, args.vae_codebook_size)
-                _, rec_index_upper = torch.max(rec_index_upper.reshape(-1, args.pose_length, args.vae_codebook_size), dim=2)
-                #rec_upper = vq_model_upper.decode(rec_index_upper)
-            else:
-                _, rec_index_upper, _, _ = vq_model_upper.quantizer(net_out_val["rec_upper"])
-                #rec_upper = vq_model_upper.decoder(rec_index_upper)
-            if args.cl != 0:
-                rec_index_lower = log_softmax(net_out_val["cls_lower"]).reshape(-1, args.vae_codebook_size)
-                _, rec_index_lower = torch.max(rec_index_lower.reshape(-1, args.pose_length, args.vae_codebook_size), dim=2)
-                #rec_lower = vq_model_lower.decode(rec_index_lower)
-            else:
-                _, rec_index_lower, _, _ = vq_model_lower.quantizer(net_out_val["rec_lower"])
-                #rec_lower = vq_model_lower.decoder(rec_index_lower)
-            if args.ch != 0:
-                rec_index_hands = log_softmax(net_out_val["cls_hands"]).reshape(-1, args.vae_codebook_size)
-                _, rec_index_hands = torch.max(rec_index_hands.reshape(-1, args.pose_length, args.vae_codebook_size), dim=2)
-                #rec_hands = vq_model_hands.decode(rec_index_hands)
-            else:
-                _, rec_index_hands, _, _ = vq_model_hands.quantizer(net_out_val["rec_hands"])
-                #rec_hands = vq_model_hands.decoder(rec_index_hands)
-            if args.cf != 0:
-                rec_index_face = log_softmax(net_out_val["cls_face"]).reshape(-1, args.vae_codebook_size)
-                _, rec_index_face = torch.max(rec_index_face.reshape(-1, args.pose_length, args.vae_codebook_size), dim=2)
-                #rec_face = vq_model_face.decoder(rec_index_face)
-            else:
-                _, rec_index_face, _, _ = vq_model_face.quantizer(net_out_val["rec_face"])
-                #rec_face = 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[:, args.pre_frames:])
-                rec_index_all_upper.append(rec_index_upper[:, args.pre_frames:])
-                rec_index_all_lower.append(rec_index_lower[:, args.pre_frames:])
-                rec_index_all_hands.append(rec_index_hands[:, args.pre_frames:])
-
-            if args.cu != 0:
-                rec_upper_last = vq_model_upper.decode(rec_index_upper)
-            else:
-                rec_upper_last = vq_model_upper.decoder(rec_index_upper)
-            if args.cl != 0:
-                rec_lower_last = vq_model_lower.decode(rec_index_lower)
-            else:
-                rec_lower_last = vq_model_lower.decoder(rec_index_lower)
-            if args.ch != 0:
-                rec_hands_last = vq_model_hands.decode(rec_index_hands)
-            else:
-                rec_hands_last = vq_model_hands.decoder(rec_index_hands)
-            # if args.cf != 0:
-            #     rec_face_last = vq_model_face.decode(rec_index_face)
-            # else:
-            #     rec_face_last = 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 = inverse_selection_tensor(rec_pose_upper, 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 = inverse_selection_tensor(rec_pose_lower, 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 = inverse_selection_tensor(rec_pose_hands, 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_hf.velocity2position(rec_trans_v_s[:, :, 0:1], 1/args.pose_fps, tar_trans[:, 0, 0:1])
-            rec_z_trans = other_tools_hf.velocity2position(rec_trans_v_s[:, :, 2:3], 1/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 args.cu != 0:
-            rec_upper = vq_model_upper.decode(rec_index_upper)
-        else:
-            rec_upper = vq_model_upper.decoder(rec_index_upper)
-        if args.cl != 0:
-            rec_lower = vq_model_lower.decode(rec_index_lower)
-        else:
-            rec_lower = vq_model_lower.decoder(rec_index_lower)
-        if args.ch != 0:
-            rec_hands = vq_model_hands.decode(rec_index_hands)
-        else:
-            rec_hands = vq_model_hands.decoder(rec_index_hands)
-        if args.cf != 0:
-            rec_face = vq_model_face.decode(rec_index_face)
-        else:
-            rec_face = 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 = inverse_selection_tensor(rec_pose_upper, 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 = inverse_selection_tensor(rec_pose_lower, 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 = inverse_selection_tensor(rec_pose_hands, 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 = global_motion(to_global)
-
-        rec_trans_v_s = rec_global["rec_pose"][:, :, 54:57]
-        rec_x_trans = other_tools_hf.velocity2position(rec_trans_v_s[:, :, 0:1], 1/args.pose_fps, tar_trans[:, 0, 0:1])
-        rec_z_trans = other_tools_hf.velocity2position(rec_trans_v_s[:, :, 2:3], 1/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)
-        
-        net_out =  {
-            '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,
-        }
-        
-
-        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], joints
-                # interpolate to 30fps  
-        if (30/args.pose_fps) != 1:
-            assert 30%args.pose_fps == 0
-            n *= int(30/args.pose_fps)
-            tar_pose = torch.nn.functional.interpolate(tar_pose.permute(0, 2, 1), scale_factor=30/args.pose_fps, mode='linear').permute(0,2,1)
-            rec_pose = torch.nn.functional.interpolate(rec_pose.permute(0, 2, 1), scale_factor=30/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)
-        
-    return tar_pose, rec_pose, tar_exps, tar_beta, rec_trans, tar_trans, rec_exps, bs, n, j
-
-    
-class BaseTrainer(object):
-    def __init__(self, args, sp, ap, tp):
-        hf_dir = "hf"
-        if not os.path.exists(args.out_path + "custom/" + hf_dir + "/"):
-            os.makedirs(args.out_path + "custom/" + hf_dir + "/")
-        sf.write(args.out_path + "custom/" + hf_dir + "/tmp.wav", ap[1][:ap[0]*8], ap[0])
-        self.audio_path = args.out_path + "custom/" + hf_dir + "/tmp.wav"
-        audio, ssr = librosa.load(self.audio_path)
-        ap = (ssr, audio)
-        self.args = args
-        self.rank = 0 # dist.get_rank()
-       
-        #self.checkpoint_path = args.out_path + "custom/" + args.name + args.notes + "/" #wandb.run.dir #args.cache_path+args.out_path+"/"+args.name
-        self.checkpoint_path = args.out_path + "custom/" + hf_dir + "/" 
-        if self.rank == 0:
-            self.test_data = __import__(f"dataloaders.{args.dataset}", fromlist=["something"]).CustomDataset(args, "test", smplx_path=sp, audio_path=ap, text_path=tp)
-            self.test_loader = torch.utils.data.DataLoader(
-                self.test_data, 
-                batch_size=1,  
-                shuffle=False,  
-                num_workers=args.loader_workers,
-                drop_last=False,
-            )
-        logger.info(f"Init test dataloader success")
-        model_module = __import__(f"models.{args.model}", fromlist=["something"])
-        
-        if args.ddp:
-            self.model = getattr(model_module, args.g_name)(args).to(self.rank)
-            process_group = torch.distributed.new_group()
-            self.model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(self.model, process_group)   
-            self.model = DDP(self.model, device_ids=[self.rank], output_device=self.rank,
-                             broadcast_buffers=False, find_unused_parameters=False)
-        else: 
-            self.model = torch.nn.DataParallel(getattr(model_module, args.g_name)(args), args.gpus).cpu()
-        
-        if self.rank == 0:
-            logger.info(self.model)
-            logger.info(f"init {args.g_name} success")
-
-        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,
-        )
-                
-        self.args = args
-        self.joints = self.test_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_hf.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).cpu()
-        # print(self.vq_model_face)
-        # other_tools_hf.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).cpu()
-        # other_tools_hf.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).cpu()
-        # other_tools_hf.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).cpu()
-        # other_tools_hf.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).cpu()
-        # other_tools_hf.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.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)
-      
-    
-    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 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): 
-            import shutil
-            shutil.rmtree(results_save_path)
-        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
-        for its, batch_data in enumerate(self.test_loader):
-            tar_pose, rec_pose, tar_exps, tar_beta, rec_trans, tar_trans, rec_exps, bs, n, j = test_demo_gpu(
-                self.model, self.vq_model_face, self.vq_model_upper, self.vq_model_hands, self.vq_model_lower, self.global_motion, self.smplx,
-                batch_data,
-                self.args,
-                self.joints, self.joint_mask_upper, self.joint_mask_lower, self.joint_mask_hands,
-                self.log_softmax,
-            )
-                           
-            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)
-                #'''
-        # its = 0
-            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
-            render_vid_path = other_tools_hf.render_one_sequence_no_gt(
-            results_save_path+"res_"+test_seq_list.iloc[its]['id']+'.npz', 
-                    # results_save_path+"gt_"+test_seq_list.iloc[its]['id']+'.npz', 
-                    results_save_path,
-                    self.audio_path,
-                    self.args.data_path_1+"smplx_models/",
-                    use_matplotlib = False,
-                    args = self.args,
-                    )
-        result = gr.Video(value=render_vid_path, visible=True)
-        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")
-        return result
-
-       
-@logger.catch
-def emage(audio_path):
-    smplx_path = None
-    text_path = None
-    rank = 0
-    world_size = 1
-    args = config.parse_args()
-    #os.environ['TRANSFORMERS_CACHE'] = args.data_path_1 + "hub/"
-    if not sys.warnoptions:
-        warnings.simplefilter("ignore")
-    # dist.init_process_group(backend="gloo", rank=rank, world_size=world_size)
-
-    #logger_tools.set_args_and_logger(args, rank)
-    other_tools_hf.set_random_seed(args)
-    other_tools_hf.print_exp_info(args)
-
-    # return one intance of trainer
-    trainer = BaseTrainer(args, sp = smplx_path, ap = audio_path, tp = text_path)
-    result = trainer.test_demo(999)
-    return result
-
-examples = [
-    ["./EMAGE/test_sequences/wave16k/2_scott_0_1_1.wav"],
-    ["./EMAGE/test_sequences/wave16k/2_scott_0_2_2.wav"],
-    ["./EMAGE/test_sequences/wave16k/2_scott_0_3_3.wav"],
-]
-
-demo = gr.Interface(
-    emage,  # function
-    inputs=[
-        # gr.File(label="Please upload SMPL-X file with npz format here.", file_types=["npz", "NPZ"]),
-        gr.Audio(),
-        # gr.File(label="Please upload textgrid format file here.", file_types=["TextGrid", "Textgrid", "textgrid"])
-    ],  # input type
-    outputs=gr.Video(format="mp4", visible=True),
-    title='\
-    <div align="center">\
-    EMAGE: Towards Unified Holistic Co-Speech Gesture Generation via Expressive Masked Audio Gesture Modeling <br/>\
-    CVPR 2024  <br/>\
-    </div>',
-    description='\
-    <div align="center">\
-    Haiyang Liu1*,  Zihao Zhu2*,  Giorgio Becherini3,  Yichen Peng4,  Mingyang Su5,<br/>\
-    You Zhou,  Xuefei Zhe,  Naoya Iwamoto,  Bo Zheng,  Michael J. Black3 <br/>\
-    (*Equal Contribution) <br/>\
-    1The University of Tokyo, 2Keio University, 4Japan Advanced Institute of Science and Technology, <br/>\
-    3Max Planck Institute for Intelligent Systems, 5Tsinghua University <br/>\
-    </div>\
-    ',
-    article="\
-    Due to the limited resources in this space, we process the first 8s of your uploaded audio. <br/>\
-    Try to develop this space locally for longer motion generation, e.g., 60s. <br/>\
-    Relevant links: [Project Page (https://pantomatrix.github.io/EMAGE/)\
-    ",
-    examples=examples,
-)
-
-            
-if __name__ == "__main__":
-    os.environ["MASTER_ADDR"]='127.0.0.1'
-    os.environ["MASTER_PORT"]='8675'
-    #os.environ["TORCH_DISTRIBUTED_DEBUG"] = "DETAIL"
-    demo.launch(share=True)

.ipynb_checkpoints/packages-checkpoint.txt

@@ -1,4 +0,0 @@
-libgl1-mesa-dev
-libglu1-mesa-dev
-freeglut3-dev
-mesa-common-dev

.ipynb_checkpoints/requirements-checkpoint.txt

@@ -1,39 +0,0 @@
-ffmpeg
-ConfigArgParse==1.7
-fasttext==0.9.2
-h5py==3.10.0
-imageio==2.31.4
-ipython==8.12.3
-joblib==1.3.2
-librosa==0.10.1
-lmdb==1.4.1
-loguru==0.7.2
-matplotlib==3.7.3
-moviepy==1.0.3
-gradio
-fasttext-wheel
-opencv_contrib_python==4.8.1.78
-opencv_python==4.8.1.78
-pandas==1.5.3
-peakutils==1.3.4
-ptflops==0.7.1.2
-python_igraph==0.11.3
-pyvirtualdisplay==3.0
-PyYAML==6.0.1
-replicate==0.15.4
-scikit_learn==1.3.2
-scipy
-soundfile==0.12.1
-termcolor==2.4.0
-textgrid==1.5
-torch==2.1.0
-torchvision
-tqdm==4.66.1
-transformers==4.35.2
-trimesh==3.23.5
-wandb==0.16.0
-pyglet<2
-smplx
-tensorboard
-pyrender
-pyarrow

.ipynb_checkpoints/test_demo-checkpoint.py

@@ -1,581 +0,0 @@
-import os
-import signal
-import time
-import csv
-import sys
-import warnings
-import random
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.distributed as dist
-from torch.nn.parallel import DistributedDataParallel as DDP
-import torch.multiprocessing as mp
-import numpy as np
-import time
-import pprint
-from loguru import logger
-import smplx
-from torch.utils.tensorboard import SummaryWriter
-import wandb
-import matplotlib.pyplot as plt
-from utils import config, logger_tools, other_tools, metric, data_transfer
-from dataloaders import data_tools
-from dataloaders.build_vocab import Vocab
-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
-from utils import rotation_conversions as rc
-
-class BaseTrainer(object):
-    def __init__(self, args):
-        self.args = args
-        self.rank = dist.get_rank()
-        self.checkpoint_path = args.out_path + "custom/" + args.name + args.notes + "/" #wandb.run.dir #args.cache_path+args.out_path+"/"+args.name
-        if self.rank == 0:
-            self.test_data = __import__(f"dataloaders.{args.dataset}", fromlist=["something"]).CustomDataset(args, "test")
-            self.test_loader = torch.utils.data.DataLoader(
-                self.test_data, 
-                batch_size=1,  
-                shuffle=False,  
-                num_workers=args.loader_workers,
-                drop_last=False,
-            )
-        logger.info(f"Init test dataloader success")
-        model_module = __import__(f"models.{args.model}", fromlist=["something"])
-        
-        if args.ddp:
-            self.model = getattr(model_module, args.g_name)(args).to(self.rank)
-            process_group = torch.distributed.new_group()
-            self.model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(self.model, process_group)   
-            self.model = DDP(self.model, device_ids=[self.rank], output_device=self.rank,
-                             broadcast_buffers=False, find_unused_parameters=False)
-        else: 
-            self.model = torch.nn.DataParallel(getattr(model_module, args.g_name)(args), args.gpus).cuda()
-        
-        if self.rank == 0:
-            logger.info(self.model)
-            logger.info(f"init {args.g_name} success")
-  
-        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,
-        ).to(self.rank).eval()
-
-        self.args = args
-        self.joints = self.test_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_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 = []
-        
-        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
-
-        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 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
-                # other_tools.render_one_sequence(
-                #     results_save_path+"res_"+test_seq_list.iloc[its]['id']+'.npz', 
-                #     results_save_path+"gt_"+test_seq_list.iloc[its]['id']+'.npz', 
-                #     results_save_path,
-                #     self.args.data_path+"wave16k/"+test_seq_list.iloc[its]['id']+".wav",
-                #     self.args.data_path_1+"smplx_models/", 
-                #     use_matplotlib = False,
-                #     args = self.args,
-                #     )
-        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")
-       
-@logger.catch
-def main_worker(rank, world_size, args):
-    #os.environ['TRANSFORMERS_CACHE'] = args.data_path_1 + "hub/"
-    if not sys.warnoptions:
-        warnings.simplefilter("ignore")
-    dist.init_process_group(backend="nccl", rank=rank, world_size=world_size)
-        
-    logger_tools.set_args_and_logger(args, rank)
-    other_tools.set_random_seed(args)
-    other_tools.print_exp_info(args)
-      
-    # return one intance of trainer
-    other_tools.write_wav_names_to_csv(args.data_path, args.data_path+"test.csv")
-    trainer = BaseTrainer(args)
-    other_tools.load_checkpoints(trainer.model, args.test_ckpt, args.g_name)
-    trainer.test_demo(999)
-    
-    
-            
-if __name__ == "__main__":
-    os.environ["MASTER_ADDR"]='127.0.0.1'
-    os.environ["MASTER_PORT"]='8675'
-    #os.environ["TORCH_DISTRIBUTED_DEBUG"] = "DETAIL"
-    args = config.parse_args()
-    if args.ddp:
-        mp.set_start_method("spawn", force=True)
-        mp.spawn(
-            main_worker,
-            args=(len(args.gpus), args,),
-            nprocs=len(args.gpus),
-                )
-    else:
-        main_worker(0, 1, args)

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-            xmax = 34.69 
-            text = "around" 
-        intervals [130]:
-            xmin = 34.69 
-            xmax = 35.08 
-            text = "enjoying" 
-        intervals [131]:
-            xmin = 35.08 
-            xmax = 35.17 
-            text = "the" 
-        intervals [132]:
-            xmin = 35.17 
-            xmax = 35.87 
-            text = "sunshine" 
-        intervals [133]:
-            xmin = 35.87 
-            xmax = 36.15 
-            text = "" 
-        intervals [134]:
-            xmin = 36.15 
-            xmax = 36.34 
-            text = "i'd" 
-        intervals [135]:
-            xmin = 36.34 
-            xmax = 36.52 
-            text = "like" 
-        intervals [136]:
-            xmin = 36.52 
-            xmax = 36.59 
-            text = "to" 
-        intervals [137]:
-            xmin = 36.59 
-            xmax = 36.74 
-            text = "have" 
-        intervals [138]:
-            xmin = 36.74 
-            xmax = 36.79 
-            text = "a" 
-        intervals [139]:
-            xmin = 36.79 
-            xmax = 37.06 
-            text = "healthy" 
-        intervals [140]:
-            xmin = 37.06 
-            xmax = 37.66 
-            text = "lifestyle" 
-        intervals [141]:
-            xmin = 37.66 
-            xmax = 38.06 
-            text = "considering" 
-        intervals [142]:
-            xmin = 38.06 
-            xmax = 38.17 
-            text = "how" 
-        intervals [143]:
-            xmin = 38.17 
-            xmax = 38.38 
-            text = "much" 
-        intervals [144]:
-            xmin = 38.38 
-            xmax = 38.74 
-            text = "time" 
-        intervals [145]:
-            xmin = 38.74 
-            xmax = 38.81 
-            text = "i" 
-        intervals [146]:
-            xmin = 38.81 
-            xmax = 39.18 
-            text = "spend" 
-        intervals [147]:
-            xmin = 39.18 
-            xmax = 39.29 
-            text = "at" 
-        intervals [148]:
-            xmin = 39.29 
-            xmax = 39.84 
-            text = "work" 
-        intervals [149]:
-            xmin = 39.84 
-            xmax = 40.29 
-            text = "" 
-        intervals [150]:
-            xmin = 40.29 
-            xmax = 40.52 
-            text = "i" 
-        intervals [151]:
-            xmin = 40.52 
-            xmax = 40.79 
-            text = "always" 
-        intervals [152]:
-            xmin = 40.79 
-            xmax = 41.28 
-            text = "try" 
-        intervals [153]:
-            xmin = 41.28 
-            xmax = 41.47 
-            text = "to" 
-        intervals [154]:
-            xmin = 41.47 
-            xmax = 41.85 
-            text = "move" 
-        intervals [155]:
-            xmin = 41.85 
-            xmax = 42 
-            text = "as" 
-        intervals [156]:
-            xmin = 42 
-            xmax = 42.22 
-            text = "much" 
-        intervals [157]:
-            xmin = 42.22 
-            xmax = 42.31 
-            text = "as" 
-        intervals [158]:
-            xmin = 42.31 
-            xmax = 42.4 
-            text = "i" 
-        intervals [159]:
-            xmin = 42.4 
-            xmax = 42.76 
-            text = "can" 
-        intervals [160]:
-            xmin = 42.76 
-            xmax = 42.89 
-            text = "when" 
-        intervals [161]:
-            xmin = 42.89 
-            xmax = 42.98 
-            text = "i'm" 
-        intervals [162]:
-            xmin = 42.98 
-            xmax = 43.18 
-            text = "not" 
-        intervals [163]:
-            xmin = 43.18 
-            xmax = 43.76 
-            text = "working" 
-        intervals [164]:
-            xmin = 43.76 
-            xmax = 44.5 
-            text = "" 
-        intervals [165]:
-            xmin = 44.5 
-            xmax = 45.19 
-            text = "and" 
-        intervals [166]:
-            xmin = 45.19 
-            xmax = 45.32 
-            text = "on" 
-        intervals [167]:
-            xmin = 45.32 
-            xmax = 45.49 
-            text = "other" 
-        intervals [168]:
-            xmin = 45.49 
-            xmax = 45.82 
-            text = "days" 
-        intervals [169]:
-            xmin = 45.82 
-            xmax = 45.96 
-            text = "when" 
-        intervals [170]:
-            xmin = 45.96 
-            xmax = 46.16 
-            text = "i'm" 
-        intervals [171]:
-            xmin = 46.16 
-            xmax = 46.65 
-            text = "free" 
-        intervals [172]:
-            xmin = 46.65 
-            xmax = 46.86 
-            text = "i" 
-        intervals [173]:
-            xmin = 46.86 
-            xmax = 47.16 
-            text = "like" 
-        intervals [174]:
-            xmin = 47.16 
-            xmax = 47.39 
-            text = "to" 
-        intervals [175]:
-            xmin = 47.39 
-            xmax = 47.86 
-            text = "listen" 
-        intervals [176]:
-            xmin = 47.86 
-            xmax = 48.03 
-            text = "to" 
-        intervals [177]:
-            xmin = 48.03 
-            xmax = 48.41 
-            text = "music" 
-        intervals [178]:
-            xmin = 48.41 
-            xmax = 48.73 
-            text = "and" 
-        intervals [179]:
-            xmin = 48.73 
-            xmax = 48.76 
-            text = "" 
-        intervals [180]:
-            xmin = 48.76 
-            xmax = 49.01 
-            text = "we're" 
-        intervals [181]:
-            xmin = 49.01 
-            xmax = 49.3 
-            text = "watch" 
-        intervals [182]:
-            xmin = 49.3 
-            xmax = 49.38 
-            text = "a" 
-        intervals [183]:
-            xmin = 49.38 
-            xmax = 50.05 
-            text = "documentary" 
-        intervals [184]:
-            xmin = 50.05 
-            xmax = 50.51 
-            text = "movies" 
-        intervals [185]:
-            xmin = 50.51 
-            xmax = 50.82 
-            text = "on" 
-        intervals [186]:
-            xmin = 50.82 
-            xmax = 51.11 
-            text = "my" 
-        intervals [187]:
-            xmin = 51.11 
-            xmax = 51.81 
-            text = "laptop" 
-        intervals [188]:
-            xmin = 51.81 
-            xmax = 52.14 
-            text = "" 
-        intervals [189]:
-            xmin = 52.14 
-            xmax = 52.44 
-            text = "but" 
-        intervals [190]:
-            xmin = 52.44 
-            xmax = 52.86 
-            text = "sometimes" 
-        intervals [191]:
-            xmin = 52.86 
-            xmax = 52.93 
-            text = "it" 
-        intervals [192]:
-            xmin = 52.93 
-            xmax = 53.13 
-            text = "just" 
-        intervals [193]:
-            xmin = 53.13 
-            xmax = 53.61 
-            text = "sleep" 
-        intervals [194]:
-            xmin = 53.61 
-            xmax = 53.65 
-            text = "" 
-        intervals [195]:
-            xmin = 53.65 
-            xmax = 53.83 
-            text = "i" 
-        intervals [196]:
-            xmin = 53.83 
-            xmax = 54.27 
-            text = "especially" 
-        intervals [197]:
-            xmin = 54.27 
-            xmax = 54.61 
-            text = "liked" 
-        intervals [198]:
-            xmin = 54.61 
-            xmax = 55.01 
-            text = "watching" 
-        intervals [199]:
-            xmin = 55.01 
-            xmax = 55.62 
-            text = "japanese" 
-        intervals [200]:
-            xmin = 55.62 
-            xmax = 55.91 
-            text = "anime" 
-        intervals [201]:
-            xmin = 55.91 
-            xmax = 56.33 
-            text = "i" 
-        intervals [202]:
-            xmin = 56.33 
-            xmax = 56.85 
-            text = "" 
-        intervals [203]:
-            xmin = 56.85 
-            xmax = 57.12 
-            text = "think" 
-        intervals [204]:
-            xmin = 57.12 
-            xmax = 57.43 
-            text = "watching" 
-        intervals [205]:
-            xmin = 57.43 
-            xmax = 57.62 
-            text = "a" 
-        intervals [206]:
-            xmin = 57.62 
-            xmax = 57.79 
-            text = "me" 
-        intervals [207]:
-            xmin = 57.79 
-            xmax = 58.09 
-            text = "is" 
-        intervals [208]:
-            xmin = 58.09 
-            xmax = 58.39 
-            text = "anime" 
-        intervals [209]:
-            xmin = 58.39 
-            xmax = 59.06 
-            text = "is" 
-        intervals [210]:
-            xmin = 59.06 
-            xmax = 59.31 
-            text = "very" 
-        intervals [211]:
-            xmin = 59.31 
-            xmax = 59.67 
-            text = "helpful" 
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-            xmin = 59.67 
-            xmax = 59.81 
-            text = "for" 
-        intervals [213]:
-            xmin = 59.81 
-            xmax = 59.98 
-            text = "me" 
-        intervals [214]:
-            xmin = 59.98 
-            xmax = 60.28 
-            text = "to" 
-        intervals [215]:
-            xmin = 60.28 
-            xmax = 60.69 
-            text = "learn" 
-        intervals [216]:
-            xmin = 60.69 
-            xmax = 60.78 
-            text = "and" 
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-            xmin = 60.78 
-            xmax = 61.21 
-            text = "express" 
-        intervals [218]:
-            xmin = 61.21 
-            xmax = 61.89 
-            text = "japanese" 
-        intervals [219]:
-            xmin = 61.89 
-            xmax = 62.42 
-            text = "better" 
-        intervals [220]:
-            xmin = 62.42 
-            xmax = 64.097375 
-            text = "" 
-    item [2]:
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-        name = "phones" 
-        xmin = 0 
-        xmax = 64.097375 
-        intervals: size = 684 
-        intervals [1]:
-            xmin = 0 
-            xmax = 1.42 
-            text = "" 
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-            xmin = 1.42 
-            xmax = 1.48 
-            text = "DH" 
-        intervals [3]:
-            xmin = 1.48 
-            xmax = 1.52 
-            text = "AH0" 
-        intervals [4]:
-            xmin = 1.52 
-            xmax = 1.62 
-            text = "F" 
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-            xmin = 1.62 
-            xmax = 1.72 
-            text = "ER1" 
-        intervals [6]:
-            xmin = 1.72 
-            xmax = 1.75 
-            text = "S" 
-        intervals [7]:
-            xmin = 1.75 
-            xmax = 1.78 
-            text = "T" 
-        intervals [8]:
-            xmin = 1.78 
-            xmax = 1.81 
-            text = "TH" 
-        intervals [9]:
-            xmin = 1.81 
-            xmax = 1.88 
-            text = "IH1" 
-        intervals [10]:
-            xmin = 1.88 
-            xmax = 1.97 
-            text = "NG" 
-        intervals [11]:
-            xmin = 1.97 
-            xmax = 2.04 
-            text = "AY1" 
-        intervals [12]:
-            xmin = 2.04 
-            xmax = 2.08 
-            text = "L" 
-        intervals [13]:
-            xmin = 2.08 
-            xmax = 2.17 
-            text = "AY1" 
-        intervals [14]:
-            xmin = 2.17 
-            xmax = 2.21 
-            text = "K" 
-        intervals [15]:
-            xmin = 2.21 
-            xmax = 2.24 
-            text = "T" 
-        intervals [16]:
-            xmin = 2.24 
-            xmax = 2.28 
-            text = "IH0" 
-        intervals [17]:
-            xmin = 2.28 
-            xmax = 2.34 
-            text = "D" 
-        intervals [18]:
-            xmin = 2.34 
-            xmax = 2.47 
-            text = "UW1" 
-        intervals [19]:
-            xmin = 2.47 
-            xmax = 2.58 
-            text = "AA1" 
-        intervals [20]:
-            xmin = 2.58 
-            xmax = 2.63 
-            text = "N" 
-        intervals [21]:
-            xmin = 2.63 
-            xmax = 2.68 
-            text = "W" 
-        intervals [22]:
-            xmin = 2.68 
-            xmax = 2.78 
-            text = "IY1" 
-        intervals [23]:
-            xmin = 2.78 
-            xmax = 2.88 
-            text = "K" 
-        intervals [24]:
-            xmin = 2.88 
-            xmax = 3.01 
-            text = "EH2" 
-        intervals [25]:
-            xmin = 3.01 
-            xmax = 3.14 
-            text = "N" 
-        intervals [26]:
-            xmin = 3.14 
-            xmax = 3.2 
-            text = "D" 
-        intervals [27]:
-            xmin = 3.2 
-            xmax = 3.32 
-            text = "Z" 
-        intervals [28]:
-            xmin = 3.32 
-            xmax = 3.47 
-            text = "IH1" 
-        intervals [29]:
-            xmin = 3.47 
-            xmax = 3.58 
-            text = "Z" 
-        intervals [30]:
-            xmin = 3.58 
-            xmax = 3.64 
-            text = "R" 
-        intervals [31]:
-            xmin = 3.64 
-            xmax = 3.7 
-            text = "IY0" 
-        intervals [32]:
-            xmin = 3.7 
-            xmax = 3.8 
-            text = "L" 
-        intervals [33]:
-            xmin = 3.8 
-            xmax = 3.96 
-            text = "AE1" 
-        intervals [34]:
-            xmin = 3.96 
-            xmax = 4.02 
-            text = "K" 
-        intervals [35]:
-            xmin = 4.02 
-            xmax = 4.11 
-            text = "S" 
-        intervals [36]:
-            xmin = 4.11 
-            xmax = 4.2 
-            text = "IH0" 
-        intervals [37]:
-            xmin = 4.2 
-            xmax = 4.41 
-            text = "NG" 
-        intervals [38]:
-            xmin = 4.41 
-            xmax = 4.52 
-            text = "" 
-        intervals [39]:
-            xmin = 4.52 
-            xmax = 4.97 
-            text = "AH0" 
-        intervals [40]:
-            xmin = 4.97 
-            xmax = 5.01 
-            text = "N" 
-        intervals [41]:
-            xmin = 5.01 
-            xmax = 5.05 
-            text = "D" 
-        intervals [42]:
-            xmin = 5.05 
-            xmax = 5.14 
-            text = "AY1" 
-        intervals [43]:
-            xmin = 5.14 
-            xmax = 5.19 
-            text = "TH" 
-        intervals [44]:
-            xmin = 5.19 
-            xmax = 5.25 
-            text = "IH1" 
-        intervals [45]:
-            xmin = 5.25 
-            xmax = 5.29 
-            text = "NG" 
-        intervals [46]:
-            xmin = 5.29 
-            xmax = 5.33 
-            text = "K" 
-        intervals [47]:
-            xmin = 5.33 
-            xmax = 5.36 
-            text = "AY1" 
-        intervals [48]:
-            xmin = 5.36 
-            xmax = 5.41 
-            text = "L" 
-        intervals [49]:
-            xmin = 5.41 
-            xmax = 5.44 
-            text = "G" 
-        intervals [50]:
-            xmin = 5.44 
-            xmax = 5.5 
-            text = "OW1" 
-        intervals [51]:
-            xmin = 5.5 
-            xmax = 5.68 
-            text = "SH" 
-        intervals [52]:
-            xmin = 5.68 
-            xmax = 5.87 
-            text = "AA1" 
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-            xmin = 5.87 
-            xmax = 5.92 
-            text = "P" 
-        intervals [54]:
-            xmin = 5.92 
-            xmax = 5.96 
-            text = "IH0" 
-        intervals [55]:
-            xmin = 5.96 
-            xmax = 6 
-            text = "NG" 
-        intervals [56]:
-            xmin = 6 
-            xmax = 6.06 
-            text = "IH0" 
-        intervals [57]:
-            xmin = 6.06 
-            xmax = 6.11 
-            text = "F" 
-        intervals [58]:
-            xmin = 6.11 
-            xmax = 6.16 
-            text = "AY1" 
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-            xmin = 6.16 
-            xmax = 6.29 
-            text = "M" 
-        intervals [60]:
-            xmin = 6.29 
-            xmax = 6.35 
-            text = "N" 
-        intervals [61]:
-            xmin = 6.35 
-            xmax = 6.48 
-            text = "AA1" 
-        intervals [62]:
-            xmin = 6.48 
-            xmax = 6.54 
-            text = "T" 
-        intervals [63]:
-            xmin = 6.54 
-            xmax = 6.58 
-            text = "DH" 
-        intervals [64]:
-            xmin = 6.58 
-            xmax = 6.64 
-            text = "AE1" 
-        intervals [65]:
-            xmin = 6.64 
-            xmax = 6.7 
-            text = "T" 
-        intervals [66]:
-            xmin = 6.7 
-            xmax = 6.78 
-            text = "T" 
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-            xmin = 6.78 
-            xmax = 6.93 
-            text = "AY1" 
-        intervals [68]:
-            xmin = 6.93 
-            xmax = 7.08 
-            text = "ER0" 
-        intervals [69]:
-            xmin = 7.08 
-            xmax = 7.19 
-            text = "D" 
-        intervals [70]:
-            xmin = 7.19 
-            xmax = 7.45 
-            text = "" 
-        intervals [71]:
-            xmin = 7.45 
-            xmax = 7.59 
-            text = "S" 
-        intervals [72]:
-            xmin = 7.59 
-            xmax = 7.62 
-            text = "OW1" 
-        intervals [73]:
-            xmin = 7.62 
-            xmax = 7.66 
-            text = "DH" 
-        intervals [74]:
-            xmin = 7.66 
-            xmax = 7.71 
-            text = "AE1" 
-        intervals [75]:
-            xmin = 7.71 
-            xmax = 7.74 
-            text = "T" 
-        intervals [76]:
-            xmin = 7.74 
-            xmax = 7.77 
-            text = "Y" 
-        intervals [77]:
-            xmin = 7.77 
-            xmax = 7.85 
-            text = "UW1" 
-        intervals [78]:
-            xmin = 7.85 
-            xmax = 7.92 
-            text = "S" 
-        intervals [79]:
-            xmin = 7.92 
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-            text = "T" 
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-            xmin = 7.97 
-            xmax = 8.02 
-            text = "AA1" 
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-            xmin = 8.02 
-            xmax = 8.05 
-            text = "R" 
-        intervals [82]:
-            xmin = 8.05 
-            xmax = 8.08 
-            text = "T" 
-        intervals [83]:
-            xmin = 8.08 
-            xmax = 8.11 
-            text = "AH0" 
-        intervals [84]:
-            xmin = 8.11 
-            xmax = 8.14 
-            text = "D" 
-        intervals [85]:
-            xmin = 8.14 
-            xmax = 8.17 
-            text = "B" 
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-            xmin = 8.17 
-            xmax = 8.24 
-            text = "AY1" 
-        intervals [87]:
-            xmin = 8.24 
-            xmax = 8.35 
-            text = "JH" 
-        intervals [88]:
-            xmin = 8.35 
-            xmax = 8.48 
-            text = "AA1" 
-        intervals [89]:
-            xmin = 8.48 
-            xmax = 8.52 
-            text = "B" 
-        intervals [90]:
-            xmin = 8.52 
-            xmax = 8.59 
-            text = "AY1" 
-        intervals [91]:
-            xmin = 8.59 
-            xmax = 8.64 
-            text = "TH" 
-        intervals [92]:
-            xmin = 8.64 
-            xmax = 8.69 
-            text = "IH1" 
-        intervals [93]:
-            xmin = 8.69 
-            xmax = 8.72 
-            text = "NG" 
-        intervals [94]:
-            xmin = 8.72 
-            xmax = 8.75 
-            text = "K" 
-        intervals [95]:
-            xmin = 8.75 
-            xmax = 8.79 
-            text = "IH1" 
-        intervals [96]:
-            xmin = 8.79 
-            xmax = 8.84 
-            text = "T" 
-        intervals [97]:
-            xmin = 8.84 
-            xmax = 8.88 
-            text = "S" 
-        intervals [98]:
-            xmin = 8.88 
-            xmax = 9.08 
-            text = "V" 
-        intervals [99]:
-            xmin = 9.08 
-            xmax = 9.2 
-            text = "EH1" 
-        intervals [100]:
-            xmin = 9.2 
-            xmax = 9.28 
-            text = "R" 
-        intervals [101]:
-            xmin = 9.28 
-            xmax = 9.35 
-            text = "IY0" 
-        intervals [102]:
-            xmin = 9.35 
-            xmax = 9.4 
-            text = "IH0" 
-        intervals [103]:
-            xmin = 9.4 
-            xmax = 9.46 
-            text = "M" 
-        intervals [104]:
-            xmin = 9.46 
-            xmax = 9.55 
-            text = "P" 
-        intervals [105]:
-            xmin = 9.55 
-            xmax = 9.63 
-            text = "AO1" 
-        intervals [106]:
-            xmin = 9.63 
-            xmax = 9.68 
-            text = "R" 
-        intervals [107]:
-            xmin = 9.68 
-            xmax = 9.71 
-            text = "T" 
-        intervals [108]:
-            xmin = 9.71 
-            xmax = 9.74 
-            text = "AH0" 
-        intervals [109]:
-            xmin = 9.74 
-            xmax = 9.77 
-            text = "N" 
-        intervals [110]:
-            xmin = 9.77 
-            xmax = 9.8 
-            text = "T" 
-        intervals [111]:
-            xmin = 9.8 
-            xmax = 9.83 
-            text = "T" 
-        intervals [112]:
-            xmin = 9.83 
-            xmax = 9.87 
-            text = "IH0" 
-        intervals [113]:
-            xmin = 9.87 
-            xmax = 9.93 
-            text = "G" 
-        intervals [114]:
-            xmin = 9.93 
-            xmax = 9.96 
-            text = "EH1" 
-        intervals [115]:
-            xmin = 9.96 
-            xmax = 9.99 
-            text = "T" 
-        intervals [116]:
-            xmin = 9.99 
-            xmax = 10.03 
-            text = "AH0" 
-        intervals [117]:
-            xmin = 10.03 
-            xmax = 10.07 
-            text = "G" 
-        intervals [118]:
-            xmin = 10.07 
-            xmax = 10.1 
-            text = "IH0" 
-        intervals [119]:
-            xmin = 10.1 
-            xmax = 10.17 
-            text = "D" 
-        intervals [120]:
-            xmin = 10.17 
-            xmax = 10.35 
-            text = "S" 
-        intervals [121]:
-            xmin = 10.35 
-            xmax = 10.43 
-            text = "L" 
-        intervals [122]:
-            xmin = 10.43 
-            xmax = 10.53 
-            text = "IY1" 
-        intervals [123]:
-            xmin = 10.53 
-            xmax = 10.56 
-            text = "P" 
-        intervals [124]:
-            xmin = 10.56 
-            xmax = 10.8 
-            text = "D" 
-        intervals [125]:
-            xmin = 10.8 
-            xmax = 10.92 
-            text = "ER1" 
-        intervals [126]:
-            xmin = 10.92 
-            xmax = 10.99 
-            text = "IH0" 
-        intervals [127]:
-            xmin = 10.99 
-            xmax = 11.14 
-            text = "NG" 
-        intervals [128]:
-            xmin = 11.14 
-            xmax = 11.2 
-            text = "Y" 
-        intervals [129]:
-            xmin = 11.2 
-            xmax = 11.23 
-            text = "UH1" 
-        intervals [130]:
-            xmin = 11.23 
-            xmax = 11.32 
-            text = "R" 
-        intervals [131]:
-            xmin = 11.32 
-            xmax = 11.4 
-            text = "W" 
-        intervals [132]:
-            xmin = 11.4 
-            xmax = 11.51 
-            text = "IY1" 
-        intervals [133]:
-            xmin = 11.51 
-            xmax = 11.6 
-            text = "K" 
-        intervals [134]:
-            xmin = 11.6 
-            xmax = 11.68 
-            text = "EH2" 
-        intervals [135]:
-            xmin = 11.68 
-            xmax = 11.74 
-            text = "N" 
-        intervals [136]:
-            xmin = 11.74 
-            xmax = 11.77 
-            text = "D" 
-        intervals [137]:
-            xmin = 11.77 
-            xmax = 11.8 
-            text = "B" 
-        intervals [138]:
-            xmin = 11.8 
-            xmax = 11.88 
-            text = "IH0" 
-        intervals [139]:
-            xmin = 11.88 
-            xmax = 12 
-            text = "K" 
-        intervals [140]:
-            xmin = 12 
-            xmax = 12.26 
-            text = "AH1" 
-        intervals [141]:
-            xmin = 12.26 
-            xmax = 12.4 
-            text = "Z" 
-        intervals [142]:
-            xmin = 12.4 
-            xmax = 12.6 
-            text = "W" 
-        intervals [143]:
-            xmin = 12.6 
-            xmax = 12.88 
-            text = "EH1" 
-        intervals [144]:
-            xmin = 12.88 
-            xmax = 12.95 
-            text = "N" 
-        intervals [145]:
-            xmin = 12.95 
-            xmax = 12.99 
-            text = "Y" 
-        intervals [146]:
-            xmin = 12.99 
-            xmax = 13.04 
-            text = "UW1" 
-        intervals [147]:
-            xmin = 13.04 
-            xmax = 13.07 
-            text = "HH" 
-        intervals [148]:
-            xmin = 13.07 
-            xmax = 13.16 
-            text = "AE1" 
-        intervals [149]:
-            xmin = 13.16 
-            xmax = 13.19 
-            text = "V" 
-        intervals [150]:
-            xmin = 13.19 
-            xmax = 13.22 
-            text = "T" 
-        intervals [151]:
-            xmin = 13.22 
-            xmax = 13.27 
-            text = "UW1" 
-        intervals [152]:
-            xmin = 13.27 
-            xmax = 13.32 
-            text = "W" 
-        intervals [153]:
-            xmin = 13.32 
-            xmax = 13.4 
-            text = "ER1" 
-        intervals [154]:
-            xmin = 13.4 
-            xmax = 13.44 
-            text = "K" 
-        intervals [155]:
-            xmin = 13.44 
-            xmax = 13.51 
-            text = "AA1" 
-        intervals [156]:
-            xmin = 13.51 
-            xmax = 13.58 
-            text = "N" 
-        intervals [157]:
-            xmin = 13.58 
-            xmax = 13.66 
-            text = "M" 
-        intervals [158]:
-            xmin = 13.66 
-            xmax = 13.76 
-            text = "AH1" 
-        intervals [159]:
-            xmin = 13.76 
-            xmax = 13.81 
-            text = "N" 
-        intervals [160]:
-            xmin = 13.81 
-            xmax = 13.85 
-            text = "D" 
-        intervals [161]:
-            xmin = 13.85 
-            xmax = 13.96 
-            text = "EY2" 
-        intervals [162]:
-            xmin = 13.96 
-            xmax = 14.01 
-            text = "TH" 
-        intervals [163]:
-            xmin = 14.01 
-            xmax = 14.04 
-            text = "R" 
-        intervals [164]:
-            xmin = 14.04 
-            xmax = 14.1 
-            text = "UW1" 
-        intervals [165]:
-            xmin = 14.1 
-            xmax = 14.17 
-            text = "F" 
-        intervals [166]:
-            xmin = 14.17 
-            xmax = 14.26 
-            text = "R" 
-        intervals [167]:
-            xmin = 14.26 
-            xmax = 14.4 
-            text = "AY1" 
-        intervals [168]:
-            xmin = 14.4 
-            xmax = 14.45 
-            text = "D" 
-        intervals [169]:
-            xmin = 14.45 
-            xmax = 14.75 
-            text = "EY2" 
-        intervals [170]:
-            xmin = 14.75 
-            xmax = 15.41 
-            text = "" 
-        intervals [171]:
-            xmin = 15.41 
-            xmax = 15.49 
-            text = "DH" 
-        intervals [172]:
-            xmin = 15.49 
-            xmax = 15.53 
-            text = "AH1" 
-        intervals [173]:
-            xmin = 15.53 
-            xmax = 15.62 
-            text = "HH" 
-        intervals [174]:
-            xmin = 15.62 
-            xmax = 15.67 
-            text = "OW1" 
-        intervals [175]:
-            xmin = 15.67 
-            xmax = 15.75 
-            text = "L" 
-        intervals [176]:
-            xmin = 15.75 
-            xmax = 15.8 
-            text = "W" 
-        intervals [177]:
-            xmin = 15.8 
-            xmax = 15.94 
-            text = "IY1" 
-        intervals [178]:
-            xmin = 15.94 
-            xmax = 16.09 
-            text = "K" 
-        intervals [179]:
-            xmin = 16.09 
-            xmax = 16.28 
-            text = "" 
-        intervals [180]:
-            xmin = 16.28 
-            xmax = 16.38 
-            text = "Y" 
-        intervals [181]:
-            xmin = 16.38 
-            xmax = 16.42 
-            text = "UW1" 
-        intervals [182]:
-            xmin = 16.42 
-            xmax = 16.49 
-            text = "ER0" 
-        intervals [183]:
-            xmin = 16.49 
-            xmax = 16.55 
-            text = "V" 
-        intervals [184]:
-            xmin = 16.55 
-            xmax = 16.58 
-            text = "EH1" 
-        intervals [185]:
-            xmin = 16.58 
-            xmax = 16.65 
-            text = "R" 
-        intervals [186]:
-            xmin = 16.65 
-            xmax = 16.73 
-            text = "IY0" 
-        intervals [187]:
-            xmin = 16.73 
-            xmax = 16.92 
-            text = "T" 
-        intervals [188]:
-            xmin = 16.92 
-            xmax = 17.08 
-            text = "AY1" 
-        intervals [189]:
-            xmin = 17.08 
-            xmax = 17.22 
-            text = "ER0" 
-        intervals [190]:
-            xmin = 17.22 
-            xmax = 17.59 
-            text = "D" 
-        intervals [191]:
-            xmin = 17.59 
-            xmax = 17.83 
-            text = "" 
-        intervals [192]:
-            xmin = 17.83 
-            xmax = 18.02 
-            text = "S" 
-        intervals [193]:
-            xmin = 18.02 
-            xmax = 18.29 
-            text = "OW1" 
-        intervals [194]:
-            xmin = 18.29 
-            xmax = 18.37 
-            text = "G" 
-        intervals [195]:
-            xmin = 18.37 
-            xmax = 18.42 
-            text = "IH1" 
-        intervals [196]:
-            xmin = 18.42 
-            xmax = 18.46 
-            text = "T" 
-        intervals [197]:
-            xmin = 18.46 
-            xmax = 18.5 
-            text = "IH0" 
-        intervals [198]:
-            xmin = 18.5 
-            xmax = 18.55 
-            text = "NG" 
-        intervals [199]:
-            xmin = 18.55 
-            xmax = 18.61 
-            text = "EY1" 
-        intervals [200]:
-            xmin = 18.61 
-            xmax = 18.67 
-            text = "G" 
-        intervals [201]:
-            xmin = 18.67 
-            xmax = 18.73 
-            text = "UH1" 
-        intervals [202]:
-            xmin = 18.73 
-            xmax = 18.78 
-            text = "D" 
-        intervals [203]:
-            xmin = 18.78 
-            xmax = 18.86 
-            text = "R" 
-        intervals [204]:
-            xmin = 18.86 
-            xmax = 18.97 
-            text = "EH1" 
-        intervals [205]:
-            xmin = 18.97 
-            xmax = 19.05 
-            text = "S" 
-        intervals [206]:
-            xmin = 19.05 
-            xmax = 19.08 
-            text = "T" 
-        intervals [207]:
-            xmin = 19.08 
-            xmax = 19.13 
-            text = "IH0" 
-        intervals [208]:
-            xmin = 19.13 
-            xmax = 19.21 
-            text = "Z" 
-        intervals [209]:
-            xmin = 19.21 
-            xmax = 19.24 
-            text = "EH1" 
-        intervals [210]:
-            xmin = 19.24 
-            xmax = 19.3 
-            text = "Z" 
-        intervals [211]:
-            xmin = 19.3 
-            xmax = 19.34 
-            text = "IH0" 
-        intervals [212]:
-            xmin = 19.34 
-            xmax = 19.38 
-            text = "M" 
-        intervals [213]:
-            xmin = 19.38 
-            xmax = 19.48 
-            text = "P" 
-        intervals [214]:
-            xmin = 19.48 
-            xmax = 19.55 
-            text = "AO1" 
-        intervals [215]:
-            xmin = 19.55 
-            xmax = 19.59 
-            text = "R" 
-        intervals [216]:
-            xmin = 19.59 
-            xmax = 19.62 
-            text = "T" 
-        intervals [217]:
-            xmin = 19.62 
-            xmax = 19.65 
-            text = "AH0" 
-        intervals [218]:
-            xmin = 19.65 
-            xmax = 19.68 
-            text = "N" 
-        intervals [219]:
-            xmin = 19.68 
-            xmax = 19.77 
-            text = "T" 
-        intervals [220]:
-            xmin = 19.77 
-            xmax = 19.94 
-            text = "AE1" 
-        intervals [221]:
-            xmin = 19.94 
-            xmax = 20.16 
-            text = "Z" 
-        intervals [222]:
-            xmin = 20.16 
-            xmax = 20.3 
-            text = "" 
-        intervals [223]:
-            xmin = 20.3 
-            xmax = 20.39 
-            text = "K" 
-        intervals [224]:
-            xmin = 20.39 
-            xmax = 20.43 
-            text = "AH0" 
-        intervals [225]:
-            xmin = 20.43 
-            xmax = 20.46 
-            text = "M" 
-        intervals [226]:
-            xmin = 20.46 
-            xmax = 20.53 
-            text = "P" 
-        intervals [227]:
-            xmin = 20.53 
-            xmax = 20.59 
-            text = "L" 
-        intervals [228]:
-            xmin = 20.59 
-            xmax = 20.63 
-            text = "EY1" 
-        intervals [229]:
-            xmin = 20.63 
-            xmax = 20.66 
-            text = "N" 
-        intervals [230]:
-            xmin = 20.66 
-            xmax = 20.69 
-            text = "T" 
-        intervals [231]:
-            xmin = 20.69 
-            xmax = 20.75 
-            text = "AH0" 
-        intervals [232]:
-            xmin = 20.75 
-            xmax = 20.87 
-            text = "JH" 
-        intervals [233]:
-            xmin = 20.87 
-            xmax = 21.09 
-            text = "AO1" 
-        intervals [234]:
-            xmin = 21.09 
-            xmax = 21.3 
-            text = "ER0" 
-        intervals [235]:
-            xmin = 21.3 
-            xmax = 21.44 
-            text = "K" 
-        intervals [236]:
-            xmin = 21.44 
-            xmax = 21.47 
-            text = "AH0" 
-        intervals [237]:
-            xmin = 21.47 
-            xmax = 21.5 
-            text = "M" 
-        intervals [238]:
-            xmin = 21.5 
-            xmax = 21.53 
-            text = "P" 
-        intervals [239]:
-            xmin = 21.53 
-            xmax = 21.6 
-            text = "L" 
-        intervals [240]:
-            xmin = 21.6 
-            xmax = 21.63 
-            text = "IY1" 
-        intervals [241]:
-            xmin = 21.63 
-            xmax = 21.66 
-            text = "T" 
-        intervals [242]:
-            xmin = 21.66 
-            xmax = 21.72 
-            text = "IH0" 
-        intervals [243]:
-            xmin = 21.72 
-            xmax = 21.79 
-            text = "NG" 
-        intervals [244]:
-            xmin = 21.79 
-            xmax = 21.83 
-            text = "AH0" 
-        intervals [245]:
-            xmin = 21.83 
-            xmax = 21.9 
-            text = "N" 
-        intervals [246]:
-            xmin = 21.9 
-            xmax = 21.98 
-            text = "EH1" 
-        intervals [247]:
-            xmin = 21.98 
-            xmax = 22.03 
-            text = "K" 
-        intervals [248]:
-            xmin = 22.03 
-            xmax = 22.07 
-            text = "S" 
-        intervals [249]:
-            xmin = 22.07 
-            xmax = 22.11 
-            text = "AH0" 
-        intervals [250]:
-            xmin = 22.11 
-            xmax = 22.14 
-            text = "L" 
-        intervals [251]:
-            xmin = 22.14 
-            xmax = 22.17 
-            text = "AH0" 
-        intervals [252]:
-            xmin = 22.17 
-            xmax = 22.2 
-            text = "N" 
-        intervals [253]:
-            xmin = 22.2 
-            xmax = 22.23 
-            text = "T" 
-        intervals [254]:
-            xmin = 22.23 
-            xmax = 22.34 
-            text = "JH" 
-        intervals [255]:
-            xmin = 22.34 
-            xmax = 22.5 
-            text = "AA1" 
-        intervals [256]:
-            xmin = 22.5 
-            xmax = 22.64 
-            text = "B" 
-        intervals [257]:
-            xmin = 22.64 
-            xmax = 23.04 
-            text = "" 
-        intervals [258]:
-            xmin = 23.04 
-            xmax = 23.14 
-            text = "IH0" 
-        intervals [259]:
-            xmin = 23.14 
-            xmax = 23.17 
-            text = "N" 
-        intervals [260]:
-            xmin = 23.17 
-            xmax = 23.2 
-            text = "M" 
-        intervals [261]:
-            xmin = 23.2 
-            xmax = 23.29 
-            text = "AY1" 
-        intervals [262]:
-            xmin = 23.29 
-            xmax = 23.36 
-            text = "S" 
-        intervals [263]:
-            xmin = 23.36 
-            xmax = 23.41 
-            text = "P" 
-        intervals [264]:
-            xmin = 23.41 
-            xmax = 23.52 
-            text = "EH1" 
-        intervals [265]:
-            xmin = 23.52 
-            xmax = 23.56 
-            text = "R" 
-        intervals [266]:
-            xmin = 23.56 
-            xmax = 23.65 
-            text = "T" 
-        intervals [267]:
-            xmin = 23.65 
-            xmax = 23.76 
-            text = "AY1" 
-        intervals [268]:
-            xmin = 23.76 
-            xmax = 23.8 
-            text = "M" 
-        intervals [269]:
-            xmin = 23.8 
-            xmax = 23.85 
-            text = "IH0" 
-        intervals [270]:
-            xmin = 23.85 
-            xmax = 23.88 
-            text = "F" 
-        intervals [271]:
-            xmin = 23.88 
-            xmax = 23.98 
-            text = "AY1" 
-        intervals [272]:
-            xmin = 23.98 
-            xmax = 24.04 
-            text = "F" 
-        intervals [273]:
-            xmin = 24.04 
-            xmax = 24.13 
-            text = "IY1" 
-        intervals [274]:
-            xmin = 24.13 
-            xmax = 24.18 
-            text = "L" 
-        intervals [275]:
-            xmin = 24.18 
-            xmax = 24.26 
-            text = "OW2" 
-        intervals [276]:
-            xmin = 24.26 
-            xmax = 24.39 
-            text = "K" 
-        intervals [277]:
-            xmin = 24.39 
-            xmax = 24.84 
-            text = "EY1" 
-        intervals [278]:
-            xmin = 24.84 
-            xmax = 25.07 
-            text = "AY1" 
-        intervals [279]:
-            xmin = 25.07 
-            xmax = 25.1 
-            text = "" 
-        intervals [280]:
-            xmin = 25.1 
-            xmax = 25.29 
-            text = "L" 
-        intervals [281]:
-            xmin = 25.29 
-            xmax = 25.35 
-            text = "AY1" 
-        intervals [282]:
-            xmin = 25.35 
-            xmax = 25.38 
-            text = "K" 
-        intervals [283]:
-            xmin = 25.38 
-            xmax = 25.41 
-            text = "T" 
-        intervals [284]:
-            xmin = 25.41 
-            xmax = 25.44 
-            text = "IH0" 
-        intervals [285]:
-            xmin = 25.44 
-            xmax = 25.5 
-            text = "G" 
-        intervals [286]:
-            xmin = 25.5 
-            xmax = 25.55 
-            text = "OW1" 
-        intervals [287]:
-            xmin = 25.55 
-            xmax = 25.59 
-            text = "F" 
-        intervals [288]:
-            xmin = 25.59 
-            xmax = 25.79 
-            text = "ER0" 
-        intervals [289]:
-            xmin = 25.79 
-            xmax = 25.83 
-            text = "AH0" 
-        intervals [290]:
-            xmin = 25.83 
-            xmax = 25.94 
-            text = "HH" 
-        intervals [291]:
-            xmin = 25.94 
-            xmax = 26.06 
-            text = "AY1" 
-        intervals [292]:
-            xmin = 26.06 
-            xmax = 26.12 
-            text = "K" 
-        intervals [293]:
-            xmin = 26.12 
-            xmax = 26.17 
-            text = "IH1" 
-        intervals [294]:
-            xmin = 26.17 
-            xmax = 26.21 
-            text = "N" 
-        intervals [295]:
-            xmin = 26.21 
-            xmax = 26.27 
-            text = "N" 
-        intervals [296]:
-            xmin = 26.27 
-            xmax = 26.4 
-            text = "EY1" 
-        intervals [297]:
-            xmin = 26.4 
-            xmax = 26.53 
-            text = "CH" 
-        intervals [298]:
-            xmin = 26.53 
-            xmax = 26.81 
-            text = "ER0" 
-        intervals [299]:
-            xmin = 26.81 
-            xmax = 27.11 
-            text = "" 
-        intervals [300]:
-            xmin = 27.11 
-            xmax = 27.21 
-            text = "S" 
-        intervals [301]:
-            xmin = 27.21 
-            xmax = 27.25 
-            text = "AH1" 
-        intervals [302]:
-            xmin = 27.25 
-            xmax = 27.28 
-            text = "M" 
-        intervals [303]:
-            xmin = 27.28 
-            xmax = 27.31 
-            text = "T" 
-        intervals [304]:
-            xmin = 27.31 
-            xmax = 27.38 
-            text = "AY2" 
-        intervals [305]:
-            xmin = 27.38 
-            xmax = 27.41 
-            text = "M" 
-        intervals [306]:
-            xmin = 27.41 
-            xmax = 27.45 
-            text = "Z" 
-        intervals [307]:
-            xmin = 27.45 
-            xmax = 27.51 
-            text = "AY1" 
-        intervals [308]:
-            xmin = 27.51 
-            xmax = 27.6 
-            text = "T" 
-        intervals [309]:
-            xmin = 27.6 
-            xmax = 27.67 
-            text = "R" 
-        intervals [310]:
-            xmin = 27.67 
-            xmax = 27.74 
-            text = "AY1" 
-        intervals [311]:
-            xmin = 27.74 
-            xmax = 27.77 
-            text = "T" 
-        intervals [312]:
-            xmin = 27.77 
-            xmax = 27.88 
-            text = "AH0" 
-        intervals [313]:
-            xmin = 27.88 
-            xmax = 28.02 
-            text = "AO1" 
-        intervals [314]:
-            xmin = 28.02 
-            xmax = 28.07 
-            text = "R" 
-        intervals [315]:
-            xmin = 28.07 
-            xmax = 28.12 
-            text = "G" 
-        intervals [316]:
-            xmin = 28.12 
-            xmax = 28.15 
-            text = "AH0" 
-        intervals [317]:
-            xmin = 28.15 
-            xmax = 28.18 
-            text = "N" 
-        intervals [318]:
-            xmin = 28.18 
-            xmax = 28.3 
-            text = "AY2" 
-        intervals [319]:
-            xmin = 28.3 
-            xmax = 28.37 
-            text = "Z" 
-        intervals [320]:
-            xmin = 28.37 
-            xmax = 28.42 
-            text = "S" 
-        intervals [321]:
-            xmin = 28.42 
-            xmax = 28.47 
-            text = "AH1" 
-        intervals [322]:
-            xmin = 28.47 
-            xmax = 28.5 
-            text = "M" 
-        intervals [323]:
-            xmin = 28.5 
-            xmax = 28.53 
-            text = "TH" 
-        intervals [324]:
-            xmin = 28.53 
-            xmax = 28.61 
-            text = "IH0" 
-        intervals [325]:
-            xmin = 28.61 
-            xmax = 28.94 
-            text = "NG" 
-        intervals [326]:
-            xmin = 28.94 
-            xmax = 28.98 
-            text = "" 
-        intervals [327]:
-            xmin = 28.98 
-            xmax = 29.08 
-            text = "F" 
-        intervals [328]:
-            xmin = 29.08 
-            xmax = 29.13 
-            text = "AO1" 
-        intervals [329]:
-            xmin = 29.13 
-            xmax = 29.19 
-            text = "R" 
-        intervals [330]:
-            xmin = 29.19 
-            xmax = 29.23 
-            text = "M" 
-        intervals [331]:
-            xmin = 29.23 
-            xmax = 29.32 
-            text = "AY1" 
-        intervals [332]:
-            xmin = 29.32 
-            xmax = 29.41 
-            text = "F" 
-        intervals [333]:
-            xmin = 29.41 
-            xmax = 29.49 
-            text = "R" 
-        intervals [334]:
-            xmin = 29.49 
-            xmax = 29.6 
-            text = "EH1" 
-        intervals [335]:
-            xmin = 29.6 
-            xmax = 29.65 
-            text = "N" 
-        intervals [336]:
-            xmin = 29.65 
-            xmax = 29.7 
-            text = "D" 
-        intervals [337]:
-            xmin = 29.7 
-            xmax = 29.89 
-            text = "Z" 
-        intervals [338]:
-            xmin = 29.89 
-            xmax = 29.92 
-            text = "" 
-        intervals [339]:
-            xmin = 29.92 
-            xmax = 29.95 
-            text = "AY1" 
-        intervals [340]:
-            xmin = 29.95 
-            xmax = 30.2 
-            text = "" 
-        intervals [341]:
-            xmin = 30.2 
-            xmax = 30.26 
-            text = "V" 
-        intervals [342]:
-            xmin = 30.26 
-            xmax = 30.39 
-            text = "AA2" 
-        intervals [343]:
-            xmin = 30.39 
-            xmax = 30.45 
-            text = "L" 
-        intervals [344]:
-            xmin = 30.45 
-            xmax = 30.48 
-            text = "AH0" 
-        intervals [345]:
-            xmin = 30.48 
-            xmax = 30.51 
-            text = "N" 
-        intervals [346]:
-            xmin = 30.51 
-            xmax = 30.6 
-            text = "T" 
-        intervals [347]:
-            xmin = 30.6 
-            xmax = 30.67 
-            text = "IH1" 
-        intervals [348]:
-            xmin = 30.67 
-            xmax = 30.73 
-            text = "R" 
-        intervals [349]:
-            xmin = 30.73 
-            xmax = 30.77 
-            text = "AE1" 
-        intervals [350]:
-            xmin = 30.77 
-            xmax = 30.86 
-            text = "T" 
-        intervals [351]:
-            xmin = 30.86 
-            xmax = 30.91 
-            text = "DH" 
-        intervals [352]:
-            xmin = 30.91 
-            xmax = 30.97 
-            text = "AH1" 
-        intervals [353]:
-            xmin = 30.97 
-            xmax = 31.13 
-            text = "B" 
-        intervals [354]:
-            xmin = 31.13 
-            xmax = 31.19 
-            text = "UW1" 
-        intervals [355]:
-            xmin = 31.19 
-            xmax = 31.24 
-            text = "D" 
-        intervals [356]:
-            xmin = 31.24 
-            xmax = 31.3 
-            text = "AH0" 
-        intervals [357]:
-            xmin = 31.3 
-            xmax = 31.35 
-            text = "S" 
-        intervals [358]:
-            xmin = 31.35 
-            xmax = 31.38 
-            text = "T" 
-        intervals [359]:
-            xmin = 31.38 
-            xmax = 31.41 
-            text = "T" 
-        intervals [360]:
-            xmin = 31.41 
-            xmax = 31.47 
-            text = "EH1" 
-        intervals [361]:
-            xmin = 31.47 
-            xmax = 31.52 
-            text = "M" 
-        intervals [362]:
-            xmin = 31.52 
-            xmax = 31.56 
-            text = "P" 
-        intervals [363]:
-            xmin = 31.56 
-            xmax = 31.61 
-            text = "AH0" 
-        intervals [364]:
-            xmin = 31.61 
-            xmax = 31.83 
-            text = "L" 
-        intervals [365]:
-            xmin = 31.83 
-            xmax = 31.9 
-            text = "AO1" 
-        intervals [366]:
-            xmin = 31.9 
-            xmax = 31.94 
-            text = "N" 
-        intervals [367]:
-            xmin = 31.94 
-            xmax = 31.97 
-            text = "DH" 
-        intervals [368]:
-            xmin = 31.97 
-            xmax = 32.01 
-            text = "AH1" 
-        intervals [369]:
-            xmin = 32.01 
-            xmax = 32.08 
-            text = "W" 
-        intervals [370]:
-            xmin = 32.08 
-            xmax = 32.17 
-            text = "IY1" 
-        intervals [371]:
-            xmin = 32.17 
-            xmax = 32.26 
-            text = "K" 
-        intervals [372]:
-            xmin = 32.26 
-            xmax = 32.45 
-            text = "EH2" 
-        intervals [373]:
-            xmin = 32.45 
-            xmax = 32.51 
-            text = "N" 
-        intervals [374]:
-            xmin = 32.51 
-            xmax = 32.6 
-            text = "D" 
-        intervals [375]:
-            xmin = 32.6 
-            xmax = 32.88 
-            text = "AO1" 
-        intervals [376]:
-            xmin = 32.88 
-            xmax = 33.01 
-            text = "R" 
-        intervals [377]:
-            xmin = 33.01 
-            xmax = 33.24 
-            text = "AY1" 
-        intervals [378]:
-            xmin = 33.24 
-            xmax = 33.36 
-            text = "K" 
-        intervals [379]:
-            xmin = 33.36 
-            xmax = 33.51 
-            text = "AE1" 
-        intervals [380]:
-            xmin = 33.51 
-            xmax = 33.62 
-            text = "N" 
-        intervals [381]:
-            xmin = 33.62 
-            xmax = 33.7 
-            text = "JH" 
-        intervals [382]:
-            xmin = 33.7 
-            xmax = 33.77 
-            text = "IH0" 
-        intervals [383]:
-            xmin = 33.77 
-            xmax = 33.8 
-            text = "S" 
-        intervals [384]:
-            xmin = 33.8 
-            xmax = 33.91 
-            text = "T" 
-        intervals [385]:
-            xmin = 33.91 
-            xmax = 33.96 
-            text = "W" 
-        intervals [386]:
-            xmin = 33.96 
-            xmax = 34.2 
-            text = "AO1" 
-        intervals [387]:
-            xmin = 34.2 
-            xmax = 34.3 
-            text = "K" 
-        intervals [388]:
-            xmin = 34.3 
-            xmax = 34.42 
-            text = "ER0" 
-        intervals [389]:
-            xmin = 34.42 
-            xmax = 34.63 
-            text = "AW1" 
-        intervals [390]:
-            xmin = 34.63 
-            xmax = 34.69 
-            text = "N" 
-        intervals [391]:
-            xmin = 34.69 
-            xmax = 34.76 
-            text = "IH0" 
-        intervals [392]:
-            xmin = 34.76 
-            xmax = 34.8 
-            text = "N" 
-        intervals [393]:
-            xmin = 34.8 
-            xmax = 34.9 
-            text = "JH" 
-        intervals [394]:
-            xmin = 34.9 
-            xmax = 34.99 
-            text = "OY1" 
-        intervals [395]:
-            xmin = 34.99 
-            xmax = 35.03 
-            text = "IH0" 
-        intervals [396]:
-            xmin = 35.03 
-            xmax = 35.08 
-            text = "NG" 
-        intervals [397]:
-            xmin = 35.08 
-            xmax = 35.12 
-            text = "DH" 
-        intervals [398]:
-            xmin = 35.12 
-            xmax = 35.17 
-            text = "AH0" 
-        intervals [399]:
-            xmin = 35.17 
-            xmax = 35.26 
-            text = "S" 
-        intervals [400]:
-            xmin = 35.26 
-            xmax = 35.33 
-            text = "AH1" 
-        intervals [401]:
-            xmin = 35.33 
-            xmax = 35.4 
-            text = "N" 
-        intervals [402]:
-            xmin = 35.4 
-            xmax = 35.53 
-            text = "SH" 
-        intervals [403]:
-            xmin = 35.53 
-            xmax = 35.69 
-            text = "AY2" 
-        intervals [404]:
-            xmin = 35.69 
-            xmax = 35.87 
-            text = "N" 
-        intervals [405]:
-            xmin = 35.87 
-            xmax = 36.15 
-            text = "" 
-        intervals [406]:
-            xmin = 36.15 
-            xmax = 36.3 
-            text = "AY1" 
-        intervals [407]:
-            xmin = 36.3 
-            xmax = 36.34 
-            text = "D" 
-        intervals [408]:
-            xmin = 36.34 
-            xmax = 36.38 
-            text = "L" 
-        intervals [409]:
-            xmin = 36.38 
-            xmax = 36.49 
-            text = "AY1" 
-        intervals [410]:
-            xmin = 36.49 
-            xmax = 36.52 
-            text = "K" 
-        intervals [411]:
-            xmin = 36.52 
-            xmax = 36.56 
-            text = "T" 
-        intervals [412]:
-            xmin = 36.56 
-            xmax = 36.59 
-            text = "AH0" 
-        intervals [413]:
-            xmin = 36.59 
-            xmax = 36.62 
-            text = "HH" 
-        intervals [414]:
-            xmin = 36.62 
-            xmax = 36.7 
-            text = "AE1" 
-        intervals [415]:
-            xmin = 36.7 
-            xmax = 36.74 
-            text = "V" 
-        intervals [416]:
-            xmin = 36.74 
-            xmax = 36.79 
-            text = "AH0" 
-        intervals [417]:
-            xmin = 36.79 
-            xmax = 36.83 
-            text = "HH" 
-        intervals [418]:
-            xmin = 36.83 
-            xmax = 36.88 
-            text = "EH1" 
-        intervals [419]:
-            xmin = 36.88 
-            xmax = 36.93 
-            text = "L" 
-        intervals [420]:
-            xmin = 36.93 
-            xmax = 37.01 
-            text = "TH" 
-        intervals [421]:
-            xmin = 37.01 
-            xmax = 37.06 
-            text = "IY0" 
-        intervals [422]:
-            xmin = 37.06 
-            xmax = 37.12 
-            text = "L" 
-        intervals [423]:
-            xmin = 37.12 
-            xmax = 37.23 
-            text = "AY1" 
-        intervals [424]:
-            xmin = 37.23 
-            xmax = 37.27 
-            text = "F" 
-        intervals [425]:
-            xmin = 37.27 
-            xmax = 37.34 
-            text = "S" 
-        intervals [426]:
-            xmin = 37.34 
-            xmax = 37.39 
-            text = "T" 
-        intervals [427]:
-            xmin = 37.39 
-            xmax = 37.56 
-            text = "AY2" 
-        intervals [428]:
-            xmin = 37.56 
-            xmax = 37.66 
-            text = "L" 
-        intervals [429]:
-            xmin = 37.66 
-            xmax = 37.73 
-            text = "K" 
-        intervals [430]:
-            xmin = 37.73 
-            xmax = 37.77 
-            text = "AH0" 
-        intervals [431]:
-            xmin = 37.77 
-            xmax = 37.82 
-            text = "N" 
-        intervals [432]:
-            xmin = 37.82 
-            xmax = 37.87 
-            text = "S" 
-        intervals [433]:
-            xmin = 37.87 
-            xmax = 37.91 
-            text = "IH1" 
-        intervals [434]:
-            xmin = 37.91 
-            xmax = 37.94 
-            text = "D" 
-        intervals [435]:
-            xmin = 37.94 
-            xmax = 37.98 
-            text = "ER0" 
-        intervals [436]:
-            xmin = 37.98 
-            xmax = 38.02 
-            text = "IH0" 
-        intervals [437]:
-            xmin = 38.02 
-            xmax = 38.06 
-            text = "NG" 
-        intervals [438]:
-            xmin = 38.06 
-            xmax = 38.13 
-            text = "HH" 
-        intervals [439]:
-            xmin = 38.13 
-            xmax = 38.17 
-            text = "AW1" 
-        intervals [440]:
-            xmin = 38.17 
-            xmax = 38.23 
-            text = "M" 
-        intervals [441]:
-            xmin = 38.23 
-            xmax = 38.27 
-            text = "AH1" 
-        intervals [442]:
-            xmin = 38.27 
-            xmax = 38.38 
-            text = "CH" 
-        intervals [443]:
-            xmin = 38.38 
-            xmax = 38.5 
-            text = "T" 
-        intervals [444]:
-            xmin = 38.5 
-            xmax = 38.67 
-            text = "AY1" 
-        intervals [445]:
-            xmin = 38.67 
-            xmax = 38.74 
-            text = "M" 
-        intervals [446]:
-            xmin = 38.74 
-            xmax = 38.81 
-            text = "AY1" 
-        intervals [447]:
-            xmin = 38.81 
-            xmax = 38.95 
-            text = "S" 
-        intervals [448]:
-            xmin = 38.95 
-            xmax = 39.02 
-            text = "P" 
-        intervals [449]:
-            xmin = 39.02 
-            xmax = 39.09 
-            text = "EH1" 
-        intervals [450]:
-            xmin = 39.09 
-            xmax = 39.12 
-            text = "N" 
-        intervals [451]:
-            xmin = 39.12 
-            xmax = 39.18 
-            text = "D" 
-        intervals [452]:
-            xmin = 39.18 
-            xmax = 39.21 
-            text = "AE1" 
-        intervals [453]:
-            xmin = 39.21 
-            xmax = 39.29 
-            text = "T" 
-        intervals [454]:
-            xmin = 39.29 
-            xmax = 39.47 
-            text = "W" 
-        intervals [455]:
-            xmin = 39.47 
-            xmax = 39.69 
-            text = "ER1" 
-        intervals [456]:
-            xmin = 39.69 
-            xmax = 39.84 
-            text = "K" 
-        intervals [457]:
-            xmin = 39.84 
-            xmax = 40.29 
-            text = "" 
-        intervals [458]:
-            xmin = 40.29 
-            xmax = 40.52 
-            text = "AY1" 
-        intervals [459]:
-            xmin = 40.52 
-            xmax = 40.56 
-            text = "AO1" 
-        intervals [460]:
-            xmin = 40.56 
-            xmax = 40.59 
-            text = "L" 
-        intervals [461]:
-            xmin = 40.59 
-            xmax = 40.66 
-            text = "W" 
-        intervals [462]:
-            xmin = 40.66 
-            xmax = 40.7 
-            text = "IY0" 
-        intervals [463]:
-            xmin = 40.7 
-            xmax = 40.79 
-            text = "Z" 
-        intervals [464]:
-            xmin = 40.79 
-            xmax = 40.94 
-            text = "T" 
-        intervals [465]:
-            xmin = 40.94 
-            xmax = 41.05 
-            text = "R" 
-        intervals [466]:
-            xmin = 41.05 
-            xmax = 41.28 
-            text = "AY1" 
-        intervals [467]:
-            xmin = 41.28 
-            xmax = 41.38 
-            text = "T" 
-        intervals [468]:
-            xmin = 41.38 
-            xmax = 41.47 
-            text = "IH0" 
-        intervals [469]:
-            xmin = 41.47 
-            xmax = 41.7 
-            text = "M" 
-        intervals [470]:
-            xmin = 41.7 
-            xmax = 41.77 
-            text = "UW1" 
-        intervals [471]:
-            xmin = 41.77 
-            xmax = 41.85 
-            text = "V" 
-        intervals [472]:
-            xmin = 41.85 
-            xmax = 41.9 
-            text = "EH1" 
-        intervals [473]:
-            xmin = 41.9 
-            xmax = 42 
-            text = "Z" 
-        intervals [474]:
-            xmin = 42 
-            xmax = 42.08 
-            text = "M" 
-        intervals [475]:
-            xmin = 42.08 
-            xmax = 42.13 
-            text = "AH1" 
-        intervals [476]:
-            xmin = 42.13 
-            xmax = 42.22 
-            text = "CH" 
-        intervals [477]:
-            xmin = 42.22 
-            xmax = 42.26 
-            text = "EH1" 
-        intervals [478]:
-            xmin = 42.26 
-            xmax = 42.31 
-            text = "Z" 
-        intervals [479]:
-            xmin = 42.31 
-            xmax = 42.4 
-            text = "AY1" 
-        intervals [480]:
-            xmin = 42.4 
-            xmax = 42.51 
-            text = "K" 
-        intervals [481]:
-            xmin = 42.51 
-            xmax = 42.64 
-            text = "AE1" 
-        intervals [482]:
-            xmin = 42.64 
-            xmax = 42.76 
-            text = "N" 
-        intervals [483]:
-            xmin = 42.76 
-            xmax = 42.81 
-            text = "W" 
-        intervals [484]:
-            xmin = 42.81 
-            xmax = 42.84 
-            text = "EH1" 
-        intervals [485]:
-            xmin = 42.84 
-            xmax = 42.89 
-            text = "N" 
-        intervals [486]:
-            xmin = 42.89 
-            xmax = 42.95 
-            text = "AH0" 
-        intervals [487]:
-            xmin = 42.95 
-            xmax = 42.98 
-            text = "M" 
-        intervals [488]:
-            xmin = 42.98 
-            xmax = 43.03 
-            text = "N" 
-        intervals [489]:
-            xmin = 43.03 
-            xmax = 43.12 
-            text = "AA1" 
-        intervals [490]:
-            xmin = 43.12 
-            xmax = 43.18 
-            text = "T" 
-        intervals [491]:
-            xmin = 43.18 
-            xmax = 43.28 
-            text = "W" 
-        intervals [492]:
-            xmin = 43.28 
-            xmax = 43.42 
-            text = "ER1" 
-        intervals [493]:
-            xmin = 43.42 
-            xmax = 43.49 
-            text = "K" 
-        intervals [494]:
-            xmin = 43.49 
-            xmax = 43.53 
-            text = "IH0" 
-        intervals [495]:
-            xmin = 43.53 
-            xmax = 43.76 
-            text = "NG" 
-        intervals [496]:
-            xmin = 43.76 
-            xmax = 44.5 
-            text = "" 
-        intervals [497]:
-            xmin = 44.5 
-            xmax = 44.86 
-            text = "AH0" 
-        intervals [498]:
-            xmin = 44.86 
-            xmax = 45.15 
-            text = "N" 
-        intervals [499]:
-            xmin = 45.15 
-            xmax = 45.19 
-            text = "D" 
-        intervals [500]:
-            xmin = 45.19 
-            xmax = 45.27 
-            text = "AA1" 
-        intervals [501]:
-            xmin = 45.27 
-            xmax = 45.32 
-            text = "N" 
-        intervals [502]:
-            xmin = 45.32 
-            xmax = 45.4 
-            text = "AH1" 
-        intervals [503]:
-            xmin = 45.4 
-            xmax = 45.46 
-            text = "DH" 
-        intervals [504]:
-            xmin = 45.46 
-            xmax = 45.49 
-            text = "ER0" 
-        intervals [505]:
-            xmin = 45.49 
-            xmax = 45.55 
-            text = "D" 
-        intervals [506]:
-            xmin = 45.55 
-            xmax = 45.74 
-            text = "EY1" 
-        intervals [507]:
-            xmin = 45.74 
-            xmax = 45.82 
-            text = "Z" 
-        intervals [508]:
-            xmin = 45.82 
-            xmax = 45.89 
-            text = "W" 
-        intervals [509]:
-            xmin = 45.89 
-            xmax = 45.92 
-            text = "EH1" 
-        intervals [510]:
-            xmin = 45.92 
-            xmax = 45.96 
-            text = "N" 
-        intervals [511]:
-            xmin = 45.96 
-            xmax = 46.09 
-            text = "AY1" 
-        intervals [512]:
-            xmin = 46.09 
-            xmax = 46.16 
-            text = "M" 
-        intervals [513]:
-            xmin = 46.16 
-            xmax = 46.29 
-            text = "F" 
-        intervals [514]:
-            xmin = 46.29 
-            xmax = 46.39 
-            text = "R" 
-        intervals [515]:
-            xmin = 46.39 
-            xmax = 46.65 
-            text = "IY1" 
-        intervals [516]:
-            xmin = 46.65 
-            xmax = 46.86 
-            text = "AY1" 
-        intervals [517]:
-            xmin = 46.86 
-            xmax = 46.94 
-            text = "L" 
-        intervals [518]:
-            xmin = 46.94 
-            xmax = 47.08 
-            text = "AY1" 
-        intervals [519]:
-            xmin = 47.08 
-            xmax = 47.16 
-            text = "K" 
-        intervals [520]:
-            xmin = 47.16 
-            xmax = 47.25 
-            text = "T" 
-        intervals [521]:
-            xmin = 47.25 
-            xmax = 47.39 
-            text = "UW1" 
-        intervals [522]:
-            xmin = 47.39 
-            xmax = 47.48 
-            text = "L" 
-        intervals [523]:
-            xmin = 47.48 
-            xmax = 47.53 
-            text = "IH1" 
-        intervals [524]:
-            xmin = 47.53 
-            xmax = 47.6 
-            text = "S" 
-        intervals [525]:
-            xmin = 47.6 
-            xmax = 47.64 
-            text = "AH0" 
-        intervals [526]:
-            xmin = 47.64 
-            xmax = 47.86 
-            text = "N" 
-        intervals [527]:
-            xmin = 47.86 
-            xmax = 47.93 
-            text = "T" 
-        intervals [528]:
-            xmin = 47.93 
-            xmax = 48.03 
-            text = "IH0" 
-        intervals [529]:
-            xmin = 48.03 
-            xmax = 48.07 
-            text = "M" 
-        intervals [530]:
-            xmin = 48.07 
-            xmax = 48.15 
-            text = "Y" 
-        intervals [531]:
-            xmin = 48.15 
-            xmax = 48.2 
-            text = "UW1" 
-        intervals [532]:
-            xmin = 48.2 
-            xmax = 48.27 
-            text = "Z" 
-        intervals [533]:
-            xmin = 48.27 
-            xmax = 48.35 
-            text = "IH0" 
-        intervals [534]:
-            xmin = 48.35 
-            xmax = 48.41 
-            text = "K" 
-        intervals [535]:
-            xmin = 48.41 
-            xmax = 48.48 
-            text = "AH0" 
-        intervals [536]:
-            xmin = 48.48 
-            xmax = 48.56 
-            text = "N" 
-        intervals [537]:
-            xmin = 48.56 
-            xmax = 48.73 
-            text = "D" 
-        intervals [538]:
-            xmin = 48.73 
-            xmax = 48.76 
-            text = "" 
-        intervals [539]:
-            xmin = 48.76 
-            xmax = 48.91 
-            text = "W" 
-        intervals [540]:
-            xmin = 48.91 
-            xmax = 49.01 
-            text = "ER1" 
-        intervals [541]:
-            xmin = 49.01 
-            xmax = 49.13 
-            text = "W" 
-        intervals [542]:
-            xmin = 49.13 
-            xmax = 49.23 
-            text = "AA1" 
-        intervals [543]:
-            xmin = 49.23 
-            xmax = 49.3 
-            text = "CH" 
-        intervals [544]:
-            xmin = 49.3 
-            xmax = 49.38 
-            text = "AH0" 
-        intervals [545]:
-            xmin = 49.38 
-            xmax = 49.46 
-            text = "D" 
-        intervals [546]:
-            xmin = 49.46 
-            xmax = 49.56 
-            text = "AA2" 
-        intervals [547]:
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EMAGE/test_sequences/textgrid/2_scott_0_2_2.TextGrid

@@ -1,3716 +0,0 @@
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-Object class = "TextGrid"
-
-xmin = 0.0
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-tiers? <exists>
-size = 2
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-				xmin = 63.7
-				xmax = 63.77
-				text = "L"
-			intervals [685]:
-				xmin = 63.77
-				xmax = 63.93
-				text = "AE1"
-			intervals [686]:
-				xmin = 63.93
-				xmax = 64.04
-				text = "S"
-			intervals [687]:
-				xmin = 64.04
-				xmax = 64.13
-				text = "W"
-			intervals [688]:
-				xmin = 64.13
-				xmax = 64.16
-				text = "IH0"
-			intervals [689]:
-				xmin = 64.16
-				xmax = 64.2
-				text = "DH"
-			intervals [690]:
-				xmin = 64.2
-				xmax = 64.3
-				text = "AW1"
-			intervals [691]:
-				xmin = 64.3
-				xmax = 64.38
-				text = "T"
-			intervals [692]:
-				xmin = 64.38
-				xmax = 64.45
-				text = "TH"
-			intervals [693]:
-				xmin = 64.45
-				xmax = 64.52
-				text = "IH1"
-			intervals [694]:
-				xmin = 64.52
-				xmax = 64.58
-				text = "NG"
-			intervals [695]:
-				xmin = 64.58
-				xmax = 64.64
-				text = "K"
-			intervals [696]:
-				xmin = 64.64
-				xmax = 64.76
-				text = "IH0"
-			intervals [697]:
-				xmin = 64.76
-				xmax = 64.83
-				text = "NG"
-			intervals [698]:
-				xmin = 64.83
-				xmax = 64.88
-				text = "AO1"
-			intervals [699]:
-				xmin = 64.88
-				xmax = 64.95
-				text = "R"
-			intervals [700]:
-				xmin = 64.95
-				xmax = 64.98
-				text = ""
-			intervals [701]:
-				xmin = 64.98
-				xmax = 65.13
-				text = "W"
-			intervals [702]:
-				xmin = 65.13
-				xmax = 65.17
-				text = "AA1"
-			intervals [703]:
-				xmin = 65.17
-				xmax = 65.21
-				text = "N"
-			intervals [704]:
-				xmin = 65.21
-				xmax = 65.24
-				text = "IH0"
-			intervals [705]:
-				xmin = 65.24
-				xmax = 65.27
-				text = "NG"
-			intervals [706]:
-				xmin = 65.27
-				xmax = 65.31
-				text = "T"
-			intervals [707]:
-				xmin = 65.31
-				xmax = 65.36
-				text = "AH0"
-			intervals [708]:
-				xmin = 65.36
-				xmax = 65.42
-				text = "G"
-			intervals [709]:
-				xmin = 65.42
-				xmax = 65.54
-				text = "OW1"
-			intervals [710]:
-				xmin = 65.54
-				xmax = 65.63
-				text = "P"
-			intervals [711]:
-				xmin = 65.63
-				xmax = 65.7
-				text = "L"
-			intervals [712]:
-				xmin = 65.7
-				xmax = 65.76
-				text = "EY1"
-			intervals [713]:
-				xmin = 65.76
-				xmax = 65.83
-				text = "S"
-			intervals [714]:
-				xmin = 65.83
-				xmax = 65.88
-				text = "IH0"
-			intervals [715]:
-				xmin = 65.88
-				xmax = 65.95
-				text = "Z"
-			intervals [716]:
-				xmin = 65.95
-				xmax = 66.0
-				text = "W"
-			intervals [717]:
-				xmin = 66.0
-				xmax = 66.03
-				text = "IH1"
-			intervals [718]:
-				xmin = 66.03
-				xmax = 66.12
-				text = "DH"
-			intervals [719]:
-				xmin = 66.12
-				xmax = 66.2
-				text = "IH0"
-			intervals [720]:
-				xmin = 66.2
-				xmax = 66.38
-				text = "M"
-			intervals [721]:
-				xmin = 66.38
-				xmax = 67
-				text = ""

EMAGE/test_sequences/wave16k/2_scott_0_1_1.wav

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EMAGE/test_sequences/wave16k/2_scott_0_2_2.wav

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EMAGE/test_sequences/wave16k/2_scott_0_3_3.wav

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EMAGE/test_sequences/wave16k/2_scott_0_4_4.wav

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EMAGE/test_sequences/weights/AESKConv_240_100.bin

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EMAGE/test_sequences/weights/mean_vel_smplxflame_30.npy

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EMAGE/test_sequences/weights/vocab.pkl

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-version https://git-lfs.github.com/spec/v1
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-size 13821361

README.md

@@ -1,24 +1,13 @@
 ---
-title: EMAGE
-emoji: 🐑
-colorFrom: green
-colorTo: gray
+title: Emagedev
+emoji: 👁
+colorFrom: red
+colorTo: indigo
 sdk: gradio
-sdk_version: 4.24.0
+sdk_version: 4.44.1
+python_version: 3.9.20
 app_file: app.py
 pinned: false
-license: cc-by-nc-4.0
-short_description: Co-Speech Gesture 3D Motion Generation
 ---
-This is a demo is a audio-only version of the approach described in the paper, ["EMAGE: Towards Unified Holistic Co-Speech Gesture Generation via Expressive Masked Audio Gesture Modeling"](https://arxiv.org/abs/2401.00374)
 
-```
-@misc{liu2023emage,
-      title={EMAGE: Towards Unified Holistic Co-Speech Gesture Generation via Expressive Masked Audio Gesture Modeling}, 
-      author={Liu, Haiyang and Zhu, Zihao and Becherini, Giorgio and Peng, Yichen and Su, Mingyang and Zhou, You and Zhe, Xuefei and Iwamoto, Naoya and Zheng, Bo and Black, Michael J},
-      year={2023},
-      eprint={2401.00374},
-      archivePrefix={arXiv},
-      primaryClass={cs.CV}
-}
-```
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

ae_trainer.py

@@ -1,375 +0,0 @@
-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", "vel", "ver", "com", "kl", "acc"], [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 = dict_data["pose"]
-            tar_beta = dict_data["beta"].cuda()
-            tar_trans = dict_data["trans"].cuda()
-            tar_pose = tar_pose.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)
-            t_data = time.time() - t_start 
-            
-            self.opt.zero_grad()
-            g_loss_final = 0
-            net_out = self.model(tar_pose)
-            rec_pose = net_out["rec_pose"]
-            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
-
-            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 
-             # vertices loss
-            if self.args.rec_ver_weight > 0:
-                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), 
-                    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=True,
-                    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), 
-                    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=True,
-                    return_joints=True,
-                    leye_pose=tar_pose[:, 69:72], 
-                    reye_pose=tar_pose[:, 72:75],
-                )  
-                vectices_loss = self.vectices_loss(vertices_rec['vertices'], vertices_tar['vertices'])
-                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
-
-                vertices_vel_loss = self.vel_loss(vertices_rec['vertices'][:, 1:] - vertices_rec['vertices'][:, :-1], vertices_tar['vertices'][:, 1:] - vertices_tar['vertices'][:, :-1]) * self.args.rec_weight
-                vertices_acc_loss = self.vel_loss(vertices_rec['vertices'][:, 2:] + vertices_rec['vertices'][:, :-2] - 2 * vertices_rec['vertices'][:, 1:-1], vertices_tar['vertices'][:, 2:] + vertices_tar['vertices'][:, :-2] - 2 * vertices_tar['vertices'][:, 1:-1]) * self.args.rec_weight
-                g_loss_final += vertices_vel_loss * self.args.rec_weight * self.args.rec_ver_weight
-                g_loss_final += vertices_acc_loss * self.args.rec_weight * self.args.rec_ver_weight 
-            
-            # if self.args.vel_weight > 0:  
-            #     pos_rec_vel = other_tools.estimate_linear_velocity(vertices_rec['joints'], 1/self.pose_fps)
-            #     pos_tar_vel = other_tools.estimate_linear_velocity(vertices_tar['joints'], 1/self.pose_fps)
-            #     vel_rec_loss = self.vel_loss(pos_rec_vel, pos_tar_vel)
-            #     tar_pose = rc.axis_angle_to_matrix(tar_pose.reshape(bs, n, j, 3))
-            #     rec_pose = rc.axis_angle_to_matrix(rec_pose.reshape(bs, n, j, 3))
-            #     rot_rec_vel = other_tools.estimate_angular_velocity(rec_pose, 1/self.pose_fps)
-            #     rot_tar_vel = other_tools.estimate_angular_velocity(tar_pose, 1/self.pose_fps)
-            #     vel_rec_loss += self.vel_loss(pos_rec_vel, pos_tar_vel)
-            #     self.tracker.update_meter("vel", "train", vel_rec_loss.item()*self.args.vel_weight)
-            #     loss += (vel_rec_loss*self.args.vel_weight)
-
-            # ---------------------- 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 = dict_data["pose"]
-                tar_beta = dict_data["beta"].cuda()
-                tar_trans = dict_data["trans"].cuda()
-                tar_pose = tar_pose.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)
-                t_data = time.time() - t_start 
-
-                #self.opt.zero_grad()
-                #g_loss_final = 0
-                net_out = self.model(tar_pose)
-                rec_pose = net_out["rec_pose"]
-                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())
-                #g_loss_final += loss_rec
-
-                 # vertices loss
-                if self.args.rec_ver_weight > 0:
-                    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), 
-                        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=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), 
-                        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=True, 
-                        leye_pose=tar_pose[:, 69:72], 
-                        reye_pose=tar_pose[:, 72:75],
-                    )  
-                    vectices_loss = self.vectices_loss(vertices_rec['vertices'], vertices_tar['vertices'])
-                    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
-            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 = dict_data["pose"]
-                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, :]
-                #print(tar_pose.shape)
-                if True:
-                    net_out = self.model(tar_pose)
-                    rec_pose = net_out["rec_pose"]
-                    n = rec_pose.shape[1]
-                    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=gt_npz["trans"][::stride][:n] - gt_npz["trans"][::stride][:n],
-                        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=gt_npz["trans"][::stride][:n] - gt_npz["trans"][::stride][:n],
-                        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")

aeface_trainer.py

@@ -1,388 +0,0 @@
-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.tracker = other_tools.EpochTracker(["rec", "vel", "acc", "com", "face", "face_vel", "face_acc", "ver", "ver_vel", "ver_acc"], [False, False, False, False, False, False, False, False, False, False])
-        self.rec_loss = get_loss_func("GeodesicLoss")
-        self.mse_loss = torch.nn.MSELoss(reduction='mean')
-        self.vel_loss = torch.nn.MSELoss(reduction='mean') #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 train(self, epoch):
-        self.model.train()
-        t_start = time.time()
-        self.tracker.reset()
-        for its, dict_data in enumerate(self.train_loader):
-            tar_pose = dict_data["pose"]
-            tar_beta = dict_data["beta"].cuda()
-            tar_trans = dict_data["trans"].cuda()
-            tar_pose = tar_pose.cuda()  
-            bs, n, j = tar_pose.shape[0], tar_pose.shape[1], self.joints
-            tar_exps = dict_data["facial"].to(self.rank)
-            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)
-            in_tar_pose = torch.cat([tar_pose, tar_exps], -1) # 103
-            t_data = time.time() - t_start 
-            
-            self.opt.zero_grad()
-            g_loss_final = 0
-            net_out = self.model(in_tar_pose)
-            # jaw open 6d loss
-            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
-            # jaw open 6d vel and acc loss
-            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 
-            # face parameter l1 loss
-            rec_exps = net_out["rec_pose"][:, :, j*6:]
-            loss_face = self.mse_loss(rec_exps, tar_exps) * self.args.rec_weight
-            self.tracker.update_meter("face", "train", loss_face.item())
-            g_loss_final += loss_face
-            # face parameter l1 vel and acc loss
-            face_velocity_loss =  self.vel_loss(rec_exps[:, 1:] - rec_exps[:, :-1], tar_exps[:, 1:] - tar_exps[:, :-1]) * self.args.rec_weight
-            face_acceleration_loss =  self.vel_loss(rec_exps[:, 2:] + rec_exps[:, :-2] - 2 * rec_exps[:, 1:-1], tar_exps[:, 2:] + tar_exps[:, :-2] - 2 * tar_exps[:, 1:-1]) * self.args.rec_weight
-            self.tracker.update_meter("face_vel", "train", face_velocity_loss.item())
-            self.tracker.update_meter("face_acc", "train", face_acceleration_loss.item())
-            g_loss_final += face_velocity_loss
-            g_loss_final += face_acceleration_loss
-
-             # vertices loss
-            if self.args.rec_ver_weight > 0:
-                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)
-                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, 
-                    global_orient=torch.zeros(bs*n, 3).cuda(), 
-                    body_pose=torch.zeros(bs*n, 21*3).cuda(), 
-                    left_hand_pose=torch.zeros(bs*n, 15*3).cuda(), 
-                    right_hand_pose=torch.zeros(bs*n, 15*3).cuda(), 
-                    return_verts=True,
-                    # return_joints=True,
-                    leye_pose=torch.zeros(bs*n, 3).cuda(), 
-                    reye_pose=torch.zeros(bs*n, 3).cuda(),
-                )
-                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=rec_exps.reshape(bs*n, 100), 
-                    jaw_pose=tar_pose, 
-                    global_orient=torch.zeros(bs*n, 3).cuda(), 
-                    body_pose=torch.zeros(bs*n, 21*3).cuda(), 
-                    left_hand_pose=torch.zeros(bs*n, 15*3).cuda(), 
-                    right_hand_pose=torch.zeros(bs*n, 15*3).cuda(), 
-                    return_verts=True,
-                    # return_joints=True,
-                    leye_pose=torch.zeros(bs*n, 3).cuda(), 
-                    reye_pose=torch.zeros(bs*n, 3).cuda(),
-                )  
-                vectices_loss = self.mse_loss(vertices_rec['vertices'], vertices_tar['vertices'])
-                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
-                # vertices vel and acc loss
-                vert_velocity_loss =  self.vel_loss(vertices_rec['vertices'][:, 1:] - vertices_rec['vertices'][:, :-1], vertices_tar['vertices'][:, 1:] - vertices_tar['vertices'][:, :-1]) * self.args.rec_weight * self.args.rec_ver_weight
-                vert_acceleration_loss =  self.vel_loss(vertices_rec['vertices'][:, 2:] + vertices_rec['vertices'][:, :-2] - 2 * vertices_rec['vertices'][:, 1:-1], vertices_tar['vertices'][:, 2:] + vertices_tar['vertices'][:, :-2] - 2 * vertices_tar['vertices'][:, 1:-1]) * self.args.rec_weight * self.args.rec_ver_weight
-                self.tracker.update_meter("ver_vel", "train", vert_velocity_loss.item())
-                self.tracker.update_meter("ver_acc", "train", vert_acceleration_loss.item())
-                g_loss_final += vert_velocity_loss
-                g_loss_final += vert_acceleration_loss
-            
-            # ---------------------- 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 = dict_data["pose"]
-                tar_beta = dict_data["beta"].cuda()
-                tar_trans = dict_data["trans"].cuda()
-                tar_pose = tar_pose.cuda()  
-                bs, n, j = tar_pose.shape[0], tar_pose.shape[1], self.joints
-                tar_exps = dict_data["facial"].to(self.rank)
-                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)
-                in_tar_pose = torch.cat([tar_pose, tar_exps], -1) # 103
-                # print(tar_pose.shape, in_tar_pose.shape, tar_exps.shape)
-                t_data = time.time() - t_start 
-
-                #self.opt.zero_grad()
-                #g_loss_final = 0
-                net_out = self.model(in_tar_pose)
-                            # jaw open 6d loss
-                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())
-                # g_loss_final += loss_rec
-                # jaw open 6d vel and acc loss
-                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", "val", velocity_loss.item())
-                self.tracker.update_meter("acc", "val", acceleration_loss.item())
-                # g_loss_final += velocity_loss 
-                # g_loss_final += acceleration_loss 
-                # face parameter l1 loss
-                rec_exps = net_out["rec_pose"][:, :, j*6:]
-                loss_face = self.vel_loss(rec_exps, tar_exps) * self.args.rec_weight
-                self.tracker.update_meter("face", "val", loss_face.item())
-                # g_loss_final += loss_face
-                # face parameter l1 vel and acc loss
-                face_velocity_loss =  self.vel_loss(rec_exps[:, 1:] - rec_exps[:, :-1], tar_exps[:, 1:] - tar_exps[:, :-1]) * self.args.rec_weight
-                face_acceleration_loss =  self.vel_loss(rec_exps[:, 2:] + rec_exps[:, :-2] - 2 * rec_exps[:, 1:-1], tar_exps[:, 2:] + tar_exps[:, :-2] - 2 * tar_exps[:, 1:-1]) * self.args.rec_weight
-                self.tracker.update_meter("face_vel", "val", face_velocity_loss.item())
-                self.tracker.update_meter("face_acc", "val", face_acceleration_loss.item())
-                # g_loss_final += face_velocity_loss
-                # g_loss_final += face_acceleration_loss
-
-                # vertices loss
-                if self.args.rec_ver_weight > 0:
-                    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)
-                    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, 
-                        global_orient=torch.zeros(bs*n, 3).cuda(), 
-                        body_pose=torch.zeros(bs*n, 21*3).cuda(), 
-                        left_hand_pose=torch.zeros(bs*n, 15*3).cuda(), 
-                        right_hand_pose=torch.zeros(bs*n, 15*3).cuda(), 
-                        return_verts=True,
-                        # return_joints=True,
-                        leye_pose=torch.zeros(bs*n, 3).cuda(), 
-                        reye_pose=torch.zeros(bs*n, 3).cuda(),
-                    )
-                    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=rec_exps.reshape(bs*n, 100), 
-                        jaw_pose=tar_pose, 
-                        global_orient=torch.zeros(bs*n, 3).cuda(), 
-                        body_pose=torch.zeros(bs*n, 21*3).cuda(), 
-                        left_hand_pose=torch.zeros(bs*n, 15*3).cuda(), 
-                        right_hand_pose=torch.zeros(bs*n, 15*3).cuda(), 
-                        return_verts=True,
-                        # return_joints=True,
-                        leye_pose=torch.zeros(bs*n, 3).cuda(), 
-                        reye_pose=torch.zeros(bs*n, 3).cuda(),
-                    )  
-                    vectices_loss = self.mse_loss(vertices_rec['vertices'], vertices_tar['vertices'])
-                    self.tracker.update_meter("ver", "val", 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
-                    # vertices vel and acc loss
-                    vert_velocity_loss =  self.vel_loss(vertices_rec['vertices'][:, 1:] - vertices_rec['vertices'][:, :-1], vertices_tar['vertices'][:, 1:] - vertices_tar['vertices'][:, :-1]) * self.args.rec_weight * self.args.rec_ver_weight
-                    vert_acceleration_loss =  self.vel_loss(vertices_rec['vertices'][:, 2:] + vertices_rec['vertices'][:, :-2] - 2 * vertices_rec['vertices'][:, 1:-1], vertices_tar['vertices'][:, 2:] + vertices_tar['vertices'][:, :-2] - 2 * vertices_tar['vertices'][:, 1:-1]) * self.args.rec_weight * self.args.rec_ver_weight
-                    self.tracker.update_meter("ver_vel", "val", vert_velocity_loss.item())
-                    self.tracker.update_meter("ver_acc", "val", vert_acceleration_loss.item())
-                    # g_loss_final += vert_velocity_loss
-                    # g_loss_final += vert_acceleration_loss
-                    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
-        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 = dict_data["pose"]
-                tar_pose = tar_pose.cuda()
-                tar_exps = dict_data["facial"].to(self.rank)
-                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, :]
-                # print(tar_exps.shape)
-                in_tar_pose = torch.cat([tar_pose, tar_exps[:, :n-remain, :]], -1) # 103
-                #print(tar_pose.shape)
-                if True:
-                    net_out = self.model(in_tar_pose)
-                    rec_pose = net_out["rec_pose"][:, :, :j*6]
-                    n = rec_pose.shape[1]
-                    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()
-                    rec_exps = net_out["rec_pose"][:, :, j*6:]
-                    rec_exps = rec_exps.cpu().numpy().reshape(bs*n, 100)
-                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"],
-                        trans=gt_npz["trans"][::stride][:n] - gt_npz["trans"][::stride][:n],
-                        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=rec_exps,
-                        trans=gt_npz["trans"][::stride][:n] - gt_npz["trans"][::stride][:n],
-                        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")

aelower_trainer.py

@@ -1,494 +0,0 @@
-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_trans
-            tar_contact_copy = tar_contact-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 = tar_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, 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=rec_xyz_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=True,
-                    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), 
-                    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=True,
-                    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['vertices'], vertices_tar['vertices'])
-                vertices_vel_loss = self.vectices_loss(
-                    vertices_rec['vertices'][:, 1:] - vertices_rec['vertices'][:, :-1],
-                    vertices_tar['vertices'][:, 1:] - vertices_tar['vertices'][:, :-1])
-                vertices_acc_loss = self.vectices_loss(
-                    vertices_rec['vertices'][:, 2:] + vertices_rec['vertices'][:, :-2] - 2 * vertices_rec['vertices'][:, 1:-1],
-                    vertices_tar['vertices'][:, 2:] + vertices_tar['vertices'][:, :-2] - 2 * vertices_tar['vertices'][:, 1:-1])
-                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*1000)
-                self.tracker.update_meter("ver", "train", vectices_loss.item()*self.args.rec_weight * self.args.rec_ver_weight)
-                g_loss_final += (vectices_loss+5*vertices_vel_loss+5*vertices_acc_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_trans
-                tar_contact_copy = tar_contact-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 = tar_pose
-                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:
-                    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=rec_xyz_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), 
-                        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
-            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_trans[:, :n-remain, :]
-                tar_contact_copy = tar_contact-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 = tar_pose #net_out["rec_pose"][:, :, :j*6]
-                    rec_trans = 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=gt_npz["trans"][::stride][:n],
-                        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,
-                        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")

aelowerfoot_trainer.py

@@ -1,491 +0,0 @@
-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")

app.py

@@ -5,675 +5,231 @@ import os
 import OpenGL.GL as gl
 os.environ["PYOPENGL_PLATFORM"] = "egl"
 os.environ["MESA_GL_VERSION_OVERRIDE"] = "4.1"
-import signal
-import time
-import csv
-import sys
-import warnings
-import random
 import gradio as gr
 import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.distributed as dist
-from torch.nn.parallel import DistributedDataParallel as DDP
-import torch.multiprocessing as mp
 import numpy as np
-import time
-import pprint
-from loguru import logger
-import smplx
-from torch.utils.tensorboard import SummaryWriter
-import wandb
-import matplotlib.pyplot as plt
-from utils import config, logger_tools, other_tools_hf, metric, data_transfer
-from dataloaders import data_tools
-from dataloaders.build_vocab import Vocab
-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
-from utils import rotation_conversions as rc
 import soundfile as sf
-import librosa 
-
-def inverse_selection_tensor(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
-
-@spaces.GPU(duration=120) 
-def test_demo_gpu(
-    model, vq_model_face, vq_model_upper, vq_model_hands, vq_model_lower, global_motion, smplx_model,
-    dict_data,
-    args,
-    joints, joint_mask_upper, joint_mask_lower, joint_mask_hands,
-    log_softmax,
-):
-    rank = 0
-    other_tools_hf.load_checkpoints(vq_model_face, args.data_path_1 + "pretrained_vq/last_790_face_v2.bin", args.e_name)
-    other_tools_hf.load_checkpoints(vq_model_upper, args.data_path_1 + "pretrained_vq/upper_vertex_1layer_710.bin", args.e_name)
-    other_tools_hf.load_checkpoints(vq_model_hands, args.data_path_1 + "pretrained_vq/hands_vertex_1layer_710.bin", args.e_name)
-    other_tools_hf.load_checkpoints(vq_model_lower, args.data_path_1 + "pretrained_vq/lower_foot_600.bin", args.e_name)
-    other_tools_hf.load_checkpoints(global_motion, args.data_path_1 + "pretrained_vq/last_1700_foot.bin", args.e_name)
-    other_tools_hf.load_checkpoints(model, args.test_ckpt, args.g_name)
-    model.to(rank).eval()
-    smplx_model.to(rank).eval()
-    vq_model_face.to(rank).eval()
-    vq_model_upper.to(rank).eval()
-    vq_model_hands.to(rank).eval()
-    vq_model_lower.to(rank).eval()
-    global_motion.to(rank).eval()
-        
-    with torch.no_grad():
-        tar_pose_raw = dict_data["pose"]
-        tar_pose = tar_pose_raw[:, :, :165].to(rank)
-        tar_contact = tar_pose_raw[:, :, 165:169].to(rank)
-        tar_trans = dict_data["trans"].to(rank)
-        tar_exps = dict_data["facial"].to(rank)
-        in_audio = dict_data["audio"].to(rank) 
-        in_word = None# dict_data["word"].to(rank)
-        tar_beta = dict_data["beta"].to(rank)
-        tar_id = dict_data["id"].to(rank).long()
-        bs, n, j = tar_pose.shape[0], tar_pose.shape[1], 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[:, :, 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[:, :, 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 = vq_model_face.map2index(tar_pose_face) # bs*n/4
-        tar_index_value_upper_top = vq_model_upper.map2index(tar_pose_upper) # bs*n/4
-        tar_index_value_hands_top = vq_model_hands.map2index(tar_pose_hands) # bs*n/4
-        tar_index_value_lower_top = vq_model_lower.map2index(tar_pose_lower) # bs*n/4
-      
-        latent_face_top = vq_model_face.map2latent(tar_pose_face) # bs*n/4
-        latent_upper_top = vq_model_upper.map2latent(tar_pose_upper) # bs*n/4
-        latent_hands_top = vq_model_hands.map2latent(tar_pose_hands) # bs*n/4
-        latent_lower_top = 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)
-
-        loaded_data = {
-            "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,
-        }
-
-        mode = 'test'
-        bs, n, j = loaded_data["tar_pose"].shape[0], loaded_data["tar_pose"].shape[1], joints 
-        tar_pose = loaded_data["tar_pose"]
-        tar_beta = loaded_data["tar_beta"]
-        in_word =None# 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[:, :, 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[:, :, 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 = []
-        
-        roundt = (n - args.pre_frames) // (args.pose_length - args.pre_frames)
-        remain = (n - args.pre_frames) % (args.pose_length - args.pre_frames)
-        round_l = args.pose_length - args.pre_frames
-
-        for i in range(0, roundt):
-            # in_word_tmp = in_word[:, i*(round_l):(i+1)*(round_l)+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*args.pre_frames]
-            in_id_tmp = loaded_data['tar_id'][:, i*(round_l):(i+1)*(round_l)+args.pre_frames]
-            mask_val = torch.ones(bs, args.pose_length, args.pose_dims+3+4).float().cuda()
-            mask_val[:, :args.pre_frames, :] = 0.0
-            if i == 0:
-                latent_all_tmp = latent_all[:, i*(round_l):(i+1)*(round_l)+args.pre_frames, :]
-            else:
-                latent_all_tmp = latent_all[:, i*(round_l):(i+1)*(round_l)+args.pre_frames, :]
-                # print(latent_all_tmp.shape, latent_last.shape)
-                latent_all_tmp[:, :args.pre_frames, :] = latent_last[:, -args.pre_frames:, :]
-            
-            net_out_val = model(
-                in_audio = in_audio_tmp,
-                in_word=None, #in_word_tmp,
-                mask=mask_val,
-                in_motion = latent_all_tmp,
-                in_id = in_id_tmp,
-                use_attentions=True,)
-            
-            if args.cu != 0:
-                rec_index_upper = log_softmax(net_out_val["cls_upper"]).reshape(-1, args.vae_codebook_size)
-                _, rec_index_upper = torch.max(rec_index_upper.reshape(-1, args.pose_length, args.vae_codebook_size), dim=2)
-                #rec_upper = vq_model_upper.decode(rec_index_upper)
-            else:
-                _, rec_index_upper, _, _ = vq_model_upper.quantizer(net_out_val["rec_upper"])
-                #rec_upper = vq_model_upper.decoder(rec_index_upper)
-            if args.cl != 0:
-                rec_index_lower = log_softmax(net_out_val["cls_lower"]).reshape(-1, args.vae_codebook_size)
-                _, rec_index_lower = torch.max(rec_index_lower.reshape(-1, args.pose_length, args.vae_codebook_size), dim=2)
-                #rec_lower = vq_model_lower.decode(rec_index_lower)
-            else:
-                _, rec_index_lower, _, _ = vq_model_lower.quantizer(net_out_val["rec_lower"])
-                #rec_lower = vq_model_lower.decoder(rec_index_lower)
-            if args.ch != 0:
-                rec_index_hands = log_softmax(net_out_val["cls_hands"]).reshape(-1, args.vae_codebook_size)
-                _, rec_index_hands = torch.max(rec_index_hands.reshape(-1, args.pose_length, args.vae_codebook_size), dim=2)
-                #rec_hands = vq_model_hands.decode(rec_index_hands)
-            else:
-                _, rec_index_hands, _, _ = vq_model_hands.quantizer(net_out_val["rec_hands"])
-                #rec_hands = vq_model_hands.decoder(rec_index_hands)
-            if args.cf != 0:
-                rec_index_face = log_softmax(net_out_val["cls_face"]).reshape(-1, args.vae_codebook_size)
-                _, rec_index_face = torch.max(rec_index_face.reshape(-1, args.pose_length, args.vae_codebook_size), dim=2)
-                #rec_face = vq_model_face.decoder(rec_index_face)
-            else:
-                _, rec_index_face, _, _ = vq_model_face.quantizer(net_out_val["rec_face"])
-                #rec_face = 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[:, args.pre_frames:])
-                rec_index_all_upper.append(rec_index_upper[:, args.pre_frames:])
-                rec_index_all_lower.append(rec_index_lower[:, args.pre_frames:])
-                rec_index_all_hands.append(rec_index_hands[:, args.pre_frames:])
-
-            if args.cu != 0:
-                rec_upper_last = vq_model_upper.decode(rec_index_upper)
-            else:
-                rec_upper_last = vq_model_upper.decoder(rec_index_upper)
-            if args.cl != 0:
-                rec_lower_last = vq_model_lower.decode(rec_index_lower)
-            else:
-                rec_lower_last = vq_model_lower.decoder(rec_index_lower)
-            if args.ch != 0:
-                rec_hands_last = vq_model_hands.decode(rec_index_hands)
-            else:
-                rec_hands_last = vq_model_hands.decoder(rec_index_hands)
-            # if args.cf != 0:
-            #     rec_face_last = vq_model_face.decode(rec_index_face)
-            # else:
-            #     rec_face_last = 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 = inverse_selection_tensor(rec_pose_upper, 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 = inverse_selection_tensor(rec_pose_lower, 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 = inverse_selection_tensor(rec_pose_hands, 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_hf.velocity2position(rec_trans_v_s[:, :, 0:1], 1/args.pose_fps, tar_trans[:, 0, 0:1])
-            rec_z_trans = other_tools_hf.velocity2position(rec_trans_v_s[:, :, 2:3], 1/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 args.cu != 0:
-            rec_upper = vq_model_upper.decode(rec_index_upper)
-        else:
-            rec_upper = vq_model_upper.decoder(rec_index_upper)
-        if args.cl != 0:
-            rec_lower = vq_model_lower.decode(rec_index_lower)
-        else:
-            rec_lower = vq_model_lower.decoder(rec_index_lower)
-        if args.ch != 0:
-            rec_hands = vq_model_hands.decode(rec_index_hands)
-        else:
-            rec_hands = vq_model_hands.decoder(rec_index_hands)
-        if args.cf != 0:
-            rec_face = vq_model_face.decode(rec_index_face)
-        else:
-            rec_face = vq_model_face.decoder(rec_index_face)
+import librosa
+from torchvision.io import write_video
+from emage_utils.motion_io import beat_format_save
+from emage_utils import fast_render
+from emage_utils.npz2pose import render2d
+from models.camn_audio import CamnAudioModel
+from models.disco_audio import DiscoAudioModel
+from models.emage_audio import EmageAudioModel, EmageVQVAEConv, EmageVAEConv, EmageVQModel
+import torch.nn.functional as F
 
-        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 = inverse_selection_tensor(rec_pose_upper, 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 = inverse_selection_tensor(rec_pose_lower, 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 = inverse_selection_tensor(rec_pose_hands, 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
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+save_folder = "./gradio_results"
+os.makedirs(save_folder, exist_ok=True)
+print(device)
 
-        to_global = rec_lower
-        to_global[:, :, 54:57] = 0.0
-        to_global[:, :, :54] = rec_lower2global
-        rec_global = global_motion(to_global)
+if not os.path.exists("./emage_evaltools/smplx_models"):
+    import subprocess
+    subprocess.run(["git", "clone", "https://huggingface.co/H-Liu1997/emage_evaltools"])
 
-        rec_trans_v_s = rec_global["rec_pose"][:, :, 54:57]
-        rec_x_trans = other_tools_hf.velocity2position(rec_trans_v_s[:, :, 0:1], 1/args.pose_fps, tar_trans[:, 0, 0:1])
-        rec_z_trans = other_tools_hf.velocity2position(rec_trans_v_s[:, :, 2:3], 1/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, :]
+model_camn = CamnAudioModel.from_pretrained("H-Liu1997/camn_audio").to(device).eval()
+model_disco = DiscoAudioModel.from_pretrained("H-Liu1997/disco_audio").to(device).eval()
 
-        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)
-        
-        net_out =  {
-            '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,
-        }
-        
+face_motion_vq = EmageVQVAEConv.from_pretrained("H-Liu1997/emage_audio", subfolder="emage_vq/face").to(device).eval()
+upper_motion_vq = EmageVQVAEConv.from_pretrained("H-Liu1997/emage_audio", subfolder="emage_vq/upper").to(device).eval()
+lower_motion_vq = EmageVQVAEConv.from_pretrained("H-Liu1997/emage_audio", subfolder="emage_vq/lower").to(device).eval()
+hands_motion_vq = EmageVQVAEConv.from_pretrained("H-Liu1997/emage_audio", subfolder="emage_vq/hands").to(device).eval()
+global_motion_ae = EmageVAEConv.from_pretrained("H-Liu1997/emage_audio", subfolder="emage_vq/global").to(device).eval()
 
-        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], joints
-                # interpolate to 30fps  
-        if (30/args.pose_fps) != 1:
-            assert 30%args.pose_fps == 0
-            n *= int(30/args.pose_fps)
-            tar_pose = torch.nn.functional.interpolate(tar_pose.permute(0, 2, 1), scale_factor=30/args.pose_fps, mode='linear').permute(0,2,1)
-            rec_pose = torch.nn.functional.interpolate(rec_pose.permute(0, 2, 1), scale_factor=30/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)
+emage_vq_model = EmageVQModel(
+    face_model=face_motion_vq, 
+    upper_model=upper_motion_vq,
+    lower_model=lower_motion_vq, 
+    hands_model=hands_motion_vq,
+    global_model=global_motion_ae
+).to(device).eval()
 
-        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)
-        
-    return tar_pose, rec_pose, tar_exps, tar_beta, rec_trans, tar_trans, rec_exps, bs, n, j
+model_emage = EmageAudioModel.from_pretrained("H-Liu1997/emage_audio").to(device).eval()
 
-    
-class BaseTrainer(object):
-    def __init__(self, args, sp, ap, tp):
-        hf_dir = "hf"
-        if not os.path.exists(args.out_path + "custom/" + hf_dir + "/"):
-            os.makedirs(args.out_path + "custom/" + hf_dir + "/")
-        sf.write(args.out_path + "custom/" + hf_dir + "/tmp.wav", ap[1], ap[0])
-        self.audio_path = args.out_path + "custom/" + hf_dir + "/tmp.wav"
-        audio, ssr = librosa.load(self.audio_path)
-        ap = (ssr, audio)
-        self.args = args
-        self.rank = 0 # dist.get_rank()
-       
-        #self.checkpoint_path = args.out_path + "custom/" + args.name + args.notes + "/" #wandb.run.dir #args.cache_path+args.out_path+"/"+args.name
-        self.checkpoint_path = args.out_path + "custom/" + hf_dir + "/" 
-        if self.rank == 0:
-            self.test_data = __import__(f"dataloaders.{args.dataset}", fromlist=["something"]).CustomDataset(args, "test", smplx_path=sp, audio_path=ap, text_path=tp)
-            self.test_loader = torch.utils.data.DataLoader(
-                self.test_data, 
-                batch_size=1,  
-                shuffle=False,  
-                num_workers=args.loader_workers,
-                drop_last=False,
-            )
-        logger.info(f"Init test dataloader success")
-        model_module = __import__(f"models.{args.model}", fromlist=["something"])
-        
-        if args.ddp:
-            self.model = getattr(model_module, args.g_name)(args).to(self.rank)
-            process_group = torch.distributed.new_group()
-            self.model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(self.model, process_group)   
-            self.model = DDP(self.model, device_ids=[self.rank], output_device=self.rank,
-                             broadcast_buffers=False, find_unused_parameters=False)
-        else: 
-            self.model = torch.nn.DataParallel(getattr(model_module, args.g_name)(args), args.gpus).cpu()
-        
-        if self.rank == 0:
-            logger.info(self.model)
-            logger.info(f"init {args.g_name} success")
 
-        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,
+def inference_camn(audio_path, sr_model, pose_fps, seed_frames):
+    audio_loaded, _ = librosa.load(audio_path, sr=sr_model)
+    audio_t = torch.from_numpy(audio_loaded).float().unsqueeze(0).to(device)
+    sid = torch.zeros(1, 1).long().to(device)
+    with torch.no_grad():
+        motion_pred = model_camn(audio_t, sid, seed_frames=seed_frames)["motion_axis_angle"]
+    t = motion_pred.shape[1]
+    motion_pred = motion_pred.cpu().numpy().reshape(t, -1)
+    npz_path = os.path.join(save_folder, "camn_output.npz")
+    beat_format_save(npz_path, motion_pred, upsample=30 // pose_fps)
+    return npz_path
+
+def inference_disco(audio_path, sr_model, pose_fps, seed_frames):
+    audio_loaded, _ = librosa.load(audio_path, sr=sr_model)
+    audio_t = torch.from_numpy(audio_loaded).float().unsqueeze(0).to(device)
+    sid = torch.zeros(1, 1).long().to(device)
+    with torch.no_grad():
+        motion_pred = model_disco(audio_t, sid, seed_frames=seed_frames, seed_motion=None)["motion_axis_angle"]
+    t = motion_pred.shape[1]
+    motion_pred = motion_pred.cpu().numpy().reshape(t, -1)
+    npz_path = os.path.join(save_folder, "disco_output.npz")
+    beat_format_save(npz_path, motion_pred, upsample=30 // pose_fps)
+    return npz_path
+
+def inference_emage(audio_path, sr_model, pose_fps):
+    audio_loaded, _ = librosa.load(audio_path, sr=sr_model)
+    audio_t = torch.from_numpy(audio_loaded).float().unsqueeze(0).to(device)
+    sid = torch.zeros(1, 1).long().to(device)
+    with torch.no_grad():
+        latent_dict = model_emage.inference(audio_t, sid, emage_vq_model, masked_motion=None, mask=None)
+        face_latent = latent_dict["rec_face"] if model_emage.cfg.lf > 0 and model_emage.cfg.cf == 0 else None
+        upper_latent = latent_dict["rec_upper"] if model_emage.cfg.lu > 0 and model_emage.cfg.cu == 0 else None
+        hands_latent = latent_dict["rec_hands"] if model_emage.cfg.lh > 0 and model_emage.cfg.ch == 0 else None
+        lower_latent = latent_dict["rec_lower"] if model_emage.cfg.ll > 0 and model_emage.cfg.cl == 0 else None
+
+        face_index = torch.max(F.log_softmax(latent_dict["cls_face"], dim=2), dim=2)[1] if model_emage.cfg.cf > 0 else None
+        upper_index = torch.max(F.log_softmax(latent_dict["cls_upper"], dim=2), dim=2)[1] if model_emage.cfg.cu > 0 else None
+        hands_index = torch.max(F.log_softmax(latent_dict["cls_hands"], dim=2), dim=2)[1] if model_emage.cfg.ch > 0 else None
+        lower_index = torch.max(F.log_softmax(latent_dict["cls_lower"], dim=2), dim=2)[1] if model_emage.cfg.cl > 0 else None
+
+        ref_trans = torch.zeros(1, 1, 3).to(device)
+        all_pred = emage_vq_model.decode(
+            face_latent=face_latent, 
+            upper_latent=upper_latent, 
+            lower_latent=lower_latent, 
+            hands_latent=hands_latent,
+            face_index=face_index, 
+            upper_index=upper_index, 
+            lower_index=lower_index, 
+            hands_index=hands_index,
+            get_global_motion=True, 
+            ref_trans=ref_trans[:, 0]
         )
-                
-        self.args = args
-        self.joints = self.test_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_hf.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).cpu()
-        # print(self.vq_model_face)
-        # other_tools_hf.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).cpu()
-        # other_tools_hf.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).cpu()
-        # other_tools_hf.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).cpu()
-        # other_tools_hf.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).cpu()
-        # other_tools_hf.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.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)
-      
-    
-    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 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): 
-            import shutil
-            shutil.rmtree(results_save_path)
-        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
-        for its, batch_data in enumerate(self.test_loader):
-            tar_pose, rec_pose, tar_exps, tar_beta, rec_trans, tar_trans, rec_exps, bs, n, j = test_demo_gpu(
-                self.model, self.vq_model_face, self.vq_model_upper, self.vq_model_hands, self.vq_model_lower, self.global_motion, self.smplx,
-                batch_data,
-                self.args,
-                self.joints, self.joint_mask_upper, self.joint_mask_lower, self.joint_mask_hands,
-                self.log_softmax,
-            )
-                           
-            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)
-                #'''
-        # its = 0
-            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
-            # render_vid_path = other_tools_hf.render_one_sequence_no_gt(
-            # results_save_path+"res_"+test_seq_list.iloc[its]['id']+'.npz', 
-            #         # results_save_path+"gt_"+test_seq_list.iloc[its]['id']+'.npz', 
-            #         results_save_path,
-            #         self.audio_path,
-            #         self.args.data_path_1+"smplx_models/",
-            #         use_matplotlib = False,
-            #         args = self.args,
-            #         )
-            render_vid_path = other_tools_hf.render_one_sequence_with_face(
-                    results_save_path+"res_"+test_seq_list.iloc[its]['id']+'.npz',
-                    results_save_path+"gt_"+test_seq_list.iloc[its]['id']+'.npz',
-                    results_save_path,
-                    self.audio_path,
-                    self.args.data_path_1+"smplx_models/",
-                    use_matplotlib = False,
-                    args = self.args,
-                    )
-        result = [
-            gr.Video(value=render_vid_path, visible=True),
-            gr.File(value=results_save_path+"res_"+test_seq_list.iloc[its]['id']+'.npz', visible=True),
-        ]
-            
-        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")
-        return result
-
-       
-@logger.catch
-def emage(audio_path):
-    smplx_path = None
-    text_path = None
-    rank = 0
-    world_size = 1
-    args = config.parse_args()
-    #os.environ['TRANSFORMERS_CACHE'] = args.data_path_1 + "hub/"
-    if not sys.warnoptions:
-        warnings.simplefilter("ignore")
-    # dist.init_process_group(backend="gloo", rank=rank, world_size=world_size)
-
-    #logger_tools.set_args_and_logger(args, rank)
-    other_tools_hf.set_random_seed(args)
-    other_tools_hf.print_exp_info(args)
-
-    # return one intance of trainer
-    trainer = BaseTrainer(args, sp = smplx_path, ap = audio_path, tp = text_path)
-    result = trainer.test_demo(999)
-    return result
+    motion_pred = all_pred["motion_axis_angle"]
+    t = motion_pred.shape[1]
+    motion_pred = motion_pred.cpu().numpy().reshape(t, -1)
+    face_pred = all_pred["expression"].cpu().numpy().reshape(t, -1)
+    trans_pred = all_pred["trans"].cpu().numpy().reshape(t, -1)
+    npz_path = os.path.join(save_folder, "emage_output.npz")
+    beat_format_save(npz_path, motion_pred, upsample=30 // pose_fps, expressions=face_pred, trans=trans_pred)
+    return npz_path
+
+
+def inference_app(audio, model_type, render_mesh=False, render_face=False, render_mesh_face=False):
+    if audio is None:
+        return [None, None, None, None, None]
+
+    sr_in, audio_data = audio
+    # --- TRUNCATE to 60 seconds if longer ---
+    max_len = int(60 * sr_in)
+    if len(audio_data) > max_len:
+        audio_data = audio_data[:max_len]
+    # ----------------------------------------
+
+    tmp_audio_path = os.path.join(save_folder, "tmp_input.wav")
+    sf.write(tmp_audio_path, audio_data, sr_in)
+
+    if model_type == "CaMN (Upper only)":
+        sr_model, pose_fps, seed_frames = model_camn.cfg.audio_sr, model_camn.cfg.pose_fps, model_camn.cfg.seed_frames
+        npz_path = inference_camn(tmp_audio_path, sr_model, pose_fps, seed_frames)
+    elif model_type == "DisCo (Upper only)":
+        sr_model, pose_fps, seed_frames = model_disco.cfg.audio_sr, model_disco.cfg.pose_fps, model_disco.cfg.seed_frames
+        npz_path = inference_disco(tmp_audio_path, sr_model, pose_fps, seed_frames)
+    else:
+        sr_model, pose_fps = model_emage.cfg.audio_sr, model_emage.cfg.pose_fps
+        npz_path = inference_emage(tmp_audio_path, sr_model, pose_fps)
+
+    motion_dict = np.load(npz_path, allow_pickle=True)
+    v2d_body = render2d(motion_dict, (720, 480), face_only=False, remove_global=True)
+    out_2d_body = npz_path.replace(".npz", "_2dbody.mp4")
+    write_video(out_2d_body, v2d_body.permute(0, 2, 3, 1), fps=30)
+    final_2d_body = out_2d_body.replace(".mp4", "_audio.mp4")
+    fast_render.add_audio_to_video(out_2d_body, tmp_audio_path, final_2d_body)
+
+    final_mesh_video = None
+    final_meshface_video = None
+    if render_mesh:
+        mesh_vid = fast_render.render_one_sequence_no_gt(
+            npz_path, save_folder, tmp_audio_path, "./emage_evaltools/smplx_models/"
+        )
+        final_mesh_video = mesh_vid
 
-examples = [
-    ["./EMAGE/test_sequences/wave16k/2_scott_0_1_1.wav"],
-    ["./EMAGE/test_sequences/wave16k/2_scott_0_2_2.wav"],
-    ["./EMAGE/test_sequences/wave16k/2_scott_0_3_3.wav"],
+    if render_mesh_face and render_mesh:
+        meshface_vid = fast_render.render_one_sequence_face_only(
+            npz_path, save_folder, tmp_audio_path, "./emage_evaltools/smplx_models/"
+        )
+        final_meshface_video = meshface_vid
+
+    final_face_video = None
+    if render_face:
+        v2d_face = render2d(motion_dict, (720, 480), face_only=True, remove_global=True)
+        out_2d_face = npz_path.replace(".npz", "_2dface.mp4")
+        write_video(out_2d_face, v2d_face.permute(0, 2, 3, 1), fps=30)
+        final_face_video = out_2d_face.replace(".mp4", "_audio.mp4")
+        fast_render.add_audio_to_video(out_2d_face, tmp_audio_path, final_face_video)
+
+    return [final_2d_body, final_mesh_video, final_face_video, final_meshface_video, npz_path]
+
+examples_data = [
+    ["./examples/audio/2_scott_0_103_103_10s.wav", "DisCo (Upper only)", True, True, True],
+    ["./examples/audio/2_scott_0_103_103_10s.wav", "CaMN (Upper only)", True, True, True],
+    ["./examples/audio/2_scott_0_103_103_10s.wav", "EMAGE (Full body + Face)", True, True, True],
 ]
 
-demo = gr.Interface(
-    emage,  # function
-    inputs=[
-        # gr.File(label="Please upload SMPL-X file with npz format here.", file_types=["npz", "NPZ"]),
-        gr.Audio(),
-        # gr.File(label="Please upload textgrid format file here.", file_types=["TextGrid", "Textgrid", "textgrid"])
-    ],  # input type
-    outputs=[
-        gr.Video(format="mp4", visible=True),
-        gr.File(label="download motion and visualize in blender"), 
-    ],
-    title='\
-    <div align="center">\
-    EMAGE: Towards Unified Holistic Co-Speech Gesture Generation via Expressive Masked Audio Gesture Modeling <br/>\
-    CVPR 2024  <br/>\
-    </div>',
-    description='\
-    <div align="center">\
-    Haiyang Liu1*,  Zihao Zhu2*,  Giorgio Becherini3,  Yichen Peng4,  Mingyang Su5,<br/>\
-    You Zhou,  Xuefei Zhe,  Naoya Iwamoto,  Bo Zheng,  Michael J. Black3 <br/>\
-    (*Equal Contribution) <br/>\
-    1The University of Tokyo, 2Keio University, 4Japan Advanced Institute of Science and Technology, <br/>\
-    3Max Planck Institute for Intelligent Systems, 5Tsinghua University <br/>\
-    </div>\
-    ',
-    article="\
-    For appling motion on your avatar: download npz file and blender v3.3 add-on on our project page, then retarget the motion. <br/> \
-    Due to the limited resources in this space, we process the first 60s of your uploaded audio，try to develop this space locally for longer motion generation, \[Project Page](https://pantomatrix.github.io/EMAGE/)\
-    ",
-    examples=examples,
-)
+with gr.Blocks() as demo:
+    with gr.Column():
+        gr.Markdown(
+        """
+        <div style="display: flex; justify-content: center; align-items: center; text-align: center;">
+          <div>
+            <h1>EMAGE</h1>
+            <span>Generating Face and Body Animation from Speech</span>
+            <br>
+            <div style="display: flex; justify-content: center; align-items: center; text-align: center;">
+              <a href="https://github.com/PantoMatrix/PantoMatrix"><img src="https://img.shields.io/badge/Project_Page-EMAGE-orange" alt="Project Page"></a>
+              &nbsp;
+              <a href="https://github.com/PantoMatrix/PantoMatrix"><img src="https://img.shields.io/badge/Github-Code-green"></a>
+              &nbsp;
+              <a href="https://github.com/PantoMatrix/PantoMatrix"><img src="https://img.shields.io/github/stars/PantoMatrix/PantoMatrix" alt="Stars"></a>
+            </div>
+          </div>
+        </div>
+        """
+        )
+        with gr.Row():
+            input_audio = gr.Audio(type="numpy", label="Upload Audio")
+            with gr.Column():
+                model_type = gr.Radio(
+                    choices=["DisCo (Upper only)", "CaMN (Upper only)", "EMAGE (Full body + Face)"],
+                    value="CaMN (Upper only)",
+                    label="Select Model: DisCo/CaMN for Upper, EMAGE for Full Body+Face"
+                )
+                render_face = gr.Checkbox(value=False, label="Render 2D Face Landmark (Fast ~4s for 7s)")
+                render_mesh = gr.Checkbox(value=False, label="Render Mesh Body (Slow ~1min for 7s)")
+                render_mesh_face = gr.Checkbox(value=False, label="Render Mesh Face (Extra Slow)")
+
+        btn = gr.Button("Run Inference")
+
+    with gr.Row():
+        vid_body = gr.Video(label="2D Body Video")
+        vid_mesh = gr.Video(label="Mesh Body Video (optional)")
+        vid_face = gr.Video(label="2D Face Video (optional)")
+        vid_meshface = gr.Video(label="Mesh Face Video (optional)")
+
+    with gr.Column():
+        gr.Markdown("Download Motion NPZ, Use Our [Blender Add-on](https://huggingface.co/datasets/H-Liu1997/BEAT2_Tools/blob/main/smplx_blender_addon_20230921.zip) for Visualization. [Demo](https://github.com/PantoMatrix/PantoMatrix/issues/178) of how to install on blender.")
+        file_npz = gr.File(label="Motion NPZ")
+
+    btn.click(
+        fn=inference_app,
+        inputs=[input_audio, model_type, render_mesh, render_face, render_mesh_face],
+        outputs=[vid_body, vid_mesh, vid_face, vid_meshface, file_npz]
+    )
+
+    gr.Examples(
+      examples=examples_data,
+      inputs=[input_audio, model_type, render_mesh, render_face, render_mesh_face],
+      outputs=[vid_body, vid_mesh, vid_face, vid_meshface, file_npz],
+      fn=inference_app,
+      cache_examples=True
+      )
 
-            
 if __name__ == "__main__":
-    os.environ["MASTER_ADDR"]='127.0.0.1'
-    os.environ["MASTER_PORT"]='8675'
-    #os.environ["TORCH_DISTRIBUTED_DEBUG"] = "DETAIL"
-    demo.launch(share=True)
+    demo.launch(share=True)

camn_trainer.py

@@ -1,361 +0,0 @@
-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
-import librosa
-
-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):
-    def __init__(self, args):
-        super().__init__(args)
-        self.joints = self.train_data.joints
-        self.tracker = other_tools.EpochTracker(["fid", "l1div", "bc", "rec", "trans", "vel", "transv", 'dis', 'gen', 'acc', 'transa', 'div_reg', "kl"], [False,True,True, 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").to(self.rank)
-        self.vel_loss = torch.nn.L1Loss(reduction='mean').to(self.rank)
-
-    def _load_data(self, dict_data):
-        tar_pose = dict_data["pose"].to(self.rank)
-        tar_trans = dict_data["trans"].to(self.rank)
-        tar_exps = dict_data["facial"].to(self.rank)
-        tar_beta = dict_data["beta"].to(self.rank)
-        tar_id = dict_data["id"].to(self.rank).long()
-        tar_word = dict_data["word"].to(self.rank)
-        in_audio = dict_data["audio"].to(self.rank)
-        in_emo = dict_data["emo"].to(self.rank) 
-        #in_sem = dict_data["sem"].to(self.rank) 
-        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)
-        in_pre_pose_cat = torch.cat([tar_pose[:, 0:self.args.pre_frames], tar_trans[:, :self.args.pre_frames]], dim=2).to(self.rank)
-
-        in_pre_pose = tar_pose.new_zeros((bs, n, j*6+1+3)).to(self.rank)
-        in_pre_pose[:, 0:self.args.pre_frames, :-1] = in_pre_pose_cat[:, 0:self.args.pre_frames]
-        in_pre_pose[:, 0:self.args.pre_frames, -1] = 1
-        return {
-            "tar_pose": tar_pose,
-            "in_audio": in_audio,
-            "in_motion": in_pre_pose,
-            "tar_trans": tar_trans,
-            "tar_exps": tar_exps,
-            "tar_beta": tar_beta,
-            "tar_word": tar_word,
-            'tar_id': tar_id,
-            'in_emo': in_emo,
-            #'in_sem': in_sem,
-        }
-    
-    def _d_training(self, loaded_data):
-        bs, n, j = loaded_data["tar_pose"].shape[0], loaded_data["tar_pose"].shape[1], self.joints 
-        net_out  = self.model(in_audio = loaded_data['in_audio'], pre_seq = loaded_data["in_motion"], in_text=loaded_data["tar_word"], in_id=loaded_data["tar_id"], in_emo=loaded_data["in_emo"], in_facial = loaded_data["tar_exps"])
-        rec_pose = net_out["rec_pose"][:, :, :j*6]
-        # rec_trans = net_out["rec_pose"][:, :, j*6:j*6+3]
-        
-        rec_pose = rec_pose.reshape(bs, n, j, 6)
-        rec_pose = rc.rotation_6d_to_matrix(rec_pose)
-        rec_pose = rc.matrix_to_rotation_6d(rec_pose).reshape(bs, n, j*6)
-        tar_pose = rc.rotation_6d_to_matrix(loaded_data["tar_pose"].reshape(bs, n, j, 6))
-        tar_pose = rc.matrix_to_rotation_6d(tar_pose).reshape(bs, n, j*6)
-        out_d_fake = self.d_model(rec_pose)
-        out_d_real = self.d_model(tar_pose)
-        
-        d_loss_adv = torch.sum(-torch.mean(torch.log(out_d_real + 1e-8) + torch.log(1 - out_d_fake + 1e-8)))
-        self.tracker.update_meter("dis", "train", d_loss_adv.item())
-        return d_loss_adv
-    
-    def _g_training(self, loaded_data, use_adv, mode="train"):
-        bs, n, j = loaded_data["tar_pose"].shape[0], loaded_data["tar_pose"].shape[1], self.joints 
-        net_out  = self.model(in_audio = loaded_data['in_audio'], pre_seq = loaded_data["in_motion"], in_text=loaded_data["tar_word"], in_id=loaded_data["tar_id"], in_emo=loaded_data["in_emo"], in_facial = loaded_data["tar_exps"])
-        rec_pose = net_out["rec_pose"][:, :, :j*6]
-        rec_trans = net_out["rec_pose"][:, :, j*6:j*6+3]
-        # print(rec_pose.shape, bs, n, j, loaded_data['in_audio'].shape, loaded_data["in_motion"].shape)
-        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(loaded_data["tar_pose"].reshape(bs, n, j, 6))
-
-        rec_loss = self.rec_loss(tar_pose, rec_pose)
-        rec_loss *= self.args.rec_weight
-        self.tracker.update_meter("rec", mode, rec_loss.item())
-        # rec_loss_vel = self.vel_loss(rec_pose[:, 1:] - rec_pose[:, :-1], tar_pose[:, 1:] - tar_pose[:, :-1])
-        # self.tracker.update_meter("vel", mode, rec_loss_vel.item())
-        # rec_loss_acc = self.vel_loss(rec_pose[:, 2:] - 2*rec_pose[:, 1:-1] + rec_pose[:, :-2], tar_pose[:, 2:] - 2*tar_pose[:, 1:-1] + tar_pose[:, :-2])
-        # self.tracker.update_meter("acc", mode, rec_loss_acc.item())
-        
-        rec_pose = rc.matrix_to_rotation_6d(rec_pose).reshape(bs, n, j*6)
-        tar_pose = rc.matrix_to_rotation_6d(tar_pose).reshape(bs, n, j*6)
-        if self.args.pose_dims < 330 and mode != "train":
-            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)
-            rec_pose = self.inverse_selection_tensor(rec_pose, self.train_data.joint_mask, rec_pose.shape[0])
-            rec_pose = rc.axis_angle_to_matrix(rec_pose.reshape(bs, n, 55, 3))
-            rec_pose = rc.matrix_to_rotation_6d(rec_pose).reshape(bs, n, 55*6)
-
-            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 = self.inverse_selection_tensor(tar_pose, self.train_data.joint_mask, tar_pose.shape[0])
-            tar_pose = rc.axis_angle_to_matrix(tar_pose.reshape(bs, n, 55, 3))
-            tar_pose = rc.matrix_to_rotation_6d(tar_pose).reshape(bs, n, 55*6)
-        if use_adv and mode == 'train':
-            out_d_fake = self.d_model(rec_pose)
-            d_loss_adv = -torch.mean(torch.log(out_d_fake + 1e-8))
-            self.tracker.update_meter("gen", mode, d_loss_adv.item())
-        else:
-            d_loss_adv = 0
-
-        if self.args.train_trans:
-            trans_loss = self.vel_loss(rec_trans, loaded_data["tar_trans"])
-            trans_loss *= self.args.rec_weight
-            self.tracker.update_meter("trans", mode, trans_loss.item())
-        else:
-            trans_loss = 0
-        # trans_loss_vel = self.vel_loss(rec_trans[:, 1:] - rec_trans[:, :-1], loaded_data["tar_trans"][:, 1:] - loaded_data["tar_trans"][:, :-1])
-        # self.tracker.update_meter("transv", mode, trans_loss_vel.item())
-        # trans_loss_acc = self.vel_loss(rec_trans[:, 2:] - 2*rec_trans[:, 1:-1] + rec_trans[:, :-2], loaded_data["tar_trans"][:, 2:] - 2*loaded_data["tar_trans"][:, 1:-1] + loaded_data["tar_trans"][:, :-2])
-        # self.tracker.update_meter("transa", mode, trans_loss_acc.item())
-
-        if mode == 'train':
-            return d_loss_adv + rec_loss + trans_loss # + rec_loss_vel + rec_loss_acc + trans_loss_vel + trans_loss_acc
-        elif mode == 'val':
-            return {
-                'rec_pose': rec_pose,
-                'rec_trans': rec_trans,
-                'tar_pose': tar_pose,
-            }
-        else:
-            return {
-                'rec_pose': rec_pose,
-                'rec_trans': rec_trans,
-                'tar_pose': tar_pose,
-                'tar_exps': loaded_data["tar_exps"],
-                'tar_beta': loaded_data["tar_beta"],
-                'tar_trans': loaded_data["tar_trans"],
-            }
-        
-    def train(self, epoch):
-        use_adv = bool(epoch>=self.args.no_adv_epoch)
-        self.model.train()
-        self.d_model.train()
-        self.tracker.reset()
-        t_start = time.time()
-        for its, batch_data in enumerate(self.train_loader):
-            loaded_data = self._load_data(batch_data)
-            t_data = time.time() - t_start
-    
-            if use_adv:
-                d_loss_final = 0
-                self.opt_d.zero_grad()
-                d_loss_adv = self._d_training(loaded_data)
-                d_loss_final += d_loss_adv
-                d_loss_final.backward()
-                self.opt_d.step() 
-    
-            self.opt.zero_grad()
-            g_loss_final = 0
-            g_loss_final += self._g_training(loaded_data, use_adv, 'train')
-            g_loss_final.backward()
-            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, lr_d=lr_d)   
-            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')
-                tar_pose = net_out['tar_pose']
-                rec_pose = net_out['rec_pose']
-                n = tar_pose.shape[1]
-                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 = []
-        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_training(loaded_data, False, 'test')
-                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']
-                bs, n, j = tar_pose.shape[0], tar_pose.shape[1], 55
-                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)
-                    tar_beta = torch.nn.functional.interpolate(tar_beta.permute(0, 2, 1), scale_factor=30/self.args.pose_fps, mode='linear').permute(0,2,1)
-                    tar_exps = torch.nn.functional.interpolate(tar_exps.permute(0, 2, 1), scale_factor=30/self.args.pose_fps, mode='linear').permute(0,2,1) 
-                    tar_trans = torch.nn.functional.interpolate(tar_trans.permute(0, 2, 1), scale_factor=30/self.args.pose_fps, mode='linear').permute(0,2,1)
-                    rec_trans = torch.nn.functional.interpolate(rec_trans.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],
-                #     )
-                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]
-                _ = 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_axis_np = tar_pose.detach().cpu().numpy()
-                rec_pose_axis_np = rec_pose.detach().cpu().numpy()
-                rec_trans_np = rec_trans.detach().cpu().numpy().reshape(bs*n, 3)
-                rec_exp_np = tar_exps.detach().cpu().numpy().reshape(bs*n, 100) - tar_exps.detach().cpu().numpy().reshape(bs*n, 100)
-                tar_exp_np = tar_exps.detach().cpu().numpy().reshape(bs*n, 100) - 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)
-                if not self.args.train_trans:
-                    tar_trans_np = tar_trans_np - tar_trans_np
-                    rec_trans_np = rec_trans_np - rec_trans_np
-                np.savez(results_save_path+"gt_"+test_seq_list.iloc[its]['id']+'.npz',
-                    betas=gt_npz["betas"],
-                    poses=tar_pose_axis_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_axis_np,
-                    expressions=rec_exp_np,
-                    trans=rec_trans_np,
-                    model='smplx2020',
-                    gender='neutral',
-                    mocap_frame_rate = 30,
-                )
-                total_length += n
-
-        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")

configs/.ipynb_checkpoints/emage_test_hf-checkpoint.yaml

@@ -1,101 +0,0 @@
-is_train: True
-ddp: False
-stat: ts
-root_path: ./
-out_path: ./outputs/audio2pose/
-project: s2g
-data_path: ./EMAGE/test_sequences/
-e_path:  weights/AESKConv_240_100.bin
-eval_model: motion_representation
-e_name: VAESKConv
-test_ckpt: ./EMAGE/emage_audio_175.bin
-data_path_1: ./EMAGE/
-vae_test_len: 32
-vae_test_dim: 330
-vae_test_stride: 20
-vae_length: 240
-vae_codebook_size: 256
-vae_layer: 4
-vae_grow: [1,1,2,1]
-variational: False
-
-# data config
-training_speakers: [2] #[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30]
-additional_data: False
-cache_path: ./datasets/beat_cache/beat_smplx_en_emage_test/
-dataset: beat_testonly_hf
-new_cache: True
-
-# motion config
-ori_joints: beat_smplx_joints
-tar_joints: beat_smplx_full
-pose_rep: smplxflame_30
-pose_norm: False
-pose_fps: 30
-rot6d: True
-pre_frames: 4
-pose_dims: 330
-pose_length: 64
-stride: 20
-test_length: 64
-motion_f: 256
-m_pre_encoder: null
-m_encoder: null
-m_fix_pre: False
-
-# audio config
-audio_rep: wave16k
-audio_sr: 16000
-audio_fps: 16000
-audio_norm: False
-audio_f: 256
-# a_pre_encoder: tcn_camn
-# a_encoder: none
-# a_fix_pre: False
-
-# text config
-# word_rep: textgrid
-# word_index_num: 11195
-# word_dims: 300
-# freeze_wordembed: False
-# word_f: 256
-# t_pre_encoder: fasttext
-# t_encoder: null
-# t_fix_pre: False
-
-# facial config
-facial_rep: smplxflame_30
-facial_dims: 100
-facial_norm: False
-facial_f: 0
-f_pre_encoder: null
-f_encoder: null
-f_fix_pre: False
-
-# speaker config
-id_rep: onehot
-speaker_f: 0
-
-# model config
-batch_size: 64
-# warmup_epochs: 1
-# warmup_lr: 1e-6
-lr_base: 5e-4
-model: emage_audio
-g_name: MAGE_Transformer
-trainer: emage
-hidden_size: 768
-n_layer: 1
- 
-rec_weight: 1
-grad_norm: 0.99
-epochs: 400
-test_period: 20
-ll: 3
-lf: 3
-lu: 3
-lh: 3
-cl: 1
-cf: 0
-cu: 1
-ch: 1

configs/camn.yaml

@@ -1,101 +0,0 @@
-is_train: True
-ddp: False
-stat: ts
-root_path: ./
-out_path: ./outputs/audio2pose/
-project: s2g
-data_path: ./BEAT2/beat_english_v2.0.0/
-e_path:  weights/AESKConv_240_100.bin
-eval_model: motion_representation
-e_name: VAESKConv
-test_ckpt: ./EMAGE/camn.bin
-data_path_1: ./EMAGE/
-vae_test_len: 64
-vae_test_dim: 330
-vae_test_stride: 20
-vae_length: 240
-vae_codebook_size: 256
-vae_layer: 4
-vae_grow: [1,1,2,1]
-variational: False
-
-# data config
-training_speakers: [2] #[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30]
-additional_data: False
-cache_path: datasets/beat_cache/beat_smplx_en_camn/
-dataset: beat_sep
-new_cache: False
-
-# motion config
-ori_joints: beat_smplx_joints
-tar_joints: beat_smplx_full
-pose_rep: smplxflame_30
-pose_norm: False
-pose_fps: 15
-rot6d: True
-pre_frames: 4
-pose_dims: 330
-pose_length: 32
-stride: 10
-test_length: 32
-motion_f: 256
-m_pre_encoder: null
-m_encoder: null
-m_fix_pre: False
-
-# audio config
-audio_rep: wave16k
-audio_sr: 16000
-audio_fps: 16000
-audio_norm: False
-audio_f: 128
-# a_pre_encoder: tcn_camn
-# a_encoder: none
-# a_fix_pre: False
-
-# text config
-word_rep: textgrid
-word_index_num: 11195
-word_dims: 300
-freeze_wordembed: False
-word_f: 128
-t_pre_encoder: fasttext
-t_encoder: null
-t_fix_pre: False
-
-# facial config
-facial_rep: smplxflame_30
-facial_dims: 100
-facial_norm: False
-facial_f: 64
-f_pre_encoder: null
-f_encoder: null
-f_fix_pre: False
-
-# speaker config
-id_rep: onehot
-speaker_f: 16
-emo_rep: emo
-emotion_f: 8
-# sem_rep: sem
-
-
-# model config
-batch_size: 128
-# warmup_epochs: 1
-# warmup_lr: 1e-6
-lr_base: 3e-4
-model: camn
-g_name: CaMN
-d_name: ConvDiscriminator
-trainer: camn
-hidden_size: 512
-n_layer: 4
-rec_weight: 500
-no_adv_epoch: 999
-# rec_pos_weight: 1
-# rec_ver_weight: 0
-# rec_fac_weight: 1
-# grad_norm: 1
-epochs: 100
-test_period: 20

configs/camn_audio.yaml

@@ -0,0 +1,71 @@
+wandb_project: 'EMAGE++'
+exp_name: 'debug'
+debug: False
+wandb_entity: ''
+wandb_key: ""
+# wandb_log_dir: '/content/outputs/wandb'
+output_dir: "../outputs/"
+log_period: 1
+seed: 42
+resume_from_checkpoint: null
+test: False
+
+
+data:
+  name_pyfile: "datasets.beat2"
+  class_name: "BEAT2Dataset"
+  train_bs: 64
+  meta_paths:
+    - "./datasets/data_json/beat2_s20_l128_speaker2.json"
+  test_meta_paths: 
+    - "./datasets/data_json/beat2_s20_l128_speaker2.json"
+  pose_norm: False
+  pose_length: 128
+  stride: 20
+  test_length: 128
+
+model:
+  name_pyfile: "models.camn_audio.modeling_camn_audio"
+  class_name: "CamnAudioModel"
+  pose_fps: 15
+  motion_f: 256
+  pose_dims: 258
+  pose_rep: "smplx"
+  body_dims: 78
+  hands_dims: 180
+  audio_rep: wave16k
+  audio_sr: 16000
+  audio_fps: 16000
+  audio_norm: False
+  audio_f: 128
+  speaker_f: 16
+  speaker_dims: 1
+  hidden_size: 512
+  n_layer: 4
+  dropout_prob: 0.1
+  seed_frames: 4 
+  joint_mask: "local_upper"
+ 
+validation:
+  validation_steps: 500
+  test_steps: 500
+  visualization: False
+  evaluation: False
+  wandb: False
+
+solver:
+  gradient_accumulation_steps: 1
+  gradient_checkpointing: False 
+  max_train_steps: 100000
+  max_grad_norm: 0.0
+  # lr
+  learning_rate: 3e-4
+  scale_lr: False 
+  lr_warmup_steps: 0
+  lr_scheduler: 'constant'
+  # optimizer
+  use_8bit_adam: False 
+  adam_beta1: 0.9
+  adam_beta2: 0.999
+  adam_weight_decay:  0.0
+  adam_epsilon: 1.0e-8

configs/cnn_vqvae_face_30.yaml

@@ -1,82 +0,0 @@
-is_train: True
-ddp: False
-stat: ts
-training_speakers: [2] #[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30]
-root_path: ./
-out_path: ./outputs/audio2pose/
-cache_path: datasets/beat_cache/beat_smplx_en_face/
-project: mage_smplx
-data_path: ./BEAT2/beat_english_v2.0.0/
-e_path:  weights/AESKConv_240_100.bin
-test_ckpt: weights/multi.bin
-data_path_1: ./EMAGE/
-#torch_hub_path: datasets/hub/
-additional_data: False
-dataset: beat_sep
-new_cache: False
-ori_joints: beat_smplx_joints
-tar_joints: beat_smplx_face
-pose_rep: smplxflame_30
-facial_rep: smplxflame_30
-pose_norm: False
-pose_fps: 30
-
-
-vae_test_len: 64
-vae_test_dim: 106
-vae_test_stride: 20
-vae_length: 256
-vae_codebook_size: 256
-vae_layer: 2
-vae_grow: [1,1,2,1]
-variational: False
-
-pose_dims: 106
-pose_length: 64
-stride: 20
-facial_dims: 100
-word_index_num: 11195
-word_dims: 300
-batch_size: 64
-lr_base: 3e-4
-model: motion_representation
-g_name: VQVAEConvZero
-#eval_model: motion_autoencoder
-#e_name: HalfEmbeddingNet
-trainer: aeface
-decay_epochs: 780
-# audio_f: 256
-# a_pre_encoder: tcn_camn
-# a_encoder: lp
-# a_fix_pre: False
-
-# freeze_wordembed: False
-# word_f: 128
-# t_pre_encoder: fasttext
-# t_encoder: lp
-# t_fix_pre: False
-
-# motion_f: 256
-# m_pre_encoder: lp
-# m_encoder: lp
-# m_fix_pre: False
-
-# facial_f: 128
-# f_pre_encoder: lp
-# f_encoder: lp
-# f_fix_pre: False
-
-#m_decoder: lstm
-#decode_fusion: cat
-#n_layer: 2
-#hidden_size: 512
-rec_weight: 1
-rec_pos_weight: 1
-rec_ver_weight: 1
-# rec_fac_weight: 1
-#ita_weight: 0
-#iwa_weight: 0
-#fusion_mode: sum
-# grad_norm: 1
-epochs: 800
-test_period: 100

configs/cnn_vqvae_hands_30.yaml

@@ -1,81 +0,0 @@
-is_train: True
-ddp: False
-stat: ts
-training_speakers: [2] #[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30]
-root_path: ./
-out_path: ./outputs/audio2pose/
-cache_path: datasets/beat_cache/beat_smplx_en_hands/
-project: mage_smplx
-data_path: ./BEAT2/beat_english_v2.0.0/
-e_path:  weights/AESKConv_240_100.bin
-test_ckpt: weights/multi.bin
-data_path_1: ./EMAGE/
-#torch_hub_path: datasets/hub/
-additional_data: False
-dataset: beat_sep
-new_cache: False
-ori_joints: beat_smplx_joints
-tar_joints: beat_smplx_hands
-pose_rep: smplxflame_30
-pose_norm: False
-pose_fps: 30
-
-
-vae_test_len: 64
-vae_test_dim: 180
-vae_test_stride: 20
-vae_length: 256
-vae_codebook_size: 256
-vae_layer: 2
-vae_grow: [1,1,2,1]
-variational: False
-
-pose_dims: 180
-pose_length: 64
-stride: 20
-facial_dims: 100
-word_index_num: 11195
-word_dims: 300
-batch_size: 64
-lr_base: 3e-4
-model: motion_representation
-g_name: VQVAEConvZero
-#eval_model: motion_autoencoder
-#e_name: HalfEmbeddingNet
-trainer: ae
-decay_epochs: 780
-# audio_f: 256
-# a_pre_encoder: tcn_camn
-# a_encoder: lp
-# a_fix_pre: False
-
-# freeze_wordembed: False
-# word_f: 128
-# t_pre_encoder: fasttext
-# t_encoder: lp
-# t_fix_pre: False
-
-# motion_f: 256
-# m_pre_encoder: lp
-# m_encoder: lp
-# m_fix_pre: False
-
-# facial_f: 128
-# f_pre_encoder: lp
-# f_encoder: lp
-# f_fix_pre: False
-
-#m_decoder: lstm
-#decode_fusion: cat
-#n_layer: 2
-#hidden_size: 512
-rec_weight: 1
-rec_pos_weight: 1
-rec_ver_weight: 1
-# rec_fac_weight: 1
-#ita_weight: 0
-#iwa_weight: 0
-#fusion_mode: sum
-# grad_norm: 1
-epochs: 800
-test_period: 100

configs/cnn_vqvae_lower_30.yaml

@@ -1,81 +0,0 @@
-is_train: True
-ddp: False
-stat: ts
-training_speakers: [2] #[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30]
-root_path: ./
-out_path: ./outputs/audio2pose/
-cache_path: datasets/beat_cache/beat_smplx_en_lower/
-project: mage_smplx
-data_path: ./BEAT2/beat_english_v2.0.0/
-e_path:  weights/AESKConv_240_100.bin
-test_ckpt: weights/multi.bin
-data_path_1: ./EMAGE/
-#torch_hub_path: datasets/hub/
-additional_data: False
-dataset: beat_sep_lower
-new_cache: False
-ori_joints: beat_smplx_joints
-tar_joints: beat_smplx_lower
-pose_rep: smplxflame_30
-pose_norm: False
-pose_fps: 30
-
-
-vae_test_len: 64
-vae_test_dim: 61
-vae_test_stride: 20
-vae_length: 256
-vae_codebook_size: 256
-vae_layer: 4
-vae_grow: [1,1,2,1]
-variational: False
-
-pose_dims: 61
-pose_length: 64
-stride: 20
-facial_dims: 100
-word_index_num: 11195
-word_dims: 300
-batch_size: 64
-lr_base: 3e-4
-model: motion_representation
-g_name: VAEConvZero
-#eval_model: motion_autoencoder
-#e_name: HalfEmbeddingNet
-trainer: aelower
-decay_epochs: 780
-# audio_f: 256
-# a_pre_encoder: tcn_camn
-# a_encoder: lp
-# a_fix_pre: False
-
-# freeze_wordembed: False
-# word_f: 128
-# t_pre_encoder: fasttext
-# t_encoder: lp
-# t_fix_pre: False
-
-# motion_f: 256
-# m_pre_encoder: lp
-# m_encoder: lp
-# m_fix_pre: False
-
-# facial_f: 128
-# f_pre_encoder: lp
-# f_encoder: lp
-# f_fix_pre: False
-
-#m_decoder: lstm
-#decode_fusion: cat
-#n_layer: 2
-#hidden_size: 512
-rec_weight: 1
-rec_pos_weight: 1
-rec_ver_weight: 1
-# rec_fac_weight: 1
-#ita_weight: 0
-#iwa_weight: 0
-#fusion_mode: sum
-# grad_norm: 1
-epochs: 800
-test_period: 100

configs/cnn_vqvae_lower_foot_30.yaml

@@ -1,81 +0,0 @@
-is_train: True
-ddp: False
-stat: ts
-training_speakers: [2]
-root_path: ./
-out_path: ./outputs/audio2pose/
-cache_path: datasets/beat_cache/beat_smplx_en_lower/
-project: mage_smplx
-data_path: ./BEAT2/beat_english_v2.0.0/
-e_path:  weights/AESKConv_240_100.bin
-test_ckpt: weights/multi.bin
-data_path_1: ./EMAGE/
-#torch_hub_path: datasets/hub/
-additional_data: False
-dataset: beat_sep_lower
-new_cache: False
-ori_joints: beat_smplx_joints
-tar_joints: beat_smplx_lower
-pose_rep: smplxflame_30
-pose_norm: False
-pose_fps: 30
-
-
-vae_test_len: 64
-vae_test_dim: 61
-vae_test_stride: 20
-vae_length: 256
-vae_codebook_size: 256
-vae_layer: 4
-vae_grow: [1,1,2,1]
-variational: False
-
-pose_dims: 61
-pose_length: 64
-stride: 20
-facial_dims: 100
-word_index_num: 11195
-word_dims: 300
-batch_size: 64
-lr_base: 3e-4
-model: motion_representation
-g_name: VQVAEConvZero
-#eval_model: motion_autoencoder
-#e_name: HalfEmbeddingNet
-trainer: aelowerfoot
-decay_epochs: 780
-# audio_f: 256
-# a_pre_encoder: tcn_camn
-# a_encoder: lp
-# a_fix_pre: False
-
-# freeze_wordembed: False
-# word_f: 128
-# t_pre_encoder: fasttext
-# t_encoder: lp
-# t_fix_pre: False
-
-# motion_f: 256
-# m_pre_encoder: lp
-# m_encoder: lp
-# m_fix_pre: False
-
-# facial_f: 128
-# f_pre_encoder: lp
-# f_encoder: lp
-# f_fix_pre: False
-
-#m_decoder: lstm
-#decode_fusion: cat
-#n_layer: 2
-#hidden_size: 512
-rec_weight: 1
-rec_pos_weight: 1
-rec_ver_weight: 1
-# rec_fac_weight: 1
-#ita_weight: 0
-#iwa_weight: 0
-#fusion_mode: sum
-# grad_norm: 1
-epochs: 800
-test_period: 100

configs/cnn_vqvae_upper_30.yaml

@@ -1,82 +0,0 @@
-is_train: True
-ddp: False
-stat: ts
-training_speakers: [2]
-root_path: ./
-out_path: ./outputs/audio2pose/
-cache_path: datasets/beat_cache/beat_smplx_en_upper/
-project: mage_smplx
-data_path: ./BEAT2/beat_english_v2.0.0/
-e_path:  weights/AESKConv_240_100.bin
-test_ckpt: weights/multi.bin
-data_path_1: ./EMAGE/
-#torch_hub_path: datasets/hub/
-additional_data: False
-dataset: beat_sep
-new_cache: False
-ori_joints: beat_smplx_joints
-tar_joints: beat_smplx_upper
-pose_rep: smplxflame_30
-pose_norm: False
-pose_fps: 30
-
-
-vae_test_len: 64
-vae_test_dim: 78
-vae_test_stride: 20
-vae_length: 256
-vae_codebook_size: 256
-vae_layer: 2
-vae_grow: [1,1,2,1]
-variational: False
-
-pose_dims: 78
-pose_length: 64
-stride: 20
-facial_dims: 100
-word_index_num: 11195
-word_dims: 300
-batch_size: 64
-lr_base: 3e-4
-decay_epochs: 9999
-model: motion_representation
-g_name: VQVAEConvZero
-#eval_model: motion_autoencoder
-#e_name: HalfEmbeddingNet
-trainer: ae
-
-# audio_f: 256
-# a_pre_encoder: tcn_camn
-# a_encoder: lp
-# a_fix_pre: False
-
-# freeze_wordembed: False
-# word_f: 128
-# t_pre_encoder: fasttext
-# t_encoder: lp
-# t_fix_pre: False
-
-# motion_f: 256
-# m_pre_encoder: lp
-# m_encoder: lp
-# m_fix_pre: False
-
-# facial_f: 128
-# f_pre_encoder: lp
-# f_encoder: lp
-# f_fix_pre: False
-
-#m_decoder: lstm
-#decode_fusion: cat
-#n_layer: 2
-#hidden_size: 512
-rec_weight: 1
-rec_pos_weight: 1
-rec_ver_weight: 1
-# rec_fac_weight: 1
-#ita_weight: 0
-#iwa_weight: 0
-#fusion_mode: sum
-# grad_norm: 1
-epochs: 500
-test_period: 100

configs/disco_audio.yaml

@@ -0,0 +1,70 @@
+wandb_project: 'EMAGE++'
+exp_name: 'debug'
+debug: False
+wandb_entity: ''
+wandb_key: ""
+# wandb_log_dir: '/content/outputs/wandb'
+output_dir: "../outputs/"
+log_period: 1
+seed: 42
+resume_from_checkpoint: null
+test: False
+
+data:
+  name_pyfile: "datasets.beat2_disco"
+  class_name: "BEAT2DatasetDisco"
+  train_bs: 64
+  meta_paths:
+    - "./datasets/data_json/beat2_s20_l128_speaker2_disco.json"
+  test_meta_paths: 
+    - "./datasets/data_json/beat2_s20_l128_speaker2.json"
+  pose_norm: False
+  pose_length: 128
+  stride: 20
+  test_length: 128
+
+model:
+  name_pyfile: "models.disco_audio.modeling_disco_audio"
+  class_name: "DiscoAudioModel"
+  pose_fps: 15
+  motion_f: 256
+  pose_dims: 258
+  pose_rep: "smplx"
+  body_dims: 78
+  hands_dims: 180
+  audio_rep: wave16k
+  audio_sr: 16000
+  audio_fps: 16000
+  audio_norm: False
+  audio_f: 128
+  speaker_f: 16
+  speaker_dims: 1
+  hidden_size: 512
+  n_layer: 4
+  dropout_prob: 0.1
+  seed_frames: 4 
+  joint_mask: "local_upper"
+ 
+validation:
+  validation_steps: 500
+  test_steps: 500
+  visualization: False
+  evaluation: False
+  wandb: False
+
+solver:
+  gradient_accumulation_steps: 1
+  gradient_checkpointing: False 
+  max_train_steps: 14500
+  max_grad_norm: 0.0
+  # lr
+  learning_rate: 3e-4
+  scale_lr: False 
+  lr_warmup_steps: 0
+  lr_scheduler: 'constant'
+  # optimizer
+  use_8bit_adam: False 
+  adam_beta1: 0.9
+  adam_beta2: 0.999
+  adam_weight_decay:  0.0
+  adam_epsilon: 1.0e-8

configs/emage.yaml

@@ -1,101 +0,0 @@
-is_train: True
-ddp: False
-stat: ts
-root_path: ./
-out_path: ./outputs/audio2pose/
-project: s2g
-data_path: ./BEAT2/beat_english_v2.0.0/
-e_path:  weights/AESKConv_240_100.bin
-eval_model: motion_representation
-e_name: VAESKConv
-test_ckpt: ./EMAGE/emage_240.bin
-data_path_1: ./EMAGE/
-vae_test_len: 32
-vae_test_dim: 330
-vae_test_stride: 20
-vae_length: 240
-vae_codebook_size: 256
-vae_layer: 4
-vae_grow: [1,1,2,1]
-variational: False
-
-# data config
-training_speakers: [2] #[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30]
-additional_data: False
-cache_path: datasets/beat_cache/beat_smplx_en_emage/
-dataset: beat_sep_lower
-new_cache: False
-
-# motion config
-ori_joints: beat_smplx_joints
-tar_joints: beat_smplx_full
-pose_rep: smplxflame_30
-pose_norm: False
-pose_fps: 30
-rot6d: True
-pre_frames: 4
-pose_dims: 330
-pose_length: 64
-stride: 20
-test_length: 64
-motion_f: 256
-m_pre_encoder: null
-m_encoder: null
-m_fix_pre: False
-
-# audio config
-audio_rep: onset+amplitude
-audio_sr: 16000
-audio_fps: 16000
-audio_norm: False
-audio_f: 256
-# a_pre_encoder: tcn_camn
-# a_encoder: none
-# a_fix_pre: False
-
-# text config
-word_rep: textgrid
-word_index_num: 11195
-word_dims: 300
-freeze_wordembed: False
-word_f: 256
-t_pre_encoder: fasttext
-t_encoder: null
-t_fix_pre: False
-
-# facial config
-facial_rep: smplxflame_30
-facial_dims: 100
-facial_norm: False
-facial_f: 0
-f_pre_encoder: null
-f_encoder: null
-f_fix_pre: False
-
-# speaker config
-id_rep: onehot
-speaker_f: 0
-
-# model config
-batch_size: 64
-# warmup_epochs: 1
-# warmup_lr: 1e-6
-lr_base: 5e-4
-model: emage
-g_name: MAGE_Transformer
-trainer: emage
-hidden_size: 768
-n_layer: 1
- 
-rec_weight: 1
-grad_norm: 0.99
-epochs: 400
-test_period: 20
-ll: 3
-lf: 3
-lu: 3
-lh: 3
-cl: 1
-cf: 0
-cu: 1
-ch: 1

configs/emage_audio.yaml

@@ -0,0 +1,78 @@
+wandb_project: 'EMAGE++'
+exp_name: 'debug'
+debug: False
+wandb_entity: ''
+wandb_key: ""
+# wandb_log_dir: '/content/outputs/wandb'
+output_dir: /content/outputs/
+log_period: 1
+seed: 222
+resume_from_checkpoint: null
+test: False
+
+data:
+  name_pyfile: "datasets.beat2"
+  class_name: "BEAT2DatasetEamgeFootContact"
+  train_bs: 56
+  meta_paths:
+    - "/content/drive/MyDrive/my-codes/PantoMatrix/datasets/data_json/beat2_s20_l64_speaker2.json"
+  test_meta_paths: 
+    - "/content/drive/MyDrive/my-codes/PantoMatrix/datasets/data_json/beat2_s20_l64_speaker2.json"
+  pose_norm: False
+  
+
+model:
+  name_pyfile: "models.emage_audio.modeling_emage_audio"
+  class_name: "EmageAudioModel"
+  pose_fps: 30
+  motion_f: 256
+  pose_dims: 330
+  pose_rep: "smplx"
+  audio_rep: wave16k
+  audio_sr: 16000
+  audio_fps: 16000
+  audio_norm: False
+  audio_f: 256
+  speaker_f: 768
+  speaker_dims: 1
+  hidden_size: 768
+  seed_frames: 4 
+  pose_length: 64
+  stride: 20
+  test_length: 64
+  joint_mask: null
+  vae_codebook_size: 256
+  ll: 3
+  lf: 3
+  lu: 3
+  lh: 3
+  cl: 1
+  cf: 0
+  cu: 1
+  ch: 1 
+  
+ 
+validation:
+  validation_steps: 500
+  test_steps: 1000
+  visualization: False
+  evaluation: False
+  wandb: False
+
+
+solver:
+  gradient_accumulation_steps: 1
+  gradient_checkpointing: False 
+  max_train_steps: 100000
+  max_grad_norm: 0.99
+  # lr
+  learning_rate: 1.5e-4
+  scale_lr: False 
+  lr_warmup_steps: 0
+  lr_scheduler: 'constant'
+  # optimizer
+  use_8bit_adam: False 
+  adam_beta1: 0.9
+  adam_beta2: 0.999
+  adam_weight_decay:  0.0
+  adam_epsilon: 1.0e-8

configs/emage_test.yaml

@@ -1,101 +0,0 @@
-is_train: True
-ddp: False
-stat: ts
-root_path: ./
-out_path: ./outputs/audio2pose/
-project: s2g
-data_path: ./EMAGE/test_sequences/
-e_path:  weights/AESKConv_240_100.bin
-eval_model: motion_representation
-e_name: VAESKConv
-test_ckpt: ./EMAGE/emage_240.bin
-data_path_1: ./EMAGE/
-vae_test_len: 32
-vae_test_dim: 330
-vae_test_stride: 20
-vae_length: 240
-vae_codebook_size: 256
-vae_layer: 4
-vae_grow: [1,1,2,1]
-variational: False
-
-# data config
-training_speakers: [2] #[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30]
-additional_data: False
-cache_path: ./datasets/beat_cache/beat_smplx_en_emage_test/
-dataset: beat_testonly
-new_cache: True
-
-# motion config
-ori_joints: beat_smplx_joints
-tar_joints: beat_smplx_full
-pose_rep: smplxflame_30
-pose_norm: False
-pose_fps: 30
-rot6d: True
-pre_frames: 4
-pose_dims: 330
-pose_length: 64
-stride: 20
-test_length: 64
-motion_f: 256
-m_pre_encoder: null
-m_encoder: null
-m_fix_pre: False
-
-# audio config
-audio_rep: onset+amplitude
-audio_sr: 16000
-audio_fps: 16000
-audio_norm: False
-audio_f: 256
-# a_pre_encoder: tcn_camn
-# a_encoder: none
-# a_fix_pre: False
-
-# text config
-word_rep: textgrid
-word_index_num: 11195
-word_dims: 300
-freeze_wordembed: False
-word_f: 256
-t_pre_encoder: fasttext
-t_encoder: null
-t_fix_pre: False
-
-# facial config
-facial_rep: smplxflame_30
-facial_dims: 100
-facial_norm: False
-facial_f: 0
-f_pre_encoder: null
-f_encoder: null
-f_fix_pre: False
-
-# speaker config
-id_rep: onehot
-speaker_f: 0
-
-# model config
-batch_size: 64
-# warmup_epochs: 1
-# warmup_lr: 1e-6
-lr_base: 5e-4
-model: emage
-g_name: MAGE_Transformer
-trainer: emage
-hidden_size: 768
-n_layer: 1
- 
-rec_weight: 1
-grad_norm: 0.99
-epochs: 400
-test_period: 20
-ll: 3
-lf: 3
-lu: 3
-lh: 3
-cl: 1
-cf: 0
-cu: 1
-ch: 1

configs/emage_test_colab.yaml

@@ -1,101 +0,0 @@
-is_train: True
-ddp: False
-stat: ts
-root_path: ./
-out_path: ./outputs/audio2pose/
-project: s2g
-data_path: ./EMAGE/test_sequences/
-e_path:  weights/AESKConv_240_100.bin
-eval_model: motion_representation
-e_name: VAESKConv
-test_ckpt: ./EMAGE/emage_240.bin
-data_path_1: ./EMAGE/
-vae_test_len: 32
-vae_test_dim: 330
-vae_test_stride: 20
-vae_length: 240
-vae_codebook_size: 256
-vae_layer: 4
-vae_grow: [1,1,2,1]
-variational: False
-
-# data config
-training_speakers: [2] #[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30]
-additional_data: False
-cache_path: ./datasets/beat_cache/beat_smplx_en_emage_test/
-dataset: beat_testonly_colab
-new_cache: True
-
-# motion config
-ori_joints: beat_smplx_joints
-tar_joints: beat_smplx_full
-pose_rep: smplxflame_30
-pose_norm: False
-pose_fps: 30
-rot6d: True
-pre_frames: 4
-pose_dims: 330
-pose_length: 64
-stride: 20
-test_length: 64
-motion_f: 256
-m_pre_encoder: null
-m_encoder: null
-m_fix_pre: False
-
-# audio config
-audio_rep: onset+amplitude
-audio_sr: 16000
-audio_fps: 16000
-audio_norm: False
-audio_f: 256
-# a_pre_encoder: tcn_camn
-# a_encoder: none
-# a_fix_pre: False
-
-# text config
-word_rep: textgrid
-word_index_num: 11195
-word_dims: 300
-freeze_wordembed: False
-word_f: 256
-t_pre_encoder: fasttext
-t_encoder: null
-t_fix_pre: False
-
-# facial config
-facial_rep: smplxflame_30
-facial_dims: 100
-facial_norm: False
-facial_f: 0
-f_pre_encoder: null
-f_encoder: null
-f_fix_pre: False
-
-# speaker config
-id_rep: onehot
-speaker_f: 0
-
-# model config
-batch_size: 64
-# warmup_epochs: 1
-# warmup_lr: 1e-6
-lr_base: 5e-4
-model: emage
-g_name: MAGE_Transformer
-trainer: emage
-hidden_size: 768
-n_layer: 1
- 
-rec_weight: 1
-grad_norm: 0.99
-epochs: 400
-test_period: 20
-ll: 3
-lf: 3
-lu: 3
-lh: 3
-cl: 1
-cf: 0
-cu: 1
-ch: 1