generate.py

import torch
import clip
import models.vqvae as vqvae
from models.vqvae_sep import VQVAE_SEP
import models.t2m_trans as trans
import models.t2m_trans_uplow as trans_uplow
import numpy as np
from exit.utils import visualize_2motions
from exit.utils import recover_from_ric
import options.option_transformer as option_trans



##### ---- CLIP ---- #####
clip_model, clip_preprocess = clip.load("ViT-B/32", device=torch.device('cpu'), jit=False)  # Must set jit=False for training
clip.model.convert_weights(clip_model)  # Actually this line is unnecessary since clip by default already on float16
clip_model.eval()
for p in clip_model.parameters():
    p.requires_grad = False

# https://github.com/openai/CLIP/issues/111
class TextCLIP(torch.nn.Module):
    def __init__(self, model) :
        super(TextCLIP, self).__init__()
        self.model = model
        
    def forward(self,text):
        with torch.no_grad():
            word_emb = self.model.token_embedding(text).type(self.model.dtype)
            word_emb = word_emb + self.model.positional_embedding.type(self.model.dtype)
            word_emb = word_emb.permute(1, 0, 2)  # NLD -> LND
            word_emb = self.model.transformer(word_emb)
            word_emb = self.model.ln_final(word_emb).permute(1, 0, 2).float()
            enctxt = self.model.encode_text(text).float()
        return enctxt, word_emb
clip_model = TextCLIP(clip_model)

def get_vqvae(args, is_upper_edit):
    if not is_upper_edit:
        return vqvae.HumanVQVAE(args, ## use args to define different parameters in different quantizers
                            args.nb_code,
                            args.code_dim,
                            args.output_emb_width,
                            args.down_t,
                            args.stride_t,
                            args.width,
                            args.depth,
                            args.dilation_growth_rate)
    else:
        return VQVAE_SEP(args, ## use args to define different parameters in different quantizers
                        args.nb_code,
                        args.code_dim,
                        args.output_emb_width,
                        args.down_t,
                        args.stride_t,
                        args.width,
                        args.depth,
                        args.dilation_growth_rate,
                        moment={'mean': torch.from_numpy(args.mean).float(), 
                            'std': torch.from_numpy(args.std).float()},
                        sep_decoder=True)

def get_maskdecoder(args, vqvae, is_upper_edit):
    tranformer = trans if not is_upper_edit else trans_uplow
    return tranformer.Text2Motion_Transformer(vqvae,
                                num_vq=args.nb_code, 
                                embed_dim=args.embed_dim_gpt, 
                                clip_dim=args.clip_dim, 
                                block_size=args.block_size, 
                                num_layers=args.num_layers, 
                                num_local_layer=args.num_local_layer, 
                                n_head=args.n_head_gpt, 
                                drop_out_rate=args.drop_out_rate, 
                                fc_rate=args.ff_rate)

class MMM(torch.nn.Module):
    def __init__(self, args=None, is_upper_edit=False):
        super().__init__()
        self.is_upper_edit = is_upper_edit


        args.dataname = args.dataset_name = 't2m'

        self.vqvae = get_vqvae(args, is_upper_edit)
        ckpt = torch.load(args.resume_pth, map_location='cpu')
        self.vqvae.load_state_dict(ckpt['net'], strict=True)
        if is_upper_edit:
            class VQVAE_WRAPPER(torch.nn.Module):
                def __init__(self, vqvae) :
                    super().__init__()
                    self.vqvae = vqvae
                    
                def forward(self, *args, **kwargs):
                    return self.vqvae(*args, **kwargs)
            self.vqvae = VQVAE_WRAPPER(self.vqvae)
        self.vqvae.eval()
        self.vqvae

        self.maskdecoder = get_maskdecoder(args, self.vqvae, is_upper_edit)
        ckpt = torch.load(args.resume_trans, map_location='cpu')
        self.maskdecoder.load_state_dict(ckpt['trans'], strict=True)
        self.maskdecoder.train()
        self.maskdecoder

    def forward(self, text, lengths=-1, rand_pos=True):
        b = len(text)
        feat_clip_text = clip.tokenize(text, truncate=True)
        feat_clip_text, word_emb = clip_model(feat_clip_text)
        index_motion = self.maskdecoder(feat_clip_text, word_emb, type="sample", m_length=lengths, rand_pos=rand_pos, if_test=False)

        m_token_length = torch.ceil((lengths)/4).int()
        pred_pose_all = torch.zeros((b, 196, 263))
        for k in range(b):
            pred_pose = self.vqvae(index_motion[k:k+1, :m_token_length[k]], type='decode')
            pred_pose_all[k:k+1, :int(lengths[k].item())] = pred_pose
        return pred_pose_all

    def inbetween_eval(self, base_pose, m_length, start_f, end_f, inbetween_text):
        bs, seq = base_pose.shape[:2]
        tokens = -1*torch.ones((bs, 50), dtype=torch.long)
        m_token_length = torch.ceil((m_length)/4).int()
        start_t = torch.round((start_f)/4).int()
        end_t = torch.round((end_f)/4).int()

        for k in range(bs):
            index_motion = self.vqvae(base_pose[k:k+1, :m_length[k]], type='encode')
            tokens[k, :start_t[k]] = index_motion[0][:start_t[k]]
            tokens[k, end_t[k]:m_token_length[k]] = index_motion[0][end_t[k]:m_token_length[k]]

        text = clip.tokenize(inbetween_text, truncate=True)
        feat_clip_text, word_emb_clip = clip_model(text)

        mask_id = self.maskdecoder.num_vq + 2
        tokens[tokens==-1] = mask_id
        inpaint_index = self.maskdecoder(feat_clip_text, word_emb_clip, type="sample", m_length=m_length, token_cond=tokens)

        pred_pose_eval = torch.zeros((bs, seq, base_pose.shape[-1]))
        for k in range(bs):
            pred_pose = self.vqvae(inpaint_index[k:k+1, :m_token_length[k]], type='decode')
            pred_pose_eval[k:k+1, :int(m_length[k].item())] = pred_pose
        return pred_pose_eval

    def long_range(self, text, lengths, num_transition_token=2, output='concat', index_motion=None):
        b = len(text)
        feat_clip_text = clip.tokenize(text, truncate=True)
        feat_clip_text, word_emb = clip_model(feat_clip_text)
        if index_motion is None:
            index_motion = self.maskdecoder(feat_clip_text, word_emb, type="sample", m_length=lengths, rand_pos=False)

        m_token_length = torch.ceil((lengths)/4).int()
        if output == 'eval':
            frame_length = m_token_length * 4
            m_token_length = m_token_length.clone()
            m_token_length = m_token_length - 2*num_transition_token
            m_token_length[[0,-1]] += num_transition_token # first and last have transition only half
        
        half_token_length = (m_token_length/2).int()
        idx_full_len = half_token_length >= 24
        half_token_length[idx_full_len] = half_token_length[idx_full_len] - 1

        mask_id = self.maskdecoder.num_vq + 2
        tokens = -1*torch.ones((b-1, 50), dtype=torch.long)
        transition_train_length = []
        
        for i in range(b-1):
            if output == 'concat':
                i_index_motion = index_motion[i]
                i1_index_motion = index_motion[i+1]
            if output == 'eval':
                if i == 0:
                    i_index_motion = index_motion[i, :m_token_length[i]]
                else:
                    i_index_motion = index_motion[i, num_transition_token:m_token_length[i] + num_transition_token]
                if i == b-1:
                    i1_index_motion = index_motion[i+1, :m_token_length[i+1]]
                else:
                    i1_index_motion = index_motion[i+1, 
                                                num_transition_token:m_token_length[i+1] + num_transition_token]
            left_end = half_token_length[i]
            right_start = left_end + num_transition_token
            end = right_start + half_token_length[i+1]

            tokens[i, :left_end] = i_index_motion[m_token_length[i]-left_end: m_token_length[i]]
            tokens[i, left_end:right_start] = mask_id
            tokens[i, right_start:end] = i1_index_motion[:half_token_length[i+1]]
            transition_train_length.append(end)
        transition_train_length = torch.tensor(transition_train_length).to(index_motion.device)
        text = clip.tokenize(text[:-1], truncate=True)
        feat_clip_text, word_emb_clip = clip_model(text)
        inpaint_index = self.maskdecoder(feat_clip_text, word_emb_clip, type="sample", m_length=transition_train_length*4, token_cond=tokens, max_steps=1)
        
        if output == 'concat':
            all_tokens = []
            for i in range(b-1):
                all_tokens.append(index_motion[i, :m_token_length[i]])
                all_tokens.append(inpaint_index[i, tokens[i] == mask_id])
            all_tokens.append(index_motion[-1, :m_token_length[-1]])
            all_tokens = torch.cat(all_tokens).unsqueeze(0)
            pred_pose = self.vqvae(all_tokens, type='decode')
            return pred_pose
        elif output == 'eval':
            all_tokens = []
            for i in range(b):
                motion_token = index_motion[i, :m_token_length[i]]
                if i == 0:
                    first_current_trans_tok = inpaint_index[i, tokens[i] == mask_id]
                    all_tokens.append(motion_token)
                    all_tokens.append(first_current_trans_tok)
                else:
                    if i < b-1:
                        first_current_trans_tok = inpaint_index[i, tokens[i] == mask_id]
                        all_tokens.append(motion_token)
                        all_tokens.append(first_current_trans_tok)
                    else:
                        all_tokens.append(motion_token)
            all_tokens = torch.cat(all_tokens)
            pred_pose_concat = self.vqvae(all_tokens.unsqueeze(0), type='decode')
            
            trans_frame = num_transition_token*4
            pred_pose = torch.zeros((b, 196, 263))
            current_point = 0
            for i in range(b):
                if i == 0:
                    start_f = torch.tensor(0)
                    end_f = frame_length[i]
                else:
                    start_f = current_point - trans_frame
                    end_f = start_f + frame_length[i]
                current_point = end_f
                pred_pose[i, :frame_length[i]] = pred_pose_concat[0, start_f: end_f]
            return pred_pose

    def upper_edit(self, pose, m_length, upper_text, lower_mask=None):
        pose = pose.clone().float() # bs, nb_joints, joints_dim, seq_len
        m_tokens_len = torch.ceil((m_length)/4)
        bs, seq = pose.shape[:2]
        max_motion_length = int(seq/4) + 1
        mot_end_idx = self.vqvae.vqvae.num_code
        mot_pad_idx = self.vqvae.vqvae.num_code + 1
        mask_id = self.vqvae.vqvae.num_code + 2
        target_lower = []
        for k in range(bs):
            target = self.vqvae(pose[k:k+1, :m_length[k]], type='encode')
            if m_tokens_len[k]+1 < max_motion_length:
                target = torch.cat([target, 
                                    torch.ones((1, 1, 2), dtype=int, device=target.device) * mot_end_idx, 
                                    torch.ones((1, max_motion_length-1-m_tokens_len[k].int().item(), 2), dtype=int, device=target.device) * mot_pad_idx], axis=1)
            else:
                target = torch.cat([target, 
                                    torch.ones((1, 1, 2), dtype=int, device=target.device) * mot_end_idx], axis=1)
            target_lower.append(target[..., 1])
        target_lower = torch.cat(target_lower, axis=0)

        ### lower mask ###
        if lower_mask is not None:
            lower_mask = torch.cat([lower_mask, torch.zeros(bs, 1, dtype=int)], dim=1).bool()
            target_lower_masked = target_lower.clone()
            target_lower_masked[lower_mask] = mask_id
            select_end = target_lower == mot_end_idx
            target_lower_masked[select_end] = target_lower[select_end]
        else:
            target_lower_masked = target_lower
        ##################

        pred_len = m_length
        pred_tok_len = m_tokens_len
        pred_pose_eval = torch.zeros((bs, seq, pose.shape[-1]))

        # __upper_text__ = ['A man punches with right hand.'] * 32
        text = clip.tokenize(upper_text, truncate=True)
        feat_clip_text, word_emb_clip = clip_model(text)
        # index_motion = trans_encoder(feat_clip_text, idx_lower=target_lower_masked, word_emb=word_emb_clip, type="sample", m_length=pred_len, rand_pos=True, CFG=-1)
        index_motion = self.maskdecoder(feat_clip_text, target_lower_masked, word_emb_clip, type="sample", m_length=pred_len, rand_pos=True)
        for i in range(bs):
            all_tokens = torch.cat([
                index_motion[i:i+1, :int(pred_tok_len[i].item()), None],
                target_lower[i:i+1, :int(pred_tok_len[i].item()), None]
            ], axis=-1)
            pred_pose = self.vqvae(all_tokens, type='decode')
            pred_pose_eval[i:i+1, :int(pred_len[i].item())] = pred_pose

        return pred_pose_eval
    

if __name__ == '__main__':
    args = option_trans.get_args_parser()

# python generate.py --resume-pth '/home/epinyoan/git/MaskText2Motion/T2M-BD/output/vq/2023-07-19-04-17-17_12_VQVAE_20batchResetNRandom_8192_32/net_last.pth' --resume-trans '/home/epinyoan/git/MaskText2Motion/T2M-BD/output/t2m/2023-10-12-10-11-15_HML3D_45_crsAtt1lyr_40breset_WRONG_THIS_20BRESET/net_last.pth' --text 'the person crouches and walks forward.' --length 156

    mmm = MMM(args)
    pred_pose = mmm([args.text], torch.tensor([args.length]), rand_pos=False)
    num_joints = 22
    
    std = np.load('./exit/t2m-std.npy')
    mean = np.load('./exit/t2m-mean.npy')
    
    norm_pose = pred_pose[0].detach().cpu().numpy() * std + mean
    norm_pose = torch.tensor(norm_pose)
    
    trimmed_pose = norm_pose[:args.length, :].unsqueeze(0).float()
    print(trimmed_pose.shape)
    
    converted_pose = recover_from_ric(trimmed_pose[0].detach().cpu(), num_joints).unsqueeze(0).numpy()
    print(converted_pose.shape)

    filename = '_'.join(args.text.split(' '))+'_'+str(args.length)
    np.save('./output/'+filename+'.npy', converted_pose)
    print('File saved successfully')

    file_name = '_'.join(args.text.split(' '))+'_'+str(args.length)
    visualize_2motions(pred_pose[0].detach().cpu().numpy(), std, mean, 't2m', args.length, save_path='./output/'+file_name+'.html')