import numpy as np import torch import torch.nn as nn import torch.nn.functional as F import clip from model.rotation2xyz import Rotation2xyz from model.BERT.BERT_encoder import load_bert from utils.misc import WeightedSum class MDM(nn.Module): def __init__(self, modeltype, njoints, nfeats, num_actions, translation, pose_rep, glob, glob_rot, latent_dim=256, ff_size=1024, num_layers=8, num_heads=4, dropout=0.1, ablation=None, activation="gelu", legacy=False, data_rep='rot6d', dataset='amass', clip_dim=512, arch='trans_enc', emb_trans_dec=False, clip_version=None, **kargs): super().__init__() self.legacy = legacy self.modeltype = modeltype self.njoints = njoints self.nfeats = nfeats self.num_actions = num_actions self.data_rep = data_rep self.dataset = dataset self.pose_rep = pose_rep self.glob = glob self.glob_rot = glob_rot self.translation = translation self.latent_dim = latent_dim self.ff_size = ff_size self.num_layers = num_layers self.num_heads = num_heads self.dropout = dropout self.ablation = ablation self.activation = activation self.clip_dim = clip_dim self.action_emb = kargs.get('action_emb', None) self.input_feats = self.njoints * self.nfeats self.normalize_output = kargs.get('normalize_encoder_output', False) self.cond_mode = kargs.get('cond_mode', 'no_cond') self.cond_mask_prob = kargs.get('cond_mask_prob', 0.) self.mask_frames = kargs.get('mask_frames', False) self.arch = arch self.gru_emb_dim = self.latent_dim if self.arch == 'gru' else 0 self.input_process = InputProcess(self.data_rep, self.input_feats+self.gru_emb_dim, self.latent_dim) self.emb_policy = kargs.get('emb_policy', 'add') self.sequence_pos_encoder = PositionalEncoding(self.latent_dim, self.dropout, max_len=kargs.get('pos_embed_max_len', 5000)) self.emb_trans_dec = emb_trans_dec self.pred_len = kargs.get('pred_len', 0) self.context_len = kargs.get('context_len', 0) self.total_len = self.pred_len + self.context_len self.is_prefix_comp = self.total_len > 0 self.all_goal_joint_names = kargs.get('all_goal_joint_names', []) self.multi_target_cond = kargs.get('multi_target_cond', False) self.multi_encoder_type = kargs.get('multi_encoder_type', 'multi') self.target_enc_layers = kargs.get('target_enc_layers', 1) if self.multi_target_cond: if self.multi_encoder_type == 'multi': self.embed_target_cond = EmbedTargetLocMulti(self.all_goal_joint_names, self.latent_dim) elif self.multi_encoder_type == 'single': self.embed_target_cond = EmbedTargetLocSingle(self.all_goal_joint_names, self.latent_dim, self.target_enc_layers) elif self.multi_encoder_type == 'split': self.embed_target_cond = EmbedTargetLocSplit(self.all_goal_joint_names, self.latent_dim, self.target_enc_layers) if self.arch == 'trans_enc': print("TRANS_ENC init") seqTransEncoderLayer = nn.TransformerEncoderLayer(d_model=self.latent_dim, nhead=self.num_heads, dim_feedforward=self.ff_size, dropout=self.dropout, activation=self.activation) self.seqTransEncoder = nn.TransformerEncoder(seqTransEncoderLayer, num_layers=self.num_layers) elif self.arch == 'trans_dec': print("TRANS_DEC init") seqTransDecoderLayer = nn.TransformerDecoderLayer(d_model=self.latent_dim, nhead=self.num_heads, dim_feedforward=self.ff_size, dropout=self.dropout, activation=activation) self.seqTransDecoder = nn.TransformerDecoder(seqTransDecoderLayer, num_layers=self.num_layers) elif self.arch == 'gru': print("GRU init") self.gru = nn.GRU(self.latent_dim, self.latent_dim, num_layers=self.num_layers, batch_first=True) else: raise ValueError('Please choose correct architecture [trans_enc, trans_dec, gru]') self.embed_timestep = TimestepEmbedder(self.latent_dim, self.sequence_pos_encoder) if self.cond_mode != 'no_cond': if 'text' in self.cond_mode: # We support CLIP encoder and DistilBERT print('EMBED TEXT') self.text_encoder_type = kargs.get('text_encoder_type', 'clip') if self.text_encoder_type == "clip": print('Loading CLIP...') self.clip_version = clip_version self.clip_model = self.load_and_freeze_clip(clip_version) self.encode_text = self.clip_encode_text elif self.text_encoder_type == 'bert': assert self.arch == 'trans_dec' # assert self.emb_trans_dec == False # passing just the time embed so it's fine print("Loading BERT...") # bert_model_path = 'model/BERT/distilbert-base-uncased' bert_model_path = 'distilbert/distilbert-base-uncased' self.clip_model = load_bert(bert_model_path) # Sorry for that, the naming is for backward compatibility self.encode_text = self.bert_encode_text self.clip_dim = 768 else: raise ValueError('We only support [CLIP, BERT] text encoders') self.embed_text = nn.Linear(self.clip_dim, self.latent_dim) if 'action' in self.cond_mode: self.embed_action = EmbedAction(self.num_actions, self.latent_dim) print('EMBED ACTION') self.output_process = OutputProcess(self.data_rep, self.input_feats, self.latent_dim, self.njoints, self.nfeats) self.rot2xyz = Rotation2xyz(device='cpu', dataset=self.dataset) def parameters_wo_clip(self): return [p for name, p in self.named_parameters() if not name.startswith('clip_model.')] def load_and_freeze_clip(self, clip_version): clip_model, clip_preprocess = clip.load(clip_version, 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 # Freeze CLIP weights clip_model.eval() for p in clip_model.parameters(): p.requires_grad = False return clip_model def mask_cond(self, cond, force_mask=False): bs = cond.shape[-2] if force_mask: return torch.zeros_like(cond) elif self.training and self.cond_mask_prob > 0.: mask = torch.bernoulli(torch.ones(bs, device=cond.device) * self.cond_mask_prob).view(1, bs, 1) # 1-> use null_cond, 0-> use real cond return cond * (1. - mask) else: return cond def clip_encode_text(self, raw_text): # raw_text - list (batch_size length) of strings with input text prompts device = next(self.parameters()).device max_text_len = 20 if self.dataset in ['humanml', 'kit'] else None # Specific hardcoding for humanml dataset if max_text_len is not None: default_context_length = 77 context_length = max_text_len + 2 # start_token + 20 + end_token assert context_length < default_context_length texts = clip.tokenize(raw_text, context_length=context_length, truncate=True).to(device) # [bs, context_length] # if n_tokens > context_length -> will truncate # print('texts', texts.shape) zero_pad = torch.zeros([texts.shape[0], default_context_length-context_length], dtype=texts.dtype, device=texts.device) texts = torch.cat([texts, zero_pad], dim=1) # print('texts after pad', texts.shape, texts) else: texts = clip.tokenize(raw_text, truncate=True).to(device) # [bs, context_length] # if n_tokens > 77 -> will truncate return self.clip_model.encode_text(texts).float().unsqueeze(0) def bert_encode_text(self, raw_text): # enc_text = self.clip_model(raw_text) # enc_text = enc_text.permute(1, 0, 2) # return enc_text enc_text, mask = self.clip_model(raw_text) # self.clip_model.get_last_hidden_state(raw_text, return_mask=True) # mask: False means no token there enc_text = enc_text.permute(1, 0, 2) mask = ~mask # mask: True means no token there, we invert since the meaning of mask for transformer is inverted https://pytorch.org/docs/stable/generated/torch.nn.MultiheadAttention.html return enc_text, mask def forward(self, x, timesteps, y=None): """ x: [batch_size, njoints, nfeats, max_frames], denoted x_t in the paper timesteps: [batch_size] (int) """ bs, njoints, nfeats, nframes = x.shape time_emb = self.embed_timestep(timesteps) # [1, bs, d] if 'target_cond' in y.keys(): # NOTE: We don't use CFG for joints - but we do wat to support uncond sampling for generation and eval! time_emb += self.mask_cond(self.embed_target_cond(y['target_cond'], y['target_joint_names'], y['is_heading'])[None], force_mask=y.get('target_uncond', False)) # For uncond support and CFG # time_emb += self.embed_target_cond(y['target_cond'], y['target_joint_names'], y['is_heading'])[None] # Build input for prefix completion if self.is_prefix_comp: x = torch.cat([y['prefix'], x], dim=-1) y['mask'] = torch.cat([torch.ones([bs, 1, 1, self.context_len], dtype=y['mask'].dtype, device=y['mask'].device), y['mask']], dim=-1) force_mask = y.get('uncond', False) if 'text' in self.cond_mode: if 'text_embed' in y.keys(): # caching option enc_text = y['text_embed'] else: enc_text = self.encode_text(y['text']) if type(enc_text) == tuple: enc_text, text_mask = enc_text if text_mask.shape[0] == 1 and bs > 1: # casting mask for the single-prompt-for-all case text_mask = torch.repeat_interleave(text_mask, bs, dim=0) text_emb = self.embed_text(self.mask_cond(enc_text, force_mask=force_mask)) # casting mask for the single-prompt-for-all case if self.emb_policy == 'add': emb = text_emb + time_emb else: emb = torch.cat([time_emb, text_emb], dim=0) text_mask = torch.cat([torch.zeros_like(text_mask[:, 0:1]), text_mask], dim=1) if 'action' in self.cond_mode: action_emb = self.embed_action(y['action']) emb = time_emb + self.mask_cond(action_emb, force_mask=force_mask) if self.cond_mode == 'no_cond': # unconstrained emb = time_emb if self.arch == 'gru': x_reshaped = x.reshape(bs, njoints*nfeats, 1, nframes) emb_gru = emb.repeat(nframes, 1, 1) #[#frames, bs, d] emb_gru = emb_gru.permute(1, 2, 0) #[bs, d, #frames] emb_gru = emb_gru.reshape(bs, self.latent_dim, 1, nframes) #[bs, d, 1, #frames] x = torch.cat((x_reshaped, emb_gru), axis=1) #[bs, d+joints*feat, 1, #frames] x = self.input_process(x) # TODO - move to collate frames_mask = None is_valid_mask = y['mask'].shape[-1] > 1 # Don't use mask with the generate script if self.mask_frames and is_valid_mask: frames_mask = torch.logical_not(y['mask'][..., :x.shape[0]].squeeze(1).squeeze(1)).to(device=x.device) if self.emb_trans_dec or self.arch == 'trans_enc': step_mask = torch.zeros((bs, 1), dtype=torch.bool, device=x.device) frames_mask = torch.cat([step_mask, frames_mask], dim=1) if self.arch == 'trans_enc': # adding the timestep embed xseq = torch.cat((emb, x), axis=0) # [seqlen+1, bs, d] xseq = self.sequence_pos_encoder(xseq) # [seqlen+1, bs, d] output = self.seqTransEncoder(xseq, src_key_padding_mask=frames_mask)[1:] # , src_key_padding_mask=~maskseq) # [seqlen, bs, d] elif self.arch == 'trans_dec': if self.emb_trans_dec: xseq = torch.cat((time_emb, x), axis=0) else: xseq = x xseq = self.sequence_pos_encoder(xseq) # [seqlen+1, bs, d] if self.text_encoder_type == 'clip': output = self.seqTransDecoder(tgt=xseq, memory=emb, tgt_key_padding_mask=frames_mask) elif self.text_encoder_type == 'bert': output = self.seqTransDecoder(tgt=xseq, memory=emb, memory_key_padding_mask=text_mask, tgt_key_padding_mask=frames_mask) # Rotem's bug fix else: raise ValueError() if self.emb_trans_dec: output = output[1:] # [seqlen, bs, d] elif self.arch == 'gru': xseq = x xseq = self.sequence_pos_encoder(xseq) # [seqlen, bs, d] output, _ = self.gru(xseq) # Extract completed suffix if self.is_prefix_comp: output = output[self.context_len:] y['mask'] = y['mask'][..., self.context_len:] output = self.output_process(output) # [bs, njoints, nfeats, nframes] return output def _apply(self, fn): super()._apply(fn) self.rot2xyz.smpl_model._apply(fn) def train(self, *args, **kwargs): super().train(*args, **kwargs) self.rot2xyz.smpl_model.train(*args, **kwargs) class PositionalEncoding(nn.Module): def __init__(self, d_model, dropout=0.1, max_len=5000): super(PositionalEncoding, self).__init__() self.dropout = nn.Dropout(p=dropout) pe = torch.zeros(max_len, d_model) position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1) div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-np.log(10000.0) / d_model)) pe[:, 0::2] = torch.sin(position * div_term) pe[:, 1::2] = torch.cos(position * div_term) pe = pe.unsqueeze(0).transpose(0, 1) self.register_buffer('pe', pe) def forward(self, x): # not used in the final model x = x + self.pe[:x.shape[0], :] return self.dropout(x) class TimestepEmbedder(nn.Module): def __init__(self, latent_dim, sequence_pos_encoder): super().__init__() self.latent_dim = latent_dim self.sequence_pos_encoder = sequence_pos_encoder time_embed_dim = self.latent_dim self.time_embed = nn.Sequential( nn.Linear(self.latent_dim, time_embed_dim), nn.SiLU(), nn.Linear(time_embed_dim, time_embed_dim), ) def forward(self, timesteps): return self.time_embed(self.sequence_pos_encoder.pe[timesteps]).permute(1, 0, 2) class InputProcess(nn.Module): def __init__(self, data_rep, input_feats, latent_dim): super().__init__() self.data_rep = data_rep self.input_feats = input_feats self.latent_dim = latent_dim self.poseEmbedding = nn.Linear(self.input_feats, self.latent_dim) if self.data_rep == 'rot_vel': self.velEmbedding = nn.Linear(self.input_feats, self.latent_dim) def forward(self, x): bs, njoints, nfeats, nframes = x.shape x = x.permute((3, 0, 1, 2)).reshape(nframes, bs, njoints*nfeats) if self.data_rep in ['rot6d', 'xyz', 'hml_vec']: x = self.poseEmbedding(x) # [seqlen, bs, d] return x elif self.data_rep == 'rot_vel': first_pose = x[[0]] # [1, bs, 150] first_pose = self.poseEmbedding(first_pose) # [1, bs, d] vel = x[1:] # [seqlen-1, bs, 150] vel = self.velEmbedding(vel) # [seqlen-1, bs, d] return torch.cat((first_pose, vel), axis=0) # [seqlen, bs, d] else: raise ValueError class OutputProcess(nn.Module): def __init__(self, data_rep, input_feats, latent_dim, njoints, nfeats): super().__init__() self.data_rep = data_rep self.input_feats = input_feats self.latent_dim = latent_dim self.njoints = njoints self.nfeats = nfeats self.poseFinal = nn.Linear(self.latent_dim, self.input_feats) if self.data_rep == 'rot_vel': self.velFinal = nn.Linear(self.latent_dim, self.input_feats) def forward(self, output): nframes, bs, d = output.shape if self.data_rep in ['rot6d', 'xyz', 'hml_vec']: output = self.poseFinal(output) # [seqlen, bs, 150] elif self.data_rep == 'rot_vel': first_pose = output[[0]] # [1, bs, d] first_pose = self.poseFinal(first_pose) # [1, bs, 150] vel = output[1:] # [seqlen-1, bs, d] vel = self.velFinal(vel) # [seqlen-1, bs, 150] output = torch.cat((first_pose, vel), axis=0) # [seqlen, bs, 150] else: raise ValueError output = output.reshape(nframes, bs, self.njoints, self.nfeats) output = output.permute(1, 2, 3, 0) # [bs, njoints, nfeats, nframes] return output class EmbedAction(nn.Module): def __init__(self, num_actions, latent_dim): super().__init__() self.action_embedding = nn.Parameter(torch.randn(num_actions, latent_dim)) def forward(self, input): idx = input[:, 0].to(torch.long) # an index array must be long output = self.action_embedding[idx] return output class EmbedTargetLocSingle(nn.Module): def __init__(self, all_goal_joint_names, latent_dim, num_layers=1): super().__init__() self.extended_goal_joint_names = all_goal_joint_names + ['traj', 'heading'] self.target_cond_dim = len(self.extended_goal_joint_names) * 4 # 4 => (x,y,z,is_valid) self.latent_dim = latent_dim _layers = [nn.Linear(self.target_cond_dim, self.latent_dim)] for _ in range(num_layers): _layers += [nn.SiLU(), nn.Linear(self.latent_dim, self.latent_dim)] self.mlp = nn.Sequential(*_layers) def forward(self, input, target_joint_names, target_heading): # TODO - generate validity from outside the model validity = torch.zeros_like(input)[..., :1] for sample_idx, sample_joint_names in enumerate(target_joint_names): sample_joint_names_w_heading = np.append(sample_joint_names, 'heading') if target_heading[sample_idx] else sample_joint_names for j in sample_joint_names_w_heading: validity[sample_idx, self.extended_goal_joint_names.index(j)] = 1. mlp_input = torch.cat([input, validity], dim=-1).view(input.shape[0], -1) return self.mlp(mlp_input) class EmbedTargetLocSplit(nn.Module): def __init__(self, all_goal_joint_names, latent_dim, num_layers=1): super().__init__() self.extended_goal_joint_names = all_goal_joint_names + ['traj', 'heading'] self.target_cond_dim = 4 self.latent_dim = latent_dim self.splited_dim = self.latent_dim // len(self.extended_goal_joint_names) assert self.latent_dim % len(self.extended_goal_joint_names) == 0 self.mini_mlps = nn.ModuleList() for _ in self.extended_goal_joint_names: _layers = [nn.Linear(self.target_cond_dim, self.splited_dim)] for _ in range(num_layers): _layers += [nn.SiLU(), nn.Linear(self.splited_dim, self.splited_dim)] self.mini_mlps.append(nn.Sequential(*_layers)) def forward(self, input, target_joint_names, target_heading): # TODO - generate validity from outside the model validity = torch.zeros_like(input)[..., :1] for sample_idx, sample_joint_names in enumerate(target_joint_names): sample_joint_names_w_heading = np.append(sample_joint_names, 'heading') if target_heading[sample_idx] else sample_joint_names for j in sample_joint_names_w_heading: validity[sample_idx, self.extended_goal_joint_names.index(j)] = 1. mlp_input = torch.cat([input, validity], dim=-1) mlp_splits = [self.mini_mlps[i](mlp_input[:, i]) for i in range(mlp_input.shape[1])] return torch.cat(mlp_splits, dim=-1) class EmbedTargetLocMulti(nn.Module): def __init__(self, all_goal_joint_names, latent_dim): super().__init__() # todo: use a tensor of weight per joint, and another one for biases, then apply a selection in one go like we to for actions self.extended_goal_joint_names = all_goal_joint_names + ['traj', 'heading'] self.extended_goal_joint_idx = {joint_name: idx for idx, joint_name in enumerate(self.extended_goal_joint_names)} self.n_extended_goal_joints = len(self.extended_goal_joint_names) self.target_loc_emb = nn.ParameterDict({joint_name: nn.Sequential( nn.Linear(3, latent_dim), nn.SiLU(), nn.Linear(latent_dim, latent_dim)) for joint_name in self.extended_goal_joint_names}) # todo: check if 3 works for heading and traj # nn.Linear(3, latent_dim) for joint_name in self.extended_goal_joint_names}) # todo: check if 3 works for heading and traj self.target_all_loc_emb = WeightedSum(self.n_extended_goal_joints) # nn.Linear(self.n_extended_goal_joints, latent_dim) self.latent_dim = latent_dim def forward(self, input, target_joint_names, target_heading): output = torch.zeros((input.shape[0], self.latent_dim), dtype=input.dtype, device=input.device) # Iterate over the batch and apply the appropriate filter for each joint for sample_idx, sample_joint_names in enumerate(target_joint_names): sample_joint_names_w_heading = np.append(sample_joint_names, 'heading') if target_heading[sample_idx] else sample_joint_names output_one_sample = torch.zeros((self.n_extended_goal_joints, self.latent_dim), dtype=input.dtype, device=input.device) for joint_name in sample_joint_names_w_heading: layer = self.target_loc_emb[joint_name] output_one_sample[self.extended_goal_joint_idx[joint_name]] = layer(input[sample_idx, self.extended_goal_joint_idx[joint_name]]) output[sample_idx] = self.target_all_loc_emb(output_one_sample) # print(torch.where(output_one_sample.sum(axis=1)!=0)[0].cpu().numpy()) return output