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# SPDX-License-Identifier: Apache-2.0
from pathlib import Path
from typing import Optional
import numpy as np
import torch
import torch as t
from torch import nn
from vector_quantize_pytorch import FSQ
from ardy.model.loading import load_checkpoint_state_dict
from ardy.motion_rep import MotionRepBase
from ardy.motion_rep.stats import Stats
from .transformer import (
DoubleCondDecoderTransformer,
EncoderTransformer,
)
def round_ste(z):
"""Round with straight through gradients."""
zhat = z.round()
return z + (zhat - z).detach()
class FSQVAETransformer(nn.Module):
ALLOWED_FEATURE_MODE = ["pose", "body", "root"]
def __init__(
self,
motion_rep: MotionRepBase,
feature_mode: list,
# fsq config
num_fsq_levels: int = 5,
fsq_level_list: int | list[int] = [16, 16, 16, 16, 16],
# encoding config
encode_with_normalization: bool = True,
encode_with_quantization: bool = True,
# network config
latent_embedding_dim: int = 128, # number of the output embedding dimension
num_frames_per_token: int = 4,
latent_dim: int = 512,
ff_size: int = 1024,
num_layers: int = 8,
num_heads: int = 4,
activation: str = "gelu",
dropout: float = 0.1,
pe_dropout: float = 0.1,
norm_first: bool = False,
causal_encoder: bool = False,
causal_decoder: bool = False,
ckpt_path: Optional[str] = None,
**kwargs,
):
super().__init__()
self._motion_rep = motion_rep
self._num_frames_per_token = num_frames_per_token
self._latent_embedding_dim = num_fsq_levels
self.encode_with_normalization = encode_with_normalization
self.encode_with_quantization = encode_with_quantization
assert len(feature_mode) == 4 and np.all([f in self.ALLOWED_FEATURE_MODE for f in feature_mode])
(
self.encoder_input_feature_mode, # body
self.decoder_output_feature_mode, # pose
self.decoder_target_cond_feature_mode, # body
self.decoder_external_cond_feature_mode, # root
) = feature_mode
# always use the local root
dim_dict = {
"pose": motion_rep.local_motion_rep_dim, # local root + body
"body": motion_rep.body_dim,
"root": motion_rep.local_root_dim,
}
encoder_state_dim = dim_dict[self.encoder_input_feature_mode]
decoder_state_dim = dim_dict[self.decoder_output_feature_mode]
decoder_target_cond_dim = dim_dict[self.decoder_target_cond_feature_mode]
decoder_external_cond_dim = dim_dict[self.decoder_external_cond_feature_mode]
latent_embedding_dim = num_fsq_levels
self.encoder = EncoderTransformer(
encoder_state_dim,
latent_embedding_dim,
num_frames_per_token,
latent_dim,
num_heads,
ff_size,
dropout,
activation,
norm_first,
num_layers,
pe_dropout,
causal_encoder,
)
self.decoder = DoubleCondDecoderTransformer(
latent_embedding_dim,
decoder_state_dim,
num_frames_per_token,
latent_dim,
num_heads,
ff_size,
dropout,
activation,
norm_first,
num_layers,
pe_dropout,
causal_decoder,
target_cond_dim=decoder_target_cond_dim,
external_cond_dim=decoder_external_cond_dim,
)
if isinstance(fsq_level_list, int):
fsq_level_list = [fsq_level_list] * num_fsq_levels
assert len(fsq_level_list) == num_fsq_levels, (
f"fsq_level_list must have num_fsq_levels={num_fsq_levels} entries, got {len(fsq_level_list)}"
)
self.quantizer = FSQ(levels=fsq_level_list, return_indices=False)
if ckpt_path:
self.load_ckpt(ckpt_path)
self.eval_and_freeze()
# make forward by default used for detokenization
self.forward = self.detokenize
def eval_and_freeze(self):
self.eval()
# freeze the autoencoder
for param in self.parameters():
param.requires_grad = False
def load_ckpt(self, ckpt_path: str) -> None:
"""Load checkpoint from path; state dict keys are stripped of 'denoiser.backbone.' prefix.
Then load the stats.
"""
# Load ckpt
state_dict = load_checkpoint_state_dict(ckpt_path)
state_dict = {
key.removeprefix("pose_net."): val for key, val in state_dict.items() if key.startswith("pose_net")
}
self.load_state_dict(state_dict)
ckpt_path = str(ckpt_path)
autoencoder_stats_dir = Path(ckpt_path).parent / "stats"
self.post_quantization_stats = Stats(folder=autoencoder_stats_dir / "post_quantization", load=True)
self.pre_quantization_stats = Stats(folder=autoencoder_stats_dir / "pre_quantization", load=True)
@property
def motion_rep(self):
return self._motion_rep
@property
def num_frames_per_token(self):
return self._num_frames_per_token
def extract_feature(
self,
x: t.Tensor,
feature: str = "",
lengths: Optional[torch.Tensor] = None,
):
"""@brief: extract the full pose / root or body from the full local features"""
assert feature in self.ALLOWED_FEATURE_MODE
if feature == "body":
return self.motion_rep.extract_body(x)
global_root = self.motion_rep.extract_root(x)
local_root = self.motion_rep.global_root_to_local_root(global_root, normalized=True, lengths=lengths)
if feature == "root":
return local_root
# pose
assert feature == "pose"
body = self.motion_rep.extract_body(x)
local_features = torch.cat([local_root, body], dim=-1)
return local_features
def tokenize(
self,
x: t.Tensor,
motion_pad_mask: t.BoolTensor,
):
"""@brief: get the embeddings of the input motion
@param x: local poses [batch_size, numFrames, feat_dim]
@param motion_pad_mask: [batch_size, numFrames], 1 means valid frames
@return embeddings: [batch_size, num_tokens, feat_dim]
"""
# create lengths
x_in = self.extract_feature(x, self.encoder_input_feature_mode, lengths=motion_pad_mask.sum(1))
x_encoder = self.encoder(x_in, motion_pad_mask)
if self.encode_with_quantization:
x_quantized, indices = self.quantizer(x_encoder)
token_embeddings = x_quantized
else:
token_embeddings = x_encoder
if self.encode_with_normalization:
# mean = self.stats['mean_after_quantization'] if encode_with_quantization else self.stats['mean_before_quantization']
# std = self.stats['std_after_quantization'] if encode_with_quantization else self.stats['std_before_quantization']
# token_embeddings = (token_embeddings - mean) / std
stats = self.post_quantization_stats if self.encode_with_quantization else self.pre_quantization_stats
token_embeddings = stats.normalize(token_embeddings)
return token_embeddings
def detokenize(
self,
token_embeddings: t.Tensor,
external_cond: t.Tensor = None,
motion_pad_mask: t.BoolTensor = None,
):
"""@brief: get the original motion from the token embeddings
@param token_embeddings: [batch_size, num_tokens, feat_dim]
@param motion_pad_mask: [batch_size, numFrames], 1 means valid frames
@return x: [batch_size, numFrames, feat_dim]
"""
if self.encode_with_normalization:
# mean = self.stats['mean_after_quantization'] if encode_with_quantization else self.stats['mean_before_quantization']
# std = self.stats['std_after_quantization'] if encode_with_quantization else self.stats['std_before_quantization']
# token_embeddings = token_embeddings * std + mean
stats = self.post_quantization_stats if self.encode_with_quantization else self.pre_quantization_stats
token_embeddings = stats.unnormalize(token_embeddings)
if not self.encode_with_quantization: # quantization not applied at encoding
token_embeddings, indices = self.quantizer(token_embeddings)
else: # redo the rounding operation do ensure quantization, but we can not directly call quantizer since it include additional operation apart from rounding
half_width = self.quantizer._levels // 2
token_embeddings = round_ste(token_embeddings.clamp(-1, 1) * half_width) / half_width
x_decoder = self.decoder(
token_embeddings,
external_cond=external_cond,
motion_pad_mask=motion_pad_mask,
)
# produce a dict instead of the raw output
output = {}
if self.decoder_output_feature_mode == "pose":
output["root"] = x_decoder[..., : self.motion_rep.local_root_dim]
output["body"] = x_decoder[..., self.motion_rep.local_root_dim :]
elif self.decoder_output_feature_mode == "body":
output["body"] = x_decoder
elif self.decoder_output_feature_mode == "root":
output["root"] = x_decoder
else:
raise NotImplementedError
return output
def requantize(
self,
token_embeddings: t.Tensor,
):
"""@brief: requantize the token embeddings to the discrete values
@param token_embeddings: [batch_size, num_tokens, feat_dim]
@return requantized_token_embeddings: [batch_size, num_tokens, feat_dim]
"""
assert self.encode_with_quantization, "Only support encode_with_quantization=True"
if self.encode_with_normalization: # unnormalize the token embeddings
stats = self.post_quantization_stats
token_embeddings = stats.unnormalize(token_embeddings)
half_width = self.quantizer._levels // 2
token_embeddings = round_ste(token_embeddings.clamp(-1, 1) * half_width) / half_width
if self.encode_with_normalization: # normalize the token embeddings
stats = self.post_quantization_stats
token_embeddings = stats.normalize(token_embeddings)
return token_embeddings
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