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import torch
from torch import nn, Tensor
from transformers import AutoModelForCausalLM, AutoConfig, AutoModel
from MeshAnything.miche.encode import load_model
from MeshAnything.models.shape_opt import ShapeOPTConfig
from einops.layers.torch import Rearrange
from einops import rearrange, repeat, reduce, pack, unpack
import torch.nn.functional as F
class NoiseResistantDecoder(nn.Module):
def __init__(self, args):
super().__init__()
self.args = args
self.pad_id = -1
self.num_quantizers = 3
self.discrete_num = 128
self.codebook_size = args.codebook_size
self.codebook_dim = args.codebook_dim
config = AutoConfig.from_pretrained("bert-base-uncased")
config.num_hidden_layers = 6
self.decoder= AutoModel.from_config(config=config).to_bettertransformer().encoder
self.n_embd = self.decoder.config.hidden_size
self.pos_embedding = nn.Embedding(18000, self.n_embd)
self.layernorm = nn.LayerNorm(self.n_embd)
self.point_layernorm = nn.LayerNorm(self.n_embd)
self.cond_length = 257
self.cond_dim = 768
self.point_pe = nn.Embedding(self.cond_length, self.n_embd)
self.cond_proj = nn.Linear(self.cond_dim, self.n_embd)
self.cond_head_proj = nn.Linear(self.cond_dim, self.n_embd)
self.project_down_codebook = nn.Linear(self.codebook_dim * 3, self.n_embd)
self.to_coor_logits = nn.Sequential(
nn.Linear(self.n_embd, self.discrete_num * 9),
Rearrange('... (v c) -> ... v c', v = 9)
)
def process_point_feature(self, encode_feature):
point_feature = torch.zeros(encode_feature.shape[0], self.cond_length, self.n_embd, device=self.cond_head_proj.weight.device, dtype=self.cond_head_proj.weight.dtype)
point_feature[:, 0] = self.cond_head_proj(encode_feature[:, 0])
point_feature[:, 1:] = self.cond_proj(encode_feature[:, 1:])
point_feature = self.point_layernorm(point_feature + self.point_pe.weight[None, :point_feature.shape[1]])
return point_feature
def forward(self, input_ids, input_embeds, point_feature = None):
input_ids = input_ids.reshape(input_ids.shape[0], -1)
point_feature = self.process_point_feature(point_feature)
face_embeds = rearrange(input_embeds, 'b (nf nv) d -> b nf (nv d)', nv = 3)
face_embeds = self.project_down_codebook(face_embeds)
face_mask = reduce(input_ids != self.pad_id, 'b (nf nv q) -> b nf', 'all', nv = 3, q = self.num_quantizers)
face_embeds[~face_mask] = 0
face_embeds = self.layernorm(face_embeds + self.pos_embedding.weight[None, :face_embeds.shape[1]])
outputs = self.decoder(
hidden_states=torch.concatenate([point_feature, face_embeds], dim=1),
)
decoded = outputs.last_hidden_state[:, self.cond_length:] # batch x nfaces x dim
decoded = decoded.masked_fill(~face_mask.unsqueeze(-1), 0.)
# batch x nfaces x 9 -> batch x nfaces x 3 x 3
pred_face_logits = self.to_coor_logits(decoded) # batch x nfaces x 9 x ndiscrete
pred_face_coords = rearrange(pred_face_logits.argmax(dim = -1), '... (v c) -> ... v c', v = 3)
continuous_coors = undiscretize(
pred_face_coords,
num_discrete = self.discrete_num,
low = -0.5,
high = 0.5
)
continuous_coors = continuous_coors.masked_fill(~rearrange(face_mask, 'b nf -> b nf 1 1'), float('nan'))
return continuous_coors
class MeshAnything(nn.Module):
def __init__(self, args):
super().__init__()
self.args = args
self.point_encoder = load_model(ckpt_path=None)
self.tokenizer = NoiseResistantDecoder(args)
self.num_quantizers = 3
self.face_per_token = self.num_quantizers * 3
self.cond_length = 257
self.cond_dim = 768
self.max_length = args.n_max_triangles * self.face_per_token + 2 + self.cond_length
self.config = ShapeOPTConfig.from_pretrained(
args.llm,
n_positions=18259,
max_position_embeddings=18259,
vocab_size=self.tokenizer.codebook_size + 3,
_attn_implementation="flash_attention_2"
)
self.bos_token_id = 0
self.eos_token_id = 1
self.pad_token_id = 2
self.config.bos_token_id = self.bos_token_id
self.config.eos_token_id = self.eos_token_id
self.config.pad_token_id = self.pad_token_id
self.config.quantize_codebook_dim = self.tokenizer.codebook_dim
self.config.face_per_token = self.face_per_token
self.config._attn_implementation="flash_attention_2"
self.config.cond_length = self.cond_length
if self.config.word_embed_proj_dim != self.config.hidden_size:
self.config.word_embed_proj_dim = self.config.hidden_size
self.transformer = AutoModelForCausalLM.from_config(
config=self.config, use_flash_attention_2 = True
)
self.transformer.to_bettertransformer()
self.transformer.model.decoder.quantize_codebooks = nn.Parameter(torch.zeros(1, self.tokenizer.codebook_size, self.tokenizer.codebook_dim))
self.cond_head_proj = nn.Linear(self.cond_dim, self.config.word_embed_proj_dim)
self.cond_proj = nn.Linear(self.cond_dim * 2, self.config.word_embed_proj_dim)
self.eval()
def process_point_feature(self, point_feature):
encode_feature = torch.zeros(point_feature.shape[0], self.cond_length, self.config.word_embed_proj_dim,
device=self.cond_head_proj.weight.device, dtype=self.cond_head_proj.weight.dtype)
encode_feature[:, 0] = self.cond_head_proj(point_feature[:, 0])
shape_latents = self.point_encoder.to_shape_latents(point_feature[:, 1:])
encode_feature[:, 1:] = self.cond_proj(torch.cat([point_feature[:, 1:], shape_latents], dim=-1))
return encode_feature
@torch.no_grad()
def forward(self, pc_normal, sampling=False) -> dict:
batch_size = pc_normal.shape[0]
point_feature = self.point_encoder.encode_latents(pc_normal)
processed_point_feature = self.process_point_feature(point_feature)
generate_length = self.max_length - self.cond_length
net_device = next(self.parameters()).device
outputs = torch.ones(batch_size, generate_length).long().to(net_device) * self.eos_token_id
if not sampling:
results = self.transformer.generate(
inputs_embeds=processed_point_feature,
max_new_tokens=generate_length, # all faces plus two
num_beams=1,
bos_token_id=self.bos_token_id,
eos_token_id=self.eos_token_id,
pad_token_id=self.pad_token_id,
)
else:
results = self.transformer.generate(
inputs_embeds = processed_point_feature,
max_new_tokens=generate_length, # all faces plus two
do_sample=True,
top_k=50,
top_p=0.95,
bos_token_id = self.bos_token_id,
eos_token_id = self.eos_token_id,
pad_token_id = self.pad_token_id,
)
assert results.shape[1] <= generate_length # B x ID bos is not included since it's predicted
outputs[:, :results.shape[1]] = results
# batch x ntokens ====> batch x ntokens x D
outputs = outputs[:, 1: -1]
outputs[outputs == self.bos_token_id] = self.tokenizer.pad_id
outputs[outputs == self.eos_token_id] = self.tokenizer.pad_id
outputs[outputs == self.pad_token_id] = self.tokenizer.pad_id
outputs[outputs != self.tokenizer.pad_id] -= 3
code_embed = self.get_codes(outputs)
decoder_output = self.tokenizer(outputs, code_embed, point_feature=point_feature)
return decoder_output
def get_codes(self, indices):
indices = indices.reshape(indices.shape[0], -1)
indices = rearrange(indices, 'b (n q) -> b n q', q=self.num_quantizers)
batch, quantize_dim = indices.shape[0], indices.shape[-1]
# may also receive indices in the shape of 'b h w q' (accept_image_fmap)
indices, ps = pack([indices], 'b * q')
# because of quantize dropout, one can pass in indices that are coarse
# and the network should be able to reconstruct
if quantize_dim < self.num_quantizers:
indices = F.pad(indices, (0, self.num_quantizers - quantize_dim), value = -1)
# take care of quantizer dropout
mask = indices == -1.
indices = indices.masked_fill(mask, 0) # have it fetch a dummy code to be masked out later
# dummy implementation for shared codebook
all_codes = self.transformer.model.decoder.quantize_codebooks[0][indices]
all_codes = all_codes.permute(2, 0, 1, 3)
# mask out any codes that were dropout-ed
all_codes = all_codes.masked_fill(rearrange(mask, 'b n q -> q b n 1'), 0.)
# if (accept_image_fmap = True) then return shape (quantize, batch, height, width, dimension)
codes, = unpack(all_codes, ps, 'q b * d')
codes_summed = reduce(codes, 'q ... -> ...', 'sum')
return codes_summed
def undiscretize(
t,
low,
high,
num_discrete
) -> Tensor:
t = t.float()
t /= num_discrete
return t * (high - low) + low
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