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import random
import torch
from torch import nn
import numpy as np
import re
from einops import rearrange
from dataclasses import dataclass
from torchvision import transforms
from diffusers.models.modeling_utils import ModelMixin
from transformers.utils import ModelOutput
from typing import Iterable, Optional, Union, List
import step1x3d_geometry
from step1x3d_geometry.utils.typing import *
from step1x3d_geometry.utils.misc import get_device
from .base import BaseLabelEncoder
DEFAULT_POSE = 0 # "unknown", "t-pose", "a-pose", uncond
NUM_POSE_CLASSES = 3
POSE_MAPPING = {"unknown": 0, "t-pose": 1, "a-pose": 2, "uncond": 3}
DEFAULT_SYMMETRY_TYPE = 0 # "asymmetry", "x", uncond
NUM_SYMMETRY_TYPE_CLASSES = 2
SYMMETRY_TYPE_MAPPING = {"asymmetry": 0, "x": 1, "y": 0, "z": 0, "uncond": 2}
DEFAULT_GEOMETRY_QUALITY = 0 # "normal", "smooth", "sharp", uncond,
NUM_GEOMETRY_QUALITY_CLASSES = 3
GEOMETRY_QUALITY_MAPPING = {"normal": 0, "smooth": 1, "sharp": 2, "uncod": 3}
@step1x3d_geometry.register("label-encoder")
class LabelEncoder(BaseLabelEncoder, ModelMixin):
"""
Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance.
Args:
num_classes (`int`): The number of classes.
hidden_size (`int`): The size of the vector embeddings.
"""
def configure(self) -> None:
super().configure()
if self.cfg.zero_uncond_embeds:
self.embedding_table_tpose = nn.Embedding(
NUM_POSE_CLASSES, self.cfg.hidden_size
)
self.embedding_table_symmetry_type = nn.Embedding(
NUM_SYMMETRY_TYPE_CLASSES, self.cfg.hidden_size
)
self.embedding_table_geometry_quality = nn.Embedding(
NUM_GEOMETRY_QUALITY_CLASSES, self.cfg.hidden_size
)
else:
self.embedding_table_tpose = nn.Embedding(
NUM_POSE_CLASSES + 1, self.cfg.hidden_size
)
self.embedding_table_symmetry_type = nn.Embedding(
NUM_SYMMETRY_TYPE_CLASSES + 1, self.cfg.hidden_size
)
self.embedding_table_geometry_quality = nn.Embedding(
NUM_GEOMETRY_QUALITY_CLASSES + 1, self.cfg.hidden_size
)
if self.cfg.zero_uncond_embeds:
self.empty_label_embeds = torch.zeros((1, 3, self.cfg.hidden_size)).detach()
else:
self.empty_label_embeds = (
self.encode_label( # the last class label is for the uncond
[{"pose": "", "symetry": "", "geometry_type": ""}]
).detach()
)
# load pretrained_model_name_or_path
if self.cfg.pretrained_model_name_or_path is not None:
print(f"Loading ckpt from {self.cfg.pretrained_model_name_or_path}")
ckpt = torch.load(
self.cfg.pretrained_model_name_or_path, map_location="cpu"
)["state_dict"]
pretrained_model_ckpt = {}
for k, v in ckpt.items():
if k.startswith("label_condition."):
pretrained_model_ckpt[k.replace("label_condition.", "")] = v
self.load_state_dict(pretrained_model_ckpt, strict=True)
def encode_label(self, labels: List[dict]) -> torch.FloatTensor:
tpose_label_embeds = []
symmetry_type_label_embeds = []
geometry_quality_label_embeds = []
for label in labels:
if "pose" in label.keys():
if label["pose"] is None or label["pose"] == "":
tpose_label_embeds.append(
torch.zeros(self.cfg.hidden_size).detach().to(get_device())
)
else:
tpose_label_embeds.append(
self.embedding_table_symmetry_type(
torch.tensor(POSE_MAPPING[label["pose"][0]]).to(
get_device()
)
)
)
else:
tpose_label_embeds.append(
self.embedding_table_tpose(
torch.tensor(DEFAULT_POSE).to(get_device())
)
)
if "symmetry" in label.keys():
if label["symmetry"] is None or label["symmetry"] == "":
symmetry_type_label_embeds.append(
torch.zeros(self.cfg.hidden_size).detach().to(get_device())
)
else:
symmetry_type_label_embeds.append(
self.embedding_table_symmetry_type(
torch.tensor(
SYMMETRY_TYPE_MAPPING[label["symmetry"]]
).to(get_device())
)
)
else:
symmetry_type_label_embeds.append(
self.embedding_table_symmetry_type(
torch.tensor(DEFAULT_SYMMETRY_TYPE).to(get_device())
)
)
if "geometry_type" in label.keys():
if label["geometry_type"] is None or label["geometry_type"] == "":
geometry_quality_label_embeds.append(
torch.zeros(self.cfg.hidden_size).detach().to(get_device())
)
else:
geometry_quality_label_embeds.append(
self.embedding_table_geometry_quality(
torch.tensor(
GEOMETRY_QUALITY_MAPPING[label["geometry_type"][0]]
).to(get_device())
)
)
else:
geometry_quality_label_embeds.append(
self.embedding_table_geometry_quality(
torch.tensor(DEFAULT_GEOMETRY_QUALITY).to(get_device())
)
)
tpose_label_embeds = torch.stack(tpose_label_embeds)
symmetry_type_label_embeds = torch.stack(symmetry_type_label_embeds)
geometry_quality_label_embeds = torch.stack(geometry_quality_label_embeds)
label_embeds = torch.stack(
[
tpose_label_embeds,
symmetry_type_label_embeds,
geometry_quality_label_embeds,
],
dim=1,
).to(self.dtype)
return label_embeds
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