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from dataclasses import dataclass, field
import numpy as np
import torch
from skimage import measure
from einops import repeat, rearrange
import craftsman
from craftsman.systems.base import BaseSystem
from craftsman.utils.ops import generate_dense_grid_points
from craftsman.utils.typing import *
from craftsman.utils.misc import get_rank
@craftsman.register("shape-autoencoder-system")
class ShapeAutoEncoderSystem(BaseSystem):
@dataclass
class Config(BaseSystem.Config):
shape_model_type: str = None
shape_model: dict = field(default_factory=dict)
sample_posterior: bool = True
cfg: Config
def configure(self):
super().configure()
self.shape_model = craftsman.find(self.cfg.shape_model_type)(self.cfg.shape_model)
def forward(self, batch: Dict[str, Any]) -> Dict[str, Any]:
if "xyz" in batch:
if "sdf" in batch:
bs = batch["sdf"].shape[0]
rand_points = torch.cat([batch["xyz"].view(bs, -1, 3), batch["patch_xyz"].view(bs, -1, 3)], dim=1)
target = torch.cat([batch["sdf"].view(bs, -1, 1), batch["patch_sdf"].view(bs, -1, 1)], dim=1).squeeze(-1)
criteria = torch.nn.MSELoss()
elif "occupancy" in batch:
bs = batch["occupancy"].shape[0]
rand_points = torch.cat([batch["xyz"].view(bs, -1, 3), batch["patch_xyz"].view(bs, -1, 3)], dim=1)
target = torch.cat([batch["occupancy"].view(bs, -1, 1), batch["patch_occupancy"].view(bs, -1, 1)], dim=1).squeeze(-1)
criteria = torch.nn.BCEWithLogitsLoss()
else:
raise NotImplementedError
else:
rand_points = batch["rand_points"]
if "sdf" in batch:
target = batch["sdf"]
criteria = torch.nn.MSELoss()
elif "occupancies" in batch:
target = batch["occupancies"]
criteria = torch.nn.BCEWithLogitsLoss()
else:
raise NotImplementedError
# forward pass
_, latents, posterior, logits = self.shape_model(
batch["surface"][..., :3 + self.cfg.shape_model.point_feats],
rand_points,
sample_posterior=self.cfg.sample_posterior
)
if self.cfg.sample_posterior:
loss_kl = posterior.kl()
loss_kl = torch.sum(loss_kl) / loss_kl.shape[0]
return {
"loss_logits": criteria(logits, target).mean(),
"loss_kl": loss_kl,
"logits": logits,
"target": target,
"latents": latents,
}
else:
return {
"loss_logits": criteria(logits, target).mean(),
"latents": latents,
"logits": logits,
}
def training_step(self, batch, batch_idx):
"""
Description:
Args:
batch:
batch_idx:
Returns:
loss:
"""
out = self(batch)
loss = 0.
for name, value in out.items():
if name.startswith("loss_"):
self.log(f"train/{name}", value)
loss += value * self.C(self.cfg.loss[name.replace("loss_", "lambda_")])
for name, value in self.cfg.loss.items():
self.log(f"train_params/{name}", self.C(value))
return {"loss": loss}
@torch.no_grad()
def validation_step(self, batch, batch_idx):
self.eval()
out = self(batch)
# self.save_state_dict("latest-weights", self.state_dict())
mesh_v_f, has_surface = self.shape_model.extract_geometry(out["latents"])
self.save_mesh(
f"it{self.true_global_step}/{batch['uid'][0]}.obj",
mesh_v_f[0][0], mesh_v_f[0][1]
)
threshold = 0
outputs = out["logits"]
labels = out["target"]
pred = torch.zeros_like(outputs)
pred[outputs>=threshold] = 1
accuracy = (pred==labels).float().sum(dim=1) / labels.shape[1]
accuracy = accuracy.mean()
intersection = (pred * labels).sum(dim=1)
union = (pred + labels).gt(0).sum(dim=1)
iou = intersection * 1.0 / union + 1e-5
iou = iou.mean()
self.log("val/accuracy", accuracy)
self.log("val/iou", iou)
torch.cuda.empty_cache()
return {"val/loss": out["loss_logits"], "val/accuracy": accuracy, "val/iou": iou}
def on_validation_epoch_end(self):
pass |