Spaces:
Runtime error
Runtime error
File size: 4,564 Bytes
0f079b2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 |
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 |