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e0a6aa0 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 | """MaskDiT-style denoiser for native packed radar wedges."""
from __future__ import annotations
import math
import json
from pathlib import Path
import numpy as np
import yaml
import torch
from torch import nn
from torch.nn import functional as F
from torch.utils.data import Dataset
def load_config(path):
with open(path, encoding="utf-8") as handle:
return yaml.safe_load(handle)
def geometry_mask(config):
data = config["data"]
mask = np.zeros((data["frames"], 4, 366, 180), dtype=bool)
for elevation, (azimuths, ranges) in enumerate(zip(data["azimuth_counts"], data["range_bins"])):
mask[:, elevation, :azimuths, :ranges] = True
return mask
def make_sample(config, seed):
rng = np.random.default_rng(seed); data = config["data"]
volume = np.zeros(tuple(data["storage_shape"]), dtype=np.float32)
phase = rng.uniform(0, 2*np.pi)
for t in range(data["frames"]):
for e, (na, nr) in enumerate(zip(data["azimuth_counts"], data["range_bins"])):
az=np.arange(na,dtype=np.float32)[:,None]; radius=np.arange(nr,dtype=np.float32)[None]
center=(80+12*t+8*e+7*np.sin(phase+t/3))%na; center_r=min(nr-15,42+4*t-5*e)
da=np.minimum(abs(az-center),na-abs(az-center)); core=np.exp(-.5*((da/(14+2*e))**2+((radius-center_r)/18)**2))
volume[t,e,:na,:nr]=np.clip(58*np.exp(-.22*e)*core+2*np.sin(radius/8+az/30+phase+t/2),0,70)
valid=geometry_mask(config); observed=valid.copy()
for t in range(data["history"]):
for e,(na,nr) in enumerate(zip(data["azimuth_counts"],data["range_bins"])):
width=max(1,int(round(na*data["missing_ratio"]))); start=int(rng.integers(0,na-width+1)); observed[t,e,start:start+width,:nr]=False
packed=volume.copy();packed[~observed]=0
return {"values":packed,"truth":volume,"validity":valid,"observed":observed}
class PackedRadarDataset(Dataset):
def __init__(self,directory,split):
self.files=sorted(Path(directory).glob(f"{split}_*.npz"))
if not self.files: raise FileNotFoundError(f"no {split} samples; run fake_data.py")
def __len__(self): return len(self.files)
def __getitem__(self,index):
with np.load(self.files[index]) as sample:
return {key:torch.from_numpy(sample[key].copy()) for key in ("values","truth","validity","observed")}
class DiffusionSchedule:
def __init__(self,steps,beta_start,beta_end,device):
self.steps=steps;self.betas=torch.linspace(beta_start,beta_end,steps,device=device);self.alphas=1-self.betas;self.alpha_bars=torch.cumprod(self.alphas,0)
def add_noise(self,clean,timestep):
noise=torch.randn_like(clean);alpha=self.alpha_bars[timestep].view(-1,1,1,1,1);return alpha.sqrt()*clean+(1-alpha).sqrt()*noise,noise
@torch.no_grad()
def sample(self,model,history,observed):
target=torch.randn(history.shape[0],5,*history.shape[2:],device=history.device)
for step in reversed(range(self.steps)):
t=torch.full((history.shape[0],),step,device=history.device,dtype=torch.long);clean=model(history,target,observed,t,None)
target=clean if step==0 else self.alpha_bars[step-1].sqrt()*clean+(1-self.alpha_bars[step-1]).sqrt()*torch.randn_like(clean)
return target
def evaluate_ensemble(ensemble,truth,valid,thresholds):
mean=ensemble.mean(0);error=mean-truth;result={"mae":float(abs(error)[valid].mean()),"rmse":float(np.sqrt((error**2)[valid].mean()))}
result["crps"]=float(abs(ensemble-truth[None])[:,valid].mean()-.5*abs(ensemble[:,None]-ensemble[None,:])[:,:,valid].mean())
result["categorical"]={}
for threshold in thresholds:
forecast,observed=mean>=threshold,truth>=threshold;hits=int((forecast&observed&valid).sum());miss=int((~forecast&observed&valid).sum());false=int((forecast&~observed&valid).sum())
result["categorical"][str(threshold)]={"csi":hits/max(hits+miss+false,1),"far":false/max(hits+false,1),"pod":hits/max(hits+miss,1)}
result["coverage_ratio"]=[[float(np.isfinite(ensemble).all(0)[lead,e][valid[lead,e]].mean()) for e in range(4)] for lead in range(5)]
return result
class DiTBlock(nn.Module):
"""Linear-cost latent attention block suitable for 24k native wedge tokens."""
def __init__(self, hidden: int, heads: int, latent_tokens: int):
super().__init__()
self.latents = nn.Parameter(torch.randn(1, latent_tokens, hidden) / math.sqrt(hidden))
self.read = nn.MultiheadAttention(hidden, heads, batch_first=True)
self.write = nn.MultiheadAttention(hidden, heads, batch_first=True)
self.norm1 = nn.LayerNorm(hidden)
self.norm2 = nn.LayerNorm(hidden)
self.mlp = nn.Sequential(nn.Linear(hidden, hidden * 4), nn.GELU(), nn.Linear(hidden * 4, hidden))
def forward(self, tokens: torch.Tensor, conditioning: torch.Tensor) -> torch.Tensor:
latent = self.latents.expand(tokens.shape[0], -1, -1) + conditioning[:, None]
latent = latent + self.read(latent, self.norm1(tokens), self.norm1(tokens), need_weights=False)[0]
tokens = tokens + self.write(self.norm1(tokens), latent, latent, need_weights=False)[0]
return tokens + self.mlp(self.norm2(tokens + conditioning[:, None]))
class EchoCast3D(nn.Module):
def __init__(self, config: dict):
super().__init__()
self.config = config
model = config["model"]
self.patch = tuple(model["patch_size"])
patch_area = self.patch[0] * self.patch[1]
self.embed = nn.Linear(config["data"]["frames"] * patch_area, model["hidden_dim"])
self.time_embed = nn.Embedding(config["diffusion"]["steps"], model["hidden_dim"])
self.elevation_embed = nn.Embedding(4, model["hidden_dim"])
self.mask_token = nn.Parameter(torch.zeros(1, 1, model["hidden_dim"]))
self.encoder = nn.ModuleList(
[DiTBlock(model["hidden_dim"], model["heads"], model["latent_tokens"]) for _ in range(model["depth"])]
)
self.decoder = nn.ModuleList(
[DiTBlock(model["hidden_dim"], model["heads"], model["latent_tokens"]) for _ in range(model["depth"])]
)
self.head = nn.Linear(model["hidden_dim"], config["data"]["target"] * patch_area)
self.layouts = []
offset = 0
for e, (na, nr) in enumerate(zip(config["data"]["azimuth_counts"], config["data"]["range_bins"])):
rows, cols = na // self.patch[0], nr // self.patch[1]
count = rows * cols
self.layouts.append((e, na, nr, rows, cols, offset, offset + count))
offset += count
self.token_count = offset
def patchify(self, volume: torch.Tensor) -> torch.Tensor:
parts = []
for e, na, nr, _, _, _, _ in self.layouts:
wedge = volume[:, :, e, :na, :nr]
patches = F.unfold(wedge, kernel_size=self.patch, stride=self.patch).transpose(1, 2)
parts.append(patches)
return torch.cat(parts, dim=1)
def token_validity(self, validity: torch.Tensor) -> torch.Tensor:
parts = []
for e, na, nr, _, _, _, _ in self.layouts:
wedge = validity[:, :, e, :na, :nr].float()
patch = F.unfold(wedge, kernel_size=self.patch, stride=self.patch)
parts.append((patch > 0.5).all(dim=1))
return torch.cat(parts, dim=1)
def unpatchify(self, patches: torch.Tensor) -> torch.Tensor:
batch = patches.shape[0]
output = patches.new_zeros((batch, 5, 4, 366, 180))
for e, na, nr, rows, cols, start, end in self.layouts:
wedge = F.fold(
patches[:, start:end].transpose(1, 2),
output_size=(na, nr),
kernel_size=self.patch,
stride=self.patch,
)
output[:, :, e, :na, :nr] = wedge
return output
def forward(
self,
history: torch.Tensor,
noisy_target: torch.Tensor,
observed: torch.Tensor,
timestep: torch.Tensor,
token_mask: torch.Tensor | None,
) -> torch.Tensor:
full = torch.cat((history, noisy_target), dim=1)
tokens = self.embed(self.patchify(full))
elevation_ids = torch.cat(
[torch.full((end - start,), e, device=full.device) for e, _, _, _, _, start, end in self.layouts]
)
tokens = tokens + self.elevation_embed(elevation_ids)[None]
if token_mask is not None:
tokens = torch.where(token_mask[..., None], self.mask_token.expand_as(tokens), tokens)
condition = self.time_embed(timestep)
for block in self.encoder:
tokens = block(tokens, condition)
for block in self.decoder:
tokens = block(tokens, condition)
return self.unpatchify(self.head(tokens))
def masked_objective(
model: EchoCast3D,
prediction: torch.Tensor,
truth: torch.Tensor,
validity: torch.Tensor,
token_mask: torch.Tensor,
reconstruction_weight: float,
) -> tuple[torch.Tensor, dict[str, float]]:
target_valid = validity[:, 3:]
token_pixels = token_mask[:, :, None].expand(-1, -1, 5 * 9)
masked_pixels = model.unpatchify(token_pixels.float()).bool()
squared = (prediction - truth) ** 2
unmasked = target_valid & ~masked_pixels
masked = target_valid & masked_pixels
score = squared[unmasked].mean()
reconstruction = squared[masked].mean()
loss = score + reconstruction_weight * reconstruction
return loss, {"score": score.item(), "reconstruction": reconstruction.item()}
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