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3549cf5 | 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 | """Train AEF reconstruction, uniformity, consistency and text objectives."""
import json
import os
import sys
from pathlib import Path
import numpy as np
import torch
import yaml
from torch.nn.parallel import DistributedDataParallel
from torch.utils.data import DataLoader, Dataset, DistributedSampler
ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(ROOT))
from model.alphaearthfoundations import AlphaEarthFoundations, compute_losses
class AEFDataset(Dataset):
def __init__(self, path, config):
self.data = np.load(path)
self.input_sources = config["data"]["input_sources"]
self.target_sources = config["data"]["target_sources"]
format_version = str(self.data["format_version"])
if format_version != config["data"]["format_version"]:
raise ValueError(f"Expected format {config['data']['format_version']}, got {format_version}")
for name, spec in self.input_sources.items():
expected = (spec["timesteps"], spec["channels"], config["data"]["image_size"], config["data"]["image_size"])
if self.data[name].shape[1:] != expected:
raise ValueError(f"{name} has shape {self.data[name].shape[1:]}, expected {expected}")
def __len__(self):
return len(self.data["valid_period"])
def __getitem__(self, index):
item = {name: torch.from_numpy(self.data[name][index]).float() for name in self.input_sources}
item.update({f"timestamps_{name}": torch.from_numpy(self.data[f"timestamps_{name}"][index]).long()
for name in self.input_sources})
for name in self.input_sources:
item[f"frame_available_{name}"] = torch.from_numpy(self.data[f"frame_available_{name}"][index])
item[f"channel_available_{name}"] = torch.from_numpy(self.data[f"channel_available_{name}"][index])
item[f"pixel_valid_{name}"] = torch.from_numpy(self.data[f"pixel_valid_{name}"][index]).float()
item[f"geometry_{name}"] = torch.from_numpy(self.data[f"geometry_{name}"][index]).float()
for name, spec in self.target_sources.items():
if name in self.input_sources:
continue
values = torch.from_numpy(self.data[f"target_sequence_{name}"][index])
item[f"target_sequence_{name}"] = values.long() if spec["type"] == "categorical" else values.float()
item[f"target_timestamps_{name}"] = torch.from_numpy(self.data[f"target_timestamps_{name}"][index]).long()
item[f"target_pixel_valid_{name}"] = torch.from_numpy(self.data[f"target_pixel_valid_{name}"][index]).float()
item[f"target_geometry_{name}"] = torch.from_numpy(self.data[f"target_geometry_{name}"][index]).float()
item[f"target_frame_available_{name}"] = torch.from_numpy(self.data[f"target_frame_available_{name}"][index])
item["support_period"] = torch.from_numpy(self.data["support_period"][index]).long()
item["valid_period"] = torch.from_numpy(self.data["valid_period"][index]).long()
item["text_target"] = torch.from_numpy(self.data["text_target"][index]).float()
return item
def device_from_config(config, local_rank=0):
requested = config["runtime"]["device"]
if requested == "auto":
return torch.device("cuda", local_rank) if torch.cuda.is_available() else torch.device("cpu")
return torch.device(requested)
def _select_indices(available):
selected = []
for row in available:
candidates = torch.nonzero(row, as_tuple=False).flatten()
choice = torch.randint(len(candidates), (), device=candidates.device)
selected.append(candidates[choice])
return torch.stack(selected).to(available.device)
def _gather(values, indices):
return values[torch.arange(len(values), device=values.device), indices]
def unpack(batch, config, device, remove_input_targets=True):
input_names, target_specs = config["data"]["input_sources"], config["data"]["target_sources"]
sources, frame_available = {}, {}
timestamps = {name: batch[f"timestamps_{name}"].to(device) for name in input_names}
targets, masks, target_times, target_periods, geometry = {}, {}, {}, {}, {}
for name in input_names:
values = batch[name].to(device)
available = batch[f"frame_available_{name}"].to(device).clone()
channel_available = batch[f"channel_available_{name}"].to(device)
values = values * channel_available[:, :, :, None, None]
indices = _select_indices(available)
targets[name] = _gather(values, indices)
masks[name] = _gather(batch[f"pixel_valid_{name}"].to(device), indices) * _gather(channel_available, indices)[:, :, None, None]
target_times[name] = _gather(timestamps[name], indices)
target_periods[name] = torch.stack([
target_times[name] - 5 * 86_400_000, target_times[name] + 5 * 86_400_000
], dim=1)
geometry[name] = _gather(batch[f"geometry_{name}"].to(device), indices)
if remove_input_targets:
available[torch.arange(len(available), device=device), indices] = False
sources[name] = values * available[:, :, None, None, None]
frame_available[name] = available
for name in target_specs:
if name in input_names:
continue
available = batch[f"target_frame_available_{name}"].to(device)
indices = _select_indices(available)
targets[name] = _gather(batch[f"target_sequence_{name}"].to(device), indices)
masks[name] = _gather(batch[f"target_pixel_valid_{name}"].to(device), indices)
target_times[name] = _gather(batch[f"target_timestamps_{name}"].to(device), indices)
target_periods[name] = torch.stack([
target_times[name] - 5 * 86_400_000, target_times[name] + 5 * 86_400_000
], dim=1)
geometry[name] = _gather(batch[f"target_geometry_{name}"].to(device), indices)
return sources, timestamps, frame_available, targets, masks, target_times, target_periods, geometry
def perturb_sources(sources, frame_available, timestamps, support_period, config):
perturbed, perturbed_available = {}, {}
source_dropout, frame_dropout = config["train"]["source_dropout"], config["train"]["frame_dropout"]
for name, values in sources.items():
output, available = values + 0.01 * torch.randn_like(values), frame_available[name].clone()
source_mask = torch.rand(values.shape[0], 1, 1, 1, 1, device=values.device) < source_dropout[name]
strategy = torch.randint(3, (1,), device=values.device).item()
if strategy == 0:
dropped = torch.rand_like(available.float()) < frame_dropout[name]
else:
midpoint = support_period[:, :1] + (support_period[:, 1:] - support_period[:, :1]) // 2
dropped = timestamps[name] >= midpoint if strategy == 1 else timestamps[name] < midpoint
available = available & ~dropped & ~source_mask[:, 0, 0, 0]
perturbed[name] = output * available[:, :, None, None, None]
perturbed_available[name] = available
return perturbed, perturbed_available
def main():
config = yaml.safe_load((ROOT / "conf/config.yaml").read_text())
torch.manual_seed(config["seed"])
distributed = int(os.environ.get("WORLD_SIZE", "1")) > 1
local_rank = int(os.environ.get("LOCAL_RANK", "0"))
if distributed:
torch.distributed.init_process_group("nccl" if torch.cuda.is_available() else "gloo")
rank = torch.distributed.get_rank() if distributed else 0
device = device_from_config(config, local_rank)
if device.type == "cuda":
torch.cuda.set_device(device)
dataset = AEFDataset(ROOT / config["data"]["root"] / "train.npz", config)
sampler = DistributedSampler(dataset, shuffle=True) if distributed else None
loader = DataLoader(dataset, batch_size=config["train"]["batch_size"], sampler=sampler,
shuffle=sampler is None, num_workers=config["train"]["num_workers"])
model = AlphaEarthFoundations(config["data"]["input_sources"], config["data"]["target_sources"], config["model"]).to(device)
if distributed:
model = DistributedDataParallel(model, device_ids=[local_rank] if device.type == "cuda" else None)
optimizer = torch.optim.Adam(model.parameters(), lr=config["train"]["learning_rate"],
weight_decay=config["train"]["weight_decay"])
weights = {name: config["train"][f"{name}_weight"] for name in ("reconstruction", "uniformity", "consistency", "text")}
history = []
for epoch in range(config["train"]["epochs"]):
if sampler:
sampler.set_epoch(epoch)
model.train()
totals = {}
for batch in loader:
sources, timestamps, frame_available, targets, masks, target_times, target_periods, geometry = unpack(batch, config, device)
valid_period = batch["valid_period"].to(device)
teacher = model(sources, timestamps, valid_period, frame_available, target_times, geometry, target_periods)
perturbed, perturbed_available = perturb_sources(
sources, frame_available, timestamps, batch["support_period"].to(device), config
)
student = model(perturbed, timestamps, valid_period, perturbed_available)
loss, components = compute_losses(teacher, student, targets, masks, batch["text_target"].to(device),
config["data"]["target_sources"], weights)
optimizer.zero_grad(set_to_none=True)
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
optimizer.step()
for name, value in components.items():
totals[name] = totals.get(name, 0.0) + float(value.detach())
metrics = {name: value / len(loader) for name, value in totals.items()}
history.append({"epoch": epoch + 1, **metrics})
if rank == 0:
print(f"epoch={epoch + 1} total_loss={metrics['total']:.6f} reconstruction={metrics['reconstruction']:.6f}")
if rank == 0:
checkpoint = ROOT / config["paths"]["checkpoint"]
metrics_path = ROOT / config["paths"]["training_metrics"]
checkpoint.parent.mkdir(parents=True, exist_ok=True)
metrics_path.parent.mkdir(parents=True, exist_ok=True)
state = model.module.state_dict() if distributed else model.state_dict()
torch.save({"model": state, "model_config": config["model"], "input_sources": config["data"]["input_sources"],
"target_sources": config["data"]["target_sources"], "format_version": config["data"]["format_version"]}, checkpoint)
metrics_path.write_text(json.dumps({"history": history}, indent=2) + "\n")
print(f"checkpoint={checkpoint.relative_to(ROOT)}")
if distributed:
torch.distributed.destroy_process_group()
if __name__ == "__main__":
main()
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