File size: 6,259 Bytes
8bf8ea0 | 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 | from __future__ import annotations
import argparse
import copy
from pathlib import Path
import torch
from common import (
append_history,
build_model,
collect_predictions,
count_parameters,
get_device,
load_config,
make_loaders,
mean_relative_l2_percent,
relative_normalized_mse,
resolve_output_path,
save_json,
set_seed,
)
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser()
parser.add_argument("--config", default=str(Path(__file__).resolve().parents[1] / "config" / "config.yaml"))
parser.add_argument("--dry-run", action="store_true", help="Run one forward/backward pass for preflight only.")
return parser.parse_args()
def main() -> None:
args = parse_args()
cfg = load_config(args.config)
set_seed(cfg["training"]["seed"], cfg["runtime"]["deterministic"], cfg["runtime"]["benchmark"])
device = get_device(cfg)
output_dir = Path(cfg["paths"]["output_dir"])
weight_path = resolve_output_path(cfg, cfg["paths"]["checkpoint"])
results_dir = resolve_output_path(cfg, cfg["paths"]["results_dir"])
output_dir.mkdir(parents=True, exist_ok=True)
weight_path.parent.mkdir(parents=True, exist_ok=True)
results_dir.mkdir(parents=True, exist_ok=True)
train_loader, val_loader, test_loader = make_loaders(cfg)
model = build_model(cfg).to(device)
params = count_parameters(model)
optimizer = torch.optim.AdamW(
model.parameters(),
lr=cfg["training"]["learning_rate"],
weight_decay=cfg["training"]["weight_decay"],
)
scheduler = torch.optim.lr_scheduler.StepLR(
optimizer,
step_size=cfg["training"]["scheduler_step_size"],
gamma=cfg["training"]["scheduler_gamma"],
)
if args.dry_run:
model.train()
x, y = next(iter(train_loader))
x = x.to(device)
y = y.to(device)
loss = relative_normalized_mse(model(x), y)
loss.backward()
print(f"DRY_RUN forward/backward passed loss={loss.item():.8f} params={params}", flush=True)
return
best_val = float("inf")
best_test = float("inf")
best_epoch = -1
best_state = None
patience = cfg["training"]["early_stopping_patience"]
stale_epochs = 0
history_path = results_dir / "history.csv"
for epoch in range(1, cfg["training"]["epochs"] + 1):
model.train()
running_loss = 0.0
batches = 0
for x, y in train_loader:
x = x.to(device, non_blocking=True)
y = y.to(device, non_blocking=True)
optimizer.zero_grad(set_to_none=True)
pred = model(x)
loss = relative_normalized_mse(pred, y)
loss.backward()
optimizer.step()
running_loss += loss.item()
batches += 1
train_loss = running_loss / max(1, batches)
val_l2 = mean_relative_l2_percent(model, val_loader, device)
test_l2 = mean_relative_l2_percent(model, test_loader, device)
lr = optimizer.param_groups[0]["lr"]
row = {
"epoch": epoch,
"train_relative_normalized_mse": train_loss,
"val_mean_relative_l2_percent": val_l2,
"test_mean_relative_l2_percent": test_l2,
"lr": lr,
}
append_history(history_path, row)
print(
f"epoch={epoch:04d} train_loss={train_loss:.8f} "
f"val_mean_relative_l2={val_l2:.6f}% test_mean_relative_l2={test_l2:.6f}% lr={lr:.8g}",
flush=True,
)
if val_l2 < best_val:
best_val = val_l2
best_test = test_l2
best_epoch = epoch
best_state = copy.deepcopy(model.state_dict())
torch.save(
{
"model_state": best_state,
"config": cfg,
"epoch": best_epoch,
"best_val_mean_relative_l2_percent": best_val,
"test_mean_relative_l2_percent_at_best_val": best_test,
"parameter_count": params,
},
weight_path,
)
stale_epochs = 0
else:
stale_epochs += 1
save_json(
results_dir / "metrics.json",
{
"status": "running",
"epoch": epoch,
"best_epoch": best_epoch,
"best_val_mean_relative_l2_percent": best_val,
"test_mean_relative_l2_percent_at_best_val": best_test,
"paper_crop_re10000_percent": cfg["experiment"]["paper_reference_test_percent"],
"paper_crop_re10000_std_percent": cfg["experiment"]["paper_reference_std_percent"],
"parameter_count": params,
},
)
scheduler.step()
if stale_epochs >= patience:
print(
f"early_stopping epoch={epoch} best_epoch={best_epoch} "
f"best_val={best_val:.6f}% best_test={best_test:.6f}%",
flush=True,
)
break
if best_state is not None:
model.load_state_dict(best_state)
inputs, preds, targets = collect_predictions(model, test_loader, device)
pred_path = results_dir / "test_predictions_from_train.npz"
import numpy as np
np.savez_compressed(pred_path, inputs=inputs, predictions=preds, targets=targets)
save_json(
results_dir / "metrics.json",
{
"status": "complete",
"best_epoch": best_epoch,
"best_val_mean_relative_l2_percent": best_val,
"test_mean_relative_l2_percent_at_best_val": best_test,
"paper_crop_re10000_percent": cfg["experiment"]["paper_reference_test_percent"],
"paper_crop_re10000_std_percent": cfg["experiment"]["paper_reference_std_percent"],
"parameter_count": params,
"checkpoint": str(weight_path),
"prediction_file": str(pred_path),
},
)
print(
f"training_complete best_epoch={best_epoch} best_val={best_val:.6f}% "
f"test_at_best_val={best_test:.6f}% checkpoint={weight_path}",
flush=True,
)
if __name__ == "__main__":
main()
|