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9f29df6 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 | """Train RainNet on contiguous windows from an RYDL-style HDF5 file."""
import json
import math
import os
from pathlib import Path
import random
import sys
import h5py
import numpy as np
import torch
from torch import nn
import torch.nn.functional as F
from torch.nn.parallel import DistributedDataParallel
from torch.utils.data import DataLoader, Dataset, DistributedSampler
import yaml
ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(ROOT))
from model.rainnet import build_rainnet
def load_config():
with (ROOT / "conf/config.yaml").open(encoding="utf-8") as handle:
return yaml.safe_load(handle)
def seed_everything(seed):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
def setup_device(config):
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(backend="nccl" if torch.cuda.is_available() else "gloo")
if torch.cuda.is_available() and config["device"] in ("auto", "cuda"):
device = torch.device("cuda", local_rank)
torch.cuda.set_device(device)
else:
device = torch.device("cpu")
return device, distributed, local_rank
class RainNetDataset(Dataset):
def __init__(self, path, keys, input_steps=4):
self.path = path
self.keys = list(keys)
self.input_steps = input_steps
if len(self.keys) <= input_steps:
raise ValueError("A split needs at least input_steps + 1 frames")
def __len__(self):
return len(self.keys) - self.input_steps
def __getitem__(self, index):
with h5py.File(self.path, "r") as handle:
inputs = np.stack(
[handle[key][...] for key in self.keys[index : index + self.input_steps]]
)
target_key = self.keys[index + self.input_steps]
target = handle[target_key][...][None]
return torch.from_numpy(inputs), torch.from_numpy(target), target_key
class LogCoshLoss(nn.Module):
def forward(self, prediction, target):
error = torch.abs(prediction - target)
return (error + F.softplus(-2.0 * error) - math.log(2.0)).mean()
def transform_and_pad(tensor, pad):
return F.pad(torch.log(tensor + 0.01), pad, mode="reflect")
def run_epoch(model, loader, criterion, device, pad, max_batches, optimizer=None):
training = optimizer is not None
model.train(training)
losses = []
parameter_updated = False
first_shapes = None
context = torch.enable_grad() if training else torch.no_grad()
with context:
for batch_index, (inputs, targets, target_keys) in enumerate(loader):
if batch_index >= max_batches:
break
inputs, targets = inputs.to(device), targets.to(device)
padded_inputs = transform_and_pad(inputs, pad)
padded_targets = transform_and_pad(targets, pad)
if training:
optimizer.zero_grad(set_to_none=True)
output = model(padded_inputs)
loss = criterion(output, padded_targets)
if not torch.isfinite(loss):
raise RuntimeError(f"Non-finite loss: {loss.item()}")
if first_shapes is None:
first_shapes = (inputs.shape, targets.shape, padded_inputs.shape, output.shape, target_keys[0])
if training:
tracked = next(model.parameters()).detach().clone()
loss.backward()
optimizer.step()
parameter_updated = parameter_updated or not torch.equal(tracked, next(model.parameters()).detach())
losses.append(loss.item())
return float(np.mean(losses)), parameter_updated, first_shapes
def save_checkpoint(path, model, optimizer, epoch, val_loss, config):
path.parent.mkdir(parents=True, exist_ok=True)
state_model = model.module if isinstance(model, DistributedDataParallel) else model
torch.save(
{
"model_state_dict": state_model.state_dict(),
"optimizer_state_dict": optimizer.state_dict(),
"epoch": epoch,
"validation_loss": val_loss,
"config": config,
},
path,
)
def main():
config = load_config()
seed_everything(config["seed"])
device, distributed, local_rank = setup_device(config)
is_main = local_rank == 0
data = config["data"]
path = ROOT / data["path"]
if not path.exists():
raise FileNotFoundError(f"Fake data not found: {path}; run scripts/fake_data.py")
with h5py.File(path, "r") as handle:
keys = sorted(handle.keys())
train_end = data["train_frames"]
val_end = train_end + data["val_frames"]
train_set = RainNetDataset(path, keys[:train_end], data["input_steps"])
val_set = RainNetDataset(path, keys[train_end:val_end], data["input_steps"])
train_sampler = DistributedSampler(train_set, shuffle=True) if distributed else None
train_loader = DataLoader(
train_set,
batch_size=config["train"]["batch_size"],
shuffle=train_sampler is None,
sampler=train_sampler,
num_workers=data["num_workers"],
)
val_loader = DataLoader(val_set, batch_size=1, shuffle=False, num_workers=data["num_workers"])
model = build_rainnet(**config["model"]).to(device)
parameter_count = sum(parameter.numel() for parameter in model.parameters())
if distributed:
model = DistributedDataParallel(model, device_ids=[local_rank] if device.type == "cuda" else None)
criterion = LogCoshLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=config["train"]["learning_rate"])
pad_h = data["padded_height"] - data["raw_height"]
pad_w = data["padded_width"] - data["raw_width"]
pad = (pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2)
history = {"train_loss": [], "validation_loss": [], "learning_rate": []}
best_loss = float("inf")
any_update = False
for epoch in range(config["train"]["epochs"]):
if train_sampler:
train_sampler.set_epoch(epoch)
train_loss, updated, shapes = run_epoch(
model, train_loader, criterion, device, pad, config["train"]["max_train_batches"], optimizer
)
val_loss, _, _ = run_epoch(
model, val_loader, criterion, device, pad, config["train"]["max_valid_batches"]
)
any_update = any_update or updated
history["train_loss"].append(train_loss)
history["validation_loss"].append(val_loss)
history["learning_rate"].append(optimizer.param_groups[0]["lr"])
if is_main:
last_path = ROOT / config["train"]["checkpoint_last"]
best_path = ROOT / config["train"]["checkpoint_best"]
save_checkpoint(last_path, model, optimizer, epoch + 1, val_loss, config)
if val_loss < best_loss:
best_loss = val_loss
save_checkpoint(best_path, model, optimizer, epoch + 1, val_loss, config)
result_dir = ROOT / config["evaluation"]["result_dir"]
result_dir.mkdir(parents=True, exist_ok=True)
with (result_dir / "train_history.json").open("w", encoding="utf-8") as handle:
json.dump(history, handle, indent=2)
print(f"Device: {device}")
print(f"Input shape: {tuple(shapes[0])}")
print(f"Target shape: {tuple(shapes[1])}")
print(f"Target key (i+4): {shapes[4]}")
print(f"Padded input shape: {tuple(shapes[2])}")
print(f"Model output shape: {tuple(shapes[3])}")
print(f"Parameter count: {parameter_count}")
print(f"Epoch: {epoch + 1}")
print(f"Train loss: {train_loss:.8f}")
print(f"Validation loss: {val_loss:.8f}")
print(f"Learning rate: {optimizer.param_groups[0]['lr']}")
print(f"parameter_update_detected: {any_update}")
print(f"Checkpoint path: {best_path}")
if not any_update:
raise RuntimeError("No model parameter changed after optimizer.step()")
if is_main:
reload_model = build_rainnet(**config["model"])
checkpoint = torch.load(
ROOT / config["train"]["checkpoint_best"], map_location="cpu", weights_only=False
)
reload_model.load_state_dict(checkpoint["model_state_dict"])
print("checkpoint_reload_after_training: True")
if distributed:
torch.distributed.destroy_process_group()
if __name__ == "__main__":
main()
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