103 lines
3.0 KiB
Python
103 lines
3.0 KiB
Python
import torch
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import pathlib
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import pandas as pd
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from aiRNN import dataloader, models, losses
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from aiRNN.preprocessors import denorm_coords
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import numpy as np
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import copy
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DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
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def split_dataset(dataset, frac=0.8, seed=42):
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n = len(dataset)
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train_n = int(frac * n)
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return torch.utils.data.random_split(
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dataset,
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[train_n, n - train_n],
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generator=torch.Generator().manual_seed(seed),
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)
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step = 10
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base_name = "LSTM"
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cls = models.ThreeInputLSTM
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hidden_size = 16
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rnn_size = 64
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hidden_layers = 0
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rnn_layers = 3
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rnn_dropout = 0.0
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altitude_weight = 1e-3
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warm = 900
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pred = 1600
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start_offset = 30 * 60 - warm
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end_offset = 30 * 60 - pred
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def load_file_list(path: pathlib.Path):
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base = path.parent
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return [
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(base / f).resolve()
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for f in path.read_text().splitlines()
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if (base / f).exists()
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]
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file_list_path = pathlib.Path("../../cpp/known_routes_and_aircraft.csv")
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file_list = load_file_list(file_list_path)
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base_ds = dataloader.EvenlySpacedDataset(
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filepaths=file_list,
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n_input=warm,
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n_output=pred,
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n_windows_per_file=5,
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step=step,
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feature_columns=("lat", "lon", "alt", "ias"),
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context_columns=("last_lat", "last_lon", "last_alt", "last_ias", "last_timestamp"),
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time_columns=("timestamp", "dt"),
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target_columns=("lat", "lon", "alt"),
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)
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train_ds, val_ds = split_dataset(base_ds)
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model = cls(
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time_in=2,
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feat_in=4,
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context_in=5,
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hidden_size=hidden_size,
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rnn_size=rnn_size,
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out_size=3,
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hidden_layers=hidden_layers,
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rnn_layers=rnn_layers,
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rnn_dropout=rnn_dropout,
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device=DEVICE,
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)
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model.load_state_dict(torch.load("LSTM_wu900_ps150_aw0.001.pt"))
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model.to(DEVICE)
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model.eval()
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with torch.no_grad():
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val_loader = torch.utils.data.DataLoader(val_ds, batch_size=64, collate_fn=dataloader.collate_to_cpu, num_workers=4)
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results = []
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for X_f, X_t, y, X_c in val_loader:
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X_f = X_f.to(DEVICE)
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X_t = X_t.to(DEVICE)
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y = y.to(DEVICE)
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if X_c is not None:
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X_c = X_c.to(DEVICE)
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y_pred, _ = model(X_t, X_f, X_c, warm // step, pred // step)
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lat_pred, lon_pred, alt_pred = denorm_coords(y_pred[...,0], y_pred[...,1], y_pred[...,2])
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lat_pred = lat_pred.flatten()
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lon_pred = lon_pred.flatten()
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alt_pred = alt_pred.flatten()
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lat_true, lon_true, alt_true = denorm_coords(y[...,0], y[...,1], y[...,2])
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lat_true = lat_true.flatten()
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lon_true = lon_true.flatten()
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alt_true = alt_true.flatten()
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results.append(torch.stack([lat_true, lon_true, alt_true, lat_pred, lon_pred, alt_pred], dim=1))
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results_tensor = torch.cat(results, dim=0)
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np_y_all = results_tensor.cpu().numpy()
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np.savetxt(
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f"predictions_{base_name}_wu{warm}_ps{pred}_aw{altitude_weight}.csv",
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np_y_all,
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delimiter=",",
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header="true_lat,true_lon,true_alt,pred_lat,pred_lon,pred_alt",
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comments="",
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)
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