File size: 5,945 Bytes
98024ab | 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 | import sys
from pathlib import Path
PROJECT_ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(PROJECT_ROOT))
import argparse
import json
import numpy as np
import pandas as pd
import torch
from torch.utils.data import DataLoader
from transformers import AutoModel
from data.dataset import YieldDataset
from data.preprocessing import daily_to_cumulative_weekly, DEFAULT_WEATHER_AGG_RULES
from hf.auto import register_yield_autoclass
def parse_args():
p = argparse.ArgumentParser()
p.add_argument("--hf_model_dir", required=True)
p.add_argument("--input_file", default=None)
p.add_argument("--single_sample_json", default=None)
p.add_argument("--cutoff", type=int, required=True)
p.add_argument("--batch_size", type=int, default=64)
p.add_argument("--output_csv", default="inference_predictions.csv")
return p.parse_args()
def load_single_sample_json(path, cfg, cutoff):
with open(path, "r") as f:
sample = json.load(f)
weather_format = sample.get("weather_format", "weekly_cumulative")
weather_cols = []
for v in cfg.weather_vars:
if v not in sample["weather"]:
raise ValueError(f"Missing weather variable in JSON: {v}")
arr = np.asarray(sample["weather"][v], dtype=np.float32)
if weather_format == "daily":
agg = DEFAULT_WEATHER_AGG_RULES.get(v, "mean")
arr = daily_to_cumulative_weekly(arr, agg=agg, week_len=7)
elif weather_format in ("weekly", "weekly_cumulative"):
pass
else:
raise ValueError(
"weather_format must be 'daily', 'weekly', "
"or 'weekly_cumulative'."
)
weather_cols.append(arr)
lengths = [len(x) for x in weather_cols]
if len(set(lengths)) != 1:
raise ValueError(f"Weather variable lengths do not match after aggregation: {lengths}")
weather = np.stack(weather_cols, axis=1).astype(np.float32)
soil = []
for v in cfg.soil_vars:
if v not in sample["soil"]:
raise ValueError(f"Missing soil variable in JSON: {v}")
soil.append(float(sample["soil"][v]))
soil = np.asarray(soil, dtype=np.float32)
w_mean = np.asarray(cfg.w_mean, dtype=np.float32)
w_std = np.asarray(cfg.w_std, dtype=np.float32)
s_mean = np.asarray(cfg.s_mean, dtype=np.float32)
s_std = np.asarray(cfg.s_std, dtype=np.float32)
weather = np.where(np.isnan(weather), w_mean[None, :], weather)
weather = (weather - w_mean[None, :]) / w_std[None, :]
soil = np.where(np.isnan(soil), s_mean, soil)
soil = (soil - s_mean) / s_std
crop_map = {"corn": 0, "maize": 0, "soybean": 1, "soy": 1}
crop = str(sample.get("crop", "corn")).strip().lower()
crop_id = crop_map.get(crop, 0)
t_eff = min(cutoff, weather.shape[0])
return {
"weather": torch.from_numpy(weather[:t_eff]).unsqueeze(0),
"soil": torch.from_numpy(soil).unsqueeze(0),
"crop_id": torch.tensor([crop_id], dtype=torch.long),
"t_eff": t_eff,
}
@torch.no_grad()
def main():
args = parse_args()
register_yield_autoclass()
if args.input_file is None and args.single_sample_json is None:
raise ValueError("Provide either --input_file or --single_sample_json.")
if args.input_file is not None and args.single_sample_json is not None:
raise ValueError("Use only one: --input_file OR --single_sample_json.")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = AutoModel.from_pretrained(args.hf_model_dir).to(device)
model.eval()
cfg = model.config
if args.single_sample_json is not None:
sample = load_single_sample_json(args.single_sample_json, cfg, args.cutoff)
weather = sample["weather"].to(device)
soil = sample["soil"].to(device)
crop_id = sample["crop_id"].to(device)
t_eff = sample["t_eff"]
out = model(
weather=weather,
soil=soil,
crop_id=crop_id,
horizon_idx=t_eff,
causal=True,
return_sequence=False,
)
pred = float(out.predictions.item())
Path(args.output_csv).parent.mkdir(parents=True, exist_ok=True)
pd.DataFrame([{
"sample_idx": 0,
"cutoff": int(args.cutoff),
"y_pred": pred,
}]).to_csv(args.output_csv, index=False)
print(f"Predicted yield: {pred:.4f}")
print(f"Saved inference prediction to {args.output_csv}")
return
ds = YieldDataset(
data_file=args.input_file,
weather_vars=cfg.weather_vars,
soil_vars=cfg.soil_vars,
split="all",
seed=1234,
crop=None,
years=None,
require_yield=False,
)
ds.set_normalization(
cfg.w_mean,
cfg.w_std,
cfg.s_mean,
cfg.s_std,
)
loader = DataLoader(ds, batch_size=args.batch_size, shuffle=False)
rows = []
sample_idx = 0
for batch in loader:
weather = batch["weather"].to(device)
soil = batch["soil"].to(device)
crop_id = batch["crop_id"].to(device)
t_eff = min(args.cutoff, weather.size(1))
out = model(
weather=weather[:, :t_eff, :],
soil=soil,
crop_id=crop_id,
horizon_idx=t_eff,
causal=True,
return_sequence=False,
)
for pred in out.predictions.detach().cpu().numpy().tolist():
rows.append({
"sample_idx": sample_idx,
"cutoff": int(args.cutoff),
"y_pred": float(pred),
})
sample_idx += 1
Path(args.output_csv).parent.mkdir(parents=True, exist_ok=True)
pd.DataFrame(rows).to_csv(args.output_csv, index=False)
print(f"Saved inference predictions to {args.output_csv}")
if __name__ == "__main__":
main() |