dev predict

fao_models/model_predict.py

@@ -0,0 +1,77 @@
+import numpy as np
+import datetime
+import logging
+from models import get_model, CmCallback
+import dataloader as dl
+import keras
+import os
+import tensorflow as tf
+import rasterio as rio
+import yaml
+from pprint import pformat
+import argparse
+
+
+logging.basicConfig(
+    format="%(asctime)s %(message)s",
+    datefmt="%Y-%m-%d %I:%M:%S %p",
+    level=logging.WARNING,
+    filename=os.path.join(
+        os.path.dirname(os.path.dirname(__file__)),
+        f'trainlog_{datetime.datetime.now().strftime("%Y-%m-%d")}.log',
+    ),  # add _%H-%M-%S if needbe
+)
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.INFO)
+
+
+def cli():
+    # initalize new cli parser
+    parser = argparse.ArgumentParser(description="Train a model with a .yml file.")
+
+    parser.add_argument(
+        "-c",
+        "--config",
+        type=str,
+        help="path to .yml file",
+    )
+    parser.add_argument(
+        "-t",
+        "--test",
+        type=bool,
+        default=False,
+        help="Run as a test. limits total examples to 5*batch_size and adds a test prefix to experiment name",
+    )
+    args = parser.parse_args()
+
+    config_file = args.config
+
+
+def main(config: str | dict):
+    # load model
+    if isinstance(config, str):
+        with open(config, "r") as file:
+            config = yaml.safe_load(file)
+    model_name = config["model_name"]
+    checkpoint = config["checkpoint"]
+    optimizer = config["optimizer"]
+    loss_function = config["loss_function"]
+
+    model = get_model(model_name, optimizer=optimizer, loss_fn=loss_function)
+    model.load_weights(checkpoint)
+    model.trainable = False
+
+    # load image
+    # local file as placeholder
+    img = "data/patch_pt9097_nonforest.tif"
+    with rio.open(img) as dst:
+        data = dst.read() / 10_000
+        profile = dst.profile
+    # convert to tensor
+    data = np.transpose(data, (1, 2, 0))
+    tfdata = tf.expand_dims(tf.convert_to_tensor(data.astype(np.float32)), axis=0)
+    o = model.predict(tfdata)
+    print(o)
+
+
+main("dev-predict-runc-resnet-jjd.yml")