code cleanup

examples/Yolo-object-detection/client/darknet/Makefile

@@ -1,4 +1,4 @@
-GPU=1
+GPU=0
 CUDNN=0
 CUDNN_HALF=0
 OPENCV=0

examples/Yolo-object-detection/client/darknet/obj.data

@@ -1,5 +1,5 @@
 classes = 1
-train = /home/sowmya/fedn/examples/Yolo-object-detection/client/data/train.txt
-valid = /home/sowmya/fedn/examples/Yolo-object-detection/client/data/val.txt
-names = /home/sowmya/fedn/examples/Yolo-object-detection/client/darknet/obj.names
+train = /Users/sowmyasriseenivasan/workspaces/fedn/examples/Yolo-object-detection/client/data/train.txt
+valid = /Users/sowmyasriseenivasan/workspaces/fedn/examples/Yolo-object-detection/client/data/val.txt
+names = /Users/sowmyasriseenivasan/workspaces/fedn/examples/Yolo-object-detection/client/data/obj.names
 backup = yolov4_tiny

examples/mnist-pytorch/client/entrypoint.py

@@ -0,0 +1,182 @@
+import json
+import os
+import fire
+import numpy as np
+
+from fedn.utils.helpers.helpers import get_helper, save_metadata, save_metrics
+
+HELPER_MODULE = "numpyhelper"
+helper = get_helper(HELPER_MODULE)
+
+NUM_CLASSES = 10
+
+dir_path = os.path.dirname(os.path.realpath(__file__))
+abs_path = os.path.abspath(dir_path)
+
+
+def _get_data_path():
+    data_path = os.environ.get("FEDN_DATA_PATH", abs_path + "/data/clients/1/mnist.npz")
+
+    return data_path
+
+def save_darknet2fedn(darkfile, fednfile    ):
+
+    fp = open(darkfile, "rb")
+    buf = np.fromfile(fp, dtype=np.float32)
+    helper.save([buf], fednfile)
+    fp.close()
+def save_fedn2darknet(fednfile, darkfile):
+
+    buf = helper.load(fednfile)[0]
+
+    with open(darkfile, "wb") as f:
+        buf.tofile(f)
+def init_seed(out_path="../seed.npz"):
+    """Initialize seed model and save it to file.
+
+    :param out_path: The path to save the seed model to.
+    :type out_path: str
+    """
+    #weights = compile_model().get_weights()
+    #helper.save(weights, out_path)
+    print("hereeee")
+    darkfile="yolov4-tiny.weights"
+    fp = open(darkfile, "rb")
+    buf = np.fromfile(fp, dtype=np.float32)
+    helper.save([buf], out_path)
+    fp.close()
+
+def train(in_model_path, out_model_path, data_path=None, batch_size=32, epochs=1):
+    """Complete a model update.
+    Load model paramters from in_model_path (managed by the FEDn client),
+    perform a model update, and write updated paramters
+    to out_model_path (picked up by the FEDn client).
+
+    :param in_model_path: The path to the input model.
+    :type in_model_path: str
+    :param out_model_path: The path to save the output model to.
+    :type out_model_path: str
+    :param data_path: The path to the data file.
+    :type data_path: str
+    :param batch_size: The batch size to use.
+    :type batch_size: int
+    :param epochs: The number of epochs to train.
+    :type epochs: int
+    """
+    darkfile = "example.weights"
+
+    save_fedn2darknet(in_model_path, darkfile)
+    #save_darknet2fedn("/Users/sowmyasriseenivasan/workspaces/fedn/examples/Yolo-object-detection/client/darknet/yolov4-tiny.weights","client")
+
+    # cli call to darknet to train darkfile and save it to e.g. darkfile_upd.weights
+    import subprocess
+
+    # Paths to your files
+    data_file = "darknet/obj.data"
+    cfg_file = "darknet/yolov4-tiny.cfg"
+    yolo_converted_weights = "example.weights"  # Pretrained weights file
+
+    # Darknet executable path
+    darknet_path = "./darknet/darknet"  # Make sure this path is correct
+
+    # Command to train YOLO using Darknet
+    command = [darknet_path, "detector", "train", data_file, cfg_file, yolo_converted_weights]
+
+    # Run the command
+    try:
+        subprocess.run(command, check=True)
+    except subprocess.CalledProcessError as e:
+        print(f"Error during training: {e}")
+
+    #save_darknet2fedn("yolov4-tiny_last.weights", out_model_path)
+
+    # saving metadata in the same way as in our example
+
+    metadata = {
+        # num_examples are mandatory
+        "num_examples": 600,
+        "batch_size": batch_size,
+        "epochs": 1,
+        "lr": 0.001,
+    }
+    # Save JSON metadata file (mandatory)
+    save_metadata(metadata, out_model_path)
+    # Save model update (mandatory)
+    save_darknet2fedn("yolov4-tiny_final.weights", "output.npz")
+    helper.save("output.npz", out_model_path)
+if __name__ == "__main__":
+    fire.Fire(
+        {
+            "init_seed": init_seed,
+            "train": train,
+            "_get_data_path": _get_data_path,  # for testing
+        }
+    )
+#if __name__ == "__main__":
+    #train("fedn/examples/Yolo-object-detection/client/yolov4-tiny.conv.29","client")
+    #save_darknet2fedn("/Users/sowmyasriseenivasan/Downloads/example_project_yolo/firstmodel/carplusbike_firstdataset.weights","/Users/sowmyasriseenivasan/workspaces/fedn/examples/Yolo-object-detection/client/output/output.npz")
+    #darkfile="example.weights"
+    #init_seed()
+    #save_fedn2darknet("/Users/sowmyasriseenivasan/workspaces/fedn/examples/Yolo-object-detection/client/output/output.npz",darkfile)
+""" def validate(in_model_path, out_json_path, data_path=None):
+"""     """Validate model.
+
+    :param in_model_path: The path to the input model.
+    :type in_model_path: str
+    :param out_json_path: The path to save the output JSON to.
+    :type out_json_path: str
+    :param data_path: The path to the data file.
+    :type data_path: str """
+"""
+    # Load data
+    x_train, y_train = load_data(data_path)
+    x_test, y_test = load_data(data_path, is_train=False)
+
+    # Load model
+    model = compile_model()
+    helper = get_helper(HELPER_MODULE)
+    weights = helper.load(in_model_path)
+    model.set_weights(weights)
+
+    # Evaluate
+    model_score = model.evaluate(x_train, y_train)
+    model_score_test = model.evaluate(x_test, y_test)
+    y_pred = model.predict(x_test)
+    y_pred = np.argmax(y_pred, axis=1)
+
+    # JSON schema
+    report = {
+        "training_loss": model_score[0],
+        "training_accuracy": model_score[1],
+        "test_loss": model_score_test[0],
+        "test_accuracy": model_score_test[1],
+    }
+
+    # Save JSON
+    save_metrics(report, out_json_path)
+
+
+def predict(in_model_path, out_json_path, data_path=None):
+    # Using test data for inference but another dataset could be loaded
+    x_test, _ = load_data(data_path, is_train=False)
+
+    # Load model
+    model = compile_model()
+    helper = get_helper(HELPER_MODULE)
+    weights = helper.load(in_model_path)
+    model.set_weights(weights)
+
+    # Infer
+    y_pred = model.predict(x_test)
+    y_pred = np.argmax(y_pred, axis=1)
+
+    # Save JSON
+    with open(out_json_path, "w") as fh:
+        fh.write(json.dumps({"predictions": y_pred.tolist()})) """
+
+
+
+""" if __name__ == "__main__":
+    #train("fedn/examples/Yolo-object-detection/client/yolov4-tiny.conv.29","fedn/examples/Yolo-object-detection/client/")
+    save_darknet2fedn("/Users/sowmyasriseenivasan/workspaces/fedn/examples/Yolo-object-detection/client/darknet/yolov4-tiny.weights","client")
+ """