Feature/SK-667 | Add new Android helper to Fedn

fedn/fedn/utils/helpers/plugins/androidhelper.py

@@ -0,0 +1,107 @@
+import os
+import struct
+import tempfile
+
+import numpy as np
+
+from .helperbase import HelperBase
+
+
+class Helper(HelperBase):
+    """FEDn helper class for android json model weights."""
+
+    def __init__(self):
+        """Initialize helper."""
+        self.name = "androidhelper"
+        super().__init__()
+
+    # function to calculate an incremental weighted average of the weights
+
+    def increment_average(
+            self, model, model_next, num_examples, total_examples
+    ):
+        """Incremental weighted average of model weights.
+
+        :param model: Current model weights.
+        :type model: list of numpy arrays.
+        :param model_next: New model weights.
+        :type model_next: list of numpy arrays.
+        :param num_examples: Number of examples in new model.
+        :type num_examples: int
+        :param total_examples: Total number of examples.
+        :type total_examples: int
+        :return: Incremental weighted average of model weights.
+        :rtype: list of numpy arrays.
+        """
+        # Incremental weighted average
+        w = num_examples / total_examples
+
+        return (1 - w) * model + w * model_next
+
+    # function to calculate an incremental weighted average of the weights using numpy.add
+    def increment_average_add(
+        self, model, model_next, num_examples, total_examples
+    ):
+        """Incremental weighted average of model weights.
+
+        :param model: Current model weights.
+        :type model: list of numpy arrays.
+        :param model_next: New model weights.
+        :type model_next: list of numpy arrays.
+        :param num_examples: Number of examples in new model.
+        :type num_examples: int
+        :param total_examples: Total number of examples.
+        :type total_examples: int
+        :return: Incremental weighted average of model weights.
+        :rtype: list of numpy arrays.
+        """
+        # Incremental weighted average
+        w = np.add(
+            model,
+            num_examples
+            * (np.array(model_next) - np.array(model))
+            / total_examples,
+        )
+        return w
+
+    def save(self, weights, path=None):
+        """Serialize weights to file. The serialized model must be a single binary object.
+
+        :param weights: weights in json format.
+        :param path: Path to file.
+        :return: Path to file.
+        """
+        if not path:
+            path = self.get_tmp_path()
+
+        byte_array = struct.pack("f"*len(weights), *weights)
+        with open(path, "wb") as file:
+            file.write(byte_array)
+
+        return path
+
+    def load(self, fh):
+        """Load weights from file or filelike.
+
+        :param fh: file path, filehandle, filelike.
+        :return: List of weights in json format.
+        """
+        print("in android helper load")
+        if isinstance(fh, str):
+            with open(fh, "rb") as file:
+                byte_data = file.read()
+        else:
+            byte_data = fh.read()
+
+        weights = np.array(struct.unpack(f'{len(byte_data) // 4}f', byte_data))
+
+        return weights
+
+    def get_tmp_path(self):
+        """Return a temporary output path compatible with save_model, load_model.
+
+        :return: Path to file.
+        """
+        fd, path = tempfile.mkstemp(suffix="")
+        os.close(fd)
+        return path