OSC Message Size Limits

2024-10-25 09:55 UTC gpt-4o Open in ChatGPT ↗

Yes, Open Sound Control (OSC) messages do have a practical size limit, though it’s not strictly defined by the OSC specification itself. The size limit for OSC messages depends primarily on the network protocol being used to transmit the messages, commonly UDP or TCP. Here’s how it typically works:

UDP Packet Size

Most OSC implementations use UDP (User Datagram Protocol), which has a maximum recommended packet size:

UDP Datagram Limit: A single UDP packet can technically hold up to 65,507 bytes of data (after accounting for IP headers).
Practical Limit: For most applications, a safe limit for UDP packets is around 512 to 1500 bytes to avoid fragmentation across networks. Packets exceeding this may get fragmented, potentially leading to data loss or reduced performance in networks not optimized for larger packets.

TCP vs. UDP

TCP: While TCP doesn’t have a strict packet size limit like UDP, larger messages are segmented and reassembled. However, TCP is less commonly used with OSC because it adds overhead and latency, which can be detrimental to real-time performance in applications like audio and live performance.

OSC Implementation Limits

Some OSC libraries may set their own limits on message size for performance reasons or may restrict message size based on UDP standards. If you’re designing an OSC-based system, it’s usually a good idea to keep messages concise to ensure compatibility and reliability across network configurations.

how to set this up so that it can accept up to 69 x 30 floats

import random

import numpy as np import pacmap import tensorflow as tf import time from pythonosc import dispatcher from pythonosc import osc_server from pythonosc import udp_client

from tensorflow.keras import backend as K from tensorflow.keras.models import Model import lstm

Custom function to move loss calculation to CPU

def categorical_crossentropy_cpu(y_true, y_pred): with tf.device(‘/CPU:0’): return K.categorical_crossentropy(y_true, y_pred)

model_loc =‘Data/LSTMWeights/2024-10-25/lstm_model_W30_O5.h5’ train_data_loc = “Data/Training/24-10-24/mocapdata_2024-10-24_W30_O5.csv” train_label_loc = “Data/Training/24-10-24/mocapdata_2024-10-24_W30_O5_labels.csv” WINDOW_SIZE = 30 FEATURES = 69

Load the trained LSTM model

model = tf.keras.models.load_model(model_loc, custom_objects={‘categorical_crossentropy_cpu’: categorical_crossentropy_cpu})

Load the data used

X_train = lstm.LSTMNeuralNet.load_data(train_data_loc,train_label_loc,“Sequence_ID”,WINDOW_SIZE)[0]

lstm_layer_output = model.layers[-2].output # Adjust index as needed activation_model = Model(inputs=model.input, outputs=lstm_layer_output) basis = activation_model.predict(X_train) basis_flat = basis.reshape(basis.shape[0], -1)

pacmap_instance = pacmap.load(“Data/Exports/2024-10-25/DR_W30_O5”)

Initialize a variable to store incoming data

global new_data_buffer new_data_buffer = None

To store the latent space projections and neighbor indices

latent_space_coordinates = [] neighbors_list = []

UNITY ON THIS COMPUTER

osc_client = udp_client.SimpleUDPClient(“172.27.144.1”, 5006)

#UNITY ON THE OTHER COMPUTER osc_client2 = udp_client.SimpleUDPClient(“169.254.202.190”, 5007)

def osc_data_handler(address, *args): global new_data_buffer

print(args)

# Assuming incoming data is an array of 70 float values
new_data_buffer = np.array(args).reshape(1, WINDOW_SIZE, FEATURES)   # Adjust shape as needed for LSTM model input

if new_data_buffer is not None:

    # Perform inference using the LSTM model
    prediction = activation_model.predict(new_data_buffer)
    prediction_flat = prediction.reshape(prediction.shape[0], -1)

    class_prediction = model.predict(new_data_buffer)

    print(class_prediction)

    latent_projection = pacmap_instance.transform(X=prediction_flat, basis=basis_flat)

    print(latent_projection[0])

    # Send the latent projection to Unity
    send_latent_projection_to_unity(latent_projection[0])

    # latent_space_coordinates.append(latent_projection[0])
    # Convert latent space to numpy array for nearest neighbor search
    # latent_space_array = np.array(latent_space_coordinates)

    new_data_buffer = None  # Reset buffer after processing

def send_latent_projection_to_unity(latent_projection): # Assuming latent_projection is a 2D array with shape (1, latent_dim) projection_values = latent_projection.flatten().tolist()

# Send the data back to Unity using the OSC client
osc_client.send_message("/unity/latent_projection", projection_values)
#osc_client.send_message("/unity/point_id", random.randint(0,100))

#Remotr PC
osc_client2.send_message("/unity/latent_projection", projection_values)

print(f"Sent latent projection to Unity: {projection_values}")

def generate_fake_data():

"""
Generate random data to simulate incoming data.
"""
return np.random.rand(1, WINDOW_SIZE, FEATURES)

def main(): global new_data_buffer global basis, activation_model

print("LSTM model is loaded and ready for real-time inference.")
print("Waiting for incoming data...")

# Setting up the OSC server
dispatcher_map = dispatcher.Dispatcher()
dispatcher_map.map("/unity/data", osc_data_handler)
server = osc_server.ThreadingOSCUDPServer(("172.27.144.1", 5010), dispatcher_map)

print("OSC server is running on port 5010.")
server.serve_forever()

try:
    while True:
        continue
        #new_data_buffer = generate_fake_data()

        # Adding a delay to simulate real-time data flow
        #time.sleep(0.1)  # Adjust as needed, e.g. to match the actual frequency of incoming data

except KeyboardInterrupt:
    print("Real-time inference terminated by user.")
    server.server_close()

if name == “main”: main()

To ensure that the OSC server can handle incoming messages of up to 69 x 30 floats (2,070 floats total), you’ll need to account for the following factors:

OSC Message Size: Sending 69 x 30 floats as a single OSC message requires a high-capacity setup, as each float occupies 4 bytes, resulting in a message size of around 8,280 bytes. This is within the maximum UDP packet size (65,507 bytes), but be cautious if intermediate networks have lower MTU limits.
Optimizing the OSC Library and Message Handling: Python OSC libraries, such as python-osc, typically support handling larger messages. However, ensure the receiving buffer is configured to accept the large incoming data size.

Here’s how you can adjust your code for this setup:

Step 1: Configure the OSC Server Buffer

You can set the OSC server’s request_queue_size and max_packet_size to handle the larger data size.

Step 2: Modify the Code for Large Incoming Data

Here’s an example modification of your code for handling up to 69 x 30 floats.

from pythonosc import dispatcher, osc_server, udp_client
import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Model

# Load the model and PaCMAP instance
model = tf.keras.models.load_model(model_loc, custom_objects={'categorical_crossentropy_cpu': categorical_crossentropy_cpu})
X_train = lstm.LSTMNeuralNet.load_data(train_data_loc, train_label_loc, "Sequence_ID", WINDOW_SIZE)[0]
lstm_layer_output = model.layers[-2].output  # Adjust index as needed
activation_model = Model(inputs=model.input, outputs=lstm_layer_output)
basis = activation_model.predict(X_train).reshape(X_train.shape[0], -1)
pacmap_instance = pacmap.load("Data/Exports/2024-10-25/DR_W30_O5")

osc_client = udp_client.SimpleUDPClient("172.27.144.1", 5006)
osc_client2 = udp_client.SimpleUDPClient("169.254.202.190", 5007)
new_data_buffer = None  # Initialize buffer

# Function to handle incoming OSC data
def osc_data_handler(address, *args):
    global new_data_buffer
    if len(args) == 2070:  # Expecting exactly 69x30 = 2070 floats
        new_data_buffer = np.array(args).reshape(1, WINDOW_SIZE, FEATURES)

        # Perform inference using the LSTM model
        prediction = activation_model.predict(new_data_buffer).reshape(1, -1)
        class_prediction = model.predict(new_data_buffer)

        latent_projection = pacmap_instance.transform(X=prediction, basis=basis)

        # Send latent projection to Unity
        send_latent_projection_to_unity(latent_projection[0])

        new_data_buffer = None  # Clear buffer after processing

def send_latent_projection_to_unity(latent_projection):
    projection_values = latent_projection.flatten().tolist()
    osc_client.send_message("/unity/latent_projection", projection_values)
    osc_client2.send_message("/unity/latent_projection", projection_values)
    print(f"Sent latent projection to Unity: {projection_values}")

def main():
    global new_data_buffer
    dispatcher_map = dispatcher.Dispatcher()
    dispatcher_map.map("/unity/data", osc_data_handler)

    server = osc_server.ThreadingOSCUDPServer(("172.27.144.1", 5010), dispatcher_map)
    server.request_queue_size = 128  # Adjust as needed for high throughput
    server.max_packet_size = 65535  # Set max packet size to accommodate large messages

    print("OSC server is running on port 5010.")
    try:
        server.serve_forever()
    except KeyboardInterrupt:
        print("Server stopped by user.")
        server.server_close()

if __name__ == "__main__":
    main()

Key Modifications

osc_data_handler Check: This verifies the exact size of the incoming data (2070 floats) before reshaping it for model inference. This ensures your handler only processes correctly-sized messages.
Server Configuration: The request_queue_size and max_packet_size attributes are set to high values to help manage incoming data and avoid overflow issues.
send_latent_projection_to_unity Simplification: This function now simply flattens the latent projection, making it easier to send over OSC.

To send a well-formatted string back to Unity based on your model_labels.yaml file and the prediction result, you’ll need to load the labels from the YAML file, map the predicted class to its label, and format the message.

Here’s how to do it:

Load model_labels.yaml: Parse the YAML file to get a dictionary of labels.
Format the Prediction Result: After inference, match the predicted class with the label, and format it as "LABEL: {PREDICTION}".
Send to Unity: Send the formatted string back to Unity using your OSC client.

Assuming model_labels.yaml is located in the same directory, here’s how to implement this:

import yaml
import numpy as np
from pythonosc import udp_client
import tensorflow as tf
from tensorflow.keras.models import Model

# Load YAML labels
with open("model_labels.yaml", 'r') as file:
    labels = yaml.safe_load(file)["MODEL"]["labels"]

# Initialize OSC clients
osc_client = udp_client.SimpleUDPClient("172.27.144.1", 5006)
osc_client2 = udp_client.SimpleUDPClient("169.254.202.190", 5007)

# Function to handle predictions and send formatted output
def handle_prediction(prediction):
    # Assuming the model outputs class probabilities, get the predicted class index
    predicted_class = np.argmax(prediction)
    label = labels[str(predicted_class)]

    # Create formatted string
    formatted_string = f"LABEL: {label}"

    # Send formatted string to Unity
    send_to_unity(formatted_string)

def send_to_unity(message):
    # Send the message to both Unity clients
    osc_client.send_message("/unity/prediction", message)
    osc_client2.send_message("/unity/prediction", message)
    print(f"Sent message to Unity: {message}")

# Example usage in your main inference function
def osc_data_handler(address, *args):
    global new_data_buffer
    if len(args) == 2070:  # Check for exact size match
        new_data_buffer = np.array(args).reshape(1, WINDOW_SIZE, FEATURES)

        # Perform inference
        prediction = model.predict(new_data_buffer)
        handle_prediction(prediction)

        new_data_buffer = None  # Clear buffer after processing

Explanation of Key Sections

handle_prediction Function: This extracts the class with the highest probability from prediction using np.argmax, finds the corresponding label in labels, and formats the output as "LABEL: {PREDICTION}".
send_to_unity Function: Sends the formatted prediction result to Unity as a string. This is useful for directly transmitting textual information.

This setup will output a clean, readable string indicating the prediction’s label for Unity to display or use.

To send all the class probabilities in a well-formatted string, we can modify the handle_prediction function to display each label alongside its probability. This requires iterating over each possible class and formatting the output as "LABEL: {PROBABILITY}" for each class.

Here’s how to update your code:

import yaml
import numpy as np
from pythonosc import udp_client
import tensorflow as tf
from tensorflow.keras.models import Model

# Load YAML labels
with open("model_labels.yaml", 'r') as file:
    labels = yaml.safe_load(file)["MODEL"]["labels"]

# Initialize OSC clients
osc_client = udp_client.SimpleUDPClient("172.27.144.1", 5006)
osc_client2 = udp_client.SimpleUDPClient("169.254.202.190", 5007)

# Function to handle predictions and send formatted output
def handle_prediction(prediction):
    # Format each label with its probability
    formatted_string = "PREDICTIONS:\n" + "\n".join(
        f"{labels[str(i)]}: {prob:.4f}" for i, prob in enumerate(prediction[0])
    )

    # Send the formatted string to Unity
    send_to_unity(formatted_string)

def send_to_unity(message):
    # Send the message to both Unity clients
    osc_client.send_message("/unity/prediction", message)
    osc_client2.send_message("/unity/prediction", message)
    print(f"Sent message to Unity: {message}")

# Example usage in your main inference function
def osc_data_handler(address, *args):
    global new_data_buffer
    if len(args) == 2070:  # Check for exact size match
        new_data_buffer = np.array(args).reshape(1, WINDOW_SIZE, FEATURES)

        # Perform inference
        prediction = model.predict(new_data_buffer)
        handle_prediction(prediction)

        new_data_buffer = None  # Clear buffer after processing

Explanation of Key Changes

handle_prediction Function:
- Loops over prediction[0], which is the array of probabilities for each class.
- Retrieves each class’s label and probability, formats them as "LABEL: PROBABILITY", and appends them to formatted_string.
Formatted Output:
- formatted_string now contains each class’s label and its corresponding probability, separated by newlines. This complete string is then sent to Unity.

This setup sends all class probabilities to Unity in a readable, formatted structure, allowing Unity to display or use the entire distribution of predicted probabilities.

To display each class probability as a percentage, you simply need to multiply each probability by 100 and format it to display with an appropriate precision, such as two decimal places. Here’s the updated code with percentages:

import yaml
import numpy as np
from pythonosc import udp_client
import tensorflow as tf
from tensorflow.keras.models import Model

# Load YAML labels
with open("model_labels.yaml", 'r') as file:
    labels = yaml.safe_load(file)["MODEL"]["labels"]

# Initialize OSC clients
osc_client = udp_client.SimpleUDPClient("172.27.144.1", 5006)
osc_client2 = udp_client.SimpleUDPClient("169.254.202.190", 5007)

# Function to handle predictions and send formatted output as percentages
def handle_prediction(prediction):
    # Format each label with its probability as a percentage
    formatted_string = "PREDICTIONS:\n" + "\n".join(
        f"{labels[str(i)]}: {prob * 100:.2f}%" for i, prob in enumerate(prediction[0])
    )

    # Send the formatted string to Unity
    send_to_unity(formatted_string)

def send_to_unity(message):
    # Send the message to both Unity clients
    osc_client.send_message("/unity/prediction", message)
    osc_client2.send_message("/unity/prediction", message)
    print(f"Sent message to Unity: {message}")

# Example usage in your main inference function
def osc_data_handler(address, *args):
    global new_data_buffer
    if len(args) == 2070:  # Check for exact size match
        new_data_buffer = np.array(args).reshape(1, WINDOW_SIZE, FEATURES)

        # Perform inference
        prediction = model.predict(new_data_buffer)
        handle_prediction(prediction)

        new_data_buffer = None  # Clear buffer after processing

Explanation of Key Changes

Percentage Conversion:
- Each probability in prediction[0] is multiplied by 100 and formatted to two decimal places using {prob * 100:.2f}%, converting it into a percentage.
Formatted Output:
- formatted_string now contains each class’s label and probability percentage in a readable, formatted structure, e.g., "Angry: 23.45%". This string is sent to Unity.

This setup will display the prediction results as percentages, making it easier to interpret confidence levels for each class in Unity.

To sort the predictions in descending order before formatting the string, we’ll modify the handle_prediction function to sort by probability. Here’s the updated code:

import yaml
import numpy as np
from pythonosc import udp_client
import tensorflow as tf
from tensorflow.keras.models import Model

# Load YAML labels
with open("model_labels.yaml", 'r') as file:
    labels = yaml.safe_load(file)["MODEL"]["labels"]

# Initialize OSC clients
osc_client = udp_client.SimpleUDPClient("172.27.144.1", 5006)
osc_client2 = udp_client.SimpleUDPClient("169.254.202.190", 5007)

# Function to handle predictions, sort, and send formatted output as percentages
def handle_prediction(prediction):
    # Get probabilities and sort by descending order
    sorted_predictions = sorted(
        enumerate(prediction[0]), key=lambda x: x[1], reverse=True
    )

    # Format each sorted label with its probability as a percentage
    formatted_string = "PREDICTIONS:\n" + "\n".join(
        f"{labels[str(i)]}: {prob * 100:.2f}%" for i, prob in sorted_predictions
    )

    # Send the formatted string to Unity
    send_to_unity(formatted_string)

def send_to_unity(message):
    # Send the message to both Unity clients
    osc_client.send_message("/unity/prediction", message)
    osc_client2.send_message("/unity/prediction", message)
    print(f"Sent message to Unity: {message}")

# Example usage in your main inference function
def osc_data_handler(address, *args):
    global new_data_buffer
    if len(args) == 2070:  # Check for exact size match
        new_data_buffer = np.array(args).reshape(1, WINDOW_SIZE, FEATURES)

        # Perform inference
        prediction = model.predict(new_data_buffer)
        handle_prediction(prediction)

        new_data_buffer = None  # Clear buffer after processing

Explanation of Key Changes

Sorting Predictions:
- The sorted_predictions variable stores the sorted list of tuples (index, probability) from prediction[0], sorted by probability in descending order.
Formatted Output:
- formatted_string now lists each label and its percentage, starting with the highest probability. This string is then sent to Unity, giving it a sorted view of predictions by confidence level.

This setup will display the class probabilities in descending order of confidence, making it easier to see which predictions the model is most confident about.