Restructure training script, use new API
This commit is contained in:
Christoph Hagen
parent
631b93872e
commit
56b74117ca
12
Training/config.sh
Normal file
12
Training/config.sh
Normal file
@ -0,0 +1,12 @@
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WORK_DIR="."
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BACKUP_DIR="./backup"
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IMAGE_DIR="../Public/images"
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MODEL_FILE="../Public/classifier.mlmodel"
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TRAINING_ITERATIONS="20"
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SSH_PORT="22"
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SERVER="pi@mydomain.com"
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SERVER_ROOT_PATH="/caps/Public"
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SERVER_PATH="https://mydomain.com/caps"
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SERVER_AUTH="mysecretkey"
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@ -25,20 +25,7 @@
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#
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########################################################################################
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########### PATHS ######################################################################
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WORK_DIR="${HOME}/Projects/Caps/Caps-Server/Training"
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BACKUP_DIR="./backup"
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IMAGE_DIR="../Public/images"
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VERSION_FILE="../Public/classifier.version"
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MODEL_FILE="../Public/classifier.mlmodel"
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TRAINING_ITERATIONS="17"
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SSH_PORT="5432"
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SERVER="pi@christophhagen.de"
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SERVER_ROOT_PATH="/data/servers/caps/Public"
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########################################################################################
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source config.sh
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echo "[INFO] Working in directory ${WORK_DIR}"
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cd $WORK_DIR
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@ -67,64 +54,4 @@ if [ $retVal -ne 0 ]; then
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return $retVal
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fi
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echo "[INFO] Getting classifier version from server..."
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scp -P $SSH_PORT ${SERVER}:/${SERVER_ROOT_PATH}/classifier.version $VERSION_FILE
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retVal=$?
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if [ $retVal -ne 0 ]; then
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echo '[ERROR] Failed to get classifier version'
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return $retVal
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fi
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# Read classifier version from file
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OLD_VERSION=$(< $VERSION_FILE)
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NEW_VERSION=$(($OLD_VERSION + 1))
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echo "[INFO] Backing up model ${OLD_VERSION}..."
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mv $MODEL_FILE "${BACKUP_DIR}/classifier${OLD_VERSION}.mlmodel"
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retVal=$?
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if [ $retVal -ne 0 ]; then
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echo '[WARNING] Failed to back up old model'
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fi
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echo "[INFO] Training model ${NEW_VERSION} ..."
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swift train.swift $IMAGE_DIR $TRAINING_ITERATIONS $MODEL_FILE
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retVal=$?
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if [ $retVal -ne 0 ]; then
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echo '[ERROR] Failed to train model'
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return $retVal
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fi
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echo "[INFO] Incrementing version file..."
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echo "${NEW_VERSION}" > $VERSION_FILE
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echo "[INFO] Copying the files to the server..."
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scp -P $SSH_PORT $MODEL_FILE $VERSION_FILE ${SERVER}:~/
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retVal=$?
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if [ $retVal -ne 0 ]; then
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echo '[ERROR] Failed to copy new files to server'
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return $retVal
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fi
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echo "[INFO] Moving server files into public directory..."
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ssh -p ${SSH_PORT} ${SERVER} "sudo mv /home/pi/classifier.* ${SERVER_ROOT_PATH}"
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retVal=$?
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if [ $retVal -ne 0 ]; then
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echo '[ERROR] Failed to move files on server'
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return $retVal
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fi
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echo "[INFO] Updating server permissions..."
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ssh -p ${SSH_PORT} ${SERVER} "sudo chown -R www-data\:www-data ${SERVER_ROOT_PATH}"
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retVal=$?
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if [ $retVal -ne 0 ]; then
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echo '[ERROR] Failed to update file permissions on server'
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return $retVal
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fi
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echo "[INFO] Process finished"
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swift train.swift $SERVER_PATH $SERVER_AUTH $IMAGE_DIR $MODEL_FILE $TRAINING_ITERATIONS
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@ -1,61 +1,157 @@
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import Foundation
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import Cocoa
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import CreateML
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let defaultIterations = 17
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let defaultWorkingDirectory = URL(fileURLWithPath: "/Users/imac/Development/CapCollectorData")
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let defaultTrainDirectory = defaultWorkingDirectory.appendingPathComponent("images")
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let defaultClassifierFile = defaultWorkingDirectory.appendingPathComponent("classifier.mlmodel")
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final class Server {
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func readArguments() -> (images: URL, classifier: URL, iterations: Int) {
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let count = CommandLine.argc
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guard count > 1 else {
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// No arguments
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return (defaultTrainDirectory, defaultClassifierFile, defaultIterations)
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let server: URL
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let authentication: String
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init(server: URL, authentication: String) {
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self.server = server
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self.authentication = authentication
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}
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// First argument is the image directory
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let imageDir = URL(fileURLWithPath: CommandLine.arguments[1])
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let classifier = imageDir.deletingLastPathComponent().appendingPathComponent("classifier.mlmodel")
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guard count > 2 else {
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// Single argument is the image directory
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return (imageDir, classifier, defaultIterations)
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private func wait(for request: URLRequest) -> Data? {
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let group = DispatchGroup()
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group.enter()
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var result: Data? = nil
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URLSession.shared.dataTask(with: request) { data, response, _ in
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defer { group.leave() }
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let code = (response as! HTTPURLResponse).statusCode
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guard code == 200 else {
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print("[ERROR] Invalid response \(code)")
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return
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}
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// Second argument is the iteration count
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guard let iterations = Int(CommandLine.arguments[2]) else {
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print("[ERROR] Invalid iterations argument '\(CommandLine.arguments[2])'")
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exit(-1)
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guard let data = data else {
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print("[ERROR] No response data")
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return
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}
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guard count > 3 else {
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return (imageDir, classifier, iterations)
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result = data
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}.resume()
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group.wait()
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return result
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}
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// Third argument is the classifier path
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let classifierPath = URL(fileURLWithPath: CommandLine.arguments[3])
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if count > 4 {
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print("[WARNING] Ignoring additional arguments")
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func getClassifierVersion() -> Int? {
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let group = DispatchGroup()
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group.enter()
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let classifierVersionUrl = server.appendingPathComponent("version")
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guard let data = wait(for: URLRequest(url: classifierVersionUrl)) else {
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return nil
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}
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guard let string = String(data: data, encoding: .utf8) else {
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print("[ERROR] Invalid classifier version \(data)")
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return nil
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}
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guard let int = Int(string) else {
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print("[ERROR] Invalid classifier version \(string)")
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return nil
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}
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return int
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}
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func upload(classifier: Data, version: Int) -> Bool {
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let classifierUrl = server
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.appendingPathComponent("classifier")
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.appendingPathComponent("\(version)")
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var request = URLRequest(url: classifierUrl)
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request.httpMethod = "POST"
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request.httpBody = classifier
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request.addValue(authentication, forHTTPHeaderField: "key")
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return wait(for: request) != nil
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}
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func upload(classes: [Int]) -> Bool {
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let classifierUrl = server
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.appendingPathComponent("classes")
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var request = URLRequest(url: classifierUrl)
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request.httpMethod = "POST"
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request.httpBody = classes.map(String.init).joined(separator: "\n").data(using: .utf8)!
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request.addValue(authentication, forHTTPHeaderField: "key")
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return wait(for: request) != nil
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}
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return (imageDir, classifierPath, iterations)
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}
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let arguments = readArguments()
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print("[INFO] Using images in \(arguments.images.path)")
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print("[INFO] Training for \(arguments.iterations) iterations")
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print("[INFO] Classifier path set to \(arguments.classifier.path)")
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let count = CommandLine.argc
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guard count == 6 else {
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print("[ERROR] Invalid number of arguments")
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exit(1)
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}
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let serverPath = CommandLine.arguments[1]
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let authenticationKey = CommandLine.arguments[2]
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let imageDirectory = URL(fileURLWithPath: CommandLine.arguments[3])
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let classifierUrl = URL(fileURLWithPath: CommandLine.arguments[4])
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let iterationsString = CommandLine.arguments[5]
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guard let serverUrl = URL(string: serverPath) else {
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print("[ERROR] Invalid server path argument")
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exit(1)
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}
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guard let iterations = Int(iterationsString) else {
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print("[ERROR] Invalid iterations argument")
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exit(1)
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}
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let server = Server(server: serverUrl, authentication: authenticationKey)
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let classes: [Int]
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do {
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classes = try FileManager.default.contentsOfDirectory(atPath: imageDirectory.path)
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.compactMap(Int.init)
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} catch {
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print("[ERROR] Failed to get model classes: \(error)")
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exit(1)
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}
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guard let oldVersion = server.getClassifierVersion() else {
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print("[ERROR] Failed to get classifier version")
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exit(1)
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}
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let newVersion = oldVersion + 1
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print("[INFO] Image directory: \(imageDirectory.path)")
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print("[INFO] Model path: \(classifierUrl.path)")
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print("[INFO] Version: \(newVersion)")
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print("[INFO] Classes: \(classes.count)")
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print("[INFO] Iterations: \(iterations)")
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var params = MLImageClassifier.ModelParameters(augmentation: [])
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params.maxIterations = arguments.iterations
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params.maxIterations = iterations
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let model = try MLImageClassifier(
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trainingData: .labeledDirectories(at: arguments.images),
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let model: MLImageClassifier
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do {
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model = try MLImageClassifier(
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trainingData: .labeledDirectories(at: imageDirectory),
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parameters: params)
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} catch {
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print("[ERROR] Failed to create classifier: \(error)")
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exit(1)
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}
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print("[INFO] Writing classifier...")
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try model.write(to: arguments.classifier)
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print("[INFO] Saving classifier...")
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do {
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try model.write(to: classifierUrl)
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} catch {
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print("[ERROR] Failed to save model to file: \(error)")
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exit(1)
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}
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/*
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let evaluation = model.evaluation(on: .labeledDirectories(at: trainDirectory))
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print("Printing evaluation:")
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print(evaluation)
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print("Finished evaluation")
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*/
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print("[INFO] Uploading classifier...")
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let modelData = try Data(contentsOf: classifierUrl)
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guard server.upload(classifier: modelData, version: newVersion) else {
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print("[ERROR] Failed to upload classifier")
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exit(1)
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}
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print("[INFO] Uploading trained classes...")
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guard server.upload(classes: classes) else {
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print("[ERROR] Failed to upload classes")
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exit(1)
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}
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print("[INFO] Done")
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exit(0)
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