Update SwiftPM pins to 0.7.0 (#39)

* Update SwiftPM pins to 0.7.0

* Update MobileNetClassifier.swift

Author: shoumikhin

mv3/apple/ExecuTorchDemo/ExecuTorchDemo.xcodeproj/project.pbxproj

@@ -18,15 +18,15 @@
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 		03672A682DE1226100147123 /* backend_mps in Frameworks */ = {isa = PBXBuildFile; productRef = 03672A672DE1226100147123 /* backend_mps */; };
 		03672A6A2DE1226100147123 /* backend_xnnpack in Frameworks */ = {isa = PBXBuildFile; productRef = 03672A692DE1226100147123 /* backend_xnnpack */; };
-		03672A732DE1231400147123 /* kernels_portable in Frameworks */ = {isa = PBXBuildFile; productRef = 03672A722DE1231400147123 /* kernels_portable */; };
 		03672A752DE144CF00147123 /* executorch_debug in Frameworks */ = {isa = PBXBuildFile; productRef = 03672A742DE144CF00147123 /* executorch_debug */; };
 		03672A772DE1457900147123 /* backend_coreml in Frameworks */ = {isa = PBXBuildFile; productRef = 03672A762DE1457900147123 /* backend_coreml */; };
 		03672A792DE1457900147123 /* backend_mps in Frameworks */ = {isa = PBXBuildFile; productRef = 03672A782DE1457900147123 /* backend_mps */; };
 		03672A7B2DE1457900147123 /* backend_xnnpack in Frameworks */ = {isa = PBXBuildFile; productRef = 03672A7A2DE1457900147123 /* backend_xnnpack */; };
-		03672A7D2DE1457900147123 /* kernels_portable in Frameworks */ = {isa = PBXBuildFile; productRef = 03672A7C2DE1457900147123 /* kernels_portable */; };
 		036834D52ACB710D00BA100F /* mv3.pte in Resources */ = {isa = PBXBuildFile; fileRef = 036834D42ACB710D00BA100F /* mv3.pte */; };
 		036834D62ACB710D00BA100F /* mv3.pte in Resources */ = {isa = PBXBuildFile; fileRef = 036834D42ACB710D00BA100F /* mv3.pte */; };
 		036834D82ACB893700BA100F /* mv3_coreml_all.pte in Resources */ = {isa = PBXBuildFile; fileRef = 036834D72ACB893700BA100F /* mv3_coreml_all.pte */; };
+		03B184962E1FA1E400246294 /* kernels_optimized in Frameworks */ = {isa = PBXBuildFile; productRef = 03B184952E1FA1E400246294 /* kernels_optimized */; };
+		03B184982E1FA1F400246294 /* kernels_optimized in Frameworks */ = {isa = PBXBuildFile; productRef = 03B184972E1FA1F400246294 /* kernels_optimized */; };
 		03C818192AC671980084CC29 /* Samoyed.jpg in Resources */ = {isa = PBXBuildFile; fileRef = 03C818182AC671980084CC29 /* Samoyed.jpg */; };
 		03C8181D2AC69C280084CC29 /* Arctic fox.jpg in Resources */ = {isa = PBXBuildFile; fileRef = 03C8181C2AC69C280084CC29 /* Arctic fox.jpg */; };
 		03C8181F2AC69DFF0084CC29 /* hot pot.jpg in Resources */ = {isa = PBXBuildFile; fileRef = 03C8181E2AC69DFF0084CC29 /* hot pot.jpg */; };
@@ -118,8 +118,8 @@
 			buildActionMask = 2147483647;
 			files = (
 				032C01EC2AC34CAC002955E1 /* libMobileNetClassifier.a in Frameworks */,
-				03672A732DE1231400147123 /* kernels_portable in Frameworks */,
 				03672A6A2DE1226100147123 /* backend_xnnpack in Frameworks */,
+				03B184962E1FA1E400246294 /* kernels_optimized in Frameworks */,
 				03672A682DE1226100147123 /* backend_mps in Frameworks */,
 				03672A662DE1226100147123 /* backend_coreml in Frameworks */,
 			);
@@ -140,9 +140,9 @@
 			files = (
 				03C818282AC760650084CC29 /* libMobileNetClassifier.a in Frameworks */,
 				03672A7B2DE1457900147123 /* backend_xnnpack in Frameworks */,
+				03B184982E1FA1F400246294 /* kernels_optimized in Frameworks */,
 				03672A792DE1457900147123 /* backend_mps in Frameworks */,
 				03672A772DE1457900147123 /* backend_coreml in Frameworks */,
-				03672A7D2DE1457900147123 /* kernels_portable in Frameworks */,
 			);
 			runOnlyForDeploymentPostprocessing = 0;
 		};
@@ -301,6 +301,7 @@
 				03672A672DE1226100147123 /* backend_mps */,
 				03672A692DE1226100147123 /* backend_xnnpack */,
 				03672A722DE1231400147123 /* kernels_portable */,
+				03B184952E1FA1E400246294 /* kernels_optimized */,
 			);
 			productName = ExecuTorchDemo;
 			productReference = 032C016F2AC228E6002955E1 /* ExecuTorchDemo.app */;
@@ -346,6 +347,7 @@
 				03672A782DE1457900147123 /* backend_mps */,
 				03672A7A2DE1457900147123 /* backend_xnnpack */,
 				03672A7C2DE1457900147123 /* kernels_portable */,
+				03B184972E1FA1F400246294 /* kernels_optimized */,
 			);
 			productName = ExecuTorchDemoTests;
 			productReference = 03C8180A2AC66FC30084CC29 /* MobileNetClassifierTest.xctest */;
@@ -560,10 +562,7 @@
 				IPHONEOS_DEPLOYMENT_TARGET = 17.0;
 				LOCALIZATION_PREFERS_STRING_CATALOGS = YES;
 				ONLY_ACTIVE_ARCH = YES;
-				OTHER_LDFLAGS = (
-					"-lc++",
-					"-all_load",
-				);
+				OTHER_LDFLAGS = "-all_load";
 				SDKROOT = iphoneos;
 				SWIFT_ACTIVE_COMPILATION_CONDITIONS = "DEBUG $(inherited)";
 				SWIFT_OPTIMIZATION_LEVEL = "-Onone";
@@ -619,10 +618,7 @@
 				GCC_WARN_UNUSED_VARIABLE = YES;
 				IPHONEOS_DEPLOYMENT_TARGET = 17.0;
 				LOCALIZATION_PREFERS_STRING_CATALOGS = YES;
-				OTHER_LDFLAGS = (
-					"-lc++",
-					"-all_load",
-				);
+				OTHER_LDFLAGS = "-all_load";
 				SDKROOT = iphoneos;
 				SWIFT_COMPILATION_MODE = wholemodule;
 				SWIFT_VERSION = 5.0;
@@ -804,7 +800,7 @@
 			isa = XCRemoteSwiftPackageReference;
 			repositoryURL = "https://github.com/pytorch/executorch";
 			requirement = {
-				branch = "swiftpm-0.6.0";
+				branch = "swiftpm-0.7.0";
 				kind = branch;
 			};
 		};
@@ -856,6 +852,16 @@
 			package = 03672A642DE1226100147123 /* XCRemoteSwiftPackageReference "executorch" */;
 			productName = kernels_portable;
 		};
+		03B184952E1FA1E400246294 /* kernels_optimized */ = {
+			isa = XCSwiftPackageProductDependency;
+			package = 03672A642DE1226100147123 /* XCRemoteSwiftPackageReference "executorch" */;
+			productName = kernels_optimized;
+		};
+		03B184972E1FA1F400246294 /* kernels_optimized */ = {
+			isa = XCSwiftPackageProductDependency;
+			package = 03672A642DE1226100147123 /* XCRemoteSwiftPackageReference "executorch" */;
+			productName = kernels_optimized;
+		};
 /* End XCSwiftPackageProductDependency section */
 	};
 	rootObject = 032C01672AC228E5002955E1 /* Project object */;

mv3/apple/ExecuTorchDemo/ExecuTorchDemo/Sources/MobileNet/MobileNetClassifier.swift

@@ -56,14 +56,10 @@ public class MobileNetClassifier: ImageClassification {
   }
 
   public func classify(image: UIImage) throws -> [Classification] {
-    let input = try normalize(rawData(from: transformed(image))).withUnsafeBytes {
-      Tensor(bytes: $0.baseAddress!, shape: [1, 3, 224, 224], dataType: .float)
-    }
-    var output: [Float] = []
-    try module.forward(input)[0].tensor?.bytes { pointer, count, _ in
-      output = Array(UnsafeBufferPointer(start: pointer.assumingMemoryBound(to: Float.self), count: count))
-    }
-    return softmax(output)
+    try normalize(transformed(image))
+    let input = Tensor<Float>(&normalizedBuffer, shape: [1, 3, 224, 224])
+    let output: Tensor<Float> = try module.forward(input)[0].tensor()!
+    return softmax(try output.scalars())
       .enumerated()
       .sorted(by: { $0.element > $1.element })
       .compactMap { index, probability in
@@ -98,7 +94,7 @@ public class MobileNetClassifier: ImageClassification {
     return resizedAndCroppedImage
   }
 
-  private func rawData(from image: UIImage) throws -> [UInt8] {
+  private func normalize(_ image: UIImage) throws {
     guard let cgImage = image.cgImage else {
       throw MobileNetClassifierError.rawData
     }
@@ -116,20 +112,15 @@ public class MobileNetClassifier: ImageClassification {
       in: CGRect(
         origin: CGPoint.zero,
         size: CGSize(width: cgImage.width, height: cgImage.height)))
-    return rawDataBuffer
-  }
-
-  private func normalize(_ rawData: [UInt8]) -> [Float] {
     let mean: [Float] = [0.485, 0.456, 0.406]
     let std: [Float] = [0.229, 0.224, 0.225]
-    let pixelCount = rawData.count / 4
+    let pixelCount = rawDataBuffer.count / 4
 
     for i in 0..<pixelCount {
-      normalizedBuffer[i] = (Float(rawData[i * 4 + 0]) / 255 - mean[0]) / std[0]
-      normalizedBuffer[i + pixelCount] = (Float(rawData[i * 4 + 1]) / 255 - mean[1]) / std[1]
-      normalizedBuffer[i + pixelCount * 2] = (Float(rawData[i * 4 + 2]) / 255 - mean[2]) / std[2]
+      normalizedBuffer[i] = (Float(rawDataBuffer[i * 4 + 0]) / 255 - mean[0]) / std[0]
+      normalizedBuffer[i + pixelCount] = (Float(rawDataBuffer[i * 4 + 1]) / 255 - mean[1]) / std[1]
+      normalizedBuffer[i + pixelCount * 2] = (Float(rawDataBuffer[i * 4 + 2]) / 255 - mean[2]) / std[2]
     }
-    return normalizedBuffer
   }
 
   private func softmax(_ input: [Float]) -> [Float] {