Replaced Python with Swift in CIFAR10 dataset loading

.gitignore

@@ -7,3 +7,4 @@
 .DS_Store
 .swiftpm
 cifar-10-batches-py/
+cifar-10-batches-bin/

CIFAR/Data.swift

@@ -1,4 +1,4 @@
-// Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+// Copyright 2019 The TensorFlow Authors. All Rights Reserved.
 //
 // Licensed under the Apache License, Version 2.0 (the "License");
 // you may not use this file except in compliance with the License.
@@ -12,27 +12,61 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-import Python
+import Foundation
 import TensorFlow
 
-/// Use Python and shell calls to download and extract the CIFAR-10 tarball if not already done
-/// This can fail for many reasons (e.g. lack of `wget`, `tar`, or an Internet connection)
+#if canImport(FoundationNetworking)
+    import FoundationNetworking
+#endif
+
 func downloadCIFAR10IfNotPresent(to directory: String = ".") {
-    let subprocess = Python.import("subprocess")
-    let path = Python.import("os.path")
-    let filepath = "\(directory)/cifar-10-batches-py"
-    let isdir = Bool(path.isdir(filepath))!
-    if !isdir {
-        print("Downloading CIFAR data...")
-        let command = "wget -nv -O- https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz | tar xzf - -C \(directory)"
-        subprocess.call(command, shell: true)
+    let downloadPath = "\(directory)/cifar-10-batches-bin"
+    let directoryExists = FileManager.default.fileExists(atPath: downloadPath)
+
+    guard !directoryExists else { return }
+
+    print("Downloading CIFAR dataset...")
+    let archivePath = "\(directory)/cifar-10-binary.tar.gz"
+    let archiveExists = FileManager.default.fileExists(atPath: archivePath)
+    if !archiveExists {
+        print("Archive missing, downloading...")
+        do {
+            let downloadedFile = try Data(
+                contentsOf: URL(
+                    string: "https://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz")!)
+            try downloadedFile.write(to: URL(fileURLWithPath: archivePath))
+        } catch {
+            print("Could not download CIFAR dataset, error: \(error)")
+            exit(-1)
+        }
+    }
+
+    print("Archive downloaded, processing...")
+
+    #if os(macOS)
+        let tarLocation = "/usr/bin/tar"
+    #else
+        let tarLocation = "/bin/tar"
+    #endif
+
+    let task = Process()
+    task.executableURL = URL(fileURLWithPath: tarLocation)
+    task.arguments = ["xzf", archivePath]
+    do {
+        try task.run()
+        task.waitUntilExit()
+    } catch {
+        print("CIFAR extraction failed with error: \(error)")
     }
-}
 
-extension Tensor where Scalar : _TensorFlowDataTypeCompatible {
-    public var _tfeTensorHandle: _AnyTensorHandle {
-        TFETensorHandle(_owning: handle._cTensorHandle)
+    do {
+        try FileManager.default.removeItem(atPath: archivePath)
+    } catch {
+        print("Could not remove archive, error: \(error)")
+        exit(-1)
     }
+
+    print("Unarchiving completed")
 }
 
 struct Example: TensorGroup {
@@ -53,52 +87,61 @@ struct Example: TensorGroup {
         label = Tensor<Int32>(handle: TensorHandle<Int32>(handle: _handles[labelIndex]))
         data = Tensor<Float>(handle: TensorHandle<Float>(handle: _handles[dataIndex]))
     }
-
-    public var _tensorHandles: [_AnyTensorHandle] { [label._tfeTensorHandle, data._tfeTensorHandle] }
 }
 
-// Each CIFAR data file is provided as a Python pickle of NumPy arrays
 func loadCIFARFile(named name: String, in directory: String = ".") -> Example {
     downloadCIFAR10IfNotPresent(to: directory)
-    let np = Python.import("numpy")
-    let pickle = Python.import("pickle")
-    let path = "\(directory)/cifar-10-batches-py/\(name)"
-    let f = Python.open(path, "rb")
-    let res = pickle.load(f, encoding: "bytes")
+    let path = "\(directory)/cifar-10-batches-bin/\(name)"
 
-    let bytes = res[Python.bytes("data", encoding: "utf8")]
-    let labels = res[Python.bytes("labels", encoding: "utf8")]
+    let imageCount = 10000
+    guard let fileContents = try? Data(contentsOf: URL(fileURLWithPath: path)) else {
+        print("Could not read dataset file: \(name)")
+        exit(-1)
+    }
+    guard fileContents.count == 30_730_000 else {
+        print(
+            "Dataset file \(name) should have 30730000 bytes, instead had \(fileContents.count)")
+        exit(-1)
+    }
+
+    var bytes: [UInt8] = []
+    var labels: [Int64] = []
+
+    let imageByteSize = 3073
+    for imageIndex in 0..<imageCount {
+        let baseAddress = imageIndex * imageByteSize
+        labels.append(Int64(fileContents[baseAddress]))
+        bytes.append(contentsOf: fileContents[(baseAddress + 1)..<(baseAddress + 3073)])
+    }
 
-    let labelTensor = Tensor<Int64>(numpy: np.array(labels))!
-    let images = Tensor<UInt8>(numpy: bytes)!
-    let imageCount = images.shape[0]
+    let labelTensor = Tensor<Int64>(shape: [imageCount], scalars: labels)
+    let images = Tensor<UInt8>(shape: [imageCount, 3, 32, 32], scalars: bytes)
 
-    // reshape and transpose from the provided N(CHW) to TF default NHWC
-    let imageTensor = Tensor<Float>(images
-        .reshaped(to: [imageCount, 3, 32, 32])
-        .transposed(withPermutations: [0, 2, 3, 1]))
+    // Transpose from the CIFAR-provided N(CHW) to TF's default NHWC.
+    let imageTensor = Tensor<Float>(images.transposed(withPermutations: [0, 2, 3, 1]))
 
     let mean = Tensor<Float>([0.485, 0.456, 0.406])
-    let std  = Tensor<Float>([0.229, 0.224, 0.225])
+    let std = Tensor<Float>([0.229, 0.224, 0.225])
     let imagesNormalized = ((imageTensor / 255.0) - mean) / std
 
     return Example(label: Tensor<Int32>(labelTensor), data: imagesNormalized)
 }
 
 func loadCIFARTrainingFiles() -> Example {
-    let data = (1..<6).map { loadCIFARFile(named: "data_batch_\($0)") }
+    let data = (1..<6).map { loadCIFARFile(named: "data_batch_\($0).bin") }
     return Example(
         label: Raw.concat(concatDim: Tensor<Int32>(0), data.map { $0.label }),
         data: Raw.concat(concatDim: Tensor<Int32>(0), data.map { $0.data })
     )
 }
 
 func loadCIFARTestFile() -> Example {
-    return loadCIFARFile(named: "test_batch")
+    return loadCIFARFile(named: "test_batch.bin")
 }
 
 func loadCIFAR10() -> (
-  training: Dataset<Example>, test: Dataset<Example>) {
+    training: Dataset<Example>, test: Dataset<Example>
+) {
     let trainingDataset = Dataset<Example>(elements: loadCIFARTrainingFiles())
     let testDataset = Dataset<Example>(elements: loadCIFARTestFile())
     return (training: trainingDataset, test: testDataset)

CIFAR/README.md

@@ -6,12 +6,7 @@ classification on the [CIFAR-10](https://www.cs.toronto.edu/~kriz/cifar.html) da
 ## Setup
 
 You'll need [the latest version][INSTALL] of Swift for TensorFlow
-installed and added to your path. Additionally, the data loader requires Python
-3.x (rather than Python 2.7), `wget`, and `numpy`.
-
-> Note: For macOS, you need to set up the `PYTHON_LIBRARY` to help the Swift for
-> TensorFlow find the `libpython3.<minor-version>.dylib` file, e.g., in
-> `homebrew`.
+installed and added to your path.
 
 To train the default model, run:
 

CIFAR/main.swift

@@ -13,8 +13,6 @@
 // limitations under the License.
 
 import TensorFlow
-import Python
-PythonLibrary.useVersion(3)
 
 let batchSize = 100
 

Package.swift

@@ -1,10 +1,13 @@
-// swift-tools-version:4.2
+// swift-tools-version:5.0
 // The swift-tools-version declares the minimum version of Swift required to build this package.
 
 import PackageDescription
 
 let package = Package(
     name: "TensorFlowModels",
+    platforms: [
+        .macOS(.v10_13)
+    ],
     products: [
         .executable(name: "MNIST", targets: ["MNIST"]),
         .executable(name: "CIFAR", targets: ["CIFAR"]),

ResNet/Data.swift

@@ -12,30 +12,61 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-import Python
+import Foundation
 import TensorFlow
 
-/// Use Python and shell calls to download and extract the CIFAR-10 tarball if not already done
-/// This can fail for many reasons (e.g. lack of `wget`, `tar`, or an Internet connection)
+#if canImport(FoundationNetworking)
+    import FoundationNetworking
+#endif
+
 func downloadCIFAR10IfNotPresent(to directory: String = ".") {
-    let subprocess = Python.import("subprocess")
-    let path = Python.import("os.path")
-    let filepath = "\(directory)/cifar-10-batches-py"
-    let isdir = Bool(path.isdir(filepath))!
-    if !isdir {
-        print("Downloading CIFAR data...")
-        let command = """
-            wget -nv -O- https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz | tar xzf - \
-            -C \(directory)
-            """
-        subprocess.call(command, shell: true)
+    let downloadPath = "\(directory)/cifar-10-batches-bin"
+    let directoryExists = FileManager.default.fileExists(atPath: downloadPath)
+
+    guard !directoryExists else { return }
+
+    print("Downloading CIFAR dataset...")
+    let archivePath = "\(directory)/cifar-10-binary.tar.gz"
+    let archiveExists = FileManager.default.fileExists(atPath: archivePath)
+    if !archiveExists {
+        print("Archive missing, downloading...")
+        do {
+            let downloadedFile = try Data(
+                contentsOf: URL(
+                    string: "https://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz")!)
+            try downloadedFile.write(to: URL(fileURLWithPath: archivePath))
+        } catch {
+            print("Could not download CIFAR dataset, error: \(error)")
+            exit(-1)
+        }
+    }
+
+    print("Archive downloaded, processing...")
+
+    #if os(macOS)
+        let tarLocation = "/usr/bin/tar"
+    #else
+        let tarLocation = "/bin/tar"
+    #endif
+
+    let task = Process()
+    task.executableURL = URL(fileURLWithPath: tarLocation)
+    task.arguments = ["xzf", archivePath]
+    do {
+        try task.run()
+        task.waitUntilExit()
+    } catch {
+        print("CIFAR extraction failed with error: \(error)")
     }
-}
 
-extension Tensor where Scalar : _TensorFlowDataTypeCompatible {
-    public var _tfeTensorHandle: _AnyTensorHandle {
-        TFETensorHandle(_owning: handle._cTensorHandle)
+    do {
+        try FileManager.default.removeItem(atPath: archivePath)
+    } catch {
+        print("Could not remove archive, error: \(error)")
+        exit(-1)
     }
+
+    print("Unarchiving completed")
 }
 
 struct Example: TensorGroup {
@@ -56,51 +87,61 @@ struct Example: TensorGroup {
         label = Tensor<Int32>(handle: TensorHandle<Int32>(handle: _handles[labelIndex]))
         data = Tensor<Float>(handle: TensorHandle<Float>(handle: _handles[dataIndex]))
     }
-
-    public var _tensorHandles: [_AnyTensorHandle] { [label._tfeTensorHandle, data._tfeTensorHandle] }
 }
 
-// Each CIFAR data file is provided as a Python pickle of NumPy arrays
 func loadCIFARFile(named name: String, in directory: String = ".") -> Example {
     downloadCIFAR10IfNotPresent(to: directory)
-    let np = Python.import("numpy")
-    let pickle = Python.import("pickle")
-    let path = "\(directory)/cifar-10-batches-py/\(name)"
-    let f = Python.open(path, "rb")
-    let res = pickle.load(f, encoding: "bytes")
+    let path = "\(directory)/cifar-10-batches-bin/\(name)"
 
-    let bytes = res[Python.bytes("data", encoding: "utf8")]
-    let labels = res[Python.bytes("labels", encoding: "utf8")]
+    let imageCount = 10000
+    guard let fileContents = try? Data(contentsOf: URL(fileURLWithPath: path)) else {
+        print("Could not read dataset file: \(name)")
+        exit(-1)
+    }
+    guard fileContents.count == 30_730_000 else {
+        print(
+            "Dataset file \(name) should have 30730000 bytes, instead had \(fileContents.count)")
+        exit(-1)
+    }
+
+    var bytes: [UInt8] = []
+    var labels: [Int64] = []
+
+    let imageByteSize = 3073
+    for imageIndex in 0..<imageCount {
+        let baseAddress = imageIndex * imageByteSize
+        labels.append(Int64(fileContents[baseAddress]))
+        bytes.append(contentsOf: fileContents[(baseAddress + 1)..<(baseAddress + 3073)])
+    }
 
-    let labelTensor = Tensor<Int64>(numpy: np.array(labels))!
-    let images = Tensor<UInt8>(numpy: bytes)!
-    let imageCount = images.shape[0]
+    let labelTensor = Tensor<Int64>(shape: [imageCount], scalars: labels)
+    let images = Tensor<UInt8>(shape: [imageCount, 3, 32, 32], scalars: bytes)
 
-    // reshape and transpose from the provided N(CHW) to TF default NHWC
-    let imageTensor = Tensor<Float>(images
-        .reshaped(to: [imageCount, 3, 32, 32])
-        .transposed(withPermutations: [0, 2, 3, 1]))
+    // Transpose from the CIFAR-provided N(CHW) to TF's default NHWC.
+    let imageTensor = Tensor<Float>(images.transposed(withPermutations: [0, 2, 3, 1]))
 
     let mean = Tensor<Float>([0.485, 0.456, 0.406])
-    let std  = Tensor<Float>([0.229, 0.224, 0.225])
+    let std = Tensor<Float>([0.229, 0.224, 0.225])
     let imagesNormalized = ((imageTensor / 255.0) - mean) / std
 
     return Example(label: Tensor<Int32>(labelTensor), data: imagesNormalized)
 }
 
 func loadCIFARTrainingFiles() -> Example {
-    let data = (1..<6).map { loadCIFARFile(named: "data_batch_\($0)") }
+    let data = (1..<6).map { loadCIFARFile(named: "data_batch_\($0).bin") }
     return Example(
         label: Raw.concat(concatDim: Tensor<Int32>(0), data.map { $0.label }),
         data: Raw.concat(concatDim: Tensor<Int32>(0), data.map { $0.data })
     )
 }
 
 func loadCIFARTestFile() -> Example {
-    return loadCIFARFile(named: "test_batch")
+    return loadCIFARFile(named: "test_batch.bin")
 }
 
-func loadCIFAR10() -> (training: Dataset<Example>, test: Dataset<Example>) {
+func loadCIFAR10() -> (
+    training: Dataset<Example>, test: Dataset<Example>
+) {
     let trainingDataset = Dataset<Example>(elements: loadCIFARTrainingFiles())
     let testDataset = Dataset<Example>(elements: loadCIFARTestFile())
     return (training: trainingDataset, test: testDataset)

ResNet/README.md

@@ -7,18 +7,13 @@ dataset.
 ## Setup
 
 You'll need [the latest version][INSTALL] of Swift for TensorFlow
-installed and added to your path. Additionally, the data loader requires Python
-3.x (rather than Python 2.7), `wget`, and `numpy`.
-
-> Note: For macOS, you need to set up the `PYTHON_LIBRARY` to help the Swift for
-> TensorFlow find the `libpython3.<minor-version>.dylib` file, e.g., in
-> `homebrew`.
+installed and added to your path.
 
 To train the model on CIFAR-10, run:
 
 ```
 cd swift-models
-swift run ResNet
+swift run -c release ResNet
 ```
 
 [INSTALL]: (https://github.com/tensorflow/swift/blob/master/Installation.md)