Updates Swift and ObjC READMEs and quickstart.

PiperOrigin-RevId: 247513844

tensorflow/lite/experimental/objc/README.md

@@ -5,7 +5,7 @@ solution for Objective-C developers. It enables low-latency inference of
 on-device machine learning models with a small binary size and fast performance
 supporting hardware acceleration.
 
-## Getting Started
+## Build TensorFlow with iOS support
 
 To build the Objective-C TensorFlow Lite library on Apple platforms,
 [install from source](https://www.tensorflow.org/install/source#setup_for_linux_and_macos)
@@ -19,9 +19,34 @@ python configure.py
 
 Follow the prompts and when asked to build TensorFlow with iOS support, enter `y`.
 
-### Bazel
+### CocoaPods developers
 
-In your `BUILD` file, add the `TensorFlowLite` dependency:
+Add the TensorFlow Lite pod to your `Podfile`:
+
+```ruby
+pod 'TensorFlowLiteObjC'
+```
+
+Then, run `pod install`.
+
+In your Objective-C files, import the umbrella header:
+
+```objectivec
+#import "TFLTensorFlowLite.h"
+```
+
+Or, the module if you set `CLANG_ENABLE_MODULES = YES` in your Xcode project:
+
+```objectivec
+@import TFLTensorFlowLite;
+```
+
+Note: To import the TensorFlow Lite module in your Objective-C files, you must
+also include `use_frameworks!` in your `Podfile`.
+
+### Bazel developers
+
+In your `BUILD` file, add the `TensorFlowLite` dependency to your target:
 
 ```python
 objc_library(
@@ -37,6 +62,12 @@ In your Objective-C files, import the umbrella header:
 #import "TFLTensorFlowLite.h"
 ```
 
+Or, the module if you set `CLANG_ENABLE_MODULES = YES` in your Xcode project:
+
+```objectivec
+@import TFLTensorFlowLite;
+```
+
 Build the `TensorFlowLite` Objective-C library target:
 
 ```shell
@@ -49,36 +80,14 @@ Build the `TensorFlowLiteTests` target:
 bazel test tensorflow/lite/experimental/objc:TensorFlowLiteTests
 ```
 
-### Tulsi
+#### Generate the Xcode project using Tulsi
 
-Open the `TensorFlowLite.tulsiproj` using the
+Open the `//tensorflow/lite/experimental/objc/TensorFlowLite.tulsiproj` using
-[TulsiApp](https://github.com/bazelbuild/tulsi) or by running the
+the [TulsiApp](https://github.com/bazelbuild/tulsi)
+or by running the
 [`generate_xcodeproj.sh`](https://github.com/bazelbuild/tulsi/blob/master/src/tools/generate_xcodeproj.sh)
 script from the root `tensorflow` directory:
 
 ```shell
 generate_xcodeproj.sh --genconfig tensorflow/lite/experimental/objc/TensorFlowLite.tulsiproj:TensorFlowLite --outputfolder ~/path/to/generated/TensorFlowLite.xcodeproj
 ```
-
-### CocoaPods
-
-Add the following to your `Podfile`:
-
-```ruby
-pod 'TensorFlowLiteObjC'
-```
-
-Then, run `pod install`.
-
-In your Objective-C files, import the umbrella header:
-
-```objectivec
-#import "TFLTensorFlowLite.h"
-```
-
-Or, the module if `CLANG_ENABLE_MODULES = YES` and `use_frameworks!` is
-specified in your `Podfile`:
-
-```objectivec
-@import TFLTensorFlowLite;
-```

tensorflow/lite/experimental/objc/TensorFlowLiteObjC.podspec

@@ -1,5 +1,3 @@
-# Run `pod lib lint TensorFlowLiteObjC.podspec` to ensure this is a valid spec.
-
 Pod::Spec.new do |s|
   s.name             = 'TensorFlowLiteObjC'
   s.version          = '0.2.0'

tensorflow/lite/experimental/swift/README.md

@@ -5,7 +5,7 @@ solution for Swift developers. It enables low-latency inference of on-device
 machine learning models with a small binary size and fast performance supporting
 hardware acceleration.
 
-## Getting Started
+## Build TensorFlow with iOS support
 
 To build the Swift TensorFlow Lite library on Apple platforms,
 [install from source](https://www.tensorflow.org/install/source#setup_for_linux_and_macos)
@@ -19,9 +19,25 @@ python configure.py
 
 Follow the prompts and when asked to build TensorFlow with iOS support, enter `y`.
 
-### Bazel
+### CocoaPods developers
 
-In your `BUILD` file, add the `TensorFlowLite` dependency:
+Add the TensorFlow Lite pod to your `Podfile`:
+
+```ruby
+pod 'TensorFlowLiteSwift'
+```
+
+Then, run `pod install`.
+
+In your Swift files, import the module:
+
+```swift
+import TensorFlowLite
+```
+
+### Bazel developers
+
+In your `BUILD` file, add the `TensorFlowLite` dependency to your target:
 
 ```python
 swift_library(
@@ -49,12 +65,12 @@ Build the `TensorFlowLiteTests` target:
 bazel test tensorflow/lite/experimental/swift:TensorFlowLiteTests --swiftcopt=-enable-testing
 ```
 
-Note that `--swiftcopt=-enable-testing` is required for optimized builds (`-c opt`).
+Note: `--swiftcopt=-enable-testing` is required for optimized builds (`-c opt`).
 
-### Tulsi
+#### Generate the Xcode project using Tulsi
 
-Open the `TensorFlowLite.tulsiproj` using the
+Open the `//tensorflow/lite/experimental/swift/TensorFlowLite.tulsiproj` using
-[TulsiApp](https://github.com/bazelbuild/tulsi)
+the [TulsiApp](https://github.com/bazelbuild/tulsi)
 or by running the
 [`generate_xcodeproj.sh`](https://github.com/bazelbuild/tulsi/blob/master/src/tools/generate_xcodeproj.sh)
 script from the root `tensorflow` directory:
@@ -62,19 +78,3 @@ script from the root `tensorflow` directory:
 ```shell
 generate_xcodeproj.sh --genconfig tensorflow/lite/experimental/swift/TensorFlowLite.tulsiproj:TensorFlowLite --outputfolder ~/path/to/generated/TensorFlowLite.xcodeproj
 ```
-
-### CocoaPods
-
-Add the following to your `Podfile`:
-
-```ruby
-pod 'TensorFlowLiteSwift'
-```
-
-Then, run `pod install`.
-
-In your Swift files, import the module:
-
-```swift
-import TensorFlowLite
-```

tensorflow/lite/experimental/swift/TensorFlowLiteSwift.podspec

@@ -1,5 +1,3 @@
-# Run `pod lib lint TensorFlowLiteSwift.podspec` to ensure this is a valid spec.
-
 Pod::Spec.new do |s|
   s.name             = 'TensorFlowLiteSwift'
   s.version          = '0.2.0'

tensorflow/lite/g3doc/guide/ios.md

@@ -30,12 +30,12 @@ To get started quickly writing your own iOS code, we recommend using our
 [Swift image classification example](https://github.com/tensorflow/examples/tree/master/lite/examples/image_classification/ios)
 as a starting point.
 
-The sections below walk you through the steps for adding TensorFlow Lite Swift
+The sections below demonstrate how to add TensorFlow Lite Swift or Objective-C
-or Objective-C to your project:
+to your project:
 
 ### CocoaPods developers
 
-In your `Podfile`, add the TensorFlow Lite pod. Then, run `pod install`:
+In your `Podfile`, add the TensorFlow Lite pod. Then, run `pod install`.
 
 #### Swift
 
@@ -52,7 +52,7 @@ pod 'TensorFlowLiteObjC'
 
 ### Bazel developers
 
-In your `BUILD` file, add the `TensorFlowLite` dependency.
+In your `BUILD` file, add the `TensorFlowLite` dependency to your target.
 
 #### Swift
 
@@ -74,7 +74,7 @@ objc_library(
 )
 ```
 
-### Importing the library
+### Import the library
 
 For Swift files, import the TensorFlow Lite module:
 
@@ -88,12 +88,11 @@ For Objective-C files, import the umbrella header:
 #import "TFLTensorFlowLite.h"
 ```
 
-Or, the TensorFlow Lite module:
+Or, the module if you set `CLANG_ENABLE_MODULES = YES` in your Xcode project:
 
 ```objectivec
 @import TFLTensorFlowLite;
 ```
 
-Note: If importing the Objective-C TensorFlow Lite module, `CLANG_ENABLE_MODULES`
+Note: For CocoaPods developers who want to import the Objective-C TensorFlow
-must be set to `YES`. Additionally, for CocoaPods developers, `use_frameworks!`
+Lite module, you must also include `use_frameworks!` in your `Podfile`.
-must be specified in your `Podfile`.