Mechanical replacement of http://storage.googleapis.com with https eq…

…uivalent.

PiperOrigin-RevId: 260023596

WORKSPACE

@@ -104,7 +104,6 @@ http_archive(
     build_file = "//:models.BUILD",
     sha256 = "7efe12a8363f09bc24d7b7a450304a15655a57a7751929b2c1593a71183bb105",
     urls = [
-        "http://storage.googleapis.com/download.tensorflow.org/models/inception_v1.zip",
         "https://storage.googleapis.com/download.tensorflow.org/models/inception_v1.zip",
     ],
 )
@@ -114,7 +113,6 @@ http_archive(
     build_file = "//:models.BUILD",
     sha256 = "bddd81ea5c80a97adfac1c9f770e6f55cbafd7cce4d3bbe15fbeb041e6b8f3e8",
     urls = [
-        "http://storage.googleapis.com/download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_android_export.zip",
         "https://storage.googleapis.com/download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_android_export.zip",
     ],
 )
@@ -124,7 +122,6 @@ http_archive(
     build_file = "//:models.BUILD",
     sha256 = "859edcddf84dddb974c36c36cfc1f74555148e9c9213dedacf1d6b613ad52b96",
     urls = [
-        "http://storage.googleapis.com/download.tensorflow.org/models/mobile_multibox_v1a.zip",
         "https://storage.googleapis.com/download.tensorflow.org/models/mobile_multibox_v1a.zip",
     ],
 )
@@ -134,7 +131,6 @@ http_archive(
     build_file = "//:models.BUILD",
     sha256 = "3d374a730aef330424a356a8d4f04d8a54277c425e274ecb7d9c83aa912c6bfa",
     urls = [
-        "http://storage.googleapis.com/download.tensorflow.org/models/stylize_v1.zip",
         "https://storage.googleapis.com/download.tensorflow.org/models/stylize_v1.zip",
     ],
 )
@@ -144,7 +140,6 @@ http_archive(
     build_file = "//:models.BUILD",
     sha256 = "c3ec4fea3158eb111f1d932336351edfe8bd515bb6e87aad4f25dbad0a600d0c",
     urls = [
-        "http://storage.googleapis.com/download.tensorflow.org/models/speech_commands_v0.01.zip",
         "https://storage.googleapis.com/download.tensorflow.org/models/speech_commands_v0.01.zip",
     ],
 )

tensorflow/examples/tutorials/deepdream/README.md

@@ -5,11 +5,18 @@ by [Alexander Mordvintsev](mailto:moralex@google.com)
 This directory contains Jupyter notebook that demonstrates a number of Convolutional Neural Network
 image generation techniques implemented with TensorFlow:
 
-- visualizing individual feature channels and their combinations to explore the space of patterns learned by the neural network (see [GoogLeNet](http://storage.googleapis.com/deepdream/visualz/tensorflow_inception/index.html) and [VGG16](http://storage.googleapis.com/deepdream/visualz/vgg16/index.html) galleries)
-- embedding TensorBoard graph visualizations into Jupyter notebooks
-- producing high-resolution images with tiled computation ([example](http://storage.googleapis.com/deepdream/pilatus_flowers.jpg))
-- using Laplacian Pyramid Gradient Normalization to produce smooth and colorful visuals at low cost
-- generating DeepDream-like images with TensorFlow
+-   visualizing individual feature channels and their combinations to explore
+    the space of patterns learned by the neural network (see
+    [GoogLeNet](https://storage.googleapis.com/deepdream/visualz/tensorflow_inception/index.html)
+    and
+    [VGG16](https://storage.googleapis.com/deepdream/visualz/vgg16/index.html)
+    galleries)
+-   embedding TensorBoard graph visualizations into Jupyter notebooks
+-   producing high-resolution images with tiled computation
+    ([example](https://storage.googleapis.com/deepdream/pilatus_flowers.jpg))
+-   using Laplacian Pyramid Gradient Normalization to produce smooth and
+    colorful visuals at low cost
+-   generating DeepDream-like images with TensorFlow
 
 You can view "deepdream.ipynb" directly on GitHub. Note that GitHub Jupyter notebook preview removes
 embedded graph visualizations. You can still see them online

tensorflow/examples/tutorials/deepdream/deepdream.ipynb

@@ -40,14 +40,14 @@
       "source": [
         "This notebook demonstrates a number of Convolutional Neural Network image generation techniques implemented with TensorFlow for fun and science:\n",
         "\n",
-        "- visualize individual feature channels and their combinations to explore the space of patterns learned by the neural network (see [GoogLeNet](http://storage.googleapis.com/deepdream/visualz/tensorflow_inception/index.html) and [VGG16](http://storage.googleapis.com/deepdream/visualz/vgg16/index.html) galleries)\n",
+        "- visualize individual feature channels and their combinations to explore the space of patterns learned by the neural network (see [GoogLeNet](https://storage.googleapis.com/deepdream/visualz/tensorflow_inception/index.html) and [VGG16](https://storage.googleapis.com/deepdream/visualz/vgg16/index.html) galleries)\n",
         "- embed TensorBoard graph visualizations into Jupyter notebooks\n",
-        "- produce high-resolution images with tiled computation ([example](http://storage.googleapis.com/deepdream/pilatus_flowers.jpg))\n",
+        "- produce high-resolution images with tiled computation ([example](https://storage.googleapis.com/deepdream/pilatus_flowers.jpg))\n",
         "- use Laplacian Pyramid Gradient Normalization to produce smooth and colorful visuals at low cost\n",
         "- generate DeepDream-like images with TensorFlow (DogSlugs included)\n",
         "\n",
         "\n",
-        "The network under examination is the [GoogLeNet architecture](http://arxiv.org/abs/1409.4842), trained to classify images into one of 1000 categories of the [ImageNet](http://image-net.org/) dataset. It consists of a set of layers that apply a sequence of transformations to the input image. The parameters of these transformations were determined during the training process by a variant of gradient descent algorithm. The internal image representations may seem obscure, but it is possible to visualize and interpret them. In this notebook we are going to present a few tricks that allow to make these visualizations both efficient to generate and even beautiful. Impatient readers can start with exploring the full galleries of images generated by the method described here for [GoogLeNet](http://storage.googleapis.com/deepdream/visualz/tensorflow_inception/index.html) and [VGG16](http://storage.googleapis.com/deepdream/visualz/vgg16/index.html) architectures."
+        "The network under examination is the [GoogLeNet architecture](http://arxiv.org/abs/1409.4842), trained to classify images into one of 1000 categories of the [ImageNet](http://image-net.org/) dataset. It consists of a set of layers that apply a sequence of transformations to the input image. The parameters of these transformations were determined during the training process by a variant of gradient descent algorithm. The internal image representations may seem obscure, but it is possible to visualize and interpret them. In this notebook we are going to present a few tricks that allow to make these visualizations both efficient to generate and even beautiful. Impatient readers can start with exploring the full galleries of images generated by the method described here for [GoogLeNet](https://storage.googleapis.com/deepdream/visualz/tensorflow_inception/index.html) and [VGG16](https://storage.googleapis.com/deepdream/visualz/vgg16/index.html) architectures."
       ]
     },
     {
@@ -1117,7 +1117,7 @@
         "id": "mYsY6_Ngpfwl"
       },
       "source": [
-        "Don't hesitate to use higher resolution inputs (also increase the number of octaves)! Here is an [example](http://storage.googleapis.com/deepdream/pilatus_flowers.jpg) of running the flower dream over the bigger image."
+        "Don't hesitate to use higher resolution inputs (also increase the number of octaves)! Here is an [example](https://storage.googleapis.com/deepdream/pilatus_flowers.jpg) of running the flower dream over the bigger image."
       ]
     },
     {

tensorflow/lite/g3doc/guide/hosted_models.md

@@ -113,7 +113,7 @@ For more information about object detection, see
 The object detection model we currently host is
 **coco_ssd_mobilenet_v1_1.0_quant_2018_06_29**.
 
-<a class="button button-primary" href="http://storage.googleapis.com/download.tensorflow.org/models/tflite/coco_ssd_mobilenet_v1_1.0_quant_2018_06_29.zip">Download
+<a class="button button-primary" href="https://storage.googleapis.com/download.tensorflow.org/models/tflite/coco_ssd_mobilenet_v1_1.0_quant_2018_06_29.zip">Download
 model and labels</a>
 
 ## Pose estimation

tensorflow/lite/g3doc/models/object_detection/overview.md

@@ -20,7 +20,7 @@ If you are using a platform other than Android or iOS, or you are already
 familiar with the <a href="https://www.tensorflow.org/api_docs/python/tf/lite">TensorFlow Lite APIs</a>, you can
 download our starter object detection model and the accompanying labels.
 
-<a class="button button-primary" href="http://storage.googleapis.com/download.tensorflow.org/models/tflite/coco_ssd_mobilenet_v1_1.0_quant_2018_06_29.zip">Download
+<a class="button button-primary" href="https://storage.googleapis.com/download.tensorflow.org/models/tflite/coco_ssd_mobilenet_v1_1.0_quant_2018_06_29.zip">Download
 starter model and labels</a>
 
 For more information about the starter model, see
@@ -185,15 +185,15 @@ Note: Object detection models accept input images of a specific size. This is li
 We recommend starting with this pre-trained quantized COCO SSD MobileNet v1
 model.
 
-<a class="button button-primary" href="http://storage.googleapis.com/download.tensorflow.org/models/tflite/coco_ssd_mobilenet_v1_1.0_quant_2018_06_29.zip">Download
+<a class="button button-primary" href="https://storage.googleapis.com/download.tensorflow.org/models/tflite/coco_ssd_mobilenet_v1_1.0_quant_2018_06_29.zip">Download
 starter model and labels</a>
 
 ### Uses and limitations
 
 The object detection model we provide can identify and locate up to 10 objects
 in an image. It is trained to recognize 80 classes of object. For a full list of
 classes, see the labels file in the
-<a href="http://storage.googleapis.com/download.tensorflow.org/models/tflite/coco_ssd_mobilenet_v1_1.0_quant_2018_06_29.zip">model
+<a href="https://storage.googleapis.com/download.tensorflow.org/models/tflite/coco_ssd_mobilenet_v1_1.0_quant_2018_06_29.zip">model
 zip</a>.
 
 If you want to train a model to recognize new classes, see
@@ -256,7 +256,7 @@ each object. There will always be 10 objects detected.
 
 The pre-trained models we provide are trained to detect 80 classes of object.
 For a full list of classes, see the labels file in the
-<a href="http://storage.googleapis.com/download.tensorflow.org/models/tflite/coco_ssd_mobilenet_v1_1.0_quant_2018_06_29.zip">model
+<a href="https://storage.googleapis.com/download.tensorflow.org/models/tflite/coco_ssd_mobilenet_v1_1.0_quant_2018_06_29.zip">model
 zip</a>.
 
 You can use a technique known as transfer learning to re-train a model to

tensorflow/workspace.bzl

@@ -829,7 +829,7 @@ def tf_repositories(path_prefix = "", tf_repo_name = ""):
         strip_prefix = "ssd_mobilenet_v1_quantized_300x300_coco14_sync_2018_07_18",
         urls = [
             "https://storage.googleapis.com/mirror.tensorflow.org/storage.googleapis.com/download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_quantized_300x300_coco14_sync_2018_07_18.tar.gz",
-            "http://storage.googleapis.com/download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_quantized_300x300_coco14_sync_2018_07_18.tar.gz",
+            "https://storage.googleapis.com/download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_quantized_300x300_coco14_sync_2018_07_18.tar.gz",
         ],
     )