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Mechanical replacement of download.tensorflow.org with https equivalent.

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PiperOrigin-RevId: 259862509
This commit is contained in:
Frank Chen 2019-07-24 18:43:43 -07:00 committed by TensorFlower Gardener
parent f6c97840e2
commit 57e0d1acc2
19 changed files with 87 additions and 85 deletions
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10
WORKSPACE
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@ -105,7 +105,7 @@ http_archive(
sha256 = "7efe12a8363f09bc24d7b7a450304a15655a57a7751929b2c1593a71183bb105",
urls = [
"http://storage.googleapis.com/download.tensorflow.org/models/inception_v1.zip",
"http://download.tensorflow.org/models/inception_v1.zip",
"https://storage.googleapis.com/download.tensorflow.org/models/inception_v1.zip",
],
)
@ -115,7 +115,7 @@ http_archive(
sha256 = "bddd81ea5c80a97adfac1c9f770e6f55cbafd7cce4d3bbe15fbeb041e6b8f3e8",
urls = [
"http://storage.googleapis.com/download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_android_export.zip",
"http://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",
],
)
@ -125,7 +125,7 @@ http_archive(
sha256 = "859edcddf84dddb974c36c36cfc1f74555148e9c9213dedacf1d6b613ad52b96",
urls = [
"http://storage.googleapis.com/download.tensorflow.org/models/mobile_multibox_v1a.zip",
"http://download.tensorflow.org/models/mobile_multibox_v1a.zip",
"https://storage.googleapis.com/download.tensorflow.org/models/mobile_multibox_v1a.zip",
],
)
@ -135,7 +135,7 @@ http_archive(
sha256 = "3d374a730aef330424a356a8d4f04d8a54277c425e274ecb7d9c83aa912c6bfa",
urls = [
"http://storage.googleapis.com/download.tensorflow.org/models/stylize_v1.zip",
"http://download.tensorflow.org/models/stylize_v1.zip",
"https://storage.googleapis.com/download.tensorflow.org/models/stylize_v1.zip",
],
)
@ -145,6 +145,6 @@ http_archive(
sha256 = "c3ec4fea3158eb111f1d932336351edfe8bd515bb6e87aad4f25dbad0a600d0c",
urls = [
"http://storage.googleapis.com/download.tensorflow.org/models/speech_commands_v0.01.zip",
"http://download.tensorflow.org/models/speech_commands_v0.01.zip",
"https://storage.googleapis.com/download.tensorflow.org/models/speech_commands_v0.01.zip",
],
)

12
tensorflow/compiler/mlir/tensorflow/ir/control_flow_ops.h
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@ -65,7 +65,7 @@ class TFControlType : public Type::TypeBase<TFControlType, Type> {
// tensor needs its own _tf.Enter to be made available inside the while loop.
//
// More details can be found in Tensorflow Controlflow white paper:
// http://download.tensorflow.org/paper/white_paper_tf_control_flow_implementation_2017_11_1.pdf
// https://storage.googleapis.com/download.tensorflow.org/paper/white_paper_tf_control_flow_implementation_2017_11_1.pdf
//
// This is defined in Tensorflow as:
//
@ -100,7 +100,7 @@ class EnterOp
// of the operand type along with the index of the first match encountered.
//
// More details can be found in Tensorflow Controlflow white paper:
// http://download.tensorflow.org/paper/white_paper_tf_control_flow_implementation_2017_11_1.pdf
// https://storage.googleapis.com/download.tensorflow.org/paper/white_paper_tf_control_flow_implementation_2017_11_1.pdf
//
// This is defined in TensorFlow as:
//
@ -130,7 +130,7 @@ class MergeOp : public Op<MergeOp, OpTrait::VariadicOperands,
// of a while loop. Each loop variable needs its own NextIteration op.
//
// More details can be found in Tensorflow Controlflow white paper:
// http://download.tensorflow.org/paper/white_paper_tf_control_flow_implementation_2017_11_1.pdf
// https://storage.googleapis.com/download.tensorflow.org/paper/white_paper_tf_control_flow_implementation_2017_11_1.pdf
//
// NextIteration op is broken into _tf.NextIteration.sink and
// _tf.NextIteration.source because NextIteration is a back-edge in Tensorflow
@ -182,7 +182,7 @@ class NextIterationSinkOp
// Tensorflow while loops.
//
// More details can be found in Tensorflow Controlflow white paper:
// http://download.tensorflow.org/paper/white_paper_tf_control_flow_implementation_2017_11_1.pdf
// https://storage.googleapis.com/download.tensorflow.org/paper/white_paper_tf_control_flow_implementation_2017_11_1.pdf
//
// This is defined in Tensorflow as:
//
@ -212,7 +212,7 @@ class LoopCondOp
// condition, and returns two values matching the type of the data predicate.
//
// More details can be found in Tensorflow Controlflow white paper:
// http://download.tensorflow.org/paper/white_paper_tf_control_flow_implementation_2017_11_1.pdf
// https://storage.googleapis.com/download.tensorflow.org/paper/white_paper_tf_control_flow_implementation_2017_11_1.pdf
//
// This is defined in TensorFlow as:
//
@ -246,7 +246,7 @@ class SwitchOp : public Op<SwitchOp, OpTrait::AtLeastNOperands<2>::Impl,
// outside of loop. Each returned tensor needs its own _tf.Exit.
//
// More details can be found in Tensorflow Controlflow white paper:
// http://download.tensorflow.org/paper/white_paper_tf_control_flow_implementation_2017_11_1.pdf
// https://storage.googleapis.com/download.tensorflow.org/paper/white_paper_tf_control_flow_implementation_2017_11_1.pdf
//
// This is defined in Tensorflow as:
//

14
tensorflow/compiler/mlir/tensorflow/ir/tf_executor_ops.td
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@ -221,7 +221,7 @@ def TfExecutor_SwitchOp : TfExecutor_Op<"Switch",
let description = [{
More details can be found in Tensorflow Control Flow white paper:
http://download.tensorflow.org/paper/white_paper_tf_control_flow_implementation_2017_11_1.pdf
https://storage.googleapis.com/download.tensorflow.org/paper/white_paper_tf_control_flow_implementation_2017_11_1.pdf
This is defined in TensorFlow as:
@ -302,7 +302,7 @@ def TfExecutor_MergeOp : TfExecutor_Op<"Merge", [NoSideEffect, ControlOperandsAf
let description = [{
More details can be found in Tensorflow Control Flow white paper:
http://download.tensorflow.org/paper/white_paper_tf_control_flow_implementation_2017_11_1.pdf
https://storage.googleapis.com/download.tensorflow.org/paper/white_paper_tf_control_flow_implementation_2017_11_1.pdf
This is defined in TensorFlow as:
@ -339,7 +339,7 @@ def TfExecutor_EnterOp : TfExecutor_Op<"Enter",
let description = [{
More details can be found in Tensorflow Control Flow white paper:
http://download.tensorflow.org/paper/white_paper_tf_control_flow_implementation_2017_11_1.pdf
https://storage.googleapis.com/download.tensorflow.org/paper/white_paper_tf_control_flow_implementation_2017_11_1.pdf
Each tensor needs its own tf_executor.Enter to be made available inside a
while loop.
@ -390,7 +390,7 @@ def TfExecutor_NextIterationSourceOp : TfExecutor_Op<"NextIteration.Source", [No
of a while loop. Each loop variable needs its own NextIteration op.
More details can be found in Tensorflow Control Flow white paper:
http://download.tensorflow.org/paper/white_paper_tf_control_flow_implementation_2017_11_1.pdf
https://storage.googleapis.com/download.tensorflow.org/paper/white_paper_tf_control_flow_implementation_2017_11_1.pdf
In the TF executor dialect, the NextIteration op is broken into
tf_executor.NextIteration.sink and tf_executor.NextIteration.source because
@ -447,7 +447,7 @@ def TfExecutor_NextIterationSinkOp : TfExecutor_Op<"NextIteration.Sink"> {
of a while loop. Each loop variable needs its own NextIteration op.
More details can be found in Tensorflow Control Flow white paper:
http://download.tensorflow.org/paper/white_paper_tf_control_flow_implementation_2017_11_1.pdf
https://storage.googleapis.com/download.tensorflow.org/paper/white_paper_tf_control_flow_implementation_2017_11_1.pdf
In the TF executor dialect, the NextIteration op is broken into
tf_executor.NextIteration.sink and tf_executor.NextIteration.source because
@ -507,7 +507,7 @@ def TfExecutor_ExitOp : TfExecutor_Op<"Exit",
let description = [{
More details can be found in Tensorflow Control Flow white paper:
http://download.tensorflow.org/paper/white_paper_tf_control_flow_implementation_2017_11_1.pdf
https://storage.googleapis.com/download.tensorflow.org/paper/white_paper_tf_control_flow_implementation_2017_11_1.pdf
This is defined in Tensorflow as:
@ -579,7 +579,7 @@ def TfExecutor_LoopCondOp : TfExecutor_Op<"LoopCond", [NoSideEffect]> {
let description = [{
More details can be found in Tensorflow Control Flow white paper:
http://download.tensorflow.org/paper/white_paper_tf_control_flow_implementation_2017_11_1.pdf
https://storage.googleapis.com/download.tensorflow.org/paper/white_paper_tf_control_flow_implementation_2017_11_1.pdf
This is defined in Tensorflow as:

2
tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb
View File

@ -117,7 +117,7 @@
"source": [
"# Download the file\n",
"path_to_zip = tf.keras.utils.get_file(\n",
" 'spa-eng.zip', origin='http://download.tensorflow.org/data/spa-eng.zip', \n",
" 'spa-eng.zip', origin='https://storage.googleapis.com/download.tensorflow.org/data/spa-eng.zip', \n",
" extract=True)\n",
"\n",
"path_to_file = os.path.dirname(path_to_zip)+\"/spa-eng/spa.txt\""

4
tensorflow/contrib/gan/python/eval/python/classifier_metrics_impl.py
View File

@ -74,7 +74,7 @@ __all__ = [
'INCEPTION_DEFAULT_IMAGE_SIZE',
]
INCEPTION_URL = 'http://download.tensorflow.org/models/frozen_inception_v1_2015_12_05.tar.gz'
INCEPTION_URL = 'https://storage.googleapis.com/download.tensorflow.org/models/frozen_inception_v1_2015_12_05.tar.gz'
INCEPTION_FROZEN_GRAPH = 'inceptionv1_for_inception_score.pb'
INCEPTION_INPUT = 'Mul:0'
INCEPTION_OUTPUT = 'logits:0'
@ -123,7 +123,7 @@ def preprocess_image(images,
"""Prepare a batch of images for evaluation.
This is the preprocessing portion of the graph from
http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz.
https://storage.googleapis.com/download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz.
Note that it expects Tensors in [0, 255]. This function maps pixel values to
[-1, 1] and resizes to match the InceptionV1 network.

2
tensorflow/contrib/makefile/download_dependencies.sh
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@ -140,7 +140,7 @@ replace_by_sed 's#static uint32x2_t p2ui_CONJ_XOR = vld1_u32( conj_XOR_DATA );#s
replace_by_sed 's#static uint64x2_t p2ul_CONJ_XOR = vld1q_u64( p2ul_conj_XOR_DATA );#static uint64x2_t p2ul_CONJ_XOR;// = vld1q_u64( p2ul_conj_XOR_DATA ); - Removed by script#' \
"${DOWNLOADS_DIR}/eigen/Eigen/src/Core/arch/NEON/Complex.h"
# TODO(satok): Remove this once protobuf/autogen.sh is fixed.
replace_by_sed 's#https://googlemock.googlecode.com/files/gmock-1.7.0.zip#http://download.tensorflow.org/deps/gmock-1.7.0.zip#' \
replace_by_sed 's#https://googlemock.googlecode.com/files/gmock-1.7.0.zip#https://storage.googleapis.com/download.tensorflow.org/deps/gmock-1.7.0.zip#' \
"${DOWNLOADS_DIR}/protobuf/autogen.sh"
cat "third_party/eigen3/gebp_neon.patch" | patch "${DOWNLOADS_DIR}/eigen/Eigen/src/Core/products/GeneralBlockPanelKernel.h"

6
tensorflow/examples/android/README.md
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@ -45,7 +45,7 @@ on API >= 14 devices.
## Prebuilt Components:
The fastest path to trying the demo is to download the [prebuilt demo APK](http://download.tensorflow.org/deps/tflite/TfLiteCameraDemo.apk).
The fastest path to trying the demo is to download the [prebuilt demo APK](https://storage.googleapis.com/download.tensorflow.org/deps/tflite/TfLiteCameraDemo.apk).
Also available are precompiled native libraries, and a jcenter package that you
may simply drop into your own applications. See
@ -109,7 +109,9 @@ protobuf compilation.
NOTE: Bazel does not currently support building for Android on Windows. Full
support for gradle/cmake builds is coming soon, but in the meantime we suggest
that Windows users download the [prebuilt demo APK](http://download.tensorflow.org/deps/tflite/TfLiteCameraDemo.apk) instead.
that Windows users download the
[prebuilt demo APK](https://storage.googleapis.com/download.tensorflow.org/deps/tflite/TfLiteCameraDemo.apk)
instead.
##### Install Bazel and Android Prerequisites

2
tensorflow/examples/speech_commands/generate_streaming_test_wav.py
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@ -174,7 +174,7 @@ if __name__ == '__main__':
'--data_url',
type=str,
# pylint: disable=line-too-long
default='http://download.tensorflow.org/data/speech_commands_v0.01.tar.gz',
default='https://storage.googleapis.com/download.tensorflow.org/data/speech_commands_v0.01.tar.gz',
# pylint: enable=line-too-long
help='Location of speech training data')
parser.add_argument(

2
tensorflow/examples/speech_commands/train.py
View File

@ -301,7 +301,7 @@ if __name__ == '__main__':
'--data_url',
type=str,
# pylint: disable=line-too-long
default='http://download.tensorflow.org/data/speech_commands_v0.02.tar.gz',
default='https://storage.googleapis.com/download.tensorflow.org/data/speech_commands_v0.02.tar.gz',
# pylint: enable=line-too-long
help='Location of speech training data archive on the web.')
parser.add_argument(

4
tensorflow/lite/examples/ios/download_models.sh
View File

@ -17,8 +17,8 @@
set -ex
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
FLOAT_MODEL_URL="http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_224.tgz"
QUANTIZED_MODEL_URL="http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224_quant.tgz"
FLOAT_MODEL_URL="https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_224.tgz"
QUANTIZED_MODEL_URL="https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224_quant.tgz"
DOWNLOADS_DIR=$(mktemp -d)
cd "$SCRIPT_DIR"

2
tensorflow/lite/examples/python/README.md
View File

@ -18,7 +18,7 @@ a good demonstration of a model trained to recognize 1,000 different objects.
# Get photo
curl https://raw.githubusercontent.com/tensorflow/tensorflow/master/tensorflow/lite/examples/label_image/testdata/grace_hopper.bmp > /tmp/grace_hopper.bmp
# Get model
curl http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_224.tgz | tar xzv -C /tmp
curl https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_224.tgz | tar xzv -C /tmp
# Get labels
curl https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_1.0_224_frozen.tgz | tar xzv -C /tmp mobilenet_v1_1.0_224/labels.txt

80
tensorflow/lite/g3doc/guide/hosted_models.md
View File

@ -21,29 +21,29 @@ For more information about image classification, see
classification models offer the smallest model size and fastest performance, at
the expense of accuracy.
Model name | Paper and model | Model size | Top-1 accuracy | Top-5 accuracy | TF Lite performance
--------------------------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------: | ---------: | -------------: | -------------: | ------------------:
Mobilenet_V1_0.25_128_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_0.25_128_quant.tgz) | 0.5 Mb | 39.5% | 64.4% | 3.7 ms
Mobilenet_V1_0.25_160_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_0.25_160_quant.tgz) | 0.5 Mb | 42.8% | 68.1% | 5.5 ms
Mobilenet_V1_0.25_192_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_0.25_192_quant.tgz) | 0.5 Mb | 45.7% | 70.8% | 7.9 ms
Mobilenet_V1_0.25_224_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_0.25_224_quant.tgz) | 0.5 Mb | 48.2% | 72.8% | 10.4 ms
Mobilenet_V1_0.50_128_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_0.5_128_quant.tgz) | 1.4 Mb | 54.9% | 78.1% | 8.8 ms
Mobilenet_V1_0.50_160_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_0.5_160_quant.tgz) | 1.4 Mb | 57.2% | 80.5% | 13.0 ms
Mobilenet_V1_0.50_192_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_0.5_192_quant.tgz) | 1.4 Mb | 59.9% | 82.1% | 18.3 ms
Mobilenet_V1_0.50_224_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_0.5_224_quant.tgz) | 1.4 Mb | 61.2% | 83.2% | 24.7 ms
Mobilenet_V1_0.75_128_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_0.75_128_quant.tgz) | 2.6 Mb | 55.9% | 79.1% | 16.2 ms
Mobilenet_V1_0.75_160_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_0.75_160_quant.tgz) | 2.6 Mb | 62.4% | 83.7% | 24.3 ms
Mobilenet_V1_0.75_192_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_0.75_192_quant.tgz) | 2.6 Mb | 66.1% | 86.2% | 33.8 ms
Mobilenet_V1_0.75_224_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_0.75_224_quant.tgz) | 2.6 Mb | 66.9% | 86.9% | 45.4 ms
Mobilenet_V1_1.0_128_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_128_quant.tgz) | 4.3 Mb | 63.3% | 84.1% | 24.9 ms
Mobilenet_V1_1.0_160_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_160_quant.tgz) | 4.3 Mb | 66.9% | 86.7% | 37.4 ms
Mobilenet_V1_1.0_192_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_192_quant.tgz) | 4.3 Mb | 69.1% | 88.1% | 51.9 ms
Mobilenet_V1_1.0_224_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224_quant.tgz) | 4.3 Mb | 70.0% | 89.0% | 70.2 ms
Mobilenet_V2_1.0_224_quant | [paper](https://arxiv.org/abs/1806.08342), [tflite&pb](http://download.tensorflow.org/models/tflite_11_05_08/mobilenet_v2_1.0_224_quant.tgz) | 3.4 Mb | 70.8% | 89.9% | 53.4 ms
Inception_V1_quant | [paper](https://arxiv.org/abs/1409.4842), [tflite&pb](http://download.tensorflow.org/models/inception_v1_224_quant_20181026.tgz) | 6.4 Mb | 70.1% | 89.8% | 154.5 ms
Inception_V2_quant | [paper](https://arxiv.org/abs/1512.00567), [tflite&pb](http://download.tensorflow.org/models/inception_v2_224_quant_20181026.tgz) | 11 Mb | 73.5% | 91.4% | 235.0 ms
Inception_V3_quant | [paper](https://arxiv.org/abs/1806.08342),[tflite&pb](http://download.tensorflow.org/models/tflite_11_05_08/inception_v3_quant.tgz) | 23 Mb | 77.5% | 93.7% | 637 ms
Inception_V4_quant | [paper](https://arxiv.org/abs/1602.07261), [tflite&pb](http://download.tensorflow.org/models/inception_v4_299_quant_20181026.tgz) | 41 Mb | 79.5% | 93.9% | 1250.8 ms
Model name | Paper and model | Model size | Top-1 accuracy | Top-5 accuracy | TF Lite performance
--------------------------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | ---------: | -------------: | -------------: | ------------------:
Mobilenet_V1_0.25_128_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_0.25_128_quant.tgz) | 0.5 Mb | 39.5% | 64.4% | 3.7 ms
Mobilenet_V1_0.25_160_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_0.25_160_quant.tgz) | 0.5 Mb | 42.8% | 68.1% | 5.5 ms
Mobilenet_V1_0.25_192_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_0.25_192_quant.tgz) | 0.5 Mb | 45.7% | 70.8% | 7.9 ms
Mobilenet_V1_0.25_224_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_0.25_224_quant.tgz) | 0.5 Mb | 48.2% | 72.8% | 10.4 ms
Mobilenet_V1_0.50_128_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_0.5_128_quant.tgz) | 1.4 Mb | 54.9% | 78.1% | 8.8 ms
Mobilenet_V1_0.50_160_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_0.5_160_quant.tgz) | 1.4 Mb | 57.2% | 80.5% | 13.0 ms
Mobilenet_V1_0.50_192_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_0.5_192_quant.tgz) | 1.4 Mb | 59.9% | 82.1% | 18.3 ms
Mobilenet_V1_0.50_224_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_0.5_224_quant.tgz) | 1.4 Mb | 61.2% | 83.2% | 24.7 ms
Mobilenet_V1_0.75_128_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_0.75_128_quant.tgz) | 2.6 Mb | 55.9% | 79.1% | 16.2 ms
Mobilenet_V1_0.75_160_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_0.75_160_quant.tgz) | 2.6 Mb | 62.4% | 83.7% | 24.3 ms
Mobilenet_V1_0.75_192_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_0.75_192_quant.tgz) | 2.6 Mb | 66.1% | 86.2% | 33.8 ms
Mobilenet_V1_0.75_224_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_0.75_224_quant.tgz) | 2.6 Mb | 66.9% | 86.9% | 45.4 ms
Mobilenet_V1_1.0_128_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_128_quant.tgz) | 4.3 Mb | 63.3% | 84.1% | 24.9 ms
Mobilenet_V1_1.0_160_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_160_quant.tgz) | 4.3 Mb | 66.9% | 86.7% | 37.4 ms
Mobilenet_V1_1.0_192_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_192_quant.tgz) | 4.3 Mb | 69.1% | 88.1% | 51.9 ms
Mobilenet_V1_1.0_224_quant | [paper](https://arxiv.org/pdf/1712.05877.pdf), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224_quant.tgz) | 4.3 Mb | 70.0% | 89.0% | 70.2 ms
Mobilenet_V2_1.0_224_quant | [paper](https://arxiv.org/abs/1806.08342), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/tflite_11_05_08/mobilenet_v2_1.0_224_quant.tgz) | 3.4 Mb | 70.8% | 89.9% | 53.4 ms
Inception_V1_quant | [paper](https://arxiv.org/abs/1409.4842), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/inception_v1_224_quant_20181026.tgz) | 6.4 Mb | 70.1% | 89.8% | 154.5 ms
Inception_V2_quant | [paper](https://arxiv.org/abs/1512.00567), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/inception_v2_224_quant_20181026.tgz) | 11 Mb | 73.5% | 91.4% | 235.0 ms
Inception_V3_quant | [paper](https://arxiv.org/abs/1806.08342),[tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/tflite_11_05_08/inception_v3_quant.tgz) | 23 Mb | 77.5% | 93.7% | 637 ms
Inception_V4_quant | [paper](https://arxiv.org/abs/1602.07261), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/inception_v4_299_quant_20181026.tgz) | 41 Mb | 79.5% | 93.9% | 1250.8 ms
Note: The model files include both TF Lite FlatBuffer and Tensorflow frozen
Graph.
@ -68,23 +68,23 @@ ResNet_V2_101 | [paper](https://arxiv.org/abs/1603.05027), [tflite&pb](h
Inception_V3 | [paper](http://arxiv.org/abs/1512.00567), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/inception_v3_2018_04_27.tgz) | 95.3 Mb | 77.9% | 93.8% | 1433 ms | 1522 ms
Inception_V4 | [paper](http://arxiv.org/abs/1602.07261), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/inception_v4_2018_04_27.tgz) | 170.7 Mb | 80.1% | 95.1% | 2986 ms | 3139 ms
Inception_ResNet_V2 | [paper](https://arxiv.org/abs/1602.07261), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/inception_resnet_v2_2018_04_27.tgz) | 121.0 Mb | 77.5% | 94.0% | 2731 ms | 2926 ms
Mobilenet_V1_0.25_128 | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.25_128.tgz) | 1.9 Mb | 41.4% | 66.2% | 6.2 ms | 13.0 ms
Mobilenet_V1_0.25_160 | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.25_160.tgz) | 1.9 Mb | 45.4% | 70.2% | 8.6 ms | 19.5 ms
Mobilenet_V1_0.25_192 | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.25_192.tgz) | 1.9 Mb | 47.1% | 72.0% | 12.1 ms | 27.8 ms
Mobilenet_V1_0.25_224 | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.25_224.tgz) | 1.9 Mb | 49.7% | 74.1% | 16.2 ms | 37.3 ms
Mobilenet_V1_0.50_128 | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.5_128.tgz) | 5.3 Mb | 56.2% | 79.3% | 18.1 ms | 29.9 ms
Mobilenet_V1_0.50_160 | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.5_160.tgz) | 5.3 Mb | 59.0% | 81.8% | 26.8 ms | 45.9 ms
Mobilenet_V1_0.50_192 | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.5_192.tgz) | 5.3 Mb | 61.7% | 83.5% | 35.6 ms | 65.3 ms
Mobilenet_V1_0.50_224 | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.5_224.tgz) | 5.3 Mb | 63.2% | 84.9% | 47.6 ms | 164.2 ms
Mobilenet_V1_0.75_128 | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.75_128.tgz) | 10.3 Mb | 62.0% | 83.8% | 34.6 ms | 48.7 ms
Mobilenet_V1_0.75_160 | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.75_160.tgz) | 10.3 Mb | 65.2% | 85.9% | 51.3 ms | 75.2 ms
Mobilenet_V1_0.75_192 | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.75_192.tgz) | 10.3 Mb | 67.1% | 87.2% | 71.7 ms | 107.0 ms
Mobilenet_V1_0.75_224 | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.75_224.tgz) | 10.3 Mb | 68.3% | 88.1% | 95.7 ms | 143.4 ms
Mobilenet_V1_1.0_128 | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_128.tgz) | 16.9 Mb | 65.2% | 85.7% | 57.4 ms | 76.8 ms
Mobilenet_V1_1.0_160 | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_160.tgz) | 16.9 Mb | 68.0% | 87.7% | 86.0 ms | 117.7 ms
Mobilenet_V1_1.0_192 | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_192.tgz) | 16.9 Mb | 69.9% | 89.1% | 118.6 ms | 167.3 ms
Mobilenet_V1_1.0_224 | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_224.tgz) | 16.9 Mb | 71.0% | 89.9% | 160.1 ms | 224.3 ms
Mobilenet_V2_1.0_224 | [paper](https://arxiv.org/pdf/1801.04381.pdf), [tflite&pb](http://download.tensorflow.org/models/tflite_11_05_08/mobilenet_v2_1.0_224.tgz) | 14.0 Mb | 71.8% | 90.6% | 117 ms |
Mobilenet_V1_0.25_128 | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.25_128.tgz) | 1.9 Mb | 41.4% | 66.2% | 6.2 ms | 13.0 ms
Mobilenet_V1_0.25_160 | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.25_160.tgz) | 1.9 Mb | 45.4% | 70.2% | 8.6 ms | 19.5 ms
Mobilenet_V1_0.25_192 | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.25_192.tgz) | 1.9 Mb | 47.1% | 72.0% | 12.1 ms | 27.8 ms
Mobilenet_V1_0.25_224 | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.25_224.tgz) | 1.9 Mb | 49.7% | 74.1% | 16.2 ms | 37.3 ms
Mobilenet_V1_0.50_128 | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.5_128.tgz) | 5.3 Mb | 56.2% | 79.3% | 18.1 ms | 29.9 ms
Mobilenet_V1_0.50_160 | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.5_160.tgz) | 5.3 Mb | 59.0% | 81.8% | 26.8 ms | 45.9 ms
Mobilenet_V1_0.50_192 | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.5_192.tgz) | 5.3 Mb | 61.7% | 83.5% | 35.6 ms | 65.3 ms
Mobilenet_V1_0.50_224 | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.5_224.tgz) | 5.3 Mb | 63.2% | 84.9% | 47.6 ms | 164.2 ms
Mobilenet_V1_0.75_128 | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.75_128.tgz) | 10.3 Mb | 62.0% | 83.8% | 34.6 ms | 48.7 ms
Mobilenet_V1_0.75_160 | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.75_160.tgz) | 10.3 Mb | 65.2% | 85.9% | 51.3 ms | 75.2 ms
Mobilenet_V1_0.75_192 | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.75_192.tgz) | 10.3 Mb | 67.1% | 87.2% | 71.7 ms | 107.0 ms
Mobilenet_V1_0.75_224 | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_0.75_224.tgz) | 10.3 Mb | 68.3% | 88.1% | 95.7 ms | 143.4 ms
Mobilenet_V1_1.0_128 | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_128.tgz) | 16.9 Mb | 65.2% | 85.7% | 57.4 ms | 76.8 ms
Mobilenet_V1_1.0_160 | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_160.tgz) | 16.9 Mb | 68.0% | 87.7% | 86.0 ms | 117.7 ms
Mobilenet_V1_1.0_192 | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_192.tgz) | 16.9 Mb | 69.9% | 89.1% | 118.6 ms | 167.3 ms
Mobilenet_V1_1.0_224 | [paper](https://arxiv.org/pdf/1704.04861.pdf), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_224.tgz) | 16.9 Mb | 71.0% | 89.9% | 160.1 ms | 224.3 ms
Mobilenet_V2_1.0_224 | [paper](https://arxiv.org/pdf/1801.04381.pdf), [tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/tflite_11_05_08/mobilenet_v2_1.0_224.tgz) | 14.0 Mb | 71.8% | 90.6% | 117 ms |
### AutoML mobile models

2
tensorflow/lite/g3doc/models/smart_reply/overview.md
View File

@ -8,7 +8,7 @@ Our smart reply model generates reply suggestions based on chat messages. The
suggestions are intended to be contextually relevant, one-touch responses that
help the user to easily reply to an incoming message.
<a class="button button-primary" href="http://download.tensorflow.org/models/tflite/smartreply_1.0_2017_11_01.zip">Download
<a class="button button-primary" href="https://storage.googleapis.com/download.tensorflow.org/models/tflite/smartreply_1.0_2017_11_01.zip">Download
starter model and labels</a>
### Sample application

8
tensorflow/lite/g3doc/performance/benchmarks.md
View File

@ -46,7 +46,7 @@ Pixel xl | 0c |
</thead>
<tr>
<td rowspan = 2>
<a href="http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224.tgz">Mobilenet_1.0_224(float)</a>
<a href="https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224.tgz">Mobilenet_1.0_224(float)</a>
</td>
<td>Pixel 2 </td>
<td>123.3 ms</td>
@ -57,7 +57,7 @@ Pixel xl | 0c |
</tr>
<tr>
<td rowspan = 2>
<a href="http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224_quant.tgz">Mobilenet_1.0_224 (quant)</a>
<a href="https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224_quant.tgz">Mobilenet_1.0_224 (quant)</a>
</td>
<td>Pixel 2 </td>
<td>65.4 ms</td>
@ -130,14 +130,14 @@ modified to set `num_threads` to 1.
</thead>
<tr>
<td>
<a href="http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224.tgz">Mobilenet_1.0_224(float)</a>
<a href="https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224.tgz">Mobilenet_1.0_224(float)</a>
</td>
<td>iPhone 8 </td>
<td>32.2 ms</td>
</tr>
<tr>
<td>
<a href="http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224_quant.tgz)">Mobilenet_1.0_224 (quant)</a>
<a href="https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224_quant.tgz)">Mobilenet_1.0_224 (quant)</a>
</td>
<td>iPhone 8 </td>
<td>24.4 ms</td>

4
tensorflow/lite/java/demo/app/build.gradle
View File

@ -60,8 +60,8 @@ dependencies {
}
def targetFolder = "src/main/assets"
def modelFloatDownloadUrl = "http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_224.tgz"
def modelQuantDownloadUrl = "http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224_quant.tgz"
def modelFloatDownloadUrl = "https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_224.tgz"
def modelQuantDownloadUrl = "https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224_quant.tgz"
def localCacheFloat = "build/intermediates/mobilenet_v1_1.0_224.tgz"
def localCacheQuant = "build/intermediates/mmobilenet_v1_1.0_224_quant.tgz"

4
tensorflow/lite/models/smartreply/g3doc/README.md
View File

@ -62,8 +62,8 @@ and [research paper](https://arxiv.org/pdf/1708.00630).
## How to use this Model?
We have provided a pre-built demo APK that you can download, install and test on
your phone ([demo APK
here](http://download.tensorflow.org/deps/tflite/SmartReplyDemo.apk)).
your phone
([demo APK here](https://storage.googleapis.com/download.tensorflow.org/deps/tflite/SmartReplyDemo.apk)).
The On-Device Smart Reply demo App works in the following way:

2
tensorflow/lite/tools/benchmark/ios/README.md
View File

@ -13,7 +13,7 @@ parameters like inputs to the model, type of inputs, number of iterations,
number of threads. The default values in the JSON file are for the
Mobilenet_1.0_224 model
([paper](https://arxiv.org/pdf/1704.04861.pdf),
[tflite&pb](http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_224.tgz))
[tflite&pb](https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_224.tgz))
## To build/install/run

4
tensorflow/tools/graph_transforms/README.md
View File

@ -111,7 +111,7 @@ unsure, the
tool can inspect the model and provide guesses about likely input and output nodes,
as well as other information that's useful for debugging. Here's an example of
how to use it on the [Inception V3
graph](http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz):
graph](https://storage.googleapis.com/download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz):
```bash
bazel build tensorflow/tools/graph_transforms:summarize_graph
@ -124,7 +124,7 @@ This section has small guides for some of the most frequently-used
transformation pipelines, aimed at users who want to quickly accomplish one of
these tasks. A lot of them will use the Inception V3 model for their examples,
which can be downloaded from
[http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz](http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz).
[https://storage.googleapis.com/download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz](https://storage.googleapis.com/download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz).
### Optimizing for Deployment

8
tensorflow/workspace.bzl
View File

@ -787,8 +787,8 @@ def tf_repositories(path_prefix = "", tf_repo_name = ""):
build_file = clean_dep("//third_party:tflite_mobilenet_float.BUILD"),
sha256 = "2fadeabb9968ec6833bee903900dda6e61b3947200535874ce2fe42a8493abc0",
urls = [
"http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224.tgz",
"http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224.tgz",
"https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224.tgz",
"https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224.tgz",
],
)
@ -797,8 +797,8 @@ def tf_repositories(path_prefix = "", tf_repo_name = ""):
build_file = clean_dep("//third_party:tflite_mobilenet_quant.BUILD"),
sha256 = "d32432d28673a936b2d6281ab0600c71cf7226dfe4cdcef3012555f691744166",
urls = [
"http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224_quant.tgz",
"http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224_quant.tgz",
"https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224_quant.tgz",
"https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224_quant.tgz",
],
)