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Bring tensorflow/models slim up to date.
Includes new gan nets. Inclues new NasNet models. PiperOrigin-RevId: 173772097
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‎research/slim/BUILD

+133-5
Original file line numberDiff line numberDiff line change
@@ -1,14 +1,14 @@
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# Description:
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# Contains files for loading, training and evaluating TF-Slim-based models.
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package(default_visibility = ["//visibility:public"])
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package(
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default_visibility = ["//visibility:public"],
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)
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licenses(["notice"]) # Apache 2.0
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exports_files(["LICENSE"])
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package_group(name = "internal")
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py_library(
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name = "dataset_utils",
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srcs = ["datasets/dataset_utils.py"],
@@ -35,6 +35,7 @@ py_binary(
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name = "build_imagenet_data",
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srcs = ["datasets/build_imagenet_data.py"],
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deps = [
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# "//numpy",
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"//tensorflow",
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],
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)
@@ -44,6 +45,7 @@ py_library(
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srcs = ["datasets/download_and_convert_cifar10.py"],
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deps = [
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":dataset_utils",
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# "//numpy",
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"//tensorflow",
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],
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)
@@ -62,6 +64,7 @@ py_library(
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srcs = ["datasets/download_and_convert_mnist.py"],
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deps = [
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":dataset_utils",
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# "//numpy",
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"//tensorflow",
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],
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)
@@ -73,6 +76,7 @@ py_binary(
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":download_and_convert_cifar10",
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":download_and_convert_flowers",
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":download_and_convert_mnist",
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"//tensorflow",
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],
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)
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@@ -137,6 +141,7 @@ py_test(
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srcs_version = "PY2AND3",
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deps = [
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":model_deploy",
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# "//numpy",
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"//tensorflow",
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],
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)
@@ -154,6 +159,7 @@ py_library(
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srcs = ["preprocessing/inception_preprocessing.py"],
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deps = [
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"//tensorflow",
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"//tensorflow/python:control_flow_ops",
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],
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)
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@@ -192,10 +198,13 @@ py_library(
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deps = [
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":alexnet",
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":cifarnet",
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":cyclegan",
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":inception",
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":lenet",
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":mobilenet_v1",
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":nasnet",
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":overfeat",
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":pix2pix",
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":resnet_v1",
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":resnet_v2",
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":vgg",
@@ -206,6 +215,7 @@ py_library(
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name = "alexnet",
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srcs = ["nets/alexnet.py"],
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srcs_version = "PY2AND3",
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deps = ["//tensorflow"],
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)
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py_test(
@@ -227,6 +237,45 @@ py_library(
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],
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)
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py_library(
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name = "cyclegan",
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srcs = ["nets/cyclegan.py"],
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deps = [
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# "//numpy",
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"//tensorflow",
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],
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)
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py_test(
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name = "cyclegan_test",
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srcs = ["nets/cyclegan_test.py"],
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shard_count = 3,
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srcs_version = "PY2AND3",
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deps = [
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":cyclegan",
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"//tensorflow",
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],
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)
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py_library(
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name = "dcgan",
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srcs = ["nets/dcgan.py"],
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deps = [
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"//tensorflow",
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],
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)
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py_test(
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name = "dcgan_test",
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srcs = ["nets/dcgan_test.py"],
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shard_count = 3,
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srcs_version = "PY2AND3",
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deps = [
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":dcgan",
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"//tensorflow",
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],
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)
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py_library(
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name = "inception",
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srcs = ["nets/inception.py"],
@@ -244,6 +293,7 @@ py_library(
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name = "inception_utils",
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srcs = ["nets/inception_utils.py"],
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srcs_version = "PY2AND3",
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deps = ["//tensorflow"],
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)
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py_library(
@@ -252,6 +302,7 @@ py_library(
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srcs_version = "PY2AND3",
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deps = [
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":inception_utils",
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"//tensorflow",
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],
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)
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@@ -261,6 +312,7 @@ py_library(
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srcs_version = "PY2AND3",
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deps = [
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":inception_utils",
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"//tensorflow",
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],
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)
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@@ -270,6 +322,7 @@ py_library(
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srcs_version = "PY2AND3",
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deps = [
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":inception_utils",
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"//tensorflow",
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],
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)
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@@ -279,13 +332,15 @@ py_library(
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srcs_version = "PY2AND3",
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deps = [
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":inception_utils",
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"//tensorflow",
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],
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)
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py_library(
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name = "inception_resnet_v2",
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srcs = ["nets/inception_resnet_v2.py"],
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srcs_version = "PY2AND3",
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deps = ["//tensorflow"],
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)
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py_test(
@@ -296,6 +351,7 @@ py_test(
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srcs_version = "PY2AND3",
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deps = [
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":inception",
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# "//numpy",
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"//tensorflow",
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],
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)
@@ -308,6 +364,7 @@ py_test(
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srcs_version = "PY2AND3",
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deps = [
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":inception",
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# "//numpy",
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"//tensorflow",
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],
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)
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srcs_version = "PY2AND3",
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deps = [
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":inception",
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# "//numpy",
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"//tensorflow",
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],
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)
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name = "mobilenet_v1",
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srcs = ["nets/mobilenet_v1.py"],
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srcs_version = "PY2AND3",
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deps = ["//tensorflow"],
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)
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py_test(
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srcs_version = "PY2AND3",
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deps = [
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":mobilenet_v1",
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# "//numpy",
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"//tensorflow",
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],
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)
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py_library(
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name = "nasnet_utils",
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srcs = ["nets/nasnet/nasnet_utils.py"],
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srcs_version = "PY2AND3",
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deps = [
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"//tensorflow",
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],
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)
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py_library(
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name = "nasnet",
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srcs = ["nets/nasnet/nasnet.py"],
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srcs_version = "PY2AND3",
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deps = [
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":nasnet_utils",
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"//tensorflow",
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],
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)
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py_test(
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name = "nasnet_utils_test",
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size = "medium",
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srcs = ["nets/nasnet/nasnet_utils_test.py"],
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srcs_version = "PY2AND3",
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deps = [
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":nasnet_utils",
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"//tensorflow",
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],
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)
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py_test(
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name = "nasnet_test",
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size = "large",
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srcs = ["nets/nasnet/nasnet_test.py"],
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shard_count = 10,
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srcs_version = "PY2AND3",
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deps = [
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":nasnet",
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"//tensorflow",
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],
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)
@@ -378,6 +480,7 @@ py_library(
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name = "overfeat",
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srcs = ["nets/overfeat.py"],
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srcs_version = "PY2AND3",
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deps = ["//tensorflow"],
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)
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py_test(
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],
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)
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py_library(
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name = "pix2pix",
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srcs = ["nets/pix2pix.py"],
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srcs_version = "PY2AND3",
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deps = ["//tensorflow"],
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)
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py_test(
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name = "pix2pix_test",
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srcs = ["nets/pix2pix_test.py"],
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srcs_version = "PY2AND3",
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deps = [
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":pix2pix",
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"//tensorflow",
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],
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)
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py_library(
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name = "resnet_utils",
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srcs = ["nets/resnet_utils.py"],
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srcs_version = "PY2AND3",
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deps = ["//tensorflow"],
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)
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py_library(
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srcs_version = "PY2AND3",
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deps = [
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":resnet_utils",
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"//tensorflow",
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],
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)
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@@ -413,7 +535,9 @@ py_test(
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shard_count = 2,
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srcs_version = "PY2AND3",
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deps = [
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":resnet_utils",
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":resnet_v1",
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# "//numpy",
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"//tensorflow",
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],
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)
@@ -424,6 +548,7 @@ py_library(
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srcs_version = "PY2AND3",
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deps = [
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":resnet_utils",
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"//tensorflow",
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],
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)
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@@ -434,7 +559,9 @@ py_test(
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shard_count = 2,
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srcs_version = "PY2AND3",
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deps = [
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":resnet_utils",
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":resnet_v2",
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# "//numpy",
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"//tensorflow",
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],
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)
@@ -443,6 +570,7 @@ py_library(
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name = "vgg",
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srcs = ["nets/vgg.py"],
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srcs_version = "PY2AND3",
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deps = ["//tensorflow"],
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)
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py_test(
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srcs = ["eval_image_classifier.py"],
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deps = [
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":dataset_factory",
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":model_deploy",
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":nets_factory",
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":preprocessing_factory",
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"//tensorflow",
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":dataset_factory",
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":nets_factory",
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"//tensorflow",
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"//tensorflow/python:platform",
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],
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)
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],
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deps = [
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":export_inference_graph",
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":nets_factory",
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"//tensorflow",
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"//tensorflow/python:platform",
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],
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)

‎research/slim/README.md

+6-1
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@@ -259,12 +259,16 @@ Model | TF-Slim File | Checkpoint | Top-1 Accuracy| Top-5 Accuracy |
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[MobileNet_v1_1.0_224](https://arxiv.org/pdf/1704.04861.pdf)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.py)|[mobilenet_v1_1.0_224_2017_06_14.tar.gz](http://download.tensorflow.org/models/mobilenet_v1_1.0_224_2017_06_14.tar.gz)|70.7|89.5|
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[MobileNet_v1_0.50_160](https://arxiv.org/pdf/1704.04861.pdf)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.py)|[mobilenet_v1_0.50_160_2017_06_14.tar.gz](http://download.tensorflow.org/models/mobilenet_v1_0.50_160_2017_06_14.tar.gz)|59.9|82.5|
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[MobileNet_v1_0.25_128](https://arxiv.org/pdf/1704.04861.pdf)|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.py)|[mobilenet_v1_0.25_128_2017_06_14.tar.gz](http://download.tensorflow.org/models/mobilenet_v1_0.25_128_2017_06_14.tar.gz)|41.3|66.2|
262+
[NASNet-A_Mobile_224](https://arxiv.org/abs/1707.07012)#|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/nasnet/nasnet.py)|[nasnet-a_mobile_04_10_2017.tar.gz](https://storage.googleapis.com/download.tensorflow.org/models/nasnet-a_mobile_04_10_2017.tar.gz)|74.0|91.6|
263+
[NASNet-A_Large_331](https://arxiv.org/abs/1707.07012)#|[Code](https://github.com/tensorflow/models/blob/master/research/slim/nets/nasnet/nasnet.py)|[nasnet-a_large_04_10_2017.tar.gz](https://storage.googleapis.com/download.tensorflow.org/models/nasnet-a_large_04_10_2017.tar.gz)|82.7|96.2|
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263265
^ ResNet V2 models use Inception pre-processing and input image size of 299 (use
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`--preprocessing_name inception --eval_image_size 299` when using
265267
`eval_image_classifier.py`). Performance numbers for ResNet V2 models are
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reported on the ImageNet validation set.
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270+
(#) More information and details about the NASNet architectures are available at this [README](nets/nasnet/README.md)
271+
268272
All 16 MobileNet Models reported in the [MobileNet Paper](https://arxiv.org/abs/1704.04861) can be found [here](https://github.com/tensorflow/models/tree/master/research/slim/nets/mobilenet_v1.md).
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(\*): Results quoted from the [paper](https://arxiv.org/abs/1603.05027).
@@ -393,7 +397,8 @@ $ python eval_image_classifier.py \
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--model_name=inception_v3
394398
```
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396-
See the [evaluation module example](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/slim#evaluation-loop) for an example of how to evaluate a model at multiple checkpoints during or after the training.
400+
See the [evaluation module example](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/slim#evaluation-loop)
401+
for an example of how to evaluate a model at multiple checkpoints during or after the training.
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# Exporting the Inference Graph
399404
<a id='Export'></a>

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