Merge branch 'master' of https://github.com/tensorflow/models into cifar_keras

research/lstm_object_detection/tf_sequence_example_decoder_test.py → research/lstm_object_detection/inputs/tf_sequence_example_decoder_test.py

@@ -21,7 +21,7 @@ from tensorflow.core.example import example_pb2
 from tensorflow.core.example import feature_pb2
 from tensorflow.python.framework import dtypes
 from tensorflow.python.ops import parsing_ops
-from lstm_object_detection import tf_sequence_example_decoder
+from lstm_object_detection.inputs import tf_sequence_example_decoder
 from object_detection.core import standard_fields as fields
 
 

research/lstm_object_detection/train.py

@@ -48,8 +48,8 @@ import os
 from absl import flags
 import tensorflow as tf
 from lstm_object_detection import model_builder
-from lstm_object_detection import seq_dataset_builder
 from lstm_object_detection import trainer
+from lstm_object_detection.inputs import seq_dataset_builder
 from lstm_object_detection.utils import config_util
 from object_detection.builders import preprocessor_builder
 

research/object_detection/dataset_tools/oid_hierarchical_labels_expansion.py

@@ -23,6 +23,8 @@ oid_hierarchical_labels_expansion.py \
 --annotation_type=<1 (for boxes) or 2 (for image-level labels)>
 """
 
+from __future__ import print_function
+
 import argparse
 import json
 
@@ -154,7 +156,7 @@ def main(parsed_args):
   if parsed_args.annotation_type == 2:
     labels_file = True
   elif parsed_args.annotation_type != 1:
-    print '--annotation_type expected value is 1 or 2.'
+    print('--annotation_type expected value is 1 or 2.')
     return -1
   with open(parsed_args.input_annotations, 'r') as source:
     with open(parsed_args.output_annotations, 'w') as target:

research/object_detection/g3doc/running_pets.md

@@ -208,7 +208,7 @@ For running the training Cloud ML job, we'll configure the cluster to use 5
 training jobs and three parameters servers. The
 configuration file can be found at `object_detection/samples/cloud/cloud.yml`.
 
-Note: This sample is supported for use with 1.8 runtime version.
+Note: The code sample below is supported for use with 1.9 runtime version.
 
 To start training and evaluation, execute the following command from the
 `tensorflow/models/research/` directory:
@@ -216,7 +216,7 @@ To start training and evaluation, execute the following command from the
 ```bash
 # From tensorflow/models/research/
 gcloud ml-engine jobs submit training `whoami`_object_detection_pets_`date +%m_%d_%Y_%H_%M_%S` \
-    --runtime-version 1.8 \
+    --runtime-version 1.9 \
     --job-dir=gs://${YOUR_GCS_BUCKET}/model_dir \
     --packages dist/object_detection-0.1.tar.gz,slim/dist/slim-0.1.tar.gz,/tmp/pycocotools/pycocotools-2.0.tar.gz \
     --module-name object_detection.model_main \
@@ -248,6 +248,8 @@ web browser. You should see something similar to the following:
 
 ![](img/tensorboard.png)
 
+Make sure your Tensorboard version is the same minor version as your Tensorflow (1.x)
+
 You will also want to click on the images tab to see example detections made by
 the model while it trains. After about an hour and a half of training, you can
 expect to see something like this:

research/object_detection/samples/cloud/cloud.yml

 trainingInput:
-  runtimeVersion: "1.8"
+  runtimeVersion: "1.9"
   scaleTier: CUSTOM
   masterType: standard_gpu
   workerCount: 5

samples/core/get_started/custom_estimator.py

@@ -27,9 +27,8 @@ parser.add_argument('--train_steps', default=1000, type=int,
                     help='number of training steps')
 
 def my_model(features, labels, mode, params):
-    """DNN with three hidden layers, and dropout of 0.1 probability."""
-    # Create three fully connected layers each layer having a dropout
-    # probability of 0.1.
+    """DNN with three hidden layers and learning_rate=0.1."""
+    # Create three fully connected layers.
     net = tf.feature_column.input_layer(features, params['feature_columns'])
     for units in params['hidden_units']:
         net = tf.layers.dense(net, units=units, activation=tf.nn.relu)