Added mobilenet support

slim/export_inference_graph.py

@@ -63,7 +63,6 @@ from tensorflow.python.platform import gfile
 from datasets import dataset_factory
 from nets import nets_factory
 
-
 slim = tf.contrib.slim
 
 tf.app.flags.DEFINE_string(
@@ -74,8 +73,8 @@ tf.app.flags.DEFINE_boolean(
     'Whether to save out a training-focused version of the model.')
 
 tf.app.flags.DEFINE_integer(
-    'default_image_size', 224,
-    'The image size to use if the model does not define it.')
+    'image_size', None,
+    'The image size to use, otherwise use the model default_image_size.')
 
 tf.app.flags.DEFINE_string('dataset_name', 'imagenet',
                            'The name of the dataset to use with the model.')
@@ -106,10 +105,7 @@ def main(_):
         FLAGS.model_name,
         num_classes=(dataset.num_classes - FLAGS.labels_offset),
         is_training=FLAGS.is_training)
-    if hasattr(network_fn, 'default_image_size'):
-      image_size = network_fn.default_image_size
-    else:
-      image_size = FLAGS.default_image_size
+    image_size = FLAGS.image_size or network_fn.default_image_size
     placeholder = tf.placeholder(name='input', dtype=tf.float32,
                                  shape=[1, image_size, image_size, 3])
     network_fn(placeholder)

slim/nets/mobilenet_v1.py

@@ -27,6 +27,8 @@ As described in https://arxiv.org/abs/1704.04861.
 
 100% Mobilenet V1 (base) with input size 224x224:
 
+See mobilenet_v1()
+
 Layer                                                     params           macs
 --------------------------------------------------------------------------------
 MobilenetV1/Conv2d_0/Conv2D:                                 864      10,838,016
@@ -62,6 +64,8 @@ Total:                                                 3,185,088     567,716,352
 
 75% Mobilenet V1 (base) with input size 128x128:
 
+See mobilenet_v1_075()
+
 Layer                                                     params           macs
 --------------------------------------------------------------------------------
 MobilenetV1/Conv2d_0/Conv2D:                                 648       2,654,208
@@ -102,6 +106,7 @@ from __future__ import division
 from __future__ import print_function
 
 from collections import namedtuple
+import functools
 
 import tensorflow as tf
 
@@ -335,6 +340,17 @@ def mobilenet_v1(inputs,
 mobilenet_v1.default_image_size = 224
 
 
+def wrapped_partial(func, *args, **kwargs):
+  partial_func = functools.partial(func, *args, **kwargs)
+  functools.update_wrapper(partial_func, func)
+  return partial_func
+
+
+mobilenet_v1_075 = wrapped_partial(mobilenet_v1, depth_multiplier=0.75)
+mobilenet_v1_050 = wrapped_partial(mobilenet_v1, depth_multiplier=0.50)
+mobilenet_v1_025 = wrapped_partial(mobilenet_v1, depth_multiplier=0.25)
+
+
 def _reduced_kernel_size_for_small_input(input_tensor, kernel_size):
   """Define kernel size which is automatically reduced for small input.
 

slim/nets/nets_factory.py

@@ -54,6 +54,9 @@ networks_map = {'alexnet_v2': alexnet.alexnet_v2,
                 'resnet_v2_152': resnet_v2.resnet_v2_152,
                 'resnet_v2_200': resnet_v2.resnet_v2_200,
                 'mobilenet_v1': mobilenet_v1.mobilenet_v1,
+                'mobilenet_v1_075': mobilenet_v1.mobilenet_v1_075,
+                'mobilenet_v1_050': mobilenet_v1.mobilenet_v1_050,
+                'mobilenet_v1_025': mobilenet_v1.mobilenet_v1_025,
                }
 
 arg_scopes_map = {'alexnet_v2': alexnet.alexnet_v2_arg_scope,
@@ -78,6 +81,9 @@ arg_scopes_map = {'alexnet_v2': alexnet.alexnet_v2_arg_scope,
                   'resnet_v2_152': resnet_v2.resnet_arg_scope,
                   'resnet_v2_200': resnet_v2.resnet_arg_scope,
                   'mobilenet_v1': mobilenet_v1.mobilenet_v1_arg_scope,
+                  'mobilenet_v1_075': mobilenet_v1.mobilenet_v1_arg_scope,
+                  'mobilenet_v1_050': mobilenet_v1.mobilenet_v1_arg_scope,
+                  'mobilenet_v1_025': mobilenet_v1.mobilenet_v1_arg_scope,
                  }
 
 

slim/scripts/export_mobilenet.sh

+#!/bin/bash
+# This script prepares the various different versions of MobileNet models for
+# use in a mobile application. If you don't specify your own trained checkpoint
+# file, it will download pretrained checkpoints for ImageNet. You'll also need
+# to have a copy of the TensorFlow source code to run some of the commands,
+# by default it will be looked for in ./tensorflow, but you can set the
+# TENSORFLOW_PATH environment variable before calling the script if your source
+# is in a different location.
+# The main slim/nets/mobilenet_v1.md description has more details about the
+# model, but the main points are that it comes in four size versions, 1.0, 0.75,
+# 0.50, and 0.25, which controls the number of parameters and so the file size
+# of the model, and the input image size, which can be 224, 192, 160, or 128
+# pixels, and affects the amount of computation needed, and the latency.
+# Here's an example generating a frozen model from pretrained weights:
+# 
+
+set -e
+
+print_usage () {
+  echo "Creates a frozen mobilenet model suitable for mobile use"
+  echo "Usage:"
+  echo "$0 <mobilenet version> <input size> [checkpoint path]"
+} 
+
+MOBILENET_VERSION=$1
+IMAGE_SIZE=$2
+CHECKPOINT=$3
+
+if [[ ${MOBILENET_VERSION} = "1.0" ]]; then
+   SLIM_NAME=mobilenet_v1
+elif [[ ${MOBILENET_VERSION} = "0.75" ]]; then
+   SLIM_NAME=mobilenet_v1_075
+elif [[ ${MOBILENET_VERSION} = "0.50" ]]; then
+   SLIM_NAME=mobilenet_v1_050
+elif [[ ${MOBILENET_VERSION} = "0.25" ]]; then
+   SLIM_NAME=mobilenet_v1_025
+else
+  echo "Bad mobilenet version, should be one of 1.0, 0.75, 0.50, or 0.25"
+  print_usage
+  exit 1
+fi
+
+if [[ ${IMAGE_SIZE} -ne "224" ]] && [[ ${IMAGE_SIZE} -ne "192" ]] && [[ ${IMAGE_SIZE} -ne "160" ]] && [[ ${IMAGE_SIZE} -ne "128" ]]; then
+  echo "Bad input image size, should be one of 224, 192, 160, or 128"
+  print_usage
+  exit 1
+fi
+
+if [[ ${TENSORFLOW_PATH} -eq "" ]]; then
+   TENSORFLOW_PATH=../tensorflow
+fi
+
+if [[ ! -d ${TENSORFLOW_PATH} ]]; then
+   echo "TensorFlow source folder not found. You should download the source and then set"
+   echo "the TENSORFLOW_PATH environment variable to point to it, like this:"
+   echo "export TENSORFLOW_PATH=/my/path/to/tensorflow"
+   print_usage
+   exit 1
+fi
+
+MODEL_FOLDER=/tmp/mobilenet_v1_${MOBILENET_VERSION}_${IMAGE_SIZE}
+if [[ -d ${MODEL_FOLDER} ]]; then
+  echo "Model folder ${MODEL_FOLDER} already exists!"
+  echo "If you want to overwrite it, then 'rm -rf ${MODEL_FOLDER}' first."
+  print_usage
+  exit 1
+fi
+mkdir ${MODEL_FOLDER}
+
+if [[ ${CHECKPOINT} = "" ]]; then
+  echo "*******"
+  echo "Downloading pretrained weights"
+  echo "*******"
+  curl "http://download.tensorflow.org/models/mobilenet_v1_${MOBILENET_VERSION}_${IMAGE_SIZE}_2017_06_14.tar.gz" \
+    -o ${MODEL_FOLDER}/checkpoints.tar.gz
+  tar xzf ${MODEL_FOLDER}/checkpoints.tar.gz --directory ${MODEL_FOLDER}
+  CHECKPOINT=${MODEL_FOLDER}/mobilenet_v1_${MOBILENET_VERSION}_${IMAGE_SIZE}.ckpt
+fi
+
+echo "*******"
+echo "Exporting graph architecture to ${MODEL_FOLDER}/unfrozen_graph.pb"
+echo "*******"
+bazel run slim:export_inference_graph -- \
+  --model_name=${SLIM_NAME} --image_size=${IMAGE_SIZE} --logtostderr \
+  --output_file=${MODEL_FOLDER}/unfrozen_graph.pb --dataset_dir=${MODEL_FOLDER}
+
+cd ../tensorflow
+
+echo "*******"
+echo "Freezing graph to ${MODEL_FOLDER}/frozen_graph.pb"
+echo "*******"
+bazel run tensorflow/python/tools:freeze_graph -- \
+  --input_graph=${MODEL_FOLDER}/unfrozen_graph.pb \
+  --input_checkpoint=${CHECKPOINT} \
+  --input_binary=true --output_graph=${MODEL_FOLDER}/frozen_graph.pb \
+  --output_node_names=MobilenetV1/Predictions/Reshape_1
+
+echo "Quantizing weights to ${MODEL_FOLDER}/quantized_graph.pb"
+bazel run tensorflow/tools/graph_transforms:transform_graph -- \
+  --in_graph=${MODEL_FOLDER}/frozen_graph.pb \
+  --out_graph=${MODEL_FOLDER}/quantized_graph.pb \
+  --inputs=input --outputs=MobilenetV1/Predictions/Reshape_1 \
+  --transforms='fold_constants fold_batch_norms quantize_weights'
+
+echo "*******"
+echo "Running label_image using the graph"
+echo "*******"
+bazel build tensorflow/examples/label_image:label_image
+bazel-bin/tensorflow/examples/label_image/label_image \
+  --input_layer=input --output_layer=MobilenetV1/Predictions/Reshape_1 \
+  --graph=${MODEL_FOLDER}/quantized_graph.pb --input_mean=-127 --input_std=127 \
+  --image=tensorflow/examples/label_image/data/grace_hopper.jpg \
+  --input_width=${IMAGE_SIZE} --input_height=${IMAGE_SIZE} --labels=${MODEL_FOLDER}/labels.txt
+
+echo "*******"
+echo "Saved graphs to ${MODEL_FOLDER}/frozen_graph.pb and ${MODEL_FOLDER}/quantized_graph.pb"
+echo "*******"