def _init_preprocessing(placeholder, preprocessing_type, image_size):
import tensorflow as tf
from preprocessing import vgg_preprocessing, inception_preprocessing
from model_tools.activations.tensorflow import load_image
preprocessing_types = {
'vgg': lambda image: vgg_preprocessing.preprocess_image(
image, image_size, image_size, resize_side_min=image_size),
'inception': lambda image: inception_preprocessing.preprocess_for_eval(
image, image_size, image_size, central_fraction=None)
}
assert preprocessing_type in preprocessing_types
preprocess_image = preprocessing_types[preprocessing_type]
preprocess = lambda image_path: preprocess_image(load_image(image_path))
preprocess = tf.map_fn(preprocess, placeholder, dtype=tf.float32)
return preprocess
def freeze_model(name):
checkpoints_dir = '/data/huang/behaviour/data/tfmodel'
OUTPUT_PB_FILENAME = 'inception_resnet_v2_behaviour_309_4_10_{}k.pb'.format(
name[0:3])
NUM_CLASSES = 309
tensorName_v4 = 'InceptionResnetV2/Logits/Predictions'
image_size = inception.inception_resnet_v2.default_image_size
with tf.Graph().as_default():
input_image_t = tf.placeholder(tf.string, name='input_image')
image = tf.image.decode_jpeg(input_image_t, channels=3)
processed_image = inception_preprocessing.preprocess_for_eval(
image, image_size, image_size, central_fraction=None)
processed_images = tf.expand_dims(processed_image, 0)
with slim.arg_scope(inception.inception_resnet_v2_arg_scope()):
logits, _ = inception.inception_resnet_v2(processed_images,
num_classes=NUM_CLASSES,
is_training=False)
# Apply softmax function to the logits (output of the last layer of the network)
probabilities = tf.nn.softmax(logits)
#model_path = tf.train.latest_checkpoint(checkpoints_dir)
model_path = '/data/huang/behaviour/data/tfmodel/model_backup/model.ckpt-{}'.format(
name)
init_fn = slim.assign_from_checkpoint_fn(model_path,
slim.get_model_variables())
with tf.Session() as sess:
# Now call the initialization function within the session
init_fn(sess)
constant_graph = convert_variables_to_constants(
sess, sess.graph_def,
["input_image", "DecodeJpeg", tensorName_v4])
tf.train.write_graph(constant_graph,
'.',
OUTPUT_PB_FILENAME,
as_text=False)
# resize input image later in the code.
image_size = inception.inception_resnet_v2.default_image_size
with tf.Graph().as_default():
# Inject placeholder into the graph
input_image_t = tf.placeholder(tf.string, name='input_image')
image = tf.image.decode_jpeg(input_image_t, channels=3)
# Resize the input image, preserving the aspect ratio
# and make a central crop of the resulted image.
# The crop will be of the size of the default image size of
# the network.
# I use the "preprocess_for_eval()" method instead of "inception_preprocessing()"
# because the latter crops all images to the center by 85% at
# prediction time (training=False).
processed_image = inception_preprocessing.preprocess_for_eval(
image, image_size, image_size, central_fraction=None)
# Networks accept images in batches.
# The first dimension usually represents the batch size.
# In our case the batch size is one.
processed_images = tf.expand_dims(processed_image, 0)
# Load the inception network structure
with slim.arg_scope(inception.inception_resnet_v2_arg_scope()):
logits, _ = inception.inception_resnet_v2(processed_images,
num_classes=NUM_CLASSES,
is_training=False)
# Apply softmax function to the logits (output of the last layer of the network)
probabilities = tf.nn.softmax(logits)
model_path = tf.train.latest_checkpoint(checkpoints_dir)
