def imagenet_input(is_training):
"""Data reader for imagenet.
Reads in imagenet data and performs pre-processing on the images.
Args:
is_training: bool specifying if train or validation dataset is needed.
Returns:
A batch of images and labels.
"""
if is_training:
dataset = dataset_factory.get_dataset('imagenet', 'train',
FLAGS.dataset_dir)
else:
dataset = dataset_factory.get_dataset('imagenet', 'validation',
FLAGS.dataset_dir)
provider = slim.dataset_data_provider.DatasetDataProvider(
dataset,
shuffle=is_training,
common_queue_capacity=2 * FLAGS.batch_size,
common_queue_min=FLAGS.batch_size)
[image, label] = provider.get(['image', 'label'])
image_preprocessing_fn = preprocessing_factory.get_preprocessing(
'mobilenet_v1', is_training=is_training)
image = image_preprocessing_fn(image, FLAGS.image_size, FLAGS.image_size)
images, labels = tf.train.batch(tensors=[image, label],
batch_size=FLAGS.batch_size,
num_threads=4,
capacity=5 * FLAGS.batch_size)
return images, labels
def parse_function(filename):
image_string = tf.read_file(filename)
image_decoded = tf.image.decode_jpeg(image_string,
channels=args.num_channel)
if args.preprocessing_name and args.preprocessing_name in preprocessing_factory.preprocessing_fn_map:
image_preprocessing_fn = preprocessing_factory.get_preprocessing(
args.preprocessing_name, is_training=False)
image_decoded = image_preprocessing_fn(image_decoded, args.input_size,
args.input_size)
else:
if not args.preprocessing_name:
args.preprocessing_name = "base_preprocessing"
preprocessing_f = util.get_attr(
'preprocessing.%s' % args.preprocessing_name,
"preprocessing_inference")
if not preprocessing_f:
preprocessing_f = util.get_attr('preprocessing.base_preprocessing',
"preprocessing_inference")
image_decoded = preprocessing_f(image_decoded,
tf.convert_to_tensor(args.input_size),
tf.convert_to_tensor(args.input_size),
args)
return image_decoded
def pre_process(self, example_proto, is_training):
features = {"image/encoded": tf.FixedLenFeature((), tf.string, default_value=""),
"image/class/label": tf.FixedLenFeature((), tf.int64, default_value=0),
'image/height': tf.FixedLenFeature((), tf.int64, default_value=0),
'image/width': tf.FixedLenFeature((), tf.int64, default_value=0)
}
parsed_features = tf.parse_single_example(example_proto, features)
if self.config.preprocessing_name:
image_preprocessing_fn = preprocessing_factory.get_preprocessing(self.config.preprocessing_name,
is_training=is_training)
image = tf.image.decode_image(parsed_features["image/encoded"], self.config.num_channel)
image = tf.clip_by_value(
image_preprocessing_fn(image, tf.convert_to_tensor(self.config.input_size),
tf.convert_to_tensor(self.config.input_size)), -1, 1.0)
else:
image = tf.clip_by_value(tf.image.per_image_standardization(
tf.image.resize_images(tf.image.decode_jpeg(parsed_features["image/encoded"], self.config.num_channel),
[tf.convert_to_tensor(self.config.input_size),
tf.convert_to_tensor(self.config.input_size)])), -1., 1.0)
if len(parsed_features["image/class/label"].get_shape()) == 0:
label = tf.one_hot(parsed_features["image/class/label"], self.config.num_class)
else:
label = parsed_features["image/class/label"]
return image, label
def inference_on_image(bot_id, suffix, setting_id, image_file, network_name='inception_v4', return_labels=1):
"""
Loads the corresponding model checkpoint, network function and preprocessing routine based on bot_id and network_name,
restores the graph and runs it to the prediction enpoint with the image as input
:param bot_id: bot_id, used to reference to correct model directory
:param image_file: reference to the temporary image file to be classified
:param network_name: name of the network type to be used
:param return_labels: number of labels to return
:return: the top n labels with probabilities, where n = return_labels
"""
# Get the model path
model_path = dirs.get_transfer_model_dir(bot_id+suffix, setting_id)
# Get number of classes to predict
protobuf_dir = dirs.get_transfer_proto_dir(bot_id, setting_id)
number_of_classes = dataset_utils.get_number_of_classes_by_labels(protobuf_dir)
# Get the preprocessing and network construction functions
preprocessing_fn = preprocessing_factory.get_preprocessing(network_name, is_training=False)
network_fn = network_factory.get_network_fn(network_name, number_of_classes)
# Process the temporary image file into a Tensor of shape [widht, height, channels]
image_tensor = tf.gfile.FastGFile(image_file, 'rb').read()
image_tensor = tf.image.decode_image(image_tensor, channels=0)
# Perform preprocessing and reshape into [network.default_width, network.default_height, channels]
network_default_size = network_fn.default_image_size
image_tensor = preprocessing_fn(image_tensor, network_default_size, network_default_size)
# Create an input batch of size one from the preprocessed image
input_batch = tf.reshape(image_tensor, [1, 299, 299, 3])
# Create the network up to the Predictions Endpoint
logits, endpoints = network_fn(input_batch)
# Create a Saver() object to restore the network from the last checkpoint
restorer = tf.train.Saver()
with tf.Session() as sess:
tf.global_variables_initializer().run()
# Restore the variables of the network from the last checkpoint and run the graph
restorer.restore(sess, tf.train.latest_checkpoint(model_path))
sess.run(endpoints)
# Get the numpy array of predictions out of the
predictions = endpoints['Predictions'].eval()[0]
return map_predictions_to_labels(protobuf_dir, predictions, return_labels)
def build_imagenet_graph(path):
tf.reset_default_graph()
print(path)
filename_queue = tf.train.string_input_producer(
tf.train.match_filenames_once(path + "/*.jpg"),
num_epochs=1,
shuffle=False,
capacity=100)
image_reader = tf.WholeFileReader()
image_file_name, image_file = image_reader.read(filename_queue)
image = tf.image.decode_jpeg(image_file, channels=3, fancy_upscaling=True)
model_name = 'inception_resnet_v2'
network_fn = nets_factory.get_network_fn(model_name,
is_training=False,
num_classes=1001)
preprocessing_name = model_name
image_preprocessing_fn = preprocessing_factory.get_preprocessing(
preprocessing_name, is_training=False)
eval_image_size = network_fn.default_image_size
image = image_preprocessing_fn(image, eval_image_size, eval_image_size)
filenames, images = tf.train.batch([image_file_name, image],
batch_size=100,
num_threads=2,
capacity=500)
logits, _ = network_fn(images)
variables_to_restore = slim.get_variables_to_restore()
predictions = tf.argmax(logits, 1)
return filenames, logits, predictions, variables_to_restore
def eval_model(candidate, N, F, save_dir, model_name):
print("eval model")
tf.logging.set_verbosity(tf.logging.INFO)
with tf.Graph().as_default():
tf_global_step = slim.get_or_create_global_step()
######################
# Select the dataset #
######################
dataset = dataset_factory.get_dataset(
FLAGS.dataset_name, 'test', FLAGS.dataset_dir)
####################
# Select the model #
####################
network_fn = nets_factory.get_network_fn(
FLAGS.model_name, candidate, N, F,
num_classes=(dataset.num_classes - FLAGS.labels_offset),
is_training=False)
##############################################################
# Create a dataset provider that loads data from the dataset #
##############################################################
provider = slim.dataset_data_provider.DatasetDataProvider(
dataset,
shuffle=False,
common_queue_capacity=2 * FLAGS.batch_size,
common_queue_min=FLAGS.batch_size)
[image, label] = provider.get(['image', 'label'])
label -= FLAGS.labels_offset
#####################################
# Select the preprocessing function #
#####################################
preprocessing_name = FLAGS.preprocessing_name or FLAGS.model_name
image_preprocessing_fn = preprocessing_factory.get_preprocessing(
preprocessing_name,
is_training=False)
eval_image_size = network_fn.default_image_size
image = image_preprocessing_fn(image, eval_image_size, eval_image_size)
FLAGS.batch_size = 100
images, labels = tf.train.batch(
[image, label],
batch_size=FLAGS.batch_size,
num_threads=FLAGS.num_preprocessing_threads,
capacity=5 * FLAGS.batch_size)
####################
# Define the model #
####################
logits, _ = network_fn(images)
if FLAGS.moving_average_decay:
variable_averages = tf.train.ExponentialMovingAverage(
FLAGS.moving_average_decay, tf_global_step)
variables_to_restore = variable_averages.variables_to_restore(
slim.get_model_variables())
variables_to_restore[tf_global_step.op.name] = tf_global_step
else:
variables_to_restore = slim.get_variables_to_restore()
predictions = tf.argmax(logits, 1)
labels = tf.squeeze(labels)
# Define the metrics:
names_to_values, names_to_updates = slim.metrics.aggregate_metric_map({
'Accuracy': slim.metrics.streaming_accuracy(predictions, labels),
# 'Recall_5': slim.metrics.streaming_recall_at_k(
# logits, labels, 5),
})
# Print the summaries to screen.
for name, value in names_to_values.items():
summary_name = 'eval/%s' % name
op = tf.summary.scalar(summary_name, value, collections=[])
op = tf.Print(op, [value], summary_name)
tf.add_to_collection(tf.GraphKeys.SUMMARIES, op)
# TODO(sguada) use num_epochs=1
if FLAGS.max_num_batches:
num_batches = FLAGS.max_num_batches
else:
# This ensures that we make a single pass over all of the data.
num_batches = math.ceil(dataset.num_samples / float(FLAGS.batch_size))
FLAGS.checkpoint_path = FLAGS.train_dir
if tf.gfile.IsDirectory(FLAGS.checkpoint_path):
checkpoint_path = tf.train.latest_checkpoint(FLAGS.checkpoint_path)
else:
checkpoint_path = FLAGS.checkpoint_path
tf.logging.info('Evaluating %s' % checkpoint_path)
final_op = [names_to_values['Accuracy']] #top1 accuracy to return
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
#time.sleep(60)
pl.start()
start_time = time.time()
a = slim.evaluation.evaluate_once(
master=FLAGS.master,
checkpoint_path=checkpoint_path,
logdir=FLAGS.eval_dir,
session_config=config,
num_evals=num_batches,
eval_op=list(names_to_updates.values()),
final_op = final_op,
variables_to_restore=variables_to_restore)
duration = time.time() - start_time
pl.stop()
data_list = pl.getDataTrace(nodeName='module/gpu', valType='power')
pickle.dump(data_list, open(os.path.join(save_dir, model_name + '_data_list_final_{}_{}.pkl'.format(N,F)),'wb'))
power_list = data_list[1]
time_list = data_list[0]
start, end = get_start_end(power_list)
integration_time = time_list[end] - time_list[start]
integration_energy = integrate_power(power_list, time_list, start, end)
return integration_time, integration_energy
def train_model(candidate, N, F):
print("train model")
print(FLAGS.dataset_name)
if not FLAGS.dataset_dir:
raise ValueError('You must supply the dataset directory with --dataset_dir')
tf.logging.set_verbosity(tf.logging.INFO)
with tf.Graph().as_default():
#######################
# Config model_deploy #
#######################
deploy_config = model_deploy.DeploymentConfig(
num_clones=FLAGS.num_clones,
clone_on_cpu=FLAGS.clone_on_cpu,
replica_id=FLAGS.task,
num_replicas=FLAGS.worker_replicas,
num_ps_tasks=FLAGS.num_ps_tasks)
# Create global_step
with tf.device(deploy_config.variables_device()):
global_step = slim.create_global_step()
######################
# Select the dataset #
######################
dataset = dataset_factory.get_dataset(
FLAGS.dataset_name, FLAGS.dataset_split_name, FLAGS.dataset_dir)
######################
# Select the network #
######################
network_fn = nets_factory.get_network_fn(
FLAGS.model_name,
candidate, N, F,
num_classes=(dataset.num_classes - FLAGS.labels_offset),
weight_decay=FLAGS.weight_decay,
is_training=True)
#####################################
# Select the preprocessing function #
#####################################
preprocessing_name = FLAGS.preprocessing_name or FLAGS.model_name
image_preprocessing_fn = preprocessing_factory.get_preprocessing(
preprocessing_name,
is_training=True)
##############################################################
# Create a dataset provider that loads data from the dataset #
##############################################################
with tf.device(deploy_config.inputs_device()):
provider = slim.dataset_data_provider.DatasetDataProvider(
dataset,
num_readers=FLAGS.num_readers,
common_queue_capacity=20 * FLAGS.batch_size,
common_queue_min=10 * FLAGS.batch_size)
[image, label] = provider.get(['image', 'label'])
label -= FLAGS.labels_offset
train_image_size = FLAGS.train_image_size or network_fn.default_image_size
image = image_preprocessing_fn(image, train_image_size, train_image_size)
images, labels = tf.train.batch(
[image, label],
batch_size=FLAGS.batch_size,
num_threads=FLAGS.num_preprocessing_threads,
capacity=5 * FLAGS.batch_size)
labels = slim.one_hot_encoding(
labels, dataset.num_classes - FLAGS.labels_offset)
batch_queue = slim.prefetch_queue.prefetch_queue(
[images, labels], capacity=2 * deploy_config.num_clones)
####################
# Define the model #
####################
def clone_fn(batch_queue):
"""Allows data parallelism by creating multiple clones of network_fn."""
images, labels = batch_queue.dequeue()
logits, end_points = network_fn(images)
#############################
# Specify the loss function #
#############################
if 'AuxLogits' in end_points:
slim.losses.softmax_cross_entropy(
end_points['AuxLogits'], labels,
label_smoothing=FLAGS.label_smoothing, weights=0.4,
scope='aux_loss')
slim.losses.softmax_cross_entropy(
logits, labels, label_smoothing=FLAGS.label_smoothing, weights=1.0)
return end_points
# Gather initial summaries.
summaries = set(tf.get_collection(tf.GraphKeys.SUMMARIES))
clones = model_deploy.create_clones(deploy_config, clone_fn, [batch_queue])
first_clone_scope = deploy_config.clone_scope(0)
# Gather update_ops from the first clone. These contain, for example,
# the updates for the batch_norm variables created by network_fn.
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS, first_clone_scope)
# Add summaries for end_points.
end_points = clones[0].outputs
for end_point in end_points:
x = end_points[end_point]
summaries.add(tf.summary.histogram('activations/' + end_point, x))
summaries.add(tf.summary.scalar('sparsity/' + end_point,
tf.nn.zero_fraction(x)))
# Add summaries for losses.
for loss in tf.get_collection(tf.GraphKeys.LOSSES, first_clone_scope):
summaries.add(tf.summary.scalar('losses/%s' % loss.op.name, loss))
# Add summaries for variables.
for variable in slim.get_model_variables():
summaries.add(tf.summary.histogram(variable.op.name, variable))
#################################
# Configure the moving averages #
#################################
if FLAGS.moving_average_decay:
moving_average_variables = slim.get_model_variables()
variable_averages = tf.train.ExponentialMovingAverage(
FLAGS.moving_average_decay, global_step)
else:
moving_average_variables, variable_averages = None, None
#########################################
# Configure the optimization procedure. #
#########################################
with tf.device(deploy_config.optimizer_device()):
learning_rate = _configure_learning_rate(dataset.num_samples, global_step)
optimizer = _configure_optimizer(learning_rate)
summaries.add(tf.summary.scalar('learning_rate', learning_rate))
if FLAGS.sync_replicas:
# If sync_replicas is enabled, the averaging will be done in the chief
# queue runner.
optimizer = tf.train.SyncReplicasOptimizer(
opt=optimizer,
replicas_to_aggregate=FLAGS.replicas_to_aggregate,
total_num_replicas=FLAGS.worker_replicas,
variable_averages=variable_averages,
variables_to_average=moving_average_variables)
elif FLAGS.moving_average_decay:
# Update ops executed locally by trainer.
update_ops.append(variable_averages.apply(moving_average_variables))
# Variables to train.
variables_to_train = _get_variables_to_train()
# and returns a train_tensor and summary_op
total_loss, clones_gradients = model_deploy.optimize_clones(
clones,
optimizer,
var_list=variables_to_train)
# Add total_loss to summary.
summaries.add(tf.summary.scalar('total_loss', total_loss))
# Create gradient updates.
grad_updates = optimizer.apply_gradients(clones_gradients,
global_step=global_step)
update_ops.append(grad_updates)
update_op = tf.group(*update_ops)
with tf.control_dependencies([update_op]):
train_tensor = tf.identity(total_loss, name='train_op')
# Add the summaries from the first clone. These contain the summaries
# created by model_fn and either optimize_clones() or _gather_clone_loss().
summaries |= set(tf.get_collection(tf.GraphKeys.SUMMARIES,
first_clone_scope))
# Merge all summaries together.
summary_op = tf.summary.merge(list(summaries), name='summary_op')
###########################
# Kicks off the training. #
###########################
slim.learning.train(
train_tensor,
logdir=FLAGS.train_dir,
master=FLAGS.master,
is_chief=(FLAGS.task == 0),
init_fn=_get_init_fn(),
summary_op=summary_op,
number_of_steps=FLAGS.max_number_of_steps,
log_every_n_steps=FLAGS.log_every_n_steps,
save_summaries_secs=FLAGS.save_summaries_secs,
save_interval_secs=FLAGS.save_interval_secs,
sync_optimizer=optimizer if FLAGS.sync_replicas else None)
def main(_):
if not FLAGS.dataset_dir:
raise ValueError(
'You must supply the dataset directory with --dataset_dir')
tf.logging.set_verbosity(tf.logging.INFO)
with tf.Graph().as_default():
tf_global_step = slim.get_or_create_global_step()
######################
# Select the dataset #
######################
dataset = dataset_factory.get_dataset(FLAGS.dataset_name,
FLAGS.dataset_split_name,
FLAGS.dataset_dir)
####################
# Select the model #
####################
network_fn = nets_factory.get_network_fn(
FLAGS.model_name,
num_classes=(dataset.num_classes - FLAGS.labels_offset),
is_training=False)
##############################################################
# Create a dataset provider that loads data from the dataset #
##############################################################
provider = slim.dataset_data_provider.DatasetDataProvider(
dataset,
shuffle=False,
common_queue_capacity=2 * FLAGS.batch_size,
common_queue_min=FLAGS.batch_size)
[image, label] = provider.get(['image', 'label'])
label -= FLAGS.labels_offset
#####################################
# Select the preprocessing function #
#####################################
preprocessing_name = FLAGS.preprocessing_name or FLAGS.model_name
image_preprocessing_fn = preprocessing_factory.get_preprocessing(
preprocessing_name, is_training=False)
eval_image_size = FLAGS.eval_image_size or network_fn.default_image_size
image = image_preprocessing_fn(image, eval_image_size, eval_image_size)
images, labels = tf.train.batch(
[image, label],
batch_size=FLAGS.batch_size,
num_threads=FLAGS.num_preprocessing_threads,
capacity=5 * FLAGS.batch_size)
####################
# Define the model #
####################
logits, _ = network_fn(images)
if FLAGS.moving_average_decay:
variable_averages = tf.train.ExponentialMovingAverage(
FLAGS.moving_average_decay, tf_global_step)
variables_to_restore = variable_averages.variables_to_restore(
slim.get_model_variables())
variables_to_restore[tf_global_step.op.name] = tf_global_step
else:
variables_to_restore = slim.get_variables_to_restore()
predictions = tf.argmax(logits, 1)
labels = tf.squeeze(labels)
# Define the metrics:
names_to_values, names_to_updates = slim.metrics.aggregate_metric_map({
'Accuracy':
slim.metrics.streaming_accuracy(predictions, labels),
'Recall_5':
slim.metrics.streaming_recall_at_k(logits, labels, 5),
})
# Print the summaries to screen.
for name, value in names_to_values.items():
summary_name = 'eval/%s' % name
op = tf.summary.scalar(summary_name, value, collections=[])
op = tf.Print(op, [value], summary_name)
tf.add_to_collection(tf.GraphKeys.SUMMARIES, op)
# TODO(sguada) use num_epochs=1
if FLAGS.max_num_batches:
num_batches = FLAGS.max_num_batches
else:
# This ensures that we make a single pass over all of the data.
num_batches = math.ceil(dataset.num_samples /
float(FLAGS.batch_size))
if tf.gfile.IsDirectory(FLAGS.checkpoint_path):
checkpoint_path = tf.train.latest_checkpoint(FLAGS.checkpoint_path)
else:
checkpoint_path = FLAGS.checkpoint_path
tf.logging.info('Evaluating %s' % checkpoint_path)
slim.evaluation.evaluate_once(
master=FLAGS.master,
checkpoint_path=checkpoint_path,
logdir=FLAGS.eval_dir,
num_evals=num_batches,
eval_op=list(names_to_updates.values()),
variables_to_restore=variables_to_restore)
def run_one_cnn(args):
num_inj_per_layer = 1
# Obtain target layer information
layer_dict = {}
with open(args.layer_path) as layer_csv:
layer_reader = csv.reader(layer_csv, delimiter='\t')
for row in layer_reader:
layer_dict[row[0]] = row[1]
print(layer_dict)
layer_names = [
layer_dict["Input tensor name:"], layer_dict["Weight tensor name:"],
layer_dict["Output tensor name:"]
]
layer_dims = [
str2list(layer_dict["Input shape:"]),
str2list(layer_dict["Weight shape:"]),
str2list(layer_dict["Output shape:"])
]
layer_stride = int(layer_dict["Layer stride:"])
layer_padding = layer_dict["Layer padding:"]
quant_min_max = str2list(layer_dict["Quant min max:"], True)
# Obtain delta
delta_set, inj_pos = delta_generator(args.network, args.precision,
args.inj_type, [layer_names[2]],
layer_dims[2], quant_min_max)
# Then start running injection
tf.reset_default_graph()
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
np_image = x_test[args.image_id]
image_label = y_test[args.image_id]
image = tf.placeholder(tf.uint8, shape=[32, 32, 3])
if 'ic' in args.network or 'mb' in args.network:
pre_fn = get_preprocessing('inception', is_training=False)
else:
pre_fn = get_preprocessing('resnet_v1_50', is_training=False)
post_image = pre_fn(image, 224, 224)
images = tf.expand_dims(post_image, 0)
# Cast the image for fp16
if 'fp16' in args.precision:
images = tf.cast(images, tf.float16)
# Deploy the network
arg_scope_fn = get_arg_scope(args.network, args.precision)
network_fn = get_network(args.network, args.precision)
# Need to feed in injection relating arguments
with tf.contrib.slim.arg_scope(arg_scope_fn()):
net, endpoints = network_fn(images,
num_classes=10,
is_training=False,
inj_type=get_network_inj_type(
args.precision, args.inj_type),
inj_layer=[layer_names[2]],
inj_pos=inj_pos,
quant_min_max=quant_min_max)
# Quantize the network if necessary
if 'int8' in args.precision:
tf.contrib.quantize.create_eval_graph()
elif 'int16' in args.precision:
tf.contrib.quantize.experimental_create_eval_graph(weight_bits=16,
activation_bits=16)
# Create saver: For FP16, need extra handling for Logits
all_variables = tf.get_collection_ref(tf.GraphKeys.GLOBAL_VARIABLES)
if 'fp16' in args.precision:
v_list = [
v for v in all_variables
if 'dense' not in v.name and 'delta' not in v.name
]
elif 'rs' in args.network and 'int' in args.precision:
v_list = [
v for v in all_variables if 'delta' not in v.name
and 'unit_1/bottleneck_v1/shortcut/act_quant' not in v.name
and 'unit_2/bottleneck_v1/conv3/act_quant' not in v.name
and 'unit_3/bottleneck_v1/conv3/act_quant' not in v.name
and 'unit_4/bottleneck_v1/conv3/act_quant' not in v.name
and 'unit_5/bottleneck_v1/conv3/act_quant' not in v.name
and 'unit_6/bottleneck_v1/conv3/act_quant' not in v.name
]
else:
v_list = [v for v in all_variables if 'delta' not in v.name]
saver = tf.train.Saver(var_list=v_list)
# Create a session and run it
with tf.Session() as sess:
saver.restore(sess, args.ckpt_path)
# For fp16, restore the dense part of the network
if 'fp16' in args.precision:
dense_var_dict = {
'mb': 'MobilenetV1/Logits/Conv2d_1c_1x1/',
'rs': 'resnet_v1_50/logits/',
'ic': 'InceptionV1/Logits/Conv2d_0c_1x1/'
}
for variable in all_variables:
if 'dense/kernel' in variable.name:
var = tf.contrib.framework.load_variable(
args.ckpt_path,
dense_var_dict[args.network[:2]] + 'weights')
sess.run(variable.assign(var[0, 0, :, :]))
if 'dense/bias' in variable.name:
var = tf.contrib.framework.load_variable(
args.ckpt_path,
dense_var_dict[args.network[:2]] + 'biases')
sess.run(variable.assign(var))
elif 'rs' in args.network and 'int' in args.precision:
for variable in all_variables:
if 'unit_1/bottleneck_v1/shortcut/act_quant' in variable.name:
var = tf.contrib.framework.load_variable(
args.ckpt_path,
re.sub('act_quant', 'conv_quant', variable.name))
sess.run(variable.assign(var))
if 'bottleneck_v1/conv3/act_quant' in variable.name and 'unit_1' not in variable.name:
var = tf.contrib.framework.load_variable(
args.ckpt_path,
re.sub('act_quant', 'conv_quant', variable.name))
sess.run(variable.assign(var))
# If we inject to input/weights or local controls
if 'INPUT' in args.inj_type or 'WEIGHT' in args.inj_type or 'RD_BFLIP' in args.inj_type:
layer = layer_names[2]
delta_np = np.zeros(shape=layer_dims[2], dtype=np.float32)
scope_string = ''
if 'mb' in args.network:
scope_string = 'MobilenetV1'
elif 'rs' in args.network:
scope_string = layer[:layer.rfind('/')]
else:
scope_string = layer[layer.find('/') + 1:layer.rfind('/')]
with tf.variable_scope(scope_string, reuse=True):
sess.run(
tf.get_variable('delta_{}'.format(
re.sub('\/', '_', layer[layer.rfind('/') + 1:])),
trainable=False).assign(delta_np))
# Get this layer's weight
weight_tensor = tf.get_default_graph().get_tensor_by_name(
layer_names[1])
if 'RD_BFLIP' in args.inj_type:
# In RD_BFLIP, input tensor means this layer's output
input_tensor = tf.get_default_graph().get_tensor_by_name(
layer + '/Conv2D:0')
else:
# Get this layer's input for injecting to input or psum
input_tensor = tf.get_default_graph().get_tensor_by_name(
layer_names[0])
# Run the golden network
wt, inp = sess.run([weight_tensor, input_tensor],
feed_dict={image: np_image})
if 'INPUT' in args.inj_type or 'RD_BFLIP' in args.inj_type:
if 'INPUT' in args.inj_type or 'RD_BFLIP' in args.inj_type:
t_a, t_b, t_c, t_d = inp.shape
else:
t_a, t_b, t_c, t_d = layer_dims[2]
p_a = np.random.randint(t_a)
p_b = np.random.randint(t_b)
p_c = np.random.randint(t_c)
p_d = np.random.randint(t_d)
golden_d = inp[p_a][p_b][p_c][p_d]
if 'RD_BFLIP' in args.inj_type:
flip_bit, perturb = get_bit_flip_perturbation(
args.network, args.precision, golden_d, layer,
'rd_bflip')
else:
flip_bit, perturb = get_bit_flip_perturbation(
args.network, args.precision, golden_d, layer, 'input')
inp_perturb = np.zeros(inp.shape)
inp_perturb[p_a][p_b][p_c][p_d] = perturb
if 'RD_BFLIP' in args.inj_type:
delta_perturb = inp_perturb
else:
delta_perturb = perturb_conv(inp_perturb, wt, layer_stride,
layer_padding == 'SAME',
layer_dims[2][-1])
else:
t_a, t_b, t_c, t_d = wt.shape
p_a = np.random.randint(t_a)
p_b = np.random.randint(t_b)
p_c = np.random.randint(t_c)
p_d = np.random.randint(t_d)
golden_d = wt[p_a][p_b][p_c][p_d]
flip_bit, perturb = get_bit_flip_perturbation(
args.network, args.precision, golden_d, layer, 'weight')
wt_perturb = np.zeros(wt.shape)
wt_perturb[p_a][p_b][p_c][p_d] = perturb
delta_perturb = perturb_conv(inp, wt_perturb, layer_stride,
layer_padding == 'SAME',
layer_dims[2][-1])
# If we only inject to 16W or 16C we need to reconfig the delta
if '16' in args.inj_type and 'PSUM' not in args.inj_type:
_, d_h, d_w, d_c = delta_perturb.shape
delta_16 = np.zeros(delta_perturb.shape)
pos_16 = []
# Injecting to input: 16 neurons in 16 channels at a time
if 'INPUT' in args.inj_type:
weight_d = layer_dims[1]
pad_type = layer_padding
if pad_type is 'VALID':
pad = 0
else:
pad = weight_d // 2
stride = layer_stride
start_h = np.random.randint(
max(0, (p_b + pad) // stride - weight_d + 1),
min((p_b + pad) // stride + 1, d_h))
start_w = np.random.randint(
max(0, (p_c + pad) // stride - weight_d + 1),
min((p_c + pad) // stride + 1, d_w))
start_c = np.random.randint(d_c // 16)
for i in range(16):
delta_16[0][start_h][start_w][
start_c +
i] = delta_perturb[0][start_h][start_w][start_c +
i]
pos_16.append(start_h)
pos_16.append(start_w)
pos_16.append(16 * start_c + i)
# Injecting to weight: 16 W at a time
else:
start_p = np.random.randint(d_h * d_w // 16)
# It will only affect neurons in p_d
start_c = p_d
for i in range(16):
# If it doesn't have 16, then just break
if start_p * 16 + i >= d_h * d_w:
break
elem_h = (start_p * 16 + i) // d_w
elem_w = (start_p * 16 + i) % d_w
delta_16[0][elem_h][elem_w][start_c] = delta_perturb[
0][elem_h][elem_w][start_c]
pos_16.append(elem_h)
pos_16.append(elem_w)
pos_16.append(start_c)
delta_perturb = delta_16
# Assign delta_perturb back to the variable delta
with tf.variable_scope(scope_string, reuse=True):
sess.run(
tf.get_variable('delta_{}'.format(
re.sub('\/', '_', layer[layer.rfind('/') + 1:])),
trainable=False).assign(delta_perturb))
# Then run the network again
if 'mb' in args.network or 'ic' in args.network:
lgt, prd = sess.run(
[endpoints['Logits'][0], endpoints['Predictions'][0]],
feed_dict={image: np_image})
else:
lgt, prd = sess.run([
endpoints['resnet_v1_50/spatial_squeeze'][0],
endpoints['predictions'][0]
],
feed_dict={image: np_image})
# Get a sorted label
network_labels = np.argsort(lgt)[::-1]
# If we inject to neuron directly
else:
layer = layer_names[2]
delta_np = np.zeros(shape=layer_dims[2], dtype=np.float32)
for n_j in range(num_inj_per_layer):
layer_pos = inj_pos[layer]
delta_np[0][layer_pos[n_j][0]][layer_pos[n_j][1]][
layer_pos[n_j][2]] = delta_set[layer][n_j]
scope_string = ''
if 'mb' in args.network:
scope_string = 'MobilenetV1'
elif 'rs' in args.network:
scope_string = layer[:layer.rfind('/')]
else:
scope_string = layer[layer.find('/') + 1:layer.rfind('/')]
with tf.variable_scope(scope_string, reuse=True):
sess.run(
tf.get_variable('delta_{}'.format(
re.sub('\/', '_', layer[layer.rfind('/') + 1:])),
trainable=False).assign(delta_np))
op_list = []
for node in tf.get_default_graph().as_graph_def().node:
if 'Conv2D' in node.name:
op_list.append(node.name + ':0')
# Run the network
if 'mb' in args.network or 'ic' in args.network:
ops, lgt, prd = sess.run([
op_list, endpoints['Logits'][0],
endpoints['Predictions'][0]
],
feed_dict={image: np_image})
else:
ops, lgt, prd = sess.run([
op_list, endpoints['resnet_v1_50/spatial_squeeze'][0],
endpoints['predictions'][0]
],
feed_dict={image: np_image})
# Get a sorted label
network_labels = np.argsort(lgt)[::-1]
print(
"After injection, the network label becomes {}".format(network_labels))
def run_transfer_learning(root_model_dir, bot_model_dir, protobuf_dir, model_name='inception_v4',
dataset_split_name='train',
dataset_name='bot',
checkpoint_exclude_scopes=None,
trainable_scopes=None,
max_train_time_sec=None,
max_number_of_steps=None,
log_every_n_steps=None,
save_summaries_secs=None,
optimization_params=None):
"""
Starts the transfer learning of a model in a tensorflow session
:param root_model_dir: Directory containing the root models pretrained checkpoint files
:param bot_model_dir: Directory where the transfer learned model's checkpoint files are written to
:param protobuf_dir: Directory for the dataset factory to load the bot's training data from
:param model_name: name of the network model for the net factory to provide the correct network and preprocesing fn
:param dataset_split_name: 'train' or 'validation'
:param dataset_name: triggers the dataset factory to load a bot dataset
:param checkpoint_exclude_scopes: Layers to exclude when restoring the models variables
:param trainable_scopes: Layers to train from the restored model
:param max_train_time_sec: time boundary to stop training after in seconds
:param max_number_of_steps: maximum number of steps to run
:param log_every_n_steps: write a log after every nth optimization step
:param save_summaries_secs: save summaries to disc every n seconds
:param optimization_params: parameters for the optimization
:return:
"""
if not optimization_params:
optimization_params = OPTIMIZATION_PARAMS
if not max_number_of_steps:
max_number_of_steps = _MAX_NUMBER_OF_STEPS
if not checkpoint_exclude_scopes:
checkpoint_exclude_scopes = _CHECKPOINT_EXCLUDE_SCOPES
if not trainable_scopes:
trainable_scopes = _TRAINABLE_SCOPES
if not max_train_time_sec:
max_train_time_sec = _MAX_TRAIN_TIME_SECONDS
if not log_every_n_steps:
log_every_n_steps = _LOG_EVERY_N_STEPS
if not save_summaries_secs:
save_summaries_secs = _SAVE_SUMMARRIES_SECS
tf.logging.set_verbosity(tf.logging.INFO)
with tf.Graph().as_default():
#######################
# Config model_deploy #
#######################
deploy_config = model_deploy.DeploymentConfig(
num_clones=_NUM_CLONES,
clone_on_cpu=_CLONE_ON_CPU,
replica_id=_TASK,
num_replicas=_WORKER_REPLICAS,
num_ps_tasks=_NUM_PS_TASKS)
# Create global_step
with tf.device(deploy_config.variables_device()):
global_step = slim.create_global_step()
######################
# Select the dataset #
######################
dataset = dataset_factory.get_dataset(
dataset_name, dataset_split_name, protobuf_dir)
######################
# Select the network #
######################
network_fn = nets_factory.get_network_fn(
model_name,
num_classes=(dataset.num_classes - _LABELS_OFFSET),
weight_decay=OPTIMIZATION_PARAMS['weight_decay'],
is_training=True,
dropout_keep_prob=OPTIMIZATION_PARAMS['dropout_keep_prob'])
#####################################
# Select the preprocessing function #
#####################################
image_preprocessing_fn = preprocessing_factory.get_preprocessing(
model_name,
is_training=True)
##############################################################
# Create a dataset provider that loads data from the dataset #
##############################################################
with tf.device(deploy_config.inputs_device()):
provider = slim.dataset_data_provider.DatasetDataProvider(
dataset,
num_readers=_NUM_READERS,
common_queue_capacity=20 * _BATCH_SIZE,
common_queue_min=10 * _BATCH_SIZE)
[image, label] = provider.get(['image', 'label'])
label -= _LABELS_OFFSET
train_image_size = network_fn.default_image_size
image = image_preprocessing_fn(image, train_image_size, train_image_size)
images, labels = tf.train.batch(
[image, label],
batch_size=_BATCH_SIZE,
num_threads=_NUM_PREPROCESSING_THREADS,
capacity=5 * _BATCH_SIZE)
labels = slim.one_hot_encoding(
labels, dataset.num_classes - _LABELS_OFFSET)
batch_queue = slim.prefetch_queue.prefetch_queue(
[images, labels], capacity=2 * deploy_config.num_clones)
####################
# Define the model #
####################
def clone_fn(batch_queue):
"""Allows data parallelism by creating multiple clones of network_fn."""
images, labels = batch_queue.dequeue()
logits, end_points = network_fn(images)
#############################
# Specify the loss function #
#############################
if 'AuxLogits' in end_points:
tf.losses.softmax_cross_entropy(
logits=end_points['AuxLogits'], onehot_labels=labels,
label_smoothing=_LABEL_SMOOTHING, weights=0.4, scope='aux_loss')
tf.losses.softmax_cross_entropy(
logits=logits, onehot_labels=labels,
label_smoothing=_LABEL_SMOOTHING, weights=1.0)
return end_points
# Gather initial summaries.
summaries = set(tf.get_collection(tf.GraphKeys.SUMMARIES))
clones = model_deploy.create_clones(deploy_config, clone_fn, [batch_queue])
first_clone_scope = deploy_config.clone_scope(0)
# Gather update_ops from the first clone. These contain, for example,
# the updates for the batch_norm variables created by network_fn.
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS, first_clone_scope)
# Add summaries for end_points.
end_points = clones[0].outputs
for end_point in end_points:
x = end_points[end_point]
summaries.add(tf.summary.histogram('activations/' + end_point, x))
summaries.add(tf.summary.scalar('sparsity/' + end_point,
tf.nn.zero_fraction(x)))
# Add summaries for losses.
for loss in tf.get_collection(tf.GraphKeys.LOSSES, first_clone_scope):
summaries.add(tf.summary.scalar('losses/%s' % loss.op.name, loss))
# Add summaries for variables.
for variable in slim.get_model_variables():
summaries.add(tf.summary.histogram(variable.op.name, variable))
#################################
# Configure the moving averages #
#################################
if OPTIMIZATION_PARAMS['moving_average_decay']:
moving_average_variables = slim.get_model_variables()
variable_averages = tf.train.ExponentialMovingAverage(
OPTIMIZATION_PARAMS['moving_average_decay'], global_step)
else:
moving_average_variables, variable_averages = None, None
#########################################
# Configure the optimization procedure. #
#########################################
with tf.device(deploy_config.optimizer_device()):
learning_rate = _configure_learning_rate(dataset.num_samples, global_step)
optimizer = _configure_optimizer(learning_rate)
summaries.add(tf.summary.scalar('learning_rate', learning_rate))
if _SYNC_REPLICAS:
# If sync_replicas is enabled, the averaging will be done in the chief
# queue runner.
optimizer = tf.train.SyncReplicasOptimizer(
opt=optimizer,
replicas_to_aggregate=_REPLICAS_TO_AGGREGATE,
variable_averages=variable_averages,
variables_to_average=moving_average_variables,
replica_id=tf.constant(_TASK, tf.int32, shape=()),
total_num_replicas=_WORKER_REPLICAS)
elif OPTIMIZATION_PARAMS['moving_average_decay']:
# Update ops executed locally by trainer.
update_ops.append(variable_averages.apply(moving_average_variables))
# Variables to train.
variables_to_train = _get_variables_to_train(trainable_scopes)
# and returns a train_tensor and summary_op
total_loss, clones_gradients = model_deploy.optimize_clones(
clones,
optimizer,
var_list=variables_to_train)
# Add total_loss to summary.
summaries.add(tf.summary.scalar('total_loss', total_loss))
# Create gradient updates.
grad_updates = optimizer.apply_gradients(clones_gradients,
global_step=global_step)
update_ops.append(grad_updates)
update_op = tf.group(*update_ops)
train_tensor = control_flow_ops.with_dependencies([update_op], total_loss,
name='train_op')
# Add the summaries from the first clone. These contain the summaries
# created by model_fn and either optimize_clones() or _gather_clone_loss().
summaries |= set(tf.get_collection(tf.GraphKeys.SUMMARIES,
first_clone_scope))
# Merge all summaries together.
summary_op = tf.summary.merge(list(summaries), name='summary_op')
###########################
# Kicks off the training. #
###########################
slim.learning.train(
train_tensor,
logdir=bot_model_dir,
train_step_fn=train_step, # Manually added a custom train step to stop after max_time
train_step_kwargs=_train_step_kwargs(logdir=bot_model_dir, max_train_time_seconds=max_train_time_sec),
master=_MASTER,
is_chief=(_TASK == 0),
init_fn=_get_init_fn(root_model_dir, bot_model_dir, checkpoint_exclude_scopes),
summary_op=summary_op,
# number_of_steps=max_number_of_steps,
log_every_n_steps=log_every_n_steps,
save_summaries_secs=save_summaries_secs,
save_interval_secs=_SAVE_INTERNAL_SECS,
sync_optimizer=optimizer if _SYNC_REPLICAS else None)
def RCNN(inputs, proposals, options, is_training=True):
"""Runs RCNN model on the `inputs`.
Args:
inputs: Input image, a [batch, height, width, 3] uint8 tensor. The pixel
values are in the range of [0, 255].
proposals: Boxes used to crop the image features, using normalized
coordinates. It should be a [batch, max_num_proposals, 4] float tensor
denoting [y1, x1, y2, x2].
options: A fast_rcnn_pb2.FastRCNN proto.
is_training: If true, the model shall be executed in training mode.
Returns:
A [batch, max_num_proposals, feature_dims] tensor.
Raises:
ValueError if options is invalid.
"""
if not isinstance(options, rcnn_pb2.RCNN):
raise ValueError('The options has to be a rcnn_pb2.RCNN proto!')
if inputs.dtype != tf.uint8:
raise ValueError('The inputs has to be a tf.uint8 tensor.')
net_fn = nets_factory.get_network_fn(name=options.feature_extractor_name,
num_classes=1001)
default_image_size = getattr(net_fn, 'default_image_size', 224)
# Preprocess image.
preprocess_fn = preprocessing_factory.get_preprocessing(
options.feature_extractor_name, is_training=False)
inputs = preprocess_fn(inputs,
output_height=None,
output_width=None,
crop_image=False)
# Crop and resize images.
batch = proposals.shape[0]
max_num_proposals = tf.shape(proposals)[1]
box_ind = tf.expand_dims(tf.range(batch), axis=-1)
box_ind = tf.tile(box_ind, [1, max_num_proposals])
cropped_inputs = tf.image.crop_and_resize(
inputs,
boxes=tf.reshape(proposals, [-1, 4]),
box_ind=tf.reshape(box_ind, [-1]),
crop_size=[default_image_size, default_image_size])
# Run CNN.
_, end_points = net_fn(cropped_inputs)
outputs = end_points[options.feature_extractor_endpoint]
outputs = tf.reshape(outputs, [batch, max_num_proposals, -1])
init_fn = slim.assign_from_checkpoint_fn(
options.feature_extractor_checkpoint,
slim.get_model_variables(options.feature_extractor_scope))
def _init_from_ckpt_fn(_, sess):
return init_fn(sess)
return outputs, _init_from_ckpt_fn
def eval(bot_id,
bot_suffix,
setting_id=None,
dataset_split='train',
dataset_name='bot',
model_name='inception_v4',
preprocessing=None,
moving_average_decay=None,
tf_master=''):
full_id = bot_id + bot_suffix
if setting_id:
protobuf_dir = dirs.get_transfer_proto_dir(bot_id, setting_id)
else:
protobuf_dir = dirs.get_protobuf_dir(bot_id)
_check_dir(protobuf_dir)
print("READIND FROM %s" % (protobuf_dir))
performance_data_dir = dirs.get_performance_data_dir(bot_id)
# if os.listdir(performance_data_dir):
# raise ValueError('%s is not empty' % performance_data_dir)
tf.logging.set_verbosity(tf.logging.INFO)
with tf.Graph().as_default():
tf_global_step = slim.get_or_create_global_step()
######################
# Select the dataset #
######################
dataset = dataset_factory.get_dataset(dataset_name, dataset_split,
protobuf_dir)
####################
# Select the model #
####################
network_fn = nets_factory.get_network_fn(
model_name,
num_classes=(dataset.num_classes - LABELS_OFFSET),
is_training=False)
##############################################################
# Create a dataset provider that loads data from the dataset #
##############################################################
provider = slim.dataset_data_provider.DatasetDataProvider(
dataset,
shuffle=False,
common_queue_capacity=2 * BATCH_SIZE,
common_queue_min=BATCH_SIZE)
[image, label] = provider.get(['image', 'label'])
label -= LABELS_OFFSET
#####################################
# Select the preprocessing function #
#####################################
preprocessing_name = preprocessing or model_name
image_preprocessing_fn = preprocessing_factory.get_preprocessing(
preprocessing_name, is_training=False)
eval_image_size = EVAL_IMAGE_SIZE or network_fn.default_image_size
image = image_preprocessing_fn(image, eval_image_size, eval_image_size)
images, labels = tf.train.batch([image, label],
batch_size=BATCH_SIZE,
num_threads=NUM_THREADS,
capacity=5 * BATCH_SIZE)
####################
# Define the model #
####################
logits, _ = network_fn(images)
if moving_average_decay:
variable_averages = tf.train.ExponentialMovingAverage(
moving_average_decay, tf_global_step)
variables_to_restore = variable_averages.variables_to_restore(
slim.get_model_variables())
variables_to_restore[tf_global_step.op.name] = tf_global_step
else:
variables_to_restore = slim.get_variables_to_restore()
predictions = tf.argmax(logits, 1)
labels = tf.squeeze(labels)
# Define the metrics:
names_to_values, names_to_updates = slim.metrics.aggregate_metric_map({
'Accuracy':
slim.metrics.streaming_accuracy(predictions, labels),
'Recall_5':
slim.metrics.streaming_recall_at_k(logits, labels, 5),
})
# Print the summaries to screen.
for name, value in names_to_values.items():
summary_name = 'eval/%s' % name
op = tf.summary.scalar(summary_name, value, collections=[])
op = tf.Print(op, [value], summary_name)
tf.add_to_collection(tf.GraphKeys.SUMMARIES, op)
# TODO(sguada) use num_epochs=1
if MAX_NUM_BATCHES:
num_batches = MAX_NUM_BATCHES
else:
# This ensures that we make a single pass over all of the data.
num_batches = math.ceil(dataset.num_samples / float(BATCH_SIZE))
print(dataset.num_samples)
print(dataset.num_classes)
