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import string
import warnings
import numpy as np
import tensorflow as tf
# attempt to import movidius V1 api, won't work in conda environment
try:
from mvnc import mvncapi as mvnc
mvnc.SetGlobalOption(mvnc.GlobalOption.LOG_LEVEL, 2)
devices = mvnc.EnumerateDevices()
if len(devices) == 0:
print('No devices found')
quit()
device = mvnc.Device(devices[0])
device.OpenDevice()
except ImportError:
warnings.warn('NCS API not installed', UserWarning)
# attempt to import movidius V2 api, might work in conda environment
try:
from openvino.inference_engine import IENetwork, IEPlugin
model_bin = './checkpoints/movidius.bin'
model_xml = './checkpoints/movidius.xml'
plugin = IEPlugin(device='MYRIAD')
network = IENetwork(model=model_xml, weights=model_bin)
exec_net = plugin.load(network=network)
input_blob = next(iter(network.inputs))
output_blob = next(iter(network.outputs))
except ImportError:
warnings.warn('NCS OpenVino API not installed', UserWarning)
# attempt to import edgetpu api, only works when ssh'd into coral dev board
try:
from edgetpu.basic.basic_engine import BasicEngine
model_path = './checkpoints/movidius_edgetpu.tflite'
tpu_engine = BasicEngine(model_path)
except ImportError:
warnings.warn('EdgeTPU API not installed', UserWarning)
class BaseModel(object):
"""Base class with utilities used by all benchmarked speech models"""
def __init__(self):
char_list = string.ascii_lowercase + '\' -'
# for mapping between int IDs and characters
self.char_to_id = {c: i for i, c in enumerate(char_list)}
self.id_to_char = {i: c for i, c in enumerate(char_list)}
self.n_chars = len(char_list)
@staticmethod
def merge(chars):
'''Merge repeated characters and strip blank CTC symbol'''
acc = ['-']
for c in chars:
if c != acc[-1]:
acc.append(c)
acc = [c for c in acc if c != '-']
return ''.join(acc)
def predict_text(self, inputs):
'''Dummy predictor getting baseline consumption values'''
return None
class TensorflowModel(BaseModel):
"""An inference-only version of speech model for doing power consumption
benchmarks on different kinds of hardware (Movidius, Jetson, CPU, GPU).
Parameters:
-----------
n_inputs : int
The dimensionality of the input to the model.
n_layers : int
The number of feedforward layers in the model.
n_per_layer : int
The dimensionality of each hidden layer in the model.
"""
def __init__(self, n_inputs, n_layers=2, n_per_layer=256):
self.n_inputs = n_inputs
self.n_layers = n_layers
self.n_per_layer = n_per_layer
self.graph = tf.Graph()
self.built = False
# set this for initializing randomly parameterized inference models
self.initializer = tf.random_uniform_initializer(-0.05, 0.05)
super().__init__()
def start_session(self):
'''Start a session instance for doing inference'''
if self.built:
with self.graph.as_default():
self.sess = tf.Session(config=self.config)
self.sess.run(tf.global_variables_initializer())
self.saver = tf.train.Saver()
else:
raise RuntimeError("No graph exists to start a session with!")
def build_branch(self, inputs, branch_scope):
'''Build a single branch from placeholder to output'''
with tf.variable_scope(branch_scope):
# create seperate variable scope for each layer
scopes = ['char_layer_' + str(n) for n in range(self.n_layers)]
x = inputs
for i, scope in enumerate(scopes):
size_in = self.n_inputs if i < 1 else self.n_per_layer
size_out = self.n_per_layer
with tf.variable_scope(scope):
w = tf.get_variable('weights', shape=[size_in, size_out],
initializer=self.initializer)
b = tf.get_variable('biases', shape=[size_out],
initializer=self.initializer)
x = tf.nn.relu(tf.nn.xw_plus_b(x, w, b))
# create variable scope for output layer
with tf.variable_scope('char_output'):
w = tf.get_variable('weights', shape=[size_in, self.n_chars],
initializer=self.initializer)
b = tf.get_variable('biases', shape=[self.n_chars],
initializer=self.initializer)
outputs = tf.nn.xw_plus_b(x, w, b, name='outputs')
return outputs
def build(self, with_gpu=False, n_copies=1):
'''Build inference-only graph for benchmarking power consumption'''
with self.graph.as_default():
self.inputs = tf.placeholder(
tf.float32, [1, self.n_inputs], name='inputs')
self.copy_scopes = ['copy_' + str(c) for c in range(n_copies)]
copy_output = []
# make n_copies of the inference graph to feed from placeholder
# (this is to load-test movidius, which doesn't allow batching)
for scope in self.copy_scopes:
output = self.build_branch(self.inputs, scope)
copy_output.append(output)
# sum outputs or take first output (copies just for load testing)
# (we don't use this kind of copying in the paper, since we can't
# replicate the required architecture on Loihi)
if n_copies > 1:
# add outputs to ensure all copies are executed
self.outputs = tf.add_n(copy_output)
else:
self.outputs = copy_output[0]
# set gpu config for starting session using proper hardware
if with_gpu:
config = tf.ConfigProto(log_device_placement=True)
config.gpu_options.per_process_gpu_memory_fraction = 0.1
self.config = config
print('Set memory allocation limit')
else:
self.config = tf.ConfigProto(
log_device_placement=True, device_count={'GPU': 0})
# flag that the model has been built, so session can be made
self.built = True
def set_weights(self, weight_dict):
'''Assign previously trained parameters to the model variables'''
with self.graph.as_default():
# need to repeat weight setting for all copies of graph
for var in tf.trainable_variables():
var_name = '/'.join(var.op.name.split('/')[1:])
val = weight_dict[var_name]
self.sess.run(var.assign(val))
print('Number of var assignments: %d'
% len(tf.trainable_variables()))
def set_tensorboard_summary(self, logdir):
'''Write a tensorboard summary to file for inspecting the graph'''
writer = tf.summary.FileWriter(logdir, graph=self.graph)
writer.flush()
print('Wrote graph definition to %s!' % logdir)
def predict_text(self, inputs):
'''Feed data through the inference graph to predict text'''
with self.graph.as_default():
feed_dict = {self.inputs: inputs}
outputs = self.sess.run(self.outputs, feed_dict=feed_dict)
ids = np.argmax(outputs, axis=1)
text = ''.join(self.id_to_char[i] for i in ids)
return text
def save(self, checkpoint):
'''Save checkpoint files for inference model'''
if self.sess is None:
raise RuntimeError('No inf session object exists to save!')
else:
with self.graph.as_default():
self.saver.save(self.sess, checkpoint)
class ScaledModel(TensorflowModel):
"""A model that scales the same way as scaling occurs on Loihi, for
benchmarking differences in power consumption with changes in compute
load under constant I/O"""
def __init__(self, n_inputs, n_copies, n_layers, n_per_layer=256):
super().__init__(n_inputs)
self.n_inputs = n_inputs
self.n_copies = n_copies
self.n_layers = n_layers
self.n_per_layer = n_per_layer
self.graph = tf.Graph()
self.built = False
def build_layer(self, inputs, scope, from_input=False):
'''Build a single branch from placeholder to output'''
# create seperate variable scope for each layer
size_in = self.n_inputs if from_input else self.n_per_layer
size_out = self.n_per_layer
with tf.variable_scope(scope):
w = tf.get_variable(
'weights', shape=[size_in, size_out],
initializer=self.initializer)
b = tf.get_variable(
'biases', shape=[size_out],
initializer=tf.zeros_initializer())
outputs = tf.nn.relu(tf.nn.xw_plus_b(inputs, w, b))
return outputs
def build(self, with_gpu=False):
'''Build with internal scaling of copies, layer depth'''
with self.graph.as_default():
self.inputs = tf.placeholder(
tf.float32, [None, self.n_inputs], name='inputs')
inp_layer = self.build_layer(
self.inputs, scope='inp_layer', from_input=True)
copy_scopes = ['copy_' + str(c) for c in range(self.n_copies)]
layer_scopes = ['layer_' + str(n) for n in range(self.n_layers)]
copy_outputs = []
# build out over layers and copies
for copy_scope in copy_scopes:
current = inp_layer
for layer_scope in layer_scopes:
scope = copy_scope + '/' + layer_scope
copy_output = self.build_layer(current, scope)
current = copy_output
copy_outputs.append(copy_output)
# project down to single output layer
with tf.variable_scope('out_layer'):
nx = self.n_copies if self.n_copies != 0 else 1
w = tf.get_variable(
'weights',
shape=[self.n_per_layer * nx, self.n_per_layer],
initializer=self.initializer)
b = tf.get_variable(
'biases', shape=[self.n_per_layer],
initializer=tf.zeros_initializer())
# handles special case with no internal scaling
# (equivalent to original spotter architecture)
if self.n_copies == 0 and self.n_layers == 0:
activities = inp_layer
else:
activities = tf.concat(copy_outputs, axis=1)
out_layer = tf.nn.relu(
tf.nn.xw_plus_b(activities, w, b))
# create variable scope for output layer
with tf.variable_scope('char_output'):
w = tf.get_variable(
'weights', shape=[self.n_per_layer, self.n_chars],
initializer=self.initializer)
b = tf.get_variable(
'biases', shape=[self.n_chars],
initializer=tf.zeros_initializer())
self.outputs = tf.nn.xw_plus_b(out_layer, w, b, name='outputs')
# set gpu config for starting session using proper hardware
if with_gpu:
config = tf.ConfigProto(log_device_placement=True)
config.gpu_options.per_process_gpu_memory_fraction = 0.1
self.config = config
print('Set memory allocation limit')
else:
self.config = tf.ConfigProto(
log_device_placement=True, device_count={'GPU': 0})
# flag that the model has been built, so session can be made
self.built = True
class MovidiusModel(BaseModel):
"""An inference-only version of speech model running on Movidius NCS"""
def load_graph(self, filename):
'''Load a previosly compiled graph to run on the NCS'''
with open(filename, mode='rb') as graph_file:
graph = graph_file.read()
self.model = device.AllocateGraph(graph)
def predict_text(self, features):
'''Predict a single character from a feature input window'''
self.model.LoadTensor(features.astype(np.float16), 'user object')
outputs, userobj = self.model.GetResult()
idx = np.argmax(outputs)
return self.id_to_char[idx]
def close_graph(self):
'''Shut everything down on the NCS'''
self.model.DeallocateGraph()
device.CloseDevice()
class MovidiusModelV2(BaseModel):
"""An inference-only version of speech model running on Movidius NCS 2"""
def predict_text(self, features):
'''Predict a single character from a feature input window'''
result = exec_net.infer(inputs={input_blob: features})
outputs = result[output_blob]
idx = np.argmax(outputs)
return self.id_to_char[idx]
def close_graph(self):
'''Shut everything down on the NCS 2'''
pass # dummy method, avoids change to experiment script
class TPUModel(BaseModel):
"""An inference-only version of speech model running on Coral Edge TPU"""
def predict_text(self, features):
'''Predict a single character from a feature input window'''
_, res = tpu_engine.RunInference(np.squeeze(features).astype(np.uint8))
idx = np.argmax(res)
return self.id_to_char[idx]
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