tensorflow 模型浮点数计算量和参数量估计

TensorFlow 模型浮点数计算量和参数量统计

2018-08-28

本博文整理了如何对一个 TensorFlow 模型的浮点数计算量（FLOPs）和参数量进行统计。

stats_graph.py

import tensorflow as tf
def stats_graph(graph):
    flops = tf.profiler.profile(graph, options=tf.profiler.ProfileOptionBuilder.float_operation())
    params = tf.profiler.profile(graph, options=tf.profiler.ProfileOptionBuilder.trainable_variables_parameter())
    print(\'FLOPs: {};    Trainable params: {}\'.format(flops.total_float_ops, params.total_parameters))

利用高斯分布对变量进行初始化会耗费一定的 FLOP

C[25,9]=A[25,16]B[16,9] FLOPs=(16+15)×(25×9)=6975FLOPs(inTFstyle)=(16+16)×(25×9)=7200total_parameters=25×16+16×9=544

with tf.Graph().as_default() as graph:
    A = tf.get_variable(initializer=tf.random_normal_initializer(dtype=tf.float32), shape=(25, 16), name=\'A\')
    B = tf.get_variable(initializer=tf.random_normal_initializer(dtype=tf.float32), shape=(16, 9), name=\'B\')
    C = tf.matmul(A, B, name=\'ouput\')
    
    stats_graph(graph)

输出为：

FLOPs: 8288; Trainable params: 544

利用常量初始化器对变量进行初始化不会耗费 FLOP

with tf.Graph().as_default() as graph:
    A = tf.get_variable(initializer=tf.constant_initializer(value=1, dtype=tf.float32), shape=(25, 16), name=\'A\')
    B = tf.get_variable(initializer=tf.zeros_initializer(dtype=tf.float32), shape=(16, 9), name=\'B\')
    C = tf.matmul(A, B, name=\'ouput\')
    
    stats_graph(graph)

输出为：

FLOPs: 7200; Trainable params: 544

Frozen graph

通常我们对耗费在初始化上的 FLOPs 并不感兴趣，因为它是发生在训练过程之前且是一次性的，我们感兴趣的是模型部署之后在生产环境下的 FLOPs。我们可以通过 Freeze 计算图的方式得到除去初始化 FLOPs 的、模型部署后推断过程中耗费的 FLOPs。

from tensorflow.python.framework import graph_util
def load_pb(pb):
    with tf.gfile.GFile(pb, "rb") as f:
        graph_def = tf.GraphDef()
        graph_def.ParseFromString(f.read())
    with tf.Graph().as_default() as graph:
        tf.import_graph_def(graph_def, name=\'\')
        return graph
with tf.Graph().as_default() as graph:
    # ***** (1) Create Graph *****
    A = tf.Variable(initial_value=tf.random_normal([25, 16]))
    B = tf.Variable(initial_value=tf.random_normal([16, 9]))
    C = tf.matmul(A, B, name=\'output\')
    
    print(\'stats before freezing\')
    stats_graph(graph)
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        # ***** (2) freeze graph *****
        output_graph = graph_util.convert_variables_to_constants(sess, graph.as_graph_def(), [\'output\'])
        with tf.gfile.GFile(\'graph.pb\', "wb") as f:
            f.write(output_graph.SerializeToString())
# ***** (3) Load frozen graph *****
graph = load_pb(\'./graph.pb\')
print(\'stats after freezing\')
stats_graph(graph)

输出为：

stats before freezing

FLOPs: 8288; Trainable params: 544

INFO:tensorflow:Froze 2 variables.

INFO:tensorflow:Converted 2 variables to const ops.

stats after freezing

FLOPs: 7200; Trainable params: 0

与 Keras 的结合

from keras import backend as K
from keras.layers import Dense
from keras.models import Sequential
from keras.initializers import Constant
model = Sequential()
model.add(Dense(32, input_dim=4, bias_initializer=Constant(value=0), kernel_initializer=Constant(value=1)))
sess = K.get_session()
graph = sess.graph
stats_graph(graph)

输出为：

FLOPs: 0; Trainable params: 160

Using TensorFlow backend.

2 ops no flops stats due to incomplete shapes.

2 ops no flops stats due to incomplete shapes.

model.summary()

Layer (type) Output Shape Param #

dense_1 (Dense) (None, 32) 160

Total params: 160

Trainable params: 160

Non-trainable params: 0

DL

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This is Robert Lexis (FengCun Li). To see the world, things dangerous to come to, to see behind walls, to draw closer, to find each other and to feel. That is the purpose of LIFE.

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