作者搜集了网上存在的利用Tensorflow计算FLOPs的代码（如下所示），但是几乎都不能准确计算，因此我们尝试通过人工计算。
CNN 模型所需的计算力（flops）和参数（parameters）数量是怎么计算的？
卷积层计算量(FLOPS)和参数量的计算
MACs和FLOPs
def get_flops_params():
sess = tf.Session()
graph = sess.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))
1.SCNN
1.1.Parameters计算
def SCNN():
# build the CNN model
in_shp = [2, 128]
L = 128 # sample points
xm_input = Input(in_shp)
xm = Reshape([128, 2], input_shape=in_shp)(xm_input)
x1 = Conv1D(128, 16, activation='relu', padding='same', input_shape=[L, 2])(xm)
x2 = BatchNormalization()(x1)
x3 = Dropout(0.5)(x2)
x4 = SeparableConv1D(64, 8, activation='relu', padding='same')(x3)
x5 = BatchNormalization()(x4)
x6 = Dropout(0.5)(x5)
x7 = Flatten()(x6)
x8 = Dense(10)(x7)
predicts = Activation('softmax')(x8)
model = Model(xm_input, predicts)
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.summary()
return model
输出每一层具体信息:
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) (None, 2, 128) 0
_________________________________________________________________
reshape_1 (Reshape) (None, 128, 2) 0
_________________________________________________________________
conv1d_1 (Conv1D) (None, 128, 128) 4224
_________________________________________________________________
batch_normalization_1 (Batch (None, 128, 128) 512
_________________________________________________________________
dropout_1 (Dropout) (None, 128, 128) 0
_________________________________________________________________
separable_conv1d_1 (Separabl (None, 128, 64) 9280
_________________________________________________________________
batch_normalization_2 (Batch (None, 128, 64) 256
_________________________________________________________________
dropout_2 (Dropout) (None, 128, 64) 0
_________________________________________________________________
flatten_1 (Flatten) (None, 8192) 0
_________________________________________________________________
dense_1 (Dense) (None, 11) 90123
_________________________________________________________________
activation_1 (Activation) (None, 11) 0
=================================================================
Total params: 104,395
Trainable params: 104,011
Non-trainable params: 384
_________________________________________________________________
- 卷积层：
以第一个conv1d_1 (Conv1D)为例，
1.2.FLOPs计算
- 卷积层：
以第一个conv1d_1 (Conv1D)为例（注意：不考虑bias时有-1，有bias时没有-1，这里的计算考虑了bias)
- 可分离卷积，参考：该捋清！卷积、可分离卷积的参数和FLOPs计算！
- 全连接dense层，参考神经网络层的FLOPs计算,
2.PET-CGDNN
def cal1(x):
y = tf.keras.backend.cos(x)
return y
def cal2(x):
y = tf.keras.backend.sin(x)
return y
def MCLDNN(weights=None,
input_shape1=[2, 128],
input_shape2=[128, 1],
classes=11,
**kwargs):
if weights is not None and not (os.path.exists(weights)):
raise ValueError('The `weights` argument should be either '
'`None` (random initialization), '
'or the path to the weights file to be loaded.')
dr = 0.5 # dropout rate (%)
input = Input(input_shape1 + [1], name='input1')
input1 = Input(input_shape2, name='input2')
input2 = Input(input_shape2, name='input3')
x1 = Flatten()(input)
x1 = Dense(1, name='fc2')(x1)
x1 = Activation('linear')(x1)
cos1 = Lambda(cal1)(x1)
sin1 = Lambda(cal2)(x1)
x11 = Multiply()([input1, cos1])
x12 = Multiply()([input2, sin1])
x21 = Multiply()([input2, cos1])
x22 = Multiply()([input1, sin1])
y1 = Add()([x11, x12])
y2 = Subtract()([x21, x22])
y1 = Reshape(target_shape=(128, 1), name='reshape1')(y1)
y2 = Reshape(target_shape=(128, 1), name='reshape2')(y2)
x11 = concatenate([y1, y2])
x3 = Reshape(target_shape=((128, 2, 1)), name='reshape3')(x11)
# spatial feature
x3 = Conv2D(75, (8, 2), padding='valid', activation="relu", name="conv1_1", kernel_initializer='glorot_uniform')(
x3)
x3 = Conv2D(25, (5, 1), padding='valid', activation="relu", name="conv1_2", kernel_initializer='glorot_uniform')(
x3)
# temporal feature
x4 = Reshape(target_shape=((117, 25)), name='reshape4')(x3)
x4 = keras.layers.GRU(units=128)(x4)
# x4 = Flatten()(x4)
#
x = Dense(classes, activation='softmax', name='softmax')(x4)
model = Model(inputs=[input, input1, input2], outputs=x)
输出每一层具体信息:
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input1 (InputLayer) (None, 2, 128, 1) 0
__________________________________________________________________________________________________
flatten_1 (Flatten) (None, 256) 0 input1[0][0]
__________________________________________________________________________________________________
fc2 (Dense) (None, 1) 257 flatten_1[0][0]
__________________________________________________________________________________________________
activation_1 (Activation) (None, 1) 0 fc2[0][0]
__________________________________________________________________________________________________
input2 (InputLayer) (None, 128, 1) 0
__________________________________________________________________________________________________
lambda_1 (Lambda) (None, 1) 0 activation_1[0][0]
__________________________________________________________________________________________________
input3 (InputLayer) (None, 128, 1) 0
__________________________________________________________________________________________________
lambda_2 (Lambda) (None, 1) 0 activation_1[0][0]
__________________________________________________________________________________________________
multiply_1 (Multiply) (None, 128, 1) 0 input2[0][0]
lambda_1[0][0]
__________________________________________________________________________________________________
multiply_2 (Multiply) (None, 128, 1) 0 input3[0][0]
lambda_2[0][0]
__________________________________________________________________________________________________
multiply_3 (Multiply) (None, 128, 1) 0 input3[0][0]
lambda_1[0][0]
__________________________________________________________________________________________________
multiply_4 (Multiply) (None, 128, 1) 0 input2[0][0]
lambda_2[0][0]
__________________________________________________________________________________________________
add_1 (Add) (None, 128, 1) 0 multiply_1[0][0]
multiply_2[0][0]
__________________________________________________________________________________________________
subtract_1 (Subtract) (None, 128, 1) 0 multiply_3[0][0]
multiply_4[0][0]
__________________________________________________________________________________________________
reshape1 (Reshape) (None, 128, 1) 0 add_1[0][0]
__________________________________________________________________________________________________
reshape2 (Reshape) (None, 128, 1) 0 subtract_1[0][0]
__________________________________________________________________________________________________
concatenate_1 (Concatenate) (None, 128, 2) 0 reshape1[0][0]
reshape2[0][0]
__________________________________________________________________________________________________
reshape3 (Reshape) (None, 128, 2, 1) 0 concatenate_1[0][0]
__________________________________________________________________________________________________
conv1_1 (Conv2D) (None, 121, 1, 75) 1275 reshape3[0][0]
__________________________________________________________________________________________________
conv1_2 (Conv2D) (None, 117, 1, 25) 9400 conv1_1[0][0]
__________________________________________________________________________________________________
reshape4 (Reshape) (None, 117, 25) 0 conv1_2[0][0]
__________________________________________________________________________________________________
gru_1 (GRU) (None, 128) 59136 reshape4[0][0]
__________________________________________________________________________________________________
softmax (Dense) (None, 11) 1419 gru_1[0][0]
==================================================================================================
Total params: 71,487
Trainable params: 71,487
Non-trainable params: 0
注：
1.我们如果用tensorflow作为backend时候，系统会默认是channel_last。
2.如何计算GRU卷积神经网络CNN中的参数量（parameters）和计算量（FLOPs ）这篇文章里面的结论直接用：FLOPs = paras * H * W * 2
我们以上面的计算结果为例
514和512差别不大。
308,550和281,325差别不大。
2,199,600和2,190,825差别不大。
因此虽然没有找到GRU的FLOPs计算公式，我们也尝试用这个公式计算
于是得到总的
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