Comments (5)
zsk-tech
commented on August 23, 2024
确实是，我也觉得这个卷积层的参数无法训练，请问您解决这个问题了吗？
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wujiayi
commented on August 23, 2024
我改写成这样可以了：
from keras.layers import Activation, Conv2D
import keras.backend as K
import tensorflow as tf
from keras.layers import Layer
class PAM(Layer):
def __init__(self,
beta_initializer = tf.zeros_initializer(),
beta_regularizer = None,
beta_constraint = None,
kernal_initializer = 'he_normal',
kernal_regularizer = None,
kernal_constraint = None,
**kwargs):
super(PAM, self).__init__(**kwargs)
self.beta_initializer = beta_initializer
self.beta_regularizer = beta_regularizer
self.beta_constraint = beta_constraint
self.kernal_initializer = kernal_initializer
self.kernal_regularizer = kernal_regularizer
self.kernal_constraint = kernal_constraint
def build(self, input_shape):
_, h, w, filters = input_shape
self.beta = self.add_weight(shape=(1, ),
initializer=self.beta_initializer,
name='beta',
regularizer=self.beta_regularizer,
constraint=self. beta_constraint,
trainable=True)
#print(self.beta)
self.kernel_b = self.add_weight(shape=(filters, filters // 8),
initializer=self.kernal_initializer,
name='kernel_b',
regularizer=self.kernal_regularizer,
constraint =self.kernal_constraint,
trainable=True)
self.kernel_c = self.add_weight(shape=(filters, filters // 8),
initializer=self.kernal_initializer ,
name='kernel_c',
regularizer=self.kernal_regularizer,
constraint=self.kernal_constraint,
trainable=True)
self.kernel_d = self.add_weight(shape=(filters, filters),
initializer=self.kernal_initializer,
name='kernel_d',
regularizer=self.kernal_regularizer,
constraint=self.kernal_constraint,
trainable=True)
self.built = True
def compute_output_shape(self, input_shape):
return input_shape
def call(self, inputs):
input_shape = inputs.get_shape().as_list()
_, h, w, filters = input_shape
b = K.dot(inputs, self.kernel_b)
c = K.dot(inputs, self.kernel_c)
d = K.dot(inputs, self.kernel_d)
vec_b = K.reshape(b, (-1, h * w, filters // 8))
vec_cT = K.permute_dimensions(K.reshape(c, (-1, h * w, filters // 8)), (0, 2, 1))
bcT = K.batch_dot(vec_b, vec_cT)
softmax_bcT = Activation('softmax')(bcT)
vec_d = K.reshape(d, (-1, h * w, filters))
bcTd = K.batch_dot(softmax_bcT, vec_d)
bcTd = K.reshape(bcTd, (-1, h, w, filters))
out = self.beta*bcTd + inputs
#print(self.beta)
return out
class CAM(Layer):
def __init__(self,
gamma_initializer=tf.zeros_initializer(),
gamma_regularizer=None,
gamma_constraint=None,
**kwargs):
super(CAM, self).__init__(**kwargs)
self.gamma_initializer = gamma_initializer
self.gamma_regularizer = gamma_regularizer
self.gamma_constraint = gamma_constraint
def build(self, input_shape):
self.gamma = self.add_weight(shape=(1, ),
initializer=self.gamma_initializer,
name='gamma',
regularizer=self.gamma_regularizer,
constraint=self.gamma_constraint)
#print(self.gamma)
self.built = True
def compute_output_shape(self, input_shape):
return input_shape
def call(self, inputs):
input_shape = inputs.get_shape().as_list()
_, h, w, filters = input_shape
vec_a = K.reshape(inputs, (-1, h * w, filters))
vec_aT = K.permute_dimensions(K.reshape(vec_a, (-1, h * w, filters)), (0, 2, 1))
aTa = K.batch_dot(vec_aT, vec_a)
softmax_aTa = Activation('softmax')(aTa)
aaTa = K.batch_dot(vec_a, softmax_aTa)
aaTa = K.reshape(aaTa, (-1, h, w, filters))
out = self.gamma*aaTa + inputs
#print(self.gamma)
return out
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AlanLu0808
commented on August 23, 2024
看起来挺好的！有测试吗？性能有提升吗？
我之前测试发现了这个问题，一直没有解决，就把这个问题搁置了。
…------------------ 原始邮件 ------------------
发件人: wujiayi <[email protected]>
发送时间: 2020年7月28日 16:35
收件人: niecongchong/DANet-keras <[email protected]>
抄送: AlanLu0808 <[email protected]>, Mention <[email protected]>
主题: 回复：[niecongchong/DANet-keras] PAM层中卷积层参数是否可以训练？ (#4)
我改写成这样可以了：
from keras.layers import Activation, Conv2D
import keras.backend as K
import tensorflow as tf
from keras.layers import Layer
class PAM(Layer):
def __init__(self, beta_initializer = tf.zeros_initializer(), beta_regularizer = None, beta_constraint = None, kernal_initializer = 'he_normal', kernal_regularizer = None, kernal_constraint = None, **kwargs): super(PAM, self).__init__(**kwargs) self.beta_initializer = beta_initializer self.beta_regularizer = beta_regularizer self.beta_constraint = beta_constraint self.kernal_initializer = kernal_initializer self.kernal_regularizer = kernal_regularizer self.kernal_constraint = kernal_constraint def build(self, input_shape): _, h, w, filters = input_shape self.beta = self.add_weight(shape=(1, ), initializer=self.beta_initializer, name='beta', regularizer=self.beta_regularizer, constraint=self. beta_constraint, trainable=True) #print(self.beta) self.kernel_b = self.add_weight(shape=(filters, filters // 8), initializer=self.kernal_initializer, name='kernel_b', regularizer=self.kernal_regularizer, constraint =self.kernal_constraint, trainable=True) self.kernel_c = self.add_weight(shape=(filters, filters // 8), initializer=self.kernal_initializer , name='kernel_c', regularizer=self.kernal_regularizer, constraint=self.kernal_constraint, trainable=True) self.kernel_d = self.add_weight(shape=(filters, filters), initializer=self.kernal_initializer, name='kernel_d', regularizer=self.kernal_regularizer, constraint=self.kernal_constraint, trainable=True) self.built = True def compute_output_shape(self, input_shape): return input_shape def call(self, inputs): input_shape = inputs.get_shape().as_list() _, h, w, filters = input_shape b = K.dot(inputs, self.kernel_b) c = K.dot(inputs, self.kernel_c) d = K.dot(inputs, self.kernel_d) vec_b = K.reshape(b, (-1, h * w, filters // 8)) vec_cT = K.permute_dimensions(K.reshape(c, (-1, h * w, filters // 8)), (0, 2, 1)) bcT = K.batch_dot(vec_b, vec_cT) softmax_bcT = Activation('softmax')(bcT) vec_d = K.reshape(d, (-1, h * w, filters)) bcTd = K.batch_dot(softmax_bcT, vec_d) bcTd = K.reshape(bcTd, (-1, h, w, filters)) out = self.beta*bcTd + inputs #print(self.beta) return out
class CAM(Layer):
def __init__(self, gamma_initializer=tf.zeros_initializer(), gamma_regularizer=None, gamma_constraint=None, **kwargs): super(CAM, self).__init__(**kwargs) self.gamma_initializer = gamma_initializer self.gamma_regularizer = gamma_regularizer self.gamma_constraint = gamma_constraint def build(self, input_shape): self.gamma = self.add_weight(shape=(1, ), initializer=self.gamma_initializer, name='gamma', regularizer=self.gamma_regularizer, constraint=self.gamma_constraint) #print(self.gamma) self.built = True def compute_output_shape(self, input_shape): return input_shape def call(self, inputs): input_shape = inputs.get_shape().as_list() _, h, w, filters = input_shape vec_a = K.reshape(inputs, (-1, h * w, filters)) vec_aT = K.permute_dimensions(K.reshape(vec_a, (-1, h * w, filters)), (0, 2, 1)) aTa = K.batch_dot(vec_aT, vec_a) softmax_aTa = Activation('softmax')(aTa) aaTa = K.batch_dot(vec_a, softmax_aTa) aaTa = K.reshape(aaTa, (-1, h, w, filters)) out = self.gamma*aaTa + inputs #print(self.gamma) return out
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wujiayi
commented on August 23, 2024
看起来挺好的！有测试吗？性能有提升吗？ 我之前测试发现了这个问题，一直没有解决，就把这个问题搁置了。
…
我试了，是可以train的，而且参数我也算了是对的。但是效果提升并不是很明显。不知道是不是还有别的什么问题。
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karryor
commented on August 23, 2024
你好，你改了之后有提升吗
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