Error:Input to reshape is a tensor with 409600 values, but the requested shape requires a multiple of 25088 [[{{node pam_3/Reshape_1}}]]

Question

I am trying to apply channel attention and position attention layer on last convolutional layer of VGG16. But stuck badly at the above error. I am new to coding in python and deep learning. I am confused about shape values in Class CAM and class PAM. Here is my code:

from keras import initializers 
from keras import regularizers 
from keras import constraints 
class PAM(Layer):**strong text**
    def __init__(self,
                 gamma_initializer=tf.zeros_initializer(),
                 gamma_regularizer=None,
                 gamma_constraint=None,
                 **kwargs):
        super(PAM, 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=(512,),
                                     initializer=self.gamma_initializer,
                                     name='gamma',
                                     regularizer=self.gamma_regularizer,
                                     constraint=self.gamma_constraint)

        self.built = True

    def compute_output_shape(self, input_shape):
        return input_shape

    def call(self, input):
        input_shape = input.get_shape().as_list()
        _, h, w, filters = input_shape

        b = Conv2D(512, 3, use_bias=False, kernel_initializer='he_normal')(att_input)
        c = Conv2D(512, 3, use_bias=False, kernel_initializer='he_normal')(att_input)
        d = Conv2D(512, 3, use_bias=False, kernel_initializer='he_normal')(att_input)

        vec_b = K.reshape(b, (-1, h * w,  512))
        vec_cT = tf.transpose(K.reshape(c, (-1, h * w,512)), (0, 2, 1))
        bcT = K.batch_dot(vec_b, vec_cT)
        softmax_bcT = Activation('softmax')(bcT)
        vec_d = K.reshape(d, (-1, h * w, 512))
        bcTd = K.batch_dot(softmax_bcT, vec_d)
        bcTd = K.reshape(bcTd, (-1, h, w, 512))

        out = self.gamma*bcTd + att_input
        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=(512,),
                                     initializer=self.gamma_initializer,
                                     name='gamma',
                                     regularizer=self.gamma_regularizer,
                                     constraint=self.gamma_constraint)

        self.built = True

    def compute_output_shape(self, input_shape):
        return input_shape

    def call(self, input):
        input_shape = input.get_shape().as_list()
        _, h, w, filters = input_shape

        vec_a = K.reshape(input, (-1, h * w, 512))
        vec_aT = tf.transpose(vec_a, (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, 512))

        out = self.gamma*aaTa + att_input
        return out

pam = PAM()(att_input)
pam = Conv2D(512, 3, padding='same', use_bias=False, kernel_initializer='he_normal')(pam)
pam = BatchNormalization(axis=3)(pam)
pam = Activation('relu')(pam)
pam = Dropout(0.5)(pam)
pam = Conv2D(512, 3, padding='same', use_bias=False, kernel_initializer='he_normal')(pam)

cam = CAM()(att_input)
cam = Conv2D(512, 3, padding='same', use_bias=False, kernel_initializer='he_normal')(cam)
cam = BatchNormalization(axis=3)(cam)
cam = Activation('relu')(cam)
cam = Dropout(0.5)(cam)
cam = Conv2D(512, 3, padding='same', use_bias=False, kernel_initializer='he_normal')(cam)

Answer 1

Make sure you have the input dimensions correct. Can't say exactly where the error is without looking at your code but whenever I've had an error like that it has almost always been a case where I overlooked one of the dimensions. Think carefully about how your input changes through the layers. Printing out the model summary might help. Good luck