P和C

import tensorflow as tf
import numpy as np
import math
import keras
from keras.layers import Conv2D,Reshape,Input
import numpy as np
import matplotlib.pyplot as plt

""" Channel attention module"""

if __name__ == '__main__':
    file = tf.read_file('img.jpg')
    x = tf.image.decode_jpeg(file)
    #print("Tensor:", x)
    sess = tf.Session()
    x1 = sess.run(x)
    print("x1:",x1)
    gamma = 0.05
    sess = tf.Session()
    x1 = sess.run(x)
    x1 = tf.expand_dims(x1, dim =0)
    print("x1.shape:", x1.shape)

    m_batchsize, height, width, C = x1.shape

    proj_query = Reshape((width * height, C))(x1)
    print("proj_query:", type(proj_query))
    print("proj_query:", proj_query.shape)
    proj_query = sess.run(proj_query)
    print(proj_query)
    proj_key = Reshape((width * height, C))(x1)
    proj_key = sess.run(proj_key).transpose(0, 2, 1)
    print(proj_key)
    print("proj_key:", type(proj_key))
    print("proj_key:", proj_key.shape)

    proj_query = proj_query.astype(np.float32)
    proj_key = proj_key.astype(np.float32)

    # N, C, C, bmm 批次矩阵乘法
    energy = tf.matmul(proj_key,proj_query)
    energy = sess.run(energy)
    print("energy:", energy)

    # 这里实现了softmax用最后一维的最大值减去了原始数据, 获得了一个不是太大的值
    # 沿着最后一维的C选择最大值, keepdim保证输出和输入形状一致, 除了指定的dim维度大小为1
    energy_new = tf.reduce_max(energy, -1, keep_dims=True)
    print("after_softmax_energy:",sess.run(energy_new))

    sess = tf.Session()
    e = energy_new
    print("b:", sess.run(energy_new))

    size = energy.shape[1]
    for i in range(size - 1):
        e = tf.concat([e, energy_new], axis=-1)

    energy_new = e
    print("energy_new2:", sess.run(energy_new))
    energy_new = energy_new - energy
    print("energy_new3:", sess.run(energy_new))

    attention = tf.nn.softmax(energy_new, axis=-1)
    print("attention:", sess.run(attention))

    proj_value = Reshape((width * height, C))(x1)
    proj_value = sess.run(proj_value)
    proj_value = proj_value.astype(np.float32)
    print("proj_value:", proj_value.shape)
    out = tf.matmul(proj_value, attention)

    out = sess.run(out)
    #plt.imshow(out)
    print("out1:", out)
    out = out.reshape(m_batchsize, width * height, C)
    #out1 = out.reshape(m_batchsize, C, height, width)
    print("out2:", out.shape)

    out = gamma * out + x
    #out = sess.run(out)
    #out = out.astype(np.int16)
    print("out3:", out)

import tensorflow as tf
import numpy as np
import math
import keras
from keras.layers import Conv2D,Reshape,Input
from keras.regularizers import l2
from keras.layers.advanced_activations import ELU, LeakyReLU
from keras import Model
import cv2

"""
Important：

1、A为CxHxW => Conv+BN+ReLU => B, C 都为CxHxW

2、Reshape B, C to CxN (N=HxW)
3、Transpose B to B’
4、Softmax(Matmul(B’, C)) => spatial attention map S为NxN(HWxHW)
5、如上式1, 其中sji测量了第i个位置在第j位置上的影响
6、也就是第i个位置和第j个位置之间的关联程度/相关性, 越大越相似.
7、A => Covn+BN+ReLU => D 为CxHxW => reshape to CxN
8、Matmul(D, S’) => CxHxW, 这里设置为DS
9、Element-wise sum(scale parameter alpha * DS, A) => the final output E 为 CxHxW (式2)
10、alpha is initialized as 0 and gradually learn to assign more weight.
"""
"""
        inputs :
            x : input feature maps( N X C X H X W)
        returns :
            out : attention value + input feature
            attention: N X (HxW) X (HxW)
"""
""" Position attention module"""
if __name__ == '__main__':
    #x = tf.random_uniform([2, 7, 7, 3],minval=0,maxval=255,dtype=tf.float32)
    file = tf.read_file('img.jpg')
    x = tf.image.decode_jpeg(file)
    #x = cv2.imread('ROIVIA3.jpg')
    print(x)
    gamma = 0.05
    sess = tf.Session()
    x1 =  sess.run(x)
    x1 = tf.expand_dims(x1, axis=0)
    print(x1.shape)
    in_dim = 3

    xlen = x1.shape[1]
    ylen = x1.shape[2]
    input = Input(shape=(xlen,ylen,3))
    query_conv = Conv2D(1, (1,1), activation='relu',kernel_initializer='he_normal')(input)
    key_conv = Conv2D(1, (1, 1), activation='relu', kernel_initializer='he_normal')(input)
    value_conv = Conv2D(3, (1, 1), activation='relu', kernel_initializer='he_normal')(input)
    print(query_conv)

    batchsize, height, width, C = x1.shape
    #print(C, height, width )
    # B => N, C, HW
    proj_query = Reshape(( width * height ,1))(query_conv)
    proj_key = Reshape(( width * height, 1))(key_conv)
    proj_value = Reshape((width * height, 3))(value_conv)
    print("proj_query:",proj_query)
    print("proj_key:", proj_key)
    print("proj_value:",proj_value.shape)
    model = Model(inputs=[input],outputs=[proj_query])
    model.compile(optimizer='adam',loss='binary_crossentropy')
    proj_query = model.predict(x1,steps=1)
    print("proj_query:",proj_query)
    # B' => N, HW, C
    proj_query = proj_query.transpose(0, 2, 1)
    print("proj_query2:", proj_query.shape)
    print("proj_query2:", type(proj_query))
    # C => N, C, HW
    model1 = Model(inputs=[input], outputs=[proj_key])
    model1.compile(optimizer='adam', loss='binary_crossentropy')
    proj_key = model1.predict(x1, steps=1)
    print("proj_key:", proj_key.shape)

    print(proj_key)
    # B'xC => N, HW, HW
    energy = tf.matmul(proj_key, proj_query)
    print("energy:",energy.shape)

    # S = softmax(B'xC) => N, HW, HW
    attention = tf.nn.softmax(energy, axis=-1)
    print("attention:", attention.shape)

    # D => N, C, HW
    model2 = Model(inputs=[input], outputs=[proj_value])
    model2.compile(optimizer='adam', loss='binary_crossentropy')
    proj_value = model2.predict(x1, steps=1)
    print("proj_value:",proj_value.shape)

    # DxS' => N, C, HW
    out = tf.matmul(proj_value, sess.run(attention).transpose(0, 2, 1))
    print("out:", out.shape)

    # N, C, H, W
    out = Reshape((height, width, 3))(out)
    print("out1:", out.shape)

    out = gamma * out + sess.run(x1)
    print("out2:", type(out))