记录使用tensorflow实现大卷积核卷积的代码

#-*- coding:utf-8 -*-import tensorflow as tfimport cv2import numpy as npimport matplotlib.pyplot as plt## read img #####tm_path = '/home/wdh/pytorch-CycleGAN-and-pix2pix1_run/datasets/maps_0/testA/1_A.jpg'ref_path = '/home/wdh/pytorch-CycleGAN-and-pix2pix1_run/datasets/maps_0/testB/1_B.jpg'img_ref_bgr = cv2.imread(ref_path)img_tm_bgr = cv2.imread(tm_path)img_ref_gray = (cv2.cvtColor(img_ref_bgr,cv2.COLOR_BGR2GRAY))/255.0img_src_gray = (cv2.cvtColor(img_tm_bgr,cv2.COLOR_BGR2GRAY))/255.0[h_ref,w_ref] = img_ref_gray.shape[h_tm,w_tm] = img_src_gray.shapeh_tm = int(h_tm/3)w_tm = int(w_tm/3)img_tm_gray = np.zeros([h_ref,w_ref])img_tm_gray[0:h_tm,0:w_tm] = np.copy(img_src_gray[h_tm:h_tm+h_tm,w_tm:w_tm+w_tm])###################################build compute graph#################Var_tm = tf.Variable(img_tm_gray,dtype=tf.float32)Var_ref = tf.Variable(img_ref_gray,dtype=tf.float32)F_tm = tf.fft2d(tf.complex(Var_tm,tf.zeros(Var_tm.shape)))F_ref = tf.fft2d(tf.complex(Var_ref,tf.zeros(Var_ref.shape)))F_m = tf.multiply(F_ref,F_tm)# F_m = F_ref * F_tmres = tf.real(tf.ifft2d(F_m))optmizer = tf.train.AdamOptimizer(0.05)loss = -tf.reduce_max(res)train = optmizer.minimize(loss)init = tf.global_variables_initializer()###################################################run gragh ##############with tf.Session() as sess:    sess.run(init)    for _ in range(500):        sess.run(train)    d = sess.run(Var_tm)    c = sess.run(Var_ref)    b = sess.run(loss)    a = np.array(sess.run(res))    print(b)    print(np.max(a),np.min(a))    plt.figure()    plt.subplot(221),plt.imshow(d,cmap='gray')    plt.subplot(222), plt.imshow(c, cmap='gray')    plt.subplot(223), plt.imshow(img_src_gray, cmap='gray')    plt.subplot(224), plt.imshow(a, cmap='gray')    plt.show()