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train_class_accuracy.py
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# -*- coding: utf-8 -*-
"""
Created on Tue Oct 23 20:01:15 2018
@author: zhang
"""
import tensorflow as tf
from model_TF import D_on_G
from config import get_config
#from data import MNISTDataHandler
from ops import mkdir
from keras.models import load_model
from utils import load_images, load_images_with_C, load__class_images, load__class_images_with_C
import numpy as np
import matplotlib.pyplot as plt
from dataset_process_attitude import jitter_maker_4sin, jitter2D, jitter_with_curve
from PIL import Image
from random import randint
import cv2
import argparse
import math
n_images = 200
def write_log(callback, names, logs, batch_no):
for name, value in zip(names, logs):
summary = tf.Summary()
summary_value = summary.value.add()
summary_value.simple_value = value
summary_value.tag = name
callback.writer.add_summary(summary, batch_no)
callback.writer.flush()
def load_image(path):
img = Image.open(path).convert('L')
return img
def preprocess(path, x_b, y_b):
img = load_image(path)
# cv2.imres
img = np.array(img)
# if randint(0,1)==0:
# img = np.rot90(img,2)
# img = img / 255
img = img[x_b:x_b+256, y_b:y_b+256]
img = (img - 127.5) / 127.5
num = 5
out = np.zeros([5,256,256,1])
for i in range(num):
out[i,:,:,0] = img
return out
def uint_img(img):
return (img*127.5+127.5).astype(np.uint8)
def psnr(img1, img2):
psnr_out = []
for k in range(img1.shape[0]):
mse = np.mean( (img1[k] - img2[k]) ** 2 )
if mse == 0:
return 100
PIXEL_MAX = 255.0
psnr_k = 20 * math.log10(PIXEL_MAX / math.sqrt(mse))
psnr_out.append(psnr_k)
return np.mean(psnr_out)
parser = argparse.ArgumentParser()
parser.add_argument("--final_layer", type=int, help="choose the number of final layers", default = 128)
parser.add_argument("--alpha", type=float, help="choose the value of alpha", default = 0.5)
parser.add_argument("--max_pooling", type=bool, help="choose whether max_pooling is used", default = True)
parser.add_argument("--kernel_size", type=int, help="choose size of the kernel", default = 3)
args = parser.parse_args()
if __name__ == "__main__":
# main()
sess = tf.Session()
config = get_config(is_train=True)
# tf.get_variable_scope().reuse_variables()
mkdir(config.tmp_dir)
mkdir(config.ckpt_dir)
restore = D_on_G(sess, config, "DIRNet", args, is_train=True)
# restore.restore(config.ckpt_dir)
# dh = MNISTDataHandler("MNIST_data", is_train=True)
data = load__class_images_with_C('..\\..\\dataset\\image_deform\\freeway\\',300,istrain = True) # load the images from different classes
# data = load_images('..\\..\\dataset\\image_deform\\',600,istrain = True) # load the images from different classes
y_train, x_train, z_train = data['B'], data['A'], data['C']
x_train = x_train[:,:,:,np.newaxis]
y_train = y_train[:,:,:,np.newaxis]
# z_train = z_train[:,:,:,np.newaxis]
# load the test dataset
data = load__class_images_with_C('..\\..\\dataset\\image_deform\\freeway\\',400,istrain = False) # load the images from different classes
# data = load_images('..\\..\\dataset\\image_deform\\',100,istrain = False)# load the images from different classes
y_test, x_test, z_test = data['B'], data['A'], data['C']
x_test = x_test[:,:,:,np.newaxis]
y_test = y_test[:,:,:,np.newaxis]
# z_test = z_test[:,:,:,np.newaxis]
train_writer = tf.summary.FileWriter('cnn/train', sess.graph)
test_writer = tf.summary.FileWriter('cnn/test')
# all_writer = tf.summary.FileWriter('cnn/all')
# merged_summary = tf.summary.merge_all()
# all_writer.add_graph(sess.graph)
batch_size = config.batch_size
kernel_past = []
validation_loss = []
# batch_x = x_train
# batch_y = y_train
# batch_x_out = jitter_with_curve(batch_y, z_train)
bo = 0
for i in range(config.iteration):
# batch_x, batch_y = dh.sample_pair(config.batch_size)
loss_train_all = []
loss_test_all = []
psnr_all = []
permutated_indexes = np.random.permutation(x_train.shape[0])
if i <= 20:
learning_rate = 1.3e-4
if i >20 and i<40:
learning_rate = 0.8e-4
if i > 40:
learning_rate = 0.3e-4
# if i >400 and i%200==0 :
# learning_rate = learning_rate-0.4e-4
for index in range(int(x_train.shape[0] / batch_size)):
batch_indexes = permutated_indexes[index*batch_size:(index+1)*batch_size]
batch_y = y_train[batch_indexes]
batch_x = x_train[batch_indexes]
if i < 10:
batch_z = z_train[batch_indexes]
batch_z = -batch_z[:,bo:bo+256,:]
else:
batch_x, batch_z = jitter2D(batch_y)
batch_x = batch_x[:,bo:bo+256,bo:bo+256,:]
batch_y = batch_y[:,bo:bo+256,bo:bo+256,:]
# =============================================================================
dis_losses = []
# for _ in range(5):
# loss, _ = restore.dis_fit(batch_x, batch_y, learning_rate,learning_rate)
# dis_losses.append(loss)
## print('gp is ', gp)
# print("iter dis loss{:>6d} : {}".format(i+1, np.mean(dis_losses)))
loss, wrap = restore.gen_fit(batch_x, batch_y, batch_z, learning_rate,learning_rate, i)
loss_train_all.append(loss)
# print("iter gen loss{:>6d} : {}".format(i+1, np.mean(loss)))
# if i % 20 == 0:
# print(kernel[0,:,:,0])
value = np.mean(loss_train_all)
summary = tf.Summary(value=[tf.Summary.Value(tag="summary_tag", simple_value=value), ])
train_writer.add_summary(summary,i)
print("iter {:>6d} : {}".format(i+1, value))
# reg.evaluate(batch_x, batch_y, i)
if (i+1) % 3 == 0:
for index in range(int(x_test.shape[0] / batch_size)):
batch_x = x_test[index*batch_size:(index+1)*batch_size,bo:bo+256,bo:bo+256,:]
batch_y = y_test[index*batch_size:(index+1)*batch_size,bo:bo+256,bo:bo+256,:]
batch_z = -z_test[index*batch_size:(index+1)*batch_size,bo:bo+256,:]
loss, y_result, _ = restore.predict(batch_x, batch_y, batch_z)
psnr_i = psnr(y_result[:,5:-5,5:-5,0], batch_y[:,5:-5,5:-5,0])
psnr_all.append(psnr_i)
loss_test_all.append(loss)
value = np.mean(loss_test_all)
psnr_mean = np.mean(psnr_all)
summary = tf.Summary(value=[tf.Summary.Value(tag="summary_tag",simple_value=value), ])
test_writer.add_summary(summary,i)
print("test iter {:>6d} : {} : {}".format(i+1, value, psnr_mean))
validation_loss.append(value)
## reg.deploy(config.tmp_dir, batch_x, batch_y)
if (i+1) % 10 == 0:
print('saving the result...')
restore.save(config.ckpt_dir)
# means = []
if (i + 1) % 3 == 0:
img_i = 0
_,_, y_result, wrap_test = restore.predict_one(batch_x[img_i], config)
plt.plot(batch_z[img_i,:,0])
plt.plot(wrap_test[:,0])
plt.title('Real Curve vs Obtained Curve')
plt.show()
plt.imshow(y_result[:,:,0], cmap='gray')
plt.grid(False)
plt.axis('off')
plt.title('Restored image')
plt.show()
plt.imshow(batch_x[img_i,:,:,0],cmap='gray')
plt.grid(False)
plt.axis('off')
plt.title('Raw image')
plt.show()
plt.imshow(batch_y[img_i,:,:,0],cmap='gray')
plt.grid(False)
plt.axis('off')
plt.title('GroundTruth')
plt.show()
#
# yaogan_x = preprocess('..//..//dataset//yaogan26//for_classification_air.png', 0, 0)
# loss1, loss2, output,wrap_yaogan = restore.predict_one(yaogan_x[0], config)
#
# plt.imshow(output[:,:,0], cmap='gray')
# plt.grid(False)
# plt.axis('off')
# plt.title('Rstored image')
## plt.savefig('results//restored1.pdf')
# cv2.imwrite('results//restored3.png', uint_img(output[:,:,0]))
#
# plt.show()
#
# plt.imshow(yaogan_x[0,:,:,0], cmap='gray')
# plt.grid(False)
# plt.axis('off')
# plt.title('Raw image')
# cv2.imwrite('results//raw3.png', uint_img(yaogan_x[0,:,:,0]))
# plt.show()
# loss_name = str(args.max_pooling)+'_'+str(args.final_layer)+'_'+str(args.alpha)+'_'+str(args.kernel_size)+'.npy'
# np.save(loss_name, validation_loss)
# plt.plot(wrap_yaogan[:,0])
# plt.title('Obtained attitude cross-track')
# plt.grid(True)
# plt.xlabel('Lines')
# plt.ylabel('Pixel')
# plt.savefig('results//cross-track-curve3.png')
# plt.show()
#
# plt.plot(wrap_yaogan[:,1])
# plt.title('Obtained attitude along-track')
# plt.grid(True)
# plt.xlabel('Lines')
# plt.ylabel('Pixel')
# plt.savefig('results//along-track-curve3.png')
# plt.show()
# plt.plot(batch_z[img_i,:,0])
# plt.plot(wrap_test[:,0])
# plt.title('Real Curve')
# plt.show()