論文pdf 地址：https://arxiv.org/pdf/1609.04802v1.pdfgit
個人實際效果
清晰度距離個人期待有距離。 顏色上面存在差距。 解決想法 增長一個顏色判別器。將顏色值反饋給生成器github
srgan論文是創建在gan基礎上的，利用gan生成式對抗網絡，將圖片重構爲高清分辨率的圖片。 github上有開源的srgan項目。因爲開源者，開發時考慮的問題更豐富，技巧更爲高明，致使其代碼都比較難以閱讀和理解。 在爲了充分理解這個論文。這裏結合論文，開源代碼，和本身的理解從新寫了個srgan高清分辨率模型。網絡
##GAN原理 在一個不斷提升判斷能力的判斷器的持續反饋下，不斷改善生成器的生成參數，直到生成器生成的結果可以經過判斷器的判斷。（見本博客其餘文章）app
##SRGAN用到的模塊，及其關係 損失值，根據的這個關係結構計算的。 注意：vgg19是使用已經訓練好的模型，這裏只是拿來提取特徵使用，dom
對於生成器，根據三個運算結果數據，進行隨機梯度的優化調整 ①斷定器生成數據的鑑定結果 ②vgg19的特徵比較狀況 ③生成圖形與理想圖形的mse差距ide
論文中，生成器和判別器的模型圖
生成器結構爲：一層卷積，16層殘差卷積，再將第一層卷積結果+16層殘差結，卷積+2倍反捲積，卷積+2倍反捲積，tanh縮放，產生生成結果。 判別器結構爲：8層卷積+reshape,全鏈接。（論文中，用了兩層。我這裏只用了一層全鏈接，參數量太大，我6G 的gpu內存不夠用） vgg19結構：在vgg19的第四層，返回獲取到的特徵結果，進行MSE對比 注意：BN處理，leaky relu等等處理技巧函數
代碼解釋
import numpy as np
import os
import tensorlayer as tl
import tensorflow as tf
#獲取vgg9.npy中vgg19的參數，
vgg19_npy_path = "./vgg19.npy"
if not os.path.isfile(vgg19_npy_path):
print("Please download vgg19.npz from : https://github.com/machrisaa/tensorflow-vgg")
exit()
npz = np.load(vgg19_npy_path, encoding='latin1').item()
w_params = []
b_params = []
for val in sorted(npz.items()):
W = np.asarray(val[1][0])
b = np.asarray(val[1][1])
# print(" Loading %s: %s, %s" % (val[0], W.shape, b.shape))
w_params.append(W, )
b_params.extend(b)
#tensorlayer加載圖片時，用於處理圖片。隨機獲取圖片中 192*192的矩陣， 內存不足時，能夠優化這裏
def crop_sub_imgs_fn(x, is_random=True):
x = tl.prepro.crop(x, wrg=192, hrg=192, is_random=is_random)
x = x / (255. / 2.)
x = x - 1.
return x
#resize矩陣 內存不足時，能夠優化這裏
def downsample_fn(x):
x = tl.prepro.imresize(x, size=[48, 48], interp='bicubic', mode=None)
x = x / (255. / 2.)
x = x - 1.
return x
# 參數
config = {
"epoch": 5,
}
# 內存不夠時，能夠減少這個
batch_size = 10
class SRGAN(object):
def __init__(self):
# with tf.device('/gpu:0'):
#佔位變量，存儲須要重構的圖片
self.x = tf.placeholder(tf.float32, shape=[batch_size, 48, 48, 3], name='train_bechanged')
#佔位變量，存儲須要學習的理想中的圖片
self.y = tf.placeholder(tf.float32, shape=[batch_size, 192, 192, 3], name='train_target')
self.init_fake_y = self.generator(self.x) # 預訓練時生成的假照片
self.fake_y = self.generator(self.x, reuse=True) # 所有訓練時生成的假照片
#佔位變量，存儲須要重構的測試圖片
self.test_x = tf.placeholder(tf.float32, shape=[1, None, None, 3], name='test_generator')
#佔位變量，存儲重構後的測試圖片
self.test_fake_y = self.generator(self.test_x, reuse=True) # 生成的假照片
#佔位變量，將生成圖片resize
self.fake_y_vgg = tf.image.resize_images(
self.fake_y, size=[224, 224], method=0,
align_corners=False)
#佔位變量，將理想圖片resize
self.real_y_vgg = tf.image.resize_images(
self.y, size=[224, 224], method=0,
align_corners=False)
#提取僞造圖片的特徵
self.fake_y_feature = self.vgg19(self.fake_y_vgg) # 假照片的特徵值
#提取理想圖片的特徵
self.real_y_feature = self.vgg19(self.real_y_vgg, reuse=True) # 真照片的特徵值
# self.pre_dis_logits = self.discriminator(self.fake_y) # 判別器生成的預測照片的判別值
self.fake_dis_logits = self.discriminator(self.fake_y, reuse=False) # 判別器生成的假照片的判別值
self.real_dis_logits = self.discriminator(self.y, reuse=True) # 判別器生成的假照片的判別值
# 預訓練時，判別器的優化根據值
self.init_mse_loss = tf.losses.mean_squared_error(self.init_fake_y, self.y)
# 關於判別器的優化根據值
self.D_loos = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=self.real_dis_logits,
labels=tf.ones_like(
self.real_dis_logits))) + \
tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=self.fake_dis_logits,
labels=tf.zeros_like(
self.fake_dis_logits)))
# 僞造數據判別器的判斷狀況，生成與目標圖像的差距，生成特徵與理想特徵的差距
self.D_loos_Ge = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=self.fake_dis_logits, labels=tf.ones_like( self.fake_dis_logits)))
self.mse_loss = tf.losses.mean_squared_error(self.fake_y, self.y)
self.loss_vgg = tf.losses.mean_squared_error(self.fake_y_feature, self.real_y_feature)
#生成器的優化根據值，上面三個值的和
self.G_loos = 1e-3 * self.D_loos_Ge + 2e-6 * self.loss_vgg + self.mse_loss
#獲取具體條件下的更新變量集合。
t_vars = tf.trainable_variables()
self.g_vars = [var for var in t_vars if var.name.startswith('trainGenerator')]
self.d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
# 生成器，16層深度殘差+1層初始的深度殘差+2次2倍反捲積+1個卷積
def generator(slef, input, reuse=False):
with tf.variable_scope('trainGenerator') as scope:
if reuse:
scope.reuse_variables()
n = tf.layers.conv2d(input, 64, 3, strides=1, padding='SAME', activation=None, use_bias=True,
bias_initializer=None)
prellu_param = tf.get_variable('p_alpha', n.get_shape()[-1], initializer=tf.constant_initializer(0.0),
dtype=tf.float32)
n = tf.nn.relu(n) + prellu_param * (n - abs(n)) * 0.02
# n = tf.nn.relu(n)
temp = n
# 開始深度殘差網絡
for i in range(16):
nn = tf.layers.conv2d(n, 64, 3, strides=1, padding='SAME', activation=None, use_bias=True,
bias_initializer=None)
nn = tf.layers.batch_normalization(nn, training=True)
prellu_param = tf.get_variable('p_alpha' + str(2 * i + 1), n.get_shape()[-1],
initializer=tf.constant_initializer(0.0),
dtype=tf.float32)
nn = tf.nn.relu(nn) + prellu_param * (nn - abs(nn)) * 0.02
nn = tf.layers.conv2d(nn, 64, 3, strides=1, padding='SAME', activation=None, use_bias=True,
bias_initializer=None)
nn = tf.layers.batch_normalization(nn, training=True)
# prellu_param = tf.get_variable('p_alpha' + str(2 * i + 2), n.get_shape()[-1],
# initializer=tf.constant_initializer(0.0),
# dtype=tf.float32)
# nn = tf.nn.relu(nn) + prellu_param * (nn - abs(nn)) * 0.02
n = nn + n
n = tf.layers.conv2d(n, 64, 3, strides=1, padding='SAME', activation=None, use_bias=True,
bias_initializer=None)
n = tf.layers.batch_normalization(n, training=True)
# prellu_param = tf.get_variable('p_alpha_34', n.get_shape()[-1],
# initializer=tf.constant_initializer(0.0),
# dtype=tf.float32)
# n = tf.nn.relu(n) + prellu_param * (n - abs(n)) * 0.02
#注意這裏的temp,看論文裏面的生成器結構圖
n = temp + n
# 將特徵還原爲圖
n = tf.layers.conv2d_transpose(n, 256, 3, strides=2, padding='SAME', activation=None, use_bias=True,
bias_initializer=None)
n = tf.layers.conv2d(n, 256, 3, strides=1, padding='SAME', activation=None, use_bias=True,
bias_initializer=None)
n = tf.layers.batch_normalization(n, training=True)
n = tf.nn.relu(n)
n = tf.layers.conv2d_transpose(n, 256, 3, strides=2, padding='SAME', activation=None, use_bias=True,
bias_initializer=None)
n = tf.layers.conv2d(n, 256, 3, strides=1, padding='SAME', activation=None, use_bias=True,
bias_initializer=None)
n = tf.layers.batch_normalization(n, training=True)
n = tf.nn.relu(n)
n = tf.layers.conv2d(n, 3, 1, strides=1, padding='SAME', activation=None, use_bias=True,
bias_initializer=None)
n = tf.nn.tanh(n)
return n
#判別器
def discriminator(self, input, reuse=False):
# input size： 384x384
with tf.variable_scope('discriminator') as scope:
if reuse:
scope.reuse_variables()
# 1
n = tf.layers.conv2d(input, 64, 3, strides=1, padding='SAME', activation=None, use_bias=True,
bias_initializer=None)
n = tf.maximum(0.01 * n, n)
# 2
n = tf.layers.conv2d(n, 64, 3, strides=2, padding='SAME', activation=None, use_bias=True,
bias_initializer=None)
n = tf.layers.batch_normalization(n, training=True)
n = tf.maximum(0.01 * n, n)
# 3
n = tf.layers.conv2d(n, 128, 3, strides=1, padding='SAME', activation=None, use_bias=True,
bias_initializer=None)
n = tf.layers.batch_normalization(n, training=True)
n = tf.maximum(0.01 * n, n)
# 4
n = tf.layers.conv2d(n, 128, 3, strides=2, padding='SAME', activation=None, use_bias=True,
bias_initializer=None)
n = tf.layers.batch_normalization(n, training=True)
n = tf.maximum(0.01 * n, n)
# 5
n = tf.layers.conv2d(n, 256, 3, strides=1, padding='SAME', activation=None, use_bias=True,
bias_initializer=None)
n = tf.layers.batch_normalization(n, training=True)
n = tf.maximum(0.01 * n, n)
# 6
n = tf.layers.conv2d(n, 256, 3, strides=2, padding='SAME', activation=None, use_bias=True,
bias_initializer=None)
n = tf.layers.batch_normalization(n, training=True)
n = tf.maximum(0.01 * n, n)
# 7
n = tf.layers.conv2d(n, 512, 3, strides=1, padding='SAME', activation=None, use_bias=True,
bias_initializer=None)
n = tf.layers.batch_normalization(n, training=True)
n = tf.maximum(0.01 * n, n)
# 8
n = tf.layers.conv2d(n, 512, 3, strides=2, padding='SAME', activation=None, use_bias=True,
bias_initializer=None)
n = tf.layers.batch_normalization(n, training=True)
n = tf.maximum(0.01 * n, n)
flatten = tf.reshape(n, (input.get_shape()[0], -1))
# 內存不夠，減少全連接數量
# f = tf.layers.dense(flatten, 1024)
# 論文裏面這裏時leaky relu，這我用的dense裏面自帶的
f = tf.layers.dense(flatten, 1, bias_initializer=tf.contrib.layers.xavier_initializer())
return f
#vgg19特徵提取
def vgg19(self, input, reuse=False):
VGG_MEAN = [103.939, 116.779, 123.68]
with tf.variable_scope('vgg19') as scope:
# if reuse:
# scope.reuse_variables()
# ====================
print("build model started")
rgb_scaled = (input + 1) * (255.0 / 2)
# Convert RGB to BGR
red, green, blue = tf.split(rgb_scaled, 3, 3)
assert red.get_shape().as_list()[1:] == [224, 224, 1]
assert green.get_shape().as_list()[1:] == [224, 224, 1]
assert blue.get_shape().as_list()[1:] == [224, 224, 1]
bgr = tf.concat(
[
blue - VGG_MEAN[0],
green - VGG_MEAN[1],
red - VGG_MEAN[2],
], axis=3)
assert bgr.get_shape().as_list()[1:] == [224, 224, 3]
# --------------------
n = tf.nn.conv2d(bgr, w_params[0], name='conv2_1', strides=(1, 1, 1, 1), padding='SAME')
n = tf.add(n, b_params[0])
n = tf.nn.relu(n)
n = tf.nn.conv2d(n, w_params[1], name='conv2_2', strides=(1, 1, 1, 1), padding='SAME')
n = tf.add(n, b_params[1])
n = tf.nn.relu(n)
n = tf.nn.max_pool(n, ksize=(1, 2, 2, 1), strides=(1, 2, 2, 1), padding='SAME')
# return n
# two
n = tf.nn.conv2d(n, w_params[2], strides=(1, 1, 1, 1), padding='SAME')
n = tf.add(n, b_params[2])
n = tf.nn.relu(n)
n = tf.nn.conv2d(n, w_params[3], strides=(1, 1, 1, 1), padding='SAME')
n = tf.add(n, b_params[3])
n = tf.nn.relu(n)
n = tf.nn.max_pool(n, ksize=(1, 2, 2, 1), strides=(1, 2, 2, 1), padding='SAME')
# three
n = tf.nn.conv2d(n, w_params[4], strides=(1, 1, 1, 1), padding='SAME')
n = tf.add(n, b_params[4])
n = tf.nn.relu(n)
n = tf.nn.conv2d(n, w_params[5], strides=(1, 1, 1, 1), padding='SAME')
n = tf.add(n, b_params[5])
n = tf.nn.relu(n)
n = tf.nn.conv2d(n, w_params[6], strides=(1, 1, 1, 1), padding='SAME')
n = tf.add(n, b_params[6])
n = tf.nn.relu(n)
n = tf.nn.conv2d(n, w_params[7], strides=(1, 1, 1, 1), padding='SAME')
n = tf.add(n, b_params[7])
n = tf.nn.relu(n)
n = tf.nn.max_pool(n, ksize=(1, 2, 2, 1), strides=(1, 2, 2, 1), padding='SAME')
# four
n = tf.nn.conv2d(n, w_params[8], strides=(1, 1, 1, 1), padding='SAME')
n = tf.add(n, b_params[8])
n = tf.nn.relu(n)
n = tf.nn.conv2d(n, w_params[9], strides=(1, 1, 1, 1), padding='SAME')
n = tf.add(n, b_params[9])
n = tf.nn.relu(n)
n = tf.nn.conv2d(n, w_params[10], strides=(1, 1, 1, 1), padding='SAME')
n = tf.add(n, b_params[10])
n = tf.nn.relu(n)
n = tf.nn.conv2d(n, w_params[11], strides=(1, 1, 1, 1), padding='SAME')
n = tf.add(n, b_params[11])
n = tf.nn.relu(n)
n = tf.nn.max_pool(n, ksize=(1, 2, 2, 1), strides=(1, 2, 2, 1), padding='SAME')
return n
# # five
# n = tf.nn.conv2d(n, w_params[12], strides=(1, 1, 1, 1), padding='SAME')
# n = tf.add(n, b_params[12])
# n = tf.nn.relu(n)
# n = tf.nn.conv2d(n, w_params[13], strides=(1, 1, 1, 1), padding='SAME')
# n = tf.add(n, b_params[13])
# n = tf.nn.relu(n)
#
# n = tf.nn.conv2d(n, w_params[14], strides=(1, 1, 1, 1), padding='SAME')
# n = tf.add(n, b_params[14])
# n = tf.nn.relu(n)
# n = tf.nn.conv2d(n, w_params[15], strides=(1, 1, 1, 1), padding='SAME')
# n = tf.add(n, b_params[15])
# n = tf.nn.relu(n)
# n = tf.nn.max_pool(n, ksize=(1, 2, 2, 1), strides=(1, 2, 2, 1), padding='SAME')
# return n
# 這裏拿特徵進行mse對比，不須要後面的全鏈接
# flatten = tf.reshape(n, (input.get_shape()[0], -1))
# f = tf.layers.dense(flatten, 4096)
# f = tf.layers.dense(f, 4096)
# f = tf.layers.dense(f, 1)
# return n
gan = SRGAN()
G_OPTIM_init = tf.train.AdamOptimizer(learning_rate=0.001, beta1=0.4).minimize(gan.init_mse_loss, var_list=gan.g_vars)
D_OPTIM = tf.train.AdamOptimizer(learning_rate=0.001, beta1=0.4).minimize(gan.D_loos, var_list=gan.d_vars)
G_OPTIM = tf.train.AdamOptimizer(learning_rate=0.001, beta1=0.4).minimize(gan.G_loos, var_list=gan.g_vars)
saver = tf.train.Saver(max_to_keep=3)
init = tf.global_variables_initializer()
#加載路徑文件夾中的訓練圖片，這裏加載的只是圖片目錄。防止內存中加載太多圖片，內存不夠
train_hr_img_list = sorted(tl.files.load_file_list(path='F:\\theRoleOfCOde\深度學習\SRGAN_PF\gaoqing', regx='.*.png', printable=False))[:100]
#加載圖片
train_hr_imgs = tl.vis.read_images(train_hr_img_list, path='F:\\theRoleOfCOde\深度學習\SRGAN_PF\gaoqing', n_threads=1)
#加載路徑文件夾中的測試圖片目錄
test_img_list = sorted( tl.files.load_file_list(path='F:\\theRoleOfCOde\深度學習\SRGAN_PF\SRGAN_PF\img\\test', regx='.*.png', printable=False))[ :6]
test_img = tl.vis.read_images(test_img_list, path='F:\\theRoleOfCOde\深度學習\SRGAN_PF\SRGAN_PF\img\\test', n_threads=1)
#分三種運行方式，
#pre，預訓練判別器
#restore,回覆訓練好的模型，繼續訓練
#訓練一下子，就測試一下效果。將生成的圖片矩陣，保存爲numpy矩陣
#經過工具函數，變化爲圖片查看
#第三種，從零開始訓練
with tf.Session() as sess:
type = 'go'
if type == 'restore':
saver.restore(sess, "./save/nets/ckpt-0-80")
print('---------------------恢復之前的訓練數據，繼續訓練-----------------------')
for epoch in range(0):
for idx in range(0, (len(train_hr_imgs) // 10), batch_size):
# print(type(train_hr_imgs[idx:idx + batch_size]))
b_imgs_384 = tl.prepro.threading_data(train_hr_imgs[idx:idx + batch_size], fn=crop_sub_imgs_fn,
is_random=True)
b_imgs_96 = tl.prepro.threading_data(b_imgs_384, fn=downsample_fn)
print('-------------pre_generator:' + str(epoch) + '_' + str(idx) + '----------------')
for i in range(40):
init_mse_loss, _ = sess.run([gan.init_mse_loss, G_OPTIM_init],
feed_dict={
gan.x: b_imgs_96,
gan.y: b_imgs_384
})
print('init_mse_loss:' + str(init_mse_loss))
saver.save(sess, "save/nets/better_ge.ckpt")
for epoch in range(config["epoch"]):
for idx in range(0, len(train_hr_imgs), batch_size):
# print(type(train_hr_imgs[idx:idx + batch_size]))
b_imgs_384 = tl.prepro.threading_data(train_hr_imgs[idx:idx + batch_size], fn=crop_sub_imgs_fn,
is_random=True)
b_imgs_96 = tl.prepro.threading_data(b_imgs_384, fn=downsample_fn)
print('-------------' + str(epoch) + '_' + str(idx) + '----------')
for i in range(25):
loss_D, _ = sess.run([gan.D_loos, D_OPTIM],
feed_dict={
gan.x: b_imgs_96,
gan.y: b_imgs_384
})
loss_G, _ = sess.run([gan.G_loos, G_OPTIM],
feed_dict={
gan.x: b_imgs_96,
gan.y: b_imgs_384
})
print(loss_D, loss_G)
if idx % 20 == 0:
saver.save(sess, "./save/nets/better_all_" + str(epoch) + "_" + str(idx) + '.ckpt')
_imgs = (np.asanyarray(test_img[0:1]) / (255. / 2.)) - 1
_imgs = _imgs[:, :, :, 0:3]
result_fake_y = sess.run([gan.test_fake_y], feed_dict={
gan.test_x: _imgs
}) # 生成的假照片
# result=sess.run(result_fake_y)
strpath = './preImg/result_' + str(epoch) + '_' + str(idx) + '_1.npy'
np.save(strpath, result_fake_y)
_imgs2 = (np.asanyarray(test_img[1:2]) / (255. / 2.)) - 1
_imgs2 = _imgs2[:, :, :, 0:3]
result_fake_y = sess.run([gan.test_fake_y], feed_dict={
gan.test_x: _imgs2
}) # 生成的假照片
# result=sess.run(result_fake_y)
strpath = './preImg/result_' + str(epoch) + '_' + str(idx) + '_2.npy'
np.save(strpath, result_fake_y)
# print(type(result_fake_y))
elif type == 'pre':
saver.restore(sess, "save/nets/better_all_1_28.ckpt")
print('---------------------恢復訓練好的模型，開始預測-----------------------')
for num in range(6):
_imgs = (np.asanyarray(test_img[num:(num + 1)]) / (255. / 2.)) - 1
print(_imgs.shape)
_imgs = _imgs[:, :, :, 0:3]
# time.sleep(1)
result_fake_y = sess.run([gan.test_fake_y], feed_dict={
gan.test_x: _imgs
}) # 生成的假照片
strpath = './preImg/pre_result_' + str(num) + '.npy'
np.save(strpath, result_fake_y)
print('ok')
else:
sess.run(init)
print('---------------------開始新的訓練-----------------------')
for epoch in range(2):
for idx in range(0, len(train_hr_imgs), batch_size):
# print(type(train_hr_imgs[idx:idx + batch_size]))
b_imgs_384 = tl.prepro.threading_data(train_hr_imgs[idx:idx + batch_size], fn=crop_sub_imgs_fn,
is_random=True)
b_imgs_96 = tl.prepro.threading_data(b_imgs_384, fn=downsample_fn)
print('-------------pre_generator:' + str(epoch) + '_' + str(idx) + '----------------')
for i in range(25):
init_mse_loss, _ = sess.run([gan.init_mse_loss, G_OPTIM_init],
feed_dict={
gan.x: b_imgs_96,
gan.y: b_imgs_384
})
print('init_mse_loss:' + str(init_mse_loss))
saver.save(sess, "save/nets/cnn_mnist_basic_generator.ckpt")
for epoch in range(config["epoch"]):
for idx in range(0, len(train_hr_imgs), batch_size):
# print(type(train_hr_imgs[idx:idx + batch_size]))
b_imgs_384 = tl.prepro.threading_data(train_hr_imgs[idx:idx + batch_size], fn=crop_sub_imgs_fn,
is_random=True)
b_imgs_96 = tl.prepro.threading_data(b_imgs_384, fn=downsample_fn)
print('-------------' + str(epoch) + '_' + str(idx) + '----------')
for i in range(25):
loss_D, _ = sess.run([gan.D_loos, D_OPTIM],
feed_dict={
gan.x: b_imgs_96,
gan.y: b_imgs_384
})
loss_G, _ = sess.run([gan.G_loos, G_OPTIM],
feed_dict={
gan.x: b_imgs_96,
gan.y: b_imgs_384
})
print(loss_D, loss_G)
if idx % 20 == 0:
_imgs = (np.asanyarray(test_img[0:1]) / (255. / 2.)) - 1
_imgs = _imgs[:, :, :, 0:3]
result_fake_y = sess.run([gan.test_fake_y], feed_dict={
gan.test_x: _imgs
}) # 生成的假照片
# result=sess.run(result_fake_y)
strpath = './preImg/result_' + str(epoch) + '_' + str(idx) + '_1.npy'
np.save(strpath, result_fake_y)
_imgs2 = (np.asanyarray(test_img[1:2]) / (255. / 2.)) - 1
_imgs2 = _imgs2[:, :, :, 0:3]
result_fake_y = sess.run([gan.test_fake_y], feed_dict={
gan.test_x: _imgs2
}) # 生成的假照片
# result=sess.run(result_fake_y)
strpath = './preImg/result_' + str(epoch) + '_' + str(idx) + '_2.npy'
np.save(strpath, result_fake_y)
saver.save(sess, "save/nets/ckpt-" + str(epoch) + '-' + str(idx))
# print(type(result_fake_y))
查看效果的工具函數
將numpy矩陣轉換爲圖片工具
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
npz = np.load('../preImg/pre_result_5.npy', encoding='latin1')
print(npz.shape)
data = ((npz[0][0]) + 1) * (255. / 2.)
print(data)
new_im = Image.fromarray(data.astype(np.uint8))
new_im.show()
new_im.save('result.png')