def evaluate(input_dir="test_input/",
output_dir="test_output/",
checkpoint_dir="save_model2",
batch_size=4):
t_image_data = read_data(input_dir)
shape = t_image_data.shape
print(shape)
t_image = tf.placeholder('float32', [None, shape[1], shape[2], shape[3]],
name='input_image')
outputs, _ = my_GAN_G2(t_image, is_train=False, reuse=False)
print(len(outputs))
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False))
tl.layers.initialize_global_variables(sess)
for i in range(len(outputs)):
R = i + 4
tl.files.load_and_assign_npz_dict(sess=sess,
name=checkpoint_dir +
'/g_%d_level_my_gan.npz' % R,
network=outputs[i])
epoch = shape[0] // batch_size
ni = int(np.sqrt(batch_size))
for i in range(epoch):
data = t_image_data[batch_size * i:batch_size * (i + 1)]
output = sess.run(outputs[-1].outputs, {t_image: data})
tl.vis.save_images(output, [ni, ni], output_dir + '/%d_output.png' % i)
def network_new2(
top_dir="sr_tanh/",
svg_dir="dataset/Test/", #test_data
pxl_dir="dataset/Train/", #train_data
output_dir="pic_smooth/",
test_output_dir='test_output/',
checkpoint_dir="save_model",
checkpoint_dir1="save_model",
model_name="model4",
big_loop=1,
scale_num=2,
epoch_init=5000,
strides=20,
batch_size=4,
max_idx=92,
data_size=92,
lr_init=1e-3,
learning_rate=1e-5,
vgg_weight_list=[1, 1, 5e-1, 1e-1],
use_vgg=False,
use_L1_loss=False,
wgan=False,
init_g=True,
init_d=True,
init_b=False,
method=0,
lowest_resolution_log2=4,
train_net=True,
generate_pics=True,
resume_network=False):
logger = logging.getLogger(__name__)
logger.setLevel(level=logging.INFO)
handler = logging.FileHandler(top_dir + "log.txt")
handler.setLevel(logging.INFO)
formatter = logging.Formatter(
'%(asctime)s - %(name)s - %(levelname)s - %(message)s')
handler.setFormatter(formatter)
logger.addHandler(handler)
for idx, val in enumerate(network_new2.__defaults__):
logger.info(
str(network_new2.__code__.co_varnames[idx]) + ' == ' + str(val))
output_dir = top_dir + output_dir
test_output_dir = top_dir + test_output_dir
checkpoint_dir = top_dir + checkpoint_dir
checkpoint_dir1 = top_dir + checkpoint_dir1
logger.info("start building the net")
if use_vgg:
print('use vgg')
t_target_image_data, image_padding_nums = read_data(pxl_dir, data_size)
resolution = t_target_image_data.shape[1] / scale_num
target_resolution = t_target_image_data.shape[1]
resolution_log2 = int(np.floor(np.log2(resolution)))
target_resolution_log2 = int(np.floor(np.log2(target_resolution)))
#image = tf.image.resize_images(t_image, size=[64, 64], method=2)
t_image_target = tf.placeholder(
'float32', [None, target_resolution, target_resolution, 3],
name='t_image_target')
t_image_ = tf.image.resize_images(
t_image_target,
size=[target_resolution // scale_num, target_resolution // scale_num],
method=method)
t_image = tf.image.resize_images(
t_image_, size=[target_resolution, target_resolution], method=method)
t_image_target_list = []
t_image_list = []
#generate list of pics from 2 ** 2 resolution to t_image_size resolution
net_Gs, mix_rates = my_GAN_G2(t_image, is_train=True, reuse=False)
print("init Gs")
net_Gs[-1].print_params(False)
net_g_test, _ = my_GAN_G2(t_image, is_train=False, reuse=True)
print("init g_test")
if use_vgg:
t_target_image_224 = tf.placeholder('float32', [None, 224, 224, 3],
name='t_image_224')
t_predict_image_224 = tf.placeholder('float32', [None, 224, 224, 3],
name='t_target_224')
net_vgg, vgg_target_emb = Vgg19_simple_api(
(t_target_image_224 + 1) / 2, reuse=False)
#initialize the list to store different level net
net_ds = []
b_outputs = []
logits_reals = []
logits_fakes = []
logits_fakes2 = []
d_loss_list = []
b_loss_list = []
d_loss3_list = []
mse_loss_list = []
g_gan_loss_list = []
g_loss_list = []
g_init_optimizer_list = []
d_init_optimizer_list = []
g_optimizer_list = []
d_optimizer_list = []
b_optimizer_list = []
w_clip_list = []
with tf.variable_scope('learning_rate'):
lr_v = tf.Variable(lr_init, trainable=False)
print("init Ds")
for i in range(lowest_resolution_log2, target_resolution_log2 + 1):
idx = i - lowest_resolution_log2
cur_resolution = 2**i
size = [cur_resolution, cur_resolution]
target_i = tf.image.resize_images(t_image_target,
size=size,
method=method)
image_i = tf.image.resize_images(t_image, size=size, method=method)
t_image_target_list += [target_i]
t_image_list += [image_i]
if use_vgg:
t_target_image_224 = tf.image.resize_images(
t_image_target, size=[224, 224], method=1, align_corners=False
) # resize_target_image_for_vgg # http://tensorlayer.readthedocs.io/en/latest/_modules/tensorlayer/layers.html#UpSampling2dLayer
add_dimens = tf.zeros_like(t_target_image_224)
print(add_dimens.dtype)
print(t_target_image_224.dtype)
t_predict_image_224 = tf.image.resize_images(
net_Gs[idx].outputs,
size=[224, 224],
method=1,
align_corners=False) # resize_generate_image_for_vgg
net_vgg, vgg_target_emb = Vgg19_simple_api(
(t_target_image_224 + 1) / 2, reuse=True)
_, vgg_predict_emb = Vgg19_simple_api(
(t_predict_image_224 + 1) / 2, reuse=True)
#initialize the D_reals and D_fake
net_d, logits_real = my_GAN_D1(target_i,
is_train=True,
reuse=False,
use_sigmoid=not wgan)
_, logits_fake = my_GAN_D1(net_Gs[idx].outputs,
is_train=True,
reuse=True,
use_sigmoid=not wgan)
_, logits_fake2 = my_GAN_D1(image_i,
is_train=True,
reuse=True,
use_sigmoid=not wgan)
blend_output = net_CT_blend(image_i, net_Gs[idx].outputs)
b_outputs += [blend_output]
net_ds += [net_d]
logits_reals += [logits_real]
logits_fakes += [logits_fake]
logits_fakes2 += [logits_fake2]
mix_factors = np.random.uniform(size=[1, 1, 1, int(target_i.shape[3])])
print(mix_factors.shape)
mix_pic = net_Gs[idx].outputs * mix_factors + target_i * (1 -
mix_factors)
_, logits_mix = my_GAN_D1(mix_pic, is_train=True, reuse=True)
d_loss1 = tl.cost.sigmoid_cross_entropy(logits_real,
tf.ones_like(logits_real),
name='d1_%d' % cur_resolution)
d_loss2 = tl.cost.sigmoid_cross_entropy(logits_fake,
tf.zeros_like(logits_fake),
name='d2_%d' % cur_resolution)
d_loss3 = tl.cost.sigmoid_cross_entropy(logits_fake2,
tf.zeros_like(logits_fake2),
name='d3_%d' % cur_resolution)
d_loss4 = (tf.reduce_mean(logits_fake2)) - (
tf.reduce_mean(logits_real))
d_loss4 = tf.nn.sigmoid(d_loss4) #make sure in [0, 1]
d_loss = 1 * (d_loss1 + d_loss2) #+ d_loss3 + d_loss4
d_loss += 0.
d_loss3 += d_loss1
use_vgg22 = True
vgg_loss = 0
if use_vgg:
for i, vgg_target in enumerate(vgg_target_emb):
vgg_loss += vgg_weight_list[i] * tl.cost.mean_squared_error(
vgg_predict_emb[i].outputs,
vgg_target.outputs,
is_mean=True)
g_gan_loss1 = tl.cost.sigmoid_cross_entropy(logits_fake,
tf.ones_like(logits_fake),
name='g_%d' %
cur_resolution)
g_gan_loss2 = (tf.reduce_mean(logits_fake2)) - (
tf.reduce_mean(logits_fake))
g_gan_loss2 = tf.nn.sigmoid(g_gan_loss2) #make sure in [0, 1]
g_gan_loss = g_gan_loss1 # + g_gan_loss2
mse_loss = tl.cost.mean_squared_error(net_Gs[idx].outputs,
target_i,
is_mean=True)
if use_L1_loss:
mes_loss = tf.reduce_mean(
tf.reduce_mean(tf.abs(net_Gs[idx].outputs - target_i)))
g_gan_loss_list += [g_gan_loss]
mse_loss_list += [mse_loss]
g_loss = 1e-3 * g_gan_loss + mse_loss
L1_norm = tf.reduce_mean(tf.reduce_mean(net_Gs[idx].outputs))
def TV_loss(x):
loss1 = x[:, :, 1:, :] - x[:, :, :-1, :]**2
loss2 = x[:, 1:, :, :] - x[:, :-1, :, :]**2
return tf.reduce_sum(tf.reduce_sum(loss1)) + tf.reduce_sum(
tf.reduce_sum(loss2))
tV_loss = TV_loss(net_Gs[idx].outputs)
b_loss = tl.cost.mean_squared_error(blend_output.outputs,
target_i,
is_mean=True)
if i >= 7: g_loss += vgg_loss
#g_loss += vgg_loss
g_vars = tl.layers.get_variables_with_name('my_GAN_G', True, True)
d_vars = tl.layers.get_variables_with_name(
'my_GAN_D_%d' % cur_resolution, True, True)
b_vars = tl.layers.get_variables_with_name(
'my_CT_blend_%d' % cur_resolution, True, True)
g_optim_init = tf.train.AdamOptimizer(lr_v,
0.9).minimize(mse_loss,
var_list=g_vars)
g_init_optimizer_list += [g_optim_init]
d_optim_init = tf.train.AdamOptimizer(lr_v,
0.9).minimize(d_loss3,
var_list=d_vars)
g_optim = tf.train.AdamOptimizer(lr_v, 0.9).minimize(g_loss,
var_list=g_vars)
d_optim = tf.train.AdamOptimizer(lr_v, 0.9).minimize(d_loss,
var_list=d_vars)
b_optim = tf.train.AdamOptimizer(lr_v, 0.9).minimize(b_loss,
var_list=b_vars)
#WGAN
if wgan:
print('mode is wgan')
g_loss = -(tf.reduce_mean(logits_fake)) + vgg_loss
d_loss = (tf.reduce_mean(logits_fake)) - (
tf.reduce_mean(logits_real))
d_loss3 = (tf.reduce_mean(logits_fake2)) - (
tf.reduce_mean(logits_real))
mix_grads = tf.gradients(tf.reduce_sum(logits_mix), mix_pic)
mix_norms = tf.sqrt(
tf.reduce_sum(tf.square(mix_grads), axis=[1, 2, 3]))
addtion = tf.reduce_mean(tf.square(mix_norms - 1.)) * 5.0
#d_loss = d_loss + d_loss3 + addtion + tl.cost.mean_squared_error(logits_real, tf.zeros_like(logits_real)) * 1e-3
d_loss = d_loss + d_loss3
g_optim = tf.train.RMSPropOptimizer(learning_rate).minimize(
g_loss, var_list=g_vars)
d_optim = tf.train.RMSPropOptimizer(learning_rate).minimize(
d_loss, var_list=d_vars)
d_optim_init = tf.train.RMSPropOptimizer(learning_rate).minimize(
d_loss3, var_list=d_vars)
clip_ops = []
for var in d_vars:
clip_bound = [-1.0, 1.0]
clip_ops.append(
tf.assign(
var, tf.clip_by_value(var, clip_bound[0],
clip_bound[1])))
clip_disc_weights = tf.group(*clip_ops)
w_clip_list += [clip_disc_weights]
d_loss_list += [d_loss]
d_loss3_list += [d_loss3]
g_loss_list += [g_loss]
b_loss_list += [b_loss]
g_optimizer_list += [g_optim]
d_optimizer_list += [d_optim]
b_optimizer_list += [b_optim]
d_init_optimizer_list += [d_optim_init]
print("init Res : %d D" % cur_resolution)
#Restore Model
config = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False)
config.gpu_options.per_process_gpu_memory_fraction = 0.8
sess = tf.Session(config=config)
tl.layers.initialize_global_variables(sess)
#......code for restore model
if use_vgg:
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()
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))
params.extend([W, b])
if (len(params) == len(net_vgg.all_params)): break
tl.files.assign_params(sess, params, net_vgg)
# net_vgg.print_params(False)
# net_vgg.print_layers()
#Read Data
#t_image_data, t_target_image_data = split_pic(read_data(svg_dir, data_size))
#initialize G
for temp_i in range(big_loop):
decay_every = epoch_init // 2
lr_decay = 0.1
logger.info("start training the net")
for R in range(lowest_resolution_log2, target_resolution_log2 + 1):
idx = R - lowest_resolution_log2
if resume_network or not train_net:
tl.files.load_and_assign_npz_dict(sess=sess,
name=checkpoint_dir1 +
'/g_%d_level_my_gan.npz' % R,
network=net_Gs[idx])
tl.files.load_and_assign_npz_dict(sess=sess,
name=checkpoint_dir1 +
'/b_%d_level_my_gan.npz' % R,
network=b_outputs[idx])
#tl.files.load_and_assign_npz_dict(sess = sess, name = checkpoint_dir1 + '/d_%d_level_my_gan.npz' % R, network = net_ds[idx])
total_mse_loss = 0
mse_loss = mse_loss_list[idx]
g_optim_init = g_init_optimizer_list[idx]
total_d3_loss = 0
d_loss3 = d_loss3_list[idx]
d_optim = d_optimizer_list[idx]
d_loss = d_loss_list[idx]
d_optim_init = d_init_optimizer_list[idx]
ni = int(np.sqrt(batch_size))
out_svg = sess.run(
t_image_list[idx],
{t_image_target: t_target_image_data[0:batch_size]})
out_pxl = sess.run(
t_image_target_list[idx],
{t_image_target: t_target_image_data[0:batch_size]})
print(out_pxl[0])
print(out_pxl.dtype)
tl.vis.save_images(out_svg, [ni, ni],
output_dir + "R_%d_svg.png" % (R))
tl.vis.save_images(out_pxl, [ni, ni],
output_dir + "R_%d_pxl.png" % (R))
f = open('log%d.txt' % R, 'w')
pre_loss_list = []
now_loss_list = []
if init_g and train_net:
#fix lr_v
print('init g')
sess.run(tf.assign(lr_v, lr_init))
for epoch in range(epoch_init + 1):
iters, data, padding_nums = batch_data(
t_target_image_data, image_padding_nums, max_idx,
batch_size)
total_mse_loss = 0
total_pre_loss = np.zeros([2])
total_now_loss = np.zeros([2])
for i in range(iters):
errM, _ = sess.run([mse_loss, g_optim_init],
{t_image_target: data[i]})
total_mse_loss += errM
if R == target_resolution_log2: #final steps
lowR_pics, output_pics, GT_pics = sess.run(
[
t_image_list[idx], net_g_test[idx].outputs,
t_image_target_list[idx]
], {t_image_target: data[i]})
pre_lowR_pics = clip_pics(lowR_pics,
padding_nums[i])
pre_output_pics = clip_pics(
output_pics, padding_nums[i])
pre_GT_pics = clip_pics(GT_pics, padding_nums[i])
for ii in range(data[i].shape[0]):
pre_loss = cal_loss(pre_lowR_pics[ii],
pre_GT_pics[ii])
now_loss = cal_loss(pre_output_pics[ii],
pre_GT_pics[ii])
total_pre_loss += pre_loss
total_now_loss += now_loss
pre_loss_list += [total_pre_loss / max_idx]
now_loss_list += [total_now_loss / max_idx]
print("[%d/%d] total_mse_loss = %f errM = %f" %
(epoch, epoch_init, total_mse_loss, errM))
## save model
if (epoch % strides == 0):
print("save img %d" % R)
out, logits_real, logits_fake, logits_fake2 = sess.run(
[
net_g_test[idx].outputs,
tf.nn.sigmoid(logits_reals[idx]),
tf.nn.sigmoid(logits_fakes[idx]),
tf.nn.sigmoid(logits_fakes2[idx])
], {
t_image_target:
t_target_image_data[0:batch_size]
})
print(out[0])
print(out.dtype)
tl.vis.save_images(
out, [ni, ni],
output_dir + "R_%d_init_%d.png" % (R, epoch))
if epoch % 10 == 0:
tl.files.save_npz_dict(
net_Gs[idx].all_params,
name=checkpoint_dir +
('/g_%d_level_{}_init.npz' % R).format(
tl.global_flag['mode']),
sess=sess)
print("R %d total_mse_loss = %f" % (2**R, total_mse_loss))
save_list(top_dir + 'init_g_pre', pre_loss_list)
save_list(top_dir + 'init_g_now', now_loss_list)
pre_loss_list = []
now_loss_list = []
if init_d and train_net:
#fix lr_v
print('init d')
sess.run(tf.assign(lr_v, lr_init))
for epoch in range(epoch_init + 1):
iters, data, padding_nums = batch_data(
t_target_image_data, image_padding_nums, max_idx,
batch_size)
for i in range(iters):
errD3, errD, _ = sess.run(
[d_loss3, d_loss, d_optim_init],
{t_image_target: data[i]})
total_d3_loss += errD3
print("[%d/%d] d_loss = %f, errD3 = %f" %
(epoch, epoch_init, errD, errD3))
## save model
if (epoch != 0) and (epoch % 5 == 0):
tl.files.save_npz_dict(
net_ds[idx].all_params,
name=checkpoint_dir +
'/d_{}_init.npz'.format(tl.global_flag['mode']),
sess=sess)
if epoch % 10 == 0:
out, logits_real, logits_fake, logits_fake2 = sess.run(
[
net_g_test[idx].outputs,
tf.nn.sigmoid(logits_reals[idx]),
tf.nn.sigmoid(logits_fakes[idx]),
tf.nn.sigmoid(logits_fakes2[idx])
], {
t_image_target:
t_target_image_data[0:batch_size]
})
print("logits_real", file=f)
print(logits_real, file=f)
print("logits_fake", file=f)
print(logits_fake, file=f)
print("logits_fake2", file=f)
print(logits_fake2, file=f)
print("R %d total_d3_loss = %f" % (2**R, total_d3_loss))
print("init g or d end", file=f)
#train GAN
g_optim = g_optimizer_list[idx]
d_optim = d_optimizer_list[idx]
d_loss = d_loss_list[idx]
g_loss = g_loss_list[idx]
mse_loss = mse_loss_list[idx]
g_gan_loss = g_gan_loss_list[idx]
mix_rate, pic_rate = mix_rates[idx]
increas = 2. / epoch_init
mix_rate_vals = np.arange(0., 1. + increas, increas)
last_errD = 0.
last_errG = 0.
if train_net:
for epoch in range(epoch_init + 1):
if epoch != 0 and (epoch % decay_every == 0):
new_lr_decay = lr_decay**(epoch // decay_every)
sess.run(tf.assign(lr_v, lr_init * new_lr_decay))
elif epoch == 0:
sess.run(tf.assign(lr_v, lr_init))
#mix_mat = np.zeros([t_image_list[idx].shape[i] for i in range(1, 4)], dtype = 'float32')
sess.run(tf.assign(mix_rate, 0))
sess.run(tf.assign(pic_rate, 0))
total_d_loss = 0
total_g_loss = 0
total_mse_loss = 0
iters, data, padding_nums = batch_data(
t_target_image_data, image_padding_nums, max_idx,
batch_size)
total_pre_loss = np.zeros([2])
total_now_loss = np.zeros([2])
for i in range(iters):
#update G
if wgan:
errG, errM, errA, _ = sess.run(
[g_loss, mse_loss, g_gan_loss, g_optim],
{t_image_target: data[i]})
#update D
if True: #last_errG * 1e3 <= last_errD * 10: # D learning too fast
flag = 1
errD, _ = sess.run([d_loss, d_optim],
{t_image_target: data[i]})
#print("[%d/%d] epoch %d times d_loss : %f" % (epoch, epoch_init, i, errD))
#update G
if not wgan:
#print("train G")
errG, errM, errA, _ = sess.run(
[g_loss, mse_loss, g_gan_loss, g_optim],
{t_image_target: data[i]})
#print("[%d/%d] epoch %d times, g_loss : %f, mse_loss : %f, g_gan_loss : %f"
# % (epoch, epoch_init, i, errG, errM, errA))
#clip var_val
if wgan:
_ = sess.run(w_clip_list[idx])
last_errD = errD
last_errG = errA
total_d_loss += errD
total_g_loss += errG
total_mse_loss += errM
if R == target_resolution_log2: #final steps
lowR_pics, output_pics, GT_pics = sess.run(
[
t_image_list[idx], net_g_test[idx].outputs,
t_image_target_list[idx]
], {t_image_target: data[i]})
pre_lowR_pics = clip_pics(lowR_pics,
padding_nums[i])
pre_output_pics = clip_pics(
output_pics, padding_nums[i])
pre_GT_pics = clip_pics(GT_pics, padding_nums[i])
for ii in range(data[i].shape[0]):
pre_loss = cal_loss(pre_lowR_pics[ii],
pre_GT_pics[ii])
now_loss = cal_loss(pre_output_pics[ii],
pre_GT_pics[ii])
total_pre_loss += pre_loss
total_now_loss += now_loss
pre_loss_list += [total_pre_loss / max_idx]
now_loss_list += [total_now_loss / max_idx]
print("lastD = %f, lastG = %f" % (last_errD, last_errG))
print("[%d/%d] epoch %d times d_loss : %f" %
(epoch, epoch_init, i, errD))
print(
"[%d/%d] epoch %d times, errM = %f, mse_loss : %f, g_gan_loss : %f"
% (epoch, epoch_init, i, errM, total_mse_loss, errA))
#save genate pic
if (epoch % strides == 0):
print("save img %d" % R)
out, logits_real, logits_fake, logits_fake2 = sess.run(
[
net_g_test[idx].outputs,
tf.nn.sigmoid(logits_reals[idx]),
tf.nn.sigmoid(logits_fakes[idx]),
tf.nn.sigmoid(logits_fakes2[idx])
], {
t_image_target:
t_target_image_data[0:batch_size]
})
print(out[0])
out = out.clip(0, 255)
print(out.dtype)
tl.vis.save_images(
out, [ni, ni],
output_dir + "R_%d_train_%d.png" % (R, epoch))
#increase the mix_rate from 0 to 1 linearly
mix_rate_val = tf.nn.sigmoid(mix_rate).eval(
session=sess)
mix_pic_val = tf.nn.sigmoid(pic_rate).eval(
session=sess)
print("logits_real")
print(logits_real)
print("logits_fake")
print(logits_fake)
print("logits_fake2")
print(logits_fake2)
print("logits_real", file=f)
print(logits_real, file=f)
print("logits_fake", file=f)
print(logits_fake, file=f)
print("logits_fake2", file=f)
print(logits_fake2, file=f)
if (logits_real == logits_fake).all():
print("optimize well")
print("optimize well", file=f)
print("mix_rate, pic_rate")
print(mix_rate_val, mix_pic_val)
print("mix_rate, pic_rate", file=f)
print(mix_rate_val, mix_pic_val, file=f)
## save model
if (epoch != 0) and (epoch % 10 == 0):
tl.files.save_npz_dict(
net_Gs[idx].all_params,
name=checkpoint_dir +
('/g_%d_level_{}.npz' % R).format(
tl.global_flag['mode']),
sess=sess)
tl.files.save_npz_dict(
net_d.all_params,
name=checkpoint_dir +
('/d_%d_level_{}.npz' % R).format(
tl.global_flag['mode']),
sess=sess)
save_list(top_dir + 'g_pre', pre_loss_list)
save_list(top_dir + 'g_now', now_loss_list)
pre_loss_list = []
now_loss_list = []
f.close()
blend_output = b_outputs[idx]
b_loss = b_loss_list[idx]
b_optim = b_optimizer_list[idx]
if not True:
#fix lr_v
sess.run(tf.assign(lr_v, lr_init))
for epoch in range(epoch_init * 3 + 1):
iters, data, padding_nums = batch_data(
t_target_image_data, image_padding_nums, max_idx,
batch_size)
for i in range(iters):
errM, _ = sess.run([b_loss, b_optim],
{t_image_target: data[i]})
total_mse_loss += errM
print("[%d/%d] total_mse_loss = %f errM = %f" %
(epoch, epoch_init, total_mse_loss, errM))
## save model
if (epoch % (strides * 3) == 0):
print("save img %d" % R)
out = sess.run(blend_output.outputs, {
t_image_target:
t_target_image_data[0:batch_size]
})
out = out.clip(0, 255)
#print(out[0])
print(out.dtype)
tl.vis.save_images(
out, [ni, ni],
output_dir + "b_%d_output_%d.png" % (R, epoch))
if epoch % 100 == 0:
tl.files.save_npz_dict(
blend_output.all_params,
name=checkpoint_dir +
('/b_%d_level_{}.npz' % R).format(
tl.global_flag['mode']),
sess=sess)
logger.info("end training the net")
if not train_net or generate_pics:
if init_b:
sess.run(tf.assign(lr_v, lr_init))
for epoch in range(epoch_init * 3 + 1):
iters, data, padding_nums = batch_data(
t_target_image_data, image_padding_nums, max_idx,
batch_size)
for i in range(iters):
errM, _ = sess.run([b_loss, b_optim],
{t_image_target: data[i]})
total_mse_loss += errM
print("[%d/%d] total_mse_loss = %f errM = %f" %
(epoch, epoch_init, total_mse_loss, errM))
## save model
if (epoch % (strides * 3) == 0):
print("save img %d" % R)
out = sess.run(blend_output.outputs, {
t_image_target:
t_target_image_data[0:batch_size]
})
out = out.clip(0, 255)
#print(out[0])
print(out.dtype)
tl.vis.save_images(
out, [ni, ni],
output_dir + "b_%d_output_%d.png" % (R, epoch))
if epoch % 100 == 0:
tl.files.save_npz_dict(
blend_output.all_params,
name=checkpoint_dir +
('/b_%d_level_{}.npz' % R).format(
tl.global_flag['mode']),
sess=sess)
logger.info("load params")
tl.files.load_and_assign_npz_dict(sess=sess,
name=checkpoint_dir1 +
'/g_%d_level_my_gan.npz' % R,
network=net_Gs[-1])
tl.files.load_and_assign_npz_dict(sess=sess,
name=checkpoint_dir1 +
'/b_%d_level_my_gan.npz' % R,
network=b_outputs[-1])
logger.info("read pics")
test_set_dir = ["Set5/", "Set14/"]
test_no = [5, 13]
for j in range(2):
data_pxl, pic_pad_nums = read_data(svg_dir + test_set_dir[j],
num=test_no[j])
iters = data_pxl.shape[0]
data_pxl = np.split(data_pxl, iters)
#iters, data = batch_data((t_image_data, t_target_image_data), 100, batch_size)
logger.info('start evaluating pics')
for i in range(iters):
print("save img %d" % R)
out = sess.run(net_g_test[idx].outputs,
{t_image_target: data_pxl[i]})
out = out.clip(0, 255)
out = np.array([clip_pic(out[0], pic_pad_nums[i])])
tl.vis.save_images(
out, [1, 1], test_output_dir + test_set_dir[j] +
"g_%d_output_%d.png" % (R, i))
out = sess.run(b_outputs[idx].outputs,
{t_image_target: data_pxl[i]})
out = out.clip(0, 255)
out = np.array([clip_pic(out[0], pic_pad_nums[i])])
tl.vis.save_images(
out, [1, 1], test_output_dir + test_set_dir[j] +
"b_%d_output_%d.png" % (R, i))
out = sess.run(t_image, {t_image_target: data_pxl[i]})
out = np.array([clip_pic(out[0], pic_pad_nums[i])])
tl.vis.save_images(
out, [1, 1], test_output_dir + test_set_dir[j] +
"svg_%d_%d.png" % (R, i))
out = sess.run(t_image_target,
{t_image_target: data_pxl[i]})
out = np.array([clip_pic(out[0], pic_pad_nums[i])])
tl.vis.save_images(
out, [1, 1], test_output_dir + test_set_dir[j] +
"pxl_%d_%d.png" % (R, i))
logger.info('end evaluating pics')