-StarGAN,AttGAN和STGAN的效果图 +
+StarGAN的效果图(图片属性分别为：origial image, Black hair, Blond Hair, Brown Hair, Male, Young)
-
-StarGAN,AttGAN和STGAN的效果图
+
+StarGAN的效果图(图片属性分别为：origial image, Black hair, Blond Hair, Brown Hair, Male, Young)
+
+AttGAN的效果图(图片属性分别为：original image, Bald, Bangs, Black Hair, Blond Hair, Brown Hair, Bushy Eyebrows, Eyeglasses, Male, Mouth Slightly Open, Mustache, No Beard, Pale Skin, Young)
+
+
+STGAN的效果图(图片属性分别为：original image, Bald, Bangs, Black Hair, Blond Hair, Brown Hair, Bushy Eyebrows, Eyeglasses, Male, Mouth Slightly Open, Mustache, No Beard, Pale Skin, Young)
+
@@ -189,7 +199,7 @@ Pix2Pix生成网络结构图[5]
@@ -197,7 +207,7 @@ CycleGAN生成网络结构图[5]
@@ -207,7 +217,7 @@ StarGAN的生成网络结构[左]和判别网络结构[右] [7]
-- AttGAN中生成网络的编码部分主要由`convolution-instance norm-ReLU`组成，解码部分由`transpose convolution-norm-ReLU`组成，判别网络主要由`convolution-leaky_ReLU`组成，详细网络结构可以查看`network/AttGAN_network.py`文件。生成网络的损失函数是由CGAN的损失函数，重构损失和分类损失组成，判别网络的损失函数由预测损失，分类损失和梯度惩罚损失组成。
+- AttGAN中生成网络的编码部分主要由`convolution-instance norm-ReLU`组成，解码部分由`transpose convolution-norm-ReLU`组成，判别网络主要由`convolution-leaky_ReLU`组成，详细网络结构可以查看`network/AttGAN_network.py`文件。生成网络的损失函数是由WGAN的损失函数，重构损失和分类损失组成，判别网络的损失函数由预测损失，分类损失和梯度惩罚损失组成。
@@ -215,7 +225,7 @@ AttGAN的网络结构[8]
@@ -228,17 +238,20 @@ STGAN的网络结构[9]
## FAQ
-**Q:** StarGAN/AttGAN/STGAN中属性没有变化，为什么?
+**Q:** StarGAN/AttGAN/STGAN中属性没有变化，为什么?
**A:** 查看是否所有的标签都转换对了。
**Q:** 预测结果不正常，是怎么回事？
**A:** 某些GAN预测的时候batch_norm的设置需要和训练的时候行为一致，查看模型库中相应的GAN中预测时batch_norm的行为和自己模型中的预测时batch_norm的
-行为是否一致。
+行为是否一致。
**Q:** 为什么STGAN和ATTGAN中变男性得到的预测结果是变女性呢？
**A:** 这是由于预测时标签的设置，目标标签是基于原本的标签进行改变，比如原本图片是男生，预测代码对标签进行转变的时候会自动变成相对立的标签，即女
性，所以得到的结果是女生。如果想要原本是男生，转变之后还是男生，保持要转变的标签不变即可。
+**Q:** 如何使用自己的数据集进行训练？
+**A:** 对于Pix2Pix来说，只要准备好类似于Cityscapes数据集的不同风格的成对的数据即可。对于CycleGAN来说，只要准备类似于Cityscapes数据集的不同风格的数据即可。对于StarGAN，AttGAN和STGAN来说，除了需要准备类似于CelebA数据集中的图片和标签文件外，还需要把模型中的selected_attrs参数设置为想要改变的目标属性，c_dim参数这是为目标属性的个数。
+
## 参考论文
[1] [Goodfellow, Ian J.; Pouget-Abadie, Jean; Mirza, Mehdi; Xu, Bing; Warde-Farley, David; Ozair, Sherjil; Courville, Aaron; Bengio, Yoshua. Generative Adversarial Networks. 2014. arXiv:1406.2661 [stat.ML].](https://arxiv.org/abs/1406.2661)
diff --git a/PaddleCV/PaddleGAN/images/attgan.jpg b/PaddleCV/PaddleGAN/images/attgan.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..b867b2885a1cb097f8a9698ce966c8fb4f6eceff
Binary files /dev/null and b/PaddleCV/PaddleGAN/images/attgan.jpg differ
diff --git a/PaddleCV/PaddleGAN/images/stargan.jpg b/PaddleCV/PaddleGAN/images/stargan.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..ff1d6ed874ddc393037e1f33f917cfb8b81f9757
Binary files /dev/null and b/PaddleCV/PaddleGAN/images/stargan.jpg differ
diff --git a/PaddleCV/PaddleGAN/images/stgan.jpg b/PaddleCV/PaddleGAN/images/stgan.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..c359780fbb10d2624d261f3903260634f0570960
Binary files /dev/null and b/PaddleCV/PaddleGAN/images/stgan.jpg differ
diff --git a/PaddleCV/PaddleGAN/infer.py b/PaddleCV/PaddleGAN/infer.py
index 45fe1f7873418aaf448ed9092dae07dabd07f91a..788fea4a57b424ea1b45157558f6120add4608c7 100644
--- a/PaddleCV/PaddleGAN/infer.py
+++ b/PaddleCV/PaddleGAN/infer.py
@@ -27,7 +27,7 @@ import imageio
import glob
from util.config import add_arguments, print_arguments
from data_reader import celeba_reader_creator
-from util.utility import check_attribute_conflict, check_gpu
+from util.utility import check_attribute_conflict, check_gpu, save_batch_image
import copy
parser = argparse.ArgumentParser(description=__doc__)
@@ -118,7 +118,6 @@ def infer(args):
print(args.init_model + '/' + model_name)
fluid.io.load_persistables(exe, args.init_model + "/" + model_name)
print('load params done')
-
if not os.path.exists(args.output):
os.makedirs(args.output)
@@ -144,7 +143,7 @@ def infer(args):
tensor_label_trg_ = fluid.LoDTensor()
tensor_img.set(real_img, place)
tensor_label_org.set(label_org, place)
- real_img_temp = np.squeeze(real_img).transpose([0, 2, 3, 1])
+ real_img_temp = save_batch_image(real_img)
images = [real_img_temp]
for i in range(args.c_dim):
label_trg_tmp = copy.deepcopy(label_trg)
@@ -152,11 +151,13 @@ def infer(args):
label_trg_tmp[j][i] = 1.0 - label_trg_tmp[j][i]
label_trg_tmp = check_attribute_conflict(
label_trg_tmp, attr_names[i], attr_names)
+ label_org_ = list(map(lambda x: ((x * 2) - 1) * 0.5, label_org))
label_trg_ = list(
map(lambda x: ((x * 2) - 1) * 0.5, label_trg_tmp))
- for j in range(len(label_org)):
- label_trg_[j][i] = label_trg_[j][i] * 2.0
- tensor_label_org_.set(label_org, place)
+ if args.model_net == 'AttGAN':
+ for k in range(len(label_org)):
+ label_trg_[k][i] = label_trg_[k][i] * 2.0
+ tensor_label_org_.set(label_org_, place)
tensor_label_trg.set(label_trg, place)
tensor_label_trg_.set(label_trg_, place)
out = exe.run(feed={
@@ -165,10 +166,11 @@ def infer(args):
"label_trg_": tensor_label_trg_
},
fetch_list=[fake.name])
- fake_temp = np.squeeze(out[0]).transpose([0, 2, 3, 1])
+ fake_temp = save_batch_image(out[0])
images.append(fake_temp)
images_concat = np.concatenate(images, 1)
- images_concat = np.concatenate(images_concat, 1)
+ if len(label_org) > 1:
+ images_concat = np.concatenate(images_concat, 1)
imageio.imwrite(args.output + "/fake_img_" + name[0], (
(images_concat + 1) * 127.5).astype(np.uint8))
elif args.model_net == 'StarGAN':
@@ -187,7 +189,7 @@ def infer(args):
tensor_label_org = fluid.LoDTensor()
tensor_img.set(real_img, place)
tensor_label_org.set(label_org, place)
- real_img_temp = np.squeeze(real_img).transpose([0, 2, 3, 1])
+ real_img_temp = save_batch_image(real_img)
images = [real_img_temp]
for i in range(args.c_dim):
label_trg_tmp = copy.deepcopy(label_org)
@@ -201,10 +203,11 @@ def infer(args):
feed={"input": tensor_img,
"label_trg_": tensor_label_trg},
fetch_list=[fake.name])
- fake_temp = np.squeeze(out[0]).transpose([0, 2, 3, 1])
+ fake_temp = save_batch_image(out[0])
images.append(fake_temp)
images_concat = np.concatenate(images, 1)
- images_concat = np.concatenate(images_concat, 1)
+ if len(label_org) > 1:
+ images_concat = np.concatenate(images_concat, 1)
imageio.imwrite(args.output + "/fake_img_" + name[0], (
(images_concat + 1) * 127.5).astype(np.uint8))
diff --git a/PaddleCV/PaddleGAN/network/AttGAN_network.py b/PaddleCV/PaddleGAN/network/AttGAN_network.py
index 7bd912186a7a6f394aa902c181d25de933477e1c..eb9a62381d6a0110fc2d9c5e05883d6d851940df 100755
--- a/PaddleCV/PaddleGAN/network/AttGAN_network.py
+++ b/PaddleCV/PaddleGAN/network/AttGAN_network.py
@@ -55,6 +55,7 @@ class AttGAN_model(object):
name=name,
dim=cfg.d_base_dims,
fc_dim=cfg.d_fc_dim,
+ norm=cfg.dis_norm,
n_layers=cfg.n_layers)
def concat(self, z, a):
@@ -149,11 +150,11 @@ class AttGAN_model(object):
d,
4,
2,
- norm=None,
+ norm=norm,
padding=1,
activation_fn='leaky_relu',
name=name + str(i),
- use_bias=True,
+ use_bias=(norm == None),
relufactor=0.01,
initial='kaiming')
diff --git a/PaddleCV/PaddleGAN/network/CGAN_network.py b/PaddleCV/PaddleGAN/network/CGAN_network.py
index 4978f6dd8e57c3a0f2a6c0fad06fcb2770a88efe..8b65b7bd32b21ed78c222658d151117b692ebc10 100644
--- a/PaddleCV/PaddleGAN/network/CGAN_network.py
+++ b/PaddleCV/PaddleGAN/network/CGAN_network.py
@@ -35,6 +35,10 @@ class CGAN_model(object):
self.gf_dim = 128
self.df_dim = 64
self.leaky_relu_factor = 0.2
+ if self.batch_size == 1:
+ self.norm = None
+ else:
+ self.norm = "batch_norm"
def network_G(self, input, label, name="generator"):
# concat noise and label
@@ -43,14 +47,14 @@ class CGAN_model(object):
o_l1 = linear(
xy,
self.gf_dim * 8,
- norm='batch_norm',
+ norm=self.norm,
activation_fn='relu',
name=name + '_l1')
o_c1 = fluid.layers.concat([o_l1, y], 1)
o_l2 = linear(
o_c1,
self.gf_dim * (self.img_w // 4) * (self.img_h // 4),
- norm='batch_norm',
+ norm=self.norm,
activation_fn='relu',
name=name + '_l2')
o_r1 = fluid.layers.reshape(
@@ -107,7 +111,7 @@ class CGAN_model(object):
o_l3 = linear(
o_c2,
self.df_dim * 16,
- norm='batch_norm',
+ norm=self.norm,
activation_fn='leaky_relu',
name=name + '_l3')
o_c3 = fluid.layers.concat([o_l3, y], 1)
diff --git a/PaddleCV/PaddleGAN/network/DCGAN_network.py b/PaddleCV/PaddleGAN/network/DCGAN_network.py
index 6c01fa449e91d338c0b24de1301c93462e9a26d4..1b455c97a40781e4d0b82f192b34a8b7e6612a21 100644
--- a/PaddleCV/PaddleGAN/network/DCGAN_network.py
+++ b/PaddleCV/PaddleGAN/network/DCGAN_network.py
@@ -31,13 +31,17 @@ class DCGAN_model(object):
self.dfc_dim = 1024
self.gf_dim = 64
self.df_dim = 64
+ if self.batch_size == 1:
+ self.norm = None
+ else:
+ self.norm = "batch_norm"
def network_G(self, input, name="generator"):
- o_l1 = linear(input, self.gfc_dim, norm='batch_norm', name=name + '_l1')
+ o_l1 = linear(input, self.gfc_dim, norm=self.norm, name=name + '_l1')
o_l2 = linear(
o_l1,
self.gf_dim * 2 * self.img_dim // 4 * self.img_dim // 4,
- norm='batch_norm',
+ norm=self.norm,
name=name + '_l2')
o_r1 = fluid.layers.reshape(
o_l2, [-1, self.df_dim * 2, self.img_dim // 4, self.img_dim // 4])
@@ -85,7 +89,7 @@ class DCGAN_model(object):
o_l1 = linear(
o_c2,
self.dfc_dim,
- norm='batch_norm',
+ norm=self.norm,
activation_fn='leaky_relu',
name=name + '_l1')
out = linear(o_l1, 1, activation_fn='sigmoid', name=name + '_l2')
diff --git a/PaddleCV/PaddleGAN/network/STGAN_network.py b/PaddleCV/PaddleGAN/network/STGAN_network.py
index 4a1111cc2431fa02c2ae41d051e98ccb15c96808..1cffecbdfc16cf53fb1a7480cec96965a4ff14f9 100755
--- a/PaddleCV/PaddleGAN/network/STGAN_network.py
+++ b/PaddleCV/PaddleGAN/network/STGAN_network.py
@@ -76,6 +76,7 @@ class STGAN_model(object):
n_atts=cfg.c_dim,
dim=cfg.d_base_dims,
fc_dim=cfg.d_fc_dim,
+ norm=cfg.dis_norm,
n_layers=cfg.n_layers,
name=name)
@@ -100,7 +101,7 @@ class STGAN_model(object):
activation_fn='leaky_relu',
name=name + str(i),
use_bias=False,
- relufactor=0.01,
+ relufactor=0.2,
initial='kaiming',
is_test=is_test)
zs.append(z)
@@ -132,7 +133,7 @@ class STGAN_model(object):
pass_state=pass_state,
name=name + str(i),
is_test=is_test)
- zs_.insert(0, output[0] + zs[n_layers - 1 - i])
+ zs_.insert(0, output[0])
if inject_layers > i:
state = self.concat(output[1], a)
else:
@@ -202,18 +203,18 @@ class STGAN_model(object):
d,
4,
2,
- norm=None,
- padding=1,
+ norm=norm,
+ padding_type="SAME",
activation_fn='leaky_relu',
name=name + str(i),
- use_bias=True,
- relufactor=0.01,
+ use_bias=(norm == None),
+ relufactor=0.2,
initial='kaiming')
logit_gan = linear(
y,
fc_dim,
- activation_fn='relu',
+ activation_fn='leaky_relu',
name=name + 'fc_adv_1',
initial='kaiming')
logit_gan = linear(
@@ -222,7 +223,7 @@ class STGAN_model(object):
logit_att = linear(
y,
fc_dim,
- activation_fn='relu',
+ activation_fn='leaky_relu',
name=name + 'fc_cls_1',
initial='kaiming')
logit_att = linear(
diff --git a/PaddleCV/PaddleGAN/network/base_network.py b/PaddleCV/PaddleGAN/network/base_network.py
index e5e9e5ab58afcca7a61c78b23202584932b9c902..4c2223e865973550f3d78c4c53893eeeded921f8 100644
--- a/PaddleCV/PaddleGAN/network/base_network.py
+++ b/PaddleCV/PaddleGAN/network/base_network.py
@@ -76,7 +76,7 @@ def norm_layer(input, norm_type='batch_norm', name=None, is_test=False):
tmp = fluid.layers.elementwise_add(tmp, offset, axis=1)
return tmp
else:
- raise NotImplementedError("norm tyoe: [%s] is not support" % norm_type)
+ raise NotImplementedError("norm type: [%s] is not support" % norm_type)
def initial_type(name,
diff --git a/PaddleCV/PaddleGAN/scripts/run_attgan.sh b/PaddleCV/PaddleGAN/scripts/run_attgan.sh
index 8477c43a55bbac8e3ffcb8d840c863c38db509d1..b001298372f78366c6a5b6697d6cceb9b8302d2d 100644
--- a/PaddleCV/PaddleGAN/scripts/run_attgan.sh
+++ b/PaddleCV/PaddleGAN/scripts/run_attgan.sh
@@ -1 +1 @@
-python train.py --model_net AttGAN --dataset celeba --crop_size 170 --image_size 128 --train_list ./data/celeba/list_attr_celeba.txt --test_list ./data/celeba/test_list_attr_celeba.txt --gan_mode wgan --batch_size 32 --print_freq 1 --num_discriminator_time 5 --epoch 90 >log.out 2>log_err
+python train.py --model_net AttGAN --dataset celeba --crop_size 170 --image_size 128 --train_list ./data/celeba/list_attr_celeba.txt --test_list ./data/celeba/test_list_attr_celeba.txt --gan_mode wgan --batch_size 32 --print_freq 1 --num_discriminator_time 5 --epoch 90 >log_out 2>log_err
diff --git a/PaddleCV/PaddleGAN/scripts/run_stgan.sh b/PaddleCV/PaddleGAN/scripts/run_stgan.sh
index ea8115a2d847ee1242bc4f7ef32d22632f4a795b..c6a83dd54af23f998b49cc6dddea04b518433df8 100644
--- a/PaddleCV/PaddleGAN/scripts/run_stgan.sh
+++ b/PaddleCV/PaddleGAN/scripts/run_stgan.sh
@@ -1 +1 @@
-python train.py --model_net STGAN --dataset celeba --crop_size 170 --image_size 128 --train_list ./data/celeba/list_attr_celeba.txt --test_list ./data/celeba/test_list_attr_celeba.txt --gan_mode wgan --batch_size 32 --print_freq 1 --num_discriminator_time 5 --epoch 20 >log.out 2>log_err
+python train.py --model_net STGAN --dataset celeba --crop_size 170 --image_size 128 --train_list ./data/celeba/list_attr_celeba.txt --test_list ./data/celeba/test_list_attr_celeba.txt --gan_mode wgan --batch_size 32 --print_freq 1 --num_discriminator_time 5 --epoch 50 >log_out 2>log_err
diff --git a/PaddleCV/PaddleGAN/trainer/AttGAN.py b/PaddleCV/PaddleGAN/trainer/AttGAN.py
index d7d84efdc3c388fb28e86bef971cb38da76198c5..cdfebf6f55d47c2413e22404e078abb6baffcbcf 100644
--- a/PaddleCV/PaddleGAN/trainer/AttGAN.py
+++ b/PaddleCV/PaddleGAN/trainer/AttGAN.py
@@ -229,6 +229,12 @@ class AttGAN(object):
type=int,
default=5,
help="default layers in the network")
+ parser.add_argument(
+ '--dis_norm',
+ type=str,
+ default=None,
+ help="the normalization in discriminator, choose in [None, instance_norm]"
+ )
return parser
diff --git a/PaddleCV/PaddleGAN/trainer/STGAN.py b/PaddleCV/PaddleGAN/trainer/STGAN.py
index 7b3c88a80207f7b5e9cb6304e86e75f1a7de7839..69a977774739c5a5f93d66382b3c7d06f57d91fa 100644
--- a/PaddleCV/PaddleGAN/trainer/STGAN.py
+++ b/PaddleCV/PaddleGAN/trainer/STGAN.py
@@ -79,7 +79,7 @@ class DTrainer():
clone_image_real = b.var('image_real')
break
self.fake_img, _ = model.network_G(
- image_real, label_org, label_trg_, cfg, name="generator")
+ image_real, label_org_, label_trg_, cfg, name="generator")
self.pred_real, self.cls_real = model.network_D(
image_real, cfg, name="discriminator")
self.pred_real.persistable = True
@@ -234,6 +234,12 @@ class STGAN(object):
type=int,
default=4,
help="default layers of GRU in generotor")
+ parser.add_argument(
+ '--dis_norm',
+ type=str,
+ default=None,
+ help="the normalization in discriminator, choose in [None, instance_norm]"
+ )
return parser
diff --git a/PaddleCV/PaddleGAN/util/utility.py b/PaddleCV/PaddleGAN/util/utility.py
index 0f4f216e392330c80f4164ebec233e18768428e0..2f55a922216d1530d23b59c07d712bf9e0ccfe82 100644
--- a/PaddleCV/PaddleGAN/util/utility.py
+++ b/PaddleCV/PaddleGAN/util/utility.py
@@ -109,13 +109,13 @@ def save_test_image(epoch,
tensor_label_org = fluid.LoDTensor()
tensor_img.set(real_img, place)
tensor_label_org.set(label_org, place)
- real_img_temp = np.squeeze(real_img).transpose([1, 2, 0])
+ real_img_temp = save_batch_image(real_img)
images = [real_img_temp]
for i in range(cfg.c_dim):
label_trg_tmp = copy.deepcopy(label_org)
label_trg_tmp[0][i] = 1.0 - label_trg_tmp[0][i]
- label_trg = check_attribute_conflict(
- label_trg_tmp, attr_names[i], attr_names)
+ label_trg = check_attribute_conflict(label_trg_tmp,
+ attr_names[i], attr_names)
tensor_label_trg = fluid.LoDTensor()
tensor_label_trg.set(label_trg, place)
fake_temp, rec_temp = exe.run(
@@ -126,8 +126,8 @@ def save_test_image(epoch,
"label_trg": tensor_label_trg
},
fetch_list=[g_trainer.fake_img, g_trainer.rec_img])
- fake_temp = np.squeeze(fake_temp[0]).transpose([1, 2, 0])
- rec_temp = np.squeeze(rec_temp[0]).transpose([1, 2, 0])
+ fake_temp = save_batch_image(fake_temp[0])
+ rec_temp = save_batch_image(rec_temp[0])
images.append(fake_temp)
images.append(rec_temp)
images_concat = np.concatenate(images, 1)
@@ -145,7 +145,7 @@ def save_test_image(epoch,
tensor_label_trg_ = fluid.LoDTensor()
tensor_img.set(real_img, place)
tensor_label_org.set(label_org, place)
- real_img_temp = np.squeeze(real_img).transpose([0, 2, 3, 1])
+ real_img_temp = save_batch_image(real_img)
images = [real_img_temp]
for i in range(cfg.c_dim):
label_trg_tmp = copy.deepcopy(label_trg)
@@ -155,12 +155,14 @@ def save_test_image(epoch,
label_trg_tmp = check_attribute_conflict(
label_trg_tmp, attr_names[i], attr_names)
+ label_org_ = list(map(lambda x: ((x * 2) - 1) * 0.5, label_org))
label_trg_ = list(
map(lambda x: ((x * 2) - 1) * 0.5, label_trg_tmp))
- for j in range(len(label_org)):
- label_trg_[j][i] = label_trg_[j][i] * 2.0
- tensor_label_org_.set(label_org, place)
+ if cfg.model_net == 'AttGAN':
+ for k in range(len(label_org)):
+ label_trg_[k][i] = label_trg_[k][i] * 2.0
+ tensor_label_org_.set(label_org_, place)
tensor_label_trg.set(label_trg, place)
tensor_label_trg_.set(label_trg_, place)
out = exe.run(test_program,
@@ -172,10 +174,11 @@ def save_test_image(epoch,
"label_trg_": tensor_label_trg_
},
fetch_list=[g_trainer.fake_img])
- fake_temp = np.squeeze(out[0]).transpose([0, 2, 3, 1])
+ fake_temp = save_batch_image(out[0])
images.append(fake_temp)
images_concat = np.concatenate(images, 1)
- images_concat = np.concatenate(images_concat, 1)
+ if len(label_org) > 1:
+ images_concat = np.concatenate(images_concat, 1)
imageio.imwrite(out_path + "/fake_img" + str(epoch) + '_' + name[0],
((images_concat + 1) * 127.5).astype(np.uint8))
@@ -266,20 +269,28 @@ def check_attribute_conflict(label_batch, attr, attrs):
return label_batch
+def save_batch_image(img):
+ if len(img) == 1:
+ res_img = np.squeeze(img).transpose([1, 2, 0])
+ else:
+ res_img = np.squeeze(img).transpose([0, 2, 3, 1])
+ return res_img
+
+
def check_gpu(use_gpu):
- """
+ """
Log error and exit when set use_gpu=true in paddlepaddle
cpu version.
"""
- err = "Config use_gpu cannot be set as true while you are " \
- "using paddlepaddle cpu version ! \nPlease try: \n" \
- "\t1. Install paddlepaddle-gpu to run model on GPU \n" \
- "\t2. Set use_gpu as false in config file to run " \
- "model on CPU"
+ err = "Config use_gpu cannot be set as true while you are " \
+ "using paddlepaddle cpu version ! \nPlease try: \n" \
+ "\t1. Install paddlepaddle-gpu to run model on GPU \n" \
+ "\t2. Set use_gpu as false in config file to run " \
+ "model on CPU"
- try:
- if use_gpu and not fluid.is_compiled_with_cuda():
- logger.error(err)
- sys.exit(1)
- except Exception as e:
- pass
+ try:
+ if use_gpu and not fluid.is_compiled_with_cuda():
+ logger.error(err)
+ sys.exit(1)
+ except Exception as e:
+ pass