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提交 862d459d 编写于 作者: u010070587's avatar u010070587 提交者： kolinwei
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add PaddleGAN models (#4184)

上级 4d33a3f0
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Showing with 111 addition and 13 deletion
PaddleCV/PaddleGAN/trainer/AttGAN.py
浏览文件 @ 862d459d
......@@ -156,8 +156,13 @@ class DTrainer():
def gradient_penalty(self, f, real, fake=None, cfg=None, name=None):
def _interpolate(a, b=None):
if b is None:
beta = fluid.layers.uniform_random_batch_size_like(
input=a, shape=a.shape, min=0.0, max=1.0)
if cfg.enable_ce:
beta = fluid.layers.uniform_random_batch_size_like(
input=a, shape=a.shape, min=0.0, max=1.0, seed=1)
else:
beta = fluid.layers.uniform_random_batch_size_like(
input=a, shape=a.shape, min=0.0, max=1.0)
mean = fluid.layers.reduce_mean(
a, dim=list(range(len(a.shape))), keep_dim=True)
input_sub_mean = fluid.layers.elementwise_sub(a, mean, axis=0)
......@@ -167,8 +172,13 @@ class DTrainer():
keep_dim=True)
b = beta * fluid.layers.sqrt(var) * 0.5 + a
shape = [a.shape[0]]
alpha = fluid.layers.uniform_random_batch_size_like(
input=a, shape=shape, min=0.0, max=1.0)
if cfg.enable_ce:
alpha = fluid.layers.uniform_random_batch_size_like(
input=a, shape=shape, min=0.0, max=1.0, seed=1)
else:
alpha = fluid.layers.uniform_random_batch_size_like(
input=a, shape=shape, min=0.0, max=1.0)
inner = fluid.layers.elementwise_mul((b-a), alpha, axis=0) + a
return inner
......@@ -254,6 +264,10 @@ class AttGAN(object):
default=None,
help="the normalization in discriminator, choose in [None, instance_norm]"
)
parser.add_argument(
'--enable_ce',
action='store_true',
help="if set, run the tasks with continuous evaluation logs")
return parser
......@@ -282,6 +296,9 @@ class AttGAN(object):
name='label_org_', shape=[None, self.cfg.c_dim], dtype='float32')
label_trg_ = fluid.data(
name='label_trg_', shape=[None, self.cfg.c_dim], dtype='float32')
# used for continuous evaluation
if self.cfg.enable_ce:
fluid.default_startup_program().random_seed = 90
py_reader = fluid.io.PyReader(
feed_list=[image_real, label_org, label_trg],
......@@ -325,7 +342,11 @@ class AttGAN(object):
dis_trainer.program).with_data_parallel(
loss_name=dis_trainer.d_loss.name,
build_strategy=build_strategy)
# used for continuous evaluation
if self.cfg.enable_ce:
gen_trainer_program.random_seed = 90
dis_trainer_program.random_seed = 90
t_time = 0
for epoch_id in range(self.cfg.epoch):
......@@ -367,6 +388,8 @@ class AttGAN(object):
d_loss_gp[0], batch_time))
sys.stdout.flush()
batch_id += 1
if self.cfg.enable_ce and batch_id == 100:
break
if self.cfg.run_test:
image_name = fluid.data(
......@@ -393,3 +416,13 @@ class AttGAN(object):
"net_G")
utility.checkpoints(epoch_id, self.cfg, exe, dis_trainer,
"net_D")
# used for continuous evaluation
if self.cfg.enable_ce:
device_num = fluid.core.get_cuda_device_count() if self.cfg.use_gpu else 1
print("kpis\tattgan_g_loss_fake_card{}\t{}".format(device_num, g_loss_fake[0]))
print("kpis\tattgan_g_loss_rec_card{}\t{}".format(device_num, g_loss_rec[0]))
print("kpis\tattgan_g_loss_cls_card{}\t{}".format(device_num, g_loss_cls[0]))
print("kpis\tattgan_d_loss_real_card{}\t{}".format(device_num, d_loss_real[0]))
print("kpis\tattgan_d_loss_fake_card{}\t{}".format(device_num,d_loss_fake[0]))
print("kpis\tattgan_d_loss_gp_card{}\t{}".format(device_num,d_loss_gp[0]))
print("kpis\tattgan_Batch_time_cost_card{}\t{}".format(device_num,batch_time))
PaddleCV/PaddleGAN/trainer/STGAN.py
浏览文件 @ 862d459d
......@@ -164,8 +164,13 @@ class DTrainer():
def gradient_penalty(self, f, real, fake=None, cfg=None, name=None):
def _interpolate(a, b=None):
if b is None:
beta = fluid.layers.uniform_random_batch_size_like(
input=a, shape=a.shape, min=0.0, max=1.0)
if cfg.enable_ce:
beta = fluid.layers.uniform_random_batch_size_like(
input=a, shape=a.shape, min=0.0, max=1.0, seed=1)
else:
beta = fluid.layers.uniform_random_batch_size_like(
input=a, shape=a.shape, min=0.0, max=1.0)
mean = fluid.layers.reduce_mean(
a, dim=list(range(len(a.shape))), keep_dim=True)
input_sub_mean = fluid.layers.elementwise_sub(a, mean, axis=0)
......@@ -175,8 +180,13 @@ class DTrainer():
keep_dim=True)
b = beta * fluid.layers.sqrt(var) * 0.5 + a
shape = [a.shape[0]]
alpha = fluid.layers.uniform_random_batch_size_like(
input=a, shape=shape, min=0.0, max=1.0)
if cfg.enable_ce:
alpha = fluid.layers.uniform_random_batch_size_like(
input=a, shape=shape, min=0.0, max=1.0, seed=1)
else:
alpha = fluid.layers.uniform_random_batch_size_like(
input=a, shape=shape, min=0.0, max=1.0)
inner = fluid.layers.elementwise_mul((b-a), alpha, axis=0) + a
return inner
......@@ -269,7 +279,10 @@ class STGAN(object):
default=None,
help="the normalization in discriminator, choose in [None, instance_norm]"
)
parser.add_argument(
'--enable_ce',
action='store_true',
help="if set, run the tasks with continuous evaluation logs")
return parser
def __init__(self,
......@@ -296,6 +309,9 @@ class STGAN(object):
name='label_org_', shape=[None, self.cfg.c_dim], dtype='float32')
label_trg_ = fluid.data(
name='label_trg_', shape=[None, self.cfg.c_dim], dtype='float32')
# used for continuous evaluation
if self.cfg.enable_ce:
fluid.default_startup_program().random_seed = 90
test_gen_trainer = GTrainer(image_real, label_org, label_org_,
label_trg, label_trg_, self.cfg,
......@@ -339,7 +355,11 @@ class STGAN(object):
dis_trainer.program).with_data_parallel(
loss_name=dis_trainer.d_loss.name,
build_strategy=build_strategy)
# used for continuous evaluation
if self.cfg.enable_ce:
gen_trainer_program.random_seed = 90
dis_trainer_program.random_seed = 90
t_time = 0
total_train_batch = 0 # used for benchmark
......@@ -382,6 +402,9 @@ class STGAN(object):
d_loss_gp[0], batch_time))
sys.stdout.flush()
batch_id += 1
if self.cfg.enable_ce and batch_id == 100:
break
total_train_batch += 1 # used for benchmark
# profiler tools
if self.cfg.profile and epoch_id == 0 and batch_id == self.cfg.print_freq:
......@@ -413,3 +436,15 @@ class STGAN(object):
"net_G")
utility.checkpoints(epoch_id, self.cfg, exe, dis_trainer,
"net_D")
# used for continuous evaluation
if self.cfg.enable_ce:
device_num = fluid.core.get_cuda_device_count() if self.cfg.use_gpu else 1
print("kpis\tstgan_g_loss_fake_card{}\t{}".format(device_num, g_loss_fake[0]))
print("kpis\tstgan_g_loss_rec_card{}\t{}".format(device_num, g_loss_rec[0]))
print("kpis\tstgan_g_loss_cls_card{}\t{}".format(device_num, g_loss_cls[0]))
print("kpis\tstgan_d_loss_card{}\t{}".format(device_num, d_loss[0]))
print("kpis\tstgan_d_loss_real_card{}\t{}".format(device_num, d_loss_real[0]))
print("kpis\tstgan_d_loss_fake_card{}\t{}".format(device_num,d_loss_fake[0]))
print("kpis\tstgan_d_loss_cls_card{}\t{}".format(device_num, d_loss_cls[0]))
print("kpis\tstgan_d_loss_gp_card{}\t{}".format(device_num,d_loss_gp[0]))
print("kpis\tstgan_Batch_time_cost_card{}\t{}".format(device_num,batch_time))
PaddleCV/PaddleGAN/trainer/StarGAN.py
浏览文件 @ 862d459d
......@@ -159,8 +159,12 @@ class DTrainer():
def gradient_penalty(self, f, real, fake, cfg=None, name=None):
def _interpolate(a, b):
shape = [a.shape[0]]
alpha = fluid.layers.uniform_random_batch_size_like(
input=a, shape=shape, min=0.0, max=1.0)
if cfg.enable_ce:
alpha = fluid.layers.uniform_random_batch_size_like(
input=a, shape=shape, min=0.0, max=1.0, seed=1)
else:
alpha = fluid.layers.uniform_random_batch_size_like(
input=a, shape=shape, min=0.0, max=1.0)
inner = fluid.layers.elementwise_mul(b, (1.0-alpha), axis=0) + fluid.layers.elementwise_mul(a, alpha, axis=0)
return inner
......@@ -245,6 +249,10 @@ class StarGAN(object):
help="the attributes we selected to change")
parser.add_argument(
'--n_samples', type=int, default=1, help="batch size when testing")
parser.add_argument(
'--enable_ce',
action='store_true',
help="if set, run the tasks with continuous evaluation logs")
return parser
......@@ -268,6 +276,9 @@ class StarGAN(object):
name='label_org', shape=[None, self.cfg.c_dim], dtype='float32')
label_trg = fluid.data(
name='label_trg', shape=[None, self.cfg.c_dim], dtype='float32')
# used for continuous evaluation
if self.cfg.enable_ce:
fluid.default_startup_program().random_seed = 90
py_reader = fluid.io.PyReader(
feed_list=[image_real, label_org, label_trg],
......@@ -304,6 +315,10 @@ class StarGAN(object):
dis_trainer.program).with_data_parallel(
loss_name=dis_trainer.d_loss.name,
build_strategy=build_strategy)
# used for continuous evaluation
if self.cfg.enable_ce:
gen_trainer_program.random_seed = 90
dis_trainer_program.random_seed = 90
t_time = 0
total_train_batch = 0 # used for benchmark
......@@ -347,6 +362,10 @@ class StarGAN(object):
sys.stdout.flush()
batch_id += 1
# used for ce
if self.cfg.enable_ce and batch_id == 100:
break
total_train_batch += 1 # used for benchmark
# profiler tools
if self.cfg.profile and epoch_id == 0 and batch_id == self.cfg.print_freq:
......@@ -378,3 +397,14 @@ class StarGAN(object):
"net_G")
utility.checkpoints(epoch_id, self.cfg, exe, dis_trainer,
"net_D")
# used for continuous evaluation
if self.cfg.enable_ce:
device_num = fluid.core.get_cuda_device_count() if self.cfg.use_gpu else 1
print("kpis\tstargan_g_loss_fake_card{}\t{}".format(device_num, g_loss_fake[0]))
print("kpis\tstargan_g_loss_rec_card{}\t{}".format(device_num, g_loss_rec[0]))
print("kpis\tstargan_g_loss_cls_card{}\t{}".format(device_num, g_loss_cls[0]))
print("kpis\tstargan_d_loss_real_card{}\t{}".format(device_num, d_loss_real[0]))
print("kpis\tstargan_d_loss_fake_card{}\t{}".format(device_num,d_loss_fake[0]))
print("kpis\tstargan_d_loss_cls_card{}\t{}".format(device_num, d_loss_cls[0]))
print("kpis\tstargan_d_loss_gp_card{}\t{}".format(device_num,d_loss_gp[0]))
print("kpis\tstargan_Batch_time_cost_card{}\t{}".format(device_num,batch_time))
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