add PaddleGAN ce (#4079)

PaddleCV/PaddleGAN/trainer/CycleGAN.py

@@ -207,7 +207,10 @@ class CycleGAN(object):
             type=int,
             default=3,
             help="only used when CycleGAN discriminator is nlayers")
-
+        parser.add_argument(
+            '--enable_ce',
+            action='store_true',
+            help="if set, run the tasks with continuous evaluation logs")
         return parser
 
     def __init__(self,
@@ -237,6 +240,9 @@ class CycleGAN(object):
             name='fake_pool_A', shape=data_shape, dtype='float32')
         fake_pool_B = fluid.data(
             name='fake_pool_B', shape=data_shape, dtype='float32')
+        # used for continuous evaluation
+        if self.cfg.enable_ce:
+            fluid.default_startup_program().random_seed = 90
 
         A_py_reader = fluid.io.PyReader(
             feed_list=[input_A],
@@ -344,6 +350,9 @@ class CycleGAN(object):
 
                 sys.stdout.flush()
                 batch_id += 1
+                # used for continuous evaluation
+                if self.cfg.enable_ce and batch_id == 10:
+                    break
 
             if self.cfg.run_test:
                 A_image_name = fluid.data(
@@ -390,3 +399,26 @@ class CycleGAN(object):
                                     "net_DA")
                 utility.checkpoints(epoch_id, self.cfg, exe, d_B_trainer,
                                     "net_DB")
+
+        # 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\tcyclegan_g_A_loss_card{}\t{}".format(device_num,
+                                                              g_A_loss[0]))
+            print("kpis\tcyclegan_g_A_cyc_loss_card{}\t{}".format(
+                device_num, g_A_cyc_loss[0]))
+            print("kpis\tcyclegan_g_A_idt_loss_card{}\t{}".format(
+                device_num, g_A_idt_loss[0]))
+            print("kpis\tcyclegan_d_A_loss_card{}\t{}".format(device_num,
+                                                              d_A_loss[0]))
+            print("kpis\tcyclegan_g_B_loss_card{}\t{}".format(device_num,
+                                                              g_B_loss[0]))
+            print("kpis\tcyclegan_g_B_cyc_loss_card{}\t{}".format(
+                device_num, g_B_cyc_loss[0]))
+            print("kpis\tcyclegan_g_B_idt_loss_card{}\t{}".format(
+                device_num, g_B_idt_loss[0]))
+            print("kpis\tcyclegan_d_B_loss_card{}\t{}".format(device_num,
+                                                              d_B_loss[0]))
+            print("kpis\tcyclegan_Batch_time_cost_card{}\t{}".format(
+                device_num, batch_time))

PaddleCV/PaddleGAN/trainer/DCGAN.py

@@ -27,6 +27,7 @@ import matplotlib
 matplotlib.use('agg')
 import matplotlib.pyplot as plt
 import paddle.fluid as fluid
+import random
 
 
 class GTrainer():
@@ -78,7 +79,10 @@ class DCGAN(object):
     def add_special_args(self, parser):
         parser.add_argument(
             '--noise_size', type=int, default=100, help="the noise dimension")
-
+        parser.add_argument(
+            '--enable_ce',
+            action='store_true',
+            help="if set, run the tasks with continuous evaluation logs")
         return parser
 
     def __init__(self, cfg=None, train_reader=None):
@@ -90,6 +94,11 @@ class DCGAN(object):
         noise = fluid.data(
             name='noise', shape=[None, self.cfg.noise_size], dtype='float32')
         label = fluid.data(name='label', shape=[None, 1], dtype='float32')
+        # used for continuous evaluation
+        if self.cfg.enable_ce:
+            fluid.default_startup_program().random_seed = 90
+            random.seed(0)
+            np.random.seed(0)
 
         g_trainer = GTrainer(noise, label, self.cfg)
         d_trainer = DTrainer(img, label, self.cfg)
@@ -200,3 +209,11 @@ class DCGAN(object):
             if self.cfg.save_checkpoints:
                 utility.checkpoints(epoch_id, self.cfg, exe, g_trainer, "net_G")
                 utility.checkpoints(epoch_id, self.cfg, exe, d_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\tdcgan_d_loss_card{}\t{}".format(device_num, d_loss[0]))
+            print("kpis\tdcgan_g_loss_card{}\t{}".format(device_num, g_loss[0]))
+            print("kpis\tdcgan_Batch_time_cost_card{}\t{}".format(device_num,
+                                                                  batch_time))

PaddleCV/PaddleGAN/trainer/Pix2pix.py

@@ -196,7 +196,10 @@ class Pix2pix(object):
             type=int,
             default=3,
             help="only used when Pix2pix discriminator is nlayers")
-
+        parser.add_argument(
+            '--enable_ce',
+            action='store_true',
+            help="if set, run the tasks with continuous evaluation logs")
         return parser
 
     def __init__(self,
@@ -218,6 +221,9 @@ class Pix2pix(object):
         input_B = fluid.data(name='input_B', shape=data_shape, dtype='float32')
         input_fake = fluid.data(
             name='input_fake', shape=data_shape, dtype='float32')
+        # used for continuous evaluation        
+        if self.cfg.enable_ce:
+            fluid.default_startup_program().random_seed = 90
 
         loader = fluid.io.DataLoader.from_generator(
             feed_list=[input_A, input_B],
@@ -336,3 +342,16 @@ class Pix2pix(object):
                                     "net_G")
                 utility.checkpoints(epoch_id, self.cfg, exe, dis_trainer,
                                     "net_D")
+        if self.cfg.enable_ce:
+            device_num = fluid.core.get_cuda_device_count(
+            ) if self.cfg.use_gpu else 1
+            print("kpis\tpix2pix_g_loss_gan_card{}\t{}".format(device_num,
+                                                               g_loss_gan[0]))
+            print("kpis\tpix2pix_g_loss_l1_card{}\t{}".format(device_num,
+                                                              g_loss_l1[0]))
+            print("kpis\tpix2pix_d_loss_real_card{}\t{}".format(device_num,
+                                                                d_loss_real[0]))
+            print("kpis\tpix2pix_d_loss_fake_card{}\t{}".format(device_num,
+                                                                d_loss_fake[0]))
+            print("kpis\tpix2pix_Batch_time_cost_card{}\t{}".format(device_num,
+                                                                    batch_time))