add spade models (#4146)

PaddleCV/PaddleGAN/trainer/SPADE.py

@@ -268,7 +268,11 @@ class SPADE(object):
             type=bool,
             default=False,
             help="Whether to use instance label.")
-
+        parser.add_argument(
+            '--enable_ce',
+            type=bool,
+            default=False,
+            help="If set True, enable continuous evaluation job.")
         return parser
 
     def __init__(self,
@@ -298,6 +302,9 @@ class SPADE(object):
             name='input_ins', shape=edge_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
 
         gen_trainer = GTrainer(input_A, input_B, input_C, self.cfg,
                                self.batch_num)
@@ -343,7 +350,11 @@ class SPADE(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):
@@ -390,7 +401,6 @@ class SPADE(object):
                                   0], batch_time))
 
                 sys.stdout.flush()
-                batch_id += 1
 
             if self.cfg.run_test:
                 test_program = gen_trainer.infer_program
@@ -422,3 +432,12 @@ class SPADE(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\tspade_g_loss_gan_card{}\t{}".format(device_num, g_loss_gan[0]))
+                print("kpis\tspade_g_loss_vgg_card{}\t{}".format(device_num,g_loss_vgg[0]))
+                print("kpis\tspade_g_loss_feat_card{}\t{}".format(device_num,g_loss_feat[0]))
+                print("kpis\tspade_d_loss_real_card{}\t{}".format(device_num,d_loss_real[0]))
+                print("kpis\tspade_d_loss_fake_card{}\t{}".format(device_num,d_loss_fake[0]))
+                print("kpis\tspade_Batch_time_cost_card{}\t{}".format(device_num,batch_time))