fix op init (#2491)

fix op init

PaddleCV/gan/network/base_network.py

@@ -79,17 +79,28 @@ def norm_layer(input, norm_type='batch_norm', name=None):
 
 
 def initial_type(name,
+                 input,
+                 op_type,
+                 fan_out,
                  init="normal",
                  use_bias=False,
-                 f_in=0,
                  filter_size=0,
                  stddev=0.02):
     if init == "kaiming":
-        fan_in = f_in * filter_size * filter_size
+        if op_type == 'conv':
+            fan_in = input.shape[1] * filter_size * filter_size
+        elif op_type == 'deconv':
+            fan_in = fan_out * filter_size * filter_size
+        else:
+            if len(input.shape) > 2:
+                fan_in = input.shape[1] * input.shape[2] * input.shape[3]
+            else:
+                fan_in = input.shape[1]
         bound = 1 / math.sqrt(fan_in)
         param_attr = fluid.ParamAttr(
             name=name + "_w",
-            initializer=fluid.initializer.MSRAInitializer(uniform=True))
+            initializer=fluid.initializer.Uniform(
+                low=-bound, high=bound))
         if use_bias == True:
             bias_attr = fluid.ParamAttr(
                 name=name + '_b',
@@ -131,9 +142,11 @@ def conv2d(input,
 
     param_attr, bias_attr = initial_type(
         name=name,
+        input=input,
+        op_type='conv',
+        fan_out=num_filters,
         init=initial,
         use_bias=use_bias,
-        f_in=input.shape[1],
         filter_size=filter_size,
         stddev=stddev)
 
@@ -210,9 +223,11 @@ def deconv2d(input,
 
     param_attr, bias_attr = initial_type(
         name=name,
+        input=input,
+        op_type='deconv',
+        fan_out=num_filters,
         init=initial,
         use_bias=use_bias,
-        f_in=input.shape[1],
         filter_size=filter_size,
         stddev=stddev)
 
@@ -286,9 +301,11 @@ def linear(input,
 
     param_attr, bias_attr = initial_type(
         name=name,
+        input=input,
+        op_type='linear',
+        fan_out=output_size,
         init=initial,
         use_bias=True,
-        f_in=input.shape[1],
         filter_size=1,
         stddev=stddev)
 

PaddleCV/gan/scripts/run_attgan.sh

-python train.py --model_net AttGAN --dataset celeba --crop_size 170 --load_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 200 >log.out #2>log_err
+python train.py --model_net AttGAN --dataset celeba --crop_size 170 --load_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 200 >log.out 2>log_err

PaddleCV/gan/scripts/run_stgan.sh

-python train.py --model_net STGAN --dataset celeba --crop_size 170 --load_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 200 >log.out #2>log_err
+python train.py --model_net STGAN --dataset celeba --crop_size 170 --load_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 200 >log.out 2>log_err

PaddleCV/gan/trainer/AttGAN.py

@@ -67,8 +67,6 @@ class GTrainer():
                 learning_rate=lr, beta1=0.5, beta2=0.999, name="net_G")
 
             optimizer.minimize(self.g_loss, parameter_list=vars)
-            with open('program_gen.txt', 'w') as f:
-                print(self.program, file=f)
 
 
 class DTrainer():
@@ -140,8 +138,6 @@ class DTrainer():
                 learning_rate=lr, beta1=0.5, beta2=0.999, name="net_D")
 
             optimizer.minimize(self.d_loss, parameter_list=vars)
-            with open('program.txt', 'w') as f:
-                print(self.program, file=f)
 
     def gradient_penalty(self, f, real, fake=None, cfg=None, name=None):
         def _interpolate(a, b=None):