fix some apis to paddle-dev

ppgan/datasets/builder.py

@@ -59,12 +59,14 @@ class DictDataLoader():
         place = paddle.CUDAPlace(ParallelEnv().dev_id) \
                     if ParallelEnv().nranks > 1 else paddle.CUDAPlace(0)
 
-        sampler = DistributedBatchSampler(self.dataset,
+        sampler = DistributedBatchSampler(
+            self.dataset,
             batch_size=batch_size,
             shuffle=True if is_train else False,
             drop_last=True if is_train else False)
 
-        self.dataloader = paddle.io.DataLoader(self.dataset,
+        self.dataloader = paddle.io.DataLoader(
+            self.dataset,
             batch_sampler=sampler,
             places=place,
             num_workers=num_workers)
@@ -92,7 +94,7 @@ class DictDataLoader():
         return len(self.dataloader)
 
     def get_items_by_indexs(self, key, indexs):
-        if isinstance(indexs, paddle.Variable):
+        if isinstance(indexs, paddle.Tensor):
             indexs = indexs.numpy()
         current_items = []
         items = getattr(self.dataset, key)

ppgan/models/generators/resnet.py

@@ -13,6 +13,7 @@ class ResnetGenerator(nn.Layer):
 
     code and idea from Justin Johnson's neural style transfer project(https://github.com/jcjohnson/fast-neural-style)
     """
+
     def __init__(self,
                  input_nc,
                  output_nc,
@@ -37,17 +38,14 @@ class ResnetGenerator(nn.Layer):
 
         norm_layer = build_norm_layer(norm_type)
         if type(norm_layer) == functools.partial:
-            use_bias = norm_layer.func == nn.InstanceNorm
+            use_bias = norm_layer.func == nn.InstanceNorm2d
         else:
-            use_bias = norm_layer == nn.InstanceNorm
+            use_bias = norm_layer == nn.InstanceNorm2d
 
         model = [
-            nn.ReflectionPad2d([3, 3, 3, 3]),
-            nn.Conv2d(input_nc,
-                      ngf,
-                      kernel_size=7,
-                      padding=0,
-                      bias_attr=use_bias),
+            nn.Pad2D(padding=[3, 3, 3, 3], mode="reflect"),
+            nn.Conv2d(
+                input_nc, ngf, kernel_size=7, padding=0, bias_attr=use_bias),
             norm_layer(ngf),
             nn.ReLU()
         ]
@@ -56,7 +54,8 @@ class ResnetGenerator(nn.Layer):
         for i in range(n_downsampling):  # add downsampling layers
             mult = 2**i
             model += [
-                nn.Conv2d(ngf * mult,
+                nn.Conv2d(
+                    ngf * mult,
                     ngf * mult * 2,
                     kernel_size=3,
                     stride=2,
@@ -70,7 +69,8 @@ class ResnetGenerator(nn.Layer):
         for i in range(n_blocks):  # add ResNet blocks
 
             model += [
-                ResnetBlock(ngf * mult,
+                ResnetBlock(
+                    ngf * mult,
                     padding_type=padding_type,
                     norm_layer=norm_layer,
                     use_dropout=use_dropout,
@@ -80,7 +80,8 @@ class ResnetGenerator(nn.Layer):
         for i in range(n_downsampling):  # add upsampling layers
             mult = 2**(n_downsampling - i)
             model += [
-                nn.ConvTranspose2d(ngf * mult,
+                nn.ConvTranspose2d(
+                    ngf * mult,
                     int(ngf * mult / 2),
                     kernel_size=3,
                     stride=2,
@@ -90,7 +91,7 @@ class ResnetGenerator(nn.Layer):
                 norm_layer(int(ngf * mult / 2)),
                 nn.ReLU()
             ]
-        model += [nn.ReflectionPad2d([3, 3, 3, 3])]
+        model += [nn.Pad2D(padding=[3, 3, 3, 3], mode="reflect")]
         model += [nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)]
         model += [nn.Tanh()]
 
@@ -103,6 +104,7 @@ class ResnetGenerator(nn.Layer):
 
 class ResnetBlock(nn.Layer):
     """Define a Resnet block"""
+
     def __init__(self, dim, padding_type, norm_layer, use_dropout, use_bias):
         """Initialize the Resnet block
 
@@ -130,15 +132,13 @@ class ResnetBlock(nn.Layer):
         """
         conv_block = []
         p = 0
-        if padding_type == 'reflect':
-            conv_block += [nn.ReflectionPad2d([1, 1, 1, 1])]
-        elif padding_type == 'replicate':
-            conv_block += [nn.ReplicationPad2d([1, 1, 1, 1])]
+        if padding_type in ['reflect', 'replicate']:
+            conv_block += [nn.Pad2D(padding=[1, 1, 1, 1], mode=padding_type)]
         elif padding_type == 'zero':
             p = 1
         else:
-            raise NotImplementedError('padding [%s] is not implemented' %
-                                      padding_type)
+            raise NotImplementedError(
+                'padding [%s] is not implemented' % padding_type)
 
         conv_block += [
             nn.Conv2d(dim, dim, kernel_size=3, padding=p, bias_attr=use_bias),
@@ -149,15 +149,13 @@ class ResnetBlock(nn.Layer):
             conv_block += [nn.Dropout(0.5)]
 
         p = 0
-        if padding_type == 'reflect':
-            conv_block += [nn.ReflectionPad2d([1, 1, 1, 1])]
-        elif padding_type == 'replicate':
-            conv_block += [nn.ReplicationPad2d([1, 1, 1, 1])]
+        if padding_type in ['reflect', 'replicate']:
+            conv_block += [nn.Pad2D(padding=[1, 1, 1, 1], mode=padding_type)]
         elif padding_type == 'zero':
             p = 1
         else:
-            raise NotImplementedError('padding [%s] is not implemented' %
-                                      padding_type)
+            raise NotImplementedError(
+                'padding [%s] is not implemented' % padding_type)
         conv_block += [
             nn.Conv2d(dim, dim, kernel_size=3, padding=p, bias_attr=use_bias),
             norm_layer(dim)

ppgan/models/losses.py

@@ -10,6 +10,7 @@ class GANLoss(nn.Layer):
     The GANLoss class abstracts away the need to create the target label tensor
     that has the same size as the input.
     """
+
     def __init__(self, gan_mode, target_real_label=1.0, target_fake_label=0.0):
         """ Initialize the GANLoss class.
 
@@ -47,17 +48,13 @@ class GANLoss(nn.Layer):
         """
         if target_is_real:
             if not hasattr(self, 'target_real_tensor'):
-                self.target_real_tensor = paddle.fill_constant(
-                    shape=paddle.shape(prediction),
-                    value=self.target_real_label,
-                    dtype='float32')
+                self.target_real_tensor = self.target_real_label * paddle.ones(
+                    paddle.shape(prediction), dtype='float32')
             target_tensor = self.target_real_tensor
         else:
             if not hasattr(self, 'target_fake_tensor'):
-                self.target_fake_tensor = paddle.fill_constant(
-                    shape=paddle.shape(prediction),
-                    value=self.target_fake_label,
-                    dtype='float32')
+                self.target_fake_tensor = self.target_fake_label * paddle.ones(
+                    paddle.shape(prediction), dtype='float32')
             target_tensor = self.target_fake_tensor
 
         # target_tensor.stop_gradient = True

ppgan/modules/init.py

@@ -80,7 +80,7 @@ def calculate_gain(nonlinearity, param=None):
 
 @paddle.no_grad()
 def constant_(x, value):
-    temp_value = paddle.fill_constant(x.shape, x.dtype, value)
+    temp_value = value * paddle.ones(x.shape, x.dtype)
     x.set_value(temp_value)
     return x
 
@@ -256,10 +256,8 @@ def kaiming_init(layer,
                  distribution='normal'):
     assert distribution in ['uniform', 'normal']
     if distribution == 'uniform':
-        kaiming_uniform_(layer.weight,
-                         a=a,
-                         mode=mode,
-                         nonlinearity=nonlinearity)
+        kaiming_uniform_(
+            layer.weight, a=a, mode=mode, nonlinearity=nonlinearity)
     else:
         kaiming_normal_(layer.weight, a=a, mode=mode, nonlinearity=nonlinearity)
     if hasattr(layer, 'bias') and layer.bias is not None:
@@ -275,6 +273,7 @@ def init_weights(net, init_type='normal', init_gain=0.02):
     We use 'normal' in the original pix2pix and CycleGAN paper. But xavier and kaiming might
     work better for some applications. Feel free to try yourself.
     """
+
     def init_func(m):  # define the initialization function
         classname = m.__class__.__name__
         if hasattr(m, 'weight') and (classname.find('Conv') != -1

ppgan/modules/norm.py

@@ -21,19 +21,20 @@ def build_norm_layer(norm_type='instance'):
     if norm_type == 'batch':
         norm_layer = functools.partial(
             nn.BatchNorm,
-            param_attr=paddle.ParamAttr(
+            weight_attr=paddle.ParamAttr(
                 initializer=nn.initializer.Normal(1.0, 0.02)),
             bias_attr=paddle.ParamAttr(
                 initializer=nn.initializer.Constant(0.0)),
             trainable_statistics=True)
     elif norm_type == 'instance':
         norm_layer = functools.partial(
-            nn.InstanceNorm,
-            param_attr=paddle.ParamAttr(
+            nn.InstanceNorm2d,
+            weight_attr=paddle.ParamAttr(
                 initializer=nn.initializer.Constant(1.0),
                 learning_rate=0.0,
                 trainable=False),
-            bias_attr=paddle.ParamAttr(initializer=nn.initializer.Constant(0.0),
+            bias_attr=paddle.ParamAttr(
+                initializer=nn.initializer.Constant(0.0),
                 learning_rate=0.0,
                 trainable=False))
     elif norm_type == 'spectral':
@@ -43,6 +44,6 @@ def build_norm_layer(norm_type='instance'):
         def norm_layer(x):
             return Identity()
     else:
-        raise NotImplementedError('normalization layer [%s] is not found' %
-                                  norm_type)
+        raise NotImplementedError(
+            'normalization layer [%s] is not found' % norm_type)
     return norm_layer