Update CycleGAN

examples/cyclegan/cyclegan.py

@@ -18,9 +18,8 @@ from __future__ import print_function
 
 import numpy as np
 
+import paddle
 import paddle.fluid as fluid
-from paddle.incubate.hapi.model import Model
-from paddle.incubate.hapi.loss import Loss
 
 from layers import ConvBN, DeConvBN
 
@@ -133,7 +132,7 @@ class NLayerDiscriminator(fluid.dygraph.Layer):
         return y
 
 
-class Generator(Model):
+class Generator(paddle.nn.Layer):
     def __init__(self, input_channel=3):
         super(Generator, self).__init__()
         self.g = ResnetGenerator(input_channel)
@@ -143,7 +142,7 @@ class Generator(Model):
         return fake
 
 
-class GeneratorCombine(Model):
+class GeneratorCombine(paddle.nn.Layer):
     def __init__(self, g_AB=None, g_BA=None, d_A=None, d_B=None,
                  is_train=True):
         super(GeneratorCombine, self).__init__()
@@ -177,16 +176,15 @@ class GeneratorCombine(Model):
         return input_A, input_B, fake_A, fake_B, cyc_A, cyc_B, idt_A, idt_B, valid_A, valid_B
 
 
-class GLoss(Loss):
+class GLoss(paddle.nn.Layer):
     def __init__(self, lambda_A=10., lambda_B=10., lambda_identity=0.5):
         super(GLoss, self).__init__()
         self.lambda_A = lambda_A
         self.lambda_B = lambda_B
         self.lambda_identity = lambda_identity
 
-    def forward(self, outputs, labels=None):
-        input_A, input_B, fake_A, fake_B, cyc_A, cyc_B, idt_A, idt_B, valid_A, valid_B = outputs
-
+    def forward(self, input_A, input_B, fake_A, fake_B, cyc_A, cyc_B, idt_A,
+                idt_B, valid_A, valid_B):
         def mse(a, b):
             return fluid.layers.reduce_mean(fluid.layers.square(a - b))
 
@@ -211,7 +209,7 @@ class GLoss(Loss):
         return loss
 
 
-class Discriminator(Model):
+class Discriminator(paddle.nn.Layer):
     def __init__(self, input_channel=3):
         super(Discriminator, self).__init__()
         self.d = NLayerDiscriminator(input_channel)
@@ -222,13 +220,11 @@ class Discriminator(Model):
         return pred_real, pred_fake
 
 
-class DLoss(Loss):
+class DLoss(paddle.nn.Layer):
     def __init__(self):
         super(DLoss, self).__init__()
 
-    def forward(self, inputs, labels=None):
-        pred_real, pred_fake = inputs
-        loss = fluid.layers.square(pred_fake) + fluid.layers.square(pred_real -
-                                                                    1.)
+    def forward(self, real, fake):
+        loss = fluid.layers.square(fake) + fluid.layers.square(real - 1.)
         loss = fluid.layers.reduce_mean(loss / 2.0)
         return loss

examples/cyclegan/infer.py

@@ -24,26 +24,32 @@ import argparse
 from PIL import Image
 from scipy.misc import imsave
 
+import paddle
 import paddle.fluid as fluid
-from paddle.incubate.hapi.model import Model, Input, set_device
+from paddle.static import InputSpec as Input
 
 from check import check_gpu, check_version
 from cyclegan import Generator, GeneratorCombine
 
 
 def main():
-    place = set_device(FLAGS.device)
+    place = paddle.set_device(FLAGS.device)
     fluid.enable_dygraph(place) if FLAGS.dynamic else None
 
+    im_shape = [-1, 3, 256, 256]
+    input_A = Input(im_shape, 'float32', 'input_A')
+    input_B = Input(im_shape, 'float32', 'input_B')
+
     # Generators
     g_AB = Generator()
     g_BA = Generator()
-    g = GeneratorCombine(g_AB, g_BA, is_train=False)
 
-    im_shape = [-1, 3, 256, 256]
-    input_A = Input(im_shape, 'float32', 'input_A')
-    input_B = Input(im_shape, 'float32', 'input_B')
-    g.prepare(inputs=[input_A, input_B], device=FLAGS.device)
+    g = paddle.Model(
+        GeneratorCombine(
+            g_AB, g_BA, is_train=False),
+        inputs=[input_A, input_B])
+    g.prepare()
+
     g.load(FLAGS.init_model, skip_mismatch=True, reset_optimizer=True)
 
     out_path = FLAGS.output + "/single"

examples/cyclegan/test.py

@@ -21,8 +21,9 @@ import argparse
 import numpy as np
 from scipy.misc import imsave
 
+import paddle
 import paddle.fluid as fluid
-from paddle.incubate.hapi.model import Model, Input, set_device
+from paddle.static import InputSpec as Input
 
 from check import check_gpu, check_version
 from cyclegan import Generator, GeneratorCombine
@@ -30,18 +31,22 @@ import data as data
 
 
 def main():
-    place = set_device(FLAGS.device)
+    place = paddle.set_device(FLAGS.device)
     fluid.enable_dygraph(place) if FLAGS.dynamic else None
 
+    im_shape = [-1, 3, 256, 256]
+    input_A = Input(im_shape, 'float32', 'input_A')
+    input_B = Input(im_shape, 'float32', 'input_B')
+
     # Generators
     g_AB = Generator()
     g_BA = Generator()
-    g = GeneratorCombine(g_AB, g_BA, is_train=False)
+    g = paddle.Model(
+        GeneratorCombine(
+            g_AB, g_BA, is_train=False),
+        inputs=[input_A, input_B])
 
-    im_shape = [-1, 3, 256, 256]
-    input_A = Input(im_shape, 'float32', 'input_A')
-    input_B = Input(im_shape, 'float32', 'input_B')
-    g.prepare(inputs=[input_A, input_B], device=FLAGS.device)
+    g.prepare()
     g.load(FLAGS.init_model, skip_mismatch=True, reset_optimizer=True)
 
     if not os.path.exists(FLAGS.output):

examples/cyclegan/train.py

@@ -24,7 +24,7 @@ import time
 
 import paddle
 import paddle.fluid as fluid
-from paddle.incubate.hapi.model import Model, Input, set_device
+from paddle.static import InputSpec as Input
 
 from check import check_gpu, check_version
 from cyclegan import Generator, Discriminator, GeneratorCombine, GLoss, DLoss
@@ -48,18 +48,29 @@ def opt(parameters):
 
 
 def main():
-    place = set_device(FLAGS.device)
+    place = paddle.set_device(FLAGS.device)
     fluid.enable_dygraph(place) if FLAGS.dynamic else None
 
+    im_shape = [None, 3, 256, 256]
+    input_A = Input(im_shape, 'float32', 'input_A')
+    input_B = Input(im_shape, 'float32', 'input_B')
+    fake_A = Input(im_shape, 'float32', 'fake_A')
+    fake_B = Input(im_shape, 'float32', 'fake_B')
+
     # Generators
     g_AB = Generator()
     g_BA = Generator()
-
-    # Discriminators
     d_A = Discriminator()
     d_B = Discriminator()
 
-    g = GeneratorCombine(g_AB, g_BA, d_A, d_B)
+    g = paddle.Model(
+        GeneratorCombine(g_AB, g_BA, d_A, d_B), inputs=[input_A, input_B])
+    g_AB = paddle.Model(g_AB, [input_A])
+    g_BA = paddle.Model(g_BA, [input_B])
+
+    # Discriminators
+    d_A = paddle.Model(d_A, [input_B, fake_B])
+    d_B = paddle.Model(d_B, [input_A, fake_A])
 
     da_params = d_A.parameters()
     db_params = d_B.parameters()
@@ -69,21 +80,12 @@ def main():
     db_optimizer = opt(db_params)
     g_optimizer = opt(g_params)
 
-    im_shape = [None, 3, 256, 256]
-    input_A = Input(im_shape, 'float32', 'input_A')
-    input_B = Input(im_shape, 'float32', 'input_B')
-    fake_A = Input(im_shape, 'float32', 'fake_A')
-    fake_B = Input(im_shape, 'float32', 'fake_B')
-
-    g_AB.prepare(inputs=[input_A], device=FLAGS.device)
-    g_BA.prepare(inputs=[input_B], device=FLAGS.device)
+    g_AB.prepare()
+    g_BA.prepare()
 
-    g.prepare(
-        g_optimizer, GLoss(), inputs=[input_A, input_B], device=FLAGS.device)
-    d_A.prepare(
-        da_optimizer, DLoss(), inputs=[input_B, fake_B], device=FLAGS.device)
-    d_B.prepare(
-        db_optimizer, DLoss(), inputs=[input_A, fake_A], device=FLAGS.device)
+    g.prepare(g_optimizer, GLoss())
+    d_A.prepare(da_optimizer, DLoss())
+    d_B.prepare(db_optimizer, DLoss())
 
     if FLAGS.resume:
         g.load(FLAGS.resume)