dy gan mostly working

test=develop

python/paddle/fluid/imperative/layers.py

@@ -27,18 +27,21 @@ class Layer(core.Layer):
     """Layers composed of operators."""
 
     def __init__(self, dtype=core.VarDesc.VarType.FP32, name=None):
-        self._once_built = False
+        self._built = False
         self._dtype = dtype
 
+    def parameters(self):
+        return []
+
     def _build_once(self, inputs):
         pass
 
     def __call__(self, *inputs):
-        if not self._once_built:
+        if not self._built:
             self._build_once(*inputs)
-            self._once_built = True
 
         outputs = self.forward(*inputs)
+        self._built = True
         return outputs
 
     def forward(self, *inputs):

python/paddle/fluid/imperative/nn.py

@@ -220,11 +220,14 @@ class FC(layers.Layer):
         self._dtype = dtype
         from ..layer_helper import LayerHelper
         self._helper = LayerHelper(
-            'FC',
-            param_attr=param_attr,
-            bias_attr=bias_attr,
-            act=act,
-            name=name)
+            'FC', param_attr=param_attr, act=act, name=name)
+        self._bias_attr = bias_attr
+
+    def parameters(self):
+        if self._bias_attr:
+            return [self._w, self._b]
+        else:
+            return [self._w]
 
     def _build_once(self, input):
         input_shape = input.shape
@@ -255,8 +258,20 @@ class FC(layers.Layer):
             inputs={"X": [tmp]},
             outputs={"Out": out},
             attrs={"use_mkldnn": False})
+        if not self._bias_attr:
+            return out
+
         # add bias
-        pre_activation = self._helper.append_bias_op(
-            out, dim_start=self._num_flatten_dims)
+        size = list(out.shape[1:])
+        if not self._built:
+            self._b = self._layer.create_parameter(
+                attr=self._bias_attr, shape=size, dtype=out.dtype, is_bias=True)
+        bias_out = self.create_variable_for_type_inference(dtype=out.dtype)
+        self.append_op(
+            type='elementwise_add',
+            inputs={'X': [out],
+                    'Y': [self._b]},
+            outputs={'Out': [bias_out]},
+            attrs={'axis': 1})
         # add activation
-        return self._helper.append_activation(pre_activation)
+        return self._helper.append_activation(bias_out)

python/paddle/fluid/tests/unittests/test_imperative_gan.py

@@ -23,6 +23,7 @@ import paddle.fluid as fluid
 from paddle.fluid.optimizer import SGDOptimizer
 from paddle.fluid.imperative.nn import Conv2D, Pool2D, FC
 from test_imperative_base import new_program_scope
+from paddle.fluid.imperative.base import to_variable
 
 
 class Discriminator(fluid.imperative.Layer):
@@ -31,6 +32,9 @@ class Discriminator(fluid.imperative.Layer):
         self._fc1 = FC(size=32, act='elu', name="d_fc1")
         self._fc2 = FC(size=1, name="d_fc2")
 
+    def parameters(self):
+        return self._fc1.parameters() + self._fc2.parameters()
+
     def forward(self, inputs):
         x = self._fc1(inputs)
         return self._fc2(x)
@@ -43,6 +47,10 @@ class Generator(fluid.imperative.Layer):
         self._fc2 = FC(size=64, act='elu', name="g_fc2")
         self._fc3 = FC(size=1, name="g_fc3")
 
+    def parameters(self):
+        return self._fc1.parameters() + self._fc2.parameters(
+        ) + self._fc3.parameters()
+
     def forward(self, inputs):
         x = self._fc1(inputs)
         x = self._fc2(x)
@@ -56,12 +64,15 @@ class TestImperativeMnist(unittest.TestCase):
         startup = fluid.Program()
         startup.random_seed = seed
         discriminate_p = fluid.Program()
+        generate_p = fluid.Program()
+        discriminate_p.random_seed = seed
+        generate_p.random_seed = seed
+
         scope = fluid.core.Scope()
         exe = fluid.Executor(fluid.CPUPlace())
+        sys.stderr.write('1111\n')
         with new_program_scope(
                 main=discriminate_p, startup=startup, scope=scope):
-            fluid.default_main_program().random_seed = seed
-
             discriminator = Discriminator()
             generator = Generator()
 
@@ -70,64 +81,92 @@ class TestImperativeMnist(unittest.TestCase):
             noise = fluid.layers.data(
                 name="noise", shape=[2, 2], append_batch_size=False)
 
-            label = fluid.layers.data(
-                name='label',
-                shape=[2, 1],
-                dtype='float32',
-                append_batch_size=False)
-
             d_real = discriminator(img)
             d_loss_real = fluid.layers.reduce_mean(
                 fluid.layers.sigmoid_cross_entropy_with_logits(
-                    x=d_real, label=label))
+                    x=d_real,
+                    label=fluid.layers.fill_constant(
+                        shape=[2, 1], dtype='float32', value=1.0)))
 
             d_fake = discriminator(generator(noise))
             d_loss_fake = fluid.layers.reduce_mean(
                 fluid.layers.sigmoid_cross_entropy_with_logits(
-                    x=d_fake, label=label))
+                    x=d_fake,
+                    label=fluid.layers.fill_constant(
+                        shape=[2, 1], dtype='float32', value=0.0)))
 
             d_loss = d_loss_real + d_loss_fake
 
             sgd = SGDOptimizer(learning_rate=1e-3)
             sgd.minimize(d_loss)
 
-        generate_p = fluid.Program()
         with new_program_scope(main=generate_p, startup=startup, scope=scope):
-            fluid.default_main_program().random_seed = seed
-
             discriminator = Discriminator()
             generator = Generator()
 
             noise = fluid.layers.data(
                 name="noise", shape=[2, 2], append_batch_size=False)
-            label = fluid.layers.data(
-                name='label',
-                shape=[2, 1],
-                dtype='float32',
-                append_batch_size=False)
 
             d_fake = discriminator(generator(noise))
             g_loss = fluid.layers.reduce_mean(
                 fluid.layers.sigmoid_cross_entropy_with_logits(
-                    x=d_fake, label=label))
+                    x=d_fake,
+                    label=fluid.layers.fill_constant(
+                        shape=[2, 1], dtype='float32', value=1.0)))
 
             sgd = SGDOptimizer(learning_rate=1e-3)
             sgd.minimize(g_loss)
 
-        img = np.ones([2, 1], np.float32)
-        label = np.ones([2, 1], np.float32)
-        noise = np.ones([2, 2], np.float32)
-        exe.run(startup)
-        d_loss_val = exe.run(discriminate_p,
-                             feed={'img': img,
-                                   'noise': noise,
-                                   'label': label},
-                             fetch_list=[d_loss])[0]
-        g_loss_val = exe.run(generate_p,
-                             feed={'noise': noise,
-                                   'label': label},
-                             fetch_list=[g_loss])[0]
-        sys.stderr.write('d_loss %s, g_loss: %s\n' % (d_loss_val, g_loss_val))
+        with fluid.scope_guard(scope):
+            img = np.ones([2, 1], np.float32)
+            noise = np.ones([2, 2], np.float32)
+            exe.run(startup)
+            d_loss_val = exe.run(discriminate_p,
+                                 feed={'img': img,
+                                       'noise': noise},
+                                 fetch_list=[d_loss])[0]
+            g_loss_val = exe.run(generate_p,
+                                 feed={'noise': noise},
+                                 fetch_list=[g_loss])[0]
+            sys.stderr.write('d_loss %s, g_loss: %s\n' %
+                             (d_loss_val, g_loss_val))
+
+            static_params = dict()
+            for param in discriminate_p.global_block().all_parameters():
+                sys.stderr.write('%s\n' % param.name)
+                static_params[param.name] = np.array(
+                    scope.find_var(param.name).get_tensor())
+
+        dy_params = dict()
+        with fluid.imperative.guard():
+            fluid.default_startup_program().random_seed = seed
+            fluid.default_main_program().random_seed = seed
+
+            discriminator = Discriminator()
+            generator = Generator()
+            sgd = SGDOptimizer(learning_rate=1e-3)
+
+            d_real = discriminator(to_variable(np.ones([2, 1], np.float32)))
+            d_loss_real = fluid.layers.reduce_mean(
+                fluid.layers.sigmoid_cross_entropy_with_logits(
+                    x=d_real, label=to_variable(np.ones([2, 1], np.float32))))
+
+            d_fake = discriminator(
+                generator(to_variable(np.ones([2, 2], np.float32))))
+            d_loss_fake = fluid.layers.reduce_mean(
+                fluid.layers.sigmoid_cross_entropy_with_logits(
+                    x=d_fake, label=to_variable(np.zeros([2, 1], np.float32))))
+
+            d_loss = d_loss_real + d_loss_fake
+            sys.stderr.write('dy_d_loss: %s\n' % d_loss._numpy())
+            d_loss._backward()
+            sgd.minimize(d_loss)
+            for p in discriminator.parameters():
+                dy_params[p.name] = p._numpy()
+
+        for k, v in six.iteritems(dy_params):
+            sys.stderr.write('dy_param_loss: %s: %s\n' % (k, np.sum(v)))
+            sys.stderr.write('static_param_loss: %s: %s\n' % (k, np.sum(v)))
 
 
 if __name__ == '__main__':