_get_optimizer() -> optimizer() (#318)

examples/A3C-Gym/train-atari.py

@@ -125,7 +125,7 @@ class Model(ModelDesc):
                                    cost, tf.reduce_mean(importance, name='importance'))
         return cost
 
-    def _get_optimizer(self):
+    def optimizer(self):
         lr = tf.get_variable('learning_rate', initializer=0.001, trainable=False)
         opt = tf.train.AdamOptimizer(lr, epsilon=1e-3)
 

examples/CTC-TIMIT/train-timit.py

@@ -70,7 +70,7 @@ class Model(ModelDesc):
         summary.add_moving_summary(err, cost)
         return cost
 
-    def _get_optimizer(self):
+    def optimizer(self):
         lr = tf.get_variable('learning_rate', initializer=5e-3, trainable=False)
         opt = tf.train.AdamOptimizer(lr, epsilon=1e-3)
         return optimizer.apply_grad_processors(

examples/Char-RNN/char-rnn.py

@@ -107,7 +107,7 @@ class Model(ModelDesc):
         summary.add_moving_summary(cost)
         return cost
 
-    def _get_optimizer(self):
+    def optimizer(self):
         lr = tf.get_variable('learning_rate', initializer=2e-3, trainable=False)
         opt = tf.train.AdamOptimizer(lr)
         return optimizer.apply_grad_processors(opt, [GlobalNormClip(5)])

examples/DeepQNetwork/DQNModel.py

@@ -81,7 +81,7 @@ class Model(ModelDesc):
         summary.add_moving_summary(cost)
         return cost
 
-    def _get_optimizer(self):
+    def optimizer(self):
         lr = tf.get_variable('learning_rate', initializer=self.learning_rate, trainable=False)
         opt = tf.train.AdamOptimizer(lr, epsilon=1e-3)
         return optimizer.apply_grad_processors(

examples/DoReFa-Net/alexnet-dorefa.py

@@ -162,7 +162,7 @@ class Model(ModelDesc):
         add_moving_summary(cost, wd_cost, total_cost)
         return total_cost
 
-    def _get_optimizer(self):
+    def optimizer(self):
         lr = tf.get_variable('learning_rate', initializer=1e-4, trainable=False)
         return tf.train.AdamOptimizer(lr, epsilon=1e-5)
 

examples/DoReFa-Net/svhn-digit-dorefa.py

@@ -123,7 +123,7 @@ class Model(ModelDesc):
         add_moving_summary(cost, wd_cost, total_cost)
         return total_cost
 
-    def _get_optimizer(self):
+    def optimizer(self):
         lr = tf.train.exponential_decay(
             learning_rate=1e-3,
             global_step=get_global_step_var(),

examples/DynamicFilterNetwork/steering-filter.py

@@ -146,7 +146,7 @@ class Model(ModelDesc):
         summary.add_moving_summary(cost)
         return cost
 
-    def _get_optimizer(self):
+    def optimizer(self):
         return tf.train.AdamOptimizer(1e-3)
 
 

examples/FasterRCNN/train.py

@@ -216,7 +216,7 @@ class Model(ModelDesc):
                 final_masks = tf.cond(tf.size(final_probs) > 0, f1, lambda: tf.zeros([0, 14, 14]))
                 tf.identity(final_masks, name='final_masks')
 
-    def _get_optimizer(self):
+    def optimizer(self):
         lr = tf.get_variable('learning_rate', initializer=0.003, trainable=False)
         tf.summary.scalar('learning_rate', lr)
 

examples/GAN/BEGAN.py

@@ -122,7 +122,7 @@ class Model(GANModelDesc):
 
         self.collect_variables()
 
-    def _get_optimizer(self):
+    def optimizer(self):
         lr = tf.get_variable('learning_rate', initializer=1e-4, trainable=False)
         opt = tf.train.AdamOptimizer(lr, beta1=0.5, beta2=0.9)
         return opt

examples/GAN/ConditionalGAN-mnist.py

@@ -103,7 +103,7 @@ class Model(GANModelDesc):
         self.build_losses(vecpos, vecneg)
         self.collect_variables()
 
-    def _get_optimizer(self):
+    def optimizer(self):
         return tf.train.AdamOptimizer(2e-4, beta1=0.5, epsilon=1e-3)
 
 

examples/GAN/CycleGAN.py

@@ -152,7 +152,7 @@ class Model(GANModelDesc):
 
         add_moving_summary(recon_loss_A, recon_loss_B, self.g_loss, self.d_loss)
 
-    def _get_optimizer(self):
+    def optimizer(self):
         lr = tf.get_variable('learning_rate', initializer=2e-4, trainable=False)
         return tf.train.AdamOptimizer(lr, beta1=0.5, epsilon=1e-3)
 

examples/GAN/DCGAN.py

@@ -94,7 +94,7 @@ class Model(GANModelDesc):
         self.build_losses(vecpos, vecneg)
         self.collect_variables()
 
-    def _get_optimizer(self):
+    def optimizer(self):
         lr = tf.get_variable('learning_rate', initializer=2e-4, trainable=False)
         return tf.train.AdamOptimizer(lr, beta1=0.5, epsilon=1e-3)
 

examples/GAN/DiscoGAN-CelebA.py

@@ -147,7 +147,7 @@ class Model(GANModelDesc):
 
         add_moving_summary(recon_loss_A, recon_loss_B, rate, g_loss, d_loss, wd_g, wd_d)
 
-    def _get_optimizer(self):
+    def optimizer(self):
         return tf.train.AdamOptimizer(2e-4, beta1=0.5, epsilon=1e-3)
 
 

examples/GAN/GAN.py

@@ -70,7 +70,7 @@ class GANModelDesc(ModelDescBase):
 
     @memoized
     def get_optimizer(self):
-        return self._get_optimizer()
+        return self.optimizer()
 
 
 class GANTrainer(TowerTrainer):

examples/GAN/Image2Image.py

@@ -143,7 +143,7 @@ class Model(GANModelDesc):
 
         self.collect_variables()
 
-    def _get_optimizer(self):
+    def optimizer(self):
         lr = tf.get_variable('learning_rate', initializer=2e-4, trainable=False)
         return tf.train.AdamOptimizer(lr, beta1=0.5, epsilon=1e-3)
 

examples/GAN/Improved-WGAN.py

@@ -77,7 +77,7 @@ class Model(DCGAN.Model):
 
         self.collect_variables()
 
-    def _get_optimizer(self):
+    def optimizer(self):
         opt = tf.train.AdamOptimizer(1e-4, beta1=0.5, beta2=0.9)
         return opt
 

examples/GAN/InfoGAN-mnist.py

@@ -201,7 +201,7 @@ class Model(GANModelDesc):
         # distinguish between variables of generator and discriminator updates
         self.collect_variables()
 
-    def _get_optimizer(self):
+    def optimizer(self):
         lr = tf.get_variable('learning_rate', initializer=2e-4, dtype=tf.float32, trainable=False)
         opt = tf.train.AdamOptimizer(lr, beta1=0.5, epsilon=1e-6)
         # generator learns 5 times faster

examples/GAN/WGAN.py

@@ -28,7 +28,7 @@ class Model(DCGAN.Model):
         self.g_loss = tf.negative(tf.reduce_mean(vecneg), name='g_loss')
         add_moving_summary(self.d_loss, self.g_loss)
 
-    def _get_optimizer(self):
+    def optimizer(self):
         opt = tf.train.RMSPropOptimizer(1e-4)
         return opt
 

examples/HED/hed.py

@@ -118,7 +118,7 @@ class Model(ModelDesc):
             add_moving_summary(costs + [wrong, total_cost])
             return total_cost
 
-    def _get_optimizer(self):
+    def optimizer(self):
         lr = tf.get_variable('learning_rate', initializer=3e-5, trainable=False)
         opt = tf.train.AdamOptimizer(lr, epsilon=1e-3)
         return optimizer.apply_grad_processors(

examples/ImageNetModels/imagenet_utils.py

@@ -180,7 +180,7 @@ class ImageNetModel(ModelDesc):
             Nx1000 logits
         """
 
-    def _get_optimizer(self):
+    def optimizer(self):
         lr = tf.get_variable('learning_rate', initializer=0.1, trainable=False)
         tf.summary.scalar('learning_rate-summary', lr)
         return tf.train.MomentumOptimizer(lr, 0.9, use_nesterov=True)

examples/ImageNetModels/inception-bn.py

@@ -114,7 +114,7 @@ class Model(ModelDesc):
         add_moving_summary(wd_cost, total_cost)
         return total_cost
 
-    def _get_optimizer(self):
+    def optimizer(self):
         lr = tf.get_variable('learning_rate', initializer=0.045, trainable=False)
         return tf.train.MomentumOptimizer(lr, 0.9)
 

examples/PennTreebank/PTB-LSTM.py

@@ -111,7 +111,7 @@ class Model(ModelDesc):
             ops.append(s[k].h.assign(z))
         return tf.group(*ops, name='reset_lstm_state')
 
-    def _get_optimizer(self):
+    def optimizer(self):
         lr = tf.get_variable('learning_rate', initializer=1.0, trainable=False)
         opt = tf.train.GradientDescentOptimizer(lr)
         return optimizer.apply_grad_processors(

examples/ResNet/cifar10-preact18-mixup.py

@@ -78,7 +78,7 @@ class ResNet_Cifar(ModelDesc):
 
         return tf.add_n([ce_cost, wd_cost], name='cost')
 
-    def _get_optimizer(self):
+    def optimizer(self):
         lr = tf.get_variable('learning_rate', initializer=0.1, trainable=False)
         opt = tf.train.MomentumOptimizer(lr, 0.9)
         return opt

examples/ResNet/cifar10-resnet.py

@@ -111,7 +111,7 @@ class Model(ModelDesc):
         add_param_summary(('.*/W', ['histogram']))   # monitor W
         return tf.add_n([cost, wd_cost], name='cost')
 
-    def _get_optimizer(self):
+    def optimizer(self):
         lr = tf.get_variable('learning_rate', initializer=0.01, trainable=False)
         opt = tf.train.MomentumOptimizer(lr, 0.9)
         return opt

examples/Saliency/CAM-resnet.py

@@ -65,7 +65,7 @@ class Model(ModelDesc):
         add_moving_summary(loss, wd_cost)
         return tf.add_n([loss, wd_cost], name='cost')
 
-    def _get_optimizer(self):
+    def optimizer(self):
         lr = tf.get_variable('learning_rate', initializer=0.1, trainable=False)
         opt = tf.train.MomentumOptimizer(lr, 0.9, use_nesterov=True)
         gradprocs = [gradproc.ScaleGradient(

examples/SimilarityLearning/mnist-embeddings.py

@@ -224,7 +224,7 @@ class EmbeddingModel(ModelDesc):
 
         return embeddings
 
-    def _get_optimizer(self):
+    def optimizer(self):
         lr = tf.get_variable('learning_rate', initializer=1e-4, trainable=False)
         return tf.train.GradientDescentOptimizer(lr)
 

examples/SpatialTransformer/mnist-addition.py

@@ -85,7 +85,7 @@ class Model(ModelDesc):
         summary.add_moving_summary(cost, wd_cost)
         return tf.add_n([wd_cost, cost], name='cost')
 
-    def _get_optimizer(self):
+    def optimizer(self):
         lr = tf.get_variable('learning_rate', initializer=5e-4, trainable=False)
         opt = tf.train.AdamOptimizer(lr, epsilon=1e-3)
         return optimizer.apply_grad_processors(

examples/SuperResolution/enet-pat.py

@@ -211,7 +211,7 @@ class Model(GANModelDesc):
 
             self.collect_variables()
 
-    def _get_optimizer(self):
+    def optimizer(self):
         lr = tf.get_variable(
             'learning_rate', initializer=1e-4, trainable=False)
         opt = tf.train.AdamOptimizer(lr)

examples/basics/cifar-convnet.py

@@ -73,7 +73,7 @@ class Model(ModelDesc):
         add_param_summary(('.*/W', ['histogram']))   # monitor W
         return tf.add_n([cost, wd_cost], name='cost')
 
-    def _get_optimizer(self):
+    def optimizer(self):
         lr = tf.get_variable('learning_rate', initializer=1e-2, trainable=False)
         tf.summary.scalar('lr', lr)
         return tf.train.AdamOptimizer(lr, epsilon=1e-3)

examples/basics/mnist-convnet.py

@@ -78,7 +78,7 @@ class Model(ModelDesc):
         summary.add_param_summary(('.*/W', ['histogram', 'rms']))
         return total_cost
 
-    def _get_optimizer(self):
+    def optimizer(self):
         lr = tf.train.exponential_decay(
             learning_rate=1e-3,
             global_step=get_global_step_var(),

examples/basics/mnist-tflayers.py

@@ -78,7 +78,7 @@ class Model(ModelDesc):
         summary.add_param_summary(('.*/kernel', ['histogram', 'rms']))
         return total_cost
 
-    def _get_optimizer(self):
+    def optimizer(self):
         lr = tf.train.exponential_decay(
             learning_rate=1e-3,
             global_step=get_global_step_var(),

examples/basics/mnist-tfslim.py

@@ -59,7 +59,7 @@ class Model(ModelDesc):
         summary.add_param_summary(('.*/weights', ['histogram', 'rms']))  # slim uses different variable names
         return cost + regularize_cost_from_collection()
 
-    def _get_optimizer(self):
+    def optimizer(self):
         lr = tf.train.exponential_decay(
             learning_rate=1e-3,
             global_step=get_global_step_var(),

examples/basics/mnist-visualizations.py

@@ -108,7 +108,7 @@ class Model(ModelDesc):
                               name='regularize_loss')
         return tf.add_n([wd_cost, cost], name='total_cost')
 
-    def _get_optimizer(self):
+    def optimizer(self):
         lr = tf.train.exponential_decay(
             learning_rate=1e-3,
             global_step=get_global_step_var(),

examples/basics/svhn-digit-convnet.py

@@ -56,7 +56,7 @@ class Model(ModelDesc):
         add_param_summary(('.*/W', ['histogram', 'rms']))   # monitor W
         return tf.add_n([cost, wd_cost], name='cost')
 
-    def _get_optimizer(self):
+    def optimizer(self):
         lr = tf.train.exponential_decay(
             learning_rate=1e-3,
             global_step=get_global_step_var(),

examples/boilerplate.py

@@ -29,7 +29,7 @@ class Model(ModelDesc):
         summary.add_moving_summary(cost)
         return cost
 
-    def _get_optimizer(self):
+    def optimizer(self):
         lr = tf.get_variable('learning_rate', initializer=5e-3, trainable=False)
         return tf.train.AdamOptimizer(lr)
 

examples/keras/mnist-keras.py

@@ -63,7 +63,7 @@ class Model(ModelDesc):
         summary.add_moving_summary(cost, wd_cost)
         return cost
 
-    def _get_optimizer(self):
+    def optimizer(self):
         lr = tf.train.exponential_decay(
             learning_rate=1e-3,
             global_step=get_global_step_var(),

tensorpack/graph_builder/model_desc.py

@@ -109,10 +109,6 @@ class ModelDescBase(object):
                 return [InputDesc.from_placeholder(p) for p in inputs]
 
     def _get_inputs(self):
-        """
-        Returns:
-            a list of :class:`InputDesc`.
-        """
         raise NotImplementedError()
 
     def inputs(self):
@@ -207,22 +203,33 @@ class ModelDesc(ModelDescBase):
     @memoized
     def get_optimizer(self):
         """
-        Return the memoized optimizer returned by `_get_optimizer`.
+        Return the memoized optimizer returned by `optimizer()`.
 
-        Users of :class:`ModelDesc` will need to implement `_get_optimizer()`,
+        Users of :class:`ModelDesc` will need to implement `optimizer()`,
         which will only be called once per each model.
 
         Returns:
             a :class:`tf.train.Optimizer` instance.
         """
+        try:
             return self._get_optimizer()
+        except NotImplementedError:
+            pass
+        return self.optimizer()
 
     def _get_optimizer(self):
         raise NotImplementedError()
 
+    def optimizer(self):
+        """
+        Returns a `tf.train.Optimizer` instance.
+        A subclass is expected to implement this method.
+        """
+        raise NotImplementedError()
+
     def _build_graph_get_cost(self, *inputs):
         """
-        Used by trainers to get the final cost for optimization.
+        Used internally by trainers to get the final cost for optimization.
         """
         ret = self.build_graph(*inputs)
         if not get_current_tower_context().is_training: