Code style fixes.

CharushS · Jul 9, 2018 · 09e6083 · 09e6083
1 parent aec1147
commit 09e6083
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Showing 7 changed files with 10 additions and 37 deletions.
diff --git a/multi_categorical_gans/methods/arae/sampler.py b/multi_categorical_gans/methods/arae/sampler.py
@@ -31,11 +31,7 @@ def sample(autoencoder, generator, num_samples, num_features, batch_size=100, no
             noise = Variable(torch.FloatTensor(batch_size, noise_size).normal_())
             noise = to_cuda_if_available(noise)
             batch_code = generator(noise)
-
-            batch_samples = autoencoder.decode(batch_code,
-                                               training=False,
-                                               temperature=temperature)
-
+            batch_samples = autoencoder.decode(batch_code, training=False, temperature=temperature)
         batch_samples = to_cpu_if_available(batch_samples)
         batch_samples = batch_samples.data.numpy()
 

diff --git a/multi_categorical_gans/methods/arae/trainer.py b/multi_categorical_gans/methods/arae/trainer.py
@@ -106,15 +106,12 @@ def regularize_ae_grad(grad):
 
                 batch_original = Variable(torch.from_numpy(batch))
                 batch_original = to_cuda_if_available(batch_original)
-                batch_code = autoencoder.encode(batch_original,
-                                                normalize_code=normalize_code)
+                batch_code = autoencoder.encode(batch_original, normalize_code=normalize_code)
                 batch_code = add_noise_to_code(batch_code, ae_noise_radius)
                 if regularization_penalty > 0:
                     batch_code.register_hook(update_last_ae_grad_norm)
 
-                batch_reconstructed = autoencoder.decode(batch_code,
-                                                         training=True,
-                                                         temperature=temperature)
+                batch_reconstructed = autoencoder.decode(batch_code, training=True, temperature=temperature)
 
                 ae_loss = categorical_variable_loss(batch_reconstructed, batch_original, variable_sizes)
                 ae_loss.backward()

diff --git a/multi_categorical_gans/methods/mc_wgan_gp/trainer.py b/multi_categorical_gans/methods/mc_wgan_gp/trainer.py
@@ -107,11 +107,7 @@ def train(generator,
                 fake_loss.backward()
 
                 # this is the magic from WGAN-GP
-                gradient_penalty = calculate_gradient_penalty(discriminator,
-                                                              penalty,
-                                                              real_features,
-                                                              fake_features)
-
+                gradient_penalty = calculate_gradient_penalty(discriminator, penalty, real_features, fake_features)
                 gradient_penalty.backward()
 
                 # finally update the discriminator weights

diff --git a/multi_categorical_gans/methods/medgan/generator.py b/multi_categorical_gans/methods/medgan/generator.py
@@ -31,12 +31,9 @@ def batch_norm_train(self, mode=True):
             batch_norm.train(mode=mode)
 
     def forward(self, noise):
-        """
-        This sums are called "shortcut connections"
-        """
         outputs = noise
 
         for module in self.modules:
             # Cannot write "outputs += module(outputs)" because it is an inplace operation (no differentiable)
-            outputs = module(outputs) + outputs
+            outputs = module(outputs) + outputs  # shortcut connection
         return outputs
diff --git a/multi_categorical_gans/methods/medgan/pre_trainer.py b/multi_categorical_gans/methods/medgan/pre_trainer.py
@@ -69,10 +69,7 @@ def pre_train_epoch(autoencoder, data, batch_size, optim=None, variable_sizes=No
         batch = Variable(torch.from_numpy(batch))
         batch = to_cuda_if_available(batch)
 
-        _, batch_reconstructed = autoencoder(batch,
-                                             training=training,
-                                             temperature=temperature,
-                                             normalize_code=False)
+        _, batch_reconstructed = autoencoder(batch, training=training, temperature=temperature, normalize_code=False)
 
         loss = categorical_variable_loss(batch_reconstructed, batch, variable_sizes)
         loss.backward()

diff --git a/multi_categorical_gans/methods/medgan/sampler.py b/multi_categorical_gans/methods/medgan/sampler.py
@@ -31,11 +31,7 @@ def sample(autoencoder, generator, num_samples, num_features, batch_size=100, co
             noise = Variable(torch.FloatTensor(batch_size, code_size).normal_())
             noise = to_cuda_if_available(noise)
             batch_code = generator(noise)
-
-            batch_samples = autoencoder.decode(batch_code,
-                                               training=False,
-                                               temperature=temperature)
-
+            batch_samples = autoencoder.decode(batch_code, training=False, temperature=temperature)
         batch_samples = to_cpu_if_available(batch_samples)
         batch_samples = batch_samples.data.numpy()
 

diff --git a/multi_categorical_gans/methods/medgan/trainer.py b/multi_categorical_gans/methods/medgan/trainer.py
@@ -98,9 +98,7 @@ def train(autoencoder,
                 noise = Variable(torch.FloatTensor(len(batch), code_size).normal_())
                 noise = to_cuda_if_available(noise)
                 fake_code = generator(noise)
-                fake_features = autoencoder.decode(fake_code,
-                                                   training=True,
-                                                   temperature=temperature)
+                fake_features = autoencoder.decode(fake_code, training=True, temperature=temperature)
                 fake_features = fake_features.detach()  # do not propagate to the generator
                 fake_pred = discriminator(fake_features)
                 fake_loss = criterion(fake_pred, label_zeros)
@@ -127,9 +125,7 @@ def train(autoencoder,
                 noise = Variable(torch.FloatTensor(len(batch), code_size).normal_())
                 noise = to_cuda_if_available(noise)
                 gen_code = generator(noise)
-                gen_features = autoencoder.decode(gen_code,
-                                                  training=True,
-                                                  temperature=temperature)
+                gen_features = autoencoder.decode(gen_code, training=True, temperature=temperature)
                 gen_pred = discriminator(gen_features)
 
                 smooth_label_ones = Variable(torch.FloatTensor(len(batch)).uniform_(0.9, 1))
@@ -167,9 +163,7 @@ def train(autoencoder,
                 noise = Variable(torch.FloatTensor(len(batch), code_size).normal_())
                 noise = to_cuda_if_available(noise)
                 fake_code = generator(noise)
-                fake_features = autoencoder.decode(fake_code,
-                                                   training=False,
-                                                   temperature=temperature)
+                fake_features = autoencoder.decode(fake_code, training=False, temperature=temperature)
                 fake_pred = discriminator(fake_features)
             fake_pred = to_cpu_if_available(fake_pred)
             correct += (fake_pred.data.numpy().ravel() < .5).sum()