Sourcery refactored master branch

dcgan/model.py

@@ -3,16 +3,14 @@
 
 
 def deconv(c_in, c_out, k_size, stride=2, pad=1, bn=True):
-    layers = []
-    layers.append(nn.ConvTranspose2d(c_in, c_out, k_size, stride, pad))
+    layers = [nn.ConvTranspose2d(c_in, c_out, k_size, stride, pad)]
     if bn:
         layers.append(nn.BatchNorm2d(c_out))
     return nn.Sequential(*layers)
 
 
 def conv(c_in, c_out, k_size, stride=2, pad=1, bn=True):
-    layers = []
-    layers.append(nn.Conv2d(c_in, c_out, k_size, stride, pad))
+    layers = [nn.Conv2d(c_in, c_out, k_size, stride, pad)]
     if bn:
         layers.append(nn.BatchNorm2d(c_out))
     return nn.Sequential(*layers)

extra/charnn/test.py

@@ -15,12 +15,11 @@ def pick_top_n(preds, vocab_size):
     p = np.squeeze(preds)
     p[np.argsort(p)[:-5]] = 0
     p = p / np.sum(p)
-    c = np.random.choice(vocab_size, 1, p=p)[0]
-    return c
+    return np.random.choice(vocab_size, 1, p=p)[0]
 
 
 def sample(checkpoint):
-    samples = [c for c in prime]
+    samples = list(prime)
     int_to_vocab, vocab_to_int, no_classes = pickle.load(
         open("./saves/data.p", "rb"))
 
@@ -45,7 +44,7 @@ def sample(checkpoint):
         samples.append(int_to_vocab[c])
 
         # Generate new samples
-        for i in range(n_samples):
+        for _ in range(n_samples):
             x[0, 0] = c
             feed = {model.inputs: x, model.initial_state: new_state}
             preds, new_state = sess.run(

extra/charnn/train.py

@@ -70,13 +70,16 @@ def train():
                 batch_loss, new_state, _ = sess.run(
                     [model.loss, model.final_state, model.train_op], feed_dict=feed)
                 end = time.time()
-                print('Epoch: {} '.format(e + 1), 'Loss: {:.4f} '.format(batch_loss),
-                      '{:.4f} sec/batch'.format((end - start)))
+                print(
+                    f'Epoch: {e + 1} ',
+                    'Loss: {:.4f} '.format(batch_loss),
+                    '{:.4f} sec/batch'.format((end - start)),
+                )
 
                 if (counter % save_every_n == 0):
-                    saver.save(sess, "saves/{}.ckpt".format(counter))
+                    saver.save(sess, f"saves/{counter}.ckpt")
 
-        saver.save(sess, "saves/{}.ckpt".format(counter))
+        saver.save(sess, f"saves/{counter}.ckpt")
 
 
 if __name__ == '__main__':

resnet/model.py

@@ -49,12 +49,9 @@ def make_layer(self, block, out_channels, blocks, stride=1):
             downsample = nn.Sequential(
                 conv3x3(self.in_channels, out_channels, stride=stride),
                 nn.BatchNorm2d(out_channels))
-        layers = []
-        layers.append(
-            block(self.in_channels, out_channels, stride, downsample))
+        layers = [block(self.in_channels, out_channels, stride, downsample)]
         self.in_channels = out_channels
-        for i in range(1, blocks):
-            layers.append(block(out_channels, out_channels))
+        layers.extend(block(out_channels, out_channels) for _ in range(1, blocks))
         return nn.Sequential(*layers)
 
     def forward(self, x):