remove unused lines (pytorch#84)

* remove unused import statements

* remove unused variable and arguments

Author: iamaziz

mnist_hogwild/main.py

@@ -1,5 +1,5 @@
 from __future__ import print_function
-import os, argparse
+import argparse
 import torch
 import torch.nn as nn
 import torch.nn.functional as F

mnist_hogwild/train.py

@@ -28,13 +28,12 @@ def train(rank, args, model):
     optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)
     for epoch in range(1, args.epochs + 1):
         train_epoch(epoch, args, model, train_loader, optimizer)
-        test_epoch(epoch, args, model, test_loader)
+        test_epoch(model, test_loader)
 
 
 def train_epoch(epoch, args, model, data_loader, optimizer):
     model.train()
     pid = os.getpid()
-    samples_seen = 0
     for batch_idx, (data, target) in enumerate(data_loader):
         data, target = Variable(data), Variable(target)
         optimizer.zero_grad()
@@ -48,7 +47,7 @@ def train_epoch(epoch, args, model, data_loader, optimizer):
                 100. * batch_idx / len(data_loader), loss.data[0]))
 
 
-def test_epoch(epoch, args, model, data_loader):
+def test_epoch(model, data_loader):
     model.eval()
     test_loss = 0
     correct = 0

reinforcement_learning/actor_critic.py

@@ -10,7 +10,6 @@
 import torch.optim as optim
 import torch.autograd as autograd
 from torch.autograd import Variable
-import torchvision.transforms as T
 
 
 parser = argparse.ArgumentParser(description='PyTorch actor-critic example')

reinforcement_learning/reinforce.py

@@ -2,15 +2,13 @@
 import gym
 import numpy as np
 from itertools import count
-from collections import namedtuple
 
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 import torch.optim as optim
 import torch.autograd as autograd
 from torch.autograd import Variable
-import torchvision.transforms as T
 
 
 parser = argparse.ArgumentParser(description='PyTorch REINFORCE example')

super_resolution/dataset.py

@@ -2,7 +2,6 @@
 
 from os import listdir
 from os.path import join
-import numpy as np
 from PIL import Image
 
 

word_language_model/generate.py

@@ -6,11 +6,8 @@
 ###############################################################################
 
 import argparse
-import time
-import math
 
 import torch
-import torch.nn as nn
 from torch.autograd import Variable
 
 import data

word_language_model/model.py

@@ -1,4 +1,3 @@
-import torch
 import torch.nn as nn
 from torch.autograd import Variable