prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  VAE loss
  According to the expression in line 95, the KL-divergence term is calculated from
  `0.5 * sum(1 + log(sigma^2) - mu^2 - sigma^2)`
  but I think the code in line 96-97 represents
  `0.5 * sum(1 + log(sigma^2) - mu^2 - sigma)`
  
  This might not be essential because whether the last term is squared or not, the loss descending behavior stays unchanged.
  
  --- END ISSUE ---
  
  Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
  --- BEGIN FILES ---
  Path: `vae/main.py`
  Content:
  ```
  1 from __future__ import print_function
  2 import argparse
  3 import torch
  4 import torch.utils.data
  5 from torch import nn, optim
  6 from torch.autograd import Variable
  7 from torchvision import datasets, transforms
  8 from torchvision.utils import save_image
  9 
  10 
  11 parser = argparse.ArgumentParser(description='VAE MNIST Example')
  12 parser.add_argument('--batch-size', type=int, default=128, metavar='N',
  13                     help='input batch size for training (default: 128)')
  14 parser.add_argument('--epochs', type=int, default=10, metavar='N',
  15                     help='number of epochs to train (default: 10)')
  16 parser.add_argument('--no-cuda', action='store_true', default=False,
  17                     help='enables CUDA training')
  18 parser.add_argument('--seed', type=int, default=1, metavar='S',
  19                     help='random seed (default: 1)')
  20 parser.add_argument('--log-interval', type=int, default=10, metavar='N',
  21                     help='how many batches to wait before logging training status')
  22 args = parser.parse_args()
  23 args.cuda = not args.no_cuda and torch.cuda.is_available()
  24 
  25 
  26 torch.manual_seed(args.seed)
  27 if args.cuda:
  28     torch.cuda.manual_seed(args.seed)
  29 
  30 
  31 kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}
  32 train_loader = torch.utils.data.DataLoader(
  33     datasets.MNIST('../data', train=True, download=True,
  34                    transform=transforms.ToTensor()),
  35     batch_size=args.batch_size, shuffle=True, **kwargs)
  36 test_loader = torch.utils.data.DataLoader(
  37     datasets.MNIST('../data', train=False, transform=transforms.ToTensor()),
  38     batch_size=args.batch_size, shuffle=True, **kwargs)
  39 
  40 
  41 class VAE(nn.Module):
  42     def __init__(self):
  43         super(VAE, self).__init__()
  44 
  45         self.fc1 = nn.Linear(784, 400)
  46         self.fc21 = nn.Linear(400, 20)
  47         self.fc22 = nn.Linear(400, 20)
  48         self.fc3 = nn.Linear(20, 400)
  49         self.fc4 = nn.Linear(400, 784)
  50 
  51         self.relu = nn.ReLU()
  52         self.sigmoid = nn.Sigmoid()
  53 
  54     def encode(self, x):
  55         h1 = self.relu(self.fc1(x))
  56         return self.fc21(h1), self.fc22(h1)
  57 
  58     def reparameterize(self, mu, logvar):
  59         if self.training:
  60           std = logvar.mul(0.5).exp_()
  61           eps = Variable(std.data.new(std.size()).normal_())
  62           return eps.mul(std).add_(mu)
  63         else:
  64           return mu
  65 
  66     def decode(self, z):
  67         h3 = self.relu(self.fc3(z))
  68         return self.sigmoid(self.fc4(h3))
  69 
  70     def forward(self, x):
  71         mu, logvar = self.encode(x.view(-1, 784))
  72         z = self.reparameterize(mu, logvar)
  73         return self.decode(z), mu, logvar
  74 
  75 
  76 model = VAE()
  77 if args.cuda:
  78     model.cuda()
  79 
  80 reconstruction_function = nn.BCELoss()
  81 
  82 
  83 def loss_function(recon_x, x, mu, logvar):
  84     BCE = reconstruction_function(recon_x, x.view(-1, 784))
  85 
  86     # see Appendix B from VAE paper:
  87     # Kingma and Welling. Auto-Encoding Variational Bayes. ICLR, 2014
  88     # https://arxiv.org/abs/1312.6114
  89     # 0.5 * sum(1 + log(sigma^2) - mu^2 - sigma^2)
  90     KLD_element = mu.pow(2).add_(logvar.exp()).mul_(-1).add_(1).add_(logvar)
  91     KLD = torch.sum(KLD_element).mul_(-0.5)
  92     # Normalise by same number of elements as in reconstruction
  93     KLD /= args.batch_size * 784
  94 
  95     return BCE + KLD
  96 
  97 
  98 optimizer = optim.Adam(model.parameters(), lr=1e-3)
  99 
  100 
  101 def train(epoch):
  102     model.train()
  103     train_loss = 0
  104     for batch_idx, (data, _) in enumerate(train_loader):
  105         data = Variable(data)
  106         if args.cuda:
  107             data = data.cuda()
  108         optimizer.zero_grad()
  109         recon_batch, mu, logvar = model(data)
  110         loss = loss_function(recon_batch, data, mu, logvar)
  111         loss.backward()
  112         train_loss += loss.data[0]
  113         optimizer.step()
  114         if batch_idx % args.log_interval == 0:
  115             print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
  116                 epoch, batch_idx * len(data), len(train_loader.dataset),
  117                 100. * batch_idx / len(train_loader),
  118                 loss.data[0] / len(data)))
  119 
  120     print('====> Epoch: {} Average loss: {:.4f}'.format(
  121           epoch, train_loss / len(train_loader.dataset)))
  122 
  123 
  124 def test(epoch):
  125     model.eval()
  126     test_loss = 0
  127     for i, (data, _) in enumerate(test_loader):
  128         if args.cuda:
  129             data = data.cuda()
  130         data = Variable(data, volatile=True)
  131         recon_batch, mu, logvar = model(data)
  132         test_loss += loss_function(recon_batch, data, mu, logvar).data[0]
  133         if i == 0:
  134           save_image(recon_batch.data.cpu().view(args.batch_size, 1, 28, 28),
  135                      'reconstruction_' + str(epoch) + '.png')
  136 
  137     test_loss /= len(test_loader.dataset)
  138     print('====> Test set loss: {:.4f}'.format(test_loss))
  139 
  140 
  141 for epoch in range(1, args.epochs + 1):
  142     train(epoch)
  143     test(epoch)
  144     sample = Variable(torch.randn(64, 20))
  145     if args.cuda:
  146        sample = sample.cuda()
  147     sample = model.decode(sample).cpu()
  148     save_image(sample.data.view(64, 1, 28, 28), 'sample_' + str(epoch) + '.png')
  149
  ```
  --- END FILES ---
  
  Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.
  
  Here is an example:
  
  ```diff
  diff --git a/examples/server_async.py b/examples/server_async.py
  --- a/examples/server_async.py
  +++ b/examples/server_async.py
  @@ -313,4 +313,4 @@
   
   
   if __name__ == "__main__":
  -    asyncio.run(run_async_server("."), debug=True)
  +    asyncio.run(run_async_server(), debug=True)
  diff --git a/examples/server_sync.py b/examples/server_sync.py
  --- a/examples/server_sync.py
  +++ b/examples/server_sync.py
  @@ -313,5 +313,5 @@
   
   
   if __name__ == "__main__":
  -    server = run_sync_server(".")
  +    server = run_sync_server()
       server.shutdown()
  
  ```
  
golden_diff: diff --git a/vae/main.py b/vae/main.py
  --- a/vae/main.py
  +++ b/vae/main.py
  @@ -4,6 +4,7 @@
   import torch.utils.data
   from torch import nn, optim
   from torch.autograd import Variable
  +from torch.nn import functional as F
   from torchvision import datasets, transforms
   from torchvision.utils import save_image
   
  @@ -77,18 +78,15 @@
   if args.cuda:
       model.cuda()
   
  -reconstruction_function = nn.BCELoss()
  -
   
   def loss_function(recon_x, x, mu, logvar):
  -    BCE = reconstruction_function(recon_x, x.view(-1, 784))
  +    BCE = F.binary_cross_entropy(recon_x, x.view(-1, 784))
   
       # see Appendix B from VAE paper:
       # Kingma and Welling. Auto-Encoding Variational Bayes. ICLR, 2014
       # https://arxiv.org/abs/1312.6114
       # 0.5 * sum(1 + log(sigma^2) - mu^2 - sigma^2)
  -    KLD_element = mu.pow(2).add_(logvar.exp()).mul_(-1).add_(1).add_(logvar)
  -    KLD = torch.sum(KLD_element).mul_(-0.5)
  +    KLD = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
       # Normalise by same number of elements as in reconstruction
       KLD /= args.batch_size * 784
   
  @@ -131,8 +129,11 @@
           recon_batch, mu, logvar = model(data)
           test_loss += loss_function(recon_batch, data, mu, logvar).data[0]
           if i == 0:
  -          save_image(recon_batch.data.cpu().view(args.batch_size, 1, 28, 28),
  -                     'reconstruction_' + str(epoch) + '.png')
  +          n = min(data.size(0), 8)
  +          comparison = torch.cat([data[:n],
  +                                  recon_batch.view(args.batch_size, 1, 28, 28)[:n]])
  +          save_image(comparison.data.cpu(),
  +                     'results/reconstruction_' + str(epoch) + '.png', nrow=n)
   
       test_loss /= len(test_loader.dataset)
       print('====> Test set loss: {:.4f}'.format(test_loss))
  @@ -145,4 +146,5 @@
       if args.cuda:
          sample = sample.cuda()
       sample = model.decode(sample).cpu()
  -    save_image(sample.data.view(64, 1, 28, 28), 'sample_' + str(epoch) + '.png')
  +    save_image(sample.data.view(64, 1, 28, 28),
  +               'results/sample_' + str(epoch) + '.png')
  
verification_info: {"golden_diff": "diff --git a/vae/main.py b/vae/main.py\n--- a/vae/main.py\n+++ b/vae/main.py\n@@ -4,6 +4,7 @@\n import torch.utils.data\n from torch import nn, optim\n from torch.autograd import Variable\n+from torch.nn import functional as F\n from torchvision import datasets, transforms\n from torchvision.utils import save_image\n \n@@ -77,18 +78,15 @@\n if args.cuda:\n     model.cuda()\n \n-reconstruction_function = nn.BCELoss()\n-\n \n def loss_function(recon_x, x, mu, logvar):\n-    BCE = reconstruction_function(recon_x, x.view(-1, 784))\n+    BCE = F.binary_cross_entropy(recon_x, x.view(-1, 784))\n \n     # see Appendix B from VAE paper:\n     # Kingma and Welling. Auto-Encoding Variational Bayes. ICLR, 2014\n     # https://arxiv.org/abs/1312.6114\n     # 0.5 * sum(1 + log(sigma^2) - mu^2 - sigma^2)\n-    KLD_element = mu.pow(2).add_(logvar.exp()).mul_(-1).add_(1).add_(logvar)\n-    KLD = torch.sum(KLD_element).mul_(-0.5)\n+    KLD = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())\n     # Normalise by same number of elements as in reconstruction\n     KLD /= args.batch_size * 784\n \n@@ -131,8 +129,11 @@\n         recon_batch, mu, logvar = model(data)\n         test_loss += loss_function(recon_batch, data, mu, logvar).data[0]\n         if i == 0:\n-          save_image(recon_batch.data.cpu().view(args.batch_size, 1, 28, 28),\n-                     'reconstruction_' + str(epoch) + '.png')\n+          n = min(data.size(0), 8)\n+          comparison = torch.cat([data[:n],\n+                                  recon_batch.view(args.batch_size, 1, 28, 28)[:n]])\n+          save_image(comparison.data.cpu(),\n+                     'results/reconstruction_' + str(epoch) + '.png', nrow=n)\n \n     test_loss /= len(test_loader.dataset)\n     print('====> Test set loss: {:.4f}'.format(test_loss))\n@@ -145,4 +146,5 @@\n     if args.cuda:\n        sample = sample.cuda()\n     sample = model.decode(sample).cpu()\n-    save_image(sample.data.view(64, 1, 28, 28), 'sample_' + str(epoch) + '.png')\n+    save_image(sample.data.view(64, 1, 28, 28),\n+               'results/sample_' + str(epoch) + '.png')\n", "issue": "VAE loss\nAccording to the expression in line 95, the KL-divergence term is calculated from\r\n`0.5 * sum(1 + log(sigma^2) - mu^2 - sigma^2)`\r\nbut I think the code in line 96-97 represents\r\n`0.5 * sum(1 + log(sigma^2) - mu^2 - sigma)`\r\n\r\nThis might not be essential because whether the last term is squared or not, the loss descending behavior stays unchanged.\n", "before_files": [{"content": "from __future__ import print_function\nimport argparse\nimport torch\nimport torch.utils.data\nfrom torch import nn, optim\nfrom torch.autograd import Variable\nfrom torchvision import datasets, transforms\nfrom torchvision.utils import save_image\n\n\nparser = argparse.ArgumentParser(description='VAE MNIST Example')\nparser.add_argument('--batch-size', type=int, default=128, metavar='N',\n                    help='input batch size for training (default: 128)')\nparser.add_argument('--epochs', type=int, default=10, metavar='N',\n                    help='number of epochs to train (default: 10)')\nparser.add_argument('--no-cuda', action='store_true', default=False,\n                    help='enables CUDA training')\nparser.add_argument('--seed', type=int, default=1, metavar='S',\n                    help='random seed (default: 1)')\nparser.add_argument('--log-interval', type=int, default=10, metavar='N',\n                    help='how many batches to wait before logging training status')\nargs = parser.parse_args()\nargs.cuda = not args.no_cuda and torch.cuda.is_available()\n\n\ntorch.manual_seed(args.seed)\nif args.cuda:\n    torch.cuda.manual_seed(args.seed)\n\n\nkwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}\ntrain_loader = torch.utils.data.DataLoader(\n    datasets.MNIST('../data', train=True, download=True,\n                   transform=transforms.ToTensor()),\n    batch_size=args.batch_size, shuffle=True, **kwargs)\ntest_loader = torch.utils.data.DataLoader(\n    datasets.MNIST('../data', train=False, transform=transforms.ToTensor()),\n    batch_size=args.batch_size, shuffle=True, **kwargs)\n\n\nclass VAE(nn.Module):\n    def __init__(self):\n        super(VAE, self).__init__()\n\n        self.fc1 = nn.Linear(784, 400)\n        self.fc21 = nn.Linear(400, 20)\n        self.fc22 = nn.Linear(400, 20)\n        self.fc3 = nn.Linear(20, 400)\n        self.fc4 = nn.Linear(400, 784)\n\n        self.relu = nn.ReLU()\n        self.sigmoid = nn.Sigmoid()\n\n    def encode(self, x):\n        h1 = self.relu(self.fc1(x))\n        return self.fc21(h1), self.fc22(h1)\n\n    def reparameterize(self, mu, logvar):\n        if self.training:\n          std = logvar.mul(0.5).exp_()\n          eps = Variable(std.data.new(std.size()).normal_())\n          return eps.mul(std).add_(mu)\n        else:\n          return mu\n\n    def decode(self, z):\n        h3 = self.relu(self.fc3(z))\n        return self.sigmoid(self.fc4(h3))\n\n    def forward(self, x):\n        mu, logvar = self.encode(x.view(-1, 784))\n        z = self.reparameterize(mu, logvar)\n        return self.decode(z), mu, logvar\n\n\nmodel = VAE()\nif args.cuda:\n    model.cuda()\n\nreconstruction_function = nn.BCELoss()\n\n\ndef loss_function(recon_x, x, mu, logvar):\n    BCE = reconstruction_function(recon_x, x.view(-1, 784))\n\n    # see Appendix B from VAE paper:\n    # Kingma and Welling. Auto-Encoding Variational Bayes. ICLR, 2014\n    # https://arxiv.org/abs/1312.6114\n    # 0.5 * sum(1 + log(sigma^2) - mu^2 - sigma^2)\n    KLD_element = mu.pow(2).add_(logvar.exp()).mul_(-1).add_(1).add_(logvar)\n    KLD = torch.sum(KLD_element).mul_(-0.5)\n    # Normalise by same number of elements as in reconstruction\n    KLD /= args.batch_size * 784\n\n    return BCE + KLD\n\n\noptimizer = optim.Adam(model.parameters(), lr=1e-3)\n\n\ndef train(epoch):\n    model.train()\n    train_loss = 0\n    for batch_idx, (data, _) in enumerate(train_loader):\n        data = Variable(data)\n        if args.cuda:\n            data = data.cuda()\n        optimizer.zero_grad()\n        recon_batch, mu, logvar = model(data)\n        loss = loss_function(recon_batch, data, mu, logvar)\n        loss.backward()\n        train_loss += loss.data[0]\n        optimizer.step()\n        if batch_idx % args.log_interval == 0:\n            print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n                epoch, batch_idx * len(data), len(train_loader.dataset),\n                100. * batch_idx / len(train_loader),\n                loss.data[0] / len(data)))\n\n    print('====> Epoch: {} Average loss: {:.4f}'.format(\n          epoch, train_loss / len(train_loader.dataset)))\n\n\ndef test(epoch):\n    model.eval()\n    test_loss = 0\n    for i, (data, _) in enumerate(test_loader):\n        if args.cuda:\n            data = data.cuda()\n        data = Variable(data, volatile=True)\n        recon_batch, mu, logvar = model(data)\n        test_loss += loss_function(recon_batch, data, mu, logvar).data[0]\n        if i == 0:\n          save_image(recon_batch.data.cpu().view(args.batch_size, 1, 28, 28),\n                     'reconstruction_' + str(epoch) + '.png')\n\n    test_loss /= len(test_loader.dataset)\n    print('====> Test set loss: {:.4f}'.format(test_loss))\n\n\nfor epoch in range(1, args.epochs + 1):\n    train(epoch)\n    test(epoch)\n    sample = Variable(torch.randn(64, 20))\n    if args.cuda:\n       sample = sample.cuda()\n    sample = model.decode(sample).cpu()\n    save_image(sample.data.view(64, 1, 28, 28), 'sample_' + str(epoch) + '.png')\n", "path": "vae/main.py"}], "after_files": [{"content": "from __future__ import print_function\nimport argparse\nimport torch\nimport torch.utils.data\nfrom torch import nn, optim\nfrom torch.autograd import Variable\nfrom torch.nn import functional as F\nfrom torchvision import datasets, transforms\nfrom torchvision.utils import save_image\n\n\nparser = argparse.ArgumentParser(description='VAE MNIST Example')\nparser.add_argument('--batch-size', type=int, default=128, metavar='N',\n                    help='input batch size for training (default: 128)')\nparser.add_argument('--epochs', type=int, default=10, metavar='N',\n                    help='number of epochs to train (default: 10)')\nparser.add_argument('--no-cuda', action='store_true', default=False,\n                    help='enables CUDA training')\nparser.add_argument('--seed', type=int, default=1, metavar='S',\n                    help='random seed (default: 1)')\nparser.add_argument('--log-interval', type=int, default=10, metavar='N',\n                    help='how many batches to wait before logging training status')\nargs = parser.parse_args()\nargs.cuda = not args.no_cuda and torch.cuda.is_available()\n\n\ntorch.manual_seed(args.seed)\nif args.cuda:\n    torch.cuda.manual_seed(args.seed)\n\n\nkwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}\ntrain_loader = torch.utils.data.DataLoader(\n    datasets.MNIST('../data', train=True, download=True,\n                   transform=transforms.ToTensor()),\n    batch_size=args.batch_size, shuffle=True, **kwargs)\ntest_loader = torch.utils.data.DataLoader(\n    datasets.MNIST('../data', train=False, transform=transforms.ToTensor()),\n    batch_size=args.batch_size, shuffle=True, **kwargs)\n\n\nclass VAE(nn.Module):\n    def __init__(self):\n        super(VAE, self).__init__()\n\n        self.fc1 = nn.Linear(784, 400)\n        self.fc21 = nn.Linear(400, 20)\n        self.fc22 = nn.Linear(400, 20)\n        self.fc3 = nn.Linear(20, 400)\n        self.fc4 = nn.Linear(400, 784)\n\n        self.relu = nn.ReLU()\n        self.sigmoid = nn.Sigmoid()\n\n    def encode(self, x):\n        h1 = self.relu(self.fc1(x))\n        return self.fc21(h1), self.fc22(h1)\n\n    def reparameterize(self, mu, logvar):\n        if self.training:\n          std = logvar.mul(0.5).exp_()\n          eps = Variable(std.data.new(std.size()).normal_())\n          return eps.mul(std).add_(mu)\n        else:\n          return mu\n\n    def decode(self, z):\n        h3 = self.relu(self.fc3(z))\n        return self.sigmoid(self.fc4(h3))\n\n    def forward(self, x):\n        mu, logvar = self.encode(x.view(-1, 784))\n        z = self.reparameterize(mu, logvar)\n        return self.decode(z), mu, logvar\n\n\nmodel = VAE()\nif args.cuda:\n    model.cuda()\n\n\ndef loss_function(recon_x, x, mu, logvar):\n    BCE = F.binary_cross_entropy(recon_x, x.view(-1, 784))\n\n    # see Appendix B from VAE paper:\n    # Kingma and Welling. Auto-Encoding Variational Bayes. ICLR, 2014\n    # https://arxiv.org/abs/1312.6114\n    # 0.5 * sum(1 + log(sigma^2) - mu^2 - sigma^2)\n    KLD = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())\n    # Normalise by same number of elements as in reconstruction\n    KLD /= args.batch_size * 784\n\n    return BCE + KLD\n\n\noptimizer = optim.Adam(model.parameters(), lr=1e-3)\n\n\ndef train(epoch):\n    model.train()\n    train_loss = 0\n    for batch_idx, (data, _) in enumerate(train_loader):\n        data = Variable(data)\n        if args.cuda:\n            data = data.cuda()\n        optimizer.zero_grad()\n        recon_batch, mu, logvar = model(data)\n        loss = loss_function(recon_batch, data, mu, logvar)\n        loss.backward()\n        train_loss += loss.data[0]\n        optimizer.step()\n        if batch_idx % args.log_interval == 0:\n            print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n                epoch, batch_idx * len(data), len(train_loader.dataset),\n                100. * batch_idx / len(train_loader),\n                loss.data[0] / len(data)))\n\n    print('====> Epoch: {} Average loss: {:.4f}'.format(\n          epoch, train_loss / len(train_loader.dataset)))\n\n\ndef test(epoch):\n    model.eval()\n    test_loss = 0\n    for i, (data, _) in enumerate(test_loader):\n        if args.cuda:\n            data = data.cuda()\n        data = Variable(data, volatile=True)\n        recon_batch, mu, logvar = model(data)\n        test_loss += loss_function(recon_batch, data, mu, logvar).data[0]\n        if i == 0:\n          n = min(data.size(0), 8)\n          comparison = torch.cat([data[:n],\n                                  recon_batch.view(args.batch_size, 1, 28, 28)[:n]])\n          save_image(comparison.data.cpu(),\n                     'results/reconstruction_' + str(epoch) + '.png', nrow=n)\n\n    test_loss /= len(test_loader.dataset)\n    print('====> Test set loss: {:.4f}'.format(test_loss))\n\n\nfor epoch in range(1, args.epochs + 1):\n    train(epoch)\n    test(epoch)\n    sample = Variable(torch.randn(64, 20))\n    if args.cuda:\n       sample = sample.cuda()\n    sample = model.decode(sample).cpu()\n    save_image(sample.data.view(64, 1, 28, 28),\n               'results/sample_' + str(epoch) + '.png')\n", "path": "vae/main.py"}]}