prompt: You will be provided with a partial code base and an issue statement explaining a problem to resolve.
  <issue>
  Incorrect inputsize for BarlowTwins Lightning Example Code
  Should the input_size in [1] be `32` instead of `224`?
  In [2], we use `input_size=32`.
  
  [1] https://github.com/lightly-ai/lightly/blob/master/examples/pytorch_lightning/barlowtwins.py#L44
  [2] https://github.com/lightly-ai/lightly/blob/master/examples/pytorch/barlowtwins.py#L35
  
  
  
  </issue>
  <code>
  [start of examples/pytorch_lightning/barlowtwins.py]
  1 import torch
  2 from torch import nn
  3 import torchvision
  4 import pytorch_lightning as pl
  5 
  6 from lightly.data import LightlyDataset
  7 from lightly.data import ImageCollateFunction
  8 from lightly.loss import BarlowTwinsLoss
  9 from lightly.models.modules import BarlowTwinsProjectionHead
  10 
  11 
  12 class BarlowTwins(pl.LightningModule):
  13     def __init__(self):
  14         super().__init__()
  15         resnet = torchvision.models.resnet18()
  16         self.backbone = nn.Sequential(*list(resnet.children())[:-1])
  17         self.projection_head = BarlowTwinsProjectionHead(512, 2048, 2048)
  18         self.criterion = BarlowTwinsLoss()
  19 
  20     def forward(self, x):
  21         x = self.backbone(x).flatten(start_dim=1)
  22         z = self.projection_head(x)
  23         return z
  24 
  25     def training_step(self, batch, batch_index):
  26         (x0, x1), _, _ = batch
  27         z0 = self.forward(x0)
  28         z1 = self.forward(x1)
  29         loss = self.criterion(z0, z1)
  30         return loss
  31 
  32     def configure_optimizers(self):
  33         optim = torch.optim.SGD(self.parameters(), lr=0.06)
  34         return optim
  35 
  36 
  37 model = BarlowTwins()
  38 
  39 cifar10 = torchvision.datasets.CIFAR10("datasets/cifar10", download=True)
  40 dataset = LightlyDataset.from_torch_dataset(cifar10)
  41 # or create a dataset from a folder containing images or videos:
  42 # dataset = LightlyDataset("path/to/folder")
  43 
  44 collate_fn = ImageCollateFunction(input_size=224)
  45 
  46 dataloader = torch.utils.data.DataLoader(
  47     dataset,
  48     batch_size=256,
  49     collate_fn=collate_fn,
  50     shuffle=True,
  51     drop_last=True,
  52     num_workers=8,
  53 )
  54 
  55 gpus = 1 if torch.cuda.is_available() else 0
  56 
  57 trainer = pl.Trainer(max_epochs=10, gpus=gpus)
  58 trainer.fit(model=model, train_dataloaders=dataloader)
  59
  [end of examples/pytorch_lightning/barlowtwins.py]
  </code>
  
  I need you to solve this issue by generating a single patch file that I can apply directly to this repository using git apply. Please respond with a single patch file in the following format.
  <patch>
  diff --git a/file.py b/file.py
  --- a/file.py
  +++ b/file.py
  @@ -1,27 +1,35 @@
   def euclidean(a, b):
  -    while b:
  -        a, b = b, a % b
  -    return a
  +    if b == 0:
  +        return a
  +    return euclidean(b, a % b)
   
   
   def bresenham(x0, y0, x1, y1):
       points = []
       dx = abs(x1 - x0)
       dy = abs(y1 - y0)
  -    sx = 1 if x0 < x1 else -1
  -    sy = 1 if y0 < y1 else -1
  -    err = dx - dy
  +    x, y = x0, y0
  +    sx = -1 if x0 > x1 else 1
  +    sy = -1 if y0 > y1 else 1
   
  -    while True:
  -        points.append((x0, y0))
  -        if x0 == x1 and y0 == y1:
  -            break
  -        e2 = 2 * err
  -        if e2 > -dy:
  -            err -= dy
  -            x0 += sx
  -        if e2 < dx:
  -            err += dx
  -            y0 += sy
  +    if dx > dy:
  +        err = dx / 2.0
  +        while x != x1:
  +            points.append((x, y))
  +            err -= dy
  +            if err < 0:
  +                y += sy
  +                err += dx
  +            x += sx
  +    else:
  +        err = dy / 2.0
  +        while y != y1:
  +            points.append((x, y))
  +            err -= dx
  +            if err < 0:
  +                x += sx
  +                err += dy
  +            y += sy
  +
  +    points.append((x, y))
       return points
  
  </patch>
golden_diff: diff --git a/examples/pytorch_lightning/barlowtwins.py b/examples/pytorch_lightning/barlowtwins.py
  --- a/examples/pytorch_lightning/barlowtwins.py
  +++ b/examples/pytorch_lightning/barlowtwins.py
  @@ -41,7 +41,7 @@
   # or create a dataset from a folder containing images or videos:
   # dataset = LightlyDataset("path/to/folder")
   
  -collate_fn = ImageCollateFunction(input_size=224)
  +collate_fn = ImageCollateFunction(input_size=32)
   
   dataloader = torch.utils.data.DataLoader(
       dataset,
  
verification_info: {"golden_diff": "diff --git a/examples/pytorch_lightning/barlowtwins.py b/examples/pytorch_lightning/barlowtwins.py\n--- a/examples/pytorch_lightning/barlowtwins.py\n+++ b/examples/pytorch_lightning/barlowtwins.py\n@@ -41,7 +41,7 @@\n # or create a dataset from a folder containing images or videos:\n # dataset = LightlyDataset(\"path/to/folder\")\n \n-collate_fn = ImageCollateFunction(input_size=224)\n+collate_fn = ImageCollateFunction(input_size=32)\n \n dataloader = torch.utils.data.DataLoader(\n     dataset,\n", "issue": "Incorrect inputsize for BarlowTwins Lightning Example Code\nShould the input_size in [1] be `32` instead of `224`?\r\nIn [2], we use `input_size=32`.\r\n\r\n[1] https://github.com/lightly-ai/lightly/blob/master/examples/pytorch_lightning/barlowtwins.py#L44\r\n[2] https://github.com/lightly-ai/lightly/blob/master/examples/pytorch/barlowtwins.py#L35\r\n\r\n\n", "before_files": [{"content": "import torch\nfrom torch import nn\nimport torchvision\nimport pytorch_lightning as pl\n\nfrom lightly.data import LightlyDataset\nfrom lightly.data import ImageCollateFunction\nfrom lightly.loss import BarlowTwinsLoss\nfrom lightly.models.modules import BarlowTwinsProjectionHead\n\n\nclass BarlowTwins(pl.LightningModule):\n    def __init__(self):\n        super().__init__()\n        resnet = torchvision.models.resnet18()\n        self.backbone = nn.Sequential(*list(resnet.children())[:-1])\n        self.projection_head = BarlowTwinsProjectionHead(512, 2048, 2048)\n        self.criterion = BarlowTwinsLoss()\n\n    def forward(self, x):\n        x = self.backbone(x).flatten(start_dim=1)\n        z = self.projection_head(x)\n        return z\n\n    def training_step(self, batch, batch_index):\n        (x0, x1), _, _ = batch\n        z0 = self.forward(x0)\n        z1 = self.forward(x1)\n        loss = self.criterion(z0, z1)\n        return loss\n\n    def configure_optimizers(self):\n        optim = torch.optim.SGD(self.parameters(), lr=0.06)\n        return optim\n\n\nmodel = BarlowTwins()\n\ncifar10 = torchvision.datasets.CIFAR10(\"datasets/cifar10\", download=True)\ndataset = LightlyDataset.from_torch_dataset(cifar10)\n# or create a dataset from a folder containing images or videos:\n# dataset = LightlyDataset(\"path/to/folder\")\n\ncollate_fn = ImageCollateFunction(input_size=224)\n\ndataloader = torch.utils.data.DataLoader(\n    dataset,\n    batch_size=256,\n    collate_fn=collate_fn,\n    shuffle=True,\n    drop_last=True,\n    num_workers=8,\n)\n\ngpus = 1 if torch.cuda.is_available() else 0\n\ntrainer = pl.Trainer(max_epochs=10, gpus=gpus)\ntrainer.fit(model=model, train_dataloaders=dataloader)\n", "path": "examples/pytorch_lightning/barlowtwins.py"}]}