prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  Did cifar10 dataset need gaussian blur
  In the https://github.com/lightly-ai/lightly/blob/master/lightly/data/collate.py,  class SimCLRCollateFunction() presents a n example for using it,
  collate_fn = SimCLRCollateFunction(
          >>>     input_size=32,
          >>>     gaussian_blur=0.,
          >>> )
  but in https://docs.lightly.ai/examples/simclr.html
  collate_fn = SimCLRCollateFunction(input_size=32)
  so I wonder which one is the one you suggested?
  
  --- END ISSUE ---
  
  Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
  --- BEGIN FILES ---
  Path: `examples/pytorch/simclr.py`
  Content:
  ```
  1 import torch
  2 from torch import nn
  3 import torchvision
  4 
  5 from lightly.data import LightlyDataset
  6 from lightly.data import SimCLRCollateFunction
  7 from lightly.loss import NTXentLoss
  8 from lightly.models.modules import SimCLRProjectionHead
  9 
  10 
  11 class SimCLR(nn.Module):
  12     def __init__(self, backbone):
  13         super().__init__()
  14         self.backbone = backbone
  15         self.projection_head = SimCLRProjectionHead(512, 512, 128)
  16 
  17     def forward(self, x):
  18         x = self.backbone(x).flatten(start_dim=1)
  19         z = self.projection_head(x)
  20         return z
  21 
  22 
  23 resnet = torchvision.models.resnet18()
  24 backbone = nn.Sequential(*list(resnet.children())[:-1])
  25 model = SimCLR(backbone)
  26 
  27 device = "cuda" if torch.cuda.is_available() else "cpu"
  28 model.to(device)
  29 
  30 cifar10 = torchvision.datasets.CIFAR10("datasets/cifar10", download=True)
  31 dataset = LightlyDataset.from_torch_dataset(cifar10)
  32 # or create a dataset from a folder containing images or videos:
  33 # dataset = LightlyDataset("path/to/folder")
  34 
  35 collate_fn = SimCLRCollateFunction(input_size=32)
  36 
  37 dataloader = torch.utils.data.DataLoader(
  38     dataset,
  39     batch_size=256,
  40     collate_fn=collate_fn,
  41     shuffle=True,
  42     drop_last=True,
  43     num_workers=8,
  44 )
  45 
  46 criterion = NTXentLoss()
  47 optimizer = torch.optim.SGD(model.parameters(), lr=0.06)
  48 
  49 print("Starting Training")
  50 for epoch in range(10):
  51     total_loss = 0
  52     for (x0, x1), _, _ in dataloader:
  53         x0 = x0.to(device)
  54         x1 = x1.to(device)
  55         z0 = model(x0)
  56         z1 = model(x1)
  57         loss = criterion(z0, z1)
  58         total_loss += loss.detach()
  59         loss.backward()
  60         optimizer.step()
  61         optimizer.zero_grad()
  62     avg_loss = total_loss / len(dataloader)
  63     print(f"epoch: {epoch:>02}, loss: {avg_loss:.5f}")
  64
  ```
  
  Path: `examples/pytorch_lightning/simclr.py`
  Content:
  ```
  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 SimCLRCollateFunction
  8 from lightly.loss import NTXentLoss
  9 from lightly.models.modules import SimCLRProjectionHead
  10 
  11 
  12 class SimCLR(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 = SimCLRProjectionHead(512, 2048, 2048)
  18         self.criterion = NTXentLoss()
  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 = SimCLR()
  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 = SimCLRCollateFunction(input_size=32)
  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 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/examples/pytorch/simclr.py b/examples/pytorch/simclr.py
  --- a/examples/pytorch/simclr.py
  +++ b/examples/pytorch/simclr.py
  @@ -32,7 +32,10 @@
   # or create a dataset from a folder containing images or videos:
   # dataset = LightlyDataset("path/to/folder")
   
  -collate_fn = SimCLRCollateFunction(input_size=32)
  +collate_fn = SimCLRCollateFunction(
  +    input_size=32,
  +    gaussian_blur=0.,
  +)
   
   dataloader = torch.utils.data.DataLoader(
       dataset,
  diff --git a/examples/pytorch_lightning/simclr.py b/examples/pytorch_lightning/simclr.py
  --- a/examples/pytorch_lightning/simclr.py
  +++ b/examples/pytorch_lightning/simclr.py
  @@ -41,7 +41,10 @@
   # or create a dataset from a folder containing images or videos:
   # dataset = LightlyDataset("path/to/folder")
   
  -collate_fn = SimCLRCollateFunction(input_size=32)
  +collate_fn = SimCLRCollateFunction(
  +    input_size=32,
  +    gaussian_blur=0.,
  +)
   
   dataloader = torch.utils.data.DataLoader(
       dataset,
  
verification_info: {"golden_diff": "diff --git a/examples/pytorch/simclr.py b/examples/pytorch/simclr.py\n--- a/examples/pytorch/simclr.py\n+++ b/examples/pytorch/simclr.py\n@@ -32,7 +32,10 @@\n # or create a dataset from a folder containing images or videos:\n # dataset = LightlyDataset(\"path/to/folder\")\n \n-collate_fn = SimCLRCollateFunction(input_size=32)\n+collate_fn = SimCLRCollateFunction(\n+    input_size=32,\n+    gaussian_blur=0.,\n+)\n \n dataloader = torch.utils.data.DataLoader(\n     dataset,\ndiff --git a/examples/pytorch_lightning/simclr.py b/examples/pytorch_lightning/simclr.py\n--- a/examples/pytorch_lightning/simclr.py\n+++ b/examples/pytorch_lightning/simclr.py\n@@ -41,7 +41,10 @@\n # or create a dataset from a folder containing images or videos:\n # dataset = LightlyDataset(\"path/to/folder\")\n \n-collate_fn = SimCLRCollateFunction(input_size=32)\n+collate_fn = SimCLRCollateFunction(\n+    input_size=32,\n+    gaussian_blur=0.,\n+)\n \n dataloader = torch.utils.data.DataLoader(\n     dataset,\n", "issue": "Did cifar10 dataset need gaussian blur\nIn the https://github.com/lightly-ai/lightly/blob/master/lightly/data/collate.py,  class SimCLRCollateFunction() presents a n example for using it,\r\ncollate_fn = SimCLRCollateFunction(\r\n        >>>     input_size=32,\r\n        >>>     gaussian_blur=0.,\r\n        >>> )\r\nbut in https://docs.lightly.ai/examples/simclr.html\r\ncollate_fn = SimCLRCollateFunction(input_size=32)\r\nso I wonder which one is the one you suggested?\n", "before_files": [{"content": "import torch\nfrom torch import nn\nimport torchvision\n\nfrom lightly.data import LightlyDataset\nfrom lightly.data import SimCLRCollateFunction\nfrom lightly.loss import NTXentLoss\nfrom lightly.models.modules import SimCLRProjectionHead\n\n\nclass SimCLR(nn.Module):\n    def __init__(self, backbone):\n        super().__init__()\n        self.backbone = backbone\n        self.projection_head = SimCLRProjectionHead(512, 512, 128)\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\nresnet = torchvision.models.resnet18()\nbackbone = nn.Sequential(*list(resnet.children())[:-1])\nmodel = SimCLR(backbone)\n\ndevice = \"cuda\" if torch.cuda.is_available() else \"cpu\"\nmodel.to(device)\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 = SimCLRCollateFunction(input_size=32)\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\ncriterion = NTXentLoss()\noptimizer = torch.optim.SGD(model.parameters(), lr=0.06)\n\nprint(\"Starting Training\")\nfor epoch in range(10):\n    total_loss = 0\n    for (x0, x1), _, _ in dataloader:\n        x0 = x0.to(device)\n        x1 = x1.to(device)\n        z0 = model(x0)\n        z1 = model(x1)\n        loss = criterion(z0, z1)\n        total_loss += loss.detach()\n        loss.backward()\n        optimizer.step()\n        optimizer.zero_grad()\n    avg_loss = total_loss / len(dataloader)\n    print(f\"epoch: {epoch:>02}, loss: {avg_loss:.5f}\")\n", "path": "examples/pytorch/simclr.py"}, {"content": "import torch\nfrom torch import nn\nimport torchvision\nimport pytorch_lightning as pl\n\nfrom lightly.data import LightlyDataset\nfrom lightly.data import SimCLRCollateFunction\nfrom lightly.loss import NTXentLoss\nfrom lightly.models.modules import SimCLRProjectionHead\n\n\nclass SimCLR(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 = SimCLRProjectionHead(512, 2048, 2048)\n        self.criterion = NTXentLoss()\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 = SimCLR()\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 = SimCLRCollateFunction(input_size=32)\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/simclr.py"}], "after_files": [{"content": "import torch\nfrom torch import nn\nimport torchvision\n\nfrom lightly.data import LightlyDataset\nfrom lightly.data import SimCLRCollateFunction\nfrom lightly.loss import NTXentLoss\nfrom lightly.models.modules import SimCLRProjectionHead\n\n\nclass SimCLR(nn.Module):\n    def __init__(self, backbone):\n        super().__init__()\n        self.backbone = backbone\n        self.projection_head = SimCLRProjectionHead(512, 512, 128)\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\nresnet = torchvision.models.resnet18()\nbackbone = nn.Sequential(*list(resnet.children())[:-1])\nmodel = SimCLR(backbone)\n\ndevice = \"cuda\" if torch.cuda.is_available() else \"cpu\"\nmodel.to(device)\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 = SimCLRCollateFunction(\n    input_size=32,\n    gaussian_blur=0.,\n)\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\ncriterion = NTXentLoss()\noptimizer = torch.optim.SGD(model.parameters(), lr=0.06)\n\nprint(\"Starting Training\")\nfor epoch in range(10):\n    total_loss = 0\n    for (x0, x1), _, _ in dataloader:\n        x0 = x0.to(device)\n        x1 = x1.to(device)\n        z0 = model(x0)\n        z1 = model(x1)\n        loss = criterion(z0, z1)\n        total_loss += loss.detach()\n        loss.backward()\n        optimizer.step()\n        optimizer.zero_grad()\n    avg_loss = total_loss / len(dataloader)\n    print(f\"epoch: {epoch:>02}, loss: {avg_loss:.5f}\")\n", "path": "examples/pytorch/simclr.py"}, {"content": "import torch\nfrom torch import nn\nimport torchvision\nimport pytorch_lightning as pl\n\nfrom lightly.data import LightlyDataset\nfrom lightly.data import SimCLRCollateFunction\nfrom lightly.loss import NTXentLoss\nfrom lightly.models.modules import SimCLRProjectionHead\n\n\nclass SimCLR(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 = SimCLRProjectionHead(512, 2048, 2048)\n        self.criterion = NTXentLoss()\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 = SimCLR()\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 = SimCLRCollateFunction(\n    input_size=32,\n    gaussian_blur=0.,\n)\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/simclr.py"}]}