prompt: You will be provided with a partial code base and an issue statement explaining a problem to resolve.
  <issue>
  ContrastiveLoss with dynamic batchsize
  **Is your feature request related to a problem? Please describe.**
  When we instantiate an instance of type class `ContrastiveLoss` we need to pass in the parameter for `batch_size`. This assumes that the batch size remains constant all through the training(which is not always the case). 
  
  **Describe the solution you'd like**
  As we know and have fixed in the [docs](https://docs.monai.io/en/latest/losses.html#monai.losses.ContrastiveLoss) that the forward accepts inputs of shape `B[F]`, why not pick up the Batch size dynamically in the forward. Is there a reason why it is not designed in that fashion?
  
  I would be happy to put a pull request if it is okay.
  
  
  </issue>
  <code>
  [start of monai/losses/contrastive.py]
  1 # Copyright (c) MONAI Consortium
  2 # Licensed under the Apache License, Version 2.0 (the "License");
  3 # you may not use this file except in compliance with the License.
  4 # You may obtain a copy of the License at
  5 #     http://www.apache.org/licenses/LICENSE-2.0
  6 # Unless required by applicable law or agreed to in writing, software
  7 # distributed under the License is distributed on an "AS IS" BASIS,
  8 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  9 # See the License for the specific language governing permissions and
  10 # limitations under the License.
  11 
  12 import torch
  13 from torch.nn import functional as F
  14 from torch.nn.modules.loss import _Loss
  15 
  16 from monai.utils import deprecated_arg
  17 
  18 
  19 class ContrastiveLoss(_Loss):
  20 
  21     """
  22     Compute the Contrastive loss defined in:
  23 
  24         Chen, Ting, et al. "A simple framework for contrastive learning of visual representations." International
  25         conference on machine learning. PMLR, 2020. (http://proceedings.mlr.press/v119/chen20j.html)
  26 
  27     Adapted from:
  28         https://github.com/Sara-Ahmed/SiT/blob/1aacd6adcd39b71efc903d16b4e9095b97dda76f/losses.py#L5
  29 
  30     """
  31 
  32     @deprecated_arg(name="reduction", since="0.8", msg_suffix="`reduction` is no longer supported.")
  33     def __init__(self, temperature: float = 0.5, batch_size: int = 1, reduction="sum") -> None:
  34         """
  35         Args:
  36             temperature: Can be scaled between 0 and 1 for learning from negative samples, ideally set to 0.5.
  37             batch_size: The number of samples.
  38 
  39         Raises:
  40             ValueError: When an input of dimension length > 2 is passed
  41             ValueError: When input and target are of different shapes
  42 
  43         .. deprecated:: 0.8.0
  44 
  45             `reduction` is no longer supported.
  46 
  47         """
  48         super().__init__()
  49 
  50         self.batch_size = batch_size
  51         self.temperature = temperature
  52 
  53     def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
  54         """
  55         Args:
  56             input: the shape should be B[F].
  57             target: the shape should be B[F].
  58         """
  59         if len(target.shape) > 2 or len(input.shape) > 2:
  60             raise ValueError(
  61                 f"Either target or input has dimensions greater than 2 where target "
  62                 f"shape is ({target.shape}) and input shape is ({input.shape})"
  63             )
  64 
  65         if target.shape != input.shape:
  66             raise ValueError(f"ground truth has differing shape ({target.shape}) from input ({input.shape})")
  67 
  68         temperature_tensor = torch.as_tensor(self.temperature).to(input.device)
  69 
  70         norm_i = F.normalize(input, dim=1)
  71         norm_j = F.normalize(target, dim=1)
  72 
  73         negatives_mask = ~torch.eye(self.batch_size * 2, self.batch_size * 2, dtype=torch.bool)
  74         negatives_mask = torch.clone(negatives_mask.type(torch.float)).to(input.device)
  75 
  76         repr = torch.cat([norm_i, norm_j], dim=0)
  77         sim_matrix = F.cosine_similarity(repr.unsqueeze(1), repr.unsqueeze(0), dim=2)
  78         sim_ij = torch.diag(sim_matrix, self.batch_size)
  79         sim_ji = torch.diag(sim_matrix, -self.batch_size)
  80 
  81         positives = torch.cat([sim_ij, sim_ji], dim=0)
  82         nominator = torch.exp(positives / temperature_tensor)
  83         denominator = negatives_mask * torch.exp(sim_matrix / temperature_tensor)
  84 
  85         loss_partial = -torch.log(nominator / torch.sum(denominator, dim=1))
  86 
  87         return torch.sum(loss_partial) / (2 * self.batch_size)
  88
  [end of monai/losses/contrastive.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/monai/losses/contrastive.py b/monai/losses/contrastive.py
  --- a/monai/losses/contrastive.py
  +++ b/monai/losses/contrastive.py
  @@ -9,6 +9,8 @@
   # See the License for the specific language governing permissions and
   # limitations under the License.
   
  +from distutils.log import warn
  +
   import torch
   from torch.nn import functional as F
   from torch.nn.modules.loss import _Loss
  @@ -30,11 +32,10 @@
       """
   
       @deprecated_arg(name="reduction", since="0.8", msg_suffix="`reduction` is no longer supported.")
  -    def __init__(self, temperature: float = 0.5, batch_size: int = 1, reduction="sum") -> None:
  +    def __init__(self, temperature: float = 0.5, batch_size: int = -1, reduction="sum") -> None:
           """
           Args:
               temperature: Can be scaled between 0 and 1 for learning from negative samples, ideally set to 0.5.
  -            batch_size: The number of samples.
   
           Raises:
               ValueError: When an input of dimension length > 2 is passed
  @@ -46,10 +47,11 @@
   
           """
           super().__init__()
  -
  -        self.batch_size = batch_size
           self.temperature = temperature
   
  +        if batch_size != -1:
  +            warn("batch_size is no longer required to be set. It will be estimated dynamically in the forward call")
  +
       def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
           """
           Args:
  @@ -66,17 +68,18 @@
               raise ValueError(f"ground truth has differing shape ({target.shape}) from input ({input.shape})")
   
           temperature_tensor = torch.as_tensor(self.temperature).to(input.device)
  +        batch_size = input.shape[0]
   
           norm_i = F.normalize(input, dim=1)
           norm_j = F.normalize(target, dim=1)
   
  -        negatives_mask = ~torch.eye(self.batch_size * 2, self.batch_size * 2, dtype=torch.bool)
  +        negatives_mask = ~torch.eye(batch_size * 2, batch_size * 2, dtype=torch.bool)
           negatives_mask = torch.clone(negatives_mask.type(torch.float)).to(input.device)
   
           repr = torch.cat([norm_i, norm_j], dim=0)
           sim_matrix = F.cosine_similarity(repr.unsqueeze(1), repr.unsqueeze(0), dim=2)
  -        sim_ij = torch.diag(sim_matrix, self.batch_size)
  -        sim_ji = torch.diag(sim_matrix, -self.batch_size)
  +        sim_ij = torch.diag(sim_matrix, batch_size)
  +        sim_ji = torch.diag(sim_matrix, -batch_size)
   
           positives = torch.cat([sim_ij, sim_ji], dim=0)
           nominator = torch.exp(positives / temperature_tensor)
  @@ -84,4 +87,4 @@
   
           loss_partial = -torch.log(nominator / torch.sum(denominator, dim=1))
   
  -        return torch.sum(loss_partial) / (2 * self.batch_size)
  +        return torch.sum(loss_partial) / (2 * batch_size)
  
verification_info: {"golden_diff": "diff --git a/monai/losses/contrastive.py b/monai/losses/contrastive.py\n--- a/monai/losses/contrastive.py\n+++ b/monai/losses/contrastive.py\n@@ -9,6 +9,8 @@\n # See the License for the specific language governing permissions and\n # limitations under the License.\n \n+from distutils.log import warn\n+\n import torch\n from torch.nn import functional as F\n from torch.nn.modules.loss import _Loss\n@@ -30,11 +32,10 @@\n     \"\"\"\n \n     @deprecated_arg(name=\"reduction\", since=\"0.8\", msg_suffix=\"`reduction` is no longer supported.\")\n-    def __init__(self, temperature: float = 0.5, batch_size: int = 1, reduction=\"sum\") -> None:\n+    def __init__(self, temperature: float = 0.5, batch_size: int = -1, reduction=\"sum\") -> None:\n         \"\"\"\n         Args:\n             temperature: Can be scaled between 0 and 1 for learning from negative samples, ideally set to 0.5.\n-            batch_size: The number of samples.\n \n         Raises:\n             ValueError: When an input of dimension length > 2 is passed\n@@ -46,10 +47,11 @@\n \n         \"\"\"\n         super().__init__()\n-\n-        self.batch_size = batch_size\n         self.temperature = temperature\n \n+        if batch_size != -1:\n+            warn(\"batch_size is no longer required to be set. It will be estimated dynamically in the forward call\")\n+\n     def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:\n         \"\"\"\n         Args:\n@@ -66,17 +68,18 @@\n             raise ValueError(f\"ground truth has differing shape ({target.shape}) from input ({input.shape})\")\n \n         temperature_tensor = torch.as_tensor(self.temperature).to(input.device)\n+        batch_size = input.shape[0]\n \n         norm_i = F.normalize(input, dim=1)\n         norm_j = F.normalize(target, dim=1)\n \n-        negatives_mask = ~torch.eye(self.batch_size * 2, self.batch_size * 2, dtype=torch.bool)\n+        negatives_mask = ~torch.eye(batch_size * 2, batch_size * 2, dtype=torch.bool)\n         negatives_mask = torch.clone(negatives_mask.type(torch.float)).to(input.device)\n \n         repr = torch.cat([norm_i, norm_j], dim=0)\n         sim_matrix = F.cosine_similarity(repr.unsqueeze(1), repr.unsqueeze(0), dim=2)\n-        sim_ij = torch.diag(sim_matrix, self.batch_size)\n-        sim_ji = torch.diag(sim_matrix, -self.batch_size)\n+        sim_ij = torch.diag(sim_matrix, batch_size)\n+        sim_ji = torch.diag(sim_matrix, -batch_size)\n \n         positives = torch.cat([sim_ij, sim_ji], dim=0)\n         nominator = torch.exp(positives / temperature_tensor)\n@@ -84,4 +87,4 @@\n \n         loss_partial = -torch.log(nominator / torch.sum(denominator, dim=1))\n \n-        return torch.sum(loss_partial) / (2 * self.batch_size)\n+        return torch.sum(loss_partial) / (2 * batch_size)\n", "issue": "ContrastiveLoss with dynamic batchsize\n**Is your feature request related to a problem? Please describe.**\r\nWhen we instantiate an instance of type class `ContrastiveLoss` we need to pass in the parameter for `batch_size`. This assumes that the batch size remains constant all through the training(which is not always the case). \r\n\r\n**Describe the solution you'd like**\r\nAs we know and have fixed in the [docs](https://docs.monai.io/en/latest/losses.html#monai.losses.ContrastiveLoss) that the forward accepts inputs of shape `B[F]`, why not pick up the Batch size dynamically in the forward. Is there a reason why it is not designed in that fashion?\r\n\r\nI would be happy to put a pull request if it is okay.\r\n\n", "before_files": [{"content": "# Copyright (c) MONAI Consortium\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#     http://www.apache.org/licenses/LICENSE-2.0\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport torch\nfrom torch.nn import functional as F\nfrom torch.nn.modules.loss import _Loss\n\nfrom monai.utils import deprecated_arg\n\n\nclass ContrastiveLoss(_Loss):\n\n    \"\"\"\n    Compute the Contrastive loss defined in:\n\n        Chen, Ting, et al. \"A simple framework for contrastive learning of visual representations.\" International\n        conference on machine learning. PMLR, 2020. (http://proceedings.mlr.press/v119/chen20j.html)\n\n    Adapted from:\n        https://github.com/Sara-Ahmed/SiT/blob/1aacd6adcd39b71efc903d16b4e9095b97dda76f/losses.py#L5\n\n    \"\"\"\n\n    @deprecated_arg(name=\"reduction\", since=\"0.8\", msg_suffix=\"`reduction` is no longer supported.\")\n    def __init__(self, temperature: float = 0.5, batch_size: int = 1, reduction=\"sum\") -> None:\n        \"\"\"\n        Args:\n            temperature: Can be scaled between 0 and 1 for learning from negative samples, ideally set to 0.5.\n            batch_size: The number of samples.\n\n        Raises:\n            ValueError: When an input of dimension length > 2 is passed\n            ValueError: When input and target are of different shapes\n\n        .. deprecated:: 0.8.0\n\n            `reduction` is no longer supported.\n\n        \"\"\"\n        super().__init__()\n\n        self.batch_size = batch_size\n        self.temperature = temperature\n\n    def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:\n        \"\"\"\n        Args:\n            input: the shape should be B[F].\n            target: the shape should be B[F].\n        \"\"\"\n        if len(target.shape) > 2 or len(input.shape) > 2:\n            raise ValueError(\n                f\"Either target or input has dimensions greater than 2 where target \"\n                f\"shape is ({target.shape}) and input shape is ({input.shape})\"\n            )\n\n        if target.shape != input.shape:\n            raise ValueError(f\"ground truth has differing shape ({target.shape}) from input ({input.shape})\")\n\n        temperature_tensor = torch.as_tensor(self.temperature).to(input.device)\n\n        norm_i = F.normalize(input, dim=1)\n        norm_j = F.normalize(target, dim=1)\n\n        negatives_mask = ~torch.eye(self.batch_size * 2, self.batch_size * 2, dtype=torch.bool)\n        negatives_mask = torch.clone(negatives_mask.type(torch.float)).to(input.device)\n\n        repr = torch.cat([norm_i, norm_j], dim=0)\n        sim_matrix = F.cosine_similarity(repr.unsqueeze(1), repr.unsqueeze(0), dim=2)\n        sim_ij = torch.diag(sim_matrix, self.batch_size)\n        sim_ji = torch.diag(sim_matrix, -self.batch_size)\n\n        positives = torch.cat([sim_ij, sim_ji], dim=0)\n        nominator = torch.exp(positives / temperature_tensor)\n        denominator = negatives_mask * torch.exp(sim_matrix / temperature_tensor)\n\n        loss_partial = -torch.log(nominator / torch.sum(denominator, dim=1))\n\n        return torch.sum(loss_partial) / (2 * self.batch_size)\n", "path": "monai/losses/contrastive.py"}]}