prompt: You will be provided with a partial code base and an issue statement explaining a problem to resolve.
  <issue>
  Perform penalty calculation after all sanity checks are completed
  **Is your feature request related to a problem? Please describe.**
  The penalty calculation takes a long time, and there are sanity checks that happen after this, which can be a pain.
  
  **Describe the solution you'd like**
  It would be great to have these checks before the penalty calculation for quality-of-life improvements.
  
  **Describe alternatives you've considered**
  N.A.
  
  **Additional context**
  From Evan C.
  
  </issue>
  <code>
  [start of GANDLF/compute/generic.py]
  1 from GANDLF.models import get_model
  2 from GANDLF.schedulers import get_scheduler
  3 from GANDLF.optimizers import get_optimizer
  4 from GANDLF.data import (
  5     get_train_loader,
  6     get_validation_loader,
  7 )
  8 from GANDLF.utils import (
  9     populate_header_in_parameters,
  10     parseTrainingCSV,
  11     send_model_to_device,
  12     get_class_imbalance_weights,
  13 )
  14 
  15 
  16 def create_pytorch_objects(parameters, train_csv=None, val_csv=None, device="cpu"):
  17     """
  18     This function creates all the PyTorch objects needed for training.
  19 
  20     Args:
  21         parameters (dict): The parameters dictionary.
  22         train_csv (str): The path to the training CSV file.
  23         val_csv (str): The path to the validation CSV file.
  24         device (str): The device to perform computations on.
  25 
  26     Returns:
  27         model (torch.nn.Module): The model to use for training.
  28         optimizer (Optimizer): The optimizer to use for training.
  29         train_loader (torch.utils.data.DataLoader): The training data loader.
  30         val_loader (torch.utils.data.DataLoader): The validation data loader.
  31         scheduler (object): The scheduler to use for training.
  32         parameters (dict): The updated parameters dictionary.
  33     """
  34     # initialize train and val loaders
  35     train_loader, val_loader = None, None
  36     headers_to_populate_train, headers_to_populate_val = None, None
  37 
  38     if train_csv is not None:
  39         # populate the data frames
  40         parameters["training_data"], headers_to_populate_train = parseTrainingCSV(
  41             train_csv, train=True
  42         )
  43         parameters = populate_header_in_parameters(
  44             parameters, headers_to_populate_train
  45         )
  46         # get the train loader
  47         train_loader = get_train_loader(parameters)
  48         parameters["training_samples_size"] = len(train_loader)
  49 
  50         # Calculate the weights here
  51         (
  52             parameters["weights"],
  53             parameters["class_weights"],
  54         ) = get_class_imbalance_weights(parameters["training_data"], parameters)
  55 
  56     if val_csv is not None:
  57         parameters["validation_data"], headers_to_populate_val = parseTrainingCSV(
  58             val_csv, train=False
  59         )
  60         if headers_to_populate_train is None:
  61             parameters = populate_header_in_parameters(
  62                 parameters, headers_to_populate_val
  63             )
  64         # get the validation loader
  65         val_loader = get_validation_loader(parameters)
  66 
  67     # get the model
  68     model = get_model(parameters)
  69     parameters["model_parameters"] = model.parameters()
  70 
  71     # get the optimizer
  72     optimizer = get_optimizer(parameters)
  73     parameters["optimizer_object"] = optimizer
  74 
  75     # send model to correct device
  76     (
  77         model,
  78         parameters["model"]["amp"],
  79         parameters["device"],
  80         parameters["device_id"],
  81     ) = send_model_to_device(
  82         model, amp=parameters["model"]["amp"], device=device, optimizer=optimizer
  83     )
  84 
  85     # only need to create scheduler if training
  86     if train_csv is not None:
  87         if not ("step_size" in parameters["scheduler"]):
  88             parameters["scheduler"]["step_size"] = (
  89                 parameters["training_samples_size"] / parameters["learning_rate"]
  90             )
  91 
  92         scheduler = get_scheduler(parameters)
  93     else:
  94         scheduler = None
  95 
  96     # these keys contain generators, and are not needed beyond this point in params
  97     generator_keys_to_remove = ["optimizer_object", "model_parameters"]
  98     for key in generator_keys_to_remove:
  99         parameters.pop(key, None)
  100 
  101     return model, optimizer, train_loader, val_loader, scheduler, parameters
  102
  [end of GANDLF/compute/generic.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/GANDLF/compute/generic.py b/GANDLF/compute/generic.py
  --- a/GANDLF/compute/generic.py
  +++ b/GANDLF/compute/generic.py
  @@ -47,12 +47,6 @@
           train_loader = get_train_loader(parameters)
           parameters["training_samples_size"] = len(train_loader)
   
  -        # Calculate the weights here
  -        (
  -            parameters["weights"],
  -            parameters["class_weights"],
  -        ) = get_class_imbalance_weights(parameters["training_data"], parameters)
  -
       if val_csv is not None:
           parameters["validation_data"], headers_to_populate_val = parseTrainingCSV(
               val_csv, train=False
  @@ -90,6 +84,13 @@
               )
   
           scheduler = get_scheduler(parameters)
  +
  +        # Calculate the weights here
  +        (
  +            parameters["weights"],
  +            parameters["class_weights"],
  +        ) = get_class_imbalance_weights(parameters["training_data"], parameters)
  +
       else:
           scheduler = None
  
verification_info: {"golden_diff": "diff --git a/GANDLF/compute/generic.py b/GANDLF/compute/generic.py\n--- a/GANDLF/compute/generic.py\n+++ b/GANDLF/compute/generic.py\n@@ -47,12 +47,6 @@\n         train_loader = get_train_loader(parameters)\n         parameters[\"training_samples_size\"] = len(train_loader)\n \n-        # Calculate the weights here\n-        (\n-            parameters[\"weights\"],\n-            parameters[\"class_weights\"],\n-        ) = get_class_imbalance_weights(parameters[\"training_data\"], parameters)\n-\n     if val_csv is not None:\n         parameters[\"validation_data\"], headers_to_populate_val = parseTrainingCSV(\n             val_csv, train=False\n@@ -90,6 +84,13 @@\n             )\n \n         scheduler = get_scheduler(parameters)\n+\n+        # Calculate the weights here\n+        (\n+            parameters[\"weights\"],\n+            parameters[\"class_weights\"],\n+        ) = get_class_imbalance_weights(parameters[\"training_data\"], parameters)\n+\n     else:\n         scheduler = None\n", "issue": "Perform penalty calculation after all sanity checks are completed\n**Is your feature request related to a problem? Please describe.**\r\nThe penalty calculation takes a long time, and there are sanity checks that happen after this, which can be a pain.\r\n\r\n**Describe the solution you'd like**\r\nIt would be great to have these checks before the penalty calculation for quality-of-life improvements.\r\n\r\n**Describe alternatives you've considered**\r\nN.A.\r\n\r\n**Additional context**\r\nFrom Evan C.\n", "before_files": [{"content": "from GANDLF.models import get_model\nfrom GANDLF.schedulers import get_scheduler\nfrom GANDLF.optimizers import get_optimizer\nfrom GANDLF.data import (\n    get_train_loader,\n    get_validation_loader,\n)\nfrom GANDLF.utils import (\n    populate_header_in_parameters,\n    parseTrainingCSV,\n    send_model_to_device,\n    get_class_imbalance_weights,\n)\n\n\ndef create_pytorch_objects(parameters, train_csv=None, val_csv=None, device=\"cpu\"):\n    \"\"\"\n    This function creates all the PyTorch objects needed for training.\n\n    Args:\n        parameters (dict): The parameters dictionary.\n        train_csv (str): The path to the training CSV file.\n        val_csv (str): The path to the validation CSV file.\n        device (str): The device to perform computations on.\n\n    Returns:\n        model (torch.nn.Module): The model to use for training.\n        optimizer (Optimizer): The optimizer to use for training.\n        train_loader (torch.utils.data.DataLoader): The training data loader.\n        val_loader (torch.utils.data.DataLoader): The validation data loader.\n        scheduler (object): The scheduler to use for training.\n        parameters (dict): The updated parameters dictionary.\n    \"\"\"\n    # initialize train and val loaders\n    train_loader, val_loader = None, None\n    headers_to_populate_train, headers_to_populate_val = None, None\n\n    if train_csv is not None:\n        # populate the data frames\n        parameters[\"training_data\"], headers_to_populate_train = parseTrainingCSV(\n            train_csv, train=True\n        )\n        parameters = populate_header_in_parameters(\n            parameters, headers_to_populate_train\n        )\n        # get the train loader\n        train_loader = get_train_loader(parameters)\n        parameters[\"training_samples_size\"] = len(train_loader)\n\n        # Calculate the weights here\n        (\n            parameters[\"weights\"],\n            parameters[\"class_weights\"],\n        ) = get_class_imbalance_weights(parameters[\"training_data\"], parameters)\n\n    if val_csv is not None:\n        parameters[\"validation_data\"], headers_to_populate_val = parseTrainingCSV(\n            val_csv, train=False\n        )\n        if headers_to_populate_train is None:\n            parameters = populate_header_in_parameters(\n                parameters, headers_to_populate_val\n            )\n        # get the validation loader\n        val_loader = get_validation_loader(parameters)\n\n    # get the model\n    model = get_model(parameters)\n    parameters[\"model_parameters\"] = model.parameters()\n\n    # get the optimizer\n    optimizer = get_optimizer(parameters)\n    parameters[\"optimizer_object\"] = optimizer\n\n    # send model to correct device\n    (\n        model,\n        parameters[\"model\"][\"amp\"],\n        parameters[\"device\"],\n        parameters[\"device_id\"],\n    ) = send_model_to_device(\n        model, amp=parameters[\"model\"][\"amp\"], device=device, optimizer=optimizer\n    )\n\n    # only need to create scheduler if training\n    if train_csv is not None:\n        if not (\"step_size\" in parameters[\"scheduler\"]):\n            parameters[\"scheduler\"][\"step_size\"] = (\n                parameters[\"training_samples_size\"] / parameters[\"learning_rate\"]\n            )\n\n        scheduler = get_scheduler(parameters)\n    else:\n        scheduler = None\n\n    # these keys contain generators, and are not needed beyond this point in params\n    generator_keys_to_remove = [\"optimizer_object\", \"model_parameters\"]\n    for key in generator_keys_to_remove:\n        parameters.pop(key, None)\n\n    return model, optimizer, train_loader, val_loader, scheduler, parameters\n", "path": "GANDLF/compute/generic.py"}]}