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103 lines
3.7 KiB
103 lines
3.7 KiB
# prep_torch_inference
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```python
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def prep_torch_inference(
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model_path: str = None,
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device: torch.device = None,
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*args,
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**kwargs,
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):
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"""
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Prepare a Torch model for inference.
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Args:
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model_path (str): Path to the model file.
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device (torch.device): Device to run the model on.
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*args: Additional positional arguments.
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**kwargs: Additional keyword arguments.
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Returns:
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torch.nn.Module: The prepared model.
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"""
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try:
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model = load_model_torch(model_path, device)
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model.eval()
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return model
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except Exception as e:
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# Add error handling code here
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print(f"Error occurred while preparing Torch model: {e}")
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return None
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```
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This method is part of the 'swarms.utils' module. It accepts a model file path and a torch device as input and returns a model that is ready for inference.
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## Detailed Functionality
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The method loads a PyTorch model from the file specified by `model_path`. This model is then moved to the specified `device` if it is provided. Subsequently, the method sets the model to evaluation mode by calling `model.eval()`. This is a crucial step when preparing a model for inference, as certain layers like dropout or batch normalization behave differently during training vs during evaluation.
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In the case of any exception (e.g., the model file not found or the device unavailable), it prints an error message and returns `None`.
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## Parameters
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| Parameter | Type | Description | Default |
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|-----------|------|-------------|---------|
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| model_path | str | Path to the model file. | None |
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| device | torch.device | Device to run the model on. | None |
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| args | tuple | Additional positional arguments. | None |
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| kwargs | dict | Additional keyword arguments. | None |
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## Returns
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| Type | Description |
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|------|-------------|
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| torch.nn.Module | The prepared model ready for inference. Returns `None` if any exception occurs. |
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## Usage Examples
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Here are some examples of how you can use the `prep_torch_inference` method. Before that, you need to import the necessary modules as follows:
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```python
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import torch
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from swarms.utils import prep_torch_inference, load_model_torch
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```
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### Example 1: Load a model for inference on CPU
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```python
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model_path = "saved_model.pth"
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model = prep_torch_inference(model_path)
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if model is not None:
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print("Model loaded successfully and is ready for inference.")
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else:
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print("Failed to load the model.")
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```
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### Example 2: Load a model for inference on CUDA device
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```python
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model_path = "saved_model.pth"
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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model = prep_torch_inference(model_path, device)
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if model is not None:
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print(f"Model loaded successfully on device {device} and is ready for inference.")
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else:
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print("Failed to load the model.")
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```
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### Example 3: Load a model with additional arguments for `load_model_torch`
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```python
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model_path = "saved_model.pth"
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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# Suppose load_model_torch accepts an additional argument, map_location
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model = prep_torch_inference(model_path, device, map_location=device)
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if model is not None:
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print(f"Model loaded successfully on device {device} and is ready for inference.")
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else:
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print("Failed to load the model.")
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```
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Please note, you need to ensure the given model path does exist and the device is available on your machine, else `prep_torch_inference` method will return `None`. Depending on the complexity and size of your models, loading them onto a specific device might take a while. So it's important that you take this into consideration when designing your machine learning workflows.
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