core

Some utility functions for working with PyTorch

source

set_seed

 set_seed (seed:int, deterministic:bool=False)

Sets the seed for generating random numbers in PyTorch, NumPy, and Python’s random module.

This function is used for reproducibility in stochastic operations, e.g. shuffling in data loaders, random initializations in neural networks, etc.

Note: The deterministic flag does not guarantee complete reproducibility. Operations which rely on CUDA might still produce non-deterministic results.

Type Default Details
seed int The seed value to be set for all random number generators.
deterministic bool False If True, uses deterministic algorithms in PyTorch where possible for reproducibility, at the cost of performance.
Returns None
seed = 1234
set_seed(seed)

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pil_to_tensor

 pil_to_tensor (img:<module'PIL.Image'from'/opt/hostedtoolcache/Python/3.9
                .18/x64/lib/python3.9/site-packages/PIL/Image.py'>,
                mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])

Converts a PIL image to a normalized and batched PyTorch tensor.

Returns: The normalized and batched tensor.

Type Default Details
img PIL.Image The input PIL image.
mean list [0.485, 0.456, 0.406] The mean values for normalization.
std list [0.229, 0.224, 0.225] The standard deviation values for normalization.

Set the path for the images directory

img_dir = Path('../images/')
img_dir
Path('../images')

Open sample image

img_path = '../images/cat.jpg'
src_img = Image.open(img_path).convert('RGB')
print(f"Image Size: {src_img.size}")
src_img
Image Size: (768, 512)

Convert image to tensor

img_tensor = pil_to_tensor(src_img, [0.5], [0.5])
img_tensor.shape, img_tensor.min(), img_tensor.max()
(torch.Size([1, 3, 512, 768]), tensor(-1.), tensor(1.))

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tensor_to_pil

 tensor_to_pil (tensor:torch.Tensor)

Convert a tensor to a PIL image.

Returns: img (PIL.Image): The PIL image

Type Details
tensor Tensor The tensor to be converted

Convert tensor to image

tensor_img = tensor_to_pil(transforms.ToTensor()(src_img))
tensor_img


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iterate_modules

 iterate_modules (module:torch.nn.modules.module.Module)

A generator function that yields the children and grandchildren of a PyTorch module.

Type Details
module Module A PyTorch module that contains child modules to be iterated over.
from torchvision import models

vgg = models.vgg16(weights=models.VGG16_Weights.IMAGENET1K_V1).features

for index, module in enumerate(iterate_modules(vgg)):
    if type(module) == torch.nn.modules.activation.ReLU:
        print(f"{index}: {module}")
1: ReLU(inplace=True)
3: ReLU(inplace=True)
6: ReLU(inplace=True)
8: ReLU(inplace=True)
11: ReLU(inplace=True)
13: ReLU(inplace=True)
15: ReLU(inplace=True)
18: ReLU(inplace=True)
20: ReLU(inplace=True)
22: ReLU(inplace=True)
25: ReLU(inplace=True)
27: ReLU(inplace=True)
29: ReLU(inplace=True)
Downloading: "https://download.pytorch.org/models/vgg16-397923af.pth" to /home/runner/.cache/torch/hub/checkpoints/vgg16-397923af.pth
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tensor_stats_df

 tensor_stats_df (tensor, attrs=['mean', 'std', 'min', 'max'], shape=True)

Calculate and return statistics of a given tensor as a pandas DataFrame.

Type Default Details
tensor Input tensor for which statistics are to be calculated.
attrs list [‘mean’, ‘std’, ‘min’, ‘max’] List of statistics to be calculated.
shape bool True If True, include shape of the tensor in the output.
tensor_stats_df(torch.randn(1, 3, 256, 256))
0
mean 0.003342
std 0.99868
min -4.558271
max 4.815985
shape (1, 3, 256, 256)

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get_torch_device

 get_torch_device ()

This function returns the device to be used for PyTorch computations.

Returns: str: “mps” if Metal Performance Shaders (MPS) for MacOS is available, “cuda” if CUDA is available, “cpu” otherwise

get_torch_device()
'cuda'

source

denorm_img_tensor

 denorm_img_tensor (img_tensor:torch.Tensor, mean:list, std:list)

Denormalize an image tensor.

Returns: torch.Tensor: The tensor representing the denormalized image.

Type Details
img_tensor Tensor The tensor representing the normalized image.
mean list The mean values used for normalization.
std list The standard deviation values used for normalization.
tensor_to_pil(img_tensor)

tensor_to_pil(denorm_img_tensor(img_tensor, [0.5], [0.5]))


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move_data_to_device

 move_data_to_device (data, device:torch.device)

Recursively move data to the specified device.

This function takes a data structure (could be a tensor, list, tuple, or dictionary) and moves all tensors within the structure to the given PyTorch device.

Type Details
data Data to move to the device.
device device The PyTorch device to move the data to.
data_tuple = (
    {
        'masks': torch.tensor([[[0, 1, 2], [3, 4, 5], [6, 7, 8]]], dtype=torch.int32),
        'boxes': torch.tensor([[19.9176, 246.6063, 512.0000, 438.8571]]),
        'labels': torch.tensor([0.]),
    },
    {
        'masks': torch.tensor([[[0, 0, 0], [0, 0, 0], [0, 0, 0]]], dtype=torch.int32),
        'boxes': torch.tensor([[19.9176, 246.6063, 512.0000, 438.8571]]),
        'labels': torch.tensor([0.]),
    },
)

move_data_to_device(data_tuple, 'cpu')
({'masks': tensor([[[0, 1, 2],
           [3, 4, 5],
           [6, 7, 8]]], dtype=torch.int32),
  'boxes': tensor([[ 19.9176, 246.6063, 512.0000, 438.8571]]),
  'labels': tensor([0.])},
 {'masks': tensor([[[0, 0, 0],
           [0, 0, 0],
           [0, 0, 0]]], dtype=torch.int32),
  'boxes': tensor([[ 19.9176, 246.6063, 512.0000, 438.8571]]),
  'labels': tensor([0.])})