Source code for torchvision.transforms.v2.functional._misc

import math
from typing import List, Optional

import PIL.Image
import torch
from torch.nn.functional import conv2d, pad as torch_pad

from torchvision import tv_tensors
from torchvision.transforms._functional_tensor import _max_value
from torchvision.transforms.functional import pil_to_tensor, to_pil_image

from torchvision.utils import _log_api_usage_once

from ._utils import _get_kernel, _register_kernel_internal

[docs]def normalize( inpt: torch.Tensor, mean: List[float], std: List[float], inplace: bool = False, ) -> torch.Tensor: """See :class:`~torchvision.transforms.v2.Normalize` for details.""" if torch.jit.is_scripting(): return normalize_image(inpt, mean=mean, std=std, inplace=inplace) _log_api_usage_once(normalize) kernel = _get_kernel(normalize, type(inpt)) return kernel(inpt, mean=mean, std=std, inplace=inplace)
@_register_kernel_internal(normalize, torch.Tensor) @_register_kernel_internal(normalize, tv_tensors.Image) def normalize_image(image: torch.Tensor, mean: List[float], std: List[float], inplace: bool = False) -> torch.Tensor: if not image.is_floating_point(): raise TypeError(f"Input tensor should be a float tensor. Got {image.dtype}.") if image.ndim < 3: raise ValueError(f"Expected tensor to be a tensor image of size (..., C, H, W). Got {image.shape}.") if isinstance(std, (tuple, list)): divzero = not all(std) elif isinstance(std, (int, float)): divzero = std == 0 else: divzero = False if divzero: raise ValueError("std evaluated to zero, leading to division by zero.") dtype = image.dtype device = image.device mean = torch.as_tensor(mean, dtype=dtype, device=device) std = torch.as_tensor(std, dtype=dtype, device=device) if mean.ndim == 1: mean = mean.view(-1, 1, 1) if std.ndim == 1: std = std.view(-1, 1, 1) if inplace: image = image.sub_(mean) else: image = image.sub(mean) return image.div_(std) @_register_kernel_internal(normalize, tv_tensors.Video) def normalize_video(video: torch.Tensor, mean: List[float], std: List[float], inplace: bool = False) -> torch.Tensor: return normalize_image(video, mean, std, inplace=inplace)
[docs]def gaussian_blur(inpt: torch.Tensor, kernel_size: List[int], sigma: Optional[List[float]] = None) -> torch.Tensor: """See :class:`~torchvision.transforms.v2.GaussianBlur` for details.""" if torch.jit.is_scripting(): return gaussian_blur_image(inpt, kernel_size=kernel_size, sigma=sigma) _log_api_usage_once(gaussian_blur) kernel = _get_kernel(gaussian_blur, type(inpt)) return kernel(inpt, kernel_size=kernel_size, sigma=sigma)
def _get_gaussian_kernel1d(kernel_size: int, sigma: float, dtype: torch.dtype, device: torch.device) -> torch.Tensor: lim = (kernel_size - 1) / (2.0 * math.sqrt(2.0) * sigma) x = torch.linspace(-lim, lim, steps=kernel_size, dtype=dtype, device=device) kernel1d = torch.softmax(x.pow_(2).neg_(), dim=0) return kernel1d def _get_gaussian_kernel2d( kernel_size: List[int], sigma: List[float], dtype: torch.dtype, device: torch.device ) -> torch.Tensor: kernel1d_x = _get_gaussian_kernel1d(kernel_size[0], sigma[0], dtype, device) kernel1d_y = _get_gaussian_kernel1d(kernel_size[1], sigma[1], dtype, device) kernel2d = kernel1d_y.unsqueeze(-1) * kernel1d_x return kernel2d @_register_kernel_internal(gaussian_blur, torch.Tensor) @_register_kernel_internal(gaussian_blur, tv_tensors.Image) def gaussian_blur_image( image: torch.Tensor, kernel_size: List[int], sigma: Optional[List[float]] = None ) -> torch.Tensor: # TODO: consider deprecating integers from sigma on the future if isinstance(kernel_size, int): kernel_size = [kernel_size, kernel_size] elif len(kernel_size) != 2: raise ValueError(f"If kernel_size is a sequence its length should be 2. Got {len(kernel_size)}") for ksize in kernel_size: if ksize % 2 == 0 or ksize < 0: raise ValueError(f"kernel_size should have odd and positive integers. Got {kernel_size}") if sigma is None: sigma = [ksize * 0.15 + 0.35 for ksize in kernel_size] else: if isinstance(sigma, (list, tuple)): length = len(sigma) if length == 1: s = float(sigma[0]) sigma = [s, s] elif length != 2: raise ValueError(f"If sigma is a sequence, its length should be 2. Got {length}") elif isinstance(sigma, (int, float)): s = float(sigma) sigma = [s, s] else: raise TypeError(f"sigma should be either float or sequence of floats. Got {type(sigma)}") for s in sigma: if s <= 0.0: raise ValueError(f"sigma should have positive values. Got {sigma}") if image.numel() == 0: return image dtype = image.dtype shape = image.shape ndim = image.ndim if ndim == 3: image = image.unsqueeze(dim=0) elif ndim > 4: image = image.reshape((-1,) + shape[-3:]) fp = torch.is_floating_point(image) kernel = _get_gaussian_kernel2d(kernel_size, sigma, dtype=dtype if fp else torch.float32, device=image.device) kernel = kernel.expand(shape[-3], 1, kernel.shape[0], kernel.shape[1]) output = image if fp else # padding = (left, right, top, bottom) padding = [kernel_size[0] // 2, kernel_size[0] // 2, kernel_size[1] // 2, kernel_size[1] // 2] output = torch_pad(output, padding, mode="reflect") output = conv2d(output, kernel, groups=shape[-3]) if ndim == 3: output = output.squeeze(dim=0) elif ndim > 4: output = output.reshape(shape) if not fp: output = output.round_().to(dtype=dtype) return output @_register_kernel_internal(gaussian_blur, PIL.Image.Image) def _gaussian_blur_image_pil( image: PIL.Image.Image, kernel_size: List[int], sigma: Optional[List[float]] = None ) -> PIL.Image.Image: t_img = pil_to_tensor(image) output = gaussian_blur_image(t_img, kernel_size=kernel_size, sigma=sigma) return to_pil_image(output, mode=image.mode) @_register_kernel_internal(gaussian_blur, tv_tensors.Video) def gaussian_blur_video( video: torch.Tensor, kernel_size: List[int], sigma: Optional[List[float]] = None ) -> torch.Tensor: return gaussian_blur_image(video, kernel_size, sigma)
[docs]def to_dtype(inpt: torch.Tensor, dtype: torch.dtype = torch.float, scale: bool = False) -> torch.Tensor: """See :func:`~torchvision.transforms.v2.ToDtype` for details.""" if torch.jit.is_scripting(): return to_dtype_image(inpt, dtype=dtype, scale=scale) _log_api_usage_once(to_dtype) kernel = _get_kernel(to_dtype, type(inpt)) return kernel(inpt, dtype=dtype, scale=scale)
def _num_value_bits(dtype: torch.dtype) -> int: if dtype == torch.uint8: return 8 elif dtype == torch.int8: return 7 elif dtype == torch.int16: return 15 elif dtype == torch.int32: return 31 elif dtype == torch.int64: return 63 else: raise TypeError(f"Number of value bits is only defined for integer dtypes, but got {dtype}.") @_register_kernel_internal(to_dtype, torch.Tensor) @_register_kernel_internal(to_dtype, tv_tensors.Image) def to_dtype_image(image: torch.Tensor, dtype: torch.dtype = torch.float, scale: bool = False) -> torch.Tensor: if image.dtype == dtype: return image elif not scale: return float_input = image.is_floating_point() if torch.jit.is_scripting(): # TODO: remove this branch as soon as `dtype.is_floating_point` is supported by JIT float_output = torch.tensor(0, dtype=dtype).is_floating_point() else: float_output = dtype.is_floating_point if float_input: # float to float if float_output: return # float to int if (image.dtype == torch.float32 and dtype in (torch.int32, torch.int64)) or ( image.dtype == torch.float64 and dtype == torch.int64 ): raise RuntimeError(f"The conversion from {image.dtype} to {dtype} cannot be performed safely.") # For data in the range `[0.0, 1.0]`, just multiplying by the maximum value of the integer range and converting # to the integer dtype is not sufficient. For example, `torch.rand(...).mul(255).to(torch.uint8)` will only # be `255` if the input is exactly `1.0`. See # for a detailed analysis. # To mitigate this, we could round before we convert to the integer dtype, but this is an extra operation. # Instead, we can also multiply by the maximum value plus something close to `1`. See # for details. eps = 1e-3 max_value = float(_max_value(dtype)) # We need to scale first since the conversion would otherwise turn the input range `[0.0, 1.0]` into the # discrete set `{0, 1}`. return image.mul(max_value + 1.0 - eps).to(dtype) else: # int to float if float_output: return / _max_value(image.dtype)) # int to int num_value_bits_input = _num_value_bits(image.dtype) num_value_bits_output = _num_value_bits(dtype) if num_value_bits_input > num_value_bits_output: return image.bitwise_right_shift(num_value_bits_input - num_value_bits_output).to(dtype) else: return - num_value_bits_input) # We encourage users to use to_dtype() instead but we keep this for BC
[docs]def convert_image_dtype(image: torch.Tensor, dtype: torch.dtype = torch.float32) -> torch.Tensor: """[DEPRECATED] Use to_dtype() instead.""" return to_dtype_image(image, dtype=dtype, scale=True)
@_register_kernel_internal(to_dtype, tv_tensors.Video) def to_dtype_video(video: torch.Tensor, dtype: torch.dtype = torch.float, scale: bool = False) -> torch.Tensor: return to_dtype_image(video, dtype, scale=scale) @_register_kernel_internal(to_dtype, tv_tensors.BoundingBoxes, tv_tensor_wrapper=False) @_register_kernel_internal(to_dtype, tv_tensors.Mask, tv_tensor_wrapper=False) def _to_dtype_tensor_dispatch(inpt: torch.Tensor, dtype: torch.dtype, scale: bool = False) -> torch.Tensor: # We don't need to unwrap and rewrap here, since preserves the type return


Access comprehensive developer documentation for PyTorch

View Docs


Get in-depth tutorials for beginners and advanced developers

View Tutorials


Find development resources and get your questions answered

View Resources