/
regression.py
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/
regression.py
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from typing import Sequence
import torch
from torch.nn import functional as F
from pytorch_lightning.metrics.functional.reduction import reduce
def mse(
pred: torch.Tensor,
target: torch.Tensor,
reduction: str = 'elementwise_mean'
) -> torch.Tensor:
"""
Computes mean squared error
Args:
pred: estimated labels
target: ground truth labels
reduction: a method to reduce metric score over labels (default: takes the mean)
Available reduction methods:
- elementwise_mean: takes the mean
- none: pass array
- sum: add elements
Return:
Tensor with MSE
Example:
>>> x = torch.tensor([0., 1, 2, 3])
>>> y = torch.tensor([0., 1, 2, 2])
>>> mse(x, y)
tensor(0.2500)
"""
mse = F.mse_loss(pred, target, reduction='none')
mse = reduce(mse, reduction=reduction)
return mse
def rmse(
pred: torch.Tensor,
target: torch.Tensor,
reduction: str = 'elementwise_mean'
) -> torch.Tensor:
"""
Computes root mean squared error
Args:
pred: estimated labels
target: ground truth labels
reduction: a method to reduce metric score over labels (default: takes the mean)
Available reduction methods:
- elementwise_mean: takes the mean
- none: pass array
- sum: add elements
Return:
Tensor with RMSE
>>> x = torch.tensor([0., 1, 2, 3])
>>> y = torch.tensor([0., 1, 2, 2])
>>> rmse(x, y)
tensor(0.5000)
"""
rmse = torch.sqrt(mse(pred, target, reduction=reduction))
return rmse
def mae(
pred: torch.Tensor,
target: torch.Tensor,
reduction: str = 'elementwise_mean'
) -> torch.Tensor:
"""
Computes mean absolute error
Args:
pred: estimated labels
target: ground truth labels
reduction: a method to reduce metric score over labels (default: takes the mean)
Available reduction methods:
- elementwise_mean: takes the mean
- none: pass array
- sum: add elements
Return:
Tensor with MAE
Example:
>>> x = torch.tensor([0., 1, 2, 3])
>>> y = torch.tensor([0., 1, 2, 2])
>>> mae(x, y)
tensor(0.2500)
"""
mae = F.l1_loss(pred, target, reduction='none')
mae = reduce(mae, reduction=reduction)
return mae
def rmsle(
pred: torch.Tensor,
target: torch.Tensor,
reduction: str = 'elementwise_mean'
) -> torch.Tensor:
"""
Computes root mean squared log error
Args:
pred: estimated labels
target: ground truth labels
reduction: a method to reduce metric score over labels (default: takes the mean)
Available reduction methods:
- elementwise_mean: takes the mean
- none: pass array
- sum: add elements
Return:
Tensor with RMSLE
Example:
>>> x = torch.tensor([0., 1, 2, 3])
>>> y = torch.tensor([0., 1, 2, 2])
>>> rmsle(x, y)
tensor(0.0207)
"""
rmsle = mse(torch.log(pred + 1), torch.log(target + 1), reduction=reduction)
return rmsle
def psnr(
pred: torch.Tensor,
target: torch.Tensor,
data_range: float = None,
base: float = 10.0,
reduction: str = 'elementwise_mean'
) -> torch.Tensor:
"""
Computes the peak signal-to-noise ratio
Args:
pred: estimated signal
target: groun truth signal
data_range: the range of the data. If None, it is determined from the data (max - min)
base: a base of a logarithm to use (default: 10)
reduction: a method to reduce metric score over labels (default: takes the mean)
Available reduction methods:
- elementwise_mean: takes the mean
- none: pass array
- sum add elements
Return:
Tensor with PSNR score
Example:
>>> from pytorch_lightning.metrics.regression import PSNR
>>> pred = torch.tensor([[0.0, 1.0], [2.0, 3.0]])
>>> target = torch.tensor([[3.0, 2.0], [1.0, 0.0]])
>>> metric = PSNR()
>>> metric(pred, target)
tensor(2.5527)
"""
if data_range is None:
data_range = max(target.max() - target.min(), pred.max() - pred.min())
else:
data_range = torch.tensor(float(data_range))
mse_score = mse(pred.view(-1), target.view(-1), reduction=reduction)
psnr_base_e = 2 * torch.log(data_range) - torch.log(mse_score)
psnr = psnr_base_e * (10 / torch.log(torch.tensor(base)))
return psnr
def _gaussian_kernel(channel, kernel_size, sigma, device):
def gaussian(kernel_size, sigma, device):
gauss = torch.arange(
start=(1 - kernel_size) / 2, end=(1 + kernel_size) / 2, step=1, dtype=torch.float32, device=device
)
gauss = torch.exp(-gauss.pow(2) / (2 * pow(sigma, 2)))
return (gauss / gauss.sum()).unsqueeze(dim=0) # (1, kernel_size)
gaussian_kernel_x = gaussian(kernel_size[0], sigma[0], device)
gaussian_kernel_y = gaussian(kernel_size[1], sigma[1], device)
kernel = torch.matmul(gaussian_kernel_x.t(), gaussian_kernel_y) # (kernel_size, 1) * (1, kernel_size)
return kernel.expand(channel, 1, kernel_size[0], kernel_size[1])
def ssim(
pred: torch.Tensor,
target: torch.Tensor,
kernel_size: Sequence[int] = (11, 11),
sigma: Sequence[float] = (1.5, 1.5),
reduction: str = "elementwise_mean",
data_range: float = None,
k1: float = 0.01,
k2: float = 0.03
) -> torch.Tensor:
"""
Computes Structual Similarity Index Measure
Args:
pred: estimated image
target: ground truth image
kernel_size: size of the gaussian kernel (default: (11, 11))
sigma: Standard deviation of the gaussian kernel (default: (1.5, 1.5))
reduction: a method to reduce metric score over labels (default: takes the mean)
Available reduction methods:
- elementwise_mean: takes the mean
- none: pass away
- sum: add elements
data_range: Range of the image. If ``None``, it is determined from the image (max - min)
k1: Parameter of SSIM. Default: 0.01
k2: Parameter of SSIM. Default: 0.03
Return:
Tensor with SSIM score
Example:
>>> pred = torch.rand([16, 1, 16, 16])
>>> target = pred * 0.75
>>> ssim(pred, target)
tensor(0.9219)
"""
if pred.dtype != target.dtype:
raise TypeError(
"Expected `pred` and `target` to have the same data type."
f" Got pred: {pred.dtype} and target: {target.dtype}."
)
if pred.shape != target.shape:
raise ValueError(
"Expected `pred` and `target` to have the same shape."
f" Got pred: {pred.shape} and target: {target.shape}."
)
if len(pred.shape) != 4 or len(target.shape) != 4:
raise ValueError(
"Expected `pred` and `target` to have BxCxHxW shape."
f" Got pred: {pred.shape} and target: {target.shape}."
)
if len(kernel_size) != 2 or len(sigma) != 2:
raise ValueError(
"Expected `kernel_size` and `sigma` to have the length of two."
f" Got kernel_size: {len(kernel_size)} and sigma: {len(sigma)}."
)
if any(x % 2 == 0 or x <= 0 for x in kernel_size):
raise ValueError(f"Expected `kernel_size` to have odd positive number. Got {kernel_size}.")
if any(y <= 0 for y in sigma):
raise ValueError(f"Expected `sigma` to have positive number. Got {sigma}.")
if data_range is None:
data_range = max(pred.max() - pred.min(), target.max() - target.min())
C1 = pow(k1 * data_range, 2)
C2 = pow(k2 * data_range, 2)
device = pred.device
channel = pred.size(1)
kernel = _gaussian_kernel(channel, kernel_size, sigma, device)
# Concatenate
# pred for mu_pred
# target for mu_target
# pred * pred for sigma_pred
# target * target for sigma_target
# pred * target for sigma_pred_target
input_list = torch.cat([pred, target, pred * pred, target * target, pred * target]) # (5 * B, C, H, W)
outputs = F.conv2d(input_list, kernel, groups=channel)
output_list = [outputs[x * pred.size(0): (x + 1) * pred.size(0)] for x in range(len(outputs))]
mu_pred_sq = output_list[0].pow(2)
mu_target_sq = output_list[1].pow(2)
mu_pred_target = output_list[0] * output_list[1]
sigma_pred_sq = output_list[2] - mu_pred_sq
sigma_target_sq = output_list[3] - mu_target_sq
sigma_pred_target = output_list[4] - mu_pred_target
UPPER = 2 * sigma_pred_target + C2
LOWER = sigma_pred_sq + sigma_target_sq + C2
ssim_idx = ((2 * mu_pred_target + C1) * UPPER) / ((mu_pred_sq + mu_target_sq + C1) * LOWER)
return reduce(ssim_idx, reduction)