Datasets:
jblitzar
/
github-python-corpus

Dataset card Data Studio Files
xet
Community
text
stringlengths
1
93.6k
"""
grad_disp_x = torch.abs(disp[:, :, :, :-1] - disp[:, :, :, 1:])
grad_disp_y = torch.abs(disp[:, :, :-1, :] - disp[:, :, 1:, :])
grad_img_x = torch.mean(torch.abs(img[:, :, :, :-1] - img[:, :, :, 1:]), 1, keepdim=True)
grad_img_y = torch.mean(torch.abs(img[:, :, :-1, :] - img[:, :, 1:, :]), 1, keepdim=True)
grad_disp_x *= torch.exp(-grad_img_x)
grad_disp_y *= torch.exp(-grad_img_y)
return grad_disp_x.mean() + grad_disp_y.mean()
class SSIM(nn.Module):
"""Layer to compute the SSIM loss between a pair of images"""
def __init__(self):
super(SSIM, self).__init__()
self.mu_x_pool = nn.AvgPool2d(3, 1)
self.mu_y_pool = nn.AvgPool2d(3, 1)
self.sig_x_pool = nn.AvgPool2d(3, 1)
self.sig_y_pool = nn.AvgPool2d(3, 1)
self.sig_xy_pool = nn.AvgPool2d(3, 1)
self.refl = nn.ReflectionPad2d(1)
self.C1 = 0.01 ** 2
self.C2 = 0.03 ** 2
def forward(self, x, y):
x = self.refl(x)
y = self.refl(y)
mu_x = self.mu_x_pool(x)
mu_y = self.mu_y_pool(y)
sigma_x = self.sig_x_pool(x ** 2) - mu_x ** 2
sigma_y = self.sig_y_pool(y ** 2) - mu_y ** 2
sigma_xy = self.sig_xy_pool(x * y) - mu_x * mu_y
SSIM_n = (2 * mu_x * mu_y + self.C1) * (2 * sigma_xy + self.C2)
SSIM_d = (mu_x ** 2 + mu_y ** 2 + self.C1) * (sigma_x + sigma_y + self.C2)
return torch.clamp((1 - SSIM_n / SSIM_d) / 2, 0, 1)
def compute_depth_errors(gt, pred):
"""Computation of error metrics between predicted and ground truth depths
"""
thresh = torch.max((gt / pred), (pred / gt))
a1 = (thresh < 1.25 ).float().mean()
a2 = (thresh < 1.25 ** 2).float().mean()
a3 = (thresh < 1.25 ** 3).float().mean()
rmse = (gt - pred) ** 2
rmse = torch.sqrt(rmse.mean())
rmse_log = (torch.log(gt) - torch.log(pred)) ** 2
rmse_log = torch.sqrt(rmse_log.mean())
abs_rel = torch.mean(torch.abs(gt - pred) / gt)
sq_rel = torch.mean((gt - pred) ** 2 / gt)
return abs_rel, sq_rel, rmse, rmse_log, a1, a2, a3
# <FILESEP>
from typing import List, Optional, Tuple, Union
from matplotlib import pyplot as plt
import numpy as np
import seaborn as sns
sns.set(style='white')
def lineplot(x: np.array, y: Union[List[float], List[List[float]]], filename: str, xlabel: str, ylabel: str, xlim: Optional[Tuple[float, float]]=None, ylim: Optional[Tuple[float, float]]=None, baseline_y: Optional[float]=None):
if isinstance(y[0], list):
y = np.transpose(np.array(y))
palette = sns.color_palette('husl', y.shape[0])
for i, y_i in enumerate(y):
sns.lineplot(x=x, y=y_i, color=palette[i])
else:
sns.lineplot(x=x, y=y, color='coral')
if baseline_y: sns.lineplot(x=x, y=np.full_like(x, baseline_y), color='gray', linestyle='--')
plt.xticks(fontsize=14)
plt.yticks(fontsize=14)
if xlabel == '':
plt.xticks([]) # Remove x-axis labels
else:
plt.xlabel(xlabel, fontsize=16)
plt.ylabel(ylabel, fontsize=16)
plt.xlim(*xlim) if xlim is not None else plt.margins(x=0)
plt.ylim(*ylim) if ylim is not None else plt.margins(y=0)
plt.tight_layout()
plt.savefig(f'results/{filename}.png')
plt.close()