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import torch
import torch.nn.functional as F
from torch.utils.data import Dataset
from typing import Tuple
from rstor.data.degradation import DegradationBlurMat, DegradationBlurGauss, DegradationNoise
from rstor.properties import DEVICE, AUGMENTATION_FLIP, DEGRADATION_BLUR_NONE, DEGRADATION_BLUR_MAT, DEGRADATION_BLUR_GAUSS
from rstor.synthetic_data.dead_leaves_cpu import cpu_dead_leaves_chart
from rstor.synthetic_data.dead_leaves_gpu import gpu_dead_leaves_chart
import cv2
from skimage.filters import gaussian
import random
import numpy as np
from rstor.utils import DEFAULT_TORCH_FLOAT_TYPE
class DeadLeavesDataset(Dataset):
def __init__(
self,
size: Tuple[int, int] = (128, 128),
length: int = 1000,
frozen_seed: int = None, # useful for validation set!
blur_kernel_half_size: int = [0, 2],
ds_factor: int = 5,
noise_stddev: float = [0., 50.],
degradation_blur=DEGRADATION_BLUR_NONE,
**config_dead_leaves
# number_of_circles: int = -1,
# background_color: Optional[Tuple[float, float, float]] = (0.5, 0.5, 0.5),
# colored: Optional[bool] = False,
# radius_mean: Optional[int] = -1,
# radius_stddev: Optional[int] = -1,
):
self.frozen_seed = frozen_seed
self.ds_factor = ds_factor
self.size = (size[0]*ds_factor, size[1]*ds_factor)
self.length = length
self.config_dead_leaves = config_dead_leaves
self.blur_kernel_half_size = blur_kernel_half_size
self.noise_stddev = noise_stddev
self.degradation_blur_type = degradation_blur
if degradation_blur == DEGRADATION_BLUR_GAUSS:
self.degradation_blur = DegradationBlurGauss(self.length,
blur_kernel_half_size,
frozen_seed)
self.blur_deg_str = "blur_kernel_half_size"
elif degradation_blur == DEGRADATION_BLUR_MAT:
self.degradation_blur = DegradationBlurMat(self.length,
frozen_seed)
self.blur_deg_str = "blur_kernel_id"
elif degradation_blur == DEGRADATION_BLUR_NONE:
pass
else:
raise ValueError(f"Unknown degradation blur {degradation_blur}")
self.degradation_noise = DegradationNoise(self.length,
noise_stddev,
frozen_seed)
self.current_degradation = {}
def __len__(self):
return self.length
def __getitem__(self, idx: int) -> Tuple[torch.Tensor, torch.Tensor]:
# TODO there is a bug on this cpu version, the dead leaved dont appear ot be right
seed = self.frozen_seed + idx if self.frozen_seed is not None else None
chart = cpu_dead_leaves_chart(self.size, seed=seed, **self.config_dead_leaves)
if self.ds_factor > 1:
# print(f"Downsampling {chart.shape} with factor {self.ds_factor}...")
sigma = 3/5
chart = gaussian(
chart, sigma=(sigma, sigma, 0), mode='nearest',
cval=0, preserve_range=True, truncate=4.0)
chart = chart[::self.ds_factor, ::self.ds_factor]
th_chart = torch.from_numpy(chart).permute(2, 0, 1).unsqueeze(0)
degraded_chart = th_chart
self.current_degradation[idx] = {}
if self.degradation_blur_type != DEGRADATION_BLUR_NONE:
degraded_chart = self.degradation_blur(degraded_chart, idx)
self.current_degradation[idx][self.blur_deg_str] = self.degradation_blur.current_degradation[idx][self.blur_deg_str]
degraded_chart = self.degradation_noise(degraded_chart, idx)
self.current_degradation[idx]["noise_stddev"] = self.degradation_noise.current_degradation[idx]["noise_stddev"]
degraded_chart = degraded_chart.squeeze(0)
th_chart = th_chart.squeeze(0)
return degraded_chart, th_chart
class DeadLeavesDatasetGPU(Dataset):
def __init__(
self,
size: Tuple[int, int] = (128, 128),
length: int = 1000,
frozen_seed: int = None, # useful for validation set!
blur_kernel_half_size: int = [0, 2],
ds_factor: int = 5,
noise_stddev: float = [0., 50.],
use_gaussian_kernel=True,
**config_dead_leaves
# number_of_circles: int = -1,
# background_color: Optional[Tuple[float, float, float]] = (0.5, 0.5, 0.5),
# colored: Optional[bool] = False,
# radius_mean: Optional[int] = -1,
# radius_stddev: Optional[int] = -1,
):
self.frozen_seed = frozen_seed
self.ds_factor = ds_factor
self.size = (size[0]*ds_factor, size[1]*ds_factor)
self.length = length
self.config_dead_leaves = config_dead_leaves
# downsample kernel
sigma = 3/5
k_size = 5 # This fits with sigma = 3/5, the cutoff value is 0.0038 (neglectable)
x = (torch.arange(k_size) - 2).to('cuda')
kernel = torch.stack(torch.meshgrid((x, x), indexing='ij'))
kernel.requires_grad = False
dist_sq = kernel[0]**2 + kernel[1]**2
kernel = (-dist_sq.square()/(2*sigma**2)).exp()
kernel = kernel / kernel.sum()
self.downsample_kernel = kernel.repeat(3, 1, 1, 1) # shape [3, 1, k_size, k_size]
self.downsample_kernel.requires_grad = False
self.use_gaussian_kernel = use_gaussian_kernel
if use_gaussian_kernel:
self.degradation_blur = DegradationBlurGauss(length,
blur_kernel_half_size,
frozen_seed)
else:
self.degradation_blur = DegradationBlurMat(length,
frozen_seed)
self.degradation_noise = DegradationNoise(length,
noise_stddev,
frozen_seed)
self.current_degradation = {}
def __len__(self) -> int:
return self.length
def __getitem__(self, idx: int) -> Tuple[torch.Tensor, torch.Tensor]:
"""Get a single deadleave chart and its degraded version.
Args:
idx (int): index of the item to retrieve
Returns:
Tuple[torch.Tensor, torch.Tensor]: degraded chart, target chart
"""
seed = self.frozen_seed + idx if self.frozen_seed is not None else None
# Return numba device array
numba_chart = gpu_dead_leaves_chart(self.size, seed=seed, **self.config_dead_leaves)
th_chart = torch.as_tensor(numba_chart, dtype=DEFAULT_TORCH_FLOAT_TYPE, device="cuda")[
None].permute(0, 3, 1, 2) # [1, c, h, w]
if self.ds_factor > 1:
# Downsample using strided gaussian conv (sigma=3/5)
th_chart = F.pad(th_chart,
pad=(2, 2, 0, 0),
mode="replicate")
th_chart = F.conv2d(th_chart,
self.downsample_kernel,
padding='valid',
groups=3,
stride=self.ds_factor)
degraded_chart = self.degradation_blur(th_chart, idx)
degraded_chart = self.degradation_noise(degraded_chart, idx)
blur_deg_str = "blur_kernel_half_size" if self.use_gaussian_kernel else "blur_kernel_id"
self.current_degradation[idx] = {
blur_deg_str: self.degradation_blur.current_degradation[idx][blur_deg_str],
"noise_stddev": self.degradation_noise.current_degradation[idx]["noise_stddev"]
}
degraded_chart = degraded_chart.squeeze(0)
th_chart = th_chart.squeeze(0)
return degraded_chart, th_chart
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