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ellipses_datamodule.py
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ellipses_datamodule.py
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#pyright: reportGeneralTypeIssues=false
import typing
from math import atan, cos, sin, tan
import matplotlib
import omegaconf
import pytorch_lightning as pl
import torch
import torch.utils.data
import torchvision.transforms
import torchvision.transforms.functional
matplotlib.use("agg")
import matplotlib.patches
import matplotlib.pyplot as plt
import numpy as np
from ct_reconstruction_dataset import CTReconstructionDataset
from fixed_noise_dataset import FixedNoiseDataset, Noise
class _EllipsesDataset(torch.utils.data.Dataset[typing.Tuple[torch.Tensor, None]]):
def __init__(self, img_count: int, img_size: int, ellipses_count: int, ellipses_size: float, ellipses_size_min: float=1, transform: typing.Union[typing.Callable[[torch.Tensor],torch.Tensor],None]=None, generator: typing.Union[torch.Generator,None]=None):
super().__init__()
self.img_count = img_count
self.img_size = img_size
self.ellipses_count = torch.poisson(torch.full((img_count,), ellipses_count).to(torch.float32), generator=generator).to(torch.int32)
real_ellipses_count = self.ellipses_count.sum()
self.ellipse_angle = torch.rand((real_ellipses_count,), generator=generator)*360.0
self.ellipse_alpha = torch.rand((real_ellipses_count,), generator=generator)*0.9+0.1
self.ellipse_width_aa = torch.rand((real_ellipses_count,), generator=generator)*max(0.0, ellipses_size-ellipses_size_min)+ellipses_size_min
self.ellipse_height_aa = torch.rand((real_ellipses_count,), generator=generator)*max(0.0, ellipses_size-ellipses_size_min)+ellipses_size_min
self.ellipse_x_raw = torch.rand((real_ellipses_count,), generator=generator)
self.ellipse_y_raw = torch.rand((real_ellipses_count,), generator=generator)
r = torch.rand((real_ellipses_count,), generator=generator)*0.3
alpha = torch.rand((real_ellipses_count,), generator=generator)*2.0*torch.pi
self.ellipse_x_raw = torch.cos(alpha)*r+0.5
self.ellipse_y_raw = torch.sin(alpha)*r+0.5
self.ellipse_width_aa = torch.rand((real_ellipses_count,), generator=generator)*0.15*img_size+0.05*img_size#max(0.0, ellipses_size-ellipses_size_min)+ellipses_size_min
self.ellipse_height_aa = torch.rand((real_ellipses_count,), generator=generator)*0.15*img_size+0.05*img_size#max(0.0, ellipses_size-ellipses_size_min)+ellipses_size_min
#self.ellipses_count = torch.ones((img_count,), dtype=torch.int32)
#self.ellipse_width_aa = torch.full((img_count,), img_size)#ellipses_size)
#self.ellipse_height_aa = torch.full((img_count,), img_size)#ellipses_size)
#self.ellipse_x_raw = torch.full((img_count,), 0.5)
#self.ellipse_y_raw = torch.full((img_count,), 0.5)
#self.ellipse_angle = torch.zeros((img_count,))
#self.ellipse_alpha = torch.ones((img_count,))
self.transform = transform
self.channel_selection_mode = "alpha"
if self[0][0].sum() == self.img_size*self.img_size:
self.channel_selection_mode = "noalpha"
def __len__(self) -> int:
return self.img_count
def __getitem__(self, idx: int) -> typing.Tuple[torch.Tensor, None]:
fig = plt.figure(figsize=(self.img_size,self.img_size), dpi=1)
ax = fig.add_axes([0.0,0.0,1.0,1.0])
ellipse_func = lambda w, h, a, t: (w/2.0*cos(t)*cos(a)-h/2.0*sin(t)*sin(a), w/2.0*cos(t)*sin(a)+h/2.0*sin(t)*cos(a))
prev_ellipses_count = self.ellipses_count[:idx].sum()
for i in range(self.ellipses_count[idx]):
e_idx = prev_ellipses_count+i
args = (self.ellipse_width_aa[e_idx].item(), self.ellipse_height_aa[e_idx].item(), self.ellipse_angle[e_idx].item()/180.0*torch.pi)
if self.ellipse_angle[idx] == 0.0:
ellipse_width = self.ellipse_width_aa[e_idx]
ellipse_height = self.ellipse_height_aa[e_idx]
else:
t = atan(-self.ellipse_height_aa[e_idx].item()*tan(self.ellipse_angle[e_idx].item()/180.0*torch.pi)/self.ellipse_width_aa[e_idx].item())
ellipse_width = max(ellipse_func(*args, t)[0], ellipse_func(*args, t+torch.pi)[0])-min(ellipse_func(*args, t)[0], ellipse_func(*args, t+torch.pi)[0])
t = atan(self.ellipse_height_aa[e_idx].item()/(tan(self.ellipse_angle[e_idx].item()/180.0*torch.pi)*self.ellipse_width_aa[e_idx].item()))
ellipse_height = max(ellipse_func(*args, t)[1], ellipse_func(*args, t+torch.pi)[1])-min(ellipse_func(*args, t)[1], ellipse_func(*args, t+torch.pi)[1])
ellipse_x = ellipse_width/2.0+self.ellipse_x_raw[e_idx].item()*(self.img_size-ellipse_width)
ellipse_y = ellipse_height/2.0+self.ellipse_y_raw[e_idx].item()*(self.img_size-ellipse_height)
ellipse = matplotlib.patches.Ellipse(xy=[ellipse_x, ellipse_y], width=self.ellipse_width_aa[e_idx].item(), height=self.ellipse_height_aa[e_idx].item(), angle=self.ellipse_angle[e_idx].item())
ax.add_artist(ellipse)
ellipse.set_clip_box(ax.bbox)
ellipse.set_alpha(self.ellipse_alpha[e_idx].item())
ellipse.set_facecolor("black")
ellipse.set_edgecolor(None)
ellipse.set_antialiased(False)
ax.axis("off")
ax.set_xlim(0.0, self.img_size)
ax.set_ylim(0.0, self.img_size)
fig.add_axes(ax)
fig.canvas.draw()
img = torch.from_numpy(np.frombuffer(fig.canvas.tostring_argb(), dtype=np.uint8).copy())
if self.channel_selection_mode == "noalpha":
img = 1.0-torch.swapaxes(img.reshape(self.img_size,self.img_size,4), 0, 2).to(torch.float32)[3:4]/255.0
elif self.channel_selection_mode == "alpha":
img = torch.swapaxes(img.reshape(self.img_size,self.img_size,4), 0, 2).to(torch.float32)[0:1]/255.0
else:
raise NotImplementedError()
plt.close()
if self.transform != None:
img = self.transform(img)
return img, None
class EllipsesDataModule(pl.LightningDataModule):
def __init__(self, config: omegaconf.DictConfig, noise: Noise) -> None:
super().__init__()
self.config = config
self.training_seed = torch.randint(0, 999_999_999_999_999, (1,)).item()
self.validation_seed = torch.randint(0, 999_999_999_999_999, (1,)).item()
self.test_seed = torch.randint(0, 999_999_999_999_999, (1,)).item()
self.__noise = noise
def train_dataloader(self) -> torch.utils.data.DataLoader[typing.Tuple[torch.Tensor,torch.Tensor,torch.Tensor,torch.Tensor]]:
training_transform = torchvision.transforms.Compose([
torchvision.transforms.Lambda(lambda x: torchvision.transforms.functional.gaussian_blur(x, 5, 2.5))
])
generator = torch.Generator()
generator.manual_seed(self.training_seed)
training_dataset = _EllipsesDataset(
(640 if self.config.training_batch_count == -1 else self.config.training_batch_count)*self.config.training_batch_size,
self.config.img_size,
self.config.trainval_dataset.ellipse_count,
self.config.trainval_dataset.ellipse_size,
self.config.trainval_dataset.ellipse_size_min,
training_transform if self.config.trainval_dataset.blurred else None,
generator
)
training_dataset = CTReconstructionDataset(training_dataset)
training_dataset = FixedNoiseDataset(training_dataset, noise=self.__noise, append_clean=True, append_noise=True)
return torch.utils.data.DataLoader(
training_dataset,
drop_last=self.config.drop_last_training_batch,
batch_size=self.config.training_batch_size,
shuffle=self.config.shuffle_training_data,
num_workers=self.config.num_workers
)
def val_dataloader(self) -> torch.utils.data.DataLoader[typing.Tuple[torch.Tensor,torch.Tensor,torch.Tensor,torch.Tensor]]:
validation_transform = torchvision.transforms.Compose([
torchvision.transforms.Lambda(lambda x: torchvision.transforms.functional.gaussian_blur(x, 5, 2.5))
])
generator = torch.Generator()
generator.manual_seed(self.validation_seed)
validation_dataset = _EllipsesDataset(
(160 if self.config.validation_batch_count == -1 else self.config.validation_batch_count)*self.config.validation_batch_size,
self.config.img_size,
self.config.trainval_dataset.ellipse_count,
self.config.trainval_dataset.ellipse_size,
self.config.trainval_dataset.ellipse_size_min,
validation_transform if self.config.trainval_dataset.blurred else None,
generator
)
#validation_dataset = CTReconstructionDataset(validation_dataset)
#validation_dataset = FixedNoiseDataset(validation_dataset, noise=self.__noise, append_clean=True, append_noise=True)
return torch.utils.data.DataLoader(
validation_dataset,
drop_last=self.config.drop_last_validation_batch,
batch_size=self.config.validation_batch_size,
shuffle=self.config.shuffle_validation_data,
num_workers=self.config.num_workers
)
def test_dataloader(self) -> torch.utils.data.DataLoader[typing.Tuple[torch.Tensor,torch.Tensor,torch.Tensor,torch.Tensor]]:
test_transform = torchvision.transforms.Compose([
torchvision.transforms.Lambda(lambda x: torchvision.transforms.functional.gaussian_blur(x, 5, 2.5))
])
generator = torch.Generator()
generator.manual_seed(self.test_seed)
test_dataset = _EllipsesDataset(
(200 if self.config.test_batch_count == -1 else self.config.test_batch_count)*self.config.test_batch_size,
self.config.img_size,
self.config.test_dataset.ellipse_count,
self.config.test_dataset.ellipse_size,
self.config.test_dataset.ellipse_size_min,
test_transform if self.config.test_dataset.blurred else None,
generator
)
test_dataset = CTReconstructionDataset(test_dataset)
test_dataset = FixedNoiseDataset(test_dataset, noise=self.__noise, append_clean=True, append_noise=True)
return torch.utils.data.DataLoader(
test_dataset,
drop_last=self.config.drop_last_test_batch,
batch_size=self.config.test_batch_size,
shuffle=self.config.shuffle_test_data,
num_workers=self.config.num_workers
)