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import functools
import os
import random
import math
from PIL import Image
import numpy as np
import torch
from einops import rearrange
from torch.utils.data import Dataset
from torchvision import transforms
from datasets import register
from utils import to_pixel_samples, to_coordinates
import torchvision.transforms.functional as TF
import random
from typing import Sequence
class MyRotateTransform:
def __init__(self, angles: Sequence[int], p=0.5):
self.angles = angles
self.p = p
def __call__(self, x):
if torch.rand(1) < self.p:
return x
angle = random.choice(self.angles)
return TF.rotate(x, angle)
@register('inr_fixed_scale_sr_warp')
class INRFixedScaleSRWarp(Dataset):
def __init__(self,
dataset, scale_ratio, patch_size=48,
augment=False, sample_q=None,
val_mode=False, test_mode=False,
encode_scale_ratio=False,
return_cell=False, # for liff
):
super(INRFixedScaleSRWarp, self).__init__()
self.dataset = dataset
self.scale_ratio = scale_ratio
self.patch_size = patch_size
self.hr_size = int(patch_size * scale_ratio)
self.augment = augment
self.sample_q = sample_q
self.test_mode = test_mode
self.val_mode = val_mode
self.encode_scale_ratio = encode_scale_ratio
self.return_cell = return_cell
def __len__(self):
return len(self.dataset)
def __getitem__(self, idx):
# import pdb
# pdb.set_trace()
img_hr, file_name = self.dataset[idx]
class_name = os.path.basename(os.path.dirname(file_name))
file_name = os.path.basename(file_name).split('.')[0]
# img_hr: 3xHxW
h, w = img_hr.shape[-2:]
# if h < 256 or w < 256:
# img_hr = transforms.Resize(256, Image.BICUBIC)(img_hr)
if self.test_mode or self.val_mode:
img_hr = transforms.CenterCrop(self.hr_size)(img_hr)
else:
img_hr = transforms.RandomCrop(self.hr_size)(img_hr)
if self.augment:
img_hr = transforms.RandomHorizontalFlip(p=0.5)(img_hr)
img_hr = transforms.RandomVerticalFlip(p=0.5)(img_hr)
img_hr = MyRotateTransform([90, 180, 270], p=0.5)(img_hr)
img_lr = transforms.Resize(self.patch_size, Image.BICUBIC)(img_hr)
hr_coord = to_coordinates(size=img_hr.shape[-2:], return_map=False)
hr_rgb = rearrange(img_hr, 'C H W -> (H W) C')
if self.sample_q is not None and not self.test_mode:
sample_lst = np.random.choice(
len(hr_coord), self.sample_q, replace=False)
hr_coord = hr_coord[sample_lst]
hr_rgb = hr_rgb[sample_lst]
return_dict = {
'inp': img_lr,
'coord': hr_coord,
'gt': hr_rgb,
'class_name': class_name,
'filename': file_name
}
if self.encode_scale_ratio:
scale_ratio = torch.ones_like(hr_coord) * self.patch_size / self.hr_size
return_dict['scale_ratio'] = scale_ratio
if self.return_cell:
cell = torch.ones_like(hr_coord)
cell[:, 0] *= 2 / img_hr.shape[-2]
cell[:, 1] *= 2 / img_hr.shape[-1]
return_dict['cell'] = cell
return return_dict
@register('inr_range_scale_sr_warp')
class INRRangeScaleSRWarp(Dataset):
def __init__(self,
dataset, max_scale_ratio, patch_size=48,
augment=False, sample_q=None,
val_mode=False, test_mode=False,
encode_scale_ratio=False,
return_cell=False, # for liff
):
super(INRRangeScaleSRWarp, self).__init__()
self.dataset = dataset
self.max_scale_ratio = max_scale_ratio
self.patch_size = patch_size
assert max_scale_ratio <= 8
self.augment = augment
self.sample_q = sample_q
self.test_mode = test_mode
self.val_mode = val_mode
self.encode_scale_ratio = encode_scale_ratio
self.return_cell = return_cell
def __len__(self):
return len(self.dataset)
def __getitem__(self, idx):
img_hr, file_name = self.dataset[idx]
class_name = os.path.basename(os.path.dirname(file_name))
h, w = img_hr.shape[-2:]
# if h < 256 or w < 256:
# img_hr = transforms.Resize(256, Image.BICUBIC)(img_hr)
hr_size = self.patch_size + self.patch_size * torch.rand([]) * (self.max_scale_ratio - 1)
hr_size = int(hr_size)
if self.test_mode or self.val_mode:
hr_size = int(self.patch_size * self.max_scale_ratio)
img_hr = transforms.CenterCrop(hr_size)(img_hr)
else:
img_hr = transforms.RandomCrop(hr_size)(img_hr)
if self.augment:
img_hr = transforms.RandomHorizontalFlip(p=0.5)(img_hr)
img_hr = transforms.RandomVerticalFlip(p=0.5)(img_hr)
img_hr = MyRotateTransform([90, 180, 270], p=0.5)(img_hr)
img_lr = transforms.Resize(self.patch_size, Image.BICUBIC)(img_hr)
hr_coord = to_coordinates(size=img_hr.shape[-2:], return_map=False)
hr_rgb = rearrange(img_hr, 'C H W -> (H W) C')
if self.sample_q is not None and not self.test_mode:
sample_lst = np.random.choice(
len(hr_coord), self.sample_q, replace=False)
hr_coord = hr_coord[sample_lst]
hr_rgb = hr_rgb[sample_lst]
return_dict = {
'inp': img_lr,
'coord': hr_coord,
'gt': hr_rgb,
'class_name': class_name
}
if self.encode_scale_ratio:
scale_ratio = torch.ones_like(hr_coord) * self.patch_size / hr_size
return_dict['scale_ratio'] = scale_ratio
if self.return_cell:
cell = torch.ones_like(hr_coord)
cell[:, 0] *= 2 / img_hr.shape[-2]
cell[:, 1] *= 2 / img_hr.shape[-1]
return_dict['cell'] = cell
return return_dict
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