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r""" FSS-1000 few-shot semantic segmentation dataset """
import os
import glob
from torch.utils.data import Dataset
import torch.nn.functional as F
import torch
import PIL.Image as Image
import numpy as np
class DatasetFSS(Dataset):
def __init__(self, datapath, fold, transform, split, shot, use_original_imgsize=False):
self.split = split
self.benchmark = 'fss'
self.shot = shot
self.base_path = os.path.join(datapath, 'FSS-1000')
# Given predefined test split, load randomly generated training/val splits:
# (reference regarding trn/val/test splits: https://github.com/HKUSTCV/FSS-1000/issues/7))
with open('./data/splits/fss/%s.txt' % split, 'r') as f:
self.categories = f.read().split('\n')[:-1]
self.categories = sorted(self.categories)
self.class_ids = self.build_class_ids()
self.img_metadata = self.build_img_metadata()
self.transform = transform
def __len__(self):
return len(self.img_metadata)
def __getitem__(self, idx):
query_name, support_names, class_sample = self.sample_episode(idx)
query_img, query_mask, support_imgs, support_masks = self.load_frame(query_name, support_names)
query_img = self.transform(query_img)
query_mask = F.interpolate(query_mask.unsqueeze(0).unsqueeze(0).float(), query_img.size()[-2:], mode='nearest').squeeze()
support_imgs = torch.stack([self.transform(support_img) for support_img in support_imgs])
support_masks_tmp = []
for smask in support_masks:
smask = F.interpolate(smask.unsqueeze(0).unsqueeze(0).float(), support_imgs.size()[-2:], mode='nearest').squeeze()
support_masks_tmp.append(smask)
support_masks = torch.stack(support_masks_tmp)
batch = {'query_img': query_img,
'query_mask': query_mask,
'query_name': query_name,
'support_imgs': support_imgs,
'support_masks': support_masks,
'support_names': support_names,
'class_id': torch.tensor(class_sample)}
return batch
def load_frame(self, query_name, support_names):
query_img = Image.open(query_name).convert('RGB')
support_imgs = [Image.open(name).convert('RGB') for name in support_names]
query_id = query_name.split('/')[-1].split('.')[0]
query_name = os.path.join(os.path.dirname(query_name), query_id) + '.png'
support_ids = [name.split('/')[-1].split('.')[0] for name in support_names]
support_names = [os.path.join(os.path.dirname(name), sid) + '.png' for name, sid in zip(support_names, support_ids)]
query_mask = self.read_mask(query_name)
support_masks = [self.read_mask(name) for name in support_names]
return query_img, query_mask, support_imgs, support_masks
def read_mask(self, img_name):
mask = torch.tensor(np.array(Image.open(img_name).convert('L')))
mask[mask < 128] = 0
mask[mask >= 128] = 1
return mask
def sample_episode(self, idx):
query_name = self.img_metadata[idx]
class_sample = self.categories.index(query_name.split('/')[-2])
if self.split == 'val':
class_sample += 520
elif self.split == 'test':
class_sample += 760
support_names = []
while True: # keep sampling support set if query == support
support_name = np.random.choice(range(1, 11), 1, replace=False)[0]
support_name = os.path.join(os.path.dirname(query_name), str(support_name)) + '.jpg'
if query_name != support_name: support_names.append(support_name)
if len(support_names) == self.shot: break
return query_name, support_names, class_sample
def build_class_ids(self):
if self.split == 'trn':
class_ids = range(0, 520)
elif self.split == 'val':
class_ids = range(520, 760)
elif self.split == 'test':
class_ids = range(760, 1000)
return class_ids
def build_img_metadata(self):
img_metadata = []
for cat in self.categories:
img_paths = sorted([path for path in glob.glob('%s/*' % os.path.join(self.base_path, cat))])
for img_path in img_paths:
if os.path.basename(img_path).split('.')[1] == 'jpg':
img_metadata.append(img_path)
return img_metadata
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