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import glob
import os
import os.path as osp
from typing import Callable, List, Optional
import torch
import torch.nn.functional as F
from torch import Tensor
from torch.utils.data import DataLoader
from torch_geometric.data import (
Data,
InMemoryDataset,
download_url,
extract_zip,
)
from torch_geometric.io import fs
class WILLOWObjectClass(InMemoryDataset):
r"""The WILLOW-ObjectClass dataset from the `"Learning Graphs to Match"
<https://www.di.ens.fr/willow/pdfscurrent/cho2013.pdf>`_ paper,
containing 10 equal keypoints of at least 40 images in each category.
The keypoints contain interpolated features from a pre-trained VGG16 model
on ImageNet (:obj:`relu4_2` and :obj:`relu5_1`).
Args:
root (str): Root directory where the dataset should be saved.
category (str): The category of the images (one of :obj:`"Car"`,
:obj:`"Duck"`, :obj:`"Face"`, :obj:`"Motorbike"`,
:obj:`"Winebottle"`).
transform (callable, optional): A function/transform that takes in an
:obj:`torch_geometric.data.Data` object and returns a transformed
version. The data object will be transformed before every access.
(default: :obj:`None`)
pre_transform (callable, optional): A function/transform that takes in
an :obj:`torch_geometric.data.Data` object and returns a
transformed version. The data object will be transformed before
being saved to disk. (default: :obj:`None`)
pre_filter (callable, optional): A function that takes in an
:obj:`torch_geometric.data.Data` object and returns a boolean
value, indicating whether the data object should be included in the
final dataset. (default: :obj:`None`)
force_reload (bool, optional): Whether to re-process the dataset.
(default: :obj:`False`)
device (str or torch.device, optional): The device to use for
processing the raw data. If set to :obj:`None`, will utilize
GPU-processing if available. (default: :obj:`None`)
"""
url = ('http://www.di.ens.fr/willow/research/graphlearning/'
'WILLOW-ObjectClass_dataset.zip')
categories = ['face', 'motorbike', 'car', 'duck', 'winebottle']
batch_size = 32
def __init__(
self,
root: str,
category: str,
transform: Optional[Callable] = None,
pre_transform: Optional[Callable] = None,
pre_filter: Optional[Callable] = None,
force_reload: bool = False,
device: Optional[str] = None,
) -> None:
if device is None:
device = 'cuda' if torch.cuda.is_available() else 'cpu'
assert category.lower() in self.categories
self.category = category
self.device = device
super().__init__(root, transform, pre_transform, pre_filter,
force_reload=force_reload)
self.load(self.processed_paths[0])
@property
def raw_dir(self) -> str:
return osp.join(self.root, 'raw')
@property
def processed_dir(self) -> str:
return osp.join(self.root, self.category.capitalize(), 'processed')
@property
def raw_file_names(self) -> List[str]:
return [category.capitalize() for category in self.categories]
@property
def processed_file_names(self) -> str:
return 'data.pt'
def download(self) -> None:
path = download_url(self.url, self.root)
extract_zip(path, self.root)
os.unlink(path)
os.unlink(osp.join(self.root, 'README'))
os.unlink(osp.join(self.root, 'demo_showAnno.m'))
fs.rm(self.raw_dir)
os.rename(osp.join(self.root, 'WILLOW-ObjectClass'), self.raw_dir)
def process(self) -> None:
import torchvision.models as models
import torchvision.transforms as T
from PIL import Image
from scipy.io import loadmat
category = self.category.capitalize()
names = glob.glob(osp.join(self.raw_dir, category, '*.png'))
names = sorted([name[:-4] for name in names])
vgg16_outputs = []
def hook(module: torch.nn.Module, x: Tensor, y: Tensor) -> None:
vgg16_outputs.append(y.to('cpu'))
vgg16 = models.vgg16(pretrained=True).to(self.device)
vgg16.eval()
vgg16.features[20].register_forward_hook(hook) # relu4_2
vgg16.features[25].register_forward_hook(hook) # relu5_1
transform = T.Compose([
T.ToTensor(),
T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
data_list = []
for name in names:
pos = loadmat(f'{name}.mat')['pts_coord']
x, y = torch.from_numpy(pos).to(torch.float)
pos = torch.stack([x, y], dim=1)
# The "face" category contains a single image with less than 10
# keypoints, so we need to skip it.
if pos.size(0) != 10:
continue
with open(f'{name}.png', 'rb') as f:
img = Image.open(f).convert('RGB')
# Rescale keypoints.
pos[:, 0] = pos[:, 0] * 256.0 / (img.size[0])
pos[:, 1] = pos[:, 1] * 256.0 / (img.size[1])
img = img.resize((256, 256), resample=Image.Resampling.BICUBIC)
img = transform(img)
data = Data(img=img, pos=pos, name=name)
data_list.append(data)
imgs = [data.img for data in data_list]
loader = DataLoader(
dataset=imgs, # type: ignore
batch_size=self.batch_size,
shuffle=False,
)
for i, batch_img in enumerate(loader):
vgg16_outputs.clear()
with torch.no_grad():
vgg16(batch_img.to(self.device))
out1 = F.interpolate(vgg16_outputs[0], (256, 256), mode='bilinear',
align_corners=False)
out2 = F.interpolate(vgg16_outputs[1], (256, 256), mode='bilinear',
align_corners=False)
for j in range(out1.size(0)):
data = data_list[i * self.batch_size + j]
assert data.pos is not None
idx = data.pos.round().long().clamp(0, 255)
x_1 = out1[j, :, idx[:, 1], idx[:, 0]].to('cpu')
x_2 = out2[j, :, idx[:, 1], idx[:, 0]].to('cpu')
data.img = None
data.x = torch.cat([x_1.t(), x_2.t()], dim=-1)
del out1
del out2
if self.pre_filter is not None:
data_list = [data for data in data_list if self.pre_filter(data)]
if self.pre_transform is not None:
data_list = [self.pre_transform(data) for data in data_list]
self.save(data_list, self.processed_paths[0])
def __repr__(self) -> str:
return (f'{self.__class__.__name__}({len(self)}, '
f'category={self.category})')
|
