GRAM-HD/datasets.py at main · JeffreyXiang/GRAM-HD · GitHub

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import os

import torch
from torch.utils.data import Dataset
import torchvision.transforms as transforms
from torchvision.transforms import functional as F
import glob
import PIL
import math
import numpy as np
import time
from scipy.io import loadmat


def read_pose(name,flip=False):
    P = loadmat(name)['angle']
    P_x = -(P[0,0] - 0.1) + math.pi/2
    if not flip:
        P_y = P[0,1] + math.pi/2
    else:
        P_y = -P[0,1] + math.pi/2

    P = torch.tensor([P_x,P_y],dtype=torch.float32)

    return P

def read_pose_npy(name,flip=False):
    P = np.load(name)
    P_x = P[0] + 0.14
    if not flip:
        P_y = P[1]
    else:
        P_y = -P[1] + math.pi

    P = torch.tensor([P_x,P_y],dtype=torch.float32)

    return P


class AFHQCats(Dataset):
    def __init__(self, path, img_size, **kwargs):
        super().__init__()
        self.img_size = img_size
        self.real_pose = False
        if 'real_pose' in kwargs and kwargs['real_pose'] == True:
            self.real_pose = True
            self.data = glob.glob(os.path.join(path,'*.png'))
            assert len(self.data) > 0, "Can't find data; make sure you specify the path to your dataset"
            if self.real_pose:
                self.pose = [os.path.join(path, 'poses', f.split('/')[-1].replace('.png','_pose.npy')) for f in self.data]

        self.transform = transforms.Compose([
            transforms.Resize((img_size, img_size),
            interpolation=transforms.InterpolationMode.LANCZOS),
            transforms.ToTensor(),
            transforms.Normalize([0.5], [0.5])
        ])

    def __len__(self):
        return len(self.data)

    def __getitem__(self, index):
        X = PIL.Image.open(self.data[index])
        X = self.transform(X)
        flip = (torch.rand(1) < 0.5)
        if flip:
            X = F.hflip(X)
        if self.real_pose:
            P = read_pose_npy(self.pose[index], flip=flip)
        else:
            P = 0

        return X, P


class FFHQ(Dataset):
    def __init__(self, path, img_size, **kwargs):
        super().__init__()
        self.img_size = img_size
        self.real_pose = False
        if 'real_pose' in kwargs and kwargs['real_pose'] == True:
            self.real_pose = True
            self.data = glob.glob(os.path.join(path,'*.png'))
            assert len(self.data) > 0, "Can't find data; make sure you specify the path to your dataset"
            if self.real_pose:
                self.pose = [os.path.join(path, 'poses', f.split('/')[-1].replace('png','mat')) for f in self.data]

        self.transform = transforms.Compose([
            transforms.Resize((img_size, img_size),
            interpolation=transforms.InterpolationMode.LANCZOS),
            transforms.ToTensor(),
            transforms.Normalize([0.5], [0.5])
        ])

    def __len__(self):
        return len(self.data)

    def __getitem__(self, index):
        X = PIL.Image.open(self.data[index])
        X = self.transform(X)
        flip = (torch.rand(1) < 0.5)
        if flip:
            X = F.hflip(X)
        if self.real_pose:
            P = read_pose(self.pose[index],flip=flip)
        else:
            P = 0

        return X, P


def get_dataset(name, subsample=None, batch_size=1, **kwargs):
    dataset = globals()[name](**kwargs)

    dataloader = torch.utils.data.DataLoader(
        dataset,
        batch_size=batch_size,
        shuffle=True,
        drop_last=True,
        pin_memory=False,
        num_workers=8
    )
    return dataloader, 3

def get_dataset_(dataset, subsample=None, batch_size=1, **kwargs):

    dataloader = torch.utils.data.DataLoader(
        dataset,
        batch_size=batch_size,
        shuffle=True,
        drop_last=True,
        pin_memory=False,
        num_workers=8
    )
    return dataloader, 3

def get_dataset_distributed(name, world_size, rank, batch_size, **kwargs):

    dataset = globals()[name](**kwargs)

    sampler = torch.utils.data.distributed.DistributedSampler(
        dataset,
        num_replicas=world_size,
        rank=rank,
    )
    dataloader = torch.utils.data.DataLoader(
        dataset,
        sampler=sampler,
        batch_size=batch_size,
        shuffle=False,
        drop_last=True,
        pin_memory=False,
        num_workers=16,
        persistent_workers=True,
    )

    return dataloader, 3

def get_dataset_distributed_(_dataset, world_size, rank, batch_size, **kwargs):

    sampler = torch.utils.data.distributed.DistributedSampler(
        _dataset,
        num_replicas=world_size,
        rank=rank,
    )
    dataloader = torch.utils.data.DataLoader(
        _dataset,
        sampler=sampler,
        batch_size=batch_size,
        shuffle=False,
        drop_last=True,
        pin_memory=False,
        num_workers=16,
        persistent_workers=True,
    )

    return dataloader, 3