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dataset.py
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dataset.py
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import pickle
import socket
from PIL import Image
import glob
import torchvision
import numpy as np
import ipdb
import torch.nn.functional as F
st = ipdb.set_trace
import torch
import time
class ClevrDataset(torch.utils.data.Dataset):
def __init__(self, opt):
self.opt = opt
root_file = opt.root_folder
self.all_files = glob.glob(f'{root_file}/clevr_train/*')
self.resize = torchvision.transforms.Resize((opt.image_height,opt.image_width))
self.resize_mask = torchvision.transforms.Resize((opt.image_height,opt.image_width),torchvision.transforms.InterpolationMode.NEAREST)
def __len__(self):
return len(self.all_files)
def __getitem__(self, idx):
if self.opt.overfit:
idx = 0
file_val = self.all_files[idx]
pickled_file = pickle.load(open(file_val,'rb'))
rgb_val = torch.from_numpy(pickled_file['image']).squeeze().float()
images = rgb_val / 256.0
# Normalize to [0, 1] range.
gt_mask_val = torch.from_numpy(np.argmax(pickled_file['mask'].squeeze(),0))
images = images.permute(2,0,1).unsqueeze(0)
images = self.resize(images)
gt_mask_val = self.resize_mask(gt_mask_val.unsqueeze(0))
images = images.squeeze()
gt_mask_val = gt_mask_val.squeeze()
max_objs = gt_mask_val.max()
gt_indices = torch.zeros(self.opt.num_slots)
gt_indices[:max_objs] = 1.0
gt_mask_val = F.one_hot(gt_mask_val, self.opt.num_slots).permute(2,0,1)
return images, gt_mask_val , gt_indices
class ClevrTexDataset(torch.utils.data.Dataset):
def __init__(self, opt):
self.opt = opt
root_file = opt.root_folder
self.all_files = glob.glob(f'{root_file}/clevr_tex/*')
self.resize = torchvision.transforms.Resize((opt.image_height,opt.image_width))
self.resize_mask = torchvision.transforms.Resize((opt.image_height,opt.image_width),torchvision.transforms.InterpolationMode.NEAREST)
def __len__(self):
return len(self.all_files)
def __getitem__(self, idx):
if self.opt.overfit:
idx = 0
if self.opt.specific_example != 'None':
idx = int(self.opt.specific_example)
file_val = self.all_files[idx]
pickled_file = pickle.load(open(file_val,'rb'))
rgb_val = torch.from_numpy(pickled_file['image']).squeeze().float()
images = rgb_val / 256.0
# Normalize to [0, 1] range.
gt_mask_val = torch.from_numpy(np.argmax(pickled_file['mask'].squeeze(),0))
images = images.permute(2,0,1).unsqueeze(0)
images = self.resize(images)
gt_mask_val = self.resize_mask(gt_mask_val.unsqueeze(0))
images = images.squeeze()
gt_mask_val = gt_mask_val.squeeze()
max_objs = gt_mask_val.max()
gt_indices = torch.zeros(self.opt.num_slots)
gt_indices[:max_objs] = 1.0
gt_mask_val = F.one_hot(gt_mask_val, self.opt.num_slots).permute(2,0,1)
return images, gt_mask_val , gt_indices
def get_dataloader(opt, dataset):
# Improve reproducibility in dataloader.
data_loader = torch.utils.data.DataLoader(
dataset,
batch_size=opt.batch_size,
drop_last=True,
shuffle=True,
num_workers=opt.num_workers,
persistent_workers=opt.persistent_worker,
)
iterator = iter(data_loader)
return data_loader, iterator
def get_data(opt):
if opt.dataset_name == "clevr_tex":
dataset = ClevrTexDataset(opt)
else:
dataset = ClevrDataset(opt)
loader, iterator = get_dataloader(opt, dataset)
return loader, iterator
def get_data_tta(opt):
if opt.dataset_name == "clevr_tex":
dataset = ClevrTexDataset(opt)
else:
dataset = ClevrDataset(opt)
return dataset
def get_input(opt, iterator, train_loader):
time_init = time.time()
try:
input = next(iterator)
except StopIteration:
iterator = iter(train_loader)
input = next(iterator)
print("stop loading")
time_init = time.time()
image, gt_mask, gt_indices = input
image = image.to(opt.device)
gt_mask = gt_mask.to(opt.device)
gt_indices = gt_indices.to(opt.device)
feed_dict = {}
feed_dict["image"] = image
feed_dict["gt_mask"] = gt_mask
feed_dict["gt_indices"] = gt_indices
return feed_dict