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## data loader
## Ackownledgement:
## We would like to thank Dr. Ibrahim Almakky (https://scholar.google.co.uk/citations?user=T9MTcK0AAAAJ&hl=en)
## for his helps in implementing cache machanism of our DIS dataloader.
from __future__ import print_function, division
import numpy as np
import random
from copy import deepcopy
import json
from tqdm import tqdm
from skimage import io
import os
from glob import glob
import torch
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, utils
from torchvision.transforms.functional import normalize
import torch.nn.functional as F
#### --------------------- DIS dataloader cache ---------------------####
def get_im_gt_name_dict(datasets, flag='valid'):
print("------------------------------", flag, "--------------------------------")
name_im_gt_list = []
for i in range(len(datasets)):
print("--->>>", flag, " dataset ",i,"/",len(datasets)," ",datasets[i]["name"],"<<<---")
tmp_im_list, tmp_gt_list = [], []
tmp_im_list = glob(datasets[i]["im_dir"]+os.sep+'*'+datasets[i]["im_ext"])
# img_name_dict[im_dirs[i][0]] = tmp_im_list
print('-im-',datasets[i]["name"],datasets[i]["im_dir"], ': ',len(tmp_im_list))
if(datasets[i]["gt_dir"]==""):
print('-gt-', datasets[i]["name"], datasets[i]["gt_dir"], ': ', 'No Ground Truth Found')
tmp_gt_list = []
else:
tmp_gt_list = [datasets[i]["gt_dir"]+os.sep+x.split(os.sep)[-1].split(datasets[i]["im_ext"])[0]+datasets[i]["gt_ext"] for x in tmp_im_list]
# lbl_name_dict[im_dirs[i][0]] = tmp_gt_list
print('-gt-', datasets[i]["name"],datasets[i]["gt_dir"], ': ',len(tmp_gt_list))
if flag=="train": ## combine multiple training sets into one dataset
if len(name_im_gt_list)==0:
name_im_gt_list.append({"dataset_name":datasets[i]["name"],
"im_path":tmp_im_list,
"gt_path":tmp_gt_list,
"im_ext":datasets[i]["im_ext"],
"gt_ext":datasets[i]["gt_ext"],
"cache_dir":datasets[i]["cache_dir"]})
else:
name_im_gt_list[0]["dataset_name"] = name_im_gt_list[0]["dataset_name"] + "_" + datasets[i]["name"]
name_im_gt_list[0]["im_path"] = name_im_gt_list[0]["im_path"] + tmp_im_list
name_im_gt_list[0]["gt_path"] = name_im_gt_list[0]["gt_path"] + tmp_gt_list
if datasets[i]["im_ext"]!=".jpg" or datasets[i]["gt_ext"]!=".png":
print("Error: Please make sure all you images and ground truth masks are in jpg and png format respectively !!!")
exit()
name_im_gt_list[0]["im_ext"] = ".jpg"
name_im_gt_list[0]["gt_ext"] = ".png"
name_im_gt_list[0]["cache_dir"] = os.sep.join(datasets[i]["cache_dir"].split(os.sep)[0:-1])+os.sep+name_im_gt_list[0]["dataset_name"]
else: ## keep different validation or inference datasets as separate ones
name_im_gt_list.append({"dataset_name":datasets[i]["name"],
"im_path":tmp_im_list,
"gt_path":tmp_gt_list,
"im_ext":datasets[i]["im_ext"],
"gt_ext":datasets[i]["gt_ext"],
"cache_dir":datasets[i]["cache_dir"]})
return name_im_gt_list
def create_dataloaders(name_im_gt_list, cache_size=[], cache_boost=True, my_transforms=[], batch_size=1, shuffle=False):
## model="train": return one dataloader for training
## model="valid": return a list of dataloaders for validation or testing
gos_dataloaders = []
gos_datasets = []
if(len(name_im_gt_list)==0):
return gos_dataloaders, gos_datasets
num_workers_ = 1
if(batch_size>1):
num_workers_ = 2
if(batch_size>4):
num_workers_ = 4
if(batch_size>8):
num_workers_ = 8
for i in range(0,len(name_im_gt_list)):
gos_dataset = GOSDatasetCache([name_im_gt_list[i]],
cache_size = cache_size,
cache_path = name_im_gt_list[i]["cache_dir"],
cache_boost = cache_boost,
transform = transforms.Compose(my_transforms))
gos_dataloaders.append(DataLoader(gos_dataset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers_))
gos_datasets.append(gos_dataset)
return gos_dataloaders, gos_datasets
def im_reader(im_path):
return io.imread(im_path)
def im_preprocess(im,size):
if len(im.shape) < 3:
im = im[:, :, np.newaxis]
if im.shape[2] == 1:
im = np.repeat(im, 3, axis=2)
im_tensor = torch.tensor(im.copy(), dtype=torch.float32)
im_tensor = torch.transpose(torch.transpose(im_tensor,1,2),0,1)
if(len(size)<2):
return im_tensor, im.shape[0:2]
else:
im_tensor = torch.unsqueeze(im_tensor,0)
im_tensor = F.upsample(im_tensor, size, mode="bilinear")
im_tensor = torch.squeeze(im_tensor,0)
return im_tensor.type(torch.uint8), im.shape[0:2]
def gt_preprocess(gt,size):
if len(gt.shape) > 2:
gt = gt[:, :, 0]
gt_tensor = torch.unsqueeze(torch.tensor(gt, dtype=torch.uint8),0)
if(len(size)<2):
return gt_tensor.type(torch.uint8), gt.shape[0:2]
else:
gt_tensor = torch.unsqueeze(torch.tensor(gt_tensor, dtype=torch.float32),0)
gt_tensor = F.upsample(gt_tensor, size, mode="bilinear")
gt_tensor = torch.squeeze(gt_tensor,0)
return gt_tensor.type(torch.uint8), gt.shape[0:2]
# return gt_tensor, gt.shape[0:2]
class GOSRandomHFlip(object):
def __init__(self,prob=0.5):
self.prob = prob
def __call__(self,sample):
imidx, image, label, shape = sample['imidx'], sample['image'], sample['label'], sample['shape']
# random horizontal flip
if random.random() >= self.prob:
image = torch.flip(image,dims=[2])
label = torch.flip(label,dims=[2])
return {'imidx':imidx,'image':image, 'label':label, 'shape':shape}
class GOSResize(object):
def __init__(self,size=[320,320]):
self.size = size
def __call__(self,sample):
imidx, image, label, shape = sample['imidx'], sample['image'], sample['label'], sample['shape']
# import time
# start = time.time()
image = torch.squeeze(F.upsample(torch.unsqueeze(image,0),self.size,mode='bilinear'),dim=0)
label = torch.squeeze(F.upsample(torch.unsqueeze(label,0),self.size,mode='bilinear'),dim=0)
# print("time for resize: ", time.time()-start)
return {'imidx':imidx,'image':image, 'label':label, 'shape':shape}
class GOSRandomCrop(object):
def __init__(self,size=[288,288]):
self.size = size
def __call__(self,sample):
imidx, image, label, shape = sample['imidx'], sample['image'], sample['label'], sample['shape']
h, w = image.shape[1:]
new_h, new_w = self.size
top = np.random.randint(0, h - new_h)
left = np.random.randint(0, w - new_w)
image = image[:,top:top+new_h,left:left+new_w]
label = label[:,top:top+new_h,left:left+new_w]
return {'imidx':imidx,'image':image, 'label':label, 'shape':shape}
class GOSNormalize(object):
def __init__(self, mean=[0.485,0.456,0.406], std=[0.229,0.224,0.225]):
self.mean = mean
self.std = std
def __call__(self,sample):
imidx, image, label, shape = sample['imidx'], sample['image'], sample['label'], sample['shape']
image = normalize(image,self.mean,self.std)
return {'imidx':imidx,'image':image, 'label':label, 'shape':shape}
class GOSDatasetCache(Dataset):
def __init__(self, name_im_gt_list, cache_size=[], cache_path='./cache', cache_file_name='dataset.json', cache_boost=False, transform=None):
self.cache_size = cache_size
self.cache_path = cache_path
self.cache_file_name = cache_file_name
self.cache_boost_name = ""
self.cache_boost = cache_boost
# self.ims_npy = None
# self.gts_npy = None
## cache all the images and ground truth into a single pytorch tensor
self.ims_pt = None
self.gts_pt = None
## we will cache the npy as well regardless of the cache_boost
# if(self.cache_boost):
self.cache_boost_name = cache_file_name.split('.json')[0]
self.transform = transform
self.dataset = {}
## combine different datasets into one
dataset_names = []
dt_name_list = [] # dataset name per image
im_name_list = [] # image name
im_path_list = [] # im path
gt_path_list = [] # gt path
im_ext_list = [] # im ext
gt_ext_list = [] # gt ext
for i in range(0,len(name_im_gt_list)):
dataset_names.append(name_im_gt_list[i]["dataset_name"])
# dataset name repeated based on the number of images in this dataset
dt_name_list.extend([name_im_gt_list[i]["dataset_name"] for x in name_im_gt_list[i]["im_path"]])
im_name_list.extend([x.split(os.sep)[-1].split(name_im_gt_list[i]["im_ext"])[0] for x in name_im_gt_list[i]["im_path"]])
im_path_list.extend(name_im_gt_list[i]["im_path"])
gt_path_list.extend(name_im_gt_list[i]["gt_path"])
im_ext_list.extend([name_im_gt_list[i]["im_ext"] for x in name_im_gt_list[i]["im_path"]])
gt_ext_list.extend([name_im_gt_list[i]["gt_ext"] for x in name_im_gt_list[i]["gt_path"]])
self.dataset["data_name"] = dt_name_list
self.dataset["im_name"] = im_name_list
self.dataset["im_path"] = im_path_list
self.dataset["ori_im_path"] = deepcopy(im_path_list)
self.dataset["gt_path"] = gt_path_list
self.dataset["ori_gt_path"] = deepcopy(gt_path_list)
self.dataset["im_shp"] = []
self.dataset["gt_shp"] = []
self.dataset["im_ext"] = im_ext_list
self.dataset["gt_ext"] = gt_ext_list
self.dataset["ims_pt_dir"] = ""
self.dataset["gts_pt_dir"] = ""
self.dataset = self.manage_cache(dataset_names)
def manage_cache(self,dataset_names):
if not os.path.exists(self.cache_path): # create the folder for cache
os.makedirs(self.cache_path)
cache_folder = os.path.join(self.cache_path, "_".join(dataset_names)+"_"+"x".join([str(x) for x in self.cache_size]))
if not os.path.exists(cache_folder): # check if the cache files are there, if not then cache
return self.cache(cache_folder)
return self.load_cache(cache_folder)
def cache(self,cache_folder):
os.mkdir(cache_folder)
cached_dataset = deepcopy(self.dataset)
# ims_list = []
# gts_list = []
ims_pt_list = []
gts_pt_list = []
for i, im_path in tqdm(enumerate(self.dataset["im_path"]), total=len(self.dataset["im_path"])):
im_id = cached_dataset["im_name"][i]
print("im_path: ", im_path)
im = im_reader(im_path)
im, im_shp = im_preprocess(im,self.cache_size)
im_cache_file = os.path.join(cache_folder,self.dataset["data_name"][i]+"_"+im_id + "_im.pt")
torch.save(im,im_cache_file)
cached_dataset["im_path"][i] = im_cache_file
if(self.cache_boost):
ims_pt_list.append(torch.unsqueeze(im,0))
# ims_list.append(im.cpu().data.numpy().astype(np.uint8))
gt = np.zeros(im.shape[0:2])
if len(self.dataset["gt_path"])!=0:
gt = im_reader(self.dataset["gt_path"][i])
gt, gt_shp = gt_preprocess(gt,self.cache_size)
gt_cache_file = os.path.join(cache_folder,self.dataset["data_name"][i]+"_"+im_id + "_gt.pt")
torch.save(gt,gt_cache_file)
if len(self.dataset["gt_path"])>0:
cached_dataset["gt_path"][i] = gt_cache_file
else:
cached_dataset["gt_path"].append(gt_cache_file)
if(self.cache_boost):
gts_pt_list.append(torch.unsqueeze(gt,0))
# gts_list.append(gt.cpu().data.numpy().astype(np.uint8))
# im_shp_cache_file = os.path.join(cache_folder,im_id + "_im_shp.pt")
# torch.save(gt_shp, shp_cache_file)
cached_dataset["im_shp"].append(im_shp)
# self.dataset["im_shp"].append(im_shp)
# shp_cache_file = os.path.join(cache_folder,im_id + "_gt_shp.pt")
# torch.save(gt_shp, shp_cache_file)
cached_dataset["gt_shp"].append(gt_shp)
# self.dataset["gt_shp"].append(gt_shp)
if(self.cache_boost):
cached_dataset["ims_pt_dir"] = os.path.join(cache_folder, self.cache_boost_name+'_ims.pt')
cached_dataset["gts_pt_dir"] = os.path.join(cache_folder, self.cache_boost_name+'_gts.pt')
self.ims_pt = torch.cat(ims_pt_list,dim=0)
self.gts_pt = torch.cat(gts_pt_list,dim=0)
torch.save(torch.cat(ims_pt_list,dim=0),cached_dataset["ims_pt_dir"])
torch.save(torch.cat(gts_pt_list,dim=0),cached_dataset["gts_pt_dir"])
try:
json_file = open(os.path.join(cache_folder, self.cache_file_name),"w")
json.dump(cached_dataset, json_file)
json_file.close()
except Exception:
raise FileNotFoundError("Cannot create JSON")
return cached_dataset
def load_cache(self, cache_folder):
json_file = open(os.path.join(cache_folder,self.cache_file_name),"r")
dataset = json.load(json_file)
json_file.close()
## if cache_boost is true, we will load the image npy and ground truth npy into the RAM
## otherwise the pytorch tensor will be loaded
if(self.cache_boost):
# self.ims_npy = np.load(dataset["ims_npy_dir"])
# self.gts_npy = np.load(dataset["gts_npy_dir"])
self.ims_pt = torch.load(dataset["ims_pt_dir"], map_location='cpu')
self.gts_pt = torch.load(dataset["gts_pt_dir"], map_location='cpu')
return dataset
def __len__(self):
return len(self.dataset["im_path"])
def __getitem__(self, idx):
im = None
gt = None
if(self.cache_boost and self.ims_pt is not None):
# start = time.time()
im = self.ims_pt[idx]#.type(torch.float32)
gt = self.gts_pt[idx]#.type(torch.float32)
# print(idx, 'time for pt loading: ', time.time()-start)
else:
# import time
# start = time.time()
# print("tensor***")
im_pt_path = os.path.join(self.cache_path,os.sep.join(self.dataset["im_path"][idx].split(os.sep)[-2:]))
im = torch.load(im_pt_path)#(self.dataset["im_path"][idx])
gt_pt_path = os.path.join(self.cache_path,os.sep.join(self.dataset["gt_path"][idx].split(os.sep)[-2:]))
gt = torch.load(gt_pt_path)#(self.dataset["gt_path"][idx])
# print(idx,'time for tensor loading: ', time.time()-start)
im_shp = self.dataset["im_shp"][idx]
# print("time for loading im and gt: ", time.time()-start)
# start_time = time.time()
im = torch.divide(im,255.0)
gt = torch.divide(gt,255.0)
# print(idx, 'time for normalize torch divide: ', time.time()-start_time)
sample = {
"imidx": torch.from_numpy(np.array(idx)),
"image": im,
"label": gt,
"shape": torch.from_numpy(np.array(im_shp)),
}
if self.transform:
sample = self.transform(sample)
return sample