ormbg/data_loader_cache.py

## 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 albumentations as A
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
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 = [], []
        im_dir = datasets[i]["im_dir"]
        gt_dir = datasets[i]["gt_dir"]
        tmp_im_list = glob(os.path.join(im_dir, "*" + "*.[jp][pn]g"))
        tmp_gt_list = glob(os.path.join(gt_dir, "*" + "*.[jp][pn]g"))

        print(
            "-im-", datasets[i]["name"], datasets[i]["im_dir"], ": ", len(tmp_im_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 GOSGridDropout(object):
    def __init__(
        self,
        ratio=0.5,
        unit_size_min=100,
        unit_size_max=100,
        holes_number_x=None,
        holes_number_y=None,
        shift_x=0,
        shift_y=0,
        random_offset=True,
        fill_value=0,
        mask_fill_value=None,
        always_apply=None,
        p=1.0,
    ):
        self.transform = A.GridDropout(
            ratio=ratio,
            unit_size_min=unit_size_min,
            unit_size_max=unit_size_max,
            holes_number_x=holes_number_x,
            holes_number_y=holes_number_y,
            shift_x=shift_x,
            shift_y=shift_y,
            random_offset=random_offset,
            fill_value=fill_value,
            mask_fill_value=mask_fill_value,
            always_apply=always_apply,
            p=p,
        )

    def __call__(self, sample):
        imidx, image, label, shape = (
            sample["imidx"],
            sample["image"],
            sample["label"],
            sample["shape"],
        )

        # Convert the torch tensors to numpy arrays
        image_np = image.permute(1, 2, 0).numpy()

        augmented = self.transform(image=image_np)

        # Convert the numpy arrays back to torch tensors
        image = torch.tensor(augmented["image"]).permute(2, 0, 1)

        return {"imidx": imidx, "image": image, "label": label, "shape": shape}


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 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