Full Code of yyliu01/PS-MT for AI

Repository: yyliu01/PS-MT
Branch: main
Commit: 058cdc3bd8fe
Files: 102
Total size: 18.9 MB

Directory structure:
gitextract_egx_o8ha/

├── CityCode/
│   ├── Base/
│   │   ├── base_dataloader.py
│   │   ├── base_dataset.py
│   │   ├── base_model.py
│   │   └── base_trainer.py
│   ├── DataLoader/
│   │   ├── city.py
│   │   └── city_splits/
│   │       ├── 1488_train_supervised.txt
│   │       ├── 1488_train_unsupervised.txt
│   │       ├── 186_train_supervised.txt
│   │       ├── 186_train_unsupervised.txt
│   │       ├── 372_train_supervised.txt
│   │       ├── 372_train_unsupervised.txt
│   │       ├── 744_train_supervised.txt
│   │       ├── 744_train_unsupervised.txt
│   │       └── val.txt
│   ├── Model/
│   │   └── Deeplabv3_plus/
│   │       ├── Backbones/
│   │       │   ├── pretrained/
│   │       │   │   ├── .gitkeep
│   │       │   │   └── nil
│   │       │   └── resnet.py
│   │       ├── EntireModel.py
│   │       ├── encoder_decoder.py
│   │       └── resnet.py
│   ├── Utils/
│   │   ├── conv_2_5d.py
│   │   ├── helpers.py
│   │   ├── logger.py
│   │   ├── losses.py
│   │   ├── lr_scheduler.py
│   │   ├── metrics.py
│   │   ├── pyt_utils.py
│   │   ├── ramps.py
│   │   ├── sliding_evaluator.py
│   │   └── tensor_board.py
│   ├── configs/
│   │   └── config_deeplab_v3+.json
│   ├── dgx/
│   │   └── download_to_pvc.py
│   ├── main.py
│   └── train.py
├── LICENSE.md
├── README.md
├── VocCode/
│   ├── Base/
│   │   ├── base_dataloader.py
│   │   ├── base_dataset.py
│   │   ├── base_model.py
│   │   └── base_trainer.py
│   ├── DataLoader/
│   │   ├── voc.py
│   │   └── voc_splits/
│   │       ├── 13230_train_supervised.txt
│   │       ├── 13230_train_unsupervised.txt
│   │       ├── 1323_train_supervised.txt
│   │       ├── 1323_train_unsupervised.txt
│   │       ├── 1464_train_supervised.txt
│   │       ├── 1464_train_unsupervised.txt
│   │       ├── 183_train_supervised.txt
│   │       ├── 183_train_unsupervised.txt
│   │       ├── 26460_train_supervised.txt
│   │       ├── 26460_train_unsupervised.txt
│   │       ├── 2646_train_supervised.txt
│   │       ├── 2646_train_unsupervised.txt
│   │       ├── 366_train_supervised.txt
│   │       ├── 366_train_unsupervised.txt
│   │       ├── 52910_train_supervised.txt
│   │       ├── 52910_train_unsupervised.txt
│   │       ├── 5291_train_supervised.txt
│   │       ├── 5291_train_unsupervised.txt
│   │       ├── 6620_train_supervised.txt
│   │       ├── 6620_train_unsupervised.txt
│   │       ├── 662_train_supervised.txt
│   │       ├── 662_train_unsupervised.txt
│   │       ├── 732_train_supervised.txt
│   │       ├── 732_train_unsupervised.txt
│   │       ├── 92_train_supervised.txt
│   │       ├── 92_train_unsupervised.txt
│   │       ├── boxes.json
│   │       ├── classes.json
│   │       ├── split_reminder.txt
│   │       └── val.txt
│   ├── Model/
│   │   └── Deeplabv3_plus/
│   │       ├── Backbones/
│   │       │   ├── pretrained/
│   │       │   │   ├── .gitkeep
│   │       │   │   └── nil
│   │       │   └── resnet.py
│   │       ├── EntireModel.py
│   │       ├── encoder_decoder.py
│   │       └── resnet.py
│   ├── Utils/
│   │   ├── conv_2_5d.py
│   │   ├── helpers.py
│   │   ├── logger.py
│   │   ├── losses.py
│   │   ├── lr_scheduler.py
│   │   ├── metrics.py
│   │   ├── pyt_utils.py
│   │   ├── ramps.py
│   │   └── tensor_board.py
│   ├── configs/
│   │   └── config_deeplab_v3+.json
│   ├── dgx/
│   │   └── download_to_pvc.py
│   ├── inference.py
│   ├── main.py
│   └── train.py
├── docs/
│   ├── before_start.md
│   └── installation.md
├── k8s_launch/
│   ├── Dockerfile
│   ├── jobs/
│   │   ├── ept_dgx_runner_city_deep_res101.yml
│   │   └── ept_dgx_runner_voc_deep_res101.yml
│   └── requirements.txt
├── ps-mt.yml
└── scripts/
    ├── train_city.sh
    ├── train_voc_aug.sh
    ├── train_voc_blender.sh
    └── train_voc_hq.sh

================================================
FILE CONTENTS
================================================

================================================
FILE: CityCode/Base/base_dataloader.py
================================================
import numpy as np
from copy import deepcopy
import torch
from torch.utils.data import DataLoader
from torch.utils.data.sampler import SubsetRandomSampler


class BaseDataLoader(DataLoader):
    def __init__(self, dataset, batch_size, shuffle, num_workers, val_split=0.0, sampler=None):
        self.shuffle = shuffle
        self.dataset = dataset
        self.nbr_examples = len(dataset)
        self.drop_last = False
        if sampler is not None:
            self.shuffle = False
            self.drop_last = True

        self.init_kwargs = {
            'dataset': self.dataset,
            'drop_last': self.drop_last,
            'batch_size': batch_size,
            'shuffle': self.shuffle,
            'num_workers': num_workers,
            'pin_memory': True
        }
        super(BaseDataLoader, self).__init__(sampler=sampler, **self.init_kwargs)

    def _split_sampler(self, split):
        if split == 0.0:
            return None, None

        self.shuffle = False

        split_indx = int(self.nbr_examples * split)
        np.random.seed(0)

        indxs = np.arange(self.nbr_examples)
        np.random.shuffle(indxs)
        train_indxs = indxs[split_indx:]
        val_indxs = indxs[:split_indx]
        self.nbr_examples = len(train_indxs)

        train_sampler = SubsetRandomSampler(train_indxs)
        val_sampler = SubsetRandomSampler(val_indxs)
        return train_sampler, val_sampler

    def get_val_loader(self):
        if self.val_sampler is None:
            return None
        return DataLoader(sampler=self.val_sampler, **self.init_kwargs)


================================================
FILE: CityCode/Base/base_dataset.py
================================================
import random
import numpy as np
import cv2
import torch
from torch.utils.data import Dataset
from PIL import Image
from torchvision import transforms


class BaseDataSet(Dataset):
    def __init__(self, data_dir, split, mean, std, ignore_index, base_size=None, augment=True, val=False,
                 jitter=False, use_weak_lables=False, weak_labels_output=None, crop_size=None, scale=False, flip=False,
                 rotate=False,
                 blur=False, return_id=False, n_labeled_examples=None):

        self.root = data_dir
        self.split = split
        self.mean = mean
        self.std = std
        self.augment = augment
        self.crop_size = crop_size
        self.jitter = jitter
        self.image_padding = (np.array(mean) * 255.).tolist()
        self.ignore_index = ignore_index
        self.return_id = return_id
        self.n_labeled_examples = n_labeled_examples
        self.val = val

        self.use_weak_lables = use_weak_lables
        self.weak_labels_output = weak_labels_output

        if self.augment:
            self.base_size = base_size
            self.scale = scale
            self.flip = flip
            self.rotate = rotate
            self.blur = blur

        self.jitter_tf = transforms.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1, hue=0.1)
        self.to_tensor = transforms.ToTensor()
        self.normalize = transforms.Normalize(mean, std)

        self.files = []
        self._set_files()

    def _set_files(self):
        raise NotImplementedError

    def _load_data(self, index):
        raise NotImplementedError

    def _rotate(self, image, label):
        # Rotate the image with an angle between -10 and 10
        h, w, _ = image.shape
        angle = random.randint(-10, 10)
        center = (w / 2, h / 2)
        rot_matrix = cv2.getRotationMatrix2D(center, angle, 1.0)
        image = cv2.warpAffine(image, rot_matrix, (w, h), flags=cv2.INTER_CUBIC)  # , borderMode=cv2.BORDER_REFLECT)
        label = cv2.warpAffine(label, rot_matrix, (w, h), flags=cv2.INTER_NEAREST)  # ,  borderMode=cv2.BORDER_REFLECT)
        return image, label

    def _crop(self, image, label):
        # Padding to return the correct crop size
        if (isinstance(self.crop_size, list) or isinstance(self.crop_size, tuple)) and len(self.crop_size) == 2:
            crop_h, crop_w = self.crop_size
        elif isinstance(self.crop_size, int):
            crop_h, crop_w = self.crop_size, self.crop_size
        else:
            raise ValueError

        h, w, _ = image.shape
        pad_h = max(crop_h - h, 0)
        pad_w = max(crop_w - w, 0)
        pad_kwargs = {
            "top": 0,
            "bottom": pad_h,
            "left": 0,
            "right": pad_w,
            "borderType": cv2.BORDER_CONSTANT, }
        if pad_h > 0 or pad_w > 0:
            image = cv2.copyMakeBorder(image, value=self.image_padding, **pad_kwargs)
            label = cv2.copyMakeBorder(label, value=self.ignore_index, **pad_kwargs)

        # Cropping 
        h, w, _ = image.shape
        start_h = random.randint(0, h - crop_h)
        start_w = random.randint(0, w - crop_w)
        end_h = start_h + crop_h
        end_w = start_w + crop_w
        image = image[start_h:end_h, start_w:end_w]
        label = label[start_h:end_h, start_w:end_w]
        return image, label

    def _flip(self, image, label):
        # Random H flip
        if random.random() > 0.5:
            image = np.fliplr(image).copy()
            label = np.fliplr(label).copy()
        return image, label

    def _resize(self, image, label, bigger_side_to_base_size=True):
        if isinstance(self.base_size, int):
            h, w, _ = image.shape
            if self.scale:
                longside = random.randint(int(self.base_size * 0.5), int(self.base_size * 2.0))
                # longside = random.randint(int(self.base_size*0.5), int(self.base_size*1))
            else:
                longside = self.base_size

            if bigger_side_to_base_size:
                h, w = (longside, int(1.0 * longside * w / h + 0.5)) if h > w else (
                int(1.0 * longside * h / w + 0.5), longside)
            else:
                h, w = (longside, int(1.0 * longside * w / h + 0.5)) if h < w else (
                int(1.0 * longside * h / w + 0.5), longside)
            image = np.asarray(Image.fromarray(np.uint8(image)).resize((w, h), Image.BICUBIC))
            label = cv2.resize(label, (w, h), interpolation=cv2.INTER_NEAREST)
            return image, label

        elif (isinstance(self.base_size, list) or isinstance(self.base_size, tuple)) and len(self.base_size) == 2:
            h, w, _ = image.shape
            if self.scale:
                scale = random.random() * 1.5 + 0.5  # Scaling between [0.5, 2]
                h, w = int(self.base_size[0] * scale), int(self.base_size[1] * scale)
            else:
                h, w = self.base_size
            image = np.asarray(Image.fromarray(np.uint8(image)).resize((w, h), Image.BICUBIC))
            label = cv2.resize(label, (w, h), interpolation=cv2.INTER_NEAREST)
            return image, label

        else:
            raise ValueError
    
    @staticmethod
    def data_aug(images, flag="weak"):
        kernel_size = int(random.random() * 4.95)
        kernel_size = kernel_size + 1 if kernel_size % 2 == 0 else kernel_size
        blurring_image = transforms.GaussianBlur(kernel_size, sigma=(0.1, 2.0))
        color_jitter = transforms.ColorJitter(brightness=0.5, contrast=0.5, saturation=0.5, hue=0.25)
        mean = [0.485, 0.456, 0.406]
        std = [0.229, 0.224, 0.225]
        normalize = transforms.Normalize(mean, std)
        to_tensor = transforms.ToTensor()

        weak_aug = normalize(to_tensor(images))
        if flag == "weak":
            return weak_aug
        # strong_aug = normalize(to_tensor(color_jitter(images)))
        strong_aug = images
        if random.random() < 0.8:
            strong_aug = color_jitter(strong_aug)
        strong_aug = transforms.RandomGrayscale(p=0.2)(strong_aug)

        if random.random() < 0.5:
            strong_aug = blurring_image(strong_aug)

        strong_aug = normalize(to_tensor(strong_aug))
        return weak_aug, strong_aug

    def _val_augmentation(self, image, label):
        if self.base_size is not None:
            image, label = self._resize(image, label)
            image = self.normalize(self.to_tensor(Image.fromarray(np.uint8(image))))
            return image, label

        image = self.normalize(self.to_tensor(Image.fromarray(np.uint8(image))))
        return image, label

    def _augmentation(self, image, label):
        h, w, _ = image.shape

        if self.base_size is not None:
            image, label = self._resize(image, label)

        if self.crop_size is not None:
            image, label = self._crop(image, label)

        if self.flip:
            image, label = self._flip(image, label)

        image = Image.fromarray(np.uint8(image))
        # image = self.jitter_tf(image) if self.jitter else image
        
        # return self.normalize(self.to_tensor(image)), label
        image_wk, image_str = self.data_aug(image, flag="both")
        return image_wk, image_str, label

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

    def __getitem__(self, index):
        image, label, image_id = self._load_data(index)
        if self.val:
            image, label = self._val_augmentation(image, label)
            label = torch.from_numpy(np.array(label, dtype=np.int32)).long()
            return image,label
        elif self.augment:
            image_wk, image_str, label = self._augmentation(image, label)
            label = torch.from_numpy(np.array(label, dtype=np.int32)).long()
            return image_wk, image_str, label
        else:
            return None

    def __repr__(self):
        fmt_str = "Dataset: " + self.__class__.__name__ + "\n"
        fmt_str += "    # data: {}\n".format(self.__len__())
        fmt_str += "    Split: {}\n".format(self.split)
        fmt_str += "    Root: {}".format(self.root)
        return fmt_str


================================================
FILE: CityCode/Base/base_model.py
================================================
import logging
import torch.nn as nn
import numpy as np


class BaseModel(nn.Module):
    def __init__(self):
        super(BaseModel, self).__init__()
        self.logger = logging.getLogger(self.__class__.__name__)

    def forward(self):
        raise NotImplementedError

    def summary(self):
        model_parameters = filter(lambda p: p.requires_grad, self.parameters())
        nbr_params = sum([np.prod(p.size()) for p in model_parameters])
        self.logger.info(f'Nbr of trainable parameters: {nbr_params}')

    def __str__(self):
        model_parameters = filter(lambda p: p.requires_grad, self.parameters())
        nbr_params = int(sum([np.prod(p.size()) for p in model_parameters]))
        return f'\nNbr of trainable parameters: {nbr_params}'
        # return super(BaseModel, self).__str__() + f'\nNbr of trainable parameters: {nbr_params}'


================================================
FILE: CityCode/Base/base_trainer.py
================================================
import os
import json
import torch
from Utils import helpers
import Utils.lr_scheduler as lr_scheduler
from torch.nn.parallel import DistributedDataParallel as DDP
import torch.distributed as dist
from itertools import chain
from Utils.helpers import group_weight, init_weight
# from dgx.download_to_pvc import *

bn_eps = 1e-5
bn_momentum = 0.1
# from utils.htmlwriter import HTML


def get_instance(module, name, config, *args):
    return getattr(module, config[name]['type'])(*args, **config[name]['args'])


class BaseTrainer:
    def __init__(self, model, config, iters_per_epoch, train_logger=None, args=None):
        self.model = model
        self.config = config
        self.args = args
        self.do_validation = self.config['trainer']['val']
        self.start_epoch = 1
        # SETTING THE DEVICE
        if self.args.local_rank <= 0:
            self.logger = train_logger
            self.logger.critical(
                "distributed data parallel training: {}".format(str("on" if args.ddp is True
                                                                    else "off")))
        if self.args.ddp:
            torch.cuda.set_device(self.args.local_rank)
            self.model.cuda(self.args.local_rank)
            if self.args.architecture == 'deeplabv3+':
                init_weight(model.encoder1.business_layer, torch.nn.init.kaiming_normal_,
                            torch.nn.BatchNorm2d, bn_eps, bn_momentum,
                            mode='fan_in', nonlinearity='relu')
                init_weight(model.encoder2.business_layer, torch.nn.init.kaiming_normal_,
                            torch.nn.BatchNorm2d, bn_eps, bn_momentum,
                            mode='fan_in', nonlinearity='relu')
                init_weight(model.decoder1.business_layer, torch.nn.init.kaiming_normal_,
                            torch.nn.BatchNorm2d, bn_eps, bn_momentum,
                            mode='fan_in', nonlinearity='relu')
                init_weight(model.decoder2.business_layer, torch.nn.init.kaiming_normal_,
                            torch.nn.BatchNorm2d, bn_eps, bn_momentum,
                            mode='fan_in', nonlinearity='relu')
            
            self.model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(self.model)

            self.model = DDP(self.model, device_ids=[self.args.local_rank], find_unused_parameters=True)
        else:
            # SETTING THE DEVICE
            self.device, availble_gpus = self._get_available_devices(self.config['n_gpu'])
            self.model = torch.nn.DataParallel(self.model, device_ids=availble_gpus)

        # CONFIGS
        cfg_trainer = self.config['trainer']
        self.epochs = cfg_trainer['epochs']
        self.save_period = cfg_trainer['save_period']

        if args.architecture == "psp":
            # OPTIMIZER 1
            trainable_params1 = [{'params': filter(lambda p: p.requires_grad, self.model.module.get_other_params(id=1)),
                                  'lr': config['optimizer']['args']['lr']},
                                 {'params': filter(lambda p: p.requires_grad, self.model.module.get_backbone_params(id=1)),
                                  'lr': config['optimizer']['args']['lr']}]

            self.optimizer1 = get_instance(torch.optim, 'optimizer', config, trainable_params1)

            # OPTIMIZER 2
            trainable_params2 = [{'params': filter(lambda p: p.requires_grad, self.model.module.get_other_params(id=2)),
                                  'lr': config['optimizer']['args']['lr']},
                                 {'params': filter(lambda p: p.requires_grad, self.model.module.get_backbone_params(id=2)),
                                  'lr': config['optimizer']['args']['lr']}]

            self.optimizer2 = get_instance(torch.optim, 'optimizer', config, trainable_params2)

            # OPTIMIZER STUDENT
            trainable_params_s = [{'params': filter(lambda p: p.requires_grad, self.model.module.get_other_params(id=3)),
                                   'lr': config['optimizer']['args']['lr']},
                                  {'params': filter(lambda p: p.requires_grad, self.model.module.get_backbone_params(id=3)),
                                   'lr': config['optimizer']['args']['lr']}]

            self.optimizer_s = get_instance(torch.optim, 'optimizer', config, trainable_params_s)
        else:
            norm_layer = torch.nn.BatchNorm2d if not args.ddp else torch.nn.SyncBatchNorm
            params_list_1 = []
            params_list_1 = group_weight(params_list_1, model.encoder1.backbone,
                                         norm_layer, config['optimizer']['args']['lr'])

            for module in chain(model.encoder1.business_layer, model.decoder1.business_layer):
                params_list_1 = group_weight(params_list_1, module, norm_layer,
                                             config['optimizer']['args']['lr'])

            params_list_2 = []
            params_list_2 = group_weight(params_list_2, model.encoder2.backbone,
                                         norm_layer, config['optimizer']['args']['lr'])

            for module in chain(model.encoder2.business_layer, model.decoder2.business_layer):
                params_list_2 = group_weight(params_list_2, module, norm_layer,
                                             config['optimizer']['args']['lr'])

            params_list_s = []
            params_list_s = group_weight(params_list_s, model.encoder_s.backbone,
                                         norm_layer, config['optimizer']['args']['lr'])

            for module in chain(model.encoder_s.business_layer, model.decoder_s.business_layer):
                params_list_s = group_weight(params_list_s, module, norm_layer,
                                             config['optimizer']['args']['lr'])

            self.optimizer1 = get_instance(torch.optim, 'optimizer', config, params_list_1)
            self.optimizer2 = get_instance(torch.optim, 'optimizer', config, params_list_2)
            self.optimizer_s = get_instance(torch.optim, 'optimizer', config, params_list_s)

        self.lr_scheduler_s = getattr(lr_scheduler, config['lr_scheduler'])(optimizer=self.optimizer_s,
                                                                            num_epochs=self.epochs,
                                                                            iters_per_epoch=iters_per_epoch)

        self.warm_up_epoch = config['model']['warm_up_epoch']
        # MONITORING
        self.mnt_current = .0

        # CHECKPOINTS
        run_name = config['experim_name']
        self.checkpoint_dir = os.path.join(cfg_trainer['save_dir'], run_name)
        if self.args.local_rank <= 0:
            helpers.dir_exists(self.checkpoint_dir)
            config_save_path = os.path.join(self.checkpoint_dir, 'config.json')
            with open(config_save_path, 'w') as handle:
                json.dump(self.config, handle, indent=4, sort_keys=True)
        if self.args.ddp:
            dist.barrier()

    def train(self):
        if self.start_epoch <= self.warm_up_epoch:
            for epoch in range(0, self.warm_up_epoch):
                _ = self._warm_up(epoch, id=1)
                _ = self._warm_up(epoch, id=2)
                _ = self._warm_up(epoch, id=3)
                if epoch == self.warm_up_epoch-1:
                    del self.optimizer1
                    del self.optimizer2
        self.model.module.freeze_teachers_parameters()
        for epoch in range(self.start_epoch, self.epochs + 1):
            curr_id = 1 if epoch % 2 != 0 else 2
            _ = self._train_epoch(epoch, id=curr_id)
            if self.args.ddp:
                dist.barrier()

            if epoch % self.config['trainer']['val_per_epochs'] == 0 or \
                    epoch > int(3/4 * (self.epochs+1)):
                results = self._valid_epoch(max(0, epoch))
                if self.args.local_rank <= 0:
                    self.logger.info('\n\n')
                    for k, v in results.items():
                        self.logger.info(f'         {str(k):15s}: {v}')
                    self.mnt_current = results["Mean_IoU"]
            else:
                continue

            # SAVE CHECKPOINT
            if epoch % self.save_period == 0 and self.args.local_rank <= 0:
                self._save_checkpoint(epoch)
            if self.args.ddp:
                dist.barrier()

    def _save_checkpoint(self, epoch, name=""):
        state = {
            'arch': type(self.model).__name__,
            'epoch': epoch,
            'state_dict': self.model.module.state_dict(),
            'monitor_best': self.mnt_current,
            'args': self.args
        }
        ckpt_name = str(self.args.labeled_examples) + '_mIoU_{}_model{}_e{}.pth'.format(str(state['monitor_best']), str(name), str(epoch))
        filename = os.path.join(self.checkpoint_dir, ckpt_name)
        self.logger.info('\nSaving a checkpoint: {} ...'.format(str(filename)))
        torch.save(state, filename)
        """
        pvc_dir = os.path.join("yy", "exercise_1", self.args.architecture,
                               "resnet{}_ckpt".format(str(self.args.backbone)), "city_cvpr_final",
                                                      str(self.args.labeled_examples))

        upload_checkpoint(local_path=self.checkpoint_dir, prefix=pvc_dir, checkpoint_filepath=ckpt_name)
        self.logger.info("Uploading current ckpt: mIoU_{}_model.pth to {}".format(str(state['monitor_best']), 
                                                                                  pvc_dir))
        """
        
    def _get_available_devices(self, n_gpu):
        sys_gpu = torch.cuda.device_count()
        if sys_gpu == 0:
            self.logger.warning('No GPUs detected, using the CPU')
            n_gpu = 0
        elif n_gpu > sys_gpu:
            self.logger.warning(f'Nbr of GPU requested is {n_gpu} but only {sys_gpu} are available')
            n_gpu = sys_gpu

        device = torch.device('cuda:0' if n_gpu > 0 else 'cpu')
        self.logger.info(f'Detected GPUs: {sys_gpu} Requested: {n_gpu}')
        available_gpus = list(range(n_gpu))
        return device, available_gpus

    def _train_epoch(self, epoch, id):
        raise NotImplementedError

    def _valid_epoch(self, epoch):
        raise NotImplementedError

    def _eval_metrics(self, output, target):
        raise NotImplementedError

    def _warm_up(self, epoch, id):
        raise NotImplementedError



================================================
FILE: CityCode/DataLoader/city.py
================================================
from Base.base_dataset import BaseDataSet
from Base.base_dataloader import BaseDataLoader

import numpy as np
import os
import torch
from PIL import Image
from torch.utils.data import Dataset


class CityDataset(BaseDataSet):
    def __init__(self, ddp_training, dgx, **kwargs):
        self.num_classes = 19
        self.ddp_training = ddp_training
        self.dgx = dgx
        # self.palette = get_voc_pallete(self.num_classes)
        super(CityDataset, self).__init__(**kwargs)

    def _set_files(self):
        # self.root = os.path.join(self.root, 'VOCdevkit/VOC2012')
        prefix = "CityCode"
        if self.split == "val":
            file_list = os.path.join(prefix, "DataLoader/city_splits", f"{self.split}" + ".txt")
        elif self.split in ["train_supervised", "train_unsupervised"]:
            file_list = os.path.join(prefix, "DataLoader/city_splits", f"{self.n_labeled_examples}_{self.split}" + ".txt")
        else:
            raise ValueError(f"Invalid split name {self.split}")

        file_list = [line.rstrip().split(' ') for line in tuple(open(file_list, "r"))]
        self.files, self.labels = list(zip(*file_list))

    def _load_data(self, index):
        image_path = os.path.join(self.root, self.files[index][1:])
        image = np.asarray(Image.open(image_path), dtype=np.float32)
        image_id = self.files[index].split("/")[-1].split(".")[0]
        if self.use_weak_lables:
            label_path = os.path.join(self.weak_labels_output, image_id + ".png")
        else:
            label_path = os.path.join(self.root, self.labels[index][1:])
        label = np.asarray(Image.open(label_path), dtype=np.int32)
        return image, label, image_id


class City(BaseDataLoader):
    def __init__(self, kwargs, ddp_training=False, dgx=False):
        self.MEAN = [0.485, 0.456, 0.406]
        self.STD = [0.229, 0.224, 0.225]
        self.batch_size = kwargs.pop('batch_size')
        kwargs['mean'] = self.MEAN
        kwargs['std'] = self.STD
        kwargs['ignore_index'] = 255
        try:
            shuffle = kwargs.pop('shuffle')
        except:
            shuffle = False
        num_workers = kwargs.pop('num_workers')
        self.dataset = CityDataset(ddp_training, **kwargs, dgx=dgx)
        if ddp_training:
            train_sampler = torch.utils.data.distributed.DistributedSampler(self.dataset)
        else:
            train_sampler = None
        super(City, self).__init__(self.dataset, self.batch_size, shuffle, num_workers, val_split=None,
                                   sampler=train_sampler)


================================================
FILE: CityCode/DataLoader/city_splits/1488_train_supervised.txt
================================================
/images/city_gt_fine/train/jena_000078_000019_leftImg8bit.png /annotation/city_gt_fine/train/jena_000078_000019_gtFine.png
/images/city_gt_fine/train/jena_000055_000019_leftImg8bit.png /annotation/city_gt_fine/train/jena_000055_000019_gtFine.png
/images/city_gt_fine/train/jena_000001_000019_leftImg8bit.png /annotation/city_gt_fine/train/jena_000001_000019_gtFine.png
/images/city_gt_fine/train/jena_000114_000019_leftImg8bit.png /annotation/city_gt_fine/train/jena_000114_000019_gtFine.png
/images/city_gt_fine/train/jena_000021_000019_leftImg8bit.png /annotation/city_gt_fine/train/jena_000021_000019_gtFine.png
/images/city_gt_fine/train/jena_000058_000019_leftImg8bit.png /annotation/city_gt_fine/train/jena_000058_000019_gtFine.png
/images/city_gt_fine/train/jena_000016_000019_leftImg8bit.png /annotation/city_gt_fine/train/jena_000016_000019_gtFine.png
/images/city_gt_fine/train/jena_000109_000019_leftImg8bit.png /annotation/city_gt_fine/train/jena_000109_000019_gtFine.png
/images/city_gt_fine/train/jena_000065_000019_leftImg8bit.png /annotation/city_gt_fine/train/jena_000065_000019_gtFine.png
/images/city_gt_fine/train/jena_000052_000019_leftImg8bit.png /annotation/city_gt_fine/train/jena_000052_000019_gtFine.png
/images/city_gt_fine/train/jena_000091_000019_leftImg8bit.png /annotation/city_gt_fine/train/jena_000091_000019_gtFine.png
/images/city_gt_fine/train/jena_000037_000019_leftImg8bit.png /annotation/city_gt_fine/train/jena_000037_000019_gtFine.png
/images/city_gt_fine/train/jena_000092_000019_leftImg8bit.png /annotation/city_gt_fine/train/jena_000092_000019_gtFine.png
/images/city_gt_fine/train/jena_000017_000019_leftImg8bit.png /annotation/city_gt_fine/train/jena_000017_000019_gtFine.png
/images/city_gt_fine/train/jena_000074_000019_leftImg8bit.png /annotation/city_gt_fine/train/jena_000074_000019_gtFine.png
/images/city_gt_fine/train/jena_000020_000019_leftImg8bit.png /annotation/city_gt_fine/train/jena_000020_000019_gtFine.png
/images/city_gt_fine/train/jena_000005_000019_leftImg8bit.png /annotation/city_gt_fine/train/jena_000005_000019_gtFine.png
/images/city_gt_fine/train/jena_000100_000019_leftImg8bit.png /annotation/city_gt_fine/train/jena_000100_000019_gtFine.png
/images/city_gt_fine/train/jena_000089_000019_leftImg8bit.png /annotation/city_gt_fine/train/jena_000089_000019_gtFine.png
/images/city_gt_fine/train/jena_000107_000019_leftImg8bit.png /annotation/city_gt_fine/train/jena_000107_000019_gtFine.png
/images/city_gt_fine/train/jena_000006_000019_leftImg8bit.png /annotation/city_gt_fine/train/jena_000006_000019_gtFine.png
/images/city_gt_fine/train/jena_000044_000019_leftImg8bit.png /annotation/city_gt_fine/train/jena_000044_000019_gtFine.png
/images/city_gt_fine/train/jena_000115_000019_leftImg8bit.png /annotation/city_gt_fine/train/jena_000115_000019_gtFine.png
/images/city_gt_fine/train/jena_000095_000019_leftImg8bit.png /annotation/city_gt_fine/train/jena_000095_000019_gtFine.png
/images/city_gt_fine/train/jena_000102_000019_leftImg8bit.png /annotation/city_gt_fine/train/jena_000102_000019_gtFine.png
/images/city_gt_fine/train/jena_000010_000019_leftImg8bit.png /annotation/city_gt_fine/train/jena_000010_000019_gtFine.png
/images/city_gt_fine/train/jena_000070_000019_leftImg8bit.png /annotation/city_gt_fine/train/jena_000070_000019_gtFine.png
/images/city_gt_fine/train/jena_000079_000019_leftImg8bit.png /annotation/city_gt_fine/train/jena_000079_000019_gtFine.png
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/images/city_gt_fine/train/jena_000043_000019_leftImg8bit.png /annotation/city_gt_fine/train/jena_000043_000019_gtFine.png
/images/city_gt_fine/train/jena_000083_000019_leftImg8bit.png /annotation/city_gt_fine/train/jena_000083_000019_gtFine.png
/images/city_gt_fine/train/jena_000108_000019_leftImg8bit.png /annotation/city_gt_fine/train/jena_000108_000019_gtFine.png
/images/city_gt_fine/train/jena_000084_000019_leftImg8bit.png /annotation/city_gt_fine/train/jena_000084_000019_gtFine.png
/images/city_gt_fine/train/jena_000024_000019_leftImg8bit.png /annotation/city_gt_fine/train/jena_000024_000019_gtFine.png
/images/city_gt_fine/train/jena_000029_000019_leftImg8bit.png /annotation/city_gt_fine/train/jena_000029_000019_gtFine.png
/images/city_gt_fine/train/jena_000117_000019_leftImg8bit.png /annotation/city_gt_fine/train/jena_000117_000019_gtFine.png
/images/city_gt_fine/train/jena_000046_000019_leftImg8bit.png /annotation/city_gt_fine/train/jena_000046_000019_gtFine.png
/images/city_gt_fine/train/jena_000035_000019_leftImg8bit.png /annotation/city_gt_fine/train/jena_000035_000019_gtFine.png
/images/city_gt_fine/train/jena_000093_000019_leftImg8bit.png /annotation/city_gt_fine/train/jena_000093_000019_gtFine.png
/images/city_gt_fine/train/jena_000025_000019_leftImg8bit.png /annotation/city_gt_fine/train/jena_000025_000019_gtFine.png
/images/city_gt_fine/train/jena_000113_000019_leftImg8bit.png /annotation/city_gt_fine/train/jena_000113_000019_gtFine.png
/images/city_gt_fine/train/jena_000098_000019_leftImg8bit.png /annotation/city_gt_fine/train/jena_000098_000019_gtFine.png
/images/city_gt_fine/train/jena_000041_000019_leftImg8bit.png /annotation/city_gt_fine/train/jena_000041_000019_gtFine.png
/images/city_gt_fine/train/stuttgart_000107_000019_leftImg8bit.png /annotation/city_gt_fine/train/stuttgart_000107_000019_gtFine.png
/images/city_gt_fine/train/stuttgart_000188_000019_leftImg8bit.png /annotation/city_gt_fine/train/stuttgart_000188_000019_gtFine.png
/images/city_gt_fine/train/stuttgart_000147_000019_leftImg8bit.png /annotation/city_gt_fine/train/stuttgart_000147_000019_gtFine.png
/images/city_gt_fine/train/stuttgart_000042_000019_leftImg8bit.png /annotation/city_gt_fine/train/stuttgart_000042_000019_gtFine.png
/images/city_gt_fine/train/stuttgart_000191_000019_leftImg8bit.png /annotation/city_gt_fine/train/stuttgart_000191_000019_gtFine.png
/images/city_gt_fine/train/stuttgart_000007_000019_leftImg8bit.png /annotation/city_gt_fine/train/stuttgart_000007_000019_gtFine.png
/images/city_gt_fine/train/stuttgart_000032_000019_leftImg8bit.png /annotation/city_gt_fine/train/stuttgart_000032_000019_gtFine.png
/images/city_gt_fine/train/stuttgart_000077_000019_leftImg8bit.png /annotation/city_gt_fine/train/stuttgart_000077_000019_gtFine.png
/images/city_gt_fine/train/stuttgart_000161_000019_leftImg8bit.png /annotation/city_gt_fine/train/stuttgart_000161_000019_gtFine.png
/images/city_gt_fine/train/stuttgart_000113_000019_leftImg8bit.png /annotation/city_gt_fine/train/stuttgart_000113_000019_gtFine.png
/images/city_gt_fine/train/stuttgart_000017_000019_leftImg8bit.png /annotation/city_gt_fine/train/stuttgart_000017_000019_gtFine.png
/images/city_gt_fine/train/stuttgart_000057_000019_leftImg8bit.png /annotation/city_gt_fine/train/stuttgart_000057_000019_gtFine.png
/images/city_gt_fine/train/stuttgart_000067_000019_leftImg8bit.png /annotation/city_gt_fine/train/stuttgart_000067_000019_gtFine.png
/images/city_gt_fine/train/stuttgart_000070_000019_leftImg8bit.png /annotation/city_gt_fine/train/stuttgart_000070_000019_gtFine.png
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/images/city_gt_fine/train/stuttgart_000128_000019_leftImg8bit.png /annotation/city_gt_fine/train/stuttgart_000128_000019_gtFine.png
/images/city_gt_fine/train/stuttgart_000100_000019_leftImg8bit.png /annotation/city_gt_fine/train/stuttgart_000100_000019_gtFine.png
/images/city_gt_fine/train/stuttgart_000125_000019_leftImg8bit.png /annotation/city_gt_fine/train/stuttgart_000125_000019_gtFine.png
/images/city_gt_fine/train/stuttgart_000168_000019_leftImg8bit.png /annotation/city_gt_fine/train/stuttgart_000168_000019_gtFine.png
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