Module monk.pytorch.finetune.level_1_dataset_base

Classes

class finetune_dataset (verbose=1)

Base class for dataset params

Args

verbose : int

Set verbosity levels 0 - Print Nothing 1 - Print desired details

Expand source code

class finetune_dataset(system):
    '''
    Base class for dataset params

    Args:
        verbose (int): Set verbosity levels
                        0 - Print Nothing
                        1 - Print desired details
    '''


    def __init__(self, verbose=1):
        super().__init__(verbose=verbose);


    ###############################################################################################################################################
    def set_dataset_dataloader(self, test=False):
        '''
        Setup the dataloader.

        Args:
            test (bool): If True then test data is loaded, else train data is loaded.

        Returns:
            None
        '''
        if(test):
            num_workers = self.system_dict["dataset"]["params"]["num_workers"];
            if(self.system_dict["dataset"]["params"]["dataset_test_type"] == "foldered"):
                test_dataset = torchvision.datasets.ImageFolder(self.system_dict["dataset"]["test_path"], self.system_dict["local"]["data_transforms"]["test"]);
                if(not self.system_dict["dataset"]["params"]["classes"]):
                    self.system_dict["dataset"]["params"]["classes"] = list(np.unique(sorted(os.listdir(self.system_dict["dataset"]["test_path"]))));
            elif(self.system_dict["dataset"]["params"]["dataset_test_type"] == "csv"):
                if(not self.system_dict["dataset"]["params"]["classes"]):
                    img_list, label_list, self.system_dict["dataset"]["params"]["classes"] = parse_csv(self.system_dict["dataset"]["csv_test"], self.system_dict["dataset"]["params"]["test_delimiter"]);
                else:
                    img_list, label_list, _ = parse_csv(self.system_dict["dataset"]["csv_test"], self.system_dict["dataset"]["params"]["test_delimiter"]);
                test_dataset = DatasetCustom(img_list, label_list, self.system_dict["dataset"]["test_path"], self.system_dict["local"]["data_transforms"]["test"]);
            self.system_dict["local"]["data_loaders"]['test'] = torch.utils.data.DataLoader(test_dataset, batch_size=1,
                                                                   shuffle=False, num_workers=num_workers);
            self.system_dict["dataset"]["params"]["num_test_images"] = len(self.system_dict["local"]["data_loaders"]['test']);
        else:
            sampled_dataset = None;
            image_datasets = {};
            dataset_type = self.system_dict["dataset"]["dataset_type"];
            dataset_train_path = self.system_dict["dataset"]["train_path"];
            dataset_val_path = self.system_dict["dataset"]["val_path"];
            csv_train = self.system_dict["dataset"]["csv_train"];
            csv_val = self.system_dict["dataset"]["csv_val"];
            train_val_split = self.system_dict["dataset"]["params"]["train_val_split"];
            delimiter = self.system_dict["dataset"]["params"]["delimiter"];
            batch_size = self.system_dict["dataset"]["params"]["batch_size"];
            shuffle = self.system_dict["dataset"]["params"]["train_shuffle"];
            num_workers = self.system_dict["dataset"]["params"]["num_workers"];

            if(dataset_type == "train"):
                sampled_dataset = torchvision.datasets.ImageFolder(dataset_train_path, self.system_dict["local"]["data_transforms"]["train"]);
            elif(dataset_type == "train-val"):
                image_datasets["train"] = torchvision.datasets.ImageFolder(dataset_train_path, self.system_dict["local"]["data_transforms"]["train"]);
                image_datasets["val"] = torchvision.datasets.ImageFolder(dataset_val_path, self.system_dict["local"]["data_transforms"]["val"]);
            elif(dataset_type == "csv_train"):
                img_list, label_list, self.system_dict["dataset"]["params"]["classes"] = parse_csv(csv_train, delimiter);
                sampled_dataset = DatasetCustom(img_list, label_list, dataset_train_path, self.system_dict["local"]["data_transforms"]["train"]);
            elif(dataset_type == "csv_train-val"):
                img_list, label_list, self.system_dict["dataset"]["params"]["classes"] = parse_csv(csv_train, delimiter);
                image_datasets["train"] = DatasetCustom(img_list, label_list, dataset_train_path, self.system_dict["local"]["data_transforms"]["train"]);
                img_list, label_list, self.system_dict["dataset"]["params"]["classes"] = parse_csv(csv_val, delimiter);
                image_datasets["val"] = DatasetCustom(img_list, label_list, dataset_val_path, self.system_dict["local"]["data_transforms"]["val"]);

            if(sampled_dataset):
                lengths = [int(len(sampled_dataset)*train_val_split), len(sampled_dataset) - int(len(sampled_dataset)*train_val_split)]
                image_datasets["train"], image_datasets["val"] = torch.utils.data.dataset.random_split(sampled_dataset, lengths)

            if("csv" not in dataset_type):
                self.system_dict["dataset"]["params"]["classes"] = list(np.unique(sorted(os.listdir(dataset_train_path))));


            self.system_dict["dataset"]["params"]["num_classes"] = len(self.system_dict["dataset"]["params"]["classes"]);

            if(self.system_dict["dataset"]["params"]["weighted_sample"]):
                self.custom_print("    Weighted sampling enabled.");
                self.custom_print("    Weighted sampling temporarily suspended. Skipping step");

            self.system_dict["dataset"]["params"]["num_train_images"] = len(image_datasets["train"]);
            self.system_dict["dataset"]["params"]["num_val_images"] = len(image_datasets["val"]);


            if(self.system_dict["dataset"]["params"]["weighted_sample"]):
                self.system_dict["local"]["data_loaders"]["train"] = torch.utils.data.DataLoader(image_datasets['train'], batch_size=batch_size,
                                                                   shuffle=True, num_workers=num_workers);
            else:
                self.system_dict["local"]["data_loaders"]["train"] = torch.utils.data.DataLoader(image_datasets['train'], batch_size=batch_size,
                                                                   shuffle=True, num_workers=num_workers);
            self.system_dict["local"]["data_loaders"]["val"] = torch.utils.data.DataLoader(image_datasets['val'], batch_size=batch_size,
                                                                   shuffle=True, num_workers=num_workers);

            self.system_dict["dataset"]["status"]= True;
    ###############################################################################################################################################











    ###############################################################################################################################################
    def set_dataset_final(self, test=False):
        '''
        Set the transforms and then invoke data loader.

        Args:
            test (bool): If True then test tranforms are set and test dataloader is prepared data, 
                        else train transforms are set and train dataloader is prepared.

        Returns:
            None
        '''
        if(test):
            self.system_dict = set_transform_test(self.system_dict);
        else:
            self.system_dict = set_transform_trainval(self.system_dict);
        self.set_dataset_dataloader(test=test);

Ancestors

• system.base_class.system

Methods

def set_dataset_dataloader(self, test=False)

Setup the dataloader.

Args

test : bool

If True then test data is loaded, else train data is loaded.

Returns

None

 

Expand source code

def set_dataset_dataloader(self, test=False):
    '''
    Setup the dataloader.

    Args:
        test (bool): If True then test data is loaded, else train data is loaded.

    Returns:
        None
    '''
    if(test):
        num_workers = self.system_dict["dataset"]["params"]["num_workers"];
        if(self.system_dict["dataset"]["params"]["dataset_test_type"] == "foldered"):
            test_dataset = torchvision.datasets.ImageFolder(self.system_dict["dataset"]["test_path"], self.system_dict["local"]["data_transforms"]["test"]);
            if(not self.system_dict["dataset"]["params"]["classes"]):
                self.system_dict["dataset"]["params"]["classes"] = list(np.unique(sorted(os.listdir(self.system_dict["dataset"]["test_path"]))));
        elif(self.system_dict["dataset"]["params"]["dataset_test_type"] == "csv"):
            if(not self.system_dict["dataset"]["params"]["classes"]):
                img_list, label_list, self.system_dict["dataset"]["params"]["classes"] = parse_csv(self.system_dict["dataset"]["csv_test"], self.system_dict["dataset"]["params"]["test_delimiter"]);
            else:
                img_list, label_list, _ = parse_csv(self.system_dict["dataset"]["csv_test"], self.system_dict["dataset"]["params"]["test_delimiter"]);
            test_dataset = DatasetCustom(img_list, label_list, self.system_dict["dataset"]["test_path"], self.system_dict["local"]["data_transforms"]["test"]);
        self.system_dict["local"]["data_loaders"]['test'] = torch.utils.data.DataLoader(test_dataset, batch_size=1,
                                                               shuffle=False, num_workers=num_workers);
        self.system_dict["dataset"]["params"]["num_test_images"] = len(self.system_dict["local"]["data_loaders"]['test']);
    else:
        sampled_dataset = None;
        image_datasets = {};
        dataset_type = self.system_dict["dataset"]["dataset_type"];
        dataset_train_path = self.system_dict["dataset"]["train_path"];
        dataset_val_path = self.system_dict["dataset"]["val_path"];
        csv_train = self.system_dict["dataset"]["csv_train"];
        csv_val = self.system_dict["dataset"]["csv_val"];
        train_val_split = self.system_dict["dataset"]["params"]["train_val_split"];
        delimiter = self.system_dict["dataset"]["params"]["delimiter"];
        batch_size = self.system_dict["dataset"]["params"]["batch_size"];
        shuffle = self.system_dict["dataset"]["params"]["train_shuffle"];
        num_workers = self.system_dict["dataset"]["params"]["num_workers"];

        if(dataset_type == "train"):
            sampled_dataset = torchvision.datasets.ImageFolder(dataset_train_path, self.system_dict["local"]["data_transforms"]["train"]);
        elif(dataset_type == "train-val"):
            image_datasets["train"] = torchvision.datasets.ImageFolder(dataset_train_path, self.system_dict["local"]["data_transforms"]["train"]);
            image_datasets["val"] = torchvision.datasets.ImageFolder(dataset_val_path, self.system_dict["local"]["data_transforms"]["val"]);
        elif(dataset_type == "csv_train"):
            img_list, label_list, self.system_dict["dataset"]["params"]["classes"] = parse_csv(csv_train, delimiter);
            sampled_dataset = DatasetCustom(img_list, label_list, dataset_train_path, self.system_dict["local"]["data_transforms"]["train"]);
        elif(dataset_type == "csv_train-val"):
            img_list, label_list, self.system_dict["dataset"]["params"]["classes"] = parse_csv(csv_train, delimiter);
            image_datasets["train"] = DatasetCustom(img_list, label_list, dataset_train_path, self.system_dict["local"]["data_transforms"]["train"]);
            img_list, label_list, self.system_dict["dataset"]["params"]["classes"] = parse_csv(csv_val, delimiter);
            image_datasets["val"] = DatasetCustom(img_list, label_list, dataset_val_path, self.system_dict["local"]["data_transforms"]["val"]);

        if(sampled_dataset):
            lengths = [int(len(sampled_dataset)*train_val_split), len(sampled_dataset) - int(len(sampled_dataset)*train_val_split)]
            image_datasets["train"], image_datasets["val"] = torch.utils.data.dataset.random_split(sampled_dataset, lengths)

        if("csv" not in dataset_type):
            self.system_dict["dataset"]["params"]["classes"] = list(np.unique(sorted(os.listdir(dataset_train_path))));


        self.system_dict["dataset"]["params"]["num_classes"] = len(self.system_dict["dataset"]["params"]["classes"]);

        if(self.system_dict["dataset"]["params"]["weighted_sample"]):
            self.custom_print("    Weighted sampling enabled.");
            self.custom_print("    Weighted sampling temporarily suspended. Skipping step");

        self.system_dict["dataset"]["params"]["num_train_images"] = len(image_datasets["train"]);
        self.system_dict["dataset"]["params"]["num_val_images"] = len(image_datasets["val"]);


        if(self.system_dict["dataset"]["params"]["weighted_sample"]):
            self.system_dict["local"]["data_loaders"]["train"] = torch.utils.data.DataLoader(image_datasets['train'], batch_size=batch_size,
                                                               shuffle=True, num_workers=num_workers);
        else:
            self.system_dict["local"]["data_loaders"]["train"] = torch.utils.data.DataLoader(image_datasets['train'], batch_size=batch_size,
                                                               shuffle=True, num_workers=num_workers);
        self.system_dict["local"]["data_loaders"]["val"] = torch.utils.data.DataLoader(image_datasets['val'], batch_size=batch_size,
                                                               shuffle=True, num_workers=num_workers);

        self.system_dict["dataset"]["status"]= True;

def set_dataset_final(self, test=False)

Set the transforms and then invoke data loader.

Args

test : bool

If True then test tranforms are set and test dataloader is prepared data, else train transforms are set and train dataloader is prepared.

Returns

None

 

Expand source code

def set_dataset_final(self, test=False):
    '''
    Set the transforms and then invoke data loader.

    Args:
        test (bool): If True then test tranforms are set and test dataloader is prepared data, 
                    else train transforms are set and train dataloader is prepared.

    Returns:
        None
    '''
    if(test):
        self.system_dict = set_transform_test(self.system_dict);
    else:
        self.system_dict = set_transform_trainval(self.system_dict);
    self.set_dataset_dataloader(test=test);