dronescapes_reader/multitask_dataset.py

#!/usr/bin/env python3
"""MultiTask Dataset module compatible with torch.utils.data.Dataset & DataLoader."""
from __future__ import annotations
import os
from pathlib import Path
from typing import Dict, List, Tuple
from argparse import ArgumentParser
from pprint import pprint
from natsort import natsorted
from loggez import loggez_logger as logger
import torch as tr
import numpy as np
from torch.utils.data import Dataset, DataLoader
from lovely_tensors import monkey_patch

from npz_representation import NpzRepresentation

monkey_patch()
BuildDatasetTuple = Tuple[Dict[str, List[Path]], List[str]]
MultiTaskItem = Tuple[Dict[str, tr.Tensor], str, List[str]] # [{task: data}, stem(name) | list[stem(name)], [tasks]]
TaskStatistics = Tuple[tr.Tensor, tr.Tensor, tr.Tensor, tr.Tensor] # (min, max, mean, std)

class MultiTaskDataset(Dataset):
    """
    MultiTaskDataset implementation. Reads data from npz files and returns them as a dict.

    Parameters:
    - path: Path to the directory containing the npz files.
    - task_names: List of tasks that are present in the dataset. If set to None, will infer from the files on disk.
    - handle_missing_data: Modes to handle missing data. Valid options are:
      - drop: Drop the data point if any of the representations is missing.
      - fill_none: Fill the missing data with Nones.

    Expected directory structure:
    path/
    - task_1/0.npz, ..., N.npz
    - ...
    - task_n/0.npz, ..., N.npz

    Names can be in a different format (i.e. 2022-01-01.npz), but must be consistent and equal across all tasks.
    """

    def __init__(self, path: Path, task_names: list[str] | None = None, handle_missing_data: str = "fill_none",
                 files_suffix: str = "npz", task_types: dict[str, type] | None = None,
                 files_per_repr_overwrites: dict[str, str] | None = None,
                 compute_statistics: bool = False):
        assert Path(path).exists(), f"Provided path '{path}' doesn't exist!"
        assert handle_missing_data in ("drop", "fill_none", "fill_zero", "fill_nan"), \
            f"Invalid handle_missing_data mode: {handle_missing_data}"
        assert files_suffix == "npz", "Only npz supported right now (though trivial to update)"
        self.path = Path(path).absolute()
        self.handle_missing_data = handle_missing_data
        self.suffix = files_suffix
        self.files_per_repr_overwrites = files_per_repr_overwrites
        self.all_files_per_repr = self._get_all_npz_files()
        self.files_per_repr, self.file_names = self._build_dataset() # these are filtered by 'drop' or 'fill_none' logic
        if task_types is None:
            logger.debug("No explicit task types. Defaulting all of them to NpzRepresentation.")
            task_types = {}

        if task_names is None:
            task_names = list(self.files_per_repr.keys())
            logger.debug(f"No explicit tasks provided. Using all of them as read from the paths ({len(task_names)}).")
        assert all(task in self.files_per_repr for task in task_names), (task_names, self.files_per_repr.keys())
        self.task_types = {k: task_types.get(k, NpzRepresentation) for k in task_names}
        assert all(isinstance(x, str) for x in task_names), tuple(zip(task_names, (type(x) for x in task_names)))
        self.task_names = sorted(task_names)
        self._data_shape: tuple[int, ...] | None = None
        self._tasks: list[NpzRepresentation] | None = None
        self.name_to_task = {task.name: task for task in self.tasks}
        logger.info(f"Tasks used in this dataset: {self.task_names}")
        self._default_vals: dict[str, tr.Tensor] | None = None
        self.statistics = None if compute_statistics is False else self._compute_statistics()

    # Public methods and properties

    @property
    def default_vals(self) -> dict[str, tr.Tensor]:
        """default values for __getitem__ if item is not on disk but we retrieve a full batch anyway"""
        if self._default_vals is None:
            _default_val = float("nan") if self.handle_missing_data == "fill_nan" else 0
            self._default_vals = {task: None if self.handle_missing_data == "fill_none" else
                                  tr.full(self.data_shape[task], _default_val) for task in self.task_names}
        return self._default_vals

    @property
    def data_shape(self) -> dict[str, tuple[int, ...]]:
        """Returns a {task: shape_tuple} for all representations. At least one npz file must exist for each."""
        first_npz = {task: [_v for _v in files if _v is not None][0] for task, files in self.files_per_repr.items()}
        data_shape = {task: self.name_to_task[task].load_from_disk(first_npz[task]).shape for task in self.task_names}
        return data_shape

    @property
    def tasks(self) -> list[NpzRepresentation]:
        """
        Returns a list of instantiated tasks in the same order as self.task_names. Overwrite this to add
        new tasks and semantics (i.e. plot_fn or doing some preprocessing after loading from disk in some tasks.
        """
        if self._tasks is not None:
            return self._tasks
        self._tasks = []
        for task_name in self.task_names:
            t = self.task_types[task_name]
            try:
                t = t(task_name) # hack for not isinstance(self.task_types, NpzRepresentation) but callable
            except Exception:
                pass
            self._tasks.append(t)
        assert all(t.name == t_n for t, t_n in zip(self._tasks, self.task_names)), (self.task_names, self._tasks)
        return self._tasks

    def collate_fn(self, items: list[MultiTaskItem]) -> MultiTaskItem:
        """
        given a list of items (i.e. from a reader[n:n+k] call), return the item batched on 1st dimension.
        Nones (missing data points) are turned into nans as per the data shape of that dim.
        """
        assert all(item[2] == self.task_names for item in items), ([item[2] for item in items], self.task_names)
        items_name = [item[1] for item in items]
        res = {k: tr.zeros(len(items), *self.data_shape[k]).float() for k in self.task_names} # float32 always
        for i in range(len(items)):
            for k in self.task_names:
                res[k][i][:] = items[i][0][k] if items[i][0][k] is not None else float("nan")
        return res, items_name, self.task_names

    # Private methods

    def _get_all_npz_files(self) -> dict[str, list[Path]]:
        """returns a dict of form: {"rgb": ["0.npz", "1.npz", ..., "N.npz"]}"""
        in_files = {}
        all_repr_dirs: list[str] = [x.name for x in self.path.iterdir() if x.is_dir()]
        for repr_dir_name in all_repr_dirs:
            dir_name = self.path / repr_dir_name
            if all(f.is_dir() for f in dir_name.iterdir()): # dataset is stored as repr/part_x/0.npz, ..., part_k/n.npz
                all_files = []
                for part in dir_name.iterdir():
                    all_files.extend(part.glob(f"*.{self.suffix}"))
            else: # dataset is stored as repr/0.npz, ..., repr/n.npz
                all_files = dir_name.glob(f"*.{self.suffix}")
            in_files[repr_dir_name] = natsorted(all_files, key=lambda x: x.name) # important: use natsorted() here
        assert not any(len(x) == 0 for x in in_files.values()), f"{ [k for k, v in in_files.items() if len(v) == 0] }"
        return in_files

    def _build_dataset(self) -> BuildDatasetTuple:
        logger.debug(f"Building dataset from: '{self.path}'")
        if self.handle_missing_data == "drop":
            files_per_repr, common = self._build_dataset_drop_missing()
        else:
            files_per_repr, common = self._build_dataset_fill_missing()
        if self.files_per_repr_overwrites is not None: # here we match for example 'hsv' to read also from 'rgb' dir
            for left, right in self.files_per_repr_overwrites.items():
                if right not in (fpr := files_per_repr):
                    logger.info(f"Overwrite: {left} => {right} provided, but {right} is not in {fpr.keys()}")
                    continue
                assert left not in fpr.keys(), f"Overwrite: {left} => {right}. {left} already exists in {fpr.keys()}"
                files_per_repr[left] = files_per_repr[right]
        return files_per_repr, common

    def _build_dataset_drop_missing(self) -> BuildDatasetTuple:
        in_files = self.all_files_per_repr
        name_to_node_path = {k: {_v.name: _v for _v in v} for k, v in in_files.items()} # {node: {name: path}}
        common = set(x.name for x in next(iter(in_files.values())))
        for node in (nodes := in_files.keys()):
            common = common.intersection([f.name for f in in_files[node]])
            assert len(common) > 0, f"Node '{node}' made the intersection null"
        common = natsorted(list(common))
        logger.info(f"Found {len(common)} data points for each node ({len(nodes)} nodes).")
        files_per_repr = {node: [name_to_node_path[node][x] for x in common] for node in nodes}
        assert len(files_per_repr) > 0
        return files_per_repr, common

    def _build_dataset_fill_missing(self) -> BuildDatasetTuple:
        in_files = self.all_files_per_repr
        name_to_node_path = {k: {_v.name: _v for _v in v} for k, v in in_files.items()}
        all_files = set(x.name for x in next(iter(in_files.values())))
        nodes = in_files.keys()
        for node in (nodes := in_files.keys()):
            all_files = all_files.union([f.name for f in in_files[node]])
        all_files = natsorted(list(all_files))
        logger.info(f"Found {len(all_files)} data points as union of all nodes' data ({len(nodes)} nodes).")

        files_per_repr = {node: [] for node in nodes}
        for node in nodes:
            for file_name in all_files:
                file_path = name_to_node_path[node].get(file_name, None)
                files_per_repr[node].append(file_path)
        assert len(files_per_repr) > 0
        return files_per_repr, all_files

    def _compute_statistics(self) -> dict[str, tr.Tensor]:
        cache_path = self.path / f".task_statistics.npz"
        res: dict[str, TaskStatistics] = {}
        if os.getenv("CACHE_IMG_STATS", "0") == "1" and cache_path.exists():
            res = np.load(cache_path, allow_pickle=True)["arr_0"].item()
            logger.info(f"Loaded task statistics: { {k: v.shape for k, v in res.items()} }")
        missing_tasks = list(set(self.task_names).difference(res.keys()))
        if len(missing_tasks) == 0:
            return res
        logger.info(f"Computing global task statistics (dataset len {len(self)}) for {missing_tasks}")
        old_tasks = self.tasks
        self._tasks = [t for t in self.tasks if t.name in missing_tasks]
        res = {**res, **self._compute_channel_level_stats(missing_tasks)}
        self._tasks = old_tasks
        logger.info(f"Computed task statistics: { {k: v[0].shape for k, v in res.items()} }")
        if os.getenv("CACHE_IMG_STATS", "0") == "1":
            np.savez(cache_path, res)
        return res

    def _compute_channel_level_stats(self, missing_tasks: list[str]) -> dict[str, TaskStatistics]:
        ch = {k: v[-1] if len(v) == 3 else 1 for k, v in self.data_shape.items()}
        sums = {task_name: tr.zeros(ch[task_name]).type(tr.float64) for task_name in missing_tasks}
        counts = {task_name: tr.zeros(ch[task_name]).long() for task_name in missing_tasks}
        mins = {task_name: tr.zeros(ch[task_name]).type(tr.float64) - 1<<31 for task_name in missing_tasks}
        maxs = {task_name: tr.zeros(ch[task_name]).type(tr.float64) + 1<<31 for task_name in missing_tasks}


        for ix in range(len(self)):
            item = self.base_dataset[ix][0]
            for task in missing_tasks:
                item_flat_ch = item[task].reshape(-1, self.ch[task])
                sums[task] += item_flat_ch.nan_to_num(0).type(tr.float64).sum(0)
                counts[task] += (item_flat_ch == item_flat_ch).long().sum(0)
        res_ch = {k: (sums[k] / counts[k]).nan_to_num(0).float() for k in missing_tasks}
        res = {k: v.reshape(-1, 1, 1).repeat(1, self.h, self.w) for k, v in res_ch.items()}
        return res

    # Python magic methods (pretty printing the reader object, reader[0], len(reader) etc.)

    def __getitem__(self, index: int | slice | list[int] | tuple) -> MultiTaskItem:
        """Read the data all the desired nodes"""
        assert isinstance(index, (int, slice, list, tuple, str)), type(index)
        if isinstance(index, slice):
            assert index.start is not None and index.stop is not None and index.step is None, "Only reader[l:r] allowed"
            index = list(range(index.stop)[index])
        if isinstance(index, (list, tuple)):
            return self.collate_fn([self.__getitem__(ix) for ix in index])
        if isinstance(index, str):
            return self.__getitem__(self.file_names.index(index))
        res = {}
        item_name = self.file_names[index]

        for task in self.tasks:
            file_path = self.files_per_repr[task.name][index]
            file_path = None if file_path is None or not (fpr := file_path.resolve()).exists() else fpr
            res[task.name] = task.load_from_disk(file_path) if file_path is not None else self.default_vals[task.name]
        return (res, item_name, self.task_names)

    def __len__(self) -> int:
        return len(self.files_per_repr[self.task_names[0]]) # all of them have the same number (filled with None or not)

    def __str__(self):
        f_str = f"[{str(type(self)).rsplit('.', maxsplit=1)[-1][0:-2]}]"
        f_str += f"\n - Path: '{self.path}'"
        f_str += f"\n - Tasks ({len(self.tasks)}): {self.tasks}"
        f_str += f"\n - Length: {len(self)}"
        f_str += f"\n - Handle missing data mode: '{self.handle_missing_data}'"
        return f_str

    def __repr__(self):
        return str(self)

def main():
    """main fn"""
    parser = ArgumentParser()
    parser.add_argument("dataset_path", type=Path)
    parser.add_argument("--handle_missing_data", choices=("drop", "fill_none"), default="fill_none")
    args = parser.parse_args()

    reader = MultiTaskDataset(args.dataset_path, task_names=None, handle_missing_data=args.handle_missing_data)
    print(reader)
    print(f"Shape: {reader.data_shape}")

    rand_ix = np.random.randint(len(reader))
    data, name, repr_names = reader[rand_ix] # get a random single data point
    print(f"Name: {name}. Nodes: {repr_names}")
    pprint({k: v for k, v in data.items()})

    data, name, repr_names = reader[rand_ix: min(len(reader), rand_ix + 5)] # get a random batch
    print(f"Name: {name}. Nodes: {repr_names}")
    pprint({k: v for k, v in data.items()}) # Nones are converted to 0s automagically

    loader = DataLoader(reader, collate_fn=reader.collate_fn, batch_size=5, shuffle=True)
    data, name, repr_names = next(iter(loader)) # get a random batch using torch DataLoader
    print(f"Name: {name}. Nodes: {repr_names}")
    pprint({k: v for k, v in data.items()}) # Nones are converted to 0s automagically

if __name__ == "__main__":
    main()