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, Iterable
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 tqdm import trange

try:
    from npz_representation import NpzRepresentation
except ImportError:
    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.
    - task_types: A dictionary of form {task_name: task_type} for the reader to call to read from disk, plot etc.
    - normalization: The normalization type used in __getitem__. Valid options are:
      - None: Reads the data as-is using task.read_from_disk(path)
      - 'min_max': Calls task.normalize(task.read_from_disk(path), mins[task], maxs[task])
      - 'standardization': Calls task.standardize(task.read_from_disk(path), means[task], stds[task])
      If normalization is not 'none', then task-level statistics will be computed. Environmental variable
      STATS_PBAR=0/1 enables tqdm during statistics computation.
    - 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,zero,nan}': Fill the missing data with Nones, zeros or NaNs.
    - files_suffix: What suffix to look for when creating the dataset. Valid values: 'npy' or 'npz'.
    - cache_task_stats: If set to True, the statistics will be cached at '{path}/.task_statistics.npz'. Can be enabled
    using the environmental variable STATS_CACHE=1. Defaults to False.
    - batch_size_stats: Controls the batch size during statistics computation. Can be enabled by environmental variable
    STATS_BATCH_SIZE. Defaults to 1.

    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],
                 task_types: dict[str, type],
                 normalization: str | None | dict[str],
                 handle_missing_data: str = "fill_none",
                 files_suffix: str = "npz",
                 cache_task_stats: bool = (os.getenv("STATS_CACHE", "0") == "1"),
                 batch_size_stats: int = int(os.getenv("STATS_BATCH_SIZE", "1")),
                 statistics: dict[str, TaskStatistics] | None = None,
    ):
        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 isinstance(task_names, Iterable), type(task_names)
        self.path = Path(path).absolute()
        self.handle_missing_data = handle_missing_data
        self.suffix = files_suffix
        self.files_per_repr, self.file_names = self._build_dataset(task_types, task_names) # + handle_missing_data
        self.cache_task_stats = cache_task_stats
        self.batch_size_stats = batch_size_stats

        assert all(isinstance(x, str) for x in task_names), tuple(zip(task_names, (type(x) for x in task_names)))
        assert (diff := set(self.files_per_repr).difference(task_names)) == set(), f"Not all tasks in files: {diff}"
        self.task_types = {k: v for k, v in task_types.items() if k in task_names} # all task_types must be provided!
        self.task_names = sorted(task_names)
        logger.info(f"Tasks used in this dataset: {self.task_names}")

        if normalization is not None:
            if isinstance(normalization, str):
                logger.info(f"Normalization provided as a string ({normalization}). Setting all tasks to this")
                normalization: dict[str, str] = {task: normalization for task in self.task_names}
            if "*" in normalization.keys(): # for the lazy, we can put {"*": "standardization", "depth": "min_max"}
                value = normalization.pop("*")
                for missing_task in set(self.task_names).difference(normalization.keys()):
                    normalization[missing_task] = value
            assert all(n in ("min_max", "standardization") for n in normalization.values()), normalization
            assert all(k in task_names for k in normalization.keys()), set(normalization).difference(task_names)
        self.normalization: dict[str, str] | None = normalization

        self._data_shape: tuple[int, ...] | None = None
        self._tasks: list[NpzRepresentation] | None = None
        self._default_vals: dict[str, tr.Tensor] | None = None
        if statistics is not None:
            self._statistics = self._load_external_statistics(statistics)
        else:
            self._statistics = None if normalization is None else self._compute_statistics()
        if self._statistics is not None:
            for task_name, task in self.name_to_task.items():
                if not task.is_classification:
                    task.set_normalization(self.normalization[task_name], self._statistics[task_name])

    # Public methods and properties

    @property
    def name_to_task(self) -> dict[str, NpzRepresentation]:
        return {task.name: task for task in self.tasks}

    @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"""
        _default_val = float("nan") if self.handle_missing_data == "fill_nan" else 0
        return {task: None if self.handle_missing_data == "fill_none" else tr.full(self.data_shape[task], _default_val)
                for task in self.task_names}

    @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 {task: tuple(shape) for task, shape in data_shape.items()}

    @property
    def mins(self) -> dict[str, tr.Tensor]:
        """returns a dict {task: mins[task]} for all the tasks if self.statistics exists"""
        assert self.normalization is not None, "No statistics for normalization is None"
        return {k: v[0] for k, v in self._statistics.items() if k in self.task_names}

    @property
    def maxs(self) -> dict[str, tr.Tensor]:
        """returns a dict {task: mins[task]} for all the tasks if self.statistics exists"""
        assert self.normalization is not None, "No statistics for normalization is None"
        return {k: v[1] for k, v in self._statistics.items() if k in self.task_names}

    @property
    def means(self) -> dict[str, tr.Tensor]:
        """returns a dict {task: mins[task]} for all the tasks if self.statistics exists"""
        assert self.normalization is not None, "No statistics for normalization is None"
        return {k: v[2] for k, v in self._statistics.items() if k in self.task_names}

    @property
    def stds(self) -> dict[str, tr.Tensor]:
        """returns a dict {task: mins[task]} for all the tasks if self.statistics exists"""
        assert self.normalization is not None, "No statistics for normalization is None"
        return {k: v[3] for k, v in self._statistics.items() if k in self.task_names}

    @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 None:
            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:
                try:
                    res[k][i][:] = items[i][0][k] if items[i][0][k] is not None else float("nan")
                except Exception as e:
                    print(k, items)
                    raise e
        return res, items_name, self.task_names

    def add_task(self, task: NpzRepresentation):
        """Safely adds a task to this reader. Most likely can be optimized"""
        logger.debug(f"Adding a new task: '{task.name}'")
        assert task.name not in self.task_names, f"Task '{task.name}' already exists: {self.task_names}"
        self.task_names = sorted([*self.task_names, task.name])
        self.task_types[task.name] = task
        self._tasks = None
        self.files_per_repr, self.file_names = self._build_dataset(self.task_types, self.task_names)

    def remove_task(self, task_name: str):
        """Safely removes a task from this reader"""
        logger.debug(f"Removing a task: '{task_name}'")
        assert task_name in self.task_names, f"Task '{task_name}' doesn't exist: {self.task_names}"
        self.task_names = sorted(name for name in self.task_names if name != task_name)
        del self.task_types[task_name]
        self._tasks = None
        self.files_per_repr, self.file_names = self._build_dataset(self.task_types, 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"]}"""
        assert self.suffix == "npz", f"Only npz supported right now (though trivial to update): {self.suffix}"
        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, task_types: dict[str, NpzRepresentation], task_names: list[str]) -> BuildDatasetTuple:
        logger.debug(f"Building dataset from: '{self.path}'")
        all_npz_files = self._get_all_npz_files()
        all_files: dict[str, dict[str, Path]] = {k: {_v.name: _v for _v in v} for k, v in all_npz_files.items()}

        relevant_tasks_for_files = set() # hsv requires only rgb, so we look at dependencies later on
        for task_name in task_names:
            relevant_tasks_for_files.update(task_types[task_name].dep_names)
        if (diff := relevant_tasks_for_files.difference(all_files)) != set():
            raise FileNotFoundError(f"Missing files for {diff}.\nFound on disk: {[*all_files]}")
        names_to_tasks: dict[str, list[str]] = {} # {name: [task]}
        for task_name in relevant_tasks_for_files: # just the relevant tasks
            for path_name in all_files[task_name].keys():
                names_to_tasks.setdefault(path_name, [])
                names_to_tasks[path_name].append(task_name)

        if self.handle_missing_data == "drop":
            b4 = len(names_to_tasks)
            names_to_tasks = {k: v for k, v in names_to_tasks.items() if len(v) == len(relevant_tasks_for_files)}
            logger.debug(f"Dropped {b4 - len(names_to_tasks)} files not in all tasks")
        all_names: list[str] = natsorted(names_to_tasks.keys())
        logger.info(f"Total files: {len(names_to_tasks)} per task across {len(task_names)} tasks")

        files_per_task: dict[str, list[str | None] | list[list[str] | None]] = {task: [] for task in task_names}
        for name in all_names:
            for task in task_names:
                all_deps_exist = set(deps := task_types[task].dep_names).issubset(names_to_tasks[name])
                if not all_deps_exist:
                    files_per_task[task].append(None) # if any of the deps don't exist for this task, skip it.
                else:
                    paths = [all_files[dep][name] for dep in deps]
                    files_per_task[task].append(paths if len(deps) > 1 else paths[0])
        return files_per_task, all_names

    def _compute_statistics(self) -> dict[str, TaskStatistics]:
        cache_path = self.path / f".task_statistics.npz"
        res: dict[str, TaskStatistics] = {}
        if self.cache_task_stats and cache_path.exists():
            res = np.load(cache_path, allow_pickle=True)["arr_0"].item()
            logger.info(f"Loaded task statistics: { {k: tuple(v[0].shape) for k, v in res.items()} } from {cache_path}")
        missing_tasks = [t for t in set(self.task_names).difference(res) if not self.name_to_task[t].is_classification]
        if len(missing_tasks) == 0:
            return res
        logger.info(f"Computing global task statistics (dataset len {len(self)}) for {missing_tasks}")
        res = {**res, **self._compute_channel_level_stats(missing_tasks)}
        logger.info(f"Computed task statistics: { {k: tuple(v[0].shape) for k, v in res.items()} }")
        np.savez(cache_path, res)
        return res

    def _compute_channel_level_stats(self, missing_tasks: list[str]) -> dict[str, TaskStatistics]:
        # kinda based on: https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Welford's_online_algorithm
        def update(counts: tr.Tensor, counts_delta: float,  mean: tr.Tensor, M2: tr.Tensor,
                   new_value: tr.Tensor) -> tuple[tr.Tensor, tr.Tensor, tr.Tensor]:
            new_count = counts + counts_delta
            batch_mean = new_value.nanmean(0)
            batch_var = ((new_value - batch_mean) ** 2).nansum(0)
            delta = batch_mean - mean
            new_count_no_zero = new_count + (new_count == 0) # add 1 (True) in case new_count is 0 to not divide by 0
            new_mean = mean + delta * counts_delta / new_count_no_zero
            new_M2 = M2 + batch_var + delta**2 * counts * counts_delta / new_count_no_zero
            assert not new_mean.isnan().any() and not new_M2.isnan().any(), (mean, new_mean, counts, counts_delta)
            return new_count, new_mean, new_M2

        assert not any(mt := [self.name_to_task[t].is_classification for t in missing_tasks]), mt
        assert len(missing_tasks) > 0, missing_tasks
        ch = {k: v[-1] if len(v) == 3 else 1 for k, v in self.data_shape.items()}
        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) + 10**10 for task_name in missing_tasks}
        maxs = {task_name: tr.zeros(ch[task_name]).type(tr.float64) - 10**10 for task_name in missing_tasks}
        means_vec = {task_name: tr.zeros(ch[task_name]).type(tr.float64) for task_name in missing_tasks}
        M2s_vec = {task_name: tr.zeros(ch[task_name]).type(tr.float64) for task_name in missing_tasks}

        old_names, old_normalization = self.task_names, self.normalization
        self.task_names, self.normalization = missing_tasks, None # for self[ix]
        res = {}
        BS = min(len(self), self.batch_size_stats)
        n = (len(self) // BS) + (len(self) % BS != 0)

        logger.debug(f"Global task statistics. Batch size: {BS}. N iterations: {n}.")
        for ix in trange(n, disable=os.getenv("STATS_PBAR", "0") == "0", desc="Computing stats"):
            item = self[ix * BS: min(len(self), (ix + 1) * BS)][0]
            for task in missing_tasks:
                item_flat_ch = item[task].reshape(-1, ch[task])
                item_no_nan = item_flat_ch.nan_to_num(0).type(tr.float64)
                mins[task] = tr.minimum(mins[task], item_no_nan.min(0)[0])
                maxs[task] = tr.maximum(maxs[task], item_no_nan.max(0)[0])
                counts_delta = (item_flat_ch == item_flat_ch).long().sum(0)
                counts[task], means_vec[task], M2s_vec[task] = \
                    update(counts[task], counts_delta, means_vec[task], M2s_vec[task], item_no_nan)

        for task in missing_tasks:
            res[task] = (mins[task], maxs[task], means_vec[task], (M2s_vec[task] / counts[task]).sqrt())
            assert not any(x[0].isnan().any() for x in res[task]), (task, res[task])
        self.task_names, self.normalization = old_names, old_normalization
        return res

    def _load_external_statistics(self, statistics: dict[str, TaskStatistics | list]) -> dict[str, TaskStatistics]:
        tasks_no_classif = [t for t in set(self.task_names) if not self.name_to_task[t].is_classification]
        assert (diff := set(tasks_no_classif).difference(statistics)) == set(), f"Missing tasks: {diff}"
        res: dict[str, TaskStatistics] = {}
        for k, v in statistics.items():
            if k in self.task_names:
                res[k] = tuple(tr.Tensor(x) for x in v)
                assert all(_stat.shape == (nd := (self.name_to_task[k].n_channels, )) for _stat in res[k]), (res[k], nd)
        logger.info(f"External statistics provided: { {k: tuple(v[0].shape) for k, v in res.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: dict[str, tr.Tensor] = {}
        for task_name in self.task_names:
            task = [t for t in self.tasks if t.name == task_name][0]
            file_path = self.files_per_repr[task_name][index]
            res[task_name] = self.default_vals[task_name] if file_path is None else task.load_from_disk(file_path)
            if not task.is_classification:
                if self.normalization is not None and self.normalization[task_name] == "min_max":
                    res[task_name] = task.normalize(res[task_name])
                if self.normalization is not None and self.normalization[task_name] == "standardization":
                    res[task_name] = task.standardize(res[task_name])
        return (res, self.file_names[index], 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}'"
        f_str += f"\n - Normalization: '{self.normalization}'"
        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()