apple/logger.py

"""

Some simple logging functionality, inspired by rllab's logging.

Logs to a tab-separated-values file (path/to/output_directory/progress.txt)

"""
import atexit
import os
import os.path as osp
import time
import warnings

import joblib
import numpy as np
import torch

import wandb

color2num = dict(gray=30, red=31, green=32, yellow=33, blue=34, magenta=35, cyan=36, white=37, crimson=38)


def setup_logger_kwargs(exp_name, seed=None, data_dir=None, datestamp=True):
    """
    Sets up the output_dir for a logger and returns a dict for logger kwargs.
    If no seed is given and datestamp is false,
    ::
        output_dir = data_dir/exp_name
    If a seed is given and datestamp is false,
    ::
        output_dir = data_dir/exp_name/exp_name_s[seed]
    If datestamp is true, amend to
    ::
        output_dir = data_dir/YY-MM-DD_exp_name/YY-MM-DD_HH-MM-SS_exp_name_s[seed]
    You can force datestamp=True by setting ``FORCE_DATESTAMP=True`` in
    ``spinup/user_config.py``.
    Args:
        exp_name (string): Name for experiment.
        seed (int): Seed for random number generators used by experiment.
        data_dir (string): Path to folder where results should be saved.
            Default is the ``DEFAULT_DATA_DIR`` in ``spinup/user_config.py``.
        datestamp (bool): Whether to include a date and timestamp in the
            name of the save directory.
    Returns:
        logger_kwargs, a dict containing output_dir and exp_name.
    """
    if data_dir is None:
        data_dir = osp.join(osp.abspath(osp.dirname(osp.dirname(osp.dirname(__file__)))), "logs")

    # Make base path
    ymd_time = time.strftime("%Y-%m-%d_") if datestamp else ""
    relpath = "".join([ymd_time, exp_name])

    if seed is not None:
        # Make a seed-specific subfolder in the experiment directory.
        if datestamp:
            hms_time = time.strftime("%Y-%m-%d_%H-%M-%S")
            subfolder = "".join([hms_time, "-", exp_name, "_s", str(seed)])
        else:
            subfolder = "".join([exp_name, "_s", str(seed)])
        relpath = osp.join(relpath, subfolder)

    logger_kwargs = dict(output_dir=osp.join(data_dir, relpath), exp_name=exp_name)
    return logger_kwargs


def colorize(string, color, bold=False, highlight=False):
    """
    Colorize a string.

    This function was originally written by John Schulman.
    """
    attr = []
    num = color2num[color]
    if highlight:
        num += 10
    attr.append(str(num))
    if bold:
        attr.append("1")
    return "\x1b[%sm%s\x1b[0m" % (";".join(attr), string)


class Logger:
    """
    A general-purpose logger.

    Makes it easy to save diagnostics, hyperparameter configurations, the
    state of a training run, and the trained model.
    """

    def __init__(
        self,
        log_to_wandb=False,
        verbose=False,
        output_dir=None,
        output_fname="progress.csv",
        delimeter=",",
        exp_name=None,
        wandbcommit=1,
    ):
        """
        Initialize a Logger.

        Args:
            log_to_wandb (bool): If True logger will log to wandb

            output_dir (string): A directory for saving results to. If
                ``None``, defaults to a temp directory of the form
                ``/tmp/experiments/somerandomnumber``.

            output_fname (string): Name for the tab-separated-value file
                containing metrics logged throughout a training run.
                Defaults to ``progress.csv``.

            exp_name (string): Experiment name. If you run multiple training
                runs and give them all the same ``exp_name``, the plotter
                will know to group them. (Use case: if you run the same
                hyperparameter configuration with multiple random seeds, you
                should give them all the same ``exp_name``.)

            delimeter (string): Used to separate logged values saved in output_fname
        """
        self.verbose = verbose
        self.log_to_wandb = log_to_wandb
        self.delimeter = delimeter
        self.wandbcommit = wandbcommit
        self.log_iter = 1
        # We assume that there's no multiprocessing.
        if output_dir is not None:
            self.output_dir = output_dir or "/tmp/experiments/%i" % int(time.time())
            if osp.exists(self.output_dir):
                print("Warning: Log dir %s already exists! Storing info there anyway." % self.output_dir)
            else:
                os.makedirs(self.output_dir)
            self.output_file = open(osp.join(self.output_dir, output_fname), "w+")
            atexit.register(self.output_file.close)
            print(colorize("Logging data to %s" % self.output_file.name, "green", bold=True))
        else:
            self.output_file = None

        self.first_row = True
        self.log_headers = []
        self.log_current_row = {}
        self.exp_name = exp_name

    def log(self, msg, color="green"):
        """Print a colorized message to stdout."""
        print(colorize(msg, color, bold=True))

    def log_tabular(self, key, val):
        """
        Log a value of some diagnostic.

        Call this only once for each diagnostic quantity, each iteration.
        After using ``log_tabular`` to store values for each diagnostic,
        make sure to call ``dump_tabular`` to write them out to file and
        stdout (otherwise they will not get saved anywhere).
        """
        if self.first_row:
            self.log_headers.append(key)
        else:
            if key not in self.log_headers:
                self.log_headers.append(key)

                if self.output_file is not None:
                    # move pointer at the beggining of the file
                    self.output_file.seek(0)
                    # skip the header
                    self.output_file.readline()
                    # keep rest of the file
                    logs = self.output_file.read()
                    # clear the file
                    self.output_file.truncate(0)
                    self.output_file.seek(0)
                    # write new headers
                    self.output_file.write(self.delimeter.join(self.log_headers) + "\n")
                    # write stored file
                    self.output_file.write(logs)
                    self.output_file.seek(0)
                    self.output_file.seek(0, 2)
            # assert key in self.log_headers, (
            #     "Trying to introduce a new key %s that you didn't include in the first iteration" % key
            # )
        assert key not in self.log_current_row, (
            "You already set %s this iteration. Maybe you forgot to call dump_tabular()" % key
        )
        self.log_current_row[key] = val

    def save_state(self, state_dict, itr=None):
        """
        Saves the state of an experiment.

        To be clear: this is about saving *state*, not logging diagnostics.
        All diagnostic logging is separate from this function. This function
        will save whatever is in ``state_dict``---usually just a copy of the
        environment---and the most recent parameters for the model you
        previously set up saving for with ``setup_tf_saver``.

        Call with any frequency you prefer. If you only want to maintain a
        single state and overwrite it at each call with the most recent
        version, leave ``itr=None``. If you want to keep all of the states you
        save, provide unique (increasing) values for 'itr'.

        Args:
            state_dict (dict): Dictionary containing essential elements to
                describe the current state of training.

            itr: An int, or None. Current iteration of training.
        """
        fname = "vars.pkl" if itr is None else "vars%d.pkl" % itr
        try:
            joblib.dump(state_dict, osp.join(self.output_dir, fname))
        except:
            self.log("Warning: could not pickle state_dict.", color="red")
        if hasattr(self, "pytorch_saver_elements"):
            self._pytorch_simple_save(itr)

    def setup_pytorch_saver(self, what_to_save):
        """
        Set up easy model saving for a single PyTorch model.

        Because PyTorch saving and loading is especially painless, this is
        very minimal; we just need references to whatever we would like to
        pickle. This is integrated into the logger because the logger
        knows where the user would like to save information about this
        training run.

        Args:
            what_to_save: Any PyTorch model or serializable object containing
                PyTorch models.
        """
        self.pytorch_saver_elements = what_to_save

    def _pytorch_simple_save(self, itr=None):
        """
        Saves the PyTorch model (or models).
        """
        assert hasattr(self, "pytorch_saver_elements"), "First have to setup saving with self.setup_pytorch_saver"
        fpath = "pyt_save"
        fpath = osp.join(self.output_dir, fpath)
        fname = "model" + ("%d" % itr if itr is not None else "") + ".pt"
        fname = osp.join(fpath, fname)
        os.makedirs(fpath, exist_ok=True)
        with warnings.catch_warnings():
            warnings.simplefilter("ignore")
            # We are using a non-recommended way of saving PyTorch models,
            # by pickling whole objects (which are dependent on the exact
            # directory structure at the time of saving) as opposed to
            # just saving network weights. This works sufficiently well
            # for the purposes of Spinning Up, but you may want to do
            # something different for your personal PyTorch project.
            # We use a catch_warnings() context to avoid the warnings about
            # not being able to save the source code.
            torch.save(self.pytorch_saver_elements, fname)

    def dump_tabular(self):
        """
        Write all of the diagnostics from the current iteration.

        Writes both to stdout, and to the output file.
        """
        vals = []
        key_lens = [len(key) for key in self.log_headers]
        max_key_len = max(15, max(key_lens))
        keystr = "%" + "%d" % max_key_len
        fmt = "| " + keystr + "s | %15s |"
        n_slashes = 22 + max_key_len
        step = self.log_current_row.get("total_env_steps")

        if self.verbose:
            print("-" * n_slashes)
            for key in self.log_headers:
                val = self.log_current_row.get(key, "")
                valstr = "%8.3g" % val if isinstance(val, float) else val
                print(fmt % (key, valstr))
                vals.append(val)
            print("-" * n_slashes, flush=True)

        if self.output_file is not None:
            if self.first_row:
                self.output_file.write(self.delimeter.join(self.log_headers) + "\n")
            self.output_file.write(self.delimeter.join(map(str, vals)) + "\n")
            self.output_file.flush()

        key_val_dict = {key: self.log_current_row.get(key, "") for key in self.log_headers}
        if self.log_to_wandb:
            if self.log_iter % self.wandbcommit == 0:
                wandb.log(key_val_dict, step=step, commit=True)
            else:
                wandb.log(key_val_dict, step=step, commit=False)

        self.log_current_row.clear()
        self.first_row = False
        self.log_iter += 1

        return key_val_dict


class EpochLogger(Logger):
    """
    A variant of Logger tailored for tracking average values over epochs.

    Typical use case: there is some quantity which is calculated many times
    throughout an epoch, and at the end of the epoch, you would like to
    report the average / std / min / max value of that quantity.

    With an EpochLogger, each time the quantity is calculated, you would
    use

    .. code-block:: python

        epoch_logger.store(NameOfQuantity=quantity_value)

    to load it into the EpochLogger's state. Then at the end of the epoch, you
    would use

    .. code-block:: python

        epoch_logger.log_tabular(NameOfQuantity, **options)

    to record the desired values.
    """

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.epoch_dict = dict()

    def store(self, d):
        """
        Save something into the epoch_logger's current state.

        Provide an arbitrary number of keyword arguments with numerical
        values.
        """
        for k, v in d.items():
            if not (k in self.epoch_dict.keys()):
                self.epoch_dict[k] = []
            self.epoch_dict[k].append(v)

    def log_tabular(self, key, val=None, with_min_and_max=False, with_median=False, with_sum=False, average_only=False):
        """
        Log a value or possibly the mean/std/min/max values of a diagnostic.

        Args:
            key (string): The name of the diagnostic. If you are logging a
                diagnostic whose state has previously been saved with
                ``store``, the key here has to match the key you used there.

            val: A value for the diagnostic. If you have previously saved
                values for this key via ``store``, do *not* provide a ``val``
                here.

            with_min_and_max (bool): If true, log min and max values of the
                diagnostic over the epoch.

            average_only (bool): If true, do not log the standard deviation
                of the diagnostic over the epoch.
        """
        if val is not None:
            super().log_tabular(key, val)
        else:
            stats = self.get_stats(key)
            super().log_tabular(key if average_only else key + "/avg", stats[0])
            if not (average_only):
                super().log_tabular(key + "/std", stats[1])
            if with_min_and_max:
                super().log_tabular(key + "/max", stats[3])
                super().log_tabular(key + "/min", stats[2])
            if with_median:
                super().log_tabular(key + "/med", stats[4])
            if with_sum:
                super().log_tabular(key + "/sum", stats[5])

        self.epoch_dict[key] = []

    def get_stats(self, key):
        """
        Lets an algorithm ask the logger for mean/std/min/max of a diagnostic.
        """
        v = self.epoch_dict.get(key)
        if not v:
            return [np.nan, np.nan, np.nan, np.nan]
        vals = np.concatenate(v) if isinstance(v[0], np.ndarray) and len(v[0].shape) > 0 else v
        return [np.mean(vals), np.std(vals), np.min(vals), np.max(vals), np.median(vals), np.sum(vals)]