Source code for spinup.utils.logx


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

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

import json
import joblib
import shutil
import numpy as np
import tensorflow as tf
import torch
import os.path as osp, time, atexit, os
import warnings
from spinup.utils.mpi_tools import proc_id, mpi_statistics_scalar
from spinup.utils.serialization_utils import convert_json

color2num = dict(

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
    if bold: attr.append('1')
    return '\x1b[%sm%s\x1b[0m' % (';'.join(attr), string)

[docs]def restore_tf_graph(sess, fpath): """ Loads graphs saved by Logger. Will output a dictionary whose keys and values are from the 'inputs' and 'outputs' dict you specified with logger.setup_tf_saver(). Args: sess: A Tensorflow session. fpath: Filepath to save directory. Returns: A dictionary mapping from keys to tensors in the computation graph loaded from ``fpath``. """ tf.saved_model.loader.load( sess, [tf.saved_model.tag_constants.SERVING], fpath ) model_info = joblib.load(osp.join(fpath, 'model_info.pkl')) graph = tf.get_default_graph() model = dict() model.update({k: graph.get_tensor_by_name(v) for k,v in model_info['inputs'].items()}) model.update({k: graph.get_tensor_by_name(v) for k,v in model_info['outputs'].items()}) return model
[docs]class Logger: """ A general-purpose logger. Makes it easy to save diagnostics, hyperparameter configurations, the state of a training run, and the trained model. """
[docs] def __init__(self, output_dir=None, output_fname='progress.txt', exp_name=None): """ Initialize a Logger. Args: 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.txt``. 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``.) """ if proc_id()==0: 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", 'green', bold=True)) else: self.output_dir = None self.output_file = None self.first_row=True self.log_headers = [] self.log_current_row = {} self.exp_name = exp_name
[docs] def log(self, msg, color='green'): """Print a colorized message to stdout.""" if proc_id()==0: print(colorize(msg, color, bold=True))
[docs] 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: 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
[docs] def save_config(self, config): """ Log an experiment configuration. Call this once at the top of your experiment, passing in all important config vars as a dict. This will serialize the config to JSON, while handling anything which can't be serialized in a graceful way (writing as informative a string as possible). Example use: .. code-block:: python logger = EpochLogger(**logger_kwargs) logger.save_config(locals()) """ config_json = convert_json(config) if self.exp_name is not None: config_json['exp_name'] = self.exp_name if proc_id()==0: output = json.dumps(config_json, separators=(',',':\t'), indent=4, sort_keys=True) print(colorize('Saving config:\n', color='cyan', bold=True)) print(output) with open(osp.join(self.output_dir, "config.json"), 'w') as out: out.write(output)
[docs] 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. """ if proc_id()==0: 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, 'tf_saver_elements'): self._tf_simple_save(itr) if hasattr(self, 'pytorch_saver_elements'): self._pytorch_simple_save(itr)
[docs] def setup_tf_saver(self, sess, inputs, outputs): """ Set up easy model saving for tensorflow. Call once, after defining your computation graph but before training. Args: sess: The Tensorflow session in which you train your computation graph. inputs (dict): A dictionary that maps from keys of your choice to the tensorflow placeholders that serve as inputs to the computation graph. Make sure that *all* of the placeholders needed for your outputs are included! outputs (dict): A dictionary that maps from keys of your choice to the outputs from your computation graph. """ self.tf_saver_elements = dict(session=sess, inputs=inputs, outputs=outputs) self.tf_saver_info = {'inputs': { for k,v in inputs.items()}, 'outputs': { for k,v in outputs.items()}}
def _tf_simple_save(self, itr=None): """ Uses simple_save to save a trained model, plus info to make it easy to associated tensors to variables after restore. """ if proc_id()==0: assert hasattr(self, 'tf_saver_elements'), \ "First have to setup saving with self.setup_tf_saver" fpath = 'tf1_save' + ('%d'%itr if itr is not None else '') fpath = osp.join(self.output_dir, fpath) if osp.exists(fpath): # simple_save refuses to be useful if fpath already exists, # so just delete fpath if it's there. shutil.rmtree(fpath) tf.saved_model.simple_save(export_dir=fpath, **self.tf_saver_elements) joblib.dump(self.tf_saver_info, osp.join(fpath, 'model_info.pkl'))
[docs] 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). """ if proc_id()==0: 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., fname)
[docs] def dump_tabular(self): """ Write all of the diagnostics from the current iteration. Writes both to stdout, and to the output file. """ if proc_id()==0: 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 print("-"*n_slashes) for key in self.log_headers: val = self.log_current_row.get(key, "") valstr = "%8.3g"%val if hasattr(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("\t".join(self.log_headers)+"\n") self.output_file.write("\t".join(map(str,vals))+"\n") self.output_file.flush() self.log_current_row.clear() self.first_row=False
[docs]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 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()
[docs] def store(self, **kwargs): """ Save something into the epoch_logger's current state. Provide an arbitrary number of keyword arguments with numerical values. """ for k,v in kwargs.items(): if not(k in self.epoch_dict.keys()): self.epoch_dict[k] = [] self.epoch_dict[k].append(v)
[docs] def log_tabular(self, key, val=None, with_min_and_max=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: v = self.epoch_dict[key] vals = np.concatenate(v) if isinstance(v[0], np.ndarray) and len(v[0].shape)>0 else v stats = mpi_statistics_scalar(vals, with_min_and_max=with_min_and_max) super().log_tabular(key if average_only else 'Average' + key, stats[0]) if not(average_only): super().log_tabular('Std'+key, stats[1]) if with_min_and_max: super().log_tabular('Max'+key, stats[3]) super().log_tabular('Min'+key, stats[2]) self.epoch_dict[key] = []
[docs] def get_stats(self, key): """ Lets an algorithm ask the logger for mean/std/min/max of a diagnostic. """ v = self.epoch_dict[key] vals = np.concatenate(v) if isinstance(v[0], np.ndarray) and len(v[0].shape)>0 else v return mpi_statistics_scalar(vals)