# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. import csv import datetime from collections import defaultdict import numpy as np import torch # import torchvision from termcolor import colored from torch.utils.tensorboard import SummaryWriter COMMON_TRAIN_FORMAT = [('frame', 'F', 'int'), ('step', 'S', 'int'), ('episode', 'E', 'int'), ('episode_length', 'L', 'int'), ('episode_reward', 'R', 'float'), ('buffer_size', 'BS', 'int'), ('fps', 'FPS', 'float'), ('total_time', 'T', 'time')] OFFLINE_TRAIN_FORMAT = [('step', 'S', 'int'), ('buffer_size', 'BS', 'int'), ('fps', 'FPS', 'float'), ('total_time', 'T', 'time')] COMMON_EVAL_FORMAT = [('frame', 'F', 'int'), ('step', 'S', 'int'), ('episode', 'E', 'int'), ('episode_length', 'L', 'int'), ('episode_reward', 'R', 'float'), ('total_time', 'T', 'time')] DISTRACTING_EVAL_FORMAT = [('frame', 'F', 'int'), ('step', 'S', 'int'), ('episode', 'E', 'int'), ('episode_length', 'L', 'int'), ('episode_reward', 'R', 'float'), ('easy_episode_reward', 'EER', 'float'), ('medium_episode_reward', 'MER', 'float'), ('hard_episode_reward', 'HER', 'float'), ('fixed_easy_episode_reward', 'FEER', 'float'), ('fixed_medium_episode_reward', 'FMER', 'float'), ('fixed_hard_episode_reward', 'FHER', 'float'), ('total_time', 'T', 'time')] MULTITASK_EVAL_FORMAT = [('frame', 'F', 'int'), ('step', 'S', 'int'), ('episode', 'E', 'int'), ('episode_length', 'L', 'int'), ('episode_reward', 'R', 'float'), ('len1_episode_reward', 'R1', 'float'), ('len2_episode_reward', 'R2', 'float'), ('len3_episode_reward', 'R3', 'float'), ('len4_episode_reward', 'R4', 'float'), ('len5_episode_reward', 'R5', 'float'), ('len6_episode_reward', 'R6', 'float'), ('len7_episode_reward', 'R7', 'float'), ('len8_episode_reward', 'R8', 'float'), ('len9_episode_reward', 'R9', 'float'), ('len10_episode_reward', 'R10', 'float'), ('total_time', 'T', 'time')] class AverageMeter(object): def __init__(self): self._sum = 0 self._count = 0 def update(self, value, n=1): self._sum += value self._count += n def value(self): return self._sum / max(1, self._count) class MetersGroup(object): def __init__(self, csv_file_name, formating): self._csv_file_name = csv_file_name self._formating = formating self._meters = defaultdict(AverageMeter) self._csv_file = None self._csv_writer = None def log(self, key, value, n=1): self._meters[key].update(value, n) def _prime_meters(self): data = dict() for key, meter in self._meters.items(): if key.startswith('train'): key = key[len('train') + 1:] else: key = key[len('eval') + 1:] key = key.replace('/', '_') data[key] = meter.value() return data def _remove_old_entries(self, data): rows = [] with self._csv_file_name.open('r') as f: reader = csv.DictReader(f) for row in reader: if float(row['step']) >= data['step']: break rows.append(row) with self._csv_file_name.open('w') as f: writer = csv.DictWriter(f, fieldnames=sorted(data.keys()), restval=0.0) writer.writeheader() for row in rows: writer.writerow(row) def _dump_to_csv(self, data): if self._csv_writer is None: should_write_header = True if self._csv_file_name.exists(): self._remove_old_entries(data) should_write_header = False self._csv_file = self._csv_file_name.open('a') self._csv_writer = csv.DictWriter(self._csv_file, fieldnames=sorted(data.keys()), restval=0.0) if should_write_header: self._csv_writer.writeheader() self._csv_writer.writerow(data) self._csv_file.flush() def _format(self, key, value, ty): if ty == 'int': value = int(value) return f'{key}: {value}' elif ty == 'float': return f'{key}: {value:.04f}' elif ty == 'time': value = str(datetime.timedelta(seconds=int(value))) return f'{key}: {value}' else: raise f'invalid format type: {ty}' def _dump_to_console(self, data, prefix): prefix = colored(prefix, 'yellow' if prefix == 'train' else 'green') pieces = [f'| {prefix: <14}'] for key, disp_key, ty in self._formating: value = data.get(key, 0) pieces.append(self._format(disp_key, value, ty)) print(' | '.join(pieces)) def dump(self, step, prefix): if len(self._meters) == 0: return data = self._prime_meters() if 'frame' in data: data['frame'] = step self._dump_to_csv(data) self._dump_to_console(data, prefix) self._meters.clear() class Logger(object): def __init__(self, log_dir, use_tb, offline=False, distracting_eval=False, multitask_eval=False): self._log_dir = log_dir train_formatting = OFFLINE_TRAIN_FORMAT if offline else COMMON_TRAIN_FORMAT if distracting_eval: eval_formatting = DISTRACTING_EVAL_FORMAT elif multitask_eval: eval_formatting = MULTITASK_EVAL_FORMAT else: eval_formatting = COMMON_EVAL_FORMAT self._train_mg = MetersGroup(log_dir / 'train.csv', formating=train_formatting) self._eval_mg = MetersGroup(log_dir / 'eval.csv', formating=eval_formatting) if use_tb: self._sw = SummaryWriter(str(log_dir / 'tb')) else: self._sw = None def _try_sw_log(self, key, value, step): if self._sw is not None: self._sw.add_scalar(key, value, step) def log(self, key, value, step): assert key.startswith('train') or key.startswith('eval') if type(value) == torch.Tensor: value = value.item() self._try_sw_log(key, value, step) mg = self._train_mg if key.startswith('train') else self._eval_mg mg.log(key, value) def log_metrics(self, metrics, step, ty): for key, value in metrics.items(): self.log(f'{ty}/{key}', value, step) def dump(self, step, ty=None): if ty is None or ty == 'eval': self._eval_mg.dump(step, 'eval') if ty is None or ty == 'train': self._train_mg.dump(step, 'train') def log_and_dump_ctx(self, step, ty): return LogAndDumpCtx(self, step, ty) class LogAndDumpCtx: def __init__(self, logger, step, ty): self._logger = logger self._step = step self._ty = ty def __enter__(self): return self def __call__(self, key, value): self._logger.log(f'{self._ty}/{key}', value, self._step) def __exit__(self, *args): self._logger.dump(self._step, self._ty)