File size: 6,277 Bytes
885b6c5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 | # 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')]
COMMON_EVAL_FORMAT = [('frame', 'F', 'int'), ('step', 'S', 'int'),
('episode', 'E', 'int'), ('episode_length', 'L', 'int'),
('episode_reward', 'R', '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['episode']) >= data['episode']:
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()
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, stage2_logger=False):
self._log_dir = log_dir
if not stage2_logger:
self._train_mg = MetersGroup(log_dir / 'train.csv',
formating=COMMON_TRAIN_FORMAT)
self._eval_mg = MetersGroup(log_dir / 'eval.csv',
formating=COMMON_EVAL_FORMAT)
else:
self._train_mg = MetersGroup(log_dir / 'train_stage2.csv',
formating=COMMON_TRAIN_FORMAT)
self._eval_mg = MetersGroup(log_dir / 'eval_stage2.csv',
formating=COMMON_EVAL_FORMAT)
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)
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