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Deterministic training (#53)

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Ayush Chaurasia 2022-11-25 02:06:14 +05:30 committed by GitHub
parent 793dde365d
commit c5f5b80c04
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3 changed files with 23 additions and 2 deletions
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7
ultralytics/yolo/engine/trainer.py
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@ -28,16 +28,19 @@ from ultralytics.yolo.utils import LOGGER, ROOT, TQDM_BAR_FORMAT
from ultralytics.yolo.utils.checks import print_args
from ultralytics.yolo.utils.files import increment_path, save_yaml
from ultralytics.yolo.utils.modeling import get_model
from ultralytics.yolo.utils.torch_utils import ModelEMA, de_parallel, one_cycle
from ultralytics.yolo.utils.torch_utils import ModelEMA, de_parallel, init_seeds, one_cycle
DEFAULT_CONFIG = ROOT / "yolo/utils/configs/default.yaml"
RANK = int(os.getenv('RANK', -1))
class BaseTrainer:
def __init__(self, config=DEFAULT_CONFIG, overrides={}):
self.console = LOGGER
self.args = self._get_config(config, overrides)
init_seeds(self.args.seed + 1 + RANK, deterministic=self.args.deterministic)
self.console = LOGGER
self.validator = None
self.model = None
self.callbacks = defaultdict(list)

1
ultralytics/yolo/utils/configs/default.yaml
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@ -22,6 +22,7 @@ pretrained: False
optimizer: 'SGD' # choices=['SGD', 'Adam', 'AdamW', 'RMSProp']
verbose: False
seed: 0
deterministic: True
local_rank: -1
single_cls: False # train multi-class data as single-class
image_weights: False # use weighted image selection for training

17
ultralytics/yolo/utils/torch_utils.py
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@ -1,11 +1,13 @@
import math
import os
import platform
import random
import time
from contextlib import contextmanager
from copy import deepcopy
from pathlib import Path
import numpy as np
import thop
import torch
import torch.distributed as dist
@ -199,6 +201,21 @@ def one_cycle(y1=0.0, y2=1.0, steps=100):
return lambda x: ((1 - math.cos(x * math.pi / steps)) / 2) * (y2 - y1) + y1
def init_seeds(seed=0, deterministic=False):
# Initialize random number generator (RNG) seeds https://pytorch.org/docs/stable/notes/randomness.html
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed) # for Multi-GPU, exception safe
# torch.backends.cudnn.benchmark = True # AutoBatch problem https://github.com/ultralytics/yolov5/issues/9287
if deterministic and check_version(torch.__version__, '1.12.0'): # https://github.com/ultralytics/yolov5/pull/8213
torch.use_deterministic_algorithms(True)
torch.backends.cudnn.deterministic = True
os.environ['CUBLAS_WORKSPACE_CONFIG'] = ':4096:8'
os.environ['PYTHONHASHSEED'] = str(seed)
class ModelEMA:
""" Updated Exponential Moving Average (EMA) from https://github.com/rwightman/pytorch-image-models
Keeps a moving average of everything in the model state_dict (parameters and buffers)