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Fix redundant outputs via Logging in DDP training (#500)

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* Change print to logging

* Clean function set_logging

* Add line spacing

* Change leftover prints to log

* Fix scanning labels output

* Fix rank naming

* Change leftover print to logging

* Reorganized DDP variables

* Fix type error

* Make quotes consistent

* Fix spelling

* Clean function call

* Add line spacing

* Update datasets.py

* Update train.py

Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
pull/1/head
NanoCode012 5 years ago committed by GitHub
parent 542833c997
commit 4949401a94
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5 changed files with 56 additions and 39 deletions
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6
models/yolo.py
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@ -1,5 +1,6 @@
import argparse import argparse
import math import math
import logging
from copy import deepcopy from copy import deepcopy
from pathlib import Path from pathlib import Path
@ -12,6 +13,7 @@ from utils.general import check_anchor_order, make_divisible, check_file
from utils.torch_utils import ( from utils.torch_utils import (
time_synchronized, fuse_conv_and_bn, model_info, scale_img, initialize_weights, select_device) time_synchronized, fuse_conv_and_bn, model_info, scale_img, initialize_weights, select_device)
logger = logging.getLogger(__name__)
class Detect(nn.Module): class Detect(nn.Module):
def __init__(self, nc=80, anchors=(), ch=()): # detection layer def __init__(self, nc=80, anchors=(), ch=()): # detection layer
@ -169,7 +171,7 @@ class Model(nn.Module):
def parse_model(d, ch): # model_dict, input_channels(3) def parse_model(d, ch): # model_dict, input_channels(3)
print('\n%3s%18s%3s%10s %-40s%-30s' % ('', 'from', 'n', 'params', 'module', 'arguments')) logger.info('\n%3s%18s%3s%10s %-40s%-30s' % ('', 'from', 'n', 'params', 'module', 'arguments'))
anchors, nc, gd, gw = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple'] anchors, nc, gd, gw = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple']
na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchors na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchors
no = na * (nc + 5) # number of outputs = anchors * (classes + 5) no = na * (nc + 5) # number of outputs = anchors * (classes + 5)
@ -224,7 +226,7 @@ def parse_model(d, ch): # model_dict, input_channels(3)
t = str(m)[8:-2].replace('__main__.', '') # module type t = str(m)[8:-2].replace('__main__.', '') # module type
np = sum([x.numel() for x in m_.parameters()]) # number params np = sum([x.numel() for x in m_.parameters()]) # number params
m_.i, m_.f, m_.type, m_.np = i, f, t, np # attach index, 'from' index, type, number params m_.i, m_.f, m_.type, m_.np = i, f, t, np # attach index, 'from' index, type, number params
print('%3s%18s%3s%10.0f %-40s%-30s' % (i, f, n, np, t, args)) # print logger.info('%3s%18s%3s%10.0f %-40s%-30s' % (i, f, n, np, t, args)) # print
save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist
layers.append(m_) layers.append(m_)
ch.append(c2) ch.append(c2)

50
train.py
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@ -3,6 +3,7 @@ import math
import os import os
import random import random
import time import time
import logging
from pathlib import Path from pathlib import Path
import numpy as np import numpy as np
@ -23,13 +24,14 @@ from utils.datasets import create_dataloader
from utils.general import ( from utils.general import (
torch_distributed_zero_first, labels_to_class_weights, plot_labels, check_anchors, labels_to_image_weights, torch_distributed_zero_first, labels_to_class_weights, plot_labels, check_anchors, labels_to_image_weights,
compute_loss, plot_images, fitness, strip_optimizer, plot_results, get_latest_run, check_dataset, check_file, compute_loss, plot_images, fitness, strip_optimizer, plot_results, get_latest_run, check_dataset, check_file,
check_git_status, check_img_size, increment_dir, print_mutation, plot_evolution) check_git_status, check_img_size, increment_dir, print_mutation, plot_evolution, set_logging)
from utils.google_utils import attempt_download from utils.google_utils import attempt_download
from utils.torch_utils import init_seeds, ModelEMA, select_device, intersect_dicts from utils.torch_utils import init_seeds, ModelEMA, select_device, intersect_dicts
logger = logging.getLogger(__name__)
def train(hyp, opt, device, tb_writer=None): def train(hyp, opt, device, tb_writer=None):
print(f'Hyperparameters {hyp}') logger.info(f'Hyperparameters {hyp}')
log_dir = Path(tb_writer.log_dir) if tb_writer else Path(opt.logdir) / 'evolve' # logging directory log_dir = Path(tb_writer.log_dir) if tb_writer else Path(opt.logdir) / 'evolve' # logging directory
wdir = str(log_dir / 'weights') + os.sep # weights directory wdir = str(log_dir / 'weights') + os.sep # weights directory
os.makedirs(wdir, exist_ok=True) os.makedirs(wdir, exist_ok=True)
@ -69,7 +71,7 @@ def train(hyp, opt, device, tb_writer=None):
state_dict = ckpt['model'].float().state_dict() # to FP32 state_dict = ckpt['model'].float().state_dict() # to FP32
state_dict = intersect_dicts(state_dict, model.state_dict(), exclude=exclude) # intersect state_dict = intersect_dicts(state_dict, model.state_dict(), exclude=exclude) # intersect
model.load_state_dict(state_dict, strict=False) # load model.load_state_dict(state_dict, strict=False) # load
print('Transferred %g/%g items from %s' % (len(state_dict), len(model.state_dict()), weights)) # report logging.info('Transferred %g/%g items from %s' % (len(state_dict), len(model.state_dict()), weights)) # report
else: else:
model = Model(opt.cfg, ch=3, nc=nc).to(device) # create model = Model(opt.cfg, ch=3, nc=nc).to(device) # create
@ -103,7 +105,7 @@ def train(hyp, opt, device, tb_writer=None):
optimizer.add_param_group({'params': pg1, 'weight_decay': hyp['weight_decay']}) # add pg1 with weight_decay optimizer.add_param_group({'params': pg1, 'weight_decay': hyp['weight_decay']}) # add pg1 with weight_decay
optimizer.add_param_group({'params': pg2}) # add pg2 (biases) optimizer.add_param_group({'params': pg2}) # add pg2 (biases)
print('Optimizer groups: %g .bias, %g conv.weight, %g other' % (len(pg2), len(pg1), len(pg0))) logger.info('Optimizer groups: %g .bias, %g conv.weight, %g other' % (len(pg2), len(pg1), len(pg0)))
del pg0, pg1, pg2 del pg0, pg1, pg2
# Scheduler https://arxiv.org/pdf/1812.01187.pdf # Scheduler https://arxiv.org/pdf/1812.01187.pdf
@ -128,7 +130,7 @@ def train(hyp, opt, device, tb_writer=None):
# Epochs # Epochs
start_epoch = ckpt['epoch'] + 1 start_epoch = ckpt['epoch'] + 1
if epochs < start_epoch: if epochs < start_epoch:
print('%s has been trained for %g epochs. Fine-tuning for %g additional epochs.' % logger.info('%s has been trained for %g epochs. Fine-tuning for %g additional epochs.' %
(weights, ckpt['epoch'], epochs)) (weights, ckpt['epoch'], epochs))
epochs += ckpt['epoch'] # finetune additional epochs epochs += ckpt['epoch'] # finetune additional epochs
@ -145,7 +147,7 @@ def train(hyp, opt, device, tb_writer=None):
# SyncBatchNorm # SyncBatchNorm
if opt.sync_bn and cuda and rank != -1: if opt.sync_bn and cuda and rank != -1:
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device) model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device)
print('Using SyncBatchNorm()') logger.info('Using SyncBatchNorm()')
# Exponential moving average # Exponential moving average
ema = ModelEMA(model) if rank in [-1, 0] else None ema = ModelEMA(model) if rank in [-1, 0] else None
@ -156,7 +158,7 @@ def train(hyp, opt, device, tb_writer=None):
# Trainloader # Trainloader
dataloader, dataset = create_dataloader(train_path, imgsz, batch_size, gs, opt, hyp=hyp, augment=True, dataloader, dataset = create_dataloader(train_path, imgsz, batch_size, gs, opt, hyp=hyp, augment=True,
cache=opt.cache_images, rect=opt.rect, local_rank=rank, cache=opt.cache_images, rect=opt.rect, rank=rank,
world_size=opt.world_size) world_size=opt.world_size)
mlc = np.concatenate(dataset.labels, 0)[:, 0].max() # max label class mlc = np.concatenate(dataset.labels, 0)[:, 0].max() # max label class
nb = len(dataloader) # number of batches nb = len(dataloader) # number of batches
@ -166,7 +168,7 @@ def train(hyp, opt, device, tb_writer=None):
if rank in [-1, 0]: if rank in [-1, 0]:
# local_rank is set to -1. Because only the first process is expected to do evaluation. # local_rank is set to -1. Because only the first process is expected to do evaluation.
testloader = create_dataloader(test_path, imgsz_test, total_batch_size, gs, opt, hyp=hyp, augment=False, testloader = create_dataloader(test_path, imgsz_test, total_batch_size, gs, opt, hyp=hyp, augment=False,
cache=opt.cache_images, rect=True, local_rank=-1, world_size=opt.world_size)[0] cache=opt.cache_images, rect=True, rank=-1, world_size=opt.world_size)[0]
# Model parameters # Model parameters
hyp['cls'] *= nc / 80. # scale coco-tuned hyp['cls'] to current dataset hyp['cls'] *= nc / 80. # scale coco-tuned hyp['cls'] to current dataset
@ -199,10 +201,9 @@ def train(hyp, opt, device, tb_writer=None):
results = (0, 0, 0, 0, 0, 0, 0) # 'P', 'R', 'mAP', 'F1', 'val GIoU', 'val Objectness', 'val Classification' results = (0, 0, 0, 0, 0, 0, 0) # 'P', 'R', 'mAP', 'F1', 'val GIoU', 'val Objectness', 'val Classification'
scheduler.last_epoch = start_epoch - 1 # do not move scheduler.last_epoch = start_epoch - 1 # do not move
scaler = amp.GradScaler(enabled=cuda) scaler = amp.GradScaler(enabled=cuda)
if rank in [0, -1]: logger.info('Image sizes %g train, %g test' % (imgsz, imgsz_test))
print('Image sizes %g train, %g test' % (imgsz, imgsz_test)) logger.info('Using %g dataloader workers' % dataloader.num_workers)
print('Using %g dataloader workers' % dataloader.num_workers) logger.info('Starting training for %g epochs...' % epochs)
print('Starting training for %g epochs...' % epochs)
# torch.autograd.set_detect_anomaly(True) # torch.autograd.set_detect_anomaly(True)
for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------ for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------
model.train() model.train()
@ -232,8 +233,8 @@ def train(hyp, opt, device, tb_writer=None):
if rank != -1: if rank != -1:
dataloader.sampler.set_epoch(epoch) dataloader.sampler.set_epoch(epoch)
pbar = enumerate(dataloader) pbar = enumerate(dataloader)
logging.info(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'GIoU', 'obj', 'cls', 'total', 'targets', 'img_size'))
if rank in [-1, 0]: if rank in [-1, 0]:
print(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'GIoU', 'obj', 'cls', 'total', 'targets', 'img_size'))
pbar = tqdm(pbar, total=nb) # progress bar pbar = tqdm(pbar, total=nb) # progress bar
optimizer.zero_grad() optimizer.zero_grad()
for i, (imgs, targets, paths, _) in pbar: # batch ------------------------------------------------------------- for i, (imgs, targets, paths, _) in pbar: # batch -------------------------------------------------------------
@ -269,7 +270,7 @@ def train(hyp, opt, device, tb_writer=None):
if rank != -1: if rank != -1:
loss *= opt.world_size # gradient averaged between devices in DDP mode loss *= opt.world_size # gradient averaged between devices in DDP mode
# if not torch.isfinite(loss): # if not torch.isfinite(loss):
# print('WARNING: non-finite loss, ending training ', loss_items) # logger.info('WARNING: non-finite loss, ending training ', loss_items)
# return results # return results
# Backward # Backward
@ -369,7 +370,7 @@ def train(hyp, opt, device, tb_writer=None):
# Finish # Finish
if not opt.evolve: if not opt.evolve:
plot_results(save_dir=log_dir) # save as results.png plot_results(save_dir=log_dir) # save as results.png
print('%g epochs completed in %.3f hours.\n' % (epoch - start_epoch + 1, (time.time() - t0) / 3600)) logger.info('%g epochs completed in %.3f hours.\n' % (epoch - start_epoch + 1, (time.time() - t0) / 3600))
dist.destroy_process_group() if rank not in [-1, 0] else None dist.destroy_process_group() if rank not in [-1, 0] else None
torch.cuda.empty_cache() torch.cuda.empty_cache()
@ -404,13 +405,19 @@ if __name__ == '__main__':
parser.add_argument('--logdir', type=str, default='runs/', help='logging directory') parser.add_argument('--logdir', type=str, default='runs/', help='logging directory')
opt = parser.parse_args() opt = parser.parse_args()
# Set DDP variables
opt.total_batch_size = opt.batch_size
opt.world_size = int(os.environ['WORLD_SIZE']) if 'WORLD_SIZE' in os.environ else 1
opt.global_rank = int(os.environ['RANK']) if 'RANK' in os.environ else -1
set_logging(opt.global_rank)
# Resume # Resume
if opt.resume: if opt.resume:
last = get_latest_run() if opt.resume == 'get_last' else opt.resume # resume from most recent run last = get_latest_run() if opt.resume == 'get_last' else opt.resume # resume from most recent run
if last and not opt.weights: if last and not opt.weights:
print(f'Resuming training from {last}') logger.info(f'Resuming training from {last}')
opt.weights = last if opt.resume and not opt.weights else opt.weights opt.weights = last if opt.resume and not opt.weights else opt.weights
if opt.local_rank == -1 or ("RANK" in os.environ and os.environ["RANK"] == "0"): if opt.global_rank in [-1,0]:
check_git_status() check_git_status()
opt.hyp = opt.hyp or ('data/hyp.finetune.yaml' if opt.weights else 'data/hyp.scratch.yaml') opt.hyp = opt.hyp or ('data/hyp.finetune.yaml' if opt.weights else 'data/hyp.scratch.yaml')
@ -419,9 +426,6 @@ if __name__ == '__main__':
opt.img_size.extend([opt.img_size[-1]] * (2 - len(opt.img_size))) # extend to 2 sizes (train, test) opt.img_size.extend([opt.img_size[-1]] * (2 - len(opt.img_size))) # extend to 2 sizes (train, test)
device = select_device(opt.device, batch_size=opt.batch_size) device = select_device(opt.device, batch_size=opt.batch_size)
opt.total_batch_size = opt.batch_size
opt.world_size = 1
opt.global_rank = -1
# DDP mode # DDP mode
if opt.local_rank != -1: if opt.local_rank != -1:
@ -429,12 +433,10 @@ if __name__ == '__main__':
torch.cuda.set_device(opt.local_rank) torch.cuda.set_device(opt.local_rank)
device = torch.device('cuda', opt.local_rank) device = torch.device('cuda', opt.local_rank)
dist.init_process_group(backend='nccl', init_method='env://') # distributed backend dist.init_process_group(backend='nccl', init_method='env://') # distributed backend
opt.world_size = dist.get_world_size()
opt.global_rank = dist.get_rank()
assert opt.batch_size % opt.world_size == 0, '--batch-size must be multiple of CUDA device count' assert opt.batch_size % opt.world_size == 0, '--batch-size must be multiple of CUDA device count'
opt.batch_size = opt.total_batch_size // opt.world_size opt.batch_size = opt.total_batch_size // opt.world_size
print(opt) logger.info(opt)
with open(opt.hyp) as f: with open(opt.hyp) as f:
hyp = yaml.load(f, Loader=yaml.FullLoader) # load hyps hyp = yaml.load(f, Loader=yaml.FullLoader) # load hyps
@ -442,7 +444,7 @@ if __name__ == '__main__':
if not opt.evolve: if not opt.evolve:
tb_writer = None tb_writer = None
if opt.global_rank in [-1, 0]: if opt.global_rank in [-1, 0]:
print('Start Tensorboard with "tensorboard --logdir %s", view at http://localhost:6006/' % opt.logdir) logger.info('Start Tensorboard with "tensorboard --logdir %s", view at http://localhost:6006/' % opt.logdir)
tb_writer = SummaryWriter(log_dir=increment_dir(Path(opt.logdir) / 'exp', opt.name)) # runs/exp tb_writer = SummaryWriter(log_dir=increment_dir(Path(opt.logdir) / 'exp', opt.name)) # runs/exp
train(hyp, opt, device, tb_writer) train(hyp, opt, device, tb_writer)

22
utils/datasets.py
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@ -47,9 +47,9 @@ def exif_size(img):
def create_dataloader(path, imgsz, batch_size, stride, opt, hyp=None, augment=False, cache=False, pad=0.0, rect=False, def create_dataloader(path, imgsz, batch_size, stride, opt, hyp=None, augment=False, cache=False, pad=0.0, rect=False,
local_rank=-1, world_size=1): rank=-1, world_size=1):
# Make sure only the first process in DDP process the dataset first, and the following others can use the cache. # Make sure only the first process in DDP process the dataset first, and the following others can use the cache.
with torch_distributed_zero_first(local_rank): with torch_distributed_zero_first(rank):
dataset = LoadImagesAndLabels(path, imgsz, batch_size, dataset = LoadImagesAndLabels(path, imgsz, batch_size,
augment=augment, # augment images augment=augment, # augment images
hyp=hyp, # augmentation hyperparameters hyp=hyp, # augmentation hyperparameters
@ -57,11 +57,12 @@ def create_dataloader(path, imgsz, batch_size, stride, opt, hyp=None, augment=Fa
cache_images=cache, cache_images=cache,
single_cls=opt.single_cls, single_cls=opt.single_cls,
stride=int(stride), stride=int(stride),
pad=pad) pad=pad,
rank=rank)
batch_size = min(batch_size, len(dataset)) batch_size = min(batch_size, len(dataset))
nw = min([os.cpu_count() // world_size, batch_size if batch_size > 1 else 0, 8]) # number of workers nw = min([os.cpu_count() // world_size, batch_size if batch_size > 1 else 0, 8]) # number of workers
train_sampler = torch.utils.data.distributed.DistributedSampler(dataset) if local_rank != -1 else None train_sampler = torch.utils.data.distributed.DistributedSampler(dataset) if rank != -1 else None
dataloader = torch.utils.data.DataLoader(dataset, dataloader = torch.utils.data.DataLoader(dataset,
batch_size=batch_size, batch_size=batch_size,
num_workers=nw, num_workers=nw,
@ -292,7 +293,7 @@ class LoadStreams: # multiple IP or RTSP cameras
class LoadImagesAndLabels(Dataset): # for training/testing class LoadImagesAndLabels(Dataset): # for training/testing
def __init__(self, path, img_size=640, batch_size=16, augment=False, hyp=None, rect=False, image_weights=False, def __init__(self, path, img_size=640, batch_size=16, augment=False, hyp=None, rect=False, image_weights=False,
cache_images=False, single_cls=False, stride=32, pad=0.0): cache_images=False, single_cls=False, stride=32, pad=0.0, rank=-1):
try: try:
f = [] # image files f = [] # image files
for p in path if isinstance(path, list) else [path]: for p in path if isinstance(path, list) else [path]:
@ -372,8 +373,10 @@ class LoadImagesAndLabels(Dataset): # for training/testing
# Cache labels # Cache labels
create_datasubset, extract_bounding_boxes, labels_loaded = False, False, False create_datasubset, extract_bounding_boxes, labels_loaded = False, False, False
nm, nf, ne, ns, nd = 0, 0, 0, 0, 0 # number missing, found, empty, datasubset, duplicate nm, nf, ne, ns, nd = 0, 0, 0, 0, 0 # number missing, found, empty, datasubset, duplicate
pbar = tqdm(self.label_files) pbar = enumerate(self.label_files)
for i, file in enumerate(pbar): if rank in [-1, 0]:
pbar = tqdm(pbar)
for i, file in pbar:
l = self.labels[i] # label l = self.labels[i] # label
if l is not None and l.shape[0]: if l is not None and l.shape[0]:
assert l.shape[1] == 5, '> 5 label columns: %s' % file assert l.shape[1] == 5, '> 5 label columns: %s' % file
@ -420,8 +423,9 @@ class LoadImagesAndLabels(Dataset): # for training/testing
ne += 1 # print('empty labels for image %s' % self.img_files[i]) # file empty ne += 1 # print('empty labels for image %s' % self.img_files[i]) # file empty
# os.system("rm '%s' '%s'" % (self.img_files[i], self.label_files[i])) # remove # os.system("rm '%s' '%s'" % (self.img_files[i], self.label_files[i])) # remove
pbar.desc = 'Scanning labels %s (%g found, %g missing, %g empty, %g duplicate, for %g images)' % ( if rank in [-1,0]:
cache_path, nf, nm, ne, nd, n) pbar.desc = 'Scanning labels %s (%g found, %g missing, %g empty, %g duplicate, for %g images)' % (
cache_path, nf, nm, ne, nd, n)
if nf == 0: if nf == 0:
s = 'WARNING: No labels found in %s. See %s' % (os.path.dirname(file) + os.sep, help_url) s = 'WARNING: No labels found in %s. See %s' % (os.path.dirname(file) + os.sep, help_url)
print(s) print(s)

7
utils/general.py
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@ -5,6 +5,7 @@ import random
import shutil import shutil
import subprocess import subprocess
import time import time
import logging
from contextlib import contextmanager from contextlib import contextmanager
from copy import copy from copy import copy
from pathlib import Path from pathlib import Path
@ -45,6 +46,12 @@ def torch_distributed_zero_first(local_rank: int):
torch.distributed.barrier() torch.distributed.barrier()
def set_logging(rank=-1):
logging.basicConfig(
format="%(message)s",
level=logging.INFO if rank in [-1, 0] else logging.WARN)
def init_seeds(seed=0): def init_seeds(seed=0):
random.seed(seed) random.seed(seed)
np.random.seed(seed) np.random.seed(seed)

10
utils/torch_utils.py
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@ -1,6 +1,7 @@
import math import math
import os import os
import time import time
import logging
from copy import deepcopy from copy import deepcopy
import torch import torch
@ -9,6 +10,7 @@ import torch.nn as nn
import torch.nn.functional as F import torch.nn.functional as F
import torchvision.models as models import torchvision.models as models
logger = logging.getLogger(__name__)
def init_seeds(seed=0): def init_seeds(seed=0):
torch.manual_seed(seed) torch.manual_seed(seed)
@ -40,12 +42,12 @@ def select_device(device='', batch_size=None):
for i in range(0, ng): for i in range(0, ng):
if i == 1: if i == 1:
s = ' ' * len(s) s = ' ' * len(s)
print("%sdevice%g _CudaDeviceProperties(name='%s', total_memory=%dMB)" % logger.info("%sdevice%g _CudaDeviceProperties(name='%s', total_memory=%dMB)" %
(s, i, x[i].name, x[i].total_memory / c)) (s, i, x[i].name, x[i].total_memory / c))
else: else:
print('Using CPU') logger.info('Using CPU')
print('') # skip a line logger.info('') # skip a line
return torch.device('cuda:0' if cuda else 'cpu') return torch.device('cuda:0' if cuda else 'cpu')
@ -142,7 +144,7 @@ def model_info(model, verbose=False):
except: except:
fs = '' fs = ''
print('Model Summary: %g layers, %g parameters, %g gradients%s' % (len(list(model.parameters())), n_p, n_g, fs)) logger.info('Model Summary: %g layers, %g parameters, %g gradients%s' % (len(list(model.parameters())), n_p, n_g, fs))
def load_classifier(name='resnet101', n=2): def load_classifier(name='resnet101', n=2):

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