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train_src.py
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train_src.py
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import argparse
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "7"
import datetime
import logging
import time
import math
import numpy as np
import torch
import torch.nn.functional as F
from core.configs import cfg
from core.datasets import build_dataset
from core.models import build_model, build_feature_extractor, build_classifier
from core.solver import adjust_learning_rate
from core.utils.misc import mkdir, AverageMeter, intersectionAndUnionGPU
from core.utils.logger import setup_logger
from core.utils.metric_logger import MetricLogger
import warnings
import random
from torch.backends import cudnn
warnings.filterwarnings('ignore')
try:
from apex import amp
APEX_AVAILABLE = True
except ModuleNotFoundError:
APEX_AVAILABLE = False
APEX_AVAILABLE = False
def amp_backward(loss, optimizer, retain_graph=False):
if APEX_AVAILABLE:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward(retain_graph=retain_graph)
else:
loss.backward(retain_graph=retain_graph)
def strip_prefix_if_present(state_dict, prefix):
from collections import OrderedDict
keys = sorted(state_dict.keys())
if not all(key.startswith(prefix) for key in keys):
return state_dict
stripped_state_dict = OrderedDict()
for key, value in state_dict.items():
if key.startswith(prefix + 'layer5'):
continue
stripped_state_dict[key.replace(prefix, "")] = value
return stripped_state_dict
def train(cfg, local_rank, distributed):
logger = logging.getLogger("FADA.trainer")
logger.info("Start training")
feature_extractor = build_feature_extractor(cfg)
device = torch.device(cfg.MODEL.DEVICE)
feature_extractor.to(device)
classifier = build_classifier(cfg)
classifier.to(device)
# if local_rank == 0:
# print(feature_extractor)
# print(classifier)
model_name, backbone_name = cfg.MODEL.NAME.split('_')
batch_size = cfg.SOLVER.BATCH_SIZE
if distributed:
pg1 = torch.distributed.new_group(range(torch.distributed.get_world_size()))
batch_size = int(cfg.SOLVER.BATCH_SIZE / torch.distributed.get_world_size())
# if not cfg.MODEL.FREEZE_BN:
# feature_extractor = torch.nn.SyncBatchNorm.convert_sync_batchnorm(feature_extractor)
feature_extractor = torch.nn.parallel.DistributedDataParallel(
feature_extractor, device_ids=[local_rank], output_device=local_rank,
find_unused_parameters=True, process_group=pg1
)
pg2 = torch.distributed.new_group(range(torch.distributed.get_world_size()))
classifier = torch.nn.parallel.DistributedDataParallel(
classifier, device_ids=[local_rank], output_device=local_rank,
find_unused_parameters=True, process_group=pg2
)
torch.autograd.set_detect_anomaly(True)
torch.distributed.barrier()
optimizer_fea = torch.optim.SGD(feature_extractor.parameters(), lr=cfg.SOLVER.BASE_LR, momentum=cfg.SOLVER.MOMENTUM,
weight_decay=cfg.SOLVER.WEIGHT_DECAY)
optimizer_fea.zero_grad()
optimizer_cls = torch.optim.SGD(classifier.parameters(), lr=cfg.SOLVER.BASE_LR * 10, momentum=cfg.SOLVER.MOMENTUM,
weight_decay=cfg.SOLVER.WEIGHT_DECAY)
optimizer_cls.zero_grad()
output_dir = cfg.OUTPUT_DIR
save_to_disk = local_rank == 0
iteration = 0
if cfg.resume:
logger.info("Loading checkpoint from {}".format(cfg.resume))
checkpoint = torch.load(cfg.resume, map_location=torch.device('cpu'))
model_weights = checkpoint['feature_extractor'] if distributed else strip_prefix_if_present(
checkpoint['feature_extractor'], 'module.')
feature_extractor.load_state_dict(model_weights)
classifier_weights = checkpoint['classifier'] if distributed else strip_prefix_if_present(
checkpoint['classifier'], 'module.')
classifier.load_state_dict(classifier_weights)
src_train_data = build_dataset(cfg, mode='train', is_source=True)
if distributed:
train_sampler = torch.utils.data.distributed.DistributedSampler(src_train_data)
else:
train_sampler = None
train_loader = torch.utils.data.DataLoader(
src_train_data,
batch_size=batch_size,
shuffle=(train_sampler is None),
num_workers=4,
pin_memory=True,
sampler=train_sampler,
drop_last=True
)
criterion = torch.nn.CrossEntropyLoss(ignore_index=255)
max_iters = cfg.SOLVER.MAX_ITER
logger.info("Start training")
meters = MetricLogger(delimiter=" ")
feature_extractor.train()
classifier.train()
start_training_time = time.time()
end = time.time()
if APEX_AVAILABLE:
[feature_extractor, classifier], [optimizer_fea,optimizer_cls] = amp.initialize(
[feature_extractor, classifier], [optimizer_fea,optimizer_cls], opt_level="O2", keep_batchnorm_fp32=True,
loss_scale="dynamic"
)
for i, (src_input, src_label, _) in enumerate(train_loader):
data_time = time.time() - end
current_lr = adjust_learning_rate(cfg.SOLVER.LR_METHOD, cfg.SOLVER.BASE_LR, iteration, max_iters,
power=cfg.SOLVER.LR_POWER)
for index in range(len(optimizer_fea.param_groups)):
optimizer_fea.param_groups[index]['lr'] = current_lr
for index in range(len(optimizer_cls.param_groups)):
optimizer_cls.param_groups[index]['lr'] = current_lr * 10
optimizer_fea.zero_grad()
optimizer_cls.zero_grad()
src_input = src_input.cuda(non_blocking=True)
src_label = src_label.cuda(non_blocking=True).long()
size = src_label.shape[-2:]
pred = classifier(feature_extractor(src_input)[1], size)
#pred = classifier(feature_extractor(src_input), size)
loss = criterion(pred, src_label)
# loss.backward()
amp_backward(loss, [optimizer_fea, optimizer_cls])
optimizer_fea.step()
optimizer_cls.step()
meters.update(loss_seg=loss.item())
iteration += 1
batch_time = time.time() - end
end = time.time()
meters.update(time=batch_time, data=data_time)
eta_seconds = meters.time.global_avg * (max_iters - iteration)
eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
if iteration % 20 == 0 or iteration == max_iters:
logger.info(
meters.delimiter.join(
[
"eta: {eta}",
"iter: {iter}",
"{meters}",
"lr: {lr:.6f}",
"max mem: {memory:.0f}",
]
).format(
eta=eta_string,
iter=iteration,
meters=str(meters),
lr=optimizer_fea.param_groups[0]["lr"],
memory=torch.cuda.max_memory_allocated() / 1024.0 / 1024.0,
)
)
if (iteration % cfg.SOLVER.CHECKPOINT_PERIOD == 0 or iteration == cfg.SOLVER.STOP_ITER):
filename = os.path.join(output_dir, "model_iter{:06d}.pth".format(iteration))
torch.save({'iteration': iteration, 'feature_extractor': feature_extractor.state_dict(),
'classifier': classifier.state_dict(), 'optimizer_fea': optimizer_fea.state_dict(),
'optimizer_cls': optimizer_cls.state_dict()}, filename)
run_test(cfg, (feature_extractor, classifier), local_rank, distributed)
if iteration == max_iters:
break
if iteration == cfg.SOLVER.STOP_ITER:
break
total_training_time = time.time() - start_training_time
total_time_str = str(datetime.timedelta(seconds=total_training_time))
logger.info(
"Total training time: {} ({:.4f} s / it)".format(
total_time_str, total_training_time / (max_iters)
)
)
return feature_extractor, classifier
def run_test(cfg, model, local_rank, distributed):
logger = logging.getLogger("FADA.tester")
if local_rank == 0:
logger.info('>>>>>>>>>>>>>>>> Start Testing >>>>>>>>>>>>>>>>')
batch_time = AverageMeter()
data_time = AverageMeter()
loss_meter = AverageMeter()
intersection_meter = AverageMeter()
union_meter = AverageMeter()
target_meter = AverageMeter()
feature_extractor, classifier = model
if distributed:
feature_extractor, classifier = feature_extractor.module, classifier.module
torch.cuda.empty_cache()
dataset_name = cfg.DATASETS.TEST
if cfg.OUTPUT_DIR:
output_folder = os.path.join(cfg.OUTPUT_DIR, "inference", dataset_name)
mkdir(output_folder)
test_data = build_dataset(cfg, mode='test', is_source=False)
if distributed:
test_sampler = torch.utils.data.distributed.DistributedSampler(test_data)
else:
test_sampler = None
test_loader = torch.utils.data.DataLoader(test_data, batch_size=cfg.TEST.BATCH_SIZE, shuffle=False, num_workers=4,
pin_memory=True, sampler=test_sampler)
feature_extractor.eval()
classifier.eval()
end = time.time()
with torch.no_grad():
for i, (x, y, _) in enumerate(test_loader):
x = x.cuda(non_blocking=True)
y = y.cuda(non_blocking=True).long()
size = y.shape[-2:]
pred = classifier(feature_extractor(x)[1])
#pred = classifier(feature_extractor(x))
pred = F.interpolate(pred, size=size, mode='bilinear', align_corners=True)
output = pred.max(1)[1]
intersection, union, target = intersectionAndUnionGPU(output, y, cfg.MODEL.NUM_CLASSES,
cfg.INPUT.IGNORE_LABEL)
if distributed:
torch.distributed.all_reduce(intersection), torch.distributed.all_reduce(
union), torch.distributed.all_reduce(target)
intersection, union, target = intersection.cpu().numpy(), union.cpu().numpy(), target.cpu().numpy()
intersection_meter.update(intersection), union_meter.update(union), target_meter.update(target)
accuracy = sum(intersection_meter.val) / (sum(target_meter.val) + 1e-10)
batch_time.update(time.time() - end)
end = time.time()
iou_class = intersection_meter.sum / (union_meter.sum + 1e-10)
accuracy_class = intersection_meter.sum / (target_meter.sum + 1e-10)
mIoU = np.mean(iou_class)
mAcc = np.mean(accuracy_class)
allAcc = sum(intersection_meter.sum) / (sum(target_meter.sum) + 1e-10)
if True:
logger.info('Val result: mIoU/mAcc/allAcc {:.4f}/{:.4f}/{:.4f}.'.format(mIoU, mAcc, allAcc))
for i in range(cfg.MODEL.NUM_CLASSES):
logger.info('Class_{} Result: iou/accuracy {:.4f}/{:.4f}.'.format(i, iou_class[i], accuracy_class[i]))
def main():
parser = argparse.ArgumentParser(description="PyTorch Semantic Segmentation Training")
parser.add_argument("-cfg",
"--config-file",
default="",
metavar="FILE",
help="path to config file",
type=str,
)
parser.add_argument("--local_rank", type=int, default=-1)
parser.add_argument(
"--skip-test",
dest="skip_test",
help="Do not test the final model",
action="store_true",
)
parser.add_argument(
"opts",
help="Modify config options using the command-line",
default=None,
nargs=argparse.REMAINDER,
)
parser.add_argument("--seed", type=int, default=8888)
args = parser.parse_args()
if args.seed is not None:
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
cudnn.deterministic = True
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
num_gpus = int(os.environ["WORLD_SIZE"]) if "WORLD_SIZE" in os.environ else 1
args.distributed = num_gpus > 1
if args.distributed:
torch.cuda.set_device(args.local_rank)
torch.distributed.init_process_group(
backend="nccl", init_method="env://"
)
# RANK = int(os.environ["RANK"])
# if 'CUDA_VISIBLE_DEVICES' in os.environ.keys():
# NGPUS_PER_NODE = len(os.environ['CUDA_VISIBLE_DEVICES'].split(','))
# else:
# NGPUS_PER_NODE = torch.cuda.device_count()
# assert NGPUS_PER_NODE > 0, "CUDA is not supported"
# GPU = RANK % NGPUS_PER_NODE
# torch.cuda.set_device(GPU)
# master_address = os.environ['MASTER_ADDR']
# master_port = int(os.environ['MASTER_PORT'])
# WORLD_SIZE = int(os.environ['WORLD_SIZE'])
# torch.distributed.init_process_group(backend='nccl',
# init_method='tcp://{}:{}'.format(
# master_address, master_port),
# rank=RANK, world_size=WORLD_SIZE)
# NUM_GPUS = WORLD_SIZE
# print(f"RANK and WORLD_SIZE in environ: {RANK}/{WORLD_SIZE}")
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
output_dir = cfg.OUTPUT_DIR
if output_dir:
mkdir(output_dir)
logger = setup_logger("FADA", output_dir, args.local_rank)
logger.info("Using {} GPUs".format(num_gpus))
logger.info(args)
logger.info("Loaded configuration file {}".format(args.config_file))
with open(args.config_file, "r") as cf:
config_str = "\n" + cf.read()
logger.info(config_str)
logger.info("Running with config:\n{}".format(cfg))
model = train(cfg, args.local_rank, args.distributed)
if not args.skip_test:
run_test(cfg, model, args.local_rank, args.distributed)
if __name__ == "__main__":
main()