def train(cfg, local_rank, distributed):
model = build_detection_model(cfg)
device = torch.device(cfg.MODEL.DEVICE)
model.to(device)
optimizer = make_optimizer(cfg, model)
scheduler = make_lr_scheduler(cfg, optimizer)
if distributed:
model = torch.nn.parallel.DistributedDataParallel(
model,
device_ids=[local_rank],
output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False,
)
arguments = {}
arguments["iteration"] = 0
output_dir = cfg.OUTPUT_DIR
save_to_disk = get_rank() == 0
checkpointer = DetectronCheckpointer(cfg, model, optimizer, scheduler,
output_dir, save_to_disk)
extra_checkpoint_data = checkpointer.load(cfg.MODEL.WEIGHT)
arguments.update(extra_checkpoint_data)
data_loader = make_data_loader(
cfg,
is_train=True,
is_distributed=distributed,
start_iter=arguments["iteration"],
)
checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD
do_train(
model,
data_loader,
optimizer,
scheduler,
checkpointer,
device,
checkpoint_period,
arguments,
)
return model
def train(cfg, local_rank, distributed):
model = build_detection_model(cfg)
device = torch.device(cfg.MODEL.DEVICE)
model.to(device)
optimizer = make_optimizer(cfg, model)
scheduler = make_lr_scheduler(cfg, optimizer)
if distributed:
model = torch.nn.parallel.deprecated.DistributedDataParallel(
model, device_ids=[local_rank], output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False,
)
arguments = {}
arguments["iteration"] = 0
output_dir = cfg.OUTPUT_DIR
save_to_disk = get_rank() == 0
checkpointer = DetectronCheckpointer(
cfg, model, optimizer, scheduler, output_dir, save_to_disk
)
extra_checkpoint_data = checkpointer.load(cfg.MODEL.WEIGHT)
arguments.update(extra_checkpoint_data)
data_loader = make_data_loader(
cfg,
is_train=True,
is_distributed=distributed,
start_iter=arguments["iteration"],
)
checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD
do_train(
model,
data_loader,
optimizer,
scheduler,
checkpointer,
device,
checkpoint_period,
arguments,
)
return model
def train(cfg, local_rank, distributed):
model = build_detection_model(cfg)
device = torch.device(cfg.MODEL.DEVICE)
model.to(device)
optimizer = make_optimizer(cfg, model)
scheduler = make_lr_scheduler(cfg, optimizer)
# Initialize mixed-precision training
use_mixed_precision = cfg.DTYPE == "float16"
amp_opt_level = 'O1' if use_mixed_precision else 'O0'
model, optimizer = amp.initialize(model,
optimizer,
opt_level=amp_opt_level)
if distributed:
model = torch.nn.parallel.DistributedDataParallel(
model,
device_ids=[local_rank],
output_device=local_rank
# this should be removed if we update BatchNorm stats
#broadcast_buffers=False,find_unused_parameters=True
)
arguments = {}
arguments["iteration"] = 0
output_dir = cfg.OUTPUT_DIR
save_to_disk = get_rank() == 0
checkpointer = DetectronCheckpointer(cfg, model, optimizer, scheduler,
output_dir, save_to_disk)
extra_checkpoint_data = checkpointer.load(cfg.MODEL.WEIGHT)
arguments.update(extra_checkpoint_data)
data_loader = make_data_loader(
cfg,
is_train=True,
is_distributed=distributed,
start_iter=arguments["iteration"],
)
checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD
do_train(model, data_loader, optimizer, scheduler, checkpointer, device,
checkpoint_period, arguments, local_rank)
return model
def train(cfg, distributed):
model = build_detection_model(cfg)
device = torch.device(cfg.MODEL.DEVICE)
model.to(device)
optimizer = make_optimizer(cfg, model)
scheduler = make_lr_scheduler(cfg, optimizer)
arguments = {}
arguments["iteration"] = 0
output_dir = cfg.OUTPUT_DIR
save_to_disk = get_rank() == 0
checkpointer = DetectronCheckpointer(
cfg, model, optimizer, scheduler, output_dir, save_to_disk
)
extra_checkpoint_data = checkpointer.load(cfg.MODEL.WEIGHT)
arguments.update(extra_checkpoint_data)
data_loader = make_data_loader(
cfg,
is_train=True,
is_distributed=distributed,
start_iter=arguments["iteration"],
)
checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD
warmup_layers = tuple(x for x in cfg.SOLVER.WARMUP_LAYERS if len(x) != 0)
warmup_iters = cfg.SOLVER.WARMUP_ITERS
do_train(
model,
data_loader,
optimizer,
scheduler,
checkpointer,
device,
checkpoint_period,
arguments,
warmup_layers,
warmup_iters
)
return model
def fit(self, train_data, test_data, classes):
self.classes = classes
optimizer = make_optimizer(self.cfg, self.model)
scheduler = make_lr_scheduler(self.cfg, optimizer)
arguments = {}
arguments["iteration"] = 0
self.checkpointer.classes = classes
self.checkpointer.optimizer = optimizer
self.checkpointer.scheduler = scheduler
train_data_loader = make_data_loader(
self.cfg,
train_data,
classes,
is_train=True,
is_distributed=False,
start_iter=arguments["iteration"],
)
test_data_loader = make_data_loader(
self.cfg,
test_data,
classes,
is_train=True,
is_distributed=False,
start_iter=arguments["iteration"],
)
self.train_meter, self.test_meter = do_train(
self.model,
train_data_loader,
test_data_loader,
optimizer,
scheduler,
self.device,
arguments,
)
def train(cfg, local_rank, distributed, random_number_generator=None):
if (torch._C, '_jit_set_profiling_executor') :
torch._C._jit_set_profiling_executor(False)
if (torch._C, '_jit_set_profiling_mode') :
torch._C._jit_set_profiling_mode(False)
# Model logging
log_event(key=constants.GLOBAL_BATCH_SIZE, value=cfg.SOLVER.IMS_PER_BATCH)
log_event(key=constants.NUM_IMAGE_CANDIDATES, value=cfg.MODEL.RPN.FPN_POST_NMS_TOP_N_TRAIN)
model = build_detection_model(cfg)
device = torch.device(cfg.MODEL.DEVICE)
model.to(device)
# Initialize mixed-precision training
is_fp16 = (cfg.DTYPE == "float16")
if is_fp16:
# convert model to FP16
model.half()
optimizer = make_optimizer(cfg, model)
# Optimizer logging
log_event(key=constants.OPT_NAME, value="sgd_with_momentum")
log_event(key=constants.OPT_BASE_LR, value=cfg.SOLVER.BASE_LR)
log_event(key=constants.OPT_LR_WARMUP_STEPS, value=cfg.SOLVER.WARMUP_ITERS)
log_event(key=constants.OPT_LR_WARMUP_FACTOR, value=cfg.SOLVER.WARMUP_FACTOR)
log_event(key=constants.OPT_LR_DECAY_FACTOR, value=cfg.SOLVER.GAMMA)
log_event(key=constants.OPT_LR_DECAY_STEPS, value=cfg.SOLVER.STEPS)
log_event(key=constants.MIN_IMAGE_SIZE, value=cfg.INPUT.MIN_SIZE_TRAIN[0])
log_event(key=constants.MAX_IMAGE_SIZE, value=cfg.INPUT.MAX_SIZE_TRAIN)
scheduler = make_lr_scheduler(cfg, optimizer)
# disable the garbage collection
gc.disable()
if distributed:
model = DDP(model, delay_allreduce=True)
arguments = {}
arguments["iteration"] = 0
arguments["nhwc"] = cfg.NHWC
output_dir = cfg.OUTPUT_DIR
save_to_disk = get_rank() == 0
checkpointer = DetectronCheckpointer(
cfg, model, optimizer, scheduler, output_dir, save_to_disk
)
arguments["save_checkpoints"] = cfg.SAVE_CHECKPOINTS
extra_checkpoint_data = checkpointer.load(cfg.MODEL.WEIGHT, cfg.NHWC)
arguments.update(extra_checkpoint_data)
if is_fp16:
optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True)
log_end(key=constants.INIT_STOP)
barrier()
log_start(key=constants.RUN_START)
barrier()
data_loader, iters_per_epoch = make_data_loader(
cfg,
is_train=True,
is_distributed=distributed,
start_iter=arguments["iteration"],
random_number_generator=random_number_generator,
)
log_event(key=constants.TRAIN_SAMPLES, value=len(data_loader))
checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD
# set the callback function to evaluate and potentially
# early exit each epoch
if cfg.PER_EPOCH_EVAL:
per_iter_callback_fn = functools.partial(
mlperf_test_early_exit,
iters_per_epoch=iters_per_epoch,
tester=functools.partial(test, cfg=cfg),
model=model,
distributed=distributed,
min_bbox_map=cfg.MLPERF.MIN_BBOX_MAP,
min_segm_map=cfg.MLPERF.MIN_SEGM_MAP)
else:
per_iter_callback_fn = None
start_train_time = time.time()
success = do_train(
model,
data_loader,
optimizer,
scheduler,
checkpointer,
device,
checkpoint_period,
arguments,
cfg.DISABLE_REDUCED_LOGGING,
per_iter_start_callback_fn=functools.partial(mlperf_log_epoch_start, iters_per_epoch=iters_per_epoch),
per_iter_end_callback_fn=per_iter_callback_fn,
)
end_train_time = time.time()
total_training_time = end_train_time - start_train_time
print(
"&&&& MLPERF METRIC THROUGHPUT={:.4f} iterations / s".format((arguments["iteration"] * cfg.SOLVER.IMS_PER_BATCH) / total_training_time)
)
return model, success
def train(cfg, local_rank, distributed, ft_flag):
# 导入模型
model = build_detection_model(cfg)
# 获取实验跑的设备
device = torch.device(cfg.MODEL.DEVICE)
# print('device', device)
model.to(device)
# 获取
optimizer = make_optimizer(cfg, model)
scheduler = make_lr_scheduler(cfg, optimizer)
if distributed:
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[local_rank], output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False,
)
arguments = {}
arguments["iteration"] = 0
output_dir = cfg.OUTPUT_DIR
save_to_disk = get_rank() == 0
#if ft_flag:
# scheduler = None
if ft_flag:
checkpointer = DetectronCheckpointer(
cfg, model, optimizer, None, output_dir, save_to_disk
)
else:
checkpointer = DetectronCheckpointer(
cfg, model, optimizer, scheduler, output_dir, save_to_disk
)
# print('cfg.MODEL.WEIGHTP:', cfg.MODEL.WEIGHT)
extra_checkpoint_data = checkpointer.load(cfg.MODEL.WEIGHT)
arguments.update(extra_checkpoint_data)
# print('extra_checkpoint_data:', extra_checkpoint_data.state_dict())
# arguments["iteration"] = 50000
arguments["iteration"] = 0 if ft_flag else arguments["iteration"]
data_loader = make_data_loader(
cfg,
is_train=True,
is_distributed=distributed,
start_iter=arguments["iteration"],
)
checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD
do_train(
model,
data_loader,
optimizer,
scheduler,
checkpointer,
device,
checkpoint_period,
arguments,
)
return model
def train(cfg, local_rank, distributed):
model = build_detection_model(cfg)
device = torch.device(cfg.MODEL.DEVICE)
model.to(device)
optimizer = make_optimizer(cfg, model)
scheduler = make_lr_scheduler(cfg, optimizer)
if distributed:
model = torch.nn.parallel.DistributedDataParallel(
model,
device_ids=[local_rank],
output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False,
)
arguments = {}
arguments["iteration"] = 0
output_dir = cfg.OUTPUT_DIR
save_to_disk = get_rank() == 0
checkpointer = DetectronCheckpointer(cfg, model, None, None, output_dir,
save_to_disk)
extra_checkpoint_data = checkpointer.load(cfg.MODEL.WEIGHT)
# arguments.update(extra_checkpoint_data)
checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD
logger = logging.getLogger("maskrcnn_benchmark.trainer")
if cfg.MODEL.META_ARCHITECTURE == 'AdaptionRCNN':
logger.info('AdaptionRCNN trainer is adapted!')
cross_do_train(
cfg,
model,
optimizer,
scheduler,
checkpointer,
device,
checkpoint_period,
arguments,
distributed,
)
elif cfg.MODEL.META_ARCHITECTURE == 'GeneralizedRCNN':
logger.info('GeneralizedRCNN trainer is adapted!')
data_loader = make_data_loader(
cfg,
is_train=True,
is_distributed=distributed,
start_iter=arguments["iteration"],
)
do_train(
cfg,
model,
data_loader,
optimizer,
scheduler,
checkpointer,
device,
checkpoint_period,
arguments,
distributed,
)
return model
def train(total_cfg, local_rank, distributed):
total_model = []
for i in reversed(range(len(total_cfg))):
model = build_detection_model(total_cfg[i])
device = torch.device(total_cfg[i].MODEL.DEVICE)
model.to(device)
if total_cfg[i].MODEL.USE_SYNCBN:
assert is_pytorch_1_1_0_or_later(), \
"SyncBatchNorm is only available in pytorch >= 1.1.0"
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
optimizer = make_optimizer(total_cfg[i], model)
scheduler = make_lr_scheduler(total_cfg[i], optimizer)
if distributed:
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[local_rank], output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False, )
arguments = {}
arguments["iteration"] = 0
output_dir = total_cfg[i].OUTPUT_DIR
save_to_disk = get_rank() == 0
checkpointer = DetectronCheckpointer(
total_cfg[i], model, optimizer, scheduler, output_dir, save_to_disk
)
extra_checkpoint_data = checkpointer.load(total_cfg[i].MODEL.WEIGHT)
if i == 0:
arguments.update(extra_checkpoint_data)
total_model.append(model)
data_loader = make_data_loader(
cfg,
is_train=True,
is_distributed=distributed,
start_iter=arguments["iteration"],
)
test_period = total_cfg[0].SOLVER.TEST_PERIOD
if test_period > 0:
data_loader_val = make_data_loader(total_cfg[0], is_train=False, is_distributed=distributed, is_for_period=True)
else:
data_loader_val = None
checkpoint_period = total_cfg[0].SOLVER.CHECKPOINT_PERIOD
if len(total_model)>1:
params = sum([np.prod(p.size()) for p in total_model[1].parameters()])
print('Number of Parameters:{:5f}M'.format(params / 1e6))
params = sum([np.prod(p.size()) for p in total_model[0].parameters()])
print('teacher_model Number of Parameters:{:5f}M'.format(params / 1e6))
else:
params = sum([np.prod(p.size()) for p in total_model[0].parameters()])
print('Number of Parameters:{:5f}M'.format(params / 1e6))
do_train(
total_cfg,
total_model,
data_loader,
data_loader_val,
optimizer,
scheduler,
checkpointer,
device,
checkpoint_period,
test_period,
arguments,
args,
)
return total_model[1]
def train(cfg, local_rank, distributed):
# 生成模型
model = build_detection_model(cfg)
# _C.MODEL.DEVICE = "cuda"
device = torch.device(cfg.MODEL.DEVICE)
model.to(device)
optimizer = make_optimizer(cfg, model)
# 调节lr，用上了预热学习率
scheduler = make_lr_scheduler(cfg, optimizer)
# Initialize mixed-precision training
use_mixed_precision = cfg.DTYPE == "float16"
amp_opt_level = 'O1' if use_mixed_precision else 'O0'
"""
opt_level有四个值选择输入："O0", "O1", "O2", "03"
00相当于原始的单精度训练。
01在大部分计算时采用半精度，但是所有的模型参数依然保持单精度，对于少数单精度较好的计算（如softmax）依然保持单精度。
02相比于01，将模型参数也变为半精度。
03基本等于最开始实验的全半精度的运算。
值得一提的是，不论在优化过程中，模型是否采用半精度，保存下来的模型均为单精度模型，能够保证模型在其他应用中的正常使用。
"""
model, optimizer = amp.initialize(model,
optimizer,
opt_level=amp_opt_level)
# 多GPU运算
if distributed:
model = torch.nn.parallel.DistributedDataParallel(
model,
device_ids=[local_rank],
output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False,
)
arguments = {}
arguments["iteration"] = 0
output_dir = cfg.OUTPUT_DIR
save_to_disk = get_rank() == 0
checkpointer = DetectronCheckpointer(cfg, model, optimizer, scheduler,
output_dir, save_to_disk)
# 加载checkpointer文件
extra_checkpoint_data = checkpointer.load(cfg.MODEL.WEIGHT)
arguments.update(extra_checkpoint_data)
# 对datasets做一些操作，返回data_loaders
data_loader = make_data_loader(
cfg,
is_train=True,
is_distributed=distributed,
start_iter=arguments["iteration"],
)
test_period = cfg.SOLVER.TEST_PERIOD
if test_period > 0:
data_loader_val = make_data_loader(cfg,
is_train=False,
is_distributed=distributed,
is_for_period=True)
else:
data_loader_val = None
# _C.SOLVER.CHECKPOINT_PERIOD = 2500
checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD
do_train(
cfg,
model,
data_loader,
data_loader_val,
optimizer,
scheduler,
checkpointer,
device,
checkpoint_period,
test_period,
arguments,
)
return model
def train(cfg, cfg_origial, local_rank, distributed):
## The one with modified number of classes
model = build_detection_model(cfg)
# cfg_origial = cfg.clone()
# cfg_origial.MODEL.ROI_BOX_HEAD.NUM_CLASSES = 81
# original_model = build_detection_model(cfg_origial) ## Original model with 81 classes
# ## Let's load weights for old class!
# save_dir = cfg.OUTPUT_DIR
# checkpointer = DetectronCheckpointer(cfg_origial, original_model, save_dir=save_dir)
# checkpointer.load(cfg_origial.MODEL.WEIGHT)
# # pretrained_model_pth = "/network/home/bhattdha/.torch/models/_detectron_35861795_12_2017_baselines_e2e_mask_rcnn_R-101-FPN_1x.yaml.02_31_37.KqyEK4tT_output_train_coco_2014_train%3Acoco_2014_valminusminival_generalized_rcnn_model_final.pkl"
# # These keys are to be removed which forms final layers of the network
# removal_keys = ['roi_heads.box.predictor.cls_score.weight', 'roi_heads.box.predictor.cls_score.bias', 'roi_heads.box.predictor.bbox_pred.weight', 'roi_heads.box.predictor.bbox_pred.bias', 'roi_heads.mask.predictor.mask_fcn_logits.weight', 'roi_heads.mask.predictor.mask_fcn_logits.bias']
# model = _transfer_pretrained_weights(new_model, original_model, removal_keys)
device = torch.device(cfg.MODEL.DEVICE)
model.to(device)
optimizer = make_optimizer(cfg, model)
scheduler = make_lr_scheduler(cfg, optimizer)
# # Initialize mixed-precision training
# use_mixed_precision = cfg.DTYPE == "float16"
# amp_opt_level = 'O1' if use_mixed_precision else 'O0'
# model, optimizer = amp.initialize(model, optimizer, opt_level=amp_opt_level)
if distributed:
model = torch.nn.parallel.DistributedDataParallel(
model,
device_ids=[local_rank],
output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False,
)
arguments = {}
arguments["iteration"] = 0
output_dir = cfg.OUTPUT_DIR
save_to_disk = get_rank() == 0
checkpointer = DetectronCheckpointer(cfg, model, optimizer, scheduler,
output_dir, save_to_disk)
# cfg.MODEL.WEIGHT = '/network/home/bhattdha/exp.pth' ## Model stored through surgery
extra_checkpoint_data = checkpointer.load(cfg.MODEL.WEIGHT)
arguments.update(extra_checkpoint_data)
data_loader = make_data_loader(
cfg,
is_train=True,
is_distributed=distributed,
start_iter=arguments["iteration"],
)
checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD
do_train(
model,
data_loader,
optimizer,
scheduler,
checkpointer,
device,
checkpoint_period,
arguments,
)
return model
def train(cfg, local_rank, distributed):
model = build_detection_model(cfg)
device = torch.device(cfg.MODEL.DEVICE)
model.to(device)
optimizer = make_optimizer(cfg, model)
scheduler = make_lr_scheduler(cfg, optimizer)
# Initialize mixed-precision training
use_mixed_precision = cfg.DTYPE == "float16"
amp_opt_level = 'O1' if use_mixed_precision else 'O0'
model, optimizer = amp.initialize(model,
optimizer,
opt_level=amp_opt_level)
if distributed:
model = torch.nn.parallel.DistributedDataParallel(
model,
device_ids=[local_rank],
output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False,
)
arguments = {}
arguments["iteration"] = 0
arguments['phase'] = 1
arguments['plot_median'], arguments['plot_global_avg'] = defaultdict(
list), defaultdict(list)
output_dir = cfg.OUTPUT_DIR
save_to_disk = get_rank() == 0
checkpointer = DetectronCheckpointer(cfg, model, optimizer, scheduler,
output_dir, save_to_disk)
extra_checkpoint_data = checkpointer.load(cfg.MODEL.WEIGHT)
arguments.update(extra_checkpoint_data)
test_period = cfg.SOLVER.TEST_PERIOD
if test_period > 0:
data_loader_val = make_data_loader(cfg,
is_train=False,
is_distributed=distributed,
is_for_period=True)
else:
data_loader_val = None
checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD
if arguments['phase'] == 1:
data_loader = make_data_loader(
cfg,
is_train=True,
is_distributed=distributed,
start_iter=arguments["iteration"],
phase=1,
)
do_train(
cfg,
model,
data_loader,
data_loader_val,
optimizer,
scheduler,
checkpointer,
device,
checkpoint_period,
test_period,
arguments,
training_phase=1,
)
arguments["iteration"] = 0
arguments["phase"] = 2
data_loader_phase2 = make_data_loader(
cfg,
is_train=True,
is_distributed=distributed,
start_iter=arguments["iteration"],
phase=2,
)
do_train(
cfg,
model,
data_loader_phase2,
data_loader_val,
optimizer,
scheduler,
checkpointer,
device,
checkpoint_period,
test_period,
arguments,
training_phase=2,
)
return model
def train(cfg, local_rank, distributed, resume, config_file):
model = build_detection_model(cfg)
device = torch.device(cfg.MODEL.DEVICE)
model.to(device)
optimizer = make_optimizer(cfg, model)
scheduler = make_lr_scheduler(cfg, optimizer)
if distributed:
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[local_rank], output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False,
)
arguments = {}
arguments["iteration"] = 0
output_dir = cfg.OUTPUT_DIR
out_split = output_dir.split("/")
tensorboard_dir = os.path.join("tensorboard", out_split[-1] if len(out_split[-1]) > 0 else out_split[-2])
print("tensorboard_dir:", tensorboard_dir)
if not os.path.isdir(tensorboard_dir):
os.makedirs(tensorboard_dir)
else:
if len(os.listdir(tensorboard_dir)) > 0:
print("Remove previous tensorboard events...")
os.system("rm " + tensorboard_dir + "/*")
result_writer = SummaryWriter(tensorboard_dir)
save_to_disk = get_rank() == 0
checkpointer = DetectronCheckpointer(
cfg, model, optimizer, scheduler, output_dir, save_to_disk
)
# print("resume:", resume)
extra_checkpoint_data = checkpointer.load(resume == "True", cfg.MODEL.WEIGHT)
arguments.update(extra_checkpoint_data)
if resume == "False":
arguments["iteration"] = 0
# print("arguments:", arguments.keys())
data_loader = make_data_loader(
cfg,
is_train=True,
is_distributed=distributed,
start_iter=arguments["iteration"],
)
checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD
do_train(
model,
data_loader,
optimizer,
scheduler,
checkpointer,
device,
checkpoint_period,
arguments,
results_recorder=result_writer,
config_file=config_file
)
return model
def train(cfg, local_rank, distributed):
model = build_detection_model(cfg)
device = torch.device(cfg.MODEL.DEVICE)
model.to(device)
if cfg.MODEL.USE_SYNCBN:
assert is_pytorch_1_1_0_or_later(), \
"SyncBatchNorm is only available in pytorch >= 1.1.0"
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
optimizer = make_optimizer(cfg, model)
scheduler = make_lr_scheduler(cfg, optimizer)
if distributed:
model = torch.nn.parallel.DistributedDataParallel(
model,
device_ids=[local_rank],
output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False,
)
arguments = {}
arguments["iteration"] = 0
output_dir = cfg.OUTPUT_DIR
save_to_disk = get_rank() == 0
checkpointer = DetectronCheckpointer(cfg, model, optimizer, scheduler,
output_dir, save_to_disk)
extra_checkpoint_data = checkpointer.load(cfg.MODEL.WEIGHT)
arguments.update(extra_checkpoint_data)
data_loader = make_data_loader(
cfg,
is_train=True,
is_distributed=distributed,
start_iter=arguments["iteration"],
)
test_period = cfg.SOLVER.TEST_PERIOD
if test_period > 0:
data_loader_val = make_data_loader(cfg,
is_train=False,
is_distributed=distributed,
is_for_period=True)
else:
data_loader_val = None
checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD
do_train(
cfg,
model,
data_loader,
data_loader_val,
optimizer,
scheduler,
checkpointer,
device,
checkpoint_period,
test_period,
arguments,
)
return model
def train(cfg, local_rank, distributed):
model = build_detection_model(cfg)
logger = logging.getLogger("maskrcnn_benchmark.train")
logger.info("The train model: \n {}".format(model))
device = torch.device(cfg.MODEL.DEVICE)
if cfg.SOLVER.USE_SYNC_BN:
model = apex.parallel.convert_syncbn_model(model)
model.to(device)
optimizer = make_optimizer(cfg, model)
model, optimizer = amp.initialize(model, optimizer, opt_level="O0")
scheduler = make_lr_scheduler(cfg, optimizer)
if distributed:
# model = torch.nn.parallel.DistributedDataParallel(
# model, device_ids=[local_rank], output_device=local_rank,
# # this should be removed if we update BatchNorm stats
# #broadcast_buffers=False,
# )
model = DDP(model, delay_allreduce=True)
arguments = {}
arguments["iteration"] = 0
output_dir = cfg.OUTPUT_DIR
save_to_disk = get_rank() == 0
checkpointer = DetectronCheckpointer(cfg, model, optimizer, scheduler,
output_dir, save_to_disk)
extra_checkpoint_data = checkpointer.load(cfg.MODEL.WEIGHT)
arguments.update(extra_checkpoint_data)
data_loader = make_data_loader(
cfg,
mode=0,
resolution=None,
is_train=True,
is_distributed=distributed,
start_iter=arguments["iteration"],
)
data_loader.collate_fn.special_deal = False
checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD
writer, arch_writer = setup_writer(output_dir, get_rank())
if arch_writer is not None:
arch_writer.write('Genotype: {}\n'.format(cfg.SEARCH.DECODER.CONFIG))
arch_writer.close()
do_train(
model,
data_loader,
optimizer,
scheduler,
checkpointer,
device,
checkpoint_period,
arguments,
writer,
)
return model
def train(cfg, local_rank, distributed):
# Model logging
print_mlperf(key=mlperf_log.INPUT_BATCH_SIZE,
value=cfg.SOLVER.IMS_PER_BATCH)
print_mlperf(key=mlperf_log.BATCH_SIZE_TEST, value=cfg.TEST.IMS_PER_BATCH)
print_mlperf(key=mlperf_log.INPUT_MEAN_SUBTRACTION,
value=cfg.INPUT.PIXEL_MEAN)
print_mlperf(key=mlperf_log.INPUT_NORMALIZATION_STD,
value=cfg.INPUT.PIXEL_STD)
print_mlperf(key=mlperf_log.INPUT_RESIZE)
print_mlperf(key=mlperf_log.INPUT_RESIZE_ASPECT_PRESERVING)
print_mlperf(key=mlperf_log.MIN_IMAGE_SIZE, value=cfg.INPUT.MIN_SIZE_TRAIN)
print_mlperf(key=mlperf_log.MAX_IMAGE_SIZE, value=cfg.INPUT.MAX_SIZE_TRAIN)
print_mlperf(key=mlperf_log.INPUT_RANDOM_FLIP)
print_mlperf(key=mlperf_log.RANDOM_FLIP_PROBABILITY, value=0.5)
print_mlperf(key=mlperf_log.FG_IOU_THRESHOLD,
value=cfg.MODEL.RPN.FG_IOU_THRESHOLD)
print_mlperf(key=mlperf_log.BG_IOU_THRESHOLD,
value=cfg.MODEL.RPN.BG_IOU_THRESHOLD)
print_mlperf(key=mlperf_log.RPN_PRE_NMS_TOP_N_TRAIN,
value=cfg.MODEL.RPN.PRE_NMS_TOP_N_TRAIN)
print_mlperf(key=mlperf_log.RPN_PRE_NMS_TOP_N_TEST,
value=cfg.MODEL.RPN.PRE_NMS_TOP_N_TEST)
print_mlperf(key=mlperf_log.RPN_POST_NMS_TOP_N_TRAIN,
value=cfg.MODEL.RPN.FPN_POST_NMS_TOP_N_TRAIN)
print_mlperf(key=mlperf_log.RPN_POST_NMS_TOP_N_TEST,
value=cfg.MODEL.RPN.FPN_POST_NMS_TOP_N_TEST)
print_mlperf(key=mlperf_log.ASPECT_RATIOS,
value=cfg.MODEL.RPN.ASPECT_RATIOS)
print_mlperf(key=mlperf_log.BACKBONE, value=cfg.MODEL.BACKBONE.CONV_BODY)
print_mlperf(key=mlperf_log.NMS_THRESHOLD, value=cfg.MODEL.RPN.NMS_THRESH)
model = build_detection_model(cfg)
device = torch.device(cfg.MODEL.DEVICE)
model.to(device)
optimizer = make_optimizer(cfg, model)
# Optimizer logging
print_mlperf(key=mlperf_log.OPT_NAME, value=mlperf_log.SGD_WITH_MOMENTUM)
print_mlperf(key=mlperf_log.OPT_LR, value=cfg.SOLVER.BASE_LR)
print_mlperf(key=mlperf_log.OPT_MOMENTUM, value=cfg.SOLVER.MOMENTUM)
print_mlperf(key=mlperf_log.OPT_WEIGHT_DECAY,
value=cfg.SOLVER.WEIGHT_DECAY)
scheduler = make_lr_scheduler(cfg, optimizer)
if distributed:
model = torch.nn.parallel.DistributedDataParallel(
model,
device_ids=[local_rank],
output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False,
)
arguments = {}
arguments["iteration"] = 0
output_dir = cfg.OUTPUT_DIR
save_to_disk = get_rank() == 0
checkpointer = DetectronCheckpointer(cfg, model, optimizer, scheduler,
output_dir, save_to_disk)
arguments["save_checkpoints"] = cfg.SAVE_CHECKPOINTS
extra_checkpoint_data = checkpointer.load(cfg.MODEL.WEIGHT)
arguments.update(extra_checkpoint_data)
data_loader, iters_per_epoch = make_data_loader(
cfg,
is_train=True,
is_distributed=distributed,
start_iter=arguments["iteration"],
)
checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD
# set the callback function to evaluate and potentially
# early exit each epoch
if cfg.PER_EPOCH_EVAL:
per_iter_callback_fn = functools.partial(
mlperf_test_early_exit,
iters_per_epoch=iters_per_epoch,
tester=functools.partial(test, cfg=cfg),
model=model,
distributed=distributed,
min_bbox_map=cfg.MLPERF.MIN_BBOX_MAP,
min_segm_map=cfg.MLPERF.MIN_SEGM_MAP)
else:
per_iter_callback_fn = None
start_train_time = time.time()
do_train(
model,
data_loader,
optimizer,
scheduler,
checkpointer,
device,
checkpoint_period,
arguments,
per_iter_start_callback_fn=functools.partial(
mlperf_log_epoch_start, iters_per_epoch=iters_per_epoch),
per_iter_end_callback_fn=per_iter_callback_fn,
)
end_train_time = time.time()
total_training_time = end_train_time - start_train_time
print("&&&& MLPERF METRIC THROUGHPUT per GPU={:.4f} iterations / s".format(
(arguments["iteration"] * 1.0) / total_training_time))
return model
def train(cfg, local_rank, distributed):
model = build_detection_model(cfg) # 梦开始的地方
device = torch.device(cfg.MODEL.DEVICE) # ！！！！！
model.to(device)
for name, value in model.backbone.body.network.named_children(
): # 冻结主干网络参数
if int(name) > 60:
for param in value.parameters():
param.requires_grad = False
optimizer = make_optimizer(cfg, model)
scheduler = make_lr_scheduler(cfg, optimizer)
# Initialize mixed-precision training
use_mixed_precision = cfg.DTYPE == "float16" # 这里可以改成float16来加速
amp_opt_level = 'O1' if use_mixed_precision else 'O0'
model, optimizer = amp.initialize(model,
optimizer,
opt_level=amp_opt_level)
if distributed:
model = torch.nn.parallel.DistributedDataParallel(
model,
device_ids=[local_rank],
output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False,
)
arguments = {}
arguments["iteration"] = 0
output_dir = cfg.OUTPUT_DIR
save_to_disk = get_rank() == 0
checkpointer = DetectronCheckpointer(cfg, model, optimizer, scheduler,
output_dir, save_to_disk)
extra_checkpoint_data = checkpointer.load(cfg.MODEL.WEIGHT)
arguments.update(extra_checkpoint_data)
data_loader = make_data_loader(
cfg,
is_train=True,
is_distributed=distributed,
start_iter=arguments["iteration"],
)
test_period = cfg.SOLVER.TEST_PERIOD
if test_period > 0:
data_loader_val = make_data_loader(cfg,
is_train=False,
is_distributed=distributed,
is_for_period=True)
else:
data_loader_val = None
checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD
do_train(
cfg,
model,
data_loader,
data_loader_val,
optimizer,
scheduler,
checkpointer,
device,
checkpoint_period,
test_period,
arguments,
)
return model
def train(cfg, local_rank, distributed):
model = build_detection_model(cfg)
device = torch.device(cfg.MODEL.DEVICE)
model.to(device)
optimizer = make_optimizer(cfg, model)
scheduler = make_lr_scheduler(cfg, optimizer)
# if use_amp:
# # Initialize mixed-precision training
# use_mixed_precision = cfg.DTYPE == "float16"
# amp_handle = amp.init(enabled=use_mixed_precision, verbose=cfg.AMP_VERBOSE)
# # wrap the optimizer for mixed precision
# if cfg.SOLVER.ACCUMULATE_GRAD:
# # also specify number of steps to accumulate over
# optimizer = amp_handle.wrap_optimizer(optimizer, num_loss=cfg.SOLVER.ACCUMULATE_STEPS)
# else:
# optimizer = amp_handle.wrap_optimizer(optimizer)
model, optimizer = amp.initialize(model, optimizer,opt_level='O1')
if distributed:
if use_apex_ddp:
model = DDP(model, delay_allreduce=True)
else:
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[local_rank], output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False,
)
arguments = {}
arguments["iteration"] = 0
output_dir = cfg.OUTPUT_DIR
save_to_disk = get_rank() == 0
checkpointer = DetectronCheckpointer(
cfg, model, optimizer, scheduler, output_dir, save_to_disk
)
extra_checkpoint_data = checkpointer.load(cfg.MODEL.WEIGHT)
arguments.update(extra_checkpoint_data)
data_loader, iters_per_epoch = make_data_loader(
cfg,
is_train=True,
is_distributed=distributed,
start_iter=arguments["iteration"],
)
checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD
# set the callback function to evaluate and potentially
# early exit each epoch
if 1==1:
per_iter_callback_fn = functools.partial(
mlperf_test_early_exit,
iters_per_epoch=iters_per_epoch,
tester=functools.partial(test, cfg=cfg),
model=model,
distributed=distributed,
min_bbox_map=cfg.MIN_BBOX_MAP,
min_segm_map=cfg.MIN_MASK_MAP)
else:
per_iter_callback_fn = None
do_train(
model,
data_loader,
optimizer,
scheduler,
checkpointer,
device,
checkpoint_period,
arguments,
use_amp,
cfg,
per_iter_end_callback_fn=per_iter_callback_fn,
)
return model
def train(cfg, local_rank, distributed, use_tensorboard=False, logger=None):
arguments = {"iteration": 0}
data_loader = make_data_loader(
cfg,
is_train=True,
is_distributed=distributed,
start_iter=arguments["iteration"],
)
model = build_detection_model(cfg)
device = torch.device(cfg.MODEL.DEVICE)
model.to(device)
if cfg.SOLVER.UNFREEZE_CONV_BODY:
for p in model.backbone.parameters():
p.requires_grad = True
optimizer = make_optimizer(cfg, model)
scheduler = make_lr_scheduler(cfg, optimizer)
if distributed:
model = torch.nn.parallel.DistributedDataParallel(
model,
device_ids=[local_rank],
output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False,
)
output_dir = cfg.OUTPUT_DIR
save_to_disk = get_rank() == 0
checkpointer = DetectronCheckpointer(cfg,
model,
optimizer,
scheduler,
output_dir,
save_to_disk,
logger=logger)
print(cfg.TRAIN.IGNORE_LIST)
extra_checkpoint_data = checkpointer.load(
cfg.MODEL.WEIGHT, ignore_list=cfg.TRAIN.IGNORE_LIST)
arguments.update(extra_checkpoint_data)
if cfg.SOLVER.KEEP_LR:
optimizer = make_optimizer(cfg, model)
scheduler = make_lr_scheduler(cfg, optimizer)
checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD
tensorboard_logdir = cfg.OUTPUT_DIR
tensorboard_exp_name = cfg.TENSORBOARD_EXP_NAME
snapshot = cfg.SOLVER.SNAPSHOT_ITERS
do_train(model,
data_loader,
optimizer,
scheduler,
checkpointer,
device,
checkpoint_period,
arguments,
snapshot,
tensorboard_logdir,
tensorboard_exp_name,
use_tensorboard=use_tensorboard)
return model
def train(cfg,
random_number_generator,
local_rank,
distributed,
args,
fp16=False):
data_loader = make_data_loader(cfg,
is_train=True,
is_distributed=distributed)
# todo sharath - undocument log below after package is updated
# print_mlperf(key=mlperf_log.INPUT_SIZE, value=len(data_loader.dataset))
print_mlperf(key=mlperf_log.INPUT_BATCH_SIZE,
value=cfg.DATALOADER.IMAGES_PER_BATCH_TRAIN)
print_mlperf(key=mlperf_log.BATCH_SIZE_TEST,
value=cfg.DATALOADER.IMAGES_PER_BATCH_TEST)
print_mlperf(key=mlperf_log.INPUT_MEAN_SUBTRACTION,
value=cfg.INPUT.PIXEL_MEAN)
print_mlperf(key=mlperf_log.INPUT_NORMALIZATION_STD,
value=cfg.INPUT.PIXEL_STD)
print_mlperf(key=mlperf_log.INPUT_RESIZE)
print_mlperf(key=mlperf_log.INPUT_RESIZE_ASPECT_PRESERVING)
print_mlperf(key=mlperf_log.MIN_IMAGE_SIZE, value=cfg.INPUT.MIN_SIZE_TRAIN)
print_mlperf(key=mlperf_log.MAX_IMAGE_SIZE, value=cfg.INPUT.MAX_SIZE_TRAIN)
print_mlperf(key=mlperf_log.INPUT_RANDOM_FLIP)
print_mlperf(key=mlperf_log.RANDOM_FLIP_PROBABILITY, value=0.5)
print_mlperf(key=mlperf_log.FG_IOU_THRESHOLD,
value=cfg.MODEL.RPN.FG_IOU_THRESHOLD)
print_mlperf(key=mlperf_log.BG_IOU_THRESHOLD,
value=cfg.MODEL.RPN.BG_IOU_THRESHOLD)
print_mlperf(key=mlperf_log.RPN_PRE_NMS_TOP_N_TRAIN,
value=cfg.MODEL.RPN.PRE_NMS_TOP_N_TRAIN)
print_mlperf(key=mlperf_log.RPN_PRE_NMS_TOP_N_TEST,
value=cfg.MODEL.RPN.PRE_NMS_TOP_N_TEST)
print_mlperf(key=mlperf_log.RPN_POST_NMS_TOP_N_TRAIN,
value=cfg.MODEL.RPN.FPN_POST_NMS_TOP_N_TRAIN)
print_mlperf(key=mlperf_log.RPN_POST_NMS_TOP_N_TEST,
value=cfg.MODEL.RPN.FPN_POST_NMS_TOP_N_TEST)
print_mlperf(key=mlperf_log.ASPECT_RATIOS,
value=cfg.MODEL.RPN.ASPECT_RATIOS)
print_mlperf(key=mlperf_log.BACKBONE, value=cfg.MODEL.BACKBONE.CONV_BODY)
print_mlperf(key=mlperf_log.NMS_THRESHOLD, value=cfg.MODEL.RPN.NMS_THRESH)
model = build_detection_model(cfg)
load_from_pretrained_checkpoint(cfg, model)
device = torch.device(cfg.MODEL.DEVICE)
model.to(device)
optimizer = make_optimizer(cfg, model)
print_mlperf(key=mlperf_log.OPT_NAME, value=mlperf_log.SGD_WITH_MOMENTUM)
print_mlperf(key=mlperf_log.OPT_LR, value=cfg.SOLVER.BASE_LR)
print_mlperf(key=mlperf_log.OPT_MOMENTUM, value=cfg.SOLVER.MOMENTUM)
print_mlperf(key=mlperf_log.OPT_WEIGHT_DECAY,
value=cfg.SOLVER.WEIGHT_DECAY)
scheduler = make_lr_scheduler(cfg, optimizer)
max_iter = cfg.SOLVER.MAX_ITER
if use_apex_amp:
amp_handle = amp.init(enabled=fp16, verbose=False)
if cfg.SOLVER.ACCUMULATE_GRAD:
# also specify number of steps to accumulate over
optimizer = amp_handle.wrap_optimizer(
optimizer, num_loss=cfg.SOLVER.ACCUMULATE_STEPS)
else:
optimizer = amp_handle.wrap_optimizer(optimizer)
if distributed:
if use_apex_ddp:
model = DDP(model, delay_allreduce=True)
else:
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[local_rank], output_device=local_rank)
arguments = {}
arguments["iteration"] = 0
arguments["use_amp"] = use_apex_amp
output_dir = cfg.OUTPUT_DIR
if cfg.SAVE_CHECKPOINTS:
checkpoint_file = cfg.CHECKPOINT
checkpointer = Checkpoint(model, optimizer, scheduler, output_dir,
local_rank)
if checkpoint_file:
extra_checkpoint_data = checkpointer.load(checkpoint_file)
arguments.update(extra_checkpoint_data)
else:
checkpointer = None
do_train(
model,
data_loader,
optimizer,
scheduler,
checkpointer,
max_iter,
device,
distributed,
arguments,
cfg,
args,
random_number_generator,
)
return model
def train(cfg,
local_rank,
distributed,
model_config=None,
use_tensorboard=True):
model = build_detection_model(cfg, model_config)
if get_rank() == 0:
if 'search' in cfg.MODEL.BACKBONE.CONV_BODY:
print('backbone search space:', blocks_key)
else:
print('backbone:', cfg.MODEL.BACKBONE)
if 'search' in cfg.MODEL.ROI_MASK_HEAD.FEATURE_EXTRACTOR or 'search' in cfg.MODEL.SEG_BRANCH.SEGMENT_BRANCH:
print('head search space:', head_ss_keys)
else:
print('head:', cfg.MODEL.ROI_MASK_HEAD.FEATURE_EXTRACTOR,
cfg.MODEL.SEG_BRANCH.SEGMENT_BRANCH)
if 'search' in cfg.MODEL.INTER_MODULE.NAME:
print('inter search space:', inter_ss_keys)
else:
print('inter:', cfg.MODEL.INTER_MODULE.NAME)
print(model)
device = torch.device(cfg.MODEL.DEVICE)
model.to(device)
optimizer, lr_dict = make_optimizer(cfg, model)
if get_rank() == 0:
for item in lr_dict:
print(item)
scheduler = make_lr_scheduler(cfg, optimizer)
# Initialize mixed-precision training
if not ('search' in cfg.MODEL.BACKBONE.CONV_BODY
or 'search' in cfg.MODEL.ROI_MASK_HEAD.FEATURE_EXTRACTOR
or 'search' in cfg.MODEL.SEG_BRANCH.SEGMENT_BRANCH):
use_mixed_precision = cfg.DTYPE == "float16"
amp_opt_level = 'O1' if use_mixed_precision else 'O0'
model, optimizer = amp.initialize(model,
optimizer,
opt_level=amp_opt_level)
if distributed:
model = torch.nn.parallel.DistributedDataParallel(
model,
device_ids=[local_rank],
output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False,
find_unused_parameters=True)
# if 'search' in cfg.MODEL.BACKBONE.CONV_BODY:
# def forward_hook(module: Module, inp: (Tensor,)):
# if module.weight is not None:
# module.weight.requires_grad = True
# if module.bias is not None:
# module.bias.requires_grad = True
# all_modules = (nn.Conv2d, nn.Linear, nn.BatchNorm2d, nn.GroupNorm, ) # group norm更新！！
# for m in model.modules():
# if isinstance(m, all_modules):
# m.register_forward_pre_hook(forward_hook)
arguments = {}
arguments["iteration"] = 0
output_dir = cfg.OUTPUT_DIR
save_to_disk = get_rank() == 0
checkpointer = DetectronCheckpointer(cfg, model, optimizer, scheduler,
output_dir, save_to_disk)
extra_checkpoint_data = checkpointer.load(cfg.MODEL.WEIGHT)
arguments.update(extra_checkpoint_data)
data_loader = make_data_loader(
cfg,
is_train=True,
is_distributed=distributed,
start_iter=arguments["iteration"],
)
test_period = cfg.SOLVER.TEST_PERIOD
if test_period > 0:
data_loader_val = make_data_loader(cfg,
is_train=False,
is_distributed=distributed,
is_for_period=True)
else:
data_loader_val = None
checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD
if use_tensorboard:
meters = TensorboardLogger(cfg=cfg,
log_dir=cfg.TENSORBOARD_EXPERIMENT,
start_iter=arguments['iteration'],
delimiter=" ")
else:
meters = MetricLogger(delimiter=" ")
do_train(
cfg,
model,
data_loader,
data_loader_val,
optimizer,
scheduler,
checkpointer,
device,
checkpoint_period,
test_period,
arguments,
meters,
)
return model
def train(cfg, local_rank, distributed, use_tensorboard=False):
model = build_detection_model(cfg)
device = torch.device(cfg.MODEL.DEVICE)
model.to(device)
optimizer = make_optimizer(cfg, model)
scheduler = make_lr_scheduler(cfg, optimizer)
# Initialize mixed-precision training
use_mixed_precision = cfg.DTYPE == "float16"
amp_opt_level = 'O1' if use_mixed_precision else 'O0'
model, optimizer = amp.initialize(model, optimizer, opt_level=amp_opt_level)
if distributed:
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[local_rank], output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False,
)
arguments = {}
arguments["iteration"] = 0
output_dir = cfg.OUTPUT_DIR
save_to_disk = get_rank() == 0
checkpointer = DetectronCheckpointer(
cfg, model, optimizer, scheduler, output_dir, save_to_disk
)
extra_checkpoint_data = checkpointer.load(cfg.MODEL.WEIGHT)
arguments.update(extra_checkpoint_data)
data_loader = make_data_loader(
cfg,
is_train=True,
is_distributed=distributed,
start_iter=arguments["iteration"],
)
test_period = cfg.SOLVER.TEST_PERIOD
if test_period > 0:
data_loader_val = make_data_loader(cfg, is_train=False, is_distributed=distributed, is_for_period=True)
else:
data_loader_val = None
checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD
if use_tensorboard:
meters = TensorboardLogger(
log_dir=cfg.TENSORBOARD_EXPERIMENT,
stage = 'train',
start_iter=arguments['iteration'],
delimiter=" ")
meters_val = TensorboardLogger(
log_dir=cfg.TENSORBOARD_EXPERIMENT,
stage = 'val',
start_iter=arguments['iteration'],
delimiter=" ")
else:
meters = MetricLogger(delimiter=" ")
meters_val = MetricLogger(delimiter=" ")
do_train(
cfg,
model,
data_loader,
data_loader_val,
optimizer,
scheduler,
checkpointer,
device,
checkpoint_period,
test_period,
arguments,
meters,
meters_val,
)
return model
def train(cfg, local_rank, distributed):
# Model logging
print_mlperf(key=mlperf_log.INPUT_BATCH_SIZE, value=cfg.SOLVER.IMS_PER_BATCH)
print_mlperf(key=mlperf_log.BATCH_SIZE_TEST, value=cfg.TEST.IMS_PER_BATCH)
print_mlperf(key=mlperf_log.INPUT_MEAN_SUBTRACTION, value = cfg.INPUT.PIXEL_MEAN)
print_mlperf(key=mlperf_log.INPUT_NORMALIZATION_STD, value=cfg.INPUT.PIXEL_STD)
print_mlperf(key=mlperf_log.INPUT_RESIZE)
print_mlperf(key=mlperf_log.INPUT_RESIZE_ASPECT_PRESERVING)
print_mlperf(key=mlperf_log.MIN_IMAGE_SIZE, value=cfg.INPUT.MIN_SIZE_TRAIN)
print_mlperf(key=mlperf_log.MAX_IMAGE_SIZE, value=cfg.INPUT.MAX_SIZE_TRAIN)
print_mlperf(key=mlperf_log.INPUT_RANDOM_FLIP)
print_mlperf(key=mlperf_log.RANDOM_FLIP_PROBABILITY, value=0.5)
print_mlperf(key=mlperf_log.FG_IOU_THRESHOLD, value=cfg.MODEL.RPN.FG_IOU_THRESHOLD)
print_mlperf(key=mlperf_log.BG_IOU_THRESHOLD, value=cfg.MODEL.RPN.BG_IOU_THRESHOLD)
print_mlperf(key=mlperf_log.RPN_PRE_NMS_TOP_N_TRAIN, value=cfg.MODEL.RPN.PRE_NMS_TOP_N_TRAIN)
print_mlperf(key=mlperf_log.RPN_PRE_NMS_TOP_N_TEST, value=cfg.MODEL.RPN.PRE_NMS_TOP_N_TEST)
print_mlperf(key=mlperf_log.RPN_POST_NMS_TOP_N_TRAIN, value=cfg.MODEL.RPN.FPN_POST_NMS_TOP_N_TRAIN)
print_mlperf(key=mlperf_log.RPN_POST_NMS_TOP_N_TEST, value=cfg.MODEL.RPN.FPN_POST_NMS_TOP_N_TEST)
print_mlperf(key=mlperf_log.ASPECT_RATIOS, value=cfg.MODEL.RPN.ASPECT_RATIOS)
print_mlperf(key=mlperf_log.BACKBONE, value=cfg.MODEL.BACKBONE.CONV_BODY)
print_mlperf(key=mlperf_log.NMS_THRESHOLD, value=cfg.MODEL.RPN.NMS_THRESH)
# /root/ssy/ssynew/maskrcnn-benchmark/maskrcnn_benchmark/modeling/detector/detectors.py
# building bare mode without doing anthing
model = build_detection_model(cfg)
device = torch.device(cfg.MODEL.DEVICE)
model.to(device)
optimizer = make_optimizer(cfg, model)
# Optimizer logging
print_mlperf(key=mlperf_log.OPT_NAME, value=mlperf_log.SGD_WITH_MOMENTUM)
print_mlperf(key=mlperf_log.OPT_LR, value=cfg.SOLVER.BASE_LR)
print_mlperf(key=mlperf_log.OPT_MOMENTUM, value=cfg.SOLVER.MOMENTUM)
print_mlperf(key=mlperf_log.OPT_WEIGHT_DECAY, value=cfg.SOLVER.WEIGHT_DECAY)
scheduler = make_lr_scheduler(cfg, optimizer)
if distributed:
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[local_rank], output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False,
)
arguments = {}
arguments["iteration"] = 0
output_dir = cfg.OUTPUT_DIR
print("output_dir "+str(output_dir))
save_to_disk = get_rank() == 0
checkpointer = DetectronCheckpointer(
cfg, model, optimizer, scheduler, output_dir, save_to_disk
)
# no such SAVE_CHECKPOINTS
#arguments["save_checkpoints"] = cfg.SAVE_CHECKPOINTS
arguments["save_checkpoints"] = False
extra_checkpoint_data = checkpointer.load(cfg.MODEL.WEIGHT)
arguments.update(extra_checkpoint_data)
data_loader, iters_per_epoch = make_data_loader(
cfg,
is_train=True,
is_distributed=distributed,
start_iter=arguments["iteration"]
)
print("SSY iters_per_epoch "+str(iters_per_epoch))
#print("SSY iters_per_epoch change to 100 ")
#iters_per_epoch = 100
checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD
# set the callback function to evaluate and potentially
# early exit each epoch
# SSY
# I already add PER_EPOCH_EVAL and MIN_BBOX_MAP MIN_SEGM_MAP to ./configs/e2e_mask_rcnn_R_50_FPN_1x.yaml
# but it still can not find it
# so I manually set them here
#if cfg.PER_EPOCH_EVAL:
# per_iter_callback_fn = functools.partial(
# mlperf_test_early_exit,
# iters_per_epoch=iters_per_epoch,
# tester=functools.partial(test, cfg=cfg),
# model=model,
# distributed=distributed,
# min_bbox_map=cfg.MLPERF.MIN_BBOX_MAP,
# min_segm_map=cfg.MLPERF.MIN_SEGM_MAP)
#else:
# per_iter_callback_fn = None
per_iter_callback_fn = functools.partial(
mlperf_test_early_exit,
iters_per_epoch=iters_per_epoch,
# /root/ssy/ssynew/maskrcnn-benchmark/maskrcnn_benchmark/engine/tester.py
tester=functools.partial(test, cfg=cfg),
model=model,
distributed=distributed,
min_bbox_map=0.377,
min_segm_map=0.339)
start_train_time = time.time()
# /root/ssy/ssynew/maskrcnn-benchmark/maskrcnn_benchmark/engine/trainer.py
do_train(
model,
data_loader,
optimizer,
scheduler,
checkpointer,
device,
checkpoint_period,
arguments,
per_iter_start_callback_fn=functools.partial(mlperf_log_epoch_start, iters_per_epoch=iters_per_epoch),
per_iter_end_callback_fn=per_iter_callback_fn,
)
end_train_time = time.time()
total_training_time = end_train_time - start_train_time
print(
"&&&& MLPERF METRIC THROUGHPUT per GPU={:.4f} iterations / s".format((arguments["iteration"] * 1.0) / total_training_time)
)
return model
def train(cfg, local_rank, distributed, fp16, dllogger):
model = build_detection_model(cfg)
device = torch.device(cfg.MODEL.DEVICE)
model.to(device)
optimizer = make_optimizer(cfg, model)
scheduler = make_lr_scheduler(cfg, optimizer)
use_amp = False
if fp16:
use_amp = True
else:
use_amp = cfg.DTYPE == "float16"
if distributed:
if use_apex_ddp:
model = DDP(model, delay_allreduce=True)
else:
model = torch.nn.parallel.DistributedDataParallel(
model,
device_ids=[local_rank],
output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False,
)
arguments = {}
arguments["iteration"] = 0
output_dir = cfg.OUTPUT_DIR
save_to_disk = get_rank() == 0
checkpointer = DetectronCheckpointer(cfg, model, optimizer, scheduler,
output_dir, save_to_disk)
extra_checkpoint_data = checkpointer.load(cfg.MODEL.WEIGHT)
arguments.update(extra_checkpoint_data)
data_loader, iters_per_epoch = make_data_loader(
cfg,
is_train=True,
is_distributed=distributed,
start_iter=arguments["iteration"],
)
checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD
# set the callback function to evaluate and potentially
# early exit each epoch
if cfg.PER_EPOCH_EVAL:
per_iter_callback_fn = functools.partial(
mlperf_test_early_exit,
iters_per_epoch=iters_per_epoch,
tester=functools.partial(test, cfg=cfg, dllogger=dllogger),
model=model,
distributed=distributed,
min_bbox_map=cfg.MIN_BBOX_MAP,
min_segm_map=cfg.MIN_MASK_MAP)
else:
per_iter_callback_fn = None
do_train(
model,
data_loader,
optimizer,
scheduler,
checkpointer,
device,
checkpoint_period,
arguments,
use_amp,
cfg,
dllogger,
per_iter_end_callback_fn=per_iter_callback_fn,
)
return model, iters_per_epoch
def train(cfg, local_rank, distributed):
# original = torch.load('/home/zoey/nas/zoey/github/maskrcnn-benchmark/checkpoints/renderpy150000/model_0025000.pth')
#
# new = {"model": original["model"]}
# torch.save(new, '/home/zoey/nas/zoey/github/maskrcnn-benchmark/checkpoints/finetune/model_0000000.pth')
# Initialize mixed-precision training
use_mixed_precision = cfg.DTYPE == "float16"
amp_opt_level = 'O1' if use_mixed_precision else 'O0'
model = build_detection_model(cfg)
device = torch.device(cfg.MODEL.DEVICE)
model.to(device)
optimizer = make_optimizer(cfg, model)
scheduler = make_lr_scheduler(cfg, optimizer)
model, optimizer = amp.initialize(model,
optimizer,
opt_level=amp_opt_level)
# if cfg.MODEL.DEPTH_ON == True:
# model_depth = build_detection_model(cfg)
# device = torch.device(cfg.MODEL.DEVICE)
# model_depth.to(device)
# optimizer_depth = make_optimizer(cfg, model_depth)
# scheduler_depth = make_lr_scheduler(cfg, optimizer_depth)
# model_depth, optimizer_depth = amp.initialize(model_depth, optimizer_depth, opt_level=amp_opt_level)
if distributed:
model = torch.nn.parallel.DistributedDataParallel(
model,
device_ids=[local_rank],
output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False,
)
arguments = {}
arguments["iteration"] = 0
output_dir = cfg.OUTPUT_DIR
save_to_disk = get_rank() == 0
data_loader = make_data_loader(
cfg,
is_train=True,
is_distributed=distributed,
start_iter=arguments["iteration"],
)
checkpointer = DetectronCheckpointer(cfg,
model,
optimizer,
scheduler,
output_dir,
save_to_disk,
logger=None,
isrgb=True,
isdepth=True)
extra_checkpoint_data = checkpointer.load(cfg.MODEL.WEIGHT)
# extra_checkpoint_data = checkpointer.load('/home/zoey/nas/zoey/github/maskrcnn-benchmark/checkpoints/renderpy150000/model_0025000.pth')
arguments.update(extra_checkpoint_data)
checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD
do_train(model, data_loader, optimizer, scheduler, checkpointer, device,
checkpoint_period, arguments)
return model
def train(cfg, local_rank, distributed, test_while_training):
model = build_detection_model(cfg)
device = torch.device(cfg.MODEL.DEVICE)
model.to(device)
optimizer = make_optimizer(cfg, model)
# ipdb.set_trace()
scheduler = make_lr_scheduler(cfg, optimizer)
logger = logging.getLogger("train_main_script")
arguments = {}
arguments["iteration"] = 0
arguments['start_save_ckpt'] = cfg.SOLVER.START_SAVE_CHECKPOINT
## define the output dir
output_dir = cfg.OUTPUT_DIR
checkpoint_output_dir = os.path.join(output_dir, 'checkpoints')
save_to_disk = get_rank() == 0
checkpointer = DetectronCheckpointer(cfg, model, optimizer, scheduler,
checkpoint_output_dir, save_to_disk)
arguments['instance_id'] = output_dir.split('/')[-1]
if len(cfg.MODEL.USE_DET_PRETRAIN) > 0:
checkpointer.load_weight_partially(cfg.MODEL.USE_DET_PRETRAIN)
elif len(cfg.MODEL.WEIGHT) > 0:
extra_checkpoint_data, ckpt_name = checkpointer.load(cfg.MODEL.WEIGHT)
arguments.update(extra_checkpoint_data)
# logger.info(str(model))
if distributed:
model = torch.nn.parallel.DistributedDataParallel(
model,
device_ids=[local_rank],
output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False,
find_unused_parameters=True,
)
data_loader = make_data_loader(
cfg,
is_train=True,
is_distributed=distributed,
start_iter=arguments["iteration"],
)
if test_while_training:
logger.info("test_while_training on ")
val_data_loader = make_data_loader(cfg,
is_train=False,
is_distributed=distributed)
else:
logger.info("test_while_training off ")
val_data_loader = None
checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD
do_train(
model,
data_loader,
optimizer,
scheduler,
checkpointer,
device,
checkpoint_period,
arguments,
distributed,
val_data_loader,
)
return model
def train(cfg, args, DatasetCatalog=None):
if len(cfg.DATASETS.TRAIN) == 0 or not args.train:
return None
local_rank = args.local_rank
distributed = args.distributed
model = build_detection_model(cfg)
# for key, value in model.named_parameters():
# print(key, value.requires_grad)
if hasattr(args, 'train_last_layer'):
if args.train_last_layer:
listofkeys = [
'cls_score.bias', 'cls_score.weight', 'bbox_pred.bias',
'bbox_pred.weight', 'mask_fcn_logits.bias',
'mask_fcn_logits.weight'
]
for key, value in model.named_parameters():
value.requires_grad = False
for k in listofkeys:
if k in key:
value.requires_grad = True
# for key, value in model.named_parameters():
# print(key, value.requires_grad)
device = torch.device(cfg.MODEL.DEVICE)
model.to(device)
optimizer = make_optimizer(cfg, model)
scheduler = make_lr_scheduler(cfg, optimizer)
# Initialize mixed-precision training
if cfg.MODEL.DEVICE == 'cuda':
use_mixed_precision = cfg.DTYPE == "float16"
amp_opt_level = 'O1' if use_mixed_precision else 'O0'
model, optimizer = amp.initialize(model,
optimizer,
opt_level=amp_opt_level)
if distributed:
model = torch.nn.parallel.DistributedDataParallel(
model,
device_ids=[local_rank],
output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False,
)
save_to_disk = get_rank() == 0
checkpointer = DetectronCheckpointer(cfg, model, optimizer, scheduler,
cfg.OUTPUT_DIR, save_to_disk)
extra_checkpoint_data = checkpointer.load(
cfg.MODEL.WEIGHT,
force_load_external_checkpoint=False,
copy_weight_from_head_box=args.copy_weight_from_head_box)
arguments = {}
arguments.update(extra_checkpoint_data)
data_loader = make_data_loader(
cfg,
args,
is_train=True,
is_distributed=distributed,
start_iter=arguments["iteration"],
DatasetCatalog=DatasetCatalog,
)
if cfg.SOLVER.TEST_PERIOD > 0:
data_loader_val = make_data_loader(
cfg,
args,
is_train=False,
is_distributed=distributed,
is_for_period=True,
start_iter=arguments["iteration"],
DatasetCatalog=DatasetCatalog,
)
else:
data_loader_val = None
do_train(
model,
cfg,
data_loader,
data_loader_val,
optimizer,
scheduler,
checkpointer,
device,
cfg.SOLVER.CHECKPOINT_PERIOD,
cfg.SOLVER.TEST_PERIOD,
arguments,
cfg.OUTPUT_DIR,
args.visualize_loss,
args.vis_title,
args.iters_per_epoch,
)
return model
def train(cfg, local_rank, distributed):
model = build_detection_model(cfg)
device = torch.device(cfg.MODEL.DEVICE)
model.to(device)
# load a reid model
reid_model = build_reid_model(cfg)
reid_model.to(device)
print('#######loading from {}#######'.format(cfg.REID.TEST.WEIGHT))
f = torch.load(
cfg.REID.TEST.WEIGHT,
map_location=torch.device("cpu"),
)
if 'model' in f:
load_state_dict(reid_model, f['model'])
else:
reid_model.load_state_dict(f, strict=False)
optimizer = make_optimizer(cfg, model)
scheduler = make_lr_scheduler(cfg, optimizer)
# model, optimizer = amp.initialize(model, optimizer,
# opt_level="O0"
# )
if distributed:
model = DDP(model, delay_allreduce=True)
# model = torch.nn.parallel.DistributedDataParallel(
# model, device_ids=[local_rank], output_device=local_rank,
# # this should be removed if we update BatchNorm stats
# broadcast_buffers=False,
# )
arguments = {}
arguments["iteration"] = 0
num_gpus = int(
os.environ["WORLD_SIZE"]) if "WORLD_SIZE" in os.environ else 1
output_dir = os.path.join(
cfg.OUTPUT_DIR, cfg.SUBDIR,
'GPU' + str(num_gpus) + '_LR' + str(cfg.SOLVER.BASE_LR))
save_to_disk = get_rank() == 0
checkpointer = DetectronCheckpointer(cfg, model, optimizer, scheduler,
output_dir, save_to_disk)
extra_checkpoint_data = checkpointer.load(cfg.MODEL.WEIGHT)
arguments.update(extra_checkpoint_data)
data_loader = make_data_loader(
cfg,
is_train=True,
is_distributed=distributed,
start_iter=arguments["iteration"],
)
checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD
do_train(
reid_model,
model,
data_loader,
optimizer,
scheduler,
checkpointer,
device,
checkpoint_period,
arguments,
)
return model
def train(cfg, local_rank, distributed, random_number_generator=None):
# Model logging
mlperf_print(key=constants.GLOBAL_BATCH_SIZE,
value=cfg.SOLVER.IMS_PER_BATCH)
mlperf_print(key=constants.NUM_IMAGE_CANDIDATES,
value=cfg.MODEL.RPN.FPN_POST_NMS_TOP_N_TRAIN)
model = build_detection_model(cfg)
device = torch.device(cfg.MODEL.DEVICE)
model.to(device)
optimizer = make_optimizer(cfg, model)
# Optimizer logging
# mlperf_print(key=constants.OPT_NAME, value="sgd_with_momentum")
mlperf_print(key=constants.OPT_BASE_LR, value=cfg.SOLVER.BASE_LR)
mlperf_print(key=constants.OPT_LR_WARMUP_STEPS,
value=cfg.SOLVER.WARMUP_ITERS)
mlperf_print(key=constants.OPT_LR_WARMUP_FACTOR,
value=cfg.SOLVER.WARMUP_FACTOR)
scheduler = make_lr_scheduler(cfg, optimizer)
# disable the garbage collection
gc.disable()
# Initialize mixed-precision training
use_mixed_precision = cfg.DTYPE == "float16"
# amp_handle = amp.init(enabled=use_mixed_precision, verbose=cfg.AMP_VERBOSE)
amp_opt_level = 'O1' if use_mixed_precision else 'O0'
model, optimizer = amp.initialize(
model, optimizer,
opt_level=amp_opt_level) # , verbose=cfg.AMP_VERBOSE)
if distributed:
model = DDP(model, delay_allreduce=True)
arguments = {}
arguments["iteration"] = 0
arguments["nhwc"] = cfg.NHWC
output_dir = cfg.OUTPUT_DIR
save_to_disk = get_rank() == 0
checkpointer = DetectronCheckpointer(cfg, model, optimizer, scheduler,
output_dir, save_to_disk)
arguments["save_checkpoints"] = cfg.SAVE_CHECKPOINTS
extra_checkpoint_data = checkpointer.load(cfg.MODEL.WEIGHT, cfg.NHWC)
arguments.update(extra_checkpoint_data)
# At this point we've loaded relevant checkpoint(s) and can now cast
# FrozenBatchNorm2d layers to half() if necessary.
# This allows us to move parameter casting logic out of the BN code itself,
# which was preventing us from annotating bn.forward with @script_method, which
# was preventing the cross-module fusion of BN with ReLU / Add-ReLU.
if use_mixed_precision:
model = cast_frozen_bn_to_half(model)
mlperf_print(key=constants.INIT_STOP, sync=True)
mlperf_print(key=constants.RUN_START, sync=True)
barrier()
data_loader, iters_per_epoch = make_data_loader(
cfg,
is_train=True,
is_distributed=distributed,
start_iter=arguments["iteration"],
random_number_generator=random_number_generator,
)
checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD
# set the callback function to evaluate and potentially
# early exit each epoch
if cfg.PER_EPOCH_EVAL:
per_iter_callback_fn = functools.partial(
mlperf_test_early_exit,
iters_per_epoch=iters_per_epoch,
tester=functools.partial(test, cfg=cfg),
model=model,
distributed=distributed,
min_bbox_map=cfg.MLPERF.MIN_BBOX_MAP,
min_segm_map=cfg.MLPERF.MIN_SEGM_MAP)
else:
per_iter_callback_fn = None
start_train_time = time.time()
success = do_train(
model,
data_loader,
optimizer,
scheduler,
checkpointer,
device,
checkpoint_period,
arguments,
cfg.DISABLE_REDUCED_LOGGING,
per_iter_start_callback_fn=functools.partial(
mlperf_log_epoch_start, iters_per_epoch=iters_per_epoch),
per_iter_end_callback_fn=per_iter_callback_fn,
)
end_train_time = time.time()
total_training_time = end_train_time - start_train_time
print("&&&& MLPERF METRIC THROUGHPUT={:.4f} iterations / s".format(
(arguments["iteration"] * cfg.SOLVER.IMS_PER_BATCH) /
total_training_time))
return model, success
def train(cfg, local_rank, distributed):
model = build_detection_model(cfg)
device = torch.device(cfg.MODEL.DEVICE)
model.to(device)
optimizer = make_optimizer(cfg, model)
scheduler = make_lr_scheduler(cfg, optimizer)
if distributed:
model = torch.nn.parallel.deprecated.DistributedDataParallel(
model,
device_ids=[local_rank],
output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False,
)
logger = logging.getLogger("Training")
with tools.TimerBlock("Loading Experimental setups", logger) as block:
exp_name = cfg.EXP.NAME
output_dir = tools.get_exp_output_dir(exp_name, cfg.OUTPUT_DIR)
checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD
validation_period = cfg.SOLVER.VALIDATION_PERIOD
with tools.TimerBlock("Loading Checkpoints...", logger) as block:
arguments = {}
save_to_disk = local_rank == 0
checkpointer = Checkpointer(
model,
save_dir=output_dir,
save_to_disk=save_to_disk,
num_class=cfg.MODEL.ROI_BOX_HEAD.NUM_CLASSES,
)
extra_checkpoint_data = checkpointer.load(cfg.MODEL.WEIGHT)
arguments.update(extra_checkpoint_data)
arguments["iteration"] = 0
with tools.TimerBlock("Initializing DAVIS Datasets", logger) as block:
logger.info("Loading training set...")
data_loader_train = make_data_loader(
cfg,
is_train=True,
is_distributed=distributed,
start_iter=arguments["iteration"],
)
logger.info("Loading valid set...")
data_loaders_valid = make_data_loader(
cfg,
is_train=False,
is_distributed=distributed,
)
do_train(
model,
data_loader_train,
data_loaders_valid[0],
optimizer,
scheduler,
checkpointer,
device,
checkpoint_period,
validation_period,
arguments,
exp_name,
)
return model
def train(cfg, local_rank, distributed):
model_det = build_detection_model(cfg)
model_G = model_det.backbone
# model_G = copy.deepcopy(model_det).backbone
# model_D = define_D(256, 64, which_model_netD='det', n_layers_D=5)
model_D = DEBUG_DESC(256, 64, n_layers=5, use_sigmoid=True)
models = [model_det, model_G, model_D]
device = torch.device(cfg.MODEL.DEVICE)
for model in models:
model.to(device)
optimizer_det = make_optimizer(cfg, model_det)
scheduler_det = make_lr_scheduler(cfg, optimizer_det)
# optimizer_G = make_optimizer_Adam(cfg, model_det.backbone)
optimizer_G = make_optimizer(cfg, model_det.backbone)
scheduler_G = None
optimizer_D = make_optimizer_Adam(cfg, model_D)
# optimizer_D = make_optimizer(cfg, model_D)
scheduler_D = None
optimizers = [optimizer_det, optimizer_D, optimizer_G]
schedulers = [scheduler_det, scheduler_D, scheduler_G]
if distributed:
for i, model in enumerate(models):
models[i] = torch.nn.parallel.deprecated.DistributedDataParallel(
models[i], device_ids=[local_rank], output_device=local_rank,
# this should be removed if we update BatchNorm stats
broadcast_buffers=False,
)
arguments = {}
manual_iter = 0
print("WARNING! MANUAL ITERATION IS", manual_iter)
arguments["iteration"] = manual_iter
output_dir = cfg.OUTPUT_DIR
save_to_disk = get_rank() == 0
checkpointer_det = DetectronCheckpointer(
cfg, model_det, optimizer_det, scheduler_det, output_dir, save_to_disk
)
checkpointer_D = Checkpointer(
model_D, optimizer_D, None, output_dir, save_to_disk
)
checkpointers = [checkpointer_det, checkpointer_D]
print('WARNING! REMOVED "iteration" from train_net.py')
extra_checkpoint_data = checkpointer_det.load(cfg.MODEL.WEIGHT)
extra_checkpoint_data = {"iteration" : 0}
arguments.update(extra_checkpoint_data)
data_loaders = make_data_loader(
cfg,
is_train=True,
is_distributed=distributed,
start_iter=arguments["iteration"],
)
checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD
data_loaders_val = make_data_loader(cfg, is_train=False, is_distributed=distributed)
arguments["need_adapt"] = False
arguments["need_train_A"] = False
arguments["need_train_B"] = True
do_train(
models,
data_loaders,
data_loaders_val,
optimizers,
schedulers,
checkpointers,
device,
checkpoint_period,
arguments,
)
return model
