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# ==============================================================================
# Copyright (c) 2022 The PersFormer Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
import os
import random
import logging
import subprocess
import numpy as np
logging.basicConfig(level=logging.INFO,
format='%(asctime)s %(levelname)-8s [%(filename)s:%(lineno)d] %(message)s',)
import torch
import torch.nn as nn
import torch.distributed as dist
import torch.multiprocessing as mp
from torch.nn import DataParallel as DP
from torch.utils.data import Dataset, DataLoader
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.utils.data.distributed import DistributedSampler as DS
from core.raft_stereo import RAFTStereo
from core.raft_stereo_disp import RAFTStereoDisp
from core.raft_stereo_mast3r import RAFTStereoMast3r
from core.raft_stereo_depthany import RAFTStereoDepthAny
from core.raft_stereo_noctx import RAFTStereoNoCTX
from core.raft_stereo_depthfusion import RAFTStereoDepthFusion
from core.raft_stereo_depthbeta import RAFTStereoDepthBeta
from core.raft_stereo_depthbeta_nolbp import RAFTStereoDepthBetaNoLBP
from core.raft_stereo_depthmatch import RAFTStereoDepthMatch
from core.raft_stereo_depthbeta_refine import RAFTStereoDepthBetaRefine
from core.raft_stereo_depth_postfusion import RAFTStereoDepthPostFusion
from core.raft_stereo_metric3d import RAFTStereoMetric3D
def setup_distributed(args):
args.rank = int(os.getenv("RANK"))
args.local_rank = int(os.getenv("LOCAL_RANK"))
args.world_size = int(os.getenv("WORLD_SIZE"))
# print("-"*10, "local_rank: {}, world_size:{}".format(args.local_rank, args.world_size),
# " - {}, {}".format(dist.get_rank(), dist.get_world_size())) # they result in the same value
dist.init_process_group(backend='nccl')
torch.cuda.set_device(args.local_rank)
torch.set_printoptions(precision=10)
def ddp_init(args):
if 'WORLD_SIZE' in os.environ:
args.distributed = int(os.environ['WORLD_SIZE']) >= 1
if args.distributed:
setup_distributed(args)
# deterministic
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
torch.manual_seed(args.local_rank)
np.random.seed(args.local_rank)
random.seed(args.local_rank)
print("complete initialization of local_rank:{}".format(args.local_rank))
def ddp_close():
dist.destroy_process_group()
def to_python_float(t):
if hasattr(t, 'item'):
return t.item()
else:
return t[0]
def reduce_tensor(tensor, world_size):
rt = tensor.clone()
dist.all_reduce(rt, op=dist.ReduceOp.SUM)
rt /= world_size
return rt
def reduce_tensors(*tensors, world_size):
return [reduce_tensor(tensor, world_size) for tensor in tensors]
def get_loader(dataset, args):
"""
create dataset from ground-truth
return a batch sampler based ont the dataset
"""
if args.distributed:
if args.local_rank == 0:
print('use distributed sampler')
data_sampler = DS(dataset, shuffle=True, drop_last=True)
data_loader = DataLoader(dataset,
batch_size=args.batch_size,
sampler=data_sampler,
num_workers=args.num_workers,
pin_memory=True,
persistent_workers=True)
else:
if args.local_rank == 0:
print("use default sampler")
# data_sampler = torch.utils.data.sampler.SubsetRandomSampler(sample_idx)
# data_loader = DataLoader(transformed_dataset,
# batch_size=args.batch_size, sampler=data_sampler,
# num_workers=args.nworkers, pin_memory=True,
# persistent_workers=True,
# worker_init_fn=seed_worker,
# generator=g)
train_loader = DataLoader(train_dataset,
batch_size=args.batch_size,
pin_memory=True, shuffle=True,
num_workers=args.num_workers,
drop_last=True)
return data_loader
NODE_RANK = os.getenv('NODE_RANK', default=0)
LOCAL_RANK = os.getenv("LOCAL_RANK", default=0)
def get_model_ddp(args):
if args.model_name.lower() == "raftstereo":
model = nn.SyncBatchNorm.convert_sync_batchnorm(RAFTStereo(args))
elif args.model_name.lower() == "raftstereodisp":
model = nn.SyncBatchNorm.convert_sync_batchnorm(RAFTStereoDisp(args))
elif args.model_name.lower() == "raftstereomast3r":
model = nn.SyncBatchNorm.convert_sync_batchnorm(RAFTStereoMast3r(args))
elif args.model_name.lower() == "raftstereodepthany":
model = nn.SyncBatchNorm.convert_sync_batchnorm(RAFTStereoDepthAny(args))
elif args.model_name.lower() == "raftstereodepthfusion":
model = nn.SyncBatchNorm.convert_sync_batchnorm(RAFTStereoDepthFusion(args))
elif args.model_name.lower() == "RAFTStereoDepthBeta".lower():
model = nn.SyncBatchNorm.convert_sync_batchnorm(RAFTStereoDepthBeta(args))
elif args.model_name.lower() == "RAFTStereoDepthBetaNoLBP".lower():
model = nn.SyncBatchNorm.convert_sync_batchnorm(RAFTStereoDepthBetaNoLBP(args))
elif args.model_name.lower() == "RAFTStereoDepthMatch".lower():
model = nn.SyncBatchNorm.convert_sync_batchnorm(RAFTStereoDepthMatch(args))
elif args.model_name.lower() == "RAFTStereoDepthBetaRefine".lower():
model = nn.SyncBatchNorm.convert_sync_batchnorm(RAFTStereoDepthBetaRefine(args))
elif args.model_name.lower() == "RAFTStereoDepthPostFusion".lower():
model = nn.SyncBatchNorm.convert_sync_batchnorm(RAFTStereoDepthPostFusion(args))
elif args.model_name.lower() == "RAFTStereoMetric3D".lower():
model = nn.SyncBatchNorm.convert_sync_batchnorm(RAFTStereoMetric3D(args))
else :
raise Exception("No such model: {}".format(args.model_name))
device = torch.device("cuda", args.local_rank)
model = model.to(device)
if args.restore_ckpt is not None:
assert args.restore_ckpt.endswith(".pth") or args.restore_ckpt.endswith(".tar")
if args.local_rank==0 :
logging.info("Loading checkpoint from {} ...".format(args.restore_ckpt))
checkpoint = torch.load(args.restore_ckpt)
new_state_dict = {}
for key, value in checkpoint.items():
new_key = key.replace('module.', '') # 去掉 'module.' 前缀
# if key.find("refinement.conf_estimate") != -1:
# continue
new_state_dict[new_key] = value
# model.load_state_dict(new_state_dict, strict=True)
model.load_state_dict(new_state_dict, strict=False)
if args.local_rank==0 :
logging.info(f"Done loading checkpoint")
dist.barrier()
# DDP setting
if args.distributed:
model = DDP(model, device_ids=[args.local_rank], output_device=args.local_rank,
find_unused_parameters=True)
else:
model = DP(RAFTStereo(args))
return model
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