import torchvision.transforms as transforms
from tensorboardX import SummaryWriter
from torch.utils.data import DataLoader # new add
import util.utils as util
from config.default_config import DefaultConfig
from config.resnet18_sunrgbd_config import RESNET18_SUNRGBD_CONFIG
from data import segmentation_dataset
from model.trecg_model import TRecgNet
from model.trecg_model_multimodal import TRecgNet_MULTIMODAL
import torch.backends.cudnn as cudnn
import sys
from datetime import datetime
# alpha = sys.argv[1]
cfg = DefaultConfig()
args = {
'resnet18': RESNET18_SUNRGBD_CONFIG().args(),
}
# args for different backbones
cfg.parse(args['resnet18'])
if len(sys.argv) > 1:
device_ids = torch.cuda.device_count()
print('device_ids:', device_ids)
arg_model, arg_target, arg_alpha, arg_loss, arg_task = sys.argv[1:]
# print(arg_model, arg_norm, arg_alpha)
cfg.MODEL = arg_model
cfg.TARGET_MODAL = arg_target
def main():
global cfg
cfg = DefaultConfig()
args = {
'resnet18': RESNET18_SUNRGBD_CONFIG().args(),
}
# args for different backbones
cfg.parse(args['resnet18'])
cfg.LR = 1e-4
cfg.EPOCHS = 200
# print('cfg.EPOCHS:',cfg.EPOCHS)
os.environ["CUDA_VISIBLE_DEVICES"] = '0,1'
# normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
# std=[0.229, 0.224, 0.225])
# dataset.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
# data
train_dataset = dataset.SingleDataset(
cfg,
data_dir=cfg.DATA_DIR_TRAIN,
transform=transforms.Compose([
dataset.Resize((cfg.LOAD_SIZE, cfg.LOAD_SIZE)),
dataset.RandomCrop((cfg.FINE_SIZE, cfg.FINE_SIZE)),
dataset.RandomHorizontalFlip(),
dataset.ToTensor(),
dataset.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
]))
val_dataset = dataset.SingleDataset(
cfg,
data_dir=cfg.DATA_DIR_VAL,
transform=transforms.Compose([
dataset.Resize((cfg.LOAD_SIZE, cfg.LOAD_SIZE)),
dataset.CenterCrop((cfg.FINE_SIZE, cfg.FINE_SIZE)),
dataset.ToTensor(),
dataset.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
]))
# train_loader = torch.utils.data.DataLoader(
# train_dataset, batch_size=cfg.BATCH_SIZE, shuffle=True,
# num_workers=4, pin_memory=True, sampler=None)
# val_loader = torch.utils.data.DataLoader(
# val_dataset,batch_size=cfg.BATCH_SIZE, shuffle=False,
# num_workers=4, pin_memory=True)
train_loader = DataProvider(cfg,
dataset=train_dataset,
batch_size=20,
shuffle=True)
val_loader = DataProvider(cfg,
dataset=val_dataset,
batch_size=5,
shuffle=False)
run_id = random.randint(1, 100000)
summary_dir = '/home/lzy/summary/generateDepth/' + 'finetuning_nofix_' + str(
run_id)
if not os.path.exists(summary_dir):
os.mkdir(summary_dir)
writer = SummaryWriter(summary_dir)
if cfg.GENERATE_Depth_DATA:
print('GENERATE_Depth_DATA model set')
cfg_generate = copy.deepcopy(cfg)
cfg_generate.CHECKPOINTS_DIR = '/home/lzy/generateDepth/checkpoints/best_AtoB/trecg_AtoB_best.pth'
cfg_generate.GENERATE_Depth_DATA = False
cfg_generate.NO_UPSAMPLE = False
checkpoint = torch.load(cfg_generate.CHECKPOINTS_DIR)
model = define_TrecgNet(cfg_generate, upsample=True, generate=True)
load_checkpoint_depth(model,
cfg_generate.CHECKPOINTS_DIR,
checkpoint,
data_para=True)
generate_model = torch.nn.DataParallel(model).cuda()
generate_model.eval()
model = ReD_Model(cfg)
policies = model.get_optim_policies()
model = torch.nn.DataParallel(model).cuda()
criterion = nn.CrossEntropyLoss().cuda()
best_mean = 0
# optimizer= torch.optim.Adam(policies, cfg.LR,
# weight_decay=cfg.WEIGHT_DECAY)
optimizer = torch.optim.SGD(policies,
cfg.LR,
momentum=cfg.MOMENTUM,
weight_decay=cfg.WEIGHT_DECAY)
for epoch in range(cfg.START_EPOCH, cfg.EPOCHS + 1):
adjust_learning_rate(optimizer, epoch)
# mean_acc=validate(val_loader,model,criterion,generate_model,epoch,writer)
train(train_loader, model, criterion, generate_model, optimizer, epoch,
writer)
mean_acc = validate(val_loader, model, criterion, generate_model,
epoch, writer)
if mean_acc > best_mean:
best_mean = mean_acc
print('best mean accuracy:', best_mean)
else:
print('best mean accuracy:', best_mean)
writer.add_scalar('mean_acc_color', mean_acc, global_step=epoch)
writer.add_scalar('best_meanacc_color', best_mean, global_step=epoch)
writer.close()
def main():
global cfg, global_all_step
global_all_step = 0
cfg = DefaultConfig()
args = {
'resnet18': RESNET18_SUNRGBD_CONFIG().args(),
}
# Setting random seed
# if cfg.MANUAL_SEED is None:
# cfg.MANUAL_SEED = random.randint(1, 10000)
# random.seed(cfg.MANUAL_SEED)
# torch.manual_seed(cfg.MANUAL_SEED)
# args for different backbones
cfg.parse(args['resnet18'])
run_id = random.randint(1, 100000)
summary_dir = '/home/lzy/summary/generateDepth/' + 'train_depth_1e-3_0909_' + str(
run_id)
if not os.path.exists(summary_dir):
os.mkdir(summary_dir)
writer = SummaryWriter(summary_dir)
cfg.LR = 0.001
os.environ["CUDA_VISIBLE_DEVICES"] = '0,1,2'
device_ids = torch.cuda.device_count()
print('device_ids:', device_ids)
# project_name = reduce(lambda x, y: str(x) + '/' + str(y), os.path.realpath(__file__).split(os.sep)[:-1])
# util.mkdir('logs')
# normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
# std=[0.229, 0.224, 0.225])
# normalize=transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
# data
train_dataset = dataset.SingleDataset(
cfg,
data_dir=cfg.DATA_DIR_TRAIN,
transform=transforms.Compose([
dataset.Resize((cfg.LOAD_SIZE, cfg.LOAD_SIZE)),
dataset.RandomCrop((cfg.FINE_SIZE, cfg.FINE_SIZE)),
dataset.RandomHorizontalFlip(),
# dataset.RandomRotate(),
# dataset.RandomFlip(),
# dataset.PILBrightness(0.4),
# dataset.PILContrast(0.4),
# dataset.PILColorBalance(0.4),
# dataset.PILSharpness(0.4),
dataset.ToTensor(),
dataset.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
]))
val_dataset = dataset.SingleDataset(
cfg,
data_dir=cfg.DATA_DIR_VAL,
transform=transforms.Compose([
dataset.Resize((cfg.LOAD_SIZE, cfg.LOAD_SIZE)),
dataset.CenterCrop((cfg.FINE_SIZE, cfg.FINE_SIZE)),
dataset.ToTensor(),
dataset.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
]))
# train_loader = torch.utils.data.DataLoader(
# train_dataset, batch_size=cfg.BATCH_SIZE, shuffle=True,
# num_workers=4, pin_memory=True, sampler=None)
# val_loader = torch.utils.data.DataLoader(
# val_dataset,batch_size=16, shuffle=False,
# num_workers=4, pin_memory=True)
train_loader = DataProvider(cfg,
dataset=train_dataset,
batch_size=150,
shuffle=True)
val_loader = DataProvider(cfg,
dataset=val_dataset,
batch_size=120,
shuffle=False)
# class weights
# num_classes_train = list(Counter([i[1] for i in train_loader.dataset.imgs]).values())
# cfg.CLASS_WEIGHTS_TRAIN = torch.FloatTensor(num_classes_train)
# writer = SummaryWriter(log_dir=cfg.LOG_PATH) # tensorboard
# net_classification_1=resnet50(pretrained=True)
# net_classification_2=resnet50(pretrained=True)
net_classification_2 = models.__dict__['resnet50'](num_classes=365)
# net_classification_2=torchvision.models.resnext101_32x8d(pretrained=True, progress=True)
# net_classification_2 = models.__dict__['resnet18'](num_classes=365)
# net_classification_2=torch.hub.load('facebookresearch/WSL-Images', 'resnext101_32x16d_wsl')
net_classification_2.fc = nn.Linear(2048, 19)
# net_classification_2=torch.hub.load('facebookresearch/WSL-Images', 'resnext101_32x16d_wsl')
# for param in net_classification_1.parameters():
# param.requires_grad = False
# for param in net_classification_2.parameters():
# param.requires_grad = True
# net_classification_1.fc = nn.Sequential(nn.Dropout(p=0.5),nn.Linear(2048, 1024),nn.LeakyReLU(inplace=True),nn.Linear(1024,67))
# net_classification_2.fc = nn.Sequential(nn.Dropout(p=0.5),nn.Linear(2048,1024),nn.LeakyReLU(inplace=True),nn.Linear(1024,67))
# net_classification_1.load_state_dict(torch.load("./bestmodel/best_model_resnext_16d_2048_1024_dropout_0.5_b.pkl"))、
# net_classification_2.load_state_dict(torch.load("./bestmodel/best_model_resnext_16d_2048_1024_dropout_0.5_b.pkl"))
# net_classification_2
load_path = "resnet50_Depth_sunrgbd_best_0909_5e-4_.pth.tar"
checkpoint = torch.load(load_path,
map_location=lambda storage, loc: storage)
state_dict = {
str.replace(k, 'module.', ''): v
for k, v in checkpoint['state_dict'].items()
}
best_mean_depth = checkpoint['best_mean_2']
print("load sunrgbd dataset:", best_mean_depth)
net_classification_2.load_state_dict(state_dict)
# print(net_classification_1)
# num_ftrs = net_classification_1.fc.in_features
# net_classification_1.fc = nn.Linear(num_ftrs, cfg.NUM_CLASSES)
# num_ftrs = net_classification_2.fc.in_features
# net_classification_2.fc = nn.Linear(num_ftrs, cfg.NUM_CLASSES)
# net_classification_2.fc = nn.Sequential(nn.Dropout(p=0.5),nn.Linear(2048,1024),nn.LeakyReLU(inplace=True),nn.Linear(1024,67))
net_classification_2.fc = nn.Linear(2048, 67)
init_weights(net_classification_2.fc, 'normal')
print(net_classification_2)
# net_classification_1.cuda()
net_classification_2.cuda()
cudnn.benchmark = True
if cfg.GENERATE_Depth_DATA:
print('GENERATE_Depth_DATA model set')
cfg_generate = copy.deepcopy(cfg)
cfg_generate.CHECKPOINTS_DIR = '/home/lzy/generateDepth/bestmodel/before_best.pth'
cfg_generate.GENERATE_Depth_DATA = False
cfg_generate.NO_UPSAMPLE = False
checkpoint = torch.load(cfg_generate.CHECKPOINTS_DIR)
model = define_TrecgNet(cfg_generate, upsample=True, generate=True)
load_checkpoint_depth(model,
cfg_generate.CHECKPOINTS_DIR,
checkpoint,
data_para=True)
generate_model = torch.nn.DataParallel(model).cuda()
generate_model.eval()
# net_classification_1 = torch.nn.DataParallel(net_classification_1).cuda()
net_classification_2 = torch.nn.DataParallel(net_classification_2).cuda()
criterion = nn.CrossEntropyLoss().cuda()
# best_mean_1=0
best_mean_2 = 0
# optimizer_2 = torch.optim.Adam(net_classification_2.parameters(), lr=cfg.LR, betas=(0.5, 0.999))
optimizer_2 = torch.optim.SGD(net_classification_2.parameters(),
lr=cfg.LR,
momentum=cfg.MOMENTUM,
weight_decay=cfg.WEIGHT_DECAY)
schedulers = get_scheduler(optimizer_2, cfg, cfg.LR_POLICY)
for epoch in range(0, 1000):
if global_all_step > cfg.NITER_TOTAL:
break
# meanacc_1,meanacc_2=validate(val_loader, net_classification_1,net_classification_2,generate_model,criterion,epoch)
# for param_group in optimizer_2.param_groups:
# lr_t = param_group['lr']
# print('/////////learning rate = %.7f' % lr_t)
# writer.add_scalar('LR',lr_t,global_step=epoch)
printlr(optimizer_2, writer, epoch)
train(train_loader, schedulers, net_classification_2, generate_model,
criterion, optimizer_2, epoch, writer)
meanacc_2 = validate(val_loader, net_classification_2, generate_model,
criterion, epoch, writer)
# meanacc_2=validate(val_loader,net_classification_2,generate_model,criterion,epoch,writer)
# train(train_loader,net_classification_2,generate_model,criterion,optimizer_2,epoch,writer)
# meanacc_2=validate(val_loader,net_classification_2,generate_model,criterion,epoch,writer)
# writer.add_image(depth_image[0])
# save best
# if meanacc_1>best_mean_1:
# best_mean_1=meanacc_1
# print('best_mean_color:',str(best_mean_1))
# save_checkpoint({
# 'epoch': epoch,
# 'arch': cfg.ARCH,
# 'state_dict': net_classification_1.state_dict(),
# 'best_mean_1': best_mean_1,
# 'optimizer' : optimizer_1.state_dict(),
# },CorD=True)
if meanacc_2 > best_mean_2:
best_mean_2 = meanacc_2
print('best_mean_depth:', str(best_mean_2))
save_checkpoint(
{
'epoch': epoch,
'arch': cfg.ARCH,
'state_dict': net_classification_2.state_dict(),
'best_mean_2': best_mean_2,
'optimizer': optimizer_2.state_dict(),
},
CorD=False)
# print('best_mean_color:',str(best_mean_1))
# writer.add_scalar('mean_acc_color', meanacc_1, global_step=epoch)
writer.add_scalar('mean_acc_depth', meanacc_2, global_step=epoch)
# writer.add_scalar('best_meanacc_color', best_mean_1, global_step=epoch)
writer.add_scalar('best_meanacc_depth', best_mean_2, global_step=epoch)
writer.close()
def main():
cfg = DefaultConfig()
args = {
'seg_resnet_sunrgbd': SEG_RESNET_SUNRGBD_CONFIG().args(),
'seg_resnet_cityscapes': SEG_RESNET_CITYSCAPE_CONFIG().args(),
'rec_resnet_sunrgbd': REC_RESNET_SUNRGBD_CONFIG().args(),
'rec_resnet_nyud2': REC_RESNET_NYUD2_CONFIG().args(),
'rec_resnet_mit67': REC_RESNET_MIT67_CONFIG().args(),
'infomax_resnet_sunrgbd': INFOMAX_RESNET_SUNRGBD_CONFIG().args(),
'infomax_resnet_nyud2': INFOMAX_RESNET_NYUD2_CONFIG().args()
}
# use shell
if len(sys.argv) > 1:
device_ids = torch.cuda.device_count()
print('device_ids:', device_ids)
gpu_ids, config_key = sys.argv[1:]
cfg.parse(args[config_key])
cfg.GPU_IDS = gpu_ids.split(',')
else:
# seg_resnet_sunrgbd
# seg_resnet_cityscapes
# infomax_resnet_sunrgbd
# rec_resnet_sunrgbd
# rec_resnet_nyud2
# rec_resnet_mit67
# infomax_resnet_nyud2
config_key = 'rec_resnet_sunrgbd'
cfg.parse(args[config_key])
os.environ['CUDA_VISIBLE_DEVICES'] = ','.join(
map(lambda x: str(x), cfg.GPU_IDS))
trans_task = '' + cfg.WHICH_DIRECTION
if not cfg.NO_TRANS:
if cfg.MULTI_MODAL:
trans_task = trans_task + '_multimodal_'
if 'SEMANTIC' in cfg.LOSS_TYPES or 'PIX2PIX' in cfg.LOSS_TYPES:
trans_task = trans_task + '_alpha_' + str(cfg.ALPHA_CONTENT)
evaluate_type = 'sliding_window' if cfg.SLIDE_WINDOWS else 'center_crop'
log_name = ''.join(
[cfg.TASK, '_', cfg.ARCH, '_', trans_task, '_', cfg.DATASET])
cfg.LOG_NAME = ''.join([
log_name, '_', '.'.join(cfg.LOSS_TYPES), '_', evaluate_type, '_gpus_',
str(len(cfg.GPU_IDS)), '_',
datetime.now().strftime('%b%d_%H-%M-%S')
])
cfg.LOG_PATH = os.path.join(cfg.LOG_PATH, cfg.MODEL, cfg.LOG_NAME)
# Setting random seed
if cfg.MANUAL_SEED is None:
cfg.MANUAL_SEED = random.randint(1, 10000)
random.seed(cfg.MANUAL_SEED)
torch.manual_seed(cfg.MANUAL_SEED)
torch.backends.cudnn.benchmark = True
# cudnn.deterministic = True
project_name = reduce(lambda x, y: str(x) + '/' + str(y),
os.path.realpath(__file__).split(os.sep)[:-1])
print('>>> task path is {0}'.format(project_name))
util.mkdir('logs')
# dataset = segmentation_dataset_cv2
train_transforms = list()
val_transforms = list()
ms_targets = []
train_transforms.append(dataset.Resize(cfg.LOAD_SIZE))
# train_transforms.append(dataset.RandomScale(cfg.RANDOM_SCALE_SIZE)) #
# train_transforms.append(dataset.RandomRotate())
# train_transforms.append(dataset.RandomCrop_Unaligned(cfg.FINE_SIZE, pad_if_needed=True, fill=0)) #
train_transforms.append(
dataset.RandomCrop(cfg.FINE_SIZE, pad_if_needed=True, fill=0)) #
train_transforms.append(dataset.RandomHorizontalFlip())
if cfg.TARGET_MODAL == 'lab':
train_transforms.append(dataset.RGB2Lab())
if cfg.MULTI_SCALE:
for item in cfg.MULTI_TARGETS:
ms_targets.append(item)
train_transforms.append(
dataset.MultiScale(size=cfg.FINE_SIZE,
scale_times=cfg.MULTI_SCALE_NUM,
ms_targets=ms_targets))
train_transforms.append(dataset.ToTensor(ms_targets=ms_targets))
train_transforms.append(
dataset.Normalize(mean=cfg.MEAN, std=cfg.STD, ms_targets=ms_targets))
val_transforms.append(dataset.Resize(cfg.LOAD_SIZE))
if not cfg.SLIDE_WINDOWS:
val_transforms.append(dataset.CenterCrop((cfg.FINE_SIZE)))
if cfg.MULTI_SCALE:
val_transforms.append(
dataset.MultiScale(size=cfg.FINE_SIZE,
scale_times=cfg.MULTI_SCALE_NUM,
ms_targets=ms_targets))
val_transforms.append(dataset.ToTensor(ms_targets=ms_targets))
val_transforms.append(
dataset.Normalize(mean=cfg.MEAN, std=cfg.STD, ms_targets=ms_targets))
train_dataset = dataset.__dict__[cfg.DATASET](
cfg=cfg,
transform=transforms.Compose(train_transforms),
data_dir=cfg.DATA_DIR_TRAIN,
phase_train=True)
val_dataset = dataset.__dict__[cfg.DATASET](
cfg=cfg,
transform=transforms.Compose(val_transforms),
data_dir=cfg.DATA_DIR_VAL,
phase_train=False)
cfg.CLASS_WEIGHTS_TRAIN = train_dataset.class_weights
cfg.IGNORE_LABEL = train_dataset.ignore_label
cfg.train_dataset = train_dataset
cfg.val_dataset = val_dataset
port = random.randint(8001, 9000)
ngpus_per_node = len(cfg.GPU_IDS)
if cfg.MULTIPROCESSING_DISTRIBUTED:
cfg.rank = 0
cfg.ngpus_per_node = ngpus_per_node
cfg.dist_url = 'tcp://127.0.0.1:' + str(port)
cfg.dist_backend = 'nccl'
cfg.opt_level = 'O0'
cfg.world_size = 1
cfg.print_args()
if cfg.MULTIPROCESSING_DISTRIBUTED:
cfg.world_size = cfg.ngpus_per_node * cfg.world_size
mp.spawn(main_worker,
nprocs=cfg.ngpus_per_node,
args=(cfg.ngpus_per_node, cfg))
else:
# Simply call main_worker function
main_worker(cfg.GPU_IDS, ngpus_per_node, cfg)
import time
from functools import reduce
import torch
import torch.nn as nn
import torchvision.transforms as transforms
from tensorboardX import SummaryWriter
import data.aligned_conc_dataset as dataset
import util.utils as util
from config.default_config import DefaultConfig
from config.evalute_resnet18_config import EVALUATE_RESNET18_CONFIG
from data import DataProvider
from model.networks import TRecgNet_Upsample_Resiual
cfg = DefaultConfig()
args = {
'resnet18': EVALUATE_RESNET18_CONFIG().args(),
}
# args for different backbones
cfg.parse(args['resnet18'])
os.environ["CUDA_VISIBLE_DEVICES"] = cfg.GPU_IDS
device_ids = torch.cuda.device_count()
print('device_ids:', device_ids)
project_name = reduce(lambda x, y: str(x) + '/' + str(y),
os.path.realpath(__file__).split(os.sep)[:-1])
util.mkdir('logs')
val_dataset = dataset.AlignedConcDataset(
def main():
global cfg
cfg = DefaultConfig()
args = {
'resnet18': RESNET18_SUNRGBD_CONFIG().args(),
}
# Setting random seed
# if cfg.MANUAL_SEED is None:
# cfg.MANUAL_SEED = random.randint(1, 10000)
# random.seed(cfg.MANUAL_SEED)
# torch.manual_seed(cfg.MANUAL_SEED)
# args for different backbones
cfg.parse(args['resnet18'])
run_id = random.randint(1, 100000)
summary_dir = '/home/lzy/summary/generateDepth/' + 'train_rgb_' + str(
run_id)
if not os.path.exists(summary_dir):
os.mkdir(summary_dir)
writer = SummaryWriter(summary_dir)
cfg.LR = 0.0001
os.environ["CUDA_VISIBLE_DEVICES"] = '1,2'
device_ids = torch.cuda.device_count()
print('device_ids:', device_ids)
# project_name = reduce(lambda x, y: str(x) + '/' + str(y), os.path.realpath(__file__).split(os.sep)[:-1])
# util.mkdir('logs')
# normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
# std=[0.229, 0.224, 0.225])
# normalize=transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
# data
train_dataset = dataset.SingleDataset(
cfg,
data_dir=cfg.DATA_DIR_TRAIN,
transform=transforms.Compose([
dataset.Resize((cfg.LOAD_SIZE, cfg.LOAD_SIZE)),
dataset.RandomCrop((cfg.FINE_SIZE, cfg.FINE_SIZE)),
dataset.RandomHorizontalFlip(),
dataset.ToTensor(),
dataset.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
]))
val_dataset = dataset.SingleDataset(
cfg,
data_dir=cfg.DATA_DIR_VAL,
transform=transforms.Compose([
dataset.Resize((cfg.LOAD_SIZE, cfg.LOAD_SIZE)),
dataset.CenterCrop((cfg.FINE_SIZE, cfg.FINE_SIZE)),
dataset.ToTensor(),
dataset.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
]))
# train_loader = torch.utils.data.DataLoader(
# train_dataset, batch_size=cfg.BATCH_SIZE, shuffle=True,
# num_workers=4, pin_memory=True, sampler=None)
# val_loader = torch.utils.data.DataLoader(
# val_dataset,batch_size=16, shuffle=False,
# num_workers=4, pin_memory=True)
train_loader = DataProvider(cfg,
dataset=train_dataset,
batch_size=40,
shuffle=True)
val_loader = DataProvider(cfg,
dataset=val_dataset,
batch_size=10,
shuffle=False)
# class weights
# num_classes_train = list(Counter([i[1] for i in train_loader.dataset.imgs]).values())
# cfg.CLASS_WEIGHTS_TRAIN = torch.FloatTensor(num_classes_train)
# writer = SummaryWriter(log_dir=cfg.LOG_PATH) # tensorboard
# net_classification_1=resnet50(pretrained=True)
# net_classification_2=resnet50(pretrained=True)
# net_classification_1 = models.__dict__['resnet18'](num_classes=365)
# net_classification_2 = models.__dict__['resnet18'](num_classes=365)
net_classification_1 = torch.hub.load('facebookresearch/WSL-Images',
'resnext101_32x16d_wsl')
# net_classification_2=torch.hub.load('facebookresearch/WSL-Images', 'resnext101_32x16d_wsl')
# for param in net_classification_1.parameters():
# param.requires_grad = False
# for param in net_classification_2.parameters():
# param.requires_grad = True
net_classification_1.fc = nn.Sequential(nn.Dropout(p=0.5),
nn.Linear(2048, 1024),
nn.LeakyReLU(inplace=True),
nn.Linear(1024, 67))
# net_classification_2.fc = nn.Sequential(nn.Dropout(p=0.5),nn.Linear(2048, 1024),nn.LeakyReLU(inplace=True),nn.Linear(1024,67))
net_classification_1.load_state_dict(
torch.load(
"./bestmodel/best_model_resnext_16d_2048_1024_dropout_0.5_b.pkl"))
# net_classification_2.load_state_dict(torch.load("./bestmodel/best_model_resnext_16d_2048_1024_dropout_0.5_b.pkl"))
# net_classification_2
# load_path = "/home/dudapeng/workspace/pretrained/place/resnet18_places365.pth"
# checkpoint = torch.load(load_path, map_location=lambda storage, loc: storage)
# state_dict = {str.replace(k, 'module.', ''): v for k, v in checkpoint['state_dict'].items()}
# net_classification_1.load_state_dict(state_dict)
# net_classification_2.load_state_dict(state_dict)
print(net_classification_1)
# num_ftrs = net_classification_1.fc.in_features
# net_classification_1.fc = nn.Linear(num_ftrs, cfg.NUM_CLASSES)
# num_ftrs = net_classification_2.fc.in_features
# net_classification_2.fc = nn.Linear(num_ftrs, cfg.NUM_CLASSES)
net_classification_1.cuda()
# net_classification_2.cuda()
cudnn.benchmark = True
# if cfg.GENERATE_Depth_DATA:
# print('GENERATE_Depth_DATA model set')
# cfg_generate = copy.deepcopy(cfg)
# cfg_generate.CHECKPOINTS_DIR='/home/lzy/generateDepth/checkpoints/best_AtoB/trecg_AtoB_best.pth'
# cfg_generate.GENERATE_Depth_DATA = False
# cfg_generate.NO_UPSAMPLE = False
# checkpoint = torch.load(cfg_generate.CHECKPOINTS_DIR)
# model = define_TrecgNet(cfg_generate, upsample=True,generate=True)
# load_checkpoint_depth(model,cfg_generate.CHECKPOINTS_DIR, checkpoint, data_para=True)
# generate_model = torch.nn.DataParallel(model).cuda()
# generate_model.eval()
net_classification_1 = torch.nn.DataParallel(net_classification_1).cuda()
# net_classification_2 = torch.nn.DataParallel(net_classification_2).cuda()
criterion = nn.CrossEntropyLoss().cuda()
best_mean_1 = 0
# best_mean_2=0
# optimizer = optim.SGD(model_ft.parameters(), lr=0.05,momentum=0.9)#,weight_decay=0.00005)
optimizer_1 = torch.optim.SGD(net_classification_1.parameters(),
lr=cfg.LR,
momentum=cfg.MOMENTUM,
weight_decay=cfg.WEIGHT_DECAY)
# optimizer_2 = torch.optim.SGD(net_classification_2.parameters(),lr=cfg.LR,momentum=cfg.MOMENTUM,weight_decay=cfg.WEIGHT_DECAY)
for epoch in range(0, 100):
adjust_learning_rate(optimizer_1, epoch)
# meanacc_1,meanacc_2=validate(val_loader, net_classification_1,net_classification_2,generate_model,criterion,epoch)
#
train(train_loader, net_classification_1, criterion, optimizer_1,
epoch, writer)
meanacc_1 = validate(val_loader, net_classification_1, criterion,
epoch, writer)
# meanacc_2=validate(val_loader,net_classification_2,generate_model,criterion,epoch,writer)
# train(train_loader,net_classification_2,generate_model,criterion,optimizer_2,epoch,writer)
# meanacc_2=validate(val_loader,net_classification_2,generate_model,criterion,epoch,writer)
# writer.add_image(depth_image[0])
# save best
if meanacc_1 > best_mean_1:
best_mean_1 = meanacc_1
print('best_mean_color:', str(best_mean_1))
save_checkpoint(
{
'epoch': epoch,
'arch': cfg.ARCH,
'state_dict': net_classification_1.state_dict(),
'best_mean_1': best_mean_1,
'optimizer': optimizer_1.state_dict(),
},
CorD=True)
# if meanacc_2>best_mean_2:
# best_mean_2=meanacc_2
# print('best_mean_depth:',str(best_mean_2))
# save_checkpoint({
# 'epoch': epoch,
# 'arch': cfg.ARCH,
# 'state_dict': net_classification_2.state_dict(),
# 'best_mean_2': best_mean_2,
# 'optimizer' : optimizer_2.state_dict(),
# },CorD=False)
print('best_mean_color:', str(best_mean_1))
writer.add_scalar('mean_acc_color', meanacc_1, global_step=epoch)
# writer.add_scalar('mean_acc_depth', meanacc_2, global_step=epoch)
writer.add_scalar('best_meanacc_color', best_mean_1, global_step=epoch)
# writer.add_scalar('best_meanacc_depth', best_mean_2, global_step=epoch)
writer.close()
# def set_log_data(self, cfg):
#
# self.loss_meters = defaultdict()
# self.log_keys = [
# 'TRAIN_GEN_LOSS',
# 'TRAIN_LOCAL_LOSS',
# 'TRAIN_LOCAL_LOSS',
# ]
# for item in self.log_keys:
# self.loss_meters[item] = AverageMeter()
if __name__ == '__main__':
cfg = DefaultConfig()
cfg.parse(RESNET_SPL10_CONFIG().args())
print(cfg.GPU_IDS)
os.environ["CUDA_VISIBLE_DEVICES"] = '4,5,6'
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
batch_size = cfg.BATCH_SIZE
writer = SummaryWriter(log_dir=cfg.LOG_PATH)
# image size 3, 32, 32
# batch size must be an even number
# shuffle must be True
# cifar_10_train_dt = CIFAR10(r'/data0/dudapeng/workspace/datasets/CIFAR10/', download=True, transform=ToTensor())
# cifar_10_train_l = DataLoader(cifar_10_train_dt, batch_size=batch_size, shuffle=True, drop_last=True,
# pin_memory=torch.cuda.is_available())
# normalize = Normalize(mean=[(0 + 100) / 2, (-86.183 + 98.233) / 2, (-107.857 + 94.478) / 2],
import random
from collections import Counter
from functools import reduce
import torch
import torchvision.transforms as transforms
from tensorboardX import SummaryWriter
import data.aligned_conc_dataset as conc_dataset
import util.utils as util
from config.default_config import DefaultConfig
from config.resnet_sunrgbd_config import RESNET_SUNRGBD_CONFIG
from data import DataProvider
from model.models import create_model
cfg = DefaultConfig()
args = {
# model should be defined as 'fusion' and set paths for RESUME_PATH_A and RESUME_PATH_B
'resnet_sunrgbd': RESNET_SUNRGBD_CONFIG().args(),
}
# Setting random seed
if cfg.MANUAL_SEED is None:
cfg.MANUAL_SEED = random.randint(1, 10000)
random.seed(cfg.MANUAL_SEED)
torch.manual_seed(cfg.MANUAL_SEED)
# args for different backbones
cfg.parse(args['resnet_sunrgbd'])
os.environ["CUDA_VISIBLE_DEVICES"] = cfg.GPU_IDS
def train():
cfg = DefaultConfig()
args = {
'resnet18': RESNET18_SUNRGBD_CONFIG().args(),
}
# Setting random seed
if cfg.MANUAL_SEED is None:
cfg.MANUAL_SEED = random.randint(1, 10000)
random.seed(cfg.MANUAL_SEED)
torch.manual_seed(cfg.MANUAL_SEED)
# args for different backbones
cfg.parse(args['resnet18'])
os.environ["CUDA_VISIBLE_DEVICES"] = cfg.GPU_IDS
device_ids = torch.cuda.device_count()
print('device_ids:', device_ids)
project_name = reduce(lambda x, y: str(x) + '/' + str(y),
os.path.realpath(__file__).split(os.sep)[:-1])
util.mkdir('logs')
# data
train_dataset = dataset.AlignedConcDataset(
cfg,
data_dir=cfg.DATA_DIR_TRAIN,
transform=transforms.Compose([
dataset.Resize((cfg.LOAD_SIZE, cfg.LOAD_SIZE)),
dataset.RandomCrop((cfg.FINE_SIZE, cfg.FINE_SIZE)),
dataset.RandomHorizontalFlip(),
dataset.ToTensor(),
dataset.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
]))
val_dataset = dataset.AlignedConcDataset(
cfg,
data_dir=cfg.DATA_DIR_VAL,
transform=transforms.Compose([
dataset.Resize((cfg.LOAD_SIZE, cfg.LOAD_SIZE)),
dataset.CenterCrop((cfg.FINE_SIZE, cfg.FINE_SIZE)),
dataset.ToTensor(),
dataset.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
]))
batch_size_val = cfg.BATCH_SIZE
unlabeled_loader = None
if cfg.UNLABELED:
unlabeled_dataset = dataset.AlignedConcDataset(
cfg,
data_dir=cfg.DATA_DIR_UNLABELED,
transform=transforms.Compose([
dataset.Resize((cfg.LOAD_SIZE, cfg.LOAD_SIZE)),
dataset.RandomCrop((cfg.FINE_SIZE, cfg.FINE_SIZE)),
dataset.RandomHorizontalFlip(),
dataset.ToTensor(),
dataset.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
]),
labeled=False)
unlabeled_loader = DataProvider(cfg, dataset=unlabeled_dataset)
train_loader = DataProvider(cfg,
dataset=train_dataset,
batch_size=batch_size_val)
val_loader = DataProvider(cfg,
dataset=val_dataset,
batch_size=batch_size_val,
shuffle=False)
# class weights
num_classes_train = list(
Counter([i[1] for i in train_loader.dataset.imgs]).values())
cfg.CLASS_WEIGHTS_TRAIN = torch.FloatTensor(num_classes_train)
writer = SummaryWriter(log_dir=cfg.LOG_PATH) # tensorboard
model = TRecgNet(cfg, writer=writer)
model.set_data_loader(train_loader, val_loader, unlabeled_loader)
if cfg.RESUME:
checkpoint_path = os.path.join(cfg.CHECKPOINTS_DIR, cfg.RESUME_PATH)
checkpoint = torch.load(checkpoint_path)
load_epoch = checkpoint['epoch']
model.load_checkpoint(model.net,
checkpoint_path,
checkpoint,
data_para=True)
cfg.START_EPOCH = load_epoch
if cfg.INIT_EPOCH:
# just load pretrained parameters
print('load checkpoint from another source')
cfg.START_EPOCH = 1
print('>>> task path is {0}'.format(project_name))
# train
model.train_parameters(cfg)
print('save model ...')
model_filename = '{0}_{1}_{2}.pth'.format(cfg.MODEL, cfg.WHICH_DIRECTION,
cfg.NITER_TOTAL)
model.save_checkpoint(cfg.NITER_TOTAL, model_filename)
if writer is not None:
writer.close()
def main():
global cfg, global_all_step
global_all_step = 0
cfg = DefaultConfig()
args = {
'resnet18': RESNET18_SUNRGBD_CONFIG().args(),
}
# Setting random seed
# if cfg.MANUAL_SEED is None:
# cfg.MANUAL_SEED = random.randint(1, 10000)
# random.seed(cfg.MANUAL_SEED)
# torch.manual_seed(cfg.MANUAL_SEED)
# args for different backbones
cfg.parse(args['resnet18'])
run_id = random.randint(1, 100000)
summary_dir = '/home/lzy/summary/generateDepth/' + 'resnet152_train_rgb_place365_2e-3_' + str(
run_id)
if not os.path.exists(summary_dir):
os.mkdir(summary_dir)
writer = SummaryWriter(summary_dir)
cfg.LR = 0.002
os.environ["CUDA_VISIBLE_DEVICES"] = '2'
device_ids = torch.cuda.device_count()
print('device_ids:', device_ids)
# project_name = reduce(lambda x, y: str(x) + '/' + str(y), os.path.realpath(__file__).split(os.sep)[:-1])
# util.mkdir('logs')
# normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
# std=[0.229, 0.224, 0.225])
# normalize=transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
# data
train_dataset = dataset.SingleDataset(
cfg,
data_dir=cfg.DATA_DIR_TRAIN,
transform=transforms.Compose([
dataset.Resize((cfg.LOAD_SIZE, cfg.LOAD_SIZE)),
dataset.RandomCrop((cfg.FINE_SIZE, cfg.FINE_SIZE)),
dataset.RandomHorizontalFlip(),
# dataset.RandomRotate(),
# dataset.RandomFlip(),
# dataset.PILBrightness(0.4),
# dataset.PILContrast(0.4),
# dataset.PILColorBalance(0.4),
# dataset.PILSharpness(0.4),
dataset.ToTensor(),
dataset.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
]))
val_dataset = dataset.SingleDataset(
cfg,
data_dir=cfg.DATA_DIR_VAL,
transform=transforms.Compose([
dataset.Resize((cfg.LOAD_SIZE, cfg.LOAD_SIZE)),
dataset.CenterCrop((cfg.FINE_SIZE, cfg.FINE_SIZE)),
dataset.ToTensor(),
dataset.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
]))
# train_loader = torch.utils.data.DataLoader(
# train_dataset, batch_size=cfg.BATCH_SIZE, shuffle=True,
# num_workers=4, pin_memory=True, sampler=None)
# val_loader = torch.utils.data.DataLoader(
# val_dataset,batch_size=16, shuffle=False,
# num_workers=4, pin_memory=True)
train_loader = DataProvider(cfg,
dataset=train_dataset,
batch_size=30,
shuffle=True)
val_loader = DataProvider(cfg,
dataset=val_dataset,
batch_size=10,
shuffle=False)
# class weights
net_classification_1 = models.__dict__['resnet152'](num_classes=365)
load_path = "./bestmodel/resnet152_places365.pth"
checkpoint = torch.load(load_path,
map_location=lambda storage, loc: storage)
weight = []
for k, v in checkpoint.items():
weight.append(v)
i = 0
from collections import OrderedDict
new_state_dict = OrderedDict()
for k in net_classification_1.state_dict():
# print(k)
new_state_dict[k] = weight[i]
i += 1
# state_dict = {str.replace(k, 'module.', ''): v for k, v in checkpoint['state_dict'].items()}
# state_dict = {str.replace(k, 'norm.', 'norm'): v for k, v in state_dict.items()}
# state_dict = {str.replace(k, 'conv.', 'conv'): v for k, v in state_dict.items()}
# state_dict = {str.replace(k, 'normweight', 'norm.weight'): v for k, v in state_dict.items()}
# state_dict = {str.replace(k, 'normbias', 'norm.bias'): v for k, v in state_dict.items()}
# state_dict = {str.replace(k, 'normrunning_var', 'norm.running_var'): v for k, v in state_dict.items()}
# state_dict = {str.replace(k, 'convweight', 'conv.weight'): v for k, v in state_dict.items()}
# state_dict = {str.replace(k, 'normrunning_mean', 'norm.running_mean'): v for k, v in state_dict.items()}
# normweight
# state_dict = {str.replace(k, 'conv.', 'conv'): v for k, v in state_dict.items()}
# best_mean_depth = checkpoint['best_mean_2']
# print("load sunrgbd dataset:",best_mean_depth)
net_classification_1.load_state_dict(new_state_dict)
# net_classification_1=torch.hub.load('facebookresearch/WSL-Images', 'resnext101_32x16d_wsl')
# net_classification_1.fc = nn.Sequential(nn.Dropout(p=0.5),nn.Linear(2048, 1024),nn.LeakyReLU(inplace=True),nn.Linear(1024,67))
# net_classification_1.fc = nn.Sequential(nn.Dropout(p=0.2), nn.Linear(2048, 1024), nn.LeakyReLU(inplace=True), nn.Dropout(p=0.2),nn.Linear(1024, 67))
# net_classification_1.fc = nn.Sequential(nn.Dropout(p=0.5),nn.Linear(1024, 67))
# net_classification_1.classifier= nn.Linear(2208, 67)
net_classification_1.fc = nn.Linear(2048, 67)
# init_weights(net_classification_1.classifier, 'normal')
init_weights(net_classification_1.fc, 'normal')
# net_classification_1.load_state_dict(torch.load("./bestmodel/best_model_resnext_16d_2048_1024_dropout_0.5_b.pkl"))
# for param in net_classification_1.parameters():
# param.requires_grad = False
# num_ftrs = net_classification_1.fc.in_features
print(net_classification_1)
net_classification_1.cuda()
# net_classification_2.cuda()
cudnn.benchmark = True
net_classification_1 = torch.nn.DataParallel(net_classification_1).cuda()
# net_classification_2 = torch.nn.DataParallel(net_classification_2).cuda()
criterion = nn.CrossEntropyLoss().cuda()
best_mean_1 = 0
# best_mean_2=0
# optimizer_1 = torch.optim.Adam(net_classification_1.parameters(), lr=cfg.LR, betas=(0.5, 0.999))
optimizer_1 = torch.optim.SGD(net_classification_1.parameters(),
lr=cfg.LR,
momentum=cfg.MOMENTUM,
weight_decay=cfg.WEIGHT_DECAY)
schedulers = get_scheduler(optimizer_1, cfg, cfg.LR_POLICY)
# optimizer_1 = torch.optim.SGD(net_classification_1.parameters(),lr=cfg.LR,momentum=cfg.MOMENTUM,weight_decay=cfg.WEIGHT_DECAY)
# optimizer_2 = torch.optim.SGD(net_classification_2.parameters(),lr=cfg.LR,momentum=cfg.MOMENTUM,weight_decay=cfg.WEIGHT_DECAY)
for epoch in range(0, 1000):
if global_all_step > cfg.NITER_TOTAL:
break
# meanacc_1,meanacc_2=validate(val_loader, net_classification_1,net_classification_2,generate_model,criterion,epoch)
printlr(optimizer_1, writer, epoch)
train(train_loader, schedulers, net_classification_1, criterion,
optimizer_1, epoch, writer)
meanacc_1 = validate(val_loader, net_classification_1, criterion,
epoch, writer)
# writer.add_image(depth_image[0])
# save best
if meanacc_1 > best_mean_1:
best_mean_1 = meanacc_1
print('best_mean_color:', str(best_mean_1))
save_checkpoint(
{
'epoch': epoch,
'arch': cfg.ARCH,
'state_dict': net_classification_1.state_dict(),
'best_mean_1': best_mean_1,
'optimizer': optimizer_1.state_dict(),
},
CorD=True)
print('best_mean_color:', str(best_mean_1))
writer.add_scalar('mean_acc_color', meanacc_1, global_step=epoch)
# writer.add_scalar('mean_acc_depth', meanacc_2, global_step=epoch)
writer.add_scalar('best_meanacc_color', best_mean_1, global_step=epoch)
# writer.add_scalar('best_meanacc_depth', best_mean_2, global_step=epoch)
writer.close()
