lmd/Pose_to_SMPL_an_230402
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

add HumanAct12, UTD_MHAD

Browse Source
This commit is contained in:
Iridoudou
2021-08-07 21:19:21 +08:00
parent 9f6274fc19
commit 2b3e65e2a2
9 changed files with 329 additions and 117 deletions
Download Patch File Download Diff File Expand all files Collapse all files

59
fit/tools/main.py
View File

@ -1,14 +1,4 @@
import matplotlib as plt
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.modules import module
from torch.optim import lr_scheduler
import torch.optim as optim
from torch.utils.data import DataLoader
from torch.utils.data import sampler
import torchvision.datasets as dset
import torchvision.transforms as T
import numpy as np
from tensorboardX import SummaryWriter
from easydict import EasyDict as edict
@ -35,17 +25,15 @@ def parse_args():
parser.add_argument('--exp', dest='exp',
help='Define exp name',
default=time.strftime('%Y-%m-%d %H-%M-%S', time.localtime(time.time())), type=str)
parser.add_argument('--config_path', dest='config_path',
help='Select configuration file',
default='fit/configs/config.json', type=str)
parser.add_argument('--dataset_path', dest='dataset_path',
parser.add_argument('--dataset_name', dest='dataset_name',
help='select dataset',
default='', type=str)
args = parser.parse_args()
return args
def get_config(args):
with open(args.config_path, 'r') as f:
config_path='fit/configs/{}.json'.format(args.dataset_name)
with open(config_path, 'r') as f:
data = json.load(f)
cfg = edict(data.copy())
return cfg
@ -100,31 +88,18 @@ if __name__ == "__main__":
gender=cfg.MODEL.GENDER,
model_root='smplpytorch/native/models')
if not cfg.DEBUG:
for root,dirs,files in os.walk(cfg.DATASET_PATH):
for file in files:
logger.info('Processing file: {}'.format(file))
target_path=os.path.join(root,file)
target = np.array(transform(np.load(target_path)))
logger.info('File shape: {}'.format(target.shape))
target = torch.from_numpy(target).float()
res = train(smpl_layer,target,
logger,writer,device,
args,cfg)
# save_pic(target,res,smpl_layer,file,logger)
save_params(res,file,logger)
else:
target = np.array(transform(load('UTD_MHAD',cfg.DATASET.TARGET_PATH),
rotate=[-1,1,-1]))
target = torch.from_numpy(target).float()
data_map_dataset=torch.tensor(cfg.DATASET.DATA_MAP.UTD_MHAD[1])
target = target.index_select(1, data_map_dataset)
print(target.shape)
res = train(smpl_layer,target,
logger,writer,device,
args,cfg)
for root,dirs,files in os.walk(cfg.DATASET.PATH):
for file in files:
logger.info('Processing file: {}'.format(file))
target = torch.from_numpy(transform(args.dataset_name,
load(args.dataset_name,
os.path.join(root,file)))).float()
res = train(smpl_layer,target,
logger,writer,device,
args,cfg)
# save_pic(res,smpl_layer,file,logger,args.dataset_name)
save_params(res,file,logger, args.dataset_name)