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train_styleVAE.py
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train_styleVAE.py
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#import argparse
import copy
import os
import re
from argparse import ArgumentParser
import pytorch_lightning as pl
import torch
from pytorch_lightning import Trainer
from pytorch_lightning import loggers
from pytorch_lightning.callbacks.model_checkpoint import ModelCheckpoint
from pytorch_lightning.profiler import SimpleProfiler
from pytorch_lightning.utilities.seed import seed_everything
from src.Datasets.BaseLoader import WindowBasedLoader
from src.Net.StyleVAENet import StyleVAENet
from src.utils import BVH_mod as BVH
def setup_seed(seed:int):
seed_everything(seed,True)
def test_model():
dict = {}
#dict['fast_dev_run'] = 1 # only run 1 train, val, test batch and program ends
dict['limit_train_batches'] = 0.1
dict['limit_val_batches'] = 0.7
return dict
def detect_nan_par():
'''track_grad_norm": 'inf'''
return { "detect_anomaly":True}
def select_gpu_par():
return {"accelerator":'gpu', "auto_select_gpus":True, "devices":-1}
def create_common_states(prefix:str):
log_name = prefix+'/'
'''test upload'''
parser = ArgumentParser()
parser.add_argument("--dev_run", action="store_true")
parser.add_argument("--version", type=str, default="-1")
parser.add_argument("--epoch",type=str,default="last")
parser.add_argument("--resume", action="store_true")
parser.add_argument("--test",action="store_true")
args = parser.parse_args()
ckpt_path_prefix = "results/"
if (args.version != "-1"):
version = args.version
else:
version = None
'''Create Loggers tensorboard'''
if args.dev_run:
log_name += "dev_run"
else:
log_name += "myResults"
tb_logger = pl.loggers.TensorBoardLogger(save_dir="tensorboard_logs/", name=log_name, version=None)
load_ckpt_path = os.path.join(ckpt_path_prefix, prefix+'/myResults', str(version))
save_ckpt_path = os.path.join(ckpt_path_prefix, log_name, str(tb_logger.version))
if (args.resume == True):
check_file = load_ckpt_path+"/"
if (args.epoch == "last"):
check_file += "last.ckpt"
else:
dirs = os.listdir(check_file)
for dir in dirs:
st = "epoch=" + args.epoch + "-step=\d+.ckpt"
out = re.findall(st, dir)
if (len(out) > 0):
check_file += out[0]
print(check_file)
break
resume_from_checkpoint = check_file # results/version/last.ckpt"
else:
resume_from_checkpoint = None
checkpoint_callback = [ModelCheckpoint(dirpath=save_ckpt_path + "/", save_top_k=-1, save_last=True, every_n_epochs=5),
ModelCheckpoint(dirpath=save_ckpt_path + "/", save_top_k=1, monitor="val_loss", save_last=False, every_n_epochs=1,save_weights_only=True),
# EMA(0.99)
]
'''Train'''
checkpoint_callback[0].CHECKPOINT_NAME_LAST = "last"
profiler = SimpleProfiler()#PyTorchProfiler(filename="profiler")
trainer_dict = {
"callbacks":checkpoint_callback,
"profiler":profiler,
"logger":tb_logger
}
return args,trainer_dict,resume_from_checkpoint,load_ckpt_path
def training_style100():
from src.Datasets.StyleVAE_DataModule import StyleVAE_DataModule
from src.Datasets.Style100Processor import StyleLoader
from src.Net.StyleVAENet import Application,VAEMode
prefix = "StyleVAE2"
data_set = "style100"
prefix += "_" + data_set
args, trainer_dict, resume_from_checkpoint, ckpt_path = create_common_states(prefix)
resume_from_checkpoint = None
loader = WindowBasedLoader(61, 21, 1)
dt = 1. / 30.
phase_dim = 10
phase_file = "+phase_gv10"
latent_size = 32
net_mode = VAEMode.SINGLE
batch_size = 32
if (args.test == False):
'''Create the model'''
style_loader = StyleLoader()
data_module = StyleVAE_DataModule(style_loader, phase_file + loader.get_postfix_str(),style_file_name=None, dt=dt, batch_size=batch_size, mirror=0.0) # when apply phase, should avoid mirror
model = StyleVAENet(data_module.skeleton, phase_dim=phase_dim, latent_size=latent_size,batch_size=batch_size,mode='pretrain',net_mode=net_mode)
if (args.dev_run):
trainer = Trainer(**trainer_dict, **test_model(),
**select_gpu_par(), precision=32, reload_dataloaders_every_n_epochs=1,#gradient_clip_val=1.0,#**detect_nan_par(),
log_every_n_steps=5, flush_logs_every_n_steps=10,
weights_summary='full')
else:
trainer = Trainer(**trainer_dict, max_epochs=10000, reload_dataloaders_every_n_epochs=1,gradient_clip_val=1.0,#**detect_nan_par(),
**select_gpu_par(), log_every_n_steps=50,
flush_logs_every_n_steps=100)
trainer.fit(model, datamodule=data_module)
else:
style_loader = StyleLoader()
data_module = StyleVAE_DataModule(style_loader, phase_file + loader.get_postfix_str(),None, dt=dt, batch_size=batch_size, mirror=0.0)
data_module.setup()
check_file = ckpt_path + "/"
if (args.epoch == "last"):
check_file += "last.ckpt"
print(check_file)
else:
dirs = os.listdir(check_file)
for dir in dirs:
st = "epoch=" + args.epoch + "-step=\d+.ckpt"
out = re.findall(st, dir)
if (len(out) > 0):
check_file += out[0]
print(check_file)
break
model = StyleVAENet.load_from_checkpoint(check_file, moe_decoder=None,pose_channels=6,net_mode=net_mode,strict=False)
model = model.cuda()
src_motion = data_module.test_set.dataset["HighKnees"][0]
source = BVH.read_bvh("source.bvh")
'''check if space can produce netural space: encoding=False, style=kick'''
data_module.mirror = 0
model = model.cpu()
model.eval()
app = Application(model, data_module)
app = app.float()
app.setSource(src_motion)
output = copy.deepcopy(source)
output.hip_pos, output.quats = app.forward(seed=3000,encoding=True)
BVH.save_bvh("test_net.bvh", output)
source.hip_pos, source.quats = app.get_source()
BVH.save_bvh("source.bvh", source)
torch.save(model, ckpt_path + "/m_save_model_" + str(args.epoch))
if __name__ == '__main__':
setup_seed(3407)
training_style100()