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train.py
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train.py
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#!/usr/bin/env python3
"""Train L-CNN
Usage:
train.py [options] <yaml-config>
train.py (-h | --help )
Arguments:
<yaml-config> Path to the yaml hyper-parameter file
Options:
-h --help Show this screen.
-d --devices <devices> Comma seperated GPU devices [default: 0]
-i --identifier <identifier> Folder identifier [default: default-identifier]
"""
import datetime
import glob
import os
import os.path as osp
import platform
import pprint
import random
import shlex
import shutil
import signal
import subprocess
import sys
import threading
import numpy as np
import torch
import yaml
from docopt import docopt
import lcnn
from lcnn.config import C, M
from lcnn.datasets import WireframeDataset, collate
from lcnn.models.line_vectorizer import LineVectorizer
from lcnn.models.multitask_learner import MultitaskHead, MultitaskLearner
def git_hash():
cmd = 'git log -n 1 --pretty="%h"'
ret = subprocess.check_output(shlex.split(cmd)).strip()
if isinstance(ret, bytes):
ret = ret.decode()
return ret
def get_outdir(identifier):
# load config
name = str(datetime.datetime.now().strftime("%y%m%d-%H%M%S"))
name += "-%s" % git_hash()
name += "-%s" % identifier
outdir = osp.join(osp.expanduser(C.io.logdir), name)
if not osp.exists(outdir):
os.makedirs(outdir)
C.io.resume_from = outdir
C.to_yaml(osp.join(outdir, "config.yaml"))
os.system(f"git diff HEAD > {outdir}/gitdiff.patch")
return outdir
def main():
args = docopt(__doc__)
config_file = args["<yaml-config>"] or "config/wireframe.yaml"
C.update(C.from_yaml(filename=config_file))
M.update(C.model)
pprint.pprint(C, indent=4)
resume_from = C.io.resume_from
# WARNING: L-CNN is still not deterministic
random.seed(0)
np.random.seed(0)
torch.manual_seed(0)
device_name = "cpu"
os.environ["CUDA_VISIBLE_DEVICES"] = args["--devices"]
if torch.cuda.is_available():
device_name = "cuda"
torch.backends.cudnn.deterministic = True
torch.cuda.manual_seed(0)
print("Let's use", torch.cuda.device_count(), "GPU(s)!")
else:
print("CUDA is not available")
device = torch.device(device_name)
# 1. dataset
# uncomment for debug DataLoader
# wireframe.datasets.WireframeDataset(datadir, split="train")[0]
# sys.exit(0)
datadir = C.io.datadir
kwargs = {
"collate_fn": collate,
"num_workers": C.io.num_workers if os.name != "nt" else 0,
"pin_memory": True,
}
train_loader = torch.utils.data.DataLoader(
WireframeDataset(datadir, split="train"),
shuffle=True,
batch_size=M.batch_size,
**kwargs,
)
val_loader = torch.utils.data.DataLoader(
WireframeDataset(datadir, split="valid"),
shuffle=False,
batch_size=M.batch_size_eval,
**kwargs,
)
epoch_size = len(train_loader)
# print("epoch_size (train):", epoch_size)
# print("epoch_size (valid):", len(val_loader))
if resume_from:
checkpoint = torch.load(osp.join(resume_from, "checkpoint_latest.pth"))
# 2. model
if M.backbone == "stacked_hourglass":
model = lcnn.models.hg(
depth=M.depth,
head=MultitaskHead,
num_stacks=M.num_stacks,
num_blocks=M.num_blocks,
num_classes=sum(sum(M.head_size, [])),
)
else:
raise NotImplementedError
model = MultitaskLearner(model)
model = LineVectorizer(model)
if resume_from:
model.load_state_dict(checkpoint["model_state_dict"])
model = model.to(device)
# 3. optimizer
if C.optim.name == "Adam":
optim = torch.optim.Adam(
model.parameters(),
lr=C.optim.lr,
weight_decay=C.optim.weight_decay,
amsgrad=C.optim.amsgrad,
)
elif C.optim.name == "SGD":
optim = torch.optim.SGD(
model.parameters(),
lr=C.optim.lr,
weight_decay=C.optim.weight_decay,
momentum=C.optim.momentum,
)
else:
raise NotImplementedError
if resume_from:
optim.load_state_dict(checkpoint["optim_state_dict"])
outdir = resume_from or get_outdir(args["--identifier"])
print("outdir:", outdir)
try:
trainer = lcnn.trainer.Trainer(
device=device,
model=model,
optimizer=optim,
train_loader=train_loader,
val_loader=val_loader,
out=outdir,
)
if resume_from:
trainer.iteration = checkpoint["iteration"]
if trainer.iteration % epoch_size != 0:
print("WARNING: iteration is not a multiple of epoch_size, reset it")
trainer.iteration -= trainer.iteration % epoch_size
trainer.best_mean_loss = checkpoint["best_mean_loss"]
del checkpoint
trainer.train()
except BaseException:
if len(glob.glob(f"{outdir}/viz/*")) <= 1:
shutil.rmtree(outdir)
raise
if __name__ == "__main__":
main()