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train_3dod.py
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train_3dod.py
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"""
Author: Guanghan Ning
Date: August, 2019
"""
import sys, os, time
sys.path.insert(0, "/export/guanghan/CenterNet-Gluon/dataset")
sys.path.insert(0, "/Users/guanghan.ning/Desktop/dev/CenterNet-Gluon/dataset")
import mxnet as mx
from mxnet import nd, gluon, init, autograd
from gluoncv.data.batchify import Tuple, Stack, Pad
from opts import opts
from models.model import create_model, load_model, save_model
from models.losses import DddLoss
from kitti_centernet import CenterKITTIDataset
from detectors.ddd_detector import DddDetector
from progress.bar import Bar
from utils.misc import AverageMeter
def get_kitti(opt, kitti_path):
"""Get coco dataset."""
train_dataset = CenterKITTIDataset(opt, split = 'train') # custom dataset
val_dataset = CenterKITTIDataset(opt, split = 'val') # custom dataset
opt.val_interval = 10
return train_dataset, val_dataset
def get_dataloader(train_dataset, data_shape, batch_size, num_workers, ctx):
"""Get dataloader."""
width, height = data_shape, data_shape
batchify_fn = Tuple(Stack(), Stack(), Stack(), Stack(), Stack(), Stack(), Stack(), Stack(), Stack(), Stack(), Stack())
train_loader = gluon.data.DataLoader(train_dataset, batch_size, True, batchify_fn=batchify_fn, last_batch='rollover', num_workers=num_workers)
return train_loader
def train(model, train_loader, val_dataset, ctx, opt):
"""Training pipeline"""
model.collect_params().reset_ctx(ctx)
trainer = gluon.Trainer(model.collect_params(),
'adam',
{'learning_rate': opt.lr})
criterion = DddLoss(opt)
for epoch in range(opt.cur_epoch, opt.num_epochs):
# training loop
print("Training Epoch: {}".format(epoch))
cumulative_train_loss = nd.zeros(1, ctx=ctx[0])
training_samples = 0
start = time.time()
for i, batch in enumerate(train_loader):
data = gluon.utils.split_and_load(batch[0], ctx_list=ctx, batch_axis=0)
targets_center = gluon.utils.split_and_load(batch[1], ctx_list=ctx, batch_axis=0) # heatmaps: (batch, num_classes, H/S, W/S)
targets_2d_wh = gluon.utils.split_and_load(batch[2], ctx_list=ctx, batch_axis=0) # scale: wh (batch, 2, H/S, W/S)
targets_2d_offset = gluon.utils.split_and_load(batch[3], ctx_list=ctx, batch_axis=0) # offset: xy (batch, 2, H/s, W/S)
targets_3d_depth = gluon.utils.split_and_load(batch[4], ctx_list=ctx, batch_axis=0)
targets_3d_dim = gluon.utils.split_and_load(batch[5], ctx_list=ctx, batch_axis=0)
targets_3d_rotbin = gluon.utils.split_and_load(batch[6], ctx_list=ctx, batch_axis=0)
targets_3d_rotres = gluon.utils.split_and_load(batch[7], ctx_list=ctx, batch_axis=0)
targets_inds = gluon.utils.split_and_load(batch[8], ctx_list=ctx, batch_axis=0)
targets_2d_wh_mask = gluon.utils.split_and_load(batch[9], ctx_list=ctx, batch_axis=0)
targets_3d_rot_mask = gluon.utils.split_and_load(batch[10], ctx_list=ctx, batch_axis=0)
with autograd.record():
losses = [criterion(model(X), hm, wh, offset, dep, dim, rotbin, rotres, inds, wh_mask, rot_mask) \
for X, hm, wh, offset, dep, dim, rotbin, rotres, inds, wh_mask, rot_mask in \
zip(data, targets_center, targets_2d_wh, targets_2d_offset, \
targets_3d_depth, targets_3d_dim, targets_3d_rotbin, targets_3d_rotres, \
targets_inds, targets_2d_wh_mask, targets_3d_rot_mask)]
for loss in losses:
loss.backward()
# normalize loss by batch-size
num_gpus = len(opt.gpus)
trainer.step(opt.batch_size // num_gpus, ignore_stale_grad=True)
for loss in losses:
cumulative_train_loss += loss.sum().as_in_context(ctx[0])
training_samples += opt.batch_size // num_gpus
if i % 20 == 1:
print("\t Iter: {}, loss: {}".format(i, losses[0].as_in_context(ctx[0]).asscalar()))
train_hours = (time.time() - start) / 3600.0 # 1 epoch training time in hours
train_loss_per_epoch = cumulative_train_loss.asscalar() / training_samples
print("Epoch {}, time: {:.2f} hours, training loss: {:.3f}".format(epoch, train_hours, train_loss_per_epoch))
# Save parameters
prefix = "3DOD_" + opt.arch
model_path = '{:s}_{:04d}.params'.format(prefix, epoch)
if not os.path.exists(model_path):
save_model(model, '{:s}_{:04d}.params'.format(prefix, epoch))
# validation loop
if epoch % opt.val_interval == 0:
validate(model, val_dataset, opt, ctx[-1])
def validate(model, dataset, opt, ctx):
"""Test on validation dataset."""
detector = DddDetector(opt)
detector.model = model
results = {}
num_iters = len(dataset)
bar = Bar('{}'.format(opt.exp_id), max=num_iters)
time_stats = ['tot', 'load', 'pre', 'net', 'dec', 'post', 'merge']
avg_time_stats = {t: AverageMeter() for t in time_stats}
print("Reporting every 1000 images...")
for ind in range(num_iters):
img_id = dataset.images[ind]
img_info = dataset.coco.loadImgs(ids=[img_id])[0] # Kitti has been transformed to COCO format
img_path = os.path.join(dataset.img_dir, img_info['file_name'])
ret = detector.run(img_path, img_info['calib'])
results[img_id] = ret['results']
Bar.suffix = '[{0}/{1}]|Tot: {total:} |ETA: {eta:} '.format(
ind, num_iters, total=bar.elapsed_td, eta=bar.eta_td)
for t in avg_time_stats:
avg_time_stats[t].update(ret[t])
Bar.suffix = Bar.suffix + '|{} {:.3f} '.format(t, avg_time_stats[t].avg)
if ind % 1000 == 0:
bar.next()
bar.finish()
val_dataset.run_eval(results = results, save_dir = './output/')
if __name__ == "__main__":
opt = opts()
opt.task = "ddd"
opt = opt.init()
ctx = [mx.gpu(int(i)) for i in opt.gpus_str.split(',') if i.strip()]
ctx = ctx if ctx else [mx.cpu()]
print("Using Devices: ", ctx)
""" 1. network """
print('Creating model...')
print("Using network architecture: ", opt.arch)
model = create_model(opt.arch, opt.heads, opt.head_conv, ctx = ctx)
opt.cur_epoch = 0
if opt.flag_finetune:
model = load_model(model, opt.pretrained_path, ctx = ctx)
#model = model.load_parameters(opt.pretrained_path, ctx=ctx, ignore_extra=True, allow_missing = True)
opt.cur_epoch = int(opt.pretrained_path.split('.')[0][-4:])
elif opt.arch != "res_18":
model.collect_params().initialize(init=init.Xavier(), ctx = ctx)
""" 2. Dataset """
train_dataset, val_dataset = get_kitti(opt, "./data/kitti")
data_shape = opt.input_res
batch_size = opt.batch_size
num_workers = opt.num_workers
train_loader = get_dataloader(train_dataset, data_shape, batch_size, num_workers, ctx)
""" 3. Training """
train(model, train_loader, val_dataset, ctx, opt)