def run():
opt = options_train_executor.parse()
print('===== arguments: program executor =====')
for key, val in vars(opt).items():
print("{:20} {}".format(key, val))
print('===== arguments: program executor =====')
if not os.path.isdir(opt.save_folder):
os.makedirs(opt.save_folder)
# build dataloader
train_loader = gdata.DataLoader(
dataset=train_set,
batch_size=opt.batch_size,
shuffle=True,
num_workers=opt.num_workers,
)
val_set = PartPrimitive(opt.val_file)
val_loader = gdata.DataLoader(
dataset=val_set,
batch_size=opt.batch_size,
shuffle=True,
num_workers=opt.num_workers,
)
# build the model
ctx = d2l.try_gpu()
model = RenderNet(opt)
model.initialize(init = init.Xavier(),ctx = ctx)
loss = gloss.SoftmaxCrossEntropyLoss(axis = 1,weight = 5)
optimizer = Trainer(model.collect_params(),"adam",
{"learning_rate":opt.learning_rate,"wd":opt.weight_decay,
'beta1':opt.beta1, 'beta2':opt.beta2})
train_from0 = False;
if train_from0:
if os.path.exists('./model of executor'):
model.load_parameters('model of executor')
print("loaded parameter of model")
if os.path.exists('./optimizer of executor'):
optimizer.load_states('optimizer of executor')
print("loaded state of trainer")
for epoch in range(1, opt.epochs+1):
adjust_learning_rate(epoch, opt, optimizer)
print("###################")
print("training")
train(epoch, train_loader, model,loss,optimizer, opt,ctx,train_loss,train_iou)
print("###################")
print("testing")
'''
gen_shapes, ori_shapes = validate(epoch, val_loader, model,
loss, opt,ctx, val_loss,val_iou, gen_shape=True)
gen_shapes = (gen_shapes > 0.5)
gen_shapes = gen_shapes.astype(np.float32)
iou = BatchIoU(ori_shapes, gen_shapes)
print("Mean IoU: {:.3f}".format(iou.mean().asscalar()))
'''
if epoch % opt.save_interval == 0:
print('Saving...')
optimizer.save_states("optimizer of executor_3"),
model.save_parameters("model of executor_3")
print('Saving...')
optimizer.save_states("optimizer of executor_3"),
model.save_parameters("model of executor_3")
bce_loss = gloss.SigmoidBinaryCrossEntropyLoss()
# initialize parameters of training
init_epoch = 0
best_iou = -1
best_epoch = -1
batch_size = cfg.CONST.BATCH_SIZE
# Load pretrained model if exists
if cfg.TRAIN.RESUME_TRAIN:
print('[INFO] %s Recovering weights' % dt.now())
checkpoint = nd.load("check_point")
init_epoch = checkpoint[0][0].asscalar()
best_iou = checkpoint[0][1].asscalar()
best_epoch = checkpoint[0][2].asscalar()
encoder.load_parameters('encoder_params')
decoder.load_parameters('decoder_params')
encoder_trainer.load_states("encoder_trainer")
decoder_trainer.load_states("decoder_trainer")
if os.path.exists(
'/home/hzx/my pix2vox model with refiner/merger_params'):
merger.load_parameters("merger_params")
refiner.load_parameters("refiner_params")
merger_trainer.load_states("merger_trainer")
refiner_trainer.load_states("refiner_trainer")
encoder.hybridize()
decoder.hybridize()
merger.hybridize()
print("net has been hybridized")
print(
'[INFO] %s Recover complete. Current epoch #%d, Best IoU = %.4f at epoch #%d.'
% (dt.now(), init_epoch, best_iou, best_epoch))