Python get_model_parameters Examples

Example #1

                                  device_ids=cuda_devices,
                                  output_device=cuda_device.index,
                                  replicate_once=True)

if multi_gpu:
    optimizer = mymodel.build_optimizer(
        Optimizer,
        lr=init_lr,
        betas=adam_betas_default,
        eps=ieps_adam_default,
        weight_decay=cnfg.weight_decay,
        amsgrad=use_ams,
        multi_gpu_optimizer=multi_gpu_optimizer,
        contiguous_parameters=contiguous_parameters)
else:
    optimizer = Optimizer(get_model_parameters(
        mymodel, contiguous_parameters=contiguous_parameters),
                          lr=init_lr,
                          betas=adam_betas_default,
                          eps=ieps_adam_default,
                          weight_decay=cnfg.weight_decay,
                          amsgrad=use_ams)
optimizer.zero_grad(set_to_none=optm_step_zero_grad_set_none)

lrsch = LRScheduler(optimizer, cnfg.isize, cnfg.warm_step, scale=cnfg.lr_scale)

state_holder = None if statesf is None and cnt_states is None else Holder(
    **{
        "optm": optimizer,
        "lrsch": lrsch,
        "pyrand": PyRandomState(),
        "thrand": THRandomState(use_cuda=use_cuda)

Example #2

File: train_doc_para.py Project: hfxunlp/transformer

	lossf.to(cuda_device)

optimizer = Optimizer(filter_para_grad(mymodel.parameters()), lr=init_lr, betas=adam_betas_default, eps=ieps_adam_default, weight_decay=cnfg.weight_decay, amsgrad=use_ams)
optimizer.zero_grad(set_to_none=optm_step_zero_grad_set_none)

use_amp = cnfg.use_amp and use_cuda
scaler = (MultiGPUGradScaler() if multi_gpu_optimizer else GradScaler()) if use_amp else None

if multi_gpu:
	mymodel = DataParallelMT(mymodel, device_ids=cuda_devices, output_device=cuda_device.index, host_replicate=True, gather_output=False)
	lossf = DataParallelCriterion(lossf, device_ids=cuda_devices, output_device=cuda_device.index, replicate_once=True)

if multi_gpu:
	optimizer = mymodel.build_optimizer(Optimizer, lr=init_lr, betas=adam_betas_default, eps=ieps_adam_default, weight_decay=cnfg.weight_decay, amsgrad=use_ams, multi_gpu_optimizer=multi_gpu_optimizer, contiguous_parameters=contiguous_parameters)
else:
	optimizer = Optimizer(get_model_parameters(mymodel, contiguous_parameters=contiguous_parameters), lr=init_lr, betas=adam_betas_default, eps=ieps_adam_default, weight_decay=cnfg.weight_decay, amsgrad=use_ams)
optimizer.zero_grad(set_to_none=optm_step_zero_grad_set_none)

lrsch = LRScheduler(optimizer, cnfg.isize, cnfg.warm_step, scale=cnfg.lr_scale)

state_holder = None if statesf is None and cnt_states is None else Holder(**{"optm": optimizer, "lrsch": lrsch, "pyrand": PyRandomState(), "thrand": THRandomState(use_cuda=use_cuda)})

num_checkpoint = cnfg.num_checkpoint
cur_checkid = 0

tminerr = inf_default

minloss, minerr = eva(vd, vl, mymodel, lossf, cuda_device, multi_gpu, use_amp)
logger.info("".join(("Init lr: ", ",".join(tostr(getlr(optimizer))), ", Dev Loss/Error: %.3f %.2f" % (minloss, minerr))))

if fine_tune_m is None: