我们从Python开源项目中，提取了以下7个代码示例，用于说明如何使用chainer.cuda.get_device_from_id()。
def init_hx(self, xs): shape = (self.n_layers * self.direction, len(xs), self.out_size) with cuda.get_device_from_id(self._device_id): hx = variable.Variable(self.xp.zeros(shape, dtype=xs[0].dtype)) return hx
def __call__(self, x, gamma_=None, beta_=None): if hasattr(self, 'gamma'): gamma = self.gamma elif gamma_ is not None: gamma = gamma_ else: with cuda.get_device_from_id(self._device_id): gamma = variable.Variable(self.xp.ones( self.avg_mean.shape, dtype=x.dtype)) if hasattr(self, 'beta'): beta = self.beta elif beta_ is not None: beta = beta_ else: with cuda.get_device_from_id(self._device_id): beta = variable.Variable(self.xp.zeros( self.avg_mean.shape, dtype=x.dtype)) decay = self.decay if (not configuration.config.train) and self.valid_test: mean = variable.Variable(self.avg_mean) var = variable.Variable(self.avg_var) ret = fixed_instance_normalization( x, gamma, beta, mean, var, self.eps) else: func = InstanceNormalizationFunction( self.eps, self.avg_mean, self.avg_var, decay) ret = func(x, gamma, beta) self.avg_mean = func.running_mean self.avg_var = func.running_var return ret
def fit(self, content_image, style_image, epoch_num, callback=None): device_id = None if self.device_id >= 0: device_id = self.device_id with cuda.get_device_from_id(device_id): return self.__fit(content_image, style_image, epoch_num, callback)
def __call__(self, x, finetune=False): if hasattr(self, 'gamma'): gamma = self.gamma else: with cuda.get_device_from_id(self._device_id): gamma = variable.Variable(self.xp.ones( self.avg_mean.shape, dtype=x.dtype)) if hasattr(self, 'beta'): beta = self.beta else: with cuda.get_device_from_id(self._device_id): beta = variable.Variable(self.xp.zeros( self.avg_mean.shape, dtype=x.dtype)) if chainer.configuration.config.train: if finetune: self.N += 1 decay = 1. - 1. / self.N else: decay = self.decay func = MultiNodeBatchNormalizationFunction( self.comm, self.eps, self.avg_mean, self.avg_var, decay) ret = func(x, gamma, beta) self.avg_mean[:] = func.running_mean self.avg_var[:] = func.running_var else: # Use running average statistics or fine-tuned statistics. mean = variable.Variable(self.avg_mean) var = variable.Variable(self.avg_var) ret = batch_normalization.fixed_batch_normalization( x, gamma, beta, mean, var, self.eps) return ret
def init_hx(self, xs): hx_shape = self.n_layers * self.direction with cuda.get_device_from_id(self._device_id): if args.chainer_version_check[0] == 2: hx = chainer.Variable( self.xp.zeros((hx_shape, xs.data.shape[1], self.out_size), dtype=xs.dtype)) else: hx = chainer.Variable( self.xp.zeros((hx_shape, xs.data.shape[1], self.out_size), dtype=xs.dtype), volatile='auto') return hx
def __init__(self, model, optimizer, gpu=None, gamma=0.99, lambd=0.95, phi=lambda x: x, value_func_coef=1.0, entropy_coef=0.01, update_interval=2048, minibatch_size=64, epochs=10, clip_eps=0.2, clip_eps_vf=None, standardize_advantages=True, average_v_decay=0.999, average_loss_decay=0.99, ): self.model = model if gpu is not None and gpu >= 0: cuda.get_device_from_id(gpu).use() self.model.to_gpu(device=gpu) self.optimizer = optimizer self.gamma = gamma self.lambd = lambd self.phi = phi self.value_func_coef = value_func_coef self.entropy_coef = entropy_coef self.update_interval = update_interval self.minibatch_size = minibatch_size self.epochs = epochs self.clip_eps = clip_eps self.clip_eps_vf = clip_eps_vf self.standardize_advantages = standardize_advantages self.average_v = 0 self.average_v_decay = average_v_decay self.average_loss_policy = 0 self.average_loss_value_func = 0 self.average_loss_entropy = 0 self.average_loss_decay = average_loss_decay self.xp = self.model.xp self.last_state = None self.memory = [] self.last_episode = []
