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Python replicate.replicate方法代碼示例

本文整理匯總了Python中torch.nn.parallel.replicate.replicate方法的典型用法代碼示例。如果您正苦於以下問題：Python replicate.replicate方法的具體用法？Python replicate.replicate怎麽用？Python replicate.replicate使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類torch.nn.parallel.replicate的用法示例。


在下文中一共展示了replicate.replicate方法的12個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。

示例1: forward

# 需要導入模塊: from torch.nn.parallel import replicate [as 別名]
# 或者: from torch.nn.parallel.replicate import replicate [as 別名]
def forward(self, *inputs, **kwargs):
        if not self.device_ids:
            return self.module(*inputs, **kwargs)

        for t in chain(self.module.parameters(), self.module.buffers()):
            if t.device != self.src_device_obj:
                raise RuntimeError(
                    "module must have its parameters and buffers "
                    "on device {} (device_ids[0]) but found one of "
                    "them on device: {}".format(
                        self.src_device_obj, t.device))
        inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids)
        if len(self.device_ids) == 1:
            return self.module(*inputs, **kwargs)
        replicas = self.replicate(self.module, self.device_ids[:len(inputs)])
        outputs = self.parallel_apply(replicas, inputs, kwargs)
        return outputs
開發者ID:PistonY，項目名稱:torch-toolbox，代碼行數:19，代碼來源:EncodingDataParallel.py

示例2: _data_parallel_wrapper

# 需要導入模塊: from torch.nn.parallel import replicate [as 別名]
# 或者: from torch.nn.parallel.replicate import replicate [as 別名]
def _data_parallel_wrapper(func_name, device_ids, output_device):
    r"""
    這個函數是用於對需要多卡執行的函數的wrapper函數。參考的nn.DataParallel的forward函數

    :param str, func_name: 對network中的這個函數進行多卡運行
    :param device_ids: nn.DataParallel中的device_ids
    :param output_device: nn.DataParallel中的output_device
    :return:
    """
    
    def wrapper(network, *inputs, **kwargs):
        inputs, kwargs = scatter_kwargs(inputs, kwargs, device_ids, dim=0)
        if len(device_ids) == 1:
            return getattr(network, func_name)(*inputs[0], **kwargs[0])
        replicas = replicate(network, device_ids[:len(inputs)])
        outputs = parallel_apply(replicas, func_name, inputs, kwargs, device_ids[:len(replicas)])
        return gather(outputs, output_device)
    
    return wrapper
開發者ID:fastnlp，項目名稱:fastNLP，代碼行數:21，代碼來源:_parallel_utils.py

示例3: replicate

# 需要導入模塊: from torch.nn.parallel import replicate [as 別名]
# 或者: from torch.nn.parallel.replicate import replicate [as 別名]
def replicate(self, module, device_ids):
        return replicate(module, device_ids, not torch.is_grad_enabled())
開發者ID:PistonY，項目名稱:torch-toolbox，代碼行數:4，代碼來源:EncodingDataParallel.py

示例4: forward

# 需要導入模塊: from torch.nn.parallel import replicate [as 別名]
# 或者: from torch.nn.parallel.replicate import replicate [as 別名]
def forward(self, *inputs, **kwargs):
        if not self.device_ids:
            return self.module(*inputs, **kwargs)
        # 分散輸入到各個設備裏
        inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids, self.chunk_sizes)
        if len(self.device_ids) == 1:
            return self.module(*inputs[0], **kwargs[0])
        # 複製啥啊
        replicas = self.replicate(self.module, self.device_ids[:len(inputs)])
        # 使用並行函數獲得輸出
        outputs = self.parallel_apply(replicas, inputs, kwargs)
        return self.gather(outputs, self.output_device)

    # 對torch裏的函數進行了再包裝
開發者ID:DataXujing，項目名稱:CornerNet-Lite-Pytorch，代碼行數:16，代碼來源:data_parallel.py

示例5: replicate

# 需要導入模塊: from torch.nn.parallel import replicate [as 別名]
# 或者: from torch.nn.parallel.replicate import replicate [as 別名]
def replicate(self, module, device_ids):
        return replicate(module, device_ids)
開發者ID:DataXujing，項目名稱:CornerNet-Lite-Pytorch，代碼行數:4，代碼來源:data_parallel.py

示例6: data_parallel

# 需要導入模塊: from torch.nn.parallel import replicate [as 別名]
# 或者: from torch.nn.parallel.replicate import replicate [as 別名]
def data_parallel(module, inputs, device_ids=None, output_device=None, dim=0, module_kwargs=None):
    r"""Evaluates module(input) in parallel across the GPUs given in device_ids.

    This is the functional version of the DataParallel module.

    Args:
        module: the module to evaluate in parallel
        inputs: inputs to the module
        device_ids: GPU ids on which to replicate module
        output_device: GPU location of the output  Use -1 to indicate the CPU.
            (default: device_ids[0])
    Returns:
        a Variable containing the result of module(input) located on
        output_device
    """
    if not isinstance(inputs, tuple):
        inputs = (inputs,)

    if device_ids is None:
        device_ids = list(range(torch.cuda.device_count()))

    if output_device is None:
        output_device = device_ids[0]

    inputs, module_kwargs = scatter_kwargs(inputs, module_kwargs, device_ids, dim)
    if len(device_ids) == 1:
        return module(*inputs[0], **module_kwargs[0])
    used_device_ids = device_ids[:len(inputs)]
    replicas = replicate(module, used_device_ids)
    outputs = parallel_apply(replicas, inputs, module_kwargs, used_device_ids)
    return gather(outputs, output_device, dim)
開發者ID:DataXujing，項目名稱:CornerNet-Lite-Pytorch，代碼行數:33，代碼來源:data_parallel.py

示例7: forward

# 需要導入模塊: from torch.nn.parallel import replicate [as 別名]
# 或者: from torch.nn.parallel.replicate import replicate [as 別名]
def forward(self, *inputs, **kwargs):
        if not self.device_ids:
            return self.module(*inputs, **kwargs)
        inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids, self.chunk_sizes)
        if len(self.device_ids) == 1:
            return self.module(*inputs[0], **kwargs[0])
        replicas = self.replicate(self.module, self.device_ids[:len(inputs)])
        outputs = self.parallel_apply(replicas, inputs, kwargs)
        return self.gather(outputs, self.output_device)
開發者ID:xingyizhou，項目名稱:ExtremeNet，代碼行數:11，代碼來源:data_parallel.py

示例8: forward

# 需要導入模塊: from torch.nn.parallel import replicate [as 別名]
# 或者: from torch.nn.parallel.replicate import replicate [as 別名]
def forward(self, *inputs, **kwargs) -> Tuple[torch.Tensor, torch.Tensor]:
        if not self.device_ids:
            return self.flow.forward(*inputs, **kwargs)
        inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids)
        if len(self.device_ids) == 1:
            return self.flow.forward(*inputs[0], **kwargs[0])
        replicas = self.replicate(self.flow, self.device_ids[:len(inputs)])
        outputs = self.parallel_apply(replicas, inputs, kwargs)
        return self.gather(outputs, self.output_device)
開發者ID:XuezheMax，項目名稱:flowseq，代碼行數:11，代碼來源:data_parallel.py

示例9: backward

# 需要導入模塊: from torch.nn.parallel import replicate [as 別名]
# 或者: from torch.nn.parallel.replicate import replicate [as 別名]
def backward(self, *inputs, **kwargs) -> Tuple[torch.Tensor, torch.Tensor]:
        if not self.device_ids:
            return self.flow.backward(*inputs, **kwargs)
        inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids)
        if len(self.device_ids) == 1:
            return self.flow.backward(*inputs[0], **kwargs[0])
        replicas = self.replicate(self.flow, self.device_ids[:len(inputs)])
        outputs = self.parallel_apply(replicas, inputs, kwargs, backward=True)
        return self.gather(outputs, self.output_device)
開發者ID:XuezheMax，項目名稱:flowseq，代碼行數:11，代碼來源:data_parallel.py

示例10: replicate

# 需要導入模塊: from torch.nn.parallel import replicate [as 別名]
# 或者: from torch.nn.parallel.replicate import replicate [as 別名]
def replicate(self, flow, device_ids):
        return replicate(flow, device_ids)
開發者ID:XuezheMax，項目名稱:flowseq，代碼行數:4，代碼來源:data_parallel.py

示例11: forward

# 需要導入模塊: from torch.nn.parallel import replicate [as 別名]
# 或者: from torch.nn.parallel.replicate import replicate [as 別名]
def forward(self, *inputs, **kwargs):
        if not self.device_ids:
            return self.module(*inputs, **kwargs)
        inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids)
        if len(self.device_ids) == 1:
            return self.module(*inputs[0], **kwargs[0])
        replicas = self.replicate(self.module, self.device_ids[: len(inputs)])
        outputs = self.parallel_apply(replicas, inputs, kwargs)
        return outputs
開發者ID:belskikh，項目名稱:kekas，代碼行數:11，代碼來源:parallel.py

示例12: replicate

# 需要導入模塊: from torch.nn.parallel import replicate [as 別名]
# 或者: from torch.nn.parallel.replicate import replicate [as 別名]
def replicate(module, device_ids):
        return replicate(module, device_ids)
開發者ID:belskikh，項目名稱:kekas，代碼行數:4，代碼來源:parallel.py


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