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Python parallel_apply.get_a_var方法代碼示例

本文整理匯總了Python中torch.nn.parallel.parallel_apply.get_a_var方法的典型用法代碼示例。如果您正苦於以下問題：Python parallel_apply.get_a_var方法的具體用法？Python parallel_apply.get_a_var怎麽用？Python parallel_apply.get_a_var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.parallel_apply的用法示例。


在下文中一共展示了parallel_apply.get_a_var方法的11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: criterion_parallel_apply

# 需要導入模塊: from torch.nn.parallel import parallel_apply [as 別名]
# 或者: from torch.nn.parallel.parallel_apply import get_a_var [as 別名]
def criterion_parallel_apply(
        modules,
        inputs,
        targets,
        kwargs_tup=None,
        devices=None):
    assert len(modules) == len(inputs)
    assert len(targets) == len(inputs)
    if kwargs_tup is not None:
        assert len(modules) == len(kwargs_tup)
    else:
        kwargs_tup = ({},) * len(modules)
    if devices is not None:
        assert len(modules) == len(devices)
    else:
        devices = [None] * len(modules)
    devices = list(map(lambda x: _get_device_index(x, True), devices))
    lock = threading.Lock()
    results = {}
    grad_enabled = torch.is_grad_enabled()

    def _worker(i, module, input, target, kwargs, device=None):
        torch.set_grad_enabled(grad_enabled)
        if device is None:
            device = get_a_var(input).get_device()
        try:
            with torch.cuda.device(device):
                if not isinstance(input, (list, tuple)):
                    input = (input,)
                if not isinstance(target, (list, tuple)):
                    target = (target,)
                output = module(*input, *target, **kwargs)
            with lock:
                results[i] = output
        except Exception:
            with lock:
                results[i] = ExceptionWrapper(
                    where="in replica {} on device {}".format(i, device))

    if len(modules) > 1:
        threads = [threading.Thread(target=_worker,
                                    args=(i, module, input, target, kwargs, device))
                   for i, (module, input, target, kwargs, device) in
                   enumerate(zip(modules, inputs, kwargs_tup, devices))]

        for thread in threads:
            thread.start()
        for thread in threads:
            thread.join()
    else:
        _worker(0, modules[0], inputs[0], kwargs_tup[0], devices[0])

    outputs = []
    for i in range(len(inputs)):
        output = results[i]
        if isinstance(output, ExceptionWrapper):
            output.reraise()
        outputs.append(output)
    return outputs
開發者ID:PistonY，項目名稱:torch-toolbox，代碼行數:61，代碼來源:EncodingDataParallel.py

示例2: _criterion_parallel_apply

# 需要導入模塊: from torch.nn.parallel import parallel_apply [as 別名]
# 或者: from torch.nn.parallel.parallel_apply import get_a_var [as 別名]
def _criterion_parallel_apply(modules, inputs, targets, kwargs_tup=None, devices=None):
    assert len(modules) == len(inputs)
    assert len(targets) == len(inputs)
    if kwargs_tup:
        assert len(modules) == len(kwargs_tup)
    else:
        kwargs_tup = ({},) * len(modules)
    if devices is not None:
        assert len(modules) == len(devices)
    else:
        devices = [None] * len(modules)

    lock = threading.Lock()
    results = {}
    if torch_ver != "0.3":
        grad_enabled = torch.is_grad_enabled()

    def _worker(i, module, input, target, kwargs, device=None):
        if torch_ver != "0.3":
            torch.set_grad_enabled(grad_enabled)
        if device is None:
            device = get_a_var(input).get_device()
        try:
            if not isinstance(input, tuple):
                input = (input,)
            with torch.cuda.device(device):
                output = module(*(input + target), **kwargs)
            with lock:
                results[i] = output
        except Exception as e:
            with lock:
                results[i] = e

    if len(modules) > 1:
        threads = [threading.Thread(target=_worker,
                                    args=(i, module, input, target,
                                          kwargs, device),)
                   for i, (module, input, target, kwargs, device) in
                   enumerate(zip(modules, inputs, targets, kwargs_tup, devices))]

        for thread in threads:
            thread.start()
        for thread in threads:
            thread.join()
    else:
        _worker(0, modules[0], inputs[0], kwargs_tup[0], devices[0])

    outputs = []
    for i in range(len(inputs)):
        output = results[i]
        if isinstance(output, Exception):
            raise output
        outputs.append(output)
    return outputs


###########################################################################
# Adapted from Synchronized-BatchNorm-PyTorch.
# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
#
開發者ID:speedinghzl，項目名稱:pytorch-segmentation-toolbox，代碼行數:62，代碼來源:encoding.py

示例3: _criterion_parallel_apply

# 需要導入模塊: from torch.nn.parallel import parallel_apply [as 別名]
# 或者: from torch.nn.parallel.parallel_apply import get_a_var [as 別名]
def _criterion_parallel_apply(modules, inputs, targets, kwargs_tup=None, devices=None):
    assert len(modules) == len(inputs)
    assert len(targets) == len(inputs)
    if kwargs_tup:
        assert len(modules) == len(kwargs_tup)
    else:
        kwargs_tup = ({},) * len(modules)
    if devices is not None:
        assert len(modules) == len(devices)
    else:
        devices = [None] * len(modules)

    lock = threading.Lock()
    results = {}
    if torch_ver != "0.3":
        grad_enabled = torch.is_grad_enabled()

    def _worker(i, module, input, target, kwargs, device=None):
        if torch_ver != "0.3":
            torch.set_grad_enabled(grad_enabled)
        if device is None:
            device = get_a_var(input).get_device()
        try:
            with torch.cuda.device(device):
                # output = module(*(input + target), **kwargs)
                if type(input) == tuple:
                    input = input[0]
                # print('module: {}\ninput: {}\ntarget:{}'.format(module, input, target))
                output = module(input, target)
            with lock:
                results[i] = output
        except Exception as e:
            with lock:
                results[i] = e

    if len(modules) > 1:
        threads = [threading.Thread(target=_worker,
                                    args=(i, module, input, target,
                                          kwargs, device), )
                   for i, (module, input, target, kwargs, device) in
                   enumerate(zip(modules, inputs, targets, kwargs_tup, devices))]

        for thread in threads:
            thread.start()
        for thread in threads:
            thread.join()
    else:
        _worker(0, modules[0], inputs[0], kwargs_tup[0], devices[0])

    outputs = []
    for i in range(len(inputs)):
        output = results[i]
        if isinstance(output, Exception):
            raise output
        outputs.append(output)
    return outputs
開發者ID:jason9693，項目名稱:MusicTransformer-pytorch，代碼行數:58，代碼來源:parallel.py

示例4: _criterion_parallel_apply

# 需要導入模塊: from torch.nn.parallel import parallel_apply [as 別名]
# 或者: from torch.nn.parallel.parallel_apply import get_a_var [as 別名]
def _criterion_parallel_apply(modules, inputs, targets, kwargs_tup=None, devices=None):
    assert len(modules) == len(inputs)
    assert len(targets) == len(inputs)
    if kwargs_tup:
        assert len(modules) == len(kwargs_tup)
    else:
        kwargs_tup = ({},) * len(modules)
    if devices is not None:
        assert len(modules) == len(devices)
    else:
        devices = [None] * len(modules)

    lock = threading.Lock()
    results = {}
    if torch_ver != "0.3":
        grad_enabled = torch.is_grad_enabled()

    def _worker(i, module, input, target, kwargs, device=None):
        if torch_ver != "0.3":
            torch.set_grad_enabled(grad_enabled)
        if device is None:
            device = get_a_var(input).get_device()
        try:
            with torch.cuda.device(device):
                output = module(*(input + target), **kwargs)
            with lock:
                results[i] = output
        except Exception as e:
            with lock:
                results[i] = e

    if len(modules) > 1:
        threads = [threading.Thread(target=_worker,
                                    args=(i, module, input, target,
                                          kwargs, device),)
                   for i, (module, input, target, kwargs, device) in
                   enumerate(zip(modules, inputs, targets, kwargs_tup, devices))]

        for thread in threads:
            thread.start()
        for thread in threads:
            thread.join()
    else:
        _worker(0, modules[0], inputs[0], kwargs_tup[0], devices[0])

    outputs = []
    for i in range(len(inputs)):
        output = results[i]
        if isinstance(output, Exception):
            raise output
        outputs.append(output)
    return outputs
開發者ID:zhanghang1989，項目名稱:PyTorch-Encoding，代碼行數:54，代碼來源:parallel.py

示例5: _criterion_parallel_apply

# 需要導入模塊: from torch.nn.parallel import parallel_apply [as 別名]
# 或者: from torch.nn.parallel.parallel_apply import get_a_var [as 別名]
def _criterion_parallel_apply(modules, inputs, targets, kwargs_tup=None, devices=None):
    assert len(modules) == len(inputs)
    assert len(targets) == len(inputs)
    if kwargs_tup:
        assert len(modules) == len(kwargs_tup)
    else:
        kwargs_tup = ({},) * len(modules)
    if devices is not None:
        assert len(modules) == len(devices)
    else:
        devices = [None] * len(modules)

    lock = threading.Lock()
    results = {}
    if torch_ver != "0.3":
        grad_enabled = torch.is_grad_enabled()

    def _worker(i, module, input, target, kwargs, device=None):
        if torch_ver != "0.3":
            torch.set_grad_enabled(grad_enabled)
        if device is None:
            device = get_a_var(input).get_device()
        try:
            with torch.cuda.device(device):
                output = module(input, *target, **kwargs)
            with lock:
                results[i] = output
        except Exception as e:
            with lock:
                results[i] = e

    if len(modules) > 1:
        threads = [threading.Thread(target=_worker,
                                    args=(i, module, input, target,
                                          kwargs, device),)
                   for i, (module, input, target, kwargs, device) in
                   enumerate(zip(modules, inputs, targets, kwargs_tup, devices))]

        for thread in threads:
            thread.start()
        for thread in threads:
            thread.join()
    else:
        _worker(0, modules[0], inputs[0], targets[0], kwargs_tup[0], devices[0])

    outputs = []
    for i in range(len(inputs)):
        output = results[i]
        if isinstance(output, Exception):
            raise output
        outputs.append(output)
    return outputs


###########################################################################
# Adapted from Synchronized-BatchNorm-PyTorch.
# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
#
開發者ID:openseg-group，項目名稱:openseg.pytorch，代碼行數:60，代碼來源:data_parallel.py

示例6: parallel_apply

# 需要導入模塊: from torch.nn.parallel import parallel_apply [as 別名]
# 或者: from torch.nn.parallel.parallel_apply import get_a_var [as 別名]
def parallel_apply(modules, func_name, inputs, kwargs_tup=None, devices=None):
    r"""Applies each `module` in :attr:`modules` in parallel on arguments
    contained in :attr:`inputs` (positional) and :attr:`kwargs_tup` (keyword)
    on each of :attr:`devices`.

    :attr:`modules`, :attr:`inputs`, :attr:`kwargs_tup` (if given), and
    :attr:`devices` (if given) should all have same length. Moreover, each
    element of :attr:`inputs` can either be a single object as the only argument
    to a module, or a collection of positional arguments.
    """
    assert len(modules) == len(inputs)
    if kwargs_tup is not None:
        assert len(modules) == len(kwargs_tup)
    else:
        kwargs_tup = ({},) * len(modules)
    if devices is not None:
        assert len(modules) == len(devices)
    else:
        devices = [None] * len(modules)
    
    lock = threading.Lock()
    results = {}
    grad_enabled = torch.is_grad_enabled()
    
    def _worker(i, module, input, kwargs, device=None):
        torch.set_grad_enabled(grad_enabled)
        if device is None:
            device = get_a_var(input).get_device()
        try:
            with torch.cuda.device(device):
                # this also avoids accidental slicing of `input` if it is a Tensor
                if not isinstance(input, (list, tuple)):
                    input = (input,)
                output = getattr(module, func_name)(*input, **kwargs)
            with lock:
                results[i] = output
        except Exception as e:
            with lock:
                results[i] = e
    
    if len(modules) > 1:
        threads = [threading.Thread(target=_worker,
                                    args=(i, module, input, kwargs, device))
                   for i, (module, input, kwargs, device) in
                   enumerate(zip(modules, inputs, kwargs_tup, devices))]
        
        for thread in threads:
            thread.start()
        for thread in threads:
            thread.join()
    else:
        _worker(0, modules[0], inputs[0], kwargs_tup[0], devices[0])
    
    outputs = []
    for i in range(len(inputs)):
        output = results[i]
        if isinstance(output, Exception):
            raise output
        outputs.append(output)
    return outputs
開發者ID:fastnlp，項目名稱:fastNLP，代碼行數:62，代碼來源:_parallel_utils.py

示例7: _criterion_parallel_apply

# 需要導入模塊: from torch.nn.parallel import parallel_apply [as 別名]
# 或者: from torch.nn.parallel.parallel_apply import get_a_var [as 別名]
def _criterion_parallel_apply(modules, inputs, targets, kwargs_tup=None, devices=None):
    assert len(modules) == len(inputs)
    assert len(targets) == len(inputs)
    if kwargs_tup:
        assert len(modules) == len(kwargs_tup)
    else:
        kwargs_tup = ({},) * len(modules)
    if devices is not None:
        assert len(modules) == len(devices)
    else:
        devices = [None] * len(modules)

    lock = threading.Lock()
    results = {}
    if torch_ver != "0.3":
        grad_enabled = torch.is_grad_enabled()

    def _worker(i, module, input, target, kwargs, device=None):
        #import pdb;pdb.set_trace()
        if torch_ver != "0.3":
            torch.set_grad_enabled(grad_enabled)
        if device is None:
            device = get_a_var(input).get_device()
        try:
            if not isinstance(input, tuple):
                input = (input,)
            if not isinstance(target, tuple):
                target = (target,)
            with torch.cuda.device(device):
                output = module(*(input + target), **kwargs)
            with lock:
                results[i] = output
        except Exception as e:
            with lock:
                results[i] = e

    if len(modules) > 1:
        threads = [threading.Thread(target=_worker,
                                    args=(i, module, input, target,
                                          kwargs, device),)
                   for i, (module, input, target, kwargs, device) in
                   enumerate(zip(modules, inputs, targets, kwargs_tup, devices))]

        for thread in threads:
            thread.start()
        for thread in threads:
            thread.join()
    else:
        _worker(0, modules[0], inputs[0], kwargs_tup[0], devices[0])

    outputs = []
    for i in range(len(inputs)):
        output = results[i]
        if isinstance(output, Exception):
            raise output
        outputs.append(output)
    return outputs
開發者ID:irfanICMLL，項目名稱:structure_knowledge_distillation，代碼行數:59，代碼來源:parallel.py

示例8: _criterion_parallel_apply

# 需要導入模塊: from torch.nn.parallel import parallel_apply [as 別名]
# 或者: from torch.nn.parallel.parallel_apply import get_a_var [as 別名]
def _criterion_parallel_apply(modules, inputs, targets, kwargs_tup=None, devices=None):
    assert len(modules) == len(inputs)
    assert len(targets) == len(inputs)
    if kwargs_tup:
        assert len(modules) == len(kwargs_tup)
    else:
        kwargs_tup = ({},) * len(modules)
    if devices is not None:
        assert len(modules) == len(devices)
    else:
        devices = [None] * len(modules)

    lock = threading.Lock()
    results = {}
    if torch_ver != "0.3":
        grad_enabled = torch.is_grad_enabled()

    def _worker(i, module, input, target, kwargs, device=None):
        if torch_ver != "0.3":
            torch.set_grad_enabled(grad_enabled)
        if device is None:
            device = get_a_var(input).get_device()
        try:
            with torch.cuda.device(device):
                output = module(input, target, **kwargs)
                # output = module(*(input + target), **kwargs)
            with lock:
                results[i] = output
        except Exception as e:
            with lock:
                results[i] = e

    if len(modules) > 1:
        threads = [threading.Thread(target=_worker,
                                    args=(i, module, input, target,
                                          kwargs, device),)
                   for i, (module, input, target, kwargs, device) in
                   enumerate(zip(modules, inputs, targets, kwargs_tup, devices))]

        for thread in threads:
            thread.start()
        for thread in threads:
            thread.join()
    else:
        _worker(0, modules[0], inputs[0], kwargs_tup[0], devices[0])

    outputs = []
    for i in range(len(inputs)):
        output = results[i]
        if isinstance(output, Exception):
            raise output
        outputs.append(output)
    return outputs


###########################################################################
# Adapted from Synchronized-BatchNorm-PyTorch.
# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
#
開發者ID:fregu856，項目名稱:evaluating_bdl，代碼行數:61，代碼來源:parallel.py

示例9: _criterion_parallel_apply

# 需要導入模塊: from torch.nn.parallel import parallel_apply [as 別名]
# 或者: from torch.nn.parallel.parallel_apply import get_a_var [as 別名]
def _criterion_parallel_apply(modules, inputs, targets, kwargs_tup=None, devices=None):
    assert len(modules) == len(inputs)
    assert len(targets) == len(inputs)

    if kwargs_tup:
        assert len(modules) == len(kwargs_tup)
    else:
        kwargs_tup = ({},) * len(modules)
    if devices is not None:
        assert len(modules) == len(devices)
    else:
        devices = [None] * len(modules)

    lock = threading.Lock()
    results = {}
    if torch_ver != "0.3":
        grad_enabled = torch.is_grad_enabled()

    def _worker(i, module, input, target, kwargs, device=None):
        if torch_ver != "0.3":
            torch.set_grad_enabled(grad_enabled)

        if device is None:
            device = get_a_var(input).get_device()
        try:
            with torch.cuda.device(device):
                output = module(input, target)
            with lock:
                results[i] = output
        except Exception as e:
            with lock:
                results[i] = e

    if len(modules) > 1:
        threads = [threading.Thread(target=_worker,
                                    args=(i, module, input, target,
                                          kwargs, device),)
                   for i, (module, input, target, kwargs, device) in
                   enumerate(zip(modules, inputs, targets, kwargs_tup, devices))]

        for thread in threads:
            thread.start()
        for thread in threads:
            thread.join()
    else:
        _worker(0, modules[0], inputs[0], kwargs_tup[0], devices[0])

    outputs = []
    for i in range(len(inputs)):
        output = results[i]
        if isinstance(output, Exception):
            raise output
        outputs.append(output)
    return outputs


###########################################################################
# Adapted from Synchronized-BatchNorm-PyTorch.
# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
#
開發者ID:ansleliu，項目名稱:LightNetPlusPlus，代碼行數:62，代碼來源:parallel.py

示例10: _criterion_parallel_apply

# 需要導入模塊: from torch.nn.parallel import parallel_apply [as 別名]
# 或者: from torch.nn.parallel.parallel_apply import get_a_var [as 別名]
def _criterion_parallel_apply(modules, inputs, targets, kwargs_tup=None, devices=None):
    assert len(modules) == len(inputs)
    assert len(targets) == len(inputs)
    if kwargs_tup:
        assert len(modules) == len(kwargs_tup)
    else:
        kwargs_tup = ({},) * len(modules)
    if devices is not None:
        assert len(modules) == len(devices)
    else:
        devices = [None] * len(modules)

    lock = threading.Lock()
    results = {}
    if torch_ver != "0.3":
        grad_enabled = torch.is_grad_enabled()

    def _worker(i, module, input, target, kwargs, device=None):
        if torch_ver != "0.3":
            torch.set_grad_enabled(grad_enabled)
        if device is None:
            device = get_a_var(input).get_device()
        try:
            with torch.cuda.device(device):
                output = module(input, target)
            with lock:
                results[i] = output
        except Exception as e:
            with lock:
                results[i] = e

    if len(modules) > 1:
        threads = [threading.Thread(target=_worker,
                                    args=(i, module, input, target,
                                          kwargs, device),)
                   for i, (module, input, target, kwargs, device) in
                   enumerate(zip(modules, inputs, targets, kwargs_tup, devices))]

        for thread in threads:
            thread.start()
        for thread in threads:
            thread.join()
    else:
        _worker(0, modules[0], inputs[0], kwargs_tup[0], devices[0])

    outputs = []
    for i in range(len(inputs)):
        output = results[i]
        if isinstance(output, Exception):
            raise output
        outputs.append(output)
    return outputs
開發者ID:lyeoni，項目名稱:pretraining-for-language-understanding，代碼行數:54，代碼來源:parallel.py

示例11: _criterion_parallel_apply

# 需要導入模塊: from torch.nn.parallel import parallel_apply [as 別名]
# 或者: from torch.nn.parallel.parallel_apply import get_a_var [as 別名]
def _criterion_parallel_apply(modules, inputs, targets, kwargs_tup=None, devices=None):
    assert len(modules) == len(inputs)
    assert len(targets) == len(inputs)
    if kwargs_tup:
        assert len(modules) == len(kwargs_tup)
    else:
        kwargs_tup = ({},) * len(modules)
    if devices is not None:
        assert len(modules) == len(devices)
    else:
        devices = [None] * len(modules)

    lock = threading.Lock()
    results = {}
    if torch_ver != "0.3":
        grad_enabled = torch.is_grad_enabled()

    def _worker(i, module, input, target, kwargs, device=None):
        if torch_ver != "0.3":
            torch.set_grad_enabled(grad_enabled)
        if device is None:
            device = get_a_var(input).get_device()
        try:
            with torch.cuda.device(device):
                output = module(*(input + target), **kwargs)
            with lock:
                results[i] = output
        except Exception as e:
            with lock:
                results[i] = e

    if len(modules) > 1:
        threads = [threading.Thread(target=_worker,
                                    args=(i, module, input, target,
                                          kwargs, device),)
                   for i, (module, input, target, kwargs, device) in
                   enumerate(zip(modules, inputs, targets, kwargs_tup, devices))]

        for thread in threads:
            thread.start()
        for thread in threads:
            thread.join()
    else:
        _worker(0, modules[0], inputs[0], kwargs_tup[0], devices[0])

    outputs = []
    for i in range(len(inputs)):
        output = results[i]
        if isinstance(output, Exception):
            raise output
        outputs.append(output)
    return outputs


###########################################################################
# Adapted from Synchronized-BatchNorm-PyTorch.
# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
#
開發者ID:minerva-ml，項目名稱:open-solution-googleai-object-detection，代碼行數:60，代碼來源:parallel.py


注：本文中的torch.nn.parallel.parallel_apply.get_a_var方法示例由純淨天空整理自Github/MSDocs等開源代碼及文檔管理平台，相關代碼片段篩選自各路編程大神貢獻的開源項目，源碼版權歸原作者所有，傳播和使用請參考對應項目的License；未經允許，請勿轉載。