mmcv/parallel/_functions.py
def scatter(input, devices, streams=None):
    """Scatters tensor across multiple GPUs.
    """
    if streams is None:
        streams = [None] * len(devices)

    if isinstance(input, list):
        chunk_size = (len(input) - 1) // len(devices) + 1
        outputs = [
            scatter(input[i], [devices[i // chunk_size]],
                    [streams[i // chunk_size]]) for i in range(len(input))
        ]
        return outputs
    elif isinstance(input, torch.Tensor):
        output = input.contiguous()
        # TODO: copy to a pinned buffer first (if copying from CPU)
        stream = streams[0] if output.numel() > 0 else None
        with torch.cuda.device(devices[0]), torch.cuda.stream(stream):
            output = output.cuda(devices[0], non_blocking=True)
        return output
    else:
        raise Exception('Unknown type {}.'.format(type(input)))
def scatter(input, devices, streams=None):
    """Scatters tensor across multiple GPUs.
    """
    if streams is None:
        streams = [None] * len(devices)

    if isinstance(input, list):
        chunk_size = (len(input) - 1) // len(devices) + 1
        outputs = [
            scatter(input[i], [devices[i // chunk_size]],
                    [streams[i // chunk_size]]) for i in range(len(input))
        ]
        return outputs
    elif isinstance(input, torch.Tensor):
        output = input.contiguous()
        # TODO: copy to a pinned buffer first (if copying from CPU)
        stream = streams[0] if output.numel() > 0 else None
        with torch.cuda.device(devices[0]), torch.cuda.stream(stream):
            output = output.cuda(devices[0], non_blocking=True)
        return output
    else:
        raise Exception('Unknown type {}.'.format(type(input)))
def scatter ( input , devices , streams = None ) : if streams is None : streams = [ None ] * len ( devices ) if isinstance ( input , list ) : chunk_size = ( len ( input ) - 1 ) // len ( devices ) + 1 outputs = [ scatter ( input [ i ] , [ devices [ i // chunk_size ] ] , [ streams [ i // chunk_size ] ] ) for i in range ( len ( input ) ) ] return outputs elif isinstance ( input , torch . Tensor ) : output = input . contiguous ( ) # TODO: copy to a pinned buffer first (if copying from CPU) stream = streams [ 0 ] if output . numel ( ) > 0 else None with torch . cuda . device ( devices [ 0 ] ) , torch . cuda . stream ( stream ) : output = output . cuda ( devices [ 0 ] , non_blocking = True ) return output else : raise Exception ( 'Unknown type {}.' . format ( type ( input ) ) )
Scatters tensor across multiple GPUs.
Scatters tensor across multiple GPUs .