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Python comm.reduce_add方法代码示例

本文整理汇总了Python中torch.cuda.comm.reduce_add方法的典型用法代码示例。如果您正苦于以下问题:Python comm.reduce_add方法的具体用法?Python comm.reduce_add怎么用?Python comm.reduce_add使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在torch.cuda.comm的用法示例。


在下文中一共展示了comm.reduce_add方法的5个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: backward

# 需要导入模块: from torch.cuda import comm [as 别名]
# 或者: from torch.cuda.comm import reduce_add [as 别名]
def backward(ctx, dz):
        z, var, weight, bias = ctx.saved_tensors
        dz = dz.contiguous()

        # Undo activation
        _act_backward(ctx, z, dz)

        if ctx.training:
            edz, eydz = _backend.edz_eydz(z, dz, weight, bias, ctx.affine, ctx.eps)

            if ctx.is_master:
                edzs, eydzs = [edz], [eydz]
                for _ in range(len(ctx.worker_queues)):
                    edz_w, eydz_w = ctx.master_queue.get()
                    ctx.master_queue.task_done()
                    edzs.append(edz_w)
                    eydzs.append(eydz_w)

                edz = comm.reduce_add(edzs) / (ctx.master_queue.maxsize + 1)
                eydz = comm.reduce_add(eydzs) / (ctx.master_queue.maxsize + 1)

                tensors = comm.broadcast_coalesced((edz, eydz), [edz.get_device()] + ctx.worker_ids)
                for ts, queue in zip(tensors[1:], ctx.worker_queues):
                    queue.put(ts)
            else:
                ctx.master_queue.put((edz, eydz))
                edz, eydz = ctx.worker_queue.get()
                ctx.worker_queue.task_done()
        else:
            edz = dz.new_zeros(dz.size(1))
            eydz = dz.new_zeros(dz.size(1))

        dx, dweight, dbias = _backend.backward(z, dz, var, weight, bias, edz, eydz, ctx.affine, ctx.eps)
        dweight = dweight if ctx.affine else None
        dbias = dbias if ctx.affine else None

        return dx, dweight, dbias, None, None, None, None, None, None, None, None 
开发者ID:miraiaroha,项目名称:ACAN,代码行数:39,代码来源:functions.py

示例2: forward

# 需要导入模块: from torch.cuda import comm [as 别名]
# 或者: from torch.cuda.comm import reduce_add [as 别名]
def forward(ctx, *inputs):
        ctx.target_gpus = [inputs[i].get_device() for i in range(len(inputs))]
        inputs = sorted(inputs, key=lambda i: i.get_device())
        return comm.reduce_add(inputs) 
开发者ID:speedinghzl,项目名称:pytorch-segmentation-toolbox,代码行数:6,代码来源:encoding.py

示例3: backward

# 需要导入模块: from torch.cuda import comm [as 别名]
# 或者: from torch.cuda.comm import reduce_add [as 别名]
def backward(ctx, dz):
        x, weight, bias, mean, var = ctx.saved_tensors
        dz = dz.contiguous()

        # 1. compute \sum(\frac{dJ}{dy_i}) and \sum(\frac{dJ}{dy_i}*\hat{x_i})
        sum_dz, sum_dz_xhat = _backend.syncbn_backward_xhat(
            dz, x, mean, var, ctx.eps)
        if ctx.is_master:
            sum_dzs, sum_dz_xhats = [sum_dz], [sum_dz_xhat]
            # master : gatther from slaves
            for _ in range(ctx.master_queue.maxsize):
                sum_dz_w, sum_dz_xhat_w = ctx.master_queue.get()
                ctx.master_queue.task_done()
                sum_dzs.append(sum_dz_w)
                sum_dz_xhats.append(sum_dz_xhat_w)
            # master : compute global stats
            sum_dz = comm.reduce_add(sum_dzs)
            sum_dz_xhat = comm.reduce_add(sum_dz_xhats)
            sum_dz /= ctx.N
            sum_dz_xhat /= ctx.N
            # master : broadcast global stats
            tensors = comm.broadcast_coalesced(
                (sum_dz, sum_dz_xhat), [mean.get_device()] + ctx.worker_ids)
            for ts, queue in zip(tensors[1:], ctx.worker_queues):
                queue.put(ts)
        else:
            # slave : send to master
            ctx.master_queue.put((sum_dz, sum_dz_xhat))
            # slave : get global stats
            sum_dz, sum_dz_xhat = ctx.worker_queue.get()
            ctx.worker_queue.task_done()

        # do batch norm backward
        dx, dweight, dbias = _backend.syncbn_backward(
            dz, x, weight, bias, mean, var, sum_dz, sum_dz_xhat,
            ctx.affine, ctx.eps)

        return dx, dweight, dbias, \
            None, None, None, None, None, None 
开发者ID:tamakoji,项目名称:pytorch-syncbn,代码行数:41,代码来源:syncbn.py

示例4: backward

# 需要导入模块: from torch.cuda import comm [as 别名]
# 或者: from torch.cuda.comm import reduce_add [as 别名]
def backward(ctx, dz):
        z, var, weight, bias = ctx.saved_tensors
        dz = dz.contiguous()

        # Undo activation
        _act_backward(ctx, z, dz)

        if ctx.training:
            edz, eydz = backend.edz_eydz(z, dz, weight, bias, ctx.affine, ctx.eps)

            if ctx.is_master:
                edzs, eydzs = [edz], [eydz]
                for _ in range(len(ctx.worker_queues)):
                    edz_w, eydz_w = ctx.master_queue.get()
                    ctx.master_queue.task_done()
                    edzs.append(edz_w)
                    eydzs.append(eydz_w)

                edz = comm.reduce_add(edzs) / (ctx.master_queue.maxsize + 1)
                eydz = comm.reduce_add(eydzs) / (ctx.master_queue.maxsize + 1)

                tensors = comm.broadcast_coalesced((edz, eydz), [edz.get_device()] + ctx.worker_ids)
                for ts, queue in zip(tensors[1:], ctx.worker_queues):
                    queue.put(ts)
            else:
                ctx.master_queue.put((edz, eydz))
                edz, eydz = ctx.worker_queue.get()
                ctx.worker_queue.task_done()
        else:
            edz = dz.new_zeros(dz.size(1))
            eydz = dz.new_zeros(dz.size(1))

        dx, dweight, dbias = backend.backward(z, dz, var, weight, bias, edz, eydz, ctx.affine, ctx.eps)
        dweight = dweight if ctx.affine else None
        dbias = dbias if ctx.affine else None

        return dx, dweight, dbias, None, None, None, None, None, None, None, None 
开发者ID:BloodAxe,项目名称:segmentation-networks-benchmark,代码行数:39,代码来源:functions.py

示例5: forward

# 需要导入模块: from torch.cuda import comm [as 别名]
# 或者: from torch.cuda.comm import reduce_add [as 别名]
def forward(ctx, x, weight, bias, running_mean, running_var,
                extra, compute_stats=True, momentum=0.1, eps=1e-05):
        def _parse_extra(ctx, extra):
            ctx.is_master = extra["is_master"]
            if ctx.is_master:
                ctx.master_queue = extra["master_queue"]
                ctx.worker_queues = extra["worker_queues"]
                ctx.worker_ids = extra["worker_ids"]
            else:
                ctx.master_queue = extra["master_queue"]
                ctx.worker_queue = extra["worker_queue"]
        # Save context
        if extra is not None:
            _parse_extra(ctx, extra)
        ctx.compute_stats = compute_stats
        ctx.momentum = momentum
        ctx.eps = eps
        ctx.affine = weight is not None and bias is not None
        if ctx.compute_stats:
            N = _count_samples(x) * (ctx.master_queue.maxsize + 1)
            assert N > 1
            # 1. compute sum(x) and sum(x^2)
            xsum, xsqsum = _backend.syncbn_sum_sqsum(x.detach())
            if ctx.is_master:
                xsums, xsqsums = [xsum], [xsqsum]
                # master : gatther all sum(x) and sum(x^2) from slaves
                for _ in range(ctx.master_queue.maxsize):
                    xsum_w, xsqsum_w = ctx.master_queue.get()
                    ctx.master_queue.task_done()
                    xsums.append(xsum_w)
                    xsqsums.append(xsqsum_w)
                xsum = comm.reduce_add(xsums)
                xsqsum = comm.reduce_add(xsqsums)
                mean = xsum / N
                sumvar = xsqsum - xsum * mean
                var = sumvar / N
                uvar = sumvar / (N - 1)
                # master : broadcast global mean, variance to all slaves
                tensors = comm.broadcast_coalesced(
                    (mean, uvar, var), [mean.get_device()] + ctx.worker_ids)
                for ts, queue in zip(tensors[1:], ctx.worker_queues):
                    queue.put(ts)
            else:
                # slave : send sum(x) and sum(x^2) to master
                ctx.master_queue.put((xsum, xsqsum))
                # slave : get global mean and variance
                mean, uvar, var = ctx.worker_queue.get()
                ctx.worker_queue.task_done()

            # Update running stats
            running_mean.mul_((1 - ctx.momentum)).add_(ctx.momentum * mean)
            running_var.mul_((1 - ctx.momentum)).add_(ctx.momentum * uvar)
            ctx.N = N
            ctx.save_for_backward(x, weight, bias, mean, var)
        else:
            mean, var = running_mean, running_var

        # do batch norm forward
        z = _backend.syncbn_forward(x, weight, bias, mean, var,
                                    ctx.affine, ctx.eps)
        return z 
开发者ID:tamakoji,项目名称:pytorch-syncbn,代码行数:63,代码来源:syncbn.py


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