From 5a7c33e31227e91818f493bb10a382c35f62496d Mon Sep 17 00:00:00 2001 From: hw_hz Date: Mon, 30 Jan 2023 15:16:58 +0800 Subject: [PATCH] batchnorm support 3d input debug --- .../nn/mindspore.nn.BatchNorm1d.rst | 8 ++-- .../nn/mindspore.nn.BatchNorm2d.rst | 4 +- .../ops/mindspore.ops.BatchNorm.rst | 2 +- .../ops/mindspore.ops.ResizeBicubic.rst | 3 -- .../kernel/mkldnn/batch_norm_cpu_kernel.cc | 16 +++---- .../mkldnn/batch_norm_grad_cpu_kernel.cc | 20 ++++----- .../gpu/kernel/nn/batch_norm_gpu_kernel.cc | 40 ++++++++--------- .../kernel/nn/batch_norm_grad_gpu_kernel.cc | 44 +++++++++---------- mindspore/core/ops/batch_norm.cc | 2 + mindspore/core/ops/grad/batch_norm_grad.cc | 5 +++ .../mindspore/nn/layer/normalization.py | 13 +++--- .../mindspore/ops/operations/image_ops.py | 5 +-- .../python/mindspore/ops/operations/nn_ops.py | 4 +- 13 files changed, 81 insertions(+), 85 deletions(-) diff --git a/docs/api/api_python/nn/mindspore.nn.BatchNorm1d.rst b/docs/api/api_python/nn/mindspore.nn.BatchNorm1d.rst index 398bdd632ca..9f2ca4dfdf3 100644 --- a/docs/api/api_python/nn/mindspore.nn.BatchNorm1d.rst +++ b/docs/api/api_python/nn/mindspore.nn.BatchNorm1d.rst @@ -3,7 +3,7 @@ mindspore.nn.BatchNorm1d .. py:class:: mindspore.nn.BatchNorm1d(num_features, eps=1e-5, momentum=0.9, affine=True, gamma_init='ones', beta_init='zeros', moving_mean_init='zeros', moving_var_init='ones', use_batch_statistics=None, data_format='NCHW') - 在二维输入（mini-batch 一维输入）上应用批归一化（Batch Normalization Layer），避免内部协变量偏移。归一化在卷积网络中被广泛的应用。请见论文 `Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift `_ 。 + 在二维或三维输入（mini-batch 一维输入或二维输入）上应用批归一化（Batch Normalization Layer），避免内部协变量偏移。归一化在卷积网络中被广泛的应用。请见论文 `Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift `_ 。 使用mini-batch数据和学习参数进行训练，计算公式如下。 @@ -14,7 +14,7 @@ mindspore.nn.BatchNorm1d BatchNorm的实现在图模式和PyNative模式下是不同的，因此不建议在网络初始化后更改其模式。 参数： - - **num_features** (int) - 通道数量，输入Tensor shape :math:`(N, C)` 中的 `C` 。 + - **num_features** (int) - 特征数量或输入 `x` 中的通道数量 `C` 。 - **eps** (float) - :math:`\epsilon` 加在分母上的值，以确保数值稳定。默认值：1e-5。 - **momentum** (float) - 动态均值和动态方差所使用的动量。默认值：0.9。 - **affine** (bool) - bool类型。设置为True时，可学习到 :math:`\gamma` 和 :math:`\beta` 值。默认值：True。 @@ -26,10 +26,10 @@ mindspore.nn.BatchNorm1d - **data_format** (str) - 数据格式可为'NHWC'或'NCHW'。默认值：'NCHW'。 输入： - - **x** (Tensor) - 输入shape为 :math:`(N, C_{in})` 的Tensor。 + - **x** (Tensor) - 输入shape为 :math:`(N, C)` 或 :math:`(N, C, L)` 的Tensor，其中 `N` 为batch， `C` 为特征数量或通道数量， `L` 为序列长度。 输出： - Tensor，归一化后的Tensor，shape为 :math:`(N, C_{out})` 。 + Tensor，归一化后的Tensor，shape为 :math:`(N, C)` 或 :math:`(N, C, L)` 。 异常： - **TypeError** - `num_features` 不是整数。 diff --git a/docs/api/api_python/nn/mindspore.nn.BatchNorm2d.rst b/docs/api/api_python/nn/mindspore.nn.BatchNorm2d.rst index 90add4fa271..5a8afd1c4df 100644 --- a/docs/api/api_python/nn/mindspore.nn.BatchNorm2d.rst +++ b/docs/api/api_python/nn/mindspore.nn.BatchNorm2d.rst @@ -36,10 +36,10 @@ mindspore.nn.BatchNorm2d - **data_format** (str) - 数据格式可为'NHWC'或'NCHW'。默认值：'NCHW'。 输入： - - **x** (Tensor) - 输入shape为 :math:`(N,C_{in},H_{in},W_{in})` 的Tensor。 + - **x** (Tensor) - 输入shape为 :math:`(N, C, H, W)` 的Tensor。 输出： - Tensor，归一化后的Tensor，shape为 :math:`(N,C_{out},H_{out},W_{out})` 。 + Tensor，归一化后的Tensor，shape为 :math:`(N, C, H, W)` 。 异常： - **TypeError** - `num_features` 不是整数。 diff --git a/docs/api/api_python/ops/mindspore.ops.BatchNorm.rst b/docs/api/api_python/ops/mindspore.ops.BatchNorm.rst index 4727b416097..f59ff039181 100644 --- a/docs/api/api_python/ops/mindspore.ops.BatchNorm.rst +++ b/docs/api/api_python/ops/mindspore.ops.BatchNorm.rst @@ -20,7 +20,7 @@ mindspore.ops.BatchNorm - **is_training** (bool) - 如果 `is_training` 为True，则在训练期间计算 `mean` 和 `variance`。如果 `is_training` 为False，则在推理期间从checkpoint加载。默认值：False。 - **epsilon** (float) - 添加到分母上的值，以确保数值稳定性。默认值：1e-5。 - **momentum** (float) - 动态均值和动态方差所使用的动量。（例如 :math:`new\_running\_mean = (1 - momentum) * running\_mean + momentum * current\_mean`）。动量值必须为[0, 1]。默认值：0.1。 - - **data_format** (str) - 输入数据格式，可选值有：'NHWC'或'NCHW'。默认值：'NCHW'。 + - **data_format** (str) - 输入数据格式，可选值有：'NHWC'或'NCHW'，'NHWC'仅在GPU上支持。默认值：'NCHW'。 输入： 如果 `is_training` 为False，则输入为多个Tensor。 diff --git a/docs/api/api_python/ops/mindspore.ops.ResizeBicubic.rst b/docs/api/api_python/ops/mindspore.ops.ResizeBicubic.rst index 196ab5ec260..1d607477ec8 100644 --- a/docs/api/api_python/ops/mindspore.ops.ResizeBicubic.rst +++ b/docs/api/api_python/ops/mindspore.ops.ResizeBicubic.rst @@ -5,9 +5,6 @@ mindspore.ops.ResizeBicubic 使用双三次插值调整图像大小到指定的大小。 - .. warning:: - 输出最大长度为1000000。 - 参数： - **align_corners** (bool，可选) - 如果为True，则输入输出图像四个角像素的中心被对齐，同时保留角像素处的值。默认值：False。 - **half_pixel_centers** (bool，可选) - 是否使用半像素中心对齐。如果设置为True，那么 `align_corners` 应该设置为False。默认值：False。 diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/batch_norm_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/batch_norm_cpu_kernel.cc index 7b3c90c36ab..5da7e85c465 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/batch_norm_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/batch_norm_cpu_kernel.cc @@ -25,23 +25,23 @@ namespace kernel { namespace { constexpr size_t kBatchNormInputsNum = 5; constexpr size_t kBatchNormOutputsNum = 5; -constexpr size_t kBatchNormInputShapeSize = 4; -constexpr size_t kBatchNormInputShapeSize2 = 2; +constexpr size_t kBatchNormInputShapeMaxSize = 4; } // namespace bool BatchNormCpuKernelMod::Init(const BaseOperatorPtr &base_operator, const std::vector &inputs, const std::vector &outputs) { + MS_EXCEPTION_IF_NULL(base_operator); auto kernel_ptr = std::dynamic_pointer_cast(base_operator); if (!kernel_ptr) { MS_LOG(ERROR) << "cast BatchNorm ops failed!"; return false; } - auto kernel_name = kernel_ptr->GetPrim()->name(); - auto kernel_attr = GetKernelAttrFromTensors(inputs, outputs); + kernel_name_ = kernel_ptr->GetPrim()->name(); + auto kernel_attr = GetKernelAttrFromTensors(inputs, outputs); bool is_match = MatchKernelAttr(kernel_attr, GetOpSupport()).first; if (!is_match) { - MS_LOG(EXCEPTION) << kernel_name << " does not support this kernel data type: " << kernel_attr; + MS_LOG(EXCEPTION) << kernel_name_ << " does not support this kernel data type: " << kernel_attr; } base_operator_ = base_operator; @@ -60,11 +60,7 @@ int BatchNormCpuKernelMod::Resize(const BaseOperatorPtr &base_operator, const st } auto x_shape = inputs[kIndex0]->GetDeviceShapeAdaptively(); - if (x_shape.size() == kBatchNormInputShapeSize2) { - (void)x_shape.insert(x_shape.end(), kBatchNormInputShapeSize - kBatchNormInputShapeSize2, 1); - } else if (x_shape.size() != kBatchNormInputShapeSize) { - MS_LOG(EXCEPTION) << "Batchnorm only support nchw input!"; - } + (void)x_shape.insert(x_shape.end(), kBatchNormInputShapeMaxSize - x_shape.size(), 1); batch_size_ = x_shape[0]; channel_ = x_shape[1]; diff --git a/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/batch_norm_grad_cpu_kernel.cc b/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/batch_norm_grad_cpu_kernel.cc index feb9cf2c904..451f55d21f3 100644 --- a/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/batch_norm_grad_cpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/cpu/kernel/mkldnn/batch_norm_grad_cpu_kernel.cc @@ -25,27 +25,27 @@ namespace kernel { namespace { constexpr size_t kBatchNormGradInputsNum = 6; constexpr size_t kBatchNormGradOutputsNum = 3; -constexpr size_t kBatchNormGradInputShapeSize = 4; -constexpr size_t kBatchNormGradInputShapeSize2 = 2; +constexpr size_t kBatchNormGradInputShapeMaxSize = 4; +constexpr size_t kBatchNormGradInputShapeMinSize = 2; constexpr size_t kScaleShiftNum = 2; } // namespace bool BatchNormGradCpuKernelMod::Init(const BaseOperatorPtr &base_operator, const std::vector &inputs, const std::vector &outputs) { + MS_EXCEPTION_IF_NULL(base_operator); base_operator_ = base_operator; auto kernel_ptr = std::dynamic_pointer_cast(base_operator); if (!kernel_ptr) { MS_LOG(ERROR) << "cast BatchNormGrad ops failed!"; return false; } - auto kernel_name = kernel_ptr->GetPrim()->name(); - auto kernel_attr = GetKernelAttrFromTensors(inputs, outputs); - is_train_ = kernel_ptr->get_is_training(); epsilon_ = kernel_ptr->get_epsilon(); + kernel_name_ = kernel_ptr->GetPrim()->name(); + auto kernel_attr = GetKernelAttrFromTensors(inputs, outputs); bool is_match = MatchKernelAttr(kernel_attr, GetOpSupport()).first; if (!is_match) { - MS_LOG(EXCEPTION) << kernel_name << " does not support this kernel data type: " << kernel_attr; + MS_LOG(EXCEPTION) << kernel_name_ << " does not support this kernel data type: " << kernel_attr; } return true; } @@ -59,11 +59,9 @@ int BatchNormGradCpuKernelMod::Resize(const BaseOperatorPtr &base_operator, cons } auto x_shape = inputs[kIndex0]->GetDeviceShapeAdaptively(); - if (x_shape.size() == NC_LEN) { - (void)x_shape.insert(x_shape.end(), (SHAPE_4D - NC_LEN), 1); - } else if (x_shape.size() != SHAPE_4D) { - MS_LOG(EXCEPTION) << "Fused batchnorm support nc or nchw input!"; - } + const size_t x_shape_size = x_shape.size(); + (void)x_shape.insert(x_shape.end(), kBatchNormGradInputShapeMaxSize - x_shape_size, 1); + batch_size_ = x_shape[N]; channel_ = x_shape[C]; hw_size_ = x_shape[H] * x_shape[W]; diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/batch_norm_gpu_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/batch_norm_gpu_kernel.cc index 969a61ddb9b..c2cc48e51a2 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/batch_norm_gpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/batch_norm_gpu_kernel.cc @@ -24,8 +24,8 @@ namespace mindspore { namespace kernel { namespace { -size_t kInputSize2 = 2; -size_t kInputSize4 = 4; +constexpr size_t kBatchNormInputShapeMaxSize = 4; +constexpr size_t kBatchNormInputShapeMinSize = 2; float kExpAvgFactorDefault = 0.1; } // namespace template @@ -73,6 +73,7 @@ bool BatchNormGpuKernelMod::LaunchKernel(const std::vector &inputs, bool BatchNormGpuKernelMod::Init(const BaseOperatorPtr &base_operator, const std::vector &inputs, const std::vector &outputs) { + MS_EXCEPTION_IF_NULL(base_operator); auto kernel_name_ = base_operator->name(); auto kernel_ptr = std::dynamic_pointer_cast(base_operator); if (kernel_ptr == nullptr) { @@ -99,6 +100,7 @@ bool BatchNormGpuKernelMod::Init(const BaseOperatorPtr &base_operator, const std << ", but got " << kernel_name_; return false; } + auto kernel_attr = GetKernelAttrFromTensors(inputs, outputs); auto [is_match, index] = MatchKernelAttr(kernel_attr, GetOpSupport()); if (!is_match) { @@ -136,13 +138,21 @@ int BatchNormGpuKernelMod::Resize(const BaseOperatorPtr &base_operator, const st if (ret != 0) { return ret; } - auto shape = inputs[kIndex0]->GetDeviceShapeAdaptively(); - if (shape.size() != kInputSize2 && shape.size() != kInputSize4) { - MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the dimension of input must be 2 or 4, but got " - << shape.size(); + + auto x_shape = inputs[kIndex0]->GetDeviceShapeAdaptively(); + const size_t x_shape_size = x_shape.size(); + + auto format = inputs[kIndex0]->GetFormat(); + if (x_shape_size == kBatchNormInputShapeMinSize) { + format = Format::NCHW; + } else if (format_ == Format::NHWC) { + format = Format::NHWC; } - if (shape.size() == kInputSize2) { + (void)x_shape.insert(x_shape.begin() + (format == Format::NHWC ? kIndex1 : x_shape_size), + kBatchNormInputShapeMaxSize - x_shape_size, 1); + + if (x_shape_size == kBatchNormInputShapeMinSize) { mode_ = CUDNN_BATCHNORM_PER_ACTIVATION; } else if (is_train_) { mode_ = CUDNN_BATCHNORM_SPATIAL_PERSISTENT; @@ -150,12 +160,8 @@ int BatchNormGpuKernelMod::Resize(const BaseOperatorPtr &base_operator, const st mode_ = CUDNN_BATCHNORM_SPATIAL; } - CheckTensorSize({shape}); - auto format = inputs[kIndex0]->GetFormat(); - if (format_ == Format::NHWC) { - format = Format::NHWC; - } - SetTensorDescriptor(format, shape); + CheckTensorSize({x_shape}); + SetTensorDescriptor(format, x_shape); InitSizeLists(); return KRET_OK; } @@ -252,13 +258,7 @@ void BatchNormGpuKernelMod::InitSizeLists() { void BatchNormGpuKernelMod::SetTensorDescriptor(const Format &format, const ShapeVector &shape) { cudnnTensorFormat_t cudnn_format; int batch, channel, height, width; - if (shape.size() == kInputSize2) { - batch = LongToInt(shape[kIndex0]); - channel = LongToInt(shape[kIndex1]); - height = 1; - width = 1; - cudnn_format = CUDNN_TENSOR_NCHW; - } else if (format == Format::NHWC) { + if (format == Format::NHWC) { batch = LongToInt(shape[kIndex0]); height = LongToInt(shape[kIndex1]); width = LongToInt(shape[kIndex2]); diff --git a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/batch_norm_grad_gpu_kernel.cc b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/batch_norm_grad_gpu_kernel.cc index 6100a2075b6..30435f62ada 100644 --- a/mindspore/ccsrc/plugin/device/gpu/kernel/nn/batch_norm_grad_gpu_kernel.cc +++ b/mindspore/ccsrc/plugin/device/gpu/kernel/nn/batch_norm_grad_gpu_kernel.cc @@ -24,11 +24,12 @@ namespace mindspore { namespace kernel { namespace { -size_t kInputSize2 = 2; -size_t kInputSize4 = 4; +constexpr size_t kBatchNormGradInputShapeMaxSize = 4; +constexpr size_t kBatchNormGradInputShapeMinSize = 2; } // namespace bool BatchNormGradGpuKernelMod::Init(const BaseOperatorPtr &base_operator, const std::vector &inputs, const std::vector &outputs) { + MS_EXCEPTION_IF_NULL(base_operator); kernel_name_ = base_operator->name(); auto kernel_ptr = std::dynamic_pointer_cast(base_operator); if (kernel_ptr == nullptr) { @@ -86,30 +87,35 @@ int BatchNormGradGpuKernelMod::Resize(const BaseOperatorPtr &base_operator, cons return ret; } - auto shape = inputs[kIndex0]->GetDeviceShapeAdaptively(); - if (shape.size() != kInputSize2 && shape.size() != kInputSize4) { - MS_LOG(EXCEPTION) << "For '" << kernel_name_ << "', the dimension of input must be 2 or 4, but got " - << shape.size(); + beta_data_diff_ = 0; + + auto x_shape = inputs[kIndex0]->GetDeviceShapeAdaptively(); + const size_t x_shape_size = x_shape.size(); + + auto format = inputs[kIndex0]->GetFormat(); + if (x_shape_size == kBatchNormGradInputShapeMinSize) { + format = Format::NCHW; + } else if (format_ == Format::NHWC) { + format = Format::NHWC; } - is_null_input_ = CHECK_SHAPE_NULL(shape, kernel_name_, "input"); + (void)x_shape.insert(x_shape.begin() + (format == Format::NHWC ? kIndex1 : x_shape_size), + kBatchNormGradInputShapeMaxSize - x_shape_size, 1); + + is_null_input_ = CHECK_SHAPE_NULL(x_shape, kernel_name_, "input"); if (is_null_input_) { InitSizeLists(); return true; } - if (shape.size() == kInputSize2) { + + if (x_shape_size == kBatchNormGradInputShapeMinSize) { mode_ = CUDNN_BATCHNORM_PER_ACTIVATION; } else { mode_ = CUDNN_BATCHNORM_SPATIAL_PERSISTENT; } - beta_data_diff_ = 0; - CheckTensorSize({shape}); - auto format = inputs[kIndex0]->GetFormat(); - if (format_ == Format::NHWC) { - format = Format::NHWC; - } - SetTensorDescriptor(format, shape); + CheckTensorSize({x_shape}); + SetTensorDescriptor(format, x_shape); InitSizeLists(); return KRET_OK; } @@ -217,13 +223,7 @@ bool BatchNormGradGpuKernelMod::LaunchKernel(const std::vector &inpu void BatchNormGradGpuKernelMod::SetTensorDescriptor(const Format &format, const ShapeVector &shape) { cudnnTensorFormat_t cudnn_format; - if (shape.size() == kInputSize2) { - batch_ = LongToInt(shape[kIndex0]); - channel_ = LongToInt(shape[kIndex1]); - height_ = 1; - width_ = 1; - cudnn_format = CUDNN_TENSOR_NCHW; - } else if (format == Format::NHWC) { + if (format == Format::NHWC) { batch_ = LongToInt(shape[kIndex0]); height_ = LongToInt(shape[kIndex1]); width_ = LongToInt(shape[kIndex2]); diff --git a/mindspore/core/ops/batch_norm.cc b/mindspore/core/ops/batch_norm.cc index e706219c3df..2fb4ab946ea 100644 --- a/mindspore/core/ops/batch_norm.cc +++ b/mindspore/core/ops/batch_norm.cc @@ -137,6 +137,8 @@ class BatchNormInfer : public abstract::OpInferBase { if (!x_shape_ptr->IsDynamic() && !scale_shape_ptr->IsDynamic()) { // auto format = GetValue(primitive->GetAttr(kFormat)); + (void)CheckAndConvertUtils::CheckInRange("rank of images", SizeToLong(x_shape.size()), kIncludeBoth, {2, 4}, + prim_name); auto format = get_format_in_infer(primitive); auto channel = format == "NHWC" ? x_shape.back() : x_shape[1]; if (scale_shape[kInputIndex0] != channel) { diff --git a/mindspore/core/ops/grad/batch_norm_grad.cc b/mindspore/core/ops/grad/batch_norm_grad.cc index 5e4c883337e..806add49c44 100644 --- a/mindspore/core/ops/grad/batch_norm_grad.cc +++ b/mindspore/core/ops/grad/batch_norm_grad.cc @@ -100,6 +100,11 @@ class BatchNormGradInfer : public abstract::OpInferBase { auto prim_name = primitive->name(); CheckAndConvertUtils::CheckInputArgs(input_args, kGreaterEqual, kInputNum, prim_name); auto x_shape_ptr = input_args[kInputIndex1]->BuildShape(); + auto x_shape = CheckAndConvertUtils::ConvertShapePtrToShapeMap(x_shape_ptr)[kShape]; + if (!IsDynamicRank(x_shape)) { + (void)CheckAndConvertUtils::CheckInRange("rank of x", SizeToLong(x_shape.size()), kIncludeBoth, {2, 4}, + prim_name); + } auto scale_shape_ptr = input_args[kInputIndex2]->BuildShape(); if (prim_name == kNameBatchNormGradWithAddAndActivation) { return std::make_shared( diff --git a/mindspore/python/mindspore/nn/layer/normalization.py b/mindspore/python/mindspore/nn/layer/normalization.py index 05a1bf07bd2..8dd3fe23b99 100644 --- a/mindspore/python/mindspore/nn/layer/normalization.py +++ b/mindspore/python/mindspore/nn/layer/normalization.py @@ -159,7 +159,7 @@ class _BatchNorm(Cell): class BatchNorm1d(_BatchNorm): r""" This layer - applies Batch Normalization over a 2D input (a mini-batch of 1D inputs) to + applies Batch Normalization over a 2D or 3D input (a mini-batch of 1D or 2D inputs) to reduce internal covariate shift. Batch Normalization is widely used in convolutional networks. For the setailed contents, refer to `Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift `_. It @@ -174,7 +174,7 @@ class BatchNorm1d(_BatchNorm): recommended to be changed after net was initialized. Args: - num_features (int): `C` from an expected input of size (N, C). + num_features (int): number of features or channels `C` of the input `x` . eps (float): A value added to the denominator for numerical stability. Default: 1e-5. momentum (float): A floating hyperparameter of the momentum for the running_mean and running_var computation. Default: 0.9. @@ -195,10 +195,11 @@ class BatchNorm1d(_BatchNorm): Default: 'NCHW'. Inputs: - - **x** (Tensor) - Tensor of shape :math:`(N, C_{in})`. + - **x** (Tensor) - Tensor of shape :math:`(N, C)` or :math:`(N, C, L)` , + where `N` is the batch size, `C` is the number of features or channels, and `L` is the sequence length. Outputs: - Tensor, the normalized, scaled, offset tensor, of shape :math:`(N, C_{out})`. + Tensor, the normalized, scaled, offset tensor, of shape :math:`(N, C)` or :math:`(N, C, L)` . Raises: TypeError: If `num_features` is not an int. @@ -276,10 +277,10 @@ class BatchNorm2d(_BatchNorm): Default: 'NCHW'. Inputs: - - **x** (Tensor) - Tensor of shape :math:`(N, C_{in}, H_{in}, W_{in})`. + - **x** (Tensor) - Tensor of shape :math:`(N, C, H, W)`. Outputs: - Tensor, the normalized, scaled, offset tensor, of shape :math:`(N, C_{out}, H_{out}, W_{out})`. + Tensor, the normalized, scaled, offset tensor, of shape :math:`(N, C, H, W)`. Raises: TypeError: If `num_features` is not an int. diff --git a/mindspore/python/mindspore/ops/operations/image_ops.py b/mindspore/python/mindspore/ops/operations/image_ops.py index c4193e1bf81..f9d66c18e34 100644 --- a/mindspore/python/mindspore/ops/operations/image_ops.py +++ b/mindspore/python/mindspore/ops/operations/image_ops.py @@ -746,9 +746,6 @@ class ResizeBicubic(Primitive): r""" Resize images to size using bicubic interpolation. - .. warning:: - The max output length is 1000000. - Args: align_corners (bool, optional):If true, the centers of the 4 corner pixels of the input and output tensors are aligned, preserving the values at the corner pixels.Default: False. @@ -758,7 +755,7 @@ class ResizeBicubic(Primitive): Inputs: - **images** (Tensor) - The input image must be a 4-D tensor of shape :math:`(batch, channels, height, width)`. - The format must be NHWC. + The format must be NCHW. Types allowed: int8, int16, int32, int64, float16, float32, float64, uint8, uint16. - **size** (Tensor) - A 1-D tensor of shape [2], with 2 elements: new_height, new_width. Types allowed: int32. diff --git a/mindspore/python/mindspore/ops/operations/nn_ops.py b/mindspore/python/mindspore/ops/operations/nn_ops.py index 1d40f2bc713..ca5a5bb3720 100644 --- a/mindspore/python/mindspore/ops/operations/nn_ops.py +++ b/mindspore/python/mindspore/ops/operations/nn_ops.py @@ -1232,8 +1232,8 @@ class BatchNorm(PrimitiveWithInfer): momentum (float): The hyper parameter to compute moving average for running_mean and running_var (e.g. :math:`new\_running\_mean = (1 - momentum) * running\_mean + momentum * current\_mean`). Momentum value must be [0, 1]. Default: 0.1. - data_format (str): The optional value for data format, is 'NHWC' or 'NCHW'. - Default: "NCHW". + data_format (str): The optional value for data format, is 'NHWC' or 'NCHW', and the 'NHWC' format + is only supported in GPU target. Default: "NCHW". Inputs: If `is_training` is False, inputs are Tensors.