batchnorm support 3d input

debug
2023-01-30 15:16:58 +08:00 · 2023-01-30 15:16:58 +08:00 · 5a7c33e312
parent fdd9dcb992
commit 5a7c33e312
13 changed files with 81 additions and 85 deletions
--- 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 <https://arxiv.org/abs/1502.03167>`_ 。
+    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 <https://arxiv.org/abs/1502.03167>`_ 。

    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。
--- 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。
--- 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。
--- 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。
--- 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<KernelTensorPtr> &inputs,
                                 const std::vector<KernelTensorPtr> &outputs) {
+  MS_EXCEPTION_IF_NULL(base_operator);
  auto kernel_ptr = std::dynamic_pointer_cast<ops::BatchNorm>(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];
--- 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<KernelTensorPtr> &inputs,
                                     const std::vector<KernelTensorPtr> &outputs) {
+  MS_EXCEPTION_IF_NULL(base_operator);
  base_operator_ = base_operator;
  auto kernel_ptr = std::dynamic_pointer_cast<ops::BatchNormGrad>(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];
--- 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 <typename T>
@ -73,6 +73,7 @@ bool BatchNormGpuKernelMod::LaunchKernel(const std::vector<AddressPtr> &inputs,

 bool BatchNormGpuKernelMod::Init(const BaseOperatorPtr &base_operator, const std::vector<KernelTensorPtr> &inputs,
                                 const std::vector<KernelTensorPtr> &outputs) {
+  MS_EXCEPTION_IF_NULL(base_operator);
  auto kernel_name_ = base_operator->name();
  auto kernel_ptr = std::dynamic_pointer_cast<ops::BatchNorm>(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]);
--- 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<KernelTensorPtr> &inputs,
                                     const std::vector<KernelTensorPtr> &outputs) {
+  MS_EXCEPTION_IF_NULL(base_operator);
  kernel_name_ = base_operator->name();
  auto kernel_ptr = std::dynamic_pointer_cast<ops::BatchNormGrad>(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<AddressPtr> &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]);
--- 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<std::string>(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) {
--- 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<abstract::TupleShape>(
--- 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 <https://arxiv.org/abs/1502.03167>`_. 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.
--- 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.
--- 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.