FM / native

/usr/local/lib64/python3.6/site-packages/torch/include/ATen/native/

/usr/local/lib64/python3.6/site-packages/torch/include/ATen/native

Name	Size	Mode	Actions
cpu/	-	0755	rm
cuda/	-	0755	rm
quantized/	-	0755	rm
Activation.h	3069	0644	edit dl rm
AdaptivePooling.h	1165	0644	edit dl rm
BatchLinearAlgebra.h	8246	0644	edit dl rm
batch_norm.h	1285	0644	edit dl rm
BinaryOps.h	4916	0644	edit dl rm
BucketizationUtils.h	4248	0644	edit dl rm
ComplexHelper.h	3797	0644	edit dl rm
CompositeRandomAccessor.h	888	0644	edit dl rm
CompositeRandomAccessorCommon.h	6713	0644	edit dl rm
ConvUtils.h	5350	0644	edit dl rm
Copy.h	356	0644	edit dl rm
CPUBlas.h	4199	0644	edit dl rm
CPUFallback.h	2404	0644	edit dl rm
Cross.h	262	0644	edit dl rm
DilatedConvolutionUtils.h	6416	0644	edit dl rm
DispatchStub.h	7672	0644	edit dl rm
Distance.h	732	0644	edit dl rm
Distributions.h	21654	0644	edit dl rm
DistributionTemplates.h	18623	0644	edit dl rm
EmbeddingBag.h	1320	0644	edit dl rm
Fill.h	384	0644	edit dl rm
ForeachUtils.h	5962	0644	edit dl rm
FunctionOfAMatrixUtils.h	436	0644	edit dl rm
GridSampler.h	10525	0644	edit dl rm
group_norm.h	896	0644	edit dl rm
Histogram.h	492	0644	edit dl rm
im2col.h	2838	0644	edit dl rm
im2col_shape_check.h	6181	0644	edit dl rm
IndexingUtils.h	5373	0644	edit dl rm
layer_norm.h	2892	0644	edit dl rm
Lerp.h	553	0644	edit dl rm
LinearAlgebra.h	603	0644	edit dl rm
LinearAlgebraUtils.h	25236	0644	edit dl rm
LossMulti.h	2197	0644	edit dl rm
Math.h	91356	0644	edit dl rm
MathBitFallThroughLists.h	4086	0644	edit dl rm
MathBitsFallback.h	7326	0644	edit dl rm
MaxPooling.h	1234	0644	edit dl rm
Normalization.h	302	0644	edit dl rm
PointwiseOps.h	749	0644	edit dl rm
Pool.h	10922	0644	edit dl rm
Pow.h	1694	0644	edit dl rm
ReduceAllOps.h	378	0644	edit dl rm
ReduceOps.h	1745	0644	edit dl rm
ReduceOpsUtils.h	12245	0644	edit dl rm
Repeat.h	1286	0644	edit dl rm
Resize.h	6501	0644	edit dl rm
ResizeCommon.h	1321	0644	edit dl rm
RNN.h	2467	0644	edit dl rm
ScatterGatherChecks.h	3641	0644	edit dl rm
SegmentReduce.h	685	0644	edit dl rm
SharedReduceOps.h	15785	0644	edit dl rm
SobolEngineOpsUtils.h	1723	0644	edit dl rm
Sorting.h	536	0644	edit dl rm
SortingUtils.h	5722	0644	edit dl rm
SpectralOpsUtils.h	3146	0644	edit dl rm
StridedRandomAccessor.h	6847	0644	edit dl rm
TensorAdvancedIndexing.h	3072	0644	edit dl rm
TensorCompare.h	1333	0644	edit dl rm
TensorDimApply.h	1832	0644	edit dl rm
TensorFactories.h	3382	0644	edit dl rm
TensorIterator.h	46	0644	edit dl rm
TensorIteratorDynamicCasting.h	2025	0644	edit dl rm
TensorShape.h	1049	0644	edit dl rm
TensorTransformations.h	938	0644	edit dl rm
TriangularOpsUtils.h	2000	0644	edit dl rm
TypeProperties.h	496	0644	edit dl rm
UnaryOps.h	4464	0644	edit dl rm
Unfold2d.h	551	0644	edit dl rm
Unfold3d.h	852	0644	edit dl rm
UnfoldBackward.h	5398	0644	edit dl rm
UpSample.h	13599	0644	edit dl rm
vol2col.h	3642	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/torch/include/ATen/native/im2col_shape_check.h (6181B)

#include <ATen/ATen.h>
#include <ATen/TensorUtils.h>

namespace at {
namespace native {

static inline void col2im_shape_check(
    const Tensor& input,
    const Tensor& grad_output,
    int64_t output_height,
    int64_t output_width,
    int64_t kernel_height,
    int64_t kernel_width,
    int64_t dilation_height,
    int64_t dilation_width,
    int64_t pad_height,
    int64_t pad_width,
    int64_t stride_height,
    int64_t stride_width) {
  TORCH_CHECK(
      kernel_width > 0 && kernel_height > 0,
      "kernel size should be greater than zero, but got kernel_height: ",
      kernel_height,
      " kernel_width: ",
      kernel_width);
  TORCH_CHECK(
      stride_width > 0 && stride_height > 0,
      "stride should be greater than zero, but got stride_height: ",
      stride_height,
      " stride_width: ",
      stride_width);
  TORCH_CHECK(
      dilation_width > 0 && dilation_height > 0,
      "dilation should be greater than zero, but got dilation_height: ",
      dilation_height,
      " dilation_width: ",
      dilation_width);

int64_t ndim = input.ndimension();
  // allow dim=0 only the batch dimension.
  TORCH_CHECK(
      (ndim == 2 && input.size(0) != 0 && input.size(1) != 0) ||
      (ndim == 3 && input.size(1) != 0 && input.size(2) != 0),
      "Expected 2D or 3D (batch mode) tensor for input with possibly 0 batch size and non-zero dimensions for input, but got: ",
      input.sizes());

int64_t batch_dim = (ndim == 3) ? 0 : -1;
  int64_t n_input_plane = input.size(batch_dim + 1);

if (n_input_plane % (kernel_width * kernel_height) != 0) {
    AT_ERROR(
        "Expected size of input's dimension 1 to be divisible by the "
        "product of kernel_size, but got input.size(1)=",
        n_input_plane,
        " and kernel_size=(",
        kernel_height,
        ", ",
        kernel_width,
        ").");
  }

int64_t input_length = input.size(batch_dim + 2);
  int64_t n_blocks_height =
      div_rtn<int64_t>(
          output_height + 2 * pad_height -
              dilation_height * (kernel_height - 1) - 1,
          stride_height) +
      1;
  int64_t n_blocks_width = div_rtn<int64_t>(
                                   output_width + 2 * pad_width -
                                       dilation_width * (kernel_width - 1) - 1,
                                   stride_width) +
      1;

if (input_length != (n_blocks_height * n_blocks_width)) {
    AT_ERROR(
        "Given output_size=(",
        output_height,
        ", ",
        output_width,
        "), kernel_size=(",
        kernel_height,
        ", ",
        kernel_width,
        "), dilation=(",
        dilation_height,
        ", ",
        dilation_width,
        "), padding=(",
        pad_height,
        ", ",
        pad_width,
        "), stride=(",
        stride_height,
        ", ",
        stride_width,
        "), expected size of input's dimension 2 to match the calculated number of ",
        "sliding blocks ",
        n_blocks_height,
        " * ",
        n_blocks_width,
        " = ",
        (n_blocks_height * n_blocks_width),
        ", but got input.size(2)=",
        input_length,
        ".");
  }

if (output_width < 1 || output_height < 1) {
    AT_ERROR(
        "Expected output spatial size to be positive, but got: output_size=(",
        output_height,
        ", ",
        output_width,
        ").");
  }
}

static inline void im2col_shape_check(
    const Tensor& input,
    const Tensor& grad_output,
    int64_t kernel_height,
    int64_t kernel_width,
    int64_t dilation_height,
    int64_t dilation_width,
    int64_t pad_height,
    int64_t pad_width,
    int64_t stride_height,
    int64_t stride_width) {
  TORCH_CHECK(
      kernel_width > 0 && kernel_height > 0,
      "kernel size should be greater than zero, but got kernel_height: ",
      kernel_height,
      " kernel_width: ",
      kernel_width);

TORCH_CHECK(
      dilation_width > 0 && dilation_height > 0,
      "dilation should be greater than zero, but got dilation_height: ",
      dilation_height,
      " dilation_width: ",
      dilation_width);

TORCH_CHECK(
      pad_width >= 0 && pad_height >= 0,
      "padding should be non-negative, but got pad_height: ",
      pad_height,
      " pad_width: ",
      pad_width);

TORCH_CHECK(
      stride_width > 0 && stride_height > 0,
      "stride should be greater than zero, but got stride_height: ",
      stride_height,
      " stride_width: ",
      stride_width);

int64_t ndim = input.ndimension();

// allow dim=0 only the batch dimension.
  bool valid_dims = input.size(1) != 0 && input.size(2) != 0;
  TORCH_CHECK(
      (ndim == 3 && input.size(0) && valid_dims) ||
      (ndim == 4 && valid_dims && input.size(3) != 0),
      "Expected 3D or 4D (batch mode) tensor with possibly 0 batch size and other non-zero dimensions for input, but got: ",
      input.sizes());

int64_t dim_batch = 0;

if (ndim == 3) {
    dim_batch = -1;
  }

int64_t input_height = input.size(dim_batch + 2);
  int64_t input_width = input.size(dim_batch + 3);
  int64_t output_height = div_rtn<int64_t>(
                              input_height + 2 * pad_height -
                                  (dilation_height * (kernel_height - 1) + 1),
                              stride_height) +
      1;
  int64_t output_width = div_rtn<int64_t>(
                             input_width + 2 * pad_width -
                                 (dilation_width * (kernel_width - 1) + 1),
                             stride_width) +
      1;

if (output_height < 1 || output_width < 1) {
    AT_ERROR(
        "Given input with spatial size (",
        input_height,
        ", ",
        input_height,
        "), kernel_size=(",
        kernel_height,
        ", ",
        kernel_width,
        "), dilation=(",
        dilation_height,
        ", ",
        dilation_width,
        "), padding=(",
        pad_height,
        ", ",
        pad_width,
        "), calculated shape of the array of sliding blocks as (",
        output_height,
        ", ",
        output_width,
        "), which is too small (non-positive).");
  }
}

} // namespace native
} // namespace at

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