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/DilatedConvolutionUtils.h (6416B)

#pragma once

#include <algorithm>
#include <vector>

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

#define TORCH_CHECK_DIM_SIZE(T, DIM, DIM_SIZE, SIZE) \
  TORCH_CHECK(                                       \
      T.dim() == DIM && T.size(DIM_SIZE) == SIZE,    \
      "Need " #T " of dimension ",                   \
      DIM,                                           \
      " and " #T ".size[",                           \
      DIM_SIZE,                                      \
      "] == ",                                       \
      SIZE,                                          \
      " but got input to be of shape ",              \
      T.sizes())

namespace at {
namespace native {
namespace internal {
namespace {
inline bool all_positive(IntArrayRef& arr) {
  return std::all_of(
      arr.begin(), arr.end(), [](int64_t item) { return item > 0; });
}

inline bool all_nonnegative(std::vector<int64_t>& arr) {
  return std::all_of(
      arr.begin(), arr.end(), [](int64_t item) { return item >= 0; });
}

} // namespace

// calculate the rear part of output tensor sizes
template <int64_t dim>
std::vector<int64_t> get_output_size(
    const Tensor& input,
    IntArrayRef kernel_size,
    IntArrayRef stride_size,
    IntArrayRef pad_size,
    IntArrayRef dilation_size) {
  std::vector<int64_t> sizes;
  for (int index = 0; index < dim; index++) {
    sizes.push_back(
        div_rtn<int64_t>(
            input.size(index + input.dim() - dim) + 2 * pad_size[index] -
                (dilation_size[index] * (kernel_size[index] - 1) + 1),
            stride_size[index]) +
        1);
  }
  return sizes;
}

// calculate the sizes of output tensor
template <int64_t dim>
std::vector<int64_t> get_output_size(
    const Tensor& input,
    const Tensor& weight,
    IntArrayRef kernel_size,
    IntArrayRef stride_size,
    IntArrayRef pad_size,
    IntArrayRef dilation_size) {
  auto output_size = get_output_size<dim>(
      input, kernel_size, stride_size, pad_size, dilation_size);
  output_size.insert(output_size.begin(), weight.size(0));
  if (input.dim() == dim + 2) {
    output_size.insert(output_size.begin(), input.size(0));
  }
  return output_size;
}
/*
  slow_conv_dilated_shape_check - check user-input to dilated convolution
  forward and backward functions.
*/
template <int64_t dim>
void slow_conv_dilated_shape_check(
    const Tensor& input,
    const Tensor& weight,
    const Tensor& bias,
    const Tensor& grad_output,
    IntArrayRef kernel_size,
    IntArrayRef stride_size,
    IntArrayRef pad_size,
    IntArrayRef dilation_size) {
  /*
    When the following tensors are defined:

bias, grad_weight, grad_output

then these are assumed to be contiguous without checking
    because of these tensors are made contiguous by calling
    .contiguous() method or by resizing of zero-sized tensors in
    forward/backward functions.

When grad_weight is defined then it is assumed without
    checking to have the same shape as weight, see backward
    functions.
   */
  // Check size arguments
  TORCH_CHECK(
      kernel_size.size() == dim,
      "kernel sizes length should be ",
      dim,
      ", but got ",
      kernel_size.size());
  TORCH_CHECK(
      stride_size.size() == dim,
      "strides length should be ",
      dim,
      ", but got ",
      stride_size.size());
  TORCH_CHECK(
      dilation_size.size() == dim,
      "dilations length should be ",
      dim,
      ", but got ",
      dilation_size.size());
  TORCH_CHECK(
      pad_size.size() == dim,
      "pads length should be ",
      dim,
      ", but got ",
      pad_size.size());

TORCH_CHECK(
      all_positive(kernel_size),
      "kernel size should be greater than zero, but got ",
      kernel_size);
  TORCH_CHECK(
      all_positive(stride_size),
      "stride should be greater than zero, but got ",
      stride_size);
  TORCH_CHECK(
      all_positive(dilation_size),
      "dilation should be greater than zero, but got ",
      dilation_size);

// check input
  TORCH_CHECK(input.defined(), "input must be defined");
  bool is_batch = input.dim() == dim + 2;
  int64_t n = (is_batch ? 2 : 1);
  int64_t ndim = n + dim;
  if (!is_batch) {
    // input dim has to be dim + 1 if not batched
    TORCH_CHECK(
        input.dim() == dim + 1,
        "input must be 4D or 5D tensor but got ",
        input.dim(),
        "D tensor");
  }

// check output sizes
  auto output_size = get_output_size<dim>(
      input, kernel_size, stride_size, pad_size, dilation_size);

TORCH_CHECK(
      all_nonnegative(output_size),
      "calculated output size ",
      output_size,
      " is too small (all sizes must be non-negative)");

// check weight
  TORCH_CHECK(weight.defined(), "weight must be defined");
  TORCH_CHECK(
      weight.dim() == dim + 2,
      "weight must be ",
      dim + 2,
      "D tensor but got ",
      weight.dim(),
      "D tensor dim=",
      dim);
  TORCH_CHECK(
      weight.sizes().slice(2) == kernel_size,
      "weight[2:] shape ",
      weight.sizes().slice(2),
      " must be equal to kernel_size ",
      kernel_size);

TORCH_CHECK_DIM_SIZE(input, input.dim(), (is_batch ? 1 : 0), weight.size(1));

// check bias when present
  if (bias.defined()) {
    TORCH_CHECK(
        bias.dim() == 1,
        "bias must be 1D tensor but got ",
        bias.dim(),
        "D tensor");
    TORCH_CHECK_DIM_SIZE(bias, 1, 0, weight.size(0));
  }

// check grad_output when present
  if (grad_output.defined()) {
    TORCH_CHECK(
        grad_output.dim() == ndim,
        "grad_output must be ",
        ndim,
        "D tensor but got ",
        grad_output.dim(),
        "D tensor");
    if (is_batch) {
      TORCH_CHECK(
          grad_output.size(0) == input.size(0),
          "grad_output.size(0)=",
          grad_output.size(0),
          " must be input.size(0)=",
          input.size(0));
    }
    TORCH_CHECK(
        grad_output.size(n - 1) == weight.size(0),
        "grad_output.size(",
        n - 1,
        ")=",
        grad_output.size(n - 1),
        " must be weight.size(0)=",
        weight.size(0));
    TORCH_CHECK(
        grad_output.sizes().slice(n) == output_size,
        "grad_output[",
        n,
        ":] shape",
        grad_output.sizes().slice(n),
        " must be equal to output size ",
        output_size);
  }
}

} // namespace internal
} // namespace native
} // namespace at

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