Skip to content

Commit

Permalink
Add logs and Rama's suggestions
Browse files Browse the repository at this point in the history
  • Loading branch information
Thiago Crepaldi committed Aug 10, 2022
1 parent 5442941 commit 21bc8f5
Show file tree
Hide file tree
Showing 5 changed files with 204 additions and 76 deletions.
9 changes: 5 additions & 4 deletions onnxruntime/core/graph/contrib_ops/contrib_defs.cc
View file
Original file line number Diff line number Diff line change
Expand Up @@ -1031,7 +1031,7 @@ ONNX_MS_OPERATOR_SET_SCHEMA(Col2Im, 1,
"The value represent the number of pixels added to the beginning "
"and end part of the corresponding axis. `pads` format should be as follow "
"[x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin is the number of pixels "
"added at the beginning of axis `i` and xi_end is the number of pixels added at the end of axis `i`. "
"added at the beginning of axis `i` and xi_end the same for the end of axis `i`. "
"If not present, the padding defaults to 0 along start and end of each spatial axis.",
AttributeProto::INTS,
OPTIONAL_VALUE)
Expand All @@ -1056,7 +1056,7 @@ ONNX_MS_OPERATOR_SET_SCHEMA(Col2Im, 1,
1,
"image_shape",
"The shape of the spatial dimensions of the image after rearranging the column blocks."
"This is a 1-dimensional tensor with size of at least 2, containing the value [H_img, W_img] "
"This is a 1-dim tensor with size of at least 2, containing the value [H_img, W_img] "
" for a 2-D image or [dim_i1, dim_i2, ..., dim_iN] for a N-D image.",
"tensor(int64)",
OpSchema::Single,
Expand All @@ -1067,8 +1067,9 @@ ONNX_MS_OPERATOR_SET_SCHEMA(Col2Im, 1,
2,
"block_shape",
"The shape of the block to apply on the input."
"This is a 1-dimensional tensor of size of at least 2, containing the value [H_block, W_block] "
" for a 2-D image or [dim_b1, dim_b2, ..., dim_bN] for a N-D block.",
"This is a 1-dim tensor of size of at least 2, containing the value [H_block, W_block] "
" for a 2-D image or [dim_b1, dim_b2, ..., dim_bN] for a N-D block."
"Dilations, pads and strides are applied to block_shape under the hood.",
"tensor(int64)",
OpSchema::Single,
true,
Expand Down
71 changes: 45 additions & 26 deletions onnxruntime/core/providers/cpu/tensor/col2im.cc
View file
Original file line number Diff line number Diff line change
Expand Up @@ -32,7 +32,6 @@ Status Col2Im<T>::Compute(OpKernelContext* context) const {

const T* col_input_data = col_input->template Data<T>();
TensorShape col_input_shape = col_input->Shape();
int64_t col_input_C = col_input_shape[1];
const auto col_input_N = col_input_shape[0];

int64_t image_shape_size = 1;
Expand All @@ -42,46 +41,52 @@ Status Col2Im<T>::Compute(OpKernelContext* context) const {
++kernel_shape_rank;
image_shape_size *= image_shape->Data<int64_t>()[i];
kernel_shape_size *= kernel_shape->Data<int64_t>()[i];
// col_input_C computed as => (C*n-ary-prod{kernel_shape}) / n-ary-prod{kernel_shape}
col_input_C /= kernel_shape->Data<int64_t>()[i];
}
const int64_t col_input_offset = col_input_C * image_shape_size;
const int64_t C = col_input_shape[1] / kernel_shape_size;
const int64_t col_output_stride = col_input_shape.SizeFromDimension(1);
const int64_t col_input_stride = C * image_shape_size;

TensorShapeVector Y_dims;
Y_dims.insert(Y_dims.begin(), {col_input_N, col_input_C});
Y_dims.insert(Y_dims.begin(), {col_input_N, C});
for (auto i=0; i < image_shape->Shape()[0]; ++i) {
Y_dims.push_back(image_shape->Data<int64_t>()[i]);
}
TensorShape Yshape(Y_dims);
Tensor* Y = context->Output(0, Yshape);
T* Ydata = Y->template MutableData<T>();
for (auto i=0; i < Yshape.Size(); ++i)
Ydata[i] = -1; // just for debug (to know what has been written to Ydata in the end)
const int64_t Y_offset = Yshape.Size() / Yshape[0];

std::cout << "\n\tInput 0: col_input = ("; for (auto i=0; i < Yshape.Size(); ++i) std::cout << col_input_data[i] << ", "; std::cout << ") with shape "<< col_input_shape << std::endl;
std::cout << "\tInput 1: image_shape = ("; for (auto i=0; i < image_shape->Shape().Size(); ++i) std::cout << image_shape->Data<int64_t>()[i] << ", "; std::cout << ")" << std::endl;
std::cout << "\tInput 2: kernel_shape = ("; for (auto i=0; i < kernel_shape->Shape().Size(); ++i) std::cout << kernel_shape->Data<int64_t>()[i] << ", "; std::cout << ")" << std::endl;
std::cout << "\tAttribute strides = ("; for (size_t i=0; i < col2im_attrs_.strides.size(); ++i) std::cout << col2im_attrs_.strides[i] << ", "; std::cout << ")"<< std::endl;
std::cout << "\tAttribute dilations = ("; for (size_t i=0; i < col2im_attrs_.dilations.size(); ++i) std::cout << col2im_attrs_.dilations[i] << ", "; std::cout << ")"<< std::endl;
std::cout << "\tAttribute pads = ("; for (size_t i=0; i < col2im_attrs_.pads.size(); ++i) std::cout << col2im_attrs_.pads[i] << ", "; std::cout << ")"<< std::endl;

std::cout << "\tVariable col_input_C: " << col_input_C << std::endl;
Ydata[i] = -1; // just for debug (to know what has been written to Ydata in the end)
// const int64_t Y_offset = Yshape.Size() / Yshape[0];

std::cout << "\n\tInput 0: col_input = ("; for (auto i=0; i < Yshape.Size(); ++i) std::cout <<
col_input_data[i] << ", "; std::cout << ") with shape "<< col_input_shape << std::endl;
std::cout << "\tInput 1: image_shape = ("; for (auto i=0; i < image_shape->Shape().Size(); ++i) std::cout <<
image_shape->Data<int64_t>()[i] << ", "; std::cout << ")" << std::endl;
std::cout << "\tInput 2: kernel_shape = ("; for (auto i=0; i < kernel_shape->Shape().Size(); ++i) std::cout <<
kernel_shape->Data<int64_t>()[i] << ", "; std::cout << ")" << std::endl;
std::cout << "\tAttribute strides = ("; for (size_t i=0; i < col2im_attrs_.strides.size(); ++i) std::cout <<
col2im_attrs_.strides[i] << ", "; std::cout << ")"<< std::endl;
std::cout << "\tAttribute dilations = ("; for (size_t i=0; i < col2im_attrs_.dilations.size(); ++i) std::cout <<
col2im_attrs_.dilations[i] << ", "; std::cout << ")"<< std::endl;
std::cout << "\tAttribute pads = ("; for (size_t i=0; i < col2im_attrs_.pads.size(); ++i) std::cout <<
col2im_attrs_.pads[i] << ", "; std::cout << ")"<< std::endl;

std::cout << "\tVariable C: " << C << std::endl;
std::cout << "\tVariable col_input_N = " << col_input_N << std::endl;
std::cout << "\tVariable image_shape_size: " << image_shape_size << std::endl;
std::cout << "\tVariable kernel_shape_size: " << kernel_shape_size << std::endl;

std::cout << "\n\tStatus Col2Im<T>::Compute() --> math::Col2imNd<>()" << std::endl;

assert(image_shape_size == Y_offset); // just for temp debug
assert(image_shape_size == Y_offset); // just for temp debug

for (auto image_id = 0; image_id < col_input_N; ++image_id) {
std::cout << "Image " << image_id+1 << " out of "<< col_input_N << std::endl;
if (image_shape->Shape()[0] == 2) {
std::cout << "image_shape->Shape()[0] == 2 --> Col2Im" << std::endl;
math::Col2im<float, CPUMathUtil, StorageOrder::NCHW>(
col_input_data + image_id * col_input_offset,
col_input_C,
col_input_data + image_id * col_output_stride,
C,
image_shape->Data<int64_t>()[0],
image_shape->Data<int64_t>()[1],
kernel_shape->Data<int64_t>()[0],
Expand All @@ -94,27 +99,41 @@ Status Col2Im<T>::Compute(OpKernelContext* context) const {
col2im_attrs_.pads[3],
col2im_attrs_.strides[0],
col2im_attrs_.strides[1],
Ydata + image_id * Y_offset,
Ydata + image_id * col_input_stride,
&CPUMathUtil::Instance());
} else {
std::cout << "image_shape->Shape()[0] != 2 --> Col2ImNd (nd=" << image_shape->Shape()[0] << ") " << std::endl;
math::Col2imNd<T, CPUMathUtil, StorageOrder::NCHW>(
col_input_data + image_id * col_input_offset, // const T* data_col,
col_input_data + image_id * col_output_stride, // const T* data_col,
image_shape->Data<int64_t>(), // const int64_t* img_shape,
Yshape.Slice(2).GetDims().data(), // const int64_t* output_shape,
col_input_C, // int64_t channels_col,
// col_input_shape[1], // int64_t channels_col,
// leads to output
// {1, -nan, 11, 16, 2.58141e+34, 2, 8.80295e+34, 12, 17, 22, 3, 4.59718e+24, 13, 18, 2.85144e+34, 4,
// -443.863, 14, -nan, 24, 5, 10, 15, 20, 25}
// that is similar to input with some spots with random values

C, // int64_t channels_col,
// leads to output {1, 6, 11, 16, 21, 2, 7, 12, 17, 22, 3, 8, 13, 18, 23, 4, 9, 14, 19, 24, 5, 10, 15, 20, 25, }
// that is identical to input

// col_input_shape[2], // int64_t channels_col,
// leads to output
// {1, 6, 1.92869e+31, 4.84145e+30, 1.88774e+31, 2, 7, 12, 17, 22, 3, 8, 1.86549e+31, 3.40686e+25,
// 2.20182e+24, 4, -2.56655e+29, 5.08551e+31, -1.05888e+29, 1.51107e+29, 5, 10, 15, 20, 7.2793e+31}
// that is very similar to input, but with some rounded numbers and corrupted "25" value
image_shape_size, // int64_t img_size,
kernel_shape->Data<int64_t>(), // const int64_t* kernel_shape,
col2im_attrs_.strides.data(), // const int64_t* stride,
col2im_attrs_.dilations.data(), // const int64_t* dilation,
col2im_attrs_.pads.data(), // const int64_t* pad,
kernel_shape->Shape().Size(), // ptrdiff_t N, --> #spatial_dims?
Ydata + image_id * Y_offset, // T* data_img,
&CPUMathUtil::Instance() // Provider* provider
);
Ydata + image_id * col_input_stride, // T* data_img,
&CPUMathUtil::Instance()); // Provider* provider
}
}
std::cout << "\n\n Return Col2Im<T>::Compute() --> "; for (auto i=0; i < Yshape.Size(); ++i) std::cout << Ydata[i] << ", "; std::cout << ") with shape " << Yshape << std::endl << std::endl;
std::cout << "\n\n Return Col2Im<T>::Compute() --> "; for (auto i=0; i < Yshape.Size(); ++i) std::cout <<
Ydata[i] << ", "; std::cout << ") with shape " << Yshape << std::endl << std::endl;

return Status::OK();
}
Expand Down
Loading

0 comments on commit 21bc8f5

Please sign in to comment.