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Hetu/python/hetu/gpu_links/Conv2dLink.py

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  1. from __future__ import absolute_import

  2. 

  3. import ctypes

  4. from .._base import _LIB

  5. from .. import ndarray as _nd

  6. 

  7. 

  8. def conv2d(in_arr_x, in_arr_f, out_arr, workspace_arr, padding=0, stride=1, stream=None):

  9.     assert isinstance(in_arr_x, _nd.NDArray)

  10.     assert isinstance(in_arr_f, _nd.NDArray)

  11.     assert isinstance(out_arr, _nd.NDArray)

  12.     assert isinstance(workspace_arr, _nd.NDArray)

  13.     _LIB.DLGpuConv2d(in_arr_x.handle, in_arr_f.handle,

  14.                      out_arr.handle, workspace_arr.handle, padding, stride, stream.handle if stream else None)

  15. 

  16. 

  17. def conv2d_gradient_of_filter(in_arr_x, in_gradient_y, out_gradient_f, workspace_im2col, workspace_batch_filter, padding=0, stride=1, stream=None):

  18.     assert isinstance(in_arr_x, _nd.NDArray)

  19.     assert isinstance(in_gradient_y, _nd.NDArray)

  20.     assert isinstance(out_gradient_f, _nd.NDArray)

  21.     assert isinstance(workspace_im2col, _nd.NDArray)

  22.     assert isinstance(workspace_batch_filter, _nd.NDArray)

  23.     _LIB.DLGpuConv2d_Gradient_of_Filter(in_arr_x.handle, in_gradient_y.handle, out_gradient_f.handle,

  24.                                         workspace_im2col.handle, workspace_batch_filter.handle, padding, stride, stream.handle if stream else None)

  25. 

  26. 

  27. def conv2d_gradient_of_data(in_arr_f, in_gradient_y, out_gradient_x, workspace_im2col, padding=0, stride=1, stream=None):

  28.     assert isinstance(in_arr_f, _nd.NDArray)

  29.     assert isinstance(in_gradient_y, _nd.NDArray)

  30.     assert isinstance(out_gradient_x, _nd.NDArray)

  31.     assert isinstance(workspace_im2col, _nd.NDArray)

  32.     _LIB.DLGpuConv2d_Gradient_of_Data(

  33.         in_arr_f.handle, in_gradient_y.handle, out_gradient_x.handle, workspace_im2col.handle, padding, stride, stream.handle if stream else None)