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121 rivejä
4.9 KiB
121 rivejä
4.9 KiB
import numpy as np
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cimport numpy as np
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cimport cython
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# DTYPE = np.float64
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# ctypedef np.float64_t DTYPE_t
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ctypedef fused DTYPE_t:
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np.float32_t
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np.float64_t
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def im2col_cython(np.ndarray[DTYPE_t, ndim=4] x, int field_height,
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int field_width, int padding, int stride):
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cdef int N = x.shape[0]
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cdef int C = x.shape[1]
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cdef int H = x.shape[2]
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cdef int W = x.shape[3]
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cdef int HH = (H + 2 * padding - field_height) / stride + 1
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cdef int WW = (W + 2 * padding - field_width) / stride + 1
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cdef int p = padding
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cdef np.ndarray[DTYPE_t, ndim=4] x_padded = np.pad(x,
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((0, 0), (0, 0), (p, p), (p, p)), mode='constant')
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cdef np.ndarray[DTYPE_t, ndim=2] cols = np.zeros(
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(C * field_height * field_width, N * HH * WW),
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dtype=x.dtype)
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# Moving the inner loop to a C function with no bounds checking works, but does
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# not seem to help performance in any measurable way.
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im2col_cython_inner(cols, x_padded, N, C, H, W, HH, WW,
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field_height, field_width, padding, stride)
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return cols
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@cython.boundscheck(False)
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cdef int im2col_cython_inner(np.ndarray[DTYPE_t, ndim=2] cols,
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np.ndarray[DTYPE_t, ndim=4] x_padded,
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int N, int C, int H, int W, int HH, int WW,
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int field_height, int field_width, int padding, int stride) except? -1:
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cdef int c, ii, jj, row, yy, xx, i, col
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for c in range(C):
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for yy in range(HH):
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for xx in range(WW):
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for ii in range(field_height):
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for jj in range(field_width):
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row = c * field_width * field_height + ii * field_height + jj
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for i in range(N):
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col = yy * WW * N + xx * N + i
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cols[row, col] = x_padded[i, c, stride * yy + ii, stride * xx + jj]
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def col2im_cython(np.ndarray[DTYPE_t, ndim=2] cols, int N, int C, int H, int W,
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int field_height, int field_width, int padding, int stride):
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cdef np.ndarray x = np.empty((N, C, H, W), dtype=cols.dtype)
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cdef int HH = (H + 2 * padding - field_height) / stride + 1
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cdef int WW = (W + 2 * padding - field_width) / stride + 1
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cdef np.ndarray[DTYPE_t, ndim=4] x_padded = np.zeros((N, C, H + 2 * padding, W + 2 * padding),
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dtype=cols.dtype)
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# Moving the inner loop to a C-function with no bounds checking improves
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# performance quite a bit for col2im.
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col2im_cython_inner(cols, x_padded, N, C, H, W, HH, WW,
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field_height, field_width, padding, stride)
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if padding > 0:
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return x_padded[:, :, padding:-padding, padding:-padding]
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return x_padded
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@cython.boundscheck(False)
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cdef int col2im_cython_inner(np.ndarray[DTYPE_t, ndim=2] cols,
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np.ndarray[DTYPE_t, ndim=4] x_padded,
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int N, int C, int H, int W, int HH, int WW,
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int field_height, int field_width, int padding, int stride) except? -1:
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cdef int c, ii, jj, row, yy, xx, i, col
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for c in range(C):
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for ii in range(field_height):
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for jj in range(field_width):
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row = c * field_width * field_height + ii * field_height + jj
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for yy in range(HH):
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for xx in range(WW):
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for i in range(N):
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col = yy * WW * N + xx * N + i
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x_padded[i, c, stride * yy + ii, stride * xx + jj] += cols[row, col]
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@cython.boundscheck(False)
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@cython.wraparound(False)
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cdef col2im_6d_cython_inner(np.ndarray[DTYPE_t, ndim=6] cols,
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np.ndarray[DTYPE_t, ndim=4] x_padded,
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int N, int C, int H, int W, int HH, int WW,
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int out_h, int out_w, int pad, int stride):
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cdef int c, hh, ww, n, h, w
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for n in range(N):
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for c in range(C):
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for hh in range(HH):
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for ww in range(WW):
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for h in range(out_h):
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for w in range(out_w):
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x_padded[n, c, stride * h + hh, stride * w + ww] += cols[c, hh, ww, n, h, w]
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def col2im_6d_cython(np.ndarray[DTYPE_t, ndim=6] cols, int N, int C, int H, int W,
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int HH, int WW, int pad, int stride):
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cdef np.ndarray x = np.empty((N, C, H, W), dtype=cols.dtype)
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cdef int out_h = (H + 2 * pad - HH) / stride + 1
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cdef int out_w = (W + 2 * pad - WW) / stride + 1
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cdef np.ndarray[DTYPE_t, ndim=4] x_padded = np.zeros((N, C, H + 2 * pad, W + 2 * pad),
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dtype=cols.dtype)
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col2im_6d_cython_inner(cols, x_padded, N, C, H, W, HH, WW, out_h, out_w, pad, stride)
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if pad > 0:
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return x_padded[:, :, pad:-pad, pad:-pad]
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return x_padded
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