def im2col(inputX, fsize, stride, pad):
assert inputX.ndim == 4
X = T.tensor4()
Xpad = lasagnepad(X, pad, batch_ndim=2)
neibs = images2neibs(Xpad, (fsize, fsize), (stride, stride), 'ignore_borders')
im2colfn = theano.function([X], neibs, allow_input_downcast=True)
return im2colfn(inputX)
def im2col(inputX, fsize, stride, pad):
assert inputX.ndim == 4
Xrows, Xcols = inputX.shape[-2:]
X = T.tensor4()
if pad is None: # 保持下和右的边界
rowpad = stride - (Xrows - fsize) % stride
colpad = stride - (Xcols - fsize) % stride
pad = ((0, rowpad), (0, colpad))
Xpad = lasagnepad(X, pad, batch_ndim=2)
neibs = images2neibs(Xpad, (fsize, fsize), (stride, stride),
'ignore_borders')
im2colfn = theano.function([X], neibs, allow_input_downcast=True)
return im2colfn(inputX)
def im2col_compfn(shape, fsize, stride, pad, ignore_border=False):
assert len(shape) == 2
assert isinstance(pad, int)
if isinstance(fsize, (int, float)): fsize = (int(fsize), int(fsize))
if isinstance(stride, (int, float)): stride = (int(stride), int(stride))
X = T.tensor4()
if not ignore_border: # 保持下和右的边界
rows, cols = shape
rows, cols = rows + 2 * pad, cols + 2 * pad
rowpad = colpad = 0
rowrem = (rows - fsize[0]) % stride[0]
if rowrem: rowpad = stride[0] - rowrem
colrem = (cols - fsize[1]) % stride[1]
if colrem: colpad = stride[1] - colrem
pad = ((pad, pad + rowpad), (pad, pad + colpad))
Xpad = lasagnepad(X, pad, batch_ndim=2)
neibs = images2neibs(Xpad, fsize, stride, 'ignore_borders')
im2colfn = theano.function([X], neibs, allow_input_downcast=True)
return im2colfn
