def kernel(A):
r = hcl.reduce_axis(0, KERNEL_SIZE)
c = hcl.reduce_axis(0, KERNEL_SIZE)
F = hcl.copy(np.random.randint(0, 10, (KERNEL_SIZE, KERNEL_SIZE)), "F")
return hcl.compute(
(SIZE - KERNEL_SIZE + 1, SIZE - KERNEL_SIZE + 1),
lambda y, x: hcl.sum(A[y + r, x + c] * F[r, c], axis=[r, c]), "B")
def max_pool2d_nchw(data, pooling, stride, padding, name='max_pool2d'):
assert len(data.shape) == 4, "only support 4-dim pooling"
assert len(stride) == 2, "only support 2-dim stride"
pooling_h, pooling_w = pooling
stride_h, stride_w = stride
batch, channel, height, width = data.shape
pad_top, pad_left, pad_bottom, pad_right = get_pad_tuple(
padding, (pooling_h, pooling_w))
pad_before = [0, 0, pad_top, pad_left]
pad_after = [0, 0, pad_bottom, pad_right]
if padding != [0, 0]:
data = pad(data,
pad_before,
pad_after,
pad_value=tvm.min_value("float32"))
out_height = simplify((height - pooling_h + pad_top + pad_bottom) //
stride_h + 1)
out_width = simplify((width - pooling_w + pad_left + pad_right) //
stride_w + 1)
dheight = hcl.reduce_axis(0, pooling_h)
dwidth = hcl.reduce_axis(0, pooling_w)
return hcl.compute(
(batch, channel, out_height, out_width),
lambda i, c, h, w: max(data[i, c, h * stride_h + dheight, w * stride_w
+ dwidth],
axis=[dheight, dwidth]),
name=name,
attrs=OrderedDict([('out_img_w', out_width), ('out_img_h', out_height),
('in_num', channel), ('kernel_h', pooling[1]),
('kernel_w', pooling[0]), ('stride_h', stride[1]),
('stride_w', stride[0]),
('app_name', tvm.make.StringImm('max_pool'))]))
def sobel(A, Gx, Gy):
B = hcl.compute((height, width),
lambda x, y: A[x][y][0] + A[x][y][1] + A[x][y][2],
"B",
dtype=hcl.Float())
r = hcl.reduce_axis(0, 3)
c = hcl.reduce_axis(0, 3)
D = hcl.compute(
(height - 2, width - 2),
lambda x, y: hcl.sum(B[x + r, y + c] * Gx[r, c], axis=[r, c]),
"D",
dtype=hcl.Float())
t = hcl.reduce_axis(0, 3)
g = hcl.reduce_axis(0, 3)
E = hcl.compute(
(height - 2, width - 2),
lambda x, y: hcl.sum(B[x + t, y + g] * Gy[t, g], axis=[t, g]),
"E",
dtype=hcl.Float())
return hcl.compute((height - 2, width - 2),
lambda x, y: hcl.sqrt(D[x][y] * D[x][y] + E[x][y] * E[x]
[y]) / 4328 * 255,
dtype=hcl.Float())
def test_conv2D_lb():
hcl.init()
A = hcl.placeholder((10, 10))
r = hcl.reduce_axis(0, 3)
c = hcl.reduce_axis(0, 3)
B = hcl.compute((8, 8), lambda y, x: hcl.sum(A[y + r, x + c], axis=[r, c]))
s = hcl.create_schedule([A, B])
LB = s.reuse_at(A, s[B], B.axis[0])
f = hcl.build(s)
np_A = np.random.randint(0, 10, size=(10, 10))
np_B = np.zeros((8, 8), dtype="int")
np_C = np.zeros((8, 8), dtype="int")
for y in range(0, 8):
for x in range(0, 8):
for r in range(0, 3):
for c in range(0, 3):
np_C[y][x] += np_A[y + r][x + c]
hcl_A = hcl.asarray(np_A)
hcl_B = hcl.asarray(np_B)
f(hcl_A, hcl_B)
np_B = hcl_B.asnumpy()
assert np.array_equal(np_B, np_C)
def softmax(x, name="softmax", axis=0, frontend='keras'):
shape = x.shape
k = hcl.reduce_axis(0, shape[axis])
new_shape = []
for i in range(len(shape)):
if i != axis:
new_shape.append(shape[i])
def _reduce_axis(axis, new_axis, keep_axis, *indices):
indices = indices[0]
new_ind = []
put_axis = False
for i in range(len(indices)):
if i == axis and keep_axis:
new_ind.append(new_axis)
put_axis = True
new_ind.append(indices[i])
elif i != axis:
new_ind.append(indices[i])
if put_axis == False and keep_axis:
new_ind.append(new_axis)
return tuple(new_ind)
max_elem = hcl.compute(
tuple(new_shape),
lambda *y: max(x[_reduce_axis(axis, k, True, y)], axis=[k]))
k = hcl.reduce_axis(0, shape[axis])
expsum = hcl.compute(
tuple(new_shape), lambda *y: sum(
tvm.exp(x[_reduce_axis(axis, k, True, y)] - max_elem[y]), axis=k))
return hcl.compute(
x.shape, lambda *y: tvm.exp(x[y] - max_elem[_reduce_axis(
axis, k, False, y)]) / expsum[_reduce_axis(axis, k, False, y)],
name)
def sobel_kernel(imgF, Gx, Gy):
def pad(x, y, z):
out = hcl.scalar(0, "out")
with hcl.if_(hcl.and_(x > 0, y > 0)):
out.v = imgF[x - 1, y - 1, z]
with hcl.else_():
out.v = 0
return out.v
P = hcl.compute((height + 2, width + 2, 3),
lambda x, y, z: pad(x, y, z), "P")
A = hcl.compute((height + 2, width + 2),
lambda x, y: P[x][y][0] + P[x][y][1] + P[x][y][2], "A")
r = hcl.reduce_axis(0, 3)
c = hcl.reduce_axis(0, 3)
resX = hcl.compute((height, width), lambda x, y: hcl.sum(
A[x + r, y + c] * Gx[r, c], axis=[r, c], name="sum1"), "X")
t = hcl.reduce_axis(0, 3)
g = hcl.reduce_axis(0, 3)
resY = hcl.compute((height, width), lambda x, y: hcl.sum(
A[x + t, y + g] * Gy[t, g], axis=[t, g], name="sum2"), "Y")
R = hcl.compute((height, width), lambda x, y: hcl.sqrt(resX[x][
y] * resX[x][y] + resY[x][y] * resY[x][y]), "R")
norm = hcl.scalar(255 / 4328)
return hcl.compute((height, width), lambda x, y: R[x][y] * norm.v, "F")
def avg_pool2d_nhwc(data,
pooling,
stride=[1, 1],
padding=[0, 0],
name='avg_pool2d'):
assert len(data.shape) == 4, "only support 4-dim pooling"
assert len(stride) == 2, "only support 2-dim stride"
pooling_h, pooling_w = pooling
stride_h, stride_w = stride
batch, height, width, channel = data.shape
pad_top, pad_left, pad_bottom, pad_right = get_pad_tuple(
padding, (pooling_h, pooling_w))
pad_before = [0, pad_top, pad_left, 0]
pad_after = [0, pad_bottom, pad_right, 0]
data = pad(data,
pad_before,
pad_after,
pad_value=tvm.const(0.0, data.dtype))
out_height = simplify((height - pooling_h + pad_top + pad_bottom) //
stride_h + 1)
out_width = simplify((width - pooling_w + pad_left + pad_right) //
stride_w + 1)
dheight = hcl.reduce_axis(0, pooling_h)
dwidth = hcl.reduce_axis(0, pooling_w)
return hcl.compute(
(batch, out_height, out_width, channel),
lambda i, h, w, c: sum(
data[i, h * stride_h + dheight, w * stride_w + dwidth, c],
axis=[dheight, dwidth]) / (pooling_w * pooling_h),
name=name,
attrs=OrderedDict([('out_img_w', out_width), ('out_img_h', out_height),
('in_num', channel), ('kernel_h', pooling[1]),
('kernel_w', pooling[0]), ('stride_h', stride[1]),
('stride_w', stride[0]),
('app_name', tvm.make.StringImm('avg_pool'))]))
def sobel(A, Gx, Gy):
r = hcl.reduce_axis(0,3)
c = hcl.reduce_axis(0,3)
A1 = hcl.compute((height,width), lambda y, x:
A[y][x][0] + A[y][x][1] + A[y][x][2], "A1")
B1 = hcl.compute((height-2,width-2),
lambda x,y: hcl.sum(A1[x+r,y+c]*Gx[r,c], axis=[r,c], name="sum1"),
name="B1", dtype=hcl.Float())
t = hcl.reduce_axis(0,3)
g = hcl.reduce_axis(0,3)
B2 = hcl.compute((height-2,width-2),
lambda x,y: hcl.sum(A1[x+t,y+g]*Gy[t,g], axis=[t,g], name="sum2"),
name="B2", dtype=hcl.Float())
def avg(in1, in2):
ll = hcl.scalar(in1, "in1")
lr = hcl.scalar(in2, "in2")
return hcl.sqrt(ll.v * ll.v + lr.v * lr.v)/4328*255
return hcl.compute((height-2,width-2),
lambda x, y : avg(B1[x,y], B2[x,y]),
name="output", dtype=hcl.Float())
def max_pool(data, kernel, stride, padding=[[0, 0], [0, 0]], name="max_pool"):
assert len(data.shape) == 4, "only support 4-dim pooling"
assert len(stride) == 2, "only support 2-dim stride"
kernel_height, kernel_width = kernel
stride_height, stride_width = stride
batch, channel, height, width = data.shape
[pad_top, pad_left], [pad_down, pad_right] = padding
pad_before = [0, 0, pad_top, pad_left]
pad_after = [0, 0, pad_down, pad_right]
if padding != [[0, 0], [0, 0]]:
data = pad(data,
pad_before,
pad_after,
pad_value=tvm.min_value("float32"))
out_height = simplify((height - kernel_height + pad_top + pad_down) //
stride_height + 1)
out_width = simplify((width - kernel_width + pad_left + pad_right) //
stride_width + 1)
dheight = hcl.reduce_axis(0, kernel_height)
dwidth = hcl.reduce_axis(0, kernel_width)
return hcl.compute(
(batch, channel, out_height, out_width),
lambda i, c, h, w: max(data[i, c, h * stride_height + dheight, w *
stride_width + dwidth],
axis=[dheight, dwidth]),
name=name,
attrs=OrderedDict([('out_img_w', out_width), ('out_img_h', out_height),
('in_num', channel), ('kernel_h', kernel[1]),
('kernel_w', kernel[0]), ('stride_h', stride[1]),
('stride_w', stride[0]),
('app_name', tvm.make.StringImm('max_pool'))]))
def unsharp(input_image, output_image):
"""
Helper Functions
"""
def clamp(val, min_, max_):
local = hcl.scalar(val)
with hcl.if_(val < min_):
local[0] = min_
with hcl.elif_(val > max_):
local[0] = max_
return local[0]
def clamp2D(tensor, min_, max_):
return hcl.compute(tensor.shape,
lambda x, y: clamp(tensor[x, y], min_, max_),
name="clamped_" + tensor.name)
def clamp3D(tensor, min_, max_):
return hcl.compute(tensor.shape,
lambda x, y, c: clamp(tensor[x, y, c], min_, max_),
name="clamped_" + tensor.name)
def kernel_f(x):
return hcl.exp(-(x * x) / (2 * 1.5 * 1.5)) / sqrt(2 * 3.14159 * 1.5)
def kernel(x):
return kernel_f(x) * 255 / (kernel_f(0) + kernel_f(1) * 2 +
kernel_f(2) * 2 + kernel_f(3) * 2 +
kernel_f(4) * 2)
rx = hcl.reduce_axis(-4, 5, "rx")
ry = hcl.reduce_axis(-4, 5, "ry")
my = hcl.reduce_axis(0, 640, "my")
gray = hcl.compute((480, 640),
lambda x, y: (input_image[x, y, 0] * 77 + input_image[
x, y, 1] * 150 + input_image[x, y, 2] * 29) >> 8,
name="gray")
blur = hcl.compute(
gray.shape,
lambda x, y: hcl.sum(gray[rx + x, ry + y] * kernel(rx) * kernel(ry),
axis=[rx, ry]),
name="blur")
sharpen = clamp2D(
hcl.compute(gray.shape,
lambda x, y: gray[x, y] * 2 - blur[x, y],
name="sharpen"), 0, 255)
ratio = clamp2D(
hcl.compute(
gray.shape,
lambda x, y: sharpen[x, y] * 32 / hcl.max(gray[x, my], axis=my),
name="ratio"), 0, 255)
out = clamp3D(
hcl.compute(output_image.shape,
lambda x, y, c: ratio[x, y] * input_image[x, y, c] >> 5,
name="out"), 0, 255)
U = hcl.update(output_image, lambda x, y, c: out[x, y, c])
return U
def conv2d_nchw(Input,
Filter,
bias=None,
stride=(1, 1),
padding='VALID',
dilation=(1, 1),
out_dtype=None,
name="conv2d_nchw"):
if out_dtype is None:
out_dtype = Input.dtype
assert isinstance(stride, int) or len(stride) == 2
assert isinstance(dilation, int) or len(dilation) == 2
if isinstance(stride, int):
stride_h = stride_w = stride
else:
stride_h, stride_w = stride
if isinstance(dilation, int):
dilation_h = dilation_w = dilation
else:
dilation_h, dilation_w = dilation
batch, in_channel, in_height, in_width = Input.shape
num_filter, channel, kernel_h, kernel_w = Filter.shape
# compute the output shape
dilated_kernel_h = (kernel_h - 1) * dilation_h + 1
dilated_kernel_w = (kernel_w - 1) * dilation_w + 1
pad_top, pad_left, pad_down, pad_right = get_pad_tuple(
padding, (dilated_kernel_h, dilated_kernel_w))
out_channel = num_filter
out_height = simplify(
(in_height - dilated_kernel_h + pad_top + pad_down) // stride_h + 1)
out_width = simplify(
(in_width - dilated_kernel_w + pad_left + pad_right) // stride_w + 1)
# compute graph
pad_before = [0, 0, pad_top, pad_left]
pad_after = [0, 0, pad_down, pad_right]
temp = pad(Input, pad_before, pad_after, name="pad_temp")
rc = hcl.reduce_axis(0, in_channel, name='rc')
ry = hcl.reduce_axis(0, kernel_h, name='ry')
rx = hcl.reduce_axis(0, kernel_w, name='rx')
conv2d = hcl.compute((batch, out_channel, out_height, out_width),
lambda nn, ff, yy, xx:
sum(temp[nn, rc, yy * stride_h + ry * dilation_h, xx *
stride_w + rx * dilation_w].astype(out_dtype)
* Filter[ff, rc, ry, rx].astype(out_dtype),
dtype=out_dtype,
axis=[rc, ry, rx]),
name=name)
if bias is not None:
conv2d = hcl.compute(conv2d.shape,
lambda i, j, k, l: conv2d[i, j, k, l] + bias[j],
name=name)
return conv2d
def sobelAlgo(A, Fx, Fy): B = hcl.compute((height, width), lambda x,y :A[x][y][0]+A[x][y][1]+A[x][y][2],"B", dtype=hcl.Float()) r = hcl.reduce_axis(0, 3) c = hcl.reduce_axis(0, 3) Gx = hcl.compute((height, width), lambda x,y: hcl.select(hcl.and_(x>0,x<(height-1),y>0,y<(width-1)), hcl.sum(B[x+r,y+c]*Fx[r,c],axis=[r,c]), B[x,y]), "Gx") t = hcl.reduce_axis(0, 3) g = hcl.reduce_axis(0, 3) Gy = hcl.compute((height, width), lambda x,y: hcl.select(hcl.and_(x>0,x<(height-1),y>0,y<(width-1)), hcl.sum(B[x+t,y+g]*Fy[t,g],axis=[t,g]), B[x,y]), "Gy") return hcl.compute((height, width), lambda x,y:(hcl.sqrt(Gx[x][y]*Gx[x][y]+Gy[x][y]*Gy[x][y]))/4328*255, dtype = hcl.Float())
def dev(gx, gy, org):
assert gx.shape == gy.shape, "mismatch"
rx = hcl.reduce_axis(0, 255, "rx")
ry = hcl.reduce_axis(0, 255, "ry")
mat_sum = hcl.compute(gx.shape, lambda nn, ff, xx, yy:
gx[nn, ff, xx, yy] + gy[nn, ff, xx, yy], name="add")
return hcl.compute(mat_sum.shape, lambda nn, ff, xx, yy:
mat_sum[nn, ff, xx, yy] * 255.0 / hcl.max(mat_sum[nn, ff, rx, ry], axis=[rx, ry]),
name = "derv")
def sobelAlgo(A, Fx, Fy):
B = hcl.compute((height+2, width+2), lambda x,y:A[x][y][0]+A[x][y][1]+A[x][y][2], "B")
r = hcl.reduce_axis(0, 3)
c = hcl.reduce_axis(0, 3)
Gx = hcl.compute((height, width), lambda y,x:hcl.sum(B[y+r, x+c]*Fx[r,c], axis = [r,c]), "Gx")
t = hcl.reduce_axis(0, 3)
g = hcl.reduce_axis(0, 3)
Gy = hcl.compute((height, width), lambda y,x:hcl.sum(B[y+t, x+g]*Fy[t,g], axis = [t,g]), "Gy")
return hcl.compute((height, width), lambda y,x:(hcl.sqrt(Gx[y][x]*Gx[y][x]+Gy[y][x]*Gy[y][x]))/4328*255)
def softmax(out, x):
assert len(x.shape) == 2, "only support 2-dim softmax"
m, n = x.shape
k = hcl.reduce_axis(0, n)
max_elem = hcl.compute((m, ), lambda i: hcl.max(x[i, k], axis=k))
k = hcl.reduce_axis(0, n)
expsum = hcl.compute(
(m, ), lambda i: hcl.sum(hcl.exp(x[i, k] - max_elem[i]), axis=k))
return hcl.update(out,
lambda i, j: hcl.exp(x[i, j] - max_elem[i]) / expsum[i])
def seidel(input_image, output_image):
dtype = hcl.Float()
rx = hcl.reduce_axis(0, 3, "rx")
ry = hcl.reduce_axis(0, 3, "ry")
tmp = hcl.compute(output_image.shape, lambda x, y: hcl.sum(
input_image[x, ry+y], axis=[ry], dtype=dtype)/3, dtype=dtype, name='tmp')
return hcl.update(output_image, lambda x, y: hcl.sum(
tmp[rx+x, y], axis=[rx], dtype=dtype)/3, name=output_image.name)
def sobel(B, G):
r = hcl.reduce_axis(0, 3)
c = hcl.reduce_axis(0, 3)
return hcl.compute(
(height, width),
lambda x, y: hcl.select(
hcl.and_(x > 0, x < (height - 1), y > 0, y < (width - 1)),
hcl.sum(B[x + r, y + c] * G[r, c], axis=[r, c]), B[x, y]),
"D",
dtype=hcl.Float())
def softmax(x, name="softmax", axis=0):
assert len(x.shape) == 2, "only support 2-dim softmax"
m, n = x.shape
k = hcl.reduce_axis(0, n)
max_elem = hcl.compute((m, ), lambda i: max(x[i, k], axis=k))
k = hcl.reduce_axis(0, n)
expsum = hcl.compute((m, ),
lambda i: sum(tvm.exp(x[i, k] - max_elem[i]), axis=k))
return hcl.compute(x.shape,
lambda i, j: tvm.exp(x[i, j] - max_elem[i]) / expsum[i],
name)
def guassian(A, G):
h = hcl.reduce_axis(0, size)
w = hcl.reduce_axis(0, size)
return hcl.compute(
(height, width),
lambda x, y: hcl.select(
hcl.and_(x > (size - 1), x < (height - size), y > (size - 1), y <
(width - size)),
hcl.sum(A[x + h, y + w] * G[h, w], axis=[h, w]), A[x, y]),
"F",
dtype=hcl.Float())
def sobel(A,Gx,Gy): B = hcl.compute((height,width), lambda x,y: A[x][y][0]+A[x][y][1]+A[x][y][2],"B") r = hcl.reduce_axis(0,3) c = hcl.reduce_axis(0,3) D = hcl.compute((height, width), lambda x,y: hcl.select(hcl.and_(x>0,x<(height-1),y>0,y<(width-1)), hcl.sum(B[x+r,y+c]*Gx[r,c],axis=[r,c]), B[x,y]), "Gx") t = hcl.reduce_axis(0, 3) g = hcl.reduce_axis(0, 3) E = hcl.compute((height, width), lambda x,y: hcl.select(hcl.and_(x>0,x<(height-1),y>0,y<(width-1)), hcl.sum(B[x+t,y+g]*Gy[t,g],axis=[t,g]), B[x,y]), "Gy") return hcl.compute((height,width), lambda x,y:hcl.sqrt(D[x][y]*D[x][y]+E[x][y]*E[x][y])/4328*255)
def test_reuse_compute_nd():
hcl.init()
nz = 1
rx = hcl.reduce_axis(0, 3, name="rx")
rz = hcl.reduce_axis(0, nz, name="rz")
A = hcl.placeholder((nz, 10, 10), name="A")
B = hcl.compute((10, 8),
lambda y, x: hcl.sum(A[rz, y, x + rx], axis=[rz, rx]), "B")
s = hcl.create_schedule([A, B])
RB = s.reuse_at(A, s[B], B.axis[1])
print(hcl.lower(s))
f = hcl.build(s)
def _conv2d_nhwc(Input,
Filter,
Bias=None,
stride=[1, 1],
padding=[1, 1],
dilation=[1, 1],
name='conv2d',
out_dtype=None):
if out_dtype is None:
out_dtype = Input.dtype
assert isinstance(stride, int) or len(stride) == 2
assert isinstance(dilation, int) or len(dilation) == 2
if isinstance(stride, int):
stride_h = stride_w = stride
else:
stride_h, stride_w = stride
if isinstance(dilation, int):
dilation_h = dilation_w = dilation
else:
dilation_h, dilation_w = dilation
batch, in_height, in_width, in_channel = Input.shape
num_filter, channel, kernel_h, kernel_w = Filter.shape
#compute output shape
dilated_kernel_h = (kernel_h - 1) * dilation_h + 1
dilated_kernel_w = (kernel_w - 1) * dilation_w + 1
pad_top, pad_left, pad_down, pad_right = hlib.nn.get_pad_tuple(
padding, (dilated_kernel_h, dilated_kernel_w))
out_channel = num_filter
out_height = hlib.nn.simplify(
(in_height - dilated_kernel_h + pad_top + pad_down) // stride_h + 1)
out_width = hlib.nn.simplify(
(in_width - dilated_kernel_w + pad_left + pad_right) // stride_w + 1)
pad_before = [0, pad_top, pad_left, 0]
pad_after = [0, pad_down, pad_right, 0]
print(pad_before, pad_after)
temp = hlib.nn.pad(Input, pad_before, pad_after, name="pad_temp")
rc = hcl.reduce_axis(0, in_channel)
ry = hcl.reduce_axis(0, kernel_h)
rx = hcl.reduce_axis(0, kernel_w)
if not Bias == None:
return hcl.compute(
(batch, out_height, out_width, out_channel),
lambda nn, yy, xx, ff: hcl.
sum(temp[nn, yy * stride_h + ry * dilation_h, xx * stride_w + rx *
dilation_w, rc].astype(out_dtype) * Filter[ff, rc, ry, rx]
.astype(out_dtype) + Bias[ff].astype(out_dtype),
axis=[ry, rx, rc]),
name=name,
)
def sobel(RGB,Gx,Gy):
B = hcl.compute((height,width), lambda x,y: RGB[x][y][8:0] + RGB[x][y][16:8] + RGB[x][y][24:16], "B")
r = hcl.reduce_axis(0,3)
c = hcl.reduce_axis(0,3)
D = hcl.compute((height-2, width-2),
lambda x,y: hcl.sum(B[x+r, y+c]*Gx[r,c], axis=[r,c], name="sum1"), "xx")
t = hcl.reduce_axis(0, 3)
g = hcl.reduce_axis(0, 3)
E = hcl.compute((height-2, width-2),
lambda x,y: hcl.sum(B[x+t, y+g]*Gy[t,g], axis=[t,g]), name="sum2"), "yy")
return hcl.compute((height-2,width-2),
lambda x,y:hcl.sqrt(D[x][y]*D[x][y]+E[x][y]*E[x][y])*0.05891867, "Fimg")
def kernel(A):
init = hcl.compute((A.shape[0]*A.shape[1],), lambda x: 11)
def freduce(x, Y):
with hcl.for_(0, Y.shape[0]) as i:
with hcl.if_(x < Y[i]):
with hcl.for_(Y.shape[0]-1, i, -1) as j:
Y[j] = Y[j-1]
Y[i] = x
hcl.break_()
my_sort = hcl.reducer(init, freduce)
rx = hcl.reduce_axis(0, 10)
ry = hcl.reduce_axis(0, 10)
return hcl.compute(init.shape, lambda _x: my_sort(A[rx, ry], axis=[rx, ry]))
def sobel(A,Gx,Gy): B = hcl.compute((height,width), lambda x,y: A[x][y][0]+A[x][y][1]+A[x][y][2], "B") r = hcl.reduce_axis(0,3) c = hcl.reduce_axis(0,3) # D = hcl.compute((height, width), lambda x,y: hcl.select(hcl.and_(x>0,x<(height-1),y>0,y<(width-1)), hcl.sum(B[x+r,y+c]*Gx[r,c],axis=[r,c]), B[x,y]), "xx") D = hcl.compute((height-2, width-2), lambda x,y: hcl.sum(B[x+r, y+c]*Gx[r,c], axis=[r,c], name="sum1"), "xx") t = hcl.reduce_axis(0, 3) g = hcl.reduce_axis(0, 3) # E = hcl.compute((height, width), lambda x,y: hcl.select(hcl.and_(x>0,x<(height-1),y>0,y<(width-1)), hcl.sum(B[x+t,y+g]*Gy[t,g],axis=[t,g]), B[x,y]), "yy") E = hcl.compute((height-2, width-2), lambda x,y: hcl.sum(B[x+t, y+g]*Gy[t,g], axis=[t,g]), "yy") return hcl.compute((height-2,width-2), lambda x,y:hcl.sqrt(D[x][y]*D[x][y]+E[x][y]*E[x][y])*0.05891867,"Fimg")
def gaussian_func_gen(input_image, output_image):
def kernel_f(x):
return hcl.exp(-(x * x) / (2 * 1.5 * 1.5)) / math.sqrt(2 * 3.14159 * 1.5)
def kernel(x):
return kernel_f(x) * 255 / (kernel_f(0) + kernel_f(1) * 2 +
kernel_f(2) * 2 + kernel_f(3) * 2 +
kernel_f(4) * 2)
rx = hcl.reduce_axis(-1, 1, "rx")
ry = hcl.reduce_axis(-1, 1, "ry")
return hcl.update(output_image, lambda x, y: hcl.sum(
input_image[rx+x, ry+y] * kernel(rx) * kernel(ry), axis=[rx, ry],
name='reduce', dtype=hcl.Float()), name=output_image.name)
def sobel_x(A, Gx):
B = hcl.compute((height, width),
lambda x, y: A[x][y][0] + A[x][y][1] + A[x][y][2],
"B",
dtype=hcl.Float())
r = hcl.reduce_axis(0, 3)
c = hcl.reduce_axis(0, 3)
return hcl.compute(
(height, width),
lambda x, y: hcl.select(
hcl.and_(x > 0, x < (height - 1), y > 0, y < (width - 1)),
hcl.sum(B[x + r, y + c] * Gx[r, c], axis=[r, c]), B[x, y]),
"X",
dtype=hcl.Float())
def kernel_conv2d(inputs, weight, stride=1, padding=1, dilation=1, groups=1):
"""Convolution 2d NCHW layout
Args:
-----------------------------
inputs : hcl.tensor.Tensor
shape [batch, channel, height, width]
weight : hcl.tensor.Tensor
shape [out_channel, channel // groups, kernel_height, kernel_width]
stride : (optional:1) int or tuple
padding : (optional:0) int or tuple
dilation: (optional:1) int
groups : (optional:1) int
-----------------------------
Returns:
-----------------------------
hcl.tensor.Tensor
shape [batch, out_channel, output_height, output_width]
-----------------------------
"""
batch_size, in_channel, in_h, in_w = inputs.shape
out_channel, channel_per_group, k_h, k_w = weight.shape
out_channel_per_group = out_channel // groups
stride = (stride, stride) if isinstance(stride, (int, )) else stride
padding = (padding, padding) if isinstance(padding, (int, )) else padding
dilation = (dilation, dilation) if isinstance(dilation, (int, )) else dilation
out_h = (in_h + 2 * padding[0] - dilation[0] * (k_h - 1) - 1) // stride[0] + 1
out_w = (in_w + 2 * padding[1] - dilation[1] * (k_w - 1) - 1) // stride[1] + 1
rc = hcl.reduce_axis(*(0, channel_per_group), name="rc")
rh = hcl.reduce_axis(*(0, k_h), name="rh")
rw = hcl.reduce_axis(*(0, k_w), name="rw")
padded = zero_pad2d(inputs, padding=padding)
output = hcl.compute(
(batch_size, out_channel, out_h, out_w),
lambda b, c, h, w: hcl.sum(
(padded[b, c // out_channel_per_group * channel_per_group + rc,
h * stride[0] + rh * dilation[0], w * stride[1] + rw * dilation[1]]
* weight[c, rc, rh, rw]),
axis=[rc, rw, rh]
), "C"
)
return output
def kernel(A, F):
name = "conv1"
ry = hcl.reduce_axis(0, 3, name=name + '_ry')
rx = hcl.reduce_axis(0, 3, name=name + '_rx')
rc_ = 0
rc = hcl.reduce_axis(0, 1, name=name + '_rc')
batch, out_height, out_width, out_channel = (1, 6, 6, 16)
return hcl.compute((batch, out_height, out_width, out_channel),
lambda nn, yy, xx, ff: hcl.sum(A[
nn, yy + ry, xx + rx, rc_] * F[ff, ry, rx, rc_],
axis=[ry, rx, rc],
name=name + "_sum",
dtype=hcl.UInt(16)),
name=name,
dtype=hcl.UInt(16))
def sobel_y(A, Gy):
B = hcl.compute((height, width),
lambda x, y: A[x][y][0] + A[x][y][1] + A[x][y][2],
"B",
dtype=hcl.Float())
t = hcl.reduce_axis(0, 3)
g = hcl.reduce_axis(0, 3)
return hcl.compute(
(height, width),
lambda x, y: hcl.select(
hcl.and_(x > 0, x < (height - 1), y > 0, y < (width - 1)),
hcl.sum(B[x + t, y + g] * Gy[t, g], axis=[t, g]), B[x, y]),
"Y",
dtype=hcl.Float())
