def top(input, filter, bias, ):
input_extent_3_required_s = (((((final_extent_2 + 31)//32) * final_extent_3) + -1)//hcl.select(((final_extent_2 + 31)//32) > 1, ((final_extent_2 + 31)//32), 1))
final_total_extent_1 = (hcl.cast(dtype = hcl.Int(bits = 64), expr = final_extent_1) * hcl.cast(dtype = hcl.Int(bits = 64), expr = final_extent_0))
final_total_extent_2 = (final_total_extent_1 * hcl.cast(dtype = hcl.Int(bits = 64), expr = final_extent_2))
final_total_extent_3 = (final_total_extent_2 * hcl.cast(dtype = hcl.Int(bits = 64), expr = final_extent_3))
f_conv_n_extent_realized_s = hcl.select(hcl.select((((final_extent_2 * final_extent_3) + -1)//hcl.select(final_extent_2 > 1, final_extent_2, 1)) > (final_extent_3 + -1), (((final_extent_2 * final_extent_3) + -1)//hcl.select(final_extent_2 > 1, final_extent_2, 1)), (final_extent_3 + -1)) > (((((final_extent_2 + 31)//32) * final_extent_3) + -1)//(hcl.select(((final_extent_2 + -1)//32) > 0, ((final_extent_2 + -1)//32), 0) + 1)), hcl.select((((final_extent_2 * final_extent_3) + -1)//hcl.select(final_extent_2 > 1, final_extent_2, 1)) > (final_extent_3 + -1), (((final_extent_2 * final_extent_3) + -1)//hcl.select(final_extent_2 > 1, final_extent_2, 1)), (final_extent_3 + -1)), (((((final_extent_2 + 31)//32) * final_extent_3) + -1)//(hcl.select(((final_extent_2 + -1)//32) > 0, ((final_extent_2 + -1)//32), 0) + 1)))
f_conv_z_extent_realized = hcl.select(((hcl.select(((final_extent_2 + -1)//32) > 0, ((final_extent_2 + -1)//32), 0) * 32) + 32) > final_extent_2, ((hcl.select(((final_extent_2 + -1)//32) > 0, ((final_extent_2 + -1)//32), 0) * 32) + 32), final_extent_2)
f_conv = hcl.compute((final_extent_0, ((((final_extent_1 + -1)//32) * 32) + 32), f_conv_z_extent_realized, (f_conv_n_extent_realized_s + 1)), lambda x, y, z, w: 0, name = "f_conv", dtype = hcl.Float(bits = 32))
with hcl.Stage("f_conv"):
with hcl.for_(0, (final_extent_2 * final_extent_3), name = "f_conv_s0_z_par") as f_conv_s0_z_par:
with hcl.for_(final_min_1, final_extent_1, name = "f_conv_s0_y") as f_conv_s0_y:
with hcl.for_(final_min_0, final_extent_0, name = "f_conv_s0_x") as f_conv_s0_x:
f_conv[f_conv_s0_x, f_conv_s0_y, ((f_conv_s0_z_par % hcl.select(final_extent_2 > 1, final_extent_2, 1)) + final_min_2), ((f_conv_s0_z_par//hcl.select(final_extent_2 > 1, final_extent_2, 1)) + final_min_3)] = bias[((f_conv_s0_z_par % hcl.select(final_extent_2 > 1, final_extent_2, 1)) + final_min_2)]
with hcl.for_(0, (((final_extent_2 + 31)//32) * final_extent_3), name = "f_conv_s1_z_z_par") as f_conv_s1_z_z_par:
f_conv_s1_z_z_t_base_s = (f_conv_s1_z_z_par % hcl.select(((final_extent_2 + 31)//32) > 1, ((final_extent_2 + 31)//32), 1))
with hcl.for_(0, 32, name = "f_conv_s1_r__z") as f_conv_s1_r__z:
with hcl.for_(0, ((final_extent_1 + 31)//32), name = "f_conv_s1_y_y") as f_conv_s1_y_y:
with hcl.for_(0, 32, name = "f_conv_s1_z_z_t") as f_conv_s1_z_z_t:
with hcl.for_(0, 32, name = "f_conv_s1_y_y_t") as f_conv_s1_y_y_t:
with hcl.for_(final_min_0, final_extent_0, name = "f_conv_s1_x") as f_conv_s1_x:
with hcl.for_(0, 3, name = "f_conv_s1_r__y_r21") as f_conv_s1_r__y_r21:
with hcl.for_(0, 3, name = "f_conv_s1_r__x_r20") as f_conv_s1_r__x_r20:
t51_s = (f_conv_s1_z_z_par//hcl.select(((final_extent_2 + 31)//32) > 1, ((final_extent_2 + 31)//32), 1))
f_conv[f_conv_s1_x, (((f_conv_s1_y_y * 32) + final_min_1) + f_conv_s1_y_y_t), (((f_conv_s1_z_z_t_base_s * 32) + final_min_2) + f_conv_s1_z_z_t), ((f_conv_s1_z_z_par//hcl.select(((final_extent_2 + 31)//32) > 1, ((final_extent_2 + 31)//32), 1)) + final_min_3)] = (f_conv[f_conv_s1_x, (((f_conv_s1_y_y * 32) + final_min_1) + f_conv_s1_y_y_t), (((f_conv_s1_z_z_t_base_s * 32) + final_min_2) + f_conv_s1_z_z_t), (final_min_3 + t51_s)] + (filter[f_conv_s1_r__x_r20, f_conv_s1_r__y_r21, f_conv_s1_r__z, (((f_conv_s1_z_z_t_base_s * 32) + final_min_2) + f_conv_s1_z_z_t)] * input[(f_conv_s1_r__x_r20 + f_conv_s1_x), ((((f_conv_s1_y_y * 32) + final_min_1) + f_conv_s1_y_y_t) + f_conv_s1_r__y_r21), f_conv_s1_r__z, (final_min_3 + t51_s)]))
final = hcl.compute((64, 64, 32, 4), lambda x, y, z, w: 0, name = "final", dtype = hcl.Float(bits = 32))
with hcl.Stage("final"):
with hcl.for_(final_min_3, final_extent_3, name = "final_s0_n") as final_s0_n:
with hcl.for_(final_min_2, final_extent_2, name = "final_s0_z") as final_s0_z:
with hcl.for_(final_min_1, final_extent_1, name = "final_s0_y") as final_s0_y:
with hcl.for_(final_min_0, final_extent_0, name = "final_s0_x") as final_s0_x:
final[final_s0_x, final_s0_y, final_s0_z, final_s0_n] = hcl.select(f_conv[final_s0_x, final_s0_y, final_s0_z, final_s0_n] > hcl.cast(dtype = hcl.Float(bits = 32), expr = 0.000000), f_conv[final_s0_x, final_s0_y, final_s0_z, final_s0_n], hcl.cast(dtype = hcl.Float(bits = 32), expr = 0.000000))
return final
def kernel_select(a, b, c, d):
use_imm = hcl.scalar(1)
with hcl.for_(0, 10, name="i") as i:
src = hcl.select(use_imm == 1, hcl.cast(hcl.Int(16),
(c[i] + b[i])),
hcl.cast(hcl.Int(32), (c[i] - b[i])))
dst = hcl.cast(hcl.Int(32), (2 * (c[i] + b[i])))
d[i] = hcl.select(dst >= (-1 * src),
hcl.select(dst <= src, a[i], src), (-1 * src))
def top(input, ):
final_total_extent_1 = (
hcl.cast(dtype=hcl.Int(bits=64), expr=final_extent_1) *
hcl.cast(dtype=hcl.Int(bits=64), expr=final_extent_0))
max_local = hcl.compute((final_extent_0, final_extent_1),
lambda x, y: 0,
name="max_local",
dtype=hcl.UInt(bits=16))
with hcl.Stage("max_local"):
with hcl.for_(final_min_1, final_extent_1,
name="max_local_s0_y") as max_local_s0_y:
with hcl.for_(final_min_0, final_extent_0,
name="max_local_s0_x") as max_local_s0_x:
maximum = hcl.compute((1, 1),
lambda x, y: 0,
name="maximum",
dtype=hcl.UInt(bits=16))
with hcl.Stage("maximum"):
maximum[max_local_s0_x,
max_local_s0_y] = hcl.cast(dtype=hcl.UInt(bits=16),
expr=0)
with hcl.for_(
0, 3,
name="maximum_s1_box__y") as maximum_s1_box__y:
with hcl.for_(
0, 3,
name="maximum_s1_box__x") as maximum_s1_box__x:
maximum[max_local_s0_x,
max_local_s0_y] = hcl.select(
maximum[max_local_s0_x, max_local_s0_y]
> input[(max_local_s0_x +
maximum_s1_box__x),
(max_local_s0_y +
maximum_s1_box__y)],
maximum[max_local_s0_x,
max_local_s0_y],
input[(max_local_s0_x +
maximum_s1_box__x),
(max_local_s0_y +
maximum_s1_box__y)])
max_local[max_local_s0_x,
max_local_s0_y] = maximum[max_local_s0_x,
max_local_s0_y]
final = hcl.compute((640, 480),
lambda x, y: 0,
name="final",
dtype=hcl.UInt(bits=16))
with hcl.Stage("final"):
with hcl.for_(final_min_1, final_extent_1,
name="final_s0_y") as final_s0_y:
with hcl.for_(final_min_0, final_extent_0,
name="final_s0_x") as final_s0_x:
final[final_s0_x, final_s0_y] = max_local[final_s0_x,
final_s0_y]
return final
def algo(A, B):
def f_mutate(i, j):
factor = hcl.scalar(B[0][0][13:11], name="factor")
idx = hcl.scalar(B[0][0][11:0], dtype=hcl.UInt(16), name="idx")
idx += i * hcl.cast(hcl.UInt(16), factor.v)
A[idx][j] = B[idx][j]
bound = hcl.scalar(5, dtype=hcl.Int(32))
domain = (hcl.cast(hcl.UInt(32),
bound.v), hcl.cast(hcl.UInt(32), bound.v))
hcl.mutate(domain, f_mutate)
def stateToIndex(sVals, iVals, bounds, ptsEachDim):
iVals[0] = ((sVals[0] - bounds[0, 0]) /
(bounds[0, 1] - bounds[0, 0])) * (ptsEachDim[0] - 1)
iVals[1] = ((sVals[1] - bounds[1, 0]) /
(bounds[1, 1] - bounds[1, 0])) * (ptsEachDim[1] - 1)
iVals[2] = ((sVals[2] - bounds[2, 0]) /
(bounds[2, 1] - bounds[2, 0])) * (ptsEachDim[2] - 1)
# NOTE: add 0.5 to simulate rounding
iVals[0] = hcl.cast(hcl.Int(), iVals[0] + 0.5)
iVals[1] = hcl.cast(hcl.Int(), iVals[1] + 0.5)
iVals[2] = hcl.cast(hcl.Int(), iVals[2] + 0.5)
def full_like(array, fill_val, dtype=None, name='full_like'):
if dtype is None:
dtype = array.dtype
hcl.init(dtype)
return hcl.compute(array.shape,
lambda *x: hcl.cast(dtype, fill_val),
name=name)
def full(shape=(1, ), fill_val=1, dtype=dtype, name='full'):
if isinstance(shape, list):
shape = tuple(shape)
return hcl.compute(shape,
lambda *x: hcl.cast(dtype, fill_val),
name=name,
dtype=dtype)
def kernel(A, B):
C = hcl.compute(A.shape,
lambda x: hcl.cast(hcl.UInt(bw), A[x]) << sl,
dtype=hcl.UInt(bw))
D = hcl.compute(A.shape, lambda x: B[x] + C[x], dtype=hcl.UInt(bw))
E = hcl.compute(A.shape, lambda x: A[x])
return E
def prelu(out, x, alpha):
assert len(x.shape) == 2, "only support 2-dim PReLU"
m, n = x.shape
k = hcl.reduce_axis(0, n)
return hcl.update(
out, lambda i, j: hcl.select(x[
i, j] < 0, hcl.cast(x.dtype, alpha[j] * x[i, j]), x[i, j]))
def thresholdedrelu(out, x, theta):
assert len(x.shape) == 2, "only support 2-dim ThresholdedReLU"
m, n = x.shape
k = hcl.reduce_axis(0, n)
return hcl.update(
out, lambda i, j: hcl.select(x[i, j] > theta, x[i, j],
hcl.cast(x.dtype, 0)))
def Sigmoid(exponent):
ret = hcl.scalar(0.0, "sigmoid", FTYPE)
with hcl.if_(exponent > hcl.cast(FTYPE, 4.0)):
ret[0] = 1.0
with hcl.elif_(exponent < hcl.cast(FTYPE, -4.0)):
ret[0] = 0.0
with hcl.else_():
with hcl.if_(exponent < hcl.cast(FTYPE, 0.0)):
num = hcl.scalar(0, dtype=hcl.UFixed(18, 8))
num[0][18:0] = exponent[29:11]
num[0] = ~(num[0] << 8) + 1
index = 2047.0 - num[0]
ret[0] = lut[hcl.cast(hcl.Int(32), index)]
with hcl.else_():
index = exponent[21:11]
ret[0] = lut[hcl.cast(hcl.Int(32), index)]
return ret[0]
def zeros(shape=(1, ), dtype=dtype, name='zeros'):
dtype = hcl.dtype_to_hcl(dtype)
shape = list(shape)
for i in range(len(shape)):
if hasattr(shape[i], 'value'):
shape[i] = shape[i].value
return hcl.compute(tuple(shape),
lambda *x: hcl.cast(dtype, 0),
name=name,
dtype=dtype)
def top(input, ):
final_total_extent_1 = (
hcl.cast(dtype=hcl.Int(bits=64), expr=final_extent_1) *
hcl.cast(dtype=hcl.Int(bits=64), expr=final_extent_0))
mean_local = hcl.compute((final_extent_0, final_extent_1),
lambda x, y: 0,
name="mean_local",
dtype=hcl.UInt(bits=16))
with hcl.Stage("mean_local"):
with hcl.for_(final_min_1, final_extent_1,
name="mean_local_s0_y") as mean_local_s0_y:
with hcl.for_(final_min_0, final_extent_0,
name="mean_local_s0_x") as mean_local_s0_x:
mean_local[mean_local_s0_x,
mean_local_s0_y] = hcl.cast(dtype=hcl.UInt(bits=16),
expr=0)
with hcl.for_(final_min_1, final_extent_1,
name="mean_local_s1_y") as mean_local_s1_y:
with hcl.for_(final_min_0, final_extent_0,
name="mean_local_s1_x") as mean_local_s1_x:
with hcl.for_(
0, 3,
name="mean_local_s1_box__y") as mean_local_s1_box__y:
with hcl.for_(0, 3, name="mean_local_s1_box__x"
) as mean_local_s1_box__x:
mean_local[mean_local_s1_x, mean_local_s1_y] = (
mean_local[mean_local_s1_x, mean_local_s1_y] +
(input[(mean_local_s1_box__x + mean_local_s1_x),
(mean_local_s1_box__y + mean_local_s1_y)] //
hcl.cast(dtype=hcl.UInt(bits=16), expr=9)))
final = hcl.compute((6418, 4818),
lambda x, y: 0,
name="final",
dtype=hcl.UInt(bits=16))
with hcl.Stage("final"):
with hcl.for_(final_min_1, final_extent_1,
name="final_s0_y") as final_s0_y:
with hcl.for_(final_min_0, final_extent_0,
name="final_s0_x") as final_s0_x:
final[final_s0_x, final_s0_y] = mean_local[final_s0_x,
final_s0_y]
return final
def prelu(data, alpha, axis=1):
def _axis_ind(axis, ind):
ind = ind[0]
new_ind = []
for i in range(len(ind)):
if i == axis:
new_ind = ind[i]
return tuple(new_ind)
return hcl.compute(
data.shape, lambda *x: hcl.select(
data[x] < 0,
hcl.cast(data.dtype, alpha[_axis_ind(axis, x)] * data[x]), data[x])
)
def top(
A,
B,
):
final_total_extent_1 = (
hcl.cast(dtype=hcl.Int(bits=64), expr=final_extent_1) *
hcl.cast(dtype=hcl.Int(bits=64), expr=final_extent_0))
prod = hcl.compute((final_extent_0, final_extent_1),
lambda x, y: 0,
name="prod",
dtype=hcl.Float(bits=32))
with hcl.Stage("prod"):
with hcl.for_(final_min_1, final_extent_1,
name="prod_s0_y") as prod_s0_y:
with hcl.for_(final_min_0, final_extent_0,
name="prod_s0_x") as prod_s0_x:
prod[prod_s0_x, prod_s0_y] = hcl.cast(dtype=hcl.Float(bits=32),
expr=0.000000)
with hcl.for_(final_min_1, final_extent_1,
name="prod_s1_y") as prod_s1_y:
with hcl.for_(final_min_0, final_extent_0,
name="prod_s1_x") as prod_s1_x:
with hcl.for_(0, 1024, name="prod_s1_r__x") as prod_s1_r__x:
prod[prod_s1_x, prod_s1_y] = (prod[prod_s1_x, prod_s1_y] +
(A[prod_s1_x, prod_s1_r__x] *
B[prod_s1_r__x, prod_s1_y]))
final = hcl.compute((1024, 1024),
lambda x, y: 0,
name="final",
dtype=hcl.Float(bits=32))
with hcl.Stage("final"):
with hcl.for_(final_min_1, final_extent_1,
name="final_s0_y") as final_s0_y:
with hcl.for_(final_min_0, final_extent_0,
name="final_s0_x") as final_s0_x:
final[final_s0_x, final_s0_y] = prod[final_s0_x, final_s0_y]
return final
def top(input, ):
final_total_extent_1 = (hcl.cast(dtype = hcl.Int(bits = 64), expr = final_extent_1) * hcl.cast(dtype = hcl.Int(bits = 64), expr = final_extent_0))
final_total_extent_2 = (final_total_extent_1 * hcl.cast(dtype = hcl.Int(bits = 64), expr = final_extent_2))
linear = hcl.compute(((final_extent_0 + 2), (final_extent_1 + 2), final_extent_2), lambda x, y, z: 0, name = "linear", dtype = hcl.Float(bits = 32))
with hcl.Stage("linear"):
with hcl.for_(final_min_2, final_extent_2, name = "linear_s0_c") as linear_s0_c:
with hcl.for_(final_min_1, (final_extent_1 + 2), name = "linear_s0_y") as linear_s0_y:
with hcl.for_(final_min_0, (final_extent_0 + 2), name = "linear_s0_x") as linear_s0_x:
t4 = input[linear_s0_x, linear_s0_y, linear_s0_c]
linear[linear_s0_x, linear_s0_y, linear_s0_c] = hcl.select((hcl.cast(dtype = hcl.Float(bits = 32), expr = 0.040450) < t4), hcl.power(((t4 * hcl.cast(dtype = hcl.Float(bits = 32), expr = 0.947867)) + hcl.cast(dtype = hcl.Float(bits = 32), expr = 0.052133)), hcl.cast(dtype = hcl.Float(bits = 32), expr = 2.400000)), (t4 * hcl.cast(dtype = hcl.Float(bits = 32), expr = 0.077399)))
blur_x = hcl.compute((final_extent_0, (final_extent_1 + 2), final_extent_2), lambda x, y, z: 0, name = "blur_x", dtype = hcl.Float(bits = 32))
with hcl.Stage("blur_x"):
with hcl.for_(final_min_2, final_extent_2, name = "blur_x_s0_c") as blur_x_s0_c:
with hcl.for_(final_min_1, (final_extent_1 + 2), name = "blur_x_s0_y") as blur_x_s0_y:
with hcl.for_(final_min_0, final_extent_0, name = "blur_x_s0_x") as blur_x_s0_x:
blur_x[blur_x_s0_x, blur_x_s0_y, blur_x_s0_c] = ((linear[(blur_x_s0_x + 2), blur_x_s0_y, blur_x_s0_c] + (linear[blur_x_s0_x, blur_x_s0_y, blur_x_s0_c] + linear[(blur_x_s0_x + 1), blur_x_s0_y, blur_x_s0_c])) * hcl.cast(dtype = hcl.Float(bits = 32), expr = 0.333333))
blur_y = hcl.compute((final_extent_0, final_extent_1, final_extent_2), lambda x, y, z: 0, name = "blur_y", dtype = hcl.Float(bits = 32))
with hcl.Stage("blur_y"):
with hcl.for_(final_min_2, final_extent_2, name = "blur_y_s0_c") as blur_y_s0_c:
with hcl.for_(final_min_1, final_extent_1, name = "blur_y_s0_y") as blur_y_s0_y:
with hcl.for_(final_min_0, final_extent_0, name = "blur_y_s0_x") as blur_y_s0_x:
blur_y[blur_y_s0_x, blur_y_s0_y, blur_y_s0_c] = ((blur_x[blur_y_s0_x, (blur_y_s0_y + 2), blur_y_s0_c] + (blur_x[blur_y_s0_x, blur_y_s0_y, blur_y_s0_c] + blur_x[blur_y_s0_x, (blur_y_s0_y + 1), blur_y_s0_c])) * hcl.cast(dtype = hcl.Float(bits = 32), expr = 0.333333))
srgb = hcl.compute((final_extent_0, final_extent_1, final_extent_2), lambda x, y, z: 0, name = "srgb", dtype = hcl.Float(bits = 32))
with hcl.Stage("srgb"):
with hcl.for_(final_min_2, final_extent_2, name = "srgb_s0_c") as srgb_s0_c:
with hcl.for_(final_min_1, final_extent_1, name = "srgb_s0_y") as srgb_s0_y:
with hcl.for_(final_min_0, final_extent_0, name = "srgb_s0_x") as srgb_s0_x:
t5 = blur_y[srgb_s0_x, srgb_s0_y, srgb_s0_c]
srgb[srgb_s0_x, srgb_s0_y, srgb_s0_c] = hcl.select((hcl.cast(dtype = hcl.Float(bits = 32), expr = 0.003131) < t5), ((hcl.power(t5, hcl.cast(dtype = hcl.Float(bits = 32), expr = 0.416667)) * hcl.cast(dtype = hcl.Float(bits = 32), expr = 1.055000)) + hcl.cast(dtype = hcl.Float(bits = 32), expr = -0.055000)), (t5 * hcl.cast(dtype = hcl.Float(bits = 32), expr = 12.920000)))
final = hcl.compute((766, 1278, 3), lambda x, y, z: 0, name = "final", dtype = hcl.Float(bits = 32))
with hcl.Stage("final"):
with hcl.for_(final_min_2, final_extent_2, name = "final_s0_c") as final_s0_c:
with hcl.for_(final_min_1, final_extent_1, name = "final_s0_y") as final_s0_y:
with hcl.for_(final_min_0, final_extent_0, name = "final_s0_x") as final_s0_x:
final[final_s0_x, final_s0_y, final_s0_c] = srgb[final_s0_x, final_s0_y, final_s0_c]
return final
def _dilate(*indices):
not_zero = []
index_tuple = []
for i in range(n):
if strides[i] != 1:
index_tuple.append(indices[i] / strides[i])
not_zero.append((indices[i] % strides[i]).equal(0))
else:
index_tuple.append(indices[i])
if not_zero:
not_zero = tvm.api.all(*not_zero)
if not_zero:
return data(*index_tuple)
else:
return hcl.cast(data.dtype, 0.0)
return data(*index_tuple)
def top(input, ):
final_total_extent_1 = (hcl.cast(dtype = hcl.Int(bits = 64), expr = final_extent_1) * hcl.cast(dtype = hcl.Int(bits = 64), expr = final_extent_0))
blur_x = hcl.compute((final_extent_0, (final_extent_1 + 2)), lambda x, y: 0, name = "blur_x", dtype = hcl.UInt(bits = 16))
with hcl.Stage("blur_x"):
with hcl.for_(final_min_1, (final_extent_1 + 2), name = "blur_x_s0_y") as blur_x_s0_y:
with hcl.for_(final_min_0, final_extent_0, name = "blur_x_s0_x") as blur_x_s0_x:
blur_x[blur_x_s0_x, blur_x_s0_y] = ((input[(blur_x_s0_x + 2), blur_x_s0_y] + (input[blur_x_s0_x, blur_x_s0_y] + input[(blur_x_s0_x + 1), blur_x_s0_y]))//hcl.cast(dtype = hcl.UInt(bits = 16), expr = 3))
blur_y = hcl.compute((final_extent_0, final_extent_1), lambda x, y: 0, name = "blur_y", dtype = hcl.UInt(bits = 16))
with hcl.Stage("blur_y"):
with hcl.for_(final_min_1, final_extent_1, name = "blur_y_s0_y") as blur_y_s0_y:
with hcl.for_(final_min_0, final_extent_0, name = "blur_y_s0_x") as blur_y_s0_x:
blur_y[blur_y_s0_x, blur_y_s0_y] = ((blur_x[blur_y_s0_x, (blur_y_s0_y + 2)] + (blur_x[blur_y_s0_x, blur_y_s0_y] + blur_x[blur_y_s0_x, (blur_y_s0_y + 1)]))//hcl.cast(dtype = hcl.UInt(bits = 16), expr = 3))
final = hcl.compute((640, 480), lambda x, y: 0, name = "final", dtype = hcl.UInt(bits = 16))
with hcl.Stage("final"):
with hcl.for_(final_min_1, final_extent_1, name = "final_s0_y") as final_s0_y:
with hcl.for_(final_min_0, final_extent_0, name = "final_s0_x") as final_s0_x:
final[final_s0_x, final_s0_y] = blur_y[final_s0_x, final_s0_y]
return final
def zeros(in_shape, dtype=dtype, name='zeros'):
return hcl.compute(in_shape, lambda *x: hcl.cast(dtype, 0), name=name)
def top(input, filter, bias):
f_conv = hcl.compute(
(output_extent_0, output_extent_1, output_extent_2, output_extent_3),
lambda x, y, z, n: 0,
name="f_conv",
dtype=hcl.Float(bits=32))
with hcl.Stage("f_conv"):
with hcl.for_(output_min_3, output_extent_3,
name="f_conv_s0_n") as f_conv_s0_n:
with hcl.for_(output_min_2, output_extent_2,
name="f_conv_s0_c") as f_conv_s0_z:
with hcl.for_(output_min_1,
output_extent_1,
name="f_conv_s0_y") as f_conv_s0_y:
with hcl.for_(output_min_0,
output_extent_0,
name="f_conv_s0_x") as f_conv_s0_x:
f_conv[f_conv_s0_x, f_conv_s0_y, f_conv_s0_z,
f_conv_s0_n] = bias[f_conv_s0_z]
with hcl.for_(output_min_3, output_extent_3,
name="f_conv_s1_n") as f_conv_s1_n:
with hcl.for_(output_min_2, output_extent_2,
name="f_conv_s1_c") as f_conv_s1_z:
with hcl.for_(output_min_1,
output_extent_1,
name="f_conv_s1_y") as f_conv_s1_y:
with hcl.for_(output_min_0,
output_extent_0,
name="f_conv_s1_x") as f_conv_s1_x:
with hcl.for_(0, 32,
name="f_conv_s1_r__z") as f_conv_s1_r__z:
with hcl.for_(
0, 3,
name="f_conv_s1_r__y") as f_conv_s1_r__y:
with hcl.for_(0, 3, name="f_conv_s1_r__x"
) as f_conv_s1_r__x:
f_conv[
f_conv_s1_x, f_conv_s1_y, f_conv_s1_z,
f_conv_s1_n] = (
f_conv[f_conv_s1_x, f_conv_s1_y,
f_conv_s1_z, f_conv_s1_n] +
(filter[
f_conv_s1_r__x, f_conv_s1_r__y,
f_conv_s1_r__z, f_conv_s1_z] *
input[
(f_conv_s1_r__x +
f_conv_s1_x),
(f_conv_s1_r__y +
f_conv_s1_y), f_conv_s1_r__z,
f_conv_s1_n]))
f_relu = hcl.compute(
(output_extent_0, output_extent_1, output_extent_2, output_extent_3),
lambda x, y, z, n: 0,
name="f_conv",
dtype=hcl.Float(bits=32))
with hcl.Stage("f_relu"):
with hcl.for_(output_min_3, output_extent_3,
name="f_relu_s0_n") as f_relu_s0_n:
with hcl.for_(output_min_2, output_extent_2,
name="f_relu_s0_c") as f_relu_s0_z:
with hcl.for_(output_min_1,
output_extent_1,
name="f_relu_s0_y") as f_relu_s0_y:
with hcl.for_(output_min_0,
output_extent_0,
name="f_relu_s0_x") as f_relu_s0_x:
f_relu[f_relu_s0_x, f_relu_s0_y, f_relu_s0_z,
f_relu_s0_n] = hcl.select(
f_conv[f_relu_s0_x, f_relu_s0_y,
f_relu_s0_z, f_relu_s0_n] > hcl.cast(
dtype=hcl.Float(bits=32),
expr=0.000000),
f_conv[f_relu_s0_x, f_relu_s0_y,
f_relu_s0_z, f_relu_s0_n],
hcl.cast(dtype=hcl.Float(bits=32),
expr=0.000000))
return f_relu
def top(
input,
filter,
bias,
):
final_total_extent_1 = (
hcl.cast(dtype=hcl.Int(bits=64), expr=final_extent_1) *
hcl.cast(dtype=hcl.Int(bits=64), expr=final_extent_0))
final_total_extent_2 = (
final_total_extent_1 *
hcl.cast(dtype=hcl.Int(bits=64), expr=final_extent_2))
final_total_extent_3 = (
final_total_extent_2 *
hcl.cast(dtype=hcl.Int(bits=64), expr=final_extent_3))
f_conv = hcl.compute(
(hcl.select(final_extent_0 > 4, final_extent_0,
4), hcl.select(final_extent_1 > 4, final_extent_1,
4), final_extent_2, final_extent_3),
lambda x, y, z, w: 0,
name="f_conv",
dtype=hcl.Float(bits=32))
with hcl.Stage("f_conv"):
with hcl.for_(final_min_3, final_extent_3,
name="f_conv_s0_n") as f_conv_s0_n:
with hcl.for_(final_min_2, final_extent_2,
name="f_conv_s0_z") as f_conv_s0_z:
with hcl.for_(0, ((final_extent_1 + 3) // 4),
name="f_conv_s0_y_y") as f_conv_s0_y_y:
f_conv_s0_y_yi_base_s = hcl.select(
(f_conv_s0_y_y * 4) < (final_extent_1 + -4),
(f_conv_s0_y_y * 4), (final_extent_1 + -4))
with hcl.for_(0, ((final_extent_0 + 3) // 4),
name="f_conv_s0_x_x") as f_conv_s0_x_x:
f_conv_s0_x_xi_base_s = hcl.select(
(f_conv_s0_x_x * 4) < (final_extent_0 + -4),
(f_conv_s0_x_x * 4), (final_extent_0 + -4))
with hcl.for_(0, 4,
name="f_conv_s0_y_yi") as f_conv_s0_y_yi:
with hcl.for_(
0, 4,
name="f_conv_s0_x_xi") as f_conv_s0_x_xi:
f_conv[((f_conv_s0_x_xi_base_s + final_min_0) +
f_conv_s0_x_xi),
((f_conv_s0_y_yi_base_s + final_min_1) +
f_conv_s0_y_yi), f_conv_s0_z,
f_conv_s0_n] = bias[f_conv_s0_z]
# with hcl.for_(final_min_3, final_extent_3, name = "f_conv_s1_n") as f_conv_s1_n:
# with hcl.for_(final_min_2, final_extent_2, name = "f_conv_s1_z") as f_conv_s1_z:
# with hcl.for_(final_min_1, final_extent_1, name = "f_conv_s1_y") as f_conv_s1_y:
# with hcl.for_(final_min_0, final_extent_0, name = "f_conv_s1_x") as f_conv_s1_x:
# with hcl.for_(0, 32, name = "f_conv_s1_r__z") as f_conv_s1_r__z:
# with hcl.for_(0, 3, name = "f_conv_s1_r__y") as f_conv_s1_r__y:
# with hcl.for_(0, 3, name = "f_conv_s1_r__x") as f_conv_s1_r__x:
# f_conv[f_conv_s1_x, f_conv_s1_y, f_conv_s1_z, f_conv_s1_n] = (f_conv[f_conv_s1_x, f_conv_s1_y, f_conv_s1_z, f_conv_s1_n] + (filter[f_conv_s1_r__x, f_conv_s1_r__y, f_conv_s1_r__z, f_conv_s1_z] * input[(f_conv_s1_r__x + f_conv_s1_x), (f_conv_s1_r__y + f_conv_s1_y), f_conv_s1_r__z, f_conv_s1_n]))
with hcl.for_(final_min_3, final_extent_3,
name="f_conv_s1_n") as f_conv_s1_n:
with hcl.for_(0, 32, name="f_conv_s1_r__z") as f_conv_s1_r__z:
with hcl.for_(0, ((final_extent_1 + 3) // 4),
name="f_conv_s1_y_y") as f_conv_s1_y_y:
f_conv_s1_y_yi_base_s = hcl.select(
(f_conv_s1_y_y * 4) < (final_extent_1 + -4),
(f_conv_s1_y_y * 4), (final_extent_1 + -4))
with hcl.for_(0, ((final_extent_0 + 3) // 4),
name="f_conv_s1_x_x") as f_conv_s1_x_x:
f_conv_s1_x_xi_base_s = hcl.select(
(f_conv_s1_x_x * 4) < (final_extent_0 + -4),
(f_conv_s1_x_x * 4), (final_extent_0 + -4))
with hcl.for_(0, 4,
name="f_conv_s1_y_yi") as f_conv_s1_y_yi:
with hcl.for_(
0, 4,
name="f_conv_s1_x_xi") as f_conv_s1_x_xi:
with hcl.for_(
final_min_2,
final_extent_2,
name="f_conv_s1_z") as f_conv_s1_z:
with hcl.for_(0, 3, name="f_conv_s1_r__y"
) as f_conv_s1_r__y:
with hcl.for_(0,
3,
name="f_conv_s1_r__x"
) as f_conv_s1_r__x:
f_conv[(
(f_conv_s1_x_xi_base_s +
final_min_0) + f_conv_s1_x_xi
), (
(f_conv_s1_y_yi_base_s +
final_min_1) + f_conv_s1_y_yi
), f_conv_s1_z, f_conv_s1_n] = (
f_conv[
((f_conv_s1_x_xi_base_s +
final_min_0) +
f_conv_s1_x_xi),
((f_conv_s1_y_yi_base_s +
final_min_1) +
f_conv_s1_y_yi),
f_conv_s1_z, f_conv_s1_n] +
(filter[f_conv_s1_r__x,
f_conv_s1_r__y,
f_conv_s1_r__z,
f_conv_s1_z] *
input[(f_conv_s1_r__x + (
(f_conv_s1_x_xi_base_s +
final_min_0) +
f_conv_s1_x_xi)),
(f_conv_s1_r__y +
((f_conv_s1_y_yi_base_s
+ final_min_1) +
f_conv_s1_y_yi)),
f_conv_s1_r__z,
f_conv_s1_n]))
f_relu = hcl.compute(
(final_extent_0, final_extent_1, final_extent_2, final_extent_3),
lambda x, y, z, w: 0,
name="f_relu",
dtype=hcl.Float(bits=32))
with hcl.Stage("f_relu"):
with hcl.for_(final_min_3, final_extent_3,
name="f_relu_s0_n") as f_relu_s0_n:
with hcl.for_(final_min_2, final_extent_2,
name="f_relu_s0_z") as f_relu_s0_z:
with hcl.for_(final_min_1, final_extent_1,
name="f_relu_s0_y") as f_relu_s0_y:
with hcl.for_(final_min_0,
final_extent_0,
name="f_relu_s0_x") as f_relu_s0_x:
f_relu[f_relu_s0_x, f_relu_s0_y, f_relu_s0_z,
f_relu_s0_n] = hcl.select(
f_conv[f_relu_s0_x, f_relu_s0_y,
f_relu_s0_z, f_relu_s0_n] > hcl.cast(
dtype=hcl.Float(bits=32),
expr=0.000000),
f_conv[f_relu_s0_x, f_relu_s0_y,
f_relu_s0_z, f_relu_s0_n],
hcl.cast(dtype=hcl.Float(bits=32),
expr=0.000000))
final = hcl.compute((64, 64, 32, 4),
lambda x, y, z, w: 0,
name="final",
dtype=hcl.Float(bits=32))
with hcl.Stage("final"):
with hcl.for_(final_min_3, final_extent_3,
name="final_s0_n") as final_s0_n:
with hcl.for_(final_min_2, final_extent_2,
name="final_s0_z") as final_s0_z:
with hcl.for_(final_min_1, final_extent_1,
name="final_s0_y") as final_s0_y:
with hcl.for_(final_min_0,
final_extent_0,
name="final_s0_x") as final_s0_x:
final[final_s0_x, final_s0_y, final_s0_z,
final_s0_n] = f_relu[final_s0_x, final_s0_y,
final_s0_z, final_s0_n]
return final
def stateToIndexInterpolants(Qopt, sVals, actions, bounds, ptsEachDim, interpV,
fillVal):
iMin = hcl.scalar(0, "iMin")
jMin = hcl.scalar(0, "jMin")
kMin = hcl.scalar(0, "kMin")
iMax = hcl.scalar(0, "iMax")
jMax = hcl.scalar(0, "jMax")
kMax = hcl.scalar(0, "kMax")
c000 = hcl.scalar(fillVal[0], "c000")
c001 = hcl.scalar(fillVal[0], "c001")
c010 = hcl.scalar(fillVal[0], "c010")
c011 = hcl.scalar(fillVal[0], "c011")
c100 = hcl.scalar(fillVal[0], "c100")
c101 = hcl.scalar(fillVal[0], "c101")
c110 = hcl.scalar(fillVal[0], "c110")
c111 = hcl.scalar(fillVal[0], "c111")
c00 = hcl.scalar(0, "c00")
c01 = hcl.scalar(0, "c01")
c10 = hcl.scalar(0, "c10")
c11 = hcl.scalar(0, "c11")
c0 = hcl.scalar(0, "c0")
c1 = hcl.scalar(0, "c1")
ia = hcl.scalar(0, "ia")
ja = hcl.scalar(0, "ja")
ka = hcl.scalar(0, "ka")
di = hcl.scalar(0, "di")
dj = hcl.scalar(0, "dj")
dk = hcl.scalar(0, "dk")
# obtain unrounded index values
ia[0] = ((sVals[0] - bounds[0, 0]) /
(bounds[0, 1] - bounds[0, 0])) * (ptsEachDim[0] - 1)
ja[0] = ((sVals[1] - bounds[1, 0]) /
(bounds[1, 1] - bounds[1, 0])) * (ptsEachDim[1] - 1)
ka[0] = ((sVals[2] - bounds[2, 0]) /
(bounds[2, 1] - bounds[2, 0])) * (ptsEachDim[2] - 1)
# obtain neighbouring state indeces in each direction
with hcl.if_(ia[0] < 0):
iMin[0] = hcl.cast(hcl.Int(), ia[0] - 1.0)
iMax[0] = hcl.cast(hcl.Int(), ia[0])
with hcl.else_():
iMin[0] = hcl.cast(hcl.Int(), ia[0])
iMax[0] = hcl.cast(hcl.Int(), ia[0] + 1.0)
with hcl.if_(ja[0] < 0):
jMin[0] = hcl.cast(hcl.Int(), ja[0] - 1.0)
jMax[0] = hcl.cast(hcl.Int(), ja[0])
with hcl.else_():
jMin[0] = hcl.cast(hcl.Int(), ja[0])
jMax[0] = hcl.cast(hcl.Int(), ja[0] + 1.0)
with hcl.if_(ka[0] < 0):
kMin[0] = hcl.cast(hcl.Int(), ka[0] - 1.0)
kMax[0] = hcl.cast(hcl.Int(), ka[0])
with hcl.else_():
kMin[0] = hcl.cast(hcl.Int(), ka[0])
kMax[0] = hcl.cast(hcl.Int(), ka[0] + 1.0)
# obtain weights in each direction
di[0] = ia[0] - iMin[0]
dj[0] = ja[0] - jMin[0]
dk[0] = ka[0] - kMin[0]
# Obtain value of each neighbour state
# Qopt[iMin, jMin, kMin]
with hcl.if_(
hcl.and_(iMin[0] < Qopt.shape[0], jMin[0] < Qopt.shape[1],
kMin[0] < Qopt.shape[2])):
with hcl.if_(hcl.and_(iMin[0] >= 0, jMin[0] >= 0, kMin[0] >= 0)):
with hcl.for_(0, actions.shape[0], name="a_") as a_:
with hcl.if_(c000[0] < Qopt[iMin[0], jMin[0], kMin[0], a_]):
c000[0] = Qopt[iMin[0], jMin[0], kMin[0], a_]
# Qopt[iMin, jMin, kMax]
with hcl.if_(
hcl.and_(iMin[0] < Qopt.shape[0], jMin[0] < Qopt.shape[1],
kMax[0] < Qopt.shape[2])):
with hcl.if_(hcl.and_(iMin[0] >= 0, jMin[0] >= 0, kMax[0] >= 0)):
with hcl.for_(0, actions.shape[0], name="a_") as a_:
with hcl.if_(c001[0] < Qopt[iMin[0], jMin[0], kMax[0], a_]):
c001[0] = Qopt[iMin[0], jMin[0], kMax[0], a_]
# Qopt[iMin, jMax, kMin]
with hcl.if_(
hcl.and_(iMin[0] < Qopt.shape[0], jMax[0] < Qopt.shape[1],
kMin[0] < Qopt.shape[2])):
with hcl.if_(hcl.and_(iMin[0] >= 0, jMax[0] >= 0, kMin[0] >= 0)):
with hcl.for_(0, actions.shape[0], name="a_") as a_:
with hcl.if_(c010[0] < Qopt[iMin[0], jMax[0], kMin[0], a_]):
c010[0] = Qopt[iMin[0], jMax[0], kMin[0], a_]
# Qopt[iMin, jMax, kMax]
with hcl.if_(
hcl.and_(iMin[0] < Qopt.shape[0], jMax[0] < Qopt.shape[1],
kMax[0] < Qopt.shape[2])):
with hcl.if_(hcl.and_(iMin[0] >= 0, jMax[0] >= 0, kMax[0] >= 0)):
with hcl.for_(0, actions.shape[0], name="a_") as a_:
with hcl.if_(c011[0] < Qopt[iMin[0], jMax[0], kMax[0], a_]):
c011[0] = Qopt[iMin[0], jMax[0], kMax[0], a_]
# Qopt[iMax, jMin, kMin]
with hcl.if_(
hcl.and_(iMax[0] < Qopt.shape[0], jMin[0] < Qopt.shape[1],
kMin[0] < Qopt.shape[2])):
with hcl.if_(hcl.and_(iMax[0] >= 0, jMin[0] >= 0, kMin[0] >= 0)):
with hcl.for_(0, actions.shape[0], name="a_") as a_:
with hcl.if_(c100[0] < Qopt[iMax[0], jMin[0], kMin[0], a_]):
c100[0] = Qopt[iMax[0], jMin[0], kMin[0], a_]
# Qopt[iMax, jMin, kMax]
with hcl.if_(
hcl.and_(iMax[0] < Qopt.shape[0], jMin[0] < Qopt.shape[1],
kMax[0] < Qopt.shape[2])):
with hcl.if_(hcl.and_(iMax[0] >= 0, jMin[0] >= 0, kMax[0] >= 0)):
with hcl.for_(0, actions.shape[0], name="a_") as a_:
with hcl.if_(c101[0] < Qopt[iMax[0], jMin[0], kMax[0], a_]):
c101[0] = Qopt[iMax[0], jMin[0], kMax[0], a_]
# Qopt[iMax, jMax, kMin]
with hcl.if_(
hcl.and_(iMax[0] < Qopt.shape[0], jMax[0] < Qopt.shape[1],
kMin[0] < Qopt.shape[2])):
with hcl.if_(hcl.and_(iMax[0] >= 0, jMax[0] >= 0, kMin[0] >= 0)):
with hcl.for_(0, actions.shape[0], name="a_") as a_:
with hcl.if_(c110[0] < Qopt[iMax[0], jMax[0], kMin[0], a_]):
c110[0] = Qopt[iMax[0], jMax[0], kMin[0], a_]
# Qopt[iMax, jMax, kMax]
with hcl.if_(
hcl.and_(iMax[0] < Qopt.shape[0], jMax[0] < Qopt.shape[1],
kMax[0] < Qopt.shape[2])):
with hcl.if_(hcl.and_(iMax[0] >= 0, jMax[0] >= 0, kMax[0] >= 0)):
with hcl.for_(0, actions.shape[0], name="a_") as a_:
with hcl.if_(c111[0] < Qopt[iMax[0], jMax[0], kMax[0], a_]):
c111[0] = Qopt[iMax[0], jMax[0], kMax[0], a_]
# perform linear interpolation
c00[0] = (c000[0] * (1 - di[0])) + (c100[0] * di[0])
c01[0] = (c001[0] * (1 - di[0])) + (c101[0] * di[0])
c10[0] = (c010[0] * (1 - di[0])) + (c110[0] * di[0])
c11[0] = (c011[0] * (1 - di[0])) + (c111[0] * di[0])
c0[0] = (c00[0] * (1 - dj[0])) + (c10[0] * dj[0])
c1[0] = (c01[0] * (1 - dj[0])) + (c11[0] * dj[0])
interpV[0] = (c0[0] * (1 - dk[0])) + (c1[0] * dk[0])
def cast(array, dtype=None, name='cast'):
return hcl.compute(array.shape,
lambda *x: hcl.cast(dtype, array[x]),
dtype=dtype,
name=name)
def dropout(data, rate=0.5):
data = hcl.compute(data.shape, lambda *x: data[x])
mask = hcl.compute(data.shape, lambda *x: hcl.cast(dtype, 1))
return data, mask
def thresholdedrelu(data, theta):
return hcl.compute(
data.shape, lambda *x: hcl.select(data[x] > theta, data[x],
hcl.cast(data.dtype, 0)))
def full(in_shape, fill_val=1, dtype=dtype, name='full'):
return hcl.compute(in_shape,
lambda *x: hcl.cast(dtype, fill_val),
name=name)
def relu(data, name='relu'):
return hcl.compute(
data.shape,
lambda *y: hcl.select(data[y] < 0, hcl.cast(data.dtype, 0), data[y]),
name)
def top(input, ):
final_total_extent_1 = (
hcl.cast(dtype=hcl.Int(bits=64), expr=final_extent_1) *
hcl.cast(dtype=hcl.Int(bits=64), expr=final_extent_0))
padded16 = hcl.compute(((final_extent_0 + 6), (final_extent_1 + 6)),
lambda x, y: 0,
name="padded16",
dtype=hcl.Int(bits=16))
with hcl.Stage("padded16"):
with hcl.for_(final_min_1, (final_extent_1 + 6),
name="padded16_s0_y") as padded16_s0_y:
with hcl.for_(final_min_0, (final_extent_0 + 6),
name="padded16_s0_x") as padded16_s0_x:
padded16[padded16_s0_x,
padded16_s0_y] = hcl.cast(dtype=hcl.Int(bits=16),
expr=input[padded16_s0_x,
padded16_s0_y])
grad_x = hcl.compute(((final_extent_0 + 4), (final_extent_1 + 4)),
lambda x, y: 0,
name="grad_x",
dtype=hcl.Int(bits=16))
with hcl.Stage("grad_x"):
with hcl.for_(final_min_1, (final_extent_1 + 4),
name="grad_x_s0_y") as grad_x_s0_y:
with hcl.for_(final_min_0, (final_extent_0 + 4),
name="grad_x_s0_x") as grad_x_s0_x:
grad_x[grad_x_s0_x, grad_x_s0_y] = (
padded16[(grad_x_s0_x + 2), (grad_x_s0_y + 2)] +
(((padded16[(grad_x_s0_x + 2), (grad_x_s0_y + 1)] *
hcl.cast(dtype=hcl.Int(bits=16), expr=2)) +
((padded16[(grad_x_s0_x + 2), grad_x_s0_y] -
padded16[grad_x_s0_x, grad_x_s0_y]) -
(padded16[grad_x_s0_x, (grad_x_s0_y + 1)] *
hcl.cast(dtype=hcl.Int(bits=16), expr=2)))) -
padded16[grad_x_s0_x, (grad_x_s0_y + 2)]))
grad_xx = hcl.compute(((final_extent_0 + 4), (final_extent_1 + 4)),
lambda x, y: 0,
name="grad_xx",
dtype=hcl.Int(bits=32))
with hcl.Stage("grad_xx"):
with hcl.for_(final_min_1, (final_extent_1 + 4),
name="grad_xx_s0_y") as grad_xx_s0_y:
with hcl.for_(final_min_0, (final_extent_0 + 4),
name="grad_xx_s0_x") as grad_xx_s0_x:
t30_s = grad_x[grad_xx_s0_x, grad_xx_s0_y]
grad_xx[grad_xx_s0_x, grad_xx_s0_y] = (
hcl.cast(dtype=hcl.Int(bits=32), expr=t30_s) *
hcl.cast(dtype=hcl.Int(bits=32), expr=t30_s))
grad_gx = hcl.compute(((final_extent_0 + 2), (final_extent_1 + 2)),
lambda x, y: 0,
name="grad_gx",
dtype=hcl.Int(bits=32))
with hcl.Stage("grad_gx"):
with hcl.for_(final_min_1, (final_extent_1 + 2),
name="grad_gx_s0_y") as grad_gx_s0_y:
with hcl.for_(final_min_0, (final_extent_0 + 2),
name="grad_gx_s0_x") as grad_gx_s0_x:
grad_gx[grad_gx_s0_x, grad_gx_s0_y] = 0
with hcl.for_(final_min_1, (final_extent_1 + 2),
name="grad_gx_s1_y") as grad_gx_s1_y:
with hcl.for_(final_min_0, (final_extent_0 + 2),
name="grad_gx_s1_x") as grad_gx_s1_x:
with hcl.for_(0, 3,
name="grad_gx_s1_box__y") as grad_gx_s1_box__y:
with hcl.for_(
0, 3,
name="grad_gx_s1_box__x") as grad_gx_s1_box__x:
grad_gx[grad_gx_s1_x, grad_gx_s1_y] = (
grad_gx[grad_gx_s1_x, grad_gx_s1_y] +
grad_xx[(grad_gx_s1_box__x + grad_gx_s1_x),
(grad_gx_s1_box__y + grad_gx_s1_y)])
grad_y = hcl.compute(((final_extent_0 + 4), (final_extent_1 + 4)),
lambda x, y: 0,
name="grad_y",
dtype=hcl.Int(bits=16))
with hcl.Stage("grad_y"):
with hcl.for_(final_min_1, (final_extent_1 + 4),
name="grad_y_s0_y") as grad_y_s0_y:
with hcl.for_(final_min_0, (final_extent_0 + 4),
name="grad_y_s0_x") as grad_y_s0_x:
grad_y[grad_y_s0_x, grad_y_s0_y] = (
(padded16[(grad_y_s0_x + 2), (grad_y_s0_y + 2)] +
(((padded16[(grad_y_s0_x + 1), (grad_y_s0_y + 2)] *
hcl.cast(dtype=hcl.Int(bits=16), expr=2)) +
(padded16[grad_y_s0_x, (grad_y_s0_y + 2)] -
padded16[grad_y_s0_x, grad_y_s0_y])) -
(padded16[(grad_y_s0_x + 1), grad_y_s0_y] *
hcl.cast(dtype=hcl.Int(bits=16), expr=2)))) -
padded16[(grad_y_s0_x + 2), grad_y_s0_y])
grad_xy = hcl.compute(((final_extent_0 + 4), (final_extent_1 + 4)),
lambda x, y: 0,
name="grad_xy",
dtype=hcl.Int(bits=32))
with hcl.Stage("grad_xy"):
with hcl.for_(final_min_1, (final_extent_1 + 4),
name="grad_xy_s0_y") as grad_xy_s0_y:
with hcl.for_(final_min_0, (final_extent_0 + 4),
name="grad_xy_s0_x") as grad_xy_s0_x:
grad_xy[grad_xy_s0_x, grad_xy_s0_y] = (
hcl.cast(dtype=hcl.Int(bits=32),
expr=grad_x[grad_xy_s0_x, grad_xy_s0_y]) *
hcl.cast(dtype=hcl.Int(bits=32),
expr=grad_y[grad_xy_s0_x, grad_xy_s0_y]))
grad_gxy = hcl.compute(((final_extent_0 + 2), (final_extent_1 + 2)),
lambda x, y: 0,
name="grad_gxy",
dtype=hcl.Int(bits=32))
with hcl.Stage("grad_gxy"):
with hcl.for_(final_min_1, (final_extent_1 + 2),
name="grad_gxy_s0_y") as grad_gxy_s0_y:
with hcl.for_(final_min_0, (final_extent_0 + 2),
name="grad_gxy_s0_x") as grad_gxy_s0_x:
grad_gxy[grad_gxy_s0_x, grad_gxy_s0_y] = 0
with hcl.for_(final_min_1, (final_extent_1 + 2),
name="grad_gxy_s1_y") as grad_gxy_s1_y:
with hcl.for_(final_min_0, (final_extent_0 + 2),
name="grad_gxy_s1_x") as grad_gxy_s1_x:
with hcl.for_(0, 3,
name="grad_gxy_s1_box__y") as grad_gxy_s1_box__y:
with hcl.for_(
0, 3,
name="grad_gxy_s1_box__x") as grad_gxy_s1_box__x:
grad_gxy[grad_gxy_s1_x, grad_gxy_s1_y] = (
grad_gxy[grad_gxy_s1_x, grad_gxy_s1_y] +
grad_xy[(grad_gxy_s1_box__x + grad_gxy_s1_x),
(grad_gxy_s1_box__y + grad_gxy_s1_y)])
grad_yy = hcl.compute(((final_extent_0 + 4), (final_extent_1 + 4)),
lambda x, y: 0,
name="grad_yy",
dtype=hcl.Int(bits=32))
with hcl.Stage("grad_yy"):
with hcl.for_(final_min_1, (final_extent_1 + 4),
name="grad_yy_s0_y") as grad_yy_s0_y:
with hcl.for_(final_min_0, (final_extent_0 + 4),
name="grad_yy_s0_x") as grad_yy_s0_x:
t31_s = grad_y[grad_yy_s0_x, grad_yy_s0_y]
grad_yy[grad_yy_s0_x, grad_yy_s0_y] = (
hcl.cast(dtype=hcl.Int(bits=32), expr=t31_s) *
hcl.cast(dtype=hcl.Int(bits=32), expr=t31_s))
grad_gy = hcl.compute(((final_extent_0 + 2), (final_extent_1 + 2)),
lambda x, y: 0,
name="grad_gy",
dtype=hcl.Int(bits=32))
with hcl.Stage("grad_gy"):
with hcl.for_(final_min_1, (final_extent_1 + 2),
name="grad_gy_s0_y") as grad_gy_s0_y:
with hcl.for_(final_min_0, (final_extent_0 + 2),
name="grad_gy_s0_x") as grad_gy_s0_x:
grad_gy[grad_gy_s0_x, grad_gy_s0_y] = 0
with hcl.for_(final_min_1, (final_extent_1 + 2),
name="grad_gy_s1_y") as grad_gy_s1_y:
with hcl.for_(final_min_0, (final_extent_0 + 2),
name="grad_gy_s1_x") as grad_gy_s1_x:
with hcl.for_(0, 3,
name="grad_gy_s1_box__y") as grad_gy_s1_box__y:
with hcl.for_(
0, 3,
name="grad_gy_s1_box__x") as grad_gy_s1_box__x:
grad_gy[grad_gy_s1_x, grad_gy_s1_y] = (
grad_gy[grad_gy_s1_x, grad_gy_s1_y] +
grad_yy[(grad_gy_s1_box__x + grad_gy_s1_x),
(grad_gy_s1_box__y + grad_gy_s1_y)])
cim = hcl.compute(((final_extent_0 + 2), (final_extent_1 + 2)),
lambda x, y: 0,
name="cim",
dtype=hcl.Float(bits=32))
with hcl.Stage("cim"):
with hcl.for_(final_min_1, (final_extent_1 + 2),
name="cim_s0_y") as cim_s0_y:
with hcl.for_(final_min_0, (final_extent_0 + 2),
name="cim_s0_x") as cim_s0_x:
t32 = grad_gx[cim_s0_x, cim_s0_y]
t33 = grad_gy[cim_s0_x, cim_s0_y]
t34 = grad_gxy[cim_s0_x, cim_s0_y]
t35 = (hcl.cast(dtype=hcl.Float(bits=32), expr=(t32 // 144)) +
hcl.cast(dtype=hcl.Float(bits=32), expr=(t33 // 144)))
cim[cim_s0_x, cim_s0_y] = (
((hcl.cast(dtype=hcl.Float(bits=32), expr=(t32 // 144)) *
hcl.cast(dtype=hcl.Float(bits=32), expr=(t33 // 144))) -
(hcl.cast(dtype=hcl.Float(bits=32), expr=(t34 // 144)) *
hcl.cast(dtype=hcl.Float(bits=32), expr=(t34 // 144)))) -
((t35 * t35) *
hcl.cast(dtype=hcl.Float(bits=32), expr=0.040000)))
output_final = hcl.compute((final_extent_0, final_extent_1),
lambda x, y: 0,
name="output_final",
dtype=hcl.UInt(bits=16))
with hcl.Stage("output_final"):
with hcl.for_(final_min_1, final_extent_1,
name="output_final_s0_y") as output_final_s0_y:
with hcl.for_(final_min_0,
final_extent_0,
name="output_final_s0_x") as output_final_s0_x:
t36 = cim[(output_final_s0_x + 1), (output_final_s0_y + 1)]
output_final[output_final_s0_x, output_final_s0_y] = hcl.select(
hcl.and_(
(hcl.cast(dtype=hcl.Float(bits=32), expr=100.000000) <=
t36), (hcl.select(
cim[(output_final_s0_x + 2),
(output_final_s0_y + 2)] >
hcl.select(
cim[(output_final_s0_x + 1),
(output_final_s0_y + 2)] >
hcl.select(
cim[output_final_s0_x,
(output_final_s0_y + 2)]
> hcl.select(
cim[(output_final_s0_x + 2),
(output_final_s0_y + 1)]
> hcl.select(
cim[output_final_s0_x,
(output_final_s0_y + 1)] >
hcl.select(
cim[output_final_s0_x,
output_final_s0_y] >
cim[(output_final_s0_x + 1),
output_final_s0_y],
cim[output_final_s0_x,
output_final_s0_y],
cim[(output_final_s0_x + 1),
output_final_s0_y]),
cim[output_final_s0_x,
(output_final_s0_y + 1)],
hcl.select(
cim[output_final_s0_x,
output_final_s0_y] >
cim[(output_final_s0_x + 1),
output_final_s0_y],
cim[output_final_s0_x,
output_final_s0_y],
cim[(output_final_s0_x + 1),
output_final_s0_y])),
cim[(output_final_s0_x + 2),
(output_final_s0_y + 1)],
hcl.select(
cim[output_final_s0_x,
(output_final_s0_y + 1)] >
hcl.select(
cim[output_final_s0_x,
output_final_s0_y] >
cim[(output_final_s0_x + 1),
output_final_s0_y],
cim[output_final_s0_x,
output_final_s0_y],
cim[(output_final_s0_x + 1),
output_final_s0_y]),
cim[output_final_s0_x,
(output_final_s0_y + 1)],
hcl.select(
cim[output_final_s0_x,
output_final_s0_y] >
cim[(output_final_s0_x + 1),
output_final_s0_y],
cim[output_final_s0_x,
output_final_s0_y],
cim[(output_final_s0_x + 1),
output_final_s0_y]))),
cim[output_final_s0_x,
(output_final_s0_y + 2)],
hcl.select(
cim[(output_final_s0_x + 2),
(output_final_s0_y + 1)] >
hcl.select(
cim[output_final_s0_x,
(output_final_s0_y + 1)] >
hcl.select(
cim[output_final_s0_x,
output_final_s0_y] >
cim[(output_final_s0_x + 1),
output_final_s0_y],
cim[output_final_s0_x,
output_final_s0_y],
cim[(output_final_s0_x + 1),
output_final_s0_y]),
cim[output_final_s0_x,
(output_final_s0_y + 1)],
hcl.select(
cim[output_final_s0_x,
output_final_s0_y] >
cim[(output_final_s0_x + 1),
output_final_s0_y],
cim[output_final_s0_x,
output_final_s0_y],
cim[(output_final_s0_x + 1),
output_final_s0_y])),
cim[(output_final_s0_x + 2),
(output_final_s0_y + 1)],
hcl.select(
cim[output_final_s0_x,
(output_final_s0_y + 1)] >
hcl.select(
cim[output_final_s0_x,
output_final_s0_y] >
cim[(output_final_s0_x + 1),
output_final_s0_y],
cim[output_final_s0_x,
output_final_s0_y],
cim[(output_final_s0_x + 1),
output_final_s0_y]),
cim[output_final_s0_x,
(output_final_s0_y + 1)],
hcl.select(
cim[output_final_s0_x,
output_final_s0_y] >
cim[(output_final_s0_x + 1),
output_final_s0_y],
cim[output_final_s0_x,
output_final_s0_y],
cim[(output_final_s0_x + 1),
output_final_s0_y])))),
cim[(output_final_s0_x + 1),
(output_final_s0_y + 2)],
hcl.select(
cim[output_final_s0_x,
(output_final_s0_y + 2)] >
hcl.select(
cim[(output_final_s0_x + 2),
(output_final_s0_y + 1)] >
hcl.select(
cim[output_final_s0_x,
(output_final_s0_y + 1)] >
hcl.select(
cim[output_final_s0_x,
output_final_s0_y] >
cim[(output_final_s0_x + 1),
output_final_s0_y],
cim[output_final_s0_x,
output_final_s0_y],
cim[(output_final_s0_x + 1),
output_final_s0_y]),
cim[output_final_s0_x,
(output_final_s0_y + 1)],
hcl.select(
cim[output_final_s0_x,
output_final_s0_y] >
cim[(output_final_s0_x + 1),
output_final_s0_y],
cim[output_final_s0_x,
output_final_s0_y],
cim[(output_final_s0_x + 1),
output_final_s0_y])),
cim[(output_final_s0_x + 2),
(output_final_s0_y + 1)],
hcl.select(
cim[output_final_s0_x,
(output_final_s0_y + 1)] >
hcl.select(
cim[output_final_s0_x,
output_final_s0_y] >
cim[(output_final_s0_x + 1),
output_final_s0_y],
cim[output_final_s0_x,
output_final_s0_y],
cim[(output_final_s0_x + 1),
output_final_s0_y]),
cim[output_final_s0_x,
(output_final_s0_y + 1)],
hcl.select(
cim[output_final_s0_x,
output_final_s0_y] >
cim[(output_final_s0_x + 1),
output_final_s0_y],
cim[output_final_s0_x,
output_final_s0_y],
cim[(output_final_s0_x + 1),
output_final_s0_y]))),
cim[output_final_s0_x,
(output_final_s0_y + 2)],
hcl.select(
cim[(output_final_s0_x + 2),
(output_final_s0_y + 1)] >
hcl.select(
cim[output_final_s0_x,
(output_final_s0_y + 1)] >
hcl.select(
cim[output_final_s0_x,
output_final_s0_y] >
cim[(output_final_s0_x + 1),
output_final_s0_y],
cim[output_final_s0_x,
output_final_s0_y],
cim[(output_final_s0_x + 1),
output_final_s0_y]),
cim[output_final_s0_x,
(output_final_s0_y + 1)],
hcl.select(
cim[output_final_s0_x,
output_final_s0_y] >
cim[(output_final_s0_x + 1),
output_final_s0_y],
cim[output_final_s0_x,
output_final_s0_y],
cim[(output_final_s0_x + 1),
output_final_s0_y])),
cim[(output_final_s0_x + 2),
(output_final_s0_y + 1)],
hcl.select(
cim[output_final_s0_x,
(output_final_s0_y + 1)] >
hcl.select(
cim[output_final_s0_x,
output_final_s0_y] >
cim[(output_final_s0_x + 1),
output_final_s0_y],
cim[output_final_s0_x,
output_final_s0_y],
cim[(output_final_s0_x + 1),
output_final_s0_y]),
cim[output_final_s0_x,
(output_final_s0_y + 1)],
hcl.select(
cim[output_final_s0_x,
output_final_s0_y] >
cim[(output_final_s0_x + 1),
output_final_s0_y],
cim[output_final_s0_x,
output_final_s0_y],
cim[(output_final_s0_x + 1),
output_final_s0_y]))))),
cim[(output_final_s0_x + 2),
(output_final_s0_y + 2)],
hcl.select(
cim[(output_final_s0_x + 1),
(output_final_s0_y + 2)] >
hcl.select(
cim[output_final_s0_x,
(output_final_s0_y + 2)] >
hcl.select(
cim[(output_final_s0_x + 2),
(output_final_s0_y + 1)] >
hcl.select(
cim[output_final_s0_x,
(output_final_s0_y + 1)] >
hcl.select(
cim[output_final_s0_x,
output_final_s0_y] >
cim[(output_final_s0_x + 1),
output_final_s0_y],
cim[output_final_s0_x,
output_final_s0_y],
cim[(output_final_s0_x + 1),
output_final_s0_y]),
cim[output_final_s0_x,
(output_final_s0_y + 1)],
hcl.select(
cim[output_final_s0_x,
output_final_s0_y] >
cim[(output_final_s0_x + 1),
output_final_s0_y],
cim[output_final_s0_x,
output_final_s0_y],
cim[(output_final_s0_x + 1),
output_final_s0_y])),
cim[(output_final_s0_x + 2),
(output_final_s0_y + 1)],
hcl.select(
cim[output_final_s0_x,
(output_final_s0_y + 1)] >
hcl.select(
cim[output_final_s0_x,
output_final_s0_y] >
cim[(output_final_s0_x + 1),
output_final_s0_y],
cim[output_final_s0_x,
output_final_s0_y],
cim[(output_final_s0_x + 1),
output_final_s0_y]),
cim[output_final_s0_x,
(output_final_s0_y + 1)],
hcl.select(
cim[output_final_s0_x,
output_final_s0_y] >
cim[(output_final_s0_x + 1),
output_final_s0_y],
cim[output_final_s0_x,
output_final_s0_y],
cim[(output_final_s0_x + 1),
output_final_s0_y]))),
cim[output_final_s0_x,
(output_final_s0_y + 2)],
hcl.select(
cim[(output_final_s0_x + 2),
(output_final_s0_y + 1)] >
hcl.select(
cim[output_final_s0_x,
(output_final_s0_y + 1)] >
hcl.select(
cim[output_final_s0_x,
output_final_s0_y] >
cim[(output_final_s0_x + 1),
output_final_s0_y],
cim[output_final_s0_x,
output_final_s0_y],
cim[(output_final_s0_x + 1),
output_final_s0_y]),
cim[output_final_s0_x,
(output_final_s0_y + 1)],
hcl.select(
cim[output_final_s0_x,
output_final_s0_y] >
cim[(output_final_s0_x + 1),
output_final_s0_y],
cim[output_final_s0_x,
output_final_s0_y],
cim[(output_final_s0_x + 1),
output_final_s0_y])),
cim[(output_final_s0_x + 2),
(output_final_s0_y + 1)],
hcl.select(
cim[output_final_s0_x,
(output_final_s0_y + 1)] >
hcl.select(
cim[output_final_s0_x,
output_final_s0_y] >
cim[(output_final_s0_x + 1),
output_final_s0_y],
cim[output_final_s0_x,
output_final_s0_y],
cim[(output_final_s0_x + 1),
output_final_s0_y]),
cim[output_final_s0_x,
(output_final_s0_y + 1)],
hcl.select(
cim[output_final_s0_x,
output_final_s0_y] >
cim[(output_final_s0_x + 1),
output_final_s0_y],
cim[output_final_s0_x,
output_final_s0_y],
cim[(output_final_s0_x + 1),
output_final_s0_y])))),
cim[(output_final_s0_x + 1),
(output_final_s0_y + 2)],
hcl.select(
cim[output_final_s0_x,
(output_final_s0_y + 2)] >
hcl.select(
cim[(output_final_s0_x + 2),
(output_final_s0_y + 1)] >
hcl.select(
cim[output_final_s0_x,
(output_final_s0_y + 1)] >
hcl.select(
cim[output_final_s0_x,
output_final_s0_y] >
cim[(output_final_s0_x + 1),
output_final_s0_y],
cim[output_final_s0_x,
output_final_s0_y],
cim[(output_final_s0_x + 1),
output_final_s0_y]),
cim[output_final_s0_x,
(output_final_s0_y + 1)],
hcl.select(
cim[output_final_s0_x,
output_final_s0_y] >
cim[(output_final_s0_x + 1),
output_final_s0_y],
cim[output_final_s0_x,
output_final_s0_y],
cim[(output_final_s0_x + 1),
output_final_s0_y])),
cim[(output_final_s0_x + 2),
(output_final_s0_y + 1)],
hcl.select(
cim[output_final_s0_x,
(output_final_s0_y + 1)] >
hcl.select(
cim[output_final_s0_x,
output_final_s0_y] >
cim[(output_final_s0_x + 1),
output_final_s0_y],
cim[output_final_s0_x,
output_final_s0_y],
cim[(output_final_s0_x + 1),
output_final_s0_y]),
cim[output_final_s0_x,
(output_final_s0_y + 1)],
hcl.select(
cim[output_final_s0_x,
output_final_s0_y] >
cim[(output_final_s0_x + 1),
output_final_s0_y],
cim[output_final_s0_x,
output_final_s0_y],
cim[(output_final_s0_x + 1),
output_final_s0_y]))),
cim[output_final_s0_x,
(output_final_s0_y + 2)],
hcl.select(
cim[(output_final_s0_x + 2),
(output_final_s0_y + 1)] >
hcl.select(
cim[output_final_s0_x,
(output_final_s0_y + 1)] >
hcl.select(
cim[output_final_s0_x,
output_final_s0_y] >
cim[(output_final_s0_x + 1),
output_final_s0_y],
cim[output_final_s0_x,
output_final_s0_y],
cim[(output_final_s0_x + 1),
output_final_s0_y]),
cim[output_final_s0_x,
(output_final_s0_y + 1)],
hcl.select(
cim[output_final_s0_x,
output_final_s0_y] >
cim[(output_final_s0_x + 1),
output_final_s0_y],
cim[output_final_s0_x,
output_final_s0_y],
cim[(output_final_s0_x + 1),
output_final_s0_y])),
cim[(output_final_s0_x + 2),
(output_final_s0_y + 1)],
hcl.select(
cim[output_final_s0_x,
(output_final_s0_y + 1)] >
hcl.select(
cim[output_final_s0_x,
output_final_s0_y] >
cim[(output_final_s0_x + 1),
output_final_s0_y],
cim[output_final_s0_x,
output_final_s0_y],
cim[(output_final_s0_x + 1),
output_final_s0_y]),
cim[output_final_s0_x,
(output_final_s0_y + 1)],
hcl.select(
cim[output_final_s0_x,
output_final_s0_y] >
cim[(output_final_s0_x + 1),
output_final_s0_y],
cim[output_final_s0_x,
output_final_s0_y],
cim[(output_final_s0_x + 1),
output_final_s0_y])))))) <
t36)),
hcl.cast(dtype=hcl.UInt(bits=16), expr=255),
hcl.cast(dtype=hcl.UInt(bits=16), expr=0))
final = hcl.compute((2442, 3258),
lambda x, y: 0,
name="final",
dtype=hcl.UInt(bits=16))
with hcl.Stage("final"):
with hcl.for_(final_min_1, final_extent_1,
name="final_s0_y") as final_s0_y:
with hcl.for_(final_min_0, final_extent_0,
name="final_s0_x") as final_s0_x:
final[final_s0_x, final_s0_y] = output_final[final_s0_x,
final_s0_y]
return final
def zeros_like(array, dtype=None, name='zeros_like'):
if dtype is None:
dtype = array.dtype
return hcl.compute(array.shape, lambda *x: hcl.cast(dtype, 0), name=name)
def ones(in_shape, dtype=dtype, name='ones'):
return hcl.compute(in_shape, lambda *x: hcl.cast(dtype, 1), name=name)