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 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 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 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 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(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 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 sobel(A, Gx, Gy):
r = hcl.reduce_axis(0,3)
c = hcl.reduce_axis(0,3)
B = hcl.compute((height-2,width-2),
lambda x,y: hcl.sum(A[x+r,y+c]*Gx[r,c], axis=[r,c], name="sum1"),
name="B", dtype=hcl.Float())
t = hcl.reduce_axis(0,3)
g = hcl.reduce_axis(0,3)
C = hcl.compute((height-2,width-2),
lambda x,y: hcl.sum(A[x+t,y+g]*Gy[t,g], axis=[t,g], name="sum2"),
name="C", dtype=hcl.Float())
return hcl.compute((height-2,width-2),
lambda x, y :hcl.sqrt(B[x,y]*B[x,y] + C[x,y]*C[x,y])/4328*255,
name="output", dtype=hcl.Float())
def kernel(matrix_1, matrix_2):
r = hcl.reduce_axis(0, k, 'k')
out_matrix = hcl.compute((m, n),
lambda x, y: hcl.sum(matrix_1[x, r] * matrix_2[r, y],
axis=r, dtype=dtype), dtype=dtype,
name="out_matrix")
return out_matrix
def learn(k, hdTrainData, prototype, prototypeCounter):
#Find samples that have the label k
match = hcl.compute(
hdTrainData.shape,
lambda x, y: hcl.select(trainLabels[x] == k, hdTrainData[x][y], 0),
"match")
#Record the number of these samples
with hcl.for_(0, hdTrainData.shape[0]) as a:
with hcl.if_(trainLabels[a] == k):
max[k] += 1
#Do hdc sum on these samples' hdv
r = hcl.reduce_axis(0, hdTrainData.shape[0], 'r')
result = hcl.compute((hdTrainData.shape[1], ),
lambda y: hcl.sum(match[r][y], axis=r), "result")
#Do the binary voting
sum1 = hcl.compute((hdTrainData.shape[1], ), lambda x: 0, "sum1")
with hcl.if_(max[k] % 2 == 0):
hcl.update(
sum1, lambda x: hcl.select(
result[x] + rdv3[k][x] - max[k] / 2 > 0, 1, 0))
with hcl.else_():
hcl.update(sum1,
lambda x: hcl.select(result[x] - max[k] / 2 > 0, 1, 0))
#Push the binary sum to prototype and the original sum to prototypeCounter
with hcl.for_(0, hdTrainData.shape[1]) as t:
prototype[k][t] = sum1[t]
prototypeCounter[k][t] = result[t]
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 kernel(A, B):
C = hcl.compute(
(M, N),
lambda x, y: hcl.sum(A[x, k] * B[k, y], axis=k, dtype=dtype),
"C",
dtype=dtype)
return C
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 update(l, prototype, prototypeCounter, max):
hcl.print((l + 1),
"%d:Use hard examples to update the prototype counters.\n")
###data preparation
distance = hcl.compute((hdTrainData.shape[1], ), lambda x: 0,
'distance')
hamming_dist = hcl.compute((numClasses, ), lambda x: 0, "hamming_dist")
m = hcl.reduce_axis(0, hdTrainData.shape[1], "m")
###
with hcl.for_(0, hdTrainData.shape[0]) as i:
with hcl.for_(0, numClasses) as n:
#Do hdc multiplication(XOR) on sample[i]'s hdv and prototype[n]'s hdv (elementwise on the high-bit data)
hcl.update(distance,
lambda x: hdTrainData[i][x] ^ prototype[n][x])
#Calculate the hamming distance of the two vectors by adding 1s
hamming_dist[n] = hcl.sum(distance[m], axis=m)
#Find the one having the least hamming distance and choose it's label as the predicted label
pred = hcl.scalar(0, 'pred')
with hcl.for_(0, hamming_dist.shape[0]) as j:
with hcl.if_(hamming_dist[j] < hamming_dist[pred]):
pred.v = j
#Adjust the proto vectors by adding the sample vector on its label proto hdv and substrct it on its predicted proto hdv
with hcl.if_(pred.v != trainLabels[i]):
max[trainLabels[i]] += 1
max[pred] -= 1
with hcl.for_(0, hdTrainData.shape[1]) as m:
prototypeCounter[trainLabels[i]][m] += hdTrainData[i][m]
prototypeCounter[pred][m] -= hdTrainData[i][m]
with hcl.if_(max[trainLabels[i]] % 2 == 0):
with hcl.if_(prototypeCounter[trainLabels[i]][m] -
max[trainLabels[i]] / 2 == 0):
prototype[trainLabels[i]][m] &= 1
with hcl.else_():
prototype[trainLabels[i]][m] = hcl.select(
prototypeCounter[trainLabels[i]][m] -
max[trainLabels[i]] / 2 > 0, 1, 0)
with hcl.if_(max[pred] % 2 == 0):
with hcl.if_(prototypeCounter[pred][m] -
max[pred] / 2 == 0):
prototype[pred][m] &= 1
with hcl.else_():
prototype[pred][m] = hcl.select(
prototypeCounter[pred][m] - max[pred] / 2 > 0, 1,
0)
#print the accuracy
hcl.mutate(
(1, ),
lambda x: test_hdc_accu(prototype, hdTrainData, trainLabels, 1),
'training_update')
hcl.mutate(
(1, ),
lambda x: test_hdc_accu(prototype, hdTestData, testLabels, 2),
'testing_update')
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 test_encoding(m, preTestData):
test_temp = hcl.compute((testData.shape[1], dim), lambda x, y: itemMem[testData[m][x]][y]^idMem[x][y], name = "test_temp")
k2 = hcl.reduce_axis(0, testData.shape[1], 'k2')
test_result = hcl.compute((dim,), lambda x: hcl.sum(test_temp[k2, x], axis = k2, dtype=hcl.Int()), name = "test_result")
with hcl.for_(0, dim) as n:
preTestData[m][n] = test_result[n]
with hcl.if_((m+1)%100 == 0):
hcl.print((m+1), "Finish encoding %d testing data\n")
def gemm_compute(matrix_1, matrix_2): m = matrix_1.shape[0]; n = matrix_2.shape[1]; k = matrix_1.shape[1]; assert matrix_1.shape[1] == matrix_2.shape[0] r = hcl.reduce_axis(0, k, 'k') temp = hcl.compute((m, n), lambda x, y: hcl.sum(matrix_1[x, r] * matrix_2[r, y], axis = r), name = 'matrix_3') return temp
def train_encoding(m, preTrainData):
train_temp = hcl.compute((trainData.shape[1], dim), lambda x, y: itemMem[trainData[m][x]][y] ^ idMem[x][y], name = "train_temp")
k1 = hcl.reduce_axis(0, trainData.shape[1], 'k1')
train_result = hcl.compute((dim,), lambda x: hcl.sum(train_temp[k1, x], axis = k1, dtype=hcl.Int()), name = "train_result")
with hcl.for_(0, dim) as n:
preTrainData[m][n] = train_result[n]
with hcl.if_((m + 1) % 1000 == 0):
hcl.print((m+1), "Finish encoding %d training data\n")
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 - 2, width - 2), lambda x, y: hcl.sum(
B[x + r, y + c] * Gx[r, c], axis=[r, c], name="sum1"), "D")
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"), "E")
# constant factor to normalize the output
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 batch_matmul(x, y, name="batch_matmul"):
out_shape = (x.shape[0], x.shape[1], y.shape[2])
k = hcl.reduce_axis(0, x.shape[2], "k")
return hcl.compute(
out_shape,
lambda b, m, n: hcl.sum(x[b, m, k] * y[b, n, k], axis=[k]),
name=name,
dtype=x.dtype)
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 SgdLR(data, label, theta, lut):
label_local = hcl.unpack(label, name="label_local")
theta_local = hcl.unpack(theta, name="theta_local")
data_local = hcl.unpack(data, name="data_local")
FTYPE = theta_local.dtype
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]
with hcl.stage("M"):
with hcl.for_(0, NUM_TRAINING) as train_id:
training_instance = hcl.compute(
(NUM_FEATURES, ),
lambda x: data_local[train_id * NUM_FEATURES + x],
"training_instance", data_local.dtype)
# Main Computation
k = hcl.reduce_axis(0, NUM_FEATURES, "k")
dot = hcl.compute(
(1, ),
lambda x: hcl.sum(theta_local[k] * training_instance[k],
axis=k,
dtype=FTYPE),
"dot",
dtype=FTYPE)
gradient = hcl.compute((NUM_FEATURES, ),
lambda x: (Sigmoid(dot[0]) - label_local[
train_id]) * training_instance[x],
"gradient",
dtype=FTYPE)
update = hcl.update(
theta_local,
lambda x: theta_local[x] - 2565.0 * gradient[x],
name="update")
theta_pack = hcl.pack(theta_local, name="theta_pack", dtype=theta.dtype)
stream_out = hcl.update(theta, lambda x: theta_pack[x], name="stream_out")
return stream_out
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 sobelAlgo(A, B, Fx, Fy):
def rgb_sum(x, y):
B[x][y] = A[x][y][0] + A[x][y][1] + A[x][y][2]
hcl.mutate(B.shape, lambda x, y: rgb_sum(x, y))
#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]) * 0.05891867))
def test_reuse_compute_sum():
hcl.init()
rx = hcl.reduce_axis(0, 3, name="rx")
A = hcl.placeholder((10, 10), name="A")
B = hcl.compute((10, 10), lambda y, x: A[y, x], "B")
C = hcl.compute((10, 8), lambda y, x: hcl.sum(B[y, x + rx], axis=rx), "C")
s = hcl.create_schedule([A, B, C])
RB = s.reuse_at(B, s[C], C.axis[1])
print(hcl.lower(s))
f = hcl.build(s)
def sobel(imgF, Gx, Gy):
A = hcl.compute((height + 2, width + 2),
lambda x, y: imgF[x][y][0] + imgF[x][y][1] + imgF[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")
return hcl.compute((height, width), lambda x, y: hcl.sqrt(resX[x][
y] * resX[x][y] + resY[x][y] * resY[x][y]) / 4328 * 255, "R")
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 update(l, prototype, prototypeCounter, max):
hcl.print((l+1),"%d:Use hard examples to update the prototype counters.\n")
###data preparation
distance = hcl.compute((in_train.shape[1],), lambda x: 0, 'distance', dtype=hcl.UInt(in_bw))
pre_dist = hcl.compute((in_train.shape[1],), lambda x: 0, "pre_dist")
hamming_dist = hcl.compute((numClasses,), lambda x: 0, "hamming_dist")
m = hcl.reduce_axis(0, in_train.shape[1], "m")
###
with hcl.for_(0, in_train.shape[0]) as i:
hcl.print((i),"%d suc\n")
# pack_proto = hcl.pack(prototype, axis=1, dtype=hcl.UInt(in_bw), name="pack_proto")
with hcl.for_(0, numClasses) as n:
#Do hdc multiplication(XOR) on sample[i]'s hdv and prototype[n]'s hdv (elementwise on the high-bit data)
hcl.update(distance, lambda x: in_train[i][x] ^ prototype[n][x])
#Calculate the hamming distance of the two vectors by adding 1s
hcl.update(pre_dist, lambda x: popcount(distance[x]))
hcl.print((),"sum of 1s suc")
hamming_dist[n] = hcl.sum(pre_dist[m], axis=m)
#Find the one having the least hamming distance and choose it's label as the predicted label
pred = hcl.scalar(0, 'pred')
with hcl.for_(0, hamming_dist.shape[0]) as j:
with hcl.if_(hamming_dist[j] < hamming_dist[pred]):
pred.v = j
#Adjust the proto vectors by adding the sample vector on its label proto hdv and substrct it on its predicted proto hdv
with hcl.if_(pred.v != trainLabels[i]):
max[trainLabels[i]] += 1
max[pred] -= 1
with hcl.for_(0, in_train.shape[1]) as m:
with hcl.for_(0, in_bw) as bit:
# with hcl.if_(in_train[i][m][bit] == 1):
# ###########
# prototypeCounter[trainLabels[i]][m*in_bw+bit] += 1
# prototypeCounter[pred][m*in_bw+bit] -= 1
prototypeCounter[trainLabels[i]][m*in_bw+bit] += in_train[i][m][bit]
prototypeCounter[pred][m*in_bw+bit] -= in_train[i][m][bit]
with hcl.if_(max[trainLabels[i]] % 2 == 0):
with hcl.if_(prototypeCounter[trainLabels[i]][m*in_bw+bit] - max[trainLabels[i]]/2 == 0):
prototype[trainLabels[i]][m][bit] &= 1
with hcl.else_():
prototype[trainLabels[i]][m][bit] = hcl.select(prototypeCounter[trainLabels[i]][m*in_bw+bit] - max[trainLabels[i]]/2 > 0, 1, 0)
with hcl.if_(max[pred] % 2 == 0):
with hcl.if_(prototypeCounter[pred][m*in_bw+bit] - max[pred]/2 == 0):
prototype[pred][m][bit] &= 1
with hcl.else_():
prototype[pred][m][bit] = hcl.select(prototypeCounter[pred][m*in_bw+bit] - max[pred]/2 > 0, 1, 0)
#print the accuracy
hcl.mutate((1,), lambda x: test_hdc_accu(prototype, in_train, trainLabels, 1), 'training_update')
hcl.mutate((1,), lambda x: test_hdc_accu(prototype, in_test, testLabels, 2), 'testing_update')
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 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)