def __init__(self, volume, template, mask, wedge, stdV, gpu=True):
self.volume = gu.to_gpu(volume)
self.template = Volume(template)
self.templatePadded = gu.zeros_like(self.volume, dtype=np.float32)
self.mask = Volume(mask)
self.maskPadded = gu.zeros_like(self.volume, dtype=np.float32)
self.sOrg = mask.shape
self.sPad = volume.shape
print(self.sPad, self.sOrg)
rotate(self.mask, [0, 0, 0], self.maskPadded, self.sPad, self.sOrg)
#paste_in_center_gpu(self.template.d_data, self.templatePadded, np.int32(self.sPad), np.int32(self.maskSize), block=(10, 10, 10), grid=(8,1,1))
#rotate(self.template, [0, 0, 0], self.templatePadded, self.sPad, self.maskSize)
print(volume.shape, stdV.shape, wedge.shape)
self.wedge = gu.to_gpu(wedge)
self.stdV = gu.to_gpu(stdV)
self.fwd_plan = Plan(volume.shape, volume.dtype, np.complex64)
self.inv_plan = Plan(volume.shape, np.complex64, volume.dtype)
self.volume_fft = gu.zeros_like(self.volume, dtype=np.complex64)
self.template_fft = gu.zeros_like(self.volume, dtype=np.complex64)
self.ccc_map = gu.zeros_like(self.volume, dtype=np.float32)
self.norm_volume = np.prod(volume.shape)
self.scores = gu.ones_like(self.volume, dtype=np.float32) * -1000
self.angles = gu.ones_like(self.volume, dtype=np.float32) * -1000
self.p = sum(self.mask.d_data)
def make_sample_data(set_: int):
np.random.seed(set_ * 4347)
if set_ == 1: # Uniform distribution
data = np.random.uniform(0, 1, size=(samples, num_features))
if set_ == 2: # 3 Gaussian distribution
data = multi_gauss_clusters(n_clusters=3)
if set_ == 3: # 10 Gaussian distribution
data = multi_gauss_clusters(n_clusters=10)
df = pd.DataFrame()
np.random.shuffle(data)
df['vec'] = data.tolist()
# find nearest neighbours
from sklearn.neighbors import NearestNeighbors
nbrs = NearestNeighbors(n_neighbors=51,
algorithm='ball_tree',
leaf_size=30).fit(data)
_, nbrs_indices = nbrs.kneighbors(data)
for n_nbr in range(10, 51, 5):
df[f"known_neighbours_{n_nbr}"] = [
x[1:(n_nbr + 1)] for x in nbrs_indices
]
# hash using random hyperplane LSH
import pycuda.gpuarray as gpuarray
import skcuda.linalg as linalg
import pycuda.autoinit
linalg.init()
os.environ['CUDA_HOME'] = "/opt/cuda/"
vec_np = np.array(df['vec'].values.tolist(), dtype=np.float32)
LSH = LSHBias(feature_dim=num_features, bits=LSH_NUM_BITS)
W = np.array(LSH.W, dtype=np.float32)
b_gpu = gpuarray.to_gpu(W)
ones = np.ones(shape=(vec_np.shape[0], 1), dtype=np.float32)
X = np.concatenate((vec_np, ones), axis=1)
# do the matrix multiplication
a_gpu = gpuarray.to_gpu(X)
mul = linalg.mdot(a_gpu, b_gpu)
# get binary: 1 if value >= 0, else 0
res = gpuarray.if_positive(
mul >= gpuarray.zeros(mul.shape, dtype=np.float32),
then_=gpuarray.ones_like(mul),
else_=gpuarray.zeros_like(mul))
res = np.array(res.get(), dtype=np.uint32)
# convert grouped bits to integers
res = np_array_binary_to_grouped_integers(res)
df[f"hash_{LSH_NUM_BITS}_bits"] = [x for x in res]
df.to_parquet(f"{config.CUDA_neighbour_search_df_dir}df-{set_}.parquet",
index=False)
print("created test-data")
def ones_like(tensor):
if tensor.device == 'cuda':
return Tensor(
data=(gpuarray.ones_like(tensor.data, dtype=dtype)
if tensor.shape != () else np.ones_like(tensor.data)),
device=tensor.device,
)
else:
return Tensor(
data=np.ones_like(tensor.data, dtype=dtype),
device=tensor.device,
)
def __init__(self,
photons,
ncopies=1,
copy_flags=True,
copy_triangles=True,
copy_weights=True):
"""Load ``photons`` onto the GPU, replicating as requested.
Args:
- photons: chroma.Event.Photons
Photon state information to load onto GPU
- ncopies: int, *optional*
Number of times to replicate the photons
on the GPU. This is used if you want
to propagate the same event many times,
for example in a likelihood calculation.
The amount of GPU storage will be proportionally
larger if ncopies > 1, so be careful.
"""
nphotons = len(photons)
self.pos = ga.empty(shape=nphotons * ncopies, dtype=ga.vec.float3)
self.dir = ga.empty(shape=nphotons * ncopies, dtype=ga.vec.float3)
self.pol = ga.empty(shape=nphotons * ncopies, dtype=ga.vec.float3)
self.wavelengths = ga.empty(shape=nphotons * ncopies, dtype=np.float32)
self.t = ga.empty(shape=nphotons * ncopies, dtype=np.float32)
self.last_hit_triangles = ga.empty(shape=nphotons * ncopies,
dtype=np.int32)
if not copy_triangles:
self.last_hit_triangles.fill(-1)
if not copy_flags:
self.flags = ga.zeros(shape=nphotons * ncopies, dtype=np.uint32)
else:
self.flags = ga.empty(shape=nphotons * ncopies, dtype=np.uint32)
if not copy_weights:
self.weights = ga.ones_like(self.last_hit_triangles,
dtype=np.float32)
else:
self.weights = ga.empty(shape=nphotons * ncopies, dtype=np.float32)
self.evidx = ga.empty(shape=nphotons, dtype=np.uint32)
# Assign the provided photons to the beginning (possibly
# the entire array if ncopies is 1
self.pos[:nphotons].set(to_float3(photons.pos))
self.dir[:nphotons].set(to_float3(photons.dir))
self.pol[:nphotons].set(to_float3(photons.pol))
self.wavelengths[:nphotons].set(photons.wavelengths.astype(np.float32))
self.t[:nphotons].set(photons.t.astype(np.float32))
if copy_triangles:
self.last_hit_triangles[:nphotons].set(
photons.last_hit_triangles.astype(np.int32))
if copy_flags:
self.flags[:nphotons].set(photons.flags.astype(np.uint32))
if copy_weights:
self.weights[:nphotons].set(photons.weights.astype(np.float32))
self.evidx[:nphotons].set(photons.evidx.astype(np.uint32))
module = get_cu_module('propagate.cu', options=cuda_options)
self.gpu_funcs = GPUFuncs(module)
# Replicate the photons to the rest of the slots if needed
if ncopies > 1:
max_blocks = 1024
nthreads_per_block = 64
for first_photon, photons_this_round, blocks in \
chunk_iterator(nphotons, nthreads_per_block, max_blocks):
self.gpu_funcs.photon_duplicate(np.int32(first_photon),
np.int32(photons_this_round),
self.pos,
self.dir,
self.wavelengths,
self.pol,
self.t,
self.flags,
self.last_hit_triangles,
self.weights,
self.evidx,
np.int32(ncopies - 1),
np.int32(nphotons),
block=(nthreads_per_block, 1,
1),
grid=(blocks, 1))
# Save the duplication information for the iterate_copies() method
self.true_nphotons = nphotons
self.ncopies = ncopies
for filename in tqdm(glob(basepath + "part-*.orc")):
df = pd.read_orc(filename)
df = df.rename(columns={"FeatureVector_all_features": "vec"})
count += 1
vec_np = np.array(df['vec'].values.tolist(), dtype=np.float32)
# add bias term
ones = np.ones(shape=(vec_np.shape[0], 1), dtype=np.float32)
X = np.concatenate((vec_np, ones), axis=1)
# do the matrix multiplication
a_gpu = gpuarray.to_gpu(X)
mul = linalg.mdot(a_gpu, b_gpu)
# get binary: 1 if value >= 0, else 0
res = gpuarray.if_positive(
mul >= gpuarray.zeros(mul.shape, dtype=np.float32),
then_=gpuarray.ones_like(mul),
else_=gpuarray.zeros_like(mul))
res = np.array(res.get(), dtype=np.uint32)
# convert grouped bits to integers
res = np_array_binary_to_grouped_integers(res)
df[f"hash_{LSH_NUM_BITS}_bits"] = [x for x in res]
df = df[["rec_MBID", f"hash_{LSH_NUM_BITS}_bits"]]
df.to_parquet(f"{config.ABz_GPU_hashed_output_dir}{count}.parquet",
index=False)
# save as a single parquet file
spark = SparkSession \
.builder \
.appName("hashed file coalesce") \
cv.namedWindow("Moving Detecting!")
cap = cv.VideoCapture(0)
W = 320
H = 240
cap.set(cv.CAP_PROP_FRAME_HEIGHT, H)
cap.set(cv.CAP_PROP_FRAME_WIDTH, W)
ret, frame = cap.read()
gray_a = cv.cvtColor(frame, cv.COLOR_RGB2GRAY)
img_ori_gpu = gpuarray.to_gpu(gray_a.astype(np.float32))
img_buf_gpu = gpuarray.empty_like(img_ori_gpu)
img_sub = gpuarray.ones_like(img_ori_gpu)
img_sub = 25 * img_sub
img_bgm = gpuarray.zeros_like(img_sub)
while True:
ret, frame = cap.read()
gray_buff = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)
img_res_gpu = gpuarray.to_gpu(gray_buff.astype(np.float32))
img_buf_gpu = cmath.fabs(img_ori_gpu - img_res_gpu)
img_buf_gpu = img_buf_gpu - img_sub
img_ori_gpu = img_res_gpu.copy()
img_res_gpu = gpuarray.if_positive(img_buf_gpu, img_bgm, img_res_gpu)
gray_buff = img_res_gpu.get()
gray_buff = gray_buff.astype(np.uint8)
frame = cv.cvtColor(gray_buff, cv.COLOR_GRAY2RGB)
cv.imshow("Moving Detecting!", frame)
if cv.waitKey(1) & 0xFF == ord('q'):
b = gpuarray.to_gpu_async(h_array, stream=stream)
print('b:\n{0}\nshape={1}\n'.format(b.get(), b.shape))
c = gpuarray.empty((100, 100), dtype=dtype)
print('c:\n{0}\nshape={1}\n'.format(c, c.shape))
d = gpuarray.zeros((100, 100), dtype=dtype)
print('d:\n{0}\nshape={1}\n'.format(d, d.shape))
e = gpuarray.arange(0.0, 100.0, 1.0, dtype=dtype)
print('e:\n{0}\nshape={1}\n'.format(e, e.shape))
f = gpuarray.if_positive(e < 50, e - 100, e + 100)
print('f:\n{0}\nshape={1}\n'.format(f, f.shape))
g = gpuarray.if_positive(e < 50, gpuarray.ones_like(e), gpuarray.zeros_like(e))
print('g:\n{0}\nshape={1}\n'.format(g, g.shape))
h = gpuarray.maximum(e, f)
print('h:\n{0}\nshape={1}\n'.format(h, h.shape))
i = gpuarray.minimum(e, f)
print('i:\n{0}\nshape={1}\n'.format(i, i.shape))
g = gpuarray.sum(a)
print(g, type(g))
k = gpuarray.max(a)
print(k, type(k))
l = gpuarray.min(a)
