def build_programs(clctx, config):
sigmas = [s['diffusion'] for s in config['signals'][:4]]
colony = genome.genome_cl(config['genome'])
colony += convolution.gaussian_cl(sigmas)
colony = cl.Program(clctx.ctx, colony).build()
feats = cl.Program(clctx.ctx, features.features_cl()).build()
sigma2, sigma4 = [np.sqrt(2.0)] * 4, [2.0] * 4
blurs2 = cl.Program(clctx.ctx, convolution.gaussian_cl(sigma2)).build()
blurs4 = cl.Program(clctx.ctx, convolution.gaussian_cl(sigma4)).build()
rdctns = cl.Program(clctx.ctx, reduction.reduction_sum_cl()).build()
colours = cl.Program(clctx.ctx, colour.colour_cl()).build()
return colony, feats, blurs2, blurs4, rdctns, colours
def profile():
import time
from iib.simulation import CLContext
from skimage import io, data, transform
gs, wgs = 256, 16
# Load some test data
r = transform.resize
sigs = np.empty((gs, gs, 4), np.float32)
sigs[:, :, 0] = r(data.coins().astype(np.float32) / 255.0, (gs, gs))
sigs[:, :, 1] = r(data.camera().astype(np.float32) / 255.0, (gs, gs))
sigs[:, :, 2] = r(data.text().astype(np.float32) / 255.0, (gs, gs))
sigs[:, :, 3] = r(data.checkerboard().astype(np.float32) / 255.0, (gs, gs))
sigs[:, :, 2] = r(io.imread("../scoring/corpus/rds/turing_001.png",
as_grey=True), (gs, gs))
sigs[:, :, 3] = io.imread("../scoring/corpus/synthetic/blobs.png",
as_grey=True)
sigs = sigs.reshape(gs*gs*4)
# Set up OpenCL
ctx = cl.create_some_context(interactive=False)
queue = cl.CommandQueue(ctx)
mf = cl.mem_flags
ifmt_f = cl.ImageFormat(cl.channel_order.RGBA, cl.channel_type.FLOAT)
bufi = cl.Image(ctx, mf.READ_ONLY, ifmt_f, (gs, gs))
cl.enqueue_copy(queue, bufi, sigs, origin=(0, 0), region=(gs, gs))
clctx = CLContext(ctx, queue, ifmt_f, gs, wgs)
# Compile the kernels
feats = cl.Program(ctx, features_cl()).build()
rdctn = cl.Program(ctx, reduction.reduction_sum_cl()).build()
blur2 = cl.Program(ctx, convolution.gaussian_cl([np.sqrt(2.0)]*4)).build()
blur4 = cl.Program(ctx, convolution.gaussian_cl([np.sqrt(4.0)]*4)).build()
iters = 500
t0 = time.time()
for i in range(iters):
get_features(clctx, feats, rdctn, blur2, blur4, bufi)
print((time.time() - t0)/iters)
def test():
import matplotlib.pyplot as plt
from skimage import io, data, transform
from iib.simulation import CLContext
gs, wgs = 256, 16
# Load some test data
r = transform.resize
sigs = np.empty((gs, gs, 4), np.float32)
sigs[:, :, 0] = r(data.coins().astype(np.float32) / 255.0, (gs, gs))
sigs[:, :, 1] = r(data.camera().astype(np.float32) / 255.0, (gs, gs))
sigs[:, :, 2] = r(data.text().astype(np.float32) / 255.0, (gs, gs))
sigs[:, :, 3] = r(data.checkerboard().astype(np.float32) / 255.0, (gs, gs))
sigs[:, :, 2] = r(io.imread("../scoring/corpus/rds/turing_001.png",
as_grey=True), (gs, gs))
sigs[:, :, 3] = io.imread("../scoring/corpus/synthetic/blobs.png",
as_grey=True)
#sq = np.arange(256).astype(np.float32).reshape((16, 16)) / 255.0
#sigs[:, :, 0] = np.tile(sq, (16, 16))
sigs = sigs.reshape(gs*gs*4)
# Set up OpenCL
ctx = cl.create_some_context(interactive=False)
queue = cl.CommandQueue(ctx)
mf = cl.mem_flags
ifmt_f = cl.ImageFormat(cl.channel_order.RGBA, cl.channel_type.FLOAT)
bufi = cl.Image(ctx, mf.READ_ONLY, ifmt_f, (gs, gs))
cl.enqueue_copy(queue, bufi, sigs, origin=(0, 0), region=(gs, gs))
clctx = CLContext(ctx, queue, ifmt_f, gs, wgs)
# Compile the kernels
feats = cl.Program(ctx, features_cl()).build()
rdctn = cl.Program(ctx, reduction.reduction_sum_cl()).build()
blur2 = cl.Program(ctx, convolution.gaussian_cl([np.sqrt(2.0)]*4)).build()
blur4 = cl.Program(ctx, convolution.gaussian_cl([np.sqrt(4.0)]*4)).build()
entropy = get_entropy(clctx, feats, rdctn, bufi)
print("Average entropy:", entropy)
variance = get_variance(clctx, feats, rdctn, bufi)
print("Variance:", variance)
edges = get_edges(clctx, feats, rdctn, blur4, bufi, summarise=False)
edge_counts = get_edges(clctx, feats, rdctn, blur4, bufi)
print("Edge pixel counts:", edge_counts)
blobs = get_blobs(clctx, feats, rdctn, blur2, bufi, summarise=False)
features = get_features(clctx, feats, rdctn, blur2, blur4, bufi)
print("Feature vector:")
print(features)
# Plot the edges and blobs
for i in range(4):
plt.subplot(4, 3, i*3+1)
img = sigs.reshape((gs, gs, 4))[:, :, i]
plt.imshow(img, cmap="gray")
plt.xticks([])
plt.yticks([])
plt.subplot(4, 3, i*3+2)
img = edges.reshape((gs, gs, 4))[:, :, i]
plt.imshow(img, cmap="gray")
plt.xticks([])
plt.yticks([])
ax = plt.subplot(4, 3, i*3+3)
img = sigs.reshape((gs, gs, 4))[:, :, i]
plt.imshow(img, cmap="gray")
plt.xticks([])
plt.yticks([])
for j in range(len(blobs)):
sblobs = blobs[j]
s = 2**(j+1)
r = np.sqrt(2.0) * s
im = sblobs[:, :, i]
posns = np.transpose(im.nonzero()) * 2**(j+1)
for xy in posns:
circ = plt.Circle((xy[1], xy[0]), r, color="green", fill=False)
ax.add_patch(circ)
plt.show()