def main():
batchsize, insize = 16, 1000
inNode = Linear(insize, 1000, name="linear1").node()
node = Activation(relu, name="relu1").node(inNode)
node1 = Linear(1000, 800, name="linear2").node(node)
node1 = Activation(relu, name="relu2").node(node1)
node2 = Linear(1000, 800, name="linear3").node(node)
node2 = Activation(relu, name="relu3").node(node2)
outNode = Add(name="add").node(node1, node2)
graph = Graph(inputs=inNode, outputs=outNode, name="graph")
data = gpuarray.to_gpu(
np.random.randn(batchsize, insize).astype(np.float32))
engine = buildVINOEngine(graph, (batchsize, insize),
savepath="../TestData")
outdata = graph(data)
enginedata = engine(data)
assert np.allclose(outdata.get(), enginedata.get())
benchModels(graph, engine, data)
def main():
net = loadResNet(modelpath="../../TestData/ResNet-50-model.hdf",
layers="50")
data = gpuarray.to_gpu(
loadResNetSample(net, "../../TestData/tarantula.jpg"))
labels = loadLabels(synpath="../../TestData/synsets.txt",
wordpath="../../TestData/synset_words.txt")
engine = buildVINOEngine(net, inshape=data.shape, savepath="../TestData")
scoreModels(net, engine, data, labels)
benchModels(net, engine, data)
def upsample2dTest():
batchsize, maps, height, width = 4, 3, 5, 8
mod = Upsample2D(scale=2, name="upsample")
data = gpuarray.to_gpu(
np.random.randn(batchsize, maps, height, width).astype(np.float32))
engine = buildVINOEngine(mod, data.shape, savepath="../TestData")
outdata = mod(data)
enginedata = engine(data)
assert np.allclose(outdata.get(), enginedata.get())
def mulAddConstTest():
batchsize, maps, height, width = 4, 3, 5, 8
mod = MulAddConst(a=1.5, b=-2.0, name="muladd")
data = gpuarray.to_gpu(
np.random.randn(batchsize, maps, height, width).astype(np.float32))
engine = buildVINOEngine(mod, data.shape, savepath="../TestData")
outdata = mod(data)
enginedata = engine(data)
assert np.allclose(outdata.get(), enginedata.get())
def batchNormTest():
batchsize, size = 16, 10
mod = BatchNorm(size, name="bn")
mod.evalMode()
data = gpuarray.to_gpu(np.random.randn(batchsize, size).astype(np.float32))
engine = buildVINOEngine(mod, data.shape, savepath="../TestData")
outdata = mod(data)
enginedata = engine(data)
assert np.allclose(outdata.get(), enginedata.get())
def splitTest():
batchsize, maps, height, width = 2, 6, 4, 5
net = Sequential(name="split")
net.append(Split(axis=1, sections=(2, 4)))
net.append(Parallel().append(SoftMax()).append(SoftMax()))
data = gpuarray.to_gpu(
np.random.randn(batchsize, maps, height, width).astype(np.float32))
engine = buildVINOEngine(net, data.shape, savepath="../TestData")
outdata = net(data)
enginedata = engine(data)
assert all(
np.allclose(outdat.get(), enginedat.get())
for outdat, enginedat in zip(outdata, enginedata))
def concatTest():
batchsize, height, width = 4, 5, 8
maps1, maps2 = 3, 2
mod = Concat(axis=1, name="concat")
data = [
gpuarray.to_gpu(
np.random.randn(batchsize, maps, height, width).astype(np.float32))
for maps in [maps1, maps2]
]
engine = buildVINOEngine(mod, [subdata.shape for subdata in data],
savepath="../TestData")
outdata = mod(data)
enginedata = engine(data)
assert np.allclose(outdata.get(), enginedata.get())