def test_amountOfComputation():
X = np.random.randn(1, 1, 4, 4)
W = np.random.randn(1, 1, 2, 2)
hparam = {"pad": 0, "stride": 1}
obj = Offload(1, 1, 1, X, W, hparam)
assert obj.amountOfComputation() == 9 * 7
def test_vecShape():
X = np.random.randn(1, 2, 4, 4)
W = np.random.randn(1, 2, 2, 2)
hparam = {"pad": 0, "stride": 1}
obj = Offload(1, 1, 1, X, W, hparam)
assert obj.vecShape() == (9, 8)
def test_outshape():
"""
X-numpy array numpy array (m,n_C, n_H, n_W)
k-kernel (n_C, n_C_prev, f, f)
out-(m,c,nh,nw)
"""
X = np.random.randn(10, 4, 5, 7)
W = np.random.randn(8, 4, 3, 3)
hparam = {"pad": 1, "stride": 2}
obj = Offload(1, 1, 1, X, W, hparam)
assert obj.outShape() == (10, 8, 3, 4)
def compute(self, n_speed, s_speed, msize, thershold, bandwidth):
"""
n_speed-node CPU speed
s_speed-server CPU speed
msize-node memory size
thershold-maximum memory limit
"""
X = self.image
for index, layer in enumerate(self.CNN):
#layer dictonary {type,kernel,bias,hparams}
#offloading decisions
kernel = kernels[layer["kernel"]]
hparam = layer["hparams"]
if (layer["l_type"] == "conv"):
#n_speed,s_speed,msize,X,kernel,hparam,bandwidth_up,bandwidth_down
off_dec = Offload(n_speed, s_speed, msize, X, kernel, hparam,
bandwidth, bandwidth)
offDec = off_dec.checkOffload(thershold)
else:
#pooling layer memory usage
offDec = self.poolingMem(X, hparam)
if X.shape[2] < 50:
pos = 0
if layer[l_type] == "conv":
pos = (0, kernel.shape[3])
else:
pos = (0, X.shape[2])
X = self.layerResult(layer, X, pos)
elif (offDec):
#get the result form the server
conv_dict = {"data": X, "index": index}
c = client(conv_dict, self.edge["ip"], self.edge["port"])
c.send()
X = c.receive_array()
else:
X = self.thread_Compute(X, layer)
#activation function and bais apply for conv
if layer[l_type] == "conv":
X += kernels[layer["bias"]]
X = ActivationFunc(X, layer["act"])