def __init__(self):
super(CalibNet48, self).__init__(
conv1=F.Convolution2D(3, 64, ksize=4, stride=1),
ln1=F.LocalResponseNormalization(n=9),
conv2=F.Convolution2D(64, 64, ksize=4, stride=1),
ln2=F.LocalResponseNormalization(n=9),
fc1=F.Linear(23104, 256),
fc2=F.Linear(256, 45), ## 384 = 256 + 128
)
def __init__(self):
super(FaceNet48, self).__init__(
net24 = FaceNet24(),
conv1=F.Convolution2D(3, 64, ksize=4, stride=1),
ln1=F.LocalResponseNormalization(n=9),
conv2=F.Convolution2D(64,64, ksize=4, stride=1),
ln2=F.LocalResponseNormalization(n=9),
fc1=F.Linear(5184, 256),
fc2=F.Linear(384, 2), ## 384 = 256 + 128
)
def check_backward(self, x_data, y_grad):
gradient_check.check_backward(functions.LocalResponseNormalization(),
x_data,
y_grad,
eps=1,
dtype=numpy.float64,
**self.check_backward_optionss)
def check_backward(self, inputs, grad_outputs, backend_config):
if backend_config.use_cuda:
inputs = cuda.to_gpu(inputs)
grad_outputs = cuda.to_gpu(grad_outputs)
with backend_config:
gradient_check.check_backward(
functions.LocalResponseNormalization(), inputs, grad_outputs,
eps=1, dtype=numpy.float64, **self.check_backward_options)
def setUp(self):
n = 5
k = 1
alpha = 1e-4
beta = .75
self.x = np.random.uniform(-1, 1,
(2, self.channel, 3, 2)).astype(self.dtype)
self.gy = np.random.uniform(-1, 1,
(2, self.channel, 3, 2)).astype(self.dtype)
self.check_forward_optionss = {}
self.check_backward_optionss = {}
if self.channel >= 8:
self.check_forward_optionss = {'atol': 1e-4, 'rtol': 1e-3}
self.check_backward_optionss = {'atol': 5e-3, 'rtol': 5e-3}
self.lrn = F.LocalResponseNormalization(n, k, alpha, beta)
def test_lrn(caculate, switchOn=True):
total_forward = 0
total_backward = 0
data = np.ndarray((10, 3, 2240, 2240), dtype=np.float32)
data.fill(333.33)
datay = np.ndarray((10, 3, 2240, 2240), dtype=np.float32)
datay.fill(333.33)
# y = np.empty(shape=(10, 3, 2240, 2240), dtype=np.float32)
# gx = np.empty(shape=(10, 3, 2240, 2240), dtype=np.float32)
total_forward = 0
count = 0
niter = 5
n_dry = 3
n = 5
k = 2
alpha = 1e-4
beta = .75
mkld.enable_lrn = switchOn
for i in range(niter):
x = np.asarray(data),
gy = np.asarray(datay),
# y = np.empty(shape=(10, 3, 2240, 2240), dtype=np.float32)
# gx = np.empty(shape=(10, 3, 2240, 2240), dtype=np.float32)
start = time.time()
lrn = F.LocalResponseNormalization(n, k, alpha, beta)
lrn.forward_cpu(x)
end = time.time()
if i > n_dry - 1:
count += 1
total_forward += (end - start) * 1000
start = time.time()
lrn.backward_cpu(x, gy)
end = time.time()
if i > n_dry - 1:
total_backward += (end - start) * 1000
# print("Mkldnn Average Forward: ", total_forward/count, "ms")
print(caculate, " Average Forward: ", total_forward / count, "ms")
print(caculate, " Average Backward: ", total_backward / count, "ms")
print(caculate, " Average Total: ",
(total_forward + total_backward) / count, "ms")
def check_backward(self, x_data, y_grad):
gradient_check.check_backward(functions.LocalResponseNormalization(),
x_data,
y_grad,
eps=1,
atol=1e-4)