def testSigmoid(self):
for input_size in self.test_configs:
op = core.CreateOperator("Sigmoid")(["X"], ["Y"])
X = np.random.rand(*input_size).astype(np.float32) - 0.5
res = device_checker.CheckSimple(op, [X], [0])
self.assertTrue(res)
for checker in gradient_checkers:
res, grad, grad_estimated = checker.CheckSimple(
op, [X], 0, [0])
self.assertTrue(res)
def testRelu(self):
for input_size in self.test_configs:
op = core.CreateOperator("Relu")(["X"], ["Y"])
X = np.random.rand(*input_size).astype(np.float32)
# go away from the origin point to avoid kink problems
X += 0.01 * np.sign(X)
X[X == 0] = 0.01
res = device_checker.CheckSimple(op, [X], [0])
self.assertTrue(res)
for checker in gradient_checkers:
res, grad, grad_estimated = checker.CheckSimple(
op, [X], 0, [0])
self.assertTrue(res)
def testLRN(self):
for input_size, depth in self.test_configs:
op = core.CreateOperator("LRN")(["X"], ["Y", "Y_scale"],
size=11,
alpha=0.001,
beta=0.5,
bias=2.0,
order="NHWC")
X = np.random.rand(2, input_size, input_size,
depth).astype(np.float32)
res = device_checker.CheckSimple(op, [X], [0])
self.assertTrue(res)
for checker in gradient_checkers:
res, grad, grad_estimated = checker.CheckSimple(
op, [X], 0, [0])
self.assertTrue(res)
def testAveragePoolingLegacyPadding(self):
for stride, kernel, legacy_pad, size, order in self.test_configs:
print 'AveragePool', stride, kernel, legacy_pad, size, order
op = core.CreateOperator("AveragePool")(["X"], ["Y"],
stride=stride,
kernel=kernel,
legacy_pad=legacy_pad,
order=order)
if order == "NHWC":
X = np.random.rand(2, size, size, 3).astype(np.float32)
else:
X = np.random.rand(2, 3, size, size).astype(np.float32)
res = device_checker.CheckSimple(op, [X], [0])
self.assertTrue(res)
for checker in gradient_checkers:
res, grad, grad_estimated = checker.CheckSimple(
op, [X], 0, [0])
self.assertTrue(res)
def testMaxPoolingLegacyPadding(self):
for stride, kernel, legacy_pad, size, order in self.test_configs:
print 'MaxPool', stride, kernel, legacy_pad, size, order
op = core.CreateOperator("MaxPool")(["X"], ["Y", "Y_maxid"],
stride=stride,
kernel=kernel,
legacy_pad=legacy_pad,
order=order)
# In order to avoid the problem of race conditions, we will do a randperm
# so that the values will be apart at least 0.01
if order == "NHWC":
X = np.random.permutation(1 * size * size * 3).reshape(
1, size, size, 3).astype(np.float32) * 0.01
else:
X = np.random.permutation(1 * size * size * 3).reshape(
1, 3, size, size).astype(np.float32) * 0.01
res = device_checker.CheckSimple(op, [X], [0])
self.assertTrue(res)
for checker in gradient_checkers:
res, grad, grad_estimated = checker.CheckSimple(
op, [X], 0, [0])
self.assertTrue(res)
def testDepthConcatNCHW(self):
for input_size, d1, d2, d3, d4 in self.test_configs:
op = core.CreateOperator("DepthConcat")(["X1", "X2", "X3", "X4"],
["Y", "Y_dims"],
order="NCHW")
Xs = [
np.random.rand(2, d1, input_size,
input_size).astype(np.float32),
np.random.rand(2, d2, input_size,
input_size).astype(np.float32),
np.random.rand(2, d3, input_size,
input_size).astype(np.float32),
np.random.rand(2, d4, input_size,
input_size).astype(np.float32)
]
for i in range(4):
res = device_checker.CheckSimple(op, Xs, [0])
self.assertTrue(res)
for checker in gradient_checkers:
res, grad, grad_estimated = checker.CheckSimple(
op, Xs, i, [0])
self.assertTrue(res)
def testConvolutionLegacyPadding(self):
for stride, kernel, legacy_pad, size, order in self.test_configs:
print 'conv', stride, kernel, legacy_pad, size, order
op = core.CreateOperator("Conv")(["X", "w", "b"], ["Y"],
stride=stride,
kernel=kernel,
legacy_pad=legacy_pad,
order=order)
if order == "NHWC":
X = np.random.rand(2, size, size, 3).astype(np.float32) - 0.5
w = np.random.rand(4, kernel, kernel, 3).astype(
np.float32) - 0.5
else:
X = np.random.rand(2, 3, size, size).astype(np.float32) - 0.5
w = np.random.rand(4, 3, kernel, kernel).astype(
np.float32) - 0.5
b = np.random.rand(4).astype(np.float32) - 0.5
res = device_checker.CheckSimple(op, [X, w, b], [0])
self.assertTrue(res)
for checker in gradient_checkers:
for i in range(3):
res, grad, grad_estimated = checker.CheckSimple(
op, [X, w, b], i, [0])
self.assertTrue(res)
