def test3DNumpy(self):
x = np.ones([3, 7, 6, 6, 5], dtype=np.float32)
ksize = 2
strides = 2
y1 = nn_ops.max_pool_v2(x, ksize, strides, "SAME")
y2 = nn_ops.max_pool3d(x, ksize, strides, "SAME")
self.assertAllEqual(self.evaluate(y1), self.evaluate(y2))
def test3DTensor(self):
x = array_ops.ones([3, 7, 6, 6, 5])
ksize = 2
strides = 2
y1 = nn_ops.max_pool_v2(x, ksize, strides, "SAME")
y2 = nn_ops.max_pool3d(x, ksize, strides, "SAME")
self.assertAllEqual(self.evaluate(y1), self.evaluate(y2))
def test3DNumpy(self):
x = np.ones([3, 7, 6, 6, 5], dtype=np.float32)
ksize = 2
strides = 2
y1 = nn_ops.max_pool_v2(x, ksize, strides, "SAME")
y2 = nn_ops.max_pool3d(x, ksize, strides, "SAME")
self.assertAllEqual(self.evaluate(y1), self.evaluate(y2))
def test3DTensor(self):
x = array_ops.ones([3, 7, 6, 6, 5])
ksize = 2
strides = 2
y1 = nn_ops.max_pool_v2(x, ksize, strides, "SAME")
y2 = nn_ops.max_pool3d(x, ksize, strides, "SAME")
self.assertAllEqual(self.evaluate(y1), self.evaluate(y2))
def _max_pool_3d(_input,
kd=2,
kh=2,
kw=2,
sd=2,
sh=2,
sw=2,
name="max_pool_3d",
padding='SAME'):
return nn_ops.max_pool3d(_input,
ksize=[1, kd, kh, kw, 1],
strides=[1, sd, sh, sw, 1],
padding=padding,
name=name)
def testMaxPool3DEmptyTensorOutputShape(self):
"""Verifies the output shape of the max pooling function when tensor is empty.
Args: none
"""
input_sizes = [0, 112, 112, 112, 64]
input_data = 1.
input_tensor = constant_op.constant(input_data,
shape=input_sizes,
name="input")
max_pool_3d = nn_ops.max_pool3d(input_tensor,
ksize=[2, 2, 2],
strides=[2, 2, 2],
padding="VALID",
data_format="NDHWC",
name="max_pool_3d")
values = self.evaluate(max_pool_3d)
self.assertEqual(values.shape, (0, 56, 56, 56, 64))