def check_avg_divisor(self, place):
with fluid.dygraph.guard(place):
input_np = np.random.random([2, 3, 32, 32, 32]).astype("float32")
input = fluid.dygraph.to_variable(input_np)
padding = 0
result = avg_pool3d(input,
kernel_size=2,
stride=2,
padding=padding,
divisor_override=8)
result_np = pool3D_forward_naive(input_np,
ksize=[2, 2, 2],
strides=[2, 2, 2],
paddings=[0, 0, 0],
pool_type='avg')
self.assertTrue(np.allclose(result.numpy(), result_np))
avg_pool3d_dg = paddle.nn.layer.AvgPool3d(kernel_size=2,
stride=2,
padding=0)
result = avg_pool3d_dg(input)
self.assertTrue(np.allclose(result.numpy(), result_np))
padding = [0, 0, 0, 0, 0, 0]
result = avg_pool3d(input,
kernel_size=2,
stride=2,
padding=padding,
divisor_override=8)
self.assertTrue(np.allclose(result.numpy(), result_np))
def check_max_dygraph_padding(self, place):
with fluid.dygraph.guard(place):
input_np = np.random.random([2, 3, 32, 32, 32]).astype("float32")
input = fluid.dygraph.to_variable(input_np)
padding = [[0, 0], [0, 0], [0, 0], [0, 0], [0, 0]]
result = max_pool3d(input,
kernel_size=2,
stride=2,
padding=padding)
result_np = pool3D_forward_naive(input_np,
ksize=[2, 2, 2],
strides=[2, 2, 2],
paddings=[0, 0, 0],
pool_type='max')
self.assertTrue(np.allclose(result.numpy(), result_np))
max_pool3d_dg = paddle.nn.layer.MaxPool3d(kernel_size=2,
stride=2,
padding=0)
result = max_pool3d_dg(input)
self.assertTrue(np.allclose(result.numpy(), result_np))
padding = [0, 0, 0, 0, 0, 0]
result = max_pool3d(input,
kernel_size=2,
stride=2,
padding=padding)
self.assertTrue(np.allclose(result.numpy(), result_np))
def check_avg_static_results(self, place):
with fluid.program_guard(fluid.Program(), fluid.Program()):
input = fluid.data(name="input",
shape=[2, 3, 32, 32, 32],
dtype="float32")
result = avg_pool3d(input, kernel_size=2, stride=2, padding=0)
input_np = np.random.random([2, 3, 32, 32, 32]).astype("float32")
result_np = pool3D_forward_naive(input_np,
ksize=[2, 2, 2],
strides=[2, 2, 2],
paddings=[0, 0, 0],
pool_type='avg')
exe = fluid.Executor(place)
fetches = exe.run(fluid.default_main_program(),
feed={"input": input_np},
fetch_list=[result])
self.assertTrue(np.allclose(fetches[0], result_np))
def check_avg_dygraph_results(self, place):
with fluid.dygraph.guard(place):
input_np = np.random.random([2, 3, 32, 32, 32]).astype("float32")
input = fluid.dygraph.to_variable(input_np)
result = avg_pool3d(input, kernel_size=2, stride=2, padding="SAME")
result_np = pool3D_forward_naive(input_np,
ksize=[2, 2, 2],
strides=[2, 2, 2],
paddings=[0, 0, 0],
pool_type='avg',
padding_algorithm="SAME")
self.assertTrue(np.allclose(result.numpy(), result_np))
avg_pool3d_dg = paddle.nn.layer.AvgPool3d(kernel_size=2,
stride=None,
padding="SAME")
result = avg_pool3d_dg(input)
self.assertTrue(np.allclose(result.numpy(), result_np))
def check_max_dygraph_ndhwc_results(self, place):
with fluid.dygraph.guard(place):
input_np = np.random.random([2, 3, 32, 32, 32]).astype("float32")
input = fluid.dygraph.to_variable(
np.transpose(input_np, [0, 2, 3, 4, 1]))
result = max_pool3d(input,
kernel_size=2,
stride=2,
padding=0,
data_format="NDHWC",
return_indices=False)
result_np = pool3D_forward_naive(input_np,
ksize=[2, 2, 2],
strides=[2, 2, 2],
paddings=[0, 0, 0],
pool_type='max')
self.assertTrue(
np.allclose(np.transpose(result.numpy(), [0, 4, 1, 2, 3]),
result_np))
def check_max_dygraph_stride_is_none(self, place):
with fluid.dygraph.guard(place):
input_np = np.random.random([2, 3, 32, 32, 32]).astype("float32")
input = fluid.dygraph.to_variable(input_np)
result, indices = max_pool3d(input,
kernel_size=2,
stride=None,
padding="SAME",
return_indices=True)
result_np = pool3D_forward_naive(input_np,
ksize=[2, 2, 2],
strides=[2, 2, 2],
paddings=[0, 0, 0],
pool_type='max',
padding_algorithm="SAME")
self.assertTrue(np.allclose(result.numpy(), result_np))
max_pool3d_dg = paddle.nn.layer.MaxPool3d(kernel_size=2,
stride=2,
padding=0)
result = max_pool3d_dg(input)
self.assertTrue(np.allclose(result.numpy(), result_np))
