def test_upper_case(self):
with fluid.dygraph.guard():
data = np.random.random((3, 32, 32, 5)).astype('float32')
x = fluid.dygraph.to_variable(data)
pool2d = fluid.dygraph.Pool2D(pool_size=2,
pool_type='MAX',
pool_stride=1,
pool_padding=[0, 0],
global_pooling=False,
data_format='nhwc')
out1 = pool2d(x)
out2 = pool2D_forward_naive(data, [2, 2], [1, 1],
paddings=[0, 0],
pool_type='max',
data_format='NHWC')
self.assertTrue(np.allclose(out1.numpy(), out2))
def check_avg_dygraph_results(self, place):
with fluid.dygraph.guard(place):
input_np = np.random.random([2, 3, 32, 32]).astype("float32")
input = fluid.dygraph.to_variable(input_np)
result = avg_pool2d(input, kernel_size=2, stride=2, padding=0)
result_np = pool2D_forward_naive(
input_np,
ksize=[2, 2],
strides=[2, 2],
paddings=[0, 0],
pool_type='avg')
self.assertTrue(np.allclose(result.numpy(), result_np))
avg_pool2d_dg = paddle.nn.layer.AvgPool2D(
kernel_size=2, stride=2, padding=0)
result = avg_pool2d_dg(input)
self.assertTrue(np.allclose(result.numpy(), result_np))
def setUp(self):
self.set_npu()
self.op_type = "pool2d"
self.init_kernel_type()
self.init_data_type()
self.init_test_case()
self.padding_algorithm = "EXPLICIT"
self.init_paddings()
self.init_global_pool()
self.init_kernel_type()
self.init_pool_type()
self.init_ceil_mode()
self.init_exclusive()
self.init_adaptive()
self.init_data_format()
self.init_shape()
input = np.random.random(self.shape).astype(self.dtype)
if self.pool_type == "max":
input = np.array([x for x in range(np.prod(self.shape))
]).reshape(self.shape).astype(self.dtype)
output = pool2D_forward_naive(
input, self.ksize, self.strides, self.paddings, self.global_pool,
self.ceil_mode, self.exclusive, self.adaptive, self.data_format,
self.pool_type, self.padding_algorithm).astype(self.dtype)
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(input)}
self.attrs = {
'strides': self.strides,
'paddings': self.paddings,
'ksize': self.ksize,
'pooling_type': self.pool_type,
'global_pooling': self.global_pool,
'use_cudnn': False,
'use_mkldnn': False,
'ceil_mode': self.ceil_mode,
'data_format': self.data_format,
'exclusive': self.exclusive,
'adaptive': self.adaptive,
"padding_algorithm": self.padding_algorithm,
}
self.outputs = {'Out': output}
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], dtype="float32")
result = avg_pool2d(input, kernel_size=2, stride=2, padding=0)
input_np = np.random.random([2, 3, 32, 32]).astype("float32")
result_np = pool2D_forward_naive(
input_np,
ksize=[2, 2],
strides=[2, 2],
paddings=[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_max_dygraph_nhwc_results(self, place):
with fluid.dygraph.guard(place):
input_np = np.random.random([2, 3, 32, 32]).astype("float32")
input = fluid.dygraph.to_variable(
np.transpose(input_np, [0, 2, 3, 1]))
result = max_pool2d(input,
kernel_size=2,
stride=2,
padding=0,
return_indices=False,
data_format="NHWC")
result_np = pool2D_forward_naive(input_np,
ksize=[2, 2],
strides=[2, 2],
paddings=[0, 0],
pool_type='max')
self.assertTrue(
np.allclose(np.transpose(result.numpy(), [0, 3, 1, 2]),
result_np))
def check_max_dygraph_padding(self, place):
with fluid.dygraph.guard(place):
input_np = np.random.random([2, 3, 32, 32]).astype("float32")
input = fluid.dygraph.to_variable(input_np)
padding = [[0, 0], [0, 0], [0, 0], [0, 0]]
result = max_pool2d(input,
kernel_size=2,
stride=2,
padding=padding,
return_indices=False)
result_np = pool2D_forward_naive(input_np,
ksize=[2, 2],
strides=[2, 2],
paddings=[0, 0],
pool_type='max')
self.assertTrue(np.allclose(result.numpy(), result_np))
max_pool2d_dg = paddle.nn.layer.MaxPool2d(kernel_size=2,
stride=2,
padding=0)
result = max_pool2d_dg(input)
self.assertTrue(np.allclose(result.numpy(), result_np))
def setUp(self):
self.place = paddle.device.MLUPlace(0)
self.__class__.use_mlu = True
self.op_type = "pool2d"
self.init_data_type()
self.init_test_case()
self.padding_algorithm = "EXPLICIT"
self.init_paddings()
self.init_global_pool()
self.init_pool_type()
self.init_ceil_mode()
self.init_exclusive()
self.init_adaptive()
self.init_data_format()
self.init_shape()
input = np.random.random(self.shape).astype(self.dtype)
output = pool2D_forward_naive(
input, self.ksize, self.strides, self.paddings, self.global_pool,
self.ceil_mode, self.exclusive, self.adaptive, self.data_format,
self.pool_type, self.padding_algorithm).astype(self.dtype)
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(input)}
self.attrs = {
'strides': self.strides,
'paddings': self.paddings,
'ksize': self.ksize,
'pooling_type': self.pool_type,
'global_pooling': self.global_pool,
'ceil_mode': self.ceil_mode,
'data_format': self.data_format,
'exclusive': self.exclusive,
'adaptive': self.adaptive,
"padding_algorithm": self.padding_algorithm,
}
self.outputs = {'Out': output}
def check_max_dygraph_stride_is_none(self, place):
with fluid.dygraph.guard(place):
input_np = np.random.random([2, 3, 32, 32]).astype("float32")
input = fluid.dygraph.to_variable(input_np)
result, indices = max_pool2d(
input,
kernel_size=2,
stride=None,
padding="SAME",
return_mask=True)
result_np = pool2D_forward_naive(
input_np,
ksize=[2, 2],
strides=[2, 2],
paddings=[0, 0],
pool_type='max',
padding_algorithm="SAME")
self.assertTrue(np.allclose(result.numpy(), result_np))
max_pool2d_dg = paddle.nn.layer.MaxPool2D(
kernel_size=2, stride=2, padding=0)
result = max_pool2d_dg(input)
self.assertTrue(np.allclose(result.numpy(), result_np))
def test_api(self):
x_NHWC = np.random.random([2, 5, 5, 3]).astype("float32")
x_NCHW = np.random.random([2, 3, 5, 5]).astype("float32")
input_NHWC = fluid.layers.data(name="input_NHWC",
shape=[2, 5, 5, 3],
append_batch_size=False,
dtype="float32")
input_NCHW = fluid.layers.data(name="input_NCHW",
shape=[2, 3, 5, 5],
append_batch_size=False,
dtype="float32")
input_NHWC_negetive = fluid.layers.data(name="input_NHWC_negetive",
shape=[2, -1, 5, 3],
append_batch_size=False,
dtype="float32")
input_NCHW_negetive = fluid.layers.data(name="input_NCHW_negetive",
shape=[2, 3, -1, -1],
append_batch_size=False,
dtype="float32")
ksize = [3, 3]
out_1 = fluid.layers.pool2d(input=input_NHWC,
pool_size=ksize,
pool_type="max",
pool_padding=[1, 1],
data_format="NHWC")
out_2 = fluid.layers.pool2d(input=input_NHWC,
pool_size=ksize,
pool_type="avg",
pool_padding=[[0, 0], [1, 1], [1, 1],
[0, 0]],
data_format="NHWC")
out_3 = fluid.layers.pool2d(input=input_NCHW,
pool_size=ksize,
pool_type="avg",
pool_padding=[[0, 0], [0, 0], [1, 1],
[1, 1]],
data_format="NCHW")
out_4 = fluid.layers.pool2d(input=input_NCHW,
pool_size=ksize,
pool_type="avg",
pool_padding=[1, 2, 1, 0],
data_format="NCHW")
# test VALID
out_5 = fluid.layers.pool2d(input=input_NCHW,
pool_size=ksize,
pool_type="avg",
pool_padding="VALID",
data_format="NCHW")
out_6 = fluid.layers.pool2d(input=input_NHWC,
pool_size=ksize,
pool_type="max",
pool_padding="VALID",
data_format="NHWC")
# test SAME
out_7 = fluid.layers.pool2d(input=input_NCHW,
pool_size=[4, 4],
pool_type="avg",
pool_padding="SAME",
data_format="NCHW")
out_8 = fluid.layers.pool2d(input=input_NHWC,
pool_size=[4, 4],
pool_type="max",
pool_padding="SAME",
data_format="NHWC")
# test negetive
out_9 = fluid.layers.pool2d(input=input_NHWC_negetive,
pool_size=ksize,
pool_type="avg",
pool_padding=[0, 0],
data_format="NHWC")
assert out_9.shape == (2, -1, 3, 3)
out_10 = fluid.layers.pool2d(input=input_NCHW_negetive,
pool_size=ksize,
pool_type="avg",
pool_padding=[0, 0],
data_format="NCHW")
assert out_10.shape == (2, 3, -1, -1)
exe = fluid.Executor(place=fluid.MLUPlace(0))
[res_1, res_2, res_3, res_4, res_5, res_6, res_7,
res_8] = exe.run(fluid.default_main_program(),
feed={
"input_NHWC": x_NHWC,
"input_NCHW": x_NCHW,
"input_NHWC_negetive": x_NHWC,
"input_NCHW_negetive": x_NCHW
},
fetch_list=[
out_1, out_2, out_3, out_4, out_5, out_6, out_7,
out_8
])
assert np.allclose(
res_1,
pool2D_forward_naive(x=x_NHWC,
ksize=ksize,
pool_type="max",
strides=[1, 1],
paddings=[1, 1],
data_format="NHWC"))
assert np.allclose(
res_2,
pool2D_forward_naive(x=x_NHWC,
ksize=ksize,
pool_type="avg",
strides=[1, 1],
paddings=[1, 1, 1, 1],
data_format="NHWC"))
assert np.allclose(res_3,
pool2D_forward_naive(x=x_NCHW,
ksize=ksize,
pool_type="avg",
strides=[1, 1],
paddings=[1, 1, 1, 1],
data_format="NCHW"),
rtol=0.07,
atol=1e-05)
assert np.allclose(res_4,
pool2D_forward_naive(x=x_NCHW,
ksize=ksize,
pool_type="avg",
strides=[1, 1],
paddings=[1, 2, 1, 0],
data_format="NCHW"),
rtol=0.07,
atol=1e-05)
# VALID
assert np.allclose(
res_5,
pool2D_forward_naive(
x=x_NCHW,
ksize=ksize,
pool_type="avg",
strides=[1, 1],
paddings=[10, 20], # any ele is ok
padding_algorithm="VALID",
data_format="NCHW"),
rtol=0.07,
atol=1e-05)
assert np.allclose(
res_6,
pool2D_forward_naive(x=x_NHWC,
ksize=ksize,
pool_type="max",
strides=[1, 1],
paddings=[10, 20],
padding_algorithm="VALID",
data_format="NHWC"))
# SAME
assert np.allclose(res_7,
pool2D_forward_naive(x=x_NCHW,
ksize=[4, 4],
pool_type="avg",
strides=[1, 1],
paddings=[10, 20],
padding_algorithm="SAME",
data_format="NCHW"),
rtol=0.07,
atol=1e-05)
assert np.allclose(
res_8,
pool2D_forward_naive(x=x_NHWC,
ksize=[4, 4],
pool_type="max",
strides=[1, 1],
paddings=[10, 20],
padding_algorithm="SAME",
data_format="NHWC"))
