wehub-resource-sync
426 lines
13 KiB
Python
426 lines
13 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
import itertools
|
|
import unittest
|
|
|
|
import numpy as np
|
|
from op_test import get_device_place, get_places, is_custom_device
|
|
|
|
import paddle
|
|
|
|
|
|
def np_pairwise_distance(x, y, p=2.0, epsilon=1e-6, keepdim=False):
|
|
distance = np.linalg.norm(x - y + epsilon, ord=p, axis=-1, keepdims=keepdim)
|
|
return distance
|
|
|
|
|
|
def call_pairwise_distance_layer(x, y, p=2.0, epsilon=1e-6, keepdim='False'):
|
|
pairwise_distance = paddle.nn.PairwiseDistance(
|
|
p=p, epsilon=epsilon, keepdim=keepdim
|
|
)
|
|
distance = pairwise_distance(x=x, y=y)
|
|
return distance
|
|
|
|
|
|
def call_pairwise_distance_layer_compatibility(
|
|
x, y, p=2.0, epsilon=1e-6, keepdim='False'
|
|
):
|
|
pairwise_distance = paddle.nn.PairwiseDistance(
|
|
p=p + 1, eps=epsilon + 1, keepdim=keepdim
|
|
)
|
|
if pairwise_distance.eps != epsilon + 1 or pairwise_distance.norm != p + 1:
|
|
raise ValueError(
|
|
f"eps and norm should be {epsilon + 1}, {p + 1}, but got {pairwise_distance.eps}, {pairwise_distance.norm}"
|
|
)
|
|
|
|
pairwise_distance.eps = epsilon
|
|
pairwise_distance.norm = p
|
|
distance = pairwise_distance(x1=x, x2=y)
|
|
return distance.numpy()
|
|
|
|
|
|
def call_pairwise_distance_functional(
|
|
x, y, p=2.0, epsilon=1e-6, keepdim='False'
|
|
):
|
|
distance = paddle.nn.functional.pairwise_distance(
|
|
x=x, y=y, p=p, epsilon=epsilon, keepdim=keepdim
|
|
)
|
|
return distance
|
|
|
|
|
|
def call_pairwise_distance_functional_compatibility(
|
|
x, y, p=2.0, epsilon=1e-6, keepdim='False'
|
|
):
|
|
distance = paddle.nn.functional.pairwise_distance(
|
|
x1=x, x2=y, p=p, eps=epsilon, keepdim=keepdim
|
|
)
|
|
return distance.numpy()
|
|
|
|
|
|
def test_static(
|
|
place, x_np, y_np, p=2.0, epsilon=1e-6, keepdim=False, functional=False
|
|
):
|
|
prog = paddle.static.Program()
|
|
startup_prog = paddle.static.Program()
|
|
place = get_device_place()
|
|
paddle.enable_static()
|
|
with paddle.static.program_guard(prog, startup_prog):
|
|
x = paddle.static.data(name='x', shape=x_np.shape, dtype=x_np.dtype)
|
|
y = paddle.static.data(name='y', shape=y_np.shape, dtype=x_np.dtype)
|
|
|
|
if functional:
|
|
distance = call_pairwise_distance_functional(
|
|
x=x, y=y, p=p, epsilon=epsilon, keepdim=keepdim
|
|
)
|
|
else:
|
|
distance = call_pairwise_distance_layer(
|
|
x=x, y=y, p=p, epsilon=epsilon, keepdim=keepdim
|
|
)
|
|
exe = paddle.static.Executor(place)
|
|
static_ret = exe.run(
|
|
prog, feed={'x': x_np, 'y': y_np}, fetch_list=[distance]
|
|
)
|
|
static_ret = static_ret[0]
|
|
paddle.disable_static()
|
|
return static_ret
|
|
|
|
|
|
def test_dygraph(
|
|
place, x_np, y_np, p=2.0, epsilon=1e-6, keepdim=False, functional=False
|
|
):
|
|
paddle.disable_static()
|
|
x = paddle.to_tensor(x_np)
|
|
y = paddle.to_tensor(y_np)
|
|
if functional:
|
|
dy_distance = call_pairwise_distance_functional(
|
|
x=x, y=y, p=p, epsilon=epsilon, keepdim=keepdim
|
|
)
|
|
else:
|
|
dy_distance = call_pairwise_distance_layer(
|
|
x=x, y=y, p=p, epsilon=epsilon, keepdim=keepdim
|
|
)
|
|
dygraph_ret = dy_distance.numpy()
|
|
paddle.enable_static()
|
|
return dygraph_ret
|
|
|
|
|
|
class TestPairwiseDistance(unittest.TestCase):
|
|
def test_pairwise_distance(self):
|
|
epsilon = 1e-6
|
|
all_shape = [[5], [100, 100]]
|
|
dtypes = ['float32', 'float64']
|
|
p_list = [-1, 0, 1, 2, np.inf, -np.inf]
|
|
places = get_places()
|
|
keeps = [False, True]
|
|
for place, shape, dtype, p, keepdim in itertools.product(
|
|
places, all_shape, dtypes, p_list, keeps
|
|
):
|
|
x_np = np.random.random(shape).astype(dtype)
|
|
y_np = np.random.random(shape).astype(dtype)
|
|
|
|
dygraph_ret = test_dygraph(
|
|
place,
|
|
x_np,
|
|
y_np,
|
|
p,
|
|
epsilon=epsilon,
|
|
keepdim=keepdim,
|
|
)
|
|
excepted_value = np_pairwise_distance(
|
|
x_np, y_np, p, epsilon=epsilon, keepdim=keepdim
|
|
)
|
|
|
|
self.assertEqual(dygraph_ret.shape, excepted_value.shape)
|
|
|
|
np.testing.assert_allclose(dygraph_ret, excepted_value, rtol=1e-05)
|
|
|
|
dygraph_functional_ret = test_dygraph(
|
|
place,
|
|
x_np,
|
|
y_np,
|
|
p,
|
|
epsilon=epsilon,
|
|
keepdim=keepdim,
|
|
)
|
|
|
|
self.assertEqual(
|
|
dygraph_functional_ret.shape,
|
|
excepted_value.shape,
|
|
)
|
|
|
|
np.testing.assert_allclose(
|
|
dygraph_functional_ret,
|
|
excepted_value,
|
|
rtol=1e-05,
|
|
)
|
|
|
|
def dynamic_and_pir_mode_test():
|
|
static_ret = test_static(
|
|
place,
|
|
x_np,
|
|
y_np,
|
|
p,
|
|
epsilon=epsilon,
|
|
keepdim=keepdim,
|
|
)
|
|
|
|
self.assertEqual(static_ret.shape, excepted_value.shape)
|
|
|
|
np.testing.assert_allclose(
|
|
static_ret, excepted_value, rtol=1e-05
|
|
)
|
|
|
|
static_functional_ret = test_static(
|
|
place,
|
|
x_np,
|
|
y_np,
|
|
p,
|
|
epsilon=epsilon,
|
|
keepdim=keepdim,
|
|
)
|
|
|
|
self.assertEqual(
|
|
static_functional_ret.shape,
|
|
excepted_value.shape,
|
|
)
|
|
|
|
np.testing.assert_allclose(
|
|
static_functional_ret,
|
|
excepted_value,
|
|
rtol=1e-05,
|
|
)
|
|
|
|
dynamic_and_pir_mode_test()
|
|
|
|
def test_pairwise_distance_broadcast_1(self):
|
|
shape_x = [100, 100]
|
|
shape_y = [100, 1]
|
|
epsilon = 1e-6
|
|
keepdim = False
|
|
place = paddle.CPUPlace()
|
|
x_np = np.random.random(shape_x).astype('float32')
|
|
y_np = np.random.random(shape_y).astype('float32')
|
|
|
|
dygraph_ret = test_dygraph(
|
|
place=place, x_np=x_np, y_np=y_np, epsilon=epsilon, keepdim=keepdim
|
|
)
|
|
excepted_value = np_pairwise_distance(
|
|
x_np, y_np, epsilon=epsilon, keepdim=keepdim
|
|
)
|
|
|
|
self.assertEqual(dygraph_ret.shape, excepted_value.shape)
|
|
|
|
np.testing.assert_allclose(dygraph_ret, excepted_value, rtol=1e-05)
|
|
|
|
dygraph_functional_ret = test_dygraph(
|
|
place=place,
|
|
x_np=x_np,
|
|
y_np=y_np,
|
|
epsilon=epsilon,
|
|
keepdim=keepdim,
|
|
functional=True,
|
|
)
|
|
|
|
self.assertEqual(dygraph_functional_ret.shape, excepted_value.shape)
|
|
|
|
np.testing.assert_allclose(
|
|
dygraph_functional_ret, excepted_value, rtol=1e-05
|
|
)
|
|
|
|
def dynamic_and_pir_mode_test():
|
|
static_ret = test_static(
|
|
place=place,
|
|
x_np=x_np,
|
|
y_np=y_np,
|
|
epsilon=epsilon,
|
|
keepdim=keepdim,
|
|
)
|
|
|
|
self.assertEqual(static_ret.shape, excepted_value.shape)
|
|
|
|
np.testing.assert_allclose(static_ret, excepted_value, rtol=1e-05)
|
|
static_functional_ret = test_static(
|
|
place=place,
|
|
x_np=x_np,
|
|
y_np=y_np,
|
|
epsilon=epsilon,
|
|
keepdim=keepdim,
|
|
functional=True,
|
|
)
|
|
|
|
self.assertEqual(static_functional_ret.shape, excepted_value.shape)
|
|
np.testing.assert_allclose(
|
|
static_functional_ret, excepted_value, rtol=1e-05
|
|
)
|
|
|
|
dynamic_and_pir_mode_test()
|
|
|
|
def test_pairwise_distance_broadcast_2(self):
|
|
shape_x = [100, 100]
|
|
shape_y = [100]
|
|
epsilon = 1e-6
|
|
keepdim = False
|
|
place = paddle.CPUPlace()
|
|
x_np = np.random.random(shape_x).astype('float32')
|
|
y_np = np.random.random(shape_y).astype('float32')
|
|
|
|
dygraph_ret = test_dygraph(
|
|
place=place, x_np=x_np, y_np=y_np, epsilon=epsilon, keepdim=keepdim
|
|
)
|
|
|
|
excepted_value = np_pairwise_distance(
|
|
x_np, y_np, epsilon=epsilon, keepdim=keepdim
|
|
)
|
|
|
|
self.assertEqual(dygraph_ret.shape, excepted_value.shape)
|
|
|
|
np.testing.assert_allclose(dygraph_ret, excepted_value, rtol=1e-05)
|
|
|
|
dygraph_functional_ret = test_dygraph(
|
|
place=place,
|
|
x_np=x_np,
|
|
y_np=y_np,
|
|
epsilon=epsilon,
|
|
keepdim=keepdim,
|
|
functional=True,
|
|
)
|
|
|
|
self.assertEqual(dygraph_functional_ret.shape, excepted_value.shape)
|
|
|
|
np.testing.assert_allclose(
|
|
dygraph_functional_ret, excepted_value, rtol=1e-05
|
|
)
|
|
|
|
def dynamic_and_pir_mode_test():
|
|
static_ret = test_static(
|
|
place=place,
|
|
x_np=x_np,
|
|
y_np=y_np,
|
|
epsilon=epsilon,
|
|
keepdim=keepdim,
|
|
)
|
|
|
|
self.assertEqual(static_ret.shape, excepted_value.shape)
|
|
|
|
np.testing.assert_allclose(static_ret, excepted_value, rtol=1e-05)
|
|
|
|
static_functional_ret = test_static(
|
|
place=place,
|
|
x_np=x_np,
|
|
y_np=y_np,
|
|
epsilon=epsilon,
|
|
keepdim=keepdim,
|
|
functional=True,
|
|
)
|
|
|
|
self.assertEqual(static_functional_ret.shape, excepted_value.shape)
|
|
|
|
np.testing.assert_allclose(
|
|
static_functional_ret, excepted_value, rtol=1e-05
|
|
)
|
|
|
|
dynamic_and_pir_mode_test()
|
|
|
|
def test_pairwise_distance_fp16(self):
|
|
shape = [100, 100]
|
|
if not (paddle.device.is_compiled_with_cuda() or is_custom_device()):
|
|
return
|
|
place = get_device_place()
|
|
x_np = np.random.random(shape).astype('float16')
|
|
y_np = np.random.random(shape).astype('float16')
|
|
static_ret = test_static(place, x_np, y_np)
|
|
|
|
def test_pairwise_distance_compatibility(self):
|
|
shape = [10, 10]
|
|
epsilon = 1e-6
|
|
keepdim = False
|
|
place = paddle.CPUPlace()
|
|
x_np = np.random.random(shape).astype('float32')
|
|
y_np = np.random.random(shape).astype('float32')
|
|
|
|
dygraph_ret = call_pairwise_distance_layer_compatibility(
|
|
x=paddle.to_tensor(x_np),
|
|
y=paddle.to_tensor(y_np),
|
|
epsilon=epsilon,
|
|
keepdim=keepdim,
|
|
)
|
|
excepted_value = np_pairwise_distance(
|
|
x_np, y_np, epsilon=epsilon, keepdim=keepdim
|
|
)
|
|
|
|
self.assertEqual(dygraph_ret.shape, excepted_value.shape)
|
|
|
|
np.testing.assert_allclose(dygraph_ret, excepted_value, rtol=1e-05)
|
|
|
|
def test_pairwise_distance_function_compatibility(self):
|
|
shape = [10, 10]
|
|
epsilon = 1e-6
|
|
keepdim = False
|
|
place = paddle.CPUPlace()
|
|
x_np = np.random.random(shape).astype('float32')
|
|
y_np = np.random.random(shape).astype('float32')
|
|
|
|
dygraph_ret = call_pairwise_distance_functional_compatibility(
|
|
x=paddle.to_tensor(x_np),
|
|
y=paddle.to_tensor(y_np),
|
|
epsilon=epsilon,
|
|
keepdim=keepdim,
|
|
)
|
|
excepted_value = np_pairwise_distance(
|
|
x_np, y_np, epsilon=epsilon, keepdim=keepdim
|
|
)
|
|
|
|
self.assertEqual(dygraph_ret.shape, excepted_value.shape)
|
|
|
|
np.testing.assert_allclose(dygraph_ret, excepted_value, rtol=1e-05)
|
|
|
|
|
|
class TestPairwiseDistance_ZeroSize(unittest.TestCase):
|
|
def test_pairwise_distance(self):
|
|
epsilon = 1e-6
|
|
all_shape = [[0], [100, 0]]
|
|
dtype = 'float32'
|
|
p = 0
|
|
places = get_places()
|
|
keeps = [False, True]
|
|
for place in places:
|
|
for shape in all_shape:
|
|
for keepdim in keeps:
|
|
x_np = np.random.random(shape).astype(dtype)
|
|
y_np = np.random.random(shape).astype(dtype)
|
|
|
|
excepted_value = np_pairwise_distance(
|
|
x_np, y_np, p, epsilon=epsilon, keepdim=keepdim
|
|
)
|
|
paddle.disable_static(place)
|
|
x = paddle.to_tensor(x_np)
|
|
x.stop_gradient = False
|
|
y = paddle.to_tensor(y_np)
|
|
ret = call_pairwise_distance_functional(
|
|
x=x, y=y, p=p, epsilon=epsilon, keepdim=keepdim
|
|
)
|
|
np.testing.assert_allclose(
|
|
ret.numpy(),
|
|
excepted_value,
|
|
rtol=1e-05,
|
|
)
|
|
loss = paddle.sum(ret)
|
|
loss.backward()
|
|
np.testing.assert_allclose(x.grad.shape, x.shape)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|