wehub-resource-sync
507 lines
17 KiB
Python
507 lines
17 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 unittest
|
|
|
|
import numpy as np
|
|
from op_test import check_out_dtype, get_device_place, is_custom_device
|
|
|
|
import paddle
|
|
import paddle.nn.functional as F
|
|
from paddle import base
|
|
from paddle.base import core
|
|
|
|
|
|
def adaptive_start_index(index, input_size, output_size):
|
|
return int(np.floor(index * input_size / output_size))
|
|
|
|
|
|
def adaptive_end_index(index, input_size, output_size):
|
|
return int(np.ceil((index + 1) * input_size / output_size))
|
|
|
|
|
|
def adaptive_pool2d_forward(
|
|
x, output_size, data_format='NCHW', pool_type="max"
|
|
):
|
|
N = x.shape[0]
|
|
C, H, W = (
|
|
[x.shape[1], x.shape[2], x.shape[3]]
|
|
if data_format == 'NCHW'
|
|
else [x.shape[3], x.shape[1], x.shape[2]]
|
|
)
|
|
|
|
if isinstance(output_size, int) or output_size is None:
|
|
H_out = output_size
|
|
W_out = output_size
|
|
output_size = [H_out, W_out]
|
|
else:
|
|
H_out, W_out = output_size
|
|
|
|
if output_size[0] is None:
|
|
output_size[0] = H
|
|
H_out = H
|
|
if output_size[1] is None:
|
|
output_size[1] = W
|
|
W_out = W
|
|
|
|
out = (
|
|
np.zeros((N, C, H_out, W_out))
|
|
if data_format == 'NCHW'
|
|
else np.zeros((N, H_out, W_out, C))
|
|
)
|
|
if x.size == 0:
|
|
return out
|
|
|
|
for i in range(H_out):
|
|
in_h_start = adaptive_start_index(i, H, output_size[0])
|
|
in_h_end = adaptive_end_index(i, H, output_size[0])
|
|
|
|
for j in range(W_out):
|
|
in_w_start = adaptive_start_index(j, W, output_size[1])
|
|
in_w_end = adaptive_end_index(j, W, output_size[1])
|
|
|
|
if data_format == 'NCHW':
|
|
x_masked = x[:, :, in_h_start:in_h_end, in_w_start:in_w_end]
|
|
if pool_type == 'avg':
|
|
field_size = (in_h_end - in_h_start) * (
|
|
in_w_end - in_w_start
|
|
)
|
|
out[:, :, i, j] = np.sum(x_masked, axis=(2, 3)) / field_size
|
|
elif pool_type == 'max':
|
|
out[:, :, i, j] = np.max(x_masked, axis=(2, 3))
|
|
elif data_format == 'NHWC':
|
|
x_masked = x[:, in_h_start:in_h_end, in_w_start:in_w_end, :]
|
|
if pool_type == 'avg':
|
|
field_size = (in_h_end - in_h_start) * (
|
|
in_w_end - in_w_start
|
|
)
|
|
out[:, i, j, :] = np.sum(x_masked, axis=(1, 2)) / field_size
|
|
elif pool_type == 'max':
|
|
out[:, i, j, :] = np.max(x_masked, axis=(1, 2))
|
|
return out
|
|
|
|
|
|
class TestAdaptiveMaxPool2DAPI(unittest.TestCase):
|
|
def setUp(self):
|
|
self.x_np = np.random.random([2, 3, 7, 7]).astype("float32")
|
|
self.res_1_np = adaptive_pool2d_forward(
|
|
x=self.x_np, output_size=[3, 3], pool_type="max"
|
|
)
|
|
|
|
self.res_2_np = adaptive_pool2d_forward(
|
|
x=self.x_np, output_size=5, pool_type="max"
|
|
)
|
|
|
|
self.res_3_np = adaptive_pool2d_forward(
|
|
x=self.x_np, output_size=[2, 5], pool_type="max"
|
|
)
|
|
"""
|
|
self.res_4_np = adaptive_pool2d_forward(
|
|
x=self.x_np,
|
|
output_size=[3, 3],
|
|
pool_type="max",
|
|
data_format="NHWC")
|
|
"""
|
|
self.res_5_np = adaptive_pool2d_forward(
|
|
x=self.x_np, output_size=[None, 3], pool_type="max"
|
|
)
|
|
|
|
def test_static_graph(self):
|
|
for use_cuda in (
|
|
[False, True]
|
|
if (core.is_compiled_with_cuda() or is_custom_device())
|
|
else [False]
|
|
):
|
|
place = get_device_place() if use_cuda else paddle.CPUPlace()
|
|
paddle.enable_static()
|
|
x = paddle.static.data(
|
|
name="x", shape=[2, 3, 7, 7], dtype="float32"
|
|
)
|
|
|
|
out_1 = paddle.nn.functional.adaptive_max_pool2d(
|
|
x=x, output_size=[3, 3]
|
|
)
|
|
|
|
out_2 = paddle.nn.functional.adaptive_max_pool2d(x=x, output_size=5)
|
|
|
|
out_3 = paddle.nn.functional.adaptive_max_pool2d(
|
|
x=x, output_size=[2, 5]
|
|
)
|
|
|
|
# out_4 = paddle.nn.functional.adaptive_max_pool2d(
|
|
# x=x, output_size=[3, 3], data_format="NHWC")
|
|
|
|
out_5 = paddle.nn.functional.adaptive_max_pool2d(
|
|
x=x, output_size=[None, 3]
|
|
)
|
|
|
|
exe = paddle.static.Executor(place=place)
|
|
[res_1, res_2, res_3, res_5] = exe.run(
|
|
base.default_main_program(),
|
|
feed={"x": self.x_np},
|
|
fetch_list=[out_1, out_2, out_3, out_5],
|
|
)
|
|
|
|
np.testing.assert_allclose(res_1, self.res_1_np)
|
|
|
|
np.testing.assert_allclose(res_2, self.res_2_np)
|
|
|
|
np.testing.assert_allclose(res_3, self.res_3_np)
|
|
|
|
# np.testing.assert_allclose(res_4, self.res_4_np)
|
|
|
|
np.testing.assert_allclose(res_5, self.res_5_np)
|
|
|
|
def test_static_graph_return_mask(self):
|
|
for use_cuda in (
|
|
[False, True]
|
|
if (core.is_compiled_with_cuda() or is_custom_device())
|
|
else [False]
|
|
):
|
|
place = get_device_place() if use_cuda else paddle.CPUPlace()
|
|
paddle.enable_static()
|
|
x = paddle.static.data(
|
|
name="x", shape=[2, 3, 7, 7], dtype="float32"
|
|
)
|
|
|
|
out_1 = paddle.nn.functional.adaptive_max_pool2d(
|
|
x=x, output_size=[3, 3], return_mask=True
|
|
)
|
|
|
|
out_2 = paddle.nn.functional.adaptive_max_pool2d(
|
|
x=x, output_size=5, return_mask=True
|
|
)
|
|
|
|
out_3 = paddle.nn.functional.adaptive_max_pool2d(
|
|
x=x, output_size=[2, 5], return_mask=True
|
|
)
|
|
|
|
# out_4 = paddle.nn.functional.adaptive_max_pool2d(
|
|
# x=x, output_size=[3, 3], data_format="NHWC"), return_mask=True
|
|
|
|
out_5 = paddle.nn.functional.adaptive_max_pool2d(
|
|
x=x, output_size=[None, 3], return_mask=True
|
|
)
|
|
|
|
exe = paddle.static.Executor(place=place)
|
|
[
|
|
res_1,
|
|
mask_1,
|
|
res_2,
|
|
mask_2,
|
|
res_3,
|
|
mask_3,
|
|
res_5,
|
|
mask_5,
|
|
] = exe.run(
|
|
base.default_main_program(),
|
|
feed={"x": self.x_np},
|
|
fetch_list=[out_1, out_2, out_3, out_5],
|
|
)
|
|
|
|
self.assertEqual(res_1.shape, mask_1.shape)
|
|
|
|
self.assertEqual(res_2.shape, mask_2.shape)
|
|
|
|
self.assertEqual(res_3.shape, mask_3.shape)
|
|
|
|
# self.assertEqual(res_4.shape, mask_4.shape)
|
|
|
|
self.assertEqual(res_5.shape, mask_5.shape)
|
|
|
|
def test_dynamic_graph(self):
|
|
for use_cuda in (
|
|
[False, True]
|
|
if (core.is_compiled_with_cuda() or is_custom_device())
|
|
else [False]
|
|
):
|
|
place = get_device_place() if use_cuda else paddle.CPUPlace()
|
|
paddle.disable_static(place=place)
|
|
x = paddle.to_tensor(self.x_np)
|
|
|
|
out_1 = paddle.nn.functional.adaptive_max_pool2d(
|
|
x=x, return_mask=False, output_size=[3, 3]
|
|
)
|
|
|
|
out_2 = paddle.nn.functional.adaptive_max_pool2d(x=x, output_size=5)
|
|
|
|
out_3 = paddle.nn.functional.adaptive_max_pool2d(
|
|
x=x, output_size=[2, 5]
|
|
)
|
|
|
|
# out_4 = paddle.nn.functional.adaptive_max_pool2d(
|
|
# x=x, output_size=[3, 3], data_format="NHWC")
|
|
|
|
out_5 = paddle.nn.functional.adaptive_max_pool2d(
|
|
x=x, output_size=[None, 3]
|
|
)
|
|
|
|
# test @param_two_alias(["x", "input"], ["return_mask", "return_indices"])
|
|
out_6 = paddle.nn.functional.adaptive_max_pool2d(
|
|
input=x, output_size=[None, 3], return_indices=False
|
|
)
|
|
np.testing.assert_allclose(out_1.numpy(), self.res_1_np)
|
|
|
|
np.testing.assert_allclose(out_2.numpy(), self.res_2_np)
|
|
|
|
np.testing.assert_allclose(out_3.numpy(), self.res_3_np)
|
|
|
|
# np.testing.assert_allclose(out_4.numpy(), self.res_4_np)
|
|
|
|
np.testing.assert_allclose(out_5.numpy(), self.res_5_np)
|
|
np.testing.assert_allclose(out_6.numpy(), self.res_5_np)
|
|
|
|
|
|
class TestAdaptiveMaxPool2DClassAPI(unittest.TestCase):
|
|
def setUp(self):
|
|
self.x_np = np.random.random([2, 3, 7, 7]).astype("float32")
|
|
self.res_1_np = adaptive_pool2d_forward(
|
|
x=self.x_np, output_size=[3, 3], pool_type="max"
|
|
)
|
|
|
|
self.res_2_np = adaptive_pool2d_forward(
|
|
x=self.x_np, output_size=5, pool_type="max"
|
|
)
|
|
|
|
self.res_3_np = adaptive_pool2d_forward(
|
|
x=self.x_np, output_size=[2, 5], pool_type="max"
|
|
)
|
|
|
|
# self.res_4_np = adaptive_pool2d_forward(
|
|
# x=self.x_np,
|
|
# output_size=[3, 3],
|
|
# pool_type="max",
|
|
# data_format="NHWC")
|
|
|
|
self.res_5_np = adaptive_pool2d_forward(
|
|
x=self.x_np, output_size=[None, 3], pool_type="max"
|
|
)
|
|
|
|
def test_static_graph(self):
|
|
for use_cuda in (
|
|
[False, True]
|
|
if (core.is_compiled_with_cuda() or is_custom_device())
|
|
else [False]
|
|
):
|
|
with paddle.static.program_guard(
|
|
paddle.static.Program(), paddle.static.Program()
|
|
):
|
|
place = get_device_place() if use_cuda else paddle.CPUPlace()
|
|
paddle.enable_static()
|
|
x = paddle.static.data(
|
|
name="x", shape=[2, 3, 7, 7], dtype="float32"
|
|
)
|
|
|
|
adaptive_max_pool = paddle.nn.AdaptiveMaxPool2D(
|
|
output_size=[3, 3]
|
|
)
|
|
out_1 = adaptive_max_pool(x=x)
|
|
|
|
adaptive_max_pool = paddle.nn.AdaptiveMaxPool2D(output_size=5)
|
|
out_2 = adaptive_max_pool(x=x)
|
|
|
|
adaptive_max_pool = paddle.nn.AdaptiveMaxPool2D(
|
|
output_size=[2, 5]
|
|
)
|
|
out_3 = adaptive_max_pool(x=x)
|
|
|
|
# adaptive_max_pool = paddle.nn.AdaptiveMaxPool2D(
|
|
# output_size=[3, 3], data_format="NHWC")
|
|
# out_4 = adaptive_max_pool(x=x)
|
|
|
|
adaptive_max_pool = paddle.nn.AdaptiveMaxPool2D(
|
|
output_size=[None, 3]
|
|
)
|
|
out_5 = adaptive_max_pool(x=x)
|
|
|
|
exe = paddle.static.Executor(place=place)
|
|
[res_1, res_2, res_3, res_5] = exe.run(
|
|
base.default_main_program(),
|
|
feed={"x": self.x_np},
|
|
fetch_list=[out_1, out_2, out_3, out_5],
|
|
)
|
|
|
|
np.testing.assert_allclose(res_1, self.res_1_np)
|
|
|
|
np.testing.assert_allclose(res_2, self.res_2_np)
|
|
|
|
np.testing.assert_allclose(res_3, self.res_3_np)
|
|
|
|
# np.testing.assert_allclose(res_4, self.res_4_np)
|
|
|
|
np.testing.assert_allclose(res_5, self.res_5_np)
|
|
|
|
def test_dynamic_graph(self):
|
|
for use_cuda in (
|
|
[False, True]
|
|
if (core.is_compiled_with_cuda() or is_custom_device())
|
|
else [False]
|
|
):
|
|
place = get_device_place() if use_cuda else paddle.CPUPlace()
|
|
paddle.disable_static(place=place)
|
|
x = paddle.to_tensor(self.x_np)
|
|
|
|
adaptive_max_pool = paddle.nn.AdaptiveMaxPool2D(output_size=[3, 3])
|
|
out_1 = adaptive_max_pool(x=x)
|
|
|
|
adaptive_max_pool = paddle.nn.AdaptiveMaxPool2D(output_size=5)
|
|
out_2 = adaptive_max_pool(x=x)
|
|
|
|
adaptive_max_pool = paddle.nn.AdaptiveMaxPool2D(output_size=[2, 5])
|
|
out_3 = adaptive_max_pool(x=x)
|
|
|
|
# adaptive_max_pool = paddle.nn.AdaptiveMaxPool2D(
|
|
# output_size=[3, 3], data_format="NHWC")
|
|
# out_4 = adaptive_max_pool(x=x)
|
|
|
|
adaptive_max_pool = paddle.nn.AdaptiveMaxPool2D(
|
|
output_size=[None, 3]
|
|
)
|
|
out_5 = adaptive_max_pool(x=x)
|
|
|
|
adaptive_max_pool = paddle.nn.AdaptiveMaxPool2d(
|
|
output_size=[None, 3], return_indices=True
|
|
)
|
|
self.assertEqual(adaptive_max_pool.return_indices, True)
|
|
adaptive_max_pool.return_indices = False
|
|
out_6 = adaptive_max_pool(input=x)
|
|
|
|
np.testing.assert_allclose(out_1.numpy(), self.res_1_np)
|
|
|
|
np.testing.assert_allclose(out_2.numpy(), self.res_2_np)
|
|
|
|
np.testing.assert_allclose(out_3.numpy(), self.res_3_np)
|
|
|
|
# np.testing.assert_allclose(out_4.numpy(), self.res_4_np)
|
|
|
|
np.testing.assert_allclose(out_5.numpy(), self.res_5_np)
|
|
|
|
np.testing.assert_allclose(out_6.numpy(), self.res_5_np)
|
|
|
|
|
|
class TestOutDtype(unittest.TestCase):
|
|
def test_max_pool(self):
|
|
api_fn = F.adaptive_max_pool2d
|
|
shape = [1, 3, 32, 32]
|
|
check_out_dtype(
|
|
api_fn,
|
|
in_specs=[(shape,)],
|
|
expect_dtypes=['float32', 'float64'],
|
|
output_size=16,
|
|
)
|
|
|
|
|
|
class TestAdaptiveMaxPool2D_ZeroSize(unittest.TestCase):
|
|
def setUp(self):
|
|
self.x_np = np.random.random([0, 3, 7, 7]).astype("float32")
|
|
self.res_1_np = adaptive_pool2d_forward(
|
|
x=self.x_np, output_size=[3, 3], pool_type="max"
|
|
)
|
|
|
|
def test_static_graph(self):
|
|
for use_cuda in (
|
|
[False, True]
|
|
if (core.is_compiled_with_cuda() or is_custom_device())
|
|
else [False]
|
|
):
|
|
place = get_device_place() if use_cuda else paddle.CPUPlace()
|
|
paddle.enable_static()
|
|
x = paddle.static.data(
|
|
name="x", shape=[0, 3, 7, 7], dtype="float32"
|
|
)
|
|
|
|
out_1 = paddle.nn.functional.adaptive_max_pool2d(
|
|
x=x, output_size=[3, 3]
|
|
)
|
|
|
|
exe = paddle.static.Executor(place=place)
|
|
[
|
|
res_1,
|
|
] = exe.run(
|
|
base.default_main_program(),
|
|
feed={"x": self.x_np},
|
|
fetch_list=[
|
|
out_1,
|
|
],
|
|
)
|
|
|
|
np.testing.assert_allclose(res_1, self.res_1_np)
|
|
|
|
def test_static_graph_return_mask(self):
|
|
for use_cuda in (
|
|
[False, True]
|
|
if (core.is_compiled_with_cuda() or is_custom_device())
|
|
else [False]
|
|
):
|
|
place = get_device_place() if use_cuda else paddle.CPUPlace()
|
|
paddle.enable_static()
|
|
x = paddle.static.data(
|
|
name="x", shape=[0, 3, 7, 7], dtype="float32"
|
|
)
|
|
|
|
out_1 = paddle.nn.functional.adaptive_max_pool2d(
|
|
x=x, output_size=[3, 3], return_mask=True
|
|
)
|
|
|
|
exe = paddle.static.Executor(place=place)
|
|
[
|
|
res_1,
|
|
mask_1,
|
|
] = exe.run(
|
|
base.default_main_program(),
|
|
feed={"x": self.x_np},
|
|
fetch_list=[
|
|
out_1,
|
|
],
|
|
)
|
|
|
|
self.assertEqual(res_1.shape, mask_1.shape)
|
|
|
|
def test_dynamic_graph(self):
|
|
for use_cuda in (
|
|
[False, True]
|
|
if (core.is_compiled_with_cuda() or is_custom_device())
|
|
else [False]
|
|
):
|
|
place = get_device_place() if use_cuda else paddle.CPUPlace()
|
|
paddle.disable_static(place=place)
|
|
x = paddle.to_tensor(self.x_np)
|
|
|
|
out_1 = paddle.nn.functional.adaptive_max_pool2d(
|
|
x=x, return_mask=False, output_size=[3, 3]
|
|
)
|
|
|
|
np.testing.assert_allclose(out_1.numpy(), self.res_1_np)
|
|
|
|
def test_grad(self):
|
|
for use_cuda in (
|
|
[False, True]
|
|
if (core.is_compiled_with_cuda() or is_custom_device())
|
|
else [False]
|
|
):
|
|
place = get_device_place() if use_cuda else paddle.CPUPlace()
|
|
paddle.disable_static(place=place)
|
|
x = paddle.to_tensor(self.x_np)
|
|
x.stop_gradient = False
|
|
out_1 = paddle.nn.functional.adaptive_max_pool2d(
|
|
x=x, return_mask=False, output_size=[3, 3]
|
|
)
|
|
loss = paddle.sum(out_1)
|
|
loss.backward()
|
|
np.testing.assert_allclose(x.grad.shape, x.shape)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|