You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
301 lines
11 KiB
301 lines
11 KiB
# 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.
|
|
|
|
from __future__ import print_function
|
|
from __future__ import division
|
|
|
|
import unittest
|
|
import numpy as np
|
|
|
|
import paddle.fluid.core as core
|
|
from op_test import OpTest
|
|
import paddle
|
|
import paddle.fluid as fluid
|
|
from paddle.fluid import Program, program_guard
|
|
|
|
|
|
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_pool3d_forward(x,
|
|
output_size,
|
|
adaptive=True,
|
|
data_format='NCDHW',
|
|
pool_type='avg'):
|
|
|
|
N = x.shape[0]
|
|
C, D, H, W = [x.shape[1], x.shape[2], x.shape[3], x.shape[4]] \
|
|
if data_format == 'NCDHW' else [x.shape[4], x.shape[1], x.shape[2],x.shape[3]]
|
|
|
|
if (isinstance(output_size, int) or output_size == None):
|
|
H_out = output_size
|
|
W_out = output_size
|
|
D_out = output_size
|
|
output_size = [D_out, H_out, W_out]
|
|
else:
|
|
D_out, H_out, W_out = output_size
|
|
|
|
if output_size[0] == None:
|
|
output_size[0] = D
|
|
D_out = D
|
|
if output_size[1] == None:
|
|
output_size[1] = H
|
|
H_out = H
|
|
if output_size[2] == None:
|
|
output_size[2] = W
|
|
W_out = W
|
|
|
|
out = np.zeros((N, C, D_out, H_out, W_out)) if data_format=='NCDHW' \
|
|
else np.zeros((N, D_out, H_out, W_out, C))
|
|
for k in range(D_out):
|
|
d_start = adaptive_start_index(k, D, output_size[0])
|
|
d_end = adaptive_end_index(k, D, output_size[0])
|
|
|
|
for i in range(H_out):
|
|
h_start = adaptive_start_index(i, H, output_size[1])
|
|
h_end = adaptive_end_index(i, H, output_size[1])
|
|
|
|
for j in range(W_out):
|
|
w_start = adaptive_start_index(j, W, output_size[2])
|
|
w_end = adaptive_end_index(j, W, output_size[2])
|
|
|
|
if data_format == 'NCDHW':
|
|
x_masked = x[:, :, d_start:d_end, h_start:h_end, w_start:
|
|
w_end]
|
|
if pool_type == 'avg':
|
|
field_size = (d_end - d_start) * (h_end - h_start) * (
|
|
w_end - w_start)
|
|
out[:, :, k, i, j] = np.sum(x_masked,
|
|
axis=(2, 3, 4)) / field_size
|
|
elif pool_type == 'max':
|
|
out[:, :, k, i, j] = np.max(x_masked, axis=(2, 3, 4))
|
|
|
|
elif data_format == 'NDHWC':
|
|
x_masked = x[:, d_start:d_end, h_start:h_end, w_start:
|
|
w_end, :]
|
|
if pool_type == 'avg':
|
|
field_size = (d_end - d_start) * (h_end - h_start) * (
|
|
w_end - w_start)
|
|
out[:, k, i, j, :] = np.sum(x_masked,
|
|
axis=(1, 2, 3)) / field_size
|
|
elif pool_type == 'max':
|
|
out[:, k, i, j, :] = np.max(x_masked, axis=(1, 2, 3))
|
|
return out
|
|
|
|
|
|
class TestAdaptiveAvgPool3DAPI(unittest.TestCase):
|
|
def setUp(self):
|
|
self.x_np = np.random.random([2, 3, 5, 7, 7]).astype("float32")
|
|
self.res_1_np = adaptive_pool3d_forward(
|
|
x=self.x_np, output_size=[3, 3, 3], pool_type="avg")
|
|
|
|
self.res_2_np = adaptive_pool3d_forward(
|
|
x=self.x_np, output_size=5, pool_type="avg")
|
|
|
|
self.res_3_np = adaptive_pool3d_forward(
|
|
x=self.x_np, output_size=[2, 3, 5], pool_type="avg")
|
|
|
|
self.res_4_np = adaptive_pool3d_forward(
|
|
x=self.x_np,
|
|
output_size=[3, 3, 3],
|
|
pool_type="avg",
|
|
data_format="NDHWC")
|
|
|
|
self.res_5_np = adaptive_pool3d_forward(
|
|
x=self.x_np, output_size=[None, 3, None], pool_type="avg")
|
|
|
|
def test_static_graph(self):
|
|
for use_cuda in ([False, True]
|
|
if core.is_compiled_with_cuda() else [False]):
|
|
place = paddle.CUDAPlace(0) if use_cuda else paddle.CPUPlace()
|
|
paddle.enable_static()
|
|
x = paddle.fluid.data(
|
|
name="x", shape=[2, 3, 5, 7, 7], dtype="float32")
|
|
|
|
out_1 = paddle.nn.functional.adaptive_avg_pool3d(
|
|
x=x, output_size=[3, 3, 3])
|
|
|
|
out_2 = paddle.nn.functional.adaptive_avg_pool3d(x=x, output_size=5)
|
|
|
|
out_3 = paddle.nn.functional.adaptive_avg_pool3d(
|
|
x=x, output_size=[2, 3, 5])
|
|
|
|
out_4 = paddle.nn.functional.adaptive_avg_pool3d(
|
|
x=x, output_size=[3, 3, 3], data_format="NDHWC")
|
|
|
|
out_5 = paddle.nn.functional.adaptive_avg_pool3d(
|
|
x=x, output_size=[None, 3, None])
|
|
|
|
exe = paddle.static.Executor(place=place)
|
|
[res_1, res_2, res_3, res_4, res_5] = exe.run(
|
|
fluid.default_main_program(),
|
|
feed={"x": self.x_np},
|
|
fetch_list=[out_1, out_2, out_3, out_4, out_5])
|
|
|
|
assert np.allclose(res_1, self.res_1_np)
|
|
|
|
assert np.allclose(res_2, self.res_2_np)
|
|
|
|
assert np.allclose(res_3, self.res_3_np)
|
|
|
|
assert np.allclose(res_4, self.res_4_np)
|
|
|
|
assert np.allclose(res_5, self.res_5_np)
|
|
|
|
def test_dynamic_graph(self):
|
|
for use_cuda in ([False, True]
|
|
if core.is_compiled_with_cuda() else [False]):
|
|
place = paddle.CUDAPlace(0) if use_cuda else paddle.CPUPlace()
|
|
paddle.disable_static(place=place)
|
|
x = paddle.to_tensor(self.x_np)
|
|
|
|
out_1 = paddle.nn.functional.adaptive_avg_pool3d(
|
|
x=x, output_size=[3, 3, 3])
|
|
|
|
out_2 = paddle.nn.functional.adaptive_avg_pool3d(x=x, output_size=5)
|
|
|
|
out_3 = paddle.nn.functional.adaptive_avg_pool3d(
|
|
x=x, output_size=[2, 3, 5])
|
|
|
|
out_4 = paddle.nn.functional.adaptive_avg_pool3d(
|
|
x=x, output_size=[3, 3, 3], data_format="NDHWC")
|
|
|
|
out_5 = paddle.nn.functional.adaptive_avg_pool3d(
|
|
x=x, output_size=[None, 3, None])
|
|
|
|
out_6 = paddle.nn.functional.interpolate(
|
|
x=x, mode="area", size=[2, 3, 5])
|
|
|
|
assert np.allclose(out_1.numpy(), self.res_1_np)
|
|
|
|
assert np.allclose(out_2.numpy(), self.res_2_np)
|
|
|
|
assert np.allclose(out_3.numpy(), self.res_3_np)
|
|
|
|
assert np.allclose(out_4.numpy(), self.res_4_np)
|
|
|
|
assert np.allclose(out_5.numpy(), self.res_5_np)
|
|
|
|
assert np.allclose(out_6.numpy(), self.res_3_np)
|
|
|
|
|
|
class TestAdaptiveAvgPool3DClassAPI(unittest.TestCase):
|
|
def setUp(self):
|
|
self.x_np = np.random.random([2, 3, 5, 7, 7]).astype("float32")
|
|
self.res_1_np = adaptive_pool3d_forward(
|
|
x=self.x_np, output_size=[3, 3, 3], pool_type="avg")
|
|
|
|
self.res_2_np = adaptive_pool3d_forward(
|
|
x=self.x_np, output_size=5, pool_type="avg")
|
|
|
|
self.res_3_np = adaptive_pool3d_forward(
|
|
x=self.x_np, output_size=[2, 3, 5], pool_type="avg")
|
|
|
|
self.res_4_np = adaptive_pool3d_forward(
|
|
x=self.x_np,
|
|
output_size=[3, 3, 3],
|
|
pool_type="avg",
|
|
data_format="NDHWC")
|
|
|
|
self.res_5_np = adaptive_pool3d_forward(
|
|
x=self.x_np, output_size=[None, 3, None], pool_type="avg")
|
|
|
|
def test_static_graph(self):
|
|
for use_cuda in ([False, True]
|
|
if core.is_compiled_with_cuda() else [False]):
|
|
place = paddle.CUDAPlace(0) if use_cuda else paddle.CPUPlace()
|
|
paddle.enable_static()
|
|
x = paddle.fluid.data(
|
|
name="x", shape=[2, 3, 5, 7, 7], dtype="float32")
|
|
|
|
adaptive_avg_pool = paddle.nn.AdaptiveAvgPool3D(
|
|
output_size=[3, 3, 3])
|
|
out_1 = adaptive_avg_pool(x=x)
|
|
|
|
adaptive_avg_pool = paddle.nn.AdaptiveAvgPool3D(output_size=5)
|
|
out_2 = adaptive_avg_pool(x=x)
|
|
|
|
adaptive_avg_pool = paddle.nn.AdaptiveAvgPool3D(
|
|
output_size=[2, 3, 5])
|
|
out_3 = adaptive_avg_pool(x=x)
|
|
|
|
adaptive_avg_pool = paddle.nn.AdaptiveAvgPool3D(
|
|
output_size=[3, 3, 3], data_format="NDHWC")
|
|
out_4 = adaptive_avg_pool(x=x)
|
|
|
|
adaptive_avg_pool = paddle.nn.AdaptiveAvgPool3D(
|
|
output_size=[None, 3, None])
|
|
out_5 = adaptive_avg_pool(x=x)
|
|
|
|
exe = paddle.static.Executor(place=place)
|
|
[res_1, res_2, res_3, res_4, res_5] = exe.run(
|
|
fluid.default_main_program(),
|
|
feed={"x": self.x_np},
|
|
fetch_list=[out_1, out_2, out_3, out_4, out_5])
|
|
|
|
assert np.allclose(res_1, self.res_1_np)
|
|
|
|
assert np.allclose(res_2, self.res_2_np)
|
|
|
|
assert np.allclose(res_3, self.res_3_np)
|
|
|
|
assert np.allclose(res_4, self.res_4_np)
|
|
|
|
assert np.allclose(res_5, self.res_5_np)
|
|
|
|
def test_dynamic_graph(self):
|
|
for use_cuda in ([False, True]
|
|
if core.is_compiled_with_cuda() else [False]):
|
|
place = paddle.CUDAPlace(0) if use_cuda else paddle.CPUPlace()
|
|
paddle.disable_static(place=place)
|
|
x = paddle.to_tensor(self.x_np)
|
|
|
|
adaptive_avg_pool = paddle.nn.AdaptiveAvgPool3D(
|
|
output_size=[3, 3, 3])
|
|
out_1 = adaptive_avg_pool(x=x)
|
|
|
|
adaptive_avg_pool = paddle.nn.AdaptiveAvgPool3D(output_size=5)
|
|
out_2 = adaptive_avg_pool(x=x)
|
|
|
|
adaptive_avg_pool = paddle.nn.AdaptiveAvgPool3D(
|
|
output_size=[2, 3, 5])
|
|
out_3 = adaptive_avg_pool(x=x)
|
|
|
|
adaptive_avg_pool = paddle.nn.AdaptiveAvgPool3D(
|
|
output_size=[3, 3, 3], data_format="NDHWC")
|
|
out_4 = adaptive_avg_pool(x=x)
|
|
|
|
adaptive_avg_pool = paddle.nn.AdaptiveAvgPool3D(
|
|
output_size=[None, 3, None])
|
|
out_5 = adaptive_avg_pool(x=x)
|
|
|
|
assert np.allclose(out_1.numpy(), self.res_1_np)
|
|
|
|
assert np.allclose(out_2.numpy(), self.res_2_np)
|
|
|
|
assert np.allclose(out_3.numpy(), self.res_3_np)
|
|
|
|
assert np.allclose(out_4.numpy(), self.res_4_np)
|
|
|
|
assert np.allclose(out_5.numpy(), self.res_5_np)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|