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.
Paddle/python/paddle/fluid/tests/unittests/test_adaptive_max_pool2d.py

287 lines
10 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, check_out_dtype
import paddle
import paddle.fluid as fluid
from paddle.fluid import Program, program_guard
import paddle.nn.functional as F
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 == 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] == None:
output_size[0] = H
H_out = H
if output_size[1] == 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))
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() else [False]):
place = paddle.CUDAPlace(0) if use_cuda else paddle.CPUPlace()
paddle.enable_static()
x = paddle.fluid.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(
fluid.default_main_program(),
feed={"x": self.x_np},
fetch_list=[out_1, out_2, out_3, 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_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])
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)
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() else [False]):
place = paddle.CUDAPlace(0) if use_cuda else paddle.CPUPlace()
paddle.enable_static()
x = paddle.fluid.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(
fluid.default_main_program(),
feed={"x": self.x_np},
fetch_list=[out_1, out_2, out_3, 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_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)
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)
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)
if __name__ == '__main__':
unittest.main()