wehub-resource-sync
324 lines
9.9 KiB
Python
324 lines
9.9 KiB
Python
# Copyright (c) 2024 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 get_device_place, is_custom_device
|
|
|
|
import paddle
|
|
import paddle.base.dygraph as dg
|
|
import paddle.nn.functional as F
|
|
from paddle import base, nn
|
|
|
|
|
|
class Conv3DTransposeTestCase(unittest.TestCase):
|
|
def __init__(
|
|
self,
|
|
methodName='runTest',
|
|
batch_size=2,
|
|
spatial_shape=(8, 8, 8),
|
|
num_channels=6,
|
|
num_filters=8,
|
|
filter_size=3,
|
|
output_size=None,
|
|
padding=0,
|
|
stride=1,
|
|
dilation=1,
|
|
groups=1,
|
|
no_bias=False,
|
|
data_format="NCDHW",
|
|
dtype="float32",
|
|
):
|
|
super().__init__(methodName)
|
|
self.batch_size = batch_size
|
|
self.num_channels = num_channels
|
|
self.num_filters = num_filters
|
|
self.spatial_shape = spatial_shape
|
|
self.filter_size = filter_size
|
|
self.output_size = output_size
|
|
|
|
self.padding = padding
|
|
self.stride = stride
|
|
self.dilation = dilation
|
|
self.groups = groups
|
|
self.no_bias = no_bias
|
|
self.data_format = data_format
|
|
self.dtype = dtype
|
|
|
|
def setUp(self):
|
|
self.channel_last = self.data_format == "NDHWC"
|
|
if self.channel_last:
|
|
input_shape = (
|
|
self.batch_size,
|
|
*self.spatial_shape,
|
|
self.num_channels,
|
|
)
|
|
else:
|
|
input_shape = (
|
|
self.batch_size,
|
|
self.num_channels,
|
|
*self.spatial_shape,
|
|
)
|
|
self.input = np.random.randn(*input_shape).astype(self.dtype)
|
|
|
|
if isinstance(self.filter_size, int):
|
|
filter_size = [self.filter_size] * 3
|
|
else:
|
|
filter_size = self.filter_size
|
|
self.weight_shape = weight_shape = (
|
|
self.num_channels,
|
|
self.num_filters // self.groups,
|
|
*filter_size,
|
|
)
|
|
self.weight = np.random.uniform(-1, 1, size=weight_shape).astype(
|
|
self.dtype
|
|
)
|
|
if self.no_bias:
|
|
self.bias = None
|
|
else:
|
|
self.bias = np.random.uniform(
|
|
-1, 1, size=(self.num_filters,)
|
|
).astype(self.dtype)
|
|
|
|
def base_layer(self, place):
|
|
main = base.Program()
|
|
start = base.Program()
|
|
with (
|
|
base.unique_name.guard(),
|
|
base.program_guard(main, start),
|
|
):
|
|
input_shape = (
|
|
(-1, -1, -1, -1, self.num_channels)
|
|
if self.channel_last
|
|
else (-1, self.num_channels, -1, -1, -1)
|
|
)
|
|
x_var = paddle.static.data("input", input_shape, dtype=self.dtype)
|
|
weight_attr = paddle.nn.initializer.Assign(self.weight)
|
|
if self.bias is None:
|
|
bias_attr = False
|
|
else:
|
|
bias_attr = paddle.nn.initializer.Assign(self.bias)
|
|
y_var = paddle.nn.Conv3DTranspose(
|
|
in_channels=self.num_channels,
|
|
out_channels=self.num_filters,
|
|
kernel_size=self.filter_size,
|
|
stride=self.stride,
|
|
padding=self.padding,
|
|
dilation=self.dilation,
|
|
groups=self.groups,
|
|
weight_attr=weight_attr,
|
|
bias_attr=bias_attr,
|
|
data_format=self.data_format,
|
|
)(x_var, self.output_size)
|
|
feed_dict = {"input": self.input}
|
|
exe = base.Executor(place)
|
|
exe.run(start)
|
|
(y_np,) = exe.run(main, feed=feed_dict, fetch_list=[y_var])
|
|
return y_np
|
|
|
|
def functional(self, place):
|
|
main = base.Program()
|
|
start = base.Program()
|
|
with (
|
|
base.unique_name.guard(),
|
|
base.program_guard(main, start),
|
|
):
|
|
input_shape = (
|
|
(-1, -1, -1, -1, self.num_channels)
|
|
if self.channel_last
|
|
else (-1, self.num_channels, -1, -1, -1)
|
|
)
|
|
x_var = paddle.static.data("input", input_shape, dtype=self.dtype)
|
|
w_var = paddle.static.data(
|
|
"weight", self.weight_shape, dtype=self.dtype
|
|
)
|
|
if not self.no_bias:
|
|
b_var = paddle.static.data(
|
|
"bias", (self.num_filters,), dtype=self.dtype
|
|
)
|
|
else:
|
|
b_var = None
|
|
y_var = F.conv3d_transpose(
|
|
x_var,
|
|
w_var,
|
|
None if self.no_bias else b_var,
|
|
output_size=self.output_size,
|
|
padding=self.padding,
|
|
stride=self.stride,
|
|
dilation=self.dilation,
|
|
groups=self.groups,
|
|
data_format=self.data_format,
|
|
)
|
|
feed_dict = {"input": self.input, "weight": self.weight}
|
|
if self.bias is not None:
|
|
feed_dict["bias"] = self.bias
|
|
exe = base.Executor(place)
|
|
exe.run(start)
|
|
(y_np,) = exe.run(main, feed=feed_dict, fetch_list=[y_var])
|
|
return y_np
|
|
|
|
def paddle_nn_layer(self):
|
|
x_var = paddle.to_tensor(self.input)
|
|
conv = nn.Conv3DTranspose(
|
|
self.num_channels,
|
|
self.num_filters,
|
|
self.filter_size,
|
|
padding=self.padding,
|
|
stride=self.stride,
|
|
dilation=self.dilation,
|
|
groups=self.groups,
|
|
data_format=self.data_format,
|
|
)
|
|
conv.weight.set_value(self.weight)
|
|
if not self.no_bias:
|
|
conv.bias.set_value(self.bias)
|
|
y_var = conv(x_var, self.output_size)
|
|
y_np = y_var.numpy()
|
|
return y_np
|
|
|
|
def _test_pir_equivalence(self, place):
|
|
place = base.CPUPlace()
|
|
with paddle.pir_utils.IrGuard():
|
|
result1 = self.base_layer(place)
|
|
result2 = self.functional(place)
|
|
with dg.guard(place):
|
|
result3 = self.paddle_nn_layer()
|
|
np.testing.assert_array_almost_equal(result1, result2)
|
|
np.testing.assert_array_almost_equal(result2, result3)
|
|
|
|
def _test_equivalence(self, place):
|
|
place = base.CPUPlace()
|
|
with paddle.pir_utils.OldIrGuard():
|
|
result1 = self.base_layer(place)
|
|
result2 = self.functional(place)
|
|
with dg.guard(place):
|
|
result3 = self.paddle_nn_layer()
|
|
np.testing.assert_array_almost_equal(result1, result2)
|
|
np.testing.assert_array_almost_equal(result2, result3)
|
|
|
|
def runTest(self):
|
|
place = base.CPUPlace()
|
|
self._test_equivalence(place)
|
|
self._test_pir_equivalence(place)
|
|
|
|
if base.core.is_compiled_with_cuda() or is_custom_device():
|
|
place = get_device_place()
|
|
self._test_equivalence(place)
|
|
self._test_pir_equivalence(place)
|
|
|
|
|
|
class Conv3DTransposeErrorTestCase(Conv3DTransposeTestCase):
|
|
def runTest(self):
|
|
place = base.CPUPlace()
|
|
with (
|
|
dg.guard(place),
|
|
self.assertRaises(ValueError),
|
|
):
|
|
self.paddle_nn_layer()
|
|
|
|
|
|
def add_cases(suite):
|
|
suite.addTest(Conv3DTransposeTestCase(methodName='runTest'))
|
|
suite.addTest(
|
|
Conv3DTransposeTestCase(
|
|
methodName='runTest', stride=[1, 2, 1], dilation=2, no_bias=True
|
|
)
|
|
)
|
|
suite.addTest(
|
|
Conv3DTransposeTestCase(
|
|
methodName='runTest',
|
|
output_size=[12, 19, 12],
|
|
stride=[1, 2, 1],
|
|
dilation=2,
|
|
)
|
|
)
|
|
suite.addTest(
|
|
Conv3DTransposeTestCase(
|
|
methodName='runTest', stride=2, dilation=(2, 1, 2)
|
|
)
|
|
)
|
|
suite.addTest(
|
|
Conv3DTransposeTestCase(methodName='runTest', padding="valid")
|
|
)
|
|
suite.addTest(
|
|
Conv3DTransposeTestCase(methodName='runTest', padding='valid')
|
|
)
|
|
suite.addTest(
|
|
Conv3DTransposeTestCase(
|
|
methodName='runTest', filter_size=1, padding=(2, 3, 1)
|
|
)
|
|
)
|
|
suite.addTest(
|
|
Conv3DTransposeTestCase(
|
|
methodName='runTest', padding=[1, 2, 2, 3, 2, 1]
|
|
)
|
|
)
|
|
suite.addTest(
|
|
Conv3DTransposeTestCase(
|
|
methodName='runTest',
|
|
padding=[[0, 0], [0, 0], [2, 3], [1, 2], [2, 1]],
|
|
)
|
|
)
|
|
suite.addTest(
|
|
Conv3DTransposeTestCase(methodName='runTest', data_format="NDHWC")
|
|
)
|
|
suite.addTest(
|
|
Conv3DTransposeTestCase(
|
|
methodName='runTest',
|
|
data_format="NDHWC",
|
|
padding=[[0, 0], [1, 1], [2, 2], [3, 3], [0, 0]],
|
|
)
|
|
)
|
|
suite.addTest(
|
|
Conv3DTransposeTestCase(methodName='runTest', groups=2, padding="valid")
|
|
)
|
|
suite.addTest(
|
|
Conv3DTransposeTestCase(
|
|
methodName='runTest',
|
|
num_filters=6,
|
|
num_channels=3,
|
|
groups=3,
|
|
padding="valid",
|
|
)
|
|
)
|
|
|
|
|
|
def add_error_cases(suite):
|
|
suite.addTest(
|
|
Conv3DTransposeErrorTestCase(
|
|
methodName='runTest', num_channels=5, groups=2
|
|
)
|
|
)
|
|
suite.addTest(
|
|
Conv3DTransposeErrorTestCase(
|
|
methodName='runTest', output_size="not_valid"
|
|
)
|
|
)
|
|
suite.addTest(
|
|
Conv3DTransposeErrorTestCase(
|
|
methodName='runTest', num_channels=5, groups=2, padding=[-1, 1, 3]
|
|
)
|
|
)
|
|
|
|
|
|
def load_tests(loader, standard_tests, pattern):
|
|
suite = unittest.TestSuite()
|
|
add_cases(suite)
|
|
add_error_cases(suite)
|
|
return suite
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|