wehub-resource-sync
409 lines
12 KiB
Python
409 lines
12 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 get_device_place, is_custom_device
|
|
from test_conv2d_op import conv2d_forward_naive
|
|
|
|
import paddle
|
|
import paddle.base.dygraph as dg
|
|
from paddle import base, nn
|
|
from paddle.base import core
|
|
|
|
|
|
def _reverse_repeat_list(t, n):
|
|
return [x for x in reversed(t) for _ in range(n)]
|
|
|
|
|
|
class Conv2DTestCase(unittest.TestCase):
|
|
def __init__(
|
|
self,
|
|
methodName='runTest',
|
|
batch_size=4,
|
|
spartial_shape=(16, 16),
|
|
num_channels=6,
|
|
num_filters=8,
|
|
filter_size=3,
|
|
padding=0,
|
|
padding_mode='zeros',
|
|
stride=1,
|
|
dilation=1,
|
|
groups=1,
|
|
no_bias=False,
|
|
data_format="NCHW",
|
|
dtype="float32",
|
|
):
|
|
super().__init__(methodName)
|
|
self.batch_size = batch_size
|
|
self.num_channels = num_channels
|
|
self.num_filters = num_filters
|
|
self.spartial_shape = spartial_shape
|
|
self.filter_size = filter_size
|
|
|
|
self.padding = padding
|
|
if padding_mode in {'reflect', 'replicate', 'circular'}:
|
|
_paired_padding = paddle.utils.convert_to_list(
|
|
padding, 2, 'padding'
|
|
)
|
|
self._reversed_padding_repeated_twice = _reverse_repeat_list(
|
|
_paired_padding, 2
|
|
)
|
|
self.padding_mode = padding_mode
|
|
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 == "NHWC"
|
|
if self.channel_last:
|
|
input_shape = (
|
|
self.batch_size,
|
|
*self.spartial_shape,
|
|
self.num_channels,
|
|
)
|
|
else:
|
|
input_shape = (
|
|
self.batch_size,
|
|
self.num_channels,
|
|
*self.spartial_shape,
|
|
)
|
|
self.input = np.random.randn(*input_shape).astype(self.dtype)
|
|
|
|
if isinstance(self.filter_size, int):
|
|
filter_size = [self.filter_size] * 2
|
|
else:
|
|
filter_size = self.filter_size
|
|
self.weight_shape = weight_shape = (
|
|
self.num_filters,
|
|
self.num_channels // self.groups,
|
|
*filter_size,
|
|
)
|
|
self.weight = np.random.uniform(-1, 1, size=weight_shape).astype(
|
|
self.dtype
|
|
)
|
|
if not self.no_bias:
|
|
self.bias = np.random.uniform(
|
|
-1, 1, size=(self.num_filters,)
|
|
).astype(self.dtype)
|
|
else:
|
|
self.bias = None
|
|
|
|
def paddle_nn_layer(self):
|
|
x_var = paddle.to_tensor(self.input)
|
|
x_var.stop_gradient = False
|
|
conv = nn.Conv2D(
|
|
self.num_channels,
|
|
self.num_filters,
|
|
self.filter_size,
|
|
padding=self.padding,
|
|
padding_mode=self.padding_mode,
|
|
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)
|
|
y_var.backward()
|
|
y_np = y_var.numpy()
|
|
t1 = x_var.gradient()
|
|
return y_np, t1
|
|
|
|
def run_Conv2D_static(self, place):
|
|
paddle.seed(2023)
|
|
main = base.Program()
|
|
start = base.Program()
|
|
with (
|
|
base.unique_name.guard(),
|
|
base.program_guard(main, start),
|
|
):
|
|
x_var = paddle.static.data(
|
|
"input", self.input.shape, dtype=self.dtype
|
|
)
|
|
conv = nn.Conv2D(
|
|
self.num_channels,
|
|
self.num_filters,
|
|
self.filter_size,
|
|
padding=self.padding,
|
|
padding_mode=self.padding_mode,
|
|
stride=self.stride,
|
|
dilation=self.dilation,
|
|
groups=self.groups,
|
|
data_format=self.data_format,
|
|
)
|
|
y_var = conv(x_var)
|
|
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 run_Conv2D_dygraph(self):
|
|
paddle.seed(2023)
|
|
x_var = paddle.to_tensor(self.input)
|
|
x_var.stop_gradient = False
|
|
conv = nn.Conv2D(
|
|
self.num_channels,
|
|
self.num_filters,
|
|
self.filter_size,
|
|
padding=self.padding,
|
|
padding_mode=self.padding_mode,
|
|
stride=self.stride,
|
|
dilation=self.dilation,
|
|
groups=self.groups,
|
|
data_format=self.data_format,
|
|
)
|
|
y_var = conv(x_var)
|
|
y_np = y_var.numpy()
|
|
return y_np
|
|
|
|
def _test_equivalence_in_pir(self, place):
|
|
with paddle.pir_utils.IrGuard():
|
|
result1 = self.run_Conv2D_static(place)
|
|
with dg.guard(place):
|
|
result2 = self.run_Conv2D_dygraph()
|
|
np.testing.assert_array_almost_equal(result1, result2)
|
|
|
|
def runTest(self):
|
|
place = base.CPUPlace()
|
|
self._test_equivalence_in_pir(place)
|
|
|
|
if base.core.is_compiled_with_cuda() or is_custom_device():
|
|
place = get_device_place()
|
|
self._test_equivalence_in_pir(place)
|
|
|
|
|
|
class Conv2DErrorTestCase(Conv2DTestCase):
|
|
def runTest(self):
|
|
place = base.CPUPlace()
|
|
with (
|
|
dg.guard(place),
|
|
self.assertRaises(ValueError),
|
|
):
|
|
self.paddle_nn_layer()
|
|
|
|
|
|
def add_cases(suite):
|
|
suite.addTest(Conv2DTestCase(methodName='runTest'))
|
|
suite.addTest(
|
|
Conv2DTestCase(methodName='runTest', stride=[1, 2], dilation=2)
|
|
)
|
|
suite.addTest(
|
|
Conv2DTestCase(methodName='runTest', stride=2, dilation=(2, 1))
|
|
)
|
|
suite.addTest(
|
|
Conv2DTestCase(methodName='runTest', padding="same", no_bias=True)
|
|
)
|
|
suite.addTest(
|
|
Conv2DTestCase(
|
|
methodName='runTest', filter_size=(3, 3), padding='valid'
|
|
)
|
|
)
|
|
suite.addTest(Conv2DTestCase(methodName='runTest', padding=(2, 3)))
|
|
suite.addTest(Conv2DTestCase(methodName='runTest', padding=[1, 2, 2, 1]))
|
|
suite.addTest(
|
|
Conv2DTestCase(
|
|
methodName='runTest', padding=[[0, 0], [0, 0], [1, 2], [2, 1]]
|
|
)
|
|
)
|
|
suite.addTest(Conv2DTestCase(methodName='runTest', data_format="NHWC"))
|
|
suite.addTest(
|
|
Conv2DTestCase(
|
|
methodName='runTest',
|
|
data_format="NHWC",
|
|
padding=[[0, 0], [1, 1], [2, 2], [0, 0]],
|
|
)
|
|
)
|
|
suite.addTest(
|
|
Conv2DTestCase(methodName='runTest', groups=2, padding="valid")
|
|
)
|
|
suite.addTest(
|
|
Conv2DTestCase(
|
|
methodName='runTest',
|
|
num_filters=6,
|
|
num_channels=3,
|
|
groups=3,
|
|
padding="valid",
|
|
)
|
|
)
|
|
suite.addTest(
|
|
Conv2DTestCase(
|
|
methodName='runTest',
|
|
filter_size=(3, 3),
|
|
padding=1,
|
|
padding_mode='reflect',
|
|
)
|
|
)
|
|
suite.addTest(
|
|
Conv2DTestCase(
|
|
methodName='runTest',
|
|
filter_size=(3, 3),
|
|
padding=1,
|
|
padding_mode='replicate',
|
|
)
|
|
)
|
|
suite.addTest(
|
|
Conv2DTestCase(
|
|
methodName='runTest',
|
|
filter_size=(3, 3),
|
|
padding=1,
|
|
padding_mode='circular',
|
|
)
|
|
)
|
|
|
|
|
|
def add_error_cases(suite):
|
|
suite.addTest(
|
|
Conv2DErrorTestCase(methodName='runTest', num_channels=5, groups=2)
|
|
)
|
|
suite.addTest(
|
|
Conv2DErrorTestCase(
|
|
methodName='runTest', num_channels=5, groups=2, stride=0
|
|
)
|
|
)
|
|
suite.addTest(
|
|
Conv2DErrorTestCase(
|
|
methodName='runTest', num_channels=5, groups=2, padding=[-1, -1]
|
|
)
|
|
)
|
|
|
|
|
|
def load_tests(loader, standard_tests, pattern):
|
|
suite = unittest.TestSuite()
|
|
add_cases(suite)
|
|
add_error_cases(suite)
|
|
return suite
|
|
|
|
|
|
def get_places():
|
|
places = []
|
|
if core.is_compiled_with_xpu():
|
|
places.append(paddle.device.XPUPlace(0))
|
|
elif core.is_compiled_with_cuda():
|
|
places.append(paddle.CUDAPlace(0))
|
|
places.append(paddle.CPUPlace())
|
|
return places
|
|
|
|
|
|
class TestConv2dAPI_Compatibility(unittest.TestCase):
|
|
def setUp(self):
|
|
np.random.seed(2025)
|
|
self.places = get_places()
|
|
self.shape_x = [2, 3, 16, 16] # NCHW
|
|
self.shape_w = [6, 3, 3, 3] # Co, Cin, kH, kW
|
|
self.dtype = "float32"
|
|
self.init_data()
|
|
|
|
def init_data(self):
|
|
self.np_x = np.random.rand(*self.shape_x).astype(self.dtype)
|
|
self.np_w = np.random.rand(*self.shape_w).astype(self.dtype)
|
|
conv_param = {"stride": [1, 1], "pad": [0, 0], "dilation": [1, 1]}
|
|
self.np_ref_out, _, _, _, _ = conv2d_forward_naive(
|
|
self.np_x, self.np_w, 1, conv_param
|
|
)
|
|
|
|
def test_dygraph_Compatibility(self):
|
|
for place in self.places:
|
|
paddle.device.set_device(place)
|
|
paddle.disable_static()
|
|
x = paddle.to_tensor(self.np_x)
|
|
w = paddle.to_tensor(self.np_w)
|
|
|
|
paddle_dygraph_out = []
|
|
# Position args (args)
|
|
out1 = paddle.nn.functional.conv2d(x, w)
|
|
paddle_dygraph_out.append(out1)
|
|
# Keywords args (kwargs) for paddle
|
|
out2 = paddle.nn.functional.conv2d(x=x, weight=w)
|
|
paddle_dygraph_out.append(out2)
|
|
# Keywords args for alias compatibility
|
|
out3 = paddle.nn.functional.conv2d(input=x, weight=w)
|
|
paddle_dygraph_out.append(out3)
|
|
# Combined args and kwargs
|
|
out4 = paddle.nn.functional.conv2d(x, weight=w)
|
|
paddle_dygraph_out.append(out4)
|
|
|
|
# refer to test/xpu/test_conv2d_op_xpu.py
|
|
if isinstance(place, core.XPUPlace):
|
|
rtol = 5e-3
|
|
atol = 5e-3
|
|
else:
|
|
rtol = 1e-5
|
|
atol = 0
|
|
|
|
# Check all dygraph results against reference
|
|
for out in paddle_dygraph_out:
|
|
np.testing.assert_allclose(
|
|
self.np_ref_out, out.numpy(), rtol=rtol, atol=atol
|
|
)
|
|
paddle.enable_static()
|
|
|
|
def test_static_Compatibility(self):
|
|
paddle.enable_static()
|
|
|
|
fetch_list = []
|
|
main = paddle.static.Program()
|
|
startup = paddle.static.Program()
|
|
with base.program_guard(main, startup):
|
|
x = paddle.static.data(
|
|
name="x", shape=self.shape_x, dtype=self.dtype
|
|
)
|
|
w = paddle.static.data(
|
|
name="w", shape=self.shape_w, dtype=self.dtype
|
|
)
|
|
|
|
# Position args (args)
|
|
out1 = paddle.nn.functional.conv2d(x, w)
|
|
fetch_list.append(out1)
|
|
# Keywords args (kwargs) for paddle
|
|
out2 = paddle.nn.functional.conv2d(x=x, weight=w)
|
|
fetch_list.append(out2)
|
|
# Keywords args for alias compatibility
|
|
out3 = paddle.nn.functional.conv2d(input=x, weight=w)
|
|
fetch_list.append(out3)
|
|
# Combined args and kwargs
|
|
out4 = paddle.nn.functional.conv2d(x, weight=w)
|
|
fetch_list.append(out4)
|
|
|
|
for place in self.places:
|
|
# refer to test/xpu/test_conv2d_op_xpu.py
|
|
if isinstance(place, core.XPUPlace):
|
|
rtol = 5e-3
|
|
atol = 5e-3
|
|
else:
|
|
rtol = 1e-5
|
|
atol = 0
|
|
|
|
exe = base.Executor(place)
|
|
fetches = exe.run(
|
|
main,
|
|
feed={"x": self.np_x, "w": self.np_w},
|
|
fetch_list=fetch_list,
|
|
)
|
|
for out in fetches:
|
|
np.testing.assert_allclose(
|
|
out, self.np_ref_out, rtol=rtol, atol=atol
|
|
)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
paddle.enable_static()
|
|
unittest.main()
|