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未验证 提交 97229944 编写于 作者: Z Zhanlue Yang 提交者： GitHub
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Fixed get_tensor method for EagerTensor (#39414) 

* Enabled Eager OpTest #1

* Enabled Eager OpTest #1

* Fixed get_tensor method for EagerTensor
上级 ec8a0c1d
隐藏空白更改
内联 并排
Showing with 223 addition and 223 deletion
paddle/fluid/pybind/eager_utils.cc
浏览文件 @ 97229944
......@@ -506,7 +506,7 @@ PyObject* ToPyObject(const paddle::framework::proto::VarType& type) {
}
PyObject* ToPyObject(const paddle::framework::LoDTensor* value) {
auto obj = ::pybind11::cast(value, py::return_value_policy::copy);
auto obj = ::pybind11::cast(value, py::return_value_policy::reference);
obj.inc_ref();
return obj.ptr();
}
......
python/paddle/fluid/tests/unittests/test_where_op.py
浏览文件 @ 97229944
#Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Copyright (c) 2022 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.
......@@ -13,7 +13,6 @@
# limitations under the License.
from __future__ import print_function
import unittest
import numpy as np
import paddle
......@@ -24,38 +23,39 @@ from op_test import OpTest
from paddle.fluid import compiler, Program, program_guard
from paddle.fluid.op import Operator
from paddle.fluid.backward import append_backward
from paddle.fluid.framework import _test_eager_guard
class TestWhereOp(OpTest):
def setUp(self):
self.op_type = "where"
self.op_type = 'where'
self.init_config()
self.inputs = {'Condition': self.cond, 'X': self.x, 'Y': self.y}
self.outputs = {'Out': np.where(self.cond, self.x, self.y)}
def test_check_output(self):
self.check_output()
self.check_output(check_eager=True)
def test_check_grad(self):
self.check_grad(['X', 'Y'], 'Out')
self.check_grad(['X', 'Y'], 'Out', check_eager=True)
def init_config(self):
self.x = np.random.uniform(-3, 5, (100)).astype("float64")
self.y = np.random.uniform(-3, 5, (100)).astype("float64")
self.cond = np.zeros((100)).astype("bool")
self.x = np.random.uniform((-3), 5, 100).astype('float64')
self.y = np.random.uniform((-3), 5, 100).astype('float64')
self.cond = np.zeros(100).astype('bool')
class TestWhereOp2(TestWhereOp):
def init_config(self):
self.x = np.random.uniform(-5, 5, (60, 2)).astype("float64")
self.y = np.random.uniform(-5, 5, (60, 2)).astype("float64")
self.cond = np.ones((60, 2)).astype("bool")
self.x = np.random.uniform((-5), 5, (60, 2)).astype('float64')
self.y = np.random.uniform((-5), 5, (60, 2)).astype('float64')
self.cond = np.ones((60, 2)).astype('bool')
class TestWhereOp3(TestWhereOp):
def init_config(self):
self.x = np.random.uniform(-3, 5, (20, 2, 4)).astype("float64")
self.y = np.random.uniform(-3, 5, (20, 2, 4)).astype("float64")
self.x = np.random.uniform((-3), 5, (20, 2, 4)).astype('float64')
self.y = np.random.uniform((-3), 5, (20, 2, 4)).astype('float64')
self.cond = np.array(np.random.randint(2, size=(20, 2, 4)), dtype=bool)
......@@ -66,15 +66,15 @@ class TestWhereAPI(unittest.TestCase):
def init_data(self):
self.shape = [10, 15]
self.cond = np.array(np.random.randint(2, size=self.shape), dtype=bool)
self.x = np.random.uniform(-2, 3, self.shape).astype(np.float32)
self.y = np.random.uniform(-2, 3, self.shape).astype(np.float32)
self.x = np.random.uniform((-2), 3, self.shape).astype(np.float32)
self.y = np.random.uniform((-2), 3, self.shape).astype(np.float32)
self.out = np.where(self.cond, self.x, self.y)
def ref_x_backward(self, dout):
return np.where(self.cond == True, dout, 0)
return np.where((self.cond == True), dout, 0)
def ref_y_backward(self, dout):
return np.where(self.cond == False, dout, 0)
return np.where((self.cond == False), dout, 0)
def test_api(self, use_cuda=False):
for x_stop_gradient in [False, True]:
......@@ -90,17 +90,17 @@ class TestWhereAPI(unittest.TestCase):
y.stop_gradient = y_stop_gradient
result = paddle.where(cond, x, y)
append_backward(layers.mean(result))
for use_cuda in [False, True]:
if use_cuda and not fluid.core.is_compiled_with_cuda():
if (use_cuda and
(not fluid.core.is_compiled_with_cuda())):
break
place = fluid.CUDAPlace(
0) if use_cuda else fluid.CPUPlace()
place = (fluid.CUDAPlace(0)
if use_cuda else fluid.CPUPlace())
exe = fluid.Executor(place)
fetch_list = [result, result.grad_name]
if x_stop_gradient is False:
if (x_stop_gradient is False):
fetch_list.append(x.grad_name)
if y_stop_gradient is False:
if (y_stop_gradient is False):
fetch_list.append(y.grad_name)
out = exe.run(
fluid.default_main_program(),
......@@ -109,13 +109,13 @@ class TestWhereAPI(unittest.TestCase):
'y': self.y},
fetch_list=fetch_list)
assert np.array_equal(out[0], self.out)
if x_stop_gradient is False:
if (x_stop_gradient is False):
assert np.array_equal(out[2],
self.ref_x_backward(out[1]))
if y.stop_gradient is False:
if (y.stop_gradient is False):
assert np.array_equal(
out[3], self.ref_y_backward(out[1]))
elif y.stop_gradient is False:
elif (y.stop_gradient is False):
assert np.array_equal(out[2],
self.ref_y_backward(out[1]))
......@@ -124,44 +124,38 @@ class TestWhereAPI(unittest.TestCase):
with fluid.program_guard(main_program):
x = fluid.layers.data(name='x', shape=[4, 1], dtype='float32')
y = fluid.layers.data(name='y', shape=[4, 2], dtype='float32')
x_i = np.array([[0.9383, 0.1983, 3.2, 1.2]]).astype("float32")
y_i = np.array([[1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0]]).astype("float32")
result = paddle.where(x > 1, x=x, y=y)
x_i = np.array([[0.9383, 0.1983, 3.2, 1.2]]).astype('float32')
y_i = np.array(
[[1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0]]).astype('float32')
result = paddle.where((x > 1), x=x, y=y)
for use_cuda in [False, True]:
if use_cuda and not fluid.core.is_compiled_with_cuda():
if (use_cuda and (not fluid.core.is_compiled_with_cuda())):
return
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
place = (fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace())
exe = fluid.Executor(place)
out = exe.run(fluid.default_main_program(),
feed={'x': x_i,
'y': y_i},
fetch_list=[result])
assert np.array_equal(out[0], np.where(x_i > 1, x_i, y_i))
assert np.array_equal(out[0], np.where((x_i > 1), x_i, y_i))
def __test_where_with_broadcast_static(self, cond_shape, x_shape, y_shape):
paddle.enable_static()
main_program = Program()
with fluid.program_guard(main_program):
cond = fluid.layers.data(
name='cond', shape=cond_shape, dtype='bool')
x = fluid.layers.data(name='x', shape=x_shape, dtype='float32')
y = fluid.layers.data(name='y', shape=y_shape, dtype='float32')
cond_data_tmp = np.random.random(size=cond_shape).astype("float32")
cond_data = cond_data_tmp < 0.3
x_data = np.random.random(size=x_shape).astype("float32")
y_data = np.random.random(size=y_shape).astype("float32")
cond_data_tmp = np.random.random(size=cond_shape).astype('float32')
cond_data = (cond_data_tmp < 0.3)
x_data = np.random.random(size=x_shape).astype('float32')
y_data = np.random.random(size=y_shape).astype('float32')
result = paddle.where(condition=cond, x=x, y=y)
for use_cuda in [False, True]:
if use_cuda and not fluid.core.is_compiled_with_cuda():
if (use_cuda and (not fluid.core.is_compiled_with_cuda())):
return
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
place = (fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace())
exe = fluid.Executor(place)
out = exe.run(
fluid.default_main_program(),
......@@ -169,9 +163,7 @@ class TestWhereAPI(unittest.TestCase):
'x': x_data,
'y': y_data},
fetch_list=[result])
expect = np.where(cond_data, x_data, y_data)
assert np.array_equal(out[0], expect)
def test_static_api_broadcast_1(self):
......@@ -198,28 +190,24 @@ class TestWhereAPI(unittest.TestCase):
b_shape = [2, 2, 4]
self.__test_where_with_broadcast_static(cond_shape, a_shape, b_shape)
# @Note Now, maybe not compatibility with old version
def test_static_api_broadcast_5(self):
cond_shape = [3, 2, 2, 4]
a_shape = [2, 2, 4]
b_shape = [2, 2, 4]
self.__test_where_with_broadcast_static(cond_shape, a_shape, b_shape)
# @Note Now, maybe not compatibility with old version
def test_static_api_broadcast_6(self):
cond_shape = [2, 2, 4]
a_shape = [2, 2, 1]
b_shape = [2, 2, 1]
self.__test_where_with_broadcast_static(cond_shape, a_shape, b_shape)
# @Note Now, maybe not compatibility with old version
def test_static_api_broadcast_7(self):
cond_shape = [2, 2, 4]
a_shape = [2, 1, 4]
b_shape = [2, 1, 4]
self.__test_where_with_broadcast_static(cond_shape, a_shape, b_shape)
# @Note Now, maybe not compatibility with old version
def test_static_api_broadcast_8(self):
cond_shape = [3, 2, 2, 4]
a_shape = [2, 2, 1]
......@@ -230,9 +218,9 @@ class TestWhereAPI(unittest.TestCase):
class TestWhereDygraphAPI(unittest.TestCase):
def test_api(self):
with fluid.dygraph.guard():
x_i = np.array([0.9383, 0.1983, 3.2, 1.2]).astype("float64")
y_i = np.array([1.0, 1.0, 1.0, 1.0]).astype("float64")
cond_i = np.array([False, False, True, True]).astype("bool")
x_i = np.array([0.9383, 0.1983, 3.2, 1.2]).astype('float64')
y_i = np.array([1.0, 1.0, 1.0, 1.0]).astype('float64')
cond_i = np.array([False, False, True, True]).astype('bool')
x = fluid.dygraph.to_variable(x_i)
y = fluid.dygraph.to_variable(y_i)
cond = fluid.dygraph.to_variable(cond_i)
......@@ -242,15 +230,12 @@ class TestWhereDygraphAPI(unittest.TestCase):
def __test_where_with_broadcast_dygraph(self, cond_shape, a_shape, b_shape):
with fluid.dygraph.guard():
cond_tmp = paddle.rand(cond_shape)
cond = cond_tmp < 0.3
cond = (cond_tmp < 0.3)
a = paddle.rand(a_shape)
b = paddle.rand(b_shape)
result = paddle.where(cond, a, b)
result = result.numpy()
expect = np.where(cond, a, b)
self.assertTrue(np.array_equal(expect, result))
def test_dygraph_api_broadcast_1(self):
......@@ -277,28 +262,24 @@ class TestWhereDygraphAPI(unittest.TestCase):
b_shape = [2, 2, 4]
self.__test_where_with_broadcast_dygraph(cond_shape, a_shape, b_shape)
# @Note Now, maybe not compatibility with old version
def test_dygraph_api_broadcast_5(self):
cond_shape = [3, 2, 2, 4]
a_shape = [2, 2, 4]
b_shape = [2, 2, 4]
self.__test_where_with_broadcast_dygraph(cond_shape, a_shape, b_shape)
# @Note Now, maybe not compatibility with old version
def test_dygraph_api_broadcast_6(self):
cond_shape = [2, 2, 4]
a_shape = [2, 2, 1]
b_shape = [2, 2, 1]
self.__test_where_with_broadcast_dygraph(cond_shape, a_shape, b_shape)
# @Note Now, maybe not compatibility with old version
def test_dygraph_api_broadcast_7(self):
cond_shape = [2, 2, 4]
a_shape = [2, 1, 4]
b_shape = [2, 1, 4]
self.__test_where_with_broadcast_dygraph(cond_shape, a_shape, b_shape)
# @Note Now, maybe not compatibility with old version
def test_dygraph_api_broadcast_8(self):
cond_shape = [3, 2, 2, 4]
a_shape = [2, 2, 1]
......@@ -308,40 +289,50 @@ class TestWhereDygraphAPI(unittest.TestCase):
def test_where_condition(self):
data = np.array([[True, False], [False, True]])
with program_guard(Program(), Program()):
x = fluid.layers.data(name='x', shape=[-1, 2])
x = fluid.layers.data(name='x', shape=[(-1), 2])
y = paddle.where(x)
self.assertEqual(type(y), tuple)
self.assertEqual(len(y), 2)
z = fluid.layers.concat(list(y), axis=1)
exe = fluid.Executor(fluid.CPUPlace())
res, = exe.run(feed={'x': data},
fetch_list=[z.name],
return_numpy=False)
(res, ) = exe.run(feed={'x': data},
fetch_list=[z.name],
return_numpy=False)
expect_out = np.array([[0, 0], [1, 1]])
self.assertTrue(np.allclose(expect_out, np.array(res)))
data = np.array([True, True, False])
with program_guard(Program(), Program()):
x = fluid.layers.data(name='x', shape=[-1])
x = fluid.layers.data(name='x', shape=[(-1)])
y = paddle.where(x)
self.assertEqual(type(y), tuple)
self.assertEqual(len(y), 1)
z = fluid.layers.concat(list(y), axis=1)
exe = fluid.Executor(fluid.CPUPlace())
res, = exe.run(feed={'x': data},
fetch_list=[z.name],
return_numpy=False)
(res, ) = exe.run(feed={'x': data},
fetch_list=[z.name],
return_numpy=False)
expect_out = np.array([[0], [1]])
self.assertTrue(np.allclose(expect_out, np.array(res)))
def test_eager(self):
with _test_eager_guard():
self.test_api()
self.test_dygraph_api_broadcast_1()
self.test_dygraph_api_broadcast_2()
self.test_dygraph_api_broadcast_3()
self.test_dygraph_api_broadcast_4()
self.test_dygraph_api_broadcast_5()
self.test_dygraph_api_broadcast_6()
self.test_dygraph_api_broadcast_7()
self.test_dygraph_api_broadcast_8()
class TestWhereOpError(unittest.TestCase):
def test_errors(self):
with program_guard(Program(), Program()):
x_i = np.array([0.9383, 0.1983, 3.2, 1.2]).astype("float64")
y_i = np.array([1.0, 1.0, 1.0, 1.0]).astype("float64")
cond_i = np.array([False, False, True, True]).astype("bool")
x_i = np.array([0.9383, 0.1983, 3.2, 1.2]).astype('float64')
y_i = np.array([1.0, 1.0, 1.0, 1.0]).astype('float64')
cond_i = np.array([False, False, True, True]).astype('bool')
def test_Variable():
paddle.where(cond_i, x_i, y_i)
......@@ -360,10 +351,14 @@ class TestWhereOpError(unittest.TestCase):
with fluid.dygraph.guard():
cond_shape = [2, 2, 4]
cond_tmp = paddle.rand(cond_shape)
cond = cond_tmp < 0.3
cond = (cond_tmp < 0.3)
a = paddle.rand(cond_shape)
self.assertRaises(ValueError, paddle.where, cond, a)
def test_eager(self):
with _test_eager_guard():
self.test_value_error()
if __name__ == '__main__':
if (__name__ == '__main__'):
unittest.main()
python/paddle/fluid/tests/unittests/test_yolo_box_op.py
浏览文件 @ 97229944
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Copyright (c) 2022 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.
......@@ -13,23 +13,22 @@
# limitations under the License.
from __future__ import division
import unittest
import numpy as np
from op_test import OpTest
import paddle
from paddle.fluid import core
from paddle.fluid.framework import _test_eager_guard
def sigmoid(x):
return 1.0 / (1.0 + np.exp(-1.0 * x))
return (1.0 / (1.0 + np.exp(((-1.0) * x))))
def YoloBox(x, img_size, attrs):
n, c, h, w = x.shape
(n, c, h, w) = x.shape
anchors = attrs['anchors']
an_num = int(len(anchors) // 2)
an_num = int((len(anchors) // 2))
class_num = attrs['class_num']
conf_thresh = attrs['conf_thresh']
downsample = attrs['downsample']
......@@ -37,60 +36,56 @@ def YoloBox(x, img_size, attrs):
scale_x_y = attrs['scale_x_y']
iou_aware = attrs['iou_aware']
iou_aware_factor = attrs['iou_aware_factor']
bias_x_y = -0.5 * (scale_x_y - 1.)
input_h = downsample * h
input_w = downsample * w
bias_x_y = ((-0.5) * (scale_x_y - 1.0))
input_h = (downsample * h)
input_w = (downsample * w)
if iou_aware:
ioup = x[:, :an_num, :, :]
ioup = np.expand_dims(ioup, axis=-1)
ioup = np.expand_dims(ioup, axis=(-1))
x = x[:, an_num:, :, :]
x = x.reshape((n, an_num, 5 + class_num, h, w)).transpose((0, 1, 3, 4, 2))
x = x.reshape((n, an_num, (5 + class_num), h, w)).transpose((0, 1, 3, 4, 2))
pred_box = x[:, :, :, :, :4].copy()
grid_x = np.tile(np.arange(w).reshape((1, w)), (h, 1))
grid_y = np.tile(np.arange(h).reshape((h, 1)), (1, w))
pred_box[:, :, :, :, 0] = (
grid_x + sigmoid(pred_box[:, :, :, :, 0]) * scale_x_y + bias_x_y) / w
pred_box[:, :, :, :, 1] = (
grid_y + sigmoid(pred_box[:, :, :, :, 1]) * scale_x_y + bias_x_y) / h
anchors = [(anchors[i], anchors[i + 1]) for i in range(0, len(anchors), 2)]
pred_box[:, :, :, :, 0] = ((
(grid_x + (sigmoid(pred_box[:, :, :, :, 0]) * scale_x_y)) + bias_x_y) /
w)
pred_box[:, :, :, :, 1] = ((
(grid_y + (sigmoid(pred_box[:, :, :, :, 1]) * scale_x_y)) + bias_x_y) /
h)
anchors = [(anchors[i], anchors[(i + 1)])
for i in range(0, len(anchors), 2)]
anchors_s = np.array(
[(an_w / input_w, an_h / input_h) for an_w, an_h in anchors])
[((an_w / input_w), (an_h / input_h)) for (an_w, an_h) in anchors])
anchor_w = anchors_s[:, 0:1].reshape((1, an_num, 1, 1))
anchor_h = anchors_s[:, 1:2].reshape((1, an_num, 1, 1))
pred_box[:, :, :, :, 2] = np.exp(pred_box[:, :, :, :, 2]) * anchor_w
pred_box[:, :, :, :, 3] = np.exp(pred_box[:, :, :, :, 3]) * anchor_h
pred_box[:, :, :, :, 2] = (np.exp(pred_box[:, :, :, :, 2]) * anchor_w)
pred_box[:, :, :, :, 3] = (np.exp(pred_box[:, :, :, :, 3]) * anchor_h)
if iou_aware:
pred_conf = sigmoid(x[:, :, :, :, 4:5])**(
1 - iou_aware_factor) * sigmoid(ioup)**iou_aware_factor
pred_conf = ((sigmoid(x[:, :, :, :, 4:5])**(1 - iou_aware_factor)) *
(sigmoid(ioup)**iou_aware_factor))
else:
pred_conf = sigmoid(x[:, :, :, :, 4:5])
pred_conf[pred_conf < conf_thresh] = 0.
pred_score = sigmoid(x[:, :, :, :, 5:]) * pred_conf
pred_box = pred_box * (pred_conf > 0.).astype('float32')
pred_box = pred_box.reshape((n, -1, 4))
pred_box[:, :, :2], pred_box[:, :, 2:4] = \
pred_box[:, :, :2] - pred_box[:, :, 2:4] / 2., \
pred_box[:, :, :2] + pred_box[:, :, 2:4] / 2.0
pred_box[:, :, 0] = pred_box[:, :, 0] * img_size[:, 1][:, np.newaxis]
pred_box[:, :, 1] = pred_box[:, :, 1] * img_size[:, 0][:, np.newaxis]
pred_box[:, :, 2] = pred_box[:, :, 2] * img_size[:, 1][:, np.newaxis]
pred_box[:, :, 3] = pred_box[:, :, 3] * img_size[:, 0][:, np.newaxis]
pred_conf[(pred_conf < conf_thresh)] = 0.0
pred_score = (sigmoid(x[:, :, :, :, 5:]) * pred_conf)
pred_box = (pred_box * (pred_conf > 0.0).astype('float32'))
pred_box = pred_box.reshape((n, (-1), 4))
(pred_box[:, :, :2], pred_box[:, :, 2:4]) = (
(pred_box[:, :, :2] - (pred_box[:, :, 2:4] / 2.0)),
(pred_box[:, :, :2] + (pred_box[:, :, 2:4] / 2.0)))
pred_box[:, :, 0] = (pred_box[:, :, 0] * img_size[:, 1][:, np.newaxis])
pred_box[:, :, 1] = (pred_box[:, :, 1] * img_size[:, 0][:, np.newaxis])
pred_box[:, :, 2] = (pred_box[:, :, 2] * img_size[:, 1][:, np.newaxis])
pred_box[:, :, 3] = (pred_box[:, :, 3] * img_size[:, 0][:, np.newaxis])
if clip_bbox:
for i in range(len(pred_box)):
pred_box[i, :, 0] = np.clip(pred_box[i, :, 0], 0, np.inf)
pred_box[i, :, 1] = np.clip(pred_box[i, :, 1], 0, np.inf)
pred_box[i, :, 2] = np.clip(pred_box[i, :, 2], -np.inf,
img_size[i, 1] - 1)
pred_box[i, :, 3] = np.clip(pred_box[i, :, 3], -np.inf,
img_size[i, 0] - 1)
return pred_box, pred_score.reshape((n, -1, class_num))
pred_box[i, :, 2] = np.clip(pred_box[i, :, 2], (-np.inf),
(img_size[(i, 1)] - 1))
pred_box[i, :, 3] = np.clip(pred_box[i, :, 3], (-np.inf),
(img_size[(i, 0)] - 1))
return (pred_box, pred_score.reshape((n, (-1), class_num)))
class TestYoloBoxOp(OpTest):
......@@ -99,42 +94,35 @@ class TestYoloBoxOp(OpTest):
self.op_type = 'yolo_box'
x = np.random.random(self.x_shape).astype('float32')
img_size = np.random.randint(10, 20, self.imgsize_shape).astype('int32')
self.attrs = {
"anchors": self.anchors,
"class_num": self.class_num,
"conf_thresh": self.conf_thresh,
"downsample": self.downsample,
"clip_bbox": self.clip_bbox,
"scale_x_y": self.scale_x_y,
"iou_aware": self.iou_aware,
"iou_aware_factor": self.iou_aware_factor
}
self.inputs = {
'X': x,
'ImgSize': img_size,
}
boxes, scores = YoloBox(x, img_size, self.attrs)
self.outputs = {
"Boxes": boxes,
"Scores": scores,
'anchors': self.anchors,
'class_num': self.class_num,
'conf_thresh': self.conf_thresh,
'downsample': self.downsample,
'clip_bbox': self.clip_bbox,
'scale_x_y': self.scale_x_y,
'iou_aware': self.iou_aware,
'iou_aware_factor': self.iou_aware_factor
}
self.inputs = {'X': x, 'ImgSize': img_size}
(boxes, scores) = YoloBox(x, img_size, self.attrs)
self.outputs = {'Boxes': boxes, 'Scores': scores}
def test_check_output(self):
self.check_output()
self.check_output(check_eager=True)
def initTestCase(self):
self.anchors = [10, 13, 16, 30, 33, 23]
an_num = int(len(self.anchors) // 2)
an_num = int((len(self.anchors) // 2))
self.batch_size = 32
self.class_num = 2
self.conf_thresh = 0.5
self.downsample = 32
self.clip_bbox = True
self.x_shape = (self.batch_size, an_num * (5 + self.class_num), 13, 13)
self.x_shape = (self.batch_size, (an_num * (5 + self.class_num)), 13,
13)
self.imgsize_shape = (self.batch_size, 2)
self.scale_x_y = 1.
self.scale_x_y = 1.0
self.iou_aware = False
self.iou_aware_factor = 0.5
......@@ -142,15 +130,16 @@ class TestYoloBoxOp(OpTest):
class TestYoloBoxOpNoClipBbox(TestYoloBoxOp):
def initTestCase(self):
self.anchors = [10, 13, 16, 30, 33, 23]
an_num = int(len(self.anchors) // 2)
an_num = int((len(self.anchors) // 2))
self.batch_size = 32
self.class_num = 2
self.conf_thresh = 0.5
self.downsample = 32
self.clip_bbox = False
self.x_shape = (self.batch_size, an_num * (5 + self.class_num), 13, 13)
self.x_shape = (self.batch_size, (an_num * (5 + self.class_num)), 13,
13)
self.imgsize_shape = (self.batch_size, 2)
self.scale_x_y = 1.
self.scale_x_y = 1.0
self.iou_aware = False
self.iou_aware_factor = 0.5
......@@ -158,13 +147,14 @@ class TestYoloBoxOpNoClipBbox(TestYoloBoxOp):
class TestYoloBoxOpScaleXY(TestYoloBoxOp):
def initTestCase(self):
self.anchors = [10, 13, 16, 30, 33, 23]
an_num = int(len(self.anchors) // 2)
an_num = int((len(self.anchors) // 2))
self.batch_size = 32
self.class_num = 2
self.conf_thresh = 0.5
self.downsample = 32
self.clip_bbox = True
self.x_shape = (self.batch_size, an_num * (5 + self.class_num), 13, 13)
self.x_shape = (self.batch_size, (an_num * (5 + self.class_num)), 13,
13)
self.imgsize_shape = (self.batch_size, 2)
self.scale_x_y = 1.2
self.iou_aware = False
......@@ -174,15 +164,16 @@ class TestYoloBoxOpScaleXY(TestYoloBoxOp):
class TestYoloBoxOpIoUAware(TestYoloBoxOp):
def initTestCase(self):
self.anchors = [10, 13, 16, 30, 33, 23]
an_num = int(len(self.anchors) // 2)
an_num = int((len(self.anchors) // 2))
self.batch_size = 32
self.class_num = 2
self.conf_thresh = 0.5
self.downsample = 32
self.clip_bbox = True
self.x_shape = (self.batch_size, an_num * (6 + self.class_num), 13, 13)
self.x_shape = (self.batch_size, (an_num * (6 + self.class_num)), 13,
13)
self.imgsize_shape = (self.batch_size, 2)
self.scale_x_y = 1.
self.scale_x_y = 1.0
self.iou_aware = True
self.iou_aware_factor = 0.5
......@@ -192,10 +183,9 @@ class TestYoloBoxDygraph(unittest.TestCase):
paddle.disable_static()
img_size = np.ones((2, 2)).astype('int32')
img_size = paddle.to_tensor(img_size)
x1 = np.random.random([2, 14, 8, 8]).astype('float32')
x1 = paddle.to_tensor(x1)
boxes, scores = paddle.vision.ops.yolo_box(
(boxes, scores) = paddle.vision.ops.yolo_box(
x1,
img_size=img_size,
anchors=[10, 13, 16, 30],
......@@ -203,12 +193,11 @@ class TestYoloBoxDygraph(unittest.TestCase):
conf_thresh=0.01,
downsample_ratio=8,
clip_bbox=True,
scale_x_y=1.)
assert boxes is not None and scores is not None
scale_x_y=1.0)
assert ((boxes is not None) and (scores is not None))
x2 = np.random.random([2, 16, 8, 8]).astype('float32')
x2 = paddle.to_tensor(x2)
boxes, scores = paddle.vision.ops.yolo_box(
(boxes, scores) = paddle.vision.ops.yolo_box(
x2,
img_size=img_size,
anchors=[10, 13, 16, 30],
......@@ -216,18 +205,21 @@ class TestYoloBoxDygraph(unittest.TestCase):
conf_thresh=0.01,
downsample_ratio=8,
clip_bbox=True,
scale_x_y=1.,
scale_x_y=1.0,
iou_aware=True,
iou_aware_factor=0.5)
paddle.enable_static()
def test_eager(self):
with _test_eager_guard():
self.test_dygraph()
class TestYoloBoxStatic(unittest.TestCase):
def test_static(self):
x1 = paddle.static.data('x1', [2, 14, 8, 8], 'float32')
img_size = paddle.static.data('img_size', [2, 2], 'int32')
boxes, scores = paddle.vision.ops.yolo_box(
(boxes, scores) = paddle.vision.ops.yolo_box(
x1,
img_size=img_size,
anchors=[10, 13, 16, 30],
......@@ -235,11 +227,10 @@ class TestYoloBoxStatic(unittest.TestCase):
conf_thresh=0.01,
downsample_ratio=8,
clip_bbox=True,
scale_x_y=1.)
assert boxes is not None and scores is not None
scale_x_y=1.0)
assert ((boxes is not None) and (scores is not None))
x2 = paddle.static.data('x2', [2, 16, 8, 8], 'float32')
boxes, scores = paddle.vision.ops.yolo_box(
(boxes, scores) = paddle.vision.ops.yolo_box(
x2,
img_size=img_size,
anchors=[10, 13, 16, 30],
......@@ -247,27 +238,27 @@ class TestYoloBoxStatic(unittest.TestCase):
conf_thresh=0.01,
downsample_ratio=8,
clip_bbox=True,
scale_x_y=1.,
scale_x_y=1.0,
iou_aware=True,
iou_aware_factor=0.5)
assert boxes is not None and scores is not None
assert ((boxes is not None) and (scores is not None))
class TestYoloBoxOpHW(TestYoloBoxOp):
def initTestCase(self):
self.anchors = [10, 13, 16, 30, 33, 23]
an_num = int(len(self.anchors) // 2)
an_num = int((len(self.anchors) // 2))
self.batch_size = 32
self.class_num = 2
self.conf_thresh = 0.5
self.downsample = 32
self.clip_bbox = False
self.x_shape = (self.batch_size, an_num * (5 + self.class_num), 13, 9)
self.x_shape = (self.batch_size, (an_num * (5 + self.class_num)), 13, 9)
self.imgsize_shape = (self.batch_size, 2)
self.scale_x_y = 1.
self.scale_x_y = 1.0
self.iou_aware = False
self.iou_aware_factor = 0.5
if __name__ == "__main__":
if (__name__ == '__main__'):
unittest.main()
python/paddle/fluid/tests/unittests/test_zeros_like_op.py
浏览文件 @ 97229944
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Copyright (c) 2022 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.
......@@ -13,13 +13,13 @@
# limitations under the License.
from __future__ import print_function
import unittest
import numpy as np
import paddle
import paddle.fluid as fluid
from paddle import zeros_like
from paddle.fluid import core, Program, program_guard
from paddle.fluid.framework import _test_eager_guard
class TestZerosLikeAPIError(unittest.TestCase):
......@@ -28,6 +28,10 @@ class TestZerosLikeAPIError(unittest.TestCase):
x = paddle.fluid.data('x', [3, 4])
self.assertRaises(TypeError, zeros_like, x, 'int8')
def test_eager(self):
with _test_eager_guard():
self.test_errors()
class TestZerosLikeAPI(unittest.TestCase):
def test_api(self):
......@@ -36,46 +40,48 @@ class TestZerosLikeAPI(unittest.TestCase):
train_program = Program()
with program_guard(train_program, startup_program):
x = paddle.fluid.data('X', shape)
# 'bool', 'float32', 'float64', 'int32', 'int64'
out1 = zeros_like(x)
out2 = zeros_like(x, np.bool)
out3 = zeros_like(x, 'float64')
out4 = zeros_like(x, 'int32')
out5 = zeros_like(x, 'int64')
place = fluid.CUDAPlace(0) if core.is_compiled_with_cuda(
) else fluid.CPUPlace()
place = (fluid.CUDAPlace(0)
if core.is_compiled_with_cuda() else fluid.CPUPlace())
exe = fluid.Executor(place)
outs = exe.run(train_program,
feed={'X': np.ones(shape).astype('float32')},
fetch_list=[out1, out2, out3, out4, out5])
for i, dtype in enumerate(
for (i, dtype) in enumerate(
[np.float32, np.bool, np.float64, np.int32, np.int64]):
self.assertEqual(outs[i].dtype, dtype)
self.assertEqual((outs[i] == np.zeros(shape, dtype)).all(), True)
def test_eager(self):
with _test_eager_guard():
self.test_api()
class TestZerosLikeImpeartive(unittest.TestCase):
def test_out(self):
shape = [3, 4]
place = fluid.CUDAPlace(0) if core.is_compiled_with_cuda(
) else fluid.CPUPlace()
place = (fluid.CUDAPlace(0)
if core.is_compiled_with_cuda() else fluid.CPUPlace())
paddle.disable_static(place)
x = paddle.to_tensor(np.ones(shape))
for dtype in [np.bool, np.float32, np.float64, np.int32, np.int64]:
out = zeros_like(x, dtype)
self.assertEqual((out.numpy() == np.zeros(shape, dtype)).all(),
True)
out = paddle.tensor.zeros_like(x)
self.assertEqual((out.numpy() == np.zeros(shape, dtype)).all(), True)
out = paddle.tensor.creation.zeros_like(x)
self.assertEqual((out.numpy() == np.zeros(shape, dtype)).all(), True)
paddle.enable_static()
def test_eager(self):
with _test_eager_guard():
self.test_out()
if __name__ == "__main__":
if (__name__ == '__main__'):
unittest.main()
python/paddle/fluid/tests/unittests/test_zeros_op.py
浏览文件 @ 97229944
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Copyright (c) 2022 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.
......@@ -13,56 +13,55 @@
# limitations under the License.
from __future__ import print_function
import unittest
import numpy as np
from op_test import OpTest
import paddle
import paddle.fluid.core as core
from paddle.fluid.op import Operator
import paddle.fluid as fluid
from paddle.fluid import compiler, Program, program_guard
from paddle.fluid.framework import _test_eager_guard
class TestZerosOpError(unittest.TestCase):
def test_errors(self):
with program_guard(Program(), Program()):
# The input dtype of zeros_op must be bool, float16, float32, float64, int32, int64.
shape = [4]
dtype = "int8"
dtype = 'int8'
self.assertRaises(TypeError, fluid.layers.zeros, shape, dtype)
def test_eager(self):
with _test_eager_guard():
self.test_errors()
class ApiZerosTest(unittest.TestCase):
def test_out(self):
with program_guard(Program()):
zeros = paddle.zeros(shape=[10], dtype="float64")
zeros = paddle.zeros(shape=[10], dtype='float64')
place = paddle.CPUPlace()
exe = paddle.static.Executor(place)
result, = exe.run(fetch_list=[zeros])
expected_result = np.zeros(10, dtype="float64")
(result, ) = exe.run(fetch_list=[zeros])
expected_result = np.zeros(10, dtype='float64')
self.assertEqual((result == expected_result).all(), True)
with paddle.static.program_guard(Program()):
zeros = paddle.zeros(shape=[10], dtype="int64")
zeros = paddle.zeros(shape=[10], dtype='int64')
place = paddle.CPUPlace()
exe = paddle.static.Executor(place)
result, = exe.run(fetch_list=[zeros])
expected_result = np.zeros(10, dtype="int64")
(result, ) = exe.run(fetch_list=[zeros])
expected_result = np.zeros(10, dtype='int64')
self.assertEqual((result == expected_result).all(), True)
with program_guard(Program()):
zeros = paddle.zeros(shape=[10], dtype="int64")
zeros = paddle.zeros(shape=[10], dtype='int64')
place = paddle.CPUPlace()
exe = paddle.static.Executor(place)
result, = exe.run(fetch_list=[zeros])
expected_result = np.zeros(10, dtype="int64")
(result, ) = exe.run(fetch_list=[zeros])
expected_result = np.zeros(10, dtype='int64')
self.assertEqual((result == expected_result).all(), True)
with program_guard(Program()):
out_np = np.zeros(shape=(1), dtype='float32')
out = paddle.zeros(shape=[1], dtype="float32")
out_np = np.zeros(shape=1, dtype='float32')
out = paddle.zeros(shape=[1], dtype='float32')
place = paddle.CPUPlace()
exe = paddle.static.Executor(place)
result = exe.run(fetch_list=[out])
......@@ -70,28 +69,37 @@ class ApiZerosTest(unittest.TestCase):
def test_fluid_out(self):
with program_guard(Program()):
zeros = fluid.layers.zeros(shape=[10], dtype="int64")
zeros = fluid.layers.zeros(shape=[10], dtype='int64')
place = paddle.CPUPlace()
exe = paddle.static.Executor(place)
result, = exe.run(fetch_list=[zeros])
expected_result = np.zeros(10, dtype="int64")
(result, ) = exe.run(fetch_list=[zeros])
expected_result = np.zeros(10, dtype='int64')
self.assertEqual((result == expected_result).all(), True)
def test_eager(self):
with _test_eager_guard():
self.test_out()
self.test_fluid_out()
class ApiZerosError(unittest.TestCase):
def test_errors(self):
def test_error1():
with paddle.static.program_guard(fluid.Program()):
ones = fluid.layers.zeros(shape=10, dtype="int64")
ones = fluid.layers.zeros(shape=10, dtype='int64')
self.assertRaises(TypeError, test_error1)
def test_error2():
with paddle.static.program_guard(fluid.Program()):
ones = fluid.layers.zeros(shape=[10], dtype="int8")
ones = fluid.layers.zeros(shape=[10], dtype='int8')
self.assertRaises(TypeError, test_error2)
def test_eager(self):
with _test_eager_guard():
self.test_errors()
if __name__ == "__main__":
if (__name__ == '__main__'):
unittest.main()
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