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.
352 lines
13 KiB
352 lines
13 KiB
# Copyright (c) 2018 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
|
|
|
|
import paddle
|
|
import unittest
|
|
import numpy as np
|
|
import paddle.fluid as fluid
|
|
import paddle.fluid.core as core
|
|
from op_test import OpTest
|
|
from paddle.fluid import compiler, Program, program_guard
|
|
|
|
|
|
class TestLRNOp(OpTest):
|
|
def get_input(self):
|
|
r''' TODO(gongweibao): why it's grad diff is so large?
|
|
x = np.ndarray(
|
|
shape=(self.N, self.C, self.H, self.W), dtype=float, order='C')
|
|
for m in range(0, self.N):
|
|
for i in range(0, self.C):
|
|
for h in range(0, self.H):
|
|
for w in range(0, self.W):
|
|
x[m][i][h][w] = m * self.C * self.H * self.W + \
|
|
i * self.H * self.W + \
|
|
h * self.W + w + 1
|
|
'''
|
|
x = np.random.rand(self.N, self.C, self.H, self.W).astype("float32")
|
|
return x + 1
|
|
|
|
def get_out(self):
|
|
start = -(self.n - 1) // 2
|
|
end = start + self.n
|
|
|
|
mid = np.empty((self.N, self.C, self.H, self.W)).astype("float32")
|
|
mid.fill(self.k)
|
|
for m in range(0, self.N):
|
|
for i in range(0, self.C):
|
|
for c in range(start, end):
|
|
ch = i + c
|
|
if ch < 0 or ch >= self.C:
|
|
continue
|
|
|
|
s = mid[m][i][:][:]
|
|
r = self.x[m][ch][:][:]
|
|
s += np.square(r) * self.alpha
|
|
|
|
mid2 = np.power(mid, -self.beta)
|
|
return np.multiply(self.x, mid2), mid
|
|
|
|
def get_attrs(self):
|
|
attrs = {
|
|
'n': self.n,
|
|
'k': self.k,
|
|
'alpha': self.alpha,
|
|
'beta': self.beta,
|
|
'data_format': self.data_format
|
|
}
|
|
return attrs
|
|
|
|
def setUp(self):
|
|
self.op_type = "lrn"
|
|
self.init_test_case()
|
|
|
|
self.N = 2
|
|
self.C = 3
|
|
self.H = 5
|
|
self.W = 5
|
|
|
|
self.n = 5
|
|
self.k = 2.0
|
|
self.alpha = 0.0001
|
|
self.beta = 0.75
|
|
self.x = self.get_input()
|
|
self.out, self.mid_out = self.get_out()
|
|
if self.data_format == 'NHWC':
|
|
self.x = np.transpose(self.x, [0, 2, 3, 1])
|
|
self.out = np.transpose(self.out, [0, 2, 3, 1])
|
|
self.mid_out = np.transpose(self.mid_out, [0, 2, 3, 1])
|
|
|
|
self.inputs = {'X': self.x}
|
|
self.outputs = {'Out': self.out, 'MidOut': self.mid_out}
|
|
self.attrs = self.get_attrs()
|
|
|
|
def init_test_case(self):
|
|
self.data_format = 'NCHW'
|
|
|
|
def test_check_output(self):
|
|
self.check_output()
|
|
|
|
def test_check_grad_normal(self):
|
|
self.check_grad(['X'], 'Out')
|
|
|
|
|
|
class TestLRNOpAttrDataFormat(TestLRNOp):
|
|
def init_test_case(self):
|
|
self.data_format = 'NHWC'
|
|
|
|
|
|
class TestLRNAPI(unittest.TestCase):
|
|
def test_case(self):
|
|
data1 = fluid.data(name='data1', shape=[2, 4, 5, 5], dtype='float32')
|
|
data2 = fluid.data(name='data2', shape=[2, 5, 5, 4], dtype='float32')
|
|
out1 = fluid.layers.lrn(data1, data_format='NCHW')
|
|
out2 = fluid.layers.lrn(data2, data_format='NHWC')
|
|
data1_np = np.random.random((2, 4, 5, 5)).astype("float32")
|
|
data2_np = np.transpose(data1_np, [0, 2, 3, 1])
|
|
|
|
if core.is_compiled_with_cuda():
|
|
place = core.CUDAPlace(0)
|
|
else:
|
|
place = core.CPUPlace()
|
|
exe = fluid.Executor(place)
|
|
exe.run(fluid.default_startup_program())
|
|
results = exe.run(fluid.default_main_program(),
|
|
feed={"data1": data1_np,
|
|
"data2": data2_np},
|
|
fetch_list=[out1, out2],
|
|
return_numpy=True)
|
|
|
|
self.assertTrue(
|
|
np.allclose(results[0], np.transpose(results[1], (0, 3, 1, 2))))
|
|
|
|
def test_exception(self):
|
|
input1 = fluid.data(name="input1", shape=[2, 4, 5, 5], dtype="float32")
|
|
input2 = fluid.data(
|
|
name="input2", shape=[2, 4, 5, 5, 5], dtype="float32")
|
|
|
|
def _attr_data_fromat():
|
|
out = fluid.layers.lrn(input1, data_format='NDHW')
|
|
|
|
def _input_dim_size():
|
|
out = fluid.layers.lrn(input2)
|
|
|
|
self.assertRaises(ValueError, _attr_data_fromat)
|
|
self.assertRaises(ValueError, _input_dim_size)
|
|
|
|
|
|
class TestLRNOpError(unittest.TestCase):
|
|
def test_errors(self):
|
|
with program_guard(Program(), Program()):
|
|
# the input must be float32
|
|
in_w = fluid.data(name="in_w", shape=[None, 3, 3, 3], dtype="int64")
|
|
self.assertRaises(TypeError, fluid.layers.lrn, in_w)
|
|
|
|
|
|
class TestLocalResponseNormFAPI(unittest.TestCase):
|
|
def setUp(self):
|
|
np.random.seed(123)
|
|
self.places = [fluid.CPUPlace()]
|
|
if core.is_compiled_with_cuda():
|
|
self.places.append(fluid.CUDAPlace(0))
|
|
|
|
def check_static_3d_input(self, place):
|
|
with fluid.program_guard(fluid.Program(), fluid.Program()):
|
|
in_np1 = np.random.random([3, 40, 40]).astype("float32")
|
|
in_np2 = np.transpose(in_np1, (0, 2, 1))
|
|
|
|
input1 = fluid.data(
|
|
name="input1", shape=[3, 40, 40], dtype="float32")
|
|
input2 = fluid.data(
|
|
name="input2", shape=[3, 40, 40], dtype="float32")
|
|
res1 = paddle.nn.functional.local_response_norm(
|
|
x=input1, size=5, data_format='NCL')
|
|
res2 = paddle.nn.functional.local_response_norm(
|
|
x=input2, size=5, data_format='NLC')
|
|
exe = fluid.Executor(place)
|
|
fetches = exe.run(fluid.default_main_program(),
|
|
feed={"input1": in_np1,
|
|
"input2": in_np2},
|
|
fetch_list=[res1, res2])
|
|
|
|
fetches1_tran = np.transpose(fetches[1], (0, 2, 1))
|
|
self.assertTrue(np.allclose(fetches[0], fetches1_tran))
|
|
|
|
def check_static_4d_input(self, place):
|
|
with fluid.program_guard(fluid.Program(), fluid.Program()):
|
|
input1 = fluid.data(
|
|
name="input1", shape=[3, 3, 40, 40], dtype="float32")
|
|
input2 = fluid.data(
|
|
name="input2", shape=[3, 40, 40, 3], dtype="float32")
|
|
|
|
res1 = paddle.nn.functional.local_response_norm(
|
|
x=input1, size=5, data_format='NCHW')
|
|
res2 = paddle.nn.functional.local_response_norm(
|
|
x=input2, size=5, data_format='NHWC')
|
|
|
|
in_np1 = np.random.random([3, 3, 40, 40]).astype("float32")
|
|
in_np2 = np.transpose(in_np1, (0, 2, 3, 1))
|
|
|
|
exe = fluid.Executor(place)
|
|
fetches = exe.run(fluid.default_main_program(),
|
|
feed={"input1": in_np1,
|
|
"input2": in_np2},
|
|
fetch_list=[res1, res2])
|
|
|
|
fetches1_tran = np.transpose(fetches[1], (0, 3, 1, 2))
|
|
self.assertTrue(np.allclose(fetches[0], fetches1_tran))
|
|
|
|
def check_static_5d_input(self, place):
|
|
with fluid.program_guard(fluid.Program(), fluid.Program()):
|
|
input1 = fluid.data(
|
|
name="input1", shape=[3, 3, 3, 40, 40], dtype="float32")
|
|
input2 = fluid.data(
|
|
name="input2", shape=[3, 3, 40, 40, 3], dtype="float32")
|
|
res1 = paddle.nn.functional.local_response_norm(
|
|
x=input1, size=5, data_format='NCDHW')
|
|
res2 = paddle.nn.functional.local_response_norm(
|
|
x=input2, size=5, data_format='NDHWC')
|
|
|
|
in_np1 = np.random.random([3, 3, 3, 40, 40]).astype("float32")
|
|
in_np2 = np.transpose(in_np1, (0, 2, 3, 4, 1))
|
|
|
|
exe = fluid.Executor(place)
|
|
fetches = exe.run(fluid.default_main_program(),
|
|
feed={"input1": in_np1,
|
|
"input2": in_np2},
|
|
fetch_list=[res1, res2])
|
|
|
|
fetches1_tran = np.transpose(fetches[1], (0, 4, 1, 2, 3))
|
|
self.assertTrue(np.allclose(fetches[0], fetches1_tran))
|
|
|
|
def test_static(self):
|
|
for place in self.places:
|
|
self.check_static_3d_input(place=place)
|
|
self.check_static_4d_input(place=place)
|
|
self.check_static_5d_input(place=place)
|
|
|
|
def check_dygraph_3d_input(self, place):
|
|
with fluid.dygraph.guard(place):
|
|
in_np1 = np.random.random([3, 40, 40]).astype("float32")
|
|
in_np2 = np.transpose(in_np1, (0, 2, 1))
|
|
|
|
in1 = paddle.to_tensor(in_np1)
|
|
in2 = paddle.to_tensor(in_np2)
|
|
|
|
res1 = paddle.nn.functional.local_response_norm(
|
|
x=in1, size=5, data_format='NCL')
|
|
res2 = paddle.nn.functional.local_response_norm(
|
|
x=in2, size=5, data_format='NLC')
|
|
|
|
res2_tran = np.transpose(res2.numpy(), (0, 2, 1))
|
|
self.assertTrue(np.allclose(res1.numpy(), res2_tran))
|
|
|
|
def check_dygraph_4d_input(self, place):
|
|
with fluid.dygraph.guard(place):
|
|
in_np1 = np.random.random([3, 3, 40, 40]).astype("float32")
|
|
in_np2 = np.transpose(in_np1, (0, 2, 3, 1))
|
|
|
|
in1 = paddle.to_tensor(in_np1)
|
|
in2 = paddle.to_tensor(in_np2)
|
|
|
|
res1 = paddle.nn.functional.local_response_norm(
|
|
x=in1, size=5, data_format='NCHW')
|
|
res2 = paddle.nn.functional.local_response_norm(
|
|
x=in2, size=5, data_format='NHWC')
|
|
|
|
res2_tran = np.transpose(res2.numpy(), (0, 3, 1, 2))
|
|
self.assertTrue(np.allclose(res1.numpy(), res2_tran))
|
|
|
|
def check_dygraph_5d_input(self, place):
|
|
with fluid.dygraph.guard(place):
|
|
in_np1 = np.random.random([3, 3, 3, 40, 40]).astype("float32")
|
|
in_np2 = np.transpose(in_np1, (0, 2, 3, 4, 1))
|
|
|
|
in1 = paddle.to_tensor(in_np1)
|
|
in2 = paddle.to_tensor(in_np2)
|
|
|
|
res1 = paddle.nn.functional.local_response_norm(
|
|
x=in1, size=5, data_format='NCDHW')
|
|
res2 = paddle.nn.functional.local_response_norm(
|
|
x=in2, size=5, data_format='NDHWC')
|
|
|
|
res2_tran = np.transpose(res2.numpy(), (0, 4, 1, 2, 3))
|
|
self.assertTrue(np.allclose(res1.numpy(), res2_tran))
|
|
|
|
def test_dygraph(self):
|
|
for place in self.places:
|
|
self.check_dygraph_3d_input(place)
|
|
self.check_dygraph_4d_input(place)
|
|
self.check_dygraph_5d_input(place)
|
|
|
|
|
|
class TestLocalResponseNormFAPIError(unittest.TestCase):
|
|
def test_errors(self):
|
|
with program_guard(Program(), Program()):
|
|
|
|
def test_Variable():
|
|
# the input of lrn must be Variable.
|
|
x1 = fluid.create_lod_tensor(
|
|
np.array([-1, 3, 5, 5]), [[1, 1, 1, 1]], fluid.CPUPlace())
|
|
paddle.nn.functional.local_response_norm(x1, size=5)
|
|
|
|
self.assertRaises(TypeError, test_Variable)
|
|
|
|
def test_datatype():
|
|
x = fluid.data(name='x', shape=[3, 4, 5, 6], dtype="int32")
|
|
paddle.nn.functional.local_response_norm(x, size=5)
|
|
|
|
self.assertRaises(TypeError, test_datatype)
|
|
|
|
def test_dataformat():
|
|
x = fluid.data(name='x', shape=[3, 4, 5, 6], dtype="float32")
|
|
paddle.nn.functional.local_response_norm(
|
|
x, size=5, data_format="NCTHW")
|
|
|
|
self.assertRaises(ValueError, test_dataformat)
|
|
|
|
def test_dim():
|
|
x = fluid.data(name='x', shape=[3, 4], dtype="float32")
|
|
paddle.nn.functional.local_response_norm(x, size=5)
|
|
|
|
self.assertRaises(ValueError, test_dim)
|
|
|
|
|
|
class TestLocalResponseNormCAPI(unittest.TestCase):
|
|
def setUp(self):
|
|
np.random.seed(123)
|
|
self.places = [fluid.CPUPlace()]
|
|
if core.is_compiled_with_cuda():
|
|
self.places.append(fluid.CUDAPlace(0))
|
|
|
|
def test_dygraph(self):
|
|
for place in self.places:
|
|
with fluid.dygraph.guard(place):
|
|
in1 = paddle.rand(shape=(3, 3, 40, 40), dtype="float32")
|
|
in2 = paddle.transpose(in1, [0, 2, 3, 1])
|
|
|
|
m1 = paddle.nn.LocalResponseNorm(size=5, data_format='NCHW')
|
|
m2 = paddle.nn.LocalResponseNorm(size=5, data_format='NHWC')
|
|
|
|
res1 = m1(in1)
|
|
res2 = m2(in2)
|
|
|
|
res2_tran = np.transpose(res2.numpy(), (0, 3, 1, 2))
|
|
self.assertTrue(np.allclose(res1.numpy(), res2_tran))
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|