test_layers.py 47.5 KB
Newer Older
1
#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
D
dzhwinter 已提交
2
#
D
dzhwinter 已提交
3 4 5
# 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
D
dzhwinter 已提交
6
#
D
dzhwinter 已提交
7
#     http://www.apache.org/licenses/LICENSE-2.0
D
dzhwinter 已提交
8
#
D
dzhwinter 已提交
9 10 11 12 13 14
# 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.

Y
Yu Yang 已提交
15
from __future__ import print_function
Q
Qiao Longfei 已提交
16 17
import unittest

18 19 20 21 22 23
import contextlib
import numpy as np
import decorators

import paddle
import paddle.fluid as fluid
24
from paddle.fluid.layers.device import get_places
25 26 27
import paddle.fluid.nets as nets
from paddle.fluid.framework import Program, program_guard, default_main_program
from paddle.fluid.param_attr import ParamAttr
28
from paddle.fluid import core
J
jerrywgz 已提交
29
from paddle.fluid.initializer import Constant
30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86
import paddle.fluid.layers as layers
from test_imperative_base import new_program_scope
from paddle.fluid.imperative import nn
from paddle.fluid.imperative import base


class LayerTest(unittest.TestCase):
    @classmethod
    def setUpClass(cls):
        cls.seed = 111

    @classmethod
    def tearDownClass(cls):
        pass

    def _get_place(self):
        if core.is_compiled_with_cuda():
            return core.CUDAPlace(0)
        return core.CPUPlace()

    @contextlib.contextmanager
    def static_graph(self):
        with new_program_scope():
            fluid.default_startup_program().random_seed = self.seed
            fluid.default_main_program().random_seed = self.seed
            yield

    def get_static_graph_result(self, feed, fetch_list):
        exe = fluid.Executor(self._get_place())
        exe.run(fluid.default_startup_program())
        return exe.run(fluid.default_main_program(),
                       feed=feed,
                       fetch_list=fetch_list)

    @contextlib.contextmanager
    def dynamic_graph(self):
        with fluid.imperative.guard(self._get_place()):
            fluid.default_startup_program().random_seed = self.seed
            fluid.default_main_program().random_seed = self.seed
            yield


class TestLayer(LayerTest):
    def test_relu(self):
        with self.static_graph():
            t = layers.data(name='t', shape=[3, 3], dtype='float32')
            ret = layers.relu(t)
            static_ret = self.get_static_graph_result(
                feed={'t': np.ones(
                    [3, 3], dtype='float32')}, fetch_list=[ret])[0]

        with self.dynamic_graph():
            t = np.ones([3, 3], dtype='float32')
            dy_ret = layers.relu(base.to_variable(t))

        self.assertTrue(np.allclose(static_ret, dy_ret._numpy()))

87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103
    def test_matmul(self):
        with self.static_graph():
            t = layers.data(name='t', shape=[3, 3], dtype='float32')
            t2 = layers.data(name='t2', shape=[3, 3], dtype='float32')
            ret = layers.matmul(t, t2)
            static_ret = self.get_static_graph_result(
                feed={
                    't': np.ones(
                        [3, 3], dtype='float32'),
                    't2': np.ones(
                        [3, 3], dtype='float32')
                },
                fetch_list=[ret])[0]

        with self.dynamic_graph():
            t = np.ones([3, 3], dtype='float32')
            t2 = np.ones([3, 3], dtype='float32')
X
polish  
Xin Pan 已提交
104
            dy_ret = layers.matmul(base.to_variable(t), base.to_variable(t2))
105 106 107

        self.assertTrue(np.allclose(static_ret, dy_ret._numpy()))

108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134
    def test_conv2d(self):
        with self.static_graph():
            images = layers.data(name='pixel', shape=[3, 5, 5], dtype='float32')
            ret = layers.conv2d(input=images, num_filters=3, filter_size=[2, 2])
            static_ret = self.get_static_graph_result(
                feed={'pixel': np.ones(
                    [2, 3, 5, 5], dtype='float32')},
                fetch_list=[ret])[0]

        with self.static_graph():
            images = layers.data(name='pixel', shape=[3, 5, 5], dtype='float32')
            conv2d = nn.Conv2D(
                'conv2d', num_channels=3, num_filters=3, filter_size=[2, 2])
            ret = conv2d(images)
            static_ret2 = self.get_static_graph_result(
                feed={'pixel': np.ones(
                    [2, 3, 5, 5], dtype='float32')},
                fetch_list=[ret])[0]

        with self.dynamic_graph():
            images = np.ones([2, 3, 5, 5], dtype='float32')
            conv2d = nn.Conv2D(
                'conv2d', num_channels=3, num_filters=3, filter_size=[2, 2])
            dy_ret = conv2d(base.to_variable(images))

        self.assertTrue(np.allclose(static_ret, dy_ret._numpy()))
        self.assertTrue(np.allclose(static_ret, static_ret2))
Y
Yu Yang 已提交
135

M
minqiyang 已提交
136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176
    def test_gru_unit(self):
        lod = [[2, 4, 3]]
        D = 5
        T = sum(lod[0])
        N = len(lod[0])

        input = np.random.rand(T, 3 * D).astype('float32')
        hidden_input = np.random.rand(T, D).astype('float32')

        with self.static_graph():
            x = layers.data(name='x', shape=[-1, D * 3], dtype='float32')
            hidden = layers.data(name='hidden', shape=[-1, D], dtype='float32')
            updated_hidden, reset_hidden_pre, gate = layers.gru_unit(
                input=x, hidden=hidden, size=D * 3)
            static_ret = self.get_static_graph_result(
                feed={'x': input,
                      'hidden': hidden_input},
                fetch_list=[updated_hidden, reset_hidden_pre, gate])

        with self.static_graph():
            x = layers.data(name='x', shape=[-1, D * 3], dtype='float32')
            hidden = layers.data(name='hidden', shape=[-1, D], dtype='float32')
            updated_hidden, reset_hidden_pre, gate = layers.gru_unit(
                input=x, hidden=hidden, size=D * 3)
            gru = nn.GRUUnit('gru', size=D * 3)
            updated_hidden, reset_hidden_pre, gate = gru(x, hidden)

            static_ret2 = self.get_static_graph_result(
                feed={'x': input,
                      'hidden': hidden_input},
                fetch_list=[updated_hidden, reset_hidden_pre, gate])

        with self.dynamic_graph():
            gru = nn.GRUUnit('gru', size=D * 3)
            dy_ret = gru(
                base.to_variable(input), base.to_variable(hidden_input))

        for i in range(len(static_ret)):
            self.assertTrue(np.allclose(static_ret[i], static_ret2[i]))
            self.assertTrue(np.allclose(static_ret[i], dy_ret[i]._numpy()))

X
Xin Pan 已提交
177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230
    def test_elementwise_math(self):
        n = np.ones([3, 3], dtype='float32')
        n2 = np.ones([3, 3], dtype='float32') * 1.1
        n3 = np.ones([3, 3], dtype='float32') * 2
        n4 = np.ones([3, 3], dtype='float32') * 3
        n5 = np.ones([3, 3], dtype='float32') * 4
        n6 = np.ones([3, 3], dtype='float32') * 5

        with self.static_graph():
            t = layers.data(name='t', shape=[3, 3], dtype='float32')
            t2 = layers.data(name='t2', shape=[3, 3], dtype='float32')
            t3 = layers.data(name='t3', shape=[3, 3], dtype='float32')
            t4 = layers.data(name='t4', shape=[3, 3], dtype='float32')
            t5 = layers.data(name='t5', shape=[3, 3], dtype='float32')
            t6 = layers.data(name='t6', shape=[3, 3], dtype='float32')

            ret = layers.elementwise_add(t, t2)
            ret = layers.elementwise_pow(ret, t3)
            ret = layers.elementwise_div(ret, t4)
            ret = layers.elementwise_sub(ret, t5)
            ret = layers.elementwise_mul(ret, t6)

            static_ret = self.get_static_graph_result(
                feed={
                    't': n,
                    't2': n2,
                    't3': n3,
                    't4': n4,
                    't5': n5,
                    't6': n6
                },
                fetch_list=[ret])[0]

        with self.dynamic_graph():
            ret = layers.elementwise_add(n, n2)
            ret = layers.elementwise_pow(ret, n3)
            ret = layers.elementwise_div(ret, n4)
            ret = layers.elementwise_sub(ret, n5)
            dy_ret = layers.elementwise_mul(ret, n6)
        self.assertTrue(
            np.allclose(static_ret, dy_ret._numpy()),
            '%s vs %s' % (static_ret, dy_ret._numpy()))

    def test_elementwise_minmax(self):
        n = np.ones([3, 3], dtype='float32')
        n2 = np.ones([3, 3], dtype='float32') * 2

        with self.dynamic_graph():
            min_ret = layers.elementwise_min(n, n2)
            max_ret = layers.elementwise_max(n, n2)

        self.assertTrue(np.allclose(n, min_ret._numpy()))
        self.assertTrue(np.allclose(n2, max_ret._numpy()))

Y
Yu Yang 已提交
231 232 233

class TestBook(unittest.TestCase):
    def test_fit_a_line(self):
234
        program = Program()
Y
Yu Yang 已提交
235 236 237 238 239
        with program_guard(program, startup_program=Program()):
            x = layers.data(name='x', shape=[13], dtype='float32')
            y_predict = layers.fc(input=x, size=1, act=None)
            y = layers.data(name='y', shape=[1], dtype='float32')
            cost = layers.square_error_cost(input=y_predict, label=y)
Y
Yu Yang 已提交
240
            avg_cost = layers.mean(cost)
Y
Yu Yang 已提交
241
            self.assertIsNotNone(avg_cost)
Y
Yu Yang 已提交
242

Y
Yu Yang 已提交
243
        print(str(program))
Y
Yu Yang 已提交
244 245

    def test_recognize_digits_mlp(self):
246
        program = Program()
Y
Yu Yang 已提交
247 248 249 250 251 252
        with program_guard(program, startup_program=Program()):
            # Change g_program, so the rest layers use `g_program`
            images = layers.data(name='pixel', shape=[784], dtype='float32')
            label = layers.data(name='label', shape=[1], dtype='int32')
            hidden1 = layers.fc(input=images, size=128, act='relu')
            hidden2 = layers.fc(input=hidden1, size=64, act='relu')
253 254 255 256
            predict = layers.fc(input=[hidden2, hidden1],
                                size=10,
                                act='softmax',
                                param_attr=["sftmax.w1", "sftmax.w2"])
Y
Yu Yang 已提交
257
            cost = layers.cross_entropy(input=predict, label=label)
Y
Yu Yang 已提交
258
            avg_cost = layers.mean(cost)
Y
Yu Yang 已提交
259 260 261
            self.assertIsNotNone(avg_cost)

        print(str(program))
262 263

    def test_simple_conv2d(self):
F
fengjiayi 已提交
264
        program = Program()
Y
Yu Yang 已提交
265
        with program_guard(program, startup_program=Program()):
266 267
            images = layers.data(
                name='pixel', shape=[3, 48, 48], dtype='float32')
Y
Yu Yang 已提交
268 269 270
            layers.conv2d(input=images, num_filters=3, filter_size=[4, 4])

        print(str(program))
Y
Yu Yang 已提交
271

272 273
    def test_conv2d_transpose(self):
        program = Program()
Y
Yu Yang 已提交
274 275 276 277
        with program_guard(program):
            img = layers.data(name='pixel', shape=[3, 2, 2], dtype='float32')
            layers.conv2d_transpose(input=img, num_filters=10, output_size=28)
        print(str(program))
278

F
fengjiayi 已提交
279
    def test_recognize_digits_conv(self):
F
fengjiayi 已提交
280
        program = Program()
Y
Yu Yang 已提交
281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301
        with program_guard(program, startup_program=Program()):
            images = layers.data(
                name='pixel', shape=[1, 28, 28], dtype='float32')
            label = layers.data(name='label', shape=[1], dtype='int32')
            conv_pool_1 = nets.simple_img_conv_pool(
                input=images,
                filter_size=5,
                num_filters=2,
                pool_size=2,
                pool_stride=2,
                act="relu")
            conv_pool_2 = nets.simple_img_conv_pool(
                input=conv_pool_1,
                filter_size=5,
                num_filters=4,
                pool_size=2,
                pool_stride=2,
                act="relu")

            predict = layers.fc(input=conv_pool_2, size=10, act="softmax")
            cost = layers.cross_entropy(input=predict, label=label)
Y
Yu Yang 已提交
302
            avg_cost = layers.mean(cost)
Y
Yu Yang 已提交
303 304

        print(str(program))
305

Q
QI JUN 已提交
306 307
    def test_word_embedding(self):
        program = Program()
Y
Yu Yang 已提交
308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347
        with program_guard(program, startup_program=Program()):
            dict_size = 10000
            embed_size = 32
            first_word = layers.data(name='firstw', shape=[1], dtype='int64')
            second_word = layers.data(name='secondw', shape=[1], dtype='int64')
            third_word = layers.data(name='thirdw', shape=[1], dtype='int64')
            forth_word = layers.data(name='forthw', shape=[1], dtype='int64')
            next_word = layers.data(name='nextw', shape=[1], dtype='int64')

            embed_first = layers.embedding(
                input=first_word,
                size=[dict_size, embed_size],
                dtype='float32',
                param_attr='shared_w')
            embed_second = layers.embedding(
                input=second_word,
                size=[dict_size, embed_size],
                dtype='float32',
                param_attr='shared_w')

            embed_third = layers.embedding(
                input=third_word,
                size=[dict_size, embed_size],
                dtype='float32',
                param_attr='shared_w')
            embed_forth = layers.embedding(
                input=forth_word,
                size=[dict_size, embed_size],
                dtype='float32',
                param_attr='shared_w')

            concat_embed = layers.concat(
                input=[embed_first, embed_second, embed_third, embed_forth],
                axis=1)

            hidden1 = layers.fc(input=concat_embed, size=256, act='sigmoid')
            predict_word = layers.fc(input=hidden1,
                                     size=dict_size,
                                     act='softmax')
            cost = layers.cross_entropy(input=predict_word, label=next_word)
Y
Yu Yang 已提交
348
            avg_cost = layers.mean(cost)
Y
Yu Yang 已提交
349 350 351
            self.assertIsNotNone(avg_cost)

        print(str(program))
Q
Qiao Longfei 已提交
352 353 354

    def test_linear_chain_crf(self):
        program = Program()
Y
Yu Yang 已提交
355
        with program_guard(program, startup_program=Program()):
Q
Qiao Longfei 已提交
356
            label_dict_len = 10
Y
Yu Yang 已提交
357 358 359
            images = layers.data(name='pixel', shape=[784], dtype='float32')
            label = layers.data(name='label', shape=[1], dtype='int32')
            hidden = layers.fc(input=images, size=128)
Q
Qiao Longfei 已提交
360 361 362 363
            crf = layers.linear_chain_crf(
                input=hidden, label=label, param_attr=ParamAttr(name="crfw"))
            crf_decode = layers.crf_decoding(
                input=hidden, param_attr=ParamAttr(name="crfw"))
Q
Qiao Longfei 已提交
364 365 366 367
            layers.chunk_eval(
                input=crf_decode,
                label=label,
                chunk_scheme="IOB",
M
minqiyang 已提交
368
                num_chunk_types=(label_dict_len - 1) // 2)
Q
qiaolongfei 已提交
369 370
            self.assertFalse(crf is None)
            self.assertFalse(crf_decode is None)
Y
Yu Yang 已提交
371 372

        print(str(program))
Q
QI JUN 已提交
373

374 375 376 377 378
    def test_sigmoid_cross_entropy(self):
        program = Program()
        with program_guard(program):
            dat = layers.data(name='data', shape=[10], dtype='float32')
            lbl = layers.data(name='label', shape=[10], dtype='float32')
379
            ignore_index = -1
380 381
            self.assertIsNotNone(
                layers.sigmoid_cross_entropy_with_logits(
J
jerrywgz 已提交
382
                    x=dat, label=lbl, ignore_index=ignore_index))
383 384
        print(str(program))

W
weixing02 已提交
385 386 387
    def test_hsigmoid(self):
        program = Program()
        with program_guard(program):
W
weixing02 已提交
388 389
            x = layers.data(name='x', shape=[2], dtype='float32')
            y = layers.data(name='y', shape=[2], dtype='int64')
W
weixing02 已提交
390 391 392 393 394
            self.assertIsNotNone(
                layers.hsigmoid(
                    input=x, label=y, num_classes=2))
        print(str(program))

J
JiabinYang 已提交
395
        # test hsigmod with custom tree structure
J
JiabinYang 已提交
396 397 398 399
        program2 = Program()
        with program_guard(program2):
            x2 = layers.data(name='x2', shape=[4, 8], dtype='float32')
            y2 = layers.data(name='y2', shape=[4], dtype='int64')
400 401 402 403
            path_table = layers.data(
                name='path_table', shape=[4, 6], dtype='int64')
            path_code = layers.data(
                name='path_code', shape=[4, 6], dtype='int64')
J
JiabinYang 已提交
404 405 406 407
            self.assertIsNotNone(
                layers.hsigmoid(
                    input=x2,
                    label=y2,
408
                    num_classes=6,
409 410 411
                    path_table=path_table,
                    path_code=path_code,
                    is_custom=True))
J
JiabinYang 已提交
412 413
            print(str(program2))

Y
yangyaming 已提交
414
    def test_sequence_expand(self):
Y
yangyaming 已提交
415 416 417 418
        program = Program()
        with program_guard(program):
            x = layers.data(name='x', shape=[10], dtype='float32')
            y = layers.data(
Y
yangyaming 已提交
419 420
                name='y', shape=[10, 20], dtype='float32', lod_level=2)
            self.assertIsNotNone(layers.sequence_expand(x=x, y=y, ref_level=1))
Y
yangyaming 已提交
421 422
        print(str(program))

Y
Yibing Liu 已提交
423 424 425 426 427 428 429 430
    def test_sequence_unpad(self):
        program = Program()
        with program_guard(program):
            x = layers.data(name='x', shape=[10, 5], dtype='float32')
            length = layers.data(name='length', shape=[1], dtype='int64')
            self.assertIsNotNone(layers.sequence_unpad(x=x, length=length))
        print(str(program))

J
JiabinYang 已提交
431 432 433 434
    def test_pool2d(self):
        program = Program()
        with program_guard(program):
            x = layers.data(name='x', shape=[3, 224, 224], dtype='float32')
J
JiabinYang 已提交
435 436 437 438 439 440
            self.assertIsNotNone(
                layers.pool2d(
                    x,
                    pool_size=[5, 3],
                    pool_stride=[1, 2],
                    pool_padding=(2, 1)))
J
JiabinYang 已提交
441

442 443 444 445 446 447 448
    def test_adaptive_pool2d(self):
        program = Program()
        with program_guard(program):
            x = layers.data(name='x', shape=[3, 224, 224], dtype='float32')
            self.assertIsNotNone(
                layers.adaptive_pool2d(
                    x, [3, 3], pool_type='avg'))
D
dengkaipeng 已提交
449 450 451
            pool, mask = layers.adaptive_pool2d(x, [3, 3], require_index=True)
            self.assertIsNotNone(pool)
            self.assertIsNotNone(mask)
452 453 454 455
            self.assertIsNotNone(layers.adaptive_pool2d(x, 3, pool_type='avg'))
            pool, mask = layers.adaptive_pool2d(x, 3, require_index=True)
            self.assertIsNotNone(pool)
            self.assertIsNotNone(mask)
456 457 458 459 460 461 462 463

    def test_adaptive_pool3d(self):
        program = Program()
        with program_guard(program):
            x = layers.data(name='x', shape=[3, 244, 224, 224], dtype='float32')
            self.assertIsNotNone(
                layers.adaptive_pool3d(
                    x, [3, 3, 3], pool_type='avg'))
D
dengkaipeng 已提交
464 465 466 467
            pool, mask = layers.adaptive_pool3d(
                x, [3, 3, 3], require_index=True)
            self.assertIsNotNone(pool)
            self.assertIsNotNone(mask)
468 469 470 471
            self.assertIsNotNone(layers.adaptive_pool3d(x, 3, pool_type='avg'))
            pool, mask = layers.adaptive_pool3d(x, 3, require_index=True)
            self.assertIsNotNone(pool)
            self.assertIsNotNone(mask)
472

Y
yangyaming 已提交
473 474 475 476 477 478 479
    def test_lstm_unit(self):
        program = Program()
        with program_guard(program):
            x_t_data = layers.data(
                name='x_t_data', shape=[10, 10], dtype='float32')
            x_t = layers.fc(input=x_t_data, size=10)
            prev_hidden_data = layers.data(
Y
yangyaming 已提交
480 481
                name='prev_hidden_data', shape=[10, 30], dtype='float32')
            prev_hidden = layers.fc(input=prev_hidden_data, size=30)
Y
yangyaming 已提交
482 483 484 485 486 487 488 489
            prev_cell_data = layers.data(
                name='prev_cell', shape=[10, 30], dtype='float32')
            prev_cell = layers.fc(input=prev_cell_data, size=30)
            self.assertIsNotNone(
                layers.lstm_unit(
                    x_t=x_t, hidden_t_prev=prev_hidden, cell_t_prev=prev_cell))
        print(str(program))

490 491 492 493 494 495 496 497 498 499 500 501
    def test_dynamic_lstmp(self):
        program = Program()
        with program_guard(program):
            hidden_dim, proj_dim = 16, 8
            seq_data = layers.data(
                name='seq_data', shape=[10, 10], dtype='float32', lod_level=1)
            fc_out = layers.fc(input=seq_data, size=4 * hidden_dim)
            self.assertIsNotNone(
                layers.dynamic_lstmp(
                    input=fc_out, size=4 * hidden_dim, proj_size=proj_dim))
        print(str(program))

Y
yangyaming 已提交
502 503 504 505 506 507
    def test_sequence_softmax(self):
        program = Program()
        with program_guard(program):
            seq_data = layers.data(
                name='seq_data', shape=[10, 10], dtype='float32', lod_level=1)
            seq = layers.fc(input=seq_data, size=20)
508
            self.assertIsNotNone(layers.sequence_softmax(seq))
Y
yangyaming 已提交
509 510
        print(str(program))

D
dangqingqing 已提交
511 512 513 514 515
    def test_softmax(self):
        program = Program()
        with program_guard(program):
            data = layers.data(name='data', shape=[10], dtype='float32')
            hid = layers.fc(input=data, size=20)
516
            self.assertIsNotNone(layers.softmax(hid))
D
dangqingqing 已提交
517 518
        print(str(program))

J
JiabinYang 已提交
519
    def test_space_to_depth(self):
J
JiabinYang 已提交
520 521 522
        program = Program()
        with program_guard(program):
            data = layers.data(
J
JiabinYang 已提交
523
                name='data',
J
JiabinYang 已提交
524 525 526
                shape=[32, 9, 6, 6],
                append_batch_size=False,
                dtype='float32')
J
JiabinYang 已提交
527
            self.assertIsNotNone(layers.space_to_depth(data, 3))
J
JiabinYang 已提交
528 529
        print(str(program))

Y
Yibing Liu 已提交
530 531 532
    def test_sequence_unsqueeze(self):
        program = Program()
        with program_guard(program):
533
            x = layers.data(name='x', shape=[8, 2], dtype='float32')
534
            out = layers.unsqueeze(input=x, axes=[1])
Y
Yibing Liu 已提交
535 536
            self.assertIsNotNone(out)
        print(str(program))
537

Y
Yibing Liu 已提交
538 539 540 541
    def test_squeeze(self):
        program = Program()
        with program_guard(program):
            x = layers.data(name='x', shape=[1, 1, 4], dtype='float32')
542
            out = layers.squeeze(input=x, axes=[2])
Y
Yibing Liu 已提交
543 544 545
            self.assertIsNotNone(out)
        print(str(program))

D
dragonwarrior 已提交
546 547 548 549 550 551 552
    def test_lrn(self):
        program = Program()
        with program_guard(program):
            data = layers.data(name='data', shape=[6, 2, 2], dtype='float32')
            self.assertIsNotNone(layers.lrn(data))
        print(str(program))

Q
qijun 已提交
553 554 555
    def test_get_places(self):
        program = Program()
        with program_guard(program):
556
            x = get_places(device_count=4)
Y
Yang Yu 已提交
557
            self.assertIsNotNone(x)
Q
qijun 已提交
558 559
        print(str(program))

560 561 562 563 564 565 566 567
    def test_sequence_reshape(self):
        program = Program()
        with program_guard(program):
            x = layers.data(name='x', shape=[8], dtype='float32', lod_level=1)
            out = layers.sequence_reshape(input=x, new_dim=16)
            self.assertIsNotNone(out)
        print(str(program))

W
wanghaoshuang 已提交
568 569 570 571
    def test_im2sequence(self):
        program = Program()
        with program_guard(program):
            x = layers.data(name='x', shape=[3, 128, 128], dtype='float32')
572
            y = layers.data(name='y', shape=[], dtype='float32')
W
wanghaoshuang 已提交
573
            output = layers.im2sequence(
574 575 576 577 578
                input=x,
                input_image_size=y,
                stride=[1, 1],
                filter_size=[2, 2],
                out_stride=[1, 1])
W
wanghaoshuang 已提交
579 580 581
            self.assertIsNotNone(output)
        print(str(program))

582
    def test_sampled_softmax_with_cross_entropy(self):
X
xuezhong 已提交
583 584 585
        program = Program()
        with program_guard(program):
            logits = layers.data(name='Logits', shape=[256], dtype='float64')
X
xuezhong 已提交
586
            label = layers.data(name='Label', shape=[1], dtype='int64')
X
xuezhong 已提交
587
            num_samples = 25
X
xuezhong 已提交
588 589
            output = layers.sampled_softmax_with_cross_entropy(logits, label,
                                                               num_samples)
X
xuezhong 已提交
590 591 592
            self.assertIsNotNone(output)
        print(str(program))

Y
Yang Yu 已提交
593 594 595 596
    @decorators.prog_scope()
    def test_nce(self):
        window_size = 5
        words = []
597
        for i in range(window_size):
Y
Yang Yu 已提交
598 599 600 601 602
            words.append(
                layers.data(
                    name='word_{0}'.format(i), shape=[1], dtype='int64'))

        dict_size = 10000
M
minqiyang 已提交
603
        label_word = int(window_size // 2) + 1
Y
Yang Yu 已提交
604 605

        embs = []
606
        for i in range(window_size):
Y
Yang Yu 已提交
607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623
            if i == label_word:
                continue

            emb = layers.embedding(
                input=words[i],
                size=[dict_size, 32],
                param_attr='emb.w',
                is_sparse=True)

            embs.append(emb)

        embs = layers.concat(input=embs, axis=1)
        loss = layers.nce(input=embs,
                          label=words[label_word],
                          num_total_classes=dict_size,
                          param_attr='nce.w',
                          bias_attr='nce.b')
Y
Yu Yang 已提交
624
        avg_loss = layers.mean(loss)
Y
Yang Yu 已提交
625 626 627
        self.assertIsNotNone(avg_loss)
        print(str(default_main_program()))

Y
yangyaming 已提交
628 629 630 631 632 633 634 635
    def test_row_conv(self):
        program = Program()
        with program_guard(program):
            x = layers.data(name='x', shape=[16], dtype='float32', lod_level=1)
            out = layers.row_conv(input=x, future_context_size=2)
            self.assertIsNotNone(out)
        print(str(program))

636 637 638 639 640 641 642 643 644 645
    def test_multiplex(self):
        program = Program()
        with program_guard(program):
            x1 = layers.data(name='x1', shape=[4], dtype='float32')
            x2 = layers.data(name='x2', shape=[4], dtype='float32')
            index = layers.data(name='index', shape=[1], dtype='int32')
            out = layers.multiplex(inputs=[x1, x2], index=index)
            self.assertIsNotNone(out)
        print(str(program))

646 647 648 649 650
    def test_softmax_with_cross_entropy(self):
        program = Program()
        with program_guard(program):
            x = layers.data(name='x', shape=[16], dtype='float32')
            y = layers.data(name='label', shape=[1], dtype='int64')
651 652 653 654
            loss, softmax = layers.softmax_with_cross_entropy(
                x, y, return_softmax=True)
            self.assertIsNotNone(loss)
            self.assertIsNotNone(softmax)
655 656 657 658 659 660 661 662 663 664 665 666 667
            loss = layers.softmax_with_cross_entropy(x, y)
            self.assertIsNotNone(loss)
        print(str(program))

    def test_smooth_l1(self):
        program = Program()
        with program_guard(program):
            x = layers.data(name='x', shape=[4], dtype='float32')
            y = layers.data(name='label', shape=[4], dtype='float32')
            loss = layers.smooth_l1(x, y)
            self.assertIsNotNone(loss)
        print(str(program))

668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686
    def test_scatter(self):
        program = Program()
        with program_guard(program):
            x = layers.data(
                name='x',
                shape=[3, 3],
                append_batch_size=False,
                dtype='float32')
            idx = layers.data(
                name='idx', shape=[2], append_batch_size=False, dtype='int32')
            updates = layers.data(
                name='updates',
                shape=[2, 3],
                append_batch_size=False,
                dtype='float32')
            out = layers.scatter(input=x, index=idx, updates=updates)
            self.assertIsNotNone(out)
        print(str(program))

Q
Qingsheng Li 已提交
687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710
    def test_sequence_scatter(self):
        program = Program()
        with program_guard(program):
            x = layers.data(
                name='x',
                shape=[3, 6],
                append_batch_size=False,
                dtype='float32')
            idx = layers.data(
                name='idx',
                shape=[12, 1],
                append_batch_size=False,
                dtype='int32',
                lod_level=1)
            updates = layers.data(
                name='updates',
                shape=[12, 1],
                append_batch_size=False,
                dtype='float32',
                lod_level=1)
            out = layers.sequence_scatter(input=x, index=idx, updates=updates)
            self.assertIsNotNone(out)
        print(str(program))

Y
Yibing Liu 已提交
711 712 713 714 715 716 717 718 719 720 721 722 723
    def test_sequence_slice(self):
        program = Program()
        with program_guard(program):
            import numpy as np
            seqs = layers.data(
                name='x', shape=[10, 5], dtype='float32', lod_level=1)
            offset = layers.assign(input=np.array([[0, 1]]).astype('int32'))
            length = layers.assign(input=np.array([[2, 1]]).astype('int32'))
            out = layers.sequence_slice(
                input=seqs, offset=offset, length=length)
            self.assertIsNotNone(out)
        print(str(program))

Y
yangyaming 已提交
724 725 726 727 728 729 730 731 732
    def test_lod_reset(self):
        program = Program()
        with program_guard(program):
            x = layers.data(name='x', shape=[10], dtype='float32')
            y = layers.data(
                name='y', shape=[10, 20], dtype='float32', lod_level=2)
            print(layers.lod_reset(x=x, y=y))
        print(str(program))

733 734 735 736 737 738 739 740 741 742
    def test_label_smooth(self):
        program = Program()
        with program_guard(program):
            label = layers.data(name="label", shape=[1], dtype="float32")
            one_hot_label = layers.one_hot(input=label, depth=10)
            smooth_label = layers.label_smooth(
                label=one_hot_label, epsilon=0.1, dtype="float32")
            self.assertIsNotNone(smooth_label)
        print(str(program))

Q
qingqing01 已提交
743 744 745 746 747 748 749 750 751
    def test_topk(self):
        program = Program()
        with program_guard(program):
            data = layers.data(name="label", shape=[200], dtype="float32")
            values, indices = layers.topk(data, k=5)
            self.assertIsNotNone(values)
            self.assertIsNotNone(indices)
        print(str(program))

752 753 754 755 756 757 758 759 760 761
    def test_roi_pool(self):
        program = Program()
        with program_guard(program):
            x = layers.data(name="x", shape=[256, 30, 30], dtype="float32")
            rois = layers.data(
                name="rois", shape=[4], dtype="float32", lod_level=1)
            output = layers.roi_pool(x, rois, 7, 7, 0.6)
            self.assertIsNotNone(output)
        print(str(program))

762 763 764 765 766 767 768 769 770 771
    def test_psroi_pool(self):
        program = Program()
        with program_guard(program):
            x = layers.data(name="x", shape=[245, 30, 30], dtype="float32")
            rois = layers.data(
                name="rois", shape=[4], dtype="float32", lod_level=1)
            output = layers.psroi_pool(x, rois, 5, 0.25, 7, 7)
            self.assertIsNotNone(output)
        print(str(program))

J
jerrywgz 已提交
772 773 774 775 776 777 778 779 780 781
    def test_roi_align(self):
        program = Program()
        with program_guard(program):
            x = layers.data(name="x", shape=[256, 30, 30], dtype="float32")
            rois = layers.data(
                name="rois", shape=[4], dtype="float32", lod_level=1)
            output = layers.roi_align(x, rois, 14, 14, 0.5, 2)
            self.assertIsNotNone(output)
        print(str(program))

B
baiyf 已提交
782
    def test_resize_bilinear(self):
783 784 785
        program = Program()
        with program_guard(program):
            x = layers.data(name='x', shape=[3, 9, 6], dtype="float32")
B
baiyf 已提交
786
            output = layers.resize_bilinear(x, out_shape=[12, 12])
787
            self.assertIsNotNone(output)
B
baiyf 已提交
788
            output = layers.resize_bilinear(x, scale=3)
789 790 791
            self.assertIsNotNone(output)
        print(str(program))

792
    def test_resize_nearest(self):
793 794 795 796 797 798 799 800 801
        program = Program()
        with program_guard(program):
            x = layers.data(name='x', shape=[3, 9, 6], dtype="float32")
            output = layers.resize_nearest(x, out_shape=[12, 12])
            self.assertIsNotNone(output)
            output = layers.resize_nearest(x, scale=3)
            self.assertIsNotNone(output)
        print(str(program))

802 803 804 805 806 807 808 809
    def test_polygon_box_transform(self):
        program = Program()
        with program_guard(program):
            x = layers.data(name='x', shape=[8, 4, 4], dtype="float32")
            output = layers.polygon_box_transform(input=x)
            self.assertIsNotNone(output)
        print(str(program))

810 811 812 813 814 815
    def test_l2_normalize(self):
        program = Program()
        with program_guard(program):
            x = layers.data(name='x', shape=[8, 7, 10], dtype="float32")
            output = layers.l2_normalize(x, axis=1)

Q
qingqing01 已提交
816 817 818 819 820 821 822 823
    def test_maxout(self):
        program = Program()
        with program_guard(program):
            data = layers.data(name='x', shape=[8, 6, 6], dtype="float32")
            output = layers.maxout(x=data, groups=2)
            self.assertIsNotNone(output)
        print(str(program))

W
whs 已提交
824
    def test_crop(self):
825 826 827 828 829 830 831 832
        program = Program()
        with program_guard(program):
            x = layers.data(name='x', shape=[3, 5], dtype="float32")
            y = layers.data(name='y', shape=[2, 3], dtype="float32")
            output = layers.crop(x, shape=y)
            self.assertIsNotNone(output)
        print(str(program))

W
whs 已提交
833 834 835 836 837 838 839 840 841
    def test_mean_iou(self):
        program = Program()
        with program_guard(program):
            x = layers.data(name='x', shape=[16], dtype='float32')
            y = layers.data(name='label', shape=[1], dtype='int64')
            iou = layers.mean_iou(x, y, 2)
            self.assertIsNotNone(iou)
        print(str(program))

842 843 844 845 846 847 848 849 850
    def test_argsort(self):
        program = Program()
        with program_guard(program):
            data = layers.data(name='x', shape=[2, 3, 3], dtype="float32")
            out, ids = layers.argsort(input=data, axis=1)
            self.assertIsNotNone(out)
            self.assertIsNotNone(ids)
        print(str(program))

851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872
    def test_rank_loss(self):
        program = Program()
        with program_guard(program):
            label = layers.data(
                name='label',
                append_batch_size=False,
                shape=[16, 1],
                dtype="float32")
            left = layers.data(
                name='left',
                append_batch_size=False,
                shape=[16, 1],
                dtype="float32")
            right = layers.data(
                name='right',
                append_batch_size=False,
                shape=[16, 1],
                dtype="float32")
            out = layers.rank_loss(label, left, right, name="rank_loss")
            self.assertIsNotNone(out)
        print(str(program))

873 874 875 876 877 878 879 880 881 882 883
    def test_flatten(self):
        program = Program()
        with program_guard(program):
            x = layers.data(
                name='x',
                append_batch_size=False,
                shape=[4, 4, 3],
                dtype="float32")
            out = layers.flatten(x, axis=1, name="flatten")
            self.assertIsNotNone(out)

B
Bai Yifan 已提交
884 885 886 887 888
    def test_shape(self):
        program = Program()
        with program_guard(program):
            input = layers.data(
                name="input", shape=[3, 100, 100], dtype="float32")
G
fix  
gongweibao 已提交
889
            out = layers.shape(input)
B
Bai Yifan 已提交
890 891 892
            self.assertIsNotNone(out)
        print(str(program))

W
whs 已提交
893 894 895 896 897
    def test_pad2d(self):
        program = Program()
        with program_guard(program):
            input = layers.data(
                name="input", shape=[3, 100, 100], dtype="float32")
898
            paddings = layers.fill_constant(shape=[4], dtype='int32', value=1)
W
whs 已提交
899 900 901 902 903 904
            out = layers.pad2d(
                input,
                paddings=[1, 2, 3, 4],
                mode='reflect',
                data_format='NCHW',
                name="shape")
905 906 907 908 909 910
            out_1 = layers.pad2d(
                input,
                paddings=paddings,
                mode='reflect',
                data_format='NCHW',
                name="shape")
W
whs 已提交
911
            self.assertIsNotNone(out)
912
            self.assertIsNotNone(out_1)
W
whs 已提交
913 914
        print(str(program))

J
jerrywgz 已提交
915 916 917 918 919 920 921 922 923 924 925 926 927 928
    def test_prelu(self):
        program = Program()
        with program_guard(program):
            input = layers.data(
                name="input", shape=[5, 200, 100, 100], dtype="float32")
            mode = 'channel'
            out = layers.prelu(
                input,
                mode,
                param_attr=ParamAttr(initializer=Constant(1.0)),
                name='prelu')
            self.assertIsNotNone(out)
        print(str(program))

T
tensor-tang 已提交
929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080
    def test_brelu(self):
        program = Program()
        with program_guard(program):
            input = layers.data(name="input", shape=[16], dtype="float32")
            out = layers.brelu(input, t_min=1.0, t_max=20.0, name='brelu')
            self.assertIsNotNone(out)
        print(str(program))

    def test_leaky_relu(self):
        program = Program()
        with program_guard(program):
            input = layers.data(name="input", shape=[16], dtype="float32")
            out = layers.leaky_relu(input, alpha=0.1, name='leaky_relu')
            self.assertIsNotNone(out)
        print(str(program))

    def test_soft_relu(self):
        program = Program()
        with program_guard(program):
            input = layers.data(name="input", shape=[16], dtype="float32")
            out = layers.soft_relu(input, threshold=30.0, name='soft_relu')
            self.assertIsNotNone(out)
        print(str(program))

    def test_sigmoid(self):
        program = Program()
        with program_guard(program):
            input = layers.data(name="input", shape=[16], dtype="float32")
            out = layers.sigmoid(input, name='sigmoid')
            self.assertIsNotNone(out)
        print(str(program))

    def test_logsigmoid(self):
        program = Program()
        with program_guard(program):
            input = layers.data(name="input", shape=[16], dtype="float32")
            out = layers.logsigmoid(input, name='logsigmoid')
            self.assertIsNotNone(out)
        print(str(program))

    def test_exp(self):
        program = Program()
        with program_guard(program):
            input = layers.data(name="input", shape=[16], dtype="float32")
            out = layers.exp(input, name='exp')
            self.assertIsNotNone(out)
        print(str(program))

    def test_tanh(self):
        program = Program()
        with program_guard(program):
            input = layers.data(name="input", shape=[16], dtype="float32")
            out = layers.tanh(input, name='tanh')
            self.assertIsNotNone(out)
        print(str(program))

    def test_tanh_shrink(self):
        program = Program()
        with program_guard(program):
            input = layers.data(name="input", shape=[16], dtype="float32")
            out = layers.tanh_shrink(input, name='tanh_shrink')
            self.assertIsNotNone(out)
        print(str(program))

    def test_sqrt(self):
        program = Program()
        with program_guard(program):
            input = layers.data(name="input", shape=[16], dtype="float32")
            out = layers.sqrt(input, name='sqrt')
            self.assertIsNotNone(out)
        print(str(program))

    def test_abs(self):
        program = Program()
        with program_guard(program):
            input = layers.data(name="input", shape=[16], dtype="float32")
            out = layers.abs(input, name='abs')
            self.assertIsNotNone(out)
        print(str(program))

    def test_ceil(self):
        program = Program()
        with program_guard(program):
            input = layers.data(name="input", shape=[16], dtype="float32")
            out = layers.ceil(input, name='ceil')
            self.assertIsNotNone(out)
        print(str(program))

    def test_floor(self):
        program = Program()
        with program_guard(program):
            input = layers.data(name="input", shape=[16], dtype="float32")
            out = layers.floor(input, name='floor')
            self.assertIsNotNone(out)
        print(str(program))

    def test_cos(self):
        program = Program()
        with program_guard(program):
            input = layers.data(name="input", shape=[16], dtype="float32")
            out = layers.cos(input, name='cos')
            self.assertIsNotNone(out)
        print(str(program))

    def test_sin(self):
        program = Program()
        with program_guard(program):
            input = layers.data(name="input", shape=[16], dtype="float32")
            out = layers.sin(input, name='sin')
            self.assertIsNotNone(out)
        print(str(program))

    def test_round(self):
        program = Program()
        with program_guard(program):
            input = layers.data(name="input", shape=[16], dtype="float32")
            out = layers.round(input, name='round')
            self.assertIsNotNone(out)
        print(str(program))

    def test_reciprocal(self):
        program = Program()
        with program_guard(program):
            input = layers.data(name="input", shape=[16], dtype="float32")
            out = layers.reciprocal(input, name='reciprocal')
            self.assertIsNotNone(out)
        print(str(program))

    def test_square(self):
        program = Program()
        with program_guard(program):
            input = layers.data(name="input", shape=[16], dtype="float32")
            out = layers.square(input, name='square')
            self.assertIsNotNone(out)
        print(str(program))

    def test_softplus(self):
        program = Program()
        with program_guard(program):
            input = layers.data(name="input", shape=[16], dtype="float32")
            out = layers.softplus(input, name='softplus')
            self.assertIsNotNone(out)
        print(str(program))

    def test_softsign(self):
        program = Program()
        with program_guard(program):
            input = layers.data(name="input", shape=[16], dtype="float32")
            out = layers.softsign(input, name='softsign')
            self.assertIsNotNone(out)
        print(str(program))

W
whs 已提交
1081 1082 1083 1084 1085 1086 1087 1088 1089 1090
    def test_roi_perspective_transform(self):
        program = Program()
        with program_guard(program):
            x = layers.data(name="x", shape=[256, 30, 30], dtype="float32")
            rois = layers.data(
                name="rois", shape=[8], dtype="float32", lod_level=1)
            output = layers.roi_perspective_transform(x, rois, 7, 7, 0.6)
            self.assertIsNotNone(output)
        print(str(program))

C
chenweihang 已提交
1091 1092 1093
    def test_sequence_enumerate(self):
        program = Program()
        with program_guard(program):
C
chenweihang 已提交
1094
            x = layers.data(name="input", shape=[1], dtype='int32', lod_level=1)
C
chenweihang 已提交
1095 1096 1097
            out = layers.sequence_enumerate(input=x, win_size=2, pad_value=0)
        print(str(program))

1098 1099 1100 1101 1102 1103 1104 1105 1106
    def test_cross_entropy(self):
        program = Program()
        with program_guard(program):
            x = layers.data(name="x", shape=[30, 10], dtype="float32")
            label = layers.data(name="label", shape=[30, 1], dtype="int32")
            mode = 'channel'
            out = layers.cross_entropy(x, label, False, 4)
            self.assertIsNotNone(out)

1107 1108 1109 1110 1111 1112 1113 1114 1115
    def test_bpr_loss(self):
        program = Program()
        with program_guard(program):
            x = layers.data(name="x", shape=[30, 10], dtype="float32")
            label = layers.data(name="label", shape=[30, 1], dtype="int32")
            out = layers.bpr_loss(x, label)
            self.assertIsNotNone(out)
        print(str(program))

W
whs 已提交
1116 1117 1118 1119 1120 1121 1122
    def test_expand(self):
        program = Program()
        with program_guard(program):
            x = layers.data(name="input", shape=[10], dtype='int32')
            out = layers.expand(x, [1, 2])
        print(str(program))

G
fix  
gongweibao 已提交
1123
    def test_uniform_random_batch_size_like(self):
G
fix  
gongweibao 已提交
1124 1125 1126 1127 1128
        program = Program()
        with program_guard(program):
            input = layers.data(name="input", shape=[13, 11], dtype='float32')
            out = layers.uniform_random_batch_size_like(input, [-1, 11])
            self.assertIsNotNone(out)
G
fix  
gongweibao 已提交
1129
        print(str(program))
G
fix  
gongweibao 已提交
1130 1131 1132 1133 1134 1135

    def test_gaussian_random(self):
        program = Program()
        with program_guard(program):
            out = layers.gaussian_random(shape=[20, 30])
            self.assertIsNotNone(out)
G
fix  
gongweibao 已提交
1136
        print(str(program))
G
fix  
gongweibao 已提交
1137 1138 1139 1140

    def test_sampling_id(self):
        program = Program()
        with program_guard(program):
G
fix  
gongweibao 已提交
1141 1142 1143 1144 1145
            x = layers.data(
                name="X",
                shape=[13, 11],
                dtype='float32',
                append_batch_size=False)
G
fix  
gongweibao 已提交
1146 1147 1148

            out = layers.sampling_id(x)
            self.assertIsNotNone(out)
G
fix  
gongweibao 已提交
1149
        print(str(program))
G
fix  
gongweibao 已提交
1150 1151 1152 1153 1154 1155 1156 1157 1158

    def test_gaussian_random_batch_size_like(self):
        program = Program()
        with program_guard(program):
            input = layers.data(name="input", shape=[13, 11], dtype='float32')

            out = layers.gaussian_random_batch_size_like(
                input, shape=[-1, 11], mean=1.0, std=2.0)
            self.assertIsNotNone(out)
G
fix  
gongweibao 已提交
1159
        print(str(program))
G
fix  
gongweibao 已提交
1160 1161 1162 1163 1164 1165 1166 1167

    def test_sum(self):
        program = Program()
        with program_guard(program):
            input = layers.data(name="input", shape=[13, 11], dtype='float32')

            out = layers.sum(input)
            self.assertIsNotNone(out)
G
fix  
gongweibao 已提交
1168
        print(str(program))
G
fix  
gongweibao 已提交
1169 1170 1171 1172 1173 1174

    def test_slice(self):
        starts = [1, 0, 2]
        ends = [3, 3, 4]
        axes = [0, 1, 2]

G
fix  
gongweibao 已提交
1175 1176 1177
        program = Program()
        with program_guard(program):
            input = layers.data(
G
fix  
gongweibao 已提交
1178 1179 1180
                name="input", shape=[3, 4, 5, 6], dtype='float32')

            out = layers.slice(input, axes=axes, starts=starts, ends=ends)
G
merge  
gongweibao 已提交
1181

B
baiyf 已提交
1182 1183 1184 1185 1186
    def test_softshrink(self):
        program = Program()
        with program_guard(program):
            input = layers.data(name="input", shape=[16], dtype="float32")
            out = layers.softshrink(input, name='softshrink')
G
fix  
gongweibao 已提交
1187
            self.assertIsNotNone(out)
G
fix  
gongweibao 已提交
1188
        print(str(program))
G
fix  
gongweibao 已提交
1189

X
Xin Pan 已提交
1190 1191 1192 1193 1194 1195 1196 1197 1198
    def iou_similarity(self):
        program = Program()
        with program_guard(program):
            x = layers.data(name="x", shape=[16], dtype="float32")
            y = layers.data(name="y", shape=[16], dtype="float32")
            out = layers.iou_similarity(x, y, name='iou_similarity')
            self.assertIsNotNone(out)
        print(str(program))

1199
    def test_grid_sampler(self):
D
dengkaipeng 已提交
1200 1201
        program = Program()
        with program_guard(program):
1202 1203
            x = layers.data(name='x', shape=[3, 5, 7], dtype='float32')
            grid = layers.data(name='grid', shape=[5, 7, 2], dtype='float32')
D
dengkaipeng 已提交
1204 1205 1206
            out = layers.grid_sampler(x, grid)
            self.assertIsNotNone(out)
        print(str(program))
1207

W
whs 已提交
1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222
    def test_affine_grid(self):
        program = Program()
        with program_guard(program):
            data = layers.data(name='data', shape=[2, 3, 3], dtype="float32")
            out, ids = layers.argsort(input=data, axis=1)

            theta = layers.data(name="theta", shape=[2, 3], dtype="float32")
            out_shape = layers.data(
                name="out_shape", shape=[-1], dtype="float32")
            data_0 = layers.affine_grid(theta, out_shape)
            data_1 = layers.affine_grid(theta, [5, 3, 28, 28])

            self.assertIsNotNone(data_0)
            self.assertIsNotNone(data_1)
        print(str(program))
D
dengkaipeng 已提交
1223

1224 1225 1226 1227 1228 1229 1230 1231 1232 1233
    def test_bilinear_tensor_product_layer(self):
        program = Program()
        with program_guard(program):
            data = layers.data(name='data', shape=[4], dtype="float32")

            theta = layers.data(name="theta", shape=[5], dtype="float32")
            out = layers.bilinear_tensor_product(data, theta, 6)

        print(str(program))

1234 1235 1236 1237 1238 1239 1240 1241 1242
    def test_batch_norm(self):
        program = Program()
        with program_guard(program):
            data = layers.data(
                name='data', shape=[32, 128, 128], dtype="float32")
            out = layers.batch_norm(data)

        print(str(program))

W
whs 已提交
1243 1244 1245 1246 1247 1248 1249 1250
    def test_range(self):
        program = Program()
        with program_guard(program):
            layers.range(0, 10, 2, 'int32')
            layers.range(0.1, 10.0, 0.2, 'float32')

        print(str(program))

1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263
    def test_spectral_norm(self):
        program = Program()
        with program_guard(program):
            weight = layers.data(
                name='weight',
                shape=[2, 3, 32, 32],
                dtype="float32",
                append_batch_size=False)
            out = layers.spectral_norm(weight, dim=1, power_iters=1)
            self.assertIsNotNone(out)

        print(str(program))

S
shippingwang 已提交
1264 1265 1266
    def test_shuffle_channel(self):
        program = Program()
        with program_guard(program):
S
shippingwang 已提交
1267 1268
            x = layers.data(name="X", shape=[16, 4, 4], dtype="float32")
            out = layers.shuffle_channel(x, group=4)
S
shippingwang 已提交
1269 1270 1271
            self.assertIsNotNone(out)
        print(str(program))

1272 1273 1274 1275 1276 1277 1278 1279 1280
    def test_fsp(self):
        program = Program()
        with program_guard(program):
            x = layers.data(name="X", shape=[16, 4, 4], dtype="float32")
            y = layers.data(name="Y", shape=[8, 4, 4], dtype="float32")
            out = layers.fsp_matrix(x, y)
            self.assertIsNotNone(out)
        print(str(program))

Y
Yu Yang 已提交
1281 1282 1283

if __name__ == '__main__':
    unittest.main()