test_layers.py 149.8 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
import contextlib
import numpy as np
20
from decorator_helper import prog_scope
21 22
import inspect
from six.moves import filter
23 24 25

import paddle
import paddle.fluid as fluid
26
from paddle.fluid.layers.device import get_places
27 28 29
import paddle.fluid.nets as nets
from paddle.fluid.framework import Program, program_guard, default_main_program
from paddle.fluid.param_attr import ParamAttr
30
from paddle.fluid import core
J
jerrywgz 已提交
31
from paddle.fluid.initializer import Constant
32 33
import paddle.fluid.layers as layers
from test_imperative_base import new_program_scope
L
lujun 已提交
34 35
from paddle.fluid.dygraph import nn
from paddle.fluid.dygraph import base
36
from paddle.fluid.dygraph import to_variable
37 38 39 40 41 42 43 44 45 46 47


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

    @classmethod
    def tearDownClass(cls):
        pass

48 49 50 51 52 53 54 55
    def _get_place(self, force_to_use_cpu=False):
        # this option for ops that only have cpu kernel
        if force_to_use_cpu:
            return core.CPUPlace()
        else:
            if core.is_compiled_with_cuda():
                return core.CUDAPlace(0)
            return core.CPUPlace()
56 57 58 59

    @contextlib.contextmanager
    def static_graph(self):
        with new_program_scope():
L
Leo Chen 已提交
60 61
            paddle.manual_seed(self.seed)
            paddle.framework.random._manual_program_seed(self.seed)
62 63
            yield

64 65 66 67 68 69
    def get_static_graph_result(self,
                                feed,
                                fetch_list,
                                with_lod=False,
                                force_to_use_cpu=False):
        exe = fluid.Executor(self._get_place(force_to_use_cpu))
70 71 72
        exe.run(fluid.default_startup_program())
        return exe.run(fluid.default_main_program(),
                       feed=feed,
73 74
                       fetch_list=fetch_list,
                       return_numpy=(not with_lod))
75 76

    @contextlib.contextmanager
77
    def dynamic_graph(self, force_to_use_cpu=False):
L
lujun 已提交
78
        with fluid.dygraph.guard(
79
                self._get_place(force_to_use_cpu=force_to_use_cpu)):
L
Leo Chen 已提交
80 81
            paddle.manual_seed(self.seed)
            paddle.framework.random._manual_program_seed(self.seed)
82 83 84 85
            yield


class TestLayer(LayerTest):
86 87
    def test_custom_layer_with_kwargs(self):
        class CustomLayer(fluid.Layer):
88 89 90 91 92 93 94 95 96 97
            def __init__(self, input_size, linear1_size=4):
                super(CustomLayer, self).__init__()
                self.linear1 = nn.Linear(
                    input_size, linear1_size, bias_attr=False)
                self.linear2 = nn.Linear(linear1_size, 1, bias_attr=False)

            def forward(self, x, do_linear2=False):
                ret = self.linear1(x)
                if do_linear2:
                    ret = self.linear2(ret)
98 99 100 101 102
                return ret

        with self.dynamic_graph():
            inp = np.ones([3, 3], dtype='float32')
            x = base.to_variable(inp)
103 104
            custom = CustomLayer(input_size=3, linear1_size=2)
            ret = custom(x, do_linear2=False)
105
            self.assertTrue(np.array_equal(ret.numpy().shape, [3, 2]))
106
            ret = custom(x, do_linear2=True)
107 108
            self.assertTrue(np.array_equal(ret.numpy().shape, [3, 1]))

109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135
    def test_dropout(self):
        inp = np.ones([3, 32, 32], dtype='float32')
        with self.static_graph():
            t = layers.data(
                name='data',
                shape=[3, 32, 32],
                dtype='float32',
                append_batch_size=False)
            dropout = nn.Dropout(p=0.35, seed=1, is_test=False)
            ret = dropout(t)
            ret2 = fluid.layers.dropout(
                t, dropout_prob=0.35, seed=1, is_test=False)
            static_ret, static_ret2 = self.get_static_graph_result(
                feed={'data': inp}, fetch_list=[ret, ret2])
        with self.dynamic_graph():
            t = base.to_variable(inp)
            dropout = nn.Dropout(p=0.35, seed=1, is_test=False)
            dy_ret = dropout(t)
            dy_ret2 = fluid.layers.dropout(
                t, dropout_prob=0.35, seed=1, is_test=False)
            dy_ret_value = dy_ret.numpy()
            dy_ret2_value = dy_ret2.numpy()

        self.assertTrue(np.array_equal(static_ret, static_ret2))
        self.assertTrue(np.array_equal(dy_ret_value, dy_ret2_value))
        self.assertTrue(np.array_equal(static_ret, dy_ret_value))

S
songyouwei 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
    def test_linear(self):
        inp = np.ones([3, 32, 32], dtype='float32')
        with self.static_graph():
            t = layers.data(
                name='data',
                shape=[3, 32, 32],
                dtype='float32',
                append_batch_size=False)
            linear = nn.Linear(
                32, 4, bias_attr=fluid.initializer.ConstantInitializer(value=1))
            ret = linear(t)
            static_ret = self.get_static_graph_result(
                feed={'data': inp}, fetch_list=[ret])[0]
        with self.dynamic_graph():
            t = base.to_variable(inp)
            linear = nn.Linear(
                32, 4, bias_attr=fluid.initializer.ConstantInitializer(value=1))
            dy_ret = linear(t)
            dy_ret_value = dy_ret.numpy()

        self.assertTrue(np.array_equal(static_ret, dy_ret_value))

158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174
        with self.static_graph():

            # the input of Linear must be Variable.
            def test_Variable():
                inp = np.ones([3, 32, 32], dtype='float32')
                linear = nn.Linear(
                    32,
                    4,
                    bias_attr=fluid.initializer.ConstantInitializer(value=1))
                linear_ret1 = linear(inp)

            self.assertRaises(TypeError, test_Variable)

            # the input dtype of Linear must be float16 or float32 or float64
            # float16 only can be set on GPU place
            def test_type():
                inp = np.ones([3, 32, 32], dtype='int32')
175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219
                linear = nn.Linear(
                    32,
                    4,
                    bias_attr=fluid.initializer.ConstantInitializer(value=1))
                linear_ret2 = linear(inp)

            self.assertRaises(TypeError, test_type)

    def test_Flatten(self):
        inp = np.ones([3, 4, 4, 5], dtype='float32')
        with self.static_graph():
            t = layers.data(
                name='data',
                shape=[3, 4, 4, 5],
                dtype='float32',
                append_batch_size=False)
            flatten = nn.Flatten()
            ret = flatten(t)
            static_ret = self.get_static_graph_result(
                feed={'data': inp}, fetch_list=[ret])[0]
        with self.dynamic_graph():
            t = base.to_variable(inp)
            flatten = nn.Flatten()
            dy_ret = flatten(t)
            dy_ret_value = dy_ret.numpy()

        self.assertTrue(np.array_equal(static_ret, dy_ret_value))

        with self.static_graph():

            # the input of Linear must be Variable.
            def test_Variable():
                inp = np.ones([3, 32, 32], dtype='float32')
                linear = nn.Linear(
                    32,
                    4,
                    bias_attr=fluid.initializer.ConstantInitializer(value=1))
                linear_ret1 = linear(inp)

            self.assertRaises(TypeError, test_Variable)

            # the input dtype of Linear must be float16 or float32 or float64
            # float16 only can be set on GPU place
            def test_type():
                inp = np.ones([3, 32, 32], dtype='int32')
220 221 222 223 224 225 226 227
                linear = nn.Linear(
                    32,
                    4,
                    bias_attr=fluid.initializer.ConstantInitializer(value=1))
                linear_ret2 = linear(inp)

            self.assertRaises(TypeError, test_type)

228 229 230 231 232 233 234 235
    def test_layer_norm(self):
        inp = np.ones([3, 32, 32], dtype='float32')
        with self.static_graph():
            t = layers.data(
                name='data',
                shape=[3, 32, 32],
                dtype='float32',
                append_batch_size=False)
236 237 238 239
            ret = layers.layer_norm(
                t,
                bias_attr=fluid.initializer.ConstantInitializer(value=1),
                act='sigmoid')
240 241 242 243 244 245 246 247
            static_ret = self.get_static_graph_result(
                feed={'data': inp}, fetch_list=[ret])[0]
        with self.static_graph():
            t = layers.data(
                name='data',
                shape=[3, 32, 32],
                dtype='float32',
                append_batch_size=False)
248
            lm = nn.LayerNorm(
249
                normalized_shape=[32, 32],
250 251
                bias_attr=fluid.initializer.ConstantInitializer(value=1),
                act='sigmoid')
252 253 254 255
            ret = lm(t)
            static_ret2 = self.get_static_graph_result(
                feed={'data': inp}, fetch_list=[ret])[0]
        with self.dynamic_graph():
256
            lm = nn.LayerNorm(
257
                normalized_shape=[32, 32],
258 259
                bias_attr=fluid.initializer.ConstantInitializer(value=1),
                act='sigmoid')
260
            dy_ret = lm(base.to_variable(inp))
261
            dy_ret_value = dy_ret.numpy()
262 263
        with self.dynamic_graph():
            lm = nn.LayerNorm(
264
                normalized_shape=[32, 32],
265 266 267 268 269 270 271 272 273
                shift=False,
                scale=False,
                param_attr=fluid.initializer.ConstantInitializer(value=1),
                bias_attr=fluid.initializer.ConstantInitializer(value=1),
                act='sigmoid')
            lm(base.to_variable(inp))

            self.assertFalse(hasattr(lm, "_scale_w"))
            self.assertFalse(hasattr(lm, "_bias_w"))
274

275
        self.assertTrue(np.array_equal(static_ret, static_ret2))
276
        self.assertTrue(np.array_equal(dy_ret_value, static_ret2))
277

278 279 280 281 282 283 284 285
        with self.dynamic_graph():
            lm = nn.LayerNorm(
                normalized_shape=[16, 32],
                bias_attr=fluid.initializer.ConstantInitializer(value=1),
                act='sigmoid')
            with self.assertRaises(ValueError):
                lm(base.to_variable(inp))

C
ceci3 已提交
286 287 288 289
    def test_SyncBatchNorm(self):
        if core.is_compiled_with_cuda():
            with self.static_graph():
                t = layers.data(name='t', shape=[-1, 3, 5, 5], dtype='float32')
C
ceci3 已提交
290
                my_sync_bn = paddle.nn.SyncBatchNorm(3)
C
ceci3 已提交
291 292 293 294 295 296 297 298 299 300 301
                ret = my_sync_bn(t)
                static_ret = self.get_static_graph_result(
                    feed={'t': np.ones(
                        [3, 3, 5, 5], dtype='float32')},
                    fetch_list=[ret])[0]

            with self.dynamic_graph():
                t = np.ones([3, 3, 5, 5], dtype='float32')
                my_syncbn = paddle.nn.SyncBatchNorm(3)
                dy_ret = my_syncbn(base.to_variable(t))
                dy_ret_value = dy_ret.numpy()
302
            self.assertTrue(np.array_equal(static_ret, dy_ret_value))
C
ceci3 已提交
303

304 305 306 307 308 309 310 311 312 313 314
    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))
315
            dy_ret_value = dy_ret.numpy()
316

317
        self.assertTrue(np.allclose(static_ret, dy_ret_value))
318

C
ceci3 已提交
319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335
    def test_pad2d(self):
        with self.static_graph():
            t = layers.data(name='t', shape=[-1, 3, 5, 5], dtype='float32')
            ret = layers.pad2d(t, paddings=[1, 1, 1, 1])
            static_ret = self.get_static_graph_result(
                feed={'t': np.ones(
                    [3, 3, 5, 5], dtype='float32')},
                fetch_list=[ret])[0]

        with self.dynamic_graph():
            t = np.ones([3, 3, 5, 5], dtype='float32')
            my_pad2d = paddle.nn.Pad2D(paddings=1)
            dy_ret = my_pad2d(base.to_variable(t))
            dy_ret_value = dy_ret.numpy()

        self.assertTrue(np.allclose(static_ret, dy_ret_value))

336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352
    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
353
            dy_ret = layers.matmul(base.to_variable(t), base.to_variable(t2))
354
            dy_ret_value = dy_ret.numpy()
355

356
        self.assertTrue(np.allclose(static_ret, dy_ret_value))
357

358 359 360 361 362 363 364 365 366 367 368
    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')
369 370
            conv2d = nn.Conv2D(
                num_channels=3, num_filters=3, filter_size=[2, 2])
371 372 373 374 375 376 377 378
            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')
379 380
            conv2d = nn.Conv2D(
                num_channels=3, num_filters=3, filter_size=[2, 2])
381
            dy_ret = conv2d(base.to_variable(images))
382
            dy_ret_value = dy_ret.numpy()
383

384 385 386
        with self.dynamic_graph():
            images = np.ones([2, 3, 5, 5], dtype='float32')
            conv2d = nn.Conv2D(
387 388 389 390
                num_channels=3,
                num_filters=3,
                filter_size=[2, 2],
                bias_attr=False)
391
            dy_ret = conv2d(base.to_variable(images))
392
            self.assertTrue(conv2d.bias is None)
393

394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414
        with self.static_graph():
            # the input of Conv2D must be Variable.
            def test_Variable():
                images = np.ones([2, 3, 5, 5], dtype='float32')
                conv2d = nn.Conv2D(
                    num_channels=3, num_filters=3, filter_size=[2, 2])
                conv2d_ret1 = conv2d(images)

            self.assertRaises(TypeError, test_Variable)

            # the input dtype of Conv2D must be float16 or float32 or float64
            # float16 only can be set on GPU place
            def test_type():
                images = layers.data(
                    name='pixel', shape=[3, 5, 5], dtype='int32')
                conv2d = nn.Conv2D(
                    num_channels=3, num_filters=3, filter_size=[2, 2])
                conv2d_ret2 = conv2d(images)

            self.assertRaises(TypeError, test_type)

415
        self.assertTrue(np.allclose(static_ret, dy_ret_value))
416
        self.assertTrue(np.allclose(static_ret, static_ret2))
Y
Yu Yang 已提交
417

418 419 420 421 422 423
        with self.dynamic_graph():
            images = np.ones([2, 3, 5, 5], dtype='float32')
            custom_weight = np.random.randn(3, 3, 2, 2).astype("float32")
            weight_attr = fluid.ParamAttr(
                initializer=fluid.initializer.NumpyArrayInitializer(
                    custom_weight))
424 425
            conv2d1 = nn.Conv2D(
                num_channels=3, num_filters=3, filter_size=[2, 2])
426
            conv2d2 = nn.Conv2D(
427
                num_channels=3,
428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453
                num_filters=3,
                filter_size=[2, 2],
                param_attr=weight_attr)
            dy_ret1 = conv2d1(base.to_variable(images))
            dy_ret2 = conv2d2(base.to_variable(images))
            self.assertFalse(np.array_equal(dy_ret1.numpy(), dy_ret2.numpy()))

            conv2d1_weight_np = conv2d1.weight.numpy()
            conv2d1_bias = conv2d1.bias
            self.assertFalse(
                np.array_equal(conv2d1_weight_np, conv2d2.weight.numpy()))
            conv2d2.weight.set_value(conv2d1_weight_np)
            self.assertTrue(
                np.array_equal(conv2d1_weight_np, conv2d2.weight.numpy()))
            conv2d2.bias.set_value(conv2d1_bias)
            dy_ret1 = conv2d1(base.to_variable(images))
            dy_ret2 = conv2d2(base.to_variable(images))
            self.assertTrue(np.array_equal(dy_ret1.numpy(), dy_ret2.numpy()))

            conv2d2.weight = conv2d1.weight
            conv2d2.bias = conv2d1.bias
            self.assertTrue(
                np.array_equal(conv2d1.weight.numpy(), conv2d2.weight.numpy()))
            self.assertTrue(
                np.array_equal(conv2d1.bias.numpy(), conv2d2.bias.numpy()))

M
minqiyang 已提交
454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477
    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)
478
            gru = nn.GRUUnit(size=D * 3)
M
minqiyang 已提交
479 480 481 482 483 484 485 486
            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():
487
            gru = nn.GRUUnit(size=D * 3)
M
minqiyang 已提交
488 489
            dy_ret = gru(
                base.to_variable(input), base.to_variable(hidden_input))
490 491 492
            dy_ret_value = []
            for i in range(len(static_ret)):
                dy_ret_value.append(dy_ret[i].numpy())
M
minqiyang 已提交
493 494 495

        for i in range(len(static_ret)):
            self.assertTrue(np.allclose(static_ret[i], static_ret2[i]))
496
            self.assertTrue(np.allclose(static_ret[i], dy_ret_value[i]))
M
minqiyang 已提交
497

498 499 500 501 502
        with self.dynamic_graph():
            custom_weight = np.random.randn(D, D * 3).astype("float32")
            weight_attr = fluid.ParamAttr(
                initializer=fluid.initializer.NumpyArrayInitializer(
                    custom_weight))
503 504
            gru1 = nn.GRUUnit(size=D * 3)
            gru2 = nn.GRUUnit(size=D * 3, param_attr=weight_attr)
505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528
            dy_ret1 = gru1(
                base.to_variable(input), base.to_variable(hidden_input))
            dy_ret2 = gru2(
                base.to_variable(input), base.to_variable(hidden_input))
            self.assertFalse(
                np.array_equal(gru1.weight.numpy(), gru2.weight.numpy()))
            for o1, o2 in zip(dy_ret1, dy_ret2):
                self.assertFalse(np.array_equal(o1.numpy(), o2.numpy()))
            gru2.weight.set_value(gru1.weight.numpy())
            gru2.bias.set_value(gru1.bias)
            dy_ret1 = gru1(
                base.to_variable(input), base.to_variable(hidden_input))
            dy_ret2 = gru2(
                base.to_variable(input), base.to_variable(hidden_input))
            for o1, o2 in zip(dy_ret1, dy_ret2):
                self.assertTrue(np.array_equal(o1.numpy(), o2.numpy()))

            gru2.weight = gru1.weight
            gru2.bias = gru1.bias
            self.assertTrue(
                np.array_equal(gru1.weight.numpy(), gru2.weight.numpy()))
            self.assertTrue(
                np.array_equal(gru1.bias.numpy(), gru2.bias.numpy()))

X
Xin Pan 已提交
529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562
    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():
563 564 565 566 567
            ret = layers.elementwise_add(to_variable(n), to_variable(n2))
            ret = layers.elementwise_pow(ret, to_variable(n3))
            ret = layers.elementwise_div(ret, to_variable(n4))
            ret = layers.elementwise_sub(ret, to_variable(n5))
            dy_ret = layers.elementwise_mul(ret, to_variable(n6))
568 569
            dy_ret_value = dy_ret.numpy()
        self.assertTrue(np.allclose(static_ret, dy_ret_value))
X
Xin Pan 已提交
570 571 572 573 574 575

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

        with self.dynamic_graph():
576 577
            min_ret = layers.elementwise_min(to_variable(n), to_variable(n2))
            max_ret = layers.elementwise_max(to_variable(n), to_variable(n2))
578 579
            min_ret_value = min_ret.numpy()
            max_ret_value = max_ret.numpy()
X
Xin Pan 已提交
580

581 582
        self.assertTrue(np.allclose(n, min_ret_value))
        self.assertTrue(np.allclose(n2, max_ret_value))
X
Xin Pan 已提交
583

584 585 586 587 588 589 590 591 592 593 594 595 596
    def test_sequence_conv(self):
        inp_np = np.arange(12).reshape([3, 4]).astype('float32')
        if core.is_compiled_with_cuda():
            place = core.CUDAPlace(0)
        else:
            place = core.CPUPlace()
        with self.static_graph():
            seq = layers.data(
                name='seq_in',
                shape=[3, 4],
                dtype='float32',
                lod_level=1,
                append_batch_size=False)
597
            out = layers.sequence_conv(seq, 2, act='sigmoid')
598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614
            static_rlt = self.get_static_graph_result(
                feed={
                    "seq_in": fluid.create_lod_tensor(
                        data=inp_np,
                        recursive_seq_lens=[[1, 1, 1]],
                        place=place)
                },
                fetch_list=[out],
                with_lod=True)[0]

        with self.static_graph():
            seq = layers.data(
                name='seq_in',
                shape=[3, 4],
                dtype='float32',
                lod_level=1,
                append_batch_size=False)
615
            seq_conv = nn.SequenceConv('seq_conv', num_filters=2, act='sigmoid')
616 617 618 619 620 621 622 623 624 625 626
            out = seq_conv(seq)
            static_rlt2 = self.get_static_graph_result(
                feed={
                    "seq_in": fluid.create_lod_tensor(
                        data=inp_np,
                        recursive_seq_lens=[[1, 1, 1]],
                        place=place)
                },
                fetch_list=[out],
                with_lod=True)[0]
        self.assertTrue(
627
            np.array_equal(np.array(static_rlt), np.array(static_rlt2)))
628 629 630 631 632 633

    def test_conv2d_transpose(self):
        inp_np = np.arange(0, 24).reshape([2, 3, 2, 2]).astype('float32')
        with self.static_graph():
            img = layers.data(name='pixel', shape=[3, 2, 2], dtype='float32')
            out = layers.conv2d_transpose(
634 635
                input=img,
                num_filters=10,
636
                filter_size=27,
637 638
                act='sigmoid',
                bias_attr=fluid.initializer.ConstantInitializer(value=1))
639 640 641 642 643
            static_rlt = self.get_static_graph_result(
                feed={'pixel': inp_np}, fetch_list=[out])[0]
        with self.static_graph():
            img = layers.data(name='pixel', shape=[3, 2, 2], dtype='float32')
            conv2d_transpose = nn.Conv2DTranspose(
644
                num_channels=3,
645
                num_filters=10,
646
                filter_size=27,
647 648
                act='sigmoid',
                bias_attr=fluid.initializer.ConstantInitializer(value=1))
649 650 651 652 653
            out = conv2d_transpose(img)
            static_rlt2 = self.get_static_graph_result(
                feed={'pixel': inp_np}, fetch_list=[out])[0]
        with self.dynamic_graph():
            conv2d_transpose = nn.Conv2DTranspose(
654
                num_channels=3,
655
                num_filters=10,
656
                filter_size=27,
657 658
                act='sigmoid',
                bias_attr=fluid.initializer.ConstantInitializer(value=1))
659
            dy_rlt = conv2d_transpose(base.to_variable(inp_np))
660
            dy_rlt_value = dy_rlt.numpy()
661
        self.assertTrue(np.allclose(static_rlt2, static_rlt))
662
        self.assertTrue(np.allclose(dy_rlt_value, static_rlt2))
663

664 665 666 667 668 669 670
        with self.dynamic_graph():
            images = np.ones([2, 3, 5, 5], dtype='float32')
            custom_weight = np.random.randn(3, 3, 2, 2).astype("float32")
            weight_attr = fluid.ParamAttr(
                initializer=fluid.initializer.NumpyArrayInitializer(
                    custom_weight))
            conv2d1 = nn.Conv2DTranspose(
671
                num_channels=3, num_filters=3, filter_size=[2, 2])
672
            conv2d2 = nn.Conv2DTranspose(
673
                num_channels=3,
674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699
                num_filters=3,
                filter_size=[2, 2],
                param_attr=weight_attr)
            dy_ret1 = conv2d1(base.to_variable(images))
            dy_ret2 = conv2d2(base.to_variable(images))
            self.assertFalse(np.array_equal(dy_ret1.numpy(), dy_ret2.numpy()))

            conv2d1_weight_np = conv2d1.weight.numpy()
            conv2d1_bias = conv2d1.bias
            self.assertFalse(
                np.array_equal(conv2d1_weight_np, conv2d2.weight.numpy()))
            conv2d2.weight.set_value(conv2d1_weight_np)
            self.assertTrue(
                np.array_equal(conv2d1_weight_np, conv2d2.weight.numpy()))
            conv2d2.bias.set_value(conv2d1_bias)
            dy_ret1 = conv2d1(base.to_variable(images))
            dy_ret2 = conv2d2(base.to_variable(images))
            self.assertTrue(np.array_equal(dy_ret1.numpy(), dy_ret2.numpy()))

            conv2d2.weight = conv2d1.weight
            conv2d2.bias = conv2d1.bias
            self.assertTrue(
                np.array_equal(conv2d1.weight.numpy(), conv2d2.weight.numpy()))
            self.assertTrue(
                np.array_equal(conv2d1.bias.numpy(), conv2d2.bias.numpy()))

700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721
        with self.static_graph():

            # the input of Conv2DTranspose must be Variable.
            def test_Variable():
                images = np.ones([2, 3, 5, 5], dtype='float32')
                conv2d = nn.Conv2DTranspose(
                    num_channels=3, num_filters=3, filter_size=[2, 2])
                conv2d_ret1 = conv2d(images)

            self.assertRaises(TypeError, test_Variable)

            # the input dtype of Conv2DTranspose must be float16 or float32 or float64
            # float16 only can be set on GPU place
            def test_type():
                images = layers.data(
                    name='pixel', shape=[3, 5, 5], dtype='int32')
                conv2d = nn.Conv2DTranspose(
                    num_channels=3, num_filters=3, filter_size=[2, 2])
                conv2d_ret2 = conv2d(images)

            self.assertRaises(TypeError, test_type)

722 723 724 725 726 727 728 729 730 731 732 733 734 735 736
    def test_bilinear_tensor_product(self):
        inp_np_x = np.array([[1, 2, 3]]).astype('float32')
        inp_np_y = np.array([[4, 5, 6]]).astype('float32')

        with self.static_graph():
            data_x = layers.data(
                name='x',
                shape=[1, 3],
                dtype="float32",
                append_batch_size=False)
            data_y = layers.data(
                name='y',
                shape=[1, 3],
                dtype="float32",
                append_batch_size=False)
737 738 739 740 741 742
            out = layers.bilinear_tensor_product(
                data_x,
                data_y,
                6,
                bias_attr=fluid.initializer.ConstantInitializer(value=1),
                act='sigmoid')
743 744 745 746

            static_rlt = self.get_static_graph_result(
                feed={'x': inp_np_x,
                      'y': inp_np_y}, fetch_list=[out])[0]
747

748 749 750 751 752 753 754 755 756 757 758
        with self.static_graph():
            data_x = layers.data(
                name='x',
                shape=[1, 3],
                dtype="float32",
                append_batch_size=False)
            data_y = layers.data(
                name='y',
                shape=[1, 3],
                dtype="float32",
                append_batch_size=False)
759
            btp = nn.BilinearTensorProduct(
760 761
                3,
                3,
762 763 764
                6,
                bias_attr=fluid.initializer.ConstantInitializer(value=1),
                act='sigmoid')
765 766 767 768 769
            out = btp(data_x, data_y)
            static_rlt2 = self.get_static_graph_result(
                feed={'x': inp_np_x,
                      'y': inp_np_y}, fetch_list=[out])[0]
        with self.dynamic_graph():
770
            btp = nn.BilinearTensorProduct(
771 772
                3,
                3,
773 774 775
                6,
                bias_attr=fluid.initializer.ConstantInitializer(value=1),
                act='sigmoid')
776
            dy_rlt = btp(base.to_variable(inp_np_x), base.to_variable(inp_np_y))
777
            dy_rlt_value = dy_rlt.numpy()
778
        with self.dynamic_graph():
779
            btp2 = nn.BilinearTensorProduct(3, 3, 6, act='sigmoid')
780 781
            dy_rlt2 = btp2(
                base.to_variable(inp_np_x), base.to_variable(inp_np_y))
782
            dy_rlt2_value = dy_rlt2.numpy()
783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800
        with self.static_graph():
            data_x2 = layers.data(
                name='x',
                shape=[1, 3],
                dtype="float32",
                append_batch_size=False)
            data_y2 = layers.data(
                name='y',
                shape=[1, 3],
                dtype="float32",
                append_batch_size=False)
            out2 = layers.bilinear_tensor_product(
                data_x2, data_y2, 6, act='sigmoid')

            static_rlt3 = self.get_static_graph_result(
                feed={'x': inp_np_x,
                      'y': inp_np_y}, fetch_list=[out2])[0]

801
        self.assertTrue(np.array_equal(dy_rlt2_value, static_rlt3))
802
        self.assertTrue(np.array_equal(static_rlt2, static_rlt))
803
        self.assertTrue(np.array_equal(dy_rlt_value, static_rlt))
804

805 806 807 808 809
        with self.dynamic_graph():
            custom_weight = np.random.randn(6, 3, 3).astype("float32")
            weight_attr = fluid.ParamAttr(
                initializer=fluid.initializer.NumpyArrayInitializer(
                    custom_weight))
810
            btp1 = nn.BilinearTensorProduct(3, 3, 6, act='sigmoid')
811
            btp2 = nn.BilinearTensorProduct(
812
                3, 3, 6, act='sigmoid', param_attr=weight_attr)
813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832
            dy_rlt1 = btp1(
                base.to_variable(inp_np_x), base.to_variable(inp_np_y))
            dy_rlt2 = btp2(
                base.to_variable(inp_np_x), base.to_variable(inp_np_y))
            self.assertFalse(np.array_equal(dy_rlt1.numpy(), dy_rlt2.numpy()))
            btp2.weight.set_value(btp1.weight.numpy())
            btp2.bias.set_value(btp1.bias)
            dy_rlt1 = btp1(
                base.to_variable(inp_np_x), base.to_variable(inp_np_y))
            dy_rlt2 = btp2(
                base.to_variable(inp_np_x), base.to_variable(inp_np_y))
            self.assertTrue(np.array_equal(dy_rlt1.numpy(), dy_rlt2.numpy()))

            btp2.weight = btp1.weight
            btp2.bias = btp1.bias
            self.assertTrue(
                np.array_equal(btp1.weight.numpy(), btp2.weight.numpy()))
            self.assertTrue(
                np.array_equal(btp1.bias.numpy(), btp2.bias.numpy()))

833
    def prelu_test(self, mode):
834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853
        inp_np = np.ones([5, 200, 100, 100]).astype('float32')
        with self.static_graph():
            data_t = layers.data(
                name="input",
                shape=[5, 200, 100, 100],
                dtype="float32",
                append_batch_size=False)
            out = layers.prelu(
                data_t, mode, param_attr=ParamAttr(initializer=Constant(1.0)))
            static_rlt = self.get_static_graph_result(
                feed={"input": inp_np}, fetch_list=[out])[0]

        with self.static_graph():
            data_t = layers.data(
                name="input",
                shape=[5, 200, 100, 100],
                dtype="float32",
                append_batch_size=False)
            prelu = nn.PRelu(
                mode=mode,
S
songyouwei 已提交
854
                channel=inp_np.shape[1],
855
                input_shape=data_t.shape,
856 857 858 859 860 861 862 863
                param_attr=ParamAttr(initializer=Constant(1.0)))
            out = prelu(data_t)
            static_rlt2 = self.get_static_graph_result(
                feed={"input": inp_np}, fetch_list=[out])[0]

        with self.dynamic_graph():
            prelu = nn.PRelu(
                mode=mode,
S
songyouwei 已提交
864
                channel=inp_np.shape[1],
865
                input_shape=inp_np.shape,
866 867
                param_attr=ParamAttr(initializer=Constant(1.0)))
            dy_rlt = prelu(base.to_variable(inp_np))
868
            dy_rlt_value = dy_rlt.numpy()
869 870

        self.assertTrue(np.allclose(static_rlt2, static_rlt))
871
        self.assertTrue(np.allclose(dy_rlt_value, static_rlt))
872

873 874 875 876 877
        with self.dynamic_graph():
            inp_np = np.random.randn(5, 200, 100, 100).astype("float32")
            inp = base.to_variable(inp_np)
            prelu1 = nn.PRelu(
                mode=mode,
S
songyouwei 已提交
878
                channel=inp_np.shape[1],
879
                input_shape=inp_np.shape,
880 881 882
                param_attr=ParamAttr(initializer=Constant(2.0)))
            prelu2 = nn.PRelu(
                mode=mode,
S
songyouwei 已提交
883
                channel=inp_np.shape[1],
884
                input_shape=inp_np.shape,
885 886 887 888 889 890 891 892 893 894 895 896 897 898 899
                param_attr=ParamAttr(initializer=Constant(1.0)))
            dy_rlt1 = prelu1(inp)
            dy_rlt2 = prelu2(inp)
            self.assertFalse(
                np.array_equal(prelu1.weight.numpy(), prelu2.weight.numpy()))
            self.assertFalse(np.array_equal(dy_rlt1.numpy(), dy_rlt2.numpy()))
            prelu2.weight.set_value(prelu1.weight.numpy())
            dy_rlt1 = prelu1(inp)
            dy_rlt2 = prelu2(inp)
            self.assertTrue(np.array_equal(dy_rlt1.numpy(), dy_rlt2.numpy()))

            prelu2.weight = prelu1.weight
            self.assertTrue(
                np.array_equal(prelu1.weight.numpy(), prelu2.weight.numpy()))

900 901 902 903 904
    def test_prelu(self):
        self.prelu_test("channel")
        self.prelu_test("element")
        self.prelu_test("all")

905 906 907 908 909 910 911 912 913 914 915 916 917 918 919
    def test_embeding(self):
        inp_word = np.array([[[1]]]).astype('int64')
        dict_size = 20
        with self.static_graph():
            data_t = layers.data(name='word', shape=[1], dtype='int64')
            emb = layers.embedding(
                input=data_t,
                size=[dict_size, 32],
                param_attr='emb.w',
                is_sparse=False)
            static_rlt = self.get_static_graph_result(
                feed={'word': inp_word}, fetch_list=[emb])[0]
        with self.static_graph():
            data_t = layers.data(name='word', shape=[1], dtype='int64')
            emb2 = nn.Embedding(
920
                size=[dict_size, 32], param_attr='emb.w', is_sparse=False)
921 922 923 924 925
            emb_rlt = emb2(data_t)
            static_rlt2 = self.get_static_graph_result(
                feed={'word': inp_word}, fetch_list=[emb_rlt])[0]
        with self.dynamic_graph():
            emb2 = nn.Embedding(
926
                size=[dict_size, 32], param_attr='emb.w', is_sparse=False)
927 928
            dy_rlt = emb2(base.to_variable(inp_word))
            dy_rlt_value = dy_rlt.numpy()
929 930

        self.assertTrue(np.allclose(static_rlt2, static_rlt))
931
        self.assertTrue(np.allclose(dy_rlt_value, static_rlt))
932

933 934 935 936 937
        with self.dynamic_graph():
            custom_weight = np.random.randn(dict_size, 32).astype("float32")
            weight_attr = fluid.ParamAttr(
                initializer=fluid.initializer.NumpyArrayInitializer(
                    custom_weight))
938
            emb1 = nn.Embedding(size=[dict_size, 32], is_sparse=False)
939
            emb2 = nn.Embedding(
940
                size=[dict_size, 32], param_attr=weight_attr, is_sparse=False)
941 942 943 944 945 946 947 948 949 950 951 952 953
            rep1 = emb1(base.to_variable(inp_word))
            rep2 = emb2(base.to_variable(inp_word))
            self.assertFalse(np.array_equal(emb1.weight.numpy(), custom_weight))
            self.assertTrue(np.array_equal(emb2.weight.numpy(), custom_weight))
            self.assertFalse(np.array_equal(rep1.numpy(), rep2.numpy()))
            emb2.weight.set_value(emb1.weight.numpy())
            rep2 = emb2(base.to_variable(inp_word))
            self.assertTrue(np.array_equal(rep1.numpy(), rep2.numpy()))

            emb2.weight = emb1.weight
            self.assertTrue(
                np.array_equal(emb1.weight.numpy(), emb2.weight.numpy()))

954 955 956 957
    def test_nce(self):
        window_size = 5
        dict_size = 20
        label_word = int(window_size // 2) + 1
958
        inp_word = np.array([[1], [2], [3], [4], [5]]).astype('int64')
959 960 961 962 963 964 965
        nid_freq_arr = np.random.dirichlet(np.ones(20) * 1000).astype('float32')
        seed = 1
        with self.static_graph():
            words = []
            for i in range(window_size):
                words.append(
                    layers.data(
966
                        name='word_{0}'.format(i), shape=[None], dtype='int64'))
967 968
            sample_weights = layers.fill_constant(
                shape=[5, 1], dtype='float32', value=1)
969 970 971 972 973
            embs = []
            for i in range(window_size):
                if i == label_word:
                    continue

974
                emb = fluid.embedding(
975 976 977 978 979 980 981
                    input=words[i],
                    size=[dict_size, 32],
                    param_attr='emb.w',
                    is_sparse=False)
                embs.append(emb)

            embs = layers.concat(input=embs, axis=1)
982
            wl = fluid.layers.unsqueeze(words[label_word], axes=[0])
983
            nce_loss = layers.nce(input=embs,
984
                                  label=wl,
985 986 987 988 989 990
                                  num_total_classes=dict_size,
                                  num_neg_samples=2,
                                  sampler="custom_dist",
                                  custom_dist=nid_freq_arr.tolist(),
                                  seed=seed,
                                  param_attr='nce.w',
991 992
                                  bias_attr='nce.b',
                                  sample_weight=sample_weights)
993 994 995 996 997 998 999 1000 1001 1002
            feed_dict = dict()
            for i in range(window_size):
                feed_dict['word_{0}'.format(i)] = inp_word[i]
            static_rlt = self.get_static_graph_result(
                feed=feed_dict, fetch_list=[nce_loss])[0]
        with self.static_graph():
            words = []
            for i in range(window_size):
                words.append(
                    layers.data(
1003
                        name='word_{0}'.format(i), shape=[None], dtype='int64'))
1004 1005
            sample_weights = layers.fill_constant(
                shape=[5, 1], dtype='float32', value=1)
1006
            emb = nn.Embedding(
1007
                size=[dict_size, 32], param_attr='emb.w', is_sparse=False)
1008 1009 1010 1011 1012 1013 1014 1015 1016 1017

            embs2 = []
            for i in range(window_size):
                if i == label_word:
                    continue

                emb_rlt = emb(words[i])
                embs2.append(emb_rlt)

            embs2 = layers.concat(input=embs2, axis=1)
1018 1019
            nce = nn.NCE(num_total_classes=dict_size,
                         dim=embs2.shape[1],
1020 1021 1022 1023 1024
                         num_neg_samples=2,
                         sampler="custom_dist",
                         custom_dist=nid_freq_arr.tolist(),
                         seed=seed,
                         param_attr='nce.w',
1025 1026
                         bias_attr='nce.b',
                         sample_weight=sample_weights)
1027

1028 1029
            wl = fluid.layers.unsqueeze(words[label_word], axes=[0])
            nce_loss2 = nce(embs2, wl)
1030 1031 1032 1033 1034 1035 1036
            feed_dict = dict()
            for i in range(len(words)):
                feed_dict['word_{0}'.format(i)] = inp_word[i]

            static_rlt2 = self.get_static_graph_result(
                feed=feed_dict, fetch_list=[nce_loss2])[0]

L
Leo Chen 已提交
1037
        with self.dynamic_graph():
1038 1039 1040
            words = []
            for i in range(window_size):
                words.append(base.to_variable(inp_word[i]))
1041 1042
            sample_weights = layers.fill_constant(
                shape=[5, 1], dtype='float32', value=1)
1043
            emb = nn.Embedding(
1044
                size=[dict_size, 32], param_attr='emb.w', is_sparse=False)
1045 1046 1047 1048 1049 1050 1051 1052 1053

            embs3 = []
            for i in range(window_size):
                if i == label_word:
                    continue

                emb_rlt = emb(words[i])
                embs3.append(emb_rlt)

S
songyouwei 已提交
1054 1055
            embs3 = layers.concat(
                input=embs3, axis=fluid.dygraph.to_variable(np.array([1])))
1056 1057
            nce = nn.NCE(num_total_classes=dict_size,
                         dim=embs3.shape[1],
1058 1059 1060 1061 1062
                         num_neg_samples=2,
                         sampler="custom_dist",
                         custom_dist=nid_freq_arr.tolist(),
                         seed=seed,
                         param_attr='nce.w',
1063 1064
                         bias_attr='nce.b',
                         sample_weight=sample_weights)
1065

1066 1067
            wl = fluid.layers.unsqueeze(words[label_word], axes=[0])
            dy_rlt = nce(embs3, wl)
1068
            dy_rlt_value = dy_rlt.numpy()
1069 1070

        self.assertTrue(np.allclose(static_rlt2, static_rlt))
1071
        self.assertTrue(np.allclose(dy_rlt_value, static_rlt))
1072

L
Leo Chen 已提交
1073
        with self.dynamic_graph():
1074 1075 1076 1077 1078 1079 1080 1081
            custom_weight = np.random.randn(dict_size, 128).astype("float32")
            weight_attr = fluid.ParamAttr(
                initializer=fluid.initializer.NumpyArrayInitializer(
                    custom_weight))
            words = []
            for i in range(window_size):
                words.append(base.to_variable(inp_word[i]))
            sample_weights = layers.fill_constant(
S
songyouwei 已提交
1082 1083 1084
                shape=fluid.dygraph.to_variable(np.array([5, 1])),
                dtype='float32',
                value=1)
1085
            emb = nn.Embedding(
1086
                size=[dict_size, 32], param_attr='emb.w', is_sparse=False)
1087 1088 1089 1090 1091 1092 1093 1094 1095 1096

            embs3 = []
            for i in range(window_size):
                if i == label_word:
                    continue

                emb_rlt = emb(words[i])
                embs3.append(emb_rlt)

            embs3 = layers.concat(input=embs3, axis=1)
1097 1098
            nce1 = nn.NCE(num_total_classes=dict_size,
                          dim=embs3.shape[1],
1099 1100 1101 1102 1103 1104 1105 1106
                          num_neg_samples=2,
                          sampler="custom_dist",
                          custom_dist=nid_freq_arr.tolist(),
                          seed=seed,
                          param_attr='nce1.w',
                          bias_attr='nce1.b',
                          sample_weight=sample_weights)

1107 1108
            nce2 = nn.NCE(num_total_classes=dict_size,
                          dim=embs3.shape[1],
1109 1110 1111 1112
                          num_neg_samples=2,
                          sampler="custom_dist",
                          custom_dist=nid_freq_arr.tolist(),
                          seed=seed,
1113
                          param_attr=weight_attr,
1114 1115 1116
                          bias_attr='nce2.b',
                          sample_weight=sample_weights)

1117 1118 1119
            wl = fluid.layers.unsqueeze(words[label_word], axes=[0])
            nce1_loss = nce1(embs3, wl)
            nce2_loss = nce2(embs3, wl)
1120 1121 1122 1123
            self.assertFalse(
                np.array_equal(nce1_loss.numpy(), nce2_loss.numpy()))
            nce2.weight.set_value(nce1.weight.numpy())
            nce2.bias.set_value(nce1.bias)
1124 1125
            nce1_loss = nce1(embs3, wl)
            nce2_loss = nce2(embs3, wl)
1126 1127 1128 1129 1130 1131 1132 1133 1134 1135
            self.assertTrue(
                np.array_equal(nce1_loss.numpy(), nce2_loss.numpy()))

            nce2.weight = nce1.weight
            nce2.bias = nce1.bias
            self.assertTrue(
                np.array_equal(nce1.weight.numpy(), nce2.weight.numpy()))
            self.assertTrue(
                np.array_equal(nce1.bias.numpy(), nce2.bias.numpy()))

S
songyouwei 已提交
1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166
    def test_one_hot(self):
        with self.dynamic_graph():
            label = fluid.dygraph.to_variable(np.array([[1], [1], [3], [0]]))
            one_hot_label1 = fluid.layers.one_hot(input=label, depth=4)
            one_hot_label2 = fluid.layers.one_hot(
                input=label, depth=fluid.dygraph.to_variable(np.array([4])))
            self.assertTrue(
                np.array_equal(one_hot_label1.numpy(), one_hot_label2.numpy()))

    def test_split(self):
        with self.dynamic_graph():
            input = fluid.dygraph.to_variable(np.random.random((3, 8, 5)))
            x0, x1 = fluid.layers.split(input, num_or_sections=2, dim=1)
            x00, x11 = fluid.layers.split(
                input,
                num_or_sections=2,
                dim=fluid.dygraph.to_variable(np.array([1])))
            self.assertTrue(np.array_equal(x0.numpy(), x00.numpy()))
            self.assertTrue(np.array_equal(x1.numpy(), x11.numpy()))

    def test_topk(self):
        with self.dynamic_graph():
            input = fluid.dygraph.to_variable(np.random.random((13, 11)))
            top5_values1, top5_indices1 = layers.topk(input, k=5)
            top5_values2, top5_indices2 = layers.topk(
                input, k=fluid.dygraph.to_variable(np.array([5])))
            self.assertTrue(
                np.array_equal(top5_values1.numpy(), top5_values2.numpy()))
            self.assertTrue(
                np.array_equal(top5_indices1.numpy(), top5_indices2.numpy()))

L
lujun 已提交
1167 1168 1169 1170
    def test_conv3d(self):
        with self.static_graph():
            images = layers.data(
                name='pixel', shape=[3, 6, 6, 6], dtype='float32')
1171
            ret = layers.conv3d(input=images, num_filters=3, filter_size=2)
L
lujun 已提交
1172 1173 1174 1175 1176 1177 1178 1179
            static_ret = self.get_static_graph_result(
                feed={'pixel': np.ones(
                    [2, 3, 6, 6, 6], dtype='float32')},
                fetch_list=[ret])[0]

        with self.static_graph():
            images = layers.data(
                name='pixel', shape=[3, 6, 6, 6], dtype='float32')
1180
            conv3d = nn.Conv3D(num_channels=3, num_filters=3, filter_size=2)
L
lujun 已提交
1181 1182 1183 1184 1185 1186 1187 1188
            ret = conv3d(images)
            static_ret2 = self.get_static_graph_result(
                feed={'pixel': np.ones(
                    [2, 3, 6, 6, 6], dtype='float32')},
                fetch_list=[ret])[0]

        with self.dynamic_graph():
            images = np.ones([2, 3, 6, 6, 6], dtype='float32')
1189
            conv3d = nn.Conv3D(num_channels=3, num_filters=3, filter_size=2)
L
lujun 已提交
1190
            dy_ret = conv3d(base.to_variable(images))
1191
            dy_rlt_value = dy_ret.numpy()
L
lujun 已提交
1192

1193
        self.assertTrue(np.allclose(static_ret, dy_rlt_value))
L
lujun 已提交
1194 1195
        self.assertTrue(np.allclose(static_ret, static_ret2))

1196 1197 1198 1199 1200 1201
        with self.dynamic_graph():
            images = np.ones([2, 3, 6, 6, 6], dtype='float32')
            custom_weight = np.random.randn(3, 3, 2, 2, 2).astype("float32")
            weight_attr = fluid.ParamAttr(
                initializer=fluid.initializer.NumpyArrayInitializer(
                    custom_weight))
1202
            conv3d1 = nn.Conv3D(num_channels=3, num_filters=3, filter_size=2)
1203
            conv3d2 = nn.Conv3D(
1204 1205 1206 1207
                num_channels=3,
                num_filters=3,
                filter_size=2,
                param_attr=weight_attr)
1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230
            dy_ret1 = conv3d1(base.to_variable(images))
            dy_ret2 = conv3d2(base.to_variable(images))
            self.assertFalse(np.array_equal(dy_ret1.numpy(), dy_ret2.numpy()))

            conv3d1_weight_np = conv3d1.weight.numpy()
            conv3d1_bias = conv3d1.bias
            self.assertFalse(
                np.array_equal(conv3d1_weight_np, conv3d2.weight.numpy()))
            conv3d2.weight.set_value(conv3d1_weight_np)
            self.assertTrue(
                np.array_equal(conv3d1_weight_np, conv3d2.weight.numpy()))
            conv3d1.bias.set_value(conv3d1_bias)
            dy_ret1 = conv3d1(base.to_variable(images))
            dy_ret2 = conv3d2(base.to_variable(images))
            self.assertTrue(np.array_equal(dy_ret1.numpy(), dy_ret2.numpy()))

            conv3d2.weight = conv3d1.weight
            conv3d2.bias = conv3d1.bias
            self.assertTrue(
                np.array_equal(conv3d1.weight.numpy(), conv3d2.weight.numpy()))
            self.assertTrue(
                np.array_equal(conv3d1.bias.numpy(), conv3d2.bias.numpy()))

L
lujun 已提交
1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265
    def test_row_conv(self):
        input = np.arange(15).reshape([3, 5]).astype('float32')
        if core.is_compiled_with_cuda():
            place = core.CUDAPlace(0)
        else:
            place = core.CPUPlace()

        with self.static_graph():
            x = layers.data(
                name='X',
                shape=[3, 5],
                dtype='float32',
                lod_level=1,
                append_batch_size=False)
            ret = layers.row_conv(input=x, future_context_size=2)
            static_ret = self.get_static_graph_result(
                feed={
                    'X': fluid.create_lod_tensor(
                        data=input, recursive_seq_lens=[[1, 1, 1]], place=place)
                },
                fetch_list=[ret],
                with_lod=True)[0]

        with self.static_graph():
            x = layers.data(
                name='X',
                shape=[3, 5],
                dtype='float32',
                lod_level=1,
                append_batch_size=False)
            rowConv = nn.RowConv('RowConv', future_context_size=2)
            ret = rowConv(x)
            static_ret2 = self.get_static_graph_result(
                feed={
                    'X': fluid.create_lod_tensor(
1266
                        data=input, recursive_seq_lens=[[1, 1, 1]], place=place)
L
lujun 已提交
1267
                },
1268 1269
                fetch_list=[ret],
                with_lod=True)[0]
L
lujun 已提交
1270

1271
        # TODO: dygraph can't support LODTensor
L
lujun 已提交
1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291

        self.assertTrue(np.allclose(static_ret, static_ret2))

    def test_group_norm(self):
        if core.is_compiled_with_cuda():
            place = core.CUDAPlace(0)
        else:
            place = core.CPUPlace()

        shape = (2, 4, 3, 3)

        input = np.random.random(shape).astype('float32')

        with self.static_graph():
            X = fluid.layers.data(
                name='X',
                shape=shape,
                dtype='float32',
                lod_level=1,
                append_batch_size=False)
1292 1293 1294 1295 1296 1297
            ret = layers.group_norm(
                input=X,
                groups=2,
                param_attr=fluid.initializer.Uniform(
                    low=-0.5, high=0.5),
                bias_attr=fluid.initializer.ConstantInitializer(value=1))
L
lujun 已提交
1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312
            static_ret = self.get_static_graph_result(
                feed={
                    'X': fluid.create_lod_tensor(
                        data=input, recursive_seq_lens=[[1, 1]], place=place)
                },
                fetch_list=[ret],
                with_lod=True)[0]

        with self.static_graph():
            X = fluid.layers.data(
                name='X',
                shape=shape,
                dtype='float32',
                lod_level=1,
                append_batch_size=False)
1313 1314 1315 1316 1317 1318
            groupNorm = nn.GroupNorm(
                channels=shape[1],
                groups=2,
                param_attr=fluid.initializer.Uniform(
                    low=-0.5, high=0.5),
                bias_attr=fluid.initializer.ConstantInitializer(value=1))
L
lujun 已提交
1319 1320 1321 1322 1323 1324 1325 1326 1327 1328
            ret = groupNorm(X)
            static_ret2 = self.get_static_graph_result(
                feed={
                    'X': fluid.create_lod_tensor(
                        data=input, recursive_seq_lens=[[1, 1]], place=place)
                },
                fetch_list=[ret],
                with_lod=True)[0]

        with self.dynamic_graph():
1329 1330 1331 1332 1333 1334
            groupNorm = nn.GroupNorm(
                channels=shape[1],
                groups=2,
                param_attr=fluid.initializer.Uniform(
                    low=-0.5, high=0.5),
                bias_attr=fluid.initializer.ConstantInitializer(value=1))
L
lujun 已提交
1335
            dy_ret = groupNorm(base.to_variable(input))
1336
            dy_rlt_value = dy_ret.numpy()
L
lujun 已提交
1337

1338
        self.assertTrue(np.allclose(static_ret, dy_rlt_value))
L
lujun 已提交
1339 1340
        self.assertTrue(np.allclose(static_ret, static_ret2))

1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395
    def test_instance_norm(self):
        if core.is_compiled_with_cuda():
            place = core.CUDAPlace(0)
        else:
            place = core.CPUPlace()

        shape = (2, 4, 3, 3)

        input = np.random.random(shape).astype('float32')

        with self.static_graph():
            X = fluid.layers.data(
                name='X', shape=shape, dtype='float32', append_batch_size=False)
            ret = layers.instance_norm(input=X)
            static_ret = self.get_static_graph_result(
                feed={'X': input}, fetch_list=[ret])[0]

        with self.static_graph():
            X = fluid.layers.data(
                name='X', shape=shape, dtype='float32', append_batch_size=False)
            instanceNorm = nn.InstanceNorm(num_channels=shape[1])
            ret = instanceNorm(X)
            static_ret2 = self.get_static_graph_result(
                feed={'X': input}, fetch_list=[ret])[0]

        with self.dynamic_graph():
            instanceNorm = nn.InstanceNorm(num_channels=shape[1])
            dy_ret = instanceNorm(base.to_variable(input))
            dy_rlt_value = dy_ret.numpy()

        with self.dynamic_graph():
            instanceNorm = paddle.nn.InstanceNorm(num_channels=shape[1])
            dy_ret = instanceNorm(base.to_variable(input))
            dy_rlt_value2 = dy_ret.numpy()

        self.assertTrue(np.allclose(static_ret, dy_rlt_value))
        self.assertTrue(np.allclose(static_ret, dy_rlt_value2))
        self.assertTrue(np.allclose(static_ret, static_ret2))

        with self.static_graph():
            # the input of InstanceNorm must be Variable.
            def test_Variable():
                instanceNorm = paddle.nn.InstanceNorm(num_channels=shape[1])
                ret1 = instanceNorm(input)

            self.assertRaises(TypeError, test_Variable)

            # the input dtype of InstanceNorm must be float32 or float64
            def test_type():
                input = np.random.random(shape).astype('int32')
                instanceNorm = paddle.nn.InstanceNorm(num_channels=shape[1])
                ret2 = instanceNorm(input)

            self.assertRaises(TypeError, test_type)

L
lujun 已提交
1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428
    def test_spectral_norm(self):
        if core.is_compiled_with_cuda():
            place = core.CUDAPlace(0)
        else:
            place = core.CPUPlace()

        shape = (2, 4, 3, 3)

        input = np.random.random(shape).astype('float32')

        with self.static_graph():
            Weight = fluid.layers.data(
                name='Weight',
                shape=shape,
                dtype='float32',
                lod_level=1,
                append_batch_size=False)
            ret = layers.spectral_norm(weight=Weight, dim=1, power_iters=2)
            static_ret = self.get_static_graph_result(
                feed={
                    'Weight': fluid.create_lod_tensor(
                        data=input, recursive_seq_lens=[[1, 1]], place=place),
                },
                fetch_list=[ret],
                with_lod=True)[0]

        with self.static_graph():
            Weight = fluid.layers.data(
                name='Weight',
                shape=shape,
                dtype='float32',
                lod_level=1,
                append_batch_size=False)
1429
            spectralNorm = nn.SpectralNorm(shape, dim=1, power_iters=2)
L
lujun 已提交
1430 1431 1432 1433 1434 1435 1436 1437 1438 1439
            ret = spectralNorm(Weight)
            static_ret2 = self.get_static_graph_result(
                feed={
                    'Weight': fluid.create_lod_tensor(
                        data=input, recursive_seq_lens=[[1, 1]], place=place)
                },
                fetch_list=[ret],
                with_lod=True)[0]

        with self.dynamic_graph():
1440
            spectralNorm = nn.SpectralNorm(shape, dim=1, power_iters=2)
L
lujun 已提交
1441
            dy_ret = spectralNorm(base.to_variable(input))
1442
            dy_rlt_value = dy_ret.numpy()
L
lujun 已提交
1443

1444
        self.assertTrue(np.allclose(static_ret, dy_rlt_value))
L
lujun 已提交
1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468
        self.assertTrue(np.allclose(static_ret, static_ret2))

    def test_tree_conv(self):
        if core.is_compiled_with_cuda():
            place = core.CUDAPlace(0)
        else:
            place = core.CPUPlace()
        adj_array = [1, 2, 1, 3, 1, 4, 1, 5, 2, 6, 2, 7, 2, 8, 4, 9, 4, 10]
        adj = np.array(adj_array).reshape((1, 9, 2)).astype('int32')
        adj = np.tile(adj, (1, 1, 1))
        vectors = np.random.random((1, 10, 5)).astype('float32')
        with self.static_graph():
            NodesVector = fluid.layers.data(
                name='NodesVector',
                shape=(1, 10, 5),
                dtype='float32',
                lod_level=1,
                append_batch_size=False)
            EdgeSet = fluid.layers.data(
                name='EdgeSet',
                shape=(1, 9, 2),
                dtype='int32',
                lod_level=1,
                append_batch_size=False)
1469
            ret = fluid.contrib.layers.tree_conv(
L
lujun 已提交
1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498
                nodes_vector=NodesVector,
                edge_set=EdgeSet,
                output_size=6,
                num_filters=1,
                max_depth=2)
            static_ret = self.get_static_graph_result(
                feed={
                    'NodesVector': fluid.create_lod_tensor(
                        data=vectors, recursive_seq_lens=[[1]], place=place),
                    'EdgeSet': fluid.create_lod_tensor(
                        data=adj, recursive_seq_lens=[[1]], place=place)
                },
                fetch_list=[ret],
                with_lod=False)[0]

        with self.static_graph():
            NodesVector = fluid.layers.data(
                name='NodesVector',
                shape=(1, 10, 5),
                dtype='float32',
                lod_level=1,
                append_batch_size=False)
            EdgeSet = fluid.layers.data(
                name='EdgeSet',
                shape=(1, 9, 2),
                dtype='int32',
                lod_level=1,
                append_batch_size=False)
            treeConv = nn.TreeConv(
1499
                feature_size=5, output_size=6, num_filters=1, max_depth=2)
L
lujun 已提交
1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512
            ret = treeConv(NodesVector, EdgeSet)
            static_ret2 = self.get_static_graph_result(
                feed={
                    'NodesVector': fluid.create_lod_tensor(
                        data=vectors, recursive_seq_lens=[[1]], place=place),
                    'EdgeSet': fluid.create_lod_tensor(
                        data=adj, recursive_seq_lens=[[1]], place=place)
                },
                fetch_list=[ret],
                with_lod=False)[0]

        with self.dynamic_graph():
            treeConv = nn.TreeConv(
1513
                feature_size=5, output_size=6, num_filters=1, max_depth=2)
L
lujun 已提交
1514
            dy_ret = treeConv(base.to_variable(vectors), base.to_variable(adj))
1515
            dy_rlt_value = dy_ret.numpy()
L
lujun 已提交
1516 1517

        self.assertTrue(np.allclose(static_ret, static_ret2))
1518
        self.assertTrue(np.allclose(static_ret, dy_rlt_value))
L
lujun 已提交
1519

1520 1521 1522 1523 1524 1525
        with self.dynamic_graph():
            custom_weight = np.random.randn(5, 3, 6, 1).astype("float32")
            weight_attr = fluid.ParamAttr(
                initializer=fluid.initializer.NumpyArrayInitializer(
                    custom_weight))
            treeConv1 = nn.TreeConv(
1526
                feature_size=5,
1527 1528 1529 1530 1531
                output_size=6,
                num_filters=1,
                max_depth=2,
                bias_attr='tc1_b')
            treeConv2 = nn.TreeConv(
1532
                feature_size=5,
1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558
                output_size=6,
                num_filters=1,
                max_depth=2,
                param_attr=weight_attr,
                bias_attr='tc2_b')
            dy_ret1 = treeConv1(
                base.to_variable(vectors), base.to_variable(adj))
            dy_ret2 = treeConv2(
                base.to_variable(vectors), base.to_variable(adj))
            self.assertFalse(np.array_equal(dy_ret1.numpy(), dy_ret2.numpy()))
            treeConv2.weight.set_value(treeConv1.weight.numpy())
            treeConv2.bias.set_value(treeConv1.bias)
            dy_ret1 = treeConv1(
                base.to_variable(vectors), base.to_variable(adj))
            dy_ret2 = treeConv2(
                base.to_variable(vectors), base.to_variable(adj))
            self.assertTrue(np.array_equal(dy_ret1.numpy(), dy_ret2.numpy()))

            treeConv2.weight = treeConv1.weight
            treeConv2.bias = treeConv1.bias
            self.assertTrue(
                np.array_equal(treeConv1.weight.numpy(),
                               treeConv2.weight.numpy()))
            self.assertTrue(
                np.array_equal(treeConv1.bias.numpy(), treeConv2.bias.numpy()))

L
lujun 已提交
1559 1560 1561 1562 1563 1564 1565
    def test_conv3d_transpose(self):
        input_array = np.arange(0, 48).reshape(
            [2, 3, 2, 2, 2]).astype('float32')

        with self.static_graph():
            img = layers.data(name='pixel', shape=[3, 2, 2, 2], dtype='float32')
            out = layers.conv3d_transpose(
1566
                input=img, num_filters=12, filter_size=12, use_cudnn=False)
L
lujun 已提交
1567 1568 1569 1570 1571
            static_rlt = self.get_static_graph_result(
                feed={'pixel': input_array}, fetch_list=[out])[0]
        with self.static_graph():
            img = layers.data(name='pixel', shape=[3, 2, 2, 2], dtype='float32')
            conv3d_transpose = nn.Conv3DTranspose(
1572
                num_channels=3, num_filters=12, filter_size=12, use_cudnn=False)
L
lujun 已提交
1573 1574 1575 1576 1577
            out = conv3d_transpose(img)
            static_rlt2 = self.get_static_graph_result(
                feed={'pixel': input_array}, fetch_list=[out])[0]
        with self.dynamic_graph():
            conv3d_transpose = nn.Conv3DTranspose(
1578
                num_channels=3, num_filters=12, filter_size=12, use_cudnn=False)
L
lujun 已提交
1579
            dy_rlt = conv3d_transpose(base.to_variable(input_array))
1580
            dy_rlt_value = dy_rlt.numpy()
L
lujun 已提交
1581
        self.assertTrue(np.allclose(static_rlt2, static_rlt))
1582
        self.assertTrue(np.allclose(dy_rlt_value, static_rlt))
L
lujun 已提交
1583

1584 1585 1586 1587 1588 1589 1590
        with self.dynamic_graph():
            images = np.ones([2, 3, 6, 6, 6], dtype='float32')
            custom_weight = np.random.randn(3, 3, 2, 2, 2).astype("float32")
            weight_attr = fluid.ParamAttr(
                initializer=fluid.initializer.NumpyArrayInitializer(
                    custom_weight))
            conv3d1 = nn.Conv3DTranspose(
1591
                num_channels=3,
1592 1593 1594 1595 1596
                num_filters=3,
                filter_size=2,
                bias_attr='conv3d1_b',
                use_cudnn=False)
            conv3d2 = nn.Conv3DTranspose(
1597
                num_channels=3,
1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625
                num_filters=3,
                filter_size=2,
                param_attr=weight_attr,
                bias_attr='conv3d2_b',
                use_cudnn=False)
            dy_ret1 = conv3d1(base.to_variable(images))
            dy_ret2 = conv3d2(base.to_variable(images))
            self.assertFalse(np.array_equal(dy_ret1.numpy(), dy_ret2.numpy()))

            conv3d1_weight_np = conv3d1.weight.numpy()
            conv3d1_bias = conv3d1.bias
            self.assertFalse(
                np.array_equal(conv3d1_weight_np, conv3d2.weight.numpy()))
            conv3d2.weight.set_value(conv3d1_weight_np)
            self.assertTrue(
                np.array_equal(conv3d1_weight_np, conv3d2.weight.numpy()))
            conv3d1.bias.set_value(conv3d1_bias)
            dy_ret1 = conv3d1(base.to_variable(images))
            dy_ret2 = conv3d2(base.to_variable(images))
            self.assertTrue(np.array_equal(dy_ret1.numpy(), dy_ret2.numpy()))

            conv3d2.weight = conv3d1.weight
            conv3d2.bias = conv3d1.bias
            self.assertTrue(
                np.array_equal(conv3d1.weight.numpy(), conv3d2.weight.numpy()))
            self.assertTrue(
                np.array_equal(conv3d1.bias.numpy(), conv3d2.bias.numpy()))

1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641
    def test_eye_op(self):
        np_eye = np.eye(3, 2)
        array_rlt1 = [np_eye for _ in range(3)]
        stack_rlt1 = np.stack(array_rlt1, axis=0)
        array_rlt2 = [stack_rlt1 for _ in range(4)]
        stack_rlt2 = np.stack(array_rlt2, axis=0)

        with self.dynamic_graph():
            eye_tensor = layers.eye(num_rows=3, num_columns=2)
            eye_tensor_rlt1 = layers.eye(num_rows=3,
                                         num_columns=2,
                                         batch_shape=[3])
            eye_tensor_rlt2 = layers.eye(num_rows=3,
                                         num_columns=2,
                                         batch_shape=[4, 3])
            diag_tensor = layers.eye(20)
1642 1643 1644 1645 1646 1647 1648 1649
            eye_tensor_value = eye_tensor.numpy()
            eye_tensor_rlt1_value = eye_tensor_rlt1.numpy()
            eye_tensor_rlt2_value = eye_tensor_rlt2.numpy()
            diag_tensor_value = diag_tensor.numpy()
        self.assertTrue(np.allclose(eye_tensor_value, np_eye))
        self.assertTrue(np.allclose(eye_tensor_rlt1_value, stack_rlt1))
        self.assertTrue(np.allclose(eye_tensor_rlt2_value, stack_rlt2))
        self.assertTrue(np.allclose(diag_tensor_value, np.eye(20)))
1650 1651 1652 1653 1654 1655 1656 1657 1658 1659

        with self.assertRaises(TypeError):
            layers.eye(num_rows=3.1)
        with self.assertRaises(TypeError):
            layers.eye(num_rows=3, num_columns=2.2)
        with self.assertRaises(TypeError):
            layers.eye(num_rows=3, batch_shape=2)
        with self.assertRaises(TypeError):
            layers.eye(num_rows=3, batch_shape=[-1])

H
huangjun12 已提交
1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670
    def test_hard_swish(self):
        with self.static_graph():
            t = layers.data(name='t', shape=[3, 3], dtype='float32')
            ret = layers.hard_swish(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.hard_swish(base.to_variable(t))
1671
            dy_ret_rlt = dy_ret.numpy()
H
huangjun12 已提交
1672

1673
        self.assertTrue(np.allclose(static_ret, dy_ret_rlt))
H
huangjun12 已提交
1674

1675 1676 1677 1678 1679 1680 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709
    def test_while_loop(self):
        with self.static_graph():
            i = layers.fill_constant(shape=[1], dtype='int64', value=0)
            ten = layers.fill_constant(shape=[1], dtype='int64', value=10)

            def cond(i):
                return layers.less_than(i, ten)

            def body(i):
                return i + 1

            out = layers.while_loop(cond, body, [i])
            static_ret = self.get_static_graph_result(feed={}, fetch_list=out)

        with self.dynamic_graph():
            i = layers.fill_constant(shape=[1], dtype='int64', value=0)
            ten = layers.fill_constant(shape=[1], dtype='int64', value=10)

            def cond(i):
                return layers.less_than(i, ten)

            def body(i):
                return i + 1

            dy_ret = layers.while_loop(cond, body, [i])
            with self.assertRaises(ValueError):
                j = layers.fill_constant(shape=[1], dtype='int64', value=0)

                def body2(i):
                    return i + 1, i + 2

                layers.while_loop(cond, body2, [j])

        self.assertTrue(np.array_equal(static_ret[0], dy_ret[0].numpy()))

1710 1711 1712 1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725
    def test_compare(self):
        value_a = np.arange(3)
        value_b = np.arange(3)
        # less than
        with self.static_graph():
            a = layers.data(name='a', shape=[1], dtype='int64')
            b = layers.data(name='b', shape=[1], dtype='int64')
            cond = layers.less_than(x=a, y=b)
            static_ret = self.get_static_graph_result(
                feed={"a": value_a,
                      "b": value_b}, fetch_list=[cond])[0]
        with self.dynamic_graph():
            da = base.to_variable(value_a)
            db = base.to_variable(value_b)
            dcond = layers.less_than(x=da, y=db)

1726 1727
            for i in range(len(static_ret)):
                self.assertTrue(dcond.numpy()[i] == static_ret[i])
1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744 1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760 1761 1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776 1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792 1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808

        # less equal
        with self.static_graph():
            a1 = layers.data(name='a1', shape=[1], dtype='int64')
            b1 = layers.data(name='b1', shape=[1], dtype='int64')
            cond1 = layers.less_equal(x=a1, y=b1)
            static_ret1 = self.get_static_graph_result(
                feed={"a1": value_a,
                      "b1": value_b}, fetch_list=[cond1])[0]
        with self.dynamic_graph():
            da1 = base.to_variable(value_a)
            db1 = base.to_variable(value_b)
            dcond1 = layers.less_equal(x=da1, y=db1)

            for i in range(len(static_ret1)):
                self.assertTrue(dcond1.numpy()[i] == static_ret1[i])

        #greater than
        with self.static_graph():
            a2 = layers.data(name='a2', shape=[1], dtype='int64')
            b2 = layers.data(name='b2', shape=[1], dtype='int64')
            cond2 = layers.greater_than(x=a2, y=b2)
            static_ret2 = self.get_static_graph_result(
                feed={"a2": value_a,
                      "b2": value_b}, fetch_list=[cond2])[0]
        with self.dynamic_graph():
            da2 = base.to_variable(value_a)
            db2 = base.to_variable(value_b)
            dcond2 = layers.greater_than(x=da2, y=db2)

            for i in range(len(static_ret2)):
                self.assertTrue(dcond2.numpy()[i] == static_ret2[i])

        #greater equal
        with self.static_graph():
            a3 = layers.data(name='a3', shape=[1], dtype='int64')
            b3 = layers.data(name='b3', shape=[1], dtype='int64')
            cond3 = layers.greater_equal(x=a3, y=b3)
            static_ret3 = self.get_static_graph_result(
                feed={"a3": value_a,
                      "b3": value_b}, fetch_list=[cond3])[0]
        with self.dynamic_graph():
            da3 = base.to_variable(value_a)
            db3 = base.to_variable(value_b)
            dcond3 = layers.greater_equal(x=da3, y=db3)

            for i in range(len(static_ret3)):
                self.assertTrue(dcond3.numpy()[i] == static_ret3[i])

        # equal
        with self.static_graph():
            a4 = layers.data(name='a4', shape=[1], dtype='int64')
            b4 = layers.data(name='b4', shape=[1], dtype='int64')
            cond4 = layers.equal(x=a4, y=b4)
            static_ret4 = self.get_static_graph_result(
                feed={"a4": value_a,
                      "b4": value_b}, fetch_list=[cond4])[0]
        with self.dynamic_graph():
            da4 = base.to_variable(value_a)
            db4 = base.to_variable(value_b)
            dcond4 = layers.equal(x=da4, y=db4)

            for i in range(len(static_ret4)):
                self.assertTrue(dcond4.numpy()[i] == static_ret4[i])

        # not equal
        with self.static_graph():
            a5 = layers.data(name='a5', shape=[1], dtype='int64')
            b5 = layers.data(name='b5', shape=[1], dtype='int64')
            cond5 = layers.equal(x=a5, y=b5)
            static_ret5 = self.get_static_graph_result(
                feed={"a5": value_a,
                      "b5": value_b}, fetch_list=[cond5])[0]
        with self.dynamic_graph():
            da5 = base.to_variable(value_a)
            db5 = base.to_variable(value_b)
            dcond5 = layers.equal(x=da5, y=db5)

            for i in range(len(static_ret5)):
                self.assertTrue(dcond5.numpy()[i] == static_ret5[i])

1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824 1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840 1841 1842 1843 1844 1845
    def test_cond(self):
        def less_than_branch(a, b):
            return fluid.layers.elementwise_add(a, b)

        def greater_equal_branch(a, b):
            return fluid.layers.elementwise_sub(a, b)

        with self.static_graph():
            a = fluid.layers.fill_constant(
                shape=[1], dtype='float32', value=0.1)
            b = fluid.layers.fill_constant(
                shape=[1], dtype='float32', value=0.23)
            out = fluid.layers.cond(a >= b, lambda: greater_equal_branch(a, b),
                                    lambda: less_than_branch(a, b))
            place = fluid.CUDAPlace(0) if core.is_compiled_with_cuda(
            ) else fluid.CPUPlace()
            exe = fluid.Executor(place)
            ret = exe.run(fetch_list=[out])
            static_res = ret[0]

        with self.dynamic_graph():
            a = fluid.dygraph.to_variable(np.array([0.1]).astype('float32'))
            b = fluid.dygraph.to_variable(np.array([0.23]).astype('float32'))
            out = layers.cond(a < b, lambda: less_than_branch(a, b),
                              lambda: greater_equal_branch(a, b))
            out2 = layers.cond(a >= b, lambda: greater_equal_branch(a, b),
                               lambda: less_than_branch(a, b))
            dynamic_res = out.numpy()
            dynamic_res2 = out2.numpy()
            self.assertTrue(np.array_equal(dynamic_res, dynamic_res2))
            with self.assertRaises(TypeError):
                layers.cond(a < b, 'str', 'str')
            with self.assertRaises(TypeError):
                layers.cond(a >= b, 'str', 'str')

        self.assertTrue(np.array_equal(static_res, dynamic_res))

1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888 1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949
    def test_case(self):
        def fn_1():
            return layers.fill_constant(shape=[1, 2], dtype='float32', value=1)

        def fn_2():
            return layers.fill_constant(shape=[2, 2], dtype='int32', value=2)

        def fn_3():
            return layers.fill_constant(shape=[3], dtype='int32', value=3)

        with self.static_graph():
            x = layers.fill_constant(shape=[1], dtype='float32', value=0.3)
            y = layers.fill_constant(shape=[1], dtype='float32', value=0.1)
            z = layers.fill_constant(shape=[1], dtype='float32', value=0.2)

            pred_1 = layers.less_than(z, x)  # true: 0.2 < 0.3
            pred_2 = layers.less_than(x, y)  # false: 0.3 < 0.1
            pred_3 = layers.equal(x, y)  # false: 0.3 == 0.1

            out_1 = layers.case(
                pred_fn_pairs=[(pred_1, fn_1), (pred_2, fn_2)], default=fn_3)
            out_2 = layers.case(pred_fn_pairs=[(pred_2, fn_2), (pred_3, fn_3)])

            place = fluid.CUDAPlace(0) if core.is_compiled_with_cuda(
            ) else fluid.CPUPlace()
            exe = fluid.Executor(place)
            static_res1, static_res2 = exe.run(fetch_list=[out_1, out_2])

        with self.dynamic_graph():
            x = layers.fill_constant(shape=[1], dtype='float32', value=0.3)
            y = layers.fill_constant(shape=[1], dtype='float32', value=0.1)
            z = layers.fill_constant(shape=[1], dtype='float32', value=0.2)

            pred_1 = layers.less_than(z, x)  # true: 0.2 < 0.3
            pred_2 = layers.less_than(x, y)  # false: 0.3 < 0.1
            pred_3 = layers.equal(x, y)  # false: 0.3 == 0.1

            out_1 = layers.case(
                pred_fn_pairs=[(pred_1, fn_1), (pred_2, fn_2)], default=fn_3)
            out_2 = layers.case(pred_fn_pairs=[(pred_2, fn_2), (pred_3, fn_3)])
            dynamic_res1 = out_1.numpy()
            dynamic_res2 = out_2.numpy()

        self.assertTrue(np.array_equal(static_res1, dynamic_res1))
        self.assertTrue(np.array_equal(static_res2, dynamic_res2))

    def test_switch_case(self):
        def fn_1():
            return layers.fill_constant(shape=[1, 2], dtype='float32', value=1)

        def fn_2():
            return layers.fill_constant(shape=[2, 2], dtype='int32', value=2)

        def fn_3():
            return layers.fill_constant(shape=[3], dtype='int32', value=3)

        with self.static_graph():
            index_1 = layers.fill_constant(shape=[1], dtype='int32', value=1)
            index_2 = layers.fill_constant(shape=[1], dtype='int32', value=2)

            out_1 = layers.switch_case(
                branch_index=index_1,
                branch_fns={1: fn_1,
                            2: fn_2},
                default=fn_3)
            out_2 = layers.switch_case(
                branch_index=index_2,
                branch_fns=[(1, fn_1), (2, fn_2)],
                default=fn_3)
            out_3 = layers.switch_case(
                branch_index=index_2,
                branch_fns=[(0, fn_1), (4, fn_2), (7, fn_3)])

            place = fluid.CUDAPlace(0) if core.is_compiled_with_cuda(
            ) else fluid.CPUPlace()
            exe = fluid.Executor(place)
            static_res1, static_res2, static_res3 = exe.run(
                fetch_list=[out_1, out_2, out_3])

        with self.dynamic_graph():
            index_1 = layers.fill_constant(shape=[1], dtype='int32', value=1)
            index_2 = layers.fill_constant(shape=[1], dtype='int32', value=2)

            out_1 = layers.switch_case(
                branch_index=index_1,
                branch_fns={1: fn_1,
                            2: fn_2},
                default=fn_3)
            out_2 = layers.switch_case(
                branch_index=index_2,
                branch_fns=[(1, fn_1), (2, fn_2)],
                default=fn_3)
            out_3 = layers.switch_case(
                branch_index=index_2,
                branch_fns=[(0, fn_1), (4, fn_2), (7, fn_3)])

            dynamic_res1 = out_1.numpy()
            dynamic_res2 = out_2.numpy()
            dynamic_res3 = out_3.numpy()

        self.assertTrue(np.array_equal(static_res1, dynamic_res1))
        self.assertTrue(np.array_equal(static_res2, dynamic_res2))
        self.assertTrue(np.array_equal(static_res3, dynamic_res3))

1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977
    def test_crop_tensor(self):
        with self.static_graph():
            x = fluid.layers.data(name="x1", shape=[6, 5, 8])

            dim1 = fluid.layers.data(
                name="dim1", shape=[1], append_batch_size=False)
            dim2 = fluid.layers.data(
                name="dim2", shape=[1], append_batch_size=False)
            crop_shape1 = (1, 2, 4, 4)
            crop_shape2 = fluid.layers.data(
                name="crop_shape", shape=[4], append_batch_size=False)
            crop_shape3 = [-1, dim1, dim2, 4]
            crop_offsets1 = [0, 0, 1, 0]
            crop_offsets2 = fluid.layers.data(
                name="crop_offset", shape=[4], append_batch_size=False)
            crop_offsets3 = [0, dim1, dim2, 0]

            out1 = fluid.layers.crop_tensor(
                x, shape=crop_shape1, offsets=crop_offsets1)
            out2 = fluid.layers.crop_tensor(
                x, shape=crop_shape2, offsets=crop_offsets2)
            out3 = fluid.layers.crop_tensor(
                x, shape=crop_shape3, offsets=crop_offsets3)

            self.assertIsNotNone(out1)
            self.assertIsNotNone(out2)
            self.assertIsNotNone(out3)

1978 1979 1980 1981 1982 1983 1984 1985
    def test_shard_index(self):
        with self.static_graph():
            x = fluid.layers.data(name="label", shape=[4, 1], dtype='int64')
            shard_label = fluid.layers.shard_index(
                input=x, index_num=20, nshards=2, shard_id=0)

        self.assertIsNotNone(shard_label)

1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998
    def test_accuracy(self):
        x = np.random.rand(3, 32, 32).astype("float32")
        y = np.array([[1], [0], [1]])
        with self.static_graph():
            data = fluid.data(name="input", shape=[-1, 32, 32], dtype="float32")
            label = fluid.data(name="label", shape=[-1, 1], dtype="int")
            fc_out = fluid.layers.fc(input=data, size=10)
            predict = fluid.layers.softmax(input=fc_out)
            result = fluid.layers.accuracy(input=predict, label=label, k=5)
            place = fluid.CPUPlace()
            exe = fluid.Executor(place)

            exe.run(fluid.default_startup_program())
L
Leo Chen 已提交
1999 2000
            # x = np.random.rand(3, 32, 32).astype("float32")
            # y = np.array([[1], [0], [1]])
2001 2002 2003 2004
            static_out = exe.run(feed={"input": x,
                                       "label": y},
                                 fetch_list=result[0])

L
Leo Chen 已提交
2005
        with self.dynamic_graph(force_to_use_cpu=True):
2006 2007 2008 2009 2010 2011 2012 2013
            data = base.to_variable(x)
            label = base.to_variable(y)
            fc_out = fluid.layers.fc(data, size=10)
            predict = fluid.layers.softmax(fc_out)
            dynamic_out = fluid.layers.accuracy(input=predict, label=label, k=5)

        self.assertTrue(np.array_equal(static_out[0], dynamic_out.numpy()))

Y
Yu Yang 已提交
2014

2015
class TestBook(LayerTest):
H
hong 已提交
2016 2017 2018 2019 2020 2021 2022 2023
    def setUp(self):
        self.only_static_set = set({"make_word_embedding"})
        self.not_compare_static_dygraph_set = set({
            "make_gaussian_random", "make_gaussian_random_batch_size_like",
            "make_kldiv_loss", "make_prelu",
            "make_sampled_softmax_with_cross_entropy", "make_sampling_id",
            "make_uniform_random_batch_size_like"
        })
2024
        self.all_close_compare = set({"make_spectral_norm"})
H
hong 已提交
2025

2026 2027 2028 2029 2030 2031
    def test_all_layers(self):
        attrs = (getattr(self, name) for name in dir(self))
        methods = filter(inspect.ismethod, attrs)
        for method in methods:
            if not method.__name__.startswith('make_'):
                continue
M
minqiyang 已提交
2032 2033 2034
            self._low_data_bound = 0
            self._high_data_bound = 2
            self._batch_size = 2
2035 2036 2037 2038 2039 2040 2041 2042 2043 2044 2045 2046 2047
            self._feed_dict = {}
            self._force_to_use_cpu = False
            with self.static_graph():
                static_var = method()
                if isinstance(static_var, tuple):
                    static_var = static_var[0]

                if static_var is not None:
                    fetch_list = [static_var.name]
                    static_result = self.get_static_graph_result(
                        feed=self._feed_dict,
                        fetch_list=fetch_list,
                        force_to_use_cpu=self._force_to_use_cpu)
H
hong 已提交
2048

2049 2050 2051
                else:
                    assert method.__name__ in ('make_get_places')
                    continue
H
hong 已提交
2052 2053
            if method.__name__ in self.only_static_set:
                continue
2054 2055 2056 2057 2058

            with self.dynamic_graph(self._force_to_use_cpu):
                dy_result = method()
                if isinstance(dy_result, tuple):
                    dy_result = dy_result[0]
2059
                dy_result_value = dy_result.numpy()
2060

2061 2062 2063 2064 2065 2066 2067 2068
            if method.__name__ in self.all_close_compare:
                self.assertTrue(
                    np.allclose(
                        static_result[0], dy_result_value, atol=0, rtol=1e-05),
                    "Result of function [{}] compare failed".format(
                        method.__name__))
                continue

H
hong 已提交
2069 2070
            if method.__name__ not in self.not_compare_static_dygraph_set:
                self.assertTrue(
2071 2072
                    np.array_equal(static_result[0], dy_result_value),
                    "Result of function [{}] not equal".format(method.__name__))
2073 2074 2075 2076

    def _get_np_data(self, shape, dtype, append_batch_size=True):
        np.random.seed(self.seed)
        if append_batch_size:
M
minqiyang 已提交
2077
            shape = [self._batch_size] + shape
2078 2079 2080 2081 2082
        if dtype == 'float32':
            return np.random.random(shape).astype(dtype)
        elif dtype == 'float64':
            return np.random.random(shape).astype(dtype)
        elif dtype == 'int32':
M
minqiyang 已提交
2083 2084
            return np.random.randint(self._low_data_bound,
                                     self._high_data_bound, shape).astype(dtype)
2085
        elif dtype == 'int64':
M
minqiyang 已提交
2086 2087
            return np.random.randint(self._low_data_bound,
                                     self._high_data_bound, shape).astype(dtype)
2088 2089 2090 2091 2092 2093 2094 2095 2096 2097

    def _get_data(self,
                  name,
                  shape,
                  dtype,
                  set_feed_dict=True,
                  append_batch_size=True):
        if base.enabled():
            return base.to_variable(
                value=self._get_np_data(shape, dtype, append_batch_size),
2098 2099
                name=name,
                zero_copy=False)
2100 2101 2102 2103 2104 2105 2106 2107 2108 2109 2110 2111 2112
        else:
            if set_feed_dict:
                self._feed_dict[name] = self._get_np_data(shape, dtype,
                                                          append_batch_size)
            return layers.data(
                name=name,
                shape=shape,
                dtype=dtype,
                append_batch_size=append_batch_size)

    def make_sampled_softmax_with_cross_entropy(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
M
minqiyang 已提交
2113
            logits = self._get_data(name='Logits', shape=[256], dtype='float32')
M
minqiyang 已提交
2114
            label = self._get_data(name='Label', shape=[1], dtype='int64')
2115 2116 2117 2118 2119 2120 2121 2122 2123 2124
            num_samples = 25
            output = layers.sampled_softmax_with_cross_entropy(logits, label,
                                                               num_samples)
            return (output)

    def make_fit_a_line(self):
        with program_guard(
                fluid.default_main_program(),
                startup_program=fluid.default_startup_program()):
            x = self._get_data(name='x', shape=[13], dtype='float32')
Y
Yu Yang 已提交
2125
            y_predict = layers.fc(input=x, size=1, act=None)
2126
            y = self._get_data(name='y', shape=[1], dtype='float32')
Y
Yu Yang 已提交
2127
            cost = layers.square_error_cost(input=y_predict, label=y)
Y
Yu Yang 已提交
2128
            avg_cost = layers.mean(cost)
2129
            return (avg_cost)
Y
Yu Yang 已提交
2130

2131 2132 2133
    def make_recognize_digits_mlp(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
Y
Yu Yang 已提交
2134
            # Change g_program, so the rest layers use `g_program`
2135 2136
            images = self._get_data(name='pixel', shape=[784], dtype='float32')
            label = self._get_data(name='label', shape=[1], dtype='int64')
Y
Yu Yang 已提交
2137 2138
            hidden1 = layers.fc(input=images, size=128, act='relu')
            hidden2 = layers.fc(input=hidden1, size=64, act='relu')
2139 2140 2141 2142
            predict = layers.fc(input=[hidden2, hidden1],
                                size=10,
                                act='softmax',
                                param_attr=["sftmax.w1", "sftmax.w2"])
Y
Yu Yang 已提交
2143
            cost = layers.cross_entropy(input=predict, label=label)
Y
Yu Yang 已提交
2144
            avg_cost = layers.mean(cost)
2145
            return (avg_cost)
Y
Yu Yang 已提交
2146

2147 2148 2149 2150 2151 2152
    def make_conv2d_transpose(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            img = self._get_data(name='pixel', shape=[3, 2, 2], dtype='float32')
            return layers.conv2d_transpose(
                input=img, num_filters=10, output_size=28)
2153

2154 2155 2156 2157
    def make_recognize_digits_conv(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            images = self._get_data(
Y
Yu Yang 已提交
2158
                name='pixel', shape=[1, 28, 28], dtype='float32')
2159
            label = self._get_data(name='label', shape=[1], dtype='int64')
Y
Yu Yang 已提交
2160 2161 2162 2163 2164 2165 2166 2167 2168 2169 2170 2171 2172 2173 2174 2175 2176
            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
2177
            avg_cost = layers.mean(cost)
2178
            return avg_cost
Y
Yu Yang 已提交
2179

2180 2181 2182
    def make_word_embedding(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
Y
Yu Yang 已提交
2183 2184
            dict_size = 10000
            embed_size = 32
2185 2186 2187 2188 2189 2190
            first_word = self._get_data(name='firstw', shape=[1], dtype='int64')
            second_word = self._get_data(
                name='secondw', shape=[1], dtype='int64')
            third_word = self._get_data(name='thirdw', shape=[1], dtype='int64')
            forth_word = self._get_data(name='forthw', shape=[1], dtype='int64')
            next_word = self._get_data(name='nextw', shape=[1], dtype='int64')
Y
Yu Yang 已提交
2191 2192 2193 2194 2195 2196 2197 2198 2199 2200 2201 2202 2203 2204 2205 2206 2207 2208 2209 2210 2211 2212 2213 2214 2215 2216 2217 2218 2219 2220 2221 2222

            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
2223
            avg_cost = layers.mean(cost)
2224
            return (avg_cost)
Y
Yu Yang 已提交
2225

2226 2227 2228 2229 2230
    def make_sigmoid_cross_entropy(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            dat = self._get_data(name='data', shape=[10], dtype='float32')
            lbl = self._get_data(name='label', shape=[10], dtype='float32')
2231
            ignore_index = -1
2232 2233 2234 2235 2236 2237 2238 2239 2240
            return (layers.sigmoid_cross_entropy_with_logits(
                x=dat, label=lbl, ignore_index=ignore_index))

    def make_hsigmoid(self):
        self._force_to_use_cpu = True
        with fluid.framework._dygraph_place_guard(place=fluid.CPUPlace()):
            x = self._get_data(name='x', shape=[2], dtype='float32')
            y = self._get_data(name='y', shape=[2], dtype='int64')
            return (layers.hsigmoid(input=x, label=y, num_classes=2))
W
weixing02 已提交
2241

J
JiabinYang 已提交
2242
        # test hsigmod with custom tree structure
J
JiabinYang 已提交
2243 2244
        program2 = Program()
        with program_guard(program2):
2245 2246 2247
            x2 = self._get_data(name='x2', shape=[4, 8], dtype='float32')
            y2 = self._get_data(name='y2', shape=[4], dtype='int64')
            path_table = self._get_data(
2248
                name='path_table', shape=[4, 6], dtype='int64')
2249
            path_code = self._get_data(
2250
                name='path_code', shape=[4, 6], dtype='int64')
2251 2252 2253 2254 2255 2256 2257
            return (layers.hsigmoid(
                input=x2,
                label=y2,
                num_classes=6,
                path_table=path_table,
                path_code=path_code,
                is_custom=True))
J
JiabinYang 已提交
2258

2259 2260 2261 2262 2263 2264 2265
    def make_pool2d(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            x = self._get_data(name='x', shape=[3, 224, 224], dtype='float32')
            return (layers.pool2d(
                x, pool_size=[5, 3], pool_stride=[1, 2], pool_padding=(2, 1)))

K
Kaipeng Deng 已提交
2266 2267 2268 2269 2270 2271 2272 2273 2274 2275 2276 2277 2278 2279 2280 2281 2282 2283 2284
    def make_pool2d_infershape(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            theta = self._get_data("theta", shape=[2, 3], dtype='float32')
            x = fluid.layers.affine_grid(theta, out_shape=[2, 3, 244, 244])
            return (layers.pool2d(
                x, pool_size=[5, 3], pool_stride=[1, 2], pool_padding=(2, 1)))

    def make_pool3d(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            x = self._get_data(
                name='x', shape=[3, 244, 244, 244], dtype='float32')
            return (layers.pool3d(
                x,
                pool_size=[5, 3, 2],
                pool_stride=[1, 2, 3],
                pool_padding=(2, 1, 1)))

2285 2286 2287 2288 2289
    def make_adaptive_pool2d(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            x = self._get_data(name='x', shape=[3, 224, 224], dtype='float32')
            return (layers.adaptive_pool2d(x, [3, 3], pool_type='avg'))
D
dengkaipeng 已提交
2290
            pool, mask = layers.adaptive_pool2d(x, [3, 3], require_index=True)
2291 2292 2293
            return (pool)
            return (mask)
            return (layers.adaptive_pool2d(x, 3, pool_type='avg'))
2294
            pool, mask = layers.adaptive_pool2d(x, 3, require_index=True)
2295 2296 2297 2298 2299 2300 2301 2302 2303
            return (pool)
            return (mask)

    def make_adaptive_pool3d(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            x = self._get_data(
                name='x', shape=[3, 244, 224, 224], dtype='float32')
            return (layers.adaptive_pool3d(x, [3, 3, 3], pool_type='avg'))
D
dengkaipeng 已提交
2304 2305
            pool, mask = layers.adaptive_pool3d(
                x, [3, 3, 3], require_index=True)
2306 2307 2308
            return (pool)
            return (mask)
            return (layers.adaptive_pool3d(x, 3, pool_type='avg'))
2309
            pool, mask = layers.adaptive_pool3d(x, 3, require_index=True)
2310 2311
            return (pool)
            return (mask)
2312

2313 2314 2315 2316
    def make_lstm_unit(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            x_t_data = self._get_data(
Y
yangyaming 已提交
2317 2318
                name='x_t_data', shape=[10, 10], dtype='float32')
            x_t = layers.fc(input=x_t_data, size=10)
2319
            prev_hidden_data = self._get_data(
Y
yangyaming 已提交
2320 2321
                name='prev_hidden_data', shape=[10, 30], dtype='float32')
            prev_hidden = layers.fc(input=prev_hidden_data, size=30)
2322
            prev_cell_data = self._get_data(
Y
yangyaming 已提交
2323 2324
                name='prev_cell', shape=[10, 30], dtype='float32')
            prev_cell = layers.fc(input=prev_cell_data, size=30)
2325 2326
            return (layers.lstm_unit(
                x_t=x_t, hidden_t_prev=prev_hidden, cell_t_prev=prev_cell))
2327

2328 2329 2330 2331
    def make_softmax(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            data = self._get_data(name='data', shape=[10], dtype='float32')
D
dangqingqing 已提交
2332
            hid = layers.fc(input=data, size=20)
2333
            return (layers.softmax(hid, axis=1))
D
dangqingqing 已提交
2334

2335 2336 2337 2338
    def make_space_to_depth(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            data = self._get_data(
J
JiabinYang 已提交
2339
                name='data',
J
JiabinYang 已提交
2340 2341 2342
                shape=[32, 9, 6, 6],
                append_batch_size=False,
                dtype='float32')
2343
            return (layers.space_to_depth(data, 3))
J
JiabinYang 已提交
2344

2345 2346 2347 2348 2349
    def make_lrn(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            data = self._get_data(name='data', shape=[6, 2, 2], dtype='float32')
            return (layers.lrn(data))
2350

2351 2352 2353 2354
    def make_get_places(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            get_places(device_count=1)
X
xuezhong 已提交
2355

2356
    @prog_scope()
2357
    def make_nce(self):
Y
Yang Yu 已提交
2358 2359
        window_size = 5
        words = []
2360
        for i in range(window_size):
Y
Yang Yu 已提交
2361
            words.append(
2362
                self._get_data(
Y
Yang Yu 已提交
2363 2364 2365
                    name='word_{0}'.format(i), shape=[1], dtype='int64'))

        dict_size = 10000
M
minqiyang 已提交
2366
        label_word = int(window_size // 2) + 1
Y
Yang Yu 已提交
2367 2368

        embs = []
2369
        for i in range(window_size):
Y
Yang Yu 已提交
2370 2371 2372 2373 2374 2375 2376 2377 2378 2379 2380 2381 2382 2383 2384 2385 2386
            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
2387
        avg_loss = layers.mean(loss)
2388
        return (avg_loss)
Y
Yang Yu 已提交
2389

2390 2391 2392 2393 2394 2395
    def make_multiplex(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            x1 = self._get_data(name='x1', shape=[4], dtype='float32')
            x2 = self._get_data(name='x2', shape=[4], dtype='float32')
            index = self._get_data(name='index', shape=[1], dtype='int32')
2396
            out = layers.multiplex(inputs=[x1, x2], index=index)
2397 2398 2399 2400 2401 2402 2403
            return (out)

    def make_softmax_with_cross_entropy(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            x = self._get_data(name='x', shape=[16], dtype='float32')
            y = self._get_data(name='label', shape=[1], dtype='int64')
2404 2405
            loss, softmax = layers.softmax_with_cross_entropy(
                x, y, return_softmax=True)
2406 2407 2408
            self.assertIsNotNone(loss)
            self.assertIsNotNone(softmax)

2409
            loss = layers.softmax_with_cross_entropy(x, y)
2410 2411 2412 2413 2414 2415 2416 2417 2418 2419 2420 2421 2422 2423 2424
            self.assertIsNotNone(loss)

            x1 = self._get_data(name='x1', shape=[16, 32, 64], dtype='float32')
            y1 = self._get_data(name='label1', shape=[1, 32, 64], dtype='int64')
            y2 = self._get_data(name='label2', shape=[16, 1, 64], dtype='int64')
            y3 = self._get_data(name='label3', shape=[16, 32, 1], dtype='int64')
            loss1 = layers.softmax_with_cross_entropy(x1, y1, axis=1)
            loss2 = layers.softmax_with_cross_entropy(x1, y2, axis=2)
            loss3 = layers.softmax_with_cross_entropy(x1, y3, axis=3)
            loss4 = layers.softmax_with_cross_entropy(x1, y3, axis=-1)
            self.assertIsNotNone(loss1)
            self.assertIsNotNone(loss2)
            self.assertIsNotNone(loss3)
            self.assertIsNotNone(loss4)
            return (loss4)
2425 2426 2427 2428 2429 2430

    def make_smooth_l1(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            x = self._get_data(name='x', shape=[4], dtype='float32')
            y = self._get_data(name='label', shape=[4], dtype='float32')
2431
            loss = layers.smooth_l1(x, y)
2432
            return (loss)
2433

2434 2435 2436 2437
    def make_scatter(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            x = self._get_data(
2438 2439 2440 2441
                name='x',
                shape=[3, 3],
                append_batch_size=False,
                dtype='float32')
2442
            idx = self._get_data(
2443
                name='idx', shape=[2], append_batch_size=False, dtype='int32')
2444
            updates = self._get_data(
2445 2446 2447 2448 2449
                name='updates',
                shape=[2, 3],
                append_batch_size=False,
                dtype='float32')
            out = layers.scatter(input=x, index=idx, updates=updates)
2450
            return (out)
Y
yangyaming 已提交
2451

2452 2453 2454 2455 2456 2457
    def make_one_hot(self):
        with fluid.framework._dygraph_place_guard(place=fluid.CPUPlace()):
            label = self._get_data(name="label", shape=[1], dtype="int32")
            one_hot_label = layers.one_hot(input=label, depth=10)
            return (one_hot_label)

2458 2459 2460 2461 2462
    def make_label_smooth(self):
        # TODO(minqiyang): support gpu ut
        self._force_to_use_cpu = True
        with fluid.framework._dygraph_place_guard(place=fluid.CPUPlace()):
            label = self._get_data(name="label", shape=[1], dtype="int32")
2463 2464
            one_hot_label = layers.one_hot(input=label, depth=10)
            smooth_label = layers.label_smooth(
2465 2466
                label=one_hot_label, epsilon=0.1, dtype="int32")
            return (smooth_label)
2467

2468 2469 2470 2471 2472 2473 2474
    def make_topk(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            data = self._get_data(name="label", shape=[200], dtype="float32")
            values, indices = layers.topk(data, k=5)
            return (values)
            return (indices)
J
jerrywgz 已提交
2475

2476 2477 2478 2479
    def make_resize_bilinear(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            x = self._get_data(name='x', shape=[3, 9, 6], dtype="float32")
B
baiyf 已提交
2480
            output = layers.resize_bilinear(x, out_shape=[12, 12])
2481
            return (output)
K
Kaipeng Deng 已提交
2482 2483 2484 2485 2486 2487

    def make_resize_bilinear_by_scale(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            x = self._get_data(name='x', shape=[3, 9, 6], dtype="float32")
            output = layers.resize_bilinear(x, scale=1.5)
2488
            return (output)
2489

2490
    def make_resize_nearest(self):
K
Kaipeng Deng 已提交
2491 2492 2493 2494 2495 2496 2497 2498 2499 2500 2501 2502 2503 2504 2505 2506 2507
        try:
            with program_guard(fluid.default_main_program(),
                               fluid.default_startup_program()):
                x = self._get_data(name='x1', shape=[3, 9, 6], dtype="float32")
                output = layers.resize_nearest(x, out_shape=[12, 12])
        except ValueError:
            pass

        try:
            with program_guard(fluid.default_main_program(),
                               fluid.default_startup_program()):
                x = self._get_data(
                    name='x2', shape=[3, 9, 6, 7], dtype="float32")
                output = layers.resize_nearest(x, out_shape=[12, 12, 12])
        except ValueError:
            pass

2508 2509 2510
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            x = self._get_data(name='x', shape=[3, 9, 6], dtype="float32")
2511
            output = layers.resize_nearest(x, out_shape=[12, 12])
2512
            return (output)
K
Kaipeng Deng 已提交
2513 2514 2515 2516 2517 2518 2519 2520 2521 2522 2523 2524 2525 2526 2527 2528 2529 2530 2531 2532 2533 2534 2535 2536 2537 2538 2539 2540 2541 2542 2543 2544 2545 2546 2547 2548 2549

    def make_resize_nearest_by_scale(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            x = self._get_data(name='x1', shape=[3, 9, 6], dtype="float32")
            output = layers.resize_nearest(x, scale=1.8)
            return (output)

    def make_resize_trilinear(self):
        try:
            with program_guard(fluid.default_main_program(),
                               fluid.default_startup_program()):
                x = self._get_data(name='x2', shape=[3, 9, 6], dtype="float32")
                output = layers.resize_trilinear(x, out_shape=[12, 12, 12])
        except ValueError:
            pass

        try:
            with program_guard(fluid.default_main_program(),
                               fluid.default_startup_program()):
                x = self._get_data(
                    name='x', shape=[3, 9, 6, 7], dtype="float32")
                output = layers.resize_trilinear(x, out_shape=[12, 12])
        except ValueError:
            pass

        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            x = self._get_data(name='x', shape=[3, 9, 6, 7], dtype="float32")
            output = layers.resize_trilinear(x, out_shape=[12, 12, 12])
            return (output)

    def make_resize_trilinear_by_scale(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            x = self._get_data(name='x', shape=[3, 9, 6, 7], dtype="float32")
            output = layers.resize_trilinear(x, scale=2.1)
2550
            return (output)
2551

2552 2553 2554 2555
    def make_polygon_box_transform(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            x = self._get_data(name='x', shape=[8, 4, 4], dtype="float32")
2556
            output = layers.polygon_box_transform(input=x)
2557
            return (output)
2558

2559 2560 2561 2562
    def make_l2_normalize(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            x = self._get_data(name='x', shape=[8, 7, 10], dtype="float32")
2563
            output = layers.l2_normalize(x, axis=1)
2564
            return output
2565

2566 2567 2568 2569
    def make_maxout(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            data = self._get_data(name='x', shape=[8, 6, 6], dtype="float32")
Q
qingqing01 已提交
2570
            output = layers.maxout(x=data, groups=2)
2571 2572 2573 2574 2575 2576 2577
            return (output)

    def make_crop(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            x = self._get_data(name='x', shape=[3, 5], dtype="float32")
            y = self._get_data(name='y', shape=[2, 3], dtype="float32")
2578
            output = layers.crop(x, shape=y)
2579 2580 2581 2582 2583
            return (output)

    def make_mean_iou(self):
        with fluid.framework._dygraph_place_guard(place=fluid.CPUPlace()):
            x = self._get_data(name='x', shape=[16], dtype='int32')
M
minqiyang 已提交
2584 2585
            y = self._get_data(name='label', shape=[16], dtype='int32')
            iou = layers.mean_iou(x, y, self._high_data_bound)
2586
            return (iou)
W
whs 已提交
2587

2588 2589 2590 2591
    def make_argsort(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            data = self._get_data(name='x', shape=[2, 3, 3], dtype="float32")
2592
            out, ids = layers.argsort(input=data, axis=1)
2593 2594 2595 2596 2597 2598 2599
            return (out)
            return (ids)

    def make_rank_loss(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            label = self._get_data(
2600 2601 2602 2603
                name='label',
                append_batch_size=False,
                shape=[16, 1],
                dtype="float32")
2604
            left = self._get_data(
2605 2606 2607 2608
                name='left',
                append_batch_size=False,
                shape=[16, 1],
                dtype="float32")
2609
            right = self._get_data(
2610 2611 2612 2613 2614
                name='right',
                append_batch_size=False,
                shape=[16, 1],
                dtype="float32")
            out = layers.rank_loss(label, left, right, name="rank_loss")
2615
            return (out)
2616

2617 2618 2619 2620
    def make_shape(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            input = self._get_data(
B
Bai Yifan 已提交
2621
                name="input", shape=[3, 100, 100], dtype="float32")
G
fix  
gongweibao 已提交
2622
            out = layers.shape(input)
2623
            return (out)
B
Bai Yifan 已提交
2624

2625 2626 2627 2628
    def make_pad2d(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            input = self._get_data(
W
whs 已提交
2629
                name="input", shape=[3, 100, 100], dtype="float32")
2630
            paddings = layers.fill_constant(shape=[4], dtype='int32', value=1)
W
whs 已提交
2631 2632 2633 2634 2635 2636
            out = layers.pad2d(
                input,
                paddings=[1, 2, 3, 4],
                mode='reflect',
                data_format='NCHW',
                name="shape")
2637 2638 2639 2640 2641 2642
            out_1 = layers.pad2d(
                input,
                paddings=paddings,
                mode='reflect',
                data_format='NCHW',
                name="shape")
2643 2644
            return (out)
            return (out_1)
W
whs 已提交
2645

2646 2647 2648 2649
    def make_prelu(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            input = self._get_data(
J
jerrywgz 已提交
2650 2651 2652 2653 2654 2655 2656
                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')
2657
            return (out)
J
jerrywgz 已提交
2658

2659 2660 2661 2662
    def make_brelu(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            input = self._get_data(name="input", shape=[16], dtype="float32")
T
tensor-tang 已提交
2663
            out = layers.brelu(input, t_min=1.0, t_max=20.0, name='brelu')
2664
            return (out)
T
tensor-tang 已提交
2665

2666 2667 2668 2669
    def make_soft_relu(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            input = self._get_data(name="input", shape=[16], dtype="float32")
T
tensor-tang 已提交
2670
            out = layers.soft_relu(input, threshold=30.0, name='soft_relu')
2671
            return (out)
T
tensor-tang 已提交
2672

2673 2674 2675 2676
    def make_sigmoid(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            input = self._get_data(name="input", shape=[16], dtype="float32")
T
tensor-tang 已提交
2677
            out = layers.sigmoid(input, name='sigmoid')
2678
            return (out)
T
tensor-tang 已提交
2679

2680 2681 2682 2683
    def make_logsigmoid(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            input = self._get_data(name="input", shape=[16], dtype="float32")
T
tensor-tang 已提交
2684
            out = layers.logsigmoid(input, name='logsigmoid')
2685
            return (out)
T
tensor-tang 已提交
2686

2687 2688 2689 2690
    def make_exp(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            input = self._get_data(name="input", shape=[16], dtype="float32")
T
tensor-tang 已提交
2691
            out = layers.exp(input, name='exp')
2692
            return (out)
T
tensor-tang 已提交
2693

2694 2695 2696 2697
    def make_tanh(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            input = self._get_data(name="input", shape=[16], dtype="float32")
T
tensor-tang 已提交
2698
            out = layers.tanh(input, name='tanh')
2699
            return (out)
T
tensor-tang 已提交
2700

2701 2702 2703 2704
    def make_tanh_shrink(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            input = self._get_data(name="input", shape=[16], dtype="float32")
T
tensor-tang 已提交
2705
            out = layers.tanh_shrink(input, name='tanh_shrink')
2706
            return (out)
T
tensor-tang 已提交
2707

2708 2709 2710 2711
    def make_sqrt(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            input = self._get_data(name="input", shape=[16], dtype="float32")
T
tensor-tang 已提交
2712
            out = layers.sqrt(input, name='sqrt')
2713
            return (out)
T
tensor-tang 已提交
2714

2715 2716 2717 2718
    def make_abs(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            input = self._get_data(name="input", shape=[16], dtype="float32")
T
tensor-tang 已提交
2719
            out = layers.abs(input, name='abs')
2720
            return (out)
T
tensor-tang 已提交
2721

2722 2723 2724 2725
    def make_ceil(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            input = self._get_data(name="input", shape=[16], dtype="float32")
T
tensor-tang 已提交
2726
            out = layers.ceil(input, name='ceil')
2727
            return (out)
T
tensor-tang 已提交
2728

2729 2730 2731 2732
    def make_floor(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            input = self._get_data(name="input", shape=[16], dtype="float32")
T
tensor-tang 已提交
2733
            out = layers.floor(input, name='floor')
2734
            return (out)
T
tensor-tang 已提交
2735

2736 2737 2738 2739
    def make_cos(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            input = self._get_data(name="input", shape=[16], dtype="float32")
T
tensor-tang 已提交
2740
            out = layers.cos(input, name='cos')
2741
            return (out)
T
tensor-tang 已提交
2742

2743 2744 2745 2746
    def make_sin(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            input = self._get_data(name="input", shape=[16], dtype="float32")
T
tensor-tang 已提交
2747
            out = layers.sin(input, name='sin')
2748
            return (out)
T
tensor-tang 已提交
2749

2750 2751 2752 2753
    def make_round(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            input = self._get_data(name="input", shape=[16], dtype="float32")
T
tensor-tang 已提交
2754
            out = layers.round(input, name='round')
2755
            return (out)
T
tensor-tang 已提交
2756

2757 2758 2759 2760
    def make_reciprocal(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            input = self._get_data(name="input", shape=[16], dtype="float32")
T
tensor-tang 已提交
2761
            out = layers.reciprocal(input, name='reciprocal')
2762
            return (out)
T
tensor-tang 已提交
2763

2764 2765 2766 2767
    def make_square(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            input = self._get_data(name="input", shape=[16], dtype="float32")
T
tensor-tang 已提交
2768
            out = layers.square(input, name='square')
2769
            return (out)
T
tensor-tang 已提交
2770

2771 2772 2773 2774
    def make_softplus(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            input = self._get_data(name="input", shape=[16], dtype="float32")
T
tensor-tang 已提交
2775
            out = layers.softplus(input, name='softplus')
2776
            return (out)
T
tensor-tang 已提交
2777

2778 2779 2780 2781
    def make_softsign(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            input = self._get_data(name="input", shape=[16], dtype="float32")
T
tensor-tang 已提交
2782
            out = layers.softsign(input, name='softsign')
2783
            return (out)
T
tensor-tang 已提交
2784

K
Kaipeng Deng 已提交
2785 2786 2787 2788 2789 2790 2791
    def make_mish(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            input = self._get_data(name="input", shape=[16], dtype="float32")
            out = layers.mish(input, name='mish')
            return (out)

2792 2793 2794 2795 2796
    def make_cross_entropy(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            x = self._get_data(name="x", shape=[30, 10], dtype="float32")
            label = self._get_data(name="label", shape=[30, 1], dtype="int64")
2797 2798
            mode = 'channel'
            out = layers.cross_entropy(x, label, False, 4)
2799
            return (out)
2800

2801 2802 2803 2804 2805
    def make_bpr_loss(self):
        self._force_to_use_cpu = True
        with fluid.framework._dygraph_place_guard(place=fluid.CPUPlace()):
            x = self._get_data(name="x", shape=[30, 10], dtype="float32")
            label = self._get_data(name="label", shape=[30, 1], dtype="int64")
2806
            out = layers.bpr_loss(x, label)
2807
            return (out)
2808

2809 2810 2811 2812
    def make_expand(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            x = self._get_data(name="input", shape=[10], dtype='int32')
W
whs 已提交
2813
            out = layers.expand(x, [1, 2])
2814
            return out
W
whs 已提交
2815

2816 2817 2818 2819 2820
    def make_uniform_random_batch_size_like(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            input = self._get_data(
                name="input", shape=[13, 11], dtype='float32')
G
fix  
gongweibao 已提交
2821
            out = layers.uniform_random_batch_size_like(input, [-1, 11])
2822
            return (out)
G
fix  
gongweibao 已提交
2823

2824 2825 2826
    def make_gaussian_random(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
G
fix  
gongweibao 已提交
2827
            out = layers.gaussian_random(shape=[20, 30])
2828
            return (out)
G
fix  
gongweibao 已提交
2829

2830 2831 2832 2833
    def make_sampling_id(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            x = self._get_data(
G
fix  
gongweibao 已提交
2834 2835 2836 2837
                name="X",
                shape=[13, 11],
                dtype='float32',
                append_batch_size=False)
G
fix  
gongweibao 已提交
2838 2839

            out = layers.sampling_id(x)
2840
            return (out)
G
fix  
gongweibao 已提交
2841

2842 2843 2844 2845 2846
    def make_gaussian_random_batch_size_like(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            input = self._get_data(
                name="input", shape=[13, 11], dtype='float32')
G
fix  
gongweibao 已提交
2847 2848 2849

            out = layers.gaussian_random_batch_size_like(
                input, shape=[-1, 11], mean=1.0, std=2.0)
2850
            return (out)
G
fix  
gongweibao 已提交
2851

2852 2853 2854 2855 2856
    def make_sum(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            input = self._get_data(
                name="input", shape=[13, 11], dtype='float32')
G
fix  
gongweibao 已提交
2857 2858

            out = layers.sum(input)
2859
            return (out)
G
fix  
gongweibao 已提交
2860

2861
    def make_slice(self):
G
fix  
gongweibao 已提交
2862 2863 2864 2865
        starts = [1, 0, 2]
        ends = [3, 3, 4]
        axes = [0, 1, 2]

2866 2867 2868
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            input = self._get_data(
G
fix  
gongweibao 已提交
2869 2870 2871
                name="input", shape=[3, 4, 5, 6], dtype='float32')

            out = layers.slice(input, axes=axes, starts=starts, ends=ends)
2872
            return out
G
merge  
gongweibao 已提交
2873

2874 2875 2876 2877 2878 2879 2880 2881 2882 2883 2884 2885 2886 2887
    def make_scale_variable(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            input = self._get_data(
                name="input", shape=[3, 4, 5, 6], dtype='float32')
            scale_var = self._get_data(
                name="scale",
                shape=[1],
                dtype='float32',
                append_batch_size=False)

            out = layers.scale(input, scale=scale_var)
            return out

2888 2889 2890 2891
    def make_softshrink(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            input = self._get_data(name="input", shape=[16], dtype="float32")
2892
            out = layers.softshrink(input, alpha=0.3)
2893
            return (out)
G
fix  
gongweibao 已提交
2894

M
minqiyang 已提交
2895
    def make_iou_similarity(self):
2896 2897
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
M
minqiyang 已提交
2898 2899
            x = self._get_data(name="x", shape=[4], dtype="float32")
            y = self._get_data(name="y", shape=[4], dtype="float32")
X
Xin Pan 已提交
2900
            out = layers.iou_similarity(x, y, name='iou_similarity')
2901 2902 2903 2904 2905 2906 2907
            return (out)

    def make_grid_sampler(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            x = self._get_data(name='x', shape=[3, 5, 7], dtype='float32')
            grid = self._get_data(name='grid', shape=[5, 7, 2], dtype='float32')
D
dengkaipeng 已提交
2908
            out = layers.grid_sampler(x, grid)
2909 2910 2911 2912 2913 2914 2915 2916 2917 2918 2919 2920 2921 2922 2923 2924 2925 2926 2927
            return (out)

    def make_bilinear_tensor_product_layer(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            data = self._get_data(name='data', shape=[4], dtype="float32")

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

    def make_batch_norm(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            data = self._get_data(
                name='data', shape=[32, 128, 128], dtype="float32")
            out = layers.batch_norm(data)
            return (out)

2928 2929 2930 2931 2932 2933 2934 2935 2936 2937 2938 2939 2940
    def make_batch_norm_momentum_variable(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            data = self._get_data(
                name='data', shape=[32, 128, 128], dtype="float32")
            momentum = self._get_data(
                name='momentum',
                shape=[1],
                dtype='float32',
                append_batch_size=False)
            out = layers.batch_norm(data, momentum=momentum)
            return (out)

K
Kaipeng Deng 已提交
2941 2942 2943 2944 2945 2946 2947 2948 2949 2950 2951 2952 2953 2954 2955 2956 2957 2958 2959 2960 2961 2962
    def make_inplace_abn(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            data = self._get_data(
                name='data', shape=[32, 128, 128], dtype="float32")
            out = layers.inplace_abn(data, act='leaky_relu', act_alpha=0.2)
            return (out)

    def make_inplace_abn_momentum_variable(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            data = self._get_data(
                name='data', shape=[32, 128, 128], dtype="float32")
            momentum = self._get_data(
                name='momentum',
                shape=[1],
                dtype='float32',
                append_batch_size=False)
            out = layers.inplace_abn(
                data, momentum=momentum, act='elu', act_alpha=2.0)
            return (out)

2963 2964 2965 2966
    def make_range(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            layers.range(0, 10, 2, 'int32')
2967 2968 2969 2970 2971 2972
            layers.range(0.1, 10.0, 0.2, 'float32')
            layers.range(0.1, 10.0, 0.2, 'float64')
            start = layers.fill_constant(shape=[1], value=0.1, dtype="float32")
            end = layers.fill_constant(shape=[1], value=10.0, dtype="float32")
            step = layers.fill_constant(shape=[1], value=0.2, dtype="float32")
            y = layers.range(start, end, step, 'float64')
2973 2974 2975 2976 2977 2978 2979 2980 2981 2982 2983 2984 2985 2986 2987 2988
            return y

    def make_spectral_norm(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            weight = self._get_data(
                name='weight',
                shape=[2, 3, 32, 32],
                dtype="float32",
                append_batch_size=False)
            out = layers.spectral_norm(weight, dim=1, power_iters=1)
            return (out)

    def make_kldiv_loss(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
M
minqiyang 已提交
2989 2990 2991 2992 2993
            x = self._get_data(
                name='x',
                shape=[32, 128, 128],
                dtype="float32",
                append_batch_size=False)
2994
            target = self._get_data(
M
minqiyang 已提交
2995 2996 2997 2998
                name='target',
                shape=[32, 128, 128],
                dtype="float32",
                append_batch_size=False)
2999 3000 3001 3002 3003 3004 3005 3006 3007 3008 3009 3010 3011 3012 3013 3014 3015
            loss = layers.kldiv_loss(x=x, target=target, reduction='batchmean')
            return (loss)

    def make_temporal_shift(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            x = self._get_data(name="X", shape=[16, 4, 4], dtype="float32")
            out = layers.temporal_shift(x, seg_num=2, shift_ratio=0.2)
            return (out)

    def make_shuffle_channel(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            x = self._get_data(name="X", shape=[16, 4, 4], dtype="float32")
            out = layers.shuffle_channel(x, group=4)
            return (out)

M
minqiyang 已提交
3016
    def make_fsp_matrix(self):
3017 3018 3019 3020 3021 3022 3023
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            x = self._get_data(name="X", shape=[16, 4, 4], dtype="float32")
            y = self._get_data(name="Y", shape=[8, 4, 4], dtype="float32")
            out = layers.fsp_matrix(x, y)
            return (out)

M
minqiyang 已提交
3024 3025 3026 3027 3028 3029 3030
    def make_pixel_shuffle(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            x = self._get_data(name="X", shape=[9, 4, 4], dtype="float32")
            out = layers.pixel_shuffle(x, upscale_factor=3)
            return (out)

R
ruri 已提交
3031 3032 3033 3034 3035 3036 3037 3038
    def make_mse_loss(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            x = self._get_data(name="X", shape=[1], dtype="float32")
            y = self._get_data(name="Y", shape=[1], dtype="float32")
            out = layers.mse_loss(input=x, label=y)
            return (out)

3039 3040 3041 3042 3043 3044 3045 3046
    def make_square_error_cost(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            x = self._get_data(name="X", shape=[1], dtype="float32")
            y = self._get_data(name="Y", shape=[1], dtype="float32")
            out = layers.square_error_cost(input=x, label=y)
            return (out)

3047 3048 3049 3050 3051 3052 3053 3054 3055 3056 3057 3058 3059 3060
    def test_dynamic_lstmp(self):
        # TODO(minqiyang): dygraph do not support lod now
        with self.static_graph():
            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))

    def test_linear_chain_crf(self):
        with self.static_graph():
            label_dict_len = 10
3061 3062 3063
            feature = layers.data(name='feature', shape=[784], dtype='float32')
            label = layers.data(name='label', shape=[1], dtype='int64')
            emission = layers.fc(input=feature, size=10)
3064
            crf = layers.linear_chain_crf(
3065 3066 3067 3068 3069 3070 3071 3072 3073 3074 3075 3076 3077 3078 3079 3080 3081 3082 3083 3084 3085 3086 3087 3088
                input=emission, label=label, param_attr=ParamAttr(name="crfw"))
            crf_decode = layers.crf_decoding(
                input=emission, param_attr=ParamAttr(name="crfw"))
            self.assertFalse(crf is None)
            self.assertFalse(crf_decode is None)
            return layers.chunk_eval(
                input=crf_decode,
                label=label,
                chunk_scheme="IOB",
                num_chunk_types=(label_dict_len - 1) // 2)

    def test_linear_chain_crf_padding(self):
        with self.static_graph():
            label_dict_len, max_len = 10, 20
            feature = layers.data(
                name='feature', shape=[max_len, 784], dtype='float32')
            label = layers.data(name='label', shape=[max_len], dtype='int64')
            length = layers.data(name='length', shape=[1], dtype='int64')
            emission = layers.fc(input=feature, size=10, num_flatten_dims=2)
            crf = layers.linear_chain_crf(
                input=emission,
                label=label,
                length=length,
                param_attr=ParamAttr(name="crfw"))
3089
            crf_decode = layers.crf_decoding(
3090 3091 3092
                input=emission,
                length=length,
                param_attr=ParamAttr(name="crfw"))
3093 3094 3095 3096 3097
            self.assertFalse(crf is None)
            self.assertFalse(crf_decode is None)
            return layers.chunk_eval(
                input=crf_decode,
                label=label,
3098
                seq_length=length,
3099 3100 3101 3102 3103 3104 3105 3106 3107 3108 3109 3110 3111 3112 3113 3114 3115 3116 3117
                chunk_scheme="IOB",
                num_chunk_types=(label_dict_len - 1) // 2)

    def test_im2sequence(self):
        # TODO(minqiyang): dygraph do not support lod now
        with self.static_graph():
            x = layers.data(name='x', shape=[3, 128, 128], dtype='float32')
            y = layers.data(name='y', shape=[], dtype='float32')
            output = layers.im2sequence(
                input=x,
                input_image_size=y,
                stride=[1, 1],
                filter_size=[2, 2],
                out_stride=[1, 1])
            return (output)

    def test_lod_reset(self):
        # TODO(minqiyang): dygraph do not support lod now
        with self.static_graph():
3118
            # case 1
3119 3120 3121
            x = layers.data(name='x', shape=[10], dtype='float32')
            y = layers.data(
                name='y', shape=[10, 20], dtype='float32', lod_level=2)
3122 3123 3124
            z = layers.lod_reset(x=x, y=y)
            self.assertTrue(z.lod_level == 2)
            # case 2
3125
            lod_tensor_in = layers.data(name='lod_in', shape=[1], dtype='int32')
3126 3127 3128 3129 3130 3131
            z = layers.lod_reset(x=x, y=lod_tensor_in)
            self.assertTrue(z.lod_level == 1)
            # case 3
            z = layers.lod_reset(x=x, target_lod=[1, 2, 3])
            self.assertTrue(z.lod_level == 1)
            return z
3132

W
whs 已提交
3133
    def test_affine_grid(self):
3134
        with self.static_graph():
W
whs 已提交
3135 3136 3137 3138
            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")
3139
            out_shape = layers.data(name="out_shape", shape=[-1], dtype="int32")
W
whs 已提交
3140 3141 3142 3143 3144
            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)
D
dengkaipeng 已提交
3145

W
wangchaochaohu 已提交
3146 3147 3148 3149 3150 3151 3152 3153 3154 3155 3156
    def test_stridedslice(self):
        axes = [0, 1, 2]
        starts = [1, 0, 2]
        ends = [3, 3, 4]
        strides = [1, 1, 1]
        with self.static_graph():
            x = layers.data(name="x", shape=[245, 30, 30], dtype="float32")
            out = layers.strided_slice(
                x, axes=axes, starts=starts, ends=ends, strides=strides)
            return out

3157 3158 3159 3160 3161 3162 3163 3164
    def test_fill_constant_batch_size_like(self):
        with self.static_graph():
            like = fluid.layers.fill_constant(
                shape=[1, 200], value=10, dtype='int64')
            out = layers.fill_constant_batch_size_like(
                input=like, shape=[2, 3300], value=1315454564656, dtype='int64')
            return out

3165 3166 3167 3168 3169 3170 3171 3172
    def test_psroi_pool(self):
        # TODO(minqiyang): dygraph do not support lod now
        with self.static_graph():
            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)
            return (output)
3173

3174 3175 3176 3177 3178 3179 3180
    def test_sequence_expand(self):
        # TODO(minqiyang): dygraph do not support lod now
        with self.static_graph():
            x = layers.data(name='x', shape=[10], dtype='float32')
            y = layers.data(
                name='y', shape=[10, 20], dtype='float32', lod_level=2)
            return (layers.sequence_expand(x=x, y=y, ref_level=1))
3181

3182 3183 3184 3185 3186 3187
    def test_sequence_reshape(self):
        # TODO(minqiyang): dygraph do not support lod now
        with self.static_graph():
            x = layers.data(name='x', shape=[8], dtype='float32', lod_level=1)
            out = layers.sequence_reshape(input=x, new_dim=16)
            return (out)
3188

3189 3190 3191 3192
    def test_sequence_unpad(self):
        # TODO(minqiyang): dygraph do not support lod now
        with self.static_graph():
            x = layers.data(name='x', shape=[10, 5], dtype='float32')
3193
            length = layers.data(name='length', shape=[], dtype='int64')
3194
            return (layers.sequence_unpad(x=x, length=length))
3195

3196 3197 3198 3199 3200 3201 3202
    def test_sequence_softmax(self):
        # TODO(minqiyang): dygraph do not support lod now
        with self.static_graph():
            seq_data = layers.data(
                name='seq_data', shape=[10, 10], dtype='float32', lod_level=1)
            seq = layers.fc(input=seq_data, size=20)
            return (layers.sequence_softmax(seq))
3203

3204 3205 3206 3207 3208 3209
    def test_sequence_unsqueeze(self):
        # TODO(minqiyang): dygraph do not support lod now
        with self.static_graph():
            x = layers.data(name='x', shape=[8, 2], dtype='float32')
            out = layers.unsqueeze(input=x, axes=[1])
            return (out)
3210

3211 3212 3213 3214 3215 3216 3217 3218 3219 3220 3221 3222 3223 3224 3225 3226 3227 3228 3229 3230 3231 3232
    def test_sequence_scatter(self):
        # TODO(minqiyang): dygraph do not support lod now
        with self.static_graph():
            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)
            return (out)
W
whs 已提交
3233

3234 3235 3236 3237 3238 3239 3240 3241 3242 3243 3244
    def test_sequence_slice(self):
        # TODO(minqiyang): dygraph do not support lod now
        with self.static_graph():
            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)
            return (out)
W
whs 已提交
3245

J
Jiawei Wang 已提交
3246 3247 3248 3249 3250 3251 3252 3253 3254 3255 3256 3257 3258 3259 3260 3261 3262 3263 3264 3265
    def test_filter_by_instag(self):
        # TODO(minqiyang): dygraph do not support lod now
        with self.static_graph():
            x1 = layers.data(
                name='Ins', shape=[32, 1], dtype='float32', lod_level=0)
            x2 = layers.data(
                name='Ins_tag',
                shape=[32, 1],
                dtype='int64',
                lod_level=0,
                stop_gradient=True)
            x3 = layers.create_global_var(
                shape=[1, 1],
                value=20,
                dtype='int64',
                persistable=True,
                force_cpu=True,
                name='Filter_tag')
            out1, out2 = layers.filter_by_instag(x1, x2, x3, is_lod=True)

Z
zhoushiyu 已提交
3266 3267 3268 3269 3270 3271 3272 3273 3274 3275 3276 3277
    def test_shuffle_batch(self):
        # TODO(minqiyang): dygraph do not support lod now
        with self.static_graph():
            x = layers.data(
                name='X', shape=[4, 50], dtype='float32', lod_level=0)
            out1 = fluid.contrib.layers.shuffle_batch(x)
            default_main_program().random_seed = 1000
            out2 = fluid.contrib.layers.shuffle_batch(x)
            self.assertIsNotNone(out1)
            self.assertIsNotNone(out2)
            return (out1)

3278 3279 3280 3281 3282 3283 3284 3285
    def test_partial_sum(self):
        with self.static_graph():
            x = fluid.data(name="x", shape=[None, 3], dtype="float32")
            y = fluid.data(name="y", shape=[None, 3], dtype="float32")
            sum = fluid.contrib.layers.partial_sum(
                [x, y], start_index=0, length=2)
            return (sum)

S
ShenLiang 已提交
3286 3287 3288 3289 3290 3291 3292 3293 3294 3295 3296 3297 3298 3299 3300 3301 3302 3303
    def test_batch_fc(self):
        with self.static_graph():
            input = fluid.data(name="input", shape=[16, 2, 3], dtype="float32")
            out = fluid.contrib.layers.batch_fc(
                input=input,
                param_size=[16, 3, 10],
                param_attr=fluid.ParamAttr(
                    learning_rate=1.0,
                    name="w_0",
                    initializer=fluid.initializer.Xavier(uniform=False)),
                bias_size=[16, 10],
                bias_attr=fluid.ParamAttr(
                    learning_rate=1.0,
                    name="b_0",
                    initializer=fluid.initializer.Xavier(uniform=False)),
                act="relu")
        return (out)

S
ShenLiang 已提交
3304 3305 3306 3307 3308 3309 3310 3311 3312 3313 3314 3315 3316 3317 3318 3319
    def test_rank_attention(self):
        with self.static_graph():
            input = fluid.data(name="input", shape=[None, 2], dtype="float32")
            rank_offset = fluid.data(
                name="rank_offset", shape=[None, 7], dtype="int32")
            out = fluid.contrib.layers.rank_attention(
                input=input,
                rank_offset=rank_offset,
                rank_param_shape=[18, 3],
                rank_param_attr=fluid.ParamAttr(
                    learning_rate=1.0,
                    name="ubm_rank_param.w_0",
                    initializer=fluid.initializer.Xavier(uniform=False)),
                max_rank=3)
            return (out)

3320 3321 3322 3323 3324 3325
    def test_roi_pool(self):
        # TODO(minqiyang): dygraph do not support lod now
        with self.static_graph():
            x = layers.data(name="x", shape=[256, 30, 30], dtype="float32")
            rois = layers.data(
                name="rois", shape=[4], dtype="float32", lod_level=1)
F
FDInSky 已提交
3326 3327 3328
            rois_lod = layers.data(
                name="rois_lod", shape=[None, ], dtype="int", lod_level=1)
            output = layers.roi_pool(x, rois, 7, 7, 0.6, rois_lod)
3329 3330 3331 3332 3333 3334 3335 3336 3337 3338 3339 3340 3341 3342
            return (output)

    def test_sequence_enumerate(self):
        # TODO(minqiyang): dygraph do not support lod now
        with self.static_graph():
            x = layers.data(name="input", shape=[1], dtype='int32', lod_level=1)
            out = layers.sequence_enumerate(input=x, win_size=2, pad_value=0)

    def test_roi_align(self):
        # TODO(minqiyang): dygraph do not support lod now
        with self.static_graph():
            x = layers.data(name="x", shape=[256, 30, 30], dtype="float32")
            rois = layers.data(
                name="rois", shape=[4], dtype="float32", lod_level=1)
F
FDInSky 已提交
3343 3344 3345 3346
            rois_lod = layers.data(
                name="rois_lod", shape=[None, ], dtype="int", lod_level=1)
            output = layers.roi_align(x, rois, 14, 14, 0.5, 2, 'roi_align',
                                      rois_lod)
3347 3348 3349 3350 3351 3352 3353 3354 3355 3356 3357 3358 3359 3360 3361 3362 3363 3364 3365 3366 3367 3368 3369 3370 3371 3372 3373 3374 3375 3376 3377 3378 3379 3380 3381 3382 3383 3384 3385 3386 3387 3388 3389
            return (output)

    def test_roi_perspective_transform(self):
        # TODO(minqiyang): dygraph do not support lod now
        with self.static_graph():
            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)
            return (output)

    def test_row_conv(self):
        # TODO(minqiyang): dygraph do not support lod now
        with self.static_graph():
            x = layers.data(name='x', shape=[16], dtype='float32', lod_level=1)
            out = layers.row_conv(input=x, future_context_size=2)
            return (out)

    def test_simple_conv2d(self):
        # TODO(minqiyang): dygraph do not support layers with param now
        with self.static_graph():
            images = layers.data(
                name='pixel', shape=[3, 48, 48], dtype='float32')
            return layers.conv2d(
                input=images, num_filters=3, filter_size=[4, 4])

    def test_squeeze(self):
        # TODO(minqiyang): dygraph do not support layers with param now
        with self.static_graph():
            x = layers.data(name='x', shape=[1, 1, 4], dtype='float32')
            out = layers.squeeze(input=x, axes=[2])
            return (out)

    def test_flatten(self):
        # TODO(minqiyang): dygraph do not support op without kernel now
        with self.static_graph():
            x = layers.data(
                name='x',
                append_batch_size=False,
                shape=[4, 4, 3],
                dtype="float32")
            out = layers.flatten(x, axis=1, name="flatten")
            return (out)
3390

Z
zhoukunsheng 已提交
3391 3392 3393 3394 3395 3396 3397
    def test_linspace(self):
        program = Program()
        with program_guard(program):
            out = layers.linspace(20, 10, 5, 'float64')
            self.assertIsNotNone(out)
        print(str(program))

3398
    def test_deformable_conv(self):
3399
        with self.static_graph():
3400 3401 3402 3403 3404 3405 3406 3407 3408 3409 3410 3411 3412 3413 3414 3415 3416 3417 3418
            input = layers.data(
                name='input',
                append_batch_size=False,
                shape=[2, 3, 32, 32],
                dtype="float32")
            offset = layers.data(
                name='offset',
                append_batch_size=False,
                shape=[2, 18, 32, 32],
                dtype="float32")
            mask = layers.data(
                name='mask',
                append_batch_size=False,
                shape=[2, 9, 32, 32],
                dtype="float32")
            out = layers.deformable_conv(
                input=input,
                offset=offset,
                mask=mask,
3419 3420 3421 3422 3423 3424 3425 3426 3427 3428 3429 3430 3431 3432 3433 3434 3435
                num_filters=2,
                filter_size=3,
                padding=1)
            return (out)

    def test_deformable_conv2(self):
        with self.static_graph():
            input = fluid.data(
                name='input', shape=[None, 3, None, None], dtype="float32")
            offset = fluid.data(
                name='offset', shape=[None, 18, None, None], dtype="float32")
            mask = fluid.data(
                name='mask', shape=[None, 9, None, None], dtype="float32")
            out = layers.deformable_conv(
                input=input,
                offset=offset,
                mask=mask,
3436 3437 3438 3439
                num_filters=2,
                filter_size=3,
                padding=1)
            return (out)
3440

3441 3442 3443 3444 3445 3446
    def test_unfold(self):
        with self.static_graph():
            x = layers.data(name='x', shape=[3, 20, 20], dtype='float32')
            out = layers.unfold(x, [3, 3], 1, 1, 1)
            return (out)

3447 3448 3449 3450 3451 3452 3453 3454 3455 3456
    def test_partial_concat(self):
        with self.static_graph():
            x = fluid.data(name="x", shape=[None, 3], dtype="float32")
            y = fluid.data(name="y", shape=[None, 3], dtype="float32")
            concat1 = fluid.contrib.layers.partial_concat(
                [x, y], start_index=0, length=2)
            concat2 = fluid.contrib.layers.partial_concat(
                x, start_index=0, length=-1)
            return concat1, concat2

C
cjt222 已提交
3457 3458 3459 3460 3461 3462 3463 3464 3465 3466 3467 3468 3469 3470 3471 3472 3473 3474 3475 3476 3477 3478 3479 3480 3481 3482 3483 3484 3485
    def test_deform_roi_pooling(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            input = layers.data(
                name='input',
                shape=[2, 3, 32, 32],
                dtype='float32',
                append_batch_size=False)
            rois = layers.data(
                name="rois", shape=[4], dtype='float32', lod_level=1)
            trans = layers.data(
                name="trans",
                shape=[2, 3, 32, 32],
                dtype='float32',
                append_batch_size=False)
            out = layers.deformable_roi_pooling(
                input=input,
                rois=rois,
                trans=trans,
                no_trans=False,
                spatial_scale=1.0,
                group_size=(1, 1),
                pooled_height=8,
                pooled_width=8,
                part_size=(8, 8),
                sample_per_part=4,
                trans_std=0.1)
        return (out)

3486 3487 3488 3489 3490 3491 3492 3493 3494 3495 3496 3497 3498 3499 3500 3501 3502 3503 3504 3505 3506 3507 3508
    def test_deformable_conv_v1(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            input = layers.data(
                name='input',
                append_batch_size=False,
                shape=[2, 3, 32, 32],
                dtype="float32")
            offset = layers.data(
                name='offset',
                append_batch_size=False,
                shape=[2, 18, 32, 32],
                dtype="float32")
            out = layers.deformable_conv(
                input=input,
                offset=offset,
                mask=None,
                num_filters=2,
                filter_size=3,
                padding=1,
                modulated=False)
            return (out)

3509 3510 3511 3512 3513 3514 3515 3516 3517 3518 3519 3520 3521 3522 3523 3524 3525 3526 3527 3528 3529 3530 3531 3532 3533 3534 3535 3536 3537 3538 3539 3540
    def test_retinanet_target_assign(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            bbox_pred = layers.data(
                name='bbox_pred',
                shape=[1, 100, 4],
                append_batch_size=False,
                dtype='float32')
            cls_logits = layers.data(
                name='cls_logits',
                shape=[1, 100, 10],
                append_batch_size=False,
                dtype='float32')
            anchor_box = layers.data(
                name='anchor_box',
                shape=[100, 4],
                append_batch_size=False,
                dtype='float32')
            anchor_var = layers.data(
                name='anchor_var',
                shape=[100, 4],
                append_batch_size=False,
                dtype='float32')
            gt_boxes = layers.data(
                name='gt_boxes',
                shape=[10, 4],
                append_batch_size=False,
                dtype='float32')
            gt_labels = layers.data(
                name='gt_labels',
                shape=[10, 1],
                append_batch_size=False,
3541
                dtype='int32')
3542 3543 3544 3545
            is_crowd = layers.data(
                name='is_crowd',
                shape=[1],
                append_batch_size=False,
3546
                dtype='int32')
3547 3548 3549 3550 3551 3552 3553 3554 3555
            im_info = layers.data(
                name='im_info',
                shape=[1, 3],
                append_batch_size=False,
                dtype='float32')
            return (layers.retinanet_target_assign(
                bbox_pred, cls_logits, anchor_box, anchor_var, gt_boxes,
                gt_labels, is_crowd, im_info, 10))

3556 3557 3558 3559 3560 3561 3562 3563 3564 3565 3566 3567 3568 3569 3570 3571 3572 3573 3574 3575 3576 3577
    def test_sigmoid_focal_loss(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            input = layers.data(
                name='data',
                shape=[10, 80],
                append_batch_size=False,
                dtype='float32')
            label = layers.data(
                name='label',
                shape=[10, 1],
                append_batch_size=False,
                dtype='int32')
            fg_num = layers.data(
                name='fg_num',
                shape=[1],
                append_batch_size=False,
                dtype='int32')
            out = fluid.layers.sigmoid_focal_loss(
                x=input, label=label, fg_num=fg_num, gamma=2., alpha=0.25)
            return (out)

3578 3579 3580 3581 3582 3583 3584 3585 3586 3587 3588 3589 3590 3591 3592 3593 3594 3595 3596 3597 3598 3599
    def test_addmm(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            input = layers.data(
                name='input_data',
                shape=[3, 3],
                append_batch_size=False,
                dtype='float32')
            x = layers.data(
                name='x',
                shape=[3, 2],
                append_batch_size=False,
                dtype='float32')
            y = layers.data(
                name='y',
                shape=[2, 3],
                append_batch_size=False,
                dtype='float32')

            out = paddle.addmm(input=input, x=x, y=y)
            return (out)

3600 3601 3602 3603 3604 3605 3606 3607 3608 3609 3610 3611 3612 3613 3614 3615 3616 3617 3618 3619 3620 3621 3622 3623 3624 3625 3626 3627 3628 3629 3630 3631 3632 3633 3634
    def test_retinanet_detection_output(self):
        with program_guard(fluid.default_main_program(),
                           fluid.default_startup_program()):
            bboxes = layers.data(
                name='bboxes',
                shape=[1, 21, 4],
                append_batch_size=False,
                dtype='float32')
            scores = layers.data(
                name='scores',
                shape=[1, 21, 10],
                append_batch_size=False,
                dtype='float32')
            anchors = layers.data(
                name='anchors',
                shape=[21, 4],
                append_batch_size=False,
                dtype='float32')
            im_info = layers.data(
                name="im_info",
                shape=[1, 3],
                append_batch_size=False,
                dtype='float32')
            nmsed_outs = layers.retinanet_detection_output(
                bboxes=[bboxes, bboxes],
                scores=[scores, scores],
                anchors=[anchors, anchors],
                im_info=im_info,
                score_threshold=0.05,
                nms_top_k=1000,
                keep_top_k=100,
                nms_threshold=0.3,
                nms_eta=1.)
            return (nmsed_outs)

3635 3636 3637 3638 3639 3640 3641 3642 3643 3644 3645 3646 3647 3648 3649 3650 3651
    def test_warpctc_with_padding(self):
        # TODO(minqiyang): dygraph do not support lod now
        with self.static_graph():
            input_length = layers.data(
                name='logits_length', shape=[11], dtype='int64')
            label_length = layers.data(
                name='labels_length', shape=[12], dtype='int64')
            label = layers.data(name='label', shape=[12, 1], dtype='int32')
            predict = layers.data(
                name='predict', shape=[4, 4, 8], dtype='float32')
            output = layers.warpctc(
                input=predict,
                label=label,
                input_length=input_length,
                label_length=label_length)
            return (output)

3652 3653 3654 3655 3656 3657 3658 3659 3660
    def test_edit_distance(self):
        with self.static_graph():
            predict = layers.data(
                name='predict', shape=[-1, 1], dtype='int64', lod_level=1)
            label = layers.data(
                name='label', shape=[-1, 1], dtype='int64', lod_level=1)
            evaluator = fluid.evaluator.EditDistance(predict, label)
            return evaluator.metrics

3661 3662 3663 3664 3665 3666 3667 3668 3669 3670 3671 3672 3673 3674 3675 3676 3677 3678 3679 3680 3681 3682 3683
    def test_basic_gru(self):
        input_size = 128
        hidden_size = 256
        with self.static_graph():
            input = fluid.data(
                name="input", shape=[None, None, input_size], dtype='float32')
            pre_hidden = fluid.data(
                name="pre_hidden", shape=[None, hidden_size], dtype='float32')
            sequence_length = fluid.data(
                name="sequence_length", shape=[None], dtype='int32')

            for bidirectional in [True, False]:
                for batch_first in [True, False]:
                    rnn_out, last_hidden = fluid.contrib.layers.basic_gru(
                        input,
                        pre_hidden,
                        hidden_size=256,
                        num_layers=2,
                        sequence_length=sequence_length,
                        dropout_prob=0.5,
                        bidirectional=bidirectional,
                        batch_first=batch_first)

Y
Yu Yang 已提交
3684

3685 3686 3687 3688 3689 3690 3691 3692 3693 3694 3695 3696 3697 3698 3699 3700 3701 3702 3703 3704 3705 3706 3707 3708 3709 3710 3711
class TestMetricsDetectionMap(unittest.TestCase):
    def test_detection_map(self):
        program = fluid.Program()
        with program_guard(program):
            detect_res = fluid.layers.data(
                name='detect_res',
                shape=[10, 6],
                append_batch_size=False,
                dtype='float32')
            label = fluid.layers.data(
                name='label',
                shape=[10, 1],
                append_batch_size=False,
                dtype='float32')
            box = fluid.layers.data(
                name='bbox',
                shape=[10, 4],
                append_batch_size=False,
                dtype='float32')
            map_eval = fluid.metrics.DetectionMAP(
                detect_res, label, box, class_num=21)
            cur_map, accm_map = map_eval.get_map_var()
            self.assertIsNotNone(cur_map)
            self.assertIsNotNone(accm_map)
        print(str(program))


Y
Yu Yang 已提交
3712 3713
if __name__ == '__main__':
    unittest.main()