test_layers.py 39.2 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
import paddle.fluid.layers as layers
19
from paddle.fluid.layers.device import get_places
20 21 22
import paddle.fluid.nets as nets
from paddle.fluid.framework import Program, program_guard, default_main_program
from paddle.fluid.param_attr import ParamAttr
23
import decorators
J
jerrywgz 已提交
24
from paddle.fluid.initializer import Constant
Y
Yu Yang 已提交
25 26 27 28


class TestBook(unittest.TestCase):
    def test_fit_a_line(self):
29
        program = Program()
Y
Yu Yang 已提交
30 31 32 33 34
        with program_guard(program, startup_program=Program()):
            x = layers.data(name='x', shape=[13], dtype='float32')
            y_predict = layers.fc(input=x, size=1, act=None)
            y = layers.data(name='y', shape=[1], dtype='float32')
            cost = layers.square_error_cost(input=y_predict, label=y)
Y
Yu Yang 已提交
35
            avg_cost = layers.mean(cost)
Y
Yu Yang 已提交
36
            self.assertIsNotNone(avg_cost)
Y
Yu Yang 已提交
37

Y
Yu Yang 已提交
38
        print(str(program))
Y
Yu Yang 已提交
39 40

    def test_recognize_digits_mlp(self):
41
        program = Program()
Y
Yu Yang 已提交
42 43 44 45 46 47
        with program_guard(program, startup_program=Program()):
            # Change g_program, so the rest layers use `g_program`
            images = layers.data(name='pixel', shape=[784], dtype='float32')
            label = layers.data(name='label', shape=[1], dtype='int32')
            hidden1 = layers.fc(input=images, size=128, act='relu')
            hidden2 = layers.fc(input=hidden1, size=64, act='relu')
48 49 50 51
            predict = layers.fc(input=[hidden2, hidden1],
                                size=10,
                                act='softmax',
                                param_attr=["sftmax.w1", "sftmax.w2"])
Y
Yu Yang 已提交
52
            cost = layers.cross_entropy(input=predict, label=label)
Y
Yu Yang 已提交
53
            avg_cost = layers.mean(cost)
Y
Yu Yang 已提交
54 55 56
            self.assertIsNotNone(avg_cost)

        print(str(program))
57 58

    def test_simple_conv2d(self):
F
fengjiayi 已提交
59
        program = Program()
Y
Yu Yang 已提交
60
        with program_guard(program, startup_program=Program()):
61 62
            images = layers.data(
                name='pixel', shape=[3, 48, 48], dtype='float32')
Y
Yu Yang 已提交
63 64 65
            layers.conv2d(input=images, num_filters=3, filter_size=[4, 4])

        print(str(program))
Y
Yu Yang 已提交
66

67 68
    def test_conv2d_transpose(self):
        program = Program()
Y
Yu Yang 已提交
69 70 71 72
        with program_guard(program):
            img = layers.data(name='pixel', shape=[3, 2, 2], dtype='float32')
            layers.conv2d_transpose(input=img, num_filters=10, output_size=28)
        print(str(program))
73

F
fengjiayi 已提交
74
    def test_recognize_digits_conv(self):
F
fengjiayi 已提交
75
        program = Program()
Y
Yu Yang 已提交
76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96
        with program_guard(program, startup_program=Program()):
            images = layers.data(
                name='pixel', shape=[1, 28, 28], dtype='float32')
            label = layers.data(name='label', shape=[1], dtype='int32')
            conv_pool_1 = nets.simple_img_conv_pool(
                input=images,
                filter_size=5,
                num_filters=2,
                pool_size=2,
                pool_stride=2,
                act="relu")
            conv_pool_2 = nets.simple_img_conv_pool(
                input=conv_pool_1,
                filter_size=5,
                num_filters=4,
                pool_size=2,
                pool_stride=2,
                act="relu")

            predict = layers.fc(input=conv_pool_2, size=10, act="softmax")
            cost = layers.cross_entropy(input=predict, label=label)
Y
Yu Yang 已提交
97
            avg_cost = layers.mean(cost)
Y
Yu Yang 已提交
98 99

        print(str(program))
100

Q
QI JUN 已提交
101 102
    def test_word_embedding(self):
        program = Program()
Y
Yu Yang 已提交
103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142
        with program_guard(program, startup_program=Program()):
            dict_size = 10000
            embed_size = 32
            first_word = layers.data(name='firstw', shape=[1], dtype='int64')
            second_word = layers.data(name='secondw', shape=[1], dtype='int64')
            third_word = layers.data(name='thirdw', shape=[1], dtype='int64')
            forth_word = layers.data(name='forthw', shape=[1], dtype='int64')
            next_word = layers.data(name='nextw', shape=[1], dtype='int64')

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

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

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

            hidden1 = layers.fc(input=concat_embed, size=256, act='sigmoid')
            predict_word = layers.fc(input=hidden1,
                                     size=dict_size,
                                     act='softmax')
            cost = layers.cross_entropy(input=predict_word, label=next_word)
Y
Yu Yang 已提交
143
            avg_cost = layers.mean(cost)
Y
Yu Yang 已提交
144 145 146
            self.assertIsNotNone(avg_cost)

        print(str(program))
Q
Qiao Longfei 已提交
147 148 149

    def test_linear_chain_crf(self):
        program = Program()
Y
Yu Yang 已提交
150
        with program_guard(program, startup_program=Program()):
Q
Qiao Longfei 已提交
151
            label_dict_len = 10
Y
Yu Yang 已提交
152 153 154
            images = layers.data(name='pixel', shape=[784], dtype='float32')
            label = layers.data(name='label', shape=[1], dtype='int32')
            hidden = layers.fc(input=images, size=128)
Q
Qiao Longfei 已提交
155 156 157 158
            crf = layers.linear_chain_crf(
                input=hidden, label=label, param_attr=ParamAttr(name="crfw"))
            crf_decode = layers.crf_decoding(
                input=hidden, param_attr=ParamAttr(name="crfw"))
Q
Qiao Longfei 已提交
159 160 161 162
            layers.chunk_eval(
                input=crf_decode,
                label=label,
                chunk_scheme="IOB",
M
minqiyang 已提交
163
                num_chunk_types=(label_dict_len - 1) // 2)
Q
qiaolongfei 已提交
164 165
            self.assertFalse(crf is None)
            self.assertFalse(crf_decode is None)
Y
Yu Yang 已提交
166 167

        print(str(program))
Q
QI JUN 已提交
168

169 170 171 172 173
    def test_sigmoid_cross_entropy(self):
        program = Program()
        with program_guard(program):
            dat = layers.data(name='data', shape=[10], dtype='float32')
            lbl = layers.data(name='label', shape=[10], dtype='float32')
174
            ignore_index = -1
175 176
            self.assertIsNotNone(
                layers.sigmoid_cross_entropy_with_logits(
J
jerrywgz 已提交
177
                    x=dat, label=lbl, ignore_index=ignore_index))
178 179
        print(str(program))

W
weixing02 已提交
180 181 182
    def test_hsigmoid(self):
        program = Program()
        with program_guard(program):
W
weixing02 已提交
183 184
            x = layers.data(name='x', shape=[2], dtype='float32')
            y = layers.data(name='y', shape=[2], dtype='int64')
W
weixing02 已提交
185 186 187 188 189
            self.assertIsNotNone(
                layers.hsigmoid(
                    input=x, label=y, num_classes=2))
        print(str(program))

J
JiabinYang 已提交
190
        # test hsigmod with custom tree structure
J
JiabinYang 已提交
191 192 193 194
        program2 = Program()
        with program_guard(program2):
            x2 = layers.data(name='x2', shape=[4, 8], dtype='float32')
            y2 = layers.data(name='y2', shape=[4], dtype='int64')
195 196 197 198
            path_table = layers.data(
                name='path_table', shape=[4, 6], dtype='int64')
            path_code = layers.data(
                name='path_code', shape=[4, 6], dtype='int64')
J
JiabinYang 已提交
199 200 201 202
            self.assertIsNotNone(
                layers.hsigmoid(
                    input=x2,
                    label=y2,
203
                    num_classes=6,
204 205 206
                    path_table=path_table,
                    path_code=path_code,
                    is_custom=True))
J
JiabinYang 已提交
207 208
            print(str(program2))

Y
yangyaming 已提交
209
    def test_sequence_expand(self):
Y
yangyaming 已提交
210 211 212 213
        program = Program()
        with program_guard(program):
            x = layers.data(name='x', shape=[10], dtype='float32')
            y = layers.data(
Y
yangyaming 已提交
214 215
                name='y', shape=[10, 20], dtype='float32', lod_level=2)
            self.assertIsNotNone(layers.sequence_expand(x=x, y=y, ref_level=1))
Y
yangyaming 已提交
216 217
        print(str(program))

Y
Yibing Liu 已提交
218 219 220 221 222 223 224 225
    def test_sequence_unpad(self):
        program = Program()
        with program_guard(program):
            x = layers.data(name='x', shape=[10, 5], dtype='float32')
            length = layers.data(name='length', shape=[1], dtype='int64')
            self.assertIsNotNone(layers.sequence_unpad(x=x, length=length))
        print(str(program))

J
JiabinYang 已提交
226 227 228 229
    def test_pool2d(self):
        program = Program()
        with program_guard(program):
            x = layers.data(name='x', shape=[3, 224, 224], dtype='float32')
J
JiabinYang 已提交
230 231 232 233 234 235
            self.assertIsNotNone(
                layers.pool2d(
                    x,
                    pool_size=[5, 3],
                    pool_stride=[1, 2],
                    pool_padding=(2, 1)))
J
JiabinYang 已提交
236

237 238 239 240 241 242 243
    def test_adaptive_pool2d(self):
        program = Program()
        with program_guard(program):
            x = layers.data(name='x', shape=[3, 224, 224], dtype='float32')
            self.assertIsNotNone(
                layers.adaptive_pool2d(
                    x, [3, 3], pool_type='avg'))
D
dengkaipeng 已提交
244 245 246
            pool, mask = layers.adaptive_pool2d(x, [3, 3], require_index=True)
            self.assertIsNotNone(pool)
            self.assertIsNotNone(mask)
247 248 249 250
            self.assertIsNotNone(layers.adaptive_pool2d(x, 3, pool_type='avg'))
            pool, mask = layers.adaptive_pool2d(x, 3, require_index=True)
            self.assertIsNotNone(pool)
            self.assertIsNotNone(mask)
251 252 253 254 255 256 257 258

    def test_adaptive_pool3d(self):
        program = Program()
        with program_guard(program):
            x = layers.data(name='x', shape=[3, 244, 224, 224], dtype='float32')
            self.assertIsNotNone(
                layers.adaptive_pool3d(
                    x, [3, 3, 3], pool_type='avg'))
D
dengkaipeng 已提交
259 260 261 262
            pool, mask = layers.adaptive_pool3d(
                x, [3, 3, 3], require_index=True)
            self.assertIsNotNone(pool)
            self.assertIsNotNone(mask)
263 264 265 266
            self.assertIsNotNone(layers.adaptive_pool3d(x, 3, pool_type='avg'))
            pool, mask = layers.adaptive_pool3d(x, 3, require_index=True)
            self.assertIsNotNone(pool)
            self.assertIsNotNone(mask)
267

Y
yangyaming 已提交
268 269 270 271 272 273 274
    def test_lstm_unit(self):
        program = Program()
        with program_guard(program):
            x_t_data = layers.data(
                name='x_t_data', shape=[10, 10], dtype='float32')
            x_t = layers.fc(input=x_t_data, size=10)
            prev_hidden_data = layers.data(
Y
yangyaming 已提交
275 276
                name='prev_hidden_data', shape=[10, 30], dtype='float32')
            prev_hidden = layers.fc(input=prev_hidden_data, size=30)
Y
yangyaming 已提交
277 278 279 280 281 282 283 284
            prev_cell_data = layers.data(
                name='prev_cell', shape=[10, 30], dtype='float32')
            prev_cell = layers.fc(input=prev_cell_data, size=30)
            self.assertIsNotNone(
                layers.lstm_unit(
                    x_t=x_t, hidden_t_prev=prev_hidden, cell_t_prev=prev_cell))
        print(str(program))

285 286 287 288 289 290 291 292 293 294 295 296
    def test_dynamic_lstmp(self):
        program = Program()
        with program_guard(program):
            hidden_dim, proj_dim = 16, 8
            seq_data = layers.data(
                name='seq_data', shape=[10, 10], dtype='float32', lod_level=1)
            fc_out = layers.fc(input=seq_data, size=4 * hidden_dim)
            self.assertIsNotNone(
                layers.dynamic_lstmp(
                    input=fc_out, size=4 * hidden_dim, proj_size=proj_dim))
        print(str(program))

Y
yangyaming 已提交
297 298 299 300 301 302
    def test_sequence_softmax(self):
        program = Program()
        with program_guard(program):
            seq_data = layers.data(
                name='seq_data', shape=[10, 10], dtype='float32', lod_level=1)
            seq = layers.fc(input=seq_data, size=20)
303
            self.assertIsNotNone(layers.sequence_softmax(seq))
Y
yangyaming 已提交
304 305
        print(str(program))

D
dangqingqing 已提交
306 307 308 309 310
    def test_softmax(self):
        program = Program()
        with program_guard(program):
            data = layers.data(name='data', shape=[10], dtype='float32')
            hid = layers.fc(input=data, size=20)
311
            self.assertIsNotNone(layers.softmax(hid))
D
dangqingqing 已提交
312 313
        print(str(program))

J
JiabinYang 已提交
314
    def test_space_to_depth(self):
J
JiabinYang 已提交
315 316 317
        program = Program()
        with program_guard(program):
            data = layers.data(
J
JiabinYang 已提交
318
                name='data',
J
JiabinYang 已提交
319 320 321
                shape=[32, 9, 6, 6],
                append_batch_size=False,
                dtype='float32')
J
JiabinYang 已提交
322
            self.assertIsNotNone(layers.space_to_depth(data, 3))
J
JiabinYang 已提交
323 324
        print(str(program))

Y
Yibing Liu 已提交
325 326 327
    def test_sequence_unsqueeze(self):
        program = Program()
        with program_guard(program):
328
            x = layers.data(name='x', shape=[8, 2], dtype='float32')
329
            out = layers.unsqueeze(input=x, axes=[1])
Y
Yibing Liu 已提交
330 331
            self.assertIsNotNone(out)
        print(str(program))
332

Y
Yibing Liu 已提交
333 334 335 336
    def test_squeeze(self):
        program = Program()
        with program_guard(program):
            x = layers.data(name='x', shape=[1, 1, 4], dtype='float32')
337
            out = layers.squeeze(input=x, axes=[2])
Y
Yibing Liu 已提交
338 339 340
            self.assertIsNotNone(out)
        print(str(program))

D
dragonwarrior 已提交
341 342 343 344 345 346 347
    def test_lrn(self):
        program = Program()
        with program_guard(program):
            data = layers.data(name='data', shape=[6, 2, 2], dtype='float32')
            self.assertIsNotNone(layers.lrn(data))
        print(str(program))

Q
qijun 已提交
348 349 350
    def test_get_places(self):
        program = Program()
        with program_guard(program):
351
            x = get_places(device_count=4)
Y
Yang Yu 已提交
352
            self.assertIsNotNone(x)
Q
qijun 已提交
353 354
        print(str(program))

355 356 357 358 359 360 361 362
    def test_sequence_reshape(self):
        program = Program()
        with program_guard(program):
            x = layers.data(name='x', shape=[8], dtype='float32', lod_level=1)
            out = layers.sequence_reshape(input=x, new_dim=16)
            self.assertIsNotNone(out)
        print(str(program))

W
wanghaoshuang 已提交
363 364 365 366
    def test_im2sequence(self):
        program = Program()
        with program_guard(program):
            x = layers.data(name='x', shape=[3, 128, 128], dtype='float32')
367
            y = layers.data(name='y', shape=[], dtype='float32')
W
wanghaoshuang 已提交
368
            output = layers.im2sequence(
369 370 371 372 373
                input=x,
                input_image_size=y,
                stride=[1, 1],
                filter_size=[2, 2],
                out_stride=[1, 1])
W
wanghaoshuang 已提交
374 375 376
            self.assertIsNotNone(output)
        print(str(program))

377
    def test_sampled_softmax_with_cross_entropy(self):
X
xuezhong 已提交
378 379 380
        program = Program()
        with program_guard(program):
            logits = layers.data(name='Logits', shape=[256], dtype='float64')
X
xuezhong 已提交
381
            label = layers.data(name='Label', shape=[1], dtype='int64')
X
xuezhong 已提交
382
            num_samples = 25
X
xuezhong 已提交
383 384
            output = layers.sampled_softmax_with_cross_entropy(logits, label,
                                                               num_samples)
X
xuezhong 已提交
385 386 387
            self.assertIsNotNone(output)
        print(str(program))

Y
Yang Yu 已提交
388 389 390 391
    @decorators.prog_scope()
    def test_nce(self):
        window_size = 5
        words = []
392
        for i in range(window_size):
Y
Yang Yu 已提交
393 394 395 396 397
            words.append(
                layers.data(
                    name='word_{0}'.format(i), shape=[1], dtype='int64'))

        dict_size = 10000
M
minqiyang 已提交
398
        label_word = int(window_size // 2) + 1
Y
Yang Yu 已提交
399 400

        embs = []
401
        for i in range(window_size):
Y
Yang Yu 已提交
402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418
            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
419
        avg_loss = layers.mean(loss)
Y
Yang Yu 已提交
420 421 422
        self.assertIsNotNone(avg_loss)
        print(str(default_main_program()))

Y
yangyaming 已提交
423 424 425 426 427 428 429 430
    def test_row_conv(self):
        program = Program()
        with program_guard(program):
            x = layers.data(name='x', shape=[16], dtype='float32', lod_level=1)
            out = layers.row_conv(input=x, future_context_size=2)
            self.assertIsNotNone(out)
        print(str(program))

431 432 433 434 435 436 437 438 439 440
    def test_multiplex(self):
        program = Program()
        with program_guard(program):
            x1 = layers.data(name='x1', shape=[4], dtype='float32')
            x2 = layers.data(name='x2', shape=[4], dtype='float32')
            index = layers.data(name='index', shape=[1], dtype='int32')
            out = layers.multiplex(inputs=[x1, x2], index=index)
            self.assertIsNotNone(out)
        print(str(program))

441 442 443 444 445
    def test_softmax_with_cross_entropy(self):
        program = Program()
        with program_guard(program):
            x = layers.data(name='x', shape=[16], dtype='float32')
            y = layers.data(name='label', shape=[1], dtype='int64')
446 447 448 449
            loss, softmax = layers.softmax_with_cross_entropy(
                x, y, return_softmax=True)
            self.assertIsNotNone(loss)
            self.assertIsNotNone(softmax)
450 451 452 453 454 455 456 457 458 459 460 461 462
            loss = layers.softmax_with_cross_entropy(x, y)
            self.assertIsNotNone(loss)
        print(str(program))

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

463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481
    def test_scatter(self):
        program = Program()
        with program_guard(program):
            x = layers.data(
                name='x',
                shape=[3, 3],
                append_batch_size=False,
                dtype='float32')
            idx = layers.data(
                name='idx', shape=[2], append_batch_size=False, dtype='int32')
            updates = layers.data(
                name='updates',
                shape=[2, 3],
                append_batch_size=False,
                dtype='float32')
            out = layers.scatter(input=x, index=idx, updates=updates)
            self.assertIsNotNone(out)
        print(str(program))

Q
Qingsheng Li 已提交
482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505
    def test_sequence_scatter(self):
        program = Program()
        with program_guard(program):
            x = layers.data(
                name='x',
                shape=[3, 6],
                append_batch_size=False,
                dtype='float32')
            idx = layers.data(
                name='idx',
                shape=[12, 1],
                append_batch_size=False,
                dtype='int32',
                lod_level=1)
            updates = layers.data(
                name='updates',
                shape=[12, 1],
                append_batch_size=False,
                dtype='float32',
                lod_level=1)
            out = layers.sequence_scatter(input=x, index=idx, updates=updates)
            self.assertIsNotNone(out)
        print(str(program))

Y
Yibing Liu 已提交
506 507 508 509 510 511 512 513 514 515 516 517 518
    def test_sequence_slice(self):
        program = Program()
        with program_guard(program):
            import numpy as np
            seqs = layers.data(
                name='x', shape=[10, 5], dtype='float32', lod_level=1)
            offset = layers.assign(input=np.array([[0, 1]]).astype('int32'))
            length = layers.assign(input=np.array([[2, 1]]).astype('int32'))
            out = layers.sequence_slice(
                input=seqs, offset=offset, length=length)
            self.assertIsNotNone(out)
        print(str(program))

Y
yangyaming 已提交
519 520 521 522 523 524 525 526 527
    def test_lod_reset(self):
        program = Program()
        with program_guard(program):
            x = layers.data(name='x', shape=[10], dtype='float32')
            y = layers.data(
                name='y', shape=[10, 20], dtype='float32', lod_level=2)
            print(layers.lod_reset(x=x, y=y))
        print(str(program))

528 529 530 531 532 533 534 535 536 537
    def test_label_smooth(self):
        program = Program()
        with program_guard(program):
            label = layers.data(name="label", shape=[1], dtype="float32")
            one_hot_label = layers.one_hot(input=label, depth=10)
            smooth_label = layers.label_smooth(
                label=one_hot_label, epsilon=0.1, dtype="float32")
            self.assertIsNotNone(smooth_label)
        print(str(program))

Q
qingqing01 已提交
538 539 540 541 542 543 544 545 546
    def test_topk(self):
        program = Program()
        with program_guard(program):
            data = layers.data(name="label", shape=[200], dtype="float32")
            values, indices = layers.topk(data, k=5)
            self.assertIsNotNone(values)
            self.assertIsNotNone(indices)
        print(str(program))

547 548 549 550 551 552 553 554 555 556
    def test_roi_pool(self):
        program = Program()
        with program_guard(program):
            x = layers.data(name="x", shape=[256, 30, 30], dtype="float32")
            rois = layers.data(
                name="rois", shape=[4], dtype="float32", lod_level=1)
            output = layers.roi_pool(x, rois, 7, 7, 0.6)
            self.assertIsNotNone(output)
        print(str(program))

557 558 559 560 561 562 563 564 565 566
    def test_psroi_pool(self):
        program = Program()
        with program_guard(program):
            x = layers.data(name="x", shape=[245, 30, 30], dtype="float32")
            rois = layers.data(
                name="rois", shape=[4], dtype="float32", lod_level=1)
            output = layers.psroi_pool(x, rois, 5, 0.25, 7, 7)
            self.assertIsNotNone(output)
        print(str(program))

J
jerrywgz 已提交
567 568 569 570 571 572 573 574 575 576
    def test_roi_align(self):
        program = Program()
        with program_guard(program):
            x = layers.data(name="x", shape=[256, 30, 30], dtype="float32")
            rois = layers.data(
                name="rois", shape=[4], dtype="float32", lod_level=1)
            output = layers.roi_align(x, rois, 14, 14, 0.5, 2)
            self.assertIsNotNone(output)
        print(str(program))

B
baiyf 已提交
577
    def test_resize_bilinear(self):
578 579 580
        program = Program()
        with program_guard(program):
            x = layers.data(name='x', shape=[3, 9, 6], dtype="float32")
B
baiyf 已提交
581
            output = layers.resize_bilinear(x, out_shape=[12, 12])
582
            self.assertIsNotNone(output)
B
baiyf 已提交
583
            output = layers.resize_bilinear(x, scale=3)
584 585 586
            self.assertIsNotNone(output)
        print(str(program))

587
    def test_resize_nearest(self):
588 589 590 591 592 593 594 595 596
        program = Program()
        with program_guard(program):
            x = layers.data(name='x', shape=[3, 9, 6], dtype="float32")
            output = layers.resize_nearest(x, out_shape=[12, 12])
            self.assertIsNotNone(output)
            output = layers.resize_nearest(x, scale=3)
            self.assertIsNotNone(output)
        print(str(program))

597 598 599 600 601 602 603 604
    def test_polygon_box_transform(self):
        program = Program()
        with program_guard(program):
            x = layers.data(name='x', shape=[8, 4, 4], dtype="float32")
            output = layers.polygon_box_transform(input=x)
            self.assertIsNotNone(output)
        print(str(program))

605 606 607 608 609 610
    def test_l2_normalize(self):
        program = Program()
        with program_guard(program):
            x = layers.data(name='x', shape=[8, 7, 10], dtype="float32")
            output = layers.l2_normalize(x, axis=1)

Q
qingqing01 已提交
611 612 613 614 615 616 617 618
    def test_maxout(self):
        program = Program()
        with program_guard(program):
            data = layers.data(name='x', shape=[8, 6, 6], dtype="float32")
            output = layers.maxout(x=data, groups=2)
            self.assertIsNotNone(output)
        print(str(program))

W
whs 已提交
619
    def test_crop(self):
620 621 622 623 624 625 626 627
        program = Program()
        with program_guard(program):
            x = layers.data(name='x', shape=[3, 5], dtype="float32")
            y = layers.data(name='y', shape=[2, 3], dtype="float32")
            output = layers.crop(x, shape=y)
            self.assertIsNotNone(output)
        print(str(program))

W
whs 已提交
628 629 630 631 632 633 634 635 636
    def test_mean_iou(self):
        program = Program()
        with program_guard(program):
            x = layers.data(name='x', shape=[16], dtype='float32')
            y = layers.data(name='label', shape=[1], dtype='int64')
            iou = layers.mean_iou(x, y, 2)
            self.assertIsNotNone(iou)
        print(str(program))

637 638 639 640 641 642 643 644 645
    def test_argsort(self):
        program = Program()
        with program_guard(program):
            data = layers.data(name='x', shape=[2, 3, 3], dtype="float32")
            out, ids = layers.argsort(input=data, axis=1)
            self.assertIsNotNone(out)
            self.assertIsNotNone(ids)
        print(str(program))

646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667
    def test_rank_loss(self):
        program = Program()
        with program_guard(program):
            label = layers.data(
                name='label',
                append_batch_size=False,
                shape=[16, 1],
                dtype="float32")
            left = layers.data(
                name='left',
                append_batch_size=False,
                shape=[16, 1],
                dtype="float32")
            right = layers.data(
                name='right',
                append_batch_size=False,
                shape=[16, 1],
                dtype="float32")
            out = layers.rank_loss(label, left, right, name="rank_loss")
            self.assertIsNotNone(out)
        print(str(program))

668 669 670 671 672 673 674 675 676 677 678
    def test_flatten(self):
        program = Program()
        with program_guard(program):
            x = layers.data(
                name='x',
                append_batch_size=False,
                shape=[4, 4, 3],
                dtype="float32")
            out = layers.flatten(x, axis=1, name="flatten")
            self.assertIsNotNone(out)

B
Bai Yifan 已提交
679 680 681 682 683
    def test_shape(self):
        program = Program()
        with program_guard(program):
            input = layers.data(
                name="input", shape=[3, 100, 100], dtype="float32")
G
fix  
gongweibao 已提交
684
            out = layers.shape(input)
B
Bai Yifan 已提交
685 686 687
            self.assertIsNotNone(out)
        print(str(program))

W
whs 已提交
688 689 690 691 692
    def test_pad2d(self):
        program = Program()
        with program_guard(program):
            input = layers.data(
                name="input", shape=[3, 100, 100], dtype="float32")
693
            paddings = layers.fill_constant(shape=[4], dtype='int32', value=1)
W
whs 已提交
694 695 696 697 698 699
            out = layers.pad2d(
                input,
                paddings=[1, 2, 3, 4],
                mode='reflect',
                data_format='NCHW',
                name="shape")
700 701 702 703 704 705
            out_1 = layers.pad2d(
                input,
                paddings=paddings,
                mode='reflect',
                data_format='NCHW',
                name="shape")
W
whs 已提交
706
            self.assertIsNotNone(out)
707
            self.assertIsNotNone(out_1)
W
whs 已提交
708 709
        print(str(program))

J
jerrywgz 已提交
710 711 712 713 714 715 716 717 718 719 720 721 722 723
    def test_prelu(self):
        program = Program()
        with program_guard(program):
            input = layers.data(
                name="input", shape=[5, 200, 100, 100], dtype="float32")
            mode = 'channel'
            out = layers.prelu(
                input,
                mode,
                param_attr=ParamAttr(initializer=Constant(1.0)),
                name='prelu')
            self.assertIsNotNone(out)
        print(str(program))

T
tensor-tang 已提交
724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875
    def test_brelu(self):
        program = Program()
        with program_guard(program):
            input = layers.data(name="input", shape=[16], dtype="float32")
            out = layers.brelu(input, t_min=1.0, t_max=20.0, name='brelu')
            self.assertIsNotNone(out)
        print(str(program))

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

W
whs 已提交
876 877 878 879 880 881 882 883 884 885
    def test_roi_perspective_transform(self):
        program = Program()
        with program_guard(program):
            x = layers.data(name="x", shape=[256, 30, 30], dtype="float32")
            rois = layers.data(
                name="rois", shape=[8], dtype="float32", lod_level=1)
            output = layers.roi_perspective_transform(x, rois, 7, 7, 0.6)
            self.assertIsNotNone(output)
        print(str(program))

C
chenweihang 已提交
886 887 888
    def test_sequence_enumerate(self):
        program = Program()
        with program_guard(program):
C
chenweihang 已提交
889
            x = layers.data(name="input", shape=[1], dtype='int32', lod_level=1)
C
chenweihang 已提交
890 891 892
            out = layers.sequence_enumerate(input=x, win_size=2, pad_value=0)
        print(str(program))

893 894 895 896 897 898 899 900 901
    def test_cross_entropy(self):
        program = Program()
        with program_guard(program):
            x = layers.data(name="x", shape=[30, 10], dtype="float32")
            label = layers.data(name="label", shape=[30, 1], dtype="int32")
            mode = 'channel'
            out = layers.cross_entropy(x, label, False, 4)
            self.assertIsNotNone(out)

902 903 904 905 906 907 908 909 910
    def test_bpr_loss(self):
        program = Program()
        with program_guard(program):
            x = layers.data(name="x", shape=[30, 10], dtype="float32")
            label = layers.data(name="label", shape=[30, 1], dtype="int32")
            out = layers.bpr_loss(x, label)
            self.assertIsNotNone(out)
        print(str(program))

W
whs 已提交
911 912 913 914 915 916 917
    def test_expand(self):
        program = Program()
        with program_guard(program):
            x = layers.data(name="input", shape=[10], dtype='int32')
            out = layers.expand(x, [1, 2])
        print(str(program))

G
fix  
gongweibao 已提交
918
    def test_uniform_random_batch_size_like(self):
G
fix  
gongweibao 已提交
919 920 921 922 923
        program = Program()
        with program_guard(program):
            input = layers.data(name="input", shape=[13, 11], dtype='float32')
            out = layers.uniform_random_batch_size_like(input, [-1, 11])
            self.assertIsNotNone(out)
G
fix  
gongweibao 已提交
924
        print(str(program))
G
fix  
gongweibao 已提交
925 926 927 928 929 930

    def test_gaussian_random(self):
        program = Program()
        with program_guard(program):
            out = layers.gaussian_random(shape=[20, 30])
            self.assertIsNotNone(out)
G
fix  
gongweibao 已提交
931
        print(str(program))
G
fix  
gongweibao 已提交
932 933 934 935

    def test_sampling_id(self):
        program = Program()
        with program_guard(program):
G
fix  
gongweibao 已提交
936 937 938 939 940
            x = layers.data(
                name="X",
                shape=[13, 11],
                dtype='float32',
                append_batch_size=False)
G
fix  
gongweibao 已提交
941 942 943

            out = layers.sampling_id(x)
            self.assertIsNotNone(out)
G
fix  
gongweibao 已提交
944
        print(str(program))
G
fix  
gongweibao 已提交
945 946 947 948 949 950 951 952 953

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

            out = layers.gaussian_random_batch_size_like(
                input, shape=[-1, 11], mean=1.0, std=2.0)
            self.assertIsNotNone(out)
G
fix  
gongweibao 已提交
954
        print(str(program))
G
fix  
gongweibao 已提交
955 956 957 958 959 960 961 962

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

            out = layers.sum(input)
            self.assertIsNotNone(out)
G
fix  
gongweibao 已提交
963
        print(str(program))
G
fix  
gongweibao 已提交
964 965 966 967 968 969

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

G
fix  
gongweibao 已提交
970 971 972
        program = Program()
        with program_guard(program):
            input = layers.data(
G
fix  
gongweibao 已提交
973 974 975
                name="input", shape=[3, 4, 5, 6], dtype='float32')

            out = layers.slice(input, axes=axes, starts=starts, ends=ends)
G
merge  
gongweibao 已提交
976

B
baiyf 已提交
977 978 979 980 981
    def test_softshrink(self):
        program = Program()
        with program_guard(program):
            input = layers.data(name="input", shape=[16], dtype="float32")
            out = layers.softshrink(input, name='softshrink')
G
fix  
gongweibao 已提交
982
            self.assertIsNotNone(out)
G
fix  
gongweibao 已提交
983
        print(str(program))
G
fix  
gongweibao 已提交
984

X
Xin Pan 已提交
985 986 987 988 989 990 991 992 993
    def iou_similarity(self):
        program = Program()
        with program_guard(program):
            x = layers.data(name="x", shape=[16], dtype="float32")
            y = layers.data(name="y", shape=[16], dtype="float32")
            out = layers.iou_similarity(x, y, name='iou_similarity')
            self.assertIsNotNone(out)
        print(str(program))

994
    def test_grid_sampler(self):
D
dengkaipeng 已提交
995 996
        program = Program()
        with program_guard(program):
997 998
            x = layers.data(name='x', shape=[3, 5, 7], dtype='float32')
            grid = layers.data(name='grid', shape=[5, 7, 2], dtype='float32')
D
dengkaipeng 已提交
999 1000 1001
            out = layers.grid_sampler(x, grid)
            self.assertIsNotNone(out)
        print(str(program))
1002

W
whs 已提交
1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017
    def test_affine_grid(self):
        program = Program()
        with program_guard(program):
            data = layers.data(name='data', shape=[2, 3, 3], dtype="float32")
            out, ids = layers.argsort(input=data, axis=1)

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

            self.assertIsNotNone(data_0)
            self.assertIsNotNone(data_1)
        print(str(program))
D
dengkaipeng 已提交
1018

1019 1020 1021 1022 1023 1024 1025 1026 1027 1028
    def test_bilinear_tensor_product_layer(self):
        program = Program()
        with program_guard(program):
            data = layers.data(name='data', shape=[4], dtype="float32")

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

        print(str(program))

1029 1030 1031 1032 1033 1034 1035 1036 1037
    def test_batch_norm(self):
        program = Program()
        with program_guard(program):
            data = layers.data(
                name='data', shape=[32, 128, 128], dtype="float32")
            out = layers.batch_norm(data)

        print(str(program))

1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050
    def test_spectral_norm(self):
        program = Program()
        with program_guard(program):
            weight = layers.data(
                name='weight',
                shape=[2, 3, 32, 32],
                dtype="float32",
                append_batch_size=False)
            out = layers.spectral_norm(weight, dim=1, power_iters=1)
            self.assertIsNotNone(out)

        print(str(program))

S
shippingwang 已提交
1051 1052 1053
    def test_shuffle_channel(self):
        program = Program()
        with program_guard(program):
S
shippingwang 已提交
1054 1055
            x = layers.data(name="X", shape=[16, 4, 4], dtype="float32")
            out = layers.shuffle_channel(x, group=4)
S
shippingwang 已提交
1056 1057 1058
            self.assertIsNotNone(out)
        print(str(program))

Y
Yu Yang 已提交
1059 1060 1061

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