test_layers.py 35.3 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 61 62 63 64
        with program_guard(program, startup_program=Program()):
            images = layers.data(name='pixel', shape=[3, 48, 48], dtype='int32')
            layers.conv2d(input=images, num_filters=3, filter_size=[4, 4])

        print(str(program))
Y
Yu Yang 已提交
65

66 67
    def test_conv2d_transpose(self):
        program = Program()
Y
Yu Yang 已提交
68 69 70 71
        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))
72

F
fengjiayi 已提交
73
    def test_recognize_digits_conv(self):
F
fengjiayi 已提交
74
        program = Program()
Y
Yu Yang 已提交
75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95
        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
96
            avg_cost = layers.mean(cost)
Y
Yu Yang 已提交
97 98

        print(str(program))
99

Q
QI JUN 已提交
100 101
    def test_word_embedding(self):
        program = Program()
Y
Yu Yang 已提交
102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141
        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
142
            avg_cost = layers.mean(cost)
Y
Yu Yang 已提交
143 144 145
            self.assertIsNotNone(avg_cost)

        print(str(program))
Q
Qiao Longfei 已提交
146 147 148

    def test_linear_chain_crf(self):
        program = Program()
Y
Yu Yang 已提交
149
        with program_guard(program, startup_program=Program()):
Q
Qiao Longfei 已提交
150
            label_dict_len = 10
Y
Yu Yang 已提交
151 152 153
            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
154 155 156 157
            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
158 159 160 161
            layers.chunk_eval(
                input=crf_decode,
                label=label,
                chunk_scheme="IOB",
M
minqiyang 已提交
162
                num_chunk_types=(label_dict_len - 1) // 2)
Q
qiaolongfei 已提交
163 164
            self.assertFalse(crf is None)
            self.assertFalse(crf_decode is None)
Y
Yu Yang 已提交
165 166

        print(str(program))
Q
QI JUN 已提交
167

168 169 170 171 172 173 174 175 176 177
    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')
            self.assertIsNotNone(
                layers.sigmoid_cross_entropy_with_logits(
                    x=dat, label=lbl))
        print(str(program))

W
weixing02 已提交
178 179 180
    def test_hsigmoid(self):
        program = Program()
        with program_guard(program):
W
weixing02 已提交
181 182
            x = layers.data(name='x', shape=[2], dtype='float32')
            y = layers.data(name='y', shape=[2], dtype='int64')
W
weixing02 已提交
183 184 185 186 187
            self.assertIsNotNone(
                layers.hsigmoid(
                    input=x, label=y, num_classes=2))
        print(str(program))

J
JiabinYang 已提交
188
        # test hsigmod with custom tree structure
J
JiabinYang 已提交
189 190 191 192
        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')
193 194 195 196
            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
197 198 199 200 201
            self.assertIsNotNone(
                layers.hsigmoid(
                    input=x2,
                    label=y2,
                    non_leaf_num=6,
202 203 204
                    path_table=path_table,
                    path_code=path_code,
                    is_custom=True))
J
JiabinYang 已提交
205 206
            print(str(program2))

Y
yangyaming 已提交
207
    def test_sequence_expand(self):
Y
yangyaming 已提交
208 209 210 211
        program = Program()
        with program_guard(program):
            x = layers.data(name='x', shape=[10], dtype='float32')
            y = layers.data(
Y
yangyaming 已提交
212 213
                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
214 215
        print(str(program))

Y
Yibing Liu 已提交
216 217 218 219 220 221 222 223
    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
224 225 226 227
    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
228 229 230 231 232 233
            self.assertIsNotNone(
                layers.pool2d(
                    x,
                    pool_size=[5, 3],
                    pool_stride=[1, 2],
                    pool_padding=(2, 1)))
J
JiabinYang 已提交
234

Y
yangyaming 已提交
235 236 237 238 239 240 241
    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
242 243
                name='prev_hidden_data', shape=[10, 30], dtype='float32')
            prev_hidden = layers.fc(input=prev_hidden_data, size=30)
Y
yangyaming 已提交
244 245 246 247 248 249 250 251
            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))

252 253 254 255 256 257 258 259 260 261 262 263
    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
264 265 266 267 268 269
    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)
270
            self.assertIsNotNone(layers.sequence_softmax(seq))
Y
yangyaming 已提交
271 272
        print(str(program))

D
dangqingqing 已提交
273 274 275 276 277
    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)
278
            self.assertIsNotNone(layers.softmax(hid))
D
dangqingqing 已提交
279 280
        print(str(program))

J
JiabinYang 已提交
281
    def test_space_to_depth(self):
J
JiabinYang 已提交
282 283 284
        program = Program()
        with program_guard(program):
            data = layers.data(
J
JiabinYang 已提交
285
                name='data',
J
JiabinYang 已提交
286 287 288
                shape=[32, 9, 6, 6],
                append_batch_size=False,
                dtype='float32')
J
JiabinYang 已提交
289
            self.assertIsNotNone(layers.space_to_depth(data, 3))
J
JiabinYang 已提交
290 291
        print(str(program))

Y
Yibing Liu 已提交
292 293 294
    def test_sequence_unsqueeze(self):
        program = Program()
        with program_guard(program):
295
            x = layers.data(name='x', shape=[8, 2], dtype='float32')
296
            out = layers.unsqueeze(input=x, axes=[1])
Y
Yibing Liu 已提交
297 298
            self.assertIsNotNone(out)
        print(str(program))
299

Y
Yibing Liu 已提交
300 301 302 303
    def test_squeeze(self):
        program = Program()
        with program_guard(program):
            x = layers.data(name='x', shape=[1, 1, 4], dtype='float32')
304
            out = layers.squeeze(input=x, axes=[2])
Y
Yibing Liu 已提交
305 306 307
            self.assertIsNotNone(out)
        print(str(program))

D
dragonwarrior 已提交
308 309 310 311 312 313 314
    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
315 316 317
    def test_get_places(self):
        program = Program()
        with program_guard(program):
318
            x = get_places(device_count=4)
Y
Yang Yu 已提交
319
            self.assertIsNotNone(x)
Q
qijun 已提交
320 321
        print(str(program))

322 323 324 325 326 327 328 329
    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
330 331 332 333
    def test_im2sequence(self):
        program = Program()
        with program_guard(program):
            x = layers.data(name='x', shape=[3, 128, 128], dtype='float32')
334
            y = layers.data(name='y', shape=[], dtype='float32')
W
wanghaoshuang 已提交
335
            output = layers.im2sequence(
336 337 338 339 340
                input=x,
                input_image_size=y,
                stride=[1, 1],
                filter_size=[2, 2],
                out_stride=[1, 1])
W
wanghaoshuang 已提交
341 342 343
            self.assertIsNotNone(output)
        print(str(program))

Y
Yang Yu 已提交
344 345 346 347
    @decorators.prog_scope()
    def test_nce(self):
        window_size = 5
        words = []
348
        for i in range(window_size):
Y
Yang Yu 已提交
349 350 351 352 353
            words.append(
                layers.data(
                    name='word_{0}'.format(i), shape=[1], dtype='int64'))

        dict_size = 10000
M
minqiyang 已提交
354
        label_word = int(window_size // 2) + 1
Y
Yang Yu 已提交
355 356

        embs = []
357
        for i in range(window_size):
Y
Yang Yu 已提交
358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374
            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
375
        avg_loss = layers.mean(loss)
Y
Yang Yu 已提交
376 377 378
        self.assertIsNotNone(avg_loss)
        print(str(default_main_program()))

Y
yangyaming 已提交
379 380 381 382 383 384 385 386
    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))

387 388 389 390 391 392 393 394 395 396
    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))

397 398 399 400 401
    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')
402 403 404 405
            loss, softmax = layers.softmax_with_cross_entropy(
                x, y, return_softmax=True)
            self.assertIsNotNone(loss)
            self.assertIsNotNone(softmax)
406 407 408 409 410 411 412 413 414 415 416 417 418
            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))

419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437
    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
438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461
    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
462 463 464 465 466 467 468 469 470 471 472 473 474
    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
475 476 477 478 479 480 481 482 483
    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))

484 485 486 487 488 489 490 491 492 493
    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
494 495 496 497 498 499 500 501 502
    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))

503 504 505 506 507 508 509 510 511 512
    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))

J
jerrywgz 已提交
513 514 515 516 517 518 519 520 521 522
    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
523
    def test_resize_bilinear(self):
524 525 526
        program = Program()
        with program_guard(program):
            x = layers.data(name='x', shape=[3, 9, 6], dtype="float32")
B
baiyf 已提交
527
            output = layers.resize_bilinear(x, out_shape=[12, 12])
528
            self.assertIsNotNone(output)
B
baiyf 已提交
529
            output = layers.resize_bilinear(x, scale=3)
530 531 532
            self.assertIsNotNone(output)
        print(str(program))

533
    def test_resize_nearest(self):
534 535 536 537 538 539 540 541 542
        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))

543 544 545 546 547 548 549 550
    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))

551 552 553 554 555 556
    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
557 558 559 560 561 562 563 564
    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
565
    def test_crop(self):
566 567 568 569 570 571 572 573
        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
574 575 576 577 578 579 580 581 582
    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))

583 584 585 586 587 588 589 590 591
    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))

592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613
    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))

614 615 616 617 618 619 620 621 622 623 624
    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
625 626 627 628 629
    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
630
            out = layers.shape(input)
B
Bai Yifan 已提交
631 632 633
            self.assertIsNotNone(out)
        print(str(program))

W
whs 已提交
634 635 636 637 638 639 640 641 642 643 644 645 646 647
    def test_pad2d(self):
        program = Program()
        with program_guard(program):
            input = layers.data(
                name="input", shape=[3, 100, 100], dtype="float32")
            out = layers.pad2d(
                input,
                paddings=[1, 2, 3, 4],
                mode='reflect',
                data_format='NCHW',
                name="shape")
            self.assertIsNotNone(out)
        print(str(program))

J
jerrywgz 已提交
648 649 650 651 652 653 654 655 656 657 658 659 660 661
    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
662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813
    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
814 815 816 817 818 819 820 821 822 823
    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
824 825 826
    def test_sequence_enumerate(self):
        program = Program()
        with program_guard(program):
C
chenweihang 已提交
827
            x = layers.data(name="input", shape=[1], dtype='int32', lod_level=1)
C
chenweihang 已提交
828 829 830
            out = layers.sequence_enumerate(input=x, win_size=2, pad_value=0)
        print(str(program))

831 832 833 834 835 836 837 838 839
    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)

W
whs 已提交
840 841 842 843 844 845 846
    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
847
    def test_uniform_random_batch_size_like(self):
G
fix  
gongweibao 已提交
848 849 850 851 852
        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
853
        print(str(program))
G
fix  
gongweibao 已提交
854 855 856 857 858 859

    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
860
        print(str(program))
G
fix  
gongweibao 已提交
861 862 863 864

    def test_sampling_id(self):
        program = Program()
        with program_guard(program):
G
fix  
gongweibao 已提交
865 866 867 868 869
            x = layers.data(
                name="X",
                shape=[13, 11],
                dtype='float32',
                append_batch_size=False)
G
fix  
gongweibao 已提交
870 871 872

            out = layers.sampling_id(x)
            self.assertIsNotNone(out)
G
fix  
gongweibao 已提交
873
        print(str(program))
G
fix  
gongweibao 已提交
874 875 876 877 878 879 880 881 882

    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
883
        print(str(program))
G
fix  
gongweibao 已提交
884 885 886 887 888 889 890 891

    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
892
        print(str(program))
G
fix  
gongweibao 已提交
893 894 895 896 897 898

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

G
fix  
gongweibao 已提交
899 900 901
        program = Program()
        with program_guard(program):
            input = layers.data(
G
fix  
gongweibao 已提交
902 903 904
                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
905

B
baiyf 已提交
906 907 908 909 910
    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
911
            self.assertIsNotNone(out)
G
fix  
gongweibao 已提交
912
        print(str(program))
G
fix  
gongweibao 已提交
913

X
Xin Pan 已提交
914 915 916 917 918 919 920 921 922
    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))

923
    def test_grid_sampler(self):
D
dengkaipeng 已提交
924 925
        program = Program()
        with program_guard(program):
926 927
            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
928 929 930
            out = layers.grid_sampler(x, grid)
            self.assertIsNotNone(out)
        print(str(program))
931

W
whs 已提交
932 933 934 935 936 937 938 939 940 941 942 943 944 945 946
    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
947

948 949 950 951 952 953 954 955 956 957
    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))

Y
Yu Yang 已提交
958 959 960

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