test_layers.py 20.2 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 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))

Y
yangyaming 已提交
188
    def test_sequence_expand(self):
Y
yangyaming 已提交
189 190 191 192
        program = Program()
        with program_guard(program):
            x = layers.data(name='x', shape=[10], dtype='float32')
            y = layers.data(
Y
yangyaming 已提交
193 194
                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
195 196
        print(str(program))

Y
yangyaming 已提交
197 198 199 200 201 202 203
    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
204 205
                name='prev_hidden_data', shape=[10, 30], dtype='float32')
            prev_hidden = layers.fc(input=prev_hidden_data, size=30)
Y
yangyaming 已提交
206 207 208 209 210 211 212 213
            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))

214 215 216 217 218 219 220 221 222 223 224 225
    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
226 227 228 229 230 231
    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)
232
            self.assertIsNotNone(layers.sequence_softmax(seq))
Y
yangyaming 已提交
233 234
        print(str(program))

D
dangqingqing 已提交
235 236 237 238 239
    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)
240
            self.assertIsNotNone(layers.softmax(hid))
D
dangqingqing 已提交
241 242
        print(str(program))

Y
Yibing Liu 已提交
243 244 245
    def test_sequence_unsqueeze(self):
        program = Program()
        with program_guard(program):
246
            x = layers.data(name='x', shape=[8, 2], dtype='float32')
247
            out = layers.unsqueeze(input=x, axes=[1])
Y
Yibing Liu 已提交
248 249
            self.assertIsNotNone(out)
        print(str(program))
250

Y
Yibing Liu 已提交
251 252 253 254
    def test_squeeze(self):
        program = Program()
        with program_guard(program):
            x = layers.data(name='x', shape=[1, 1, 4], dtype='float32')
255
            out = layers.squeeze(input=x, axes=[2])
Y
Yibing Liu 已提交
256 257 258
            self.assertIsNotNone(out)
        print(str(program))

D
dragonwarrior 已提交
259 260 261 262 263 264 265
    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
266 267 268
    def test_get_places(self):
        program = Program()
        with program_guard(program):
269
            x = get_places(device_count=4)
Y
Yang Yu 已提交
270
            self.assertIsNotNone(x)
Q
qijun 已提交
271 272
        print(str(program))

273 274 275 276 277 278 279 280
    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
281 282 283 284
    def test_im2sequence(self):
        program = Program()
        with program_guard(program):
            x = layers.data(name='x', shape=[3, 128, 128], dtype='float32')
285
            y = layers.data(name='y', shape=[], dtype='float32')
W
wanghaoshuang 已提交
286
            output = layers.im2sequence(
287 288 289 290 291
                input=x,
                input_image_size=y,
                stride=[1, 1],
                filter_size=[2, 2],
                out_stride=[1, 1])
W
wanghaoshuang 已提交
292 293 294
            self.assertIsNotNone(output)
        print(str(program))

Y
Yang Yu 已提交
295 296 297 298
    @decorators.prog_scope()
    def test_nce(self):
        window_size = 5
        words = []
299
        for i in range(window_size):
Y
Yang Yu 已提交
300 301 302 303 304
            words.append(
                layers.data(
                    name='word_{0}'.format(i), shape=[1], dtype='int64'))

        dict_size = 10000
M
minqiyang 已提交
305
        label_word = int(window_size // 2) + 1
Y
Yang Yu 已提交
306 307

        embs = []
308
        for i in range(window_size):
Y
Yang Yu 已提交
309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325
            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
326
        avg_loss = layers.mean(loss)
Y
Yang Yu 已提交
327 328 329
        self.assertIsNotNone(avg_loss)
        print(str(default_main_program()))

Y
yangyaming 已提交
330 331 332 333 334 335 336 337
    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))

338 339 340 341 342 343 344 345 346 347
    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))

348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365
    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')
            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))

366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384
    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))

Y
yangyaming 已提交
385 386 387 388 389 390 391 392 393
    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))

394 395 396 397 398 399 400 401 402 403
    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
404 405 406 407 408 409 410 411 412
    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))

413 414 415 416 417 418 419 420 421 422
    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))

B
baiyf 已提交
423
    def test_resize_bilinear(self):
424 425 426
        program = Program()
        with program_guard(program):
            x = layers.data(name='x', shape=[3, 9, 6], dtype="float32")
B
baiyf 已提交
427
            output = layers.resize_bilinear(x, out_shape=[12, 12])
428
            self.assertIsNotNone(output)
B
baiyf 已提交
429
            output = layers.resize_bilinear(x, scale=3)
430 431 432
            self.assertIsNotNone(output)
        print(str(program))

433 434 435 436 437 438 439 440
    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))

441 442 443 444 445 446
    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
447 448 449 450 451 452 453 454
    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
455
    def test_crop(self):
456 457 458 459 460 461 462 463
        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
464 465 466 467 468 469 470 471 472
    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))

473 474 475 476 477 478 479 480 481
    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))

482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503
    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))

504 505 506 507 508 509 510 511 512 513 514
    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
515 516 517 518 519 520 521 522 523
    def test_shape(self):
        program = Program()
        with program_guard(program):
            input = layers.data(
                name="input", shape=[3, 100, 100], dtype="float32")
            out = layers.shape(input, name="shape")
            self.assertIsNotNone(out)
        print(str(program))

J
jerrywgz 已提交
524 525 526 527 528 529 530 531 532 533 534 535 536 537
    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))

Y
Yu Yang 已提交
538 539 540

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