test_layers.py 15.9 KB
Newer Older
1
#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
D
dzhwinter 已提交
2
#
D
dzhwinter 已提交
3 4 5
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
D
dzhwinter 已提交
6
#
D
dzhwinter 已提交
7
#     http://www.apache.org/licenses/LICENSE-2.0
D
dzhwinter 已提交
8
#
D
dzhwinter 已提交
9 10 11 12 13 14
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

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

18 19 20 21
import paddle.fluid.layers as layers
import paddle.fluid.nets as nets
from paddle.fluid.framework import Program, program_guard, default_main_program
from paddle.fluid.param_attr import ParamAttr
Y
Yang Yu 已提交
22
import decorators
Y
Yu Yang 已提交
23 24 25 26


class TestBook(unittest.TestCase):
    def test_fit_a_line(self):
27
        program = Program()
Y
Yu Yang 已提交
28 29 30 31 32
        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
33
            avg_cost = layers.mean(cost)
Y
Yu Yang 已提交
34
            self.assertIsNotNone(avg_cost)
Y
Yu Yang 已提交
35

Y
Yu Yang 已提交
36
        print(str(program))
Y
Yu Yang 已提交
37 38

    def test_recognize_digits_mlp(self):
39
        program = Program()
Y
Yu Yang 已提交
40 41 42 43 44 45
        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')
46 47 48 49
            predict = layers.fc(input=[hidden2, hidden1],
                                size=10,
                                act='softmax',
                                param_attr=["sftmax.w1", "sftmax.w2"])
Y
Yu Yang 已提交
50
            cost = layers.cross_entropy(input=predict, label=label)
Y
Yu Yang 已提交
51
            avg_cost = layers.mean(cost)
Y
Yu Yang 已提交
52 53 54
            self.assertIsNotNone(avg_cost)

        print(str(program))
55 56

    def test_simple_conv2d(self):
F
fengjiayi 已提交
57
        program = Program()
Y
Yu Yang 已提交
58 59 60 61 62
        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
63

64 65
    def test_conv2d_transpose(self):
        program = Program()
Y
Yu Yang 已提交
66 67 68 69
        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))
70

F
fengjiayi 已提交
71
    def test_recognize_digits_conv(self):
F
fengjiayi 已提交
72
        program = Program()
Y
Yu Yang 已提交
73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93
        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
94
            avg_cost = layers.mean(cost)
Y
Yu Yang 已提交
95 96

        print(str(program))
97

Q
QI JUN 已提交
98 99
    def test_word_embedding(self):
        program = Program()
Y
Yu Yang 已提交
100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139
        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
140
            avg_cost = layers.mean(cost)
Y
Yu Yang 已提交
141 142 143
            self.assertIsNotNone(avg_cost)

        print(str(program))
Q
Qiao Longfei 已提交
144 145 146

    def test_linear_chain_crf(self):
        program = Program()
Y
Yu Yang 已提交
147
        with program_guard(program, startup_program=Program()):
Q
Qiao Longfei 已提交
148
            label_dict_len = 10
Y
Yu Yang 已提交
149 150 151
            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
152 153 154 155
            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
156 157 158 159 160
            layers.chunk_eval(
                input=crf_decode,
                label=label,
                chunk_scheme="IOB",
                num_chunk_types=(label_dict_len - 1) / 2)
Q
qiaolongfei 已提交
161 162
            self.assertFalse(crf is None)
            self.assertFalse(crf_decode is None)
Y
Yu Yang 已提交
163 164

        print(str(program))
Q
QI JUN 已提交
165

166 167 168 169 170 171 172 173 174 175
    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
176 177 178 179
    def test_hsigmoid(self):
        program = Program()
        with program_guard(program):
            x = layers.data(name='x', shape=[2, 2], dtype='float32')
W
weixing02 已提交
180
            y = layers.data(name='y', shape=[1, 2], dtype='int64')
W
weixing02 已提交
181 182 183 184 185
            self.assertIsNotNone(
                layers.hsigmoid(
                    input=x, label=y, num_classes=2))
        print(str(program))

Y
yangyaming 已提交
186
    def test_sequence_expand(self):
Y
yangyaming 已提交
187 188 189 190
        program = Program()
        with program_guard(program):
            x = layers.data(name='x', shape=[10], dtype='float32')
            y = layers.data(
Y
yangyaming 已提交
191 192
                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
193 194
        print(str(program))

Y
yangyaming 已提交
195 196 197 198 199 200 201
    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
202 203
                name='prev_hidden_data', shape=[10, 30], dtype='float32')
            prev_hidden = layers.fc(input=prev_hidden_data, size=30)
Y
yangyaming 已提交
204 205 206 207 208 209 210 211
            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))

212 213 214 215 216 217 218 219 220 221 222 223
    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
224 225 226 227 228 229
    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)
230
            self.assertIsNotNone(layers.sequence_softmax(seq))
Y
yangyaming 已提交
231 232
        print(str(program))

D
dangqingqing 已提交
233 234 235 236 237
    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)
238
            self.assertIsNotNone(layers.softmax(hid))
D
dangqingqing 已提交
239 240
        print(str(program))

D
dragonwarrior 已提交
241 242 243 244 245 246 247
    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
248 249 250
    def test_get_places(self):
        program = Program()
        with program_guard(program):
Q
qijun 已提交
251
            x = layers.get_places(device_count=4)
Y
Yang Yu 已提交
252
            self.assertIsNotNone(x)
Q
qijun 已提交
253 254
        print(str(program))

255 256 257 258 259 260 261 262
    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
263 264 265 266 267 268 269 270 271 272
    def test_im2sequence(self):
        print("test_im2sequence")
        program = Program()
        with program_guard(program):
            x = layers.data(name='x', shape=[3, 128, 128], dtype='float32')
            output = layers.im2sequence(
                input=x, stride=[1, 1], filter_size=[2, 2])
            self.assertIsNotNone(output)
        print(str(program))

Y
Yang Yu 已提交
273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303
    @decorators.prog_scope()
    def test_nce(self):
        window_size = 5
        words = []
        for i in xrange(window_size):
            words.append(
                layers.data(
                    name='word_{0}'.format(i), shape=[1], dtype='int64'))

        dict_size = 10000
        label_word = int(window_size / 2) + 1

        embs = []
        for i in xrange(window_size):
            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
304
        avg_loss = layers.mean(loss)
Y
Yang Yu 已提交
305 306 307
        self.assertIsNotNone(avg_loss)
        print(str(default_main_program()))

Y
yangyaming 已提交
308 309 310 311 312 313 314 315
    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))

316 317 318 319 320 321 322 323 324 325
    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))

326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343
    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))

Y
yangyaming 已提交
344 345 346 347 348 349 350 351 352
    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))

353 354 355 356 357 358 359 360 361 362
    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
363 364 365 366 367 368 369 370 371
    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))

372 373 374 375 376 377 378 379 380 381
    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
382
    def test_resize_bilinear(self):
383 384 385
        program = Program()
        with program_guard(program):
            x = layers.data(name='x', shape=[3, 9, 6], dtype="float32")
B
baiyf 已提交
386
            output = layers.resize_bilinear(x, out_shape=[12, 12])
387
            self.assertIsNotNone(output)
B
baiyf 已提交
388
            output = layers.resize_bilinear(x, scale=3)
389 390 391
            self.assertIsNotNone(output)
        print(str(program))

392 393 394 395 396 397 398 399
    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))

400 401 402 403 404 405
    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
406 407 408 409 410
    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)
411 412 413
            self.assertIsNotNone(output)
        print(str(program))

Y
Yu Yang 已提交
414 415 416

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