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1038 lines
38 KiB
1038 lines
38 KiB
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import print_function
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import unittest
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import paddle.fluid.layers as layers
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from paddle.fluid.layers.device import get_places
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import paddle.fluid.nets as nets
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from paddle.fluid.framework import Program, program_guard, default_main_program
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from paddle.fluid.param_attr import ParamAttr
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import decorators
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from paddle.fluid.initializer import Constant
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class TestBook(unittest.TestCase):
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def test_fit_a_line(self):
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program = Program()
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with program_guard(program, startup_program=Program()):
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x = layers.data(name='x', shape=[13], dtype='float32')
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y_predict = layers.fc(input=x, size=1, act=None)
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y = layers.data(name='y', shape=[1], dtype='float32')
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cost = layers.square_error_cost(input=y_predict, label=y)
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avg_cost = layers.mean(cost)
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self.assertIsNotNone(avg_cost)
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print(str(program))
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def test_recognize_digits_mlp(self):
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program = Program()
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with program_guard(program, startup_program=Program()):
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# Change g_program, so the rest layers use `g_program`
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images = layers.data(name='pixel', shape=[784], dtype='float32')
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label = layers.data(name='label', shape=[1], dtype='int32')
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hidden1 = layers.fc(input=images, size=128, act='relu')
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hidden2 = layers.fc(input=hidden1, size=64, act='relu')
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predict = layers.fc(input=[hidden2, hidden1],
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size=10,
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act='softmax',
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param_attr=["sftmax.w1", "sftmax.w2"])
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cost = layers.cross_entropy(input=predict, label=label)
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avg_cost = layers.mean(cost)
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self.assertIsNotNone(avg_cost)
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print(str(program))
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def test_simple_conv2d(self):
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program = Program()
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with program_guard(program, startup_program=Program()):
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images = layers.data(
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name='pixel', shape=[3, 48, 48], dtype='float32')
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layers.conv2d(input=images, num_filters=3, filter_size=[4, 4])
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print(str(program))
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def test_conv2d_transpose(self):
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program = Program()
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with program_guard(program):
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img = layers.data(name='pixel', shape=[3, 2, 2], dtype='float32')
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layers.conv2d_transpose(input=img, num_filters=10, output_size=28)
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print(str(program))
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def test_recognize_digits_conv(self):
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program = Program()
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with program_guard(program, startup_program=Program()):
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images = layers.data(
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name='pixel', shape=[1, 28, 28], dtype='float32')
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label = layers.data(name='label', shape=[1], dtype='int32')
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conv_pool_1 = nets.simple_img_conv_pool(
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input=images,
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filter_size=5,
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num_filters=2,
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pool_size=2,
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pool_stride=2,
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act="relu")
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conv_pool_2 = nets.simple_img_conv_pool(
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input=conv_pool_1,
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filter_size=5,
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num_filters=4,
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pool_size=2,
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pool_stride=2,
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act="relu")
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predict = layers.fc(input=conv_pool_2, size=10, act="softmax")
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cost = layers.cross_entropy(input=predict, label=label)
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avg_cost = layers.mean(cost)
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print(str(program))
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def test_word_embedding(self):
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program = Program()
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with program_guard(program, startup_program=Program()):
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dict_size = 10000
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embed_size = 32
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first_word = layers.data(name='firstw', shape=[1], dtype='int64')
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second_word = layers.data(name='secondw', shape=[1], dtype='int64')
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third_word = layers.data(name='thirdw', shape=[1], dtype='int64')
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forth_word = layers.data(name='forthw', shape=[1], dtype='int64')
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next_word = layers.data(name='nextw', shape=[1], dtype='int64')
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embed_first = layers.embedding(
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input=first_word,
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size=[dict_size, embed_size],
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dtype='float32',
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param_attr='shared_w')
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embed_second = layers.embedding(
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input=second_word,
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size=[dict_size, embed_size],
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dtype='float32',
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param_attr='shared_w')
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embed_third = layers.embedding(
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input=third_word,
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size=[dict_size, embed_size],
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dtype='float32',
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param_attr='shared_w')
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embed_forth = layers.embedding(
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input=forth_word,
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size=[dict_size, embed_size],
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dtype='float32',
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param_attr='shared_w')
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concat_embed = layers.concat(
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input=[embed_first, embed_second, embed_third, embed_forth],
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axis=1)
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hidden1 = layers.fc(input=concat_embed, size=256, act='sigmoid')
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predict_word = layers.fc(input=hidden1,
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size=dict_size,
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act='softmax')
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cost = layers.cross_entropy(input=predict_word, label=next_word)
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avg_cost = layers.mean(cost)
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self.assertIsNotNone(avg_cost)
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print(str(program))
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def test_linear_chain_crf(self):
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program = Program()
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with program_guard(program, startup_program=Program()):
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label_dict_len = 10
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images = layers.data(name='pixel', shape=[784], dtype='float32')
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label = layers.data(name='label', shape=[1], dtype='int32')
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hidden = layers.fc(input=images, size=128)
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crf = layers.linear_chain_crf(
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input=hidden, label=label, param_attr=ParamAttr(name="crfw"))
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crf_decode = layers.crf_decoding(
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input=hidden, param_attr=ParamAttr(name="crfw"))
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layers.chunk_eval(
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input=crf_decode,
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label=label,
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chunk_scheme="IOB",
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num_chunk_types=(label_dict_len - 1) // 2)
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self.assertFalse(crf is None)
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self.assertFalse(crf_decode is None)
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print(str(program))
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def test_sigmoid_cross_entropy(self):
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program = Program()
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with program_guard(program):
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dat = layers.data(name='data', shape=[10], dtype='float32')
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lbl = layers.data(name='label', shape=[10], dtype='float32')
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ignore_index = -1
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self.assertIsNotNone(
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layers.sigmoid_cross_entropy_with_logits(
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x=dat, label=lbl, ignore_index=ignore_index))
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print(str(program))
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def test_hsigmoid(self):
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program = Program()
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with program_guard(program):
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x = layers.data(name='x', shape=[2], dtype='float32')
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y = layers.data(name='y', shape=[2], dtype='int64')
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self.assertIsNotNone(
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layers.hsigmoid(
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input=x, label=y, num_classes=2))
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print(str(program))
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# test hsigmod with custom tree structure
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program2 = Program()
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with program_guard(program2):
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x2 = layers.data(name='x2', shape=[4, 8], dtype='float32')
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y2 = layers.data(name='y2', shape=[4], dtype='int64')
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path_table = layers.data(
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name='path_table', shape=[4, 6], dtype='int64')
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path_code = layers.data(
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name='path_code', shape=[4, 6], dtype='int64')
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self.assertIsNotNone(
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layers.hsigmoid(
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input=x2,
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label=y2,
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num_classes=6,
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path_table=path_table,
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path_code=path_code,
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is_custom=True))
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print(str(program2))
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def test_sequence_expand(self):
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program = Program()
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with program_guard(program):
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x = layers.data(name='x', shape=[10], dtype='float32')
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y = layers.data(
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name='y', shape=[10, 20], dtype='float32', lod_level=2)
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self.assertIsNotNone(layers.sequence_expand(x=x, y=y, ref_level=1))
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print(str(program))
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def test_sequence_unpad(self):
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program = Program()
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with program_guard(program):
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x = layers.data(name='x', shape=[10, 5], dtype='float32')
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length = layers.data(name='length', shape=[1], dtype='int64')
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self.assertIsNotNone(layers.sequence_unpad(x=x, length=length))
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print(str(program))
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def test_pool2d(self):
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program = Program()
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with program_guard(program):
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x = layers.data(name='x', shape=[3, 224, 224], dtype='float32')
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self.assertIsNotNone(
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layers.pool2d(
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x,
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pool_size=[5, 3],
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pool_stride=[1, 2],
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pool_padding=(2, 1)))
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def test_adaptive_pool2d(self):
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program = Program()
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with program_guard(program):
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x = layers.data(name='x', shape=[3, 224, 224], dtype='float32')
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self.assertIsNotNone(
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layers.adaptive_pool2d(
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x, [3, 3], pool_type='avg'))
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pool, mask = layers.adaptive_pool2d(x, [3, 3], require_index=True)
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self.assertIsNotNone(pool)
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self.assertIsNotNone(mask)
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self.assertIsNotNone(layers.adaptive_pool2d(x, 3, pool_type='avg'))
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pool, mask = layers.adaptive_pool2d(x, 3, require_index=True)
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self.assertIsNotNone(pool)
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self.assertIsNotNone(mask)
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def test_adaptive_pool3d(self):
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program = Program()
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with program_guard(program):
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x = layers.data(name='x', shape=[3, 244, 224, 224], dtype='float32')
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self.assertIsNotNone(
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layers.adaptive_pool3d(
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x, [3, 3, 3], pool_type='avg'))
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pool, mask = layers.adaptive_pool3d(
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x, [3, 3, 3], require_index=True)
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self.assertIsNotNone(pool)
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self.assertIsNotNone(mask)
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self.assertIsNotNone(layers.adaptive_pool3d(x, 3, pool_type='avg'))
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pool, mask = layers.adaptive_pool3d(x, 3, require_index=True)
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self.assertIsNotNone(pool)
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self.assertIsNotNone(mask)
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def test_lstm_unit(self):
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program = Program()
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with program_guard(program):
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x_t_data = layers.data(
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name='x_t_data', shape=[10, 10], dtype='float32')
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x_t = layers.fc(input=x_t_data, size=10)
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prev_hidden_data = layers.data(
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name='prev_hidden_data', shape=[10, 30], dtype='float32')
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prev_hidden = layers.fc(input=prev_hidden_data, size=30)
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prev_cell_data = layers.data(
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name='prev_cell', shape=[10, 30], dtype='float32')
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prev_cell = layers.fc(input=prev_cell_data, size=30)
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self.assertIsNotNone(
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layers.lstm_unit(
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x_t=x_t, hidden_t_prev=prev_hidden, cell_t_prev=prev_cell))
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print(str(program))
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def test_dynamic_lstmp(self):
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program = Program()
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with program_guard(program):
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hidden_dim, proj_dim = 16, 8
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seq_data = layers.data(
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name='seq_data', shape=[10, 10], dtype='float32', lod_level=1)
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fc_out = layers.fc(input=seq_data, size=4 * hidden_dim)
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self.assertIsNotNone(
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layers.dynamic_lstmp(
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input=fc_out, size=4 * hidden_dim, proj_size=proj_dim))
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print(str(program))
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def test_sequence_softmax(self):
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program = Program()
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with program_guard(program):
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seq_data = layers.data(
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name='seq_data', shape=[10, 10], dtype='float32', lod_level=1)
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seq = layers.fc(input=seq_data, size=20)
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self.assertIsNotNone(layers.sequence_softmax(seq))
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print(str(program))
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def test_softmax(self):
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program = Program()
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with program_guard(program):
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data = layers.data(name='data', shape=[10], dtype='float32')
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hid = layers.fc(input=data, size=20)
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self.assertIsNotNone(layers.softmax(hid))
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print(str(program))
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def test_space_to_depth(self):
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program = Program()
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with program_guard(program):
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data = layers.data(
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name='data',
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shape=[32, 9, 6, 6],
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append_batch_size=False,
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dtype='float32')
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self.assertIsNotNone(layers.space_to_depth(data, 3))
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print(str(program))
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def test_sequence_unsqueeze(self):
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program = Program()
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with program_guard(program):
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x = layers.data(name='x', shape=[8, 2], dtype='float32')
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out = layers.unsqueeze(input=x, axes=[1])
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self.assertIsNotNone(out)
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print(str(program))
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def test_squeeze(self):
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program = Program()
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with program_guard(program):
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x = layers.data(name='x', shape=[1, 1, 4], dtype='float32')
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out = layers.squeeze(input=x, axes=[2])
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self.assertIsNotNone(out)
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print(str(program))
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def test_lrn(self):
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program = Program()
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with program_guard(program):
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data = layers.data(name='data', shape=[6, 2, 2], dtype='float32')
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self.assertIsNotNone(layers.lrn(data))
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print(str(program))
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def test_get_places(self):
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program = Program()
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with program_guard(program):
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x = get_places(device_count=4)
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self.assertIsNotNone(x)
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print(str(program))
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def test_sequence_reshape(self):
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program = Program()
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with program_guard(program):
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x = layers.data(name='x', shape=[8], dtype='float32', lod_level=1)
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out = layers.sequence_reshape(input=x, new_dim=16)
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self.assertIsNotNone(out)
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print(str(program))
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def test_im2sequence(self):
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program = Program()
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with program_guard(program):
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x = layers.data(name='x', shape=[3, 128, 128], dtype='float32')
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y = layers.data(name='y', shape=[], dtype='float32')
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output = layers.im2sequence(
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input=x,
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input_image_size=y,
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stride=[1, 1],
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filter_size=[2, 2],
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out_stride=[1, 1])
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self.assertIsNotNone(output)
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print(str(program))
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@decorators.prog_scope()
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def test_nce(self):
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window_size = 5
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words = []
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for i in range(window_size):
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words.append(
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layers.data(
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name='word_{0}'.format(i), shape=[1], dtype='int64'))
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dict_size = 10000
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label_word = int(window_size // 2) + 1
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embs = []
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for i in range(window_size):
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if i == label_word:
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continue
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emb = layers.embedding(
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input=words[i],
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size=[dict_size, 32],
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param_attr='emb.w',
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is_sparse=True)
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embs.append(emb)
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embs = layers.concat(input=embs, axis=1)
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loss = layers.nce(input=embs,
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label=words[label_word],
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num_total_classes=dict_size,
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param_attr='nce.w',
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bias_attr='nce.b')
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avg_loss = layers.mean(loss)
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self.assertIsNotNone(avg_loss)
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print(str(default_main_program()))
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def test_row_conv(self):
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program = Program()
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with program_guard(program):
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x = layers.data(name='x', shape=[16], dtype='float32', lod_level=1)
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out = layers.row_conv(input=x, future_context_size=2)
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self.assertIsNotNone(out)
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print(str(program))
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def test_multiplex(self):
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program = Program()
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with program_guard(program):
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x1 = layers.data(name='x1', shape=[4], dtype='float32')
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x2 = layers.data(name='x2', shape=[4], dtype='float32')
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index = layers.data(name='index', shape=[1], dtype='int32')
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out = layers.multiplex(inputs=[x1, x2], index=index)
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self.assertIsNotNone(out)
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print(str(program))
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def test_softmax_with_cross_entropy(self):
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program = Program()
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with program_guard(program):
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x = layers.data(name='x', shape=[16], dtype='float32')
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y = layers.data(name='label', shape=[1], dtype='int64')
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loss, softmax = layers.softmax_with_cross_entropy(
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x, y, return_softmax=True)
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self.assertIsNotNone(loss)
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self.assertIsNotNone(softmax)
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loss = layers.softmax_with_cross_entropy(x, y)
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self.assertIsNotNone(loss)
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print(str(program))
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def test_smooth_l1(self):
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program = Program()
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with program_guard(program):
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x = layers.data(name='x', shape=[4], dtype='float32')
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y = layers.data(name='label', shape=[4], dtype='float32')
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loss = layers.smooth_l1(x, y)
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self.assertIsNotNone(loss)
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print(str(program))
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def test_scatter(self):
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program = Program()
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with program_guard(program):
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x = layers.data(
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name='x',
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shape=[3, 3],
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append_batch_size=False,
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dtype='float32')
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idx = layers.data(
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name='idx', shape=[2], append_batch_size=False, dtype='int32')
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updates = layers.data(
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name='updates',
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shape=[2, 3],
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append_batch_size=False,
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dtype='float32')
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out = layers.scatter(input=x, index=idx, updates=updates)
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self.assertIsNotNone(out)
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print(str(program))
|
|
|
|
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))
|
|
|
|
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))
|
|
|
|
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))
|
|
|
|
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))
|
|
|
|
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))
|
|
|
|
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))
|
|
|
|
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))
|
|
|
|
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))
|
|
|
|
def test_resize_bilinear(self):
|
|
program = Program()
|
|
with program_guard(program):
|
|
x = layers.data(name='x', shape=[3, 9, 6], dtype="float32")
|
|
output = layers.resize_bilinear(x, out_shape=[12, 12])
|
|
self.assertIsNotNone(output)
|
|
output = layers.resize_bilinear(x, scale=3)
|
|
self.assertIsNotNone(output)
|
|
print(str(program))
|
|
|
|
def test_resize_nearest(self):
|
|
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))
|
|
|
|
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))
|
|
|
|
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)
|
|
|
|
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))
|
|
|
|
def test_crop(self):
|
|
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))
|
|
|
|
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))
|
|
|
|
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))
|
|
|
|
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))
|
|
|
|
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)
|
|
|
|
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)
|
|
self.assertIsNotNone(out)
|
|
print(str(program))
|
|
|
|
def test_pad2d(self):
|
|
program = Program()
|
|
with program_guard(program):
|
|
input = layers.data(
|
|
name="input", shape=[3, 100, 100], dtype="float32")
|
|
paddings = layers.fill_constant(shape=[4], dtype='int32', value=1)
|
|
out = layers.pad2d(
|
|
input,
|
|
paddings=[1, 2, 3, 4],
|
|
mode='reflect',
|
|
data_format='NCHW',
|
|
name="shape")
|
|
out_1 = layers.pad2d(
|
|
input,
|
|
paddings=paddings,
|
|
mode='reflect',
|
|
data_format='NCHW',
|
|
name="shape")
|
|
self.assertIsNotNone(out)
|
|
self.assertIsNotNone(out_1)
|
|
print(str(program))
|
|
|
|
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))
|
|
|
|
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))
|
|
|
|
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))
|
|
|
|
def test_sequence_enumerate(self):
|
|
program = Program()
|
|
with program_guard(program):
|
|
x = layers.data(name="input", shape=[1], dtype='int32', lod_level=1)
|
|
out = layers.sequence_enumerate(input=x, win_size=2, pad_value=0)
|
|
print(str(program))
|
|
|
|
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)
|
|
|
|
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))
|
|
|
|
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))
|
|
|
|
def test_uniform_random_batch_size_like(self):
|
|
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)
|
|
print(str(program))
|
|
|
|
def test_gaussian_random(self):
|
|
program = Program()
|
|
with program_guard(program):
|
|
out = layers.gaussian_random(shape=[20, 30])
|
|
self.assertIsNotNone(out)
|
|
print(str(program))
|
|
|
|
def test_sampling_id(self):
|
|
program = Program()
|
|
with program_guard(program):
|
|
x = layers.data(
|
|
name="X",
|
|
shape=[13, 11],
|
|
dtype='float32',
|
|
append_batch_size=False)
|
|
|
|
out = layers.sampling_id(x)
|
|
self.assertIsNotNone(out)
|
|
print(str(program))
|
|
|
|
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)
|
|
print(str(program))
|
|
|
|
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)
|
|
print(str(program))
|
|
|
|
def test_slice(self):
|
|
starts = [1, 0, 2]
|
|
ends = [3, 3, 4]
|
|
axes = [0, 1, 2]
|
|
|
|
program = Program()
|
|
with program_guard(program):
|
|
input = layers.data(
|
|
name="input", shape=[3, 4, 5, 6], dtype='float32')
|
|
|
|
out = layers.slice(input, axes=axes, starts=starts, ends=ends)
|
|
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def test_softshrink(self):
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program = Program()
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with program_guard(program):
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input = layers.data(name="input", shape=[16], dtype="float32")
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out = layers.softshrink(input, name='softshrink')
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self.assertIsNotNone(out)
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print(str(program))
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def iou_similarity(self):
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program = Program()
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with program_guard(program):
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x = layers.data(name="x", shape=[16], dtype="float32")
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y = layers.data(name="y", shape=[16], dtype="float32")
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out = layers.iou_similarity(x, y, name='iou_similarity')
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self.assertIsNotNone(out)
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print(str(program))
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def test_grid_sampler(self):
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program = Program()
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with program_guard(program):
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x = layers.data(name='x', shape=[3, 5, 7], dtype='float32')
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grid = layers.data(name='grid', shape=[5, 7, 2], dtype='float32')
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out = layers.grid_sampler(x, grid)
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self.assertIsNotNone(out)
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|
print(str(program))
|
|
|
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def test_affine_grid(self):
|
|
program = Program()
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|
with program_guard(program):
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|
data = layers.data(name='data', shape=[2, 3, 3], dtype="float32")
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out, ids = layers.argsort(input=data, axis=1)
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|
|
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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)
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|
data_1 = layers.affine_grid(theta, [5, 3, 28, 28])
|
|
|
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self.assertIsNotNone(data_0)
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|
self.assertIsNotNone(data_1)
|
|
print(str(program))
|
|
|
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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))
|
|
|
|
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))
|
|
|
|
def test_shuffle_channel(self):
|
|
program = Program()
|
|
with program_guard(program):
|
|
x = layers.data(name="X", shape=[16, 4, 4], dtype="float32")
|
|
out = layers.shuffle_channel(x, group=4)
|
|
self.assertIsNotNone(out)
|
|
print(str(program))
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|