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1616 lines
60 KiB
1616 lines
60 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 contextlib
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import numpy as np
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import decorators
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import paddle
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import paddle.fluid as fluid
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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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from paddle.fluid import core
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from paddle.fluid.initializer import Constant
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import paddle.fluid.layers as layers
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from test_imperative_base import new_program_scope
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from paddle.fluid.imperative import nn
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from paddle.fluid.imperative import base
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class LayerTest(unittest.TestCase):
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@classmethod
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def setUpClass(cls):
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cls.seed = 111
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@classmethod
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def tearDownClass(cls):
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pass
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def _get_place(self, force_to_use_cpu=False):
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# this option for ops that only have cpu kernel
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if force_to_use_cpu:
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return core.CPUPlace()
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else:
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if core.is_compiled_with_cuda():
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return core.CUDAPlace(0)
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return core.CPUPlace()
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@contextlib.contextmanager
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def static_graph(self):
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with new_program_scope():
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fluid.default_startup_program().random_seed = self.seed
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fluid.default_main_program().random_seed = self.seed
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yield
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def get_static_graph_result(self, feed, fetch_list, with_lod=False):
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exe = fluid.Executor(self._get_place())
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exe.run(fluid.default_startup_program())
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return exe.run(fluid.default_main_program(),
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feed=feed,
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fetch_list=fetch_list,
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return_numpy=(not with_lod))
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@contextlib.contextmanager
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def dynamic_graph(self, force_to_use_cpu=False):
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with fluid.imperative.guard(
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self._get_place(force_to_use_cpu=force_to_use_cpu)):
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fluid.default_startup_program().random_seed = self.seed
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fluid.default_main_program().random_seed = self.seed
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yield
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class TestLayer(LayerTest):
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def test_layer_norm(self):
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inp = np.ones([3, 32, 32], dtype='float32')
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with self.static_graph():
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t = layers.data(
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name='data',
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shape=[3, 32, 32],
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dtype='float32',
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append_batch_size=False)
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ret = layers.layer_norm(t)
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static_ret = self.get_static_graph_result(
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feed={'data': inp}, fetch_list=[ret])[0]
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with self.static_graph():
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t = layers.data(
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name='data',
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shape=[3, 32, 32],
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dtype='float32',
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append_batch_size=False)
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lm = nn.LayerNorm('layer_norm')
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ret = lm(t)
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static_ret2 = self.get_static_graph_result(
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feed={'data': inp}, fetch_list=[ret])[0]
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with self.dynamic_graph():
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lm = nn.LayerNorm('layer_norm')
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dy_ret = lm(base.to_variable(inp))
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self.assertTrue(np.allclose(static_ret, static_ret2))
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self.assertTrue(np.allclose(dy_ret._numpy(), static_ret2))
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def test_relu(self):
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with self.static_graph():
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t = layers.data(name='t', shape=[3, 3], dtype='float32')
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ret = layers.relu(t)
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static_ret = self.get_static_graph_result(
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feed={'t': np.ones(
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[3, 3], dtype='float32')}, fetch_list=[ret])[0]
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with self.dynamic_graph():
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t = np.ones([3, 3], dtype='float32')
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dy_ret = layers.relu(base.to_variable(t))
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self.assertTrue(np.allclose(static_ret, dy_ret._numpy()))
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def test_matmul(self):
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with self.static_graph():
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t = layers.data(name='t', shape=[3, 3], dtype='float32')
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t2 = layers.data(name='t2', shape=[3, 3], dtype='float32')
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ret = layers.matmul(t, t2)
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static_ret = self.get_static_graph_result(
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feed={
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't': np.ones(
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[3, 3], dtype='float32'),
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't2': np.ones(
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[3, 3], dtype='float32')
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},
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fetch_list=[ret])[0]
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with self.dynamic_graph():
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t = np.ones([3, 3], dtype='float32')
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t2 = np.ones([3, 3], dtype='float32')
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dy_ret = layers.matmul(base.to_variable(t), base.to_variable(t2))
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self.assertTrue(np.allclose(static_ret, dy_ret._numpy()))
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def test_conv2d(self):
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with self.static_graph():
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images = layers.data(name='pixel', shape=[3, 5, 5], dtype='float32')
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ret = layers.conv2d(input=images, num_filters=3, filter_size=[2, 2])
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static_ret = self.get_static_graph_result(
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feed={'pixel': np.ones(
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[2, 3, 5, 5], dtype='float32')},
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fetch_list=[ret])[0]
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with self.static_graph():
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images = layers.data(name='pixel', shape=[3, 5, 5], dtype='float32')
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conv2d = nn.Conv2D(
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'conv2d', num_channels=3, num_filters=3, filter_size=[2, 2])
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ret = conv2d(images)
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static_ret2 = self.get_static_graph_result(
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feed={'pixel': np.ones(
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[2, 3, 5, 5], dtype='float32')},
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fetch_list=[ret])[0]
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with self.dynamic_graph():
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images = np.ones([2, 3, 5, 5], dtype='float32')
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conv2d = nn.Conv2D(
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'conv2d', num_channels=3, num_filters=3, filter_size=[2, 2])
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dy_ret = conv2d(base.to_variable(images))
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self.assertTrue(np.allclose(static_ret, dy_ret._numpy()))
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self.assertTrue(np.allclose(static_ret, static_ret2))
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def test_gru_unit(self):
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lod = [[2, 4, 3]]
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D = 5
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T = sum(lod[0])
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N = len(lod[0])
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input = np.random.rand(T, 3 * D).astype('float32')
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hidden_input = np.random.rand(T, D).astype('float32')
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with self.static_graph():
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x = layers.data(name='x', shape=[-1, D * 3], dtype='float32')
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hidden = layers.data(name='hidden', shape=[-1, D], dtype='float32')
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updated_hidden, reset_hidden_pre, gate = layers.gru_unit(
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input=x, hidden=hidden, size=D * 3)
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static_ret = self.get_static_graph_result(
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feed={'x': input,
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'hidden': hidden_input},
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fetch_list=[updated_hidden, reset_hidden_pre, gate])
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with self.static_graph():
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x = layers.data(name='x', shape=[-1, D * 3], dtype='float32')
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hidden = layers.data(name='hidden', shape=[-1, D], dtype='float32')
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updated_hidden, reset_hidden_pre, gate = layers.gru_unit(
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input=x, hidden=hidden, size=D * 3)
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gru = nn.GRUUnit('gru', size=D * 3)
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updated_hidden, reset_hidden_pre, gate = gru(x, hidden)
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static_ret2 = self.get_static_graph_result(
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feed={'x': input,
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'hidden': hidden_input},
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fetch_list=[updated_hidden, reset_hidden_pre, gate])
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with self.dynamic_graph():
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gru = nn.GRUUnit('gru', size=D * 3)
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dy_ret = gru(
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base.to_variable(input), base.to_variable(hidden_input))
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for i in range(len(static_ret)):
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self.assertTrue(np.allclose(static_ret[i], static_ret2[i]))
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self.assertTrue(np.allclose(static_ret[i], dy_ret[i]._numpy()))
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def test_elementwise_math(self):
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n = np.ones([3, 3], dtype='float32')
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n2 = np.ones([3, 3], dtype='float32') * 1.1
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n3 = np.ones([3, 3], dtype='float32') * 2
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n4 = np.ones([3, 3], dtype='float32') * 3
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n5 = np.ones([3, 3], dtype='float32') * 4
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n6 = np.ones([3, 3], dtype='float32') * 5
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with self.static_graph():
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t = layers.data(name='t', shape=[3, 3], dtype='float32')
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t2 = layers.data(name='t2', shape=[3, 3], dtype='float32')
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t3 = layers.data(name='t3', shape=[3, 3], dtype='float32')
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t4 = layers.data(name='t4', shape=[3, 3], dtype='float32')
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t5 = layers.data(name='t5', shape=[3, 3], dtype='float32')
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t6 = layers.data(name='t6', shape=[3, 3], dtype='float32')
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ret = layers.elementwise_add(t, t2)
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ret = layers.elementwise_pow(ret, t3)
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ret = layers.elementwise_div(ret, t4)
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ret = layers.elementwise_sub(ret, t5)
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ret = layers.elementwise_mul(ret, t6)
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static_ret = self.get_static_graph_result(
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feed={
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't': n,
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't2': n2,
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't3': n3,
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't4': n4,
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't5': n5,
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't6': n6
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},
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fetch_list=[ret])[0]
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with self.dynamic_graph():
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ret = layers.elementwise_add(n, n2)
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ret = layers.elementwise_pow(ret, n3)
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ret = layers.elementwise_div(ret, n4)
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ret = layers.elementwise_sub(ret, n5)
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dy_ret = layers.elementwise_mul(ret, n6)
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self.assertTrue(
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np.allclose(static_ret, dy_ret._numpy()),
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'%s vs %s' % (static_ret, dy_ret._numpy()))
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def test_elementwise_minmax(self):
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n = np.ones([3, 3], dtype='float32')
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n2 = np.ones([3, 3], dtype='float32') * 2
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with self.dynamic_graph():
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min_ret = layers.elementwise_min(n, n2)
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max_ret = layers.elementwise_max(n, n2)
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self.assertTrue(np.allclose(n, min_ret._numpy()))
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self.assertTrue(np.allclose(n2, max_ret._numpy()))
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def test_sequence_conv(self):
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inp_np = np.arange(12).reshape([3, 4]).astype('float32')
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if core.is_compiled_with_cuda():
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place = core.CUDAPlace(0)
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else:
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place = core.CPUPlace()
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with self.static_graph():
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seq = layers.data(
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name='seq_in',
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shape=[3, 4],
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dtype='float32',
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lod_level=1,
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append_batch_size=False)
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out = layers.sequence_conv(seq, 2)
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static_rlt = self.get_static_graph_result(
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feed={
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"seq_in": fluid.create_lod_tensor(
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data=inp_np,
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recursive_seq_lens=[[1, 1, 1]],
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place=place)
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},
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fetch_list=[out],
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with_lod=True)[0]
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with self.static_graph():
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seq = layers.data(
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name='seq_in',
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shape=[3, 4],
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dtype='float32',
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lod_level=1,
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append_batch_size=False)
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seq_conv = nn.SequenceConv('seq_conv', num_filters=2)
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out = seq_conv(seq)
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static_rlt2 = self.get_static_graph_result(
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feed={
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"seq_in": fluid.create_lod_tensor(
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data=inp_np,
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recursive_seq_lens=[[1, 1, 1]],
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place=place)
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},
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fetch_list=[out],
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with_lod=True)[0]
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self.assertTrue(
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np.allclose(np.array(static_rlt), np.array(static_rlt2)))
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def test_conv2d_transpose(self):
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inp_np = np.arange(0, 24).reshape([2, 3, 2, 2]).astype('float32')
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with self.static_graph():
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img = layers.data(name='pixel', shape=[3, 2, 2], dtype='float32')
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out = layers.conv2d_transpose(
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input=img, num_filters=10, output_size=28)
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static_rlt = self.get_static_graph_result(
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feed={'pixel': inp_np}, fetch_list=[out])[0]
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with self.static_graph():
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img = layers.data(name='pixel', shape=[3, 2, 2], dtype='float32')
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conv2d_transpose = nn.Conv2DTranspose(
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'conv2d_transpose', num_filters=10, output_size=28)
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out = conv2d_transpose(img)
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static_rlt2 = self.get_static_graph_result(
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feed={'pixel': inp_np}, fetch_list=[out])[0]
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with self.dynamic_graph():
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conv2d_transpose = nn.Conv2DTranspose(
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'conv2d_transpose', num_filters=10, output_size=28)
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dy_rlt = conv2d_transpose(base.to_variable(inp_np))
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self.assertTrue(np.allclose(static_rlt2, static_rlt))
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self.assertTrue(np.allclose(dy_rlt._numpy(), static_rlt))
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def test_bilinear_tensor_product(self):
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inp_np_x = np.array([[1, 2, 3]]).astype('float32')
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inp_np_y = np.array([[4, 5, 6]]).astype('float32')
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with self.static_graph():
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data_x = layers.data(
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name='x',
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shape=[1, 3],
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dtype="float32",
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append_batch_size=False)
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data_y = layers.data(
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name='y',
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shape=[1, 3],
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dtype="float32",
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append_batch_size=False)
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out = layers.bilinear_tensor_product(data_x, data_y, 6)
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static_rlt = self.get_static_graph_result(
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feed={'x': inp_np_x,
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'y': inp_np_y}, fetch_list=[out])[0]
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with self.static_graph():
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data_x = layers.data(
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name='x',
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shape=[1, 3],
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dtype="float32",
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append_batch_size=False)
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data_y = layers.data(
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name='y',
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shape=[1, 3],
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dtype="float32",
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append_batch_size=False)
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btp = nn.BilinearTensorProduct('btp', 6)
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out = btp(data_x, data_y)
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static_rlt2 = self.get_static_graph_result(
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feed={'x': inp_np_x,
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'y': inp_np_y}, fetch_list=[out])[0]
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with self.dynamic_graph():
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btp = nn.BilinearTensorProduct('btp', 6)
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dy_rlt = btp(base.to_variable(inp_np_x), base.to_variable(inp_np_y))
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self.assertTrue(np.allclose(static_rlt2, static_rlt))
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self.assertTrue(np.allclose(dy_rlt._numpy(), static_rlt))
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def test_prelu(self):
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inp_np = np.ones([5, 200, 100, 100]).astype('float32')
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with self.static_graph():
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data_t = layers.data(
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name="input",
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shape=[5, 200, 100, 100],
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dtype="float32",
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append_batch_size=False)
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mode = 'channel'
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out = layers.prelu(
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data_t, mode, param_attr=ParamAttr(initializer=Constant(1.0)))
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static_rlt = self.get_static_graph_result(
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feed={"input": inp_np}, fetch_list=[out])[0]
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with self.static_graph():
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data_t = layers.data(
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name="input",
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shape=[5, 200, 100, 100],
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dtype="float32",
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append_batch_size=False)
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mode = 'channel'
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prelu = nn.PRelu(
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'prelu',
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mode=mode,
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param_attr=ParamAttr(initializer=Constant(1.0)))
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out = prelu(data_t)
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static_rlt2 = self.get_static_graph_result(
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feed={"input": inp_np}, fetch_list=[out])[0]
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with self.dynamic_graph():
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mode = 'channel'
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prelu = nn.PRelu(
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'prelu',
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mode=mode,
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param_attr=ParamAttr(initializer=Constant(1.0)))
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dy_rlt = prelu(base.to_variable(inp_np))
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self.assertTrue(np.allclose(static_rlt2, static_rlt))
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self.assertTrue(np.allclose(dy_rlt._numpy(), static_rlt))
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def test_embeding(self):
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inp_word = np.array([[[1]]]).astype('int64')
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dict_size = 20
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with self.static_graph():
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data_t = layers.data(name='word', shape=[1], dtype='int64')
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emb = layers.embedding(
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input=data_t,
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size=[dict_size, 32],
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param_attr='emb.w',
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is_sparse=False)
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static_rlt = self.get_static_graph_result(
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feed={'word': inp_word}, fetch_list=[emb])[0]
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with self.static_graph():
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data_t = layers.data(name='word', shape=[1], dtype='int64')
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emb2 = nn.Embedding(
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name_scope='embedding',
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size=[dict_size, 32],
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param_attr='emb.w',
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is_sparse=False)
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emb_rlt = emb2(data_t)
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static_rlt2 = self.get_static_graph_result(
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feed={'word': inp_word}, fetch_list=[emb_rlt])[0]
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with self.dynamic_graph():
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emb2 = nn.Embedding(
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name_scope='embedding',
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size=[dict_size, 32],
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param_attr='emb.w',
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is_sparse=False)
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static_rlt3 = emb2(base.to_variable(inp_word))
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self.assertTrue(np.allclose(static_rlt2, static_rlt))
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self.assertTrue(np.allclose(static_rlt3._numpy(), static_rlt))
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def test_nce(self):
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window_size = 5
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dict_size = 20
|
|
label_word = int(window_size // 2) + 1
|
|
inp_word = np.array([[[1]], [[2]], [[3]], [[4]], [[5]]]).astype('int64')
|
|
nid_freq_arr = np.random.dirichlet(np.ones(20) * 1000).astype('float32')
|
|
seed = 1
|
|
with self.static_graph():
|
|
words = []
|
|
for i in range(window_size):
|
|
words.append(
|
|
layers.data(
|
|
name='word_{0}'.format(i), shape=[1], dtype='int64'))
|
|
|
|
embs = []
|
|
for i in range(window_size):
|
|
if i == label_word:
|
|
continue
|
|
|
|
emb = layers.embedding(
|
|
input=words[i],
|
|
size=[dict_size, 32],
|
|
param_attr='emb.w',
|
|
is_sparse=False)
|
|
embs.append(emb)
|
|
|
|
embs = layers.concat(input=embs, axis=1)
|
|
nce_loss = layers.nce(input=embs,
|
|
label=words[label_word],
|
|
num_total_classes=dict_size,
|
|
num_neg_samples=2,
|
|
sampler="custom_dist",
|
|
custom_dist=nid_freq_arr.tolist(),
|
|
seed=seed,
|
|
param_attr='nce.w',
|
|
bias_attr='nce.b')
|
|
feed_dict = dict()
|
|
for i in range(window_size):
|
|
feed_dict['word_{0}'.format(i)] = inp_word[i]
|
|
static_rlt = self.get_static_graph_result(
|
|
feed=feed_dict, fetch_list=[nce_loss])[0]
|
|
with self.static_graph():
|
|
words = []
|
|
for i in range(window_size):
|
|
words.append(
|
|
layers.data(
|
|
name='word_{0}'.format(i), shape=[1], dtype='int64'))
|
|
|
|
emb = nn.Embedding(
|
|
'embedding',
|
|
size=[dict_size, 32],
|
|
param_attr='emb.w',
|
|
is_sparse=False)
|
|
|
|
embs2 = []
|
|
for i in range(window_size):
|
|
if i == label_word:
|
|
continue
|
|
|
|
emb_rlt = emb(words[i])
|
|
embs2.append(emb_rlt)
|
|
|
|
embs2 = layers.concat(input=embs2, axis=1)
|
|
nce = nn.NCE('nce',
|
|
num_total_classes=dict_size,
|
|
num_neg_samples=2,
|
|
sampler="custom_dist",
|
|
custom_dist=nid_freq_arr.tolist(),
|
|
seed=seed,
|
|
param_attr='nce.w',
|
|
bias_attr='nce.b')
|
|
|
|
nce_loss2 = nce(embs2, words[label_word])
|
|
feed_dict = dict()
|
|
for i in range(len(words)):
|
|
feed_dict['word_{0}'.format(i)] = inp_word[i]
|
|
|
|
static_rlt2 = self.get_static_graph_result(
|
|
feed=feed_dict, fetch_list=[nce_loss2])[0]
|
|
|
|
with self.dynamic_graph(force_to_use_cpu=True):
|
|
words = []
|
|
for i in range(window_size):
|
|
words.append(base.to_variable(inp_word[i]))
|
|
|
|
emb = nn.Embedding(
|
|
'embedding',
|
|
size=[dict_size, 32],
|
|
param_attr='emb.w',
|
|
is_sparse=False)
|
|
|
|
embs3 = []
|
|
for i in range(window_size):
|
|
if i == label_word:
|
|
continue
|
|
|
|
emb_rlt = emb(words[i])
|
|
embs3.append(emb_rlt)
|
|
|
|
embs3 = layers.concat(input=embs3, axis=1)
|
|
nce = nn.NCE('nce',
|
|
num_total_classes=dict_size,
|
|
num_neg_samples=2,
|
|
sampler="custom_dist",
|
|
custom_dist=nid_freq_arr.tolist(),
|
|
seed=seed,
|
|
param_attr='nce.w',
|
|
bias_attr='nce.b')
|
|
|
|
nce_loss3 = nce(embs3, words[label_word])
|
|
|
|
self.assertTrue(np.allclose(static_rlt2, static_rlt))
|
|
self.assertTrue(np.allclose(nce_loss3._numpy(), static_rlt))
|
|
|
|
|
|
class TestBook(unittest.TestCase):
|
|
def test_fit_a_line(self):
|
|
program = Program()
|
|
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)
|
|
avg_cost = layers.mean(cost)
|
|
self.assertIsNotNone(avg_cost)
|
|
|
|
print(str(program))
|
|
|
|
def test_recognize_digits_mlp(self):
|
|
program = Program()
|
|
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')
|
|
predict = layers.fc(input=[hidden2, hidden1],
|
|
size=10,
|
|
act='softmax',
|
|
param_attr=["sftmax.w1", "sftmax.w2"])
|
|
cost = layers.cross_entropy(input=predict, label=label)
|
|
avg_cost = layers.mean(cost)
|
|
self.assertIsNotNone(avg_cost)
|
|
|
|
print(str(program))
|
|
|
|
def test_simple_conv2d(self):
|
|
program = Program()
|
|
with program_guard(program, startup_program=Program()):
|
|
images = layers.data(
|
|
name='pixel', shape=[3, 48, 48], dtype='float32')
|
|
layers.conv2d(input=images, num_filters=3, filter_size=[4, 4])
|
|
|
|
print(str(program))
|
|
|
|
def test_conv2d_transpose(self):
|
|
program = Program()
|
|
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))
|
|
|
|
def test_recognize_digits_conv(self):
|
|
program = Program()
|
|
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)
|
|
avg_cost = layers.mean(cost)
|
|
|
|
print(str(program))
|
|
|
|
def test_word_embedding(self):
|
|
program = Program()
|
|
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)
|
|
avg_cost = layers.mean(cost)
|
|
self.assertIsNotNone(avg_cost)
|
|
|
|
print(str(program))
|
|
|
|
def test_linear_chain_crf(self):
|
|
program = Program()
|
|
with program_guard(program, startup_program=Program()):
|
|
label_dict_len = 10
|
|
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)
|
|
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"))
|
|
layers.chunk_eval(
|
|
input=crf_decode,
|
|
label=label,
|
|
chunk_scheme="IOB",
|
|
num_chunk_types=(label_dict_len - 1) // 2)
|
|
self.assertFalse(crf is None)
|
|
self.assertFalse(crf_decode is None)
|
|
|
|
print(str(program))
|
|
|
|
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')
|
|
ignore_index = -1
|
|
self.assertIsNotNone(
|
|
layers.sigmoid_cross_entropy_with_logits(
|
|
x=dat, label=lbl, ignore_index=ignore_index))
|
|
print(str(program))
|
|
|
|
def test_hsigmoid(self):
|
|
program = Program()
|
|
with program_guard(program):
|
|
x = layers.data(name='x', shape=[2], dtype='float32')
|
|
y = layers.data(name='y', shape=[2], dtype='int64')
|
|
self.assertIsNotNone(
|
|
layers.hsigmoid(
|
|
input=x, label=y, num_classes=2))
|
|
print(str(program))
|
|
|
|
# test hsigmod with custom tree structure
|
|
program2 = Program()
|
|
with program_guard(program2):
|
|
x2 = layers.data(name='x2', shape=[4, 8], dtype='float32')
|
|
y2 = layers.data(name='y2', shape=[4], dtype='int64')
|
|
path_table = layers.data(
|
|
name='path_table', shape=[4, 6], dtype='int64')
|
|
path_code = layers.data(
|
|
name='path_code', shape=[4, 6], dtype='int64')
|
|
self.assertIsNotNone(
|
|
layers.hsigmoid(
|
|
input=x2,
|
|
label=y2,
|
|
num_classes=6,
|
|
path_table=path_table,
|
|
path_code=path_code,
|
|
is_custom=True))
|
|
print(str(program2))
|
|
|
|
def test_sequence_expand(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)
|
|
self.assertIsNotNone(layers.sequence_expand(x=x, y=y, ref_level=1))
|
|
print(str(program))
|
|
|
|
def test_sequence_unpad(self):
|
|
program = Program()
|
|
with program_guard(program):
|
|
x = layers.data(name='x', shape=[10, 5], dtype='float32')
|
|
length = layers.data(name='length', shape=[1], dtype='int64')
|
|
self.assertIsNotNone(layers.sequence_unpad(x=x, length=length))
|
|
print(str(program))
|
|
|
|
def test_pool2d(self):
|
|
program = Program()
|
|
with program_guard(program):
|
|
x = layers.data(name='x', shape=[3, 224, 224], dtype='float32')
|
|
self.assertIsNotNone(
|
|
layers.pool2d(
|
|
x,
|
|
pool_size=[5, 3],
|
|
pool_stride=[1, 2],
|
|
pool_padding=(2, 1)))
|
|
|
|
def test_adaptive_pool2d(self):
|
|
program = Program()
|
|
with program_guard(program):
|
|
x = layers.data(name='x', shape=[3, 224, 224], dtype='float32')
|
|
self.assertIsNotNone(
|
|
layers.adaptive_pool2d(
|
|
x, [3, 3], pool_type='avg'))
|
|
pool, mask = layers.adaptive_pool2d(x, [3, 3], require_index=True)
|
|
self.assertIsNotNone(pool)
|
|
self.assertIsNotNone(mask)
|
|
self.assertIsNotNone(layers.adaptive_pool2d(x, 3, pool_type='avg'))
|
|
pool, mask = layers.adaptive_pool2d(x, 3, require_index=True)
|
|
self.assertIsNotNone(pool)
|
|
self.assertIsNotNone(mask)
|
|
|
|
def test_adaptive_pool3d(self):
|
|
program = Program()
|
|
with program_guard(program):
|
|
x = layers.data(name='x', shape=[3, 244, 224, 224], dtype='float32')
|
|
self.assertIsNotNone(
|
|
layers.adaptive_pool3d(
|
|
x, [3, 3, 3], pool_type='avg'))
|
|
pool, mask = layers.adaptive_pool3d(
|
|
x, [3, 3, 3], require_index=True)
|
|
self.assertIsNotNone(pool)
|
|
self.assertIsNotNone(mask)
|
|
self.assertIsNotNone(layers.adaptive_pool3d(x, 3, pool_type='avg'))
|
|
pool, mask = layers.adaptive_pool3d(x, 3, require_index=True)
|
|
self.assertIsNotNone(pool)
|
|
self.assertIsNotNone(mask)
|
|
|
|
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(
|
|
name='prev_hidden_data', shape=[10, 30], dtype='float32')
|
|
prev_hidden = layers.fc(input=prev_hidden_data, size=30)
|
|
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))
|
|
|
|
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))
|
|
|
|
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)
|
|
self.assertIsNotNone(layers.sequence_softmax(seq))
|
|
print(str(program))
|
|
|
|
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)
|
|
self.assertIsNotNone(layers.softmax(hid))
|
|
print(str(program))
|
|
|
|
def test_space_to_depth(self):
|
|
program = Program()
|
|
with program_guard(program):
|
|
data = layers.data(
|
|
name='data',
|
|
shape=[32, 9, 6, 6],
|
|
append_batch_size=False,
|
|
dtype='float32')
|
|
self.assertIsNotNone(layers.space_to_depth(data, 3))
|
|
print(str(program))
|
|
|
|
def test_sequence_unsqueeze(self):
|
|
program = Program()
|
|
with program_guard(program):
|
|
x = layers.data(name='x', shape=[8, 2], dtype='float32')
|
|
out = layers.unsqueeze(input=x, axes=[1])
|
|
self.assertIsNotNone(out)
|
|
print(str(program))
|
|
|
|
def test_squeeze(self):
|
|
program = Program()
|
|
with program_guard(program):
|
|
x = layers.data(name='x', shape=[1, 1, 4], dtype='float32')
|
|
out = layers.squeeze(input=x, axes=[2])
|
|
self.assertIsNotNone(out)
|
|
print(str(program))
|
|
|
|
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))
|
|
|
|
def test_get_places(self):
|
|
program = Program()
|
|
with program_guard(program):
|
|
x = get_places(device_count=4)
|
|
self.assertIsNotNone(x)
|
|
print(str(program))
|
|
|
|
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))
|
|
|
|
def test_im2sequence(self):
|
|
program = Program()
|
|
with program_guard(program):
|
|
x = layers.data(name='x', shape=[3, 128, 128], dtype='float32')
|
|
y = layers.data(name='y', shape=[], dtype='float32')
|
|
output = layers.im2sequence(
|
|
input=x,
|
|
input_image_size=y,
|
|
stride=[1, 1],
|
|
filter_size=[2, 2],
|
|
out_stride=[1, 1])
|
|
self.assertIsNotNone(output)
|
|
print(str(program))
|
|
|
|
def test_sampled_softmax_with_cross_entropy(self):
|
|
program = Program()
|
|
with program_guard(program):
|
|
logits = layers.data(name='Logits', shape=[256], dtype='float64')
|
|
label = layers.data(name='Label', shape=[1], dtype='int64')
|
|
num_samples = 25
|
|
output = layers.sampled_softmax_with_cross_entropy(logits, label,
|
|
num_samples)
|
|
self.assertIsNotNone(output)
|
|
print(str(program))
|
|
|
|
@decorators.prog_scope()
|
|
def test_nce(self):
|
|
window_size = 5
|
|
words = []
|
|
for i in range(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 range(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')
|
|
avg_loss = layers.mean(loss)
|
|
self.assertIsNotNone(avg_loss)
|
|
print(str(default_main_program()))
|
|
|
|
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))
|
|
|
|
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))
|
|
|
|
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, softmax = layers.softmax_with_cross_entropy(
|
|
x, y, return_softmax=True)
|
|
self.assertIsNotNone(loss)
|
|
self.assertIsNotNone(softmax)
|
|
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))
|
|
|
|
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))
|
|
|
|
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)
|
|
|
|
def test_softshrink(self):
|
|
program = Program()
|
|
with program_guard(program):
|
|
input = layers.data(name="input", shape=[16], dtype="float32")
|
|
out = layers.softshrink(input, name='softshrink')
|
|
self.assertIsNotNone(out)
|
|
print(str(program))
|
|
|
|
def iou_similarity(self):
|
|
program = Program()
|
|
with program_guard(program):
|
|
x = layers.data(name="x", shape=[16], dtype="float32")
|
|
y = layers.data(name="y", shape=[16], dtype="float32")
|
|
out = layers.iou_similarity(x, y, name='iou_similarity')
|
|
self.assertIsNotNone(out)
|
|
print(str(program))
|
|
|
|
def test_grid_sampler(self):
|
|
program = Program()
|
|
with program_guard(program):
|
|
x = layers.data(name='x', shape=[3, 5, 7], dtype='float32')
|
|
grid = layers.data(name='grid', shape=[5, 7, 2], dtype='float32')
|
|
out = layers.grid_sampler(x, grid)
|
|
self.assertIsNotNone(out)
|
|
print(str(program))
|
|
|
|
def test_affine_grid(self):
|
|
program = Program()
|
|
with program_guard(program):
|
|
data = layers.data(name='data', shape=[2, 3, 3], dtype="float32")
|
|
out, ids = layers.argsort(input=data, axis=1)
|
|
|
|
theta = layers.data(name="theta", shape=[2, 3], dtype="float32")
|
|
out_shape = layers.data(
|
|
name="out_shape", shape=[-1], dtype="float32")
|
|
data_0 = layers.affine_grid(theta, out_shape)
|
|
data_1 = layers.affine_grid(theta, [5, 3, 28, 28])
|
|
|
|
self.assertIsNotNone(data_0)
|
|
self.assertIsNotNone(data_1)
|
|
print(str(program))
|
|
|
|
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_range(self):
|
|
program = Program()
|
|
with program_guard(program):
|
|
layers.range(0, 10, 2, 'int32')
|
|
layers.range(0.1, 10.0, 0.2, 'float32')
|
|
|
|
print(str(program))
|
|
|
|
def test_spectral_norm(self):
|
|
program = Program()
|
|
with program_guard(program):
|
|
weight = layers.data(
|
|
name='weight',
|
|
shape=[2, 3, 32, 32],
|
|
dtype="float32",
|
|
append_batch_size=False)
|
|
out = layers.spectral_norm(weight, dim=1, power_iters=1)
|
|
self.assertIsNotNone(out)
|
|
|
|
print(str(program))
|
|
|
|
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))
|
|
|
|
def test_fsp(self):
|
|
program = Program()
|
|
with program_guard(program):
|
|
x = layers.data(name="X", shape=[16, 4, 4], dtype="float32")
|
|
y = layers.data(name="Y", shape=[8, 4, 4], dtype="float32")
|
|
out = layers.fsp_matrix(x, y)
|
|
self.assertIsNotNone(out)
|
|
print(str(program))
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|