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206 lines
7.2 KiB
206 lines
7.2 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 numpy as np
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from op_test import OpTest
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import paddle.fluid as fluid
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from paddle.fluid import compiler, Program, program_guard
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class TestConcatOp(OpTest):
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def setUp(self):
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self.op_type = "concat"
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self.dtype = self.get_dtype()
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self.init_test_data()
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self.inputs = {'X': [('x0', self.x0), ('x1', self.x1), ('x2', self.x2)]}
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self.attrs = {'axis': self.axis}
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if self.axis < 0:
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self.actual_axis = self.axis + len(self.x0.shape)
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self.actual_axis = self.actual_axis if self.actual_axis > 0 else 0
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else:
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self.actual_axis = self.axis
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self.outputs = {
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'Out': np.concatenate(
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(self.x0, self.x1, self.x2), axis=self.actual_axis)
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}
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def get_dtype(self):
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return "float32"
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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self.check_grad(['x0'], 'Out')
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self.check_grad(['x1'], 'Out')
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self.check_grad(['x2'], 'Out')
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def init_test_data(self):
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self.x0 = np.random.random((2, 1, 4, 5)).astype(self.dtype)
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self.x1 = np.random.random((2, 2, 4, 5)).astype(self.dtype)
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self.x2 = np.random.random((2, 3, 4, 5)).astype(self.dtype)
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self.axis = 1
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class TestConcatOp2(TestConcatOp):
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def init_test_data(self):
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self.x0 = np.random.random((2, 3, 4, 5)).astype(self.dtype)
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self.x1 = np.random.random((2, 3, 4, 5)).astype(self.dtype)
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self.x2 = np.random.random((2, 3, 4, 5)).astype(self.dtype)
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self.axis = 1
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class TestConcatOp3(TestConcatOp):
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def init_test_data(self):
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self.x0 = np.random.random((1, 256, 170, 256)).astype(self.dtype)
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self.x1 = np.random.random((1, 128, 170, 256)).astype(self.dtype)
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self.x2 = np.random.random((1, 128, 170, 256)).astype(self.dtype)
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self.axis = 1
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def test_check_grad(self):
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pass
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class TestConcatOp4(TestConcatOp):
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def init_test_data(self):
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self.x0 = np.random.random((2, 3, 4, 5)).astype(self.dtype)
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self.x1 = np.random.random((2, 3, 4, 5)).astype(self.dtype)
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self.x2 = np.random.random((0, 3, 4, 5)).astype(self.dtype)
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self.axis = 0
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def test_check_grad(self):
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pass
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class TestConcatOp5(TestConcatOp):
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def init_test_data(self):
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self.x0 = np.random.random((2, 1, 4, 5)).astype(self.dtype)
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self.x1 = np.random.random((2, 2, 4, 5)).astype(self.dtype)
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self.x2 = np.random.random((2, 3, 4, 5)).astype(self.dtype)
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self.axis = -3
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def create_test_AxisTensor(parent):
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class TestConcatAxisTensor(parent):
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def setUp(self):
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self.op_type = "concat"
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self.dtype = self.get_dtype()
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self.init_test_data()
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self.inputs = {
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'X': [('x0', self.x0), ('x1', self.x1), ('x2', self.x2)],
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'AxisTensor': np.array([self.axis]).astype("int32")
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}
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self.attrs = {}
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if self.axis < 0:
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self.actual_axis = self.axis + len(self.x0.shape)
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self.actual_axis = self.actual_axis if self.actual_axis > 0 else 0
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else:
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self.actual_axis = self.axis
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self.outputs = {
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'Out': np.concatenate(
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(self.x0, self.x1, self.x2), axis=self.actual_axis)
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}
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cls_name = "{0}_{1}".format(parent.__name__, "AxisTensor")
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TestConcatAxisTensor.__name__ = cls_name
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globals()[cls_name] = TestConcatAxisTensor
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create_test_AxisTensor(TestConcatOp)
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create_test_AxisTensor(TestConcatOp2)
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create_test_AxisTensor(TestConcatOp3)
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create_test_AxisTensor(TestConcatOp4)
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create_test_AxisTensor(TestConcatOp5)
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#----------------Concat Fp16----------------
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def create_test_fp16(parent):
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class TestConcatFp16(parent):
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def get_dtype(self):
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return np.float16
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cls_name = "{0}_{1}".format(parent.__name__, "Fp16")
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TestConcatFp16.__name__ = cls_name
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globals()[cls_name] = TestConcatFp16
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create_test_fp16(TestConcatOp)
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create_test_fp16(TestConcatOp2)
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create_test_fp16(TestConcatOp3)
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create_test_fp16(TestConcatOp4)
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create_test_fp16(TestConcatOp5)
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class TestConcatOpError(OpTest):
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def test_errors(self):
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with program_guard(Program(), Program()):
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# The input type of concat_op should be list.
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x1 = fluid.layers.data(shape=[4], dtype='int32', name='x1')
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fluid.layers.concat(x1)
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# The item in input must be Variable.
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x2 = fluid.create_lod_tensor(
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np.array([[-1]]), [[1]], fluid.CPUPlace())
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x3 = fluid.create_lod_tensor(
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np.array([[-1]]), [[1]], fluid.CPUPlace())
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self.assertRaises(TypeError, fluid.layers.concat, [x2])
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# The input dtype of concat_op must be float16(only support on GPU), float32, float64, int32, int64.
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x4 = fluid.layers.data(shape=[4], dtype='uint8', name='x4')
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x5 = fluid.layers.data(shape=[4], dtype='uint8', name='x5')
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self.assertRaises(TypeError, fluid.layers.concat, [x4, x5])
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x6 = fluid.layers.data(shape=[4], dtype='float16', name='x6')
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x7 = fluid.layers.data(shape=[4], dtype='float16', name='x7')
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fluid.layers.concat([x6, x7])
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# The type of axis in concat_op should be int or Variable.
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def test_axis_type():
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fluid.layers.concat([x6, x7], 3.2)
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self.assertRaises(TypeError, test_axis_type)
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class TestConcatAPI(OpTest):
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def test_api(self):
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x_1 = fluid.data(shape=[None, 1, 4, 5], dtype='int32', name='x_1')
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fluid.layers.concat([x_1, x_1], 0)
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input_2 = np.random.random([2, 1, 4, 5]).astype("int32")
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input_3 = np.random.random([2, 2, 4, 5]).astype("int32")
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x_2 = fluid.data(shape=[2, 1, 4, 5], dtype='int32', name='x_2')
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x_3 = fluid.data(shape=[2, 2, 4, 5], dtype='int32', name='x_3')
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positive_1 = fluid.layers.fill_constant([1], "int32", 1)
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out_1 = fluid.layers.concat(input=[x_2, x_3], axis=1)
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out_2 = fluid.layers.concat(input=[x_2, x_3], axis=positive_1)
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exe = fluid.Executor(place=fluid.CPUPlace())
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[res_1, res_2] = exe.run(
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fluid.default_main_program(),
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feed={"x_1": input_2,
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"x_2": input_2,
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"x_3": input_3},
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fetch_list=[out_1, out_2])
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assert np.array_equal(res_1, np.concatenate((input_2, input_3), axis=1))
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assert np.array_equal(res_2, np.concatenate((input_2, input_3), axis=1))
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if __name__ == '__main__':
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unittest.main()
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