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393 lines
15 KiB
393 lines
15 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, skip_check_grad_ci
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import paddle.fluid as fluid
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from paddle.fluid import compiler, Program, program_guard, core
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import paddle
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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 "float64"
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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((5, 1, 4, 5)).astype(self.dtype)
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self.x1 = np.random.random((5, 2, 4, 5)).astype(self.dtype)
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self.x2 = np.random.random((5, 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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@skip_check_grad_ci(
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reason="The function 'check_grad' for large inputs is too slow.")
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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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@skip_check_grad_ci(
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reason="This test will meet fetch error when there is a null grad. The detailed information is in PR#17015."
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)
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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((5, 1, 4, 5)).astype(self.dtype)
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self.x1 = np.random.random((5, 2, 4, 5)).astype(self.dtype)
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self.x2 = np.random.random((5, 3, 4, 5)).astype(self.dtype)
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self.axis = -3
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class TestConcatOp6(TestConcatOp):
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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.lod = [[20, 80]]
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self.out_lod = [[20, 80, 20, 80, 20, 80]]
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self.inputs = {
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'X': [('x0', (self.x0, self.lod)), ('x1', (self.x1, self.lod)),
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('x2', (self.x2, self.lod))]
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}
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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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out = np.concatenate((self.x0, self.x1, self.x2), axis=self.actual_axis)
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self.outputs = {'Out': (out, self.out_lod)}
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def test_check_output(self):
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self.check_output(check_dygraph=False)
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def test_check_grad(self):
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self.check_grad(['x0'], 'Out', check_dygraph=False)
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self.check_grad(['x1'], 'Out', check_dygraph=False)
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self.check_grad(['x2'], 'Out', check_dygraph=False)
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def init_test_data(self):
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self.x0 = np.random.random([100]).astype(self.dtype)
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self.x1 = np.random.random([100]).astype(self.dtype)
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self.x2 = np.random.random([100]).astype(self.dtype)
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self.axis = 0
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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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create_test_AxisTensor(TestConcatOp6)
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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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create_test_fp16(TestConcatOp6)
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class TestConcatOpError(unittest.TestCase):
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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, 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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x8 = fluid.layers.data(shape=[4], dtype='float32', name='x8')
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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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def test_input_same_dtype():
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fluid.layers.concat([x7, x8])
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self.assertRaises(TypeError, test_input_same_dtype)
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class TestConcatAPI(unittest.TestCase):
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def test_fluid_api(self):
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paddle.enable_static()
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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_int32 = fluid.layers.fill_constant([1], "int32", 1)
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positive_1_int64 = fluid.layers.fill_constant([1], "int64", 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_int32)
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out_3 = fluid.layers.concat(input=[x_2, x_3], axis=positive_1_int64)
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exe = fluid.Executor(place=fluid.CPUPlace())
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[res_1, res_2, res_3] = 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, out_3])
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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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assert np.array_equal(res_3, np.concatenate((input_2, input_3), axis=1))
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def test_api(self):
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paddle.enable_static()
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x_1 = paddle.fluid.data(
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shape=[None, 1, 4, 5], dtype='int32', name='x_1')
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paddle.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_int32 = paddle.fluid.layers.fill_constant([1], "int32", 1)
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positive_1_int64 = paddle.fluid.layers.fill_constant([1], "int64", 1)
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negative_int64 = paddle.fluid.layers.fill_constant([1], "int64", -3)
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out_1 = paddle.concat(x=[x_2, x_3], axis=1)
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out_2 = paddle.concat(x=[x_2, x_3], axis=positive_1_int32)
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out_3 = paddle.concat(x=[x_2, x_3], axis=positive_1_int64)
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out_4 = paddle.concat(x=[x_2, x_3], axis=negative_int64)
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exe = paddle.static.Executor(place=paddle.CPUPlace())
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[res_1, res_2, res_3, res_4] = exe.run(
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paddle.static.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, out_3, out_4])
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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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assert np.array_equal(res_3, np.concatenate((input_2, input_3), axis=1))
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assert np.array_equal(res_4, np.concatenate((input_2, input_3), axis=1))
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def test_imperative(self):
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in1 = np.array([[1, 2, 3], [4, 5, 6]])
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in2 = np.array([[11, 12, 13], [14, 15, 16]])
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in3 = np.array([[21, 22], [23, 24]])
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paddle.disable_static()
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x1 = paddle.to_tensor(in1)
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x2 = paddle.to_tensor(in2)
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x3 = paddle.to_tensor(in3)
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out1 = fluid.layers.concat(input=[x1, x2, x3], axis=-1)
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out2 = paddle.concat(x=[x1, x2], axis=0)
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np_out1 = np.concatenate([in1, in2, in3], axis=-1)
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np_out2 = np.concatenate([in1, in2], axis=0)
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paddle.enable_static()
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self.assertEqual((out1.numpy() == np_out1).all(), True)
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self.assertEqual((out2.numpy() == np_out2).all(), True)
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def test_errors(self):
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with program_guard(Program(), Program()):
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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, paddle.concat, [x2])
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# The input dtype of concat_op must be float16, float32, float64, int32, int64.
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x4 = paddle.fluid.data(shape=[4], dtype='uint8', name='x4')
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x5 = paddle.fluid.data(shape=[4], dtype='uint8', name='x5')
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self.assertRaises(TypeError, fluid.layers.concat, [x4, x5])
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# The type of axis in concat_op should be int or Variable.
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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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x8 = fluid.layers.data(shape=[4], dtype='float32', name='x8')
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def test_axis_type():
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paddle.concat([x6, x7], 3.2)
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self.assertRaises(TypeError, test_axis_type)
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def test_input_same_dtype():
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paddle.concat([x7, x8])
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self.assertRaises(TypeError, test_input_same_dtype)
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class TestConcatAPIWithLoDTensorArray(unittest.TestCase):
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"""
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Test concat api when the input(x) is a LoDTensorArray.
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"""
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def setUp(self):
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self.axis = 1
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self.iter_num = 3
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self.input_shape = [2, 3]
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self.x = np.random.random(self.input_shape).astype("float32")
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self.place = fluid.CUDAPlace(0) \
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if fluid.is_compiled_with_cuda() else fluid.CPUPlace()
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def set_program(self, use_fluid_api):
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paddle.enable_static()
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if use_fluid_api:
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self.program = fluid.Program()
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with fluid.program_guard(self.program):
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input = fluid.layers.assign(self.x)
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tensor_array = fluid.layers.create_array(dtype='float32')
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zero = fluid.layers.fill_constant(
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shape=[1], value=0, dtype="int64")
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for i in range(self.iter_num):
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fluid.layers.array_write(input, zero + i, tensor_array)
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self.out_var = fluid.layers.concat(tensor_array, axis=self.axis)
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else:
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self.program = paddle.static.Program()
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with paddle.static.program_guard(self.program):
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input = paddle.assign(self.x)
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tensor_array = fluid.layers.create_array(
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dtype='float32'
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) # Api create_array is not supported in paddle 2.0 yet.
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zero = paddle.zeros(shape=[1], dtype="int64")
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for i in range(self.iter_num):
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# Api array_write is not supported in paddle 2.0 yet.
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fluid.layers.array_write(input, zero + i, tensor_array)
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self.out_var = paddle.concat(tensor_array, axis=self.axis)
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def test_fluid_api(self):
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self._run_static_mode(use_fluid_api=True)
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def test_paddle_api(self):
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self._run_static_mode(use_fluid_api=False)
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def _run_static_mode(self, use_fluid_api):
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self.set_program(use_fluid_api)
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self.assertTrue(self.out_var.shape[self.axis] == -1)
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exe = fluid.Executor(self.place)
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res = exe.run(self.program, fetch_list=self.out_var)
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self.assertTrue(
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np.array_equal(
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res[0],
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np.concatenate(
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[self.x] * self.iter_num, axis=self.axis)))
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if __name__ == '__main__':
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unittest.main()
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