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307 lines
9.8 KiB
307 lines
9.8 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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import paddle.fluid.core as core
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from op_test import OpTest, randomize_probability
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class TestCrossEntropyOp(OpTest):
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"""Test cross-entropy with discrete one-hot labels.
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"""
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def setUp(self):
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self.op_type = "cross_entropy"
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self.soft_label = False
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self.ignore_index = -100
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self.dtype = np.float64
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self.batch_size = 30
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self.class_num = 10
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self.init_dtype_type()
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self.init_attr_type()
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self.init_bs_class_num()
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self.init_x()
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self.init_label()
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self.get_cross_entropy()
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self.inputs = {"X": self.x, "Label": self.label}
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self.outputs = {"Y": self.cross_entropy}
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self.attrs = {
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"soft_label": self.soft_label,
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"ignore_index": self.ignore_index
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}
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def init_x(self):
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self.x = randomize_probability(
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self.batch_size, self.class_num, dtype=self.dtype)
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def init_label(self):
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self.label = np.random.randint(
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0, self.class_num, (self.batch_size, 1), dtype="int64")
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def get_cross_entropy(self):
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self.cross_entropy = np.asmatrix(
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[[-np.log(self.x[i][self.label[i][0]])]
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for i in range(self.x.shape[0])],
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dtype="float64")
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def init_attr_type(self):
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pass
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def init_dtype_type(self):
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pass
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def init_bs_class_num(self):
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pass
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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(["X"], "Y", numeric_grad_delta=0.001)
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class TestCrossEntropyOp2(TestCrossEntropyOp):
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"""Test cross-entropy with vectorized soft labels.
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"""
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def init_label(self):
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self.label = np.random.uniform(
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0.1, 1.0, [self.batch_size, self.class_num]).astype(self.dtype)
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self.label /= self.label.sum(axis=1, keepdims=True)
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def get_cross_entropy(self):
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self.cross_entropy = (-self.label * np.log(self.x)).sum(
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axis=1, keepdims=True).astype(self.dtype)
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def init_attr_type(self):
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self.soft_label = True
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def init_dtype_type(self):
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self.dtype = np.float32
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def init_bs_class_num(self):
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self.batch_size = 5
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self.class_num = 37
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def test_check_grad(self):
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self.check_grad(
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["X"], "Y", max_relative_error=0.05, numeric_grad_delta=0.001)
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class TestCrossEntropyOp3(TestCrossEntropyOp):
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"""Test cross-entropy with vectorized one-hot representation of labels.
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"""
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def init_label(self):
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self.label_index = np.random.randint(0, self.class_num,
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(self.batch_size))
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self.label = np.zeros(self.x.shape).astype(self.dtype)
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self.label[np.arange(self.batch_size), self.label_index] = 1
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def get_cross_entropy(self):
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self.cross_entropy = np.asmatrix(
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[[-np.log(self.x[i][self.label_index[i]])]
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for i in range(self.x.shape[0])]).astype(self.dtype)
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def init_attr_type(self):
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self.soft_label = True
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def init_dtype_type(self):
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self.dtype = np.float32
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def init_bs_class_num(self):
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self.batch_size = 5
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self.class_num = 17
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def test_check_grad(self):
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self.check_grad(
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["X"], "Y", max_relative_error=0.05, numeric_grad_delta=0.001)
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class TestCrossEntropyOp4(TestCrossEntropyOp):
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"""Test high rank tensor cross-entropy with discrete one-hot labels.
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"""
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def init_x(self):
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self.shape = [10, 2, 4]
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self.ins_num = np.prod(np.array(self.shape))
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self.X_2d = randomize_probability(self.ins_num,
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self.class_num).astype(self.dtype)
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self.x = self.X_2d.reshape(self.shape + [self.class_num])
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def init_label(self):
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self.label_2d = np.random.randint(
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0, self.class_num, (self.ins_num, 1), dtype="int64")
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self.label = self.label_2d.reshape(self.shape + [1])
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def get_cross_entropy(self):
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cross_entropy_2d = np.asmatrix(
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[[-np.log(self.X_2d[i][self.label_2d[i][0]])]
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for i in range(self.X_2d.shape[0])]).astype(self.dtype)
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self.cross_entropy = np.array(cross_entropy_2d).reshape(self.shape +
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[1])
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def init_attr_type(self):
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self.soft_label = False
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def init_dtype_type(self):
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self.dtype = np.float64
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def init_bs_class_num(self):
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self.class_num = 10
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class TestCrossEntropyOp5(TestCrossEntropyOp):
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"""Test high rank tensor cross-entropy with vectorized soft labels.
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"""
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def init_x(self):
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self.shape = [4, 3]
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self.ins_num = np.prod(np.array(self.shape))
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self.X_2d = randomize_probability(self.ins_num,
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self.class_num).astype(self.dtype)
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self.x = self.X_2d.reshape(self.shape + [self.class_num])
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def init_label(self):
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self.label_2d = np.random.uniform(
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0.1, 1.0, [self.ins_num, self.class_num]).astype(self.dtype)
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self.label_2d /= self.label_2d.sum(axis=1, keepdims=True)
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self.label = self.label_2d.reshape(self.shape + [self.class_num])
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def get_cross_entropy(self):
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cross_entropy_2d = (-self.label_2d * np.log(self.X_2d)).sum(
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axis=1, keepdims=True).astype(self.dtype)
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self.cross_entropy = np.array(cross_entropy_2d).reshape(self.shape +
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[1])
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def init_attr_type(self):
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self.soft_label = True
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def init_dtype_type(self):
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self.dtype = np.float32
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def init_bs_class_num(self):
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self.class_num = 37
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def test_check_grad(self):
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self.check_grad(
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["X"], "Y", max_relative_error=0.05, numeric_grad_delta=0.001)
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class TestCrossEntropyOp6(TestCrossEntropyOp):
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"""Test high rank tensor cross-entropy with vectorized one-hot representation of labels.
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"""
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def init_x(self):
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self.shape = [4, 3, 2]
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self.ins_num = np.prod(np.array(self.shape))
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self.X_2d = randomize_probability(self.ins_num,
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self.class_num).astype(self.dtype)
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self.x = self.X_2d.reshape(self.shape + [self.class_num])
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def init_label(self):
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self.label_index_2d = np.random.randint(
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0, self.class_num, (self.ins_num), dtype="int64")
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label_2d = np.zeros(self.X_2d.shape)
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label_2d[np.arange(self.ins_num), self.label_index_2d] = 1
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self.label = label_2d.reshape(self.shape + [self.class_num]).astype(
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self.dtype)
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def get_cross_entropy(self):
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cross_entropy_2d = np.asmatrix(
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[[-np.log(self.X_2d[i][self.label_index_2d[i]])]
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for i in range(self.X_2d.shape[0])])
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self.cross_entropy = np.array(cross_entropy_2d).reshape(
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self.shape + [1]).astype(self.dtype)
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def init_attr_type(self):
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self.soft_label = True
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def init_dtype_type(self):
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self.dtype = np.float32
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def init_bs_class_num(self):
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self.class_num = 17
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def test_check_grad(self):
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self.check_grad(
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["X"], "Y", max_relative_error=0.05, numeric_grad_delta=0.001)
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class TestCrossEntropyOp7(TestCrossEntropyOp):
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"""Test cross-entropy with ignore index.
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"""
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def init_label(self):
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self.label = np.random.randint(
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0, self.class_num, (self.batch_size, 1), dtype="int64")
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def get_cross_entropy(self):
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self.cross_entropy = np.asmatrix(
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[[-np.log(self.x[i][self.label[i][0]])]
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if self.label[i][0] != self.ignore_index else [0]
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for i in range(self.x.shape[0])]).astype(self.dtype)
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def init_attr_type(self):
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self.soft_label = False
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self.ignore_index = 3
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def init_dtype_type(self):
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self.dtype = np.float64
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def init_bs_class_num(self):
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self.batch_size = 30
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self.class_num = 10
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# Add Fp16 test
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def create_test_class(parent, cls_name):
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@unittest.skipIf(not core.is_compiled_with_cuda(),
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"core is not compiled with CUDA")
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class TestCrossEntropyFP16Op(parent):
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def init_dtype_type(self):
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return np.float16
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def test_check_output(self):
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place = core.CUDAPlace(0)
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if core.is_float16_supported(place):
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self.check_output_with_place(place, atol=2e-1)
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def test_check_grad(self):
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place = core.CUDAPlace(0)
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if core.is_float16_supported(place):
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self.check_grad_with_place(
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place, ['X'], 'Y', max_relative_error=0.9)
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cls_name = "{0}".format(cls_name)
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TestCrossEntropyFP16Op.__name__ = cls_name
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globals()[cls_name] = TestCrossEntropyFP16Op
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create_test_class(TestCrossEntropyOp, "TestCrossEntropyF16Op")
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#create_test_class(TestCrossEntropyOp2, "TestCrossEntropyF16Op2")
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create_test_class(TestCrossEntropyOp3, "TestCrossEntropyF16Op3")
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create_test_class(TestCrossEntropyOp4, "TestCrossEntropyF16Op4")
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#create_test_class(TestCrossEntropyOp5, "TestCrossEntropyF16Op5")
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create_test_class(TestCrossEntropyOp6, "TestCrossEntropyF16Op6")
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create_test_class(TestCrossEntropyOp7, "TestCrossEntropyF16Op7")
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if __name__ == "__main__":
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unittest.main()
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