#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import paddle.fluid as fluid
import paddle.fluid.core as core
import os
import unittest
import paddle.fluid.layers as layers
from paddle.fluid.layers.nn import _pull_box_sparse


class TestDataFeed(unittest.TestCase):
    """  TestBaseCase(Merge PV)   """

    def setUp(self):
        self.batch_size = 10
        self.pv_batch_size = 10
        self.enable_pv_merge = True
        self.merge_by_sid = True

    def set_data_config(self):
        self.dataset = fluid.DatasetFactory().create_dataset("BoxPSDataset")
        self.dataset.set_feed_type("PaddleBoxDataFeed")
        self.dataset.set_parse_logkey(True)
        self.dataset.set_thread(1)
        self.dataset.set_enable_pv_merge(self.enable_pv_merge)
        self.dataset.set_batch_size(self.batch_size)
        if self.enable_pv_merge:
            self.dataset.set_merge_by_sid(self.merge_by_sid)
            self.dataset.set_rank_offset("rank_offset")
            self.dataset.set_pv_batch_size(self.pv_batch_size)

    def test_pboxdatafeed(self):
        self.run_dataset(False)

    def test_pboxdatafeed(self):
        self.run_dataset(True)

    def run_dataset(self, is_cpu):
        x = fluid.layers.data(name='x', shape=[1], dtype='int64', lod_level=0)
        y = fluid.layers.data(name='y', shape=[1], dtype='int64', lod_level=0)
        rank_offset = fluid.layers.data(
            name="rank_offset",
            shape=[-1, 7],
            dtype="int32",
            lod_level=0,
            append_batch_size=False)

        emb_x, emb_y = _pull_box_sparse([x, y], size=2)
        emb_xp = _pull_box_sparse(x, size=2)
        concat = layers.concat([emb_x, emb_y], axis=1)
        fc = layers.fc(input=concat,
                       name="fc",
                       size=1,
                       num_flatten_dims=1,
                       bias_attr=False)
        loss = layers.reduce_mean(fc)
        place = fluid.CPUPlace() if is_cpu or not core.is_compiled_with_cuda(
        ) else fluid.CUDAPlace(0)
        exe = fluid.Executor(place)

        with open("test_run_with_dump_a.txt", "w") as f:
            data = "1 1702f830eee19501ad7429505f714c1d 1 1 1 9\n"
            data += "1 1702f830eee19502ad7429505f714c1d 1 2 1 8\n"
            data += "1 1702f830eee19503ad7429505f714c1d 1 3 1 7\n"
            data += "1 1702f830eee0de01ad7429505f714c2d 1 4 1 6\n"
            data += "1 1702f830eee0df01ad7429505f714c3d 1 5 1 5\n"
            data += "1 1702f830eee0df02ad7429505f714c3d 1 6 1 4\n"
            f.write(data)
        with open("test_run_with_dump_b.txt", "w") as f:
            data = "1 1702f830fff22201ad7429505f715c1d 1 1 1 1\n"
            data += "1 1702f830fff22202ad7429505f715c1d 1 2 1 2\n"
            data += "1 1702f830fff22203ad7429505f715c1d 1 3 1 3\n"
            data += "1 1702f830fff22101ad7429505f714ccd 1 4 1 4\n"
            data += "1 1702f830fff22102ad7429505f714ccd 1 5 1 5\n"
            data += "1 1702f830fff22103ad7429505f714ccd 1 6 1 6\n"
            data += "1 1702f830fff22104ad7429505f714ccd 1 6 1 7\n"
            f.write(data)

        self.set_data_config()
        self.dataset.set_use_var([x, y])
        self.dataset.set_filelist(
            ["test_run_with_dump_a.txt", "test_run_with_dump_b.txt"])

        optimizer = fluid.optimizer.SGD(learning_rate=0.5)
        optimizer = fluid.optimizer.PipelineOptimizer(
            optimizer,
            cut_list=[],
            place_list=[place],
            concurrency_list=[1],
            queue_size=1,
            sync_steps=-1)
        optimizer.minimize(loss)
        exe.run(fluid.default_startup_program())
        self.dataset.set_current_phase(1)
        self.dataset.load_into_memory()
        self.dataset.preprocess_instance()
        self.dataset.begin_pass()
        pv_num = self.dataset.get_pv_data_size()

        exe.train_from_dataset(
            program=fluid.default_main_program(),
            dataset=self.dataset,
            print_period=1)
        self.dataset.set_current_phase(0)
        self.dataset.postprocess_instance()
        exe.train_from_dataset(
            program=fluid.default_main_program(),
            dataset=self.dataset,
            print_period=1)
        self.dataset.end_pass(True)
        os.remove("test_run_with_dump_a.txt")
        os.remove("test_run_with_dump_b.txt")


class TestDataFeed2(TestDataFeed):
    """  TestBaseCase(Merge PV not merge by sid)   """

    def setUp(self):
        self.batch_size = 10
        self.pv_batch_size = 10
        self.enable_pv_merge = True
        self.merge_by_sid = False


class TestDataFeed3(TestDataFeed):
    """  TestBaseCase(Not Merge PV)   """

    def setUp(self):
        self.batch_size = 10
        self.pv_batch_size = 10
        self.enable_pv_merge = False


if __name__ == '__main__':
    unittest.main()

#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import paddle.fluid as fluid
import paddle.fluid.core as core
import os
import unittest
import paddle.fluid.layers as layers
from paddle.fluid.layers.nn import _pull_box_sparse


class TestDataFeed(unittest.TestCase):
    """  TestBaseCase(Merge PV)   """

    def setUp(self):
        self.batch_size = 10
        self.pv_batch_size = 10
        self.enable_pv_merge = True
        self.merge_by_sid = True

    def set_data_config(self):
        self.dataset = fluid.DatasetFactory().create_dataset("BoxPSDataset")
        self.dataset.set_feed_type("PaddleBoxDataFeed")
        self.dataset.set_parse_logkey(True)
        self.dataset.set_thread(1)
        self.dataset.set_enable_pv_merge(self.enable_pv_merge)
        self.dataset.set_batch_size(self.batch_size)
        if self.enable_pv_merge:
            self.dataset.set_merge_by_sid(self.merge_by_sid)
            self.dataset.set_rank_offset("rank_offset")
            self.dataset.set_pv_batch_size(self.pv_batch_size)

    def test_pboxdatafeed(self):
        self.run_dataset(False)

    def test_pboxdatafeed(self):
        self.run_dataset(True)

    def run_dataset(self, is_cpu):
        x = fluid.layers.data(name='x', shape=[1], dtype='int64', lod_level=0)
        y = fluid.layers.data(name='y', shape=[1], dtype='int64', lod_level=0)
        rank_offset = fluid.layers.data(
            name="rank_offset",
            shape=[-1, 7],
            dtype="int32",
            lod_level=0,
            append_batch_size=False)

        emb_x, emb_y = _pull_box_sparse([x, y], size=2)
        emb_xp = _pull_box_sparse(x, size=2)
        concat = layers.concat([emb_x, emb_y], axis=1)
        fc = layers.fc(input=concat,
                       name="fc",
                       size=1,
                       num_flatten_dims=1,
                       bias_attr=False)
        loss = layers.reduce_mean(fc)
        place = fluid.CPUPlace() if is_cpu or not core.is_compiled_with_cuda(
        ) else fluid.CUDAPlace(0)
        exe = fluid.Executor(place)

        with open("test_run_with_dump_a.txt", "w") as f:
            data = "1 1702f830eee19501ad7429505f714c1d 1 1 1 9\n"
            data += "1 1702f830eee19502ad7429505f714c1d 1 2 1 8\n"
            data += "1 1702f830eee19503ad7429505f714c1d 1 3 1 7\n"
            data += "1 1702f830eee0de01ad7429505f714c2d 1 4 1 6\n"
            data += "1 1702f830eee0df01ad7429505f714c3d 1 5 1 5\n"
            data += "1 1702f830eee0df02ad7429505f714c3d 1 6 1 4\n"
            f.write(data)
        with open("test_run_with_dump_b.txt", "w") as f:
            data = "1 1702f830fff22201ad7429505f715c1d 1 1 1 1\n"
            data += "1 1702f830fff22202ad7429505f715c1d 1 2 1 2\n"
            data += "1 1702f830fff22203ad7429505f715c1d 1 3 1 3\n"
            data += "1 1702f830fff22101ad7429505f714ccd 1 4 1 4\n"
            data += "1 1702f830fff22102ad7429505f714ccd 1 5 1 5\n"
            data += "1 1702f830fff22103ad7429505f714ccd 1 6 1 6\n"
            data += "1 1702f830fff22104ad7429505f714ccd 1 6 1 7\n"
            f.write(data)

        self.set_data_config()
        self.dataset.set_use_var([x, y])
        self.dataset.set_filelist(
            ["test_run_with_dump_a.txt", "test_run_with_dump_b.txt"])

        optimizer = fluid.optimizer.SGD(learning_rate=0.5)
        optimizer = fluid.optimizer.PipelineOptimizer(
            optimizer,
            cut_list=[],
            place_list=[place],
            concurrency_list=[1],
            queue_size=1,
            sync_steps=-1)
        optimizer.minimize(loss)
        exe.run(fluid.default_startup_program())
        self.dataset.set_current_phase(1)
        self.dataset.load_into_memory()
        self.dataset.preprocess_instance()
        self.dataset.begin_pass()
        pv_num = self.dataset.get_pv_data_size()

        exe.train_from_dataset(
            program=fluid.default_main_program(),
            dataset=self.dataset,
            print_period=1)
        self.dataset.set_current_phase(0)
        self.dataset.postprocess_instance()
        exe.train_from_dataset(
            program=fluid.default_main_program(),
            dataset=self.dataset,
            print_period=1)
        self.dataset.end_pass(True)
        os.remove("test_run_with_dump_a.txt")
        os.remove("test_run_with_dump_b.txt")


class TestDataFeed2(TestDataFeed):
    """  TestBaseCase(Merge PV not merge by sid)   """

    def setUp(self):
        self.batch_size = 10
        self.pv_batch_size = 10
        self.enable_pv_merge = True
        self.merge_by_sid = False


class TestDataFeed3(TestDataFeed):
    """  TestBaseCase(Not Merge PV)   """

    def setUp(self):
        self.batch_size = 10
        self.pv_batch_size = 10
        self.enable_pv_merge = False


if __name__ == '__main__':
    unittest.main()

#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # #     http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. TestBaseCase(Merge PV) TestBaseCase(Merge PV not merge by sid) TestBaseCase(Not Merge PV)