test_model.py 29.5 KB
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# 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 division
from __future__ import print_function

import unittest

import os
import numpy as np
import shutil
import tempfile

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import paddle
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from paddle import fluid
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from paddle import to_tensor
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from paddle.nn import Conv2D, Linear, ReLU, Sequential, Softmax
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from paddle import Model
from paddle.static import InputSpec
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from paddle.nn.layer.loss import CrossEntropyLoss
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from paddle.metric import Accuracy
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from paddle.vision.datasets import MNIST
from paddle.vision.models import LeNet
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from paddle.io import DistributedBatchSampler, Dataset
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from paddle.hapi.model import prepare_distributed_context
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from paddle.fluid.dygraph.jit import declarative
from paddle.fluid.dygraph.dygraph_to_static.program_translator import ProgramTranslator
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class LeNetDygraph(paddle.nn.Layer):
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    def __init__(self, num_classes=10):
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        super(LeNetDygraph, self).__init__()
        self.num_classes = num_classes
        self.features = Sequential(
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            Conv2D(
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                1, 6, 3, stride=1, padding=1),
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            ReLU(),
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            paddle.fluid.dygraph.Pool2D(2, 'max', 2),
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            Conv2D(
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                6, 16, 5, stride=1, padding=0),
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            ReLU(),
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            paddle.fluid.dygraph.Pool2D(2, 'max', 2))
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        if num_classes > 0:
            self.fc = Sequential(
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                Linear(400, 120), Linear(120, 84), Linear(84, 10))
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    def forward(self, inputs):
        x = self.features(inputs)

        if self.num_classes > 0:
            x = fluid.layers.flatten(x, 1)
            x = self.fc(x)
        return x


class MnistDataset(MNIST):
    def __init__(self, mode, return_label=True, sample_num=None):
        super(MnistDataset, self).__init__(mode=mode)
        self.return_label = return_label
        if sample_num:
            self.images = self.images[:sample_num]
            self.labels = self.labels[:sample_num]

    def __getitem__(self, idx):
        img, label = self.images[idx], self.labels[idx]
        img = np.reshape(img, [1, 28, 28])
        if self.return_label:
            return img, np.array(self.labels[idx]).astype('int64')
        return img,

    def __len__(self):
        return len(self.images)


def compute_acc(pred, label):
    pred = np.argmax(pred, -1)
    label = np.array(label)
    correct = pred[:, np.newaxis] == label
    return np.sum(correct) / correct.shape[0]


def dynamic_train(model, dataloader):
    optim = fluid.optimizer.Adam(
        learning_rate=0.001, parameter_list=model.parameters())
    model.train()
    for inputs, labels in dataloader:
        outputs = model(inputs)
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        loss = CrossEntropyLoss(reduction="sum")(outputs, labels)
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        avg_loss = fluid.layers.reduce_sum(loss)
        avg_loss.backward()
        optim.minimize(avg_loss)
        model.clear_gradients()


def dynamic_evaluate(model, dataloader):
    with fluid.dygraph.no_grad():
        model.eval()
        cnt = 0
        for inputs, labels in dataloader:
            outputs = model(inputs)

            cnt += (np.argmax(outputs.numpy(), -1)[:, np.newaxis] ==
                    labels.numpy()).astype('int').sum()

    return cnt / len(dataloader.dataset)


@unittest.skipIf(not fluid.is_compiled_with_cuda(),
                 'CPU testing is not supported')
class TestModel(unittest.TestCase):
    @classmethod
    def setUpClass(cls):
        if not fluid.is_compiled_with_cuda():
            self.skipTest('module not tested when ONLY_CPU compling')
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        cls.device = paddle.set_device('gpu')
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        fluid.enable_dygraph(cls.device)

        sp_num = 1280
        cls.train_dataset = MnistDataset(mode='train', sample_num=sp_num)
        cls.val_dataset = MnistDataset(mode='test', sample_num=sp_num)
        cls.test_dataset = MnistDataset(
            mode='test', return_label=False, sample_num=sp_num)

        cls.train_loader = fluid.io.DataLoader(
            cls.train_dataset, places=cls.device, batch_size=64)
        cls.val_loader = fluid.io.DataLoader(
            cls.val_dataset, places=cls.device, batch_size=64)
        cls.test_loader = fluid.io.DataLoader(
            cls.test_dataset, places=cls.device, batch_size=64)

        seed = 333
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        paddle.seed(seed)
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        paddle.framework.random._manual_program_seed(seed)
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        dy_lenet = LeNetDygraph()
        cls.init_param = dy_lenet.state_dict()
        dynamic_train(dy_lenet, cls.train_loader)

        cls.acc1 = dynamic_evaluate(dy_lenet, cls.val_loader)

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        cls.inputs = [InputSpec([-1, 1, 28, 28], 'float32', 'image')]
        cls.labels = [InputSpec([None, 1], 'int64', 'label')]
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        cls.save_dir = tempfile.mkdtemp()
        cls.weight_path = os.path.join(cls.save_dir, 'lenet')
        fluid.dygraph.save_dygraph(dy_lenet.state_dict(), cls.weight_path)

        fluid.disable_dygraph()

    @classmethod
    def tearDownClass(cls):
        shutil.rmtree(cls.save_dir)

    def test_fit_dygraph(self):
        self.fit(True)

    def test_fit_static(self):
        self.fit(False)

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    def test_fit_dynamic_with_rank(self):
        self.fit(True, 2, 0)

    def test_fit_static_with_rank(self):
        self.fit(False, 2, 0)

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    def test_evaluate_dygraph(self):
        self.evaluate(True)

    def test_evaluate_static(self):
        self.evaluate(False)

    def test_predict_dygraph(self):
        self.predict(True)

    def test_predict_static(self):
        self.predict(False)

    def test_prepare_context(self):
        prepare_distributed_context()

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    def fit(self, dynamic, num_replicas=None, rank=None):
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        fluid.enable_dygraph(self.device) if dynamic else None
        seed = 333
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        paddle.seed(seed)
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        paddle.framework.random._manual_program_seed(seed)
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        net = LeNet()
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        optim_new = fluid.optimizer.Adam(
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            learning_rate=0.001, parameter_list=net.parameters())
        model = Model(net, inputs=self.inputs, labels=self.labels)
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        model.prepare(
            optim_new,
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            loss=CrossEntropyLoss(reduction="sum"),
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            metrics=Accuracy())
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        model.fit(self.train_dataset, batch_size=64, shuffle=False)

        result = model.evaluate(self.val_dataset, batch_size=64)
        np.testing.assert_allclose(result['acc'], self.acc1)

        train_sampler = DistributedBatchSampler(
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            self.train_dataset,
            batch_size=64,
            shuffle=False,
            num_replicas=num_replicas,
            rank=rank)
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        val_sampler = DistributedBatchSampler(
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            self.val_dataset,
            batch_size=64,
            shuffle=False,
            num_replicas=num_replicas,
            rank=rank)
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        train_loader = fluid.io.DataLoader(
            self.train_dataset,
            batch_sampler=train_sampler,
            places=self.device,
            return_list=True)

        val_loader = fluid.io.DataLoader(
            self.val_dataset,
            batch_sampler=val_sampler,
            places=self.device,
            return_list=True)

        model.fit(train_loader, val_loader)
        fluid.disable_dygraph() if dynamic else None

    def evaluate(self, dynamic):
        fluid.enable_dygraph(self.device) if dynamic else None
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        model = Model(LeNet(), self.inputs, self.labels)
        model.prepare(metrics=Accuracy())
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        model.load(self.weight_path)
        result = model.evaluate(self.val_dataset, batch_size=64)
        np.testing.assert_allclose(result['acc'], self.acc1)

        sampler = DistributedBatchSampler(
            self.val_dataset, batch_size=64, shuffle=False)

        val_loader = fluid.io.DataLoader(
            self.val_dataset,
            batch_sampler=sampler,
            places=self.device,
            return_list=True)

        model.evaluate(val_loader)

        fluid.disable_dygraph() if dynamic else None

    def predict(self, dynamic):
        fluid.enable_dygraph(self.device) if dynamic else None
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        model = Model(LeNet(), self.inputs)
        model.prepare()
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        model.load(self.weight_path)
        output = model.predict(
            self.test_dataset, batch_size=64, stack_outputs=True)
        np.testing.assert_equal(output[0].shape[0], len(self.test_dataset))

        acc = compute_acc(output[0], self.val_dataset.labels)
        np.testing.assert_allclose(acc, self.acc1)

        sampler = DistributedBatchSampler(
            self.test_dataset, batch_size=64, shuffle=False)

        test_loader = fluid.io.DataLoader(
            self.test_dataset,
            batch_sampler=sampler,
            places=self.device,
            return_list=True)

        model.evaluate(test_loader)

        fluid.disable_dygraph() if dynamic else None

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    def test_predict_without_inputs(self):
        fluid.enable_dygraph(self.device)
        model = Model(LeNet())
        model.prepare()
        model.load(self.weight_path)
        model._inputs = None
        output = model.predict(
            self.test_dataset, batch_size=64, stack_outputs=True)
        np.testing.assert_equal(output[0].shape[0], len(self.test_dataset))
        fluid.disable_dygraph()

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    def test_summary_gpu(self):
        paddle.disable_static(self.device)
        rnn = paddle.nn.LSTM(16, 32, 2)
        params_info = paddle.summary(
            rnn, [(-1, 23, 16), ((2, None, 32), (2, -1, 32))])

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class MyModel(paddle.nn.Layer):
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    def __init__(self):
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        super(MyModel, self).__init__()
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        self._fc = Linear(20, 10)
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    def forward(self, x):
        y = self._fc(x)
        return y


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class MyDataset(Dataset):
    def __getitem__(self, idx):
        return np.random.random(size=(20,)).astype(np.float32), \
               np.random.randint(0, 10, size=(1,)).astype(np.int64)

    def __len__(self):
        return 40


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class TestModelFunction(unittest.TestCase):
    def set_seed(self, seed=1024):
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        paddle.seed(seed)
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        paddle.framework.random._manual_program_seed(seed)
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    def test_train_batch(self, dynamic=True):
        dim = 20
        data = np.random.random(size=(4, dim)).astype(np.float32)
        label = np.random.randint(0, 10, size=(4, 1)).astype(np.int64)

        def get_expect():
            fluid.enable_dygraph(fluid.CPUPlace())
            self.set_seed()
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            m = MyModel()
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            optim = fluid.optimizer.SGD(learning_rate=0.001,
                                        parameter_list=m.parameters())
            m.train()
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            output = m(to_tensor(data))
            loss = CrossEntropyLoss(reduction='sum')(output, to_tensor(label))
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            avg_loss = fluid.layers.reduce_sum(loss)
            avg_loss.backward()
            optim.minimize(avg_loss)
            m.clear_gradients()
            fluid.disable_dygraph()
            return avg_loss.numpy()

        ref = get_expect()
        for dynamic in [True, False]:
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            device = paddle.set_device('cpu')
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            fluid.enable_dygraph(device) if dynamic else None
            self.set_seed()

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            net = MyModel()
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            optim2 = fluid.optimizer.SGD(learning_rate=0.001,
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                                         parameter_list=net.parameters())
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            inputs = [InputSpec([None, dim], 'float32', 'x')]
            labels = [InputSpec([None, 1], 'int64', 'label')]
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            model = Model(net, inputs, labels)
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            model.prepare(optim2, loss=CrossEntropyLoss(reduction="sum"))
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            loss, = model.train_batch([data], [label])
            np.testing.assert_allclose(loss.flatten(), ref.flatten())
            fluid.disable_dygraph() if dynamic else None

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    def test_test_batch(self):
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        dim = 20
        data = np.random.random(size=(4, dim)).astype(np.float32)

        def get_expect():
            fluid.enable_dygraph(fluid.CPUPlace())
            self.set_seed()
            m = MyModel()
            m.eval()
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            output = m(to_tensor(data))
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            fluid.disable_dygraph()
            return output.numpy()

        ref = get_expect()
        for dynamic in [True, False]:
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            device = paddle.set_device('cpu')
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            fluid.enable_dygraph(device) if dynamic else None
            self.set_seed()
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            net = MyModel()
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            inputs = [InputSpec([None, dim], 'float32', 'x')]
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            model = Model(net, inputs)
            model.prepare()
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            out, = model.predict_batch([data])
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            np.testing.assert_allclose(out, ref, rtol=1e-6)
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            fluid.disable_dygraph() if dynamic else None

    def test_save_load(self):
        path = tempfile.mkdtemp()
        for dynamic in [True, False]:
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            device = paddle.set_device('cpu')
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            fluid.enable_dygraph(device) if dynamic else None
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            net = MyModel()
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            inputs = [InputSpec([None, 20], 'float32', 'x')]
            labels = [InputSpec([None, 1], 'int64', 'label')]
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            optim = fluid.optimizer.SGD(learning_rate=0.001,
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                                        parameter_list=net.parameters())
            model = Model(net, inputs, labels)
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            model.prepare(
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                optimizer=optim, loss=CrossEntropyLoss(reduction="sum"))
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            model.save(path + '/test')
            model.load(path + '/test')
            shutil.rmtree(path)
            fluid.disable_dygraph() if dynamic else None

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    def test_dynamic_load(self):
        mnist_data = MnistDataset(mode='train')
        for new_optimizer in [True, False]:
            path = tempfile.mkdtemp()
            paddle.disable_static()
            net = LeNet()
            inputs = [InputSpec([None, 1, 28, 28], 'float32', 'x')]
            labels = [InputSpec([None, 1], 'int64', 'label')]
            if new_optimizer:
                optim = paddle.optimizer.Adam(
                    learning_rate=0.001, parameters=net.parameters())
            else:
                optim = fluid.optimizer.Adam(
                    learning_rate=0.001, parameter_list=net.parameters())
            model = Model(net, inputs, labels)
            model.prepare(
                optimizer=optim, loss=CrossEntropyLoss(reduction="sum"))
            model.fit(mnist_data, batch_size=64, verbose=0)
            model.save(path + '/test')
            model.load(path + '/test')
            shutil.rmtree(path)
            paddle.enable_static()

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    def test_dynamic_save_static_load(self):
        path = tempfile.mkdtemp()
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        # dynamic saving
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        device = paddle.set_device('cpu')
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        fluid.enable_dygraph(device)
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        model = Model(MyModel())
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        optim = fluid.optimizer.SGD(learning_rate=0.001,
                                    parameter_list=model.parameters())
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        model.prepare(optimizer=optim, loss=CrossEntropyLoss(reduction="sum"))
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        model.save(path + '/test')
        fluid.disable_dygraph()
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        inputs = [InputSpec([None, 20], 'float32', 'x')]
        labels = [InputSpec([None, 1], 'int64', 'label')]
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        model = Model(MyModel(), inputs, labels)
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        optim = fluid.optimizer.SGD(learning_rate=0.001,
                                    parameter_list=model.parameters())
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        model.prepare(optimizer=optim, loss=CrossEntropyLoss(reduction="sum"))
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        model.load(path + '/test')
        shutil.rmtree(path)

    def test_static_save_dynamic_load(self):
        path = tempfile.mkdtemp()

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        net = MyModel()
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        inputs = [InputSpec([None, 20], 'float32', 'x')]
        labels = [InputSpec([None, 1], 'int64', 'label')]
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        optim = fluid.optimizer.SGD(learning_rate=0.001,
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                                    parameter_list=net.parameters())
        model = Model(net, inputs, labels)
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        model.prepare(optimizer=optim, loss=CrossEntropyLoss(reduction="sum"))
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        model.save(path + '/test')

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        device = paddle.set_device('cpu')
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        fluid.enable_dygraph(device)  #if dynamic else None

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        net = MyModel()
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        inputs = [InputSpec([None, 20], 'float32', 'x')]
        labels = [InputSpec([None, 1], 'int64', 'label')]
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        optim = fluid.optimizer.SGD(learning_rate=0.001,
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                                    parameter_list=net.parameters())
        model = Model(net, inputs, labels)
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        model.prepare(optimizer=optim, loss=CrossEntropyLoss(reduction="sum"))
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        model.load(path + '/test')
        shutil.rmtree(path)
        fluid.disable_dygraph()

    def test_parameters(self):
        for dynamic in [True, False]:
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            device = paddle.set_device('cpu')
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            fluid.enable_dygraph(device) if dynamic else None
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            net = MyModel()
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            inputs = [InputSpec([None, 20], 'float32', 'x')]
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            model = Model(net, inputs)
            model.prepare()
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            params = model.parameters()
            self.assertTrue(params[0].shape[0] == 20)
            self.assertTrue(params[0].shape[1] == 10)
            fluid.disable_dygraph() if dynamic else None

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    def test_summary(self):
        def _get_param_from_state_dict(state_dict):
            params = 0
            for k, v in state_dict.items():
                params += np.prod(v.numpy().shape)
            return params

        for dynamic in [True, False]:
            device = paddle.set_device('cpu')
            fluid.enable_dygraph(device) if dynamic else None
            net = MyModel()
            inputs = [InputSpec([None, 20], 'float32', 'x')]
            model = Model(net, inputs)
            model.prepare()
            params_info = model.summary()
            gt_params = _get_param_from_state_dict(net.state_dict())

            np.testing.assert_allclose(params_info['total_params'], gt_params)
            print(params_info)

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            model.summary(input_size=(20))
            model.summary(input_size=[(20)])
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            model.summary(input_size=(20), dtype='float32')
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    def test_summary_nlp(self):
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        def _get_param_from_state_dict(state_dict):
            params = 0
            for k, v in state_dict.items():
                params += np.prod(v.numpy().shape)
            return params

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        nlp_net = paddle.nn.GRU(input_size=2,
                                hidden_size=3,
                                num_layers=3,
                                direction="bidirectional")
        paddle.summary(nlp_net, (1, 1, 2))
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        rnn = paddle.nn.LSTM(16, 32, 2)
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        params_info = paddle.summary(
            rnn, [(-1, 23, 16), ((2, None, 32), (2, -1, 32))])
        gt_params = _get_param_from_state_dict(rnn.state_dict())
        np.testing.assert_allclose(params_info['total_params'], gt_params / 2.0)

        rnn = paddle.nn.GRU(16, 32, 2, direction='bidirectional')
        params_info = paddle.summary(rnn, (4, 23, 16))
        gt_params = _get_param_from_state_dict(rnn.state_dict())
        np.testing.assert_allclose(params_info['total_params'], gt_params / 2.0)

        rnn = paddle.nn.SimpleRNN(16, 32, 2, direction='bidirectional')
        params_info = paddle.summary(rnn, (4, 23, 16))
        gt_params = _get_param_from_state_dict(rnn.state_dict())
        np.testing.assert_allclose(params_info['total_params'], gt_params / 2.0)
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    def test_summary_dtype(self):
        input_shape = (3, 1)
        net = paddle.nn.Embedding(10, 3, sparse=True)
        paddle.summary(net, input_shape, dtypes='int64')

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    def test_summary_error(self):
        with self.assertRaises(TypeError):
            nlp_net = paddle.nn.GRU(input_size=2, hidden_size=3, num_layers=3)
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            paddle.summary(nlp_net, (1, 1, '2'))
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        with self.assertRaises(ValueError):
            nlp_net = paddle.nn.GRU(input_size=2, hidden_size=3, num_layers=3)
            paddle.summary(nlp_net, (-1, -1))

        paddle.disable_static()
        nlp_net = paddle.nn.GRU(input_size=2, hidden_size=3, num_layers=3)
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        paddle.summary(nlp_net, (1, 1, 2))
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    def test_export_deploy_model(self):
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        self.set_seed()
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        np.random.seed(201)
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        for dynamic in [True, False]:
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            paddle.disable_static() if dynamic else None
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            prog_translator = ProgramTranslator()
            prog_translator.enable(False) if not dynamic else None
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            net = LeNet()
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            inputs = [InputSpec([None, 1, 28, 28], 'float32', 'x')]
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            model = Model(net, inputs)
            model.prepare()
            save_dir = tempfile.mkdtemp()
            if not os.path.exists(save_dir):
                os.makedirs(save_dir)
            tensor_img = np.array(
                np.random.random((1, 1, 28, 28)), dtype=np.float32)
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            model.save(save_dir, training=False)
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            ori_results = model.predict_batch(tensor_img)
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            fluid.disable_dygraph() if dynamic else None
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            place = fluid.CPUPlace() if not fluid.is_compiled_with_cuda(
            ) else fluid.CUDAPlace(0)
            new_scope = fluid.Scope()
            with fluid.scope_guard(new_scope):
                exe = fluid.Executor(place)
                [inference_program, feed_target_names, fetch_targets] = (
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                    paddle.static.io.load_inference_model(
                        path_prefix=save_dir, executor=exe))
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                results = exe.run(inference_program,
                                  feed={feed_target_names[0]: tensor_img},
                                  fetch_list=fetch_targets)
                np.testing.assert_allclose(
                    results, ori_results, rtol=1e-5, atol=1e-7)
                shutil.rmtree(save_dir)
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            paddle.enable_static()
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    def test_dygraph_export_deploy_model_about_inputs(self):
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        mnist_data = MnistDataset(mode='train')
        paddle.disable_static()
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        # without inputs
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        for initial in ["fit", "train_batch", "eval_batch", "predict_batch"]:
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            save_dir = tempfile.mkdtemp()
            if not os.path.exists(save_dir):
                os.makedirs(save_dir)
            net = LeNet()
            model = Model(net)
            optim = fluid.optimizer.Adam(
                learning_rate=0.001, parameter_list=model.parameters())
            model.prepare(
                optimizer=optim, loss=CrossEntropyLoss(reduction="sum"))
            if initial == "fit":
                model.fit(mnist_data, batch_size=64, verbose=0)
            else:
                img = np.array(
                    np.random.random((1, 1, 28, 28)), dtype=np.float32)
                label = np.array(np.random.rand(1, 1), dtype=np.int64)
                if initial == "train_batch":
                    model.train_batch([img], [label])
                elif initial == "eval_batch":
                    model.eval_batch([img], [label])
                else:
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                    model.predict_batch([img])
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            model.save(save_dir, training=False)
            shutil.rmtree(save_dir)
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        # with inputs, and the type of inputs is InputSpec
        save_dir = tempfile.mkdtemp()
        if not os.path.exists(save_dir):
            os.makedirs(save_dir)
        net = LeNet()
        inputs = InputSpec([None, 1, 28, 28], 'float32', 'x')
        model = Model(net, inputs)
        optim = fluid.optimizer.Adam(
            learning_rate=0.001, parameter_list=model.parameters())
        model.prepare(optimizer=optim, loss=CrossEntropyLoss(reduction="sum"))
        model.save(save_dir, training=False)
        shutil.rmtree(save_dir)
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class TestModelWithLRScheduler(unittest.TestCase):
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    def test_fit_by_step(self):
        base_lr = 1e-3
        boundaries = [5, 8]

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        def make_optimizer(parameters=None):
            momentum = 0.9
            weight_decay = 5e-4
            values = [base_lr * (0.1**i) for i in range(len(boundaries) + 1)]
            learning_rate = paddle.optimizer.lr.PiecewiseDecay(
                boundaries=boundaries, values=values)
            learning_rate = paddle.optimizer.lr.LinearWarmup(
                learning_rate=learning_rate,
                warmup_steps=4,
                start_lr=base_lr / 5.,
                end_lr=base_lr,
                verbose=True)
            optimizer = paddle.optimizer.Momentum(
                learning_rate=learning_rate,
                weight_decay=weight_decay,
                momentum=momentum,
                parameters=parameters)
            return optimizer

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        # dynamic test
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        device = paddle.set_device('cpu')
        fluid.enable_dygraph(device)
        net = MyModel()
        inputs = [InputSpec([None, 20], 'float32', 'x')]
        labels = [InputSpec([None, 1], 'int64', 'label')]
        optim = make_optimizer(net.parameters())
        model = Model(net, inputs, labels)
        model.prepare(optimizer=optim, loss=CrossEntropyLoss(reduction="sum"))

        dataset = MyDataset()
        model.fit(dataset, dataset, batch_size=4, epochs=10, num_workers=0)

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        np.testing.assert_allclose(model._optimizer._learning_rate.last_lr,
                                   base_lr * (0.1**len(boundaries)))
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        # static test
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        paddle.enable_static()

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        net = MyModel()
        inputs = [InputSpec([None, 20], 'float32', 'x')]
        labels = [InputSpec([None, 1], 'int64', 'label')]
        optim = make_optimizer(net.parameters())
        model = Model(net, inputs, labels)
        model.prepare(optimizer=optim, loss=CrossEntropyLoss(reduction="sum"))

        dataset = MyDataset()
        model.fit(dataset, dataset, batch_size=4, epochs=10, num_workers=0)

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        np.testing.assert_allclose(model._optimizer._learning_rate.last_lr,
                                   base_lr * (0.1**len(boundaries)))

    def test_fit_by_epoch(self):
        base_lr = 1e-3
        boundaries = [5, 8]
        epochs = 10
        wamup_epochs = 4

        def make_optimizer(parameters=None):
            momentum = 0.9
            weight_decay = 5e-4
            values = [base_lr * (0.1**i) for i in range(len(boundaries) + 1)]
            learning_rate = paddle.optimizer.lr.PiecewiseDecay(
                boundaries=boundaries, values=values)
            learning_rate = paddle.optimizer.lr.LinearWarmup(
                learning_rate=learning_rate,
                warmup_steps=wamup_epochs,
                start_lr=base_lr / 5.,
                end_lr=base_lr,
                verbose=True)
            optimizer = paddle.optimizer.Momentum(
                learning_rate=learning_rate,
                weight_decay=weight_decay,
                momentum=momentum,
                parameters=parameters)
            return optimizer

        # dynamic test
        device = paddle.set_device('cpu')
        fluid.enable_dygraph(device)
        net = MyModel()
        inputs = [InputSpec([None, 20], 'float32', 'x')]
        labels = [InputSpec([None, 1], 'int64', 'label')]
        optim = make_optimizer(net.parameters())
        model = Model(net, inputs, labels)
        model.prepare(optimizer=optim, loss=CrossEntropyLoss(reduction="sum"))

        dataset = MyDataset()

        lr_scheduler_callback = paddle.callbacks.LRScheduler(
            by_step=False, by_epoch=True)

        model.fit(dataset,
                  dataset,
                  batch_size=4,
                  epochs=epochs,
                  num_workers=0,
                  callbacks=lr_scheduler_callback)

        cnt = 0
        for b in boundaries:
            if b + wamup_epochs <= epochs:
                cnt += 1

        np.testing.assert_allclose(model._optimizer._learning_rate.last_lr,
                                   base_lr * (0.1**cnt))
        # static test
        paddle.enable_static()

        net = MyModel()
        inputs = [InputSpec([None, 20], 'float32', 'x')]
        labels = [InputSpec([None, 1], 'int64', 'label')]
        optim = make_optimizer(net.parameters())
        model = Model(net, inputs, labels)
        model.prepare(optimizer=optim, loss=CrossEntropyLoss(reduction="sum"))

        dataset = MyDataset()

        lr_scheduler_callback = paddle.callbacks.LRScheduler(
            by_step=False, by_epoch=True)

        model.fit(dataset,
                  dataset,
                  batch_size=4,
                  epochs=epochs,
                  num_workers=0,
                  callbacks=lr_scheduler_callback)

        cnt = 0
        for b in boundaries:
            if b + wamup_epochs <= epochs:
                cnt += 1

        np.testing.assert_allclose(model._optimizer._learning_rate.last_lr,
                                   base_lr * (0.1**cnt))

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class TestRaiseError(unittest.TestCase):
    def test_input_without_name(self):
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        net = MyModel()
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        inputs = [InputSpec([None, 10], 'float32')]
        labels = [InputSpec([None, 1], 'int64', 'label')]
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        with self.assertRaises(ValueError):
            model = Model(net, inputs, labels)

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    def test_static_without_inputs(self):
        paddle.enable_static()
        net = MyModel()
        with self.assertRaises(TypeError):
            model = Model(net)

    def test_save_infer_model_without_inputs_and_run_in_dygraph(self):
        paddle.disable_static()
        net = MyModel()
        save_dir = tempfile.mkdtemp()
        if not os.path.exists(save_dir):
            os.makedirs(save_dir)
        with self.assertRaises(RuntimeError):
            model = Model(net)
            model.save(save_dir, training=False)
        paddle.enable_static()
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    def test_save_infer_model_without_file_prefix(self):
        paddle.enable_static()
        net = LeNet()
        inputs = [InputSpec([None, 1, 28, 28], 'float32', 'x')]
        model = Model(net, inputs)
        model.prepare()
        path = ""
        tensor_img = np.array(
            np.random.random((1, 1, 28, 28)), dtype=np.float32)
        with self.assertRaises(ValueError):
            model.save(path, training=False)

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