wehub-resource-sync
1176 lines
38 KiB
Python
1176 lines
38 KiB
Python
# copyright (c) 2020 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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import os
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import shutil
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import tempfile
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import unittest
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import numpy as np
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from op_test import get_device, get_device_place, is_custom_device
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import paddle
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from paddle import Model, base, jit, to_tensor
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from paddle.hapi.model import prepare_distributed_context
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from paddle.io import Dataset, DistributedBatchSampler
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from paddle.metric import Accuracy
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from paddle.nn import Conv2D, Linear, ReLU, Sequential
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from paddle.nn.layer.loss import CrossEntropyLoss
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from paddle.static import InputSpec
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from paddle.vision import models
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from paddle.vision.datasets import MNIST
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from paddle.vision.models import LeNet
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class LeNetDygraph(paddle.nn.Layer):
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def __init__(self, num_classes=10):
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super().__init__()
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self.num_classes = num_classes
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self.features = Sequential(
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Conv2D(1, 6, 3, stride=1, padding=1),
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ReLU(),
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paddle.nn.MaxPool2D(2, 2),
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Conv2D(6, 16, 5, stride=1, padding=0),
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ReLU(),
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paddle.nn.MaxPool2D(2, 2),
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)
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if num_classes > 0:
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self.fc = Sequential(
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Linear(400, 120), Linear(120, 84), Linear(84, 10)
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)
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def forward(self, inputs):
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x = self.features(inputs)
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if self.num_classes > 0:
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x = paddle.flatten(x, 1, -1)
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x = self.fc(x)
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return x
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class ModelInner(paddle.nn.Layer):
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def __init__(self):
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super().__init__()
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self.fc = paddle.nn.Linear(3, 4)
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def forward(self, x):
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y = self.fc(x)
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return y, 0
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class ModelOuter(paddle.nn.Layer):
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def __init__(self):
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super().__init__()
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self.module1 = ModelInner()
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self.module2 = paddle.nn.Linear(4, 5)
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def forward(self, x):
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y, _ = self.module1(x)
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y = self.module2(y)
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return y, 3
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class LeNetListInput(paddle.nn.Layer):
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def __init__(self, num_classes=10):
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super().__init__()
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self.num_classes = num_classes
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self.cov = Conv2D(1, 6, 3, stride=1, padding=1)
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for param in self.cov.parameters():
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param.trainable = False
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self.features = Sequential(
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self.cov,
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ReLU(),
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paddle.nn.MaxPool2D(2, 2),
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Conv2D(6, 16, 5, stride=1, padding=0),
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ReLU(),
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paddle.nn.MaxPool2D(2, 2),
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)
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if num_classes > 0:
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self.fc = Sequential(
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Linear(400, 120), Linear(120, 84), Linear(84, 10)
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)
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def forward(self, inputs):
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x = inputs[0]
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x = self.features(x)
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if self.num_classes > 0:
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x = paddle.flatten(x, 1)
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x = self.fc(x + inputs[1])
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return x
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class LeNetDictInput(LeNetDygraph):
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def forward(self, inputs):
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x = self.features(inputs['x1'])
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if self.num_classes > 0:
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x = paddle.flatten(x, 1)
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x = self.fc(x + inputs['x2'])
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return x
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class MnistDataset(MNIST):
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def __init__(self, mode, return_label=True, sample_num=None):
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super().__init__(mode=mode)
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self.return_label = return_label
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if sample_num:
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self.images = self.images[:sample_num]
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self.labels = self.labels[:sample_num]
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def __getitem__(self, idx):
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img, label = self.images[idx], self.labels[idx]
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img = np.reshape(img, [1, 28, 28])
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if self.return_label:
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return img, np.array(self.labels[idx]).astype('int64')
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return (img,)
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def __len__(self):
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return len(self.images)
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def compute_acc(pred, label):
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pred = np.argmax(pred, -1)
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label = np.array(label)
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correct = pred[:, np.newaxis] == label
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return np.sum(correct) / correct.shape[0]
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def dynamic_train(model, dataloader):
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optim = paddle.optimizer.Adam(
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learning_rate=0.001, parameters=model.parameters()
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)
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model.train()
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for inputs, labels in dataloader:
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outputs = model(inputs)
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loss = CrossEntropyLoss(reduction="sum")(outputs, labels)
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avg_loss = paddle.sum(loss)
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avg_loss.backward()
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optim.minimize(avg_loss)
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model.clear_gradients()
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def dynamic_evaluate(model, dataloader):
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with base.dygraph.no_grad():
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model.eval()
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cnt = 0
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for inputs, labels in dataloader:
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outputs = model(inputs)
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cnt += (
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(
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np.argmax(outputs.numpy(), -1)[:, np.newaxis]
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== labels.numpy()
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)
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.astype('int')
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.sum()
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)
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return cnt / len(dataloader.dataset)
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@unittest.skipIf(
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not (base.is_compiled_with_cuda() or is_custom_device()),
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'CPU testing is not supported',
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)
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class TestModel(unittest.TestCase):
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@classmethod
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def setUpClass(cls):
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if not (base.is_compiled_with_cuda() or is_custom_device()):
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cls().skipTest('module not tested when ONLY_CPU compiling')
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cls.device = paddle.set_device(get_device())
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base.enable_dygraph(cls.device)
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sp_num = 1280
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cls.train_dataset = MnistDataset(mode='train', sample_num=sp_num)
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cls.val_dataset = MnistDataset(mode='test', sample_num=sp_num)
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cls.test_dataset = MnistDataset(
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mode='test', return_label=False, sample_num=sp_num
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)
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cls.train_loader = paddle.io.DataLoader(
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cls.train_dataset, places=cls.device, batch_size=64
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)
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cls.val_loader = paddle.io.DataLoader(
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cls.val_dataset, places=cls.device, batch_size=64
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)
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cls.test_loader = paddle.io.DataLoader(
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cls.test_dataset, places=cls.device, batch_size=64
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)
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seed = 555
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paddle.seed(seed)
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if paddle.framework.use_pir_api():
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with paddle.pir_utils.OldIrGuard():
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paddle.framework.random._manual_program_seed(seed)
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paddle.framework.random._manual_program_seed(seed)
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else:
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paddle.framework.random._manual_program_seed(seed)
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dy_lenet = LeNetDygraph()
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cls.init_param = dy_lenet.state_dict()
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dynamic_train(dy_lenet, cls.train_loader)
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cls.acc1 = dynamic_evaluate(dy_lenet, cls.val_loader)
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cls.inputs = [InputSpec([-1, 1, 28, 28], 'float32', 'image')]
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cls.labels = [InputSpec([None, 1], 'int64', 'label')]
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cls.save_dir = os.path.join(tempfile.mkdtemp(), '.cache_test_model')
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if not os.path.exists(cls.save_dir):
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os.makedirs(cls.save_dir)
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cls.weight_path = os.path.join(cls.save_dir, 'lenet')
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paddle.save(dy_lenet.state_dict(), cls.weight_path + '.pdparams')
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base.disable_dygraph()
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@classmethod
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def tearDownClass(cls):
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shutil.rmtree(cls.save_dir)
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def test_fit_dygraph(self):
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self.fit(True)
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def test_fit_static(self):
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self.fit(False)
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def test_fit_dynamic_with_tuple_input(self):
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self.fit_with_tuple_input(True)
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def test_fit_static_with_tuple_input(self):
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self.fit_with_tuple_input(False)
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def test_fit_dynamic_with_rank(self):
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self.fit(True, 2, 0)
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def test_fit_static_with_rank(self):
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self.fit(False, 2, 0)
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def test_fit_dynamic_with_num_iters(self):
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self.fit(True, num_iters=1)
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def test_fit_static_with_num_iters(self):
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self.fit(False, num_iters=1)
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def test_evaluate_dygraph(self):
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self.evaluate(True)
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def test_evaluate_static(self):
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self.evaluate(False)
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def test_predict_dygraph(self):
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self.predict(True)
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def test_predict_static(self):
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self.predict(False)
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def test_prepare_context(self):
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prepare_distributed_context()
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def fit(self, dynamic, num_replicas=None, rank=None, num_iters=None):
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base.enable_dygraph(self.device) if dynamic else None
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seed = 555
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paddle.seed(seed)
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if paddle.framework.use_pir_api():
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with paddle.pir_utils.OldIrGuard():
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paddle.framework.random._manual_program_seed(seed)
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paddle.framework.random._manual_program_seed(seed)
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else:
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paddle.framework.random._manual_program_seed(seed)
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net = LeNet()
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optim_new = paddle.optimizer.Adam(
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learning_rate=0.001, parameters=net.parameters()
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)
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model = Model(net, inputs=self.inputs, labels=self.labels)
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model.prepare(
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optim_new,
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loss=CrossEntropyLoss(reduction="sum"),
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metrics=Accuracy(),
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)
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model.fit(self.train_dataset, batch_size=64, shuffle=False)
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result = model.evaluate(self.val_dataset, batch_size=64)
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np.testing.assert_allclose(result['acc'], self.acc1)
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model.fit(
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self.train_dataset,
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batch_size=64,
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shuffle=False,
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num_iters=num_iters,
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)
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result = model.evaluate(
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self.val_dataset, batch_size=64, num_iters=num_iters
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)
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model.fit(self.train_dataset, batch_size=(64, 64), shuffle=False)
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train_sampler = DistributedBatchSampler(
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self.train_dataset,
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batch_size=64,
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shuffle=False,
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num_replicas=num_replicas,
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rank=rank,
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)
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val_sampler = DistributedBatchSampler(
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self.val_dataset,
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batch_size=64,
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shuffle=False,
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num_replicas=num_replicas,
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rank=rank,
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)
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train_loader = paddle.io.DataLoader(
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self.train_dataset,
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batch_sampler=train_sampler,
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places=self.device,
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return_list=True,
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)
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val_loader = paddle.io.DataLoader(
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self.val_dataset,
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batch_sampler=val_sampler,
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places=self.device,
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return_list=True,
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)
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model.fit(train_loader, val_loader)
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base.disable_dygraph() if dynamic else None
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def fit_with_tuple_input(self, dynamic, num_replicas=None, rank=None):
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base.enable_dygraph(self.device) if dynamic else None
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seed = 555
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paddle.seed(seed)
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if paddle.framework.use_pir_api():
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with paddle.pir_utils.OldIrGuard():
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paddle.framework.random._manual_program_seed(seed)
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paddle.framework.random._manual_program_seed(seed)
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else:
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paddle.framework.random._manual_program_seed(seed)
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net = LeNet()
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optim_new = paddle.optimizer.Adam(
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learning_rate=0.001, parameters=net.parameters()
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)
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model = Model(net, inputs=tuple(self.inputs), labels=tuple(self.labels))
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model.prepare(
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optim_new,
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loss=CrossEntropyLoss(reduction="sum"),
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metrics=Accuracy(),
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)
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model.fit(self.train_dataset, batch_size=64, shuffle=False)
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result = model.evaluate(self.val_dataset, batch_size=64)
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np.testing.assert_allclose(result['acc'], self.acc1)
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train_sampler = DistributedBatchSampler(
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self.train_dataset,
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batch_size=64,
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shuffle=False,
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num_replicas=num_replicas,
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rank=rank,
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)
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val_sampler = DistributedBatchSampler(
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self.val_dataset,
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batch_size=64,
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shuffle=False,
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num_replicas=num_replicas,
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rank=rank,
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)
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train_loader = paddle.io.DataLoader(
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self.train_dataset,
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batch_sampler=train_sampler,
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places=self.device,
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return_list=True,
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)
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val_loader = paddle.io.DataLoader(
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self.val_dataset,
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batch_sampler=val_sampler,
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places=self.device,
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return_list=True,
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)
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model.fit(train_loader, val_loader)
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base.disable_dygraph() if dynamic else None
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def evaluate(self, dynamic):
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base.enable_dygraph(self.device) if dynamic else None
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model = Model(LeNet(), self.inputs, self.labels)
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model.prepare(metrics=Accuracy())
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model.load(self.weight_path)
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result = model.evaluate(self.val_dataset, batch_size=64)
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np.testing.assert_allclose(result['acc'], self.acc1)
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sampler = DistributedBatchSampler(
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self.val_dataset, batch_size=64, shuffle=False
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)
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val_loader = paddle.io.DataLoader(
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self.val_dataset,
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batch_sampler=sampler,
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places=self.device,
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return_list=True,
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)
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model.evaluate(val_loader)
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base.disable_dygraph() if dynamic else None
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def predict(self, dynamic):
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base.enable_dygraph(self.device) if dynamic else None
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model = Model(LeNet(), self.inputs)
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model.prepare()
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model.load(self.weight_path)
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output = model.predict(
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self.test_dataset, batch_size=64, stack_outputs=True
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)
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np.testing.assert_equal(output[0].shape[0], len(self.test_dataset))
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acc = compute_acc(output[0], self.val_dataset.labels)
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np.testing.assert_allclose(acc, self.acc1)
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sampler = DistributedBatchSampler(
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self.test_dataset, batch_size=64, shuffle=False
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)
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test_loader = paddle.io.DataLoader(
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self.test_dataset,
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batch_sampler=sampler,
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places=self.device,
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return_list=True,
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)
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model.evaluate(test_loader)
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base.disable_dygraph() if dynamic else None
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def test_predict_without_inputs(self):
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base.enable_dygraph(self.device)
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model = Model(LeNet())
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model.prepare()
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model.load(self.weight_path)
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model._inputs = None
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output = model.predict(
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self.test_dataset, batch_size=64, stack_outputs=True
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)
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np.testing.assert_equal(output[0].shape[0], len(self.test_dataset))
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base.disable_dygraph()
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def test_summary_gpu(self):
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paddle.disable_static(self.device)
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rnn = paddle.nn.LSTM(16, 32, 2)
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params_info = paddle.summary(
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rnn, [(-1, 23, 16), ((2, None, 32), (2, -1, 32))]
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)
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class MyModel(paddle.nn.Layer):
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def __init__(self):
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super().__init__()
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self._fc = Linear(20, 10)
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def forward(self, x):
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y = self._fc(x)
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return y
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class MyDataset(Dataset):
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def __getitem__(self, idx):
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return np.random.random(size=(20,)).astype(
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np.float32
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), np.random.randint(0, 10, size=(1,)).astype(np.int64)
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def __len__(self):
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return 40
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class TestModelFunction(unittest.TestCase):
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def set_seed(self, seed=1024):
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paddle.seed(seed)
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if paddle.framework.use_pir_api():
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with paddle.pir_utils.OldIrGuard():
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paddle.framework.random._manual_program_seed(seed)
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paddle.framework.random._manual_program_seed(seed)
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else:
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paddle.framework.random._manual_program_seed(seed)
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def test_train_batch(self, dynamic=True):
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dim = 20
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data = np.random.random(size=(4, dim)).astype(np.float32)
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label = np.random.randint(0, 10, size=(4, 1)).astype(np.int64)
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def get_expect():
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base.enable_dygraph(base.CPUPlace())
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self.set_seed()
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m = MyModel()
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optim = paddle.optimizer.SGD(
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learning_rate=0.001, parameters=m.parameters()
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)
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m.train()
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output = m(to_tensor(data))
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loss = CrossEntropyLoss(reduction='sum')(output, to_tensor(label))
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avg_loss = paddle.sum(loss)
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avg_loss.backward()
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optim.minimize(avg_loss)
|
|
m.clear_gradients()
|
|
base.disable_dygraph()
|
|
return avg_loss.numpy()
|
|
|
|
ref = get_expect()
|
|
for dynamic in [True, False]:
|
|
device = paddle.set_device('cpu')
|
|
base.enable_dygraph(device) if dynamic else None
|
|
self.set_seed()
|
|
|
|
net = MyModel()
|
|
optim2 = paddle.optimizer.SGD(
|
|
learning_rate=0.001, parameters=net.parameters()
|
|
)
|
|
|
|
inputs = [InputSpec([None, dim], 'float32', 'x')]
|
|
labels = [InputSpec([None, 1], 'int64', 'label')]
|
|
model = Model(net, inputs, labels)
|
|
model.prepare(optim2, loss=CrossEntropyLoss(reduction="sum"))
|
|
(loss,) = model.train_batch([data], [label])
|
|
np.testing.assert_allclose(loss.flatten(), ref.flatten())
|
|
base.disable_dygraph() if dynamic else None
|
|
|
|
def test_test_batch(self):
|
|
dim = 20
|
|
data = np.random.random(size=(4, dim)).astype(np.float32)
|
|
|
|
def get_expect():
|
|
base.enable_dygraph(base.CPUPlace())
|
|
self.set_seed()
|
|
m = MyModel()
|
|
m.eval()
|
|
output = m(to_tensor(data))
|
|
base.disable_dygraph()
|
|
return output.numpy()
|
|
|
|
ref = get_expect()
|
|
for dynamic in [True, False]:
|
|
device = paddle.set_device('cpu')
|
|
base.enable_dygraph(device) if dynamic else None
|
|
self.set_seed()
|
|
startup = paddle.base.default_startup_program()
|
|
net = MyModel()
|
|
inputs = [InputSpec([None, dim], 'float32', 'x')]
|
|
model = Model(net, inputs)
|
|
model.prepare()
|
|
(out,) = model.predict_batch([data])
|
|
if dynamic:
|
|
out_dy = out
|
|
else:
|
|
out_st = out
|
|
np.testing.assert_allclose(out, ref, rtol=1e-6)
|
|
base.disable_dygraph() if dynamic else None
|
|
|
|
def test_save_load(self):
|
|
path = os.path.join(tempfile.mkdtemp(), '.cache_test_save_load')
|
|
if not os.path.exists(path):
|
|
os.makedirs(path)
|
|
for dynamic in [True, False]:
|
|
device = paddle.set_device('cpu')
|
|
base.enable_dygraph(device) if dynamic else None
|
|
|
|
net = MyModel()
|
|
inputs = [InputSpec([None, 20], 'float32', 'x')]
|
|
labels = [InputSpec([None, 1], 'int64', 'label')]
|
|
optim = paddle.optimizer.SGD(
|
|
learning_rate=0.001, parameters=net.parameters()
|
|
)
|
|
model = Model(net, inputs, labels)
|
|
model.prepare(
|
|
optimizer=optim, loss=CrossEntropyLoss(reduction="sum")
|
|
)
|
|
model.save(path)
|
|
model.load(path)
|
|
base.disable_dygraph() if dynamic else None
|
|
shutil.rmtree(path)
|
|
|
|
def test_dynamic_load(self):
|
|
mnist_data = MnistDataset(mode='train')
|
|
|
|
path = os.path.join(tempfile.mkdtemp(), '.cache_dynamic_load')
|
|
if not os.path.exists(path):
|
|
os.makedirs(path)
|
|
|
|
for new_optimizer in [True, False]:
|
|
paddle.disable_static()
|
|
net = LeNet()
|
|
inputs = [InputSpec([None, 1, 28, 28], 'float32', 'x')]
|
|
labels = [InputSpec([None, 1], 'int64', 'label')]
|
|
optim = paddle.optimizer.Adam(
|
|
learning_rate=0.001, parameters=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)
|
|
model.load(path)
|
|
paddle.enable_static()
|
|
shutil.rmtree(path)
|
|
|
|
def test_dynamic_save_static_load(self):
|
|
path = os.path.join(
|
|
tempfile.mkdtemp(), '.cache_dynamic_save_static_load'
|
|
)
|
|
if not os.path.exists(path):
|
|
os.makedirs(path)
|
|
# dynamic saving
|
|
device = paddle.set_device('cpu')
|
|
base.enable_dygraph(device)
|
|
model = Model(MyModel())
|
|
optim = paddle.optimizer.SGD(
|
|
learning_rate=0.001, parameters=model.parameters()
|
|
)
|
|
model.prepare(optimizer=optim, loss=CrossEntropyLoss(reduction="sum"))
|
|
model.save(path)
|
|
base.disable_dygraph()
|
|
|
|
inputs = [InputSpec([None, 20], 'float32', 'x')]
|
|
labels = [InputSpec([None, 1], 'int64', 'label')]
|
|
model = Model(MyModel(), inputs, labels)
|
|
optim = paddle.optimizer.SGD(
|
|
learning_rate=0.001, parameters=model.parameters()
|
|
)
|
|
model.prepare(optimizer=optim, loss=CrossEntropyLoss(reduction="sum"))
|
|
model.load(path)
|
|
shutil.rmtree(path)
|
|
|
|
def test_static_save_dynamic_load(self):
|
|
path = os.path.join(
|
|
tempfile.mkdtemp(), '.cache_test_static_save_dynamic_load'
|
|
)
|
|
if not os.path.exists(path):
|
|
os.makedirs(path)
|
|
|
|
net = MyModel()
|
|
inputs = [InputSpec([None, 20], 'float32', 'x')]
|
|
labels = [InputSpec([None, 1], 'int64', 'label')]
|
|
optim = paddle.optimizer.SGD(
|
|
learning_rate=0.001, parameters=net.parameters()
|
|
)
|
|
model = Model(net, inputs, labels)
|
|
model.prepare(optimizer=optim, loss=CrossEntropyLoss(reduction="sum"))
|
|
model.save(path)
|
|
|
|
device = paddle.set_device('cpu')
|
|
base.enable_dygraph(device) # if dynamic else None
|
|
|
|
net = MyModel()
|
|
inputs = [InputSpec([None, 20], 'float32', 'x')]
|
|
labels = [InputSpec([None, 1], 'int64', 'label')]
|
|
optim = paddle.optimizer.SGD(
|
|
learning_rate=0.001, parameters=net.parameters()
|
|
)
|
|
model = Model(net, inputs, labels)
|
|
model.prepare(optimizer=optim, loss=CrossEntropyLoss(reduction="sum"))
|
|
model.load(path)
|
|
shutil.rmtree(path)
|
|
base.disable_dygraph()
|
|
|
|
def test_parameters(self):
|
|
for dynamic in [True, False]:
|
|
device = paddle.set_device('cpu')
|
|
base.enable_dygraph(device) if dynamic else None
|
|
|
|
net = MyModel()
|
|
inputs = [InputSpec([None, 20], 'float32', 'x')]
|
|
model = Model(net, inputs)
|
|
model.prepare()
|
|
params = model.parameters()
|
|
self.assertTrue(params[0].shape[0] == 20)
|
|
self.assertTrue(params[0].shape[1] == 10)
|
|
base.disable_dygraph() if dynamic else None
|
|
|
|
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')
|
|
base.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)
|
|
|
|
model.summary(input_size=(20))
|
|
model.summary(input_size=[20])
|
|
model.summary(input_size=(20), dtype='float32')
|
|
|
|
def test_summary_non_tensor(self):
|
|
paddle.summary(ModelOuter(), input_size=(-1, 3))
|
|
|
|
def test_summary_nlp(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
|
|
|
|
nlp_net = paddle.nn.GRU(
|
|
input_size=2, hidden_size=3, num_layers=3, direction="bidirectional"
|
|
)
|
|
paddle.summary(nlp_net, (1, 1, 2))
|
|
|
|
rnn = paddle.nn.LSTM(16, 32, 2)
|
|
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)
|
|
|
|
def test_summary_input(self):
|
|
paddle.enable_static()
|
|
mymodel = MyModel()
|
|
input_data = paddle.rand([1, 20])
|
|
paddle.summary(mymodel, input=input_data)
|
|
paddle.disable_static()
|
|
|
|
rnn = paddle.nn.SimpleRNN(16, 32, 2, direction='bidirectional')
|
|
input_data = paddle.rand([4, 23, 16])
|
|
paddle.summary(rnn, input=input_data)
|
|
|
|
lenet_List_input = LeNetListInput()
|
|
input_data = [paddle.rand([1, 1, 28, 28]), paddle.rand([1, 400])]
|
|
paddle.summary(lenet_List_input, input=input_data)
|
|
|
|
lenet_dict_input = LeNetDictInput()
|
|
input_data = {
|
|
'x1': paddle.rand([1, 1, 28, 28]),
|
|
'x2': paddle.rand([1, 400]),
|
|
}
|
|
paddle.summary(lenet_dict_input, input=input_data)
|
|
|
|
def test_summary_dtype(self):
|
|
input_shape = (3, 1)
|
|
net = paddle.nn.Embedding(10, 3, sparse=True)
|
|
paddle.summary(net, input_shape, dtypes='int64')
|
|
|
|
def test_summary_error(self):
|
|
with self.assertRaises(TypeError):
|
|
nlp_net = paddle.nn.GRU(input_size=2, hidden_size=3, num_layers=3)
|
|
paddle.summary(nlp_net, (1, 1, '2'))
|
|
|
|
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)
|
|
paddle.summary(nlp_net, (1, 1, 2))
|
|
|
|
def test_static_flops(self):
|
|
if True:
|
|
return
|
|
paddle.disable_static()
|
|
net = models.__dict__['mobilenet_v2'](pretrained=False)
|
|
inputs = paddle.randn([1, 3, 224, 224])
|
|
static_program = jit._trace(net, inputs=[inputs])[1]
|
|
paddle.flops(static_program, [1, 3, 224, 224], print_detail=True)
|
|
|
|
def test_dynamic_flops(self):
|
|
net = models.__dict__['mobilenet_v2'](pretrained=False)
|
|
|
|
def customize_dropout(m, x, y):
|
|
m.total_ops += 0
|
|
|
|
paddle.flops(
|
|
net,
|
|
[1, 3, 224, 224],
|
|
custom_ops={paddle.nn.Dropout: customize_dropout},
|
|
print_detail=True,
|
|
)
|
|
|
|
def test_dynamic_flops_with_multiple_outputs(self):
|
|
net = paddle.nn.MaxPool2D(
|
|
kernel_size=2, stride=2, padding=0, return_mask=True
|
|
)
|
|
|
|
def customize_dropout(m, x, y):
|
|
m.total_ops += 0
|
|
|
|
paddle.flops(
|
|
net,
|
|
[1, 2, 32, 32],
|
|
custom_ops={paddle.nn.Dropout: customize_dropout},
|
|
print_detail=True,
|
|
)
|
|
|
|
def test_export_deploy_model(self):
|
|
self.set_seed()
|
|
np.random.seed(201)
|
|
|
|
save_dir = os.path.join(
|
|
tempfile.mkdtemp(), '.cache_test_export_deploy_model'
|
|
)
|
|
if not os.path.exists(save_dir):
|
|
os.makedirs(save_dir)
|
|
|
|
for dynamic in [True, False]:
|
|
paddle.disable_static() if dynamic else None
|
|
|
|
net = LeNet()
|
|
inputs = [InputSpec([None, 1, 28, 28], 'float32', 'x')]
|
|
model = Model(net, inputs)
|
|
model.prepare()
|
|
np.random.seed(201)
|
|
tensor_img = np.array(
|
|
np.random.random((1, 1, 28, 28)), dtype=np.float32
|
|
)
|
|
|
|
model.save(save_dir, training=False)
|
|
ori_results = model.predict_batch(tensor_img)
|
|
base.disable_dygraph() if dynamic else None
|
|
|
|
place = get_device_place()
|
|
new_scope = base.Scope()
|
|
with base.scope_guard(new_scope):
|
|
exe = base.Executor(place)
|
|
[
|
|
inference_program,
|
|
feed_target_names,
|
|
fetch_targets,
|
|
] = paddle.static.io.load_inference_model(
|
|
path_prefix=save_dir, executor=exe
|
|
)
|
|
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-6
|
|
)
|
|
|
|
paddle.enable_static()
|
|
|
|
shutil.rmtree(save_dir)
|
|
|
|
def test_dygraph_export_deploy_model_about_inputs(self):
|
|
self.set_seed()
|
|
np.random.seed(201)
|
|
mnist_data = MnistDataset(mode='train')
|
|
paddle.disable_static()
|
|
# without inputs
|
|
save_dir = os.path.join(
|
|
tempfile.mkdtemp(), '.cache_test_dygraph_export_deploy'
|
|
)
|
|
if not os.path.exists(save_dir):
|
|
os.makedirs(save_dir)
|
|
for initial in ["fit", "train_batch", "eval_batch", "predict_batch"]:
|
|
net = LeNet()
|
|
model = Model(net)
|
|
optim = paddle.optimizer.Adam(
|
|
learning_rate=0.001, parameters=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:
|
|
model.predict_batch([img])
|
|
|
|
model.save(save_dir, training=False)
|
|
shutil.rmtree(save_dir)
|
|
# with inputs, and the type of inputs is InputSpec
|
|
save_dir = os.path.join(
|
|
tempfile.mkdtemp(), '.cache_test_dygraph_export_deploy_2'
|
|
)
|
|
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 = paddle.optimizer.Adam(
|
|
learning_rate=0.001, parameters=model.parameters()
|
|
)
|
|
model.prepare(optimizer=optim, loss=CrossEntropyLoss(reduction="sum"))
|
|
model.save(save_dir, training=False)
|
|
shutil.rmtree(save_dir)
|
|
|
|
def test_accumulate(
|
|
self,
|
|
):
|
|
dim = 20
|
|
data = np.random.random(size=(4, dim)).astype(np.float32)
|
|
label = np.random.randint(0, 10, size=(4, 1)).astype(np.int64)
|
|
net = MyModel()
|
|
optim = paddle.optimizer.SGD(
|
|
learning_rate=0.001, parameters=net.parameters()
|
|
)
|
|
inputs = [InputSpec([None, dim], 'float32', 'x')]
|
|
labels = [InputSpec([None, 1], 'int64', 'label')]
|
|
|
|
for amp_cfg in [None, 'O1']:
|
|
model = Model(net, inputs, labels)
|
|
model.prepare(
|
|
optim,
|
|
loss=CrossEntropyLoss(reduction="sum"),
|
|
amp_configs=amp_cfg,
|
|
)
|
|
losses, grads = [], []
|
|
for stat in [False, False, True]:
|
|
(loss,) = model.train_batch([data], [label], update=stat)
|
|
losses.append(loss)
|
|
grads.append([p.grad.numpy() for p in net.parameters()])
|
|
|
|
for grad1, grad2, grad3 in zip(*grads):
|
|
np.testing.assert_almost_equal(grad1 * 2, grad2, decimal=4)
|
|
np.testing.assert_almost_equal(
|
|
grad3, np.zeros_like(grad3), decimal=4
|
|
)
|
|
|
|
np.testing.assert_almost_equal(losses[0], losses[1], decimal=4)
|
|
np.testing.assert_almost_equal(losses[0], losses[2], decimal=4)
|
|
|
|
|
|
class TestModelWithLRScheduler(unittest.TestCase):
|
|
def test_fit_by_step(self):
|
|
base_lr = 1e-3
|
|
boundaries = [5, 8]
|
|
|
|
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 / 5.0,
|
|
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')
|
|
base.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)
|
|
|
|
np.testing.assert_allclose(
|
|
model._optimizer._learning_rate.last_lr,
|
|
base_lr * (0.1 ** len(boundaries)),
|
|
)
|
|
# 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()
|
|
model.fit(dataset, dataset, batch_size=4, epochs=10, num_workers=0)
|
|
|
|
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
|
|
warmup_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=warmup_epochs,
|
|
start_lr=base_lr / 5.0,
|
|
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')
|
|
base.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 + warmup_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 + warmup_epochs <= epochs:
|
|
cnt += 1
|
|
|
|
np.testing.assert_allclose(
|
|
model._optimizer._learning_rate.last_lr, base_lr * (0.1**cnt)
|
|
)
|
|
|
|
|
|
class TestRaiseError(unittest.TestCase):
|
|
def test_input_without_name(self):
|
|
net = MyModel()
|
|
inputs = [InputSpec([None, 10], 'float32')]
|
|
labels = [InputSpec([None, 1], 'int64', 'label')]
|
|
with self.assertRaises(ValueError):
|
|
model = Model(net, inputs, labels)
|
|
|
|
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 = os.path.join(tempfile.mkdtemp(), '.cache_test_save_infer')
|
|
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()
|
|
shutil.rmtree(save_dir)
|
|
|
|
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)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|