# 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 import paddle from paddle import fluid from paddle import to_tensor from paddle.nn import Conv2d, Pool2D, Linear, ReLU, Sequential, Softmax from paddle import Model from paddle.static import InputSpec from paddle.nn.layer.loss import CrossEntropyLoss from paddle.metric import Accuracy from paddle.vision.datasets import MNIST from paddle.vision.models import LeNet from paddle.io import DistributedBatchSampler from paddle.hapi.model import prepare_distributed_context from paddle.fluid.dygraph.jit import declarative from paddle.fluid.dygraph.dygraph_to_static.program_translator import ProgramTranslator class LeNetDygraph(paddle.nn.Layer): def __init__(self, num_classes=10, classifier_activation=None): super(LeNetDygraph, self).__init__() self.num_classes = num_classes self.features = Sequential( Conv2d( 1, 6, 3, stride=1, padding=1), ReLU(), Pool2D(2, 'max', 2), Conv2d( 6, 16, 5, stride=1, padding=0), ReLU(), Pool2D(2, 'max', 2)) if num_classes > 0: self.fc = Sequential( Linear(400, 120), Linear(120, 84), Linear(84, 10), Softmax()) #Todo: accept any activation 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) loss = CrossEntropyLoss(reduction="sum")(outputs, labels) 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') cls.device = paddle.set_device('gpu') 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 paddle.manual_seed(seed) paddle.framework.random._manual_program_seed(seed) 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) cls.inputs = [InputSpec([-1, 1, 28, 28], 'float32', 'image')] cls.labels = [InputSpec([None, 1], 'int64', 'label')] 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) def test_fit_dynamic_with_rank(self): self.fit(True, 2, 0) def test_fit_static_with_rank(self): self.fit(False, 2, 0) 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() def fit(self, dynamic, num_replicas=None, rank=None): fluid.enable_dygraph(self.device) if dynamic else None seed = 333 paddle.manual_seed(seed) paddle.framework.random._manual_program_seed(seed) net = LeNet(classifier_activation=None) optim_new = fluid.optimizer.Adam( learning_rate=0.001, parameter_list=net.parameters()) model = Model(net, inputs=self.inputs, labels=self.labels) model.prepare( optim_new, loss=CrossEntropyLoss(reduction="sum"), metrics=Accuracy()) 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( self.train_dataset, batch_size=64, shuffle=False, num_replicas=num_replicas, rank=rank) val_sampler = DistributedBatchSampler( self.val_dataset, batch_size=64, shuffle=False, num_replicas=num_replicas, rank=rank) 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 model = Model(LeNet(), self.inputs, self.labels) model.prepare(metrics=Accuracy()) 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 model = Model(LeNet(), self.inputs) model.prepare() 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 class MyModel(paddle.nn.Layer): def __init__(self, classifier_activation='softmax'): super(MyModel, self).__init__() self._fc = Linear(20, 10) self._act = Softmax() #Todo: accept any activation def forward(self, x): y = self._fc(x) y = self._act(y) return y class TestModelFunction(unittest.TestCase): def set_seed(self, seed=1024): paddle.manual_seed(seed) paddle.framework.random._manual_program_seed(seed) 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() m = MyModel(classifier_activation=None) optim = fluid.optimizer.SGD(learning_rate=0.001, parameter_list=m.parameters()) m.train() output = m(to_tensor(data)) loss = CrossEntropyLoss(reduction='sum')(output, to_tensor(label)) 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]: device = paddle.set_device('cpu') fluid.enable_dygraph(device) if dynamic else None self.set_seed() net = MyModel(classifier_activation=None) optim2 = fluid.optimizer.SGD(learning_rate=0.001, parameter_list=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()) fluid.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(): fluid.enable_dygraph(fluid.CPUPlace()) self.set_seed() m = MyModel() m.eval() output = m(to_tensor(data)) fluid.disable_dygraph() return output.numpy() ref = get_expect() for dynamic in [True, False]: device = paddle.set_device('cpu') fluid.enable_dygraph(device) if dynamic else None self.set_seed() net = MyModel() inputs = [InputSpec([None, dim], 'float32', 'x')] model = Model(net, inputs) model.prepare() out, = model.test_batch([data]) np.testing.assert_allclose(out, ref, rtol=1e-6) fluid.disable_dygraph() if dynamic else None def test_save_load(self): path = tempfile.mkdtemp() for dynamic in [True, False]: device = paddle.set_device('cpu') fluid.enable_dygraph(device) if dynamic else None net = MyModel(classifier_activation=None) inputs = [InputSpec([None, 20], 'float32', 'x')] labels = [InputSpec([None, 1], 'int64', 'label')] optim = fluid.optimizer.SGD(learning_rate=0.001, parameter_list=net.parameters()) model = Model(net, inputs, labels) model.prepare( optimizer=optim, loss=CrossEntropyLoss(reduction="sum")) model.save(path + '/test') model.load(path + '/test') shutil.rmtree(path) fluid.disable_dygraph() if dynamic else None 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() def test_dynamic_save_static_load(self): path = tempfile.mkdtemp() # dynamic saving device = paddle.set_device('cpu') fluid.enable_dygraph(device) model = Model(MyModel(classifier_activation=None)) optim = fluid.optimizer.SGD(learning_rate=0.001, parameter_list=model.parameters()) model.prepare(optimizer=optim, loss=CrossEntropyLoss(reduction="sum")) model.save(path + '/test') fluid.disable_dygraph() inputs = [InputSpec([None, 20], 'float32', 'x')] labels = [InputSpec([None, 1], 'int64', 'label')] model = Model(MyModel(classifier_activation=None), inputs, labels) optim = fluid.optimizer.SGD(learning_rate=0.001, parameter_list=model.parameters()) model.prepare(optimizer=optim, loss=CrossEntropyLoss(reduction="sum")) model.load(path + '/test') shutil.rmtree(path) def test_static_save_dynamic_load(self): path = tempfile.mkdtemp() net = MyModel(classifier_activation=None) inputs = [InputSpec([None, 20], 'float32', 'x')] labels = [InputSpec([None, 1], 'int64', 'label')] optim = fluid.optimizer.SGD(learning_rate=0.001, parameter_list=net.parameters()) model = Model(net, inputs, labels) model.prepare(optimizer=optim, loss=CrossEntropyLoss(reduction="sum")) model.save(path + '/test') device = paddle.set_device('cpu') fluid.enable_dygraph(device) #if dynamic else None net = MyModel(classifier_activation=None) inputs = [InputSpec([None, 20], 'float32', 'x')] labels = [InputSpec([None, 1], 'int64', 'label')] optim = fluid.optimizer.SGD(learning_rate=0.001, parameter_list=net.parameters()) model = Model(net, inputs, labels) model.prepare(optimizer=optim, loss=CrossEntropyLoss(reduction="sum")) model.load(path + '/test') shutil.rmtree(path) fluid.disable_dygraph() def test_parameters(self): 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 = model.parameters() self.assertTrue(params[0].shape[0] == 20) self.assertTrue(params[0].shape[1] == 10) fluid.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') 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) model.summary(input_size=(20)) model.summary(input_size=[(20)]) model.summary(input_size=(20), dtype='float32') def test_summary_nlp(self): paddle.enable_static() 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) paddle.summary(rnn, [(-1, 23, 16), ((2, None, 32), (2, -1, 32))]) 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_export_deploy_model(self): for dynamic in [True, False]: paddle.disable_static() if dynamic else None prog_translator = ProgramTranslator() prog_translator.enable(False) if not dynamic else None net = LeNet() inputs = [InputSpec([None, 1, 28, 28], 'float32', 'x')] 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) model.save(save_dir, training=False) ori_results = model.test_batch(tensor_img) fluid.disable_dygraph() if dynamic else None 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] = ( fluid.io.load_inference_model( dirname=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-7) shutil.rmtree(save_dir) paddle.enable_static() def test_export_deploy_model_without_inputs_in_dygraph(self): mnist_data = MnistDataset(mode='train') paddle.disable_static() for initial in ["fit", "train_batch", "eval_batch", "test_batch"]: save_dir = tempfile.mkdtemp() if not os.path.exists(save_dir): os.makedirs(save_dir) net = LeNet() model = Model(net) 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.test_batch(img) model.save(save_dir, training=False) shutil.rmtree(save_dir) class TestRaiseError(unittest.TestCase): def test_input_without_name(self): net = MyModel(classifier_activation=None) inputs = [InputSpec([None, 10], 'float32')] labels = [InputSpec([None, 1], 'int64', 'label')] with self.assertRaises(ValueError): model = Model(net, inputs, labels) def test_export_deploy_model_without_inputs_and_run_in_dygraph(self): paddle.disable_static() net = MyModel(classifier_activation=None) 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() if __name__ == '__main__': unittest.main()