# 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, 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 import paddle.vision.models as models import paddle.fluid.dygraph.jit as jit from paddle.io import DistributedBatchSampler, Dataset 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): super(LeNetDygraph, self).__init__() self.num_classes = num_classes self.features = Sequential( Conv2D( 1, 6, 3, stride=1, padding=1), ReLU(), paddle.fluid.dygraph.Pool2D(2, 'max', 2), Conv2D( 6, 16, 5, stride=1, padding=0), ReLU(), paddle.fluid.dygraph.Pool2D(2, 'max', 2)) if num_classes > 0: self.fc = Sequential( Linear(400, 120), Linear(120, 84), Linear(84, 10)) 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 ModelInner(paddle.nn.Layer): def __init__(self): super(ModelInner, self).__init__() self.fc = paddle.nn.Linear(3, 4) def forward(self, x): y = self.fc(x) return y, 0 class ModelOutter(paddle.nn.Layer): def __init__(self): super(ModelOutter, self).__init__() self.module1 = ModelInner() self.module2 = paddle.nn.Linear(4, 5) def forward(self, x): y, dummpy = self.module1(x) y = self.module2(y) return y, 3 class LeNetListInput(LeNetDygraph): def forward(self, inputs): x = inputs[0] x = self.features(x) if self.num_classes > 0: x = paddle.flatten(x, 1) x = self.fc(x + inputs[1]) return x class LeNetDictInput(LeNetDygraph): def forward(self, inputs): x = self.features(inputs['x1']) if self.num_classes > 0: x = paddle.flatten(x, 1) x = self.fc(x + inputs['x2']) 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(): cls().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.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_tuple_input(self): self.fit_with_tuple_input(True) def test_fit_static_with_tuple_input(self): self.fit_with_tuple_input(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_fit_dynamic_with_num_iters(self): self.fit(True, num_iters=1) def test_fit_static_with_num_iters(self): self.fit(False, num_iters=1) 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, num_iters=None): fluid.enable_dygraph(self.device) if dynamic else None seed = 333 paddle.seed(seed) paddle.framework.random._manual_program_seed(seed) net = LeNet() 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) model.fit(self.train_dataset, batch_size=64, shuffle=False, num_iters=num_iters) result = model.evaluate( self.val_dataset, batch_size=64, num_iters=num_iters) 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 fit_with_tuple_input(self, dynamic, num_replicas=None, rank=None): fluid.enable_dygraph(self.device) if dynamic else None seed = 333 paddle.seed(seed) paddle.framework.random._manual_program_seed(seed) net = LeNet() optim_new = fluid.optimizer.Adam( learning_rate=0.001, parameter_list=net.parameters()) model = Model(net, inputs=tuple(self.inputs), labels=tuple(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 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() 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))]) class MyModel(paddle.nn.Layer): def __init__(self): super(MyModel, self).__init__() self._fc = Linear(20, 10) def forward(self, x): y = self._fc(x) return y 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 class TestModelFunction(unittest.TestCase): def set_seed(self, seed=1024): paddle.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() 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() 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.predict_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() 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()) 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(), 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() 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() 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_non_tensor(self): paddle.summary(ModelOutter(), 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): 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_export_deploy_model(self): self.set_seed() np.random.seed(201) 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.predict_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] = ( 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-7) shutil.rmtree(save_dir) paddle.enable_static() 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 for initial in ["fit", "train_batch", "eval_batch", "predict_batch"]: 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: 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 = 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) 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 = fluid.optimizer.SGD(learning_rate=0.001, parameter_list=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., 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() 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 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)) 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 = 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() 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()