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')] 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) model.load(path) paddle.enable_static() 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 = 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) 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) shutil.rmtree(path) fluid.disable_dygraph()
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 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 _calc_output(self, place, mode="test", dygraph=True): if dygraph: fluid.enable_dygraph(place) else: fluid.disable_dygraph() gen = paddle.seed(self._random_seed) paddle.framework.random._manual_program_seed(self._random_seed) scope = fluid.core.Scope() with fluid.scope_guard(scope): layer = self.model_cls(**self.attrs) if isinstance( self.attrs, dict) else self.model_cls(*self.attrs) model = Model(layer, inputs=self.make_inputs()) model.prepare() if self.param_states: model.load(self.param_states, optim_state=None) return model.predict_batch(self.inputs)
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