def test_export_deploy_model(self): for dynamic in [True, False]: fluid.enable_dygraph() if dynamic else None # paddle.disable_static() if dynamic else None prog_translator = ProgramTranslator() prog_translator.enable(False) if not dynamic else None net = LeNetDeclarative() 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) ori_results = model.test_batch(tensor_img) model.save(save_dir, training=False) 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)
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 _calc_output(self, place, mode="test", dygraph=True): if dygraph: fluid.enable_dygraph(place) else: fluid.disable_dygraph() gen = paddle.manual_seed(self._random_seed) gen._is_init_py = False 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.test_batch(self.inputs)