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_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 = 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 = 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 = 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 _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 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
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()
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-6)
paddle.enable_static()
shutil.rmtree(save_dir)
