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Update hapi.model._save_inference_model by using new features of dy2stat in 2.0-beta API (#27272)

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* update model.save_inference_model

* update doc for _save_inference_model, delete useless class in unittests

* make users not be able to set model._inputs be None

* update usage of Model class in unittests

* fix bugs of _verify_spec

* fix bugs of _verify_spec

* add unittest to increase coverage rate

* delete http.log

* update doc for save, remove requirments and limitations for using

* update doc for class Model
revert-27520-disable_pr
LiuChiachi 4 years ago committed by GitHub
parent f0a5eef58f
commit 69279207eb
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2 changed files with 26 additions and 70 deletions
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46
python/paddle/hapi/model.py
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@ -792,15 +792,14 @@ class Model(object):
switched by `paddle.disable_static()`. The usage is as follows.
But note, the switching between dynamic and static should be before
instantiating a Model. The input description, i.e, paddle.static.InputSpec,
must be required for static graph.
must be required.
Args:
network (paddle.nn.Layer): The network is an instance of
paddle.nn.Layer.
inputs (InputSpec|list|dict|None): `inputs`, entry points of network,
could be a InputSpec instance, or lits of InputSpec instances,
or dict ({name: InputSpec}), or None. For static graph,
inputs must be set. For dynamic graph, it could be None.
or dict ({name: InputSpec}), and it couldn't be None.
labels (InputSpec|list|None): `labels`, entry points of network,
could be a InputSpec instnace or lits of InputSpec instances,
or None. For static graph, if labels is required in loss,
@ -849,10 +848,9 @@ class Model(object):
self._optimizer = None
self._test_dataloader = None
if not in_dygraph_mode():
if not isinstance(inputs, (list, dict, Input)):
raise TypeError(
"'inputs' must be list or dict in static graph mode")
if not isinstance(inputs, (list, dict, Input)):
raise TypeError(
"'inputs' must be list or dict, and couldn't be None.")
self._inputs = self._verify_spec(inputs, True)
self._labels = self._verify_spec(labels)
@ -1004,11 +1002,7 @@ class Model(object):
have no variable need to save (like SGD), the fill will not generated).
This function will silently overwrite existing file at the target location.
If `training` is set to False, only inference model will be saved. It
should be noted that before using `save`, you should run the model, and
the shape of input you saved is as same as the input of its running.
`@paddle.jit.to_static` must be added on `forward` function of your layer
in dynamic mode now and these will be optimized later.
If `training` is set to False, only inference model will be saved.
Args:
path (str): The file prefix to save model. The format is
@ -1037,8 +1031,6 @@ class Model(object):
nn.Linear(200, 10),
nn.Softmax())
# If save for inference in dygraph, need this
@paddle.jit.to_static
def forward(self, x):
return self.net(x)
@ -1046,7 +1038,7 @@ class Model(object):
device = paddle.set_device('cpu')
# if use static graph, do not set
paddle.disable_static(device) if dynamic else None
# inputs and labels are not required for dynamic graph.
input = InputSpec([None, 784], 'float32', 'x')
label = InputSpec([None, 1], 'int64', 'label')
model = paddle.Model(Mnist(), input, label)
@ -1649,10 +1641,6 @@ class Model(object):
model_only=False):
"""
Save inference model can be in static or dynamic mode.
It should be noted that before using `save_inference_model`, you should
run the model, and the shape you saved is as same as the input of its
running. `@paddle.jit.to_static` must be added on `forward` function of
your layer in dynamic mode now and these will be optimized later.
Args:
save_dir (str): The directory path to save the inference model.
@ -1678,14 +1666,11 @@ class Model(object):
return result_list
# TODO:
# 1. Make it Unnecessary to run model before calling `save_inference_model` for users in dygraph.
# 2. Save correct shape of input, now the interface stores the shape that the user sent to
# the inputs of the model in running.
# 3. Make it Unnecessary to add `@paddle.jit.to_static` for users in dynamic mode.
if fluid.in_dygraph_mode():
with fluid.framework._dygraph_guard(None):
layer = self.network
layer.forward = paddle.jit.to_static(
layer.forward, input_spec=self._inputs)
# 1. input check
prog_translator = ProgramTranslator()
@ -1879,18 +1864,7 @@ class Model(object):
def _verify_spec(self, specs, is_input=False):
out_specs = []
if specs is None:
# Note(Aurelius84): If not specific specs of `Input`, using argument names of `forward` function
# to generate `Input`. But how can we know the actual shape of each input tensor?
if is_input:
out_specs = [
Input(
name=n, shape=[None])
for n in extract_args(self.network.forward) if n != 'self'
]
else:
out_specs = to_list(specs)
elif isinstance(specs, dict):
if isinstance(specs, dict):
assert is_input == False
out_specs = [specs[n] \
for n in extract_args(self.network.forward) if n != 'self']

50
python/paddle/tests/test_model.py
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@ -67,35 +67,6 @@ class LeNetDygraph(paddle.nn.Layer):
return x
class LeNetDeclarative(fluid.dygraph.Layer):
def __init__(self, num_classes=10, classifier_activation=None):
super(LeNetDeclarative, 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
@declarative
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)
@ -444,7 +415,9 @@ class TestModelFunction(unittest.TestCase):
# dynamic saving
device = paddle.set_device('cpu')
fluid.enable_dygraph(device)
model = Model(MyModel(classifier_activation=None))
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"))
@ -543,11 +516,10 @@ class TestModelFunction(unittest.TestCase):
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
paddle.disable_static() if dynamic else None
prog_translator = ProgramTranslator()
prog_translator.enable(False) if not dynamic else None
net = LeNetDeclarative()
net = LeNet()
inputs = [InputSpec([None, 1, 28, 28], 'float32', 'x')]
model = Model(net, inputs)
model.prepare()
@ -556,8 +528,9 @@ class TestModelFunction(unittest.TestCase):
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)
ori_results = model.test_batch(tensor_img)
fluid.disable_dygraph() if dynamic else None
place = fluid.CPUPlace() if not fluid.is_compiled_with_cuda(
@ -574,6 +547,7 @@ class TestModelFunction(unittest.TestCase):
np.testing.assert_allclose(
results, ori_results, rtol=1e-5, atol=1e-7)
shutil.rmtree(save_dir)
paddle.enable_static()
class TestRaiseError(unittest.TestCase):
@ -585,6 +559,14 @@ class TestRaiseError(unittest.TestCase):
with self.assertRaises(ValueError):
model = Model(net, inputs, labels)
def test_input_without_input_spec(self):
for dynamic in [True, False]:
paddle.disable_static() if dynamic else None
net = MyModel(classifier_activation=None)
with self.assertRaises(TypeError):
model = Model(net)
paddle.enable_static()
if __name__ == '__main__':
unittest.main()

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