From 4c5dead62b4d8465a5f5477c571c2965e5ab1e2a Mon Sep 17 00:00:00 2001
From: LiuChiaChi <709153940@qq.com>
Date: Fri, 25 Sep 2020 05:43:25 +0000
Subject: [PATCH 01/14] remove input requirment in dygraph Model

---
 python/paddle/hapi/model.py | 25 +++++++++++++++++++------
 1 file changed, 19 insertions(+), 6 deletions(-)

diff --git a/python/paddle/hapi/model.py b/python/paddle/hapi/model.py
index 53928ebed1b25f..f502a1dfc340de 100644
--- a/python/paddle/hapi/model.py
+++ b/python/paddle/hapi/model.py
@@ -792,14 +792,15 @@ 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.
+    must be required for static graph.
 
     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}), and it couldn't be None.
+            or dict ({name: InputSpec}), and it couldn't be None in static
+            graph.
         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,
@@ -848,9 +849,10 @@ def __init__(self, network, inputs=None, labels=None):
         self._optimizer = None
         self._test_dataloader = None
 
-        if not isinstance(inputs, (list, dict, Input)):
-            raise TypeError(
-                "'inputs' must be list or dict, and couldn't be None.")
+        if not in_dygraph_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)
 
@@ -1863,7 +1865,18 @@ def summary(self, input_size=None, dtype=None):
     def _verify_spec(self, specs, is_input=False):
         out_specs = []
 
-        if isinstance(specs, dict):
+        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):
             assert is_input == False
             out_specs = [specs[n] \
                 for n in extract_args(self.network.forward) if n != 'self']

From 5d2fad7b7938f4acf65988c07af02c260bbd34b2 Mon Sep 17 00:00:00 2001
From: LiuChiaChi <709153940@qq.com>
Date: Fri, 25 Sep 2020 05:47:10 +0000
Subject: [PATCH 02/14] correct unittest

---
 python/paddle/tests/test_model.py | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/python/paddle/tests/test_model.py b/python/paddle/tests/test_model.py
index c89cbbbfbdae9e..b20dfa37b665a0 100644
--- a/python/paddle/tests/test_model.py
+++ b/python/paddle/tests/test_model.py
@@ -415,9 +415,7 @@ def test_dynamic_save_static_load(self):
         # dynamic saving
         device = paddle.set_device('cpu')
         fluid.enable_dygraph(device)
-        inputs = [InputSpec([None, 20], 'float32', 'x')]
-        labels = [InputSpec([None, 1], 'int64', 'label')]
-        model = Model(MyModel(classifier_activation=None), inputs, labels)
+        model = Model(MyModel(classifier_activation=None))
         optim = fluid.optimizer.SGD(learning_rate=0.001,
                                     parameter_list=model.parameters())
         model.prepare(optimizer=optim, loss=CrossEntropyLoss(reduction="sum"))

From f7f4063521921ff1c2d5853337d0256734f4e419 Mon Sep 17 00:00:00 2001
From: LiuChiaChi <709153940@qq.com>
Date: Sun, 27 Sep 2020 06:13:44 +0000
Subject: [PATCH 03/14] upadte save inference model in dygraph without input

---
 python/paddle/hapi/model.py       | 70 +++++++++++++++++++++++++------
 python/paddle/tests/test_model.py | 44 +++++++++++++++----
 2 files changed, 95 insertions(+), 19 deletions(-)

diff --git a/python/paddle/hapi/model.py b/python/paddle/hapi/model.py
index f502a1dfc340de..d00cdf1044bdcd 100644
--- a/python/paddle/hapi/model.py
+++ b/python/paddle/hapi/model.py
@@ -598,6 +598,7 @@ def __init__(self, model):
             'test_batch': 0
         }
 
+        self._shapes = None
         if self._nranks > 1:
             stradegy = fluid.dygraph.parallel.ParallelStrategy()
             stradegy.nranks = ParallelEnv().nranks
@@ -617,11 +618,13 @@ def mode(self, value):
 
     # TODO multi device in dygraph mode not implemented at present time
     def train_batch(self, inputs, labels=None):
+
         assert self.model._optimizer, \
             "model not ready, please call `model.prepare()` first"
         self.model.network.train()
         self.mode = 'train'
         inputs = to_list(inputs)
+        self._shapes = [list(input.shape) for input in inputs]
         labels = labels or []
         labels = [to_variable(l) for l in to_list(labels)]
 
@@ -656,6 +659,7 @@ def eval_batch(self, inputs, labels=None):
         self.model.network.eval()
         self.mode = 'eval'
         inputs = to_list(inputs)
+        self._shapes = [list(input.shape) for input in inputs]
         labels = labels or []
         labels = [to_variable(l) for l in to_list(labels)]
 
@@ -704,6 +708,7 @@ def test_batch(self, inputs):
         self.model.network.eval()
         self.mode = 'test'
         inputs = [to_variable(x) for x in to_list(inputs)]
+        self._shapes = [list(input.shape) for input in inputs]
         outputs = self.model.network.forward(*inputs)
         if self._nranks > 1 and isinstance(self.model._place, fluid.CUDAPlace):
             outputs = [_all_gather(o, self._nranks) for o in to_list(outputs)]
@@ -778,7 +783,7 @@ def load(self, param_state_pairs, optim_state):
 
         if not hasattr(self.model._optimizer, 'set_state_dict'):
             warnings.warn(
-                "paddle.fluid.optimizer is deprecated in API 2.0, please use paddle.optimizer instead"
+                "paddle.fluid.optimizer is deprecated in API 2.0, please use paddle.optimizer instead."
             )
             self.model._optimizer.set_dict(converted_state)
         else:
@@ -846,14 +851,18 @@ def __init__(self, network, inputs=None, labels=None):
         self._loss = None
         self._loss_weights = None
         self._optimizer = None
-        self._optimizer = None
+        self._shapes = None
+        self._is_shape_inferred = False
         self._test_dataloader = None
 
         if not in_dygraph_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)
+        elif inputs:
+            self._shapes = [list(input.shape) for input in inputs]
+
+        self._inputs = self._verify_spec(inputs, is_input=True)
         self._labels = self._verify_spec(labels)
 
         # init backend
@@ -905,7 +914,12 @@ def train_batch(self, inputs, labels=None):
               loss = model.train_batch([data], [label])
               print(loss)
         """
-        return self._adapter.train_batch(inputs, labels)
+        loss = self._adapter.train_batch(inputs, labels)
+        if fluid.in_dygraph_mode() and self._shapes is None:
+            self._shapes = self._adapter._shapes
+            self._is_shape_inferred = True
+            self._inputs = self._verify_spec(None, self._shapes, True)
+        return loss
 
     def eval_batch(self, inputs, labels=None):
         """
@@ -951,7 +965,12 @@ def eval_batch(self, inputs, labels=None):
               loss = model.eval_batch([data], [label])
               print(loss)
         """
-        return self._adapter.eval_batch(inputs, labels)
+        loss = self._adapter.eval_batch(inputs, labels)
+        if fluid.in_dygraph_mode() and self._shapes is None:
+            self._shapes = self._adapter._shapes
+            self._is_shape_inferred = True
+            self._inputs = self._verify_spec(None, self._shapes, True)
+        return loss
 
     def test_batch(self, inputs):
         """
@@ -988,7 +1007,12 @@ def test_batch(self, inputs):
               out = model.test_batch([data])
               print(out)
         """
-        return self._adapter.test_batch(inputs)
+        loss = self._adapter.test_batch(inputs)
+        if fluid.in_dygraph_mode() and self._shapes is None:
+            self._shapes = self._adapter._shapes
+            self._is_shape_inferred = True
+            self._inputs = self._verify_spec(None, self._shapes, True)
+        return loss
 
     def save(self, path, training=True):
         """  
@@ -1671,6 +1695,14 @@ def get_inout_spec(all_vars, return_name=False):
         if fluid.in_dygraph_mode():
             with fluid.framework._dygraph_guard(None):
                 layer = self.network
+                if self._shapes is None:  # No provided or inferred
+                    raise RuntimeError(
+                        "Saving inference model needs `inputs` or running before saving."
+                    )
+                if self._is_shape_inferred:
+                    warnings.warn(
+                        'Saving actual input shapes only if `inputs` is provided, otherwise variable input dimension is immutable.'
+                    )
                 layer.forward = paddle.jit.to_static(
                     layer.forward, input_spec=self._inputs)
 
@@ -1769,6 +1801,7 @@ def _run_one_epoch(self, data_loader, callbacks, mode, logs={}):
             data = flatten(data)
             # LoDTensor.shape is callable, where LoDTensor comes from
             # DataLoader in static graph
+
             batch_size = data[0].shape()[0] if callable(data[
                 0].shape) else data[0].shape[0]
 
@@ -1811,6 +1844,14 @@ def _run_one_epoch(self, data_loader, callbacks, mode, logs={}):
             callbacks.on_batch_end(mode, step, logs)
         self._reset_metrics()
 
+        if self._shapes is None and mode == "train":
+            self._shapes = [
+                data[i].shape() if callable(data[i].shape) else data[i].shape
+                for i in range(len(data) - 1)
+            ]
+            self._is_shape_inferred = True
+            self._inputs = self._verify_spec(None, self._shapes, True)
+
         if mode == 'test':
             return logs, outputs
         return logs
@@ -1862,18 +1903,23 @@ def summary(self, input_size=None, dtype=None):
             _input_size = self._inputs
         return summary(self.network, _input_size, dtype)
 
-    def _verify_spec(self, specs, is_input=False):
+    def _verify_spec(self, specs, shapes=None, 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'
-                ]
+                arg_names = extract_args(self.network.forward)[1:]
+                if shapes is not None and fluid.in_dygraph_mode():
+                    out_specs = [
+                        Input(
+                            name=n, shape=shapes[i])
+                        for i, n in enumerate(arg_names)
+                    ]
+                else:
+                    out_specs = [Input(name=n, shape=[None]) for n in arg_names]
             else:
                 out_specs = to_list(specs)
         elif isinstance(specs, dict):
diff --git a/python/paddle/tests/test_model.py b/python/paddle/tests/test_model.py
index b20dfa37b665a0..e88a4951ec39ae 100644
--- a/python/paddle/tests/test_model.py
+++ b/python/paddle/tests/test_model.py
@@ -552,6 +552,33 @@ def test_export_deploy_model(self):
                 shutil.rmtree(save_dir)
             paddle.enable_static()
 
+    def test_export_deploy_model_without_inputs_in_dygraph(self):
+        mnist_data = MnistDataset(mode='train')
+        paddle.disable_static()
+        for initial in ["fit", "train_batch", "eval_batch", "test_batch"]:
+            save_dir = tempfile.mkdtemp()
+            if not os.path.exists(save_dir):
+                os.makedirs(save_dir)
+                net = LeNet()
+                model = Model(net)
+                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.test_batch(img)
+
+                model.save(save_dir, training=False)
+                shutil.rmtree(save_dir)
+
 
 class TestRaiseError(unittest.TestCase):
     def test_input_without_name(self):
@@ -562,13 +589,16 @@ def test_input_without_name(self):
         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()
+    def test_export_deploy_model_without_inputs_and_run_in_dygraph(self):
+        paddle.disable_static()
+        net = MyModel(classifier_activation=None)
+        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()
 
 
 if __name__ == '__main__':

From 87a1b53bba2df3a76e3eb392aaad0227cf00e1c9 Mon Sep 17 00:00:00 2001
From: LiuChiaChi <709153940@qq.com>
Date: Sun, 27 Sep 2020 06:24:20 +0000
Subject: [PATCH 04/14] fix unittets for test_model.py

---
 python/paddle/tests/http.log      | 0
 python/paddle/tests/test_model.py | 6 +++---
 2 files changed, 3 insertions(+), 3 deletions(-)
 create mode 100644 python/paddle/tests/http.log

diff --git a/python/paddle/tests/http.log b/python/paddle/tests/http.log
new file mode 100644
index 00000000000000..e69de29bb2d1d6
diff --git a/python/paddle/tests/test_model.py b/python/paddle/tests/test_model.py
index e88a4951ec39ae..2de39c709d899e 100644
--- a/python/paddle/tests/test_model.py
+++ b/python/paddle/tests/test_model.py
@@ -570,11 +570,11 @@ def test_export_deploy_model_without_inputs_in_dygraph(self):
                         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)
+                        model.train_batch([img], [label])
                     elif initial == "eval_batch":
-                        model.eval_batch(img, label)
+                        model.eval_batch([img], [label])
                     else:
-                        model.test_batch(img)
+                        model.test_batch([img])
 
                 model.save(save_dir, training=False)
                 shutil.rmtree(save_dir)

From 6396fceda48020676c8163591e35ec6bcee29762 Mon Sep 17 00:00:00 2001
From: LiuChiaChi <709153940@qq.com>
Date: Sun, 27 Sep 2020 06:28:09 +0000
Subject: [PATCH 05/14] solve conflicts

---
 python/paddle/tests/test_model.py | 10 ----------
 1 file changed, 10 deletions(-)

diff --git a/python/paddle/tests/test_model.py b/python/paddle/tests/test_model.py
index 34e7065cd2391f..69a888de9c61fd 100644
--- a/python/paddle/tests/test_model.py
+++ b/python/paddle/tests/test_model.py
@@ -614,7 +614,6 @@ def test_input_without_name(self):
         with self.assertRaises(ValueError):
             model = Model(net, inputs, labels)
 
-<<<<<<< HEAD
     def test_export_deploy_model_without_inputs_and_run_in_dygraph(self):
         paddle.disable_static()
         net = MyModel(classifier_activation=None)
@@ -625,15 +624,6 @@ def test_export_deploy_model_without_inputs_and_run_in_dygraph(self):
             model = Model(net)
             model.save(save_dir, training=False)
         paddle.enable_static()
-=======
-    def test_input_without_input_spec(self):
-        for dynamic in [True, False]:
-            paddle.disable_static() if dynamic else None
-            net = MyModel()
-            with self.assertRaises(TypeError):
-                model = Model(net)
-            paddle.enable_static()
->>>>>>> 6b727e08b1f38b3f4acc1708c163ed6ae5df8d58
 
 
 if __name__ == '__main__':

From 67bac4892822290a1bcc215ba12d4933d6c90f39 Mon Sep 17 00:00:00 2001
From: LiuChiaChi <709153940@qq.com>
Date: Sun, 27 Sep 2020 06:29:33 +0000
Subject: [PATCH 06/14] solve conflicts

---
 python/paddle/hapi/model.py | 8 --------
 1 file changed, 8 deletions(-)

diff --git a/python/paddle/hapi/model.py b/python/paddle/hapi/model.py
index fd6161f1cf8ec4..ea6d310ad53785 100644
--- a/python/paddle/hapi/model.py
+++ b/python/paddle/hapi/model.py
@@ -854,7 +854,6 @@ def __init__(self, network, inputs=None, labels=None):
         self._is_shape_inferred = False
         self._test_dataloader = None
 
-<<<<<<< HEAD
         if not in_dygraph_mode():
             if not isinstance(inputs, (list, dict, Input)):
                 raise TypeError(
@@ -863,13 +862,6 @@ def __init__(self, network, inputs=None, labels=None):
             self._shapes = [list(input.shape) for input in inputs]
 
         self._inputs = self._verify_spec(inputs, is_input=True)
-=======
-        if not isinstance(inputs, (list, dict, Input)):
-            raise TypeError(
-                "'inputs' must be list or dict in static graph mode")
-
-        self._inputs = self._verify_spec(inputs, True)
->>>>>>> 6b727e08b1f38b3f4acc1708c163ed6ae5df8d58
         self._labels = self._verify_spec(labels)
 
         # init backend

From 8dcc03ae1ae5f8bd0c7c92665fc496e8bd826da3 Mon Sep 17 00:00:00 2001
From: LiuChiaChi <709153940@qq.com>
Date: Sun, 27 Sep 2020 06:31:31 +0000
Subject: [PATCH 07/14] delete http.log

---
 python/paddle/tests/http.log | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 delete mode 100644 python/paddle/tests/http.log

diff --git a/python/paddle/tests/http.log b/python/paddle/tests/http.log
deleted file mode 100644
index e69de29bb2d1d6..00000000000000

From cc71c7b9ec58e4eeb9d6da95f73cdda264081685 Mon Sep 17 00:00:00 2001
From: LiuChiaChi <709153940@qq.com>
Date: Sun, 27 Sep 2020 07:33:44 +0000
Subject: [PATCH 08/14] fix test_model.py bug, correct initialization of
 MyModel

---
 python/paddle/tests/test_model.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/paddle/tests/test_model.py b/python/paddle/tests/test_model.py
index 69a888de9c61fd..c5a0e820fe793f 100644
--- a/python/paddle/tests/test_model.py
+++ b/python/paddle/tests/test_model.py
@@ -616,7 +616,7 @@ def test_input_without_name(self):
 
     def test_export_deploy_model_without_inputs_and_run_in_dygraph(self):
         paddle.disable_static()
-        net = MyModel(classifier_activation=None)
+        net = MyModel()
         save_dir = tempfile.mkdtemp()
         if not os.path.exists(save_dir):
             os.makedirs(save_dir)

From 7afd7d4b257a1efe93648d7295d2b4203c648634 Mon Sep 17 00:00:00 2001
From: LiuChiaChi <709153940@qq.com>
Date: Sun, 27 Sep 2020 08:53:35 +0000
Subject: [PATCH 09/14] fix unittests bugs

---
 python/paddle/tests/test_model.py | 70 ++++++++++---------------------
 1 file changed, 22 insertions(+), 48 deletions(-)

diff --git a/python/paddle/tests/test_model.py b/python/paddle/tests/test_model.py
index c5a0e820fe793f..6f5163f0583a95 100644
--- a/python/paddle/tests/test_model.py
+++ b/python/paddle/tests/test_model.py
@@ -66,34 +66,6 @@ def forward(self, inputs):
         return x
 
 
-class LeNetDeclarative(fluid.dygraph.Layer):
-    def __init__(self, num_classes=10):
-        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))
-
-    @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)
@@ -577,32 +549,34 @@ def test_export_deploy_model(self):
                 shutil.rmtree(save_dir)
             paddle.enable_static()
 
-    def test_export_deploy_model_without_inputs_in_dygraph(self):
+    def test_dygraph_export_deploy_model_without_inputs(self):
         mnist_data = MnistDataset(mode='train')
         paddle.disable_static()
         for initial in ["fit", "train_batch", "eval_batch", "test_batch"]:
             save_dir = tempfile.mkdtemp()
             if not os.path.exists(save_dir):
                 os.makedirs(save_dir)
-                net = LeNet()
-                model = Model(net)
-                model.prepare(
-                    optimizer=optim, loss=CrossEntropyLoss(reduction="sum"))
-                if initial == "fit":
-                    model.fit(mnist_data, batch_size=64, verbose=0)
+            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:
-                    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.test_batch([img])
-
-                model.save(save_dir, training=False)
-                shutil.rmtree(save_dir)
+                    model.test_batch([img])
+
+            model.save(save_dir, training=False)
+            shutil.rmtree(save_dir)
 
 
 class TestRaiseError(unittest.TestCase):
@@ -614,7 +588,7 @@ def test_input_without_name(self):
         with self.assertRaises(ValueError):
             model = Model(net, inputs, labels)
 
-    def test_export_deploy_model_without_inputs_and_run_in_dygraph(self):
+    def test_save_infer_model_without_inputs_and_run_in_dygraph(self):
         paddle.disable_static()
         net = MyModel()
         save_dir = tempfile.mkdtemp()

From 707a42135f259e136423b8d07f1df464399f06c4 Mon Sep 17 00:00:00 2001
From: LiuChiaChi <709153940@qq.com>
Date: Sun, 27 Sep 2020 09:21:51 +0000
Subject: [PATCH 10/14] set paddle manual seed for unittest

---
 python/paddle/hapi/model.py       | 2 --
 python/paddle/tests/test_model.py | 1 +
 2 files changed, 1 insertion(+), 2 deletions(-)

diff --git a/python/paddle/hapi/model.py b/python/paddle/hapi/model.py
index ea6d310ad53785..936bc77d491eed 100644
--- a/python/paddle/hapi/model.py
+++ b/python/paddle/hapi/model.py
@@ -618,7 +618,6 @@ def mode(self, value):
 
     # TODO multi device in dygraph mode not implemented at present time
     def train_batch(self, inputs, labels=None):
-
         assert self.model._optimizer, \
             "model not ready, please call `model.prepare()` first"
         self.model.network.train()
@@ -1858,7 +1857,6 @@ def _run_one_epoch(self, data_loader, callbacks, mode, logs={}):
             ]
             self._is_shape_inferred = True
             self._inputs = self._verify_spec(None, self._shapes, True)
-
         if mode == 'test':
             return logs, outputs
         return logs
diff --git a/python/paddle/tests/test_model.py b/python/paddle/tests/test_model.py
index 6f5163f0583a95..809b25db5e47f8 100644
--- a/python/paddle/tests/test_model.py
+++ b/python/paddle/tests/test_model.py
@@ -515,6 +515,7 @@ def test_summary_error(self):
         paddle.summary(nlp_net, (1, 1, 2))
 
     def test_export_deploy_model(self):
+        self.set_seed()
         for dynamic in [True, False]:
             paddle.disable_static() if dynamic else None
             prog_translator = ProgramTranslator()

From 7d8d7910505886761851ca88c63915153f028d81 Mon Sep 17 00:00:00 2001
From: LiuChiaChi <709153940@qq.com>
Date: Sun, 27 Sep 2020 11:40:02 +0000
Subject: [PATCH 11/14] fix Model bugs, because inputs can be list or dict when
 it is provided.

---
 python/paddle/hapi/model.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/python/paddle/hapi/model.py b/python/paddle/hapi/model.py
index 936bc77d491eed..ef06e7e0b5b7a7 100644
--- a/python/paddle/hapi/model.py
+++ b/python/paddle/hapi/model.py
@@ -858,7 +858,10 @@ def __init__(self, network, inputs=None, labels=None):
                 raise TypeError(
                     "'inputs' must be list or dict, and couldn't be None.")
         elif inputs:
-            self._shapes = [list(input.shape) for input in inputs]
+            if isinstance(inputs, list):
+                self._shapes = [list(input.shape) for input in inputs]
+            elif isinstance(inputs, dict):
+                self._shapes = [list(inputs[name]) for name in inputs]
 
         self._inputs = self._verify_spec(inputs, is_input=True)
         self._labels = self._verify_spec(labels)

From 3e56bfbfc534c946cda92583b746706fe167b80c Mon Sep 17 00:00:00 2001
From: LiuChiaChi <709153940@qq.com>
Date: Sun, 27 Sep 2020 12:33:11 +0000
Subject: [PATCH 12/14] add random seed for test_export_deploy_model

---
 python/paddle/tests/test_model.py | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/python/paddle/tests/test_model.py b/python/paddle/tests/test_model.py
index 809b25db5e47f8..96c4483a35ba83 100644
--- a/python/paddle/tests/test_model.py
+++ b/python/paddle/tests/test_model.py
@@ -516,6 +516,7 @@ def test_summary_error(self):
 
     def test_export_deploy_model(self):
         self.set_seed()
+        np.random.seed(2020)
         for dynamic in [True, False]:
             paddle.disable_static() if dynamic else None
             prog_translator = ProgramTranslator()
@@ -589,6 +590,12 @@ def test_input_without_name(self):
         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()

From 5ecba2acab18e2b89899eb0e6cdae7515fb52d83 Mon Sep 17 00:00:00 2001
From: LiuChiaChi <709153940@qq.com>
Date: Mon, 28 Sep 2020 06:57:47 +0000
Subject: [PATCH 13/14] delete redundant codes, because  calls

---
 python/paddle/hapi/model.py | 9 +--------
 1 file changed, 1 insertion(+), 8 deletions(-)

diff --git a/python/paddle/hapi/model.py b/python/paddle/hapi/model.py
index ef06e7e0b5b7a7..2fdb6be54f6602 100644
--- a/python/paddle/hapi/model.py
+++ b/python/paddle/hapi/model.py
@@ -861,7 +861,7 @@ def __init__(self, network, inputs=None, labels=None):
             if isinstance(inputs, list):
                 self._shapes = [list(input.shape) for input in inputs]
             elif isinstance(inputs, dict):
-                self._shapes = [list(inputs[name]) for name in inputs]
+                self._shapes = [list(inputs[name].shape) for name in inputs]
 
         self._inputs = self._verify_spec(inputs, is_input=True)
         self._labels = self._verify_spec(labels)
@@ -1853,13 +1853,6 @@ def _run_one_epoch(self, data_loader, callbacks, mode, logs={}):
             callbacks.on_batch_end(mode, step, logs)
         self._reset_metrics()
 
-        if self._shapes is None and mode == "train":
-            self._shapes = [
-                data[i].shape() if callable(data[i].shape) else data[i].shape
-                for i in range(len(data) - 1)
-            ]
-            self._is_shape_inferred = True
-            self._inputs = self._verify_spec(None, self._shapes, True)
         if mode == 'test':
             return logs, outputs
         return logs

From b151fba973e00942bd140f4b29e53a18b20c7606 Mon Sep 17 00:00:00 2001
From: LiuChiaChi <709153940@qq.com>
Date: Mon, 28 Sep 2020 11:36:37 +0000
Subject: [PATCH 14/14] Code optimization, error information optimization

---
 python/paddle/hapi/model.py | 50 +++++++++++++++++++++----------------
 1 file changed, 28 insertions(+), 22 deletions(-)

diff --git a/python/paddle/hapi/model.py b/python/paddle/hapi/model.py
index 2fdb6be54f6602..8505544a71f58e 100644
--- a/python/paddle/hapi/model.py
+++ b/python/paddle/hapi/model.py
@@ -200,6 +200,15 @@ def _init_context():
     return strategy
 
 
+def _update_input_shapes(inputs):
+    shapes = None
+    if isinstance(inputs, list):
+        shapes = [list(input.shape) for input in inputs]
+    elif isinstance(inputs, dict):
+        shapes = [list(inputs[name].shape) for name in inputs]
+    return shapes
+
+
 class StaticGraphAdapter(object):
     """
     Model traning/inference with a static graph.
@@ -598,7 +607,7 @@ def __init__(self, model):
             'test_batch': 0
         }
 
-        self._shapes = None
+        self._input_shapes = None
         if self._nranks > 1:
             stradegy = fluid.dygraph.parallel.ParallelStrategy()
             stradegy.nranks = ParallelEnv().nranks
@@ -623,7 +632,7 @@ def train_batch(self, inputs, labels=None):
         self.model.network.train()
         self.mode = 'train'
         inputs = to_list(inputs)
-        self._shapes = [list(input.shape) for input in inputs]
+        self._input_shapes = _update_input_shapes(inputs)
         labels = labels or []
         labels = [to_variable(l) for l in to_list(labels)]
 
@@ -658,7 +667,7 @@ def eval_batch(self, inputs, labels=None):
         self.model.network.eval()
         self.mode = 'eval'
         inputs = to_list(inputs)
-        self._shapes = [list(input.shape) for input in inputs]
+        self._input_shapes = _update_input_shapes(inputs)
         labels = labels or []
         labels = [to_variable(l) for l in to_list(labels)]
 
@@ -707,7 +716,7 @@ def test_batch(self, inputs):
         self.model.network.eval()
         self.mode = 'test'
         inputs = [to_variable(x) for x in to_list(inputs)]
-        self._shapes = [list(input.shape) for input in inputs]
+        self._input_shapes = _update_input_shapes(inputs)
         outputs = self.model.network.forward(*inputs)
         if self._nranks > 1 and isinstance(self.model._place, fluid.CUDAPlace):
             outputs = [_all_gather(o, self._nranks) for o in to_list(outputs)]
@@ -849,7 +858,7 @@ def __init__(self, network, inputs=None, labels=None):
         self._loss = None
         self._loss_weights = None
         self._optimizer = None
-        self._shapes = None
+        self._input_shapes = None
         self._is_shape_inferred = False
         self._test_dataloader = None
 
@@ -858,10 +867,7 @@ def __init__(self, network, inputs=None, labels=None):
                 raise TypeError(
                     "'inputs' must be list or dict, and couldn't be None.")
         elif inputs:
-            if isinstance(inputs, list):
-                self._shapes = [list(input.shape) for input in inputs]
-            elif isinstance(inputs, dict):
-                self._shapes = [list(inputs[name].shape) for name in inputs]
+            self._input_shapes = _update_input_shapes(inputs)
 
         self._inputs = self._verify_spec(inputs, is_input=True)
         self._labels = self._verify_spec(labels)
@@ -915,10 +921,10 @@ def train_batch(self, inputs, labels=None):
               print(loss)
         """
         loss = self._adapter.train_batch(inputs, labels)
-        if fluid.in_dygraph_mode() and self._shapes is None:
-            self._shapes = self._adapter._shapes
+        if fluid.in_dygraph_mode() and self._input_shapes is None:
+            self._input_shapes = self._adapter._input_shapes
             self._is_shape_inferred = True
-            self._inputs = self._verify_spec(None, self._shapes, True)
+            self._inputs = self._verify_spec(None, self._input_shapes, True)
         return loss
 
     def eval_batch(self, inputs, labels=None):
@@ -965,10 +971,10 @@ def eval_batch(self, inputs, labels=None):
               print(loss)
         """
         loss = self._adapter.eval_batch(inputs, labels)
-        if fluid.in_dygraph_mode() and self._shapes is None:
-            self._shapes = self._adapter._shapes
+        if fluid.in_dygraph_mode() and self._input_shapes is None:
+            self._input_shapes = self._adapter._input_shapes
             self._is_shape_inferred = True
-            self._inputs = self._verify_spec(None, self._shapes, True)
+            self._inputs = self._verify_spec(None, self._input_shapes, True)
         return loss
 
     def test_batch(self, inputs):
@@ -1010,10 +1016,10 @@ def test_batch(self, inputs):
               print(out)
         """
         loss = self._adapter.test_batch(inputs)
-        if fluid.in_dygraph_mode() and self._shapes is None:
-            self._shapes = self._adapter._shapes
+        if fluid.in_dygraph_mode() and self._input_shapes is None:
+            self._input_shapes = self._adapter._input_shapes
             self._is_shape_inferred = True
-            self._inputs = self._verify_spec(None, self._shapes, True)
+            self._inputs = self._verify_spec(None, self._input_shapes, True)
         return loss
 
     def save(self, path, training=True):
@@ -1704,14 +1710,14 @@ def get_inout_spec(all_vars, return_name=False):
         if fluid.in_dygraph_mode():
             with fluid.framework._dygraph_guard(None):
                 layer = self.network
-                if self._shapes is None:  # No provided or inferred
+                if self._input_shapes is None:  # No provided or inferred
                     raise RuntimeError(
-                        "Saving inference model needs `inputs` or running before saving."
+                        "Saving inference model needs 'inputs' or running before saving. Please specify 'inputs' in Model initialization or input training zqqdata and perform a training for shape derivation."
                     )
                 if self._is_shape_inferred:
                     warnings.warn(
-                        'Saving actual input shapes only if `inputs` is provided, otherwise variable input dimension is immutable.'
-                    )
+                        "'inputs' was not specified when Model initialization, so the input shape to be saved will be the shape derived from the user's actual inputs. The input shape to be saved is %s. For saving correct input shapes, please provide 'inputs' for Model initialization."
+                        % self._input_shapes)
                 layer.forward = paddle.jit.to_static(
                     layer.forward, input_spec=self._inputs)