[remove fluid] under unittesets of linear api (PaddlePaddle#48564)

* [remove fluid] under unittesets of linear api * [remove fluid] under unittesets of linear api * [remove fluid] under unittesets of linear api * [remove fluid] under unittesets of linear api * [remove fluid] under unittesets of linear api * [remove fluid] under unittesets of linear api * [remove fluid] fluid dygrapn linear api * [remove fluid] fluid dygrapn linear api * [remove fluid] fluid dygrapn linear api
lxsbupt · Dec 8, 2022 · 99a9dcd · 99a9dcd
1 parent 082886c
commit 99a9dcd
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diff --git a/python/paddle/distributed/sharding/group_sharded.py b/python/paddle/distributed/sharding/group_sharded.py
@@ -91,7 +91,7 @@ def group_sharded_parallel(
 
             # required: distributed
             import paddle
-            from paddle.fluid.dygraph.nn import Linear
+            from paddle.nn import Linear
             from paddle.distributed import fleet
             from paddle.distributed.sharding import group_sharded_parallel
 
@@ -238,7 +238,7 @@ def save_group_sharded_model(model, output, optimizer=None):
 
             # required: distributed
             import paddle
-            from paddle.fluid.dygraph.nn import Linear
+            from paddle.nn import Linear
             from paddle.distributed import fleet
             from paddle.distributed.sharding import group_sharded_parallel, save_group_sharded_model
 

diff --git a/python/paddle/fluid/contrib/slim/tests/test_imperative_qat_user_defined.py b/python/paddle/fluid/contrib/slim/tests/test_imperative_qat_user_defined.py
@@ -23,7 +23,7 @@
 from paddle.fluid.contrib.slim.quantization import ImperativeQuantAware
 from paddle.fluid.contrib.slim.quantization import QuantizationTransformPass
 from paddle.nn import Sequential
-from paddle.fluid.dygraph import Linear
+from paddle.nn import Linear
 from paddle.nn.quant.quant_layers import QuantizedConv2DTranspose
 from paddle.fluid.log_helper import get_logger
 from paddle.fluid.framework import _test_eager_guard
@@ -111,7 +111,7 @@ class ModelForConv2dT(nn.Layer):
     def __init__(self, num_classes=10):
         super().__init__()
         self.features = nn.Conv2DTranspose(4, 6, (3, 3))
-        self.fc = Linear(input_dim=600, output_dim=num_classes)
+        self.fc = Linear(600, num_classes)
 
     def forward(self, inputs):
         x = self.features(inputs)
@@ -143,11 +143,9 @@ def __init__(self, num_classes=10, classifier_activation='softmax'):
         )
 
         self.fc = Sequential(
-            Linear(input_dim=400, output_dim=120),
-            Linear(input_dim=120, output_dim=84),
-            Linear(
-                input_dim=84, output_dim=num_classes, act=classifier_activation
-            ),
+            Linear(400, 120),
+            Linear(120, 84),
+            Linear(84, num_classes),
         )
 
     def forward(self, inputs):

diff --git a/python/paddle/fluid/dygraph/learning_rate_scheduler.py b/python/paddle/fluid/dygraph/learning_rate_scheduler.py
@@ -821,11 +821,12 @@ class ReduceLROnPlateau(LearningRateDecay):
     .. code-block:: python
 
         import paddle.fluid as fluid
+        import paddle
         import numpy as np
 
         with fluid.dygraph.guard():
             x = np.random.uniform(-1, 1, [10, 10]).astype("float32")
-            linear = fluid.dygraph.Linear(10, 10)
+            linear = paddle.nn.Linear(10, 10)
             input = fluid.dygraph.to_variable(x)
 
             reduce_lr = fluid.dygraph.ReduceLROnPlateau(
@@ -842,7 +843,7 @@ class ReduceLROnPlateau(LearningRateDecay):
                 total_loss = 0
                 for bath_id in range(5):
                     out = linear(input)
-                    loss = fluid.layers.reduce_mean(out)
+                    loss = paddle.mean(out)
                     total_loss += loss
                     adam.minimize(loss)
 
@@ -1090,17 +1091,18 @@ class StepDecay(_LearningRateEpochDecay):
 
             import paddle.fluid as fluid
             import numpy as np
+            import paddle
             with fluid.dygraph.guard():
                 x = np.random.uniform(-1, 1, [10, 10]).astype("float32")
-                linear = fluid.dygraph.Linear(10, 10)
+                linear = paddle.nn.Linear(10, 10)
                 input = fluid.dygraph.to_variable(x)
                 scheduler = fluid.dygraph.StepDecay(0.5, step_size=3)
                 adam = fluid.optimizer.Adam(learning_rate = scheduler, parameter_list = linear.parameters())
 
                 for epoch in range(9):
                     for batch_id in range(5):
                         out = linear(input)
-                        loss = fluid.layers.reduce_mean(out)
+                        loss = paddle.mean(out)
                         adam.minimize(loss)
                     scheduler.epoch()
 
@@ -1170,17 +1172,18 @@ class MultiStepDecay(_LearningRateEpochDecay):
 
             import paddle.fluid as fluid
             import numpy as np
+            import paddle
             with fluid.dygraph.guard():
                 x = np.random.uniform(-1, 1, [10, 10]).astype("float32")
-                linear = fluid.dygraph.Linear(10, 10)
+                linear = paddle.nn.Linear(10, 10)
                 input = fluid.dygraph.to_variable(x)
                 scheduler = fluid.dygraph.MultiStepDecay(0.5, milestones=[3, 5])
                 adam = fluid.optimizer.Adam(learning_rate = scheduler, parameter_list = linear.parameters())
 
                 for epoch in range(6):
                     for batch_id in range(5):
                         out = linear(input)
-                        loss = fluid.layers.reduce_mean(out)
+                        loss = paddle.mean(out)
                         adam.minimize(loss)
                     scheduler.epoch()
 
@@ -1255,17 +1258,18 @@ class LambdaDecay(_LearningRateEpochDecay):
 
             import paddle.fluid as fluid
             import numpy as np
+            import paddle
             with fluid.dygraph.guard():
                 x = np.random.uniform(-1, 1, [10, 10]).astype("float32")
-                linear = fluid.dygraph.Linear(10, 10)
+                linear = paddle.nn.Linear(10, 10)
                 input = fluid.dygraph.to_variable(x)
                 scheduler = fluid.dygraph.LambdaDecay(0.5, lr_lambda=lambda x: 0.95**x)
                 adam = fluid.optimizer.Adam(learning_rate = scheduler, parameter_list = linear.parameters())
 
                 for epoch in range(6):
                     for batch_id in range(5):
                         out = linear(input)
-                        loss = fluid.layers.reduce_mean(out)
+                        loss = paddle.mean(out)
                         adam.minimize(loss)
                     scheduler.epoch()