hardtanh prelu softmax, test=develop

python/paddle/fluid/layers/nn.py

@@ -1189,6 +1189,7 @@ def chunk_eval(input,
             num_correct_chunks)
 
 
+@deprecated(since="2.0.0", update_to="paddle.nn.functional.softmax")
 def softmax(input, use_cudnn=False, name=None, axis=-1):
     """
     This operator implements the softmax layer. The calculation process is as follows:
@@ -8601,7 +8602,7 @@ def log(x, name=None):
     return out
 
 
-@templatedoc()
+@deprecated(since="2.0.0", update_to="paddle.nn.functional.relu")
 def relu(x, name=None):
     """
     ${comment}
@@ -9260,7 +9261,7 @@ def pad2d(input,
     return out
 
 
-@templatedoc()
+@deprecated(since="2.0.0", update_to="paddle.nn.functional.elu")
 def elu(x, alpha=1.0, name=None):
     """
     :alias_main: paddle.nn.functional.elu
@@ -9576,6 +9577,7 @@ def swish(x, beta=1.0, name=None):
     return out
 
 
+@deprecated(since="2.0.0", update_to="paddle.nn.functional.prelu")
 def prelu(x, mode, param_attr=None, name=None):
     """
     :api_attr: Static Graph

python/paddle/fluid/tests/unittests/test_activation_op.py

@@ -525,6 +525,63 @@ def test_errors(self):
             F.hardshrink(x_fp16)
 
 
+def ref_hardtanh(x, min=-1.0, max=1.0):
+    out = np.copy(x)
+    out[np.abs(x - min) < 0.005] = min + 0.02
+    out[np.abs(x - max) < 0.005] = max + 0.02
+    out = np.minimum(np.maximum(x, min), max)
+    return out
+
+
+class TestHardtanhAPI(unittest.TestCase):
+    # test paddle.nn.Hardtanh, paddle.nn.functional.hardtanh
+    def setUp(self):
+        self.x_np = np.random.uniform(-3, 3, [10, 12]).astype('float32')
+        self.place=paddle.CUDAPlace(0) if core.is_compiled_with_cuda() \
+            else paddle.CPUPlace()
+
+    def test_static_api(self):
+        with paddle.static.program_guard(paddle.static.Program()):
+            x = paddle.data('X', [10, 12])
+            out1 = F.hardtanh(x)
+            m = paddle.nn.Hardtanh()
+            out2 = m(x)
+            exe = paddle.static.Executor(self.place)
+            res = exe.run(feed={'X': self.x_np}, fetch_list=[out1, out2])
+        out_ref = ref_hardtanh(self.x_np)
+        for r in res:
+            self.assertEqual(np.allclose(out_ref, r), True)
+
+    def test_dygraph_api(self):
+        paddle.disable_static(self.place)
+        x = paddle.to_variable(self.x_np)
+        out1 = F.hardtanh(x)
+        m = paddle.nn.Hardtanh()
+        out2 = m(x)
+        out_ref = ref_hardtanh(self.x_np)
+        for r in [out1, out2]:
+            self.assertEqual(np.allclose(out_ref, r.numpy()), True)
+
+        out1 = F.hardtanh(x, -2.0, 2.0)
+        m = paddle.nn.Hardtanh(-2.0, 2.0)
+        out2 = m(x)
+        out_ref = ref_hardtanh(self.x_np, -2.0, 2.0)
+        for r in [out1, out2]:
+            self.assertEqual(np.allclose(out_ref, r.numpy()), True)
+        paddle.enable_static()
+
+    def test_errors(self):
+        with paddle.static.program_guard(paddle.static.Program()):
+            # The input type must be Variable.
+            self.assertRaises(TypeError, F.hardtanh, 1)
+            # The input dtype must be float16, float32, float64.
+            x_int32 = paddle.data(name='x_int32', shape=[12, 10], dtype='int32')
+            self.assertRaises(TypeError, F.hardtanh, x_int32)
+            # support the input dtype is float16
+            x_fp16 = paddle.data(name='x_fp16', shape=[12, 10], dtype='float16')
+            F.hardtanh(x_fp16)
+
+
 def ref_softshrink(x, threshold=0.5):
     out = np.copy(x)
     out = (out < -threshold) * (out + threshold) + (out > threshold) * (

python/paddle/fluid/tests/unittests/test_prelu_op.py

@@ -18,23 +18,134 @@
 import numpy as np
 import paddle.fluid as fluid
 import six
-import paddle.fluid as fluid
+import paddle.fluid.core as core
 from paddle.fluid import Program, program_guard
 from op_test import OpTest, skip_check_grad_ci
+import paddle
+import paddle.nn.functional as F
+
+
+def ref_prelu(x, weight):
+    x_t = x.copy()
+    weight = weight.reshape(1, -1, 1, 1)
+    neg_indices = x <= 0
+    assert x.shape == neg_indices.shape
+    x_t[neg_indices] = (x_t * weight)[neg_indices]
+    return (x_t, )
+
+
+def ref_prelu_nn(x, num_parameters, init):
+    weight_np = np.full((num_parameters), init)
+    return ref_prelu(x, weight_np)
 
 
-class TestPReluOpError(unittest.TestCase):
-    def test_errors(self):
-        with program_guard(Program()):
+class TestFunctionalPReluAPI(unittest.TestCase):
+    def setUp(self):
+        self.place = paddle.CUDAPlace(0) if core.is_compiled_with_cuda(
+        ) else paddle.CPUPlace()
+        self.x_np = np.random.uniform(-1., 1., [1, 2, 3, 4]).astype('float32')
+        self.weight_np_0 = np.random.randn(1).astype('float32')
+        self.weight_np_1 = np.random.randn(self.x_np.shape[1]).astype('float32')
+
+    def static_check(self, weight_np):
+        with paddle.static.program_guard(paddle.static.Program()):
+            x = paddle.data('X', self.x_np.shape, 'float32')
+            weight = paddle.data('Alpha', weight_np.shape, 'float32')
+            out = F.prelu(x, weight)
+            exe = paddle.static.Executor(self.place)
+            res = exe.run(feed={'X': self.x_np,
+                                'Alpha': weight_np},
+                          fetch_list=[out])
+        out_ref = ref_prelu(self.x_np, weight_np)
+        self.assertEqual(np.allclose(out_ref, res[0]), True)
+
+    def dygraph_check(self, weight_np):
+        paddle.disable_static(self.place)
+        x = paddle.to_tensor(self.x_np)
+        weight = paddle.to_tensor(weight_np)
+        out = F.prelu(x, weight)
+        out_ref = ref_prelu(self.x_np, weight_np)
+        self.assertEqual(np.allclose(out_ref, out.numpy()), True)
+        paddle.enable_static()
+
+    def test_static_api(self):
+        self.static_check(self.weight_np_0)
+        self.static_check(self.weight_np_1)
+
+    def test_dygraph_api(self):
+        self.dygraph_check(self.weight_np_0)
+        self.dygraph_check(self.weight_np_1)
+
+    def test_error(self):
+        with paddle.static.program_guard(paddle.static.Program()):
+            weight_fp32 = paddle.data(
+                name='weight_fp32', shape=[1], dtype='float32')
             # The input type must be Variable.
-            self.assertRaises(TypeError, fluid.layers.prelu, 0.1, 'all')
+            self.assertRaises(TypeError, F.prelu, x=1, weight=weight_fp32)
             # The input dtype must be float16, float32, float64.
-            x_int32 = fluid.data(name='x_int32', shape=[12, 10], dtype='int32')
-            self.assertRaises(TypeError, fluid.layers.prelu, x_int32, 'all')
-            # support the input dtype is float32
-            x_fp16 = fluid.layers.data(
-                name='x_fp16', shape=[12, 10], dtype='float32')
-            fluid.layers.prelu(x_fp16, 'all')
+            x_int32 = paddle.data(name='x_int32', shape=[2, 3], dtype='int32')
+            self.assertRaises(TypeError, F.prelu, x=x_int32, weight=weight_fp32)
+            # support the input dtype is float16
+            x_fp16 = paddle.data(name='x_fp16', shape=[2, 3], dtype='float16')
+            F.prelu(x=x_fp16, weight=weight_fp32)
+
+
+class TestNNPReluAPI(unittest.TestCase):
+    def setUp(self):
+        self.place = paddle.CUDAPlace(0) if core.is_compiled_with_cuda(
+        ) else paddle.CPUPlace()
+        self.x_np = np.ones([1, 2, 3, 4]).astype('float32')
+
+    def test_static_api(self):
+        startup_program = paddle.static.Program()
+        train_program = paddle.static.Program()
+        with paddle.static.program_guard(train_program, startup_program):
+            x = paddle.data(name='X', shape=self.x_np.shape, dtype='float32')
+            m = paddle.nn.PReLU()
+            out = m(x)
+            exe = paddle.static.Executor(self.place)
+            exe.run(startup_program)
+            res = exe.run(train_program,
+                          feed={'X': self.x_np},
+                          fetch_list=[out])
+        out_ref = ref_prelu_nn(self.x_np, 1, 0.25)
+        self.assertEqual(np.allclose(out_ref, res[0]), True)
+
+    def test_dygraph_api(self):
+        paddle.disable_static(self.place)
+
+        x = paddle.to_tensor(self.x_np)
+        m = paddle.nn.PReLU()
+        out = m(x)
+        out_ref = ref_prelu_nn(self.x_np, 1, 0.25)
+        self.assertEqual(np.allclose(out_ref, out.numpy()), True)
+
+        x = paddle.to_tensor(self.x_np)
+        m = paddle.nn.PReLU(num_parameters=self.x_np.shape[1])
+        out = m(x)
+        out_ref = ref_prelu_nn(self.x_np, self.x_np.shape[1], 0.25)
+        self.assertEqual(np.allclose(out_ref, out.numpy()), True)
+
+        x = paddle.to_tensor(self.x_np)
+        m = paddle.nn.PReLU(init=0.5)
+        out = m(x)
+        out_ref = ref_prelu_nn(self.x_np, 1, 0.5)
+        self.assertEqual(np.allclose(out_ref, out.numpy()), True)
+
+        x = paddle.to_tensor(self.x_np)
+        m = paddle.nn.PReLU(weight_attr=fluid.ParamAttr(name="weight"))
+        out = m(x)
+        out_ref = ref_prelu_nn(self.x_np, 1, 0.25)
+        self.assertEqual(np.allclose(out_ref, out.numpy()), True)
+
+        x = paddle.to_tensor(self.x_np)
+        m = paddle.nn.PReLU(weight_attr=fluid.ParamAttr(
+            initializer=fluid.initializer.Constant(0.5)))
+        out = m(x)
+        out_ref = ref_prelu_nn(self.x_np, 1, 0.5)
+        self.assertEqual(np.allclose(out_ref, out.numpy()), True)
+
+        paddle.enable_static()
 
 
 class PReluTest(OpTest):

python/paddle/fluid/tests/unittests/test_softmax_op.py

@@ -35,6 +35,15 @@ def stable_softmax(x):
     return exps / np.sum(exps)
 
 
+def ref_softmax(x, axis=None, dtype=None):
+    x_t = x.copy()
+    if dtype is not None:
+        x_t = x_t.astype(dtype)
+    if axis is None:
+        axis = -1
+    return np.apply_along_axis(stable_softmax, axis, x_t)
+
+
 class TestSoftmaxOp(OpTest):
     def get_x_shape(self):
         return [10, 10]
@@ -93,20 +102,6 @@ def test_check_grad(self):
                 check_dygraph=(self.use_mkldnn == False))
 
 
-class TestSoftmaxOpError(unittest.TestCase):
-    def test_errors(self):
-        with program_guard(Program(), Program()):
-            # The input type of softmax_op must be Variable.
-            x1 = fluid.create_lod_tensor(
-                np.array([[-1]]), [[1]], fluid.CPUPlace())
-            self.assertRaises(TypeError, fluid.layers.softmax, x1)
-            # The input dtype of softmax_op must be float16, float32 or float64.
-            x2 = fluid.layers.data(name='x2', shape=[4], dtype="int32")
-            self.assertRaises(TypeError, fluid.layers.softmax, x2)
-            x3 = fluid.layers.data(name='x3', shape=[4], dtype="float16")
-            fluid.layers.softmax(x3)
-
-
 class TestSoftmaxOp2(TestSoftmaxOp):
     def get_x_shape(self):
         return [2, 3, 4, 5]
@@ -224,41 +219,59 @@ def get_x_shape(self):
         return [2, 3, 4, 5]
 
 
-class TestNnFunctionalSoftmaxApi(unittest.TestCase):
+class TestSoftmaxAPI(unittest.TestCase):
     def setUp(self):
         self.place = paddle.CUDAPlace(0) if core.is_compiled_with_cuda(
         ) else paddle.CPUPlace()
         self.x_np = np.random.uniform(-1., 1., [2, 3, 4, 5]).astype('float32')
         self.out_ref = np.apply_along_axis(stable_softmax, -1, self.x_np)
 
-    def test_api_static(self):
-        with program_guard(Program()):
+    def test_static_check(self):
+        with paddle.static.program_guard(paddle.static.Program()):
             x = paddle.data('X', self.x_np.shape, 'float32')
-            out = F.softmax(x)
+            out1 = F.softmax(x)
+            m = paddle.nn.Softmax()
+            out2 = m(x)
             exe = paddle.static.Executor(self.place)
-            res = exe.run(feed={'X': self.x_np}, fetch_list=[out])
-        self.assertEqual(np.allclose(self.out_ref, res[0]), True)
+            res = exe.run(feed={'X': self.x_np}, fetch_list=[out1, out2])
+        out_ref = ref_softmax(self.x_np, axis=-1, dtype=None)
+        for r in res:
+            self.assertEqual(np.allclose(out_ref, r), True)
 
-    def test_api_imperative(self):
+    def test_dygraph_check(self):
         paddle.disable_static(self.place)
 
-        x = paddle.to_variable(self.x_np)
-        out = F.softmax(x)
-        self.assertEqual(np.allclose(self.out_ref, out.numpy()), True)
-
-        out = F.softmax(x, axis=0)
-        out_ref = np.apply_along_axis(stable_softmax, 0, self.x_np)
+        x = paddle.to_tensor(self.x_np)
+        out1 = F.softmax(x)
+        m = paddle.nn.Softmax()
+        out2 = m(x)
+        out_ref = ref_softmax(self.x_np, axis=-1, dtype=None)
+        for r in [out1, out2]:
+            self.assertEqual(np.allclose(out_ref, r.numpy()), True)
+
+        out1 = F.softmax(x, axis=0)
+        m = paddle.nn.Softmax(axis=0)
+        out2 = m(x)
+        out_ref = ref_softmax(self.x_np, axis=0, dtype=None)
+        for r in [out1, out2]:
+            self.assertEqual(np.allclose(out_ref, r.numpy()), True)
+
+        out = F.softmax(x, dtype=np.float64)
+        out_ref = ref_softmax(self.x_np, axis=-1, dtype=np.float64)
         self.assertEqual(np.allclose(out_ref, out.numpy()), True)
 
         paddle.enable_static()
 
     def test_error(self):
-        with program_guard(Program(), Program()):
-            # The x should be variable and its dtype should be float32, float64.
-            self.assertRaises(TypeError, F.softmax, [1])
-
-            x = paddle.data(name='x', shape=[2, 3], dtype='int32')
-            self.assertRaises(TypeError, F.softmax, x)
+        with paddle.static.program_guard(paddle.static.Program()):
+            # The input type must be Variable.
+            self.assertRaises(TypeError, F.softmax, 1)
+            # The input dtype must be float16, float32, float64.
+            x_int32 = paddle.data(name='x_int32', shape=[2, 3], dtype='int32')
+            self.assertRaises(TypeError, F.softmax, x_int32)
+            # support the input dtype is float16
+            x_fp16 = paddle.data(name='x_fp16', shape=[2, 3], dtype='float16')
+            F.softmax(x_fp16)
 
 
 if __name__ == "__main__":

python/paddle/nn/__init__.py

@@ -55,14 +55,15 @@
 from .layer.activation import ELU
 from .layer.activation import GELU
 from .layer.activation import Hardshrink
-# from .layer.activation import PReLU        #DEFINE_ALIAS
+from .layer.activation import Hardtanh
+from .layer.activation import PReLU
 from .layer.activation import ReLU
 from .layer.activation import ReLU6  #DEFINE_ALIAS
 from .layer.activation import SELU  #DEFINE_ALIAS
 from .layer.activation import LeakyReLU  #DEFINE_ALIAS
 from .layer.activation import Sigmoid  #DEFINE_ALIAS
 from .layer.activation import LogSigmoid
-# from .layer.activation import Softmax        #DEFINE_ALIAS
+from .layer.activation import Softmax  #DEFINE_ALIAS
 from .layer.activation import Softplus  #DEFINE_ALIAS
 from .layer.activation import Softshrink  #DEFINE_ALIAS
 from .layer.activation import Softsign  #DEFINE_ALIAS

python/paddle/nn/functional/__init__.py

@@ -30,13 +30,14 @@
 from .activation import erf  #DEFINE_ALIAS
 from .activation import gelu  #DEFINE_ALIAS
 from .activation import hardshrink  #DEFINE_ALIAS
+from .activation import hardtanh  #DEFINE_ALIAS
 from .activation import hard_sigmoid  #DEFINE_ALIAS
 from .activation import hard_swish  #DEFINE_ALIAS
 from .activation import hsigmoid  #DEFINE_ALIAS
 from .activation import leaky_relu  #DEFINE_ALIAS
 from .activation import logsigmoid  #DEFINE_ALIAS
 from .activation import maxout  #DEFINE_ALIAS
-# from .activation import prelu        #DEFINE_ALIAS
+from .activation import prelu  #DEFINE_ALIAS
 from .activation import relu  #DEFINE_ALIAS
 from .activation import relu6  #DEFINE_ALIAS
 from .activation import selu  #DEFINE_ALIAS