skip cases with small shape (#22318)

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

@@ -231,12 +231,12 @@ class OpTest(unittest.TestCase):
                     "This test of %s op needs check_grad with fp64 precision." %
                     cls.op_type)
 
-        if not get_numeric_gradient.is_large_shape \
+            if not get_numeric_gradient.is_large_shape \
-            and cls.op_type not in check_shape_white_list.NOT_CHECK_OP_LIST \
+                and cls.op_type not in check_shape_white_list.NOT_CHECK_OP_LIST \
-            and cls.op_type not in check_shape_white_list.NEED_TO_FIX_OP_LIST:
+                and cls.op_type not in check_shape_white_list.NEED_TO_FIX_OP_LIST:
-            raise AssertionError(
+                raise AssertionError(
-                "Input's shape should be large than or equal to 100 for " +
+                    "Input's shape should be large than or equal to 100 for " +
-                cls.op_type + " Op.")
+                    cls.op_type + " Op.")
 
     def try_call_once(self, data_type):
         if not self.call_once:

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

@@ -17,15 +17,16 @@ from __future__ import print_function
 import unittest
 import numpy as np
 import six
-from op_test import OpTest
+from op_test import OpTest, skip_check_grad_ci
 
 
 class PReluTest(OpTest):
     def setUp(self):
+        self.init_input_shape()
+        self.init_attr()
         self.op_type = "prelu"
-        self.initTestCase()
-        x_np = np.random.normal(size=(3, 5, 5, 10)).astype("float32")
 
+        x_np = np.random.uniform(-1, 1, self.x_shape).astype("float32")
         # Since zero point in prelu is not differentiable, avoid randomize
         # zero.
         x_np[np.abs(x_np) < 0.005] = 0.02
@@ -37,8 +38,8 @@ class PReluTest(OpTest):
             alpha_np = np.random.rand(1, x_np.shape[1], 1, 1).astype("float32")
             self.inputs = {'X': x_np, 'Alpha': alpha_np}
         else:
-            alpha_np = np.random.rand(1, x_np.shape[1], x_np.shape[2], \
+            alpha_np = np.random.rand(1, x_np.shape[1], x_np.shape[2],
-                x_np.shape[3]).astype("float32")
+                                      x_np.shape[3]).astype("float32")
             self.inputs = {'X': x_np, 'Alpha': alpha_np}
 
         out_np = np.maximum(self.inputs['X'], 0.)
@@ -47,7 +48,10 @@ class PReluTest(OpTest):
         assert out_np is not self.inputs['X']
         self.outputs = {'Out': out_np}
 
-    def initTestCase(self):
+    def init_input_shape(self):
+        self.x_shape = (2, 100, 3, 4)
+
+    def init_attr(self):
         self.attrs = {'mode': "channel"}
 
     def test_check_output(self):
@@ -66,16 +70,21 @@ class PReluTest(OpTest):
 # TODO(minqiyang): Resume these test cases after fixing Python3 CI job issues
 if six.PY2:
 
-    class TestCase1(PReluTest):
+    @skip_check_grad_ci(
-        def initTestCase(self):
+        reason="[skip shape check] Input(Alpha) must be 1-D and only has one data in 'all' mode"
+    )
+    class TestModeAll(PReluTest):
+        def init_input_shape(self):
+            self.x_shape = (2, 3, 4, 5)
+
+        def init_attr(self):
             self.attrs = {'mode': "all"}
 
-    class TestCase2(PReluTest):
+    class TestModeElt(PReluTest):
-        def initTestCase(self):
+        def init_input_shape(self):
-            self.attrs = {'mode': "channel"}
+            self.x_shape = (3, 2, 5, 10)
 
-    class TestCase3(PReluTest):
+        def init_attr(self):
-        def initTestCase(self):
             self.attrs = {'mode': "element"}
 
 

python/paddle/fluid/tests/unittests/white_list/check_shape_white_list.py

@@ -24,8 +24,6 @@ NOT_CHECK_OP_LIST = [
     'elementwise_min',
     'elementwise_pow',
     'fused_elemwise_activation',
-    # prelu op's input alpha must be 1-d and only has one data in 'all' mode
-    'prelu'
 ]
 
 NEED_TO_FIX_OP_LIST = [