all cases use large shape (#22241)

enhanced ops: concat, nearest_interp, deformable_conv_v1, sequence_conv, transpose2, conv2d

python/paddle/fluid/tests/unittests/mkldnn/test_conv2d_mkldnn_op.py

@@ -156,6 +156,14 @@ class TestWithStride(TestConv2dMKLDNNOp):
 
 
 class TestWithGroup(TestConv2dMKLDNNOp):
+    def init_test_case(self):
+        self.pad = [0, 0]
+        self.stride = [1, 1]
+        self.input_size = [2, 6, 5, 5]  # NCHW
+        assert np.mod(self.input_size[1], self.groups) == 0
+        f_c = self.input_size[1] // self.groups
+        self.filter_size = [6, f_c, 3, 3]
+
     def init_group(self):
         self.groups = 3
 
@@ -163,13 +171,13 @@ class TestWithGroup(TestConv2dMKLDNNOp):
 class TestWith1x1(TestConv2dMKLDNNOp):
     def init_test_case(self):
         TestConv2dMKLDNNOp.init_test_case(self)
-        self.filter_size = [6, 3, 1, 1]
+        self.filter_size = [40, 3, 1, 1]
 
 
 class TestWithInput1x1Filter1x1(TestConv2dMKLDNNOp):
     def init_test_case(self):
         TestConv2dMKLDNNOp.init_test_case(self)
-        self.input_size = [2, 3, 1, 1]  # NCHW
+        self.input_size = [2, 60, 1, 1]  # NCHW
         assert np.mod(self.input_size[1], self.groups) == 0
         f_c = self.input_size[1] // self.groups
         self.filter_size = [6, f_c, 1, 1]

python/paddle/fluid/tests/unittests/mkldnn/test_transpose_mkldnn_op.py

@@ -60,13 +60,13 @@ class TestCase0MKLDNN(TestTransposeMKLDNN):
 
 class TestCase1a(TestTransposeMKLDNN):
     def initTestCase(self):
-        self.shape = (3, 4, 5)
+        self.shape = (3, 4, 10)
         self.axis = (0, 2, 1)
 
 
 class TestCase1b(TestTransposeMKLDNN):
     def initTestCase(self):
-        self.shape = (3, 4, 5)
+        self.shape = (3, 4, 10)
         self.axis = (2, 1, 0)
 
 

python/paddle/fluid/tests/unittests/sequence/test_sequence_conv.py

@@ -194,7 +194,7 @@ class TestSeqProjectCase1(TestSeqProject):
         self.padding_trainable = True
         self.context_stride = 1
 
-        self.input_size = [self.input_row, 23]
+        self.input_size = [self.input_row, 50]
         offset_lod = [[0, 4, 5, 8, self.input_row]]
         self.lod = [[]]
         # convert from offset-based lod to length-based lod
@@ -211,7 +211,7 @@ class TestSeqProjectCase2Len0(TestSeqProject):
         self.padding_trainable = True
         self.context_stride = 1
 
-        self.input_size = [self.input_row, 23]
+        self.input_size = [self.input_row, 50]
         offset_lod = [[0, 0, 4, 5, 5, 8, self.input_row, self.input_row]]
         self.lod = [[]]
         # convert from offset-based lod to length-based lod
@@ -228,7 +228,7 @@ class TestSeqProjectCase3(TestSeqProject):
         self.padding_trainable = True
         self.context_stride = 1
 
-        self.input_size = [self.input_row, 23]
+        self.input_size = [self.input_row, 25]
         idx = list(range(self.input_size[0]))
         del idx[0]
         offset_lod = [[0] + np.sort(random.sample(idx, 8)).tolist() +

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

@@ -51,9 +51,9 @@ class TestConcatOp(OpTest):
         self.check_grad(['x2'], 'Out')
 
     def init_test_data(self):
-        self.x0 = np.random.random((2, 1, 4, 5)).astype(self.dtype)
-        self.x1 = np.random.random((2, 2, 4, 5)).astype(self.dtype)
-        self.x2 = np.random.random((2, 3, 4, 5)).astype(self.dtype)
+        self.x0 = np.random.random((5, 1, 4, 5)).astype(self.dtype)
+        self.x1 = np.random.random((5, 2, 4, 5)).astype(self.dtype)
+        self.x2 = np.random.random((5, 3, 4, 5)).astype(self.dtype)
         self.axis = 1
 
 
@@ -94,9 +94,9 @@ class TestConcatOp4(TestConcatOp):
 
 class TestConcatOp5(TestConcatOp):
     def init_test_data(self):
-        self.x0 = np.random.random((2, 1, 4, 5)).astype(self.dtype)
-        self.x1 = np.random.random((2, 2, 4, 5)).astype(self.dtype)
-        self.x2 = np.random.random((2, 3, 4, 5)).astype(self.dtype)
+        self.x0 = np.random.random((5, 1, 4, 5)).astype(self.dtype)
+        self.x1 = np.random.random((5, 2, 4, 5)).astype(self.dtype)
+        self.x2 = np.random.random((5, 3, 4, 5)).astype(self.dtype)
         self.axis = -3
 
 

python/paddle/fluid/tests/unittests/test_deformable_conv_v1_op.py

@@ -198,7 +198,7 @@ class TestWithDilation(TestModulatedDeformableConvOp):
     def init_test_case(self):
         self.pad = [2, 2]
         self.stride = [1, 1]
-        self.input_size = [2, 3, 4, 4]  # NCHW
+        self.input_size = [5, 3, 4, 4]  # NCHW
         assert np.mod(self.input_size[1], self.groups) == 0
         f_c = self.input_size[1] // self.groups
         self.filter_size = [6, f_c, 3, 3]
@@ -221,7 +221,7 @@ class TestWith1x1(TestModulatedDeformableConvOp):
         self.input_size = [2, 3, 5, 5]  # NCHW
         assert np.mod(self.input_size[1], self.groups) == 0
         f_c = self.input_size[1] // self.groups
-        self.filter_size = [6, f_c, 1, 1]
+        self.filter_size = [40, f_c, 1, 1]
         self.im2col_step = 1
         self.deformable_groups = 1
         offset_c = 2 * self.deformable_groups * self.filter_size[
@@ -232,6 +232,22 @@ class TestWith1x1(TestModulatedDeformableConvOp):
 
 
 class TestWithGroup(TestModulatedDeformableConvOp):
+    def init_test_case(self):
+        self.pad = [1, 1]
+        self.stride = [1, 1]
+        self.dilations = [1, 1]
+        self.input_size = [2, 8, 4, 4]  # NCHW
+        assert np.mod(self.input_size[1], self.groups) == 0
+        f_c = self.input_size[1] // self.groups
+        self.filter_size = [4, f_c, 3, 3]
+        self.im2col_step = 1
+        self.deformable_groups = 1
+        offset_c = 2 * self.deformable_groups * self.filter_size[
+            2] * self.filter_size[3]
+        self.offset_size = [
+            self.input_size[0], offset_c, self.input_size[2], self.input_size[3]
+        ]
+
     def init_group(self):
         self.groups = 2
 

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

@@ -377,7 +377,7 @@ class TestNearestInterpOp_attr_tensor(OpTest):
 
     def init_test_case(self):
         self.interp_method = 'nearest'
-        self.input_shape = [2, 3, 4, 4]
+        self.input_shape = [2, 5, 4, 4]
         self.out_h = 3
         self.out_w = 3
         self.scale = 0.