Clean unittest code.

test=develop

python/paddle/fluid/tests/unittests/test_pool2d_int8_mkldnn_op.py

@@ -20,217 +20,91 @@ import numpy as np
 
 import paddle.fluid.core as core
 from op_test import OpTest
+from test_pool2d_op import TestPool2D_Op, avg_pool2D_forward_naive, max_pool2D_forward_naive
 
 
-def adaptive_start_index(index, input_size, output_size):
-    return int(np.floor(index * input_size / output_size))
-
-
-def adaptive_end_index(index, input_size, output_size):
-    return int(np.ceil((index + 1) * input_size / output_size))
-
-
-def max_pool2D_forward_naive(x,
-                             ksize,
-                             strides,
-                             paddings,
-                             global_pool=0,
-                             ceil_mode=False,
-                             exclusive=True,
-                             adaptive=False):
-    N, C, H, W = x.shape
-    if global_pool == 1:
-        ksize = [H, W]
-    if adaptive:
-        H_out, W_out = ksize
-    else:
-        H_out = (H - ksize[0] + 2 * paddings[0] + strides[0] - 1
-                 ) // strides[0] + 1 if ceil_mode else (
-                     H - ksize[0] + 2 * paddings[0]) // strides[0] + 1
-        W_out = (W - ksize[1] + 2 * paddings[1] + strides[1] - 1
-                 ) // strides[1] + 1 if ceil_mode else (
-                     W - ksize[1] + 2 * paddings[1]) // strides[1] + 1
-    out = np.zeros((N, C, H_out, W_out))
-    for i in range(H_out):
-        for j in range(W_out):
-            if adaptive:
-                r_start = adaptive_start_index(i, H, ksize[0])
-                r_end = adaptive_end_index(i, H, ksize[0])
-                c_start = adaptive_start_index(j, W, ksize[1])
-                c_end = adaptive_end_index(j, W, ksize[1])
-            else:
-                r_start = np.max((i * strides[0] - paddings[0], 0))
-                r_end = np.min((i * strides[0] + ksize[0] - paddings[0], H))
-                c_start = np.max((j * strides[1] - paddings[1], 0))
-                c_end = np.min((j * strides[1] + ksize[1] - paddings[1], W))
-            x_masked = x[:, :, r_start:r_end, c_start:c_end]
-
-            out[:, :, i, j] = np.max(x_masked, axis=(2, 3))
-    return out
-
-
-def avg_pool2D_forward_naive(x,
-                             ksize,
-                             strides,
-                             paddings,
-                             global_pool=0,
-                             ceil_mode=False,
-                             exclusive=True,
-                             adaptive=False):
-    N, C, H, W = x.shape
-    if global_pool == 1:
-        ksize = [H, W]
-    if adaptive:
-        H_out, W_out = ksize
-    else:
-        H_out = (H - ksize[0] + 2 * paddings[0] + strides[0] - 1
-                 ) // strides[0] + 1 if ceil_mode else (
-                     H - ksize[0] + 2 * paddings[0]) // strides[0] + 1
-        W_out = (W - ksize[1] + 2 * paddings[1] + strides[1] - 1
-                 ) // strides[1] + 1 if ceil_mode else (
-                     W - ksize[1] + 2 * paddings[1]) // strides[1] + 1
-    out = np.zeros((N, C, H_out, W_out))
-    for i in range(H_out):
-        for j in range(W_out):
-            if adaptive:
-                r_start = adaptive_start_index(i, H, ksize[0])
-                r_end = adaptive_end_index(i, H, ksize[0])
-                c_start = adaptive_start_index(j, W, ksize[1])
-                c_end = adaptive_end_index(j, W, ksize[1])
-            else:
-                r_start = np.max((i * strides[0] - paddings[0], 0))
-                r_end = np.min((i * strides[0] + ksize[0] - paddings[0], H))
-                c_start = np.max((j * strides[1] - paddings[1], 0))
-                c_end = np.min((j * strides[1] + ksize[1] - paddings[1], W))
-            x_masked = x[:, :, r_start:r_end, c_start:c_end]
-
-            field_size = ((r_end - r_start) * (c_end - c_start)) \
-                        if (exclusive or adaptive) else (ksize[0] * ksize[1])
-            out[:, :, i, j] = np.sum(x_masked, axis=(2, 3)) / field_size
-    return out
-
-
-class TestPool2D_Op(OpTest):
-    def setUp(self):
-        self.op_type = "pool2d"
-        self.use_cudnn = False
+class TestPool2dMKLDNNInt8_Op(TestPool2D_Op):
+    def init_kernel_type(self):
         self.use_mkldnn = True
-        self.dtype = np.int8
-        self.init_test_case()
-        self.init_global_pool()
-        self.init_pool_type()
-        self.init_ceil_mode()
-        self.init_exclusive()
-        self.init_adaptive()
-        if self.global_pool:
-            self.paddings = [0 for _ in range(len(self.paddings))]
-        input = np.random.random(self.shape).astype(self.dtype)
-        output = self.pool2D_forward_naive(
-            input, self.ksize, self.strides, self.paddings, self.global_pool,
-            self.ceil_mode, self.exclusive, self.adaptive).astype(self.dtype)
-        self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(input)}
-
-        self.attrs = {
-            'strides': self.strides,
-            'paddings': self.paddings,
-            'ksize': self.ksize,
-            'pooling_type': self.pool_type,
-            'global_pooling': self.global_pool,
-            'use_cudnn': self.use_cudnn,
-            'use_mkldnn': self.use_mkldnn,
-            'ceil_mode': self.ceil_mode,
-            'data_format':
-            'AnyLayout',  # TODO(dzhwinter) : should be fix latter
-            'exclusive': self.exclusive,
-            'adaptive': self.adaptive
-        }
-
-        self.outputs = {'Out': output}
-
-    def test_check_output(self):
-        self.check_output_with_place(core.CPUPlace(), atol=1e-5)
 
-    def init_test_case(self):
-        self.shape = [2, 3, 5, 5]
-        self.ksize = [3, 3]
-        self.strides = [1, 1]
-        self.paddings = [0, 0]
+    def init_data_type(self):
         self.dtype = np.int8
 
-    def init_pool_type(self):
-        self.pool_type = "avg"
-        self.pool2D_forward_naive = avg_pool2D_forward_naive
-
-    def init_global_pool(self):
-        self.global_pool = True
-
-    def init_ceil_mode(self):
-        self.ceil_mode = False
+    def setUp(self):
+        TestPool2D_Op.setUp(self)
+        assert self.dtype in [np.int8, np.uint8
+                              ], 'Dtype should be int8 or uint8'
 
-    def init_exclusive(self):
-        self.exclusive = True
+    def test_check_output(self):
+        self.check_output_with_place(core.CPUPlace(), atol=1e-5)
 
-    def init_adaptive(self):
-        self.adaptive = False
+    def test_check_grad(self):
+        pass
 
 
-class TestCase1(TestPool2D_Op):
+class TestCase1Avg(TestPool2dMKLDNNInt8_Op):
     def init_test_case(self):
         self.shape = [2, 3, 7, 7]
         self.ksize = [3, 3]
         self.strides = [1, 1]
         self.paddings = [0, 0]
-        self.dtype = np.int8
-
-    def init_pool_type(self):
-        self.pool_type = "avg"
-        self.pool2D_forward_naive = avg_pool2D_forward_naive
 
     def init_global_pool(self):
         self.global_pool = False
 
 
-class TestCase2(TestPool2D_Op):
+class TestCase2Avg(TestPool2dMKLDNNInt8_Op):
     def init_test_case(self):
         self.shape = [2, 3, 7, 7]
         self.ksize = [3, 3]
         self.strides = [1, 1]
         self.paddings = [1, 1]
-        self.dtype = np.uint8
-
-    def init_pool_type(self):
-        self.pool_type = "avg"
-        self.pool2D_forward_naive = avg_pool2D_forward_naive
 
     def init_global_pool(self):
         self.global_pool = False
 
 
-class TestCase3(TestPool2D_Op):
-    def init_test_case(self):
-        self.shape = [2, 3, 7, 7]
-        self.ksize = [3, 3]
-        self.strides = [1, 1]
-        self.paddings = [0, 0]
-        self.dtype = np.int8
-
+class TestCase0Max(TestPool2dMKLDNNInt8_Op):
     def init_pool_type(self):
         self.pool_type = "max"
         self.pool2D_forward_naive = max_pool2D_forward_naive
 
 
-class TestCase4(TestCase1):
-    def init_test_case(self):
-        self.shape = [2, 3, 7, 7]
-        self.ksize = [3, 3]
-        self.strides = [1, 1]
-        self.paddings = [1, 1]
-        self.dtype = np.uint8
+class TestCase1Max(TestCase1Avg):
+    def init_pool_type(self):
+        self.pool_type = "max"
+        self.pool2D_forward_naive = max_pool2D_forward_naive
+
 
+class TestCase2Max(TestCase2Avg):
     def init_pool_type(self):
         self.pool_type = "max"
         self.pool2D_forward_naive = max_pool2D_forward_naive
 
 
+def create_test_s8_u8_class(parent):
+    class TestS8Case(parent):
+        def init_data_type(self):
+            self.dtype = np.int8
+
+    class TestU8Case(parent):
+        def init_data_type(self):
+            self.dtype = np.uint8
+
+    cls_name_s8 = "{0}_{1}".format(parent.__name__, "mkldnn_s8")
+    cls_name_u8 = "{0}_{1}".format(parent.__name__, "mkldnn_u8")
+    TestS8Case.__name__ = cls_name_s8
+    TestU8Case.__name__ = cls_name_u8
+    globals()[cls_name_s8] = TestS8Case
+    globals()[cls_name_u8] = TestU8Case
+
+
+create_test_s8_u8_class(TestPool2dMKLDNNInt8_Op)
+create_test_s8_u8_class(TestCase1Avg)
+create_test_s8_u8_class(TestCase2Avg)
+create_test_s8_u8_class(TestCase0Max)
+create_test_s8_u8_class(TestCase1Max)
+create_test_s8_u8_class(TestCase2Max)
+
 if __name__ == '__main__':
     unittest.main()

python/paddle/fluid/tests/unittests/test_pool2d_mkldnn_op.py

@@ -18,35 +18,22 @@ import unittest
 from test_pool2d_op import TestPool2D_Op, TestCase1, TestCase2, TestCase3, TestCase4, TestCase5
 
 
-class TestMKLDNNCase1(TestPool2D_Op):
-    def init_kernel_type(self):
-        self.use_mkldnn = True
-
-
-class TestMKLDNNCase2(TestCase1):
-    def init_kernel_type(self):
-        self.use_mkldnn = True
-
-
-class TestMKLDNNCase3(TestCase2):
-    def init_kernel_type(self):
-        self.use_mkldnn = True
-
-
-class TestMKLDNNCase4(TestCase3):
-    def init_kernel_type(self):
-        self.use_mkldnn = True
-
-
-class TestMKLDNNCase5(TestCase4):
-    def init_kernel_type(self):
-        self.use_mkldnn = True
-
-
-class TestMKLDNNCase6(TestCase5):
-    def init_kernel_type(self):
-        self.use_mkldnn = True
-
+def create_test_mkldnn_class(parent):
+    class TestMKLDNNCase(parent):
+        def init_kernel_type(self):
+            self.use_mkldnn = True
+
+    cls_name = "{0}_{1}".format(parent.__name__, "MKLDNNOp")
+    TestMKLDNNCase.__name__ = cls_name
+    globals()[cls_name] = TestMKLDNNCase
+
+
+create_test_mkldnn_class(TestPool2D_Op)
+create_test_mkldnn_class(TestCase1)
+create_test_mkldnn_class(TestCase2)
+create_test_mkldnn_class(TestCase3)
+create_test_mkldnn_class(TestCase4)
+create_test_mkldnn_class(TestCase5)
 
 if __name__ == '__main__':
     unittest.main()

python/paddle/fluid/tests/unittests/test_pool2d_op.py

@@ -115,7 +115,7 @@ class TestPool2D_Op(OpTest):
         self.op_type = "pool2d"
         self.use_cudnn = False
         self.use_mkldnn = False
-        self.dtype = np.float32
+        self.init_data_type()
         self.init_test_case()
         self.init_global_pool()
         self.init_kernel_type()
@@ -177,6 +177,9 @@ class TestPool2D_Op(OpTest):
     def init_kernel_type(self):
         pass
 
+    def init_data_type(self):
+        self.dtype = np.float32
+
     def init_pool_type(self):
         self.pool_type = "avg"
         self.pool2D_forward_naive = avg_pool2D_forward_naive