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提交 cce9af0e 编写于 作者: Z zhupengyang 提交者： Tao Luo
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all cases use large shape (#22106) 

enhanced ops: conv2d, conv3d
elementwise_pow: change to a reasonable shape
上级 6c20e7c4
隐藏空白更改
内联 并排
Showing with 56 addition and 44 deletion
python/paddle/fluid/tests/unittests/test_conv2d_op.py
浏览文件 @ cce9af0e
......@@ -430,8 +430,14 @@ class TestWithStride(TestConv2dOp):
class TestWithGroup(TestConv2dOp):
def init_group(self):
self.groups = 3
def init_test_case(self):
self.pad = [0, 0]
self.stride = [1, 1]
self.input_size = [2, 3, 5, 5] # NCHW
self.group = 3
assert np.mod(self.input_size[1], self.groups) == 0
f_c = self.input_size[1] // self.groups
self.filter_size = [18, f_c, 3, 3]
class TestWith1x1(TestConv2dOp):
......@@ -441,7 +447,7 @@ class TestWith1x1(TestConv2dOp):
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 = [120, f_c, 1, 1]
def init_group(self):
self.groups = 3
......@@ -454,7 +460,7 @@ class TestWithDepthWise3x3(TestConv2dOp):
self.input_size = [3, 4, 10, 10] # NCHW
assert np.mod(self.input_size[1], self.groups) == 0
f_c = self.input_size[1] // self.groups
self.filter_size = [8, f_c, 3, 3]
self.filter_size = [12, f_c, 3, 3]
def init_dilation(self):
self.dilations = [2, 2]
......@@ -496,7 +502,7 @@ class TestWithDilation(TestConv2dOp):
self.input_size = [2, 3, 10, 10] # 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]
self.filter_size = [12, f_c, 3, 3]
def init_dilation(self):
self.dilations = [2, 2]
......@@ -509,10 +515,10 @@ class TestWithInput1x1Filter1x1(TestConv2dOp):
def init_test_case(self):
self.pad = [0, 0]
self.stride = [1, 1]
self.input_size = [2, 3, 1, 1] # NCHW
self.input_size = [100, 3, 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]
self.filter_size = [120, f_c, 1, 1]
def init_group(self):
self.groups = 3
......@@ -548,7 +554,7 @@ class TestDepthwiseConv(TestConv2dOp):
self.groups = 3
assert np.mod(self.input_size[1], self.groups) == 0
f_c = self.input_size[1] // self.groups
self.filter_size = [3, f_c, 3, 3]
self.filter_size = [12, f_c, 3, 3]
self.op_type = "depthwise_conv2d"
......@@ -561,7 +567,7 @@ class TestDepthwiseConv2(TestConv2dOp):
self.groups = 3
assert np.mod(self.input_size[1], self.groups) == 0
f_c = self.input_size[1] // self.groups
self.filter_size = [3, f_c, 3, 3]
self.filter_size = [12, f_c, 3, 3]
self.op_type = "depthwise_conv2d"
......@@ -574,7 +580,7 @@ class TestDepthwiseConv3(TestConv2dOp):
self.groups = 3
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]
self.filter_size = [24, f_c, 3, 3]
self.op_type = "depthwise_conv2d"
......@@ -588,7 +594,7 @@ class TestDepthwiseConvWithDilation(TestConv2dOp):
self.dilations = [2, 2]
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]
self.filter_size = [24, f_c, 3, 3]
self.op_type = "depthwise_conv2d"
......@@ -602,7 +608,7 @@ class TestDepthwiseConvWithDilation2(TestConv2dOp):
self.dilations = [2, 2]
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]
self.filter_size = [24, f_c, 3, 3]
self.op_type = "depthwise_conv2d"
......@@ -616,7 +622,7 @@ class TestDepthwiseConvandFuse(TestConv2dOp):
self.groups = 3
assert np.mod(self.input_size[1], self.groups) == 0
f_c = self.input_size[1] // self.groups
self.filter_size = [3, f_c, 3, 3]
self.filter_size = [12, f_c, 3, 3]
self.op_type = "depthwise_conv2d"
......@@ -630,7 +636,7 @@ class TestDepthwiseConv2andFuse(TestConv2dOp):
self.groups = 3
assert np.mod(self.input_size[1], self.groups) == 0
f_c = self.input_size[1] // self.groups
self.filter_size = [3, f_c, 3, 3]
self.filter_size = [12, f_c, 3, 3]
self.op_type = "depthwise_conv2d"
......@@ -644,7 +650,7 @@ class TestDepthwiseConv3andFuse(TestConv2dOp):
self.groups = 3
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]
self.filter_size = [24, f_c, 3, 3]
self.op_type = "depthwise_conv2d"
......@@ -659,7 +665,7 @@ class TestDepthwiseConvWithDilationandFuse(TestConv2dOp):
self.dilations = [2, 2]
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]
self.filter_size = [24, f_c, 3, 3]
self.op_type = "depthwise_conv2d"
......@@ -674,7 +680,7 @@ class TestDepthwiseConvWithDilation2andFuse(TestConv2dOp):
self.dilations = [2, 2]
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]
self.filter_size = [24, f_c, 3, 3]
self.op_type = "depthwise_conv2d"
......@@ -878,8 +884,14 @@ class TestWithStride_AsyPadding(TestConv2dOp_v2):
class TestWithGroup_AsyPadding(TestConv2dOp_v2):
def init_group(self):
self.groups = 3
def init_test_case(self):
self.pad = [0, 0]
self.stride = [1, 2]
self.input_size = [2, 3, 5, 5] # NCHW
self.group = 3
assert np.mod(self.input_size[1], self.groups) == 0
f_c = self.input_size[1] // self.groups
self.filter_size = [24, f_c, 4, 3]
class TestWith1x1_AsyPadding(TestConv2dOp_v2):
......@@ -888,7 +900,7 @@ class TestWith1x1_AsyPadding(TestConv2dOp_v2):
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 = [120, f_c, 1, 1]
def init_group(self):
self.groups = 3
......@@ -904,7 +916,7 @@ class TestWithDepthWise3x3_AsyPadding(TestConv2dOp_v2):
self.input_size = [3, 4, 10, 10] # NCHW
assert np.mod(self.input_size[1], self.groups) == 0
f_c = self.input_size[1] // self.groups
self.filter_size = [8, f_c, 3, 3]
self.filter_size = [16, f_c, 3, 3]
def init_dilation(self):
self.dilations = [2, 2]
......@@ -955,7 +967,7 @@ class TestWithDilation_AsyPadding(TestConv2dOp_v2):
self.input_size = [2, 3, 10, 10] # 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]
self.filter_size = [24, f_c, 3, 3]
def init_dilation(self):
self.dilations = [2, 2]
......@@ -971,10 +983,10 @@ class TestWithDilation_AsyPadding(TestConv2dOp_v2):
class TestWithInput1x1Filter1x1_AsyPadding(TestConv2dOp_v2):
def init_test_case(self):
self.stride = [1, 1]
self.input_size = [2, 3, 1, 1] # NCHW
self.input_size = [40, 3, 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]
self.filter_size = [120, f_c, 1, 1]
def init_group(self):
self.groups = 3
......@@ -1000,7 +1012,7 @@ class TestDepthwiseConv_AsyPadding(TestConv2dOp_v2):
self.groups = 3
assert np.mod(self.input_size[1], self.groups) == 0
f_c = self.input_size[1] // self.groups
self.filter_size = [3, f_c, 3, 3]
self.filter_size = [12, f_c, 3, 3]
self.op_type = "depthwise_conv2d"
def init_paddings(self):
......@@ -1016,7 +1028,7 @@ class TestDepthwiseConv2_AsyPadding(TestConv2dOp_v2):
self.groups = 3
assert np.mod(self.input_size[1], self.groups) == 0
f_c = self.input_size[1] // self.groups
self.filter_size = [3, f_c, 3, 3]
self.filter_size = [12, f_c, 3, 3]
self.op_type = "depthwise_conv2d"
def init_paddings(self):
......@@ -1032,7 +1044,7 @@ class TestDepthwiseConv3_AsyPadding(TestConv2dOp_v2):
self.groups = 3
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]
self.filter_size = [24, f_c, 3, 3]
self.op_type = "depthwise_conv2d"
def init_paddings(self):
......@@ -1050,7 +1062,7 @@ class TestDepthwiseConvWithDilation_AsyPadding(TestConv2dOp_v2):
self.dilations = [2, 2]
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]
self.filter_size = [24, f_c, 3, 3]
self.op_type = "depthwise_conv2d"
def init_paddings(self):
......@@ -1068,7 +1080,7 @@ class TestDepthwiseConvWithDilation2_AsyPadding(TestConv2dOp_v2):
self.dilations = [2, 2]
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]
self.filter_size = [24, f_c, 3, 3]
self.op_type = "depthwise_conv2d"
def init_paddings(self):
......@@ -1086,7 +1098,7 @@ class TestDepthwiseConvandFuse_AsyPadding(TestConv2dOp_v2):
self.groups = 3
assert np.mod(self.input_size[1], self.groups) == 0
f_c = self.input_size[1] // self.groups
self.filter_size = [3, f_c, 3, 3]
self.filter_size = [12, f_c, 3, 3]
self.op_type = "depthwise_conv2d"
def init_paddings(self):
......@@ -1104,7 +1116,7 @@ class TestDepthwiseConv2andFuse_AsyPadding(TestConv2dOp_v2):
self.groups = 3
assert np.mod(self.input_size[1], self.groups) == 0
f_c = self.input_size[1] // self.groups
self.filter_size = [3, f_c, 3, 3]
self.filter_size = [12, f_c, 3, 3]
self.op_type = "depthwise_conv2d"
def init_paddings(self):
......@@ -1122,7 +1134,7 @@ class TestDepthwiseConv3andFuse_AsyPadding(TestConv2dOp_v2):
self.groups = 3
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]
self.filter_size = [24, f_c, 3, 3]
self.op_type = "depthwise_conv2d"
def init_paddings(self):
......@@ -1141,7 +1153,7 @@ class TestDepthwiseConvWithDilationandFuse_AsyPadding(TestConv2dOp_v2):
self.dilations = [2, 2]
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]
self.filter_size = [24, f_c, 3, 3]
self.op_type = "depthwise_conv2d"
def init_paddings(self):
......@@ -1160,7 +1172,7 @@ class TestDepthwiseConvWithDilation2andFuse_AsyPadding(TestConv2dOp_v2):
self.dilations = [2, 2]
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]
self.filter_size = [24, f_c, 3, 3]
self.op_type = "depthwise_conv2d"
def init_paddings(self):
......
python/paddle/fluid/tests/unittests/test_conv3d_op.py
浏览文件 @ cce9af0e
......@@ -361,7 +361,7 @@ class TestWith1x1(TestConv3dOp):
self.input_size = [2, 3, 4, 4, 4]
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, 1]
self.filter_size = [120, f_c, 1, 1, 1]
def init_dilation(self):
self.dilations = [1, 1, 1]
......@@ -374,10 +374,10 @@ class TestWithInput1x1Filter1x1(TestConv3dOp):
def init_test_case(self):
self.pad = [0, 0, 0]
self.stride = [1, 1, 1]
self.input_size = [2, 3, 1, 1, 1]
self.input_size = [40, 3, 1, 1, 1]
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, 1]
self.filter_size = [120, f_c, 1, 1, 1]
def init_dilation(self):
self.dilations = [1, 1, 1]
......@@ -393,7 +393,7 @@ class TestWithDilation(TestConv3dOp):
self.input_size = [2, 3, 6, 6, 6]
assert np.mod(self.input_size[1], self.groups) == 0
f_c = self.input_size[1] // self.groups
self.filter_size = [6, f_c, 2, 2, 2]
self.filter_size = [24, f_c, 2, 2, 2]
def init_dilation(self):
self.dilations = [2, 2, 2]
......@@ -699,7 +699,7 @@ class TestWith1x1_AsyPadding(TestConv3dOp_2):
self.input_size = [2, 3, 4, 4, 4]
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, 1]
self.filter_size = [120, f_c, 1, 1, 1]
def init_dilation(self):
self.dilations = [1, 1, 1]
......@@ -718,7 +718,7 @@ class TestWithDilation_AsyPadding(TestConv3dOp_2):
self.input_size = [2, 3, 6, 6, 6]
assert np.mod(self.input_size[1], self.groups) == 0
f_c = self.input_size[1] // self.groups
self.filter_size = [6, f_c, 2, 2, 2]
self.filter_size = [24, f_c, 2, 2, 2]
def init_dilation(self):
self.dilations = [2, 2, 2]
......
python/paddle/fluid/tests/unittests/test_elementwise_pow_op.py
浏览文件 @ cce9af0e
......@@ -39,8 +39,8 @@ class TestElementwisePowOp_big_shape_1(TestElementwisePowOp):
def setUp(self):
self.op_type = "elementwise_pow"
self.inputs = {
'X': np.random.uniform(0.1, 1, [100, 100]).astype("float64"),
'Y': np.random.uniform(0.1, 1, [100, 100]).astype("float64")
'X': np.random.uniform(0.1, 1, [10, 10]).astype("float64"),
'Y': np.random.uniform(0.1, 1, [10, 10]).astype("float64")
}
self.outputs = {'Out': np.power(self.inputs['X'], self.inputs['Y'])}
......@@ -49,8 +49,8 @@ class TestElementwisePowOp_big_shape_2(TestElementwisePowOp):
def setUp(self):
self.op_type = "elementwise_pow"
self.inputs = {
'X': np.random.uniform(0.1, 1, [100, 100]).astype("float64"),
'Y': np.random.uniform(0.1, 1, [100, 100]).astype("float64") * 20
'X': np.random.uniform(0.1, 1, [10, 10]).astype("float64"),
'Y': np.random.uniform(0.1, 1, [10, 10]).astype("float64") * 20
}
self.outputs = {'Out': np.power(self.inputs['X'], self.inputs['Y'])}
......
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