reformat python style

PaddlePaddle · Oct 23, 2023 · a6016d5 · a6016d5
1 parent ff39e41
commit a6016d5
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Showing 2 changed files with 20 additions and 22 deletions.
diff --git a/test/mkldnn/test_fc_add_int8_mkldnn_op.py b/test/mkldnn/test_fc_add_int8_mkldnn_op.py
@@ -22,7 +22,6 @@
 
 @OpTestTool.skip_if_not_cpu()
 class TestFCAddINT8OneDNNOp(OpTest):
-
     def setUp(self):
         self.op_type = "fc"
         self._cpu_only = True
@@ -31,8 +30,9 @@ def setUp(self):
         self.generate_data()
         self.set_inputs()
 
-        y_scales_size = (self.bias_shape
-                         if self.per_channel_quantize_weight else 1)
+        y_scales_size = (
+            self.bias_shape if self.per_channel_quantize_weight else 1
+        )
 
         self.attrs = {
             'use_mkldnn': True,
@@ -77,16 +77,17 @@ def quantize(self, tensor):
         return scale, quantized
 
     def generate_data(self):
-        self.x_float = np.random.random(
-            self.input_shape).astype("float32") * 10
+        self.x_float = np.random.random(self.input_shape).astype("float32") * 10
         self.x_scale, self.x = self.quantize(self.x_float)
 
-        self.y_float = (np.random.random(self.weight_shape).astype("float32") *
-                        10)
+        self.y_float = (
+            np.random.random(self.weight_shape).astype("float32") * 10
+        )
         self.y_scale, self.y = self.quantize(self.y_float)
 
-        self.residual_float = np.random.random(
-            self.residual_shape).astype("float32") * 10
+        self.residual_float = (
+            np.random.random(self.residual_shape).astype("float32") * 10
+        )
         self.residual_scale, self.residual = self.quantize(self.residual_float)
 
         flatten_shape = [1, 1]
@@ -96,11 +97,11 @@ def generate_data(self):
             else:
                 flatten_shape[1] *= self.input_shape[i]
 
-        self.out_float = np.dot(self.x_float.reshape(flatten_shape),
-                                self.y_float)
+        self.out_float = np.dot(
+            self.x_float.reshape(flatten_shape), self.y_float
+        )
         if self.use_bias:
-            self.bias = np.random.random(
-                self.bias_shape).astype("float32") * 10
+            self.bias = np.random.random(self.bias_shape).astype("float32") * 10
             self.out_float += self.bias
         # Dont' add residual
         # self.out_float += self.residual_float
@@ -114,7 +115,6 @@ def test_check_output(self):
 
 
 class TestFCINT8NoBiasOneDNNOp(TestFCAddINT8OneDNNOp):
-
     def configure(self):
         self.use_bias = False
         self.force_fp32_output = False
@@ -130,7 +130,6 @@ def set_inputs(self):
 
 
 class TestFCINT8ForceFP32OutputOneDNNOp(TestFCINT8NoBiasOneDNNOp):
-
     def configure(self):
         self.use_bias = False
         self.force_fp32_output = True
@@ -139,7 +138,6 @@ def configure(self):
 
 
 class TestFCINT8ForceFP32OutputPerChannelWeightOneDNNOp(TestFCAddINT8OneDNNOp):
-
     def configure(self):
         self.use_bias = True
         self.force_fp32_output = True

diff --git a/test/mkldnn/test_fc_add_mkldnn_op.py b/test/mkldnn/test_fc_add_mkldnn_op.py
@@ -28,15 +28,13 @@ def fully_connected_naive(input, weights, bias_data, residual_data):
 
 
 class MatrixGenerate:
-
     def __init__(self, mb, ic, oc, h, w):
         self.input = np.random.random((mb, ic * h * w)).astype("float32")
         self.weights = np.random.random((ic * h * w, oc)).astype("float32")
         self.residual = np.random.random((mb, oc)).astype("float32")
 
 
 class TestFCAddMKLDNNOp(OpTest):
-
     def create_data(self):
         self.matrix = MatrixGenerate(1, 10, 15, 3, 3)
         self.bias = np.random.random(15).astype("float32")
@@ -60,9 +58,12 @@ def setUp(self):
         # self.attrs = {'use_mkldnn': self.use_mkldnn, 'fuse_residual_connection' : True}
 
         self.outputs = {
-            'Out':
-            fully_connected_naive(self.matrix.input, self.matrix.weights,
-                                  self.bias, self.matrix.residual)
+            'Out': fully_connected_naive(
+                self.matrix.input,
+                self.matrix.weights,
+                self.bias,
+                self.matrix.residual,
+            )
         }
 
     def test_check_output(self):
@@ -76,7 +77,6 @@ def test_check_grad_no_weight(self):
 
 
 class TestFCAddMKLDNNOp1(TestFCAddMKLDNNOp):
-
     def create_data(self):
         self.matrix = MatrixGenerate(2, 15, 48, 2, 2)
         self.bias = np.random.random(48).astype("float32")