[CodeStyle][Ruff][BUAA][G-[171-180]] Fix ruff RUF005 diagnostic for 10 files

test/dygraph_to_static/simnet_dygraph_model.py

@@ -340,8 +340,9 @@ def _build_once(self, input):
             param_shape = [
                 reduce(
                     lambda a, b: a * b, input_shape[self._num_flatten_dims :], 1
-                )
-            ] + [self._size]
+                ),
+                self._size,
+            ]
             self.__w.append(
                 self.add_parameter(
                     '_w%d' % i,

test/dygraph_to_static/test_cycle_gan.py

@@ -544,7 +544,7 @@ def train(args):
 
     with base.dygraph.guard(place):
         max_images_num = args.max_images_num
-        data_shape = [-1] + args.image_shape
+        data_shape = [-1, *args.image_shape]
 
         random.seed(SEED)
         np.random.seed(SEED)

test/dygraph_to_static/test_set_static_op_arg_pre_cast_hook.py

@@ -53,7 +53,7 @@ def __init__(self):
     # at transform time.
     @paddle.jit.not_to_static
     def forward_impl(self, x):
-        return paddle.concat([x] + self.extra_inputs, axis=0)
+        return paddle.concat([x, *self.extra_inputs], axis=0)
 
     def forward(self, x):
         return self.forward_impl(x)

test/dygraph_to_static/test_tsm.py

@@ -204,7 +204,7 @@ def __init__(self, name_scope, config, mode):
         )
 
     def forward(self, inputs):
-        y = paddle.reshape(inputs, [-1] + self.reshape_list)
+        y = paddle.reshape(inputs, [-1, *self.reshape_list])
         y = self.conv(y)
         y = self.pool2d_max(y)
         for bottleneck_block in self.bottleneck_block_list:

test/dygraph_to_static/transformer_dygraph_model.py

@@ -708,7 +708,7 @@ def beam_search(
     ):
         def expand_to_beam_size(tensor, beam_size):
             tensor = paddle.reshape(
-                tensor, [tensor.shape[0], 1] + list(tensor.shape[1:])
+                tensor, [tensor.shape[0], 1, *list(tensor.shape[1:])]
             )
             tile_dims = [-1] * len(tensor.shape)
             tile_dims[1] = beam_size

test/ir/inference/test_mkldnn_cpu_bfloat16_pass.py

@@ -27,7 +27,7 @@ def setUp(self):
         self.init_data()
         with base.program_guard(self.main_program, self.startup_program):
             x = paddle.static.data(
-                name='x', shape=[-1] + self.shape_x, dtype=self.d_type
+                name='x', shape=[-1, *self.shape_x], dtype=self.d_type
             )
 
             out = paddle.transpose(x, perm=[0, 1, 2, 3])
@@ -36,7 +36,7 @@ def setUp(self):
             out = paddle.static.nn.fc(out, size=1)
 
             self.feeds = {
-                "x": np.random.random([self.bs] + self.shape_x).astype(
+                "x": np.random.random([self.bs, *self.shape_x]).astype(
                     self.d_type
                 )
             }

test/ir/inference/test_mkldnn_matmul_op_output_fuse_pass.py

@@ -33,10 +33,10 @@ def init_data(self):
     def make_network(self):
         with base.program_guard(self.main_program, self.startup_program):
             x = paddle.static.data(
-                name='x', shape=[-1] + self.shape_x, dtype=self.d_type
+                name='x', shape=[-1, *self.shape_x], dtype=self.d_type
             )
             y = paddle.static.data(
-                name='y', shape=[-1] + self.shape_y, dtype=self.d_type
+                name='y', shape=[-1, *self.shape_y], dtype=self.d_type
             )
             out = paddle.matmul(x, y)
             out = paddle.transpose(out, perm=[0, 2, 1, 3])
@@ -52,8 +52,8 @@ def setUp(self):
 
     def set_feeds(self, out):
         self.feeds = {
-            "x": np.random.random([self.bs] + self.shape_x).astype(self.d_type),
-            "y": np.random.random([self.bs] + self.shape_y).astype(self.d_type),
+            "x": np.random.random([self.bs, *self.shape_x]).astype(self.d_type),
+            "y": np.random.random([self.bs, *self.shape_y]).astype(self.d_type),
         }
         self.fetch_list = [out]
 
@@ -75,10 +75,10 @@ class TestMKLDNNMatmulOpNotFusedWrongTransposeAxis(TestMKLDNNMatmulFuseOp):
     def make_network(self):
         with base.program_guard(self.main_program, self.startup_program):
             x = paddle.static.data(
-                name='x', shape=[-1] + self.shape_x, dtype=self.d_type
+                name='x', shape=[-1, *self.shape_x], dtype=self.d_type
             )
             y = paddle.static.data(
-                name='y', shape=[-1] + self.shape_y, dtype=self.d_type
+                name='y', shape=[-1, *self.shape_y], dtype=self.d_type
             )
             out = paddle.matmul(x, y)
             out = paddle.transpose(out, perm=[0, 1, 2, 3])
@@ -98,10 +98,10 @@ def init_data(self):
     def make_network(self):
         with base.program_guard(self.main_program, self.startup_program):
             x = paddle.static.data(
-                name='x', shape=[-1] + self.shape_x, dtype=self.d_type
+                name='x', shape=[-1, *self.shape_x], dtype=self.d_type
             )
             y = paddle.static.data(
-                name='y', shape=[-1] + self.shape_y, dtype=self.d_type
+                name='y', shape=[-1, *self.shape_y], dtype=self.d_type
             )
             out = paddle.matmul(x, y)
             out = paddle.transpose(out, perm=[0, 2, 1, 3])

test/ir/inference/test_reshape2_matmul_fuse_pass.py

@@ -48,7 +48,7 @@ def sample_program_config(self, draw):
                 st.integers(min_value=1, max_value=10), min_size=2, max_size=2
             )
         )
-        x_shape = reshape + [1, 1]
+        x_shape = [*reshape, 1, 1]
 
         # 2. Generate attr:transpose_X/transpose_Y/alpha of matmul
         alpha = 1.0

test/ir/inference/test_trt_convert_deformable_conv.py

@@ -62,7 +62,7 @@ def generate_input1(
             kernel_sizes: List[int],
             attrs: List[Dict[str, Any]],
         ):
-            return np.random.random([batch, 3] + input_size).astype(np.float32)
+            return np.random.random([batch, 3, *input_size]).astype(np.float32)
 
         def generate_offset1(
             batch: int,
@@ -72,7 +72,7 @@ def generate_offset1(
         ):
             output_size = compute_output_size(input_size, kernel_sizes, attrs)
             return np.random.random(
-                [batch, 2 * np.prod(kernel_sizes)] + output_size
+                [batch, 2 * np.prod(kernel_sizes), *output_size]
             ).astype(np.float32)
 
         def generate_mask1(
@@ -83,7 +83,7 @@ def generate_mask1(
         ):
             output_size = compute_output_size(input_size, kernel_sizes, attrs)
             return np.random.random(
-                [batch, np.prod(kernel_sizes)] + output_size
+                [batch, np.prod(kernel_sizes), *output_size]
             ).astype(np.float32)
 
         def generate_filter1(
@@ -92,7 +92,7 @@ def generate_filter1(
             kernel_sizes: List[int],
             attrs: List[Dict[str, Any]],
         ):
-            filter = np.random.random([6, 3] + kernel_sizes)
+            filter = np.random.random([6, 3, *kernel_sizes])
             filter[0][0][0][0] = 8.8978638e-08
             return filter.astype(np.float32)
 

test/ir/inference/test_trt_skip_layernorm_fuse_pass.py

@@ -28,14 +28,14 @@
 class SkipLayernormFusePassTest(InferencePassTest):
     def setUp(self):
         self.set_args()
-        input_shape_with_batch = [self.batch_size] + self.input_shape
-        min_input_shape_with_batch = [1] + self.min_input_shape
+        input_shape_with_batch = [self.batch_size, *self.input_shape]
+        min_input_shape_with_batch = [1, *self.min_input_shape]
         with base.program_guard(self.main_program, self.startup_program):
             data1 = paddle.static.data(
-                name='data1', shape=[-1] + self.input_shape, dtype='float32'
+                name='data1', shape=[-1, *self.input_shape], dtype='float32'
             )
             data2 = paddle.static.data(
-                name='data2', shape=[-1] + self.input_shape, dtype='float32'
+                name='data2', shape=[-1, *self.input_shape], dtype='float32'
             )
             eltwise_out = paddle.add(data1, data2)
             out = paddle.nn.LayerNorm(eltwise_out.shape[-1:])(eltwise_out)