Fix inconsistent indentation in flow_matching.py

Aligned code indentation for improved readability and maintainability. Adjusted spacing issues in method signatures and data pointer lists to ensure uniform formatting across the file.

Author: hexisyztem

Diff for: cosyvoice/flow/flow_matching.py

@@ -11,14 +11,15 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+import queue
 import threading
 import torch
 import torch.nn.functional as F
 from matcha.models.components.flow_matching import BASECFM
-import queue
+
 
 class EstimatorWrapper:
-    def __init__(self, estimator_engine, estimator_count=2,):
+    def __init__(self, estimator_engine, estimator_count=2, ):
         self.estimators = queue.Queue()
         self.estimator_engine = estimator_engine
         for _ in range(estimator_count):
@@ -36,6 +37,7 @@ def release_estimator(self, estimator):
         self.estimators.put(estimator)
         return
 
+
 class ConditionalCFM(BASECFM):
     def __init__(self, in_channels, cfm_params, n_spks=1, spk_emb_dim=64, estimator: torch.nn.Module = None):
         super().__init__(
@@ -53,7 +55,8 @@ def __init__(self, in_channels, cfm_params, n_spks=1, spk_emb_dim=64, estimator:
         self.lock = threading.Lock()
 
     @torch.inference_mode()
-    def forward(self, mu, mask, n_timesteps, temperature=1.0, spks=None, cond=None, prompt_len=0, flow_cache=torch.zeros(1, 80, 0, 2)):
+    def forward(self, mu, mask, n_timesteps, temperature=1.0, spks=None, cond=None, prompt_len=0,
+                flow_cache=torch.zeros(1, 80, 0, 2)):
         """Forward diffusion
 
         Args:
@@ -155,12 +158,12 @@ def forward_estimator(self, x, mask, mu, t, spks, cond):
                 estimator.set_input_shape('cond', (2, 80, x.size(2)))
 
                 data_ptrs = [x.contiguous().data_ptr(),
-                                            mask.contiguous().data_ptr(),
-                                            mu.contiguous().data_ptr(),
-                                            t.contiguous().data_ptr(),
-                                            spks.contiguous().data_ptr(),
-                                            cond.contiguous().data_ptr(),
-                                            x.data_ptr()]
+                             mask.contiguous().data_ptr(),
+                             mu.contiguous().data_ptr(),
+                             t.contiguous().data_ptr(),
+                             spks.contiguous().data_ptr(),
+                             cond.contiguous().data_ptr(),
+                             x.data_ptr()]
 
                 for idx, data_ptr in enumerate(data_ptrs):
                     estimator.set_tensor_address(engine.get_tensor_name(idx), data_ptr)
@@ -181,12 +184,12 @@ def forward_estimator(self, x, mask, mu, t, spks, cond):
                     self.estimator.set_input_shape('cond', (2, 80, x.size(2)))
                     # run trt engine
                     self.estimator.execute_v2([x.contiguous().data_ptr(),
-                                            mask.contiguous().data_ptr(),
-                                            mu.contiguous().data_ptr(),
-                                            t.contiguous().data_ptr(),
-                                            spks.contiguous().data_ptr(),
-                                            cond.contiguous().data_ptr(),
-                                            x.data_ptr()])
+                                               mask.contiguous().data_ptr(),
+                                               mu.contiguous().data_ptr(),
+                                               t.contiguous().data_ptr(),
+                                               spks.contiguous().data_ptr(),
+                                               cond.contiguous().data_ptr(),
+                                               x.data_ptr()])
                     return x
 
     def compute_loss(self, x1, mask, mu, spks=None, cond=None):