format

speechx/examples/ds2_ol/onnx/README.md

@@ -34,4 +34,4 @@ For more details please see `run.sh`.
 ## Outputs
 The optimized onnx model is `exp/model.opt.onnx`.
 
-To show the graph, please using `local/netron.sh`.
+To show the graph, please using `local/netron.sh`.

speechx/examples/ds2_ol/onnx/local/infer_check.py

@@ -12,13 +12,14 @@
 # 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 argparse
+import os
+import pickle
+
 import numpy as np
 import onnxruntime
 import paddle
-import os
-import pickle
+
 
 def parse_args():
     parser = argparse.ArgumentParser(description=__doc__)
@@ -26,26 +27,19 @@ def parse_args():
         '--input_file',
         type=str,
         default="static_ds2online_inputs.pickle",
-        help="ds2 input pickle file.",
-    )
+        help="ds2 input pickle file.", )
     parser.add_argument(
-        '--model_dir',
-        type=str,
-        default=".",
-        help="paddle model dir."
-    )
+        '--model_dir', type=str, default=".", help="paddle model dir.")
     parser.add_argument(
         '--model_prefix',
         type=str,
         default="avg_1.jit",
-        help="paddle model prefix."
-    )
+        help="paddle model prefix.")
     parser.add_argument(
         '--onnx_model',
         type=str,
         default='./model.old.onnx',
-        help="onnx model."
-    )
+        help="onnx model.")
 
     return parser.parse_args()
 
@@ -69,19 +63,19 @@ if __name__ == '__main__':
         paddle.to_tensor(audio_chunk),
         paddle.to_tensor(audio_chunk_lens),
         paddle.to_tensor(chunk_state_h_box),
-        paddle.to_tensor(chunk_state_c_box),
-    )
+        paddle.to_tensor(chunk_state_c_box), )
 
     # onnxruntime
     options = onnxruntime.SessionOptions()
-    options.enable_profiling=True
+    options.enable_profiling = True
     sess = onnxruntime.InferenceSession(FLAGS.onnx_model, sess_options=options)
     ort_res_chunk, ort_res_lens, ort_chunk_state_h, ort_chunk_state_c = sess.run(
-                ['softmax_0.tmp_0', 'tmp_5', 'concat_0.tmp_0', 'concat_1.tmp_0'],
-                {"audio_chunk": audio_chunk,
-                 "audio_chunk_lens":audio_chunk_lens,
-                 "chunk_state_h_box": chunk_state_h_box,
-                 "chunk_state_c_box":chunk_state_c_box})
+        ['softmax_0.tmp_0', 'tmp_5', 'concat_0.tmp_0', 'concat_1.tmp_0'], {
+            "audio_chunk": audio_chunk,
+            "audio_chunk_lens": audio_chunk_lens,
+            "chunk_state_h_box": chunk_state_h_box,
+            "chunk_state_c_box": chunk_state_c_box
+        })
 
     print(sess.end_profiling())
 
@@ -89,4 +83,4 @@ if __name__ == '__main__':
     print(np.allclose(ort_res_chunk, res_chunk, atol=1e-6))
     print(np.allclose(ort_res_lens, res_lens, atol=1e-6))
     print(np.allclose(ort_chunk_state_h, chunk_state_h, atol=1e-6))
-    print(np.allclose(ort_chunk_state_c, chunk_state_c, atol=1e-6))
+    print(np.allclose(ort_chunk_state_c, chunk_state_c, atol=1e-6))

speechx/examples/ds2_ol/onnx/local/onnx_infer_shape.py

@@ -1,5 +1,6 @@
 # Copyright (c) Microsoft Corporation. All rights reserved.
 # Licensed under the MIT License.
+# flake8: noqa
 import argparse
 import logging
 
@@ -491,9 +492,6 @@ class SymbolicShapeInference:
         skip_infer = node.op_type in [
             'If', 'Loop', 'Scan', 'SplitToSequence', 'ZipMap', \
             # contrib ops
-
-
-
             'Attention', 'BiasGelu', \
             'EmbedLayerNormalization', \
             'FastGelu', 'Gelu', 'LayerNormalization', \
@@ -1605,8 +1603,8 @@ class SymbolicShapeInference:
     def _infer_Scan(self, node):
         subgraph = get_attribute(node, 'body')
         num_scan_inputs = get_attribute(node, 'num_scan_inputs')
-        scan_input_axes = get_attribute(node, 'scan_input_axes', [0] *
-                                        num_scan_inputs)
+        scan_input_axes = get_attribute(node, 'scan_input_axes',
+                                        [0] * num_scan_inputs)
         num_scan_states = len(node.input) - num_scan_inputs
         scan_input_axes = [
             handle_negative_axis(
@@ -1627,8 +1625,8 @@ class SymbolicShapeInference:
             si.name = subgraph_name
         self._onnx_infer_subgraph(node, subgraph)
         num_scan_outputs = len(node.output) - num_scan_states
-        scan_output_axes = get_attribute(node, 'scan_output_axes', [0] *
-                                         num_scan_outputs)
+        scan_output_axes = get_attribute(node, 'scan_output_axes',
+                                         [0] * num_scan_outputs)
         scan_input_dim = get_shape_from_type_proto(
             self.known_vi_[node.input[-1]].type)[scan_input_axes[-1]]
         for i, o in enumerate(node.output):
@@ -1821,8 +1819,8 @@ class SymbolicShapeInference:
         split = get_attribute(node, 'split')
         if not split:
             num_outputs = len(node.output)
-            split = [input_sympy_shape[axis] / sympy.Integer(num_outputs)
-                     ] * num_outputs
+            split = [input_sympy_shape[axis] /
+                     sympy.Integer(num_outputs)] * num_outputs
             self._update_computed_dims(split)
         else:
             split = [sympy.Integer(s) for s in split]
@@ -2174,8 +2172,8 @@ class SymbolicShapeInference:
             subgraphs = []
             if 'If' == node.op_type:
                 subgraphs = [
-                    get_attribute(node, 'then_branch'), get_attribute(
-                        node, 'else_branch')
+                    get_attribute(node, 'then_branch'),
+                    get_attribute(node, 'else_branch')
                 ]
             elif node.op_type in ['Loop', 'Scan']:
                 subgraphs = [get_attribute(node, 'body')]
@@ -2330,8 +2328,8 @@ class SymbolicShapeInference:
                                 'LessOrEqual', 'GreaterOrEqual'
                         ]:
                             shapes = [
-                                self._get_shape(node, i) for i in range(
-                                    len(node.input))
+                                self._get_shape(node, i)
+                                for i in range(len(node.input))
                             ]
                             if node.op_type in [
                                     'MatMul', 'MatMulInteger', 'MatMulInteger16'

speechx/examples/ds2_ol/onnx/local/onnx_prune_model.py

@@ -1,13 +1,12 @@
 #!/usr/bin/env python3 -W ignore::DeprecationWarning
-
 # prune model by output names
-
 import argparse
 import copy
 import sys
 
 import onnx
 
+
 def parse_arguments():
     parser = argparse.ArgumentParser()
     parser.add_argument(

speechx/examples/ds2_ol/onnx/local/onnx_rename_model.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python3 -W ignore::DeprecationWarning
-
 # rename node to new names
 import argparse
 import sys

speechx/examples/ds2_ol/onnx/local/pd_infer_shape.py

@@ -4,6 +4,7 @@ import argparse
 
 # paddle inference shape
 
+
 def process_old_ops_desc(program):
     """set matmul op head_number attr to 1 is not exist.
 

speechx/examples/ds2_ol/onnx/local/pd_prune_model.py

@@ -6,6 +6,7 @@ from typing import List
 
 # paddle prune model.
 
+
 def prepend_feed_ops(program,
                      feed_target_names: List[str],
                      feed_holder_name='feed'):

utils/zh_tn.py

@@ -747,7 +747,7 @@ def num2chn(number_string,
                         previous_symbol, (CNU, type(None))):
                     if next_symbol.power != 1 and (
                         (previous_symbol is None) or
-                        (previous_symbol.power != 1)):
+                        (previous_symbol.power != 1)):  # noqa: E129
                         result_symbols[i] = liang
 
     # if big is True, '两' will not be used and `alt_two` has no impact on output