Update .pre-commit-config.yaml (#2019)

* Update .pre-commit-config.yaml

* Update __init__.py

* Update .pre-commit-config.yaml

* Precommit updates

models/common.py

@@ -380,11 +380,11 @@ class DetectMultiBackend(nn.Module):
                 w = next(Path(w).glob('*.xml'))  # get *.xml file from *_openvino_model dir
             network = ie.read_model(model=w, weights=Path(w).with_suffix('.bin'))
             if network.get_parameters()[0].get_layout().empty:
-                network.get_parameters()[0].set_layout(Layout("NCHW"))
+                network.get_parameters()[0].set_layout(Layout('NCHW'))
             batch_dim = get_batch(network)
             if batch_dim.is_static:
                 batch_size = batch_dim.get_length()
-            executable_network = ie.compile_model(network, device_name="CPU")  # device_name="MYRIAD" for Intel NCS2
+            executable_network = ie.compile_model(network, device_name='CPU')  # device_name="MYRIAD" for Intel NCS2
             stride, names = self._load_metadata(Path(w).with_suffix('.yaml'))  # load metadata
         elif engine:  # TensorRT
             LOGGER.info(f'Loading {w} for TensorRT inference...')
@@ -431,7 +431,7 @@ class DetectMultiBackend(nn.Module):
             import tensorflow as tf
 
             def wrap_frozen_graph(gd, inputs, outputs):
-                x = tf.compat.v1.wrap_function(lambda: tf.compat.v1.import_graph_def(gd, name=""), [])  # wrapped
+                x = tf.compat.v1.wrap_function(lambda: tf.compat.v1.import_graph_def(gd, name=''), [])  # wrapped
                 ge = x.graph.as_graph_element
                 return x.prune(tf.nest.map_structure(ge, inputs), tf.nest.map_structure(ge, outputs))
 
@@ -445,7 +445,7 @@ class DetectMultiBackend(nn.Module):
             gd = tf.Graph().as_graph_def()  # TF GraphDef
             with open(w, 'rb') as f:
                 gd.ParseFromString(f.read())
-            frozen_func = wrap_frozen_graph(gd, inputs="x:0", outputs=gd_outputs(gd))
+            frozen_func = wrap_frozen_graph(gd, inputs='x:0', outputs=gd_outputs(gd))
         elif tflite or edgetpu:  # https://www.tensorflow.org/lite/guide/python#install_tensorflow_lite_for_python
             try:  # https://coral.ai/docs/edgetpu/tflite-python/#update-existing-tf-lite-code-for-the-edge-tpu
                 from tflite_runtime.interpreter import Interpreter, load_delegate
@@ -467,9 +467,9 @@ class DetectMultiBackend(nn.Module):
             output_details = interpreter.get_output_details()  # outputs
             # load metadata
             with contextlib.suppress(zipfile.BadZipFile):
-                with zipfile.ZipFile(w, "r") as model:
+                with zipfile.ZipFile(w, 'r') as model:
                     meta_file = model.namelist()[0]
-                    meta = ast.literal_eval(model.read(meta_file).decode("utf-8"))
+                    meta = ast.literal_eval(model.read(meta_file).decode('utf-8'))
                     stride, names = int(meta['stride']), meta['names']
         elif tfjs:  # TF.js
             raise NotImplementedError('ERROR: YOLOv3 TF.js inference is not supported')
@@ -491,7 +491,7 @@ class DetectMultiBackend(nn.Module):
             check_requirements('tritonclient[all]')
             from utils.triton import TritonRemoteModel
             model = TritonRemoteModel(url=w)
-            nhwc = model.runtime.startswith("tensorflow")
+            nhwc = model.runtime.startswith('tensorflow')
         else:
             raise NotImplementedError(f'ERROR: {w} is not a supported format')
 
@@ -608,7 +608,7 @@ class DetectMultiBackend(nn.Module):
         url = urlparse(p)  # if url may be Triton inference server
         types = [s in Path(p).name for s in sf]
         types[8] &= not types[9]  # tflite &= not edgetpu
-        triton = not any(types) and all([any(s in url.scheme for s in ["http", "grpc"]), url.netloc])
+        triton = not any(types) and all([any(s in url.scheme for s in ['http', 'grpc']), url.netloc])
         return types + [triton]
 
     @staticmethod

models/segment/yolov5m-seg.yaml

@@ -45,4 +45,4 @@ head:
    [-1, 3, C3, [1024, False]],  # 23 (P5/32-large)
 
    [[17, 20, 23], 1, Segment, [nc, anchors, 32, 256]],  # Detect(P3, P4, P5)
-  ]
+  ]

models/segment/yolov5s-seg.yaml

@@ -45,4 +45,4 @@ head:
    [-1, 3, C3, [1024, False]],  # 23 (P5/32-large)
 
    [[17, 20, 23], 1, Segment, [nc, anchors, 32, 256]],  # Detect(P3, P4, P5)
-  ]
+  ]

models/tf.py

@@ -356,7 +356,7 @@ class TFUpsample(keras.layers.Layer):
     # TF version of torch.nn.Upsample()
     def __init__(self, size, scale_factor, mode, w=None):  # warning: all arguments needed including 'w'
         super().__init__()
-        assert scale_factor % 2 == 0, "scale_factor must be multiple of 2"
+        assert scale_factor % 2 == 0, 'scale_factor must be multiple of 2'
         self.upsample = lambda x: tf.image.resize(x, (x.shape[1] * scale_factor, x.shape[2] * scale_factor), mode)
         # self.upsample = keras.layers.UpSampling2D(size=scale_factor, interpolation=mode)
         # with default arguments: align_corners=False, half_pixel_centers=False
@@ -371,7 +371,7 @@ class TFConcat(keras.layers.Layer):
     # TF version of torch.concat()
     def __init__(self, dimension=1, w=None):
         super().__init__()
-        assert dimension == 1, "convert only NCHW to NHWC concat"
+        assert dimension == 1, 'convert only NCHW to NHWC concat'
         self.d = 3
 
     def call(self, inputs):
@@ -523,17 +523,17 @@ class AgnosticNMS(keras.layers.Layer):
         selected_boxes = tf.gather(boxes, selected_inds)
         padded_boxes = tf.pad(selected_boxes,
                               paddings=[[0, topk_all - tf.shape(selected_boxes)[0]], [0, 0]],
-                              mode="CONSTANT",
+                              mode='CONSTANT',
                               constant_values=0.0)
         selected_scores = tf.gather(scores_inp, selected_inds)
         padded_scores = tf.pad(selected_scores,
                                paddings=[[0, topk_all - tf.shape(selected_boxes)[0]]],
-                               mode="CONSTANT",
+                               mode='CONSTANT',
                                constant_values=-1.0)
         selected_classes = tf.gather(class_inds, selected_inds)
         padded_classes = tf.pad(selected_classes,
                                 paddings=[[0, topk_all - tf.shape(selected_boxes)[0]]],
-                                mode="CONSTANT",
+                                mode='CONSTANT',
                                 constant_values=-1.0)
         valid_detections = tf.shape(selected_inds)[0]
         return padded_boxes, padded_scores, padded_classes, valid_detections
@@ -603,6 +603,6 @@ def main(opt):
     run(**vars(opt))
 
 
-if __name__ == "__main__":
+if __name__ == '__main__':
     opt = parse_opt()
     main(opt)