Merge remote-tracking branch 'origin/docstrings' into docstrings

models/tf.py

@@ -69,7 +69,12 @@ def call(self, inputs):
 class TFPad(keras.layers.Layer):
     # Pad inputs in spatial dimensions 1 and 2
     def __init__(self, pad):
-        """Initializes a padding layer for spatial dimensions 1 and 2 with specified padding, supporting both int and tuple inputs. Inputs are """
+        """
+        Initializes a padding layer for spatial dimensions 1 and 2 with specified padding, supporting both int and tuple
+        inputs.
+
+        Inputs are
+        """
         super().__init__()
         if isinstance(pad, int):
             self.pad = tf.constant([[0, 0], [pad, pad], [pad, pad], [0, 0]])
@@ -84,7 +89,12 @@ def call(self, inputs):
 class TFConv(keras.layers.Layer):
     # Standard convolution
     def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None):
-        """Initializes a standard convolution layer with optional batch normalization and activation; supports only group=1. Inputs are ch_in, ch_out, weights, kernel, stride, padding, groups."""
+        """
+        Initializes a standard convolution layer with optional batch normalization and activation; supports only
+        group=1.
+
+        Inputs are ch_in, ch_out, weights, kernel, stride, padding, groups.
+        """
         super().__init__()
         assert g == 1, "TF v2.2 Conv2D does not support 'groups' argument"
         # TensorFlow convolution padding is inconsistent with PyTorch (e.g. k=3 s=2 'SAME' padding)
@@ -110,7 +120,12 @@ def call(self, inputs):
 class TFDWConv(keras.layers.Layer):
     # Depthwise convolution
     def __init__(self, c1, c2, k=1, s=1, p=None, act=True, w=None):
-        """Initializes a depthwise convolution layer with optional batch normalization and activation for TensorFlow models. Input are ch_in, ch_out, weights, kernel, stride, padding, groups."""
+        """
+        Initializes a depthwise convolution layer with optional batch normalization and activation for TensorFlow
+        models.
+
+        Input are ch_in, ch_out, weights, kernel, stride, padding, groups.
+        """
         super().__init__()
         assert c2 % c1 == 0, f"TFDWConv() output={c2} must be a multiple of input={c1} channels"
         conv = keras.layers.DepthwiseConv2D(
@@ -134,7 +149,11 @@ def call(self, inputs):
 class TFDWConvTranspose2d(keras.layers.Layer):
     # Depthwise ConvTranspose2d
     def __init__(self, c1, c2, k=1, s=1, p1=0, p2=0, w=None):
-        """Initializes depthwise ConvTranspose2D layer with specific channel, kernel, stride, and padding settings. Inputs are ch_in, ch_out, weights, kernel, stride, padding, groups."""
+        """
+        Initializes depthwise ConvTranspose2D layer with specific channel, kernel, stride, and padding settings.
+
+        Inputs are ch_in, ch_out, weights, kernel, stride, padding, groups.
+        """
         super().__init__()
         assert c1 == c2, f"TFDWConv() output={c2} must be equal to input={c1} channels"
         assert k == 4 and p1 == 1, "TFDWConv() only valid for k=4 and p1=1"
@@ -162,7 +181,12 @@ def call(self, inputs):
 class TFFocus(keras.layers.Layer):
     # Focus wh information into c-space
     def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None):
-        """Initializes TFFocus layer to focus width and height information into channel space with custom convolution parameters. Inputs are ch_in, ch_out, kernel, stride, padding, groups."""
+        """
+        Initializes TFFocus layer to focus width and height information into channel space with custom convolution
+        parameters.
+
+        Inputs are ch_in, ch_out, kernel, stride, padding, groups.
+        """
         super().__init__()
         self.conv = TFConv(c1 * 4, c2, k, s, p, g, act, w.conv)
 
@@ -182,7 +206,9 @@ def __init__(self, c1, c2, shortcut=True, g=1, e=0.5, w=None):  # ch_in, ch_out,
         self.add = shortcut and c1 == c2
 
     def call(self, inputs):
-        """Performs forward pass; if shortcut is True & input/output channels match, adds input to the convolution result."""
+        """Performs forward pass; if shortcut is True & input/output channels match, adds input to the convolution
+        result.
+        """
         return inputs + self.cv2(self.cv1(inputs)) if self.add else self.cv2(self.cv1(inputs))
 
 
@@ -204,7 +230,9 @@ def call(self, inputs):
 class TFConv2d(keras.layers.Layer):
     # Substitution for PyTorch nn.Conv2D
     def __init__(self, c1, c2, k, s=1, g=1, bias=True, w=None):
-        """Initializes a TensorFlow 2D convolution layer, mimicking PyTorch's nn.Conv2D functionality for given filter sizes and stride."""
+        """Initializes a TensorFlow 2D convolution layer, mimicking PyTorch's nn.Conv2D functionality for given filter
+        sizes and stride.
+        """
         super().__init__()
         assert g == 1, "TF v2.2 Conv2D does not support 'groups' argument"
         self.conv = keras.layers.Conv2D(
@@ -225,7 +253,12 @@ def call(self, inputs):
 class TFBottleneckCSP(keras.layers.Layer):
     # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
     def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None):
-        """Initializes CSP bottleneck layer with specified channel sizes, count, shortcut option, groups, and expansion ratio. Inputs are ch_in, ch_out, number, shortcut, groups, expansion."""
+        """
+        Initializes CSP bottleneck layer with specified channel sizes, count, shortcut option, groups, and expansion
+        ratio.
+
+        Inputs are ch_in, ch_out, number, shortcut, groups, expansion.
+        """
         super().__init__()
         c_ = int(c2 * e)  # hidden channels
         self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)
@@ -237,7 +270,9 @@ def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None):
         self.m = keras.Sequential([TFBottleneck(c_, c_, shortcut, g, e=1.0, w=w.m[j]) for j in range(n)])
 
     def call(self, inputs):
-        """Processes input through the model layers, concatenates, normalizes, activates, and reduces the output dimensions."""
+        """Processes input through the model layers, concatenates, normalizes, activates, and reduces the output
+        dimensions.
+        """
         y1 = self.cv3(self.m(self.cv1(inputs)))
         y2 = self.cv2(inputs)
         return self.cv4(self.act(self.bn(tf.concat((y1, y2), axis=3))))
@@ -246,7 +281,11 @@ def call(self, inputs):
 class TFC3(keras.layers.Layer):
     # CSP Bottleneck with 3 convolutions
     def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None):
-        """Initializes CSP Bottleneck with 3 convolutions, supporting optional shortcuts and group convolutions. Inputs are ch_in, ch_out, number, shortcut, groups, expansion."""
+        """
+        Initializes CSP Bottleneck with 3 convolutions, supporting optional shortcuts and group convolutions.
+
+        Inputs are ch_in, ch_out, number, shortcut, groups, expansion.
+        """
         super().__init__()
         c_ = int(c2 * e)  # hidden channels
         self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)
@@ -255,14 +294,22 @@ def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None):
         self.m = keras.Sequential([TFBottleneck(c_, c_, shortcut, g, e=1.0, w=w.m[j]) for j in range(n)])
 
     def call(self, inputs):
-        """Processes input through a sequence of transformations for object detection (YOLOv5). See https://github.com/ultralytics/yolov5."""
+        """
+        Processes input through a sequence of transformations for object detection (YOLOv5).
+
+        See https://github.com/ultralytics/yolov5.
+        """
         return self.cv3(tf.concat((self.m(self.cv1(inputs)), self.cv2(inputs)), axis=3))
 
 
 class TFC3x(keras.layers.Layer):
     # 3 module with cross-convolutions
     def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None):
-        """Initializes layer with cross-convolutions for enhanced feature extraction in object detection models. Inputs are ch_in, ch_out, number, shortcut, groups, expansion."""
+        """
+        Initializes layer with cross-convolutions for enhanced feature extraction in object detection models.
+
+        Inputs are ch_in, ch_out, number, shortcut, groups, expansion.
+        """
         super().__init__()
         c_ = int(c2 * e)  # hidden channels
         self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)
@@ -296,15 +343,19 @@ def call(self, inputs):
 class TFSPPF(keras.layers.Layer):
     # Spatial pyramid pooling-Fast layer
     def __init__(self, c1, c2, k=5, w=None):
-        """Initializes a fast spatial pyramid pooling layer with customizable in/out channels, kernel size, and weights."""
+        """Initializes a fast spatial pyramid pooling layer with customizable in/out channels, kernel size, and
+        weights.
+        """
         super().__init__()
         c_ = c1 // 2  # hidden channels
         self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)
         self.cv2 = TFConv(c_ * 4, c2, 1, 1, w=w.cv2)
         self.m = keras.layers.MaxPool2D(pool_size=k, strides=1, padding="SAME")
 
     def call(self, inputs):
-        """Executes the model's forward pass, concatenating input features with three max-pooled versions before final convolution."""
+        """Executes the model's forward pass, concatenating input features with three max-pooled versions before final
+        convolution.
+        """
         x = self.cv1(inputs)
         y1 = self.m(x)
         y2 = self.m(y1)
@@ -365,7 +416,9 @@ def _make_grid(nx=20, ny=20):
 class TFSegment(TFDetect):
     # YOLOv5 Segment head for segmentation models
     def __init__(self, nc=80, anchors=(), nm=32, npr=256, ch=(), imgsz=(640, 640), w=None):
-        """Initializes YOLOv5 Segment head with specified channel depths, anchors, and input size for segmentation models."""
+        """Initializes YOLOv5 Segment head with specified channel depths, anchors, and input size for segmentation
+        models.
+        """
         super().__init__(nc, anchors, ch, imgsz, w)
         self.nm = nm  # number of masks
         self.npr = npr  # number of protos
@@ -385,7 +438,9 @@ def call(self, x):
 
 class TFProto(keras.layers.Layer):
     def __init__(self, c1, c_=256, c2=32, w=None):
-        """Initializes TFProto layer with convolutional and upsampling layers for feature extraction and transformation."""
+        """Initializes TFProto layer with convolutional and upsampling layers for feature extraction and
+        transformation.
+        """
         super().__init__()
         self.cv1 = TFConv(c1, c_, k=3, w=w.cv1)
         self.upsample = TFUpsample(None, scale_factor=2, mode="nearest")
@@ -566,7 +621,9 @@ def predict(
 
     @staticmethod
     def _xywh2xyxy(xywh):
-        """Converts bounding box format from [x, y, w, h] to [x1, y1, x2, y2], where xy1=top-left and xy2=bottom-right."""
+        """Converts bounding box format from [x, y, w, h] to [x1, y1, x2, y2], where xy1=top-left and xy2=bottom-
+        right.
+        """
         x, y, w, h = tf.split(xywh, num_or_size_splits=4, axis=-1)
         return tf.concat([x - w / 2, y - h / 2, x + w / 2, y + h / 2], axis=-1)
 
@@ -628,7 +685,9 @@ def activations(act=nn.SiLU):
 
 
 def representative_dataset_gen(dataset, ncalib=100):
-    """Generates a representative dataset for calibration by yielding transformed numpy arrays from the input dataset."""
+    """Generates a representative dataset for calibration by yielding transformed numpy arrays from the input
+    dataset.
+    """
     for n, (path, img, im0s, vid_cap, string) in enumerate(dataset):
         im = np.transpose(img, [1, 2, 0])
         im = np.expand_dims(im, axis=0).astype(np.float32)
@@ -664,7 +723,9 @@ def run(
 
 
 def parse_opt():
-    """Parses and returns command-line options for model inference, including weights path, image size, batch size, and dynamic batching."""
+    """Parses and returns command-line options for model inference, including weights path, image size, batch size, and
+    dynamic batching.
+    """
     parser = argparse.ArgumentParser()
     parser.add_argument("--weights", type=str, default=ROOT / "yolov5s.pt", help="weights path")
     parser.add_argument("--imgsz", "--img", "--img-size", nargs="+", type=int, default=[640], help="inference size h,w")