Coding style changes

yolov3.py

@@ -48,7 +48,7 @@ class WeightReader:
 
     def load_weights(self, model):
         """
-        Load weights into created model
+        Load weights into created model.
         """
         for i in range(106):
             try:
@@ -81,21 +81,26 @@ class WeightReader:
 
     def reset(self):
         """
-        Resets offset to restart loading weights
+        Resets offset to restart loading weights.
         """
         self.offset = 0
 
 # Step 2:
-# _conv_block(input, convs, skip=True) is a function to create convolutional layer
 def _conv_block(input_layer, convs, skip=True):
+    """
+    Function to create convolutional layer.
+    """
     tmp = input_layer
     count = 0
     for conv in convs:
         if count == (len(convs) - 2) and skip:
             skip_connection = tmp
         count += 1
+
         # Peculiar padding as darknet prefer left and top
-        if conv['stride'] > 1: tmp = ZeroPadding2D(((1,0),(1,0)))(tmp)
+        if conv['stride'] > 1:
+            tmp = ZeroPadding2D(((1,0),(1,0)))(tmp)
+
         tmp = Conv2D(conv['filter'],
                      conv['kernel'],
                      strides=conv['stride'],
@@ -104,76 +109,77 @@ def _conv_block(input_layer, convs, skip=True):
                      name='conv_' + str(conv['layer_idx']),
                      use_bias=False if conv['bnorm'] else True)(tmp)
 
-        if conv['bnorm']: tmp = BatchNormalization(epsilon=0.001, name='bnorm_'
+        if conv['bnorm']:
-            + str(conv['layer_idx']))(tmp)
+            tmp = BatchNormalization(epsilon=0.001, name='bnorm_' + str(conv['layer_idx']))(tmp)
-        if conv['leaky']: tmp = LeakyReLU(alpha=0.1, name='leaky_'
+        if conv['leaky']:
-            + str(conv['layer_idx']))(tmp)
+            tmp = LeakyReLU(alpha=0.1, name='leaky_' + str(conv['layer_idx']))(tmp)
 
     return add([skip_connection, tmp]) if skip else tmp
 
-# make_yolov3_model() is a function to create layers of convoluational and stack together as a
-# whole yolo model
 def make_yolov3_model():
+    """
+    Function to create layers of convoluational and stack together as a whole yolo model.
+    """
     input_image = Input(shape=(None, None, 3))
 
     # Layer  0 => 4
-    x = _conv_block(input_image,
+    tmp = _conv_block(input_image,
                       [{'filter': 32, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 0},
                        {'filter': 64, 'kernel': 3, 'stride': 2, 'bnorm': True, 'leaky': True, 'layer_idx': 1},
                        {'filter': 32, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 2},
                        {'filter': 64, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 3}])
 
     # Layer  5 => 8
-    x = _conv_block(x,
+    tmp = _conv_block(tmp,
                       [{'filter': 128, 'kernel': 3, 'stride': 2, 'bnorm': True, 'leaky': True, 'layer_idx': 5},
                        {'filter':  64, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 6},
                        {'filter': 128, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 7}])
 
     # Layer  9 => 11
-    x = _conv_block(x,
+    tmp = _conv_block(tmp,
                       [{'filter':  64, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 9},
                        {'filter': 128, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 10}])
 
     # Layer 12 => 15
-    x = _conv_block(x,
+    tmp = _conv_block(tmp,
                       [{'filter': 256, 'kernel': 3, 'stride': 2, 'bnorm': True, 'leaky': True, 'layer_idx': 12},
                        {'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 13},
                        {'filter': 256, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 14}])
 
     # Layer 16 => 36
     for i in range(7):
-        x = _conv_block(x,
+        tmp = _conv_block(tmp,
                           [{'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 16+i*3},
                            {'filter': 256, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 17+i*3}])
-    skip_36 = x
+    skip_36 = tmp
 
     # Layer 37 => 40
-    x = _conv_block(x,
+    tmp = _conv_block(tmp,
                       [{'filter': 512, 'kernel': 3, 'stride': 2, 'bnorm': True, 'leaky': True, 'layer_idx': 37},
                        {'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 38},
                        {'filter': 512, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 39}])
 
     # Layer 41 => 61
     for i in range(7):
-        x = _conv_block(x,
+        tmp = _conv_block(tmp,
                           [{'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 41+i*3},
                            {'filter': 512, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 42+i*3}])
-    skip_61 = x
+    skip_61 = tmp
 
     # Layer 62 => 65
-    x = _conv_block(x,
+    tmp = _conv_block(tmp,
                       [{'filter': 1024, 'kernel': 3, 'stride': 2, 'bnorm': True, 'leaky': True, 'layer_idx': 62},
                        {'filter':  512, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 63},
                        {'filter': 1024, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 64}])
 
     # Layer 66 => 74
     for i in range(3):
-        x = _conv_block(x,
+        tmp = _conv_block(tmp,
                           [{'filter':  512, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 66+i*3},
                            {'filter': 1024, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 67+i*3}])
 
     # Layer 75 => 79
-    x = _conv_block(x,
+    tmp = _conv_block(tmp,
                       [{'filter':  512, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 75},
                        {'filter': 1024, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 76},
                        {'filter':  512, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 77},
@@ -182,21 +188,21 @@ def make_yolov3_model():
                       skip=False)
 
     # Layer 80 => 82
-    yolo_82 = _conv_block(x,
+    yolo_82 = _conv_block(tmp,
                           [{'filter': 1024, 'kernel': 3, 'stride': 1, 'bnorm': True,  'leaky': True,  'layer_idx': 80},
                            {'filter':  255, 'kernel': 1, 'stride': 1, 'bnorm': False, 'leaky': False, 'layer_idx': 81}],
                           skip=False)
 
     # Layer 83 => 86
-    x = _conv_block(x,
+    tmp = _conv_block(tmp,
                       [{'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 84}],
                       skip=False)
 
-    x = UpSampling2D(2)(x)
+    tmp = UpSampling2D(2)(tmp)
-    x = concatenate([x, skip_61])
+    tmp = concatenate([tmp, skip_61])
 
     # Layer 87 => 91
-    x = _conv_block(x,
+    tmp = _conv_block(tmp,
                     [{'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 87},
                      {'filter': 512, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 88},
                      {'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 89},
@@ -205,21 +211,21 @@ def make_yolov3_model():
                     skip=False)
 
     # Layer 92 => 94
-    yolo_94 = _conv_block(x,
+    yolo_94 = _conv_block(tmp,
                           [{'filter': 512, 'kernel': 3, 'stride': 1, 'bnorm': True,  'leaky': True,  'layer_idx': 92},
                            {'filter': 255, 'kernel': 1, 'stride': 1, 'bnorm': False, 'leaky': False, 'layer_idx': 93}],
                           skip=False)
 
     # Layer 95 => 98
-    x = _conv_block(x,
+    tmp = _conv_block(tmp,
                       [{'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True,   'layer_idx': 96}],
                       skip=False)
 
-    x = UpSampling2D(2)(x)
+    tmp = UpSampling2D(2)(tmp)
-    x = concatenate([x, skip_36])
+    tmp = concatenate([tmp, skip_36])
 
     # Layer 99 => 106
-    yolo_106 = _conv_block(x,
+    yolo_106 = _conv_block(tmp,
                            [{'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True,  'leaky': True,  'layer_idx': 99},
                             {'filter': 256, 'kernel': 3, 'stride': 1, 'bnorm': True,  'leaky': True,  'layer_idx': 100},
                             {'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True,  'leaky': True,  'layer_idx': 101},
@@ -233,33 +239,35 @@ def make_yolov3_model():
 
     return model
 
-"""**step 4:** Prediction
+# Step 4:
- by loading the image to model and make prediction
+# Prediction
-"""
-
 def load_image_pixels(filename, shape):
-    # load image to get its shape
+    """
+    Loading the image to model and make prediction
+    """
+
+    # Load image to get its shape
     image = load_img(filename)
     width, height = image.size
 
-    # load image with required size
+    # Load image with required size
     image = load_img(filename, target_size=shape)
     image = img_to_array(image)
 
-    # grayscale image normalization
+    # Grayscale image normalization
     image = image.astype('float32')
     image /= 255.0
 
-    # add a dimension so that we have one sample
+    # Add a dimension so that we have one sample
     image = expand_dims(image, 0)
     return image, width, height
 
-"""**Step 4:** Decode the prediction output to rectangle coordinates
+# Step 4: Decode the prediction output to rectangle coordinates
-- `BoundBox` class is used to return object bounding box coordinates, object name and threshold score
-- `decode_netout` function is used to decode the prediction output to rectangle coordinates
-"""
-
 class BoundBox:
+    """
+    BoundBox class is used to return object bounding box coordinates, object name and threshold
+    score decode_netout` function is used to decode the prediction output to rectangle coordinates
+    """
     def __init__(self, xmin, ymin, xmax, ymax, objness = None, classes = None):
         self.xmin = xmin
         self.ymin = ymin