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Coding style changes

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This commit is contained in:
Heiko J Schick
2020-10-21 10:52:24 +02:00
parent 423982c9f9
commit 935d1d3f1a
1 changed files with 87 additions and 79 deletions
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yolov3.py
+87 -79
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@@ -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'],
@@ -104,99 +109,100 @@ 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_'
+ str(conv['layer_idx']))(tmp)
if conv['leaky']: tmp = LeakyReLU(alpha=0.1, name='leaky_'
+ str(conv['layer_idx']))(tmp)
if conv['bnorm']:
tmp = BatchNormalization(epsilon=0.001, name='bnorm_' + str(conv['layer_idx']))(tmp)
if conv['leaky']:
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,
[{'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}])
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,
[{'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}])
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,
[{'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}])
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,
[{'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}])
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,
[{'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
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 = tmp
# Layer 37 => 40
x = _conv_block(x,
[{'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}])
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,
[{'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
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 = tmp
# Layer 62 => 65
x = _conv_block(x,
[{'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}])
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,
[{'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}])
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,
[{'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},
{'filter': 1024, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 78},
{'filter': 512, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 79}],
skip=False)
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},
{'filter': 1024, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 78},
{'filter': 512, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 79}],
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,
[{'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 84}],
skip=False)
tmp = _conv_block(tmp,
[{'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 84}],
skip=False)
x = UpSampling2D(2)(x)
x = concatenate([x, skip_61])
tmp = UpSampling2D(2)(tmp)
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,
[{'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 96}],
skip=False)
tmp = _conv_block(tmp,
[{'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 96}],
skip=False)
x = UpSampling2D(2)(x)
x = concatenate([x, skip_36])
tmp = UpSampling2D(2)(tmp)
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
by loading the image to model and make prediction
"""
# Step 4:
# 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
- `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
"""
# Step 4: 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