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