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densenet.py
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densenet.py
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from tensorflow.python.keras import layers
from tensorflow.python.keras import backend
from tensorflow.python.keras.engine import training
from tensorflow.python.keras.utils import layer_utils
''' part of code from https://github.com/taki0112/Densenet-Tensorflow'''
def bottleneck_layer(x, name, filters, dropout_rate):
x = layers.BatchNormalization()(x)
x = layers.Activation('relu', name=name + '_relu1')(x)
x = layers.Conv2D(4 * filters, 1, padding='same', name=name + '_conv1')(x)
x = layers.Dropout(rate=dropout_rate)(x)
x = layers.BatchNormalization()(x)
x = layers.Activation('relu', name=name + '_relu2')(x)
x = layers.Conv2D(filters, 1, padding='same', name=name + '_conv2')(x)
x = layers.Dropout(rate=dropout_rate)(x)
return x
def transition_block(x, name, dropout_rate, reduction=0.5):
x = layers.BatchNormalization(name=name + '_bn')(x)
x = layers.Activation('relu', name=name + '_relu')(x)
x = layers.Conv2D(int(x.get_shape().as_list()[-1] * reduction),
1,
padding='same',
name=name + '_conv')(x)
x = layers.Dropout(rate=dropout_rate)(x)
x = layers.AveragePooling2D(strides=2, name=name + '_pool')(x)
return x
def dense_block(x, blocks, filters, dropout_rate, name):
for i in range(blocks):
x1 = bottleneck_layer(x,
filters=filters,
dropout_rate=dropout_rate,
name=name + '_bottleN_' + str(i))
x = layers.Concatenate(axis=3)([x, x1])
return x
def DenseNet(blocks,
classes,
filters,
dropout_rate,
include_top=True,
weights=None,
input_tensor=None,
input_shape=None,
pooling=None,
classifier_activation=None):
# Determine proper input shape
if input_tensor is None:
img_input = layers.Input(shape=input_shape)
else:
if not backend.is_keras_tensor(input_tensor):
img_input = layers.Input(tensor=input_tensor, shape=input_shape)
else:
img_input = input_tensor
# x = layers.ZeroPadding2D(padding=((3, 3), (3, 3)))(x)
x = layers.Conv2D(2 * filters,
3,
strides=2,
padding='same',
name='conv1/conv')(img_input)
# x = layers.BatchNormalization(axis=bn_axis,
# epsilon=1.001e-5,
# name='conv1/bn')(x)
# x = layers.Activation('relu', name='conv1/relu')(x)
# x = layers.ZeroPadding2D(padding=((1, 1), (1, 1)))(x)
x = layers.MaxPooling2D(pool_size=(3, 3), strides=2, name='pool1')(x)
# x = dense_block(x,
# blocks=6,
# filters=filters,
# dropout_rate=dropout_rate,
# name='dense_1')
# x = transition_block(x, dropout_rate=dropout_rate, name='trans_2')
# x = dense_block(x,
# blocks=12,
# filters=filters,
# dropout_rate=dropout_rate,
# name='dense_2')
# x = transition_block(x, dropout_rate=dropout_rate, name='trans_3')
# x = dense_block(x,
# blocks=24,
# filters=filters,
# dropout_rate=dropout_rate,
# name='dense_3')
# x = transition_block(x, dropout_rate=dropout_rate, name='trans_4')
# x = dense_block(x,
# blocks=16,
# filters=filters,
# dropout_rate=dropout_rate,
# name='dense_4')
for i in range(blocks):
x = dense_block(x,
blocks=6,
filters=filters,
dropout_rate=dropout_rate,
name='dense_' + str(i))
x = transition_block(x,
dropout_rate=dropout_rate,
name='trans_' + str(i))
x = dense_block(x,
blocks=12,
filters=filters,
dropout_rate=dropout_rate,
name='dense_final')
x = layers.BatchNormalization()(x)
x = layers.Activation('relu', name='relu')(x)
if include_top:
x = layers.GlobalAveragePooling2D(name='avg_pool')(x)
x = layers.Flatten()(x)
x = layers.Dense(classes,
activation=classifier_activation,
name='predictions')(x)
else:
if pooling == 'avg':
x = layers.GlobalAveragePooling2D(name='avg_pool')(x)
elif pooling == 'max':
x = layers.GlobalMaxPooling2D(name='max_pool')(x)
# Ensure that the model takes into account
# any potential predecessors of `input_tensor`.
if input_tensor is not None:
inputs = layer_utils.get_source_inputs(input_tensor)
else:
inputs = img_input
# Create model.
model = training.Model(inputs, x, name='NET.model')
return model