def fcn_VGG16_32s(INPUT_SIZE,nb_classes):
""" Returns Keras FCN-32 model definition.
"""
# Input and output layers for FCN:
inputs = Input(shape=(INPUT_SIZE, INPUT_SIZE, 3))
# Start from VGG16 layers
vgg16 = VGG16(weights='imagenet', include_top=False, input_tensor=inputs)
# score from the top vgg16 layer:
score7 = vgg16.output
#score7 = Dropout(0.5)(score7) # (optional)
score7c = Conv2D(filters=nb_classes,kernel_size=(1, 1), name='score7c')(score7)
#
score7c_upsample_32 = Conv2DTranspose(filters=nb_classes,
kernel_size=(64, 64),
strides=(32, 32),
padding='same',
activation=None,
kernel_initializer=Constant(bilinear_upsample_weights(32, nb_classes)),
name="score_pool7c_upsample_32")(score7c)
fcn_output = (Activation('softmax'))(score7c_upsample_32)
#fcn_output = score7c_upsample_32
model = Model(inputs=inputs, output=fcn_output, name='fcn_VGG16_32s')
# Fixing weighs in lower layers
# for layer in model.layers[:15]: # sometimes I use it, sometimes not.
# layer.trainable = False
return model
def fcn_RESNET50_32s(INPUT_SIZE,nb_classes):
""" Returns Keras FCN-32 + based on ResNet50 model definition.
"""
# Input and output layers for FCN:
inputs = Input(shape=(INPUT_SIZE, INPUT_SIZE, 3))
# Start from ResNet50 layers
resnet50 = ResNet50(weights='imagenet', include_top=False, input_tensor=inputs)
# score from the top resnet50 layer:
act49 = resnet50.output # equivalent to: resnet50.get_layer('activation_49').output
act49 = Dropout(0.5)(act49) # (optional)
# add classifier:
pred32 = Conv2D(filters=nb_classes,kernel_size=(1, 1), name='pred_32')(act49)
# add upsampler:
score_pred32_upsample = Conv2DTranspose(filters=nb_classes,
kernel_size=(64, 64),
strides=(32, 32),
padding='same',
activation=None,
kernel_initializer=Constant(bilinear_upsample_weights(32, nb_classes)),
name="score_pred32_upsample")(pred32)
output = (Activation('softmax'))(score_pred32_upsample)
model = Model(inputs=inputs, outputs=output, name='fcn_RESNET50_32s')
# fine-tune
train_layers = ['pred_32',
'score_pred32_upsample'
'bn5c_branch2c',
'res5c_branch2c',
'bn5c_branch2b',
'res5c_branch2b',
'bn5c_branch2a',
'res5c_branch2a',
'bn5b_branch2c',
'res5b_branch2c',
'bn5b_branch2b',
'res5b_branch2b',
'bn5b_branch2a',
'res5b_branch2a',
'bn5a_branch2c',
'res5a_branch2c',
'bn5a_branch2b',
'res5a_branch2b',
'bn5a_branch2a',
'res5a_branch2a']
# for l in model.layers:
# if l.name in train_layers:
# l.trainable = True
# else:
# l.trainable = False
return model
def fcn_32s_orig(nb_classes):
inputs = Input(shape=(None, None, 3))
vgg16 = VGG16(weights='imagenet', include_top=False, input_tensor=inputs)
x = Conv2D(filters=nb_classes,
kernel_size=(1, 1))(vgg16.output)
x = Conv2DTranspose(filters=nb_classes,
kernel_size=(64, 64),
strides=(32, 32),
padding='same',
activation='sigmoid',
kernel_initializer=Constant(bilinear_upsample_weights(32, nb_classes)))(x)
model = Model(inputs=inputs, outputs=x)
for layer in model.layers[:15]:
layer.trainable = False
return model
def fcn_VGG16_8s(INPUT_SIZE,nb_classes): # previous name: fcn8s_take2
""" Returns Keras FCN-8 model definition.
"""
fcn32_flag = False
inputs = Input(shape=(INPUT_SIZE, INPUT_SIZE, 3))
# Start from VGG16 layers
vgg16 = VGG16(weights='imagenet', include_top=False, input_tensor=inputs)
# Skip connections from pool3, 256 channels
vgg16_upto_intermediate_layer_pool3 = Model(inputs=vgg16.input, outputs=vgg16.get_layer('block3_pool').output)
score_pool3 = vgg16_upto_intermediate_layer_pool3.output
# 1x1 conv layer to reduce number of channels to nb_classes:
score_pool3c = Conv2D(filters=nb_classes,kernel_size=(1, 1),name="score_pool3c")(score_pool3)
# Skip connections from pool4, 512 channels
vgg16_upto_intermediate_layer_pool4 = Model(inputs=vgg16.input, outputs=vgg16.get_layer('block4_pool').output)
score_pool4 = vgg16_upto_intermediate_layer_pool4.output
# 1x1 conv layer to reduce number of channels to nb_classes:
score_pool4c = Conv2D(filters=nb_classes, kernel_size=(1, 1))(score_pool4)
# score from the top vgg16 layer:
score_pool5 = vgg16.output
#score_pool5 = Dropout(0.5)(score_pool5) # (optional)
#n = 4096
score6c = Conv2D(filters=4096, kernel_size=(7, 7), padding='same', name="conv6")(score_pool5)
score6c = Dropout(0.5)(score6c) # we need dropout as regularization and weight decays for RNNs
score7c = Conv2D(filters=4096, kernel_size=(1, 1), padding='same', name="conv7")(score6c)
score7c = Dropout(0.5)(score7c) # we need dropout as regularization and weight decays for RNNs
#score7c = Conv2D(filters=nb_classes,kernel_size=(1, 1))(score6c)
score7c_upsample = Conv2DTranspose(filters=nb_classes,
kernel_size=(4, 4),
strides=(2, 2),
padding='same',
activation = None,
kernel_initializer = Constant(bilinear_upsample_weights(2, nb_classes)),
name="score_pool7c_upsample")(score7c)
# Fuse scores
score_7_4 = Add()([score7c_upsample, score_pool4c])
# upsample:
score_7_4_up = Conv2DTranspose(filters=nb_classes,
kernel_size=(4, 4),
strides=(2, 2),
padding='same',
activation= None,
kernel_initializer=Constant(bilinear_upsample_weights(2, nb_classes)),
name="score_7_4_up")(score_7_4)
# Fuse scores
score_7_4_3 = Add()([score_7_4_up, score_pool3c])
# upsample:
score_7_4_3_up = Conv2DTranspose(filters=nb_classes,
kernel_size=(16, 16),
strides=(8, 8),
padding='same',
activation=None,
kernel_initializer=Constant(bilinear_upsample_weights(8, nb_classes)),
name="score_7_4_3_up")(score_7_4_3)
# Batch Normalization: (optional) -- another way to large weights and exploding gradient.
score_7_4_3_up = BatchNormalization()(score_7_4_3_up)
output = (Activation('softmax'))(score_7_4_3_up)
# # -- There's another way to match the tensor sizes from earlier layers, using a Cropping2D layer --
# # e.g., for fcn-16, we can crop layer 'score_pool4c' to get the same size as layer 'score_7c'
# score_pool4c_cropped = Cropping2D((5+3, 5+3))(score_pool4c)
# # fuse layers,
# score_7_4_cropped = Add()([score7c, score_pool4c_cropped])
# # then upsample to input size:
# x = Conv2DTranspose(filters=nb_classes,
# kernel_size=(64, 64),
# strides=(32+2,32+2),
# padding='same',
# activation='sigmoid',
# kernel_initializer=Constant(bilinear_upsample_weights(32, nb_classes)))(score_7_4_cropped)
# Creating the model:
model = Model(inputs=inputs, outputs=output, name='fcn_VGG16_8s')
# Fixing weighs in lower layers
# for layer in model.layers[:15]: # sometimes I use it, sometimes not.
# layer.trainable = False
return model
