def get_model(n_classes, class_weights, unsuper_weight=0):
i = Input((None, None, 3))
t = bn_block(i)
feat_list = [t]
for layer in range(0, 3):
t = conv_block(t, 32, (3, 3))
feat_list.append(t)
t = Dropout(0.15)(t)
t = conv_block(t, 128, (1, 1))
t = conv_block(t, 32, (1, 1))
t = Conv2D(n_classes, (1, 1))(t)
o1 = Softmax4D()(t)
model = Model(inputs=[i], outputs=[o1])
loss = M.loss(weights=class_weights, unsuper_weight=unsuper_weight, unsuper_channel=-1)
wcce = M.wcceOA(weights=class_weights)
uentr = M.entrONA(unsuper_channel=-1)
wacc = M.waccOA(weights=class_weights)
model.compile(optimizer=Adam(0.01), loss=loss, metrics=[wcce, uentr, wacc])
return model
def get_model(n_classes, class_weights, unsuper_weight=0):
atrous_rates = [(1, 1), (1, 1), (2, 2), (3, 3), (5, 5), (8, 8), (13, 13),
(21, 21), (34, 34), (55, 55)]
i = Input((None, None, 3))
t = bn_block(i)
feat_list = [t]
for layer in range(0, len(atrous_rates)):
t = conv_block(t, 32, (3, 3), atrous_rate=atrous_rates[layer])
feat_list.append(t)
t = Concatenate(axis=-1)(feat_list)
t = Dropout(0.5)(t)
t = conv_block(t, 128, (1, 1))
t = conv_block(t, 32, (1, 1))
t = Conv2D(n_classes, (1, 1), kernel_initializer='he_normal')(t)
o1 = Softmax4D()(t)
model = Model(inputs=[i], outputs=[o1])
loss = M.loss(weights=class_weights,
unsuper_weight=unsuper_weight,
unsuper_channel=-1)
wcce = M.wcceOA(weights=class_weights)
uentr = M.entrONA(unsuper_channel=-1)
wacc = M.waccOA(weights=class_weights)
model.compile(optimizer=Adam(0.01), loss=loss, metrics=[wcce, uentr, wacc])
return model
def get_model(n_classes, class_weights, unsuper_weight=0):
model = models.Sequential()
model.add(
layers.Conv2D(64, (3, 3),
activation='relu',
input_shape=(None, None, 3),
padding='same'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu', padding='same'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu', padding='same'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (1, 1), activation='relu', padding='same'))
model.add(layers.Conv2D(n_classes, (1, 1), activation='relu'))
model.add(UpSampling2D((8, 8)))
model.add(Softmax4D())
#model = Model(inputs=[i], outputs=[o1])
loss = M.loss(weights=class_weights,
unsuper_weight=unsuper_weight,
unsuper_channel=-1)
wcce = M.wcceOA(weights=class_weights)
uentr = M.entrONA(unsuper_channel=-1)
wacc = M.waccOA(weights=class_weights)
model.compile(optimizer=Adam(0.01), loss=loss, metrics=[wcce, uentr, wacc])
return model
def get_model(n_classes, class_weights, unsuper_weight=0):
i = Input((None, None, 3))
#encoding layers
o = (ZeroPadding2D((1, 1)))(i)
o = (Convolution2D(64, (3, 3), padding='valid'))(o)
o = (BatchNormalization())(o)
o = (MaxPooling2D(pool_size=(2, 2)))(o)
o = (ZeroPadding2D((1, 1)))(o)
o = (Convolution2D(128, (3, 3), padding='valid'))(o)
o = (Activation('relu'))(o)
o = (BatchNormalization())(o)
o = (MaxPooling2D(pool_size=(2, 2)))(o)
o = (ZeroPadding2D((1, 1)))(o)
o = (Convolution2D(256, (3, 3), padding='valid'))(o)
o = (Activation('relu'))(o)
o = (BatchNormalization())(o)
o = (MaxPooling2D(pool_size=(2, 2)))(o)
o = (ZeroPadding2D((1, 1)))(o)
o = (Convolution2D(512, (3, 3), padding='valid'))(o)
o = (Activation('relu'))(o)
o = (BatchNormalization())(o)
#decoding layers
o = (ZeroPadding2D((1, 1)))(o)
o = (Convolution2D(512, (3, 3), padding='valid'))(o)
o = (BatchNormalization())(o)
o = (UpSampling2D((2, 2)))(o)
o = (ZeroPadding2D((1, 1)))(o)
o = (Convolution2D(256, (3, 3), padding='valid'))(o)
o = (BatchNormalization())(o)
o = (UpSampling2D((2, 2)))(o)
o = (ZeroPadding2D((1, 1)))(o)
o = (Convolution2D(128, (3, 3), padding='valid'))(o)
o = (BatchNormalization())(o)
o = (UpSampling2D((2, 2)))(o)
o = (ZeroPadding2D((1, 1)))(o)
o = (Convolution2D(64, (3, 3), padding='valid'))(o)
o = (BatchNormalization())(o)
o = (Convolution2D(n_classes, (1, 1), padding='valid'))(o)
o1 = Softmax4D()(o)
model = Model(inputs=[i], outputs=[o1])
loss = M.loss(weights=class_weights,
unsuper_weight=unsuper_weight,
unsuper_channel=-1)
wcce = M.wcceOA(weights=class_weights)
uentr = M.entrONA(unsuper_channel=-1)
wacc = M.waccOA(weights=class_weights)
model.compile(optimizer=Adam(0.01), loss=loss, metrics=[wcce, uentr, wacc])
return model