def create_model(input_shape, n_out):
input_tensor = Input(shape=(SIZE, SIZE, 3))
#bn = BatchNormalization()(input_tensor)
#conv = Conv2D(3,(3,3),padding='same',activation='relu')(bn)
r = Lambda(lambda x: x[:, :, :, 0:1])(input_tensor)
g = Lambda(lambda x: x[:, :, :, 1:2])(input_tensor)
b = Lambda(lambda x: x[:, :, :, 2:])(input_tensor)
r = Concatenate(axis=-1)([r, r, r])
g = Concatenate(axis=-1)([g, g, g])
b = Concatenate(axis=-1)([b, b, b])
r_base = ResNet18(include_top=False,
weights='imagenet',
input_shape=(SIZE, SIZE, 3),
input_tensor=r)
for layer in r_base.layers:
layer.name += 'r'
r = r_base.get_layer('stage3_unit1_relu1r').output
g_base = ResNet18(include_top=False,
weights='imagenet',
input_shape=(SIZE, SIZE, 3),
input_tensor=g)
for layer in g_base.layers:
layer.name += 'g'
g = g_base.get_layer('stage3_unit1_relu1g').output
b_base = ResNet18(include_top=False,
weights='imagenet',
input_shape=(SIZE, SIZE, 3),
input_tensor=b)
for layer in b_base.layers:
layer.name += 'b'
b = b_base.get_layer('stage3_unit1_relu1b').output
rgb = Add()([r, g, b])
rgb = Conv2D(256, kernel_size=(3, 3), activation='relu',
padding='same')(rgb)
rgb = MaxPool2D()(rgb)
rgb = Conv2D(28, kernel_size=(3, 3), activation='sigmoid',
padding='same')(rgb)
x_pooled = NoisyAndPooling2D()(rgb)
output = Dense(28, activation='sigmoid')(x_pooled)
model = Model(input_tensor, output)
return model
def create_model(input_shape, n_out):
input_tensor = Input(shape=(SIZE, SIZE, 3))
#bn = BatchNormalization()(input_tensor)
#conv = Conv2D(3,(3,3),padding='same',activation='relu')(bn)
base_model = ResNet18(include_top=False,
weights='imagenet',
input_shape=(SIZE, SIZE, 3),
input_tensor=input_tensor)
enc_out, short_cuts = encoder(base_model)
x0 = GlobalAveragePooling2D()(squeeze_excite_block(enc_out))
x1 = GlobalAveragePooling2D()(squeeze_excite_block(short_cuts[0]))
x2 = GlobalAveragePooling2D()(squeeze_excite_block(short_cuts[1]))
x3 = GlobalAveragePooling2D()(squeeze_excite_block(short_cuts[2]))
x = Concatenate()([x0, x1, x2, x3])
x = Dropout(0.3)(x)
x = Dense(256, activation='relu')(x)
x = Dropout(0.3)(x)
x = Dense(256, activation='relu')(x)
x = Dropout(0.3)(x)
output = Dense(n_out, activation='sigmoid')(x)
model = Model(input_tensor, output)
# transfer imagenet weights
#res_img = ResNet34(include_top=False, weights='imagenet', input_shape=(SIZE, SIZE, 3))
#offset = 2
#for i, l in enumerate(base_model.layers[offset+1:]):
# l.set_weights(res_img.layers[i + 1].get_weights())
return model
def create_model(input_shape, n_out):
input_tensor = Input(shape=(SIZE, SIZE, 3))
#bn = BatchNormalization()(input_tensor)
#conv = Conv2D(3,(3,3),padding='same',activation='relu')(bn)
base_model = ResNet18(include_top=False,
weights='imagenet',
input_shape=(SIZE, SIZE, 3),input_tensor=input_tensor)
enc_out, short_cuts = encoder(base_model)
x0 = GlobalAveragePooling2D()(squeeze_excite_block(enc_out))
x1 = GlobalAveragePooling2D()(squeeze_excite_block(short_cuts[0]))
x2 = GlobalAveragePooling2D()(squeeze_excite_block(short_cuts[1]))
x3 = GlobalAveragePooling2D()(squeeze_excite_block(short_cuts[2]))
x = Concatenate()([x0,x1,x2,x3])
x = BatchNormalization()(x)
x = Dropout(0.5)(x)
x = Dense(256, activation='relu')(x)
#x = BatchNormalization()(x)
#x = Dropout(0.5)(x)
#x = Dense(256, activation='relu')(x)
x = BatchNormalization()(x)
x = Dropout(0.5)(x)
output = Dense(n_out, activation='sigmoid')(x)
model = Model(input_tensor, output)
return model
def create_model(input_shape, n_out):
input_tensor2 = Input(shape=(SIZE, SIZE, 4))
#bn = BatchNormalization()(input_tensor)
#conv = Conv2D(3,(3,3),padding='same',activation='relu')(bn)
base_model2 = ResNet18(include_top=False,
weights=None,
input_shape=(SIZE, SIZE, 4),
input_tensor=input_tensor2)
# transfer weights of deeper layers
for k, layer in enumerate(base_model2.layers):
if k > 4:
base_model2.layers[k].set_weights(
base_model.layers[k].get_weights())
# get first conv weight matrix
conv_w = base_model.layers[3].get_weights()[0]
new_part = np.zeros((7, 7, 1, 64))
fused = np.concatenate([conv_w, new_part], axis=2)
base_model2.layers[3].set_weights([fused])
enc_out, short_cuts = encoder(base_model2)
x0 = GlobalAveragePooling2D()(squeeze_excite_block(enc_out))
x1 = GlobalAveragePooling2D()(squeeze_excite_block(short_cuts[0]))
x2 = GlobalAveragePooling2D()(squeeze_excite_block(short_cuts[1]))
x3 = GlobalAveragePooling2D()(squeeze_excite_block(short_cuts[2]))
x4 = GlobalAveragePooling2D()(squeeze_excite_block(short_cuts[3]))
x = Concatenate()([x0, x1, x2, x3, x4])
x = BatchNormalization()(x)
x = Dropout(0.5)(x)
x = Dense(256, activation='relu')(x)
#x = BatchNormalization()(x)
#x = Dropout(0.5)(x)
#x = Dense(256, activation='relu')(x)
x = BatchNormalization()(x)
x = Dropout(0.5)(x)
output = Dense(n_out, activation='sigmoid')(x)
model = Model(input_tensor2, output)
# transfer imagenet weights
#res_img = ResNet34(include_top=False, weights='imagenet', input_shape=(SIZE, SIZE, 3))
#offset = 2
#for i, l in enumerate(base_model.layers[offset+1:]):
# l.set_weights(res_img.layers[i + 1].get_weights())
return model
def create_model(input_shape, n_out):
input_tensor = Input(shape=(SIZE, SIZE, 3))
#bn = BatchNormalization()(input_tensor)
#conv = Conv2D(3,(3,3),padding='same',activation='relu')(bn)
base_model = ResNet18(include_top=False,
weights='imagenet',
input_shape=(SIZE, SIZE, 3),input_tensor=input_tensor)
x = base_model.get_layer('stage4_unit1_relu1').output
#x = Dropout(0.5)(x)
x_map32 = Conv2D(28, kernel_size=(1, 1), activation='sigmoid',name='map32')(x)
x_pooled = NoisyAndPooling2D()(x_map32)
output = Dense(n_out,activation='sigmoid',name = 'pred')(x_pooled)
model = Model(input_tensor, [output,x_map32])
# transfer imagenet weights
#res_img = ResNet34(include_top=False, weights='imagenet', input_shape=(SIZE, SIZE, 3))
#offset = 2
#for i, l in enumerate(base_model.layers[offset+1:]):
# l.set_weights(res_img.layers[i + 1].get_weights())
return model
c0 = backbone.get_layer('relu0').output
c1 = backbone.get_layer('stage2_unit1_relu1').get_output_at(0) # 128
c2 = backbone.get_layer('stage3_unit1_relu1').output # 63
enc_out = backbone.get_layer('stage4_unit1_relu1').output # 32
#c4 = backbone.get_layer('stage4_unit1_relu1').output # 16
#enc_out = backbone.output # 8
short_cuts = [c0, c1, c2]
return enc_out, short_cuts
input_tensor = Input(shape=(SIZE, SIZE, 3))
base_model = ResNet18(include_top=False,
weights='imagenet',
input_shape=(SIZE, SIZE, 3),
input_tensor=input_tensor)
def create_model(input_shape, n_out):
input_tensor2 = Input(shape=(SIZE, SIZE, 4))
#bn = BatchNormalization()(input_tensor)
#conv = Conv2D(3,(3,3),padding='same',activation='relu')(bn)
base_model2 = ResNet18(include_top=False,
weights=None,
input_shape=(SIZE, SIZE, 4),
input_tensor=input_tensor2)
# transfer weights of deeper layers