def check_print():
base_model = resnet.ResNet101(include_top=False,
weights='imagenet',
input_shape=(224, 224, 3),
classes=17,
backend=keras.backend,
layers=keras.layers,
models=keras.models,
utils=keras.utils)
base_model.trainable = False
x = base_model.output
#x = GlobalMaxPooling2D()(x)
#x = Dense(2048, activation='relu')(x)
#x = BatchNormalization()(x)
x = Flatten()(x)
#x = Dropout(0.5)(x)
#x = Dense(2048,activation='relu')(x)
x = Dropout(0.5)(x)
#x = Dense(2048,activation='relu',kernel_regularizer=l2(0.0003))(x)
predictions = Dense(NB_CLASS, activation='softmax')(x)
model = Model(inputs=base_model.input, outputs=predictions)
model.summary()
model.compile(optimizer=Adam(lr=0.00005,
beta_1=0.9,
beta_2=0.99,
epsilon=1e-08,
decay=1e-6),
loss='categorical_crossentropy',
metrics=['accuracy'])
return model
def rmac(input_shape, num_rois):
#load ResNet101
resnet101_model = resnet.ResNet101(include_top=True, weights='imagenet', input_tensor=None, input_shape=(3, 224, 224),
pooling=None, classes=1000, backend=keras.backend, layers=keras.layers, models=keras.models, utils=keras.utils)
# Load VGG16
#vgg16_model = VGG16('', input_shape)
# vgg16_model = VGG16(include_top=True, weights='imagenet', input_tensor=None, input_shape=(3, 224, 224), pooling=None, classes=1000)
# Regions as input
in_roi = Input(shape=(num_rois, 4), name='input_roi')
#reshape
# xxx = K.permute_dimensions(vgg16_model.layers[-5].output, (0, 3, 1, 2))
# ROI pooling
layer_name = resnet101_model.layers[-4].name
layer_output = resnet101_model.layers[-4].output
print("layer name : " + layer_name)
print(layer_output)
# print('layer name : ' + vgg16_model.layers[-5].name)
# print(vgg16_model.layers[-5].output)
x = RoiPooling([1], num_rois)([layer_output, in_roi])
# Normalization
x = Lambda(lambda x: K.l2_normalize(x, axis=2), name='norm1')(x)
# PCA
x = TimeDistributed(Dense(2048, name='pca',
kernel_initializer='identity',
bias_initializer='zeros'))(x)
# Normalization
x = Lambda(lambda x: K.l2_normalize(x, axis=2), name='pca_norm')(x)
# Addition
rmac = Lambda(addition, output_shape=(2048,), name='rmac')(x)
# # Normalization
rmac_norm = Lambda(lambda x: K.l2_normalize(x, axis=1), name='rmac_norm')(rmac)
# Define model
model = Model([resnet101_model.input, in_roi], rmac_norm)
# Load PCA weights
#todo pca layer is trained by data ???
mat = scipy.io.loadmat(utils.DATA_DIR + utils.PCA_FILE)
b = np.squeeze(mat['bias'], axis=1)
w = np.transpose(mat['weights'])
model.layers[-4].set_weights([w, b])
return model
def ResNet101(*args, **kwargs):
return resnet.ResNet101(*args, **kwargs)