| from config import DatasetName, DatasetType, W300Conf, InputDataSize, LearningConfig |
| import tensorflow as tf |
| from tensorflow import keras |
| from skimage.transform import resize |
| from keras.regularizers import l2, l1 |
| from keras.models import Model |
| from keras.applications import mobilenet_v2, mobilenet, densenet |
| from keras.layers import Dense, MaxPooling2D, Conv2D, Flatten, \ |
| BatchNormalization, Activation, GlobalAveragePooling2D, DepthwiseConv2D, Dropout, ReLU, Concatenate, Input, \ |
| GlobalMaxPool2D |
| import efficientnet.tfkeras as efn |
|
|
|
|
| class CNNModel: |
| def get_model(self, arch, output_len): |
|
|
| if arch == 'mobileNetV2': |
| model = self.create_MobileNet(inp_shape=[224, 224, 3], output_len=output_len) |
|
|
| elif arch == 'efNb0': |
| model = self.create_efficientNet_b0(inp_shape=[224, 224, 3], input_tensor=None, output_len=output_len) |
|
|
| elif arch == 'efNb3': |
| model = self.create_efficientNet_b3(inp_shape=[224, 224, 3], input_tensor=None, output_len=output_len) |
|
|
| return |
|
|
| def create_MobileNet(self, inp_shape, output_len): |
| mobilenet_model = mobilenet_v2.MobileNetV2(input_shape=inp_shape, |
| alpha=1.0, |
| include_top=True, |
| weights=None, |
| input_tensor=None, |
| pooling=None) |
| mobilenet_model.layers.pop() |
|
|
| x = mobilenet_model.get_layer('global_average_pooling2d').output |
| x = Dropout(0.1)(x) |
| out_landmarks = Dense(output_len, activation=keras.activations.linear, kernel_initializer=initializer, |
| use_bias=True, name='O_L')(x) |
| inp = mobilenet_model.input |
|
|
| revised_model = Model(inp, [out_landmarks]) |
|
|
| revised_model.summary() |
| model_json = revised_model.to_json() |
|
|
| with open("mobileNet_v2.json", "w") as json_file: |
| json_file.write(model_json) |
|
|
| return revised_model |
|
|
| def create_efficientNet_b0(self, inp_shape, input_tensor, output_len): |
| initializer = tf.keras.initializers.he_uniform() |
|
|
| eff_net = efn.EfficientNetB0(include_top=True, |
| weights=None, |
| input_tensor=input_tensor, |
| input_shape=inp_shape, |
| pooling=None) |
| eff_net.layers.pop() |
| inp = eff_net.input |
|
|
| x = eff_net.get_layer('top_activation').output |
| x = GlobalAveragePooling2D()(x) |
| x = keras.layers.Dropout(rate=0.5)(x) |
| output = Dense(output_len, activation='linear', use_bias=True, name='out', |
| kernel_initializer=initializer)(x) |
|
|
| eff_net = Model(inp, output) |
| eff_net.summary() |
|
|
| model_json = eff_net.to_json() |
| with open("eff_net_b0.json", "w") as json_file: |
| json_file.write(model_json) |
|
|
| return eff_net |
|
|
| def create_efficientNet_b3(self, inp_shape, input_tensor, output_len): |
| initializer = tf.keras.initializers.he_uniform() |
|
|
| eff_net = efn.EfficientNetB3(include_top=True, |
| weights=None, |
| input_tensor=input_tensor, |
| input_shape=inp_shape, |
| pooling=None) |
| eff_net.layers.pop() |
| inp = eff_net.input |
|
|
| x = eff_net.get_layer('top_activation').output |
| x = GlobalAveragePooling2D()(x) |
| x = keras.layers.Dropout(rate=0.5)(x) |
|
|
| output = Dense(output_len, activation='linear', use_bias=True, name='out', |
| kernel_initializer=initializer)(x) |
|
|
| eff_net = Model(inp, output) |
| eff_net.summary() |
|
|
| model_json = eff_net.to_json() |
| with open("eff_net_b3.json", "w") as json_file: |
| json_file.write(model_json) |
|
|
| return eff_net |
|
|
