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