File size: 3,772 Bytes
3dbf98e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 | from configuration import DatasetName, DatasetType, \
D300wConf, InputDataSize, LearningConfig
import tensorflow as tf
# tf.compat.v1.disable_eager_execution()
from tensorflow import keras
from skimage.transform import resize
from keras.regularizers import l2
# tf.logging.set_verbosity(tf.logging.ERROR)
from tensorflow.keras.models import Model
from tensorflow.keras.applications import mobilenet_v2, mobilenet, resnet50, densenet
from tensorflow.keras.layers import Dense, MaxPooling2D, Conv2D, Flatten, \
BatchNormalization, Activation, GlobalAveragePooling2D, DepthwiseConv2D, Dropout, ReLU, Concatenate, Input, Conv2DTranspose
from keras.callbacks import ModelCheckpoint
from keras import backend as K
from keras.optimizers import Adam
import numpy as np
import matplotlib.pyplot as plt
import math
from keras.callbacks import CSVLogger
from datetime import datetime
import cv2
import os.path
from keras.utils.vis_utils import plot_model
from scipy.spatial import distance
import scipy.io as sio
import efficientnet.tfkeras as efn
class CNNModel:
def get_model(self, arch, input_tensor, output_len,
inp_shape=[InputDataSize.image_input_size, InputDataSize.image_input_size, 3],
weight_path=None):
if arch == 'efficientNet':
model = self.create_efficientNet(inp_shape=inp_shape, input_tensor=input_tensor, output_len=output_len)
elif arch == 'mobileNetV2':
model = self.create_MobileNet(inp_shape=inp_shape, inp_tensor=input_tensor)
return model
def create_MobileNet(self, inp_shape, inp_tensor):
mobilenet_model = mobilenet_v2.MobileNetV2(input_shape=inp_shape,
alpha=1.0,
include_top=True,
weights=None,
input_tensor=inp_tensor,
pooling=None)
inp = mobilenet_model.input
out_landmarks = mobilenet_model.get_layer('O_L').output
revised_model = Model(inp, [out_landmarks])
model_json = revised_model.to_json()
with open("mobileNet_v2_stu.json", "w") as json_file:
json_file.write(model_json)
return revised_model
def create_efficientNet(self, inp_shape, input_tensor, output_len, is_teacher=True):
if is_teacher: # for teacher we use a heavier network
eff_net = efn.EfficientNetB3(include_top=True,
weights=None,
input_tensor=None,
input_shape=[InputDataSize.image_input_size, InputDataSize.image_input_size,
3],
pooling=None,
classes=output_len)
else: # for student we use the small network
eff_net = efn.EfficientNetB0(include_top=True,
weights=None,
input_tensor=None,
input_shape=inp_shape,
pooling=None,
classes=output_len) # or weights='noisy-student'
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', name='out')(x)
eff_net = Model(inp, output)
eff_net.summary()
return eff_net
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