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from configuration import DatasetName, DatasetType, \
AffectnetConf, D300wConf, W300Conf, InputDataSize, LearningConfig, CofwConf, WflwConf
from tf_record_utility import TFRecordUtility
from clr_callback import CyclicLR
from cnn_model import CNNModel
from custom_Losses import Custom_losses
from Data_custom_generator import CustomHeatmapGenerator
from PW_Data_custom_generator import PWCustomHeatmapGenerator
from image_utility import ImageUtility
import img_printer as imgpr
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import math
from datetime import datetime
from sklearn.utils import shuffle
from sklearn.model_selection import train_test_split
from numpy import save, load, asarray
import csv
from skimage.io import imread
import pickle
# tf.compat.v1.enable_eager_execution()
class TeacherTrainer:
def __init__(self, dataset_name, use_augmneted):
self.dataset_name = dataset_name
if dataset_name == DatasetName.w300:
self.num_landmark = D300wConf.num_of_landmarks * 2
if use_augmneted:
self.img_path = D300wConf.augmented_train_image
self.annotation_path = D300wConf.augmented_train_annotation
else:
self.img_path = D300wConf.no_aug_train_image
self.annotation_path = D300wConf.no_aug_train_annotation
if dataset_name == DatasetName.cofw:
self.num_landmark = CofwConf.num_of_landmarks * 2
self.img_path = CofwConf.augmented_train_image
self.annotation_path = CofwConf.augmented_train_annotation
if dataset_name == DatasetName.wflw:
self.num_landmark = WflwConf.num_of_landmarks * 2
if use_augmneted:
self.img_path = WflwConf.augmented_train_image
self.annotation_path = WflwConf.augmented_train_annotation
else:
self.img_path = WflwConf.no_aug_train_image
self.annotation_path = WflwConf.no_aug_train_annotation
def train(self, arch, weight_path):
""""""
'''create loss'''
c_loss = Custom_losses()
'''create summary writer'''
summary_writer = tf.summary.create_file_writer(
"./train_logs/fit/" + datetime.now().strftime("%Y%m%d-%H%M%S"))
'''making models'''
model = self.make_model(arch=arch, w_path=weight_path, is_old=False)
'''create optimizer'''
_lr = 1e-3
optimizer_student = self._get_optimizer(lr=_lr)
'''create sample generator'''
x_train_filenames, x_val_filenames, y_train_filenames, y_val_filenames = self._create_generators()
'''create train configuration'''
step_per_epoch = len(x_train_filenames) // LearningConfig.batch_size
'''start train:'''
for epoch in range(LearningConfig.epochs):
x_train_filenames, y_train_filenames = self._shuffle_data(x_train_filenames, y_train_filenames)
for batch_index in range(step_per_epoch):
'''load annotation and images'''
images, annotation_gr = self._get_batch_sample(
batch_index=batch_index, x_train_filenames=x_train_filenames,
y_train_filenames=y_train_filenames, model=model)
'''convert to tensor'''
images = tf.cast(images, tf.float32)
annotation_gr = tf.cast(annotation_gr, tf.float32)
'''train step'''
self.train_step(epoch=epoch, step=batch_index, total_steps=step_per_epoch, images=images,
model=model,
annotation_gr=annotation_gr,
optimizer=optimizer_student,
summary_writer=summary_writer, c_loss=c_loss)
'''evaluating part'''
img_batch_eval, pn_batch_eval = self._create_evaluation_batch(x_val_filenames, y_val_filenames)
# loss_eval, loss_eval_tol_dif_stu, loss_eval_tol_dif_gt, loss_eval_tou_dif_stu, loss_eval_tou_dif_gt = \
loss_eval = self._eval_model(img_batch_eval, pn_batch_eval, model)
with summary_writer.as_default():
tf.summary.scalar('Eval-LOSS', loss_eval, step=epoch)
'''save weights'''
model.save(
'./models/teacher_model_' + str(epoch) + '_' + self.dataset_name + '_' + str(loss_eval) + '.h5')
# @tf.function
def train_step(self, epoch, step, total_steps, images,
model, annotation_gr,
optimizer, summary_writer, c_loss):
with tf.GradientTape() as tape:
'''create annotation_predicted'''
annotation_predicted = model(images, training=True)
'''calculate loss'''
loss = c_loss.MSE(x_pr=annotation_predicted, x_gt=annotation_gr)
'''calculate gradient'''
gradients = tape.gradient(loss, model.trainable_variables)
'''apply Gradients:'''
optimizer.apply_gradients(zip(gradients, model.trainable_variables))
'''printing loss Values: '''
tf.print("->EPOCH: ", str(epoch), "->STEP: ", str(step) + '/' + str(total_steps), ' -> : LOSS: ', loss)
with summary_writer.as_default():
tf.summary.scalar('LOSS', loss, step=epoch)
def make_model(self, arch, w_path, is_old=False):
cnn = CNNModel()
model = cnn.get_model(arch=arch, output_len=self.num_landmark, input_tensor=None, weight_path=w_path,
is_old=is_old)
if w_path is not None and arch != 'mobileNetV2_d' and not is_old:
model.load_weights(w_path)
return model
def _eval_model(self, img_batch_eval, pn_batch_eval, model):
# annotation_predicted, pr_tol_dif_stu, pr_tol_dif_gt, pr_tou_dif_stu, pr_tou_dif_gt = model(img_batch_eval)
annotation_predicted = model(img_batch_eval)
loss_eval = np.array(tf.reduce_mean(tf.abs(pn_batch_eval - annotation_predicted)))
# loss_eval_tol_dif_stu = np.array(tf.reduce_mean(tf.abs(pn_batch_eval - annotation_predicted)))
# loss_eval_tol_dif_gt = np.array(tf.reduce_mean(tf.abs(pn_batch_eval - annotation_predicted)))
# loss_eval_tou_dif_stu = np.array(tf.reduce_mean(tf.abs(pn_batch_eval - annotation_predicted)))
# loss_eval_tou_dif_gt = np.array(tf.reduce_mean(tf.abs(pn_batch_eval - annotation_predicted)))
# return loss_eval, loss_eval_tol_dif_stu, loss_eval_tol_dif_gt, loss_eval_tou_dif_stu, loss_eval_tou_dif_gt
return loss_eval
def _get_optimizer(self, lr=1e-2, beta_1=0.9, beta_2=0.999, decay=1e-4):
return tf.keras.optimizers.Adam(lr=lr, beta_1=beta_1, beta_2=beta_2, decay=decay)
def _shuffle_data(self, filenames, labels):
filenames_shuffled, y_labels_shuffled = shuffle(filenames, labels)
return filenames_shuffled, y_labels_shuffled
def _create_generators(self):
fn_prefix = './file_names/' + self.dataset_name + '_'
# x_trains_path = fn_prefix + 'x_train_fns.npy'
# x_validations_path = fn_prefix + 'x_val_fns.npy'
tf_utils = TFRecordUtility(number_of_landmark=self.num_landmark)
filenames, labels = tf_utils.create_image_and_labels_name(img_path=self.img_path,
annotation_path=self.annotation_path)
filenames_shuffled, y_labels_shuffled = shuffle(filenames, labels)
x_train_filenames, x_val_filenames, y_train, y_val = train_test_split(
filenames_shuffled, y_labels_shuffled, test_size=LearningConfig.batch_size, random_state=1)
# save(x_trains_path, filenames_shuffled)
# save(x_validations_path, y_labels_shuffled)
# save(x_trains_path, x_train_filenames)
# save(x_validations_path, x_val_filenames)
# save(y_trains_path, y_train)
# save(y_validations_path, y_val)
# return filenames_shuffled, y_labels_shuffled
return x_train_filenames, x_val_filenames, y_train, y_val
def _create_evaluation_batch(self, x_eval_filenames, y_eval_filenames):
img_path = self.img_path
pn_tr_path = self.annotation_path
'''create batch data and normalize images'''
batch_x = x_eval_filenames[0:LearningConfig.batch_size]
batch_y = y_eval_filenames[0:LearningConfig.batch_size]
'''create img and annotations'''
img_batch = np.array([imread(img_path + file_name) for file_name in batch_x]) / 255.0
if self.dataset_name == DatasetName.cofw: # this ds is not normalized
pn_batch = np.array([load(pn_tr_path + file_name) for file_name in batch_y])
else:
pn_batch = np.array([self._load_and_normalize(pn_tr_path + file_name) for file_name in batch_y])
return img_batch, pn_batch
def _get_batch_sample(self, batch_index, x_train_filenames, y_train_filenames):
img_path = self.img_path
pn_tr_path = self.annotation_path
'''create batch data and normalize images'''
batch_x = x_train_filenames[
batch_index * LearningConfig.batch_size:(batch_index + 1) * LearningConfig.batch_size]
batch_y = y_train_filenames[
batch_index * LearningConfig.batch_size:(batch_index + 1) * LearningConfig.batch_size]
img_batch = np.array([imread(img_path + file_name) for file_name in batch_x]) / 255.0
pn_batch = np.array([self._load_and_normalize(pn_tr_path + file_name) for file_name in batch_y])
return img_batch, pn_batch
def _load_and_normalize(self, point_path):
annotation = load(point_path)
"""for training we dont normalize COFW"""
'''normalize landmarks based on hyperface method'''
width = InputDataSize.image_input_size
height = InputDataSize.image_input_size
x_center = width / 2
y_center = height / 2
annotation_norm = []
for p in range(0, len(annotation), 2):
annotation_norm.append((x_center - annotation[p]) / width)
annotation_norm.append((y_center - annotation[p + 1]) / height)
return annotation_norm
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