from configuration import DatasetName, WflwConf, W300Conf, DatasetType, LearningConfig, InputDataSize from cnn_model import CNNModel import tensorflow as tf import tensorflow.keras as keras 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 from image_utility import ImageUtility from tqdm import tqdm import os from Asm_assisted_loss import ASMLoss from cnn_model import CNNModel class Train: def __init__(self, arch, dataset_name, save_path, asm_accuracy=90): """ :param arch: :param dataset_name: :param save_path: :param asm_accuracy: """ self.dataset_name = dataset_name self.save_path = save_path self.arch = arch self.asm_accuracy = asm_accuracy if dataset_name == DatasetName.w300: self.num_landmark = W300Conf.num_of_landmarks * 2 self.img_path = W300Conf.train_image self.annotation_path = W300Conf.train_annotation self.pose_path = W300Conf.train_pose if dataset_name == DatasetName.wflw: self.num_landmark = WflwConf.num_of_landmarks * 2 self.img_path = WflwConf.train_image self.annotation_path = WflwConf.train_annotation self.pose_path = WflwConf.train_pose def train(self, weight_path): """ :param weight_path: :return: """ '''create loss''' c_loss = ASMLoss(dataset_name=self.dataset_name, accuracy=90) cnn = CNNModel() '''making models''' model = cnn.get_model(arch=self.arch, output_len=self.num_landmark) if weight_path is not None: model.load_weights(weight_path) '''create sample generator''' image_names, landmark_names, pose_names = self._create_generators() '''create train configuration''' step_per_epoch = len(image_names) // LearningConfig.batch_size '''start train:''' optimizer = tf.keras.optimizers.Adam(lr=1e-2, decay=1e-5) for epoch in range(LearningConfig.epochs): image_names, landmark_names, pose_names = shuffle(image_names, landmark_names, pose_names) for batch_index in range(step_per_epoch): '''load annotation and images''' images, annotation_gr, poses_gr = self._get_batch_sample( batch_index=batch_index, img_filenames=image_names, landmark_filenames=landmark_names, pose_filenames=pose_names) '''convert to tensor''' images = tf.cast(images, tf.float32) annotation_gr = tf.cast(annotation_gr, tf.float32) poses_gr = tf.cast(poses_gr, tf.float32) '''train step''' self.train_step(epoch=epoch, step=batch_index, total_steps=step_per_epoch, model=model, images=images, annotation_gt=annotation_gr, poses_gt=poses_gr, optimizer=optimizer, c_loss=c_loss) '''save weights''' model.save(self.save_path + self.arch + str(epoch) + '_' + self.dataset_name) def train_step(self, epoch, step, total_steps, model, images, annotation_gt, poses_gt, optimizer, c_loss): """ :param epoch: :param step: :param total_steps: :param model: :param images: :param annotation_gt: :param poses_gt: :param optimizer: :param c_loss: :return: """ with tf.GradientTape() as tape: '''create annotation_predicted''' annotation_predicted, pose_predicted = model(images, training=True) '''calculate loss''' mse_loss, asm_loss = c_loss.calculate_landmark_ASM_assisted_loss(landmark_pr=annotation_predicted, landmark_gt=annotation_gt, current_epoch=epoch, total_steps=total_steps) pose_loss = c_loss.calculate_pose_loss(x_pr=pose_predicted, x_gt=poses_gt) '''calculate loss''' total_loss = mse_loss + asm_loss + pose_loss '''calculate gradient''' gradients_of_model = tape.gradient(total_loss, model.trainable_variables) '''apply Gradients:''' optimizer.apply_gradients(zip(gradients_of_model, model.trainable_variables)) '''printing loss Values: ''' tf.print("->EPOCH: ", str(epoch), "->STEP: ", str(step) + '/' + str(total_steps), ' -> : total_loss: ', total_loss) def _create_generators(self): """ :return: """ image_names, landmark_filenames, pose_names = \ self._create_image_and_labels_name(img_path=self.img_path, annotation_path=self.annotation_path, pose_path=self.pose_path) return image_names, landmark_filenames, pose_names def _create_image_and_labels_name(self, img_path, annotation_path, pose_path): """ :param img_path: :param annotation_path: :param pose_path: :return: """ img_filenames = [] landmark_filenames = [] poses_filenames = [] for file in os.listdir(img_path): if file.endswith(".jpg") or file.endswith(".png"): lbl_file = str(file)[:-3] + "npy" # just name pose_file = str(file)[:-3] + "npy" # just name if os.path.exists(annotation_path + lbl_file) and os.path.exists(pose_path + lbl_file): img_filenames.append(str(file)) landmark_filenames.append(lbl_file) poses_filenames.append(pose_file) return np.array(img_filenames), np.array(landmark_filenames), np.array(poses_filenames) def _get_batch_sample(self, batch_index, img_filenames, landmark_filenames, pose_filenames): """ :param batch_index: :param img_filenames: :param landmark_filenames: :param pose_filenames: :return: """ '''create batch data and normalize images''' batch_img = img_filenames[ batch_index * LearningConfig.batch_size:(batch_index + 1) * LearningConfig.batch_size] batch_lnd = landmark_filenames[ batch_index * LearningConfig.batch_size:(batch_index + 1) * LearningConfig.batch_size] batch_pose = pose_filenames[ batch_index * LearningConfig.batch_size:(batch_index + 1) * LearningConfig.batch_size] '''create img and annotations''' img_batch = np.array([imread(self.img_path + file_name) for file_name in batch_img]) / 255.0 lnd_batch = np.array([self._load_and_normalize(self.annotation_path + file_name) for file_name in batch_lnd]) pose_batch = np.array([load(self.pose_path + file_name) for file_name in batch_pose]) return img_batch, lnd_batch, pose_batch def _load_and_normalize(self, point_path): """ :param point_path: :return: """ annotation = load(point_path) '''normalize landmarks''' 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