from __future__ import absolute_import, division, print_function, unicode_literals import tensorflow as tf #import tensorflow_probability as tfp #tf.enable_eager_execution() import os import bz2 import PIL.Image from PIL import ImageFilter import numpy as np from keras.models import Model from keras.utils import get_file from keras.applications.vgg16 import VGG16, preprocess_input import keras.backend as K import traceback import dnnlib.tflib as tflib def load_images(images_list, image_size=256, sharpen=False): loaded_images = list() for img_path in images_list: img = PIL.Image.open(img_path).convert('RGB') if image_size is not None: img = img.resize((image_size,image_size),PIL.Image.LANCZOS) if (sharpen): img = img.filter(ImageFilter.DETAIL) img = np.array(img) img = np.expand_dims(img, 0) loaded_images.append(img) loaded_images = np.vstack(loaded_images) return loaded_images def tf_custom_adaptive_loss(a,b): from adaptive import lossfun shape = a.get_shape().as_list() dim = np.prod(shape[1:]) a = tf.reshape(a, [-1, dim]) b = tf.reshape(b, [-1, dim]) loss, _, _ = lossfun(b-a, var_suffix='1') return tf.math.reduce_mean(loss) def tf_custom_adaptive_rgb_loss(a,b): from adaptive import image_lossfun loss, _, _ = image_lossfun(b-a, color_space='RGB', representation='PIXEL') return tf.math.reduce_mean(loss) def tf_custom_l1_loss(img1,img2): return tf.math.reduce_mean(tf.math.abs(img2-img1), axis=None) def tf_custom_logcosh_loss(img1,img2): return tf.math.reduce_mean(tf.keras.losses.logcosh(img1,img2)) def create_stub(batch_size): return tf.constant(0, dtype='float32', shape=(batch_size, 0)) def unpack_bz2(src_path): data = bz2.BZ2File(src_path).read() dst_path = src_path[:-4] with open(dst_path, 'wb') as fp: fp.write(data) return dst_path class PerceptualModel: def __init__(self, args, batch_size=1, perc_model=None, sess=None): self.sess = tf.compat.v1.get_default_session() if sess is None else sess K.set_session(self.sess) self.epsilon = 0.00000001 self.lr = args.lr self.decay_rate = args.decay_rate self.decay_steps = args.decay_steps self.img_size = args.image_size self.layer = args.use_vgg_layer self.vgg_loss = args.use_vgg_loss self.face_mask = args.face_mask self.use_grabcut = args.use_grabcut self.scale_mask = args.scale_mask self.mask_dir = args.mask_dir if (self.layer <= 0 or self.vgg_loss <= self.epsilon): self.vgg_loss = None self.pixel_loss = args.use_pixel_loss if (self.pixel_loss <= self.epsilon): self.pixel_loss = None self.mssim_loss = args.use_mssim_loss if (self.mssim_loss <= self.epsilon): self.mssim_loss = None self.lpips_loss = args.use_lpips_loss if (self.lpips_loss <= self.epsilon): self.lpips_loss = None self.l1_penalty = args.use_l1_penalty if (self.l1_penalty <= self.epsilon): self.l1_penalty = None self.adaptive_loss = args.use_adaptive_loss self.sharpen_input = args.sharpen_input self.batch_size = batch_size if perc_model is not None and self.lpips_loss is not None: self.perc_model = perc_model else: self.perc_model = None self.ref_img = None self.ref_weight = None self.perceptual_model = None self.ref_img_features = None self.features_weight = None self.loss = None self.discriminator_loss = args.use_discriminator_loss if (self.discriminator_loss <= self.epsilon): self.discriminator_loss = None if self.discriminator_loss is not None: self.discriminator = None self.stub = create_stub(batch_size) if self.face_mask: import dlib self.detector = dlib.get_frontal_face_detector() landmarks_model_path = unpack_bz2('shape_predictor_68_face_landmarks.dat.bz2') self.predictor = dlib.shape_predictor(landmarks_model_path) def add_placeholder(self, var_name): var_val = getattr(self, var_name) setattr(self, var_name + "_placeholder", tf.compat.v1.placeholder(var_val.dtype, shape=var_val.get_shape())) setattr(self, var_name + "_op", var_val.assign(getattr(self, var_name + "_placeholder"))) def assign_placeholder(self, var_name, var_val): self.sess.run(getattr(self, var_name + "_op"), {getattr(self, var_name + "_placeholder"): var_val}) def build_perceptual_model(self, generator, discriminator=None): # Learning rate global_step = tf.Variable(0, dtype=tf.int32, trainable=False, name="global_step") incremented_global_step = tf.compat.v1.assign_add(global_step, 1) self._reset_global_step = tf.assign(global_step, 0) self.learning_rate = tf.compat.v1.train.exponential_decay(self.lr, incremented_global_step, self.decay_steps, self.decay_rate, staircase=True) self.sess.run([self._reset_global_step]) if self.discriminator_loss is not None: self.discriminator = discriminator generated_image_tensor = generator.generated_image generated_image = tf.compat.v1.image.resize_nearest_neighbor(generated_image_tensor, (self.img_size, self.img_size), align_corners=True) self.ref_img = tf.get_variable('ref_img', shape=generated_image.shape, dtype='float32', initializer=tf.initializers.zeros()) self.ref_weight = tf.get_variable('ref_weight', shape=generated_image.shape, dtype='float32', initializer=tf.initializers.zeros()) self.add_placeholder("ref_img") self.add_placeholder("ref_weight") if (self.vgg_loss is not None): vgg16 = VGG16(include_top=False, weights='vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5', input_shape=(self.img_size, self.img_size, 3)) # https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5 self.perceptual_model = Model(vgg16.input, vgg16.layers[self.layer].output) generated_img_features = self.perceptual_model(preprocess_input(self.ref_weight * generated_image)) self.ref_img_features = tf.get_variable('ref_img_features', shape=generated_img_features.shape, dtype='float32', initializer=tf.initializers.zeros()) self.features_weight = tf.get_variable('features_weight', shape=generated_img_features.shape, dtype='float32', initializer=tf.initializers.zeros()) self.sess.run([self.features_weight.initializer, self.features_weight.initializer]) self.add_placeholder("ref_img_features") self.add_placeholder("features_weight") if self.perc_model is not None and self.lpips_loss is not None: img1 = tflib.convert_images_from_uint8(self.ref_weight * self.ref_img, nhwc_to_nchw=True) img2 = tflib.convert_images_from_uint8(self.ref_weight * generated_image, nhwc_to_nchw=True) self.loss = 0 # L1 loss on VGG16 features if (self.vgg_loss is not None): if self.adaptive_loss: self.loss += self.vgg_loss * tf_custom_adaptive_loss(self.features_weight * self.ref_img_features, self.features_weight * generated_img_features) else: self.loss += self.vgg_loss * tf_custom_logcosh_loss(self.features_weight * self.ref_img_features, self.features_weight * generated_img_features) # + logcosh loss on image pixels if (self.pixel_loss is not None): if self.adaptive_loss: self.loss += self.pixel_loss * tf_custom_adaptive_rgb_loss(self.ref_weight * self.ref_img, self.ref_weight * generated_image) else: self.loss += self.pixel_loss * tf_custom_logcosh_loss(self.ref_weight * self.ref_img, self.ref_weight * generated_image) # + MS-SIM loss on image pixels if (self.mssim_loss is not None): self.loss += self.mssim_loss * tf.math.reduce_mean(1-tf.image.ssim_multiscale(self.ref_weight * self.ref_img, self.ref_weight * generated_image, 1)) # + extra perceptual loss on image pixels if self.perc_model is not None and self.lpips_loss is not None: self.loss += self.lpips_loss * tf.math.reduce_mean(self.perc_model.get_output_for(img1, img2)) # + L1 penalty on dlatent weights if self.l1_penalty is not None: self.loss += self.l1_penalty * 512 * tf.math.reduce_mean(tf.math.abs(generator.dlatent_variable-generator.get_dlatent_avg())) # discriminator loss (realism) if self.discriminator_loss is not None: self.loss += self.discriminator_loss * tf.math.reduce_mean(self.discriminator.get_output_for(tflib.convert_images_from_uint8(generated_image_tensor, nhwc_to_nchw=True), self.stub)) # - discriminator_network.get_output_for(tflib.convert_images_from_uint8(ref_img, nhwc_to_nchw=True), stub) def generate_face_mask(self, im): from imutils import face_utils import cv2 rects = self.detector(im, 1) # loop over the face detections for (j, rect) in enumerate(rects): """ Determine the facial landmarks for the face region, then convert the facial landmark (x, y)-coordinates to a NumPy array """ shape = self.predictor(im, rect) shape = face_utils.shape_to_np(shape) # we extract the face vertices = cv2.convexHull(shape) mask = np.zeros(im.shape[:2],np.uint8) cv2.fillConvexPoly(mask, vertices, 1) if self.use_grabcut: bgdModel = np.zeros((1,65),np.float64) fgdModel = np.zeros((1,65),np.float64) rect = (0,0,im.shape[1],im.shape[2]) (x,y),radius = cv2.minEnclosingCircle(vertices) center = (int(x),int(y)) radius = int(radius*self.scale_mask) mask = cv2.circle(mask,center,radius,cv2.GC_PR_FGD,-1) cv2.fillConvexPoly(mask, vertices, cv2.GC_FGD) cv2.grabCut(im,mask,rect,bgdModel,fgdModel,5,cv2.GC_INIT_WITH_MASK) mask = np.where((mask==2)|(mask==0),0,1) return mask def set_reference_images(self, images_list): assert(len(images_list) != 0 and len(images_list) <= self.batch_size) loaded_image = load_images(images_list, self.img_size, sharpen=self.sharpen_input) image_features = None if self.perceptual_model is not None: image_features = self.perceptual_model.predict_on_batch(preprocess_input(np.array(loaded_image))) weight_mask = np.ones(self.features_weight.shape) if self.face_mask: image_mask = np.zeros(self.ref_weight.shape) for (i, im) in enumerate(loaded_image): try: _, img_name = os.path.split(images_list[i]) mask_img = os.path.join(self.mask_dir, f'{img_name}') if (os.path.isfile(mask_img)): print("Loading mask " + mask_img) imask = PIL.Image.open(mask_img).convert('L') mask = np.array(imask)/255 mask = np.expand_dims(mask,axis=-1) else: mask = self.generate_face_mask(im) imask = (255*mask).astype('uint8') imask = PIL.Image.fromarray(imask, 'L') print("Saving mask " + mask_img) imask.save(mask_img, 'PNG') mask = np.expand_dims(mask,axis=-1) mask = np.ones(im.shape,np.float32) * mask except Exception as e: print("Exception in mask handling for " + mask_img) traceback.print_exc() mask = np.ones(im.shape[:2],np.uint8) mask = np.ones(im.shape,np.float32) * np.expand_dims(mask,axis=-1) image_mask[i] = mask img = None else: image_mask = np.ones(self.ref_weight.shape) if len(images_list) != self.batch_size: if image_features is not None: features_space = list(self.features_weight.shape[1:]) existing_features_shape = [len(images_list)] + features_space empty_features_shape = [self.batch_size - len(images_list)] + features_space existing_examples = np.ones(shape=existing_features_shape) empty_examples = np.zeros(shape=empty_features_shape) weight_mask = np.vstack([existing_examples, empty_examples]) image_features = np.vstack([image_features, np.zeros(empty_features_shape)]) images_space = list(self.ref_weight.shape[1:]) existing_images_space = [len(images_list)] + images_space empty_images_space = [self.batch_size - len(images_list)] + images_space existing_images = np.ones(shape=existing_images_space) empty_images = np.zeros(shape=empty_images_space) image_mask = image_mask * np.vstack([existing_images, empty_images]) loaded_image = np.vstack([loaded_image, np.zeros(empty_images_space)]) if image_features is not None: self.assign_placeholder("features_weight", weight_mask) self.assign_placeholder("ref_img_features", image_features) self.assign_placeholder("ref_weight", image_mask) self.assign_placeholder("ref_img", loaded_image) def optimize(self, vars_to_optimize, iterations=200, use_optimizer='adam'): vars_to_optimize = vars_to_optimize if isinstance(vars_to_optimize, list) else [vars_to_optimize] if use_optimizer == 'lbfgs': optimizer = tf.contrib.opt.ScipyOptimizerInterface(self.loss, var_list=vars_to_optimize, method='L-BFGS-B', options={'maxiter': iterations}) else: if use_optimizer == 'ggt': optimizer = tf.contrib.opt.GGTOptimizer(learning_rate=self.learning_rate) else: optimizer = tf.train.AdamOptimizer(learning_rate=self.learning_rate) min_op = optimizer.minimize(self.loss, var_list=[vars_to_optimize]) self.sess.run(tf.variables_initializer(optimizer.variables())) fetch_ops = [min_op, self.loss, self.learning_rate] #min_op = optimizer.minimize(self.sess) #optim_results = tfp.optimizer.lbfgs_minimize(make_val_and_grad_fn(get_loss), initial_position=vars_to_optimize, num_correction_pairs=10, tolerance=1e-8) self.sess.run(self._reset_global_step) #self.sess.graph.finalize() # Graph is read-only after this statement. for _ in range(iterations): if use_optimizer == 'lbfgs': optimizer.minimize(self.sess, fetches=[vars_to_optimize, self.loss]) yield {"loss":self.loss.eval()} else: _, loss, lr = self.sess.run(fetch_ops) yield {"loss":loss,"lr":lr}