def __init__(self, flags, model_name=""): super().__init__(flags) # Model Parameters self.scale = flags.scale self.layers = flags.layers self.depth_wise_convolution = flags.depth_wise_convolution self.resampling_method = BICUBIC_METHOD_STRING self.self_ensemble = flags.self_ensemble # Training Parameters self.optimizer = flags.optimizer self.beta1 = flags.beta1 self.beta2 = flags.beta2 self.momentum = flags.momentum self.batch_num = flags.batch_num self.batch_image_size = flags.batch_image_size self.clipping_norm = flags.clipping_norm # Learning Rate Control for Training self.initial_lr = flags.initial_lr self.lr_decay = flags.lr_decay self.lr_decay_epoch = flags.lr_decay_epoch # Dataset or Others self.training_images = int( math.ceil(flags.training_images / flags.batch_num) * flags.batch_num) # Image Processing Parameters self.max_value = flags.max_value self.channels = flags.channels self.output_channels = flags.channels self.psnr_calc_border_size = flags.psnr_calc_border_size if self.psnr_calc_border_size < 0: self.psnr_calc_border_size = 2 + self.scale # initialize variables self.name = self.get_model_name(model_name) self.total_epochs = 0 lr = self.initial_lr while lr > flags.end_lr: self.total_epochs += self.lr_decay_epoch lr *= self.lr_decay # initialize environment util.make_dir(self.checkpoint_dir) util.make_dir(flags.graph_dir) util.make_dir(self.tf_log_dir) if flags.initialize_tf_log: util.clean_dir(self.tf_log_dir) util.set_logging(flags.log_filename, stream_log_level=logging.INFO, file_log_level=logging.INFO, tf_log_level=tf.logging.WARN) logging.info("\nLFFN-------------------------------------") logging.info("%s [%s]" % (util.get_now_date(), self.name)) self.init_train_step()
def build_batch(self, data_dir): """ Build batch images and. """ print("Building batch images for %s..." % self.batch_dir) filenames = util.get_files_in_directory(data_dir) images_count = 0 util.make_dir(self.batch_dir) util.clean_dir(self.batch_dir) util.make_dir(self.batch_dir + "/" + INPUT_IMAGE_DIR) util.make_dir(self.batch_dir + "/" + INTERPOLATED_IMAGE_DIR) util.make_dir(self.batch_dir + "/" + TRUE_IMAGE_DIR) processed_images = 0 for filename in filenames: output_window_size = self.batch_image_size * self.scale output_window_stride = self.stride * self.scale input_image, input_interpolated_image, true_image = \ build_image_set(filename, channels=self.channels, resampling_method=self.resampling_method, scale=self.scale, print_console=False) # split into batch images input_batch_images = util.get_split_images(input_image, self.batch_image_size, stride=self.stride) input_interpolated_batch_images = util.get_split_images(input_interpolated_image, output_window_size, stride=output_window_stride) if input_batch_images is None or input_interpolated_batch_images is None: # if the original image size * scale is less than batch image size continue input_count = input_batch_images.shape[0] true_batch_images = util.get_split_images(true_image, output_window_size, stride=output_window_stride) for i in range(input_count): self.save_input_batch_image(images_count, input_batch_images[i]) self.save_interpolated_batch_image(images_count, input_interpolated_batch_images[i]) self.save_true_batch_image(images_count, true_batch_images[i]) images_count += 1 processed_images += 1 if processed_images % 10 == 0: print('.', end='', flush=True) print("Finished") self.count = images_count print("%d mini-batch images are built(saved)." % images_count) config = configparser.ConfigParser() config.add_section("batch") config.set("batch", "count", str(images_count)) config.set("batch", "scale", str(self.scale)) config.set("batch", "batch_image_size", str(self.batch_image_size)) config.set("batch", "stride", str(self.stride)) config.set("batch", "channels", str(self.channels)) with open(self.batch_dir + "/batch_images.ini", "w") as configfile: config.write(configfile)
def build_batch(self, data_dir, batch_dir): """ load from input files. Then save batch images on file to reduce memory consumption. """ print("Building batch images for %s..." % batch_dir) filenames = util.get_files_in_directory(data_dir) images_count = 0 util.make_dir(batch_dir) util.clean_dir(batch_dir) util.make_dir(batch_dir + "/" + INPUT_IMAGE_DIR) util.make_dir(batch_dir + "/" + INTERPOLATED_IMAGE_DIR) util.make_dir(batch_dir + "/" + TRUE_IMAGE_DIR) for filename in filenames: output_window_size = self.batch_image_size * self.scale output_window_stride = self.stride * self.scale input_image, input_interpolated_image = self.input.load_input_image(filename, rescale=True, resampling_method=self.resampling_method) test_image = self.true.load_test_image(filename) # split into batch images input_batch_images = util.get_split_images(input_image, self.batch_image_size, stride=self.stride) input_interpolated_batch_images = util.get_split_images(input_interpolated_image, output_window_size, stride=output_window_stride) if input_batch_images is None or input_interpolated_batch_images is None: continue input_count = input_batch_images.shape[0] test_batch_images = util.get_split_images(test_image, output_window_size, stride=output_window_stride) for i in range(input_count): save_input_batch_image(batch_dir, images_count, input_batch_images[i]) save_interpolated_batch_image(batch_dir, images_count, input_interpolated_batch_images[i]) save_true_batch_image(batch_dir, images_count, test_batch_images[i]) images_count += 1 print("%d mini-batch images are built(saved)." % images_count) config = configparser.ConfigParser() config.add_section("batch") config.set("batch", "count", str(images_count)) config.set("batch", "scale", str(self.scale)) config.set("batch", "batch_image_size", str(self.batch_image_size)) config.set("batch", "stride", str(self.stride)) config.set("batch", "channels", str(self.channels)) config.set("batch", "jpeg_mode", str(self.jpeg_mode)) config.set("batch", "max_value", str(self.max_value)) with open(batch_dir + "/batch_images.ini", "w") as configfile: config.write(configfile)
def load_datasets(self, data_dir, batch_dir, batch_image_size, stride_size=0): """ build input patch images and loads as a datasets Opens image directory as a datasets. Each images are splitted into patch images and converted to input image. Since loading (especially from PNG/JPG) and building input-LR images needs much computation in the training phase, building pre-processed images makes training much faster. However, images are limited by divided grids. """ batch_dir += "/scale%d" % self.scale self.train = loader.BatchDataSets(self.scale, batch_dir, batch_image_size, stride_size, channels=self.channels, resampling_method=self.resampling_method) if not self.train.is_batch_exist(): util.make_dir(batch_dir) util.clean_dir(batch_dir) util.make_dir(batch_dir + "/" + INPUT_IMAGE_DIR) util.make_dir(batch_dir + "/" + INTERPOLATED_IMAGE_DIR) util.make_dir(batch_dir + "/" + TRUE_IMAGE_DIR) self.train.build_batch_threaded(data_dir, batch_dir, self.threads) else: self.train.load_batch_counts() self.train.load_all_batch_images(self.threads)
def __init__(self, flags, model_name=""): super().__init__(flags) # Model Parameters self.layers = flags.layers self.filters = flags.filters self.min_filters = min(flags.filters, flags.min_filters) self.filters_decay_gamma = flags.filters_decay_gamma self.use_nin = flags.use_nin self.nin_filters = flags.nin_filters self.nin_filters2 = flags.nin_filters2 self.reconstruct_layers = max(flags.reconstruct_layers, 1) self.reconstruct_filters = flags.reconstruct_filters self.resampling_method = BICUBIC_METHOD_STRING self.pixel_shuffler = flags.pixel_shuffler self.self_ensemble = flags.self_ensemble # Training Parameters self.l2_decay = flags.l2_decay self.optimizer = flags.optimizer self.beta1 = flags.beta1 self.beta2 = flags.beta2 self.momentum = flags.momentum self.batch_num = flags.batch_num self.batch_image_size = flags.batch_image_size if flags.stride_size == 0: self.stride_size = flags.batch_image_size // 2 else: self.stride_size = flags.stride_size self.clipping_norm = flags.clipping_norm # Learning Rate Control for Training self.initial_lr = flags.initial_lr self.lr_decay = flags.lr_decay self.lr_decay_epoch = flags.lr_decay_epoch # Dataset or Others self.dataset = flags.dataset self.test_dataset = flags.test_dataset self.training_image_count = max( 1, (flags.training_images // flags.batch_num)) * flags.batch_num self.train = None self.test = None # Image Processing Parameters self.scale = flags.scale self.max_value = flags.max_value self.channels = flags.channels self.jpeg_mode = flags.jpeg_mode self.output_channels = 1 # Environment (all directory name should not contain '/' after ) self.batch_dir = flags.batch_dir # initialize variables self.name = self.get_model_name(model_name) self.total_epochs = 0 lr = self.initial_lr while lr > flags.end_lr: self.total_epochs += self.lr_decay_epoch lr *= self.lr_decay # initialize environment util.make_dir(self.checkpoint_dir) util.make_dir(flags.graph_dir) util.make_dir(self.tf_log_dir) if flags.initialise_tf_log: util.clean_dir(self.tf_log_dir) util.set_logging(flags.log_filename, stream_log_level=logging.INFO, file_log_level=logging.INFO, tf_log_level=tf.logging.WARN) logging.info("\nDCSCN v2-------------------------------------") logging.info("%s [%s]" % (util.get_now_date(), self.name)) self.init_train_step()
def __init__(self, flags, model_name=""): super().__init__(flags) # Model Parameters self.scale = flags.scale self.layers = flags.layers self.filters = flags.filters self.min_filters = min(flags.filters, flags.min_filters) self.filters_decay_gamma = flags.filters_decay_gamma self.use_nin = flags.use_nin self.nin_filters = flags.nin_filters self.nin_filters2 = flags.nin_filters2 self.reconstruct_layers = max(flags.reconstruct_layers, 1) self.reconstruct_filters = flags.reconstruct_filters self.resampling_method = flags.resampling_method self.pixel_shuffler = flags.pixel_shuffler self.pixel_shuffler_filters = flags.pixel_shuffler_filters self.self_ensemble = flags.self_ensemble self.depthwise_seperable = flags.depthwise_seperable self.bottleneck = flags.bottleneck # Training Parameters self.l2_decay = flags.l2_decay self.optimizer = flags.optimizer self.beta1 = flags.beta1 self.beta2 = flags.beta2 self.epsilon = flags.epsilon self.momentum = flags.momentum self.batch_num = flags.batch_num self.batch_image_size = flags.batch_image_size if flags.stride_size == 0: self.stride_size = flags.batch_image_size // 2 else: self.stride_size = flags.stride_size self.clipping_norm = flags.clipping_norm self.use_l1_loss = flags.use_l1_loss # Learning Rate Control for Training self.initial_lr = flags.initial_lr self.lr_decay = flags.lr_decay self.lr_decay_epoch = flags.lr_decay_epoch # Dataset or Others self.training_images = int( math.ceil(flags.training_images / flags.batch_num) * flags.batch_num) self.train = None self.test = None self.gpu_device_id = flags.gpu_device_id # Image Processing Parameters self.max_value = flags.max_value self.channels = flags.channels self.output_channels = 1 self.psnr_calc_border_size = flags.psnr_calc_border_size if self.psnr_calc_border_size < 0: self.psnr_calc_border_size = self.scale self.input_image_width = flags.input_image_width self.input_image_height = flags.input_image_height # Environment (all directory name should not contain tailing '/' ) self.batch_dir = flags.batch_dir # initialize variables self.name = self.get_model_name(model_name, name_postfix=flags.name_postfix) self.total_epochs = 0 lr = self.initial_lr while lr > flags.end_lr: self.total_epochs += self.lr_decay_epoch lr *= self.lr_decay # initialize environment util.make_dir(self.checkpoint_dir) util.make_dir(flags.graph_dir) util.make_dir(self.tf_log_dir) if flags.initialize_tf_log: util.clean_dir(self.tf_log_dir) util.set_logging(flags.log_filename, stream_log_level=logging.INFO, file_log_level=logging.INFO, tf_log_level=tf.logging.WARN) logging.info("\nDCSCN v2-------------------------------------") logging.info("%s [%s]" % (util.get_now_date(), self.name)) self.init_train_step()
def __init__(self, flags, model_name=""): # Model Parameters self.filters = flags.filters self.min_filters = flags.min_filters self.nin_filters = flags.nin_filters self.nin_filters2 = flags.nin_filters2 if flags.nin_filters2 != 0 else flags.nin_filters // 2 self.cnn_size = flags.cnn_size self.last_cnn_size = flags.last_cnn_size self.cnn_stride = 1 self.layers = flags.layers self.nin = flags.nin self.bicubic_init = flags.bicubic_init self.dropout = flags.dropout self.activator = flags.activator self.filters_decay_gamma = flags.filters_decay_gamma # Training Parameters self.initializer = flags.initializer self.weight_dev = flags.weight_dev self.l2_decay = flags.l2_decay self.optimizer = flags.optimizer self.beta1 = flags.beta1 self.beta2 = flags.beta2 self.momentum = flags.momentum self.batch_num = flags.batch_num self.batch_image_size = flags.batch_image_size if flags.stride_size == 0: self.stride_size = flags.batch_image_size // 2 else: self.stride_size = flags.stride_size # Learning Rate Control for Training self.initial_lr = flags.initial_lr self.lr_decay = flags.lr_decay self.lr_decay_epoch = flags.lr_decay_epoch # Dataset or Others self.dataset = flags.dataset self.test_dataset = flags.test_dataset # Image Processing Parameters self.scale = flags.scale self.max_value = flags.max_value self.channels = flags.channels self.jpeg_mode = flags.jpeg_mode self.output_channels = self.scale * self.scale # Environment (all directory name should not contain '/' after ) self.checkpoint_dir = flags.checkpoint_dir self.tf_log_dir = flags.tf_log_dir # Debugging or Logging self.debug = flags.debug self.save_loss = flags.save_loss self.save_weights = flags.save_weights self.save_images = flags.save_images self.save_images_num = flags.save_images_num self.log_weight_image_num = 32 # initialize variables self.name = self.get_model_name(model_name) self.batch_input = self.batch_num * [None] self.batch_input_quad = self.batch_num * [None] self.batch_true_quad = self.batch_num * [None] self.receptive_fields = 2 * self.layers + self.cnn_size - 2 self.complexity = 0 # initialize environment util.make_dir(self.checkpoint_dir) util.make_dir(flags.graph_dir) util.make_dir(self.tf_log_dir) if flags.initialise_tf_log: util.clean_dir(self.tf_log_dir) util.set_logging(flags.log_filename, stream_log_level=logging.INFO, file_log_level=logging.INFO, tf_log_level=tf.logging.WARN) config = tf.ConfigProto() config.gpu_options.allow_growth = True self.sess = tf.InteractiveSession(config=config) self.init_train_step() logging.info("\nDCSCN -------------------------------------") logging.info("%s [%s]" % (util.get_now_date(), self.name))