print(">> LOAD VALID DATASET FORM:", valid_annotation_json_filepath) dataloader_valid = DataLoader( images_dir_path=valid_images_dir_path, annotation_json_path=valid_annotation_json_filepath, config_training=config_training, config_model=config_model, config_preproc=config_preproc) number_of_keypoints = dataloader_train.number_of_keypoints # 17 # train dataset dataset_train = dataloader_train.input_fn() dataset_valid = dataloader_valid.input_fn() # validation images val_images, val_heatmaps = dataloader_valid.get_images( 0, batch_size=25) # from 22 index 6 images and 6 labels # ================================================ # =============== build model ==================== # ================================================ from model_provider import get_model model = get_model(model_name=model_name, model_subname=model_subname, number_of_keypoints=number_of_keypoints, config_extra=config_extra) loss_object = tf.keras.losses.MeanSquaredError() optimizer = tf.keras.optimizers.Adam( config_training["learning_rate"], epsilon=config_training["epsilon"]) train_loss = tf.keras.metrics.Mean(name="train_loss") valid_loss = tf.keras.metrics.Mean(name="valid_loss")
config_model=config_model, config_preproc=config_preproc) number_of_keypoints = dataloader_train.number_of_keypoints # 17 # train dataset dataset_train = dataloader_train.input_fn() dataset_valid = dataloader_valid.input_fn( ) if dataloader_valid is not None else None #dataset_train = strategy.experimental_distribute_dataset(dataset_train) #dataset_valid = strategy.experimental_distribute_dataset(dataset_valid) # validation images val_images, val_heatmaps = dataloader_valid.get_images( 0, batch_size=25 ) if dataloader_valid is not None else None, None # from 22 index 6 images and 6 labels # ================================================ # =============== build model ==================== # ================================================ from model_provider import get_model if strategy is not None: with strategy.scope(): model = get_model(model_name=model_name, model_subname=model_subname, number_of_keypoints=number_of_keypoints, config_extra=config_extra, backbone_name=model_backbone_name) else: model = get_model(model_name=model_name,