best_lips_IoU_ft = [ 77, 79, 120, 104, 141, 0, 34, 125, 15, 89, 49, 237, 174, 39, 210, 112, 111, 201, 149, 165, 80, 42, 128, 74, 131, 193, 133, 44, 154, 101, 173, 6, 148, 61, 27, 249, 209, 19, 247, 90, 1, 255, 182, 251, 186, 248 ] # load config config = load_config(config_path, path='configs/norm_base_config') # create directory if non existant save_path = os.path.join("models/saved", config["config_name"]) if not os.path.exists(save_path): os.mkdir(save_path) # load and define model v4_model = load_extraction_model(config, input_shape=tuple(config["input_shape"])) v4_model = tf.keras.Model(inputs=v4_model.input, outputs=v4_model.get_layer( config['v4_layer']).output) size_ft = tuple(np.shape(v4_model.output)[1:3]) print("[LOAD] size_ft", size_ft) print("[LOAD] Model loaded") print() nb_model = NormBase(config, tuple(config['input_shape'])) # ------------------------------------------------------------------------------------------------------------------- # train # load data data = load_data(config)
# df["category"] = df["category"].astype(int) # get only human c2 = category 1 category = "1" df = df[df['category'].isin([category])] # load data data = load_from_csv(df, config) print("shape data", np.shape(data[0])) sequence = np.copy(data[0]) data[0] = tf.keras.applications.vgg19.preprocess_input(data[0]) # declare model config["v4_layer"] = "block3_pool" model = load_extraction_model(config, tuple(config['input_shape'])) model = tf.keras.Model(inputs=model.input, outputs=model.get_layer(config['v4_layer']).output) # predict model preds = model.predict(data) print("shape preds", np.shape(preds)) # # plot feature maps # plot_cnn_output(preds, os.path.join("models/saved", config["config_name"]), # config['v4_layer'] + ".gif", # image=raw_data, # video=True) # test model test_features_map_variability(preds, sequence, "06_" + config["v4_layer"])