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"])