def get_wb_from_convnet2_checkpoint(name, sp, params, item_type): """ params = [check_point_path, layer_name, ] """ sys.path.append('/home/grads/sijinli2/Projects/cuda-convnet2/') checkpath = params[0] layer_name = name if len(params) == 1 else params[1] filepath = os.path.join( checkpath, sorted(os.listdir(checkpath), key=alphanum_key)[-1]) saved = mio.unpickle(filepath) model_state = saved['model_state'] layer = model_state['layers'][layer_name] if item_type == 'weights': # weights n_w = len(sp) print ' init from convnet {}--------'.format(checkpath) idx_list = range(n_w) if len(params) < 3 else iu.get_int_list_from_str( params[2]) print ','.join( ['{}'.format(layer['weights'][k].shape) for k in idx_list]) print '--------------\n---------\n' return [layer['weights'][k] for k in idx_list] else: return [layer['biases']]
def gwfp(name, sp_list, params): """ get weights from saved model params = [model_path, layer_name, [idx,...]] """ n_w = len(sp_list) model_path = params[0] layer_name = name if len(params) == 1 else params[1] model = Solver.get_saved_model(model_path) layers = get_layers(model) lay = layers[layer_name][2] if len(params) < 3: idx_list = range(n_w) else: idx_list = iu.get_int_list_from_str(params[2]) return [lay['weights'][k] for k in idx_list]
def get_wb_from_convnet2_checkpoint(name, sp, params, item_type): """ params = [check_point_path, layer_name, ] """ sys.path.append('/home/grads/sijinli2/Projects/cuda-convnet2/') checkpath= params[0] layer_name= name if len(params) == 1 else params[1] filepath = os.path.join(checkpath, sorted(os.listdir(checkpath), key=alphanum_key)[-1]) saved = mio.unpickle(filepath) model_state = saved['model_state'] layer = model_state['layers'][layer_name] if item_type == 'weights': # weights n_w = len(sp) print ' init from convnet {}--------'.format(checkpath) idx_list = range(n_w) if len(params) < 3 else iu.get_int_list_from_str(params[2]) print ','.join(['{}'.format(layer['weights'][k].shape) for k in idx_list]) print '--------------\n---------\n' return [layer['weights'][k] for k in idx_list] else: return [layer['biases']]