def check_data_integrity(bot): '''Checks and fixes the file integrity of Data.\n A.K.A Replaces files which are currupt, missing, ETC.\n Also replaces values that have been set incorrectly.''' for server in bot.servers: server_path = "Data/" + server.id #This is for the main server folder. if not os.path.exists(server_path): os.makedirs(server_path) #And this is for the json files contained within. check_json(server_path + "/RolesConfig.json", {}) check_json(server_path + "/CommandConfig.json", {}) check_json(server_path + "/EventConfig.json", {}) check_json(server_path + "/FunctionConfig.json", {}) check_json(server_path + "/UserConfig.json", {}) if check_json(server_path + "/ServerConfig.json", server_config): set_default_config_values(server) update_config_keys(server) replace_invalid_values(server) remove_absent_servers(bot) for member in server.members: if member.server.get_channel(configs.get_config(configs.server_config, server.id)["MainChannel"]).permissions_for(member).administrator: if member.id not in configs.get_config(configs.user_config, server.id): configs.set_config(server.id, "Users", member.id) configs.set_config(server.id, "Users", member.id + " / GodMode / true")
def replace_invalid_values(server): "Replaces values in servers configs that are invalid" #Replacing main_channel if channel does not exist main_channel = configs.get_config(configs.server_config, server.id)["MainChannel"] reset = True for channel in server.channels: if channel.id == main_channel: reset = False if reset: configs.set_config(server.id, "Server", "MainChannel / " + get_default_config(server)["MainChannel"])
def set_default_config_values(server): "Sets server's ServerConfig.json to default values." default_config = get_default_config(server) configs.set_config(server.id, "Server", "MainChannel / " + default_config["MainChannel"]) configs.set_config(server.id, "Server", "JoinMessage / " + default_config["JoinMessage"]) configs.set_config(server.id, "Server", "StartMessage / " + default_config["StartMessage"]) configs.set_config(server.id, "Server", "NoPermissonMessage / " + default_config["NoPermissonMessage"])
if __name__ == '__main__': # Set env torch.backends.cudnn.benchmark = False torch.backends.cudnn.deterministic = True torch.backends.cudnn.enabled = True # Set seed seed = 2019 np.random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed_all(seed) # Config args = set_config() enable_dump_neuron_per_layer = False # should be fa enable_hidden_sum = False assert not enable_hidden_sum if args.checkpoint_dir is not None: if args.enable_train: args.checkpoint_dir = os.path.join(args.checkpoint_dir, 'sz{}_d{}_s{}'.format(args.spatial_size, args.dim, args.scale)) if args.number_of_fmaps != 4: args.checkpoint_dir = args.checkpoint_dir + '_depth{}'.format(args.number_of_fmaps) if args.enable_neuron_prune: if (args.layer_sparsity_list is not None) and (args.layer_sparsity_list > 0):
async def main_bot_loop(): await bot_client.bot.wait_until_ready() await asyncio.sleep(1) while True: CURRENT_TIME = time.time() #Checking each server for server in bot_client.bot.servers: helpers.check_data_integrity(bot_client.bot) #Events events = configs.get_config(configs.event_config, server.id) events_to_delete = [] for event in events: if events[event]["Enabled"]: do_execute = False last_execution = events[event]["LastExecuted"] time_since_last_execution = CURRENT_TIME - last_execution #Execute on TimeOfExecution if events[event]["Repeat"] == "None": try: event_time = datetime.datetime(events[event]["TimeOfExecution"]["Year"],events[event]["TimeOfExecution"]["Month"],events[event]["TimeOfExecution"]["Day"], events[event]["TimeOfExecution"]["Hour"], events[event]["TimeOfExecution"]["Min"], events[event]["TimeOfExecution"]["Second"]) if CURRENT_TIME > event_time.timestamp(): do_execute = True except: await bot_client.bot.send_message(server.get_channel(configs.get_config(configs.server_config, server.id)["MainChannel"]), "**Invalid time set in event " + event + ". Disabling.**") configs.set_config(server.id, "Events", event + " / Enabled / false") #Execute every minute if events[event]["Repeat"] == "Min": if (time_since_last_execution / 60) > 1: do_execute = True #Execute every hour if events[event]["Repeat"] == "Hour": if ((time_since_last_execution / 60) / 60) > 1: do_execute = True #Execute every day if events[event]["Repeat"] == "Day": if (((time_since_last_execution / 60) / 60) / 24) > 1: do_execute = True #Execute every week if events[event]["Repeat"] == "Week": if ((((time_since_last_execution / 60) / 60) / 24) / 7) > 1: do_execute = True #Execute every month if events[event]["Repeat"] == "Month": if ((((time_since_last_execution / 60) / 60) / 24) / 30) > 1: do_execute = True if do_execute: print("Executing", event, "in", server.id) await logic.execute_function(bot_client.bot, server.get_channel(configs.get_config(configs.server_config, server.id)["MainChannel"]), events[event], events[event]["Args"]) configs.set_config(server.id, "Events", event + " / LastExecuted / " + str(int(CURRENT_TIME))) #If event is not repeating if events[event]["Repeat"] != "Min" and "Hour" and "Day" and "Week" and "Month": events_to_delete.append(event) #Writing Changes configs.set_config(server.id, "Events", events_to_delete) await asyncio.sleep(1)
print('Using device:', device, ' - ', torch.cuda.get_device_name(0)) story_selector = ['st1', 'st2', 'st3'] task_name = 'ComParE2020_USOMS-e' executable_models = [ 'frozen-bert-gmax', 'frozen-bert-rnnatt', 'frozen-bert-pos-fuse-rnnatt' ] labels = ['V_cat_no', 'A_cat_no' ] #'V_self_cat_no', 'A_self_cat_no', 'V_exp_cat_no', 'A_exp_cat_no' partitions = ['train', 'devel', 'test'] for model_name in executable_models: print('-- ', model_name, ' --') for label in labels: # load config config = configs.set_config(label, story_selector, model_name) torch.cuda.empty_cache() # load data df, temp_numpy_data_splits, labels_df_cat, labels_numpy_hot = data.get_data( config) # create and add part-of-speech tagging if config.pos_embedding: if config.verbose > 0: print("Create pos embedding") part_of_speech = pos.add_pos_embeddings( task_name=task_name, temp_numpy_data_splits=temp_numpy_data_splits, MAX_LEN=config.max_seq_length,
import os from third_party.PSM.models import * from third_party.efficient_3DCNN.model import generate_model from third_party.unet.model import UNet3D from third_party.thop.thop.profile import profile from pruning.pytorch_snip.prune import pruning from configs import set_config from aux.utils import weight_init if __name__ == "__main__": torch.backends.cudnn.benchmark = False torch.backends.cudnn.deterministic = True torch.backends.cudnn.enabled = True min_sparsity, max_sparsity = 0., 1. opt = set_config() opt.network_name = opt.model if opt.dataset == 'ucf101': if opt.network_name == 'i3d': opt.sample_duration = 16 opt.sample_size = 224 opt.prune_spatial_size = opt.sample_size elif opt.network_name == 'mobilenetv2': opt.sample_duration = 16 opt.sample_size = 112 opt.groups = 3 opt.width_mult = 1.0 opt.prune_spatial_size = opt.sample_size else: assert False
def main(Param): train_df, val_df, test_df = read_text_file() if args.fine_tuning: weights_list = class_weights(train_df) model = ClassificationModel(args.model_type, args.model_name, num_labels=3, weight=weights_list, use_cuda=CUDA, args=Param) model.train_model(train_df, eval_df=val_df, f1=f1, uar=uar, verbose=False) model = ClassificationModel(args.model_type, Param['best_model_dir'], num_labels=3, use_cuda=CUDA) evaluate(model, train_df, val_df, test_df) extract_dutch_bert_embedding(train_df, 'train', Param['best_model_dir']) extract_dutch_bert_embedding(val_df, 'devel', Param['best_model_dir']) extract_dutch_bert_embedding(test_df, 'test', Param['best_model_dir']) else: extract_dutch_bert_embedding(train_df, 'train') extract_dutch_bert_embedding(val_df, 'devel') extract_dutch_bert_embedding(test_df, 'test') if args.fusion_type == 'rnnatt': config = set_config() X_train, y_train = load_feature_vectors(train_df, config) X_devel, y_devel = load_feature_vectors(val_df, config) X_test, _ = load_feature_vectors(test_df, config) y_train_df = train_df['label'] model = models.create_bert_rnn_att(config) model, history = train_model(config, model, X_train, y_train, X_devel, y_devel, y_train_df) export_results(model, config, X_train, X_devel, y_train, y_devel) output_model_features(model, data={ 'train': X_train, 'devel': X_devel, 'test': X_test }, df={ 'train': train_df, 'devel': val_df, 'test': test_df }) # to be save - free memory del model torch.cuda.empty_cache()
async def execute_attributes(bot, dic): global target_member global remaining_args #Executing layer one attributes if "addroles" in dic: await helpers.give_roles(bot, target_member, dic["addroles"]) if "say" in dic: amount = None try: amount = dic["say"][0] except: if len(remaining_args) > 0: amount = remaining_args[0] del remaining_args[0] try: send_channel = channel.server.get_channel(dic["say"][1]) except: send_channel = channel if amount != None: await bot.send_message(send_channel, amount) else: await missing_args("No message set for say.") return False if "removemessages" in dic: amount = None try: amount = int(dic["removemessages"][0]) except: if len(remaining_args) > 0: amount = int(remaining_args[0]) del remaining_args[0] try: send_channel = channel.server.get_channel( dic["removemessages"][1]) except: send_channel = channel if amount != None: async for amount in bot.logs_from(channel, amount): await bot.delete_message(amount) else: await missing_args("Amount not specified for removemessages") return False if "addevent" in dic: remaining_args = dic["addevent"][1:] + remaining_args i = 0 event_name = "Event_0" while str(event_name) in configs.get_config( configs.event_config, channel.server.id): event_name = "Event_" + str(i) i += 1 configs.set_config(channel.server.id, "Events", event_name) #Functions configs.set_config( channel.server.id, "Events", event_name + " / Functions / " + dic["addevent"][0]) #Execution time if remaining_args[0][0] == "+": seconds = int(remaining_args[1]) if len(remaining_args[0]) > 1: if remaining_args[0][1] == "m": seconds = int(remaining_args[1]) * 60 if remaining_args[0][1] == "h": seconds = int(remaining_args[1]) * 3600 if remaining_args[0][1] == "d": seconds = int(remaining_args[1]) * 86400 if remaining_args[0][1] == "w": seconds = int(remaining_args[1]) * 604800 if remaining_args[0][1] == "mo": seconds = int(remaining_args[1]) * 2592000 if remaining_args[0][1] == "y": seconds = int(remaining_args[1]) * 31536000 del remaining_args[0] del remaining_args[0] time_of_execution = datetime.datetime.fromtimestamp( time.time() + seconds) configs.set_config( channel.server.id, "Events", event_name + " / TimeOfExecution / " + str(time_of_execution.year) + " " + str(time_of_execution.month) + " " + str(time_of_execution.day) + " " + str(time_of_execution.hour) + " " + str(time_of_execution.minute) + " " + str(time_of_execution.second)) else: if len(remaining_args) < 6: configs.set_config( channel.server.id, "Events", event_name + " / TimeOfExecution / " + remaining_args[0] + " " + remaining_args[1] + " " + remaining_args[2] + " " + remaining_args[3] + " " + remaining_args[4] + " " + remaining_args[5]) remaining_args = remaining_args[6:] else: await missing_args("No time set for addevent.") return False #Args #Checks if addevent needs target_member if so adds it to it's args. attributes_in_event = get_required_attributes(dic["addevent"][0]) if "containsroles" in attributes_in_event or "haspermisson" in attributes_in_event or "addroles" in attributes_in_event: remaining_args = [target_member.id] + remaining_args configs.set_config( channel.server.id, "Events", event_name + " / Args / " + " ".join(remaining_args)) remaining_args.clear() configs.set_config(channel.server.id, "Events", event_name + " / Enabled / true")