def load_model_file(model_file): print(model_file) model_stuff = data.load_checkpoint(model_file) opt = model_stuff["opt"] state_dict = model_stuff["state_dict"] return opt, state_dict
args = parser.parse_args() split = args.split # Generate configuration files depending on experiment being run utils.generate_config_files("atomic", args.experiment_num, eval_mode=True) # Loads the correct configuration file config_file = "config/atomic/config_{}.json".format(args.experiment_num) # Read config file to option config = cfg.read_config(cfg.load_config(config_file)) cfg.device = args.device eval_opt = cfg.get_eval_parameters(config) model_stuff = data.load_checkpoint(args.model_name) opt = model_stuff["opt"] opt.eval.update(eval_opt) # Set the random seeds torch.manual_seed(args.seed) random.seed(args.seed) if config.gpu_mode: torch.cuda.manual_seed_all(args.seed) opt.train.dynamic.epoch = 0 print("Loading Data") f = open("atomic_generate.txt", "w")
# eval_mode = True means changes are taken from config/atomic/eval_changes.json utils.generate_config_files("atomic", args.experiment_num, eval_mode=True) # Loads the correct configuration file config_file = "config/atomic/config_{}.json".format(args.experiment_num) print(config_file) # Read config file to option config = cfg.read_config(cfg.load_config(config_file)) cfg.device = config.gpu_index eval_opt = cfg.get_eval_parameters(config) # Batch multiple models model_file = data.load_checkpoint(args.model_name) opt = model_file["opt"] opt.eval.update(eval_opt) print("Loading Data") # Do multiple sets of categories: # compute individual perplexity of categories in addition to total perplexity if len(opt.data.categories) == 1: set_of_categories = [opt.data.categories] else: set_of_categories = [opt.data.categories] + [[i] for i in opt.data.categories] print(set_of_categories)
parser = argparse.ArgumentParser() parser.add_argument("--device", type=int, default=0) parser.add_argument( "--model_file", type=str, default= "models/conceptnet-generation/iteration-500-100000/transformer/rel_language-trainsize_100-devversion_12-maxe1_10-maxe2_15/model_transformer-nL_12-nH_12-hSize_768-edpt_0.1-adpt_0.1-rdpt_0.1-odpt_0.1-pt_gpt-afn_gelu-init_pt-vSize_40545/exp_generation-seed_123-l2_0.01-vl2_T-lrsched_warmup_linear-lrwarm_0.002-clip_1-loss_nll-b2_0.999-b1_0.9-e_1e-08/bs_1-smax_40-sample_greedy-numseq_1-gs_full-es_full-categories_None/1e-05_adam_64_15500.pickle" ) parser.add_argument("--output_file", type=str, default="tmp/output.json") parser.add_argument("--input", type=str, default="") parser.add_argument("--sampling_algorithm", type=str, default="greedy") args = parser.parse_args() model_stuff = data.load_checkpoint(args.model_file) opt = model_stuff["opt"] relations = data.conceptnet_data.conceptnet_relations if opt.data.get("maxr", None) is None: if opt.data.rel == "language": opt.data.maxr = 5 else: opt.data.maxr = 1 path = "comet-commonsense/data/conceptnet/processed/generation/{}.pickle".format( utils.make_name_string(opt.data)) data_loader = data.make_data_loader(opt) loaded = data_loader.load_data(path)
if args.model_name == None: print("Please enter model name.") exit() split = args.split # configure evaluation run config = ac_conf.load_default() config.train.dynamic.bs = 32 #config.gpu_index = int(args.gpu_num) meta = ac_conf.get_meta(config) eval_opt = cfg.get_eval_parameters(config) checkpoint = data.load_checkpoint(abs_path(args.model_name), gpu=False) opt = checkpoint["opt"] opt.eval.update(eval_opt) # Set the random seeds torch.manual_seed(opt.train.static.seed) random.seed(opt.train.static.seed) #if config.gpu_mode: # torch.cuda.manual_seed_all(opt.train.static.seed) opt.train.dynamic.epoch = 0 print("Loading Data") # load data relations = encode.get_relations()
args = parser.parse_args() split = args.split # Generate configuration files depending on experiment being run #utils.generate_config_files("conceptnet", args.experiment_num, eval_mode=True) # Loads the correct configuration file config_file = "config/conceptnet/config_{}.json".format(args.experiment_num) # Read config file to option config = cfg.read_config(cfg.load_config(config_file)) cfg.device = args.device eval_opt = cfg.get_eval_parameters(config) model_stuff = data.load_checkpoint(args.model_name) model_know_stuff = data.load_checkpoint(args.model_knowledge_name) opt = model_stuff["opt"] opt.eval.update(eval_opt) # Set the random seeds torch.manual_seed(opt.train.static.seed) random.seed(opt.train.static.seed) if config.gpu_mode: torch.cuda.manual_seed_all(opt.train.static.seed) # Where to find the data splits = ["train", "dev", "test"] opt.train.dynamic.epoch = 0
"sentence": (True, True), "reiss": (False, False), "maslow": (False, False), "plutchik": (False, False), "plutchik16": (False, False), "entity": (True, False) } splits = ["test"] split = splits[0] config_file = "config/class_config.json" config = cfg.read_config(cfg.load_config(config_file)) print("Loading model from: {}".format(config.load_model_name)) loaded_model = data.load_checkpoint(config.load_model_name) opt = loaded_model["opt"] print("Doing task: {}".format(opt.task)) print("Doig granularity: {}".format(opt.granularity)) if opt.net.enc.model in ["ren", "npn"]: data_loader = data.MemoryModelDataLoader() data_loader.load_vocabs(vocab_paths, vocab_text) data_loader = data_loader.load_data( opt, splits, opt.task, dl_type="memory", granularity=opt.granularity, exist=True) else: data_loader = data.NeuralModelDataLoader() data_loader.load_vocabs(vocab_paths, vocab_text) data_loader = data_loader.load_data(
} splits = ["dev", "test"] print("Loading Data") print opt # Don't save models meta.save = True opt.epochs = 100 # Load model print 'Loading model from: {}'.format(config["load_model_{}_{}".format( opt.net.enc.model, opt.task)]) loaded_model = data.load_checkpoint(config["load_model_{}_{}".format( opt.net.enc.model, opt.task)]) # Load configuration old_opt = loaded_model["opt"] if old_opt.net.enc.model != opt.net.enc.model: print "Not the same model being run. Ending" raise # Save number of epochs trained for pretrained model # Good for tracking the pretrained models we used opt.net.enc["prev#"] = config["load_model_{}_{}".format( opt.net.enc.model, opt.task)][:-7].split("_")[-1] opt.net.gendec = old_opt.net.gendec