restore args json

app.py

@@ -25,7 +25,7 @@ def process_examples(samples):
         processed.append(example)
     return processed
 
-args, args_list, lng_names = parse_args(ckpt='./ckpt/model_fixed.pt')
+args, args_list, lng_names = parse_args(ckpt='./ckpt/model.pt')
 
 tokenizer = T5Tokenizer.from_pretrained(args.model_name)
 device = 'cuda' if torch.cuda.is_available() else 'cpu'
@@ -46,14 +46,6 @@ ling_collection_scaled = scaler.transform(ling_collection)
 
 model, ling_disc, sem_emb = get_model(args, tokenizer, device)
 
-# state = torch.load(args.ckpt, map_location=torch.device('cpu'))
-# model.load_state_dict(state['model'], strict=True)
-# model.eval()
-# ling_disc.eval()
-
-# state = torch.load(args.sem_ckpt, map_location=torch.device('cpu'))
-# sem_emb.load_state_dict(state['model'], strict=True)
-# sem_emb.eval()
 
 ############# Start demo code
 def round_ling(x):

ckpt/model.json

@@ -0,0 +1,82 @@
+{
+    "data": "ling_conversion",
+    "data_sources": ["qqp", "mrpc", "stsb"],
+    "data_type": "text",
+    "kld_annealing": "cyclic",
+    "lingpred_annealing": "mono",
+    "ling_embed_type": "one-layer",
+    "combine_weight": 1,
+    "alpha_kld": 1,
+    "alpha_lingpred": 1,
+    "alpha_sem": 1,
+    "max_grad_norm": 10,
+    "sem_loss_tao": 0.5,
+    "sem_loss_eps": 1,
+    "ckpt": "./ckpt/model.pt",
+    "disc_type": "deberta",
+    "disc_ckpt": "./ckpt/ling_disc",
+    "sem_ckpt": "./ckpt/sem_emb.pt",
+    "lng_ids": null,
+    "lng_ids_idx": null,
+    "model_name": "google/flan-t5-base",
+    "aim_exp": "lingconv-0606",
+    "sem_loss_type": "dedicated",
+    "combine_method": "decoder_add_first",
+    "train_log": 200,
+    "val_log": 2000,
+    "batch_size": 80,
+    "eval_batch_size": 200,
+    "max_eval_samples": 1000,
+    "test_batch_size": 1,
+    "hidden_dim": 500,
+    "latent_dim": 150,
+    "lng_dim": 40,
+    "disc_lng_dim": 40,
+    "use_lora": false,
+    "lora_r": 64,
+    "gpu": "4",
+    "epochs": 20,
+    "grad_accumulation": 1,
+    "n_ica": 10,
+    "max_length": 200,
+    "total_steps": null,
+    "kld_const": 1,
+    "lr": 0.001,
+    "kl_weight": 0.1,
+    "weight_decay": 0.01,
+    "ling_dropout": 0.1,
+    "predict_fn": "logs/test.txt",
+    "save_predict": false,
+    "use_ica": false,
+    "pretrain_gen": false,
+    "pretrain_sem": false,
+    "pretrain_disc": false,
+    "linggen_type": "none",
+    "linggen_input": "s+l",
+    "aug_same": false,
+    "ling_vae": false,
+    "process_lingpred": false,
+    "fudge_lambda": 1.0,
+    "use_lingpred": false,
+    "ling2_only": true,
+    "cycle_loss": false,
+    "disc_loss": false,
+    "sem_loss": false,
+    "sim_loss": false,
+    "optuna": false,
+    "debug": false,
+    "demo": false,
+    "fudge": false,
+    "out_fn": "logs/default",
+    "eval_only": false,
+    "predict_with_feedback": false,
+    "feedback_param": "s",
+    "eval_ling": false,
+    "seed": 0,
+    "major_arg": 0,
+    "quantize_lng": false,
+    "quant_nbins": 20,
+    "src_lng": "ling",
+    "to_restore": [],
+    "disc_steps": 0
+}