Improved model

data/test.txt

@@ -1 +1 @@
-CTCAAGCTGAGCAGTGGGTTTGCTCTGGAGGGGAAGCTCAACGGTGGCGACAAGGAAGAATCTGCTTGCGAGGCGAGCCCTGACGCCGCTGATAGCGACCAAAGGTGGATTAAACAACCCATTTCATCATTCTTCTTCCTTGTTAGTTATGATTCCCACGCTTGCCTTTCATGAATCATGATCCTATATGTATATTGATATTAATCAGTTCTAGAAAGTTCAACAACATTTGAGCATGTCAAAACCTGATCGTTGCCTGTTCCATGTCAACAGTGGATTATAACACGTGCAAATGTAGCTATTTGTGTGAGAAGACGTGTGATCGACTCTTTTTTTATATAGATAGCATTGAGATCAACTGTTTGTATATATCTTGTCATAACATTTTTACTTCGTAGCAACGTACGAGCGTTCACCTATTTGTATATAAGTTATCATGATATTTATAAGTTACCGTTGCAACGCACGGACACTCACCTAGTATAGTTTATGTATTACAGTACTAGGAGCCCTAGGCTTCCAATAACTAGAAAAAGTCCTGGTCAGTCGAACCAAACCACAATCCGACGTATACATTCTGGTTCCCCCACGCCCCCATCCGTTCGATTCA
+ATGGACAAACTCTAGTAACGGT

models/transformer/prediction-model/saved_model.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1b398b2fb6306ba1659ba6aefe6e280cc2b65d61bd15ed4a6234b0a8df43e0cc
+size 191081761

module/transformers_utility.py

@@ -1,6 +1,6 @@
 from pathlib import PosixPath
 from typing import Union, Optional
-
+import torch
 from transformers import (
     RobertaConfig,
     RobertaTokenizerFast,
@@ -81,8 +81,13 @@ def load_model(model_name: str,
     )
     if pretrained_model:
         # print(f"Loading from pretrained model {pretrained_model}")
-        model = model_class.from_pretrained(
-            str(pretrained_model), config=config_obj)
+        model = model_class(config=config_obj)
+        state_dict = torch.load(pretrained_model)
+        state_dict = {k.replace('module.', ''): v for k, v in state_dict.items()}
+        unexpected_keys = [k for k in state_dict.keys() if 'position_ids' in k]
+        for key in unexpected_keys:
+            del state_dict[key]
+        model.load_state_dict(state_dict)
     else:
         print("Loading untrained model")
         model = model_class(config=config_obj)

prediction.py

@@ -1,10 +1,11 @@
 from module import config, transformers_utility as tr, utils, metrics, dataio
 from prettytable import PrettyTable
+import numpy as np
 
 table = PrettyTable()
 table.field_names = config.tissues
 TOKENIZER_DIR = config.models / "byte-level-bpe-tokenizer"
-PRETRAINED_MODEL = config.models / "transformer" / "prediction-model"
+PRETRAINED_MODEL = config.models / "transformer" / "prediction-model" / "saved_model.pth"
 DATA_DIR = config.data
 
 def load_model(args, settings):
@@ -49,7 +50,7 @@ def main(TEST_DATA):
     dataset_test = datasets["train"]
 
     print("Getting predictions:")
-    preds = metrics.get_predictions(model, dataset_test)
+    preds = np.exp(np.array(metrics.get_predictions(model, dataset_test))) - 1
     for e in preds:
         table.add_row(e)
     print(table)