Update deepbind.py

deepbind.py

@@ -40,15 +40,15 @@ import pandas as pd
 from typing import List
 from functools import partial
 
-MODEL_CONFIG = datasets.load_dataset(path="thewall/deepbindweight", split="all")
-SELEX_CONFIG = pd.read_excel(MODEL_CONFIG[0]['selex'], index_col=0)
+DEEPBIND_MODEL_CONFIG = datasets.load_dataset(path="thewall/deepbindweight", split="all")
+SELEX_CONFIG = pd.read_excel(DEEPBIND_MODEL_CONFIG[0]['selex'], index_col=0)
 
 class DeepBind(nn.Module):
     ALPHABET = "ATGCN"
     ALPHABET_MAP = {key: i for i, key in enumerate(ALPHABET)}
     ALPHABET_MAP["U"] = 1
     ALPHABET_COMPLEMENT = "TACGN"
-    COMPLEMENT_ID_MAP = np.array([1, 0, 3, 2, 4])
+    COMPLEMENT_ID_MAP = torch.IntTensor([1, 0, 3, 2, 4])
 
     def __init__(self, reverse_complement=True, num_detectors=16, detector_len=24, has_avg_pooling=True, num_hidden=1,
                  tokenizer=None):
@@ -79,7 +79,7 @@ class DeepBind(nn.Module):
 
     @classmethod
     def complement_idxs_encode_batch(cls, idxs, reverse=False):
-        return np.array(list(map(partial(cls.complement_idxs_encode, reverse=reverse), idxs)))
+        return torch.stack(list(map(partial(cls.complement_idxs_encode, reverse=reverse), idxs)))
 
     @classmethod
     def complement_idxs_encode(cls, idxs, reverse=False):
@@ -143,7 +143,7 @@ class DeepBind(nn.Module):
             if ID is None:
                 data = SELEX_CONFIG
                 ID = data.loc[sra_id]["ID"]
-            file = os.path.join(MODEL_CONFIG['config'][0], f"{ID}.txt")
+            file = os.path.join(DEEPBIND_MODEL_CONFIG['config'][0], f"{ID}.txt")
         keys = [("reverse_complement", lambda x: bool(eval(x))), ("num_detectors", int), ("detector_len", int),
                 ("has_avg_pooling", lambda x: bool(eval(x))), ("num_hidden", int)]
 
@@ -184,8 +184,8 @@ class DeepBind(nn.Module):
         seq_len = mask.sum(dim=1)
         score = self.batch_scan_model(ids, seq_len, window_size, average_flag)
         if self.reverse_complement:
-            rev_seq = self.complement_idxs_encode_batch(ids.cpu().int(), reverse=True)
-            rev_seq = torch.Tensor(rev_seq).to(device=self.device)
+            rev_seq = self.complement_idxs_encode_batch(ids.cpu().long(), reverse=True)
+            rev_seq = torch.Tensor(rev_seq).to(device=self.device).float()
             rev_score = self.batch_scan_model(rev_seq, seq_len, window_size, average_flag)
             score = torch.stack([rev_score, score], dim=-1).max(dim=-1)[0]
         return score.cpu().tolist()
@@ -197,7 +197,8 @@ class DeepBind(nn.Module):
         masked = seq_len <= window_size
         for idx in torch.where(masked)[0]:
             scores[idx] = self.forward(ids[idx:idx + 1, :seq_len[idx]].int())
-
+        if torch.all(masked):
+            return scores
         fold_ids = F.unfold(ids[~masked].unsqueeze(1).unsqueeze(1), kernel_size=(1, window_size), stride=1)
         B, W, G = fold_ids.shape
         fold_ids = fold_ids.permute(0, 2, 1).reshape(-1, W)
@@ -218,7 +219,7 @@ class DeepBind(nn.Module):
     def test(self, seq: torch.IntTensor, window_size=0, average_flag=False):
         score = self.scan_model(seq, window_size, average_flag)
         if self.reverse_complement:
-            rev_seq = self.complement_idxs_encode_batch(seq.cpu(), reverse=True)
+            rev_seq = self.complement_idxs_encode_batch(seq.cpu().long(), reverse=True)
             rev_seq = torch.IntTensor(rev_seq).to(device=seq.device)
             rev_score = self.scan_model(rev_seq, window_size, average_flag)
             score = torch.cat([rev_score, score], dim=-1).max(dim=-1)[0]