Delete utils.py

utils.py

@@ -1,132 +0,0 @@
-import torch.nn as nn
-import copy, math
-import torch
-import numpy as np
-import torch.nn.functional as F
-
-from vocab import PepVocab
-
-def create_vocab():
-    vocab_mlm = PepVocab()
-    vocab_mlm.vocab_from_txt('/home/ubuntu/work/gecheng/conoGen_final/vocab.txt')
-    # vocab_mlm.token_to_idx['-'] = 23
-    return vocab_mlm
-
-def show_parameters(model: nn.Module, show_all=False, show_trainable=True):
-
-    mlp_pa = {name:param.requires_grad for name, param in model.named_parameters()}
-    
-    if show_all:
-        print('All parameters:')
-        print(mlp_pa)
-
-    if show_trainable:
-        print('Trainable parameters:')
-        print(list(filter(lambda x: x[1], list(mlp_pa.items()))))
-
-class ContraLoss(nn.Module):
-    def __init__(self, *args, **kwargs) -> None:
-        super(ContraLoss, self).__init__(*args, **kwargs)
-        
-        self.temp = 0.07
-
-    def contrastive_loss(self, proj1, proj2):
-        proj1 = F.normalize(proj1, dim=1)
-        proj2 = F.normalize(proj2, dim=1)
-        dot = torch.matmul(proj1, proj2.T) / self.temp
-        dot_max, _ = torch.max(dot, dim=1, keepdim=True)
-        dot = dot - dot_max.detach()
-
-        exp_dot = torch.exp(dot)
-        log_prob = torch.diag(dot, 0) - torch.log(exp_dot.sum(1))
-        cont_loss = -log_prob.mean()
-        return cont_loss
-    
-    def forward(self, x, y, label=None):
-        return self.contrastive_loss(x, y)
-
-
-import numpy as np
-from tqdm import tqdm
-import torch
-import torch.nn as nn
-import random
-from transformers import set_seed
-
-def show_parameters(model: nn.Module, show_all=False, show_trainable=True):
-
-    mlp_pa = {name:param.requires_grad for name, param in model.named_parameters()}
-    
-    if show_all:
-        print('All parameters:')
-        print(mlp_pa)
-
-    if show_trainable:
-        print('Trainable parameters:')
-        print(list(filter(lambda x: x[1], list(mlp_pa.items()))))
-
-def extract_args(text):
-    str_list = []
-    substr = ""
-    for s in text:
-        if s in ('(', ')', '=', ',', ' ', '\n', "'"):
-            if substr != '':
-                str_list.append(substr)
-                substr = ''
-        else:
-            substr += s
-
-def eval_one_epoch(loader, cono_encoder):
-    cono_encoder.eval()
-    batch_loss = []
-    for i, data in enumerate(tqdm(loader)):
-        
-        loss = cono_encoder.contra_forward(data)
-        batch_loss.append(loss.item())
-        print(f'[INFO] Test batch {i} loss: {loss.item()}')
-
-    total_loss = np.mean(batch_loss)    
-    print(f'[INFO] Total loss: {total_loss}')    
-    return total_loss
-
-def setup_seed(seed):
-    torch.manual_seed(seed)
-    torch.cuda.manual_seed_all(seed)
-    np.random.seed(seed)
-    random.seed(seed)
-    torch.backends.cudnn.deterministic = True
-    set_seed(seed)
-
-class CrossEntropyLossWithMask(torch.nn.Module):
-    def __init__(self, weight=None):
-        super(CrossEntropyLossWithMask, self).__init__()
-        self.criterion = nn.CrossEntropyLoss(reduction='none')
-
-    def forward(self, y_pred, y_true, mask):
-        (pos_mask, label_mask, seq_mask) = mask
-        loss = self.criterion(y_pred, y_true) # (6912)
-        
-        pos_loss = (loss * pos_mask).sum() / torch.sum(pos_mask)
-        label_loss = (loss * label_mask).sum() / torch.sum(label_mask)
-        seq_loss = (loss * seq_mask).sum() / torch.sum(seq_mask)
-        
-        loss = pos_loss + label_loss/2 + seq_loss/3
-
-        return loss
-
-
-def mask(x, start, end, time):
-    ske_pos = np.where(np.array(x)=='C')[0] - start
-    lables_pos = np.array([1, 2]) - start
-    ske_pos = list(filter(lambda x: end-start >= x >= 0, ske_pos))
-    lables_pos = list(filter(lambda x: x >= 0, lables_pos))
-    weight = np.ones(end - start+1)
-    rand = np.random.rand()
-    if rand < 0.5:
-        weight[lables_pos] = 100000
-    else:
-        weight[lables_pos] = 1
-    mask_pos = np.random.choice(range(start, end+1), time, p=weight/np.sum(weight), replace=False)
-    for idx in mask_pos:
-        x[idx]  = '[MASK]'
-    return x