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import numpy as np
import gradio as gr
from transformers import AutoTokenizer,AutoModelForSequenceClassification
from transformers import set_seed
from torch.utils.data import Dataset,DataLoader
import torch
import torch.nn as nn
import numpy as np
import warnings
warnings.filterwarnings('ignore')
set_seed(4)
device = "cpu"
model_checkpoint = "facebook/esm2_t6_8M_UR50D"
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
def AMP(file):
test_sequences = file
class MyDataset(Dataset):
def __init__(self,dict_data) -> None:
super(MyDataset,self).__init__()
self.data=dict_data
def __getitem__(self, index):
return self.data['text'][index]
def __len__(self):
return len(self.data['text'])
def collate_fn(batch):
max_len = 30
pt_batch=tokenizer([b[0] for b in batch], max_length=max_len, padding="max_length",truncation=True, return_tensors='pt')
return {'input_ids':pt_batch['input_ids'],
'attention_mask':pt_batch['attention_mask']}
test_dict = {"text":test_sequences}
test_data=MyDataset(test_dict)
test_dataloader=DataLoader(test_data,batch_size=len(test_sequences), collate_fn=collate_fn)
class MyModel(nn.Module):
def __init__(self):
super().__init__()
self.bert = AutoModelForSequenceClassification.from_pretrained(model_checkpoint,num_labels=320)
self.bn1 = nn.BatchNorm1d(256)
self.bn2 = nn.BatchNorm1d(128)
self.bn3 = nn.BatchNorm1d(64)
self.relu = nn.ReLU()
self.fc1 = nn.Linear(320,256)
self.fc2 = nn.Linear(256,128)
self.fc3 = nn.Linear(128,64)
self.output_layer = nn.Linear(64,2)
self.dropout = nn.Dropout(0)
def forward(self,x):
with torch.no_grad():
bert_output = self.bert(input_ids=x['input_ids'].to(device),attention_mask=x['attention_mask'].to(device))
output_feature = self.dropout(bert_output["logits"])
output_feature = self.relu(self.bn1(self.fc1(output_feature)))
output_feature = self.relu(self.bn2(self.fc2(output_feature)))
output_feature = self.relu(self.bn3(self.fc3(output_feature)))
output_feature = self.output_layer(output_feature)
return torch.softmax(output_feature,dim=1)
model = MyModel()
model.load_state_dict(torch.load("best_model.pth"))
model = model.to(device)
model.eval()
out = []
out_probability = []
with torch.no_grad():
for index, batch in enumerate(test_dataloader):
batchs = {k: v for k, v in batch.items()}
predict = model(batchs)
# tsne_plot(output_feature,batchs)
out_probability.extend(np.max(np.array(predict.cpu()),axis=1).tolist())
# out_probability.extend(np.array(predict.cpu())[:, -1].tolist())
test_argmax = np.argmax(predict.cpu(), axis=1).tolist()
out.extend(test_argmax)
return out, out_probability
iface = gr.Interface(fn=AMP,
inputs="text"(label="input sequence"),
outputs= ["text"(label="class"), "text"(label="probability")])
iface.launch() |