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# -*- coding: utf-8 -*-
import torch
from torch import nn
import torch.nn.functional as F
from transformers import AutoTokenizer, AutoModelWithLMHead
from functools import lru_cache
from tokenizers import ByteLevelBPETokenizer
from tokenizers.processors import BertProcessing
def setup_tokenizer():
tokenizer = AutoTokenizer.from_pretrained('distilroberta-base')
tokenizer.save_pretrained("tokenizer")
import os
os.system("mkdir -p tokenizer")
setup_tokenizer()
# from https://github.com/digantamisra98/Mish/blob/b5f006660ac0b4c46e2c6958ad0301d7f9c59651/Mish/Torch/mish.py
@torch.jit.script
def mish(input):
return input * torch.tanh(F.softplus(input))
class Mish(nn.Module):
def forward(self, input):
return mish(input)
class EmoModel(nn.Module):
def __init__(self, base_model, n_classes=2, base_model_output_size=768, dropout=0.05):
super().__init__()
self.base_model = base_model
self.classifier = nn.Sequential(
nn.Dropout(dropout),
nn.Linear(base_model_output_size, base_model_output_size),
Mish(),
nn.Dropout(dropout),
# originally, n_classes = 6
# now, we want to use VA, change it to 2
nn.Linear(base_model_output_size, n_classes)
)
for layer in self.classifier:
if isinstance(layer, nn.Linear):
layer.weight.data.normal_(mean=0.0, std=0.02)
if layer.bias is not None:
layer.bias.data.zero_()
def forward(self, input_, *args):
X, attention_mask = input_
hidden_states = self.base_model(X, attention_mask=attention_mask)
return self.classifier(hidden_states[0][:, 0, :])
from pathlib import Path
pretrained_path = "on_plurk_new_fix_data_arch_1_epoch_2_bs_16.pt"
assert Path(pretrained_path).is_file()
model = EmoModel(AutoModelWithLMHead.from_pretrained("distilroberta-base").base_model)
model.load_state_dict(torch.load(pretrained_path,map_location=torch.device('cpu')))
model.eval()
from functools import lru_cache
@lru_cache(maxsize=1)
def get_tokenizer(max_tokens=512):
from tokenizers import ByteLevelBPETokenizer
from tokenizers.processors import BertProcessing
# add error checking
voc_file = "tokenizer/vocab.json"
merg_file = "tokenizer/merges.txt"
import os.path
if not os.path.isfile(voc_file) or not os.path.isfile(merg_file):
setup_tokenizer()
t = ByteLevelBPETokenizer(
voc_file,
merg_file
)
t._tokenizer.post_processor = BertProcessing(
("</s>", t.token_to_id("</s>")),
("<s>", t.token_to_id("<s>")),
)
t.enable_truncation(max_tokens)
t.enable_padding(length=max_tokens, pad_id=t.token_to_id("<pad>"))
return t
# Cell
def convert_text_to_tensor(text, tokenizer=None):
if tokenizer is None:
tokenizer = get_tokenizer()
enc = tokenizer.encode(text)
X = torch.tensor(enc.ids).unsqueeze(0)
Attn = torch.tensor(enc.attention_mask).unsqueeze(0)
return (X, Attn)
def get_output(text, model, tokenizer=None, return_tensor=False):
# we should add try/Except error handling for "model" argument
# , but i consider it to be ugly
import torch
with torch.no_grad():
model.eval()
out = model(convert_text_to_tensor(text, tokenizer))
if return_tensor == True:
return out
else: # return [float, float]
# remember to make it a 1-D tensor
tt = out[0]
return float(tt[0]), float(tt[1])
import gradio as gr
def fn2(text, model=model, return_tensor=False):
out = get_output(text,model, return_tensor=return_tensor)
return out
interface = gr.Interface(
fn = fn2,
inputs="text",
outputs=["number", "number"]
)
interface.launch()
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