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FastAPI app for IMDB Transformer
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#!/usr/local/bin/python3 | |
import logging, sys, os | |
logging.basicConfig(stream=sys.stdout, | |
format='%(asctime)s : %(levelname)s : %(message)s', | |
level=logging.INFO) | |
import torch | |
import torch.nn.functional as F | |
from pytorch_transformers import BertTokenizer | |
from utils import TransformerWithClfHead | |
from types import SimpleNamespace | |
from pydantic import BaseModel | |
from fastapi import FastAPI | |
logger = logging.getLogger("app.py") | |
LOG_DIR = os.getenv("LOG_PATH", "/logs") | |
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") | |
class Input(BaseModel): | |
text: str | |
def load_files(): | |
# load metadata, parameters and tokenizer | |
metadata = torch.load(LOG_DIR + "/metadata.bin") | |
state_dict = torch.load(LOG_DIR + "/model_weights.pth", | |
map_location=device) | |
tokenizer = BertTokenizer.from_pretrained('bert-base-cased', | |
do_lower_case=False) | |
# create model | |
model = TransformerWithClfHead(SimpleNamespace(**metadata["config"]), | |
SimpleNamespace(**metadata["config_ft"])) | |
model.load_state_dict(state_dict) | |
return model, tokenizer, metadata | |
def predict(model, tokenizer, int2label, device=None, input="test"): | |
"predict `input` with `model`" | |
if device is None: | |
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') | |
tok = tokenizer.tokenize(input) | |
ids = tokenizer.convert_tokens_to_ids(tok) + [tokenizer.vocab['[CLS]']] | |
tensor = torch.tensor(ids, dtype=torch.long) | |
tensor = tensor.to(device) | |
tensor = tensor.reshape(1, -1) | |
tensor_in = tensor.transpose(0, 1).contiguous() # [S, 1] | |
logits = model(tensor_in, | |
clf_tokens_mask=(tensor_in == tokenizer.vocab['[CLS]']), | |
padding_mask=(tensor == tokenizer.vocab['[PAD]'])) | |
val, _ = torch.max(logits, 0) | |
val = F.softmax(val, dim=0).detach().cpu().numpy() | |
return { | |
int2label[val.argmax()]: val.max(), | |
int2label[val.argmin()]: val.min() | |
} | |
app = FastAPI() | |
model, tokenizer, metadata = load_files() | |
@app.get("/") | |
def root(): | |
return {"message": "Not much here. Check host:port/docs!"} | |
@app.post("/inference/") | |
async def inference(input: Input): | |
output = predict(model, | |
tokenizer, | |
metadata['int2label'], | |
device=device, | |
input=input.text) | |
return {k: str(v) for k, v in output.items()} |
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