153 lines
6.2 KiB
Python
153 lines
6.2 KiB
Python
#!/usr/bin/env python
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# -*- coding: UTF-8 -*-
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"""
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@Project ：trinity_client
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@File ：service_att_recognition.py
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@Author ：周成融
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@Date ：2023/7/26 12:01:05
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@detail ：
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"""
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import json
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import logging
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import time
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from io import BytesIO
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import cv2
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import minio
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import redis
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import tritonclient.grpc as grpcclient
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import numpy as np
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from minio import Minio
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from tritonclient.utils import np_to_triton_dtype
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from app.core.config import *
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from app.schemas.generate_image import GenerateImageModel
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from app.service.generate_image.utils.upload_sd_image import upload_png_sd
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logger = logging.getLogger()
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class GenerateImage:
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def __init__(self, request_data):
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self.minio_client = Minio(MINIO_URL, access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=MINIO_SECURE)
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self.grpc_client = grpcclient.InferenceServerClient(url=GI_MODEL_URL)
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self.redis_client = redis.StrictRedis(host=REDIS_HOST, port=REDIS_PORT, db=REDIS_DB, decode_responses=True)
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self.connection = pika.BlockingConnection(pika.ConnectionParameters(**RABBITMQ_PARAMS))
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self.channel = self.connection.channel()
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if request_data.mode == "img2img":
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self.image = self.get_image(request_data.image_url)
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self.prompt = request_data.prompt
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else:
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self.image = np.random.randint(0, 256, (1024, 1024, 3), dtype=np.uint8)
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self.prompt = request_data.prompt
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self.tasks_id = request_data.tasks_id
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self.user_id = self.tasks_id[self.tasks_id.rfind('-') + 1:]
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self.mode = request_data.mode
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self.batch_size = 1
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self.category = request_data.category
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self.index = 0
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self.generate_data = {'tasks_id': self.tasks_id, 'status': 'PENDING', 'message': "pending", 'data': ''}
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self.redis_client.set(self.tasks_id, json.dumps(self.generate_data))
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self.redis_client.expire(self.tasks_id, 600)
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def get_image(self, image_url):
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# Get data of an object.
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# Read data from response.
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try:
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response = self.minio_client.get_object(image_url.split('/')[0], image_url[image_url.find('/') + 1:])
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image_file = BytesIO(response.data)
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image_array = np.asarray(bytearray(image_file.read()), dtype=np.uint8)
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image_cv2 = cv2.imdecode(image_array, cv2.IMREAD_COLOR)
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except minio.error.S3Error:
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image_cv2 = np.random.randint(0, 256, (1024, 1024, 3), dtype=np.uint8)
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return image_cv2
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def callback(self, result, error):
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if error:
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self.generate_data['status'] = "FAILURE"
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self.generate_data['message'] = str(error)
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self.generate_data['data'] = str(error)
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self.redis_client.set(self.tasks_id, json.dumps(self.generate_data))
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else:
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image_result = result.as_numpy("generated_image")[0]
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image_url = upload_png_sd(image_result, user_id=self.user_id, category=f"{self.category}", object_name=f"{self.tasks_id}.png")
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self.generate_data['status'] = "SUCCESS"
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self.generate_data['message'] = "success"
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self.generate_data['data'] = str(image_url)
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self.redis_client.set(self.tasks_id, json.dumps(self.generate_data))
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def read_tasks_status(self):
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status_data = self.redis_client.get(self.tasks_id)
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return json.loads(status_data), status_data
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def infer(self, inputs):
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return self.grpc_client.async_infer(
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model_name=GI_MODEL_NAME,
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inputs=inputs,
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callback=self.callback
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)
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def get_result(self):
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try:
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prompts = [self.prompt] * self.batch_size
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modes = [self.mode] * self.batch_size
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images = [self.image.astype(np.float16)] * self.batch_size
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text_obj = np.array(prompts, dtype="object").reshape((-1, 1))
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mode_obj = np.array(modes, dtype="object").reshape((-1, 1))
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image_obj = np.array(images, dtype=np.float16).reshape((-1, 1024, 1024, 3))
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input_text = grpcclient.InferInput("prompt", text_obj.shape, np_to_triton_dtype(text_obj.dtype))
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input_image = grpcclient.InferInput("input_image", image_obj.shape, "FP16")
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input_mode = grpcclient.InferInput("mode", mode_obj.shape, np_to_triton_dtype(text_obj.dtype))
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input_text.set_data_from_numpy(text_obj)
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input_image.set_data_from_numpy(image_obj)
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input_mode.set_data_from_numpy(mode_obj)
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inputs = [input_text, input_image, input_mode]
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ctx = self.infer(inputs)
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time_out = 60
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generate_data = None
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while time_out > 0:
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generate_data, _ = self.read_tasks_status()
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if generate_data['status'] in ["REVOKED", "FAILURE"]:
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ctx.cancel()
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break
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elif generate_data['status'] == "SUCCESS":
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break
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time_out -= 1
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time.sleep(0.1)
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return generate_data
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except Exception as e:
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self.generate_data['status'] = "FAILURE"
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self.generate_data['message'] = "failure"
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self.generate_data['data'] = str(e)
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self.redis_client.set(self.tasks_id, json.dumps(self.generate_data))
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raise Exception(str(e))
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finally:
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dict_generate_data, str_generate_data = self.read_tasks_status()
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self.channel.basic_publish(exchange='', routing_key=GI_RABBITMQ_QUEUES, body=str_generate_data)
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logger.info(f" [x] Sent {json.dumps(dict_generate_data, indent=4)}")
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def infer_cancel(tasks_id):
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redis_client = redis.StrictRedis(host=REDIS_HOST, port=REDIS_PORT, db=REDIS_DB, decode_responses=True)
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data = {'tasks_id': tasks_id, 'status': 'REVOKED', 'message': "revoked", 'data': 'revoked'}
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generate_data = json.dumps(data)
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redis_client.set(tasks_id, generate_data)
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return data
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if __name__ == '__main__':
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rd = GenerateImageModel(
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tasks_id="123-89",
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prompt='skeleton sitting by the side of a river looking soulful, concert poster, 4k, artistic',
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image_url="",
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mode='txt2img',
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category="test"
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)
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server = GenerateImage(rd)
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print(server.get_result())
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