LangDaoAI
commented
3 years ago 以下是我这边客户端：
coding='utf-8'
import json import traceback from functools import partial
import paddle from flask import Flask, request, Response import numpy as np import paddlenlp as ppnlp from paddlenlp.data import Tuple, Pad from paddle import inference import paddle.nn.functional as F from gevent.pywsgi import WSGIServer
from data import processor_dict from task_label_description import TASK_LABELS_DESC, honorcrisis_labels
app = Flask(name) app.config['JSON_AS_ASCII'] = False
task_name = "honorcrisis"
def convert_example(example, tokenizer, max_seq_length=512, is_test=False): sentence1 = example["sentence1"] sentence2 = example["sentence2"] encoded_inputs = tokenizer( text=sentence1, text_pair=sentence2, max_seq_len=max_seq_length, truncation_strategy="only_first")
src_ids = encoded_inputs["input_ids"]
token_type_ids = encoded_inputs["token_type_ids"]
return src_ids, token_type_idsdef create_dataloader(dataset, batch_size=1, batchify_fn=None, trans_fn=None): if trans_fn: dataset = dataset.map(trans_fn)
batch_sampler = paddle.io.BatchSampler(
dataset, batch_size=batch_size, shuffle=False)
return paddle.io.DataLoader(
dataset=dataset,
batch_sampler=batch_sampler,
collate_fn=batchify_fn,
return_list=True)@app.route("/", methods=['GET', 'POST']) @app.route("/cn/crisis", methods=['GET', 'POST']) def crisis_cls_client(): try: text = [] if request.method == "GET": text.append(request.args.get("text")) if request.method == "POST": if request.content_type.startswith('application/json'): text.append(request.json.get('text')) elif request.content_type.startswith('multipart/form-data'): text.append(request.form.get('content')) else: text.append(request.values.get("content"))
test_ds = []
for sentence in text:
new_sentence = dict()
new_sentence['sentence'] = sentence
test_ds.append(new_sentence)
processor = processor_dict[task_name]()
test_ds = processor.get_test_datasets(test_ds, TASK_LABELS_DESC[task_name])
# [src_ids, token_type_ids]
predict_batchify_fn = lambda samples, fn=Tuple(
Pad(axis=0, pad_val=tokenizer.pad_token_id), # src_ids
Pad(axis=0, pad_val=tokenizer.pad_token_type_id), # token_type_ids
): [data for data in fn(samples)]
predict_trans_func = partial(
convert_example,
tokenizer=tokenizer,
max_seq_length=256,
is_test=True)
test_data_loader = create_dataloader(
test_ds,
batch_size=1,
batchify_fn=predict_batchify_fn,
trans_fn=predict_trans_func)
y_pred_labels, prediction_probs = predictor.predict(test_data_loader, task_label_description=TASK_LABELS_DESC[task_name])
return Response(
json.dumps({'status': '200', 'result': {'类别': y_pred_labels[0], '类别描述': honorcrisis_labels[
y_pred_labels[0]], '概率': prediction_probs[0][
int(y_pred_labels[
0]) - 1]}},
ensure_ascii=False), mimetype='application/json')
except Exception as e:
tb = traceback.format_exc() #
print('handle_request_failed\t%s\t%s' % (tb, str(e)))
return Response(json.dumps({'status': '500', 'result': str(e)}), mimetype='application/json')class Predictor(object): def init(self, model_file, params_file, device, max_seq_length): self.max_seq_length = max_seq_length
config = inference.Config(model_file, params_file)
if device == "gpu":
# set GPU configs accordingly
config.enable_use_gpu(100, 0)
elif device == "cpu":
# set CPU configs accordingly,
# such as enable_mkldnn, set_cpu_math_library_num_threads
config.disable_gpu()
elif device == "xpu":
# set XPU configs accordingly
config.enable_xpu(100)
config.switch_use_feed_fetch_ops(False)
self.predictor = inference.create_predictor(config)
self.input_handles = [
self.predictor.get_input_handle(name)
for name in self.predictor.get_input_names()
]
self.output_handle = self.predictor.get_output_handle(
self.predictor.get_output_names()[0])
@paddle.no_grad()
def predict(self, data_loader, task_label_description):
index2label = {
idx: label
for idx, label in enumerate(task_label_description.keys())
}
class_num = len(task_label_description)
all_prediction_probs = []
for batch in data_loader:
src_ids, token_type_ids = batch
self.input_handles[0].copy_from_cpu(src_ids)
self.input_handles[1].copy_from_cpu(token_type_ids)
self.predictor.run()
prediction_scores = self.output_handle.copy_to_cpu()
prediction_probs = prediction_scores.numpy()
all_prediction_probs.append(prediction_probs)
all_prediction_probs = np.concatenate(all_prediction_probs, axis=0)
all_prediction_probs = np.reshape(all_prediction_probs, (-1, class_num, 2))
prediction_pos_probs = all_prediction_probs[:, :, 1]
prediction_pos_probs = np.reshape(prediction_pos_probs, (-1, class_num))
prediction_pos_probs1 = paddle.to_tensor(prediction_pos_probs)
probs = F.softmax(prediction_pos_probs1)
y_pred_index = np.argmax(prediction_pos_probs, axis=-1)
y_preds = [index2label[idx] for idx in y_pred_index]
return y_preds, probs.numpy()if name == "main": predictor = Predictor("./static_graph_test/static_graph_params.pdmodel", "./static_graph_test/static_graph_params.pdiparams", 'gpu', 256) tokenizer = ppnlp.transformers.ErnieGramTokenizer.from_pretrained('ernie-gram-zh') http_server = WSGIServer(('0.0.0.0', 8021), app) http_server.serve_forever()
tianxin1860
few shot EFL模型推理部署后， 出现DataType of equal Op's duplicable Variable Y must be consistent的异常：
我使用的部署推理脚本如下：
请求Team验证， 比较急， 感谢！