234 lines
7.7 KiB
Python
234 lines
7.7 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import argparse
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import base64
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import shutil
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import cv2
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import numpy as np
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from paddle.inference import Config
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from paddle.inference import create_predictor
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def parse_args():
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def str2bool(v):
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return v.lower() in ("true", "t", "1")
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# general params
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parser = argparse.ArgumentParser()
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parser.add_argument("-i", "--image_file", type=str)
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parser.add_argument("--use_gpu", type=str2bool, default=True)
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parser.add_argument("--multilabel", type=str2bool, default=False)
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# params for preprocess
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parser.add_argument("--resize_short", type=int, default=256)
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parser.add_argument("--resize", type=int, default=224)
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parser.add_argument("--normalize", type=str2bool, default=True)
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# params for predict
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parser.add_argument("--model_file", type=str)
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parser.add_argument("--params_file", type=str)
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parser.add_argument("-b", "--batch_size", type=int, default=1)
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parser.add_argument("--use_fp16", type=str2bool, default=False)
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parser.add_argument("--ir_optim", type=str2bool, default=True)
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parser.add_argument("--use_tensorrt", type=str2bool, default=False)
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parser.add_argument("--gpu_mem", type=int, default=8000)
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parser.add_argument("--enable_profile", type=str2bool, default=False)
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parser.add_argument("--enable_benchmark", type=str2bool, default=False)
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parser.add_argument("--top_k", type=int, default=1)
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parser.add_argument("--enable_mkldnn", type=str2bool, default=False)
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parser.add_argument("--cpu_num_threads", type=int, default=10)
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parser.add_argument("--hubserving", type=str2bool, default=False)
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# params for infer
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parser.add_argument("--model", type=str)
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parser.add_argument("--pretrained_model", type=str)
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parser.add_argument("--class_num", type=int, default=1000)
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parser.add_argument(
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"--load_static_weights",
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type=str2bool,
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default=False,
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help='Whether to load the pretrained weights saved in static mode')
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# parameters for pre-label the images
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parser.add_argument(
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"--pre_label_image",
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type=str2bool,
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default=False,
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help="Whether to pre-label the images using the loaded weights")
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parser.add_argument("--pre_label_out_idr", type=str, default=None)
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# parameters for test hubserving
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parser.add_argument("--server_url", type=str)
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return parser.parse_args()
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def create_paddle_predictor(args):
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config = Config(args.model_file, args.params_file)
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if args.use_gpu:
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config.enable_use_gpu(args.gpu_mem, 0)
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else:
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config.disable_gpu()
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if args.enable_mkldnn:
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# cache 10 different shapes for mkldnn to avoid memory leak
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config.set_mkldnn_cache_capacity(10)
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config.enable_mkldnn()
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config.set_cpu_math_library_num_threads(args.cpu_num_threads)
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if args.enable_profile:
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config.enable_profile()
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config.disable_glog_info()
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config.switch_ir_optim(args.ir_optim) # default true
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if args.use_tensorrt:
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config.enable_tensorrt_engine(
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precision_mode=Config.Precision.Half
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if args.use_fp16 else Config.Precision.Float32,
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max_batch_size=args.batch_size)
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config.enable_memory_optim()
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# use zero copy
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config.switch_use_feed_fetch_ops(False)
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predictor = create_predictor(config)
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return predictor
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def preprocess(img, args):
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resize_op = ResizeImage(resize_short=args.resize_short)
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img = resize_op(img)
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crop_op = CropImage(size=(args.resize, args.resize))
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img = crop_op(img)
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if args.normalize:
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img_mean = [0.485, 0.456, 0.406]
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img_std = [0.229, 0.224, 0.225]
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img_scale = 1.0 / 255.0
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normalize_op = NormalizeImage(
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scale=img_scale, mean=img_mean, std=img_std)
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img = normalize_op(img)
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tensor_op = ToTensor()
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img = tensor_op(img)
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return img
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def postprocess(batch_outputs, topk=5, multilabel=False):
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batch_results = []
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for probs in batch_outputs:
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results = []
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if multilabel:
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index = np.where(probs >= 0.5)[0].astype('int32')
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else:
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index = probs.argsort(axis=0)[-topk:][::-1].astype("int32")
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clas_id_list = []
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score_list = []
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for i in index:
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clas_id_list.append(i.item())
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score_list.append(probs[i].item())
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batch_results.append({"clas_ids": clas_id_list, "scores": score_list})
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return batch_results
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def get_image_list(img_file):
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imgs_lists = []
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if img_file is None or not os.path.exists(img_file):
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raise Exception("not found any img file in {}".format(img_file))
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img_end = ['jpg', 'png', 'jpeg', 'JPEG', 'JPG', 'bmp']
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if os.path.isfile(img_file) and img_file.split('.')[-1] in img_end:
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imgs_lists.append(img_file)
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elif os.path.isdir(img_file):
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for single_file in os.listdir(img_file):
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if single_file.split('.')[-1] in img_end:
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imgs_lists.append(os.path.join(img_file, single_file))
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if len(imgs_lists) == 0:
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raise Exception("not found any img file in {}".format(img_file))
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return imgs_lists
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def save_prelabel_results(class_id, input_file_path, output_dir):
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output_dir = os.path.join(output_dir, str(class_id))
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if not os.path.isdir(output_dir):
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os.makedirs(output_dir)
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shutil.copy(input_file_path, output_dir)
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class ResizeImage(object):
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def __init__(self, resize_short=None):
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self.resize_short = resize_short
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def __call__(self, img):
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img_h, img_w = img.shape[:2]
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percent = float(self.resize_short) / min(img_w, img_h)
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w = int(round(img_w * percent))
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h = int(round(img_h * percent))
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return cv2.resize(img, (w, h))
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class CropImage(object):
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def __init__(self, size):
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if type(size) is int:
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self.size = (size, size)
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else:
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self.size = size
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def __call__(self, img):
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w, h = self.size
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img_h, img_w = img.shape[:2]
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w_start = (img_w - w) // 2
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h_start = (img_h - h) // 2
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w_end = w_start + w
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h_end = h_start + h
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return img[h_start:h_end, w_start:w_end, :]
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class NormalizeImage(object):
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def __init__(self, scale=None, mean=None, std=None):
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self.scale = np.float32(scale if scale is not None else 1.0 / 255.0)
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mean = mean if mean is not None else [0.485, 0.456, 0.406]
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std = std if std is not None else [0.229, 0.224, 0.225]
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shape = (1, 1, 3)
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self.mean = np.array(mean).reshape(shape).astype('float32')
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self.std = np.array(std).reshape(shape).astype('float32')
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def __call__(self, img):
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return (img.astype('float32') * self.scale - self.mean) / self.std
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class ToTensor(object):
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def __init__(self):
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pass
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def __call__(self, img):
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img = img.transpose((2, 0, 1))
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return img
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def b64_to_np(b64str, revert_params):
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shape = revert_params["shape"]
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dtype = revert_params["dtype"]
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dtype = getattr(np, dtype) if isinstance(str, type(dtype)) else dtype
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data = base64.b64decode(b64str.encode('utf8'))
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data = np.fromstring(data, dtype).reshape(shape)
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return data
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def np_to_b64(images):
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img_str = base64.b64encode(images).decode('utf8')
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return img_str, images.shape
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