# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import sys __dir__ = os.path.dirname(__file__) sys.path.append(os.path.join(__dir__, '')) import cv2 import numpy as np import tarfile import requests from tqdm import tqdm import tools.infer.utils as utils import shutil __all__ = ['PaddleClas'] BASE_DIR = os.path.expanduser("~/.paddleclas/") BASE_INFERENCE_MODEL_DIR = os.path.join(BASE_DIR, 'inference_model') BASE_IMAGES_DIR = os.path.join(BASE_DIR, 'images') model_names = { 'Xception71', 'SE_ResNeXt101_32x4d', 'ShuffleNetV2_x0_5', 'ResNet34', 'ShuffleNetV2_x2_0', 'ResNeXt101_32x4d', 'HRNet_W48_C_ssld', 'ResNeSt50_fast_1s1x64d', 'MobileNetV2_x2_0', 'MobileNetV3_large_x1_0', 'Fix_ResNeXt101_32x48d_wsl', 'MobileNetV2_ssld', 'ResNeXt101_vd_64x4d', 'ResNet34_vd_ssld', 'MobileNetV3_small_x1_0', 'VGG11', 'ResNeXt50_vd_32x4d', 'MobileNetV3_large_x1_25', 'MobileNetV3_large_x1_0_ssld', 'MobileNetV2_x0_75', 'MobileNetV3_small_x0_35', 'MobileNetV1_x0_75', 'MobileNetV1_ssld', 'ResNeXt50_32x4d', 'GhostNet_x1_3_ssld', 'Res2Net101_vd_26w_4s', 'ResNet152', 'Xception65', 'EfficientNetB0', 'ResNet152_vd', 'HRNet_W18_C', 'Res2Net50_14w_8s', 'ShuffleNetV2_x0_25', 'HRNet_W64_C', 'Res2Net50_vd_26w_4s_ssld', 'HRNet_W18_C_ssld', 'ResNet18_vd', 'ResNeXt101_32x16d_wsl', 'SE_ResNeXt50_32x4d', 'SqueezeNet1_1', 'SENet154_vd', 'SqueezeNet1_0', 'GhostNet_x1_0', 'ResNet50_vc', 'DPN98', 'HRNet_W48_C', 'DenseNet264', 'SE_ResNet34_vd', 'HRNet_W44_C', 'MobileNetV3_small_x1_25', 'MobileNetV1_x0_5', 'ResNet200_vd', 'VGG13', 'EfficientNetB3', 'EfficientNetB2', 'ShuffleNetV2_x0_33', 'MobileNetV3_small_x0_75', 'ResNeXt152_vd_32x4d', 'ResNeXt101_32x32d_wsl', 'ResNet18', 'MobileNetV3_large_x0_35', 'Res2Net50_26w_4s', 'MobileNetV2_x0_5', 'EfficientNetB0_small', 'ResNet101_vd_ssld', 'EfficientNetB6', 'EfficientNetB1', 'EfficientNetB7', 'ResNeSt50', 'ShuffleNetV2_x1_0', 'MobileNetV3_small_x1_0_ssld', 'InceptionV4', 'GhostNet_x0_5', 'SE_HRNet_W64_C_ssld', 'ResNet50_ACNet_deploy', 'Xception41', 'ResNet50', 'Res2Net200_vd_26w_4s_ssld', 'Xception41_deeplab', 'SE_ResNet18_vd', 'SE_ResNeXt50_vd_32x4d', 'HRNet_W30_C', 'HRNet_W40_C', 'VGG19', 'Res2Net200_vd_26w_4s', 'ResNeXt101_32x8d_wsl', 'ResNet50_vd', 'ResNeXt152_64x4d', 'DarkNet53', 'ResNet50_vd_ssld', 'ResNeXt101_64x4d', 'MobileNetV1_x0_25', 'Xception65_deeplab', 'AlexNet', 'ResNet101', 'DenseNet121', 'ResNet50_vd_v2', 'Res2Net50_vd_26w_4s', 'ResNeXt101_32x48d_wsl', 'MobileNetV3_large_x0_5', 'MobileNetV2_x0_25', 'DPN92', 'ResNet101_vd', 'MobileNetV2_x1_5', 'DPN131', 'ResNeXt50_vd_64x4d', 'ShuffleNetV2_x1_5', 'ResNet34_vd', 'MobileNetV1', 'ResNeXt152_vd_64x4d', 'DPN107', 'VGG16', 'ResNeXt50_64x4d', 'RegNetX_4GF', 'DenseNet161', 'GhostNet_x1_3', 'HRNet_W32_C', 'Fix_ResNet50_vd_ssld_v2', 'Res2Net101_vd_26w_4s_ssld', 'DenseNet201', 'DPN68', 'EfficientNetB4', 'ResNeXt152_32x4d', 'InceptionV3', 'ShuffleNetV2_swish', 'GoogLeNet', 'ResNet50_vd_ssld_v2', 'SE_ResNet50_vd', 'MobileNetV2', 'ResNeXt101_vd_32x4d', 'MobileNetV3_large_x0_75', 'MobileNetV3_small_x0_5', 'DenseNet169', 'EfficientNetB5' } def download_with_progressbar(url, save_path): response = requests.get(url, stream=True) total_size_in_bytes = int(response.headers.get('content-length', 0)) block_size = 1024 # 1 Kibibyte progress_bar = tqdm(total=total_size_in_bytes, unit='iB', unit_scale=True) with open(save_path, 'wb') as file: for data in response.iter_content(block_size): progress_bar.update(len(data)) file.write(data) progress_bar.close() if total_size_in_bytes == 0 or progress_bar.n != total_size_in_bytes: raise Exception("Something went wrong while downloading models") def maybe_download(model_storage_directory, url): # using custom model tar_file_name_list = [ 'inference.pdiparams', 'inference.pdiparams.info', 'inference.pdmodel' ] if not os.path.exists( os.path.join(model_storage_directory, 'inference.pdiparams') ) or not os.path.exists( os.path.join(model_storage_directory, 'inference.pdmodel')): tmp_path = os.path.join(model_storage_directory, url.split('/')[-1]) print('download {} to {}'.format(url, tmp_path)) os.makedirs(model_storage_directory, exist_ok=True) download_with_progressbar(url, tmp_path) with tarfile.open(tmp_path, 'r') as tarObj: for member in tarObj.getmembers(): filename = None for tar_file_name in tar_file_name_list: if tar_file_name in member.name: filename = tar_file_name if filename is None: continue file = tarObj.extractfile(member) with open( os.path.join(model_storage_directory, filename), 'wb') as f: f.write(file.read()) os.remove(tmp_path) def save_prelabel_results(class_id, input_filepath, output_idr): output_dir = os.path.join(output_idr, str(class_id)) if not os.path.isdir(output_dir): os.makedirs(output_dir) shutil.copy(input_filepath, output_dir) def load_label_name_dict(path): result = {} if not os.path.exists(path): print( 'Warning: If want to use your own label_dict, please input legal path!\nOtherwise label_names will be empty!' ) else: for line in open(path, 'r'): partition = line.split('\n')[0].partition(' ') try: result[int(partition[0])] = str(partition[-1]) except: result = {} break return result def parse_args(mMain=True, add_help=True): import argparse def str2bool(v): return v.lower() in ("true", "t", "1") if mMain == True: # general params parser = argparse.ArgumentParser(add_help=add_help) parser.add_argument("--model_name", type=str) parser.add_argument("-i", "--image_file", type=str) parser.add_argument("--use_gpu", type=str2bool, default=False) # params for preprocess parser.add_argument("--resize_short", type=int, default=256) parser.add_argument("--resize", type=int, default=224) parser.add_argument("--normalize", type=str2bool, default=True) parser.add_argument("-b", "--batch_size", type=int, default=1) # params for predict parser.add_argument( "--model_file", type=str, default='') ## inference.pdmodel parser.add_argument( "--params_file", type=str, default='') ## inference.pdiparams parser.add_argument("--ir_optim", type=str2bool, default=True) parser.add_argument("--use_fp16", type=str2bool, default=False) parser.add_argument("--use_tensorrt", type=str2bool, default=False) parser.add_argument("--gpu_mem", type=int, default=8000) parser.add_argument("--enable_profile", type=str2bool, default=False) parser.add_argument("--top_k", type=int, default=1) parser.add_argument("--enable_mkldnn", type=str2bool, default=False) parser.add_argument("--enable_benchmark", type=str2bool, default=False) parser.add_argument("--cpu_num_threads", type=int, default=10) parser.add_argument("--hubserving", type=str2bool, default=False) # parameters for pre-label the images parser.add_argument("--label_name_path", type=str, default='') parser.add_argument( "--pre_label_image", type=str2bool, default=False, help="Whether to pre-label the images using the loaded weights") parser.add_argument("--pre_label_out_idr", type=str, default=None) return parser.parse_args() else: return argparse.Namespace( model_name='', image_file='', use_gpu=False, use_fp16=False, use_tensorrt=False, resize_short=256, resize=224, normalize=True, batch_size=1, model_file='', params_file='', ir_optim=True, gpu_mem=8000, enable_profile=False, top_k=1, enable_mkldnn=False, enable_benchmark=False, cpu_num_threads=10, hubserving=False, label_name_path='', pre_label_image=False, pre_label_out_idr=None) class PaddleClas(object): print('Inference models that Paddle provides are listed as follows:\n\n{}'. format(model_names), '\n') def __init__(self, **kwargs): process_params = parse_args(mMain=False, add_help=False) process_params.__dict__.update(**kwargs) if not os.path.exists(process_params.model_file): if process_params.model_name is None: raise Exception( 'Please input model name that you want to use!') if process_params.model_name in model_names: url = 'https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/{}_infer.tar'.format( process_params.model_name) if not os.path.exists( os.path.join(BASE_INFERENCE_MODEL_DIR, process_params.model_name)): os.makedirs( os.path.join(BASE_INFERENCE_MODEL_DIR, process_params.model_name)) download_path = os.path.join(BASE_INFERENCE_MODEL_DIR, process_params.model_name) maybe_download(model_storage_directory=download_path, url=url) process_params.model_file = os.path.join(download_path, 'inference.pdmodel') process_params.params_file = os.path.join( download_path, 'inference.pdiparams') process_params.label_name_path = os.path.join( __dir__, 'ppcls/utils/imagenet1k_label_list.txt') else: raise Exception( 'If you want to use your own model, Please input model_file as model path!' ) else: print('Using user-specified model and params!') print("process params are as follows: \n{}".format(process_params)) self.label_name_dict = load_label_name_dict( process_params.label_name_path) self.args = process_params self.predictor = utils.create_paddle_predictor(process_params) def predict(self, img): """ predict label of img with paddleclas Args: img: input image for clas, support single image , internet url, folder path containing series of images Returns: dict：{image_name: "", class_id: [], scores: [], label_names: []}，if label name path == None，label_names will be empty. """ assert isinstance(img, (str, np.ndarray)) input_names = self.predictor.get_input_names() input_tensor = self.predictor.get_input_handle(input_names[0]) output_names = self.predictor.get_output_names() output_tensor = self.predictor.get_output_handle(output_names[0]) if isinstance(img, str): # download internet image if img.startswith('http'): if not os.path.exists(BASE_IMAGES_DIR): os.makedirs(BASE_IMAGES_DIR) image_path = os.path.join(BASE_IMAGES_DIR, 'tmp.jpg') download_with_progressbar(img, image_path) print("Current using image from Internet:{}, renamed as: {}". format(img, image_path)) img = image_path image_list = utils.get_image_list(img) else: if isinstance(img, np.ndarray): image_list = [img] else: print('Please input legal image!') total_result = [] for filename in image_list: if isinstance(filename, str): image = cv2.imread(filename)[:, :, ::-1] assert image is not None, "Error in loading image: {}".format( filename) inputs = utils.preprocess(image, self.args) inputs = np.expand_dims( inputs, axis=0).repeat( 1, axis=0).copy() else: inputs = filename input_tensor.copy_from_cpu(inputs) self.predictor.run() outputs = output_tensor.copy_to_cpu() classes, scores = utils.postprocess(outputs, self.args) label_names = [] if len(self.label_name_dict) != 0: label_names = [self.label_name_dict[c] for c in classes] result = { "filename": filename if isinstance(filename, str) else 'image', "class_ids": classes.tolist(), "scores": scores.tolist(), "label_names": label_names, } total_result.append(result) if self.args.pre_label_image: save_prelabel_results(classes[0], filename, self.args.pre_label_out_idr) print("\tSaving prelabel results in {}".format( os.path.join(self.args.pre_label_out_idr, str(classes[ 0])))) return total_result def main(): # for cmd args = parse_args(mMain=True) clas_engine = PaddleClas(**(args.__dict__)) print('{}{}{}'.format('*' * 10, args.image_file, '*' * 10)) result = clas_engine.predict(args.image_file) if result is not None: print(result) if __name__ == '__main__': main()