# Copyright (c) 2022 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 from typing import Union, Generator import argparse import shutil import textwrap import tarfile import requests from functools import partial from difflib import SequenceMatcher import cv2 import numpy as np from tqdm import tqdm from prettytable import PrettyTable import paddle from .ppcls.arch import backbone from .ppcls.utils import logger from .deploy.python.predict_cls import ClsPredictor from .deploy.python.predict_system import SystemPredictor from .deploy.python.build_gallery import GalleryBuilder from .deploy.utils.get_image_list import get_image_list from .deploy.utils import config # for the PaddleClas Project from . import deploy from . import ppcls # for building model with loading pretrained weights from backbone logger.init_logger() __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") IMN_MODEL_BASE_DOWNLOAD_URL = "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/{}_infer.tar" IMN_MODEL_SERIES = { "AlexNet": ["AlexNet"], "ConvNeXt": ["ConvNeXt_tiny"], "CSPNet": ["CSPDarkNet53"], "CSWinTransformer": [ "CSWinTransformer_tiny_224", "CSWinTransformer_small_224", "CSWinTransformer_base_224", "CSWinTransformer_base_384", "CSWinTransformer_large_224", "CSWinTransformer_large_384" ], "DarkNet": ["DarkNet53"], "DeiT": [ "DeiT_base_distilled_patch16_224", "DeiT_base_distilled_patch16_384", "DeiT_base_patch16_224", "DeiT_base_patch16_384", "DeiT_small_distilled_patch16_224", "DeiT_small_patch16_224", "DeiT_tiny_distilled_patch16_224", "DeiT_tiny_patch16_224" ], "DenseNet": [ "DenseNet121", "DenseNet161", "DenseNet169", "DenseNet201", "DenseNet264" ], "DLA": [ "DLA46_c", "DLA60x_c", "DLA34", "DLA60", "DLA60x", "DLA102", "DLA102x", "DLA102x2", "DLA169" ], "DPN": ["DPN68", "DPN92", "DPN98", "DPN107", "DPN131"], "EfficientNet": [ "EfficientNetB0", "EfficientNetB0_small", "EfficientNetB1", "EfficientNetB2", "EfficientNetB3", "EfficientNetB4", "EfficientNetB5", "EfficientNetB6", "EfficientNetB7" ], "ESNet": ["ESNet_x0_25", "ESNet_x0_5", "ESNet_x0_75", "ESNet_x1_0"], "GhostNet": ["GhostNet_x0_5", "GhostNet_x1_0", "GhostNet_x1_3", "GhostNet_x1_3_ssld"], "HarDNet": ["HarDNet39_ds", "HarDNet68_ds", "HarDNet68", "HarDNet85"], "HRNet": [ "HRNet_W18_C", "HRNet_W30_C", "HRNet_W32_C", "HRNet_W40_C", "HRNet_W44_C", "HRNet_W48_C", "HRNet_W64_C", "HRNet_W18_C_ssld", "HRNet_W48_C_ssld" ], "Inception": ["GoogLeNet", "InceptionV3", "InceptionV4"], "LeViT": ["LeViT_128S", "LeViT_128", "LeViT_192", "LeViT_256", "LeViT_384"], "MixNet": ["MixNet_S", "MixNet_M", "MixNet_L"], "MobileNetV1": [ "MobileNetV1_x0_25", "MobileNetV1_x0_5", "MobileNetV1_x0_75", "MobileNetV1", "MobileNetV1_ssld" ], "MobileNetV2": [ "MobileNetV2_x0_25", "MobileNetV2_x0_5", "MobileNetV2_x0_75", "MobileNetV2", "MobileNetV2_x1_5", "MobileNetV2_x2_0", "MobileNetV2_ssld" ], "MobileNetV3": [ "MobileNetV3_small_x0_35", "MobileNetV3_small_x0_5", "MobileNetV3_small_x0_75", "MobileNetV3_small_x1_0", "MobileNetV3_small_x1_25", "MobileNetV3_large_x0_35", "MobileNetV3_large_x0_5", "MobileNetV3_large_x0_75", "MobileNetV3_large_x1_0", "MobileNetV3_large_x1_25", "MobileNetV3_small_x1_0_ssld", "MobileNetV3_large_x1_0_ssld" ], "MobileViT": ["MobileViT_XXS", "MobileViT_XS", "MobileViT_S"], "PeleeNet": ["PeleeNet"], "PPHGNet": [ "PPHGNet_tiny", "PPHGNet_small", "PPHGNet_tiny_ssld", "PPHGNet_small_ssld", ], "PPLCNet": [ "PPLCNet_x0_25", "PPLCNet_x0_35", "PPLCNet_x0_5", "PPLCNet_x0_75", "PPLCNet_x1_0", "PPLCNet_x1_5", "PPLCNet_x2_0", "PPLCNet_x2_5" ], "PPLCNetV2": ["PPLCNetV2_base"], "PVTV2": [ "PVT_V2_B0", "PVT_V2_B1", "PVT_V2_B2", "PVT_V2_B2_Linear", "PVT_V2_B3", "PVT_V2_B4", "PVT_V2_B5" ], "RedNet": ["RedNet26", "RedNet38", "RedNet50", "RedNet101", "RedNet152"], "RegNet": ["RegNetX_4GF"], "Res2Net": [ "Res2Net50_14w_8s", "Res2Net50_26w_4s", "Res2Net50_vd_26w_4s", "Res2Net200_vd_26w_4s", "Res2Net101_vd_26w_4s", "Res2Net50_vd_26w_4s_ssld", "Res2Net101_vd_26w_4s_ssld", "Res2Net200_vd_26w_4s_ssld" ], "ResNeSt": ["ResNeSt50", "ResNeSt50_fast_1s1x64d"], "ResNet": [ "ResNet18", "ResNet18_vd", "ResNet34", "ResNet34_vd", "ResNet50", "ResNet50_vc", "ResNet50_vd", "ResNet50_vd_v2", "ResNet101", "ResNet101_vd", "ResNet152", "ResNet152_vd", "ResNet200_vd", "ResNet34_vd_ssld", "ResNet50_vd_ssld", "ResNet50_vd_ssld_v2", "ResNet101_vd_ssld", "Fix_ResNet50_vd_ssld_v2", "ResNet50_ACNet_deploy" ], "ResNeXt": [ "ResNeXt50_32x4d", "ResNeXt50_vd_32x4d", "ResNeXt50_64x4d", "ResNeXt50_vd_64x4d", "ResNeXt101_32x4d", "ResNeXt101_vd_32x4d", "ResNeXt101_32x8d_wsl", "ResNeXt101_32x16d_wsl", "ResNeXt101_32x32d_wsl", "ResNeXt101_32x48d_wsl", "Fix_ResNeXt101_32x48d_wsl", "ResNeXt101_64x4d", "ResNeXt101_vd_64x4d", "ResNeXt152_32x4d", "ResNeXt152_vd_32x4d", "ResNeXt152_64x4d", "ResNeXt152_vd_64x4d" ], "ReXNet": ["ReXNet_1_0", "ReXNet_1_3", "ReXNet_1_5", "ReXNet_2_0", "ReXNet_3_0"], "SENet": [ "SENet154_vd", "SE_HRNet_W64_C_ssld", "SE_ResNet18_vd", "SE_ResNet34_vd", "SE_ResNet50_vd", "SE_ResNeXt50_32x4d", "SE_ResNeXt50_vd_32x4d", "SE_ResNeXt101_32x4d" ], "ShuffleNetV2": [ "ShuffleNetV2_swish", "ShuffleNetV2_x0_25", "ShuffleNetV2_x0_33", "ShuffleNetV2_x0_5", "ShuffleNetV2_x1_0", "ShuffleNetV2_x1_5", "ShuffleNetV2_x2_0" ], "SqueezeNet": ["SqueezeNet1_0", "SqueezeNet1_1"], "SwinTransformer": [ "SwinTransformer_large_patch4_window7_224_22kto1k", "SwinTransformer_large_patch4_window12_384_22kto1k", "SwinTransformer_base_patch4_window7_224_22kto1k", "SwinTransformer_base_patch4_window12_384_22kto1k", "SwinTransformer_base_patch4_window12_384", "SwinTransformer_base_patch4_window7_224", "SwinTransformer_small_patch4_window7_224", "SwinTransformer_tiny_patch4_window7_224" ], "Twins": [ "pcpvt_small", "pcpvt_base", "pcpvt_large", "alt_gvt_small", "alt_gvt_base", "alt_gvt_large" ], "TNT": ["TNT_small"], "VAN": ["VAN_B0"], "VGG": ["VGG11", "VGG13", "VGG16", "VGG19"], "VisionTransformer": [ "ViT_base_patch16_224", "ViT_base_patch16_384", "ViT_base_patch32_384", "ViT_large_patch16_224", "ViT_large_patch16_384", "ViT_large_patch32_384", "ViT_small_patch16_224" ], "Xception": [ "Xception41", "Xception41_deeplab", "Xception65", "Xception65_deeplab", "Xception71" ] } PULC_MODEL_BASE_DOWNLOAD_URL = "https://paddleclas.bj.bcebos.com/models/PULC/inference/{}_infer.tar" PULC_MODELS = [ "car_exists", "language_classification", "person_attribute", "person_exists", "safety_helmet", "text_image_orientation", "textline_orientation", "traffic_sign", "vehicle_attribute", "table_attribute" ] SHITU_MODEL_BASE_DOWNLOAD_URL = "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/{}_infer.tar" SHITU_MODELS = [ # "picodet_PPLCNet_x2_5_mainbody_lite_v1.0", # ShiTuV1(V2)_mainbody_det # "general_PPLCNet_x2_5_lite_v1.0" # ShiTuV1_general_rec # "PP-ShiTuV2/general_PPLCNetV2_base_pretrained_v1.0", # ShiTuV2_general_rec TODO(hesensen): add lite model "PP-ShiTuV2" ] class ImageTypeError(Exception): """ImageTypeError. """ def __init__(self, message=""): super().__init__(message) class InputModelError(Exception): """InputModelError. """ def __init__(self, message=""): super().__init__(message) def init_config(model_type, model_name, inference_model_dir, **kwargs): if kwargs.get("build_gallery", False): cfg_path = "deploy/configs/inference_general.yaml" elif model_type == "pulc": cfg_path = f"deploy/configs/PULC/{model_name}/inference_{model_name}.yaml" elif model_type == "shitu": cfg_path = "deploy/configs/inference_general.yaml" else: cfg_path = "deploy/configs/inference_cls.yaml" __dir__ = os.path.dirname(__file__) cfg_path = os.path.join(__dir__, cfg_path) cfg = config.get_config( cfg_path, overrides=kwargs.get("override", None), show=False) if cfg.Global.get("inference_model_dir"): cfg.Global.inference_model_dir = inference_model_dir else: cfg.Global.rec_inference_model_dir = os.path.join( inference_model_dir, "PP-ShiTuV2/general_PPLCNetV2_base_pretrained_v1.0") cfg.Global.det_inference_model_dir = os.path.join( inference_model_dir, "picodet_PPLCNet_x2_5_mainbody_lite_v1.0") if "batch_size" in kwargs and kwargs["batch_size"]: cfg.Global.batch_size = kwargs["batch_size"] if "use_gpu" in kwargs and kwargs["use_gpu"]: cfg.Global.use_gpu = kwargs["use_gpu"] if cfg.Global.use_gpu and not paddle.device.is_compiled_with_cuda(): msg = "The current running environment does not support the use of GPU. CPU has been used instead." logger.warning(msg) cfg.Global.use_gpu = False if "infer_imgs" in kwargs and kwargs["infer_imgs"]: cfg.Global.infer_imgs = kwargs["infer_imgs"] if "index_dir" in kwargs and kwargs["index_dir"]: cfg.IndexProcess.index_dir = kwargs["index_dir"] if "data_file" in kwargs and kwargs["data_file"]: cfg.IndexProcess.data_file = kwargs["data_file"] if "enable_mkldnn" in kwargs and kwargs["enable_mkldnn"]: cfg.Global.enable_mkldnn = kwargs["enable_mkldnn"] if "cpu_num_threads" in kwargs and kwargs["cpu_num_threads"]: cfg.Global.cpu_num_threads = kwargs["cpu_num_threads"] if "use_fp16" in kwargs and kwargs["use_fp16"]: cfg.Global.use_fp16 = kwargs["use_fp16"] if "use_tensorrt" in kwargs and kwargs["use_tensorrt"]: cfg.Global.use_tensorrt = kwargs["use_tensorrt"] if "gpu_mem" in kwargs and kwargs["gpu_mem"]: cfg.Global.gpu_mem = kwargs["gpu_mem"] if "resize_short" in kwargs and kwargs["resize_short"]: cfg.PreProcess.transform_ops[0]["ResizeImage"][ "resize_short"] = kwargs["resize_short"] if "crop_size" in kwargs and kwargs["crop_size"]: cfg.PreProcess.transform_ops[1]["CropImage"]["size"] = kwargs[ "crop_size"] # TODO(gaotingquan): not robust if "thresh" in kwargs and kwargs[ "thresh"] and "ThreshOutput" in cfg.PostProcess: cfg.PostProcess.ThreshOutput.thresh = kwargs["thresh"] if cfg.get("PostProcess"): if "Topk" in cfg.PostProcess: if "topk" in kwargs and kwargs["topk"]: cfg.PostProcess.Topk.topk = kwargs["topk"] if "class_id_map_file" in kwargs and kwargs["class_id_map_file"]: cfg.PostProcess.Topk.class_id_map_file = kwargs[ "class_id_map_file"] else: class_id_map_file_path = os.path.relpath( cfg.PostProcess.Topk.class_id_map_file, "../") cfg.PostProcess.Topk.class_id_map_file = os.path.join( __dir__, class_id_map_file_path) if "VehicleAttribute" in cfg.PostProcess: if "color_threshold" in kwargs and kwargs["color_threshold"]: cfg.PostProcess.VehicleAttribute.color_threshold = kwargs[ "color_threshold"] if "type_threshold" in kwargs and kwargs["type_threshold"]: cfg.PostProcess.VehicleAttribute.type_threshold = kwargs[ "type_threshold"] if "TableAttribute" in cfg.PostProcess: if "source_threshold" in kwargs and kwargs["source_threshold"]: cfg.PostProcess.VehicleAttribute.color_threshold = kwargs[ "source_threshold"] if "number_threshold" in kwargs and kwargs["number_threshold"]: cfg.PostProcess.VehicleAttribute.color_threshold = kwargs[ "number_threshold"] if "color_threshold" in kwargs and kwargs["color_threshold"]: cfg.PostProcess.VehicleAttribute.color_threshold = kwargs[ "color_threshold"] if "clarity_threshold" in kwargs and kwargs["clarity_threshold"]: cfg.PostProcess.VehicleAttribute.color_threshold = kwargs[ "clarity_threshold"] if "obstruction_threshold" in kwargs and kwargs[ "obstruction_threshold"]: cfg.PostProcess.VehicleAttribute.color_threshold = kwargs[ "obstruction_threshold"] if "angle_threshold" in kwargs and kwargs["angle_threshold"]: cfg.PostProcess.VehicleAttribute.color_threshold = kwargs[ "angle_threshold"] if "save_dir" in kwargs and kwargs["save_dir"]: cfg.PostProcess.SavePreLabel.save_dir = kwargs["save_dir"] return cfg def args_cfg(): def str2bool(v): return v.lower() in ("true", "t", "1") parser = argparse.ArgumentParser() parser.add_argument( "--infer_imgs", type=str, required=False, help="The image(s) to be predicted.") parser.add_argument( "--model_name", type=str, help="The model name to be used.") parser.add_argument( "--predict_type", type=str, default="cls", help="The predict type to be selected.") parser.add_argument( "--inference_model_dir", type=str, help="The directory of model files. Valid when model_name not specifed." ) parser.add_argument( "--index_dir", type=str, required=False, help="The index directory path.") parser.add_argument( "--data_file", type=str, required=False, help="The label file path.") parser.add_argument("--use_gpu", type=str2bool, help="Whether use GPU.") parser.add_argument( "--gpu_mem", type=int, help="The memory size of GPU allocated to predict.") parser.add_argument( "--enable_mkldnn", type=str2bool, help="Whether use MKLDNN. Valid when use_gpu is False") parser.add_argument( "--cpu_num_threads", type=int, help="The threads number when predicting on CPU.") parser.add_argument( "--use_tensorrt", type=str2bool, help="Whether use TensorRT to accelerate.") parser.add_argument( "--use_fp16", type=str2bool, help="Whether use FP16 to predict.") parser.add_argument("--batch_size", type=int, help="Batch size.") parser.add_argument( "--topk", type=int, help="Return topk score(s) and corresponding results when Topk postprocess is used." ) parser.add_argument( "--class_id_map_file", type=str, help="The path of file that map class_id and label.") parser.add_argument( "--threshold", type=float, help="The threshold of ThreshOutput when postprocess is used.") parser.add_argument("--color_threshold", type=float, help="") parser.add_argument("--type_threshold", type=float, help="") parser.add_argument( "--save_dir", type=str, help="The directory to save prediction results as pre-label.") parser.add_argument( "--resize_short", type=int, help="Resize according to short size.") parser.add_argument("--crop_size", type=int, help="Centor crop size.") parser.add_argument( "--build_gallery", type=str2bool, default=False, help="Whether build gallery.") parser.add_argument( '-o', '--override', action='append', default=[], help='config options to be overridden') args = parser.parse_args() return vars(args) def print_info(): """Print list of supported models in formatted. """ imn_table = PrettyTable(["IMN Model Series", "Model Name"]) pulc_table = PrettyTable(["PULC Models"]) shitu_table = PrettyTable(["PP-ShiTu Models"]) try: sz = os.get_terminal_size() total_width = sz.columns first_width = 30 second_width = total_width - first_width if total_width > 50 else 10 except OSError: total_width = 100 second_width = 100 for series in IMN_MODEL_SERIES: names = textwrap.fill( " ".join(IMN_MODEL_SERIES[series]), width=second_width) imn_table.add_row([series, names]) table_width = len(str(imn_table).split("\n")[0]) pulc_table.add_row([ textwrap.fill( " ".join(PULC_MODELS), width=total_width).center(table_width - 4) ]) shitu_table.add_row([ textwrap.fill( " ".join(SHITU_MODELS), width=total_width).center(table_width - 4) ]) print("{}".format("-" * table_width)) print("Models supported by PaddleClas".center(table_width)) print(imn_table) print(pulc_table) print(shitu_table) print("Powered by PaddlePaddle!".rjust(table_width)) print("{}".format("-" * table_width)) def get_imn_model_names(): """Get the model names list. """ model_names = [] for series in IMN_MODEL_SERIES: model_names += (IMN_MODEL_SERIES[series]) return model_names def similar_model_names(name="", names=[], thresh=0.1, topk=5): """Find the most similar topk model names. """ scores = [] for idx, n in enumerate(names): if n.startswith("__"): continue score = SequenceMatcher(None, n.lower(), name.lower()).quick_ratio() if score > thresh: scores.append((idx, score)) scores.sort(key=lambda x: x[1], reverse=True) similar_names = [names[s[0]] for s in scores[:min(topk, len(scores))]] return similar_names def download_with_progressbar(url, save_path): """Download from url with progressbar. """ if os.path.isfile(save_path): os.remove(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 or not os.path.isfile( save_path): raise Exception( f"Something went wrong while downloading file from {url}") def check_model_file(model_type, model_name): """Check the model files exist and download and untar when no exist. """ if model_type == "pulc": storage_directory = partial(os.path.join, BASE_INFERENCE_MODEL_DIR, "PULC", model_name) url = PULC_MODEL_BASE_DOWNLOAD_URL.format(model_name) elif model_type == "shitu": storage_directory = partial(os.path.join, BASE_INFERENCE_MODEL_DIR, "PP-ShiTu", model_name) url = SHITU_MODEL_BASE_DOWNLOAD_URL.format(model_name) else: storage_directory = partial(os.path.join, BASE_INFERENCE_MODEL_DIR, "IMN", model_name) url = IMN_MODEL_BASE_DOWNLOAD_URL.format(model_name) tar_file_name_list = [ "inference.pdiparams", "inference.pdiparams.info", "inference.pdmodel" ] model_file_path = storage_directory("inference.pdmodel") params_file_path = storage_directory("inference.pdiparams") if not os.path.exists(model_file_path) or not os.path.exists( params_file_path): tmp_path = storage_directory(url.split("/")[-1]) logger.info(f"download {url} to {tmp_path}") os.makedirs(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(storage_directory(filename), "wb") as f: f.write(file.read()) os.remove(tmp_path) if not os.path.exists(model_file_path) or not os.path.exists( params_file_path): raise Exception( f"Something went wrong while praparing the model[{model_name}] files!" ) return storage_directory() class PaddleClas(object): """PaddleClas. """ def __init__(self, build_gallery: bool=False, gallery_image_root: str=None, gallery_data_file: str=None, index_dir: str=None, model_name: str=None, inference_model_dir: str=None, **kwargs): """Init PaddleClas with config. Args: model_name (str, optional): The model name supported by PaddleClas. If specified, override config. Defaults to None. inference_model_dir (str, optional): The directory that contained model file and params file to be used. If specified, override config. Defaults to None. use_gpu (bool, optional): Whether use GPU. If specified, override config. Defaults to True. batch_size (int, optional): The batch size to pridict. If specified, override config. Defaults to 1. topk (int, optional): Return the top k prediction results with the highest score. Defaults to 5. """ super().__init__() if build_gallery: self.model_type, inference_model_dir = self._check_input_model( model_name if model_name else "PP-ShiTuV2", inference_model_dir) self._config = init_config(self.model_type, model_name if model_name else "PP-ShiTuV2", inference_model_dir, **kwargs) if gallery_image_root: self._config.IndexProcess.image_root = gallery_image_root if gallery_data_file: self._config.IndexProcess.data_file = gallery_data_file if index_dir: self._config.IndexProcess.index_dir = index_dir logger.info("Building Gallery...") GalleryBuilder(self._config) else: self.model_type, inference_model_dir = self._check_input_model( model_name, inference_model_dir) self._config = init_config(self.model_type, model_name, inference_model_dir, **kwargs) if self.model_type == "shitu": if index_dir: self._config.IndexProcess.index_dir = index_dir self.predictor = SystemPredictor(self._config) else: self.predictor = ClsPredictor(self._config) def get_config(self): """Get the config. """ return self._config def _check_input_model(self, model_name, inference_model_dir): """Check input model name or model files. """ all_imn_model_names = get_imn_model_names() all_pulc_model_names = PULC_MODELS all_shitu_model_names = SHITU_MODELS if model_name: if model_name in all_imn_model_names: inference_model_dir = check_model_file("imn", model_name) return "imn", inference_model_dir elif model_name in all_pulc_model_names: inference_model_dir = check_model_file("pulc", model_name) return "pulc", inference_model_dir elif model_name in all_shitu_model_names: inference_model_dir = check_model_file( "shitu", "PP-ShiTuV2/general_PPLCNetV2_base_pretrained_v1.0") inference_model_dir = check_model_file( "shitu", "picodet_PPLCNet_x2_5_mainbody_lite_v1.0") inference_model_dir = os.path.abspath( os.path.dirname(inference_model_dir)) return "shitu", inference_model_dir else: similar_imn_names = similar_model_names(model_name, all_imn_model_names) similar_pulc_names = similar_model_names(model_name, all_pulc_model_names) similar_names_str = ", ".join(similar_imn_names + similar_pulc_names) err = f"{model_name} is not provided by PaddleClas. \nMaybe you want the : [{similar_names_str}]. \nIf you want to use your own model, please specify inference_model_dir!" raise InputModelError(err) elif inference_model_dir: model_file_path = os.path.join(inference_model_dir, "inference.pdmodel") params_file_path = os.path.join(inference_model_dir, "inference.pdiparams") if not os.path.isfile(model_file_path) or not os.path.isfile( params_file_path): err = f"There is no model file or params file in this directory: {inference_model_dir}" raise InputModelError(err) return "custom", inference_model_dir else: err = "Please specify the model name supported by PaddleClas or directory contained model files(inference.pdmodel, inference.pdiparams)." raise InputModelError(err) return None def predict_cls(self, input_data: Union[str, np.array], print_pred: bool=False) -> Generator[list, None, None]: """Predict input_data. Args: input_data (Union[str, np.array]): When the type is str, it is the path of image, or the directory containing images, or the URL of image from Internet. When the type is np.array, it is the image data whose channel order is RGB. print_pred (bool, optional): Whether print the prediction result. Defaults to False. Raises: ImageTypeError: Illegal input_data. Yields: Generator[list, None, None]: The prediction result(s) of input_data by batch_size. For every one image, prediction result(s) is zipped as a dict, that includs topk "class_ids", "scores" and "label_names". The format of batch prediction result(s) is as follow: [{"class_ids": [...], "scores": [...], "label_names": [...]}, ...] """ if isinstance(input_data, np.ndarray): yield self.predictor.predict(input_data) elif isinstance(input_data, str): if input_data.startswith("http") or input_data.startswith("https"): image_storage_dir = partial(os.path.join, BASE_IMAGES_DIR) if not os.path.exists(image_storage_dir()): os.makedirs(image_storage_dir()) image_save_path = image_storage_dir("tmp.jpg") download_with_progressbar(input_data, image_save_path) logger.info( f"Image to be predicted from Internet: {input_data}, has been saved to: {image_save_path}" ) input_data = image_save_path image_list = get_image_list(input_data) batch_size = self._config.Global.get("batch_size", 1) img_list = [] img_path_list = [] cnt = 0 for idx_img, img_path in enumerate(image_list): img = cv2.imread(img_path) if img is None: logger.warning( f"Image file failed to read and has been skipped. The path: {img_path}" ) continue img = img[:, :, ::-1] img_list.append(img) img_path_list.append(img_path) cnt += 1 if cnt % batch_size == 0 or (idx_img + 1) == len(image_list): preds = self.predictor.predict(img_list) if preds: for idx_pred, pred in enumerate(preds): pred["filename"] = img_path_list[idx_pred] if print_pred: logger.info(", ".join( [f"{k}: {pred[k]}" for k in pred])) img_list = [] img_path_list = [] yield preds else: err = "Please input legal image! The type of image supported by PaddleClas are: NumPy.ndarray and string of local path or Ineternet URL" raise ImageTypeError(err) return def predict_shitu(self, input_data: Union[str, np.array], print_pred: bool=False) -> Generator[list, None, None]: """Predict input_data. Args: input_data (Union[str, np.array]): When the type is str, it is the path of image, or the directory containing images, or the URL of image from Internet. When the type is np.array, it is the image data whose channel order is RGB. print_pred (bool, optional): Whether print the prediction result. Defaults to False. Raises: ImageTypeError: Illegal input_data. Yields: Generator[list, None, None]: The prediction result(s) of input_data by batch_size. For every one image, prediction result(s) is zipped as a dict, that includs topk "class_ids", "scores" and "label_names". The format of batch prediction result(s) is as follow: [{"class_ids": [...], "scores": [...], "label_names": [...]}, ...] """ if input_data == None and self._config.Global.infer_imgs: input_data = self._config.Global.infer_imgs if isinstance(input_data, np.ndarray): yield self.predictor.predict(input_data) elif isinstance(input_data, str): if input_data.startswith("http") or input_data.startswith("https"): image_storage_dir = partial(os.path.join, BASE_IMAGES_DIR) if not os.path.exists(image_storage_dir()): os.makedirs(image_storage_dir()) image_save_path = image_storage_dir("tmp.jpg") download_with_progressbar(input_data, image_save_path) logger.info( f"Image to be predicted from Internet: {input_data}, has been saved to: {image_save_path}" ) input_data = image_save_path image_list = get_image_list(input_data) cnt = 0 for idx_img, img_path in enumerate(image_list): img = cv2.imread(img_path) if img is None: logger.warning( f"Image file failed to read and has been skipped. The path: {img_path}" ) continue img = img[:, :, ::-1] cnt += 1 preds = self.predictor.predict( img) # [dict1, dict2, ..., dictn] if preds: if print_pred: logger.info(f"{preds}, filename: {img_path}") yield preds else: err = "Please input legal image! The type of image supported by PaddleClas are: NumPy.ndarray and string of local path or Ineternet URL" raise ImageTypeError(err) return def predict(self, input_data: Union[str, np.array], print_pred: bool=False, predict_type="cls"): assert predict_type in ["cls", "shitu" ], "Predict type should be 'cls' or 'shitu'." if predict_type == "cls": return self.predict_cls(input_data, print_pred) elif predict_type == "shitu": assert not isinstance(input_data, ( list, tuple )), "PP-ShiTu predictor only support single image as input now." return self.predict_shitu(input_data, print_pred) else: raise ModuleNotFoundError # for CLI def main(): """Function API used for commad line. """ print_info() cfg = args_cfg() clas_engine = PaddleClas(**cfg) if cfg["build_gallery"] == False: res = clas_engine.predict( cfg["infer_imgs"], print_pred=True, predict_type=cfg["predict_type"]) for _ in res: pass logger.info("Predict complete!") return if __name__ == "__main__": main()