376 lines
16 KiB
Python
376 lines
16 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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# 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 sys
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__dir__ = os.path.dirname(__file__)
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sys.path.append(os.path.join(__dir__, ''))
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import argparse
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import shutil
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import cv2
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import numpy as np
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import tarfile
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import requests
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from tqdm import tqdm
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from tools.infer.utils import get_image_list, preprocess, save_prelabel_results
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from tools.infer.predict import Predictor
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__all__ = ['PaddleClas']
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BASE_DIR = os.path.expanduser("~/.paddleclas/")
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BASE_INFERENCE_MODEL_DIR = os.path.join(BASE_DIR, 'inference_model')
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BASE_IMAGES_DIR = os.path.join(BASE_DIR, 'images')
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model_names = {
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'Xception71', 'SE_ResNeXt101_32x4d', 'ShuffleNetV2_x0_5', 'ResNet34',
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'ShuffleNetV2_x2_0', 'ResNeXt101_32x4d', 'HRNet_W48_C_ssld',
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'ResNeSt50_fast_1s1x64d', 'MobileNetV2_x2_0', 'MobileNetV3_large_x1_0',
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'Fix_ResNeXt101_32x48d_wsl', 'MobileNetV2_ssld', 'ResNeXt101_vd_64x4d',
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'ResNet34_vd_ssld', 'MobileNetV3_small_x1_0', 'VGG11',
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'ResNeXt50_vd_32x4d', 'MobileNetV3_large_x1_25',
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'MobileNetV3_large_x1_0_ssld', 'MobileNetV2_x0_75',
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'MobileNetV3_small_x0_35', 'MobileNetV1_x0_75', 'MobileNetV1_ssld',
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'ResNeXt50_32x4d', 'GhostNet_x1_3_ssld', 'Res2Net101_vd_26w_4s',
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'ResNet152', 'Xception65', 'EfficientNetB0', 'ResNet152_vd', 'HRNet_W18_C',
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'Res2Net50_14w_8s', 'ShuffleNetV2_x0_25', 'HRNet_W64_C',
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'Res2Net50_vd_26w_4s_ssld', 'HRNet_W18_C_ssld', 'ResNet18_vd',
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'ResNeXt101_32x16d_wsl', 'SE_ResNeXt50_32x4d', 'SqueezeNet1_1',
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'SENet154_vd', 'SqueezeNet1_0', 'GhostNet_x1_0', 'ResNet50_vc', 'DPN98',
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'HRNet_W48_C', 'DenseNet264', 'SE_ResNet34_vd', 'HRNet_W44_C',
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'MobileNetV3_small_x1_25', 'MobileNetV1_x0_5', 'ResNet200_vd', 'VGG13',
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'EfficientNetB3', 'EfficientNetB2', 'ShuffleNetV2_x0_33',
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'MobileNetV3_small_x0_75', 'ResNeXt152_vd_32x4d', 'ResNeXt101_32x32d_wsl',
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'ResNet18', 'MobileNetV3_large_x0_35', 'Res2Net50_26w_4s',
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'MobileNetV2_x0_5', 'EfficientNetB0_small', 'ResNet101_vd_ssld',
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'EfficientNetB6', 'EfficientNetB1', 'EfficientNetB7', 'ResNeSt50',
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'ShuffleNetV2_x1_0', 'MobileNetV3_small_x1_0_ssld', 'InceptionV4',
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'GhostNet_x0_5', 'SE_HRNet_W64_C_ssld', 'ResNet50_ACNet_deploy',
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'Xception41', 'ResNet50', 'Res2Net200_vd_26w_4s_ssld',
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'Xception41_deeplab', 'SE_ResNet18_vd', 'SE_ResNeXt50_vd_32x4d',
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'HRNet_W30_C', 'HRNet_W40_C', 'VGG19', 'Res2Net200_vd_26w_4s',
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'ResNeXt101_32x8d_wsl', 'ResNet50_vd', 'ResNeXt152_64x4d', 'DarkNet53',
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'ResNet50_vd_ssld', 'ResNeXt101_64x4d', 'MobileNetV1_x0_25',
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'Xception65_deeplab', 'AlexNet', 'ResNet101', 'DenseNet121',
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'ResNet50_vd_v2', 'Res2Net50_vd_26w_4s', 'ResNeXt101_32x48d_wsl',
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'MobileNetV3_large_x0_5', 'MobileNetV2_x0_25', 'DPN92', 'ResNet101_vd',
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'MobileNetV2_x1_5', 'DPN131', 'ResNeXt50_vd_64x4d', 'ShuffleNetV2_x1_5',
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'ResNet34_vd', 'MobileNetV1', 'ResNeXt152_vd_64x4d', 'DPN107', 'VGG16',
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'ResNeXt50_64x4d', 'RegNetX_4GF', 'DenseNet161', 'GhostNet_x1_3',
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'HRNet_W32_C', 'Fix_ResNet50_vd_ssld_v2', 'Res2Net101_vd_26w_4s_ssld',
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'DenseNet201', 'DPN68', 'EfficientNetB4', 'ResNeXt152_32x4d',
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'InceptionV3', 'ShuffleNetV2_swish', 'GoogLeNet', 'ResNet50_vd_ssld_v2',
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'SE_ResNet50_vd', 'MobileNetV2', 'ResNeXt101_vd_32x4d',
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'MobileNetV3_large_x0_75', 'MobileNetV3_small_x0_5', 'DenseNet169',
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'EfficientNetB5', 'DeiT_base_distilled_patch16_224',
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'DeiT_base_distilled_patch16_384', 'DeiT_base_patch16_224',
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'DeiT_base_patch16_384', 'DeiT_small_distilled_patch16_224',
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'DeiT_small_patch16_224', 'DeiT_tiny_distilled_patch16_224',
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'DeiT_tiny_patch16_224', 'ViT_base_patch16_224', 'ViT_base_patch16_384',
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'ViT_base_patch32_384', 'ViT_large_patch16_224', 'ViT_large_patch16_384',
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'ViT_large_patch32_384', 'ViT_small_patch16_224'
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}
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def download_with_progressbar(url, save_path):
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response = requests.get(url, stream=True)
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total_size_in_bytes = int(response.headers.get('content-length', 0))
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block_size = 1024 # 1 Kibibyte
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progress_bar = tqdm(total=total_size_in_bytes, unit='iB', unit_scale=True)
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with open(save_path, 'wb') as file:
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for data in response.iter_content(block_size):
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progress_bar.update(len(data))
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file.write(data)
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progress_bar.close()
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if total_size_in_bytes == 0 or progress_bar.n != total_size_in_bytes:
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raise Exception(
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"Something went wrong while downloading model/image from {}".
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format(url))
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def maybe_download(model_storage_directory, url):
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# using custom model
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tar_file_name_list = [
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'inference.pdiparams', 'inference.pdiparams.info', 'inference.pdmodel'
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]
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if not os.path.exists(
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os.path.join(model_storage_directory, 'inference.pdiparams')
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) or not os.path.exists(
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os.path.join(model_storage_directory, 'inference.pdmodel')):
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tmp_path = os.path.join(model_storage_directory, url.split('/')[-1])
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print('download {} to {}'.format(url, tmp_path))
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os.makedirs(model_storage_directory, exist_ok=True)
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download_with_progressbar(url, tmp_path)
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with tarfile.open(tmp_path, 'r') as tarObj:
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for member in tarObj.getmembers():
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filename = None
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for tar_file_name in tar_file_name_list:
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if tar_file_name in member.name:
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filename = tar_file_name
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if filename is None:
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continue
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file = tarObj.extractfile(member)
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with open(
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os.path.join(model_storage_directory, filename),
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'wb') as f:
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f.write(file.read())
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os.remove(tmp_path)
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def load_label_name_dict(path):
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if not os.path.exists(path):
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print(
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"Warning: If want to use your own label_dict, please input legal path!\nOtherwise label_names will be empty!"
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)
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return None
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else:
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result = {}
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for line in open(path, 'r'):
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partition = line.split('\n')[0].partition(' ')
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try:
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result[int(partition[0])] = str(partition[-1])
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except:
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result = {}
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break
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return result
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def parse_args(mMain=True, add_help=True):
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def str2bool(v):
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return v.lower() in ("true", "t", "1")
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if mMain == True:
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# general params
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parser = argparse.ArgumentParser(add_help=add_help)
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parser.add_argument("--model_name", type=str)
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parser.add_argument("-i", "--image_file", type=str)
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parser.add_argument("--use_gpu", 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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parser.add_argument("-b", "--batch_size", type=int, default=1)
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# params for predict
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parser.add_argument(
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"--model_file", type=str, default='') ## inference.pdmodel
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parser.add_argument(
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"--params_file", type=str, default='') ## inference.pdiparams
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parser.add_argument("--ir_optim", type=str2bool, default=True)
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parser.add_argument("--use_fp16", type=str2bool, default=False)
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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("--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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# parameters for pre-label the images
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parser.add_argument("--label_name_path", type=str, default='')
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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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return parser.parse_args()
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else:
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return argparse.Namespace(
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model_name='',
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image_file='',
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use_gpu=False,
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use_fp16=False,
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use_tensorrt=False,
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is_preprocessed=False,
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resize_short=256,
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resize=224,
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normalize=True,
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batch_size=1,
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model_file='',
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params_file='',
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ir_optim=True,
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gpu_mem=8000,
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enable_profile=False,
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top_k=1,
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enable_mkldnn=False,
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cpu_num_threads=10,
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label_name_path='',
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pre_label_image=False,
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pre_label_out_idr=None)
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class PaddleClas(object):
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print('Inference models that Paddle provides are listed as follows:\n\n{}'.
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format(model_names), '\n')
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def __init__(self, **kwargs):
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process_params = parse_args(mMain=False, add_help=False)
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process_params.__dict__.update(**kwargs)
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if not os.path.exists(process_params.model_file):
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if process_params.model_name is None:
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raise Exception(
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'Please input model name that you want to use!')
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if process_params.model_name in model_names:
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url = 'https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/{}_infer.tar'.format(
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process_params.model_name)
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if not os.path.exists(
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os.path.join(BASE_INFERENCE_MODEL_DIR,
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process_params.model_name)):
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os.makedirs(
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os.path.join(BASE_INFERENCE_MODEL_DIR,
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process_params.model_name))
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download_path = os.path.join(BASE_INFERENCE_MODEL_DIR,
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process_params.model_name)
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maybe_download(model_storage_directory=download_path, url=url)
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process_params.model_file = os.path.join(download_path,
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'inference.pdmodel')
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process_params.params_file = os.path.join(
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download_path, 'inference.pdiparams')
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process_params.label_name_path = os.path.join(
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__dir__, 'ppcls/utils/imagenet1k_label_list.txt')
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else:
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raise Exception(
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'The model inputed is {}, not provided by PaddleClas. If you want to use your own model, please input model_file as model path!'.
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format(process_params.model_name))
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else:
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print('Using user-specified model and params!')
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print("process params are as follows: \n{}".format(process_params))
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self.label_name_dict = load_label_name_dict(
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process_params.label_name_path)
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self.args = process_params
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self.predictor = Predictor(process_params)
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def postprocess(self, output):
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output = output.flatten()
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classes = np.argpartition(output, -self.args.top_k)[-self.args.top_k:]
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class_ids = classes[np.argsort(-output[classes])]
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scores = output[class_ids]
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label_names = [self.label_name_dict[c]
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for c in class_ids] if self.label_name_dict else []
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return {
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"class_ids": class_ids,
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"scores": scores,
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"label_names": label_names
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}
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def predict(self, input_data):
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"""
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predict label of img with paddleclas
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Args:
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input_data(string, NumPy.ndarray): image to be classified, support:
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string: local path of image file, internet URL, directory containing series of images;
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NumPy.ndarray: preprocessed image data that has 3 channels and accords with [C, H, W], or raw image data that has 3 channels and accords with [H, W, C]
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Returns:
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dict: {image_name: "", class_id: [], scores: [], label_names: []}，if label name path == None，label_names will be empty.
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"""
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if isinstance(input_data, np.ndarray):
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if not self.args.is_preprocessed:
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input_data = input_data[:, :, ::-1]
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input_data = preprocess(input_data, self.args)
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input_data = np.expand_dims(input_data, axis=0)
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batch_outputs = self.predictor.predict(input_data)
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result = {"filename": "image"}
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result.update(self.postprocess(batch_outputs[0]))
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return result
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elif isinstance(input_data, str):
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input_path = input_data
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# download internet image
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if input_path.startswith('http'):
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if not os.path.exists(BASE_IMAGES_DIR):
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os.makedirs(BASE_IMAGES_DIR)
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file_path = os.path.join(BASE_IMAGES_DIR, 'tmp.jpg')
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download_with_progressbar(input_path, file_path)
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print("Current using image from Internet:{}, renamed as: {}".
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format(input_path, file_path))
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input_path = file_path
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image_list = get_image_list(input_path)
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total_result = []
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batch_input_list = []
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img_path_list = []
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cnt = 0
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for idx, img_path in enumerate(image_list):
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img = cv2.imread(img_path)
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if img is None:
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print(
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"Warning: Image file failed to read and has been skipped. The path: {}".
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format(img_path))
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continue
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else:
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img = img[:, :, ::-1]
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data = preprocess(img, self.args)
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batch_input_list.append(data)
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img_path_list.append(img_path)
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cnt += 1
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if cnt % self.args.batch_size == 0 or (idx + 1
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) == len(image_list):
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batch_outputs = self.predictor.predict(
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np.array(batch_input_list))
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for number, output in enumerate(batch_outputs):
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result = {"filename": img_path_list[number]}
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result.update(self.postprocess(output))
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result_str = "top-{} result: {}".format(
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self.args.top_k, result)
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print(result_str)
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total_result.append(result)
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if self.args.pre_label_image:
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save_prelabel_results(result["class_ids"][0],
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img_path_list[number],
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self.args.pre_label_out_idr)
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batch_input_list = []
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img_path_list = []
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return total_result
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else:
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print(
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"Error: Please input legal image! The type of image supported by PaddleClas are: NumPy.ndarray and string of local path or Ineternet URL"
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)
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return []
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def main():
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# for cmd
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args = parse_args(mMain=True)
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clas_engine = PaddleClas(**(args.__dict__))
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print('{}{}{}'.format('*' * 10, args.image_file, '*' * 10))
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total_result = clas_engine.predict(args.image_file)
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print("Predict complete!")
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if __name__ == '__main__':
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main()
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