# 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 import yaml import glob from functools import reduce import cv2 import numpy as np import math import paddle from paddle.inference import Config from paddle.inference import create_predictor import sys # add deploy path of PadleDetection to sys.path parent_path = os.path.abspath(os.path.join(__file__, *(['..']))) sys.path.insert(0, parent_path) from benchmark_utils import PaddleInferBenchmark from preprocess import preprocess, Resize, NormalizeImage, Permute, PadStride, LetterBoxResize, WarpAffine from visualize import visualize_attr from utils import argsparser, Timer, get_current_memory_mb from infer import Detector, get_test_images, print_arguments, load_predictor from PIL import Image, ImageDraw, ImageFont class AttrDetector(Detector): """ Args: model_dir (str): root path of model.pdiparams, model.pdmodel and infer_cfg.yml device (str): Choose the device you want to run, it can be: CPU/GPU/XPU, default is CPU run_mode (str): mode of running(paddle/trt_fp32/trt_fp16) batch_size (int): size of pre batch in inference trt_min_shape (int): min shape for dynamic shape in trt trt_max_shape (int): max shape for dynamic shape in trt trt_opt_shape (int): opt shape for dynamic shape in trt trt_calib_mode (bool): If the model is produced by TRT offline quantitative calibration, trt_calib_mode need to set True cpu_threads (int): cpu threads enable_mkldnn (bool): whether to open MKLDNN output_dir (str): The path of output threshold (float): The threshold of score for visualization """ def __init__( self, model_dir, device='CPU', run_mode='paddle', batch_size=1, trt_min_shape=1, trt_max_shape=1280, trt_opt_shape=640, trt_calib_mode=False, cpu_threads=1, enable_mkldnn=False, output_dir='output', threshold=0.5, ): super(AttrDetector, self).__init__( model_dir=model_dir, device=device, run_mode=run_mode, batch_size=batch_size, trt_min_shape=trt_min_shape, trt_max_shape=trt_max_shape, trt_opt_shape=trt_opt_shape, trt_calib_mode=trt_calib_mode, cpu_threads=cpu_threads, enable_mkldnn=enable_mkldnn, output_dir=output_dir, threshold=threshold, ) def get_label(self): return self.pred_config.labels def postprocess(self, inputs, result): # postprocess output of predictor im_results = result['output'] labels = self.pred_config.labels age_list = ['AgeLess18', 'Age18-60', 'AgeOver60'] direct_list = ['Front', 'Side', 'Back'] bag_list = ['HandBag', 'ShoulderBag', 'Backpack'] upper_list = ['UpperStride', 'UpperLogo', 'UpperPlaid', 'UpperSplice'] lower_list = [ 'LowerStripe', 'LowerPattern', 'LongCoat', 'Trousers', 'Shorts', 'Skirt&Dress' ] glasses_threshold = 0.3 hold_threshold = 0.6 batch_res = [] for res in im_results: res = res.tolist() label_res = [] # gender gender = 'Female' if res[22] > self.threshold else 'Male' label_res.append(gender) # age age = age_list[np.argmax(res[19:22])] label_res.append(age) # direction direction = direct_list[np.argmax(res[23:])] label_res.append(direction) # glasses glasses = 'Glasses: ' if res[1] > glasses_threshold: glasses += 'True' else: glasses += 'False' label_res.append(glasses) # hat hat = 'Hat: ' if res[0] > self.threshold: hat += 'True' else: hat += 'False' label_res.append(hat) # hold obj hold_obj = 'HoldObjectsInFront: ' if res[18] > hold_threshold: hold_obj += 'True' else: hold_obj += 'False' label_res.append(hold_obj) # bag bag = bag_list[np.argmax(res[15:18])] bag_score = res[15 + np.argmax(res[15:18])] bag_label = bag if bag_score > self.threshold else 'No bag' label_res.append(bag_label) # upper upper_res = res[4:8] upper_label = 'Upper:' sleeve = 'LongSleeve' if res[3] > res[2] else 'ShortSleeve' upper_label += ' {}'.format(sleeve) for i, r in enumerate(upper_res): if r > self.threshold: upper_label += ' {}'.format(upper_list[i]) label_res.append(upper_label) # lower lower_res = res[8:14] lower_label = 'Lower: ' has_lower = False for i, l in enumerate(lower_res): if l > self.threshold: lower_label += ' {}'.format(lower_list[i]) has_lower = True if not has_lower: lower_label += ' {}'.format(lower_list[np.argmax(lower_res)]) label_res.append(lower_label) # shoe shoe = 'Boots' if res[14] > self.threshold else 'No boots' label_res.append(shoe) batch_res.append(label_res) result = {'output': batch_res} return result def predict(self, repeats=1): ''' Args: repeats (int): repeats number for prediction Returns: result (dict): include 'boxes': np.ndarray: shape:[N,6], N: number of box, matix element:[class, score, x_min, y_min, x_max, y_max] MaskRCNN's result include 'masks': np.ndarray: shape: [N, im_h, im_w] ''' # model prediction for i in range(repeats): self.predictor.run() output_names = self.predictor.get_output_names() output_tensor = self.predictor.get_output_handle(output_names[0]) np_output = output_tensor.copy_to_cpu() result = dict(output=np_output) return result def predict_image(self, image_list, run_benchmark=False, repeats=1, visual=True): batch_loop_cnt = math.ceil(float(len(image_list)) / self.batch_size) results = [] for i in range(batch_loop_cnt): start_index = i * self.batch_size end_index = min((i + 1) * self.batch_size, len(image_list)) batch_image_list = image_list[start_index:end_index] if run_benchmark: # preprocess inputs = self.preprocess(batch_image_list) # warmup self.det_times.preprocess_time_s.start() inputs = self.preprocess(batch_image_list) self.det_times.preprocess_time_s.end() # model prediction result = self.predict(repeats=repeats) # warmup self.det_times.inference_time_s.start() result = self.predict(repeats=repeats) self.det_times.inference_time_s.end(repeats=repeats) # postprocess result_warmup = self.postprocess(inputs, result) # warmup self.det_times.postprocess_time_s.start() result = self.postprocess(inputs, result) self.det_times.postprocess_time_s.end() self.det_times.img_num += len(batch_image_list) cm, gm, gu = get_current_memory_mb() self.cpu_mem += cm self.gpu_mem += gm self.gpu_util += gu else: # preprocess self.det_times.preprocess_time_s.start() inputs = self.preprocess(batch_image_list) self.det_times.preprocess_time_s.end() # model prediction self.det_times.inference_time_s.start() result = self.predict() self.det_times.inference_time_s.end() # postprocess self.det_times.postprocess_time_s.start() result = self.postprocess(inputs, result) self.det_times.postprocess_time_s.end() self.det_times.img_num += len(batch_image_list) if visual: visualize( batch_image_list, result, output_dir=self.output_dir) results.append(result) if visual: print('Test iter {}'.format(i)) results = self.merge_batch_result(results) return results def merge_batch_result(self, batch_result): if len(batch_result) == 1: return batch_result[0] res_key = batch_result[0].keys() results = {k: [] for k in res_key} for res in batch_result: for k, v in res.items(): results[k].extend(v) return results def visualize(image_list, batch_res, output_dir='output'): # visualize the predict result batch_res = batch_res['output'] for image_file, res in zip(image_list, batch_res): im = visualize_attr(image_file, [res]) if not os.path.exists(output_dir): os.makedirs(output_dir) img_name = os.path.split(image_file)[-1] out_path = os.path.join(output_dir, img_name) cv2.imwrite(out_path, im) print("save result to: " + out_path) def main(): detector = AttrDetector( FLAGS.model_dir, device=FLAGS.device, run_mode=FLAGS.run_mode, batch_size=FLAGS.batch_size, trt_min_shape=FLAGS.trt_min_shape, trt_max_shape=FLAGS.trt_max_shape, trt_opt_shape=FLAGS.trt_opt_shape, trt_calib_mode=FLAGS.trt_calib_mode, cpu_threads=FLAGS.cpu_threads, enable_mkldnn=FLAGS.enable_mkldnn, threshold=FLAGS.threshold, output_dir=FLAGS.output_dir) # predict from image if FLAGS.image_dir is None and FLAGS.image_file is not None: assert FLAGS.batch_size == 1, "batch_size should be 1, when image_file is not None" img_list = get_test_images(FLAGS.image_dir, FLAGS.image_file) detector.predict_image(img_list, FLAGS.run_benchmark, repeats=10) if not FLAGS.run_benchmark: detector.det_times.info(average=True) else: mems = { 'cpu_rss_mb': detector.cpu_mem / len(img_list), 'gpu_rss_mb': detector.gpu_mem / len(img_list), 'gpu_util': detector.gpu_util * 100 / len(img_list) } perf_info = detector.det_times.report(average=True) model_dir = FLAGS.model_dir mode = FLAGS.run_mode model_info = { 'model_name': model_dir.strip('/').split('/')[-1], 'precision': mode.split('_')[-1] } data_info = { 'batch_size': FLAGS.batch_size, 'shape': "dynamic_shape", 'data_num': perf_info['img_num'] } det_log = PaddleInferBenchmark(detector.config, model_info, data_info, perf_info, mems) det_log('Attr') if __name__ == '__main__': paddle.enable_static() parser = argsparser() FLAGS = parser.parse_args() print_arguments(FLAGS) FLAGS.device = FLAGS.device.upper() assert FLAGS.device in ['CPU', 'GPU', 'XPU' ], "device should be CPU, GPU or XPU" assert not FLAGS.use_gpu, "use_gpu has been deprecated, please use --device" main()