349 lines
13 KiB
Python
349 lines
13 KiB
Python
# Copyright (c) 2022 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 yaml
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import glob
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from functools import reduce
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import cv2
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import numpy as np
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import math
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import paddle
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from paddle.inference import Config
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from paddle.inference import create_predictor
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import sys
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# add deploy path of PaddleDetection to sys.path
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parent_path = os.path.abspath(os.path.join(__file__, *(['..'])))
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sys.path.insert(0, parent_path)
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from python.benchmark_utils import PaddleInferBenchmark
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from python.preprocess import preprocess, Resize, NormalizeImage, Permute, PadStride, LetterBoxResize, WarpAffine
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from python.visualize import visualize_attr
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from python.utils import argsparser, Timer, get_current_memory_mb
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from python.infer import Detector, get_test_images, print_arguments, load_predictor
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from PIL import Image, ImageDraw, ImageFont
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class AttrDetector(Detector):
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"""
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Args:
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model_dir (str): root path of model.pdiparams, model.pdmodel and infer_cfg.yml
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device (str): Choose the device you want to run, it can be: CPU/GPU/XPU/NPU, default is CPU
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run_mode (str): mode of running(paddle/trt_fp32/trt_fp16)
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batch_size (int): size of pre batch in inference
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trt_min_shape (int): min shape for dynamic shape in trt
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trt_max_shape (int): max shape for dynamic shape in trt
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trt_opt_shape (int): opt shape for dynamic shape in trt
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trt_calib_mode (bool): If the model is produced by TRT offline quantitative
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calibration, trt_calib_mode need to set True
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cpu_threads (int): cpu threads
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enable_mkldnn (bool): whether to open MKLDNN
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output_dir (str): The path of output
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threshold (float): The threshold of score for visualization
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"""
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def __init__(
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self,
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model_dir,
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device='CPU',
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run_mode='paddle',
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batch_size=1,
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trt_min_shape=1,
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trt_max_shape=1280,
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trt_opt_shape=640,
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trt_calib_mode=False,
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cpu_threads=1,
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enable_mkldnn=False,
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output_dir='output',
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threshold=0.5, ):
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super(AttrDetector, self).__init__(
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model_dir=model_dir,
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device=device,
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run_mode=run_mode,
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batch_size=batch_size,
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trt_min_shape=trt_min_shape,
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trt_max_shape=trt_max_shape,
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trt_opt_shape=trt_opt_shape,
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trt_calib_mode=trt_calib_mode,
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cpu_threads=cpu_threads,
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enable_mkldnn=enable_mkldnn,
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output_dir=output_dir,
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threshold=threshold, )
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@classmethod
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def init_with_cfg(cls, args, cfg):
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return cls(model_dir=cfg['model_dir'],
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batch_size=cfg['batch_size'],
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device=args.device,
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run_mode=args.run_mode,
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trt_min_shape=args.trt_min_shape,
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trt_max_shape=args.trt_max_shape,
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trt_opt_shape=args.trt_opt_shape,
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trt_calib_mode=args.trt_calib_mode,
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cpu_threads=args.cpu_threads,
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enable_mkldnn=args.enable_mkldnn)
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def get_label(self):
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return self.pred_config.labels
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def postprocess(self, inputs, result):
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# postprocess output of predictor
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im_results = result['output']
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labels = self.pred_config.labels
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age_list = ['AgeLess18', 'Age18-60', 'AgeOver60']
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direct_list = ['Front', 'Side', 'Back']
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bag_list = ['HandBag', 'ShoulderBag', 'Backpack']
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upper_list = ['UpperStride', 'UpperLogo', 'UpperPlaid', 'UpperSplice']
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lower_list = [
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'LowerStripe', 'LowerPattern', 'LongCoat', 'Trousers', 'Shorts',
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'Skirt&Dress'
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]
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glasses_threshold = 0.3
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hold_threshold = 0.6
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batch_res = []
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for res in im_results:
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res = res.tolist()
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label_res = []
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# gender
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gender = 'Female' if res[22] > self.threshold else 'Male'
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label_res.append(gender)
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# age
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age = age_list[np.argmax(res[19:22])]
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label_res.append(age)
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# direction
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direction = direct_list[np.argmax(res[23:])]
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label_res.append(direction)
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# glasses
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glasses = 'Glasses: '
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if res[1] > glasses_threshold:
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glasses += 'True'
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else:
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glasses += 'False'
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label_res.append(glasses)
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# hat
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hat = 'Hat: '
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if res[0] > self.threshold:
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hat += 'True'
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else:
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hat += 'False'
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label_res.append(hat)
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# hold obj
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hold_obj = 'HoldObjectsInFront: '
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if res[18] > hold_threshold:
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hold_obj += 'True'
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else:
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hold_obj += 'False'
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label_res.append(hold_obj)
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# bag
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bag = bag_list[np.argmax(res[15:18])]
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bag_score = res[15 + np.argmax(res[15:18])]
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bag_label = bag if bag_score > self.threshold else 'No bag'
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label_res.append(bag_label)
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# upper
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upper_label = 'Upper:'
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sleeve = 'LongSleeve' if res[3] > res[2] else 'ShortSleeve'
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upper_label += ' {}'.format(sleeve)
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upper_res = res[4:8]
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if np.max(upper_res) > self.threshold:
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upper_label += ' {}'.format(upper_list[np.argmax(upper_res)])
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label_res.append(upper_label)
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# lower
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lower_res = res[8:14]
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lower_label = 'Lower: '
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has_lower = False
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for i, l in enumerate(lower_res):
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if l > self.threshold:
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lower_label += ' {}'.format(lower_list[i])
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has_lower = True
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if not has_lower:
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lower_label += ' {}'.format(lower_list[np.argmax(lower_res)])
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label_res.append(lower_label)
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# shoe
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shoe = 'Boots' if res[14] > self.threshold else 'No boots'
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label_res.append(shoe)
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batch_res.append(label_res)
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result = {'output': batch_res}
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return result
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def predict(self, repeats=1):
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'''
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Args:
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repeats (int): repeats number for prediction
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Returns:
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result (dict): include 'boxes': np.ndarray: shape:[N,6], N: number of box,
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matix element:[class, score, x_min, y_min, x_max, y_max]
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MaskRCNN's result include 'masks': np.ndarray:
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shape: [N, im_h, im_w]
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'''
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# model prediction
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for i in range(repeats):
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self.predictor.run()
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output_names = self.predictor.get_output_names()
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output_tensor = self.predictor.get_output_handle(output_names[0])
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np_output = output_tensor.copy_to_cpu()
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result = dict(output=np_output)
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return result
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def predict_image(self,
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image_list,
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run_benchmark=False,
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repeats=1,
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visual=True):
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batch_loop_cnt = math.ceil(float(len(image_list)) / self.batch_size)
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results = []
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for i in range(batch_loop_cnt):
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start_index = i * self.batch_size
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end_index = min((i + 1) * self.batch_size, len(image_list))
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batch_image_list = image_list[start_index:end_index]
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if run_benchmark:
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# preprocess
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inputs = self.preprocess(batch_image_list) # warmup
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self.det_times.preprocess_time_s.start()
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inputs = self.preprocess(batch_image_list)
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self.det_times.preprocess_time_s.end()
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# model prediction
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result = self.predict(repeats=repeats) # warmup
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self.det_times.inference_time_s.start()
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result = self.predict(repeats=repeats)
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self.det_times.inference_time_s.end(repeats=repeats)
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# postprocess
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result_warmup = self.postprocess(inputs, result) # warmup
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self.det_times.postprocess_time_s.start()
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result = self.postprocess(inputs, result)
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self.det_times.postprocess_time_s.end()
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self.det_times.img_num += len(batch_image_list)
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cm, gm, gu = get_current_memory_mb()
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self.cpu_mem += cm
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self.gpu_mem += gm
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self.gpu_util += gu
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else:
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# preprocess
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self.det_times.preprocess_time_s.start()
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inputs = self.preprocess(batch_image_list)
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self.det_times.preprocess_time_s.end()
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# model prediction
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self.det_times.inference_time_s.start()
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result = self.predict()
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self.det_times.inference_time_s.end()
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# postprocess
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self.det_times.postprocess_time_s.start()
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result = self.postprocess(inputs, result)
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self.det_times.postprocess_time_s.end()
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self.det_times.img_num += len(batch_image_list)
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if visual:
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visualize(
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batch_image_list, result, output_dir=self.output_dir)
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results.append(result)
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if visual:
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print('Test iter {}'.format(i))
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results = self.merge_batch_result(results)
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return results
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def merge_batch_result(self, batch_result):
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if len(batch_result) == 1:
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return batch_result[0]
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res_key = batch_result[0].keys()
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results = {k: [] for k in res_key}
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for res in batch_result:
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for k, v in res.items():
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results[k].extend(v)
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return results
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def visualize(image_list, batch_res, output_dir='output'):
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# visualize the predict result
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batch_res = batch_res['output']
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for image_file, res in zip(image_list, batch_res):
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im = visualize_attr(image_file, [res])
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if not os.path.exists(output_dir):
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os.makedirs(output_dir)
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img_name = os.path.split(image_file)[-1]
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out_path = os.path.join(output_dir, img_name)
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cv2.imwrite(out_path, im)
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print("save result to: " + out_path)
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def main():
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detector = AttrDetector(
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FLAGS.model_dir,
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device=FLAGS.device,
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run_mode=FLAGS.run_mode,
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batch_size=FLAGS.batch_size,
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trt_min_shape=FLAGS.trt_min_shape,
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trt_max_shape=FLAGS.trt_max_shape,
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trt_opt_shape=FLAGS.trt_opt_shape,
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trt_calib_mode=FLAGS.trt_calib_mode,
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cpu_threads=FLAGS.cpu_threads,
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enable_mkldnn=FLAGS.enable_mkldnn,
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threshold=FLAGS.threshold,
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output_dir=FLAGS.output_dir)
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# predict from image
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if FLAGS.image_dir is None and FLAGS.image_file is not None:
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assert FLAGS.batch_size == 1, "batch_size should be 1, when image_file is not None"
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img_list = get_test_images(FLAGS.image_dir, FLAGS.image_file)
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detector.predict_image(img_list, FLAGS.run_benchmark, repeats=10)
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if not FLAGS.run_benchmark:
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detector.det_times.info(average=True)
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else:
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mems = {
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'cpu_rss_mb': detector.cpu_mem / len(img_list),
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'gpu_rss_mb': detector.gpu_mem / len(img_list),
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'gpu_util': detector.gpu_util * 100 / len(img_list)
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}
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perf_info = detector.det_times.report(average=True)
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model_dir = FLAGS.model_dir
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mode = FLAGS.run_mode
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model_info = {
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'model_name': model_dir.strip('/').split('/')[-1],
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'precision': mode.split('_')[-1]
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}
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data_info = {
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'batch_size': FLAGS.batch_size,
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'shape': "dynamic_shape",
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'data_num': perf_info['img_num']
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}
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det_log = PaddleInferBenchmark(detector.config, model_info, data_info,
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perf_info, mems)
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det_log('Attr')
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if __name__ == '__main__':
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paddle.enable_static()
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parser = argsparser()
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FLAGS = parser.parse_args()
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print_arguments(FLAGS)
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FLAGS.device = FLAGS.device.upper()
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assert FLAGS.device in ['CPU', 'GPU', 'XPU', 'NPU'
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], "device should be CPU, GPU, XPU or NPU"
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assert not FLAGS.use_gpu, "use_gpu has been deprecated, please use --device"
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main()
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